Laser matrix bombardment water quality detection method and system based on multi-pulse fusion algorithm

Abstract

The embodiment of the application relates to the technical fields of image processing and water quality detection, and provides a laser matrix bombardment water quality detection method and system based on a multi-pulse fusion algorithm, the method comprising: performing contour extraction on an image corresponding to a to-be-detected sample to obtain first contour information, extracting coordinate information corresponding to each pixel point in the first contour information, determining the coordinate information corresponding to each pixel point in the first contour information as position information corresponding to the to-be-detected sample to obtain first position information, reconstructing a preset laser matrix according to the first position information and a preset reflection loss rate threshold to obtain a first laser matrix, and performing water quality detection on the to-be-detected sample by using the first laser matrix according to a multi-pulse fusion algorithm to obtain a target detection result, the multi-pulse fusion algorithm comprising dynamic weight distribution based on a signal-to-noise ratio and spectrum information fusion, and the accuracy of water quality detection on the to-be-detected sample is improved.

Description

Technical Field

[0001] This application relates to the fields of image processing and water quality detection technology, specifically to a laser matrix bombardment water quality detection method and system based on a multi-pulse fusion algorithm. Background Technology

[0002] Water is a necessity for both humans and natural systems, and monitoring water quality is of great significance in protecting public health, the environment and economy, agricultural irrigation, and industrial manufacturing.

[0003] Currently, commonly used water quality testing methods are divided into two main categories: physical testing and chemical testing. Among them, physical testing is the most commonly used. It involves bombarding the sample with a laser and extracting the spectral information of the sample after laser bombardment. Then, the concentration of elements in the sample is calculated in reverse using the spectral information of the sample, thereby realizing water quality testing of the sample to be tested.

[0004] In existing solutions, a fixed laser matrix is ​​typically used to bombard the sample to be tested. However, the position of the sample changes each time water quality testing is performed, resulting in varying reflection loss rates of the laser matrix. This leads to insufficient accuracy in water quality testing. Furthermore, when fusing the spectral signals generated by multi-pulse lasers, a simple time-series superposition of the spectral signals with fixed weights is used, resulting in insufficient accuracy of the fused spectral signal. Consequently, the accuracy of the fused spectral signal is also insufficient, further contributing to the inaccuracy of water quality testing. Summary of the Invention

[0005] This application provides a laser matrix bombardment water quality detection method and system based on a multi-pulse fusion algorithm. It reconstructs a preset laser matrix according to the reflection loss rate of the laser emitter in the laser matrix, avoiding the inaccuracy caused by varying reflection loss rates during each water quality detection. Furthermore, by assigning dynamic weights to the spectral information in the spectral information set based on the signal-to-noise ratio, it avoids the inaccuracy of the fused spectral signal caused by simply using fixed weights to sequentially superimpose the spectral signals generated by the multi-pulse laser when fusing the spectral information in the spectral information set. This improves the accuracy of water quality detection.

[0006] A first aspect of this application provides a laser matrix bombardment water quality detection method based on a multi-pulse fusion algorithm, the method comprising:

[0007] Contour extraction is performed on the image corresponding to the sample to be detected to obtain the first contour information. The sample to be detected is a water sample that has been heated and dried.

[0008] Extract the coordinate information corresponding to each pixel in the first contour information;

[0009] The coordinate information corresponding to each pixel in the first contour information is determined as the position information of the sample to be detected, and the first position information is obtained.

[0010] The preset laser matrix is ​​reconstructed based on the first position information and the preset reflection loss rate threshold to obtain the first laser matrix;

[0011] The first laser matrix is ​​used to perform water quality detection on the sample to be detected according to the multi-pulse fusion algorithm to obtain the target detection result. The multi-pulse fusion algorithm includes dynamic weight allocation based on signal-to-noise ratio and spectral information fusion.

[0012] In this example, contour extraction is performed on the image corresponding to the sample to be detected to obtain first contour information. The sample to be detected is a water sample that has been heated and dried. The coordinate information corresponding to each pixel in the first contour information is extracted, and the coordinate information corresponding to each pixel in the first contour information is determined as the position information corresponding to the sample to be detected to obtain first position information. Based on the first position information and a preset reflection loss rate threshold, a preset laser matrix is ​​reconstructed to obtain a first laser matrix. The first laser matrix is ​​used to perform water quality detection on the sample to be detected according to a multi-pulse fusion algorithm to obtain the target detection result. This avoids the decrease in accuracy of water quality detection caused by changes in the position of the sample to be detected. At the same time, the multi-pulse fusion algorithm is used to improve the accuracy of fusing the spectral information of the sample to be detected, further improving the accuracy of the target detection result, thereby improving the accuracy of the micro-water sample stratification and air isolation of the water sample to be detected.

[0013] A second aspect of this application provides a laser matrix bombardment water quality detection system based on a multi-pulse fusion algorithm, the system comprising:

[0014] The extraction unit is used to extract the contour of the image corresponding to the sample to be detected to obtain the first contour information. The sample to be detected is a water sample that has been heated and dried. The unit extracts the coordinate information corresponding to each pixel in the first contour information and determines the coordinate information corresponding to each pixel in the first contour information as the position information corresponding to the sample to be detected to obtain the first position information.

[0015] The reconstruction unit is used to reconstruct the preset laser matrix based on the first position information and the preset reflection loss rate threshold to obtain the first laser matrix;

[0016] The detection unit is used to perform water quality detection on the sample to be detected using the first laser matrix according to the multi-pulse fusion algorithm, and obtain the target detection result. The multi-pulse fusion algorithm includes dynamic weight allocation based on signal-to-noise ratio and spectral information fusion.

[0017] A third aspect of this application provides a laser water quality analyzer, characterized in that the laser water quality analyzer is used to execute the laser matrix bombardment water quality detection method based on a multi-pulse fusion algorithm as described in any one of the first aspects. The laser water quality analyzer includes: a laser bombardment unit, a water sample preparation unit, and a detection unit. The laser output from the laser bombardment unit bombards spots on the water sample preparation area of ​​the water sample preparation unit through the end of the laser bombardment unit. The detection unit monitors the light generated by the laser bombardment unit bombarding the spots.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0019] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A schematic diagram of a laser water quality analyzer is provided for an embodiment of this application;

[0022] Figure 2 This application provides a schematic flowchart of a laser matrix bombardment water quality detection method based on a multi-pulse fusion algorithm.

[0023] Figure 3 A schematic diagram of an illumination angle is provided for an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0025] Figure 5 This application provides a schematic diagram of a laser matrix bombardment water quality detection system based on a multi-pulse fusion algorithm. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below 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.

