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

Abstract

The invention relates to the technical field of data processing and water quality detection, and provides a signal processing method of a multi-parameter water quality analyzer and a related device, the method comprises the following steps: using a photoelectric detection module to obtain a first detection light signal generated when a to-be-treated water sample plate is subjected to laser irradiation; performing conversion processing on the first detection optical signal to obtain a first detection electric signal; optimizing the first detection electric signal to obtain a second detection electric signal; performing spectrum construction according to the second detection electric signal to obtain target spectrum information; and determining pollutant information of the to-be-treated water sample plate according to the target spectral information. By adopting the method provided by the invention, the accuracy of the signal processing process can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing and water quality detection, in particular to a signal processing method of a multi-parameter water quality analyzer and related devices. BACKGROUND

[0002] In the field of multi-parameter water quality analysis technology, the accuracy of water quality analysis is directly related to environmental governance effectiveness and public health safety. As the core link of multi-parameter water quality analysis, signal processing plays a key role in providing high-quality data support. However, the accuracy of the signal processing process in the prior art is insufficient, which cannot meet the actual needs of high-precision water quality detection. The core problem is that there is a lack of systematic optimization scheme for the acquired detection light signal, resulting in inaccurate signals for analysis and processing, which ultimately leads to misjudgment in pollutant type identification and large concentration calculation errors, seriously restricting the application reliability and detection accuracy of the multi-parameter water quality analyzer. SUMMARY

[0003] The embodiments of the present application provide a signal processing method of a multi-parameter water quality analyzer and related devices, which is beneficial to improving the accuracy of the signal processing process, helps to realize a more accurate pollutant information identification process, and can provide high-precision and high-reliability technical support for multi-parameter water quality analysis.

[0004] The first aspect of the embodiments of the present application provides a signal processing method of a multi-parameter water quality analyzer, which comprises: acquiring a first detection light signal generated when a water sample plate is irradiated with laser light by using a photoelectric detection module; performing conversion processing on the first detection light signal to obtain a first detection electrical signal; performing optimization processing on the first detection electrical signal to obtain a second detection electrical signal; performing spectrum construction according to the second detection electrical signal to obtain target spectrum information; determining pollutant information of the water sample plate to be processed according to the target spectrum information.

[0005] In this example, by acquiring a first detection light signal generated when a water sample plate is irradiated with laser light by using a photoelectric detection module, the first detection light signal can be further converted to obtain a first detection electrical signal, which is optimized to obtain a second detection electrical signal. The second detection electrical signal can be used for spectrum construction to obtain target spectrum information, and the target spectrum information can be used to determine the pollutant information of the water sample plate to be processed. Through accurate acquisition, efficient conversion, multiple optimization, and deep adaptation of the spectrum construction process, the accuracy of the signal processing process and the pollutant information identification process can be improved, and high-precision and high-reliability technical support can be provided for multi-parameter water quality analysis.

[0006] The second aspect of the embodiment of the present application provides a signal processing device of a multi-parameter water quality analyzer, the device comprising: An acquisition unit configured to acquire a first detection light signal generated when a water sample plate to be processed is irradiated with laser light; A first processing unit configured to perform conversion processing on the first detection light signal to obtain a first detection electrical signal; A second processing unit configured to perform optimization processing on the first detection electrical signal to obtain a second detection electrical signal; A third processing unit configured to perform spectrum construction based on the second detection electrical signal to obtain target spectrum information; A determination unit configured to determine pollutant information of the water sample plate to be processed based on the target spectrum information.

[0007] The third aspect of the embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are connected with each other, wherein the memory is configured to store a computer program, the computer program comprises program instructions, the processor is configured to invoke the program instructions, and execute the step instructions as in the first aspect of the embodiment of the present application.

[0008] The fourth aspect of the embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0009] The fifth aspect of the embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product can be a software installation package. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor.

[0011] Figure 1 A structural schematic diagram of a laser liquid analysis system is provided for the embodiment of the present application; Figure 2A flowchart of a signal processing method of a multi-parameter water quality analyzer is provided for the embodiments of the present application. Figure 3 A structural diagram of a terminal is provided for the embodiments of the present application. Figure 4 A structural diagram of a signal processing device of a multi-parameter water quality analyzer is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0013] The terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or device.

[0014] In the present application, the phrase "embodiments" means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiments, nor does it necessarily refer to mutually exclusive or alternative embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in the present application can be combined with other embodiments.

[0015] In order to better understand the signal processing method of the multi-parameter water quality analyzer provided by the embodiments of the present application, first of all, the existing signal processing method of the multi-parameter water quality analyzer will be briefly introduced. In the prior art, after the conversion of the obtained original detection light signal, there is a lack of systematic electric signal optimization scheme, and single filtering or simple amplification processing is mostly used, which cannot effectively eliminate the noise interference and waveform distortion in the signal, resulting in the problems of low purity and fuzzy characteristics of the optimized electric signal; at the same time, the background light interference correction is not fully combined in the spectrum construction process, and there is a lack of double correction mechanism for water environment and test environment, so that the constructed spectrum information deviates from the real characteristics of the water sample. In addition, the connection of each link of signal processing lacks collaborative design. From the whole process of light signal acquisition to the determination of pollutant information, the error is accumulated continuously, which finally leads to the problem of insufficient accuracy of signal processing result, and the characteristic properties of pollutants cannot be accurately reflected, which further causes the problems of misjudgment of pollutant type identification and large error of concentration calculation, and it is difficult to meet the actual demand of water quality monitoring for high-precision detection, and the application reliability of the multi-parameter water quality analyzer in the scene of environmental monitoring and drinking water safety guarantee is limited.

[0016] To solve the above problems, the embodiments of the present application provide a signal processing method of a multi-parameter water quality analyzer, which acquires a detection light signal and converts and optimizes an electric signal, constructs a spectrum matrix to obtain target spectrum information in combination with a background light transmittance curve, accurately determines pollutant information after double-stage environmental correction, and can realize accurate identification and quantification of the pollutant information. The whole process from signal acquisition, conversion and optimization to spectrum construction and correction improves the processing accuracy, and can provide high-precision data support for the multi-parameter water quality analysis.

[0017] The laser liquid analysis system can include a control platform and a multi-parameter water quality analyzer, and the control platform is in communication connection with at least one multi-parameter water quality analyzer; the multi-parameter water quality analyzer can include a water sample drying spot forming device (which can include a laser irradiation unit, a water sample making unit and a detection unit) and a pressurized frame type water sample plate leveling and fixing device with pressing ribs. The liquid that can be analyzed by the multi-parameter water quality analyzer includes but is not limited to water, oil and liquid medicine. The control platform performs data backup and subsequent application processing according to the analysis results of the multi-parameter water quality analyzer on the liquid.

[0018] Please refer to Figure 1 , Figure 1 A schematic diagram of a laser liquid analysis system is shown. As Figure 1A partial structural diagram of the laser liquid analysis system is shown, which includes a laser irradiation unit (also referred to as a laser bombardment unit, which is not limited in the present application) 11, a water sample preparation unit 12, and a detection unit (not shown in the figure). The laser output by the laser irradiation unit 11 irradiates a spot on the water sample preparation area 121 on the water sample preparation unit 12 through the end 111 of the laser irradiation unit 11. It should be noted that, Figure 1 The detection unit in the above can be regarded as the photoelectric detection module of the present application. The detection unit can use a spectrometer. The multi-parameter water quality analyzer mainly detects heavy metals, non-metals, etc. in the spot formed after the liquid is dried.

[0019] The pressurized ribbed pressurized frame water sample plate flat fixing device is a grid-shaped rigid frame, and the bottom surface is provided with uniformly distributed pressing ribs, which can cover and press the zinc film bearing the water sample spot. The device is used twice: first, the zinc film is flatly fixed on the support platform before the water sample is added to ensure the horizontality of the film surface; second, the zinc film is flatly fixed again after the water sample is dried into a spot to avoid spot deformation or displacement during laser irradiation. Through the grid structure, the laser can penetrate and press uniformly, and the device ensures the stability of the spot shape, thereby improving the repeatability and accuracy of the subsequent spectral signal, and providing a reliable sample preparation basis for high-precision water quality analysis.

