Signal processing method of multi-parameter water quality analyzer and related device

Abstract

The application relates to the technical fields of data processing and water quality detection, and provides a signal processing method of a multi-parameter water quality analyzer and related devices, which comprises the following steps: an optoelectronic detection module is used to obtain a first detection light signal generated when a water sample plate is irradiated with laser; the first detection light signal is converted and processed to obtain a first detection electric signal; the first detection electric signal is optimized to obtain a second detection electric signal; target spectrum information is obtained by constructing a spectrum according to the second detection electric signal; and the pollutant information of the water sample plate is determined according to the target spectrum information. The method provided by the application is beneficial to improving the accuracy of the signal processing process.

Description

Technical Field

[0001] This application relates to the fields of data processing and water quality testing technology, specifically to a signal processing method and related apparatus for a multi-parameter water quality analyzer. Background Technology

[0002] In the field of multi-parameter water quality analysis, the accuracy of water quality analysis directly affects the effectiveness of environmental governance and public health and safety. Signal processing, as a core component of multi-parameter water quality analysis, plays a crucial role in providing high-quality data support. However, the accuracy of signal processing in existing technologies is insufficient, making it difficult to meet the actual needs of high-precision water quality detection. The core problem lies in the lack of a systematic optimization scheme for the acquired detection optical signals, resulting in inaccurate signals for analysis and processing. Ultimately, this leads to misidentification of pollutant types and large errors in concentration calculation, severely restricting the reliability and detection accuracy of multi-parameter water quality analyzers. Summary of the Invention

[0003] This application provides a signal processing method and related apparatus for a multi-parameter water quality analyzer, which helps to improve the accuracy of the signal processing process, facilitates a more precise pollutant information identification process, and provides high-precision and high-reliability technical support for multi-parameter water quality analysis.

[0004] A first aspect of this application provides a signal processing method for a multi-parameter water quality analyzer, the method comprising: The photoelectric detection module is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with laser. The first detection optical signal is converted and processed to obtain the first detection electrical signal; The first detection electrical signal is optimized to obtain the second detection electrical signal; The target spectral information is obtained by constructing a spectrum based on the second detection electrical signal. The pollutant information of the water sample to be treated is determined based on the target spectral information.

[0005] In this example, a photoelectric detection module is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with a laser. The first detection light signal can be further converted to obtain a first detection electrical signal. The first detection electrical signal is then optimized to obtain a second detection electrical signal. Spectral construction can be performed based on the second detection electrical signal to obtain target spectral information. Furthermore, the pollutant information of the water sample to be treated can be determined based on the target spectral information. Through precise acquisition, efficient conversion, multiple optimizations, and a deeply adapted spectral construction process, the accuracy of the signal processing and pollutant information identification processes can be improved, providing high-precision and high-reliability technical support for multi-parameter water quality analysis.

[0006] A second aspect of this application provides a signal processing device for a multi-parameter water quality analyzer, the device comprising: The acquisition unit is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with laser; The first processing unit is used to convert the first detection optical signal to obtain the first detection electrical signal; The second processing unit is used to optimize the first detection electrical signal to obtain the second detection electrical signal; The third processing unit is used to construct a spectrum based on the second detection electrical signal to obtain target spectral information; The determining unit is used to determine the pollutant information of the water sample to be treated based on the target spectral information.

[0007] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0008] 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.

[0009] 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

[0010] 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.

[0011] Figure 1 This application provides a schematic diagram of the structure of a laser liquid analysis system. Figure 2This application provides a schematic flowchart of a signal processing method for a multi-parameter water quality analyzer. Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 4 This application provides a schematic diagram of the signal processing device of a multi-parameter water quality analyzer. Detailed Implementation

[0012] 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.

[0013] 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.

[0014] 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.

[0015] To better understand the signal processing method of a multi-parameter water quality analyzer provided in this application embodiment, a brief introduction to existing signal processing methods for multi-parameter water quality analyzers is given below. In the prior art, after converting the acquired raw detection optical signal, there is a lack of a systematic electrical signal optimization scheme. Most methods employ single filtering or simple amplification, which cannot effectively remove noise interference and waveform distortion from the signal. This results in the optimized electrical signal still having problems such as low purity and blurred features. Simultaneously, the spectral construction process does not fully incorporate background light interference correction and lacks a dual correction mechanism for the aquatic environment and the testing environment, causing deviations between the constructed spectral information and the true characteristics of the water sample. Furthermore, the connection between various stages of signal processing lacks coordinated design. Errors accumulate continuously throughout the entire process from optical signal acquisition to pollutant information determination, ultimately leading to insufficient accuracy in the signal processing results. This results in an inability to accurately reflect the characteristic attributes of pollutants, causing misidentification of pollutant types and large errors in concentration calculation. This makes it difficult to meet the actual needs of water quality monitoring for high-precision detection, limiting the reliability of multi-parameter water quality analyzers in environmental monitoring, drinking water safety assurance, and other scenarios.

[0016] To address the aforementioned issues, this application provides a signal processing method for a multi-parameter water quality analyzer. By acquiring and converting the detection optical signal into an electrical signal and constructing a spectral matrix based on the background light transmittance curve, the target spectral information is obtained. After two-stage environmental correction, pollutant information is accurately determined, enabling accurate identification and quantification of pollutant information. The entire process, from signal acquisition, conversion, and optimization to spectral construction and correction, improves processing accuracy and provides high-precision data support for multi-parameter water quality analysis.

[0017] The laser liquid analysis system may include a control platform and a multi-parameter water quality analyzer. The control platform is communicatively connected to at least one multi-parameter water quality analyzer. The multi-parameter water quality analyzer may include a water sample drying and spot formation device (which may include a laser irradiation unit, a water sample preparation unit, and a detection unit) and a pressure frame-type water sample plate leveling and fixing device with pressing ribs. The liquids that the multi-parameter 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 liquid analysis results from the multi-parameter water quality analyzer.

[0018] Please see Figure 1 , Figure 1 A schematic diagram of a laser liquid analysis system is shown. Figure 1The diagram shows a partial structural representation of a laser liquid analysis system. This structure includes: a laser irradiation unit (also referred to as a laser bombardment unit, which is not limited to this definition) 11, a water sample preparation unit 12, and a detection unit (not shown in the diagram). The laser output from the laser irradiation unit 11 irradiates the spots on the water sample preparation area 121 of the water sample preparation unit 12 through its end 111. It should be noted that... Figure 1 The detection unit in this application can be considered as the photoelectric detection module. The detection unit can be a spectrometer. The multi-parameter water quality analyzer mainly detects heavy metals and non-metals in spots formed after liquid drying.

[0019] The pressure-frame water sample flattening and fixing device with pressing ribs is a grid-like rigid frame with evenly distributed pressing ribs on its bottom surface, which can cover and press firmly the zinc film supporting the water sample spots. The device is used in two stages: first, before adding the water sample, the zinc film is flattened and fixed to the support platform to ensure the film surface is horizontal; second, after the water sample has dried and formed spots, it is flattened and fixed again to prevent spot deformation or displacement during laser irradiation. The grid structure allows laser penetration and uniform pressure, ensuring stable spot morphology, thereby improving the repeatability and accuracy of subsequent spectral signals and providing a reliable sample preparation basis for high-precision water quality analysis.

[0020] Understandably, a predetermined volume (e.g., 10 μL) of liquid is placed on the water sample preparation area 121 of the flexible membrane in the water sample preparation unit 12, and the liquid in the water sample preparation area 121 is dried to form spots. Before drying the liquid in the water sample preparation area 121 to form spots, a pressure frame-type water sample plate leveling and fixing device with pressure ribs (not shown in the figure) performs an initial flatness check and fixation on the flexible membrane that will bear the water sample (i.e., the flexible membrane where the water sample preparation area 121 is located), ensuring the water sample plate remains level, which is beneficial for more uniform spots after the water sample is dried. After drying the liquid in the water sample preparation area 121 to form spots, the pressure frame-type water sample plate leveling and fixing device with pressure ribs performs a secondary flatness check and fixation on the flexible membrane bearing the water sample spots, ensuring the flexible membrane is flat and stable, preventing spot morphology shift or flexible membrane displacement during subsequent laser irradiation by the laser irradiation unit 11, thereby ensuring accurate spectral detection data.

[0021] The laser generated by the laser in the laser irradiation unit 11 contacts the dried residue of the liquid (i.e., the water sample spot mentioned later). The water sample spot forms 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 which point the energy is emitted in the form of light. The light emitted by each element is different. The spectrometer of the detection module (which can be regarded as the core functional component of the photoelectric detection module mentioned in this application) monitors the light generated by the laser irradiation unit 11 irradiating the spot (i.e., the "first detection light signal generated during laser irradiation" mentioned below). The liquid result can be analyzed based on the data detected by the detection unit. In other words, the multi-parameter water quality analyzer can quickly detect multiple elements without consuming chemical reagents.