[0027] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0028] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0029] To better understand the laser matrix bombardment water quality detection method based on a multi-pulse fusion algorithm provided in this application, a brief introduction to existing laser matrix bombardment water quality detection methods based on multi-pulse fusion algorithms is given below. In existing methods, a fixed laser matrix is ​​typically used to bombard the sample to be tested. However, the position of the sample changes each time water quality is tested, resulting in varying reflection loss rates of the laser matrix each time, leading to insufficient accuracy in water quality detection. Furthermore, when fusing the spectral signals generated by the multi-pulse laser, a simple time-series superposition of the spectral signals with fixed weights is used, resulting in insufficient accuracy of the fused spectral signal, thus contributing to insufficient accuracy in water quality detection.

[0030] To address the aforementioned technical problems, this application provides a laser matrix bombardment water quality detection method based on a multi-pulse fusion algorithm. The method extracts the position information of the sample to be tested on a water sample platform to obtain first position information. It calculates the reflection loss rate corresponding to each laser emitter in a preset laser matrix and determines a second set of laser emitters within the preset laser matrix based on the reflection loss rate of each laser emitter and the first position information. The laser matrix formed by the second laser emitters in the second set is then defined as the first laser matrix. Using the first laser matrix for water quality detection of the sample avoids the decrease in accuracy caused by changes in the position of the sample. A calibration model is constructed to correct the spectral information of the sample, and then a multi-pulse fusion algorithm is used to fuse the corrected spectral information to obtain target spectral information. This avoids the inaccuracy of the fused spectral signal caused by simply using fixed weights to temporally superimpose the spectral signals generated by multi-pulse lasers. Regression training is performed on the target spectral information to obtain the target detection result, thereby improving the accuracy of water quality detection of the sample.

[0031] To better understand the micro-level water sample drying spot formation method provided in this application, the existing water sample drying and formation methods are briefly introduced below. Current technologies are mainly designed for water samples with volumes of milliliters and above, making it difficult to adapt to the processing needs of 1-100 μL micro-level water samples, thus exhibiting significant technical limitations. Specifically, traditional constant-temperature drying technology uses a single temperature (e.g., 60-70℃) to heat the water sample. While simple to operate, micro-level water samples have a significantly higher surface area to volume ratio. A single high temperature can easily cause a sudden rise in moisture and violent boiling, leading to solute splashing and forming incomplete spots, or causing excessive concentration and caking of local solutes, resulting in irregular morphology. Traditional natural air-drying technology relies on ambient temperature and humidity for natural evaporation of moisture, which is not only time-consuming (several minutes to tens of minutes) and inefficient, but also easily affected by ambient airflow, causing solute to accumulate in large quantities at the edge of the spot, while the central area becomes empty due to insufficient solute, further affecting subsequent detection. Meanwhile, existing technologies lack effective spot position calibration mechanisms. Slight substrate displacement and solute shrinkage during water sample drying can easily cause the dried spots to deviate from the initial water sample position, resulting in inaccurate focusing during laser detection and causing signal deviation. Furthermore, spot defect repair often relies on manual operation, such as manually adjusting the substrate position with a microscope, wiping rough edges with cotton swabs, or adding exogenous solutes to fill gaps. This not only takes 5-10 minutes per sample, making it inefficient for batch analysis, but also easily introduces exogenous impurities to contaminate the sample, or damages the spot structure due to human error. Ultimately, this limits the accuracy and scope of laser water quality analysis in micro-scale water sample scenarios (such as trace heavy metal detection and trace organic matter analysis).

[0032] To address the aforementioned issues, this application provides a method for forming micro-level water sample drying spots. This method utilizes a gradient temperature-controlled drying mechanism, position calibration processing, and morphology optimization processing to achieve regular formation of micro-level water sample drying spots, thereby reducing detection errors and external interference, and ultimately improving the accuracy and efficiency of laser water quality analysis.

[0033] Please see Figure 1 , Figure 1 A schematic diagram of a laser liquid analysis system is shown. Figure 1 As shown, the laser liquid analysis system may include a control platform and a laser water quality analyzer. The control platform is communicatively connected to at least one laser water quality analyzer. The laser water quality analyzer may include a micro-level water sample drying and spot formation device. Liquids that the laser water quality analyzer can analyze include, but are not limited to, water, oil, and pharmaceutical solutions. The control platform performs data backup and subsequent application processing based on the analysis results of the liquid from the laser water quality analyzer.

[0034] Figure 1 The text describes a laser water quality analyzer as a micro-scale water sample drying and spot formation device. (The provided text is incomplete and requires further context.) Figure 1 The diagram shows a partial structural representation of a micro-level water sample drying and spot formation device. This device includes a laser bombardment unit 11, a water sample preparation unit 12, and a detection unit (not shown). The laser output from the laser bombardment unit 11 bombards the spots on the water sample preparation area 121 of the water sample preparation unit 12 through its end 111. The detection unit uses a spectrometer. The laser water quality analyzer primarily detects heavy metals and non-metals in the spots formed after liquid drying.

[0035] Understandably, a predetermined volume (e.g., 10 μL) of liquid is placed in the water sample preparation area 121 (hereinafter referred to as the substrate), and the liquid in the water sample preparation area 121 is dried to form spots. The laser generated by the laser instrument of the laser bombardment unit 11 contacts the dried residue (spots) of the liquid. The spots form plasma at high temperature, achieving a transition from a low-energy state to a high-energy state. However, the high-energy state is unstable and immediately returns to the ground state (i.e., the original state). At this time, energy is emitted in the form of light, and the light emitted by each element is different. The spectrometer of the detection module monitors the light generated by the laser bombardment unit 11 bombarding the spots. The liquid results can be analyzed based on the data detected by the detection unit. In other words, the laser water quality analyzer can quickly detect multiple elements without consuming chemical reagents.

[0036] Optionally, the water sample preparation unit 12 includes a support platform, an unwinding mechanism, a winding structure, and a flexible membrane. The flexible membrane is unwound from the unwinding mechanism, passes over the support platform, and a predetermined volume (e.g., 10 μL) of liquid is placed in the water sample preparation area 121 on the support platform. Before the next test, the unwinding mechanism and the winding structure work together to wind the discarded flexible membrane onto the winding structure, and the unused flexible membrane is placed on the support platform so that a predetermined volume (e.g., 10 μL) of liquid can be placed in the water sample preparation area 121 on the support platform. The implementation of the unwinding mechanism and the winding structure can be selected from existing technologies and will not be described in detail here.

[0037] Optionally, the flexible membrane may be made of zinc.

[0038] Please see Figure 2 , Figure 2 This application provides a schematic flowchart of a laser matrix bombardment water quality detection method based on a multi-pulse fusion algorithm, as an embodiment of the present application. Figure 2 As shown, the method includes:

[0039] 201. Extract the contour of the image corresponding to the sample to be tested to obtain the first contour information. The sample to be tested is a water sample that has been heated and dried.