[0020] It can be understood that a predetermined volume (such as 10 μL) of liquid is placed on the water sample preparation area 121 of the flexible film of the water sample preparation unit 12, and the liquid on the water sample preparation area 121 is dried to form a spot. Before the liquid on the water sample preparation area 121 is dried to form a spot, the pressurized ribbed pressurized frame water sample plate flat fixing device (not shown in the figure) can perform first flatness detection and fixation on the flexible film that will bear the water sample (i.e. the flexible film on which the above water sample preparation area 121 is located), so that the water sample plate remains horizontal, thereby facilitating more uniform spot after water sample drying; after the liquid on the water sample preparation area 121 is dried to form a spot, the pressurized ribbed pressurized frame water sample plate flat fixing device can perform second flatness detection and fixation on the flexible film bearing the water sample spot, so as to ensure the flatness and stability of the flexible film, avoid the spot shape deviation or the flexible film displacement during subsequent laser irradiation by the laser irradiation unit 11, and further ensure the accuracy of the spectral detection data.

[0021] The laser instrument of the laser irradiation unit 11 generates laser light that contacts the dried residue of the liquid (i.e. the water-like spot to be mentioned later), and the water-like spot forms plasma at high temperature, realizes transition from a low-energy state to a high-energy state, but the high-energy state is unstable and returns to the ground state (i.e. the original state) immediately, at which time the energy is emitted in the form of light, and each element emits light of a different color; the spectrometer of the detection module (which can be regarded as the core functional component of the photoelectric detection module mentioned in the present application) monitors the light generated by the laser irradiation unit 11 irradiating the spot (i.e. the "first detection light signal generated when laser irradiation is performed" to be mentioned later), and according to the data detected by the detection unit, the result of the liquid can be analyzed. That is, the multi-parameter water quality analyzer rapidly detects multiple elements without consuming chemical reagents.

[0022] Optionally, the water sample preparation unit 12 comprises a support platform, a pay-off mechanism, a winding mechanism and a flexible film. The flexible film is paid out from the pay-off mechanism through the support platform, and a liquid of a preset volume (for example, 10 μL) is placed on the water sample preparation area 121 on the support platform; after the completion of the present detection and before the next detection, the pay-off mechanism and the winding mechanism cooperate to wind the waste flexible film to the winding mechanism, and the unused flexible film is placed on the support platform so as to place a liquid of a preset volume (for example, 10 μL) on the water sample preparation area 121 on the support platform. The implementation mode of the pay-off mechanism and the winding mechanism can be selected from the prior art, and will not be described here. Optionally, the flexible film adopts a zinc film.

[0023] Please refer to Figure 2 , Figure 2 A flowchart of a signal processing method of a multi-parameter water quality analyzer is provided for the embodiment of the present application. As Figure 2 shown, the method can be applied to the signal processing system of the multi-parameter water quality analyzer, and the method comprises the following steps: S10: acquiring, by using a photoelectric detection module, a first detection light signal generated when laser irradiation is performed on a water sample plate to be processed.

[0024] The photoelectric detection module refers to a core component for converting a light signal into a processable electrical signal, and the photoelectric detection module usually comprises a photoelectric sensor (such as a photodiode, a photomultiplier tube, etc.), a signal receiving light path and a preliminary signal adaptation circuit, and can accurately capture a light signal of a specific waveband. In the process of laser water quality analysis, the photoelectric detection module can usually be used in cooperation with the detection unit of the multi-parameter water quality analyzer, and the present application does not limit this. Optionally, the photodiode in the photoelectric sensor can adopt a Hamamatsu S1337 series photodiode.

[0025] The water sample to be processed plate refers to a sample carrier bearing a water sample spot to be detected. The sample carrier can be a rigid substrate on which a flexible film (such as a zinc film) is laid, and the surface of the flexible film forms a solid water sample spot after the water sample is dropped and dried. The water sample to be processed plate can be the direct object of laser irradiation, and can be subjected to flat fixing treatment before laser irradiation to ensure stable form, which is not limited in the present application.

[0026] Laser irradiation refers to irradiating the water sample spot with a specific wavelength laser (such as ultraviolet or near-infrared waveband) with good monochromaticity and high intensity, so that the pollutant molecules in the spot are excited to produce characteristic light responses (such as absorption, scattering, fluorescence, etc.), thereby providing a signal source for subsequent detection.

[0027] The first detection light signal refers to the original light signal (such as transmitted light or scattered light) obtained by light signal collection when the water sample spot on the water sample to be processed plate is subjected to laser irradiation. The first detection light signal can include the original light signal of the characteristic information of the pollutants carried by the water sample spot, which is not limited in the present application. The first detection light signal can be regarded as a basic data source in the subsequent signal processing process.

[0028] Optionally, the first detection light signal can be the original light signal (such as plasma emission light or scattered light) obtained by light signal collection by the detection unit (the core component is a spectrometer, i.e. the core functional component of the photoelectric detection module in the present application) when the laser output by the laser irradiation unit 11 is precisely irradiated on the water sample spot carried on the water sample preparation area 121 of the water sample preparation unit 12. The collection quality of the first detection light signal can be directly affected by the irradiation accuracy of the laser irradiation unit, the spot forming effect of the water sample preparation unit and the signal capture ability of the detection unit, which is not limited in the present application.

[0029] The laser light source with a corresponding wavelength (such as 254 nm ultraviolet laser for detecting organic matter) can be selected according to the characteristics of the water sample spot and in combination with the characteristics of the pollutants to be detected, so that the laser light is vertically irradiated on the flat water sample spot. Further, the photoelectric detection module is started to converge the characteristic light signal generated by the spot to the photoelectric sensor through the light path focusing assembly, so as to capture and output the first detection light signal.

[0030] In the process of acquiring the first detection light signal generated by laser irradiation on the water sample plate to be processed by using the photoelectric detection module, the signal acquisition link is prone to signal crosstalk problem caused by electromagnetic interference. For example, the control board of the multi-parameter water quality analyzer can integrate multiple functional modules such as laser driving, spectrum acquisition, motion control, etc. Among them, the high-voltage signal of the laser driving module and the digital switch signal of the motion control module can cause interference to the weak first detection light signal generated by the spectrum acquisition module through spatial radiation or line conduction, form signal crosstalk, and then cause the signal-to-noise ratio to decrease, affecting the accuracy of subsequent electrical signal conversion and processing.

[0031] To solve this core problem, the multi-parameter water quality analyzer anti-electromagnetic interference integrated control board of the present application can adopt targeted hardware design: the control board selects a 6-layer FR-4 printed circuit board (PCB), and can be divided into laser driving area (high voltage area), spectrum acquisition area (analog area), and motion control area (digital area) according to function. Different types of signals can be prevented from crossing and interfering with each other by spatial partition isolation from the source. Differential shielding design can be used between layers, such as wrapping the high-voltage area with 2oz copper foil to block the diffusion of strong electromagnetic radiation, and laying a grid ground in the analog area to provide a stable ground reference for weak spectral signals, further weakening the interference conduction path. The core signal transmission line can adopt a differential wiring method with an impedance of 100Ω to control the crosstalk below -90dB, and a ferrite bead with an impedance of 600Ω at a frequency of 100MHz is loaded on the board to accurately filter the high-frequency noise generated by the motion control module, thereby achieving a stable effect with a signal-to-noise ratio greater than 80dB.

[0032] Optionally, the laser driving area can correspond to the laser irradiation unit in the laser liquid analysis system; the spectrum acquisition area can correspond to the detection unit such as the spectrometer in the laser liquid analysis system; and the motion control area can correspond to the unwinding mechanism and winding structure of the water sample preparation unit in the laser liquid analysis system, etc. The present application does not limit this.

[0033] In the water quality detection scene, the characteristic light signal (such as fluorescence signal, plasma emission signal) generated by laser irradiation of water sample spots is extremely weak, and the environmental interference in actual application is complex and changeable (such as on-site power grid fluctuation, surrounding equipment electromagnetic radiation, water sample matrix interference, etc.). Even through the above high-strength hardware anti-interference design, it is still difficult to completely eliminate residual noise and system error, and only the interference can be suppressed within a reasonable range that can be processed by the algorithm. Therefore, the 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, and laying a reliable foundation for algorithm processing; and the subsequent electrical signal optimization, spectrum reconstruction and double-environment correction precise algorithms are the finishing of the above data raw materials, which realizes high-precision pollutant identification and quantification by suppressing residual noise, correcting system bias and compensating environmental impact. The two can form a coordinated system of hardware anti-interference and algorithm precise optimization, which is indispensable to jointly ensure the ultra-high precision and stability of multi-parameter water quality analysis.

[0034] It can be understood that the 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 acquisition and transmission, which is conducive to laying a high-quality data foundation for subsequent electrical signal optimization, spectrum construction and pollutant information determination, which is not limited by the present application.