[0022] 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. Optionally, the flexible membrane is a zinc membrane.

[0023] Please see Figure 2 , Figure 2 This application provides a schematic flowchart of a signal processing method for a multi-parameter water quality analyzer, as illustrated in this embodiment. Figure 2 As shown, this method can be applied to the signal processing system of a multi-parameter water quality analyzer. The method includes: S10: The photoelectric detection module is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with laser.

[0024] The photoelectric detection module refers to the core component that converts optical signals into processable electrical signals. This module typically includes a photoelectric sensor (such as a photodiode or photomultiplier tube), a signal receiving optical path, and a preliminary signal adaptation circuit, enabling it to accurately capture optical signals in a specific wavelength band. In laser water quality analysis, the photoelectric detection module can usually be used in conjunction with the detection unit of a multi-parameter water quality analyzer; this application does not impose any limitations on this. Optionally, the photodiode in the photoelectric sensor can be a Hamamatsu S1337 series photodiode.

[0025] The water sample plate to be processed refers to the sample carrier that holds the water sample spots to be tested. This sample carrier can be a rigid substrate covered with a flexible membrane (such as a zinc membrane), and the surface of the flexible membrane is treated with water sample and then dried to form solid water sample spots. This water sample plate can be the direct target of laser irradiation; it can be flattened and fixed before laser irradiation to ensure morphological stability, and this application does not impose any restrictions on this.

[0026] Laser irradiation refers to the use of a specific wavelength laser (such as the ultraviolet or near-infrared band) with good monochromaticity and high intensity to irradiate water sample spots, thereby exciting the pollutant molecules in the spots to produce characteristic light responses (such as absorption, scattering, fluorescence, etc.), providing a signal source for subsequent detection.

[0027] The first detection optical signal refers to the raw optical signal (such as transmitted light or scattered light) obtained by optical signal acquisition when a water sample spot on the water sample plate is irradiated with a laser. This first detection optical signal may include the raw optical signal containing the characteristic information of the pollutants carried by the water sample spot; this application does not limit this. This first detection optical signal can be considered as the basic data source for subsequent signal processing.

[0028] Optionally, the first detection optical signal can be the raw optical signal obtained by the detection unit (whose core component is a spectrometer, i.e., the core functional component of the photoelectric detection module of this application) when the laser emitted by the laser irradiation unit 11 precisely irradiates the water sample spot on the water sample preparation area 121 of the water sample preparation unit 12 via its end 111, such as plasma emitted light or scattered light. The acquisition quality of this first detection optical signal can be directly affected by the irradiation accuracy of the laser irradiation unit, the spot formation effect of the water sample preparation unit, and the signal capture capability of the detection unit, and this application does not impose any limitations on this.

[0029] Based on the characteristics of the water sample spots and in combination with the characteristics of the pollutants to be detected, a laser source of the corresponding wavelength can be selected (e.g., a 254nm ultraviolet laser can be used to detect organic matter) so that the laser is vertically irradiated onto the flattened water sample spots; then the photoelectric detection module is activated to focus the characteristic light signal generated by the spots onto the photoelectric sensor through the optical path focusing component, thereby capturing and outputting the first detection light signal.

[0030] In the process of acquiring the first detection light signal generated when a water sample is irradiated with a laser using a photoelectric detection module, the signal acquisition stage is susceptible to electromagnetic interference, leading to signal crosstalk. For example, the control board of a multi-parameter water quality analyzer can integrate multiple functional modules such as laser drive, spectral acquisition, and motion control. Among them, the high-voltage signal of the laser drive module and the digital switching signal of the motion control module may interfere with the weak first detection light signal generated by the spectral acquisition module through spatial radiation or line conduction, forming signal crosstalk, which in turn leads to a decrease in the signal-to-noise ratio and affects the accuracy of subsequent electrical signal conversion and processing.

[0031] To address this core issue, the multi-parameter water quality analyzer's integrated electromagnetic interference-resistant control board of this application employs a targeted hardware design: the control board uses a 6-layer FR-4 printed circuit board (PCB) and can be functionally divided into a laser drive area (high-voltage area), a spectrum acquisition area (analog area), and a motion control area (digital area). Spatial partitioning and isolation can prevent cross-interference between different types of signals from the source; differentiated shielding designs can be used between layers, such as using 2oz copper foil to fully wrap the high-voltage area to block the diffusion of strong electromagnetic radiation, and laying a grid ground in the analog area to provide a stable grounding reference for weak spectral signals, further weakening the interference conduction path; the core signal transmission line can use a differential routing method with an impedance of 100Ω to control crosstalk below -90dB, while onboard ferrite beads with an impedance of 600Ω at a frequency of 100MHz are used to accurately filter out high-frequency noise generated by the motion control module, thereby achieving a stable overall signal-to-noise ratio of greater than 80dB.

[0032] Optionally, the laser driving area can correspond to the laser irradiation unit in the laser liquid analysis system; the spectral acquisition area can correspond to the detection unit such as a spectrometer in the laser liquid analysis system; and the motion control area can correspond to the unwinding mechanism and rewinding structure of the water sample preparation unit in the laser liquid analysis system. This application does not impose any restrictions on these aspects.

[0033] In water quality testing scenarios, the characteristic light signals (such as fluorescence signals and plasma emission signals) generated by laser irradiation of water sample spots are extremely weak. Furthermore, environmental interference in practical applications is complex and variable (such as fluctuations in the power grid, electromagnetic radiation from surrounding equipment, and interference from the water sample matrix). Even with the aforementioned high-strength hardware anti-interference design, it is difficult to completely eliminate residual noise and system errors; interference can only be suppressed within a reasonable range that the algorithm can handle. Therefore, hardware design can provide high-quality data raw materials for subsequent signal processing, such as ensuring that the original light signal is not severely distorted during acquisition and transmission, laying a reliable foundation for algorithm processing. Subsequent precise algorithms, such as electrical signal optimization, spectral reconstruction, and dual-environment correction, refine these data raw materials by suppressing residual noise, correcting system biases, and compensating for environmental impacts, thereby achieving high-precision pollutant identification and quantification. These two aspects form a synergistic system of hardware anti-interference and precise algorithm optimization, neither of which can be dispensed with, jointly ensuring the ultra-high accuracy and stability of multi-parameter water quality analysis.

[0034] It is understandable that this hardware design can provide a reliable anti-interference environment for the photoelectric detection module to accurately capture the first detection light signal, and can ensure that the original light signal is not affected by electromagnetic interference during the acquisition and transmission process. This is conducive to laying a high-quality data foundation for subsequent electrical signal optimization, spectrum construction and pollutant information determination. This application does not limit this.

[0035] Optionally, a wideband noise monitoring circuit can be added to the control board to acquire the high-frequency noise spectrum of the spectral acquisition area in real time. The signal processing algorithm (such as the filter cutoff frequency and regularization parameter β) dynamically and adaptively adjusts itself based on the monitored noise dominant frequency and intensity. For example, when a sudden increase in motor interference noise at a specific frequency is detected, the digital filter weights of that frequency band are automatically strengthened. This establishes a closed-loop correlation between hardware feedback and algorithm parameters, forming a collaborative solution not found in existing technologies.

[0036] S20: The first detection optical signal is converted to obtain the first detection electrical signal.

[0037] The conversion process, based on the photoelectric effect, transforms the optical signals (physical quantities such as light intensity and luminous flux) captured by the photoelectric detection module into electrical signals (voltage and current) that can be recognized by electronic devices. The conversion process can be viewed as a fundamental transformation of the signal form.

[0038] The first detection electrical signal refers to the raw electrical signal directly converted from the first detection optical signal. The amplitude and frequency of this first detection electrical signal are related to the characteristics of the raw optical signal. This first detection electrical signal may contain impurities such as equipment noise and electromagnetic interference, and is an unoptimized and unpurified electrical signal.

[0039] The above conversion process can be achieved through the photoelectric conversion element (such as a photodiode) built into the photoelectric detection module. When the first detection light signal shines on the photoelectric conversion element, the light energy can excite the movement of charge carriers to form a weak current signal that is proportional to the intensity of the light signal. The weak current signal is further converted into a stable voltage signal by the pre-conversion circuit in the module, and the first detection electrical signal can be obtained (for example, the stronger the light intensity, the greater the voltage amplitude after conversion).

[0040] S30: Optimize the first detection electrical signal to obtain the second detection electrical signal.