[0040] Since the dried sample is usually in the form of salt crystals, solid residues or precipitates, there is a significant contrast between the dried sample and the water sample platform in the background. Therefore, the location information of the sample can be determined by extracting the contour of the sample in the image corresponding to the sample to be tested.

[0041] Specifically, the image corresponding to the sample to be detected can be binarized using a general binarization method to obtain a first image; the first image can be filtered using a Gaussian filtering method to remove random noise and obtain a second image; the second image can be eroded and dilated using a general erosion and dilation method (such as using morphological opening to process the image, eliminate noise and smooth contours) to refine the contours corresponding to the sample to be detected in the second image and obtain a third image; the third image can be edge-detected using an edge detection algorithm to extract the contour information corresponding to the sample to be detected in the third image and obtain the first contour information.

[0042] 202. Extract the coordinate information corresponding to each pixel in the first contour information.

[0043] Specifically, after obtaining the first contour information, the coordinate information corresponding to each pixel in the first contour information can be obtained by extracting the coordinate position information of all pixels within the range of the first contour information based on the pixel matrix of the image of the sample to be detected.

[0044] 203. Determine the coordinate information corresponding to each pixel in the first contour information as the position information corresponding to the sample to be detected, and obtain the first position information.

[0045] Specifically, after obtaining the coordinate information corresponding to each pixel in the first contour information, the first position information can be obtained by determining the coordinate information corresponding to each pixel in the first contour information as the position information corresponding to the sample to be detected.

[0046] 204. Based on the first position information and the preset reflection loss rate threshold, the preset laser matrix is ​​reconstructed to obtain the first laser matrix.

[0047] Specifically, this can be achieved by extracting the coverage area of ​​each laser emitter in a preset laser matrix, selecting laser emitters that can cover the first position information from the preset laser matrix based on the coverage area, thus obtaining a first set of laser emitters; determining the coordinate information corresponding to the center point of the sample to be detected based on the first position information, and calculating the illumination angle of each first laser emitter in the first set of laser emitters based on the coordinate information corresponding to the center point of the sample to be detected, thus obtaining a set of illumination angles; calculating the reflection loss rate corresponding to each first laser emitter in the first set of laser emitters based on the illumination angles in the set of illumination angles, and identifying the first laser emitters in the first set of laser emitters with a reflection loss rate less than a preset reflection loss rate threshold as second laser emitters, thus obtaining a second set of laser emitters; adjusting the working state of the second laser emitters in the preset laser matrix to the working state, and adjusting the working state of the remaining laser emitters to the standby state or sleep state, thus obtaining the first laser matrix.

[0048] 205. Using the first laser matrix to perform water quality detection on the sample to be detected according to the multi-pulse fusion algorithm, the target detection result is obtained. The multi-pulse fusion algorithm includes dynamic weight allocation based on signal-to-noise ratio and spectral information fusion.

[0049] Specifically, the process can be as follows: The position of the first laser matrix is ​​adjusted so that its center point coincides with the center point of the sample to be detected in a horizontal two-dimensional plane. The sample is then bombarded with the first laser matrix, and its spectral information is recorded to obtain a first spectral information set. A calibration model is then constructed to correct the first spectral information in the first spectral information set, resulting in a second spectral information set. The signal-to-noise ratio (SNR) of each second spectral information in the second spectral information set is calculated, and second spectral information with a SNR less than a preset threshold is removed, resulting in a third spectral information set. A multi-pulse fusion algorithm is then used to fuse the third spectral information in the third spectral information set to obtain the target spectral information. Finally, the concentration of each element in the sample to be detected is calculated based on the target spectral information to obtain the target detection result.

[0050] In this example, the location information corresponding to the sample to be tested is extracted to obtain the first location information. The sample to be tested is a water sample that has been heated and dried. The preset laser matrix is ​​reconstructed according to the first location information and the preset reflection loss rate threshold to obtain the first laser matrix. The first laser matrix is ​​used to perform water quality detection on the sample to be tested according to the multi-pulse fusion algorithm to obtain the target detection result. This avoids the decrease in accuracy of water quality detection caused by changes in the location of the sample to be tested. At the same time, the multi-pulse fusion algorithm improves the accuracy of fusing the spectral information of the sample to be tested, further improving the accuracy of the target detection result. This improves the accuracy of the micro-water sample stratification and air isolation of the water sample to be tested.

[0051] In one possible implementation, a method for reconstructing a preset laser matrix based on the first location information and a preset reflection loss rate threshold to obtain a first laser matrix includes:

[0052] A1. Extract the coverage area corresponding to each laser emitter in the preset laser matrix to obtain the first coverage area information set;

[0053] A2. Based on the first coverage information in the first coverage information set, select laser emitters whose coverage is within the first position information in the preset laser matrix to obtain the first laser emitter set;

[0054] A3. Calculate the reflection loss rate of each first laser emitter based on the position information and first position information of each first laser emitter in the first laser emitter set, and obtain the first reflection loss rate set;

[0055] A4. Based on the preset reflection loss rate threshold and the first reflection loss rate in the first reflection loss rate set, select the laser emitter used to bombard the sample to be tested from the first laser emitter set to obtain the second laser emitter set.

[0056] A5. The laser matrix formed by the second laser emitters in the second laser emitter set is determined as the first laser matrix.

[0057] This can be achieved by querying the product description corresponding to each laser emitter in a preset laser matrix, extracting the coverage area recorded in the product description, and obtaining a first set of coverage area information.

[0058] After obtaining the first set of coverage areas, the first set of laser emitters can be obtained by extracting the laser emitters in the preset laser matrix that are within the first location information of the first coverage area.

[0059] After obtaining the first set of laser emitters, the propagation distance of the laser emitted by each laser emitter in the air can be extracted by querying the product description corresponding to each laser emitter in the preset laser matrix, thus obtaining the first propagation distance set; the position information corresponding to the center point of the sample to be tested can be determined based on the first position information, thus obtaining the first coordinate information; the angle between the laser emission direction of each first laser emitter in the first set of laser emitters and the normal to the surface of the sample to be tested can be calculated based on the first coordinate information using a general angle calculation method, thus obtaining the illumination angle set; (See also...) Figure 3 , Figure 3This application provides a schematic diagram of the illumination angle for an embodiment; a simulation experiment is performed on the first laser emitter in the first laser emitter set to determine the reflection loss rate when the first laser emitter in the first laser emitter set bombards a smooth standard sample (such as a solution sample of elements such as Cu and Fe with known concentration) at an illumination angle of 0°, thus obtaining the baseline reflection loss rate; a simulation experiment is then performed on the first laser emitter in the first laser emitter set to determine the reflection loss rate when the first laser emitter in the first laser emitter set bombards a smooth standard sample at different illumination angles, thus obtaining a reference reflection loss rate set; the baseline reflection loss rate and the reference reflection rate are then calculated. The ratio between each reference reflection loss rate in the loss rate set is used to obtain the reflection loss correction coefficient set, and the average value corresponding to the reflection loss correction coefficient in the reflection loss correction coefficient set is calculated to obtain the target reflection loss correction coefficient; the roughness of the sample to be tested is extracted by equipment such as microscopic imaging and laser surface profilometer to obtain the first roughness information; the position information and the first position information of each first laser emitter in the first laser emitter set are calculated based on the first propagation distance in the first propagation distance set, the illumination angle in the illumination angle set, the base station reflection loss rate, the reflection loss correction coefficient, and the first roughness information to obtain the first reflection loss rate set.