[0035] Optionally, a wideband noise monitoring circuit can be added to the control board to real-time collect the high-frequency noise spectrum of the spectrum collection area. The signal processing algorithm (such as the cutoff frequency of the filter, the regularization parameter β) is dynamically self-adaptive adjusted according to the monitored noise main frequency and intensity. For example, when a sudden increase in motor interference noise at a specific frequency is detected, the digital filter weight of the frequency band is automatically strengthened. Thus, a closed-loop correlation between hardware feedback and algorithm parameters is established, forming a coordinated scheme that does not exist in the prior art.

[0036] S20: converting and processing the first detection light signal to obtain a first detection electrical signal.

[0037] The conversion and processing is a process of converting the light signal (physical quantities such as light intensity and light flux) captured by the photoelectric detection module into an electrical signal (voltage and current) recognizable by the electronic device based on the photoelectric effect. The conversion and processing can be regarded as an essential conversion of signal form.

[0038] The first detection electrical signal refers to the original electrical signal directly converted from the first detection light signal. The amplitude and frequency of the first detection electrical signal are related to the characteristics of the original light signal. The first detection electrical signal may contain impurities such as device noise and electromagnetic interference, and is an electrical signal that has not been optimized and purified.

[0039] The conversion process can be realized by a photoelectric conversion element (such as a photodiode) built in the photoelectric detection module. When the first detection light signal irradiates on the photoelectric conversion element, the photoenergy can excite the carrier movement to form a weak current signal proportional to the light signal intensity. Further, through the pre-conversion circuit in the module, the weak current signal is converted into a stable voltage signal, and the first detection electric signal is obtained (for example, the stronger the light intensity, the greater the converted voltage amplitude).

[0040] S30: optimizing the first detection electric signal to obtain a second detection electric signal.

[0041] The optimization process is a process of improving the purity, regularity and recognition of the electric signal by combining operations such as filtering, amplifying and shaping, aiming at the problems of high-frequency noise, waveform distortion and weak signal in the first detection electric signal.

[0042] The second detection electric signal refers to a high-quality electric signal with suppressed noise, regular waveform and appropriate amplitude after optimization processing. The second detection electric signal can accurately reflect the core characteristics of the original light signal and can provide reliable data support for spectrum construction.

[0043] The process of primary filtering, amplifying, shaping and secondary filtering can be used, for example, a low-pass filter is used to filter the high-frequency electromagnetic noise (such as the noise generated by the motion control module) in the first detection electric signal; an operational amplifier is used to amplify the weak filtered signal to the voltage level (such as amplifying the millivolt level signal by 1000 times), which is convenient for subsequent processing; a shaping circuit is used to correct the distorted waveform of the signal, so that the signal edge is steep and the amplitude is stable; a band-pass filter is used for secondary filtering to eliminate the residual noise introduced in the amplification process, thereby obtaining the second detection electric signal. The present application does not make any limitation.

[0044] S40: constructing a spectrum according to the second detection electric signal to obtain target spectrum information.

[0045] The spectrum construction is a process of forming a spectrum curve reflecting the optical characteristics of the water sample spot by combining the second detection electric signal with the wavelength-intensity correspondence of the light signal, such as matrix construction, function expansion and reconstruction.

[0046] The target spectrum information refers to the spectrum data obtained after spectrum construction and optimization, such as absorption spectrum and fluorescence spectrum. The target spectrum information can contain characteristic spectrum peaks (such as position, intensity, half-width, etc.) of pollutant molecules, which can be regarded as the core basis for identifying pollutants.

[0047] The background light transmittance curve during laser irradiation can be obtained, and the target spectrum matrix is constructed based on the background light transmittance curve to eliminate the interference of the background light; the spectrum function is constructed according to the amplitude change of the second detection electric signal and the light signal intensity at different wavelengths; the spectrum function is linearly expanded and decomposed into a Gaussian basis function and a weight coefficient; and the complete target spectrum curve is formed and obtained through the reconstruction of the weight coefficient and the Gaussian basis function. For example, a certain heavy metal ion has a characteristic absorption peak at a specific wavelength, and the spectrum curve will have a significant amplitude drop at this wavelength position, which is not limited by the present application.

[0048] S50: determining the pollutant information of the water sample to be processed according to the target spectrum information.

[0049] The pollutant information refers to the core data of the pollutants in the water sample to be processed. Optionally, the pollutant information can include but is not limited to the type (such as organic matter, heavy metal ions, bacteria, etc.), concentration, content ratio, etc. It can be understood that the pollutant information can be regarded as the detection result of water quality detection.

[0050] The water environment parameters (such as water temperature, pH value, and dissolved oxygen content) and the test environment interference information (such as external electromagnetic interference and light interference) of the water sample to be processed can be obtained to determine the corresponding spectrum correction parameters; the environmental errors in the target spectrum information are corrected through two-stage correction (i.e., water environment parameter correction and test environment interference correction); the corrected spectrum information is compared with the standard spectrum library of known pollutants (such as matching the characteristic peak position and intensity) to identify the type of pollutants; and the specific concentration of the pollutants is calculated according to the quantitative relationship between the characteristic peak intensity and the pollutant concentration (such as the Lambert-Beer law), so as to obtain the complete pollutant information, which is not limited by the present application.

[0051] The above signal processing procedure realizes high precision and high stability of water quality detection through the progressive design of accurate light signal acquisition, photoelectric conversion, signal optimization and purification, spectrum construction, and pollutant identification; the water sample spot shape can be guaranteed to be regular through a flat and fixed device, and the characteristic light signal can be efficiently captured in cooperation with the photoelectric detection module; after the light signal is converted into a processable electric signal through photoelectric conversion, noise and distortion can be removed through multi-stage optimization processing to improve the signal purity; the target spectrum can be constructed in combination with background correction and environmental parameter correction to ensure that the spectrum information truly reflects the characteristics of the water sample; and the accurate identification of the type and concentration of pollutants is realized through standard spectrum comparison. The entire procedure takes into account signal integrity, anti-interference, and scene adaptability, effectively solves the detection error problems caused by signal crosstalk, shape deviation, and environmental interference in water quality detection, and provides stable and reliable technical support for multi-parameter water quality analysis.

[0052] In this example, by adopting the photoelectric detection module to obtain the first detection light signal generated when the water sample plate is irradiated with laser, the first detection light signal can be further converted and processed to obtain a first detection electrical signal, and the first detection electrical signal can be optimized to obtain a second detection electrical signal, so that the target spectral information can be constructed according to the second detection electrical signal, and the pollutant information of the water sample plate can be determined according to the target spectral information, which is beneficial to improve the accuracy of the signal processing process, helps to realize more accurate pollutant information identification process, and provides high-precision and high-reliability technical support for multi-parameter water quality analysis.

[0053] In one possible implementation, when performing optimization processing, the first detection electrical signal after photoelectric conversion can be subjected to primary filtering to remove high-frequency interference to obtain a first intermediate detection electrical signal; the signal amplitude can be improved through amplification processing to form a second intermediate detection electrical signal; the waveform distortion and baseline can be corrected through shaping processing to obtain a third intermediate detection electrical signal; and residual noise can be removed through secondary filtering to further obtain a high-quality second detection electrical signal, which can lay a foundation for subsequent spectral construction and pollutant detection. One possible method for optimizing the first detection electrical signal to obtain a second detection electrical signal includes: A1, performing primary filtering processing on the first detection electrical signal to obtain a first intermediate detection electrical signal; A2, performing amplification processing on the first intermediate detection electrical signal to obtain a second intermediate detection electrical signal; A3, performing shaping processing on the second intermediate detection electrical signal to obtain a third intermediate detection electrical signal; A4, performing secondary filtering processing on the third intermediate detection electrical signal to obtain a second detection electrical signal.

[0054] The primary filtering processing is a process of using a specific filtering method to preliminarily reduce noise for high-frequency interference in the first detection electrical signal. The primary filtering processing can quickly remove most of the noise that is greatly different from the signal frequency range, which is beneficial to reduce the interference burden for subsequent amplification processing.

[0055] The first intermediate detection electrical signal refers to an electrical signal obtained after primary filtering processing, in which high-frequency noise is significantly suppressed and the core characteristic signal is retained. The purity of the first intermediate detection electrical signal is improved compared with the first detection electrical signal, which can provide a more stable basic signal for subsequent amplification processing.