[0041] The optimization process addresses issues such as high-frequency noise, waveform distortion, and weak signal in the first detection electrical signal by using a combination of operations such as filtering, amplification, and shaping to improve the purity, regularity, and recognizability of the electrical signal.

[0042] The second detection electrical signal refers to a high-quality electrical signal that has undergone optimized processing, resulting in suppressed noise, a regular waveform, and an appropriate amplitude. This second detection electrical signal can accurately reflect the core characteristics of the original optical signal and provide reliable data support for spectral construction.

[0043] The process can involve initial filtering, amplification, shaping, and secondary filtering. For example, a low-pass filter can be used to filter out high-frequency electromagnetic noise (such as noise generated by the motion control module) in the first detection signal; an operational amplifier can be used to amplify the weak filtered signal to the volt level (such as amplifying a millivolt signal by 1000 times) to facilitate subsequent processing; a shaping circuit can be used to correct the waveform distortion of the signal, making the signal edges steep and the amplitude stable; and a band-pass filter can be used for secondary filtering to remove residual noise introduced during the amplification process, thereby obtaining the aforementioned second detection signal. This application does not impose any limitations on this process.

[0044] S40: Construct a spectrum based on the second detection electrical signal to obtain the target spectral information.

[0045] Among them, spectrum construction is the process of combining the wavelength-intensity correspondence between the second detection electrical signal and the optical signal, such as through matrix construction, function expansion and reconstruction, to form a spectral curve that reflects the optical characteristics of the water sample spot.

[0046] Target spectral information refers to the spectral data obtained after spectral construction and optimization, such as absorption spectra and fluorescence spectra. This target spectral information may contain characteristic spectral peaks of pollutant molecules (such as position, intensity, and full width at half maximum), and can be regarded as the core basis for identifying pollutants.

[0047] The background light transmittance curve during laser irradiation can be obtained, and a target spectral matrix can be constructed using this curve to eliminate background light interference. Based on the amplitude change of the second detection electrical signal, a spectral function is constructed corresponding to the light signal intensity at different wavelengths. This spectral function is then linearly expanded and decomposed into a Gaussian function and weighting coefficients. Through reconstruction using the weighting coefficients and the Gaussian function, a complete target spectral curve is formed and obtained. For example, a certain heavy metal ion may have a characteristic absorption peak at a specific wavelength, resulting in a significant amplitude decrease in the spectral curve at that wavelength; this application does not impose any limitations on this.

[0048] S50: Determine the pollutant information of the water sample to be treated based on the target spectral information.

[0049] The pollutant information refers to the core data of pollutants in the water sample to be treated. Optionally, this pollutant information may include, but is not limited to, type (such as organic matter, heavy metal ions, bacteria, etc.), concentration, and percentage of content. It can be understood that this pollutant information can be considered as the test results of water quality testing.

[0050] The system can acquire aquatic environmental parameters (such as water temperature, pH value, and dissolved oxygen content) and test environment interference information (such as external electromagnetic interference and light interference) of the water sample to be treated, so as to determine the corresponding spectral correction parameters respectively; through two-stage correction (i.e., aquatic environmental parameter correction and test environment interference correction), the environmental error in the target spectral information is corrected; the corrected spectral information is compared with the standard spectral library of known pollutants (such as matching characteristic peak positions and intensities) to identify the types of pollutants; based on the quantitative relationship between characteristic peak intensity and pollutant concentration (such as Lambert-Beer law), the specific concentration of pollutants is calculated, thereby obtaining complete pollutant information. This application does not impose any limitations on this.

[0051] The aforementioned signal processing flow achieves high precision and stability in water quality detection through a progressive design involving precise optical signal acquisition, photoelectric conversion, signal optimization and purification, spectral construction, and pollutant identification. It utilizes a flat, fixed device to ensure the regularity of water sample spot morphology and, in conjunction with a photoelectric detection module, efficiently captures characteristic optical signals. After photoelectric conversion transforms the optical signal into a processable electrical signal, multi-stage optimization processing removes noise and distortion, improving signal purity. Subsequently, background correction and environmental parameter correction are combined to construct the target spectrum, ensuring that the spectral information accurately reflects the water sample characteristics. Accurate identification of pollutant types and concentrations is achieved through standard spectral comparison. The entire process balances signal integrity, anti-interference capabilities, and scene adaptability, effectively solving detection errors caused by signal crosstalk, morphological deviations, and environmental interference in water quality detection, providing stable and reliable technical support for multi-parameter water quality analysis.

[0052] In this example, a photoelectric detection module is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with a laser. The first detection light signal can be further converted and processed to obtain a first detection electrical signal. The first detection electrical signal is then optimized to obtain a second detection electrical signal. Spectral construction can be performed based on the second detection electrical signal to obtain target spectral information. Furthermore, the pollutant information of the water sample to be treated can be determined based on the target spectral information. This improves the accuracy of the signal processing process, facilitates a more precise pollutant information identification process, and provides high-precision and high-reliability technical support for multi-parameter water quality analysis.

[0053] In one possible implementation, during optimization, the first detection signal after photoelectric conversion can be initially filtered to remove high-frequency interference, resulting in a first intermediate detection signal; the signal amplitude can be increased through amplification to form a second intermediate detection signal; waveform distortion can be corrected and the baseline stabilized through shaping to obtain a third intermediate detection signal; and residual noise can be removed through secondary filtering to further obtain a high-quality second detection signal, which can lay the foundation for subsequent spectral construction and pollutant detection. A possible method for optimizing the first detection signal to obtain the second detection signal includes: A1. Perform initial filtering on the first detection electrical signal to obtain the first intermediate detection electrical signal; A2. Amplify the first intermediate detection electrical signal to obtain the second intermediate detection electrical signal; A3. The second intermediate detection electrical signal is shaped to obtain the third intermediate detection electrical signal; A4. Perform secondary filtering on the third intermediate detection electrical signal to obtain the second detection electrical signal.

[0054] The initial filtering process targets high-frequency interference in the first detected electrical signal and employs a specific filtering method for preliminary noise reduction. This initial filtering can quickly eliminate most noise that differs significantly from the signal's frequency range, thus reducing interference burden for subsequent amplification processing.

[0055] The first intermediate detection signal refers to the electrical signal obtained after the initial filtering process, in which high-frequency noise is significantly suppressed and core characteristic signals are preserved. Compared with the first detection signal, the purity of the first intermediate detection signal is improved, which can provide a more stable base signal for subsequent amplification processing.

[0056] The initial filtering described above can be achieved using a low-pass filter (such as a Butterworth low-pass filter). For example, the filter cutoff frequency (e.g., 1kHz) can be set based on the frequency range of the characteristic electrical signals in water quality detection (typically low to mid-frequency). This allows only electrical signals within the target frequency range to pass through, filtering out high-frequency noise (such as electromagnetic interference above 100MHz). For instance, if the first detection electrical signal contains high-frequency pulse noise generated by the motion control module, after low-pass filtering, this high-frequency pulse noise is filtered out, leaving only the core electrical signal related to the light response of the water sample spot, thus obtaining the aforementioned first intermediate detection electrical signal.

[0057] Optionally, the motion control module can be a core component of the multi-parameter water quality analyzer, responsible for driving the mechanical movements of the equipment, such as moving the platform carrying the water sample plate, controlling the position calibration of the laser emitting device, and driving the pressure frame to flatten and fix the water sample spots. This motion control module can be integrated with the laser drive module and the spectral acquisition module on the equipment's control board. Amplification is the process of increasing the amplitude of a weak first intermediate detection electrical signal to a range suitable for subsequent processing using amplification devices, while preserving as much of the original signal characteristics (such as waveform and frequency) as possible without distortion. Amplification can solve the problem of the original signal amplitude being too small to be recognized and processed by subsequent circuits.

[0058] The second intermediate detection signal refers to an amplified signal with an amplitude reaching the volt level (e.g., 1-5V) and more prominent characteristics. It can be understood that this second intermediate detection signal retains the core information related to the pollutants while possessing sufficient strength to support subsequent shaping and secondary filtering processes.

[0059] An operational amplifier can be used to build the amplification circuit. The amplification factor (e.g., 1000-10000 times) can be set according to the amplitude of the first intermediate detection signal (e.g., 0.1-1mV) to ensure that the amplified signal amplitude is within the input range of the subsequent processing module. For example, if the amplitude of the first intermediate detection signal is 0.5mV, after passing through a 1000x amplification circuit, the amplitude is increased to 0.5V, thus forming and obtaining the aforementioned second intermediate detection signal. The characteristics of this second intermediate detection signal are more easily identified, and the problem of excessive noise due to a small amplitude can be avoided.