[0060] Specifically, the reflection loss rate of each first laser emitter in the first laser emitter set can be calculated using the method shown in the following formula, based on the first propagation distance in the first propagation distance set, the illumination angle in the illumination angle set, the reference reflection loss rate, the reflection loss correction coefficient, and the first roughness information, thereby obtaining the first reflection loss rate set:

[0061]

[0062] In the formula This represents the i-th first reflection loss rate in the set of first reflection loss rates; denoted as the baseline reflection loss rate; cos represents the cosine function in the trigonometric functions; j represents the reflection loss correction coefficient in the set of reflection loss correction coefficients, which can be understood as the reflection loss correction coefficient in the set of reflection loss correction coefficients corresponding to the i-th first reflection loss rate in the set of first reflection loss rates, and can be determined through simulation experiments; This represents the i-th illumination angle in the set of illumination angles; The roughness sensitivity coefficient can be determined by user input or by system default; R represents the first roughness information; e represents the natural constant, which is approximately equal to 2.71828. This represents the i-th first propagation distance in the first propagation distance set.

[0063] After obtaining the first set of reflection loss rates, the second set of laser emitters can be obtained by extracting the first reflection loss rates that are less than a preset reflection loss rate threshold from the first set of reflection loss rates and determining the corresponding first laser emitter as the second laser emitter.

[0064] After obtaining the second set of laser emitters, the working state of the second laser emitter in the second set of laser emitters can be adjusted to the working state by adjusting the working state of each laser emitting unit in the preset laser matrix, and the working state of the other laser emitters in the preset laser matrix can be adjusted to the standby or sleep state, thereby reducing the energy consumption information when performing water quality testing on the sample to be tested, and thus obtaining the first laser matrix.

[0065] In this example, by selecting a suitable laser emitter from the preset laser matrix based on the coverage and reflection loss rate of each laser emitter in the preset laser matrix to bombard the sample to be tested, the accuracy of water quality testing of the sample to be tested is ensured. At the same time, the energy consumption of water quality testing of the sample to be tested is reduced by adjusting the working state of each laser emitter in the preset laser matrix.

[0066] In one possible implementation, a method for performing water quality detection on a sample to be detected using the first laser matrix according to a multi-pulse fusion algorithm to obtain a target detection result includes:

[0067] B1. Extract the spectral information of the sample to be tested when the first laser matrix is ​​used to bombard the sample to be tested, and obtain the first spectral information set;

[0068] B2. Correct the first spectral information in the first spectral information set to obtain the spectral information set to be fused. The correction process includes a spectral correction model based on the bombardment angle.

[0069] B3. Using a multi-pulse fusion algorithm, the spectral information to be fused in the spectral information set to be fused is fused according to the dynamic weights determined by the preset first signal-to-noise ratio threshold and the preset second signal-to-noise ratio threshold to obtain the target spectral information. The preset first signal-to-noise ratio threshold is greater than the preset second signal-to-noise ratio threshold.

[0070] B4. Calculate the concentration of each element in the sample to be detected based on the target spectral information to obtain the target detection result.

[0071] The process can be as follows: First coordinate information is obtained by extracting the coordinate position information corresponding to the center point of the sample to be detected; the distance between each pixel in the first position information and the first coordinate information is calculated to obtain a first distance information set; a user-input distance threshold is extracted to obtain a preset distance threshold information; first distance information greater than or equal to the preset distance threshold information is extracted from the first distance information set, and the first distance information greater than or equal to the preset distance threshold information is determined as second distance information to obtain a second distance information set; k pixels to be detected are determined from the first position information based on the second distance information in the second distance information set, and the coordinate position information of the k pixels to be detected and the first coordinate information are determined as detection points corresponding to the sample to be detected to obtain a detection point set; wherein the mutual distance between the k pixels to be detected is the largest; the position of the first laser matrix is ​​adjusted so that the coordinate position information of the center point of the first laser matrix coincides with the first coordinate information on the two-dimensional plane, and the first laser matrix is ​​used to emit high-frequency pulsed laser (such as a 100mJ high-frequency laser) to bombard each detection point in the detection point set m times, and a spectrometer is used to extract the spectral information of the sample to be detected after each bombardment to obtain a first spectral information set. The values ​​of k and m are determined by user input or by system default.

[0072] After obtaining the first spectral information set, the first reference spectral information set can be obtained by extracting the spectral information corresponding to different standard samples at different bombardment angles. Then, the bombardment angle corresponding to each first reference spectral information in the first reference spectral information set can be extracted to obtain the first bombardment angle information set. By fitting the first reference spectral information in the first reference spectral information set with the first bombardment angle information in the first bombardment angle information set, the correction factor corresponding to each first spectral information in the first reference spectral information set can be determined to obtain the correction factor set. Based on the correction factors in the correction factor set, a correction model can be constructed to obtain the first correction model. The first correction model can be used to correct the first spectral information in the first spectral information set to obtain the spectral information set to be fused.

[0073] After obtaining the set of spectral information to be fused, the signal-to-noise ratio (SNR) of each spectral information to be fused in the set of spectral information to be fused can be extracted to obtain a first SNR set; the weight information corresponding to each spectral information to be fused in the set of spectral information to be fused can be determined according to the first SNR in the first SNR set and a preset SNR threshold to obtain a target weight information set; and a multi-pulse fusion algorithm can be used to fuse the spectral information to be fused in the set of spectral information to be fused according to the target weight information in the target weight information set to obtain the target spectral information.

[0074] After obtaining the target spectral information, it can be filtered using an SG (Savitzky-Golay) filter, and then the filtered target spectral information can be input into a PLS (Partial Least Squares Regression) model for regression training to calculate the concentration information of each element in the sample to be detected, thereby obtaining the target analysis results.

[0075] In this example, by extracting the location information of the center point of the sample to be tested and determining k test points based on the location information of the center point of the sample to be tested, random errors are eliminated and the accuracy of water quality test results is improved.