[0056] A low-pass filter (e.g., a Butterworth low-pass filter) can be used to implement the above-mentioned primary filtering. For example, the filter can be set to have a cutoff frequency (e.g., 1 kHz) according to the frequency range (e.g., low to medium frequencies) of the characteristic electrical signal in water quality detection, so that only electrical signals within the target frequency range are allowed to pass through, and high-frequency noise (e.g., electromagnetic interference above 100 MHz) is filtered out. For example, the first detection electrical signal is mixed with high-frequency pulse noise generated by the motion control module. After low-pass filtering, the high-frequency pulse noise is filtered out, and only the core electrical signal related to the light response of the water sample spot is retained, i.e., the above-mentioned first intermediate detection electrical signal is obtained.

[0057] Optionally, the motion control module can be one of the core components of the multi-parameter water quality analyzer, and can be used to drive the mechanical movement of the device, 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 complete the flattening and fixing of the water sample spot. The motion control module can be integrated with the laser driving module and the spectrum acquisition module on the control board of the device. The amplification process is a process of lifting the amplitude of the weak first intermediate detection electrical signal to a range suitable for subsequent processing by an amplifier, while maintaining the original characteristics (e.g., waveform, frequency) of the signal as much as possible. The amplification process can solve the problem that the original signal amplitude is too small to be recognized and processed by subsequent circuits.

[0058] The second intermediate detection electrical signal refers to an electrical signal with a voltage level (e.g., 1-5 V) and more prominent characteristics after amplification processing. It can be understood that the second intermediate detection electrical signal not only retains the core information related to the pollutants, but also has sufficient strength to support subsequent shaping and secondary filtering processing.

[0059] An operational amplifier can be used to build an amplification circuit. For example, the amplification factor (e.g., 1000-10000 times) can be set according to the amplitude (e.g., 0.1-1 mV) of the first intermediate detection electrical signal, to ensure that the amplitude of the amplified signal is within the input adaptation range of the subsequent processing module. For example, the amplitude of the first intermediate detection electrical signal is 0.5 mV, and after passing through a 1000 times amplification circuit, the amplitude is increased to 0.5 V, thereby forming and obtaining the above-mentioned second intermediate detection electrical signal. At this time, the characteristics of the second intermediate detection electrical signal are more easily recognized, and the problem of high noise ratio caused by too small amplitude can be avoided.

[0060] The shaping process is a process of correcting the distorted waveform (e.g., edge blur, peak fluctuation, baseline drift, etc.) of the second intermediate detection electrical signal by circuit design, so that the signal waveform is regular, the edge is steep, and the amplitude is stable. The shaping process can ensure the consistency of signal parameters (e.g., peak value, pulse width).

[0061] The third intermediate detection electrical signal refers to an electrical signal obtained after shaping processing, which has regular waveform, stable baseline and clear characteristic parameters. It can be understood that the third intermediate detection electrical signal eliminates waveform distortion that may be generated in the amplification process, and can provide high-quality signals for secondary filtering and subsequent spectrum construction.

[0062] A combination of a voltage comparator and a shaping circuit can be used to achieve the above-mentioned shaping processing. A reasonable reference voltage threshold can be set to correct the blurred edges in the second intermediate detection electrical signal to steep rectangular waves or pulse waves, and the baseline drift of the signal can be further calibrated to control the signal amplitude fluctuation within a permissible range in each period. For example, the second intermediate detection electrical signal has baseline offset and peak value fluctuation due to temperature drift of the amplification circuit. After shaping processing, the baseline is stable and the peak value is uniform within a set range, and the waveform edge is clear, thereby forming the above-mentioned third intermediate detection electrical signal.

[0063] The secondary filtering processing is a process of secondary purification of small amplitude noise (such as inherent noise introduced by the amplification circuit and spurs generated in the shaping process) that may remain after the shaping processing, using high-precision filtering.

[0064] The second detection electrical signal refers to an optimal electrical signal obtained after full-process optimization processing, which has noise suppressed to the maximum extent, regular waveform and accurate characteristics. The second detection electrical signal can truly reflect the light response characteristics of water sample spots, and can be regarded as the core data basis for subsequent spectrum construction and pollutant identification.

[0065] A band-pass filter can be selected for the above-mentioned secondary filtering. For example, the passband width of the filter can be set according to the accurate frequency range of the target signal (such as 0.5-2 kHz), and only the core signal is allowed to pass, further eliminating residual noise that is not completely eliminated in the initial filtering and spurs generated in the shaping process. For example, a small amount of low-frequency noise introduced by the amplification circuit remains in the third intermediate detection electrical signal. After band-pass filtering, the residual noise is completely filtered, and the second detection electrical signal with stable amplitude, regular waveform and no obvious interference can be finally obtained, which is not limited in the present application.

[0066] In this example, through the progressive design of primary filtering, amplification, shaping and secondary filtering, the accurate purification from the original electrical signal to the high-quality final signal is realized; the primary filtering quickly eliminates high-frequency strong interference first, clearing the way for amplification processing; the amplification processing enhances the signal amplitude, solving the problem of weak signal difficult to identify; the shaping processing corrects the waveform distortion, ensuring the consistency of signal characteristics; the secondary filtering accurately eliminates residual noise, realizing the final purification of the signal. The whole process not only avoids the problem that noise is amplified synchronously in the amplification process, but also ensures the integrity and accuracy of the signal through multi-stage optimization, effectively improving the accuracy of subsequent spectrum construction and the reliability of pollutant detection, providing stable signal support for high-precision detection of multi-parameter water quality analyzers.

[0067] In one possible implementation, when performing spectrum construction, the background light transmittance curve when the laser irradiates the water sample plate to be processed can be obtained to construct a target spectrum matrix based on the background light transmittance curve, and further combined with the optimized second detection electrical signal, through matrix operation, function expansion and reconstruction, etc., to determine the target spectrum information reflecting the characteristics of the water sample spot. A possible method for performing spectrum construction based on the second detection electrical signal to obtain target spectrum information, comprising: B1, obtaining a background light transmittance curve when the water sample plate to be processed is irradiated by laser; B2, performing spectrum matrix construction according to the background light transmittance curve to obtain a target spectrum matrix; B3, determining target spectrum information using the target spectrum matrix and the second detection electrical signal.

[0068] Wherein, the background light transmittance curve refers to the curve obtained for describing the change of the transmittance proportion of different wavelengths of laser with wavelength when there is no pollutant influence, only laser penetrates the blank water sample plate (without water sample spot) and environmental medium.

[0069] Before irradiating the water sample plate to be processed containing water sample spots with laser, a blank water sample plate without water sample spots (material, size consistent with the water sample plate to be processed) can be placed in the detection light path; start the laser light source, emit laser by wavelength in the preset wavelength range (such as 200-800nm), to collect the light signal penetrating the blank water sample plate at each wavelength through the photoelectric detection module, and further convert the electrical signal to calculate the transmittance (ratio of transmitted light intensity to incident light intensity), and draw the curve with wavelength as the horizontal axis and transmittance as the vertical axis, that is, the above-mentioned background light transmittance curve can be obtained. For example, when detecting an industrial wastewater sample, a blank water sample plate of blank zinc film rigid substrate can be laid to collect the corresponding background light transmittance curve, which can reflect the transmittance characteristics of zinc film itself to different wavelengths of laser.

[0070] The spectral matrix construction is a process of discretizing the continuous data of the background light transmittance curve, arranging the data in a two-dimensional matrix in order of wavelength. The matrix elements in the constructed spectral matrix can be transmittance values at corresponding wavelengths, so as to facilitate subsequent numerical operations with electrical signals.

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

[0072] The wavelength range related to detection in the background light transmittance curve can be selected, and the transmittance data can be extracted at a fixed wavelength interval (such as 1 nm); the wavelength serial number is used as the matrix row index, and the transmittance value is used as the matrix element to construct a two-dimensional matrix (the number of rows is the number of wavelength points, and the number of columns is 1); if multi-channel detection is required, it can be expanded to a multi-row and multi-column matrix to ensure that the wavelength data of each detection channel corresponds to the transmittance one by one. For example, 601 transmittance data can be extracted at an interval of 1 nm in the wavelength range of 200-800 nm to construct a two-dimensional matrix of 601x1, which can be the above-mentioned target spectral matrix.

[0073] As can be seen from the foregoing, the second detection electrical signal refers to a high-quality electrical signal obtained after primary filtering, amplification, shaping, and secondary filtering optimization. It can be understood that the second detection electrical signal can be an electrical signal with suppressed noise and regular waveform, which can more accurately reflect the light response characteristics of the water sample spot. After correcting the background interference by the target spectral matrix, the spectral data (such as absorption spectrum, fluorescence spectrum) related to the pollutants in the water sample spot is obtained, which is the target spectral information.