[0060] Shaping is a process of correcting waveform distortions (such as blurred edges, peak fluctuations, and baseline drift) in the second intermediate detection signal through circuit design, so as to make the signal waveform regular, with steep edges and stable amplitude. This shaping process can ensure the consistency of signal parameters (such as peak value and pulse width).

[0061] The third intermediate detection signal refers to the electrical signal obtained after shaping, which has a regular waveform, stable baseline, and clear characteristic parameters. It can be understood that this third intermediate detection signal eliminates waveform distortion that may occur during amplification, providing a high-quality signal for secondary filtering and subsequent spectral construction.

[0062] The above-mentioned shaping process can be achieved by combining a voltage comparator and a shaping circuit. A reasonable reference voltage threshold can be set to correct the blurred edges in the second intermediate detection signal into a steep rectangular wave or pulse wave. At the same time, the baseline drift of the signal can be further calibrated so that the signal amplitude fluctuation within each cycle is controlled within an allowable range. For example, if the second intermediate detection signal has a baseline shift and fluctuating peak values ​​due to temperature drift in the amplifier circuit, after shaping, the baseline returns to stability, the peak values ​​are uniform within the set range, and the waveform edges are clear, thus forming the third intermediate detection signal.

[0063] The secondary filtering process is a process of refining small noises that may remain after the shaping process (such as inherent noise introduced by the amplifier circuit and noise generated during the shaping process) using a high-precision filtering method.

[0064] The second detection electrical signal refers to the optimal electrical signal obtained after full-process optimization, where noise is suppressed to the maximum extent, the waveform is regularized, and the features are accurate. This second detection electrical signal can truly reflect the light response characteristics of the water sample spot and can be regarded as the core data foundation for subsequent spectral construction and pollutant identification.

[0065] A bandpass filter can be used for the above-mentioned secondary filtering. For example, the passband width of the filter can be set according to the precise frequency range of the target signal (e.g., 0.5-2kHz), allowing only the core signal to pass through, further eliminating residual noise not completely eliminated by the initial filtering and clutter generated during the shaping process. For example, a small amount of low-frequency noise introduced by the amplifier circuit may still remain in the third intermediate detection signal. After bandpass filtering, the residual noise is completely filtered out, and finally a second detection signal with stable amplitude, regular waveform, and no obvious interference can be obtained. This application does not impose any restrictions on this.

[0066] In this example, a progressive design involving initial filtering, amplification, shaping, and secondary filtering achieves precise purification from the raw electrical signal to a high-quality final signal. Initial filtering quickly removes high-frequency interference, clearing obstacles for amplification. Amplification increases the signal amplitude, addressing the difficulty in identifying weak signals. Shaping corrects waveform distortion, ensuring consistent signal characteristics. Secondary filtering precisely removes residual noise, achieving final signal purification. This entire process avoids the problem of noise being amplified simultaneously during amplification and ensures signal integrity and accuracy through multi-stage optimization. It effectively improves the accuracy of subsequent spectral construction and the reliability of pollutant detection, providing stable signal support for high-precision detection in multi-parameter water quality analyzers.

[0067] In one possible implementation, during spectral construction, the background light transmittance curve of the water sample under laser irradiation can be obtained. A target spectral matrix can then be constructed based on this background light transmittance curve. Further, combined with an optimized second detection electrical signal, matrix operations, function expansion, and reconstruction are performed to determine the target spectral information reflecting the characteristics of the water sample spots. A possible method for constructing the target spectral information based on the second detection electrical signal includes: B1. Obtain the background light transmittance curve when the water sample to be treated is irradiated with laser; B2. Construct the spectral matrix based on the background light transmittance curve to obtain the target spectral matrix; B3. The target spectral information is determined using the target spectral matrix and the second detection electrical signal.

[0068] Among them, the background light transmittance curve refers to the curve obtained when there is no pollutant influence and only the laser penetrates the blank water sample plate (without water sample spots) and the environmental medium, which is used to describe the transmission ratio of different wavelength lasers as a function of wavelength.

[0069] Before irradiating the water sample containing water spots with a laser, a blank water sample (with the same material and size as the sample) without water spots can be placed in the detection optical path. The laser source is then activated, emitting laser light wavelength by wavelength within a preset range (e.g., 200-800nm). A photoelectric detection module collects the light signals penetrating the blank water sample at each wavelength, converts them into electrical signals, and calculates the transmittance (the ratio of transmitted light intensity to incident light intensity). A curve is plotted with wavelength on the horizontal axis and transmittance on the vertical axis to obtain the aforementioned background light transmittance curve. For example, when testing an industrial wastewater sample, a rigid substrate with a blank zinc film can be used as the blank water sample to collect its corresponding background light transmittance curve. This background light transmittance curve reflects the transmission characteristics of the zinc film itself to lasers of different wavelengths.

[0070] Constructing a spectral matrix involves discretizing the continuous data of the background light transmittance curve and arranging it in wavelength order to form a two-dimensional matrix. The matrix elements in the constructed spectral matrix can be transmittance values ​​at corresponding wavelengths, facilitating subsequent numerical calculations with electrical signals.

[0071] The target spectral matrix refers to the discretized matrix obtained after constructing the spectral matrix based on the background light transmittance curve. This target spectral matrix can contain background transmittance information at each detection wavelength and can be regarded as the core data carrier for correcting background interference and accurately extracting the target signal.

[0072] The relevant wavelength range from the background light transmittance curve can be selected, and transmittance data can be extracted at fixed wavelength intervals (e.g., 1 nm). A two-dimensional matrix (rows equal to wavelength points, columns equal to 1) is constructed using the wavelength index as the matrix row index and the transmittance value as the matrix element. For multi-channel detection, this can be expanded into a multi-row, multi-column matrix to ensure a one-to-one correspondence between wavelength data and transmittance for each detection channel. For example, 601 transmittance data points can be extracted at 1 nm intervals within the 200-800 nm wavelength range to construct a 601×1 two-dimensional matrix, which can serve as the target spectral matrix.

[0073] As mentioned above, the second detection electrical signal refers to the high-quality electrical signal obtained after initial filtering, amplification, shaping, and secondary filtering optimization. It can be understood that this second detection electrical signal can be a noise-suppressed, waveform-regularized signal, which can more accurately reflect the light response characteristics of the water sample spot. After correcting for background interference through the target spectral matrix, the resulting spectral data (such as absorption spectra and fluorescence spectra) related to pollutants in the water sample spot constitutes the target spectral information.

[0074] The second detection electrical signal can be discretized into an electrical signal matrix according to the corresponding wavelength, and its dimension can be consistent with the target spectral matrix. Through matrix operations (such as multiplying the electrical signal matrix with the inverse of the target spectral matrix), the influence of background light transmittance is eliminated, and the original spectral data reflecting the true light response of the water sample spot is obtained. The original spectral data is further linearly expanded, such as by using the Gaussian function and weighting coefficients for fitting and reconstruction, to fill data gaps and smooth noise, thereby forming and obtaining the aforementioned target spectral information. For example, after discretizing the second detection electrical signal, a 601×1 electrical signal matrix can be obtained. After further matrix operations with the aforementioned 601×1 target spectrum, an absorption spectrum containing the characteristic absorption peaks of pollutants can be reconstructed, which is the aforementioned target spectral information.

[0075] In this example, by first acquiring the background light transmittance curve, then constructing the target spectral matrix, and finally combining the optimized electrical signal to reconstruct the spectrum, the influence of background interference such as substrate material and ambient light on the detection is effectively eliminated. The discretization of the target spectral matrix realizes the numerical and accurate background correction, and the combination of Gaussian function reconstruction further improves the integrity and smoothness of the spectral data. The final target spectral information can truly reflect the characteristics of pollutants in the water sample spot, providing high-quality data support for subsequent pollutant identification and concentration calculation, and significantly improving the detection accuracy and reliability of the multi-parameter water quality analyzer.

[0076] In one possible implementation, when determining the target spectral information, a first spectral function can be constructed by combining the correspondence between the target spectral matrix and the second detection electrical signal. This function can then be linearly expanded and decomposed into weighting coefficients and a Gaussian function. The target spectral information that accurately reflects the characteristics of the pollutants can then be reconstructed through a combination of the weighting coefficients and the Gaussian function. A possible method for determining the target spectral information using the target spectral matrix and the second detection electrical signal includes: C1. Construct a first spectral function based on the target spectral matrix and the second detection electrical signal; C2. Perform a linear expansion on the first spectral function to obtain the weighting coefficients and the Gaussian function; C3. Reconstruct the target spectral information based on the weighting coefficients and the Gaussian function.