[0076] In one possible implementation, a method for correcting first spectral information in a first spectral information set to obtain a spectral information set to be fused includes:

[0077] C1. Extract the spectral information corresponding to different standard samples at different bombardment angles to obtain the first reference spectral information set. The standard samples are dried solutions of Cu, Fe, etc. with known concentrations.

[0078] C2. Extract the bombardment angle corresponding to each first reference spectrum information in the first reference spectrum information set to obtain the first bombardment angle information set;

[0079] C3. Perform a quadratic polynomial fitting on the first reference spectral information in the first reference spectral information set and the first bombardment angle information in the first bombardment angle information set to obtain the first fitting function;

[0080] C4. Solve the first fitting function using the least squares method to determine the correction factor corresponding to each first reference spectral information in the first reference spectral information set, and obtain the correction factor set;

[0081] C5. Construct a correction model based on the correction factors in the set of correction factors to obtain the first correction model;

[0082] C6. Use the first correction model to correct the first spectral information in the first spectral information set to obtain the spectral information set to be fused.

[0083] Because the bombardment angle of the first laser matrix at each test point is different when bombarding the test sample with the first laser matrix, the absorption efficiency of the high-frequency laser emitted by the first laser matrix by the test sample is different. This results in a difference between the obtained spectral information and the spectral information obtained by bombarding the test sample at a standard angle (90° perpendicular to the plane of the test sample). Therefore, it is necessary to correct the first spectral information in the first spectral information set to ensure the accuracy of water quality testing of the test sample.

[0084] Specifically, this can be achieved by extracting spectral information from different standard samples (dryed solutions of known concentrations of Cu, Fe, etc.) at different bombardment angles to obtain a first reference spectral information set; extracting the bombardment angle corresponding to each first reference spectral information in the first reference spectral information set to obtain a first bombardment angle information set; constructing a correction model based on the first reference spectral information in the first reference spectral information set and the first bombardment angle information in the first bombardment angle information set to obtain a first correction model; and using the first correction model to correct the first spectral information in the first spectral information set to obtain the spectral information set to be fused.

[0085] Specifically, a first fitting function can be obtained by performing a quadratic polynomial fitting on the first reference spectral information in the first reference spectral information set and the first bombardment angle information in the first bombardment angle information set, thereby describing the correlation between them; the first fitting function can be expressed as: , This represents the first reference spectral information in the first reference spectral information set. Let a, b, and c represent the first bombardment angle information in the first bombardment angle information set, and let c represent the fitting parameters. The fitting parameters in the first fitting function are solved using the general least squares method, and the correction factor corresponding to each first reference spectral information in the first reference spectral information set is calculated based on the fitting parameters to obtain the correction factor set. The correction model is constructed based on the correction factors in the correction factor set to obtain the first correction model.

[0086] The mathematical expression for the first correction model is: , This indicates the corrected spectral information. This represents the input spectral information that needs to be corrected. This represents the correction factor in the set of correction factors.

[0087] Specifically, the correction factor can be calculated based on the fitting parameters using the following formula to obtain the correction factor set:

[0088]

[0089] In the formula This indicates the corrected spectral information; This indicates the input spectral information that needs to be corrected; This represents the correction factor in the set of correction factors; denoted as the first bombardment angle information in the first bombardment angle information set; a, b, and c represent the fitting parameters.

[0090] In this example, a calibration model is established using spectral information from different standard samples at different bombardment angles to correct the first spectral information in the first spectral information set. This eliminates the difference between the spectral information obtained due to different bombardment angles and the spectral information obtained by bombarding the sample to be tested at a standard angle (vertical 90°), thereby improving the accuracy in water quality testing of the sample to be tested. In one possible implementation, a method using a multi-pulse fusion algorithm to fuse the spectral information to be fused in the spectral information set to be fused according to a preset first signal-to-noise ratio threshold and a preset second signal-to-noise ratio threshold to obtain the target spectral information includes:

[0091] D1. Fit the spectral information to be fused in the set of spectral information to be fused to obtain a second set of spectral information;

[0092] D2. Extract the signal intensity of each second spectral information from the second spectral information set to obtain the first signal intensity information set;

[0093] D3. Calculate the signal-to-noise ratio of the corresponding second spectral information based on the first signal intensity information in the first signal intensity information set to obtain the first signal-to-noise ratio set;

[0094] D4. Extract the signal-to-noise ratios that are higher than the preset second signal-to-noise ratio threshold from the first signal-to-noise ratio set to obtain the second signal-to-noise ratio set;

[0095] D5. Extract the second spectral information corresponding to the second signal-to-noise ratio set from the second spectral information set to obtain the third spectral information set;

[0096] D6. Determine the dynamic weight corresponding to each third spectral information in the third spectral information set based on the second signal-to-noise ratio in the second signal-to-noise ratio set, the preset first signal-to-noise ratio threshold, and the preset second signal-to-noise ratio threshold, to obtain the target weight information set;

[0097] D7. Based on the target weight information in the target weight information set, use the multi-pulse fusion algorithm to fuse the third spectral information in the third spectral information set to obtain the target spectral information.

[0098] Since the spectral information to be fused in the spectral information set may experience baseline drift due to factors such as fluorescence background or instrument dark current, which reduces the accuracy of the spectral information to be fused, it is necessary to process the spectral information to be fused in the spectral information set to eliminate the impact of baseline drift on the accuracy of the spectral information to be fused in the spectral information set.

[0099] Specifically, this can be achieved by extracting the peak intensity corresponding to each spectral information to be fused from the set of spectral information to be fused, obtaining N first peak intensity information sets; where N represents the quantity of spectral information to be fused in the set of spectral information to be fused; determining two data points as the baseline intensity corresponding to each first peak intensity information in the N first peak intensity information sets as the baseline intensity corresponding to each spectral information to be fused in the set of spectral information to be fused, obtaining N baseline intensity information sets; wherein, in each baseline intensity information set, one baseline intensity information is greater than the first peak intensity information, and the other baseline intensity information is less than the first peak intensity information; fitting the baseline intensity information in the N baseline intensity information sets using a general linear fitting or nonlinear fitting method, obtaining first baseline information; and performing baseline correction on the spectral information to be fused in the set of spectral information to be fused based on the first baseline information to eliminate the influence of baseline drift on the accuracy of the spectral information to be fused in the set of spectral information to be fused, obtaining a second spectral information set.

[0100] After obtaining the second spectral information set, the characteristic peak intensity value corresponding to each second spectral information in the second spectral information set can be extracted using a general characteristic peak intensity value extraction method, and the characteristic peak intensity value corresponding to each second spectral information can be determined as the signal intensity corresponding to the second spectral information to obtain the first signal intensity information set.