[0074] The second detection electrical signal can be discretized into an electrical signal matrix according to corresponding wavelengths, and the dimensions thereof can be consistent with those of the target spectral matrix; through matrix operation (such as multiplying the electrical signal matrix by the inverse matrix of the target spectral matrix), the influence of the background light transmittance is eliminated, and the original spectral data reflecting the real light response of the water sample spot is obtained; the original spectral data is further linearly expanded, such as being fitted and reconstructed by using Gaussian basis functions and weight coefficients, to fill the data gap and smooth the noise, thereby forming and obtaining the above-mentioned target spectral information. For example, after discretization, the second detection electrical signal can obtain an electrical signal matrix of 601x1, and after further matrix operation with the above-mentioned target spectral matrix of 601x1, an absorption spectrum containing the characteristic absorption peak of the pollutant can be reconstructed, which is the above-mentioned target spectral information.

[0075] In this example, by first collecting the background light transmittance curve, then constructing the target spectrum 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 processing of the target spectrum matrix realizes the numerical and precise correction of the background, and the reconstruction combined with the Gaussian basis function further improves the integrity and smoothness of the spectrum data, and the final target spectrum information can truly reflect the characteristics of the pollutants in the water sample spot, providing high-quality data support for subsequent pollutant type 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 spectrum information, a first spectrum function can be constructed in combination with the corresponding relationship between the target spectrum matrix and the second detection electrical signal, and the function can be further linearly expanded and decomposed into a weight coefficient and a Gaussian basis function, so that the target spectrum information that can accurately reflect the characteristics of the pollutants can be reconstructed by the combination of the weight coefficient and the Gaussian basis function. A possible method for determining the target spectrum information by using the target spectrum matrix and the second detection electrical signal includes: C1, constructing a spectrum function according to the target spectrum matrix and the second detection electrical signal to obtain a first spectrum function; C2, linearly expanding the first spectrum function to obtain a weight coefficient and a Gaussian basis function; C3, reconstructing according to the weight coefficient and the Gaussian basis function to obtain target spectrum information.

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

[0078] The first spectrum function refers to a continuous function obtained by mathematical modeling, which is related to the transmittance and the electrical signal intensity. It can be understood that the first spectrum function integrates the background correction information and the light response characteristics of the water sample spot, and can be regarded as the basis for subsequent spectrum expansion and reconstruction.

[0079] The transmittance values corresponding to each wavelength in the target spectrum matrix can be extracted, and can be paired with the amplitudes of the corresponding wavelengths in the second detection electrical signal to obtain multiple groups of (wavelength-transmittance-electrical signal intensity) data; a continuous mathematical model between the three can be established by using the least square method, with wavelength as independent variable, electrical signal intensity as dependent variable, and transmittance as correction coefficient, so as to fit the function reflecting the correlation between the three, i.e. the above-mentioned first spectrum function. For example, within the wavelength interval of 200-800 nm, 601 groups of data can be paired, and a quadratic continuous function can be further fitted by using the least square method, i.e. the above-mentioned first spectrum function.

[0080] Linear expansion is to decompose the complex first spectrum function into a linear combination of multiple simple base functions, which simplifies the function operation and reconstruction difficulty. The present application takes the decomposition into multiple Gaussian base functions as an example for illustration, which does not limit the present application.

[0081] The Gaussian base function can be a continuous mathematical curve with smooth and symmetric shape, and its core feature is high in the middle and low on both sides, similar to the profile of a gentle mountain peak. Optionally, a certain specific wavelength (such as the center wavelength) is taken as the highest point of the peak, and the wavelength gradually decreases smoothly to both sides, without sharp corners or abrupt changes, which can well fit the common characteristic peak shape in the spectrum curve. The present application selects the Gaussian base function as the base function because of its simple form, continuous differentiability, and good fitting of common smooth peak shapes in the spectrum. The center wavelength is uniformly distributed according to the known characteristic spectral range of the pollutant to be detected, and the standard deviation is set according to the resolution of the spectrometer.

[0082] The weight coefficient refers to the coefficient corresponding to each Gaussian base function after linear expansion. The weight coefficient of each Gaussian base function can reflect the contribution proportion of the Gaussian base function in the overall spectrum. It can be understood that the greater the absolute value of the weight coefficient, the more significant the influence of the corresponding Gaussian base function on the spectrum feature; the smaller the absolute value of the weight coefficient, the weaker the influence of the corresponding Gaussian base function on the spectrum feature. For example, when detecting a certain organic matter in water, its characteristic spectrum peak is a wide peak, at this time, 3-5 Gaussian base functions with similar center wavelengths, different widths and narrowness can be combined to perfectly reproduce the shape of this wide peak by adjusting their respective weight coefficients, so as to make the spectrum feature clearer and more easily identifiable.

[0083] The number of Gaussian base functions (such as 50), center wavelengths (uniformly distributed in the detection wavelength interval) and standard deviations can be set according to the detection wavelength range and spectral resolution requirements. Optionally, the number of Gaussian base functions, center wavelengths and standard deviations can also be determined by optimization algorithm according to historical spectrum data. The first spectrum function can be decomposed into a linear combination of the Gaussian base functions by using orthogonal decomposition method, and the corresponding coefficient of each Gaussian base function, i.e. the weight coefficient, can be determined by solving the linear equation set. For example, the first spectrum function in the 200-800nm interval can be decomposed into a linear combination of 50 Gaussian base functions with center wavelength interval of 12nm, and 50 corresponding weight coefficients can be further calculated to complete the above linear expansion process.

[0084] Reconstruction is the process of weighting and summing the weight coefficients obtained by linear expansion with the corresponding Gaussian basis functions to restore and optimize the spectral curve. After reconstruction, smooth and clear spectral data (such as absorption spectrum, fluorescence spectrum) can be obtained, which is the target spectral information. The target spectral information can contain key information such as the characteristic spectral peak position, intensity, and half-width of the pollutant.

[0085] Each Gaussian basis function can be multiplied by its corresponding weight coefficient to obtain a plurality of weighted basis functions; all weighted basis functions are summed to obtain a reconstructed continuous spectral curve; and the spectral curve is smoothed to eliminate minor fluctuations and highlight characteristic peaks, thereby forming and obtaining the above-mentioned target spectral information. For example, the sum of 50 weighted Gaussian basis functions obtains a preliminary reconstructed spectrum, and after smoothing, the smoothed spectrum can clearly present the characteristic absorption peak of a heavy metal ion at 280 nm, that is, the smoothed spectrum is the target spectral information.

[0086] Optionally, after linear expansion of the weight coefficients and Gaussian basis functions, to achieve accurate reconstruction of the target spectral information, the scene characteristics of the multi-parameter water quality analysis of the present application can be fully combined to solve the problems of insufficient signal and detection scene adaptability and noise-induced reconstruction distortion in the traditional reconstruction process. For example, the present application is based on the first spectral function, deeply correlates the actual detection conditions of the water sample plate to be processed, combines the characteristics of the second detection electrical signal with the morphological characteristics of the water sample spot and the type characteristics of the water quality to be detected, and introduces a regularization process to suppress the interference of residual noise, to further ensure that the reconstructed spectral information can truly and accurately reflect the characteristic properties of the pollutants in the water sample plate to be processed.

[0087] Optionally, the present application can use the following reconstruction optimization equation to solve the optimal weight coefficient and obtain the target spectral information: wherein, α refers to the weight coefficient vector of the Gaussian basis function; R refers to the real number set, which can represent that all elements of the weight coefficient (i.e., the weight coefficient corresponding to each Gaussian basis function) are real numbers; n is the number of Gaussian basis functions; A refers to the coupling matrix; I corr refers to the second detection electrical signal vector; refers to Hadamard product (i.e., Hadamard product), which refers to the multiplication of corresponding elements of the vectors; refers to the spot shape correction coefficient vector; refers to scalar multiplication; K water refers to the water quality type adaptation factor; represents the square of the L2 norm (i.e., the square of the Euclidean norm), which is used to calculate the sum of squares of each element of the vector, and the operation result is a non-negative scalar; β is a regularization parameter. Optionally, β can be determined by cross-validation method, and the value range can be 0.01-0.1.

[0088] The coupling matrix A described above can be obtained by the product of the target spectrum matrix T(λ) and the Gaussian basis function matrix φ(λ), that is, A=T(λ)×φ(λ), wherein × represents matrix multiplication. The coupling matrix integrates the background correction information of the background light transmittance and the spectral characteristics of the Gaussian basis function, and can provide data support for the correlation of the signal and the spectrum.