[0077] The spectral function construction involves establishing a mathematical mapping relationship between the transmittance data in the target spectral matrix and the amplitude of the second detection electrical signal, in order to form a continuous function describing the wavelength-signal intensity correlation.

[0078] The first spectral function refers to a continuous function derived through mathematical modeling that correlates transmittance with electrical signal intensity. It can be understood that this first spectral function integrates background correction information and the optical response characteristics of the water sample spot, and can be considered the basis for subsequent spectral unfolding and reconstruction.

[0079] The transmittance values ​​corresponding to each wavelength in the target spectral matrix can be extracted and paired with the amplitudes of the corresponding wavelengths in the second detection electrical signal to obtain multiple sets of (wavelength-transmittance-electrical signal intensity) data. A continuous mathematical model relating these three factors can be established using the least squares method, with wavelength as the independent variable, electrical signal intensity as the dependent variable, and transmittance as the correction coefficient. This allows for the fitting of a function reflecting the correlation between the three factors, i.e., the aforementioned first spectral function. For example, within the 200-800nm ​​wavelength range, 601 sets of data can be obtained through pairing, and further fitting using the least squares method yields a univariate quadratic continuous function, which is the aforementioned first spectral function.

[0080] Linear expansion decomposes the complex first spectral function into a linear combination of multiple simple basis functions, simplifying the process of function computation and reconstruction. This application uses the decomposition into multiple Gaussian functions as an example for illustration, which does not constitute a limitation on this application.

[0081] The Gaussian function can be a smooth, symmetrical, continuous mathematical curve, characterized by a high center and low edges, resembling a gentle mountain peak. Optionally, it can have a peak at a specific wavelength (e.g., the center wavelength) and gradually decrease in peak value towards both sides, without sharp angles or abrupt changes, thus closely mimicking common characteristic peak shapes in spectral curves. This application selects the Gaussian function as the basis function because of its simple form, continuous differentiability, and ability to well fit smooth peak shapes commonly found in spectra. Its center wavelength is uniformly distributed within the known characteristic spectral range of the analyte, and the standard deviation is set according to the spectrometer resolution.

[0082] Weighting coefficients refer to the coefficients of each Gaussian Gaussian function after linear expansion. The weighting coefficient of each Gaussian Gaussian function reflects its contribution to the overall spectrum. It can be understood that the larger the absolute value of the weighting coefficient, the more significant the influence of the corresponding Gaussian Gaussian function on the spectral characteristics; conversely, the smaller the absolute value of the weighting coefficient, the weaker the influence of the corresponding Gaussian Gaussian function on the spectral characteristics. For example, when detecting an organic compound in water, its characteristic spectral peak is a broad peak. In this case, 3-5 Gaussian Gaussian functions with similar center wavelengths but different widths can be combined. By adjusting their respective weighting coefficients, the shape of this broad peak can be perfectly replicated, making the spectral characteristics clearer and easier to identify.

[0083] The number of Gaussian functions (e.g., 50), the center wavelength (uniformly distributed within the detection wavelength range), and the standard deviation can be set according to the required detection wavelength range and spectral resolution. Optionally, the number of Gaussian functions, the center wavelength, and the standard deviation can also be determined using historical spectral data through optimization algorithms. An orthogonal decomposition method can be used to decompose the first spectral function into a linear combination of these Gaussian functions. By solving a system of linear equations, the coefficients corresponding to each Gaussian function, i.e., the weighting coefficients, can be determined. For example, the first spectral function in the 200-800nm ​​range can be decomposed into a linear combination of 50 Gaussian functions with a center wavelength interval of 12nm. Further calculation of 50 corresponding weighting coefficients can then complete the aforementioned linear expansion process.

[0084] Reconstruction involves weighting the coefficients obtained from linear expansion with the corresponding Gausky functions to restore and optimize the spectral curve. After reconstruction, smooth and clearly defined spectral data (such as absorption and fluorescence spectra) are obtained, which constitutes the target spectral information. This target spectral information may contain key information such as the position, intensity, and full width at half maximum (FWHM) of the pollutant's characteristic spectral peaks.

[0085] Each Gaussian function can be multiplied by its corresponding weighting coefficient to obtain multiple weighted basis functions. Summing all weighted basis functions yields a reconstructed continuous spectral curve. Smoothing this spectral curve removes minor fluctuations and highlights characteristic peaks, thus forming and obtaining the target spectral information. For example, summing 50 weighted Gaussian functions yields a preliminary reconstructed spectrum. After smoothing, this smoothed spectrum clearly shows the characteristic absorption peak of a heavy metal ion at 280 nm; that is, this smoothed spectrum is the target spectral information.

[0086] Optionally, after completing the linear expansion of the weighting coefficients and the Gaussian function, to achieve accurate reconstruction of the target spectral information, the scenario characteristics of the multi-parameter water quality analysis in this application can be fully utilized to solve the problems of insufficient signal-detection scenario adaptability and noise-induced reconstruction distortion in traditional reconstruction processes. For example, this application uses the first spectral function as a basis, deeply correlates it with the actual detection conditions of the water sample to be treated, combines the characteristics of the second detection electrical signal with the morphological characteristics of the water sample spots and the type characteristics of the water quality to be detected, and introduces regularization processing to suppress the interference of residual noise, further ensuring that the reconstructed spectral information can truly and accurately reflect the characteristic attributes of pollutants in the water sample to be treated.

[0087] Optionally, this application may employ the following reconstruction optimization equation to solve for the optimal weighting coefficients, thereby obtaining the target spectral information: Where α refers to the weight coefficient vector of the Gaussian functions; R is a set of real numbers, meaning that all elements representing the weight coefficients (i.e., the weight coefficients corresponding to each Gaussian function) are real numbers; n is the number of Gaussian functions; A refers to the coupling matrix; I corr The second detected electrical signal vector; ⊙ refers to the Hadamard product, which is the element-wise multiplication of quantities; ζspot refers to the speckle morphology correction coefficient vector; · refers to quantity multiplication; K water Water quality type suitability factor; represents the square of the L2 norm (i.e., the square of the Euclidean norm), used to calculate the sum of squares of the elements of a vector, and the result is a non-negative scalar; β is the regularization parameter. Optionally, β can be determined by cross-validation, and its value can range from 0.01 to 0.1.

[0088] The coupling matrix A described above can be obtained by multiplying the target spectral matrix T(λ) and the Gaussian function matrix φ(λ), i.e., A = T(λ) × φ(λ), where × denotes matrix multiplication. This coupling matrix integrates background correction information of background light transmittance with the spectral characteristics of the Gaussian function, and can provide data support for the correlation between signal and spectrum.

[0089] This refers to the signal-morphology-water quality coupling term, which can be seen as deeply associating the reconstruction process with the water quality detection scenario involved in this application, to ensure that the target spectral information obtained after reconstruction can meet the actual needs of water quality analysis. Specifically, (Aα) I corr This can be a basic signal fitting term, used to ensure that the combination of weighting coefficients and the Gaussian function best matches the characteristics of the second detected electrical signal. spot This can be a vector of spot morphology correction coefficients, whose dimension can be the same as the second detection electrical signal vector I. corr Consistency means that each element corresponds to a spot morphology correction value at a detection wavelength. This spot morphology correction value is determined based on the flatness detection results of the flexible membrane using a pressure frame water sample plate flattening and fixing device with pressing ribs. It can be understood that when the flatness deviation of the flexible membrane increases, the vector element corresponding to that wavelength is adjusted synchronously to accurately offset the influence of spot morphology shift on laser transmission and optical signal acquisition at that wavelength, thereby ensuring that the reconstructed spectrum is not affected by the difference in spot flatness and thickness across the entire detection band.

[0090] Optionally, the spot morphology correction coefficient vector can be quantitatively calculated based on the detection data of the laser displacement sensor mounted on the pressure frame water sample flattening and fixing device with pressing ribs, specifically using the following formula: Where, ζ spot This is a vector of spot morphology correction coefficients, whose dimension is the same as the total number of detection wavelengths (m is the total number of detection wavelengths); Δ h1 The average height deviation (unit: μm) of the spot area corresponding to the first detection wavelength, measured by the laser displacement sensor, can represent the height difference between the actual morphology and the ideal flat state of the flexible film in the spot area corresponding to the first detection wavelength. The detection resolution can be no less than 0.01 μm; Δ h2 The average height deviation (in μm) of the spot area corresponding to the second detection wavelength, as measured by the laser displacement sensor; Δhm denoted by , is the average height deviation (in μm) of the spot area corresponding to the m-th detection wavelength, measured by the laser displacement sensor; k is an empirical attenuation coefficient, which can be calibrated according to the flexible film material (such as zinc film) and the characteristics of the laser wavelength, and its value range can be 0.08~0.12 μm. - ¹, In this application, k=0.1μm is preferred. - ¹, to ensure that the correction coefficient is linearly negatively correlated with the height deviation, conforming to the physical law of light intensity attenuation; [ ] T This represents the vector transpose, used to construct a column vector with the same dimensions as the second detection electrical signal vector. Using this formula, a specific correction value can be calculated for the spot region corresponding to each detection wavelength, thereby constructing a complete spot morphology correction coefficient vector.