[0101] After obtaining the first signal intensity information set, the noise information corresponding to each second spectral information in the second spectral information set can be determined based on the first signal intensity information in the first signal intensity information set, resulting in N first noise information sets; the standard deviation corresponding to each of the N first noise information sets can be calculated, and the standard deviation can be used as the noise intensity corresponding to the second spectral information in the second spectral information set, resulting in the second signal intensity set; the ratio between each first intensity information in the first intensity information set and its corresponding second intensity information can be calculated, and this ratio can be used as the signal-to-noise ratio of the corresponding second spectral information in the second spectral information set, resulting in the first signal-to-noise ratio set.

[0102] After obtaining the first signal-to-noise ratio set, the second signal-to-noise ratio set can be obtained by extracting the signal-to-noise ratios in the first signal-to-noise ratio set that are greater than a preset second signal-to-noise ratio threshold.

[0103] After obtaining the second signal-to-noise ratio set, the third spectral information set can be obtained by extracting the second spectral information corresponding to the second signal-to-noise ratio set from the second spectral information set.

[0104] After obtaining the third spectral information set, the third spectral information in the third spectral information set can be classified according to the second signal-to-noise ratio in the second signal-to-noise ratio set, a preset first signal-to-noise ratio threshold, and a preset second signal-to-noise ratio threshold. Third spectral information with a first signal-to-noise ratio greater than the preset first signal-to-noise ratio threshold is identified as fourth spectral information, thus obtaining the fourth spectral information set. Third spectral information with a first signal-to-noise ratio less than or equal to the preset first signal-to-noise ratio threshold and greater than or equal to the preset second signal-to-noise ratio threshold is identified as fifth spectral information, thus obtaining the fifth spectral information set. A preset first weight is used as the weight information corresponding to each fourth spectral information in the fourth spectral information set, and a preset second weight is used as the weight information corresponding to each fifth spectral information in the fifth spectral information set, thereby determining the dynamic weight corresponding to each third spectral information in the third spectral information set, and obtaining the target weight information set.

[0105] In a specific example, the first signal-to-noise ratio set corresponding to the third spectral information set is (31, 30, 26, 17, 10), and the preset first signal-to-noise ratio threshold is 30, the preset second signal-to-noise ratio threshold is 10, the preset target weight information is 0.15, and the preset second weight information is 0.1. Then the target weight information set is (0.15, 0.1, 0.1, 0.1, 0.1).

[0106] After obtaining the target weight information set, the number of pulses corresponding to each third spectral information in the third spectral information set can be extracted to obtain a pulse count set. Then, a multi-pulse fusion algorithm can be used to fuse the third spectral information in the third spectral information set based on the pulse count in the pulse count set and the target weight information in the target weight information set to obtain the target spectral information.

[0107] Specifically, the target spectral information can be obtained by fusing the third spectral information in the third spectral information set with the pulse count in the pulse count set and the target weight information in the target weight information set, as shown in the following formula:

[0108]

[0109] In the formula This represents the target's spectral information; S represents the maximum number of pulses in the pulse count set. This represents the i-th third spectral information in the third spectral information set; This represents the i-th target weight information in the target weight information set.

[0110] In this example, by fitting the first spectral information in the first spectral information set, the problem of decreased accuracy of the first spectral information in the first spectral information set due to fluorescence background or instrument dark current is eliminated. Then, the first spectral information with too low a signal-to-noise ratio in the first spectral information set is removed according to the preset second signal-to-noise ratio threshold, resulting in a third spectral information set. By performing multi-pulse fusion on the third spectral information in the third spectral information set according to the signal-to-noise ratio corresponding to each third spectral information in the third spectral information set, the preset first signal-to-noise ratio threshold, and the preset second signal-to-noise ratio threshold, the accuracy of the fusion processing of the third spectral information in the third spectral information set is improved, thereby improving the accuracy when conducting water quality testing on the sample to be tested.

[0111] For examples consistent with the above embodiments, please refer to... Figure 4 , Figure 4 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 4 As shown, it includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0112] Contour extraction is performed on the image corresponding to the sample to be detected to obtain the first contour information. The sample to be detected is a water sample that has been heated and dried.

[0113] Extract the coordinate information corresponding to each pixel in the first contour information;

[0114] The coordinate information corresponding to each pixel in the first contour information is determined as the position information of the sample to be detected, and the first position information is obtained.

[0115] The preset laser matrix is ​​reconstructed based on the first position information and the preset reflection loss rate threshold to obtain the first laser matrix;

[0116] The first laser matrix is ​​used to perform water quality detection on the sample to be detected according to the multi-pulse fusion algorithm to obtain the target detection result. The multi-pulse fusion algorithm includes dynamic weight allocation based on signal-to-noise ratio and spectral information fusion.

[0117] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0118] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0119] For those consistent with the above, please refer to Figure 5 , Figure 5 This application provides a schematic diagram of a laser matrix bombardment water quality detection system based on a multi-pulse fusion algorithm, as an embodiment of the present application. Figure 5 As shown, the system includes:

[0120] Extraction unit 501 is used to extract the contour of the image corresponding to the sample to be detected to obtain first contour information. The sample to be detected is a water sample that has been heated and dried. It extracts the coordinate information corresponding to each pixel in the first contour information and determines the coordinate information corresponding to each pixel in the first contour information as the position information corresponding to the sample to be detected to obtain first position information.

[0121] The reconstruction unit 502 is used to reconstruct a preset laser matrix based on the first position information and a preset reflection loss rate threshold to obtain a first laser matrix;

[0122] The detection unit 503 is used to perform water quality detection on the sample to be detected using the first laser matrix according to the multi-pulse fusion algorithm, and obtain the target detection result. The multi-pulse fusion algorithm includes dynamic weight allocation based on signal-to-noise ratio and spectral information fusion.

[0123] In one possible implementation, the reconstruction unit 502 is specifically used for:

[0124] Extract the coverage area corresponding to each laser emitter in the preset laser matrix to obtain the first coverage area information set;

[0125] Based on the first coverage information in the first coverage information set, select laser emitters whose coverage is within the first location information from the preset laser matrix to obtain the first laser emitter set;

[0126] The reflection loss rate of each first laser emitter is calculated based on the position information and first position information of each first laser emitter in the first laser emitter set, and the first reflection loss rate set is obtained.

[0127] Based on the preset reflection loss rate threshold and the first reflection loss rate in the first reflection loss rate set, a laser emitter for bombarding the sample to be tested is selected from the first laser emitter set to obtain the second laser emitter set;

[0128] The laser matrix formed by the second laser emitters in the second set of laser emitters is defined as the first laser matrix.

[0129] In one possible implementation, the detection unit 503 is specifically used for:

[0130] Extract the spectral information of the sample to be tested when bombarded with the first laser matrix to obtain the first spectral information set;

[0131] The first spectral information in the first spectral information set is corrected to obtain the spectral information set to be fused. The correction process includes a spectral correction model based on the bombardment angle.