[0089] The signal-morphology-water quality coupling term can be regarded as deeply associating the reconstruction process with the water quality detection scene involved in the present application to ensure that the target spectrum information obtained after reconstruction can meet the actual needs of water quality analysis. Wherein (Aα I corr ) can be a basic signal fitting term, which can be used to ensure that the combination of the weight coefficient and the Gaussian basis function can best fit the characteristics of the second detection electrical signal. ζ spot may be a spot morphology correction coefficient vector, the dimension of which can be consistent with the second detection electrical signal vector I corr , that is, each element corresponds to a spot morphology correction value at a detection wavelength; the spot morphology correction value is determined according to the flatness detection result of the flexible film by the flat fixing device of the pressurized ribbed pressurized frame water sample plate; it can be understood that when the flatness deviation of the flexible film increases, the vector element corresponding to the wavelength is adjusted synchronously to accurately offset the influence of the spot morphology deviation on the laser transmission and light signal collection at the wavelength, so as to ensure that the reconstructed spectrum is not disturbed by the spot flatness and thickness difference in the whole detection wavelength band.

[0090] Optionally, the spot morphology correction coefficient vector can be quantitatively calculated based on the detection data of the laser displacement sensor carried by the flat fixing device of the pressurized ribbed pressurized frame water sample plate, and the following formula can be used: Wherein, ζ spot is a spot morphology correction coefficient vector, the dimension of which is consistent with the total number of detection wavelengths (m is the total number of detection wavelengths); Δ h1 is the average height deviation (unit: μm) of the light spot area corresponding to the first detection wavelength measured by the laser displacement sensor, which can represent the height difference between the actual morphology of the flexible film and the ideal flat state in the light spot area corresponding to the first detection wavelength, and the detection resolution can be not less than 0.01 μm; Δ h2 is the average height deviation (unit: μm) of the light spot area corresponding to the second detection wavelength measured by the laser displacement sensor; Δhm is the average height deviation (unit: pm) of the light spot area corresponding to the mth 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 wavelength characteristics of the laser, and the value range can be 0.08~0.12 pm - ¹, the present application can preferably k=0.1 pm - ¹, to ensure that the correction coefficient is linearly and negatively correlated with the height deviation, which conforms to the physical law of light intensity attenuation; T represents vector transposition, which is used to construct a column vector with the same dimension as the second detection electric signal vector. Through the formula, the exclusive correction value can be calculated for the light spot area corresponding to each detection wavelength, and then a complete spot shape correction coefficient vector is constructed.

[0091] K water may be a water quality type adaptation factor, which is a scalar parameter, and the adaptation value can be selected according to the water quality type (such as drinking water, industrial wastewater, surface water, etc.) corresponding to the water sample plate to be processed; for example, for drinking water with simple matrix composition, the factor value is close to 1.0; for industrial wastewater, surface water containing complex organic matter, suspended solids and other interference components, the factor is appropriately adjusted according to the interference degree to offset the influence of different water quality matrixes on the spectral characteristics, so as to adapt to the core demand of the present application for multi-parameter water quality analysis.

[0092] Optionally, the following typical water quality corresponding to the water quality type adaptation factor suggested value range can be referred to, and the turbidity, organic matter content and other specific parameters of the water quality can be further fine-tuned in actual application: Further, refers to the L2 regularization term, wherein β is the regularization parameter, which can be determined by the generalized cross-validation method to suppress the amplification of the residual small noise in the second detection electric signal in the reconstruction process, thereby reducing the multicollinearity between the weight coefficients and avoiding distortion of the reconstructed spectrum.

[0093] Further, refers to the constraint condition of the weight coefficient, wherein 0≤α conforms to the physical characteristics of non-negative spectral intensity, to ensure that the reconstructed spectral intensity has actual physical meaning; α max may be an upper limit vector of the weight coefficient vector, and each element thereof can be set based on spectral physical constraints (such as non-negative and less than the theoretical maximum absorbance). Through the constraint condition, it can not only prevent the weight coefficient from being too large to cause the reconstructed spectrum to exceed the physically reasonable range, but also avoid the appearance of false high-intensity spectral signals, thereby ensuring the scientificity and practicality of the reconstruction result.

[0094] Optionally, the optimal weight coefficient vector a is obtained by solving the above reconstruction optimization equation Then, the target spectral information can be further calculated by the following formula: Wherein, F(λ) refers to the target spectral information; n refers to the number of Gaussian basis functions; j refers to the index of the Gaussian basis function; refers to the optimal weight coefficient corresponding to the jth Gaussian basis function centered at wavelength λ; φ j (λ) is the jth Gaussian basis function; ζ spot,λ refers to the element corresponding to wavelength λ in the spot morphology correction coefficient vector. It can be understood that the formula can obtain the target spectral information which can truly reflect the characteristics of the pollutants in the water sample plate to be processed by the weighted sum of the optimal weight coefficient and the corresponding Gaussian basis function, and superimposes the spot morphology correction.

[0095] Optionally, to further improve the accuracy of spot morphology correction, a scattering and optical path distribution model of laser on a non-flat spot surface, referred to as a spot morphology-spectrum compensation model, can be established. The three-dimensional topography data of the flexible film detected by the water sample plate flat fixing device with a pressing rib is input into the spot morphology-spectrum compensation model, and a prediction vector of the light intensity distribution distortion and spectral shift caused by the irregular spot morphology is calculated by the model, referred to as a spot morphology correction compensation term. The spot morphology correction compensation term can replace the spot morphology correction coefficient (scalar) in the foregoing spectral reconstruction equation, and is directly integrated into the spectral reconstruction equation as a compensation term to realize the quantitative compensation of the morphology interference. This way can upgrade the spot morphology correction in the reconstruction process from "empirical scalar correction" to "physical model driven accurate vector compensation", which can significantly improve the scientificity and accuracy of the morphology correction.

[0096] In this example, through the layer-by-layer progressive design of function construction, linear expansion and reconstruction optimization, efficient conversion from discrete data to accurate spectrum is realized; the spectral function construction integrates background correction and signal characteristics, laying a foundation for subsequent processing; linear expansion decomposes complex functions into simple base function combinations, reducing the operation difficulty; the reconstruction based on Gaussian basis function not only retains the characteristic spectral information of pollutants, but also improves the regularity and readability of the spectrum through smoothing processing. The whole process effectively solves the problems of discrete original data and fuzzy spectral characteristics, greatly improves the accuracy and reliability of spectral information, and provides core technical support for multi-parameter water quality analyzers to accurately identify pollutant types and calculate pollutant concentrations.

[0097] In a possible implementation, when determining the pollutant information of the water sample panel to be processed, the region information of the sample to be detected in the water sample panel to be processed can be acquired, and the first environmental parameter related to the water environment can be extracted, and the first spectrum correction parameter can be determined according to the first environmental parameter to preliminarily correct the target spectrum information; the test environment interference information when the laser irradiation is acquired, and the second spectrum correction parameter can be determined to further correct the preliminarily corrected spectrum, so that the pollutant information can be determined based on the twice corrected spectrum information. A possible method for determining the pollutant information of the water sample panel to be processed according to the target spectrum information, comprising: D1, acquiring the region information of the sample to be detected in the water sample panel to be processed; D2, extracting environmental information according to the region information to obtain the first environmental parameter; D3, determining the first spectrum correction parameter according to the first environmental parameter; D4, preliminarily correcting the target spectrum information by using the first spectrum correction parameter to obtain the first corrected spectrum information; D5, acquiring the environmental interference information when the water sample panel to be processed is irradiated by the laser; D6, determining the second spectrum correction parameter according to the environmental interference information; D7, correcting the first corrected spectrum information by using the second spectrum correction parameter to obtain the second corrected spectrum information; D8, determining the pollutant information of the water sample panel to be processed by using the second corrected spectrum information.

[0098] The sample to be detected refers to the water sample spot on the water sample panel to be processed, which can include but is not limited to the solute in the water sample, and the pollutant that can be contained. The sample to be detected can be regarded as the direct object of detection. The region information refers to the spatial position, range and the like of the sample to be detected on the water sample panel to be processed, such as the coordinate range, the area size and the like. The region information can be used for accurately positioning the detection region.

[0099] The water sample panel to be processed can be photographed by an industrial camera, and the edge contour of the water sample spot can be extracted by combining an image recognition algorithm to determine the coordinate range (such as taking the lower left corner of the substrate as the origin) and the area size of the water sample spot on the substrate; the region of the sample to be detected can also be positioned by the infrared positioning point pre-set on the edge of the substrate to ensure the accuracy of the region information.