[0091] K water It can be a water quality type adaptation factor, which is a scalar parameter. The appropriate value can be selected according to the water quality type (such as drinking water, industrial wastewater, surface water, etc.) of the water sample to be treated. For example, for drinking water with simple matrix composition, the value of this factor is close to 1.0. For industrial wastewater and surface water containing complex organic matter, suspended solids and other interfering components, this factor is appropriately adjusted according to the degree of interference to offset the influence of different water quality matrices on spectral characteristics, thereby adapting to the core requirements of multi-parameter water quality analysis in this application.

[0092] Optionally, you can refer to the following recommended range of water quality type adaptation factors for typical water quality. In practical applications, you can further fine-tune the values ​​based on specific parameters such as turbidity and organic matter content of the water: Furthermore, The L2 regularization term refers to the regularization parameter β, which can be determined by the generalized cross-validation method. This term is used to suppress the amplification of residual small noise in the second detection electrical signal during the reconstruction process, thereby reducing multicollinearity among the weighting coefficients and avoiding distortion of the reconstructed spectrum.

[0093] Furthermore, This refers to the constraint condition for the weighting coefficients, where 0 ≤ α satisfies the physical property of non-negativity of spectral intensity, ensuring that the reconstructed spectral intensity has practical physical meaning; α max This can be an upper bound vector for the weighting coefficient vector, where each element can be set based on spectral physical constraints (such as absorbance being non-negative and less than the theoretical maximum value). This constraint prevents the weighting coefficients from being too large, causing the reconstructed spectrum to exceed the physically reasonable range, and also avoids the appearance of spurious high-intensity spectral signals, thus ensuring the scientific validity and practicality of the reconstruction results.

[0094] Optionally, the optimal weight coefficient vector α can be obtained by solving the above reconstruction optimization equation. Then, the target spectral information can be further calculated using the following formula: Where F(λ) refers to the target spectral information; n refers to the number of Gaussian functions; and j refers to the index of the Gaussian function. φ refers to the optimal weighting coefficient corresponding to the j-th Gaussian function centered at wavelength λ; j (λ) is the j-th Gaussian function; ζ spot,λ This refers to the element in the spot morphology correction coefficient vector corresponding to the wavelength λ. It can be understood that this formula, through the weighted summation of the optimal weighting coefficients and the corresponding Gaussian functions, and the superposition of spot morphology corrections, can obtain the target spectral information that truly reflects the characteristics of pollutants in the water sample to be treated.

[0095] Optionally, to further improve the accuracy of spot morphology correction, a model of laser scattering and optical path distribution on the uneven spot surface can be established, such as a spot morphology-spectrum compensation model. The three-dimensional morphology data of the flexible membrane, obtained using a pressure-frame water sample flattening and fixing device with pressure ribs, is input into this spot morphology-spectrum compensation model. The model calculates the predicted vectors of light intensity distribution distortion and spectral shift caused by the irregular spot morphology, such as the spot morphology correction compensation term. This spot morphology correction compensation term can replace the spot morphology correction coefficient (scalar) in the aforementioned spectral reconstruction equation, directly integrating it into the spectral reconstruction equation as a compensation term to achieve quantitative compensation for morphological interference. This approach upgrades spot morphology correction in the reconstruction process from "empirical scalar correction" to "precise vector compensation driven by a physical model," significantly improving the scientific rigor and accuracy of morphology correction.

[0096] In this example, a progressive design involving function construction, linear expansion, and reconstruction optimization achieves efficient transformation from discrete data to accurate spectra. The spectral function construction integrates background correction and signal features, laying the foundation for subsequent processing. Linear expansion decomposes complex functions into combinations of simple basis functions, reducing computational complexity. Reconstruction based on the Gaussian function preserves the characteristic spectral information of pollutants while improving spectral regularity and readability through smoothing. The entire process effectively solves the problems of discrete raw data and blurred spectral features, significantly improving the accuracy and reliability of spectral information and providing core technical support for multi-parameter water quality analyzers to accurately identify pollutant types and calculate pollutant concentrations.

[0097] In one possible implementation, when determining the pollutant information of the water sample to be treated, regional information of the sample to be tested in the water sample to be treated can be obtained and first environmental parameters related to the aquatic environment can be extracted. Based on these parameters, first spectral correction parameters can be determined to preliminarily correct the target spectral information. Then, test environment interference information during laser irradiation can be obtained, and second spectral correction parameters can be determined to further correct the preliminarily corrected spectrum. Thus, pollutant information can be determined based on the second-corrected spectral information. A possible method for determining the pollutant information of the water sample to be treated based on the target spectral information includes: D1. Obtain the area information of the sample to be tested in the water sample plate to be processed; D2. Extract environmental information based on the area information to obtain the first environmental parameters; D3. Determine the first spectral correction parameter based on the first environmental parameter; D4. Perform preliminary correction on the target spectral information using the first spectral correction parameters to obtain the first corrected spectral information; D5. Obtain environmental interference information when the water sample to be treated is irradiated with laser; D6. Determine the second spectral correction parameters based on the environmental interference information; D7. The first corrected spectral information is corrected using the second spectral correction parameters to obtain the second corrected spectral information; D8. Use the second corrected spectral information to determine the pollutant information of the water sample to be treated.

[0098] The sample to be tested refers to the water sample spot on the water sample plate to be treated, which may include, but is not limited to, solutes in the water sample, as well as any potential contaminants. This sample to be tested can be considered the direct object of the test. The area information refers to the spatial location and extent of the sample to be tested on the water sample plate to be treated, such as coordinate range and area size. This area information can be used to accurately locate the testing area.

[0099] The water sample to be processed can be photographed using an industrial camera, and the edge contour of the water sample spot can be extracted by combining image recognition algorithms to determine its coordinate range (e.g., with the lower left corner of the substrate as the origin) and area size on the substrate; the area of ​​the sample to be tested can also be located by pre-setting infrared positioning points on the edge of the substrate to ensure accurate area information.

[0100] Environmental information refers to the natural environmental data of the original water body corresponding to the sample to be tested. This environmental information can be correlated with regional information and can reflect the original environmental characteristics of the water sample. The primary environmental parameter refers to the key aquatic environmental indicators extracted from the environmental information that affect spectral characteristics, such as water temperature, pH value, dissolved oxygen content, and turbidity. This primary environmental parameter can be used to specifically correct spectral biases caused by the aquatic environment. For high-precision water quality analysis, the aquatic environment (such as pH) can significantly affect the spectral morphology of certain pollutants. Therefore, taking correction measures based on environmental information can ensure that the target spectral information truly reflects the properties of the pollutants themselves, thereby achieving accurate calibration of the pollutant characteristic spectra.

[0101] Based on the regional information of the sample to be tested, environmental monitoring data of its original water intake area can be linked (e.g., retrieved from a water quality monitoring database), or real-time monitoring can be performed using sensors integrated into the testing equipment (if the sample was collected on-site and has not left its original environment). Key indicators such as water temperature, pH value, and dissolved oxygen content can then be extracted as the primary environmental parameters. For example, if the regional information of an industrial wastewater sample is linked to its water intake, the environmental data of that water intake can be retrieved, yielding a water temperature of 25℃, a pH value of 7.2, and a dissolved oxygen content of 5 mg / L, thus obtaining the primary environmental parameters.

[0102] The first spectral correction parameter refers to a quantitative parameter calculated based on the first environmental parameter, used to correct the influence of the aquatic environment on the spectrum, such as wavelength shift correction value and intensity attenuation coefficient. This first spectral correction parameter can ensure that the spectral information can more accurately reflect the characteristics of pollutants.

[0103] A pre-defined calibration model (which can be established by combining a large amount of experimental data and linking different aquatic environmental parameters with spectral deviations) can be used to input the first environmental parameter into the model, thereby calculating the corresponding spectral correction value. For example, based on the parameters of water temperature 25℃ and pH value 7.2, the wavelength shift correction value calculated by the calibration model is 0.1nm and the intensity attenuation coefficient is 1.02, which are the aforementioned first spectral correction parameters.