[0132] A multi-pulse fusion algorithm is used to fuse the spectral information to be fused in the spectral information set to be fused according to the dynamic weights determined by the preset first signal-to-noise ratio threshold and the preset second signal-to-noise ratio threshold, so as to obtain the target spectral information. The preset first signal-to-noise ratio threshold is greater than the preset second signal-to-noise ratio threshold.

[0133] The concentration of each element in the sample to be detected is calculated based on the target spectral information to obtain the target detection result.

[0134] In one possible implementation, in correcting the first spectral information in the first spectral information set to obtain the spectral information set to be fused, the detection unit 503 is specifically used for:

[0135] The spectral information corresponding to different standard samples under different bombardment angles is extracted to obtain the first reference spectral information set. The standard samples are dried solutions of Cu, Fe, etc. with known concentrations.

[0136] Extract the bombardment angle corresponding to each first reference spectrum information in the first reference spectrum information set to obtain the first bombardment angle information set;

[0137] A first fitting function is obtained by performing a quadratic polynomial fitting on the first reference spectral information in the first reference spectral information set and the first bombardment angle information in the first bombardment angle information set;

[0138] The first fitting function is solved using the least squares method to determine the correction factor corresponding to each first reference spectral information in the first reference spectral information set, thus obtaining the correction factor set.

[0139] The specific formula for calculating the correction factor is as follows:

[0140]

[0141] Among them, in the formula This indicates the corrected spectral information; This indicates the input spectral information that needs to be corrected; This represents the correction factor in the set of correction factors; This represents the first bombardment angle information in the first bombardment angle information set; a, b, and c represent the fitting parameters, which can be used to fit the first fitting function using the least squares method. Solving for the results

[0142] A correction model is constructed based on the correction factors in the set of correction factors to obtain the first correction model;

[0143] The first correction model is used to correct the first spectral information in the first spectral information set to obtain a spectral information set to be fused. In one possible implementation, regarding the use of a multi-pulse fusion algorithm to fuse the spectral information to be fused in the spectral information set to be fused according to a preset first signal-to-noise ratio threshold and a preset second signal-to-noise ratio threshold to obtain target spectral information, the detection unit 503 is specifically used for:

[0144] The spectral information to be fused in the set of spectral information to be fused is fitted to obtain a second set of spectral information.

[0145] Extract the signal intensity of each second spectral information from the second spectral information set to obtain the first signal intensity information set;

[0146] The signal-to-noise ratio of the corresponding second spectral information is calculated based on the first signal intensity information in the first signal intensity information set to obtain the first signal-to-noise ratio set.

[0147] The signal-to-noise ratios that are higher than the preset second signal-to-noise ratio threshold are extracted from the first signal-to-noise ratio set to obtain the second signal-to-noise ratio set;

[0148] The second spectral information corresponding to the second signal-to-noise ratio set is extracted from the second spectral information set to obtain the third spectral information set;

[0149] The dynamic weight corresponding to each third spectral information in the third spectral information set is determined based on the second signal-to-noise ratio in the second signal-to-noise ratio set, the preset first signal-to-noise ratio threshold, and the preset second signal-to-noise ratio threshold, thereby obtaining the target weight information set;

[0150] Based on the target weight information in the target weight information set, a multi-pulse fusion algorithm is used to fuse the third spectral information in the third spectral information set to obtain the target spectral information.

[0151] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the laser matrix bombardment water quality detection methods based on a multi-pulse fusion algorithm as described in the above method embodiments.

[0152] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the laser matrix bombardment water quality detection methods based on multi-pulse fusion algorithms described in the above method embodiments.

[0153] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0154] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0156] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0158] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0159] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0160] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A laser matrix bombardment method for water quality detection based on a multi-pulse fusion algorithm, characterized in that, The method includes: Contour extraction is performed on the image corresponding to the sample to be detected to obtain the first contour information. The sample to be detected is a water sample that has been heated and dried. Extract the coordinate information corresponding to each pixel in the first contour information; The coordinate information corresponding to each pixel in the first contour information is determined as the position information of the sample to be detected, and the first position information is obtained. The preset laser matrix is ​​reconstructed based on the first position information and the preset reflection loss rate threshold to obtain the first laser matrix; The first laser matrix is ​​used to perform water quality detection on the sample to be detected according to the multi-pulse fusion algorithm to obtain the target detection result. The multi-pulse fusion algorithm includes dynamic weight allocation based on signal-to-noise ratio and spectral information fusion.

2. The laser matrix bombardment water quality detection method based on multi-pulse fusion algorithm according to claim 1, characterized in that, The step of reconstructing a preset laser matrix based on the first position information and a preset reflection loss rate threshold to obtain a first laser matrix includes: Extract the coverage area corresponding to each laser emitter in the preset laser matrix to obtain the first coverage area information set; Based on the first coverage information in the first coverage information set, select laser emitters whose coverage is within the first location information from the preset laser matrix to obtain the first laser emitter set; The reflection loss rate of each first laser emitter is calculated based on the position information and first position information of each first laser emitter in the first laser emitter set, and the first reflection loss rate set is obtained. Based on the preset reflection loss rate threshold and the first reflection loss rate in the first reflection loss rate set, a laser emitter for bombarding the sample to be tested is selected from the first laser emitter set to obtain the second laser emitter set; The laser matrix formed by the second laser emitters in the second set of laser emitters is defined as the first laser matrix.

3. The laser matrix bombardment water quality detection method based on multi-pulse fusion algorithm according to claim 2, characterized in that, The step of using the first laser matrix to perform water quality detection on the sample to be detected according to the multi-pulse fusion algorithm to obtain the target detection result includes: Extract the spectral information of the sample to be tested when bombarded with the first laser matrix to obtain the first spectral information set; The first spectral information in the first spectral information set is corrected to obtain the spectral information set to be fused. The correction process includes a spectral correction model based on the bombardment angle. A multi-pulse fusion algorithm is used to fuse the spectral information to be fused in the spectral information set to be fused according to the dynamic weights determined by the preset first signal-to-noise ratio threshold and the preset second signal-to-noise ratio threshold, so as to obtain the target spectral information. The preset first signal-to-noise ratio threshold is greater than the preset second signal-to-noise ratio threshold. The concentration of each element in the sample to be detected is calculated based on the target spectral information to obtain the target detection result.