[0100] The environmental information refers to natural environment data of an original water area corresponding to the sample to be detected. The environmental information can be associated with the regional information and can reflect the original environmental characteristics of the water sample. The first environmental parameter refers to a key water area environmental index affecting the spectral characteristics extracted from the environmental information, such as water temperature, pH value, dissolved oxygen content, turbidity, and the like. The first environmental parameter can be used to correct the spectral deviation caused by the water area environment in a targeted manner. For high-precision water quality analysis, the water area environment (such as pH) can significantly affect the spectral morphology of certain pollutants, and therefore, according to the environmental information, a correction measure can be taken to ensure that the target spectral information can truly reflect the properties of the pollutants themselves, so as to achieve accurate calibration of the spectral characteristics of the pollutants.

[0101] The first environmental parameter can be extracted according to the regional information of the sample to be detected, the environmental monitoring data of the original water sampling area (such as being retrieved through a water quality monitoring database), or being detected in real time through a sensor integrated on the detection device (if the sample is collected on site and not separated from the original environment) to extract key indexes such as water temperature, pH value, and dissolved oxygen content. For example, the regional information of a certain industrial wastewater sample is associated with a water inlet, and then the environmental data of the water inlet can be retrieved to obtain a water temperature of 25°C, a pH value of 7.2, and a dissolved oxygen content of 5 mg / L, thereby obtaining the first environmental parameter.

[0102] The first spectral correction parameter refers to a quantitative parameter for correcting the influence of the water area environment on the spectrum, such as a wavelength shift correction value and an intensity attenuation coefficient, which is calculated based on the first environmental parameter. The first spectral correction parameter can ensure that the spectral information can more truly reflect the characteristics of the pollutants.

[0103] The first spectral correction parameter can be calculated by inputting the first environmental parameter into a preset correction model (which can be established in combination with a large amount of experimental data and is associated with the corresponding relationship between different water area environmental parameters and spectral deviations) to obtain the corresponding spectral correction value. For example, according to the parameters of a water temperature of 25°C and a pH value of 7.2, a wavelength shift correction value of 0.1 nm and an intensity attenuation coefficient of 1.02 are calculated through the correction model, which are the first spectral correction parameters described above.

[0104] The preliminary correction is a process of adjusting the target spectral information by using the first spectral correction parameter to eliminate the spectral deviation (such as wavelength shift and intensity distortion) caused by the water area environment. The first corrected spectral information refers to spectral data that is closer to the true spectral characteristics of the pollutants after the preliminary correction and eliminates the interference of the water area environment.

[0105] The first spectrum correction parameter can be applied to the target spectrum information to correct the wavelength and intensity of the spectrum, such as adjusting the wavelength position of a characteristic peak according to the wavelength offset correction value, and correcting the amplitude of the spectrum signal according to the intensity attenuation coefficient. For example, the wavelength of a certain characteristic peak in the target spectrum is 280 nm, and the intensity is 0.8. After applying the first spectrum correction parameter (wavelength offset 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, obtaining the first corrected spectrum information.

[0106] The environmental interference information refers to the data of interference factors existing in the test environment that may affect the spectrum detection when the laser irradiates the water sample plate to be processed, such as the intensity of external electromagnetic interference, the intensity of ambient light, and the temperature of the detection equipment. The test environment refers to the on-site environment (such as a laboratory or an outdoor detection point) for laser irradiation and spectrum detection. It can be understood that the test environment and the original water environment of the sample to be detected can be different environments, which are not limited in the present application.

[0107] The environmental interference information can be collected in real time by sensors (such as electromagnetic interference sensors, light sensors, and temperature sensors) integrated on the detection equipment to extract the intensity of external electromagnetic interference, the intensity of ambient light, and the temperature of the equipment. For example, when detecting in a laboratory, the electromagnetic interference intensity of the laboratory can be collected as 5 V / m, the ambient light intensity can be collected as 100 lux, and the equipment temperature can be collected as 30°C, which is the above-mentioned environmental interference information.

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

[0109] The environmental interference information can be input into a preset interference correction model (such as a model that can be established by experimental data, which relates different test environment interference factors to spectrum deviation) to calculate the corresponding correction value. For example, according to the information of the external electromagnetic interference intensity of 5 V / m and the ambient light intensity of 100 lux, the noise suppression coefficient can be calculated as 0.98 and the light intensity compensation value can be calculated as 0.03 by the model, which is the above-mentioned second spectrum correction parameter.

[0110] The correction process refers to the process of using the second spectrum correction parameter to adjust the first corrected spectrum information twice to eliminate the spectrum deviation (such as noise superposition and light intensity interference) caused by the test environment interference. The second corrected spectrum information refers to the final spectrum data that completely eliminates the double interference of the water environment and the test environment and more truly and accurately reflects the characteristics of the pollutants after two corrections.

[0111] The second spectrum correction parameter can be applied to the first corrected spectrum information to further correct the noise and light intensity of the spectrum, such as filtering the interference noise in the spectrum according to the noise suppression coefficient, adjusting the signal amplitude according to the light intensity compensation value, etc. For example, there is a small amount of noise in the first corrected spectrum due to electromagnetic interference, and after applying the second spectrum correction parameter (noise suppression coefficient 0.98, light intensity compensation value 0.03), the noise in the first corrected spectrum is filtered, and the spectrum intensity is corrected to 0.846, thereby obtaining the second corrected spectrum information with regular waveform and clear characteristics.

[0112] Further, the second corrected spectrum information can be compared with a standard spectrum library of known pollutants to identify the type of pollutants through the matching degree of parameters such as characteristic peak position, intensity, and half-width; the concentration of pollutants can be calculated based on the quantitative relationship between the characteristic peak intensity and the concentration of pollutants (such as the Lambert-Beer law), and the corrected spectrum intensity can be combined to form complete pollutant information. For example, after comparing the second corrected spectrum with the standard spectrum library, the characteristic peak of heavy metal cadmium can be matched, and its concentration can be calculated by intensity, i.e. the above-mentioned pollutant information can be formed and obtained.

[0113] In this example, the two-stage correction design of water environment correction and test environment correction realizes accurate purification of spectrum information; the first environment parameter is used to eliminate the spectrum deviation caused by the original water environment of the water sample, and the second spectrum correction parameter is used to eliminate the environmental interference in the detection site, so that the second corrected spectrum information can truly reflect the characteristics of the pollutants. The whole process not only considers the difference in the original environment of the water sample, but also takes into account the interference factors in the detection site, effectively solves the problem of spectrum distortion caused by environmental interference, significantly improves the accuracy of pollutant type identification and the reliability of concentration calculation, and provides a key guarantee for high-precision detection of multi-parameter water quality analyzers.

[0114] For the above-mentioned embodiments, please refer to Figure 3 , Figure 3 A structure schematic diagram of a terminal provided by the embodiments of the present application is shown in the figure, which includes a processor, an input device, an output device, and a memory, and the processor, the input device, the output device, and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions, and the above-mentioned program includes instructions for executing 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 and processed to obtain a second detection electric signal; constructing spectrum according to the second detection electric signal to obtain target spectrum information; determining the pollutant information of the water sample to be processed according to the target spectrum information.

[0115] In this example, by using the photoelectric detection module to obtain the first detection light signal generated when the water sample to be processed is irradiated with laser, the first detection light signal can be further converted and processed to obtain the first detection electric signal, the first detection electric signal can be optimized and processed to obtain the second detection electric signal, and then the spectrum can be constructed according to the second detection electric signal to obtain the target spectrum information, and the pollutant information of the water sample to be processed can be determined according to the target spectrum information, which is beneficial to improve the accuracy of the signal processing process and helps to realize more accurate pollutant information identification process, and provides high-precision and high-reliability technical support for multi-parameter water quality analysis.

[0116] The above mainly introduces the scheme of the embodiments of the present application from the perspective of the execution process of the method. It can be understood that the terminal includes hardware structure and / or software modules corresponding to the execution of each function in order to realize the above functions. Those skilled in the art should easily realize that the units and algorithm steps of each example described in combination with the embodiments provided in the present text can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0117] The embodiments of the present application can divide the functional units of the terminal according to the above method examples, for example, each functional unit can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be realized in the form of hardware or software functional unit. The division of units in the embodiments of the present application is illustrative, and is only a logical function division. When actually implemented, there can be another division method.