[0104] Preliminary calibration is the process of adjusting the target spectral information using first spectral calibration parameters to eliminate spectral deviations (such as wavelength shifts and intensity distortions) caused by the aquatic environment. The first-calibrated spectral information refers to the spectral data that, after preliminary calibration, eliminates interference from the aquatic environment and more closely approximates the true spectral characteristics of the pollutants.

[0105] The first spectral correction parameters can be applied to the target spectral information to correct the wavelength and intensity of the spectrum. For example, the wavelength position of the characteristic peak can be adjusted according to the wavelength shift correction value, and the amplitude of the spectral signal can be corrected according to the intensity attenuation coefficient. For instance, if the wavelength of a characteristic peak in the target spectrum is 280 nm and the intensity is 0.8, after applying the first spectral correction parameters (wavelength shift 0.1 nm, intensity attenuation coefficient 1.02), the wavelength of the target spectrum is adjusted to 280.1 nm and the intensity is corrected to 0.816, thus obtaining the first corrected spectral information.

[0106] Environmental interference information refers to data on interfering factors present in the testing environment during laser irradiation of the water sample to be treated, which may affect spectral detection, such as the intensity of external electromagnetic interference, ambient light intensity, and the temperature of the testing equipment. The testing environment refers to the on-site environment where laser irradiation and spectral detection are performed (e.g., a laboratory or outdoor testing site). It is understood that this testing environment can be different from the original aquatic environment of the sample to be tested, and this application does not impose any restrictions on this.

[0107] By using sensors integrated into the testing equipment (such as electromagnetic interference sensors, light sensors, and temperature sensors), real-time data on the test environment during laser irradiation can be collected to extract information on interference factors such as external electromagnetic interference intensity, ambient light intensity, and equipment operating temperature. For example, when conducting testing in a laboratory, data such as an electromagnetic interference intensity of 5V / m, an ambient light intensity of 100 lux, and an equipment temperature of 30℃ can be collected, which represents the aforementioned environmental interference information.

[0108] The second spectral correction parameter refers to a quantitative parameter calculated based on interference information from the test environment, used to correct the influence of test environment interference on the spectrum, such as noise suppression coefficient and light intensity compensation value. This spectral correction parameter can further improve spectral accuracy.

[0109] An interference correction model can be established based on pre-set data (e.g., by establishing a model that correlates the relationship between interference factors in different test environments and spectral deviations). Environmental interference information can be input into the model to calculate the corresponding correction values. For example, based on information such as an external electromagnetic interference intensity of 5V / m and an ambient light intensity of 100lux, the noise suppression coefficient can be calculated to be 0.98 and the light intensity compensation value to be 0.03, which are the aforementioned second spectral correction parameters.

[0110] The calibration process refers to the secondary adjustment of the first calibrated spectral information using a second spectral calibration parameter to eliminate spectral deviations caused by interference from the testing environment (such as noise superposition and light intensity interference). The second calibrated spectral information refers to the final spectral data that, after two calibrations, completely eliminates the dual interference from the aquatic environment and the testing environment, and more realistically and accurately reflects the characteristics of pollutants.

[0111] The second spectral correction parameters can be applied to the first corrected spectral information to further correct the noise and light intensity of the spectrum. For example, interference noise in the spectrum can be filtered out based on the noise suppression coefficient, and the signal amplitude can be adjusted based on the light intensity compensation value. For instance, if there is a small amount of noise caused by electromagnetic interference in the first corrected spectrum, after applying the second spectral correction parameters (noise suppression coefficient 0.98, light intensity compensation value 0.03), the noise in the first corrected spectrum is filtered out, and the spectral intensity is corrected to 0.846, thus obtaining second corrected spectral information with a regular waveform and clear characteristics.

[0112] Furthermore, the second calibrated spectral information can be compared with a standard spectral library of known pollutants. The pollutant type can be identified by the matching degree of parameters such as characteristic peak position, intensity, and full width at half maximum (FWHM). Based on the quantitative relationship between characteristic peak intensity and pollutant concentration (such as the Lambert-Beer law), the pollutant concentration can be calculated by combining the calibrated spectral intensity, thus forming complete pollutant information. For example, after comparing the second calibrated spectrum with the standard spectral library, the characteristic peak of the heavy metal cadmium can be matched, and its concentration can be calculated to be 0.05 mg / L, thus forming and obtaining the aforementioned pollutant information.

[0113] In this example, a two-stage calibration design, employing both aquatic environment calibration and testing environment calibration, achieves precise purification of spectral information. The first environmental parameter eliminates spectral deviations caused by the original aquatic environment of the water sample, while the second spectral calibration parameter removes environmental interference from the testing site. The resulting second-calibrated spectral information accurately reflects the characteristics of pollutants. This entire process considers both the differences in the original environment of the water sample and interference factors at the testing site, effectively solving the problem of spectral distortion caused by environmental interference. It significantly improves the accuracy of pollutant identification and the reliability of concentration calculation, providing crucial support for high-precision detection by multi-parameter water quality analyzers.

[0114] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory 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. The photoelectric detection module is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with laser. The first detection optical signal is converted to obtain the first detection electrical signal; The first detection electrical signal is optimized to obtain the second detection electrical signal; The target spectral information is obtained by constructing a spectrum based on the second detected electrical signal. The pollutant information of the water sample to be treated is determined based on the target spectral information.

[0115] In this example, a photoelectric detection module is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with a laser. The first detection light signal can be further converted and processed to obtain a first detection electrical signal. The first detection electrical signal is then optimized to obtain a second detection electrical signal. Spectral construction can be performed based on the second detection electrical signal to obtain target spectral information. Furthermore, the pollutant information of the water sample to be treated can be determined based on the target spectral information. This improves the accuracy of the signal processing process, facilitates a more precise pollutant information identification process, and provides high-precision and high-reliability technical support for multi-parameter water quality analysis.

[0116] 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.

[0117] 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. 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.

[0118] For those consistent with the above, please refer to Figure 4 , Figure 4 This application provides a schematic diagram of the signal processing device for a multi-parameter water quality analyzer. (See attached diagram.) Figure 4 As shown, the device includes: The acquisition unit 101 is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with laser. The first processing unit 102 is used to convert the first detection optical signal to obtain a first detection electrical signal. The second processing unit 103 is used to optimize the first detection electrical signal to obtain a second detection electrical signal. The third processing unit 104 is used to construct a spectrum based on the second detection electrical signal to obtain target spectral information; The determining unit 105 is used to determine the pollutant information of the water sample to be treated based on the target spectral information.

[0119] In one possible implementation, the second processing unit 103 is configured to optimize the first detection electrical signal to obtain a second detection electrical signal, specifically for: The first detection electrical signal is subjected to initial filtering to obtain the first intermediate detection electrical signal; The first intermediate detection electrical signal is amplified to obtain the second intermediate detection electrical signal; The second intermediate detection electrical signal is shaped to obtain the third intermediate detection electrical signal; The third intermediate detection signal is subjected to secondary filtering to obtain the second detection signal.

[0120] In one possible implementation, the third processing unit 104 is used to construct a spectrum based on the second detection electrical signal to obtain target spectral information, specifically for: Obtain the background light transmittance curve when the water sample to be treated is irradiated with laser; The target spectral matrix is ​​obtained by constructing a spectral matrix based on the background light transmittance curve. The target spectral information is determined using the target spectral matrix and the second detection electrical signal.

[0121] In one possible implementation, the third processing unit 104 is used to determine target spectral information using the target spectral matrix and the second detection electrical signal, specifically for: A first spectral function is obtained by constructing a spectral function based on the target spectral matrix and the second detection electrical signal; The first spectral function is linearly expanded to obtain the weighting coefficients and the Gaussian function; The target spectral information is obtained by reconstructing the data based on the weighting coefficients and the Gaussian function.

[0122] In one possible implementation, the determining unit 105 is configured to determine the pollutant information of the water sample to be treated based on the target spectral information, specifically for: Obtain the regional information of the sample to be tested in the water sample plate to be treated; Environmental information is extracted based on the area information to obtain the first environmental parameter; The first spectral correction parameter is determined based on the first environmental parameter; The target spectral information is initially corrected using the first spectral correction parameter to obtain the first corrected spectral information; Obtain environmental interference information when the water sample to be treated is irradiated with laser; The second spectral correction parameters are determined based on the environmental interference information. The first corrected spectral information is corrected using the second spectral correction parameter to obtain the second corrected spectral information; The pollutant information of the water sample to be treated is determined using the second corrected spectral information.

[0123] This application also provides a computer storage medium storing a computer program for electronic data exchange, which causes a computer to perform some or all of the steps of the signal processing method of any of the multi-parameter water quality analyzers described in the above method embodiments.