4. The laser matrix bombardment water quality detection method based on multi-pulse fusion algorithm according to claim 3, characterized in that, The step of correcting the first spectral information in the first spectral information set to obtain the spectral information set to be fused includes: Extract the spectral information of different standard samples at different bombardment angles to obtain the first reference spectral information set; Extract the bombardment angle corresponding to each first reference spectrum information in the first reference spectrum information set to obtain the first bombardment angle information set; A first fitting function is obtained by performing a quadratic polynomial fitting on the first reference spectral information in the first reference spectral information set and the first bombardment angle information in the first bombardment angle information set; The first fitting function is solved using the least squares method to determine the correction factor corresponding to each first reference spectral information in the first reference spectral information set, thus obtaining the correction factor set. The specific formula for calculating the correction factor is as follows: Among them, in the formula This indicates the corrected spectral information; This indicates the input spectral information that needs to be corrected; This represents the correction factor in the set of correction factors; This represents the first bombardment angle information in the first bombardment angle information set; a, b, and c represent the fitting parameters, which can be used to fit the first fitting function using the least squares method. The solution is obtained; A correction model is constructed based on the correction factors in the set of correction factors to obtain the first correction model; The first spectral information in the first spectral information set is corrected using the first correction model to obtain the spectral information set to be fused.

5. The laser matrix bombardment water quality detection method based on multi-pulse fusion algorithm according to claim 4, characterized in that, The multi-pulse fusion algorithm is used to fuse the spectral information to be fused in the spectral information set to be fused according to a preset first signal-to-noise ratio threshold and a preset second signal-to-noise ratio threshold to obtain the target spectral information, including: The spectral information to be fused in the set of spectral information to be fused is fitted to obtain a second set of spectral information. Extract the signal intensity of each second spectral information from the second spectral information set to obtain the first signal intensity information set; The signal-to-noise ratio of the corresponding second spectral information in the second spectral information set is calculated based on the first signal intensity information in the first signal intensity information set to obtain the first signal-to-noise ratio set; The signal-to-noise ratios that are higher than the preset second signal-to-noise ratio threshold are extracted from the first signal-to-noise ratio set to obtain the second signal-to-noise ratio set; Extract the second spectral information corresponding to the second signal-to-noise ratio set from the second spectral information set to obtain the third spectral information set; The dynamic weight corresponding to each third spectral information in the third spectral information set is determined based on the second signal-to-noise ratio in the second signal-to-noise ratio set, the preset first signal-to-noise ratio threshold, and the preset second signal-to-noise ratio threshold, thereby obtaining the target weight information set; Based on the target weight information in the target weight information set, a multi-pulse fusion algorithm is used to fuse the third spectral information in the third spectral information set to obtain the target spectral information.

6. A laser matrix bombardment water quality detection system based on a multi-pulse fusion algorithm, characterized in that, The system includes: The extraction unit is used to extract the contour of the image corresponding to the sample to be detected to obtain the first contour information. The sample to be detected is a water sample that has been heated and dried. The unit extracts the coordinate information corresponding to each pixel in the first contour information and determines the coordinate information corresponding to each pixel in the first contour information as the position information corresponding to the sample to be detected to obtain the first position information. The reconstruction unit is used to reconstruct the preset laser matrix based on the first position information and the preset reflection loss rate threshold to obtain the first laser matrix; The detection unit is used to perform water quality detection on the sample to be detected using the first laser matrix according to the multi-pulse fusion algorithm, and obtain the target detection result. The multi-pulse fusion algorithm includes dynamic weight allocation based on signal-to-noise ratio and spectral information fusion.

7. The laser matrix bombardment water quality detection system based on multi-pulse fusion algorithm according to claim 6, characterized in that, In reconstructing a preset laser matrix based on the first position information and a preset reflection loss rate threshold to obtain a first laser matrix, the reconstruction unit is specifically used for: Extract the coverage area corresponding to each laser emitter in the preset laser matrix to obtain the first coverage area information set; Based on the first coverage information in the first coverage information set, select laser emitters whose coverage is within the first location information from the preset laser matrix to obtain the first laser emitter set; The reflection loss rate of each first laser emitter is calculated based on the position information and first position information of each first laser emitter in the first laser emitter set, and the first reflection loss rate set is obtained. Based on the preset reflection loss rate threshold and the first reflection loss rate in the first reflection loss rate set, a laser emitter for bombarding the sample to be tested is selected from the first laser emitter set to obtain the second laser emitter set; The laser matrix formed by the second laser emitters in the second set of laser emitters is defined as the first laser matrix.

8. The laser matrix bombardment water quality detection system based on multi-pulse fusion algorithm according to claim 7, characterized in that, In terms of using the first laser matrix to perform water quality detection on the sample to be detected according to the multi-pulse fusion algorithm to obtain the target detection result, the detection unit is specifically used for: Extract the spectral information of the sample to be tested when bombarded with the first laser matrix to obtain the first spectral information set; The first spectral information in the first spectral information set is corrected to obtain the spectral information set to be fused. The correction process includes a spectral correction model based on the bombardment angle. A multi-pulse fusion algorithm is used to fuse the spectral information to be fused in the spectral information set to be fused according to the dynamic weights determined by the preset first signal-to-noise ratio threshold and the preset second signal-to-noise ratio threshold, so as to obtain the target spectral information. The preset first signal-to-noise ratio threshold is greater than the preset second signal-to-noise ratio threshold. The concentration of each element in the sample to be detected is calculated based on the target spectral information to obtain the target detection result.

9. The laser matrix bombardment water quality detection system based on multi-pulse fusion algorithm according to claim 8, characterized in that, In correcting the first spectral information in the first spectral information set to obtain the spectral information set to be fused, the detection unit is specifically used for: Extract the spectral information of different standard samples at different bombardment angles to obtain the first reference spectral information set; Extract the bombardment angle corresponding to each first reference spectrum information in the first reference spectrum information set to obtain the first bombardment angle information set; A first fitting function is obtained by performing a quadratic polynomial fitting on the first reference spectral information in the first reference spectral information set and the first bombardment angle information in the first bombardment angle information set; The first fitting function is solved using the least squares method to determine the correction factor corresponding to each first reference spectral information in the first reference spectral information set, thus obtaining the correction factor set. The specific formula for calculating the correction factor is as follows: Among them, in the formula This indicates the corrected spectral information; This indicates the input spectral information that needs to be corrected; This represents the correction factor in the set of correction factors; This represents the first bombardment angle information in the first bombardment angle information set; a, b, and c represent the fitting parameters, which can be used to fit the first fitting function using the least squares method: The solution is obtained; A correction model is constructed based on the correction factors in the set of correction factors to obtain the first correction model; The first spectral information in the first spectral information set is corrected using the first correction model to obtain the spectral information set to be fused.

10. A laser water quality analyzer, characterized in that, The laser water quality analyzer is used to perform the laser matrix bombardment water quality detection method based on the multi-pulse fusion algorithm as described in any one of claims 1-5. The laser water quality analyzer includes: a laser bombardment unit, a water sample preparation unit, and a detection unit. The laser output from the laser bombardment unit bombards spots on the water sample preparation area of ​​the water sample preparation unit through the end of the laser bombardment unit. The detection unit monitors the light generated by the laser bombardment unit bombarding the spots.

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