[0118] Consistent with the above, please refer to Figure 4 , Figure 4 The present application provides a structural schematic diagram of a signal processing device of a multi-parameter water quality analyzer. As shown in Figure 4 , the device comprises: The acquisition unit 101 is configured to acquire a first detection light signal generated when a water sample to be processed is irradiated with laser; The first processing unit 102 is configured to convert and process the first detection light signal to obtain a first detection electric signal; The second processing unit 103 is configured to perform optimization processing on the first detection electric signal to obtain a second detection electric signal. The third processing unit 104 is configured to perform spectrum construction according to the second detection electric signal to obtain target spectrum information. The determining unit 105 is configured to determine pollutant information of the water sample to be processed according to the target spectrum information.

[0119] In a possible implementation, the second processing unit 103 is configured to perform optimization processing on the first detection electric signal to obtain a second detection electric signal, and specifically configured to: perform primary filtering processing on the first detection electric signal to obtain a first intermediate detection electric signal; perform amplification processing on the first intermediate detection electric signal to obtain a second intermediate detection electric signal; perform shaping processing on the second intermediate detection electric signal to obtain a third intermediate detection electric signal; perform secondary filtering processing on the third intermediate detection electric signal to obtain a second detection electric signal.

[0120] In a possible implementation, the third processing unit 104 is configured to perform spectrum construction according to the second detection electric signal to obtain target spectrum information, and specifically configured to: obtain a background light transmittance curve when the water sample to be processed is irradiated by laser; perform spectrum matrix construction according to the background light transmittance curve to obtain a target spectrum matrix; determine target spectrum information by using the target spectrum matrix and the second detection electric signal.

[0121] In a possible implementation, the third processing unit 104 is configured to determine target spectrum information by using the target spectrum matrix and the second detection electric signal, and specifically configured to: perform spectrum function construction according to the target spectrum matrix and the second detection electric signal to obtain a first spectrum function; perform linear expansion on the first spectrum function to obtain a weight coefficient and a Gaussian base function; perform reconstruction according to the weight coefficient and the Gaussian base function to obtain target spectrum information.

[0122] In a possible implementation, the determining unit 105 is configured to determine pollutant information of the water sample to be processed according to the target spectrum information, and specifically configured to: obtain region information of a sample to be detected in the water sample to be processed; extract environmental information according to the region information to obtain a first environmental parameter; determine a first spectral correction parameter according to the first environmental parameter; perform preliminary correction on the target spectral information by using the first spectral correction parameter to obtain first corrected spectral information; obtain environmental interference information when the laser irradiation is performed on the water sample to be processed; determine a second spectral correction parameter according to the environmental interference information; perform correction processing on the first corrected spectral information by using the second spectral correction parameter to obtain second corrected spectral information; determine the pollutant information of the water sample to be processed by using the second corrected spectral information.

[0123] The embodiment of the present application further provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program causes the computer to perform part or all steps of the signal processing method of the multi-parameter water quality analyzer described in any one of the above method embodiments.

[0124] The embodiment of the present application further provides a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, and the computer program causes the computer to perform part or all steps of the signal processing method of the multi-parameter water quality analyzer described in any one of the above method embodiments.

[0125] For each of the above method embodiments, in order to simply describe, each is described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the action order described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0126] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0127] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical or other forms.

[0128] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e., may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0129] In addition, the functional units in each embodiment of the application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software program module.

[0130] The integrated unit, if realized in the form of a software program module and sold or used as an independent product, can be stored in a computer readable memory. Based on this understanding, the technical solutions of the application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a memory and includes instructions for making a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the application. The aforementioned memory includes: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0131] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by programs instructing related hardware, and the programs can be stored in a computer readable memory, which can include a flash disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0132] The embodiments of the application are described in detail above, and the principles and implementation modes of the application are described by applying specific examples. The above description of the embodiments is only used to help understand the method of the application and its core idea; at the same time, for those skilled in the art, according to the idea of the application, the specific implementation mode and application range will be changed; in view of the above, the content of the specification should not be understood as a limitation of the application.

Claims

1. A signal processing method for a multi-parameter water quality analyzer, characterized by, The method comprises: The photoelectric detection module is used to obtain a first detection light signal generated when the water sample plate is irradiated with laser; The first detection light signal is converted to obtain a first detection electrical signal; The first detection electrical signal is optimized to obtain a second detection electrical signal; Spectrum construction is performed according to the second detection electrical signal to obtain target spectrum information; The target spectrum information is used to determine the pollutant information of the water sample plate.

2. The signal processing method of a multi-parameter water quality analyzer according to claim 1, characterized by, The first detection electrical signal is optimized to obtain a second detection electrical signal, which comprises: The first detection electrical signal is first filtered to obtain a first intermediate detection electrical signal; The first intermediate detection electrical signal is amplified to obtain a second intermediate detection electrical signal; The second intermediate detection electrical signal is shaped to obtain a third intermediate detection electrical signal; The third intermediate detection electrical signal is second filtered to obtain a second detection electrical signal.

3. The signal processing method of a multi-parameter water quality analyzer according to claim 2, characterized by, The spectrum construction is performed according to the second detection electrical signal to obtain target spectrum information, which comprises: A background light transmittance curve when the water sample plate is irradiated with laser is obtained; A spectrum matrix is constructed according to the background light transmittance curve to obtain a target spectrum matrix; The target spectrum matrix and the second detection electrical signal are used to determine target spectrum information.

4. The signal processing method of a multi-parameter water quality analyzer according to claim 3, characterized by, The target spectrum matrix and the second detection electrical signal are used to determine target spectrum information, which comprises: A spectrum function is constructed according to the target spectrum matrix and the second detection electrical signal to obtain a first spectrum function; The first spectrum function is linearly expanded to obtain a weight coefficient and a Gaussian basis function; The target spectrum information is reconstructed according to the weight coefficient and the Gaussian basis function.

5. The signal processing method of a multi-parameter water quality analyzer according to claim 3, wherein, The target spectrum information is used to determine the pollutant information of the water sample plate, which comprises: The region information of a sample to be detected in the water sample plate is obtained; The environment information is extracted according to the region information to obtain a first environment parameter; The first spectrum correction parameter is determined according to the first environment parameter; The first spectrum correction parameter is used to preliminarily correct the target spectrum information to obtain first corrected spectrum information; The environment interference information when the water sample plate is irradiated with laser is obtained; The second spectrum correction parameter is determined according to the environment interference information; The second spectrum correction parameter is used to correct the first corrected spectrum information to obtain second corrected spectrum information; The second corrected spectrum information is used to determine the pollutant information of the water sample plate.

6. A signal processing device for a multi-parameter water quality analyzer, characterized by The device comprises: An acquisition unit is configured to obtain a first detection light signal generated when a water sample plate is irradiated with laser; A first processing unit is configured to convert the first detection light signal to obtain a first detection electrical signal; A second processing unit is configured to optimize the first detection electrical signal to obtain a second detection electrical signal; A third processing unit is configured to perform spectrum construction according to the second detection electrical signal to obtain target spectrum information; A determination unit is configured to determine the pollutant information of the water sample plate according to the target spectrum information.

7. The signal processing device for a multiparameter water quality analyzer according to claim 6, wherein, The second processing unit is configured to perform optimization processing on the first detection electric signal to obtain a second detection electric signal, and specifically configured to: perform primary filtering processing on the first detection electric signal to obtain a first intermediate detection electric signal; perform amplification processing on the first intermediate detection electric signal to obtain a second intermediate detection electric signal; perform shaping processing on the second intermediate detection electric signal to obtain a third intermediate detection electric signal; perform secondary filtering processing on the third intermediate detection electric signal to obtain the second detection electric signal.

8. The signal processing device for a multiparameter water quality analyzer according to claim 7, wherein The third processing unit is configured to perform spectrum construction according to the second detection electric signal to obtain target spectrum information, and specifically configured to: obtain a background light transmittance curve when the laser irradiation is performed on the water sample plate to be processed; perform spectrum matrix construction according to the background light transmittance curve to obtain a target spectrum matrix; determine the target spectrum information by using the target spectrum matrix and the second detection electric signal.

9. A multi-parameter water quality analyzer characterized by, The multi-parameter water quality analyzer is configured to perform the signal processing method of the multi-parameter water quality analyzer according to any one of claims 1-5, and the multi-parameter water quality analyzer comprises a laser irradiation unit, a water sample preparation unit and a detection unit. The laser output by the laser irradiation unit irradiates a spot on the water sample preparation area on the water sample preparation unit through the end of the laser irradiation unit. The detection unit monitors spectrum information generated by the laser irradiation unit irradiating the spot.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program comprises program instructions. When the program instructions are executed by the processor, the processor executes the method according to any one of claims 1-5.

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