[0124] 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 the signal processing method of any of the multi-parameter water quality analyzers described in the above method embodiments.

[0125] For the foregoing method embodiments, in order to simplify the description, they 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, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 signal processing method for a multi-parameter water quality analyzer, characterized in that, The method includes: The photoelectric detection module is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with laser. The first detection optical signal is converted to obtain the first detection electrical signal; The first detection electrical signal is optimized to obtain the second detection electrical signal; The target spectral information is obtained by constructing a spectrum based on the second detected electrical signal. The pollutant information of the water sample to be treated is determined based on the target spectral information; The step of optimizing the first detection electrical signal to obtain the second detection electrical signal includes: The first detection electrical signal is subjected to initial filtering to obtain the first intermediate detection electrical signal; The first intermediate detection electrical signal is amplified to obtain the second intermediate detection electrical signal; The second intermediate detection electrical signal is shaped to obtain the third intermediate detection electrical signal; The third intermediate detection electrical signal is subjected to secondary filtering to obtain the second detection electrical signal; wherein, the primary filtering process uses a low-pass filter to filter out high-frequency noise, and the secondary filtering process uses a band-pass filter to remove residual noise that was not completely eliminated in the primary filtering process and noise generated during the shaping process; The step of constructing a spectrum based on the second detected electrical signal to obtain target spectral information includes: Obtain the background light transmittance curve when the water sample to be treated is irradiated with laser; The target spectral matrix is ​​obtained by constructing a spectral matrix based on the background light transmittance curve. The target spectral information is determined using the target spectral matrix and the second detection electrical signal; The step of determining the target spectral information using the target spectral matrix and the second detection electrical signal includes: A first spectral function is obtained by constructing a spectral function based on the target spectral matrix and the second detected electrical signal. The spectral function is constructed by establishing a mathematical mapping relationship between the transmittance data in the target spectral matrix and the amplitude of the second detected electrical signal, forming a continuous function describing the wavelength-signal intensity correlation. The first spectral function is linearly expanded to obtain the weighting coefficients and the Gaussian function. After completing the linear expansion of the weighting coefficients and the Gaussian function, based on the first spectral function and according to the actual detection conditions of the water sample to be processed, the characteristics of the second detection electrical signal are combined with the morphological characteristics of the water sample spot and the type characteristics of the water quality to be detected, and regularization is introduced to suppress the interference of residual noise. The target spectral information is obtained by reconstructing the spectrum based on the weighting coefficients and the Gaussian function; wherein, the reconstruction based on the weighting coefficients and the Gaussian function includes: multiplying each Gaussian function by its corresponding weighting coefficient to obtain multiple weighted basis functions; summing all the weighted basis functions to obtain the reconstructed continuous spectral curve; and smoothing the reconstructed continuous spectral curve. The step of determining the pollutant information of the water sample to be treated based on the target spectral information includes: Obtain the regional information of the sample to be tested in the water sample plate to be treated; Environmental information is extracted based on the area information to obtain the first environmental parameter; The first spectral correction parameter is determined based on the first environmental parameter; the first spectral correction parameter is a quantitative parameter calculated based on the first environmental parameter to correct the influence of the aquatic environment on the spectrum, including wavelength shift correction value and intensity attenuation coefficient; The target spectral information is initially corrected using the first spectral correction parameter to obtain the first corrected spectral information; The environmental interference information is obtained when the water sample to be treated is irradiated with laser; the environmental interference information is the data of interference factors that exist in the on-site test environment and affect the spectral detection when the water sample to be treated is irradiated with laser, including the intensity of external electromagnetic interference, the intensity of ambient light, and the temperature of the detection equipment; The second spectral correction parameter is determined based on the environmental interference information; the second spectral correction parameter is a quantitative parameter calculated based on the interference information of the test environment to correct the influence of the test environment interference on the spectrum, including noise suppression coefficient and light intensity compensation value; The first corrected spectral information is corrected using the second spectral correction parameter to obtain the second corrected spectral information; The pollutant information of the water sample to be treated is determined using the second corrected spectral information.

2. A signal processing device for a multi-parameter water quality analyzer, characterized in that, The device includes: The acquisition unit is used to acquire the first detection light signal generated when the water sample to be treated is irradiated with laser; The first processing unit is used to convert the first detection optical signal to obtain the first detection electrical signal; The second processing unit is used to optimize the first detection electrical signal to obtain the second detection electrical signal; The third processing unit is used to construct a spectrum based on the second detection electrical signal to obtain target spectral information; The determining unit is used to determine the pollutant information of the water sample to be treated based on the target spectral information; The second processing unit is used to optimize the first detection electrical signal to obtain a second detection electrical signal, specifically for: The first detection electrical signal is subjected to initial filtering to obtain the first intermediate detection electrical signal; The first intermediate detection electrical signal is amplified to obtain the second intermediate detection electrical signal; The second intermediate detection electrical signal is shaped to obtain the third intermediate detection electrical signal; The third intermediate detection electrical signal is subjected to secondary filtering to obtain the second detection electrical signal; wherein, the primary filtering process uses a low-pass filter to filter out high-frequency noise, and the secondary filtering process uses a band-pass filter to remove residual noise that was not completely eliminated in the primary filtering process and noise generated during the shaping process; The third processing unit is used to construct a spectrum based on the second detection electrical signal to obtain target spectral information, specifically for: Obtain the background light transmittance curve when the water sample to be treated is irradiated with laser; The target spectral matrix is ​​obtained by constructing a spectral matrix based on the background light transmittance curve. The target spectral information is determined using the target spectral matrix and the second detection electrical signal; The third processing unit is used to determine the target spectral information using the target spectral matrix and the second detection electrical signal, specifically for: A first spectral function is obtained by constructing a spectral function based on the target spectral matrix and the second detected electrical signal. The spectral function is constructed by establishing a mathematical mapping relationship between the transmittance data in the target spectral matrix and the amplitude of the second detected electrical signal, forming a continuous function describing the wavelength-signal intensity correlation. The first spectral function is linearly expanded to obtain the weighting coefficients and the Gaussian function. After completing the linear expansion of the weighting coefficients and the Gaussian function, based on the first spectral function and according to the actual detection conditions of the water sample to be processed, the characteristics of the second detection electrical signal are combined with the morphological characteristics of the water sample spot and the type characteristics of the water quality to be detected, and regularization is introduced to suppress the interference of residual noise. The target spectral information is obtained by reconstructing the spectrum based on the weighting coefficients and the Gaussian function; wherein, the reconstruction based on the weighting coefficients and the Gaussian function includes: multiplying each Gaussian function by its corresponding weighting coefficient to obtain multiple weighted basis functions; summing all the weighted basis functions to obtain the reconstructed continuous spectral curve; and smoothing the reconstructed continuous spectral curve. The determining unit is used to determine the pollutant information of the water sample to be treated based on the target spectral information, specifically for: Obtain the regional information of the sample to be tested in the water sample plate to be treated; Environmental information is extracted based on the area information to obtain the first environmental parameter; The first spectral correction parameter is determined based on the first environmental parameter; the first spectral correction parameter is a quantitative parameter calculated based on the first environmental parameter to correct the influence of the aquatic environment on the spectrum, including wavelength shift correction value and intensity attenuation coefficient; The target spectral information is initially corrected using the first spectral correction parameter to obtain the first corrected spectral information; The environmental interference information is obtained when the water sample to be treated is irradiated with laser; the environmental interference information is the data of interference factors that exist in the on-site test environment and affect the spectral detection when the water sample to be treated is irradiated with laser, including the intensity of external electromagnetic interference, the intensity of ambient light, and the temperature of the detection equipment; The second spectral correction parameter is determined based on the environmental interference information; the second spectral correction parameter is a quantitative parameter calculated based on the interference information of the test environment to correct the influence of the test environment interference on the spectrum, including noise suppression coefficient and light intensity compensation value; The first corrected spectral information is corrected using the second spectral correction parameter to obtain the second corrected spectral information; The pollutant information of the water sample to be treated is determined using the second corrected spectral information.

3. A multi-parameter water quality analyzer, characterized in that, The multi-parameter water quality analyzer is used to execute the signal processing method of the multi-parameter water quality analyzer as described in claim 1. The multi-parameter water quality analyzer includes: a laser irradiation unit, a water sample preparation unit, and a detection unit. The laser output by the laser irradiation unit irradiates the spots on the water sample preparation area of ​​the water sample preparation unit through the end of the laser irradiation unit. The detection unit monitors the spectral information generated by the spots irradiated by the laser irradiation unit.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in claim 1.

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