Intelligent water quality detection method and system for marine water supply systems

Abstract

This invention discloses an intelligent water quality detection method and system for marine water supply systems, relating to the field of water quality detection technology. The method includes: detecting first water quality information of a target water sample for marine water supply, and combining this with a marine water supply water quality database to detect second water quality information, thus forming target water quality information; retrieving a total alkalinity analysis strategy to analyze the target water quality information to obtain a total alkalinity coefficient; dynamically detecting the marine water supply based on a predetermined detection frequency to obtain a real-time water quality parameter set for the real-time water sample; combining the total alkalinity coefficient and the real-time water quality parameter set to obtain the real-time total alkalinity of the marine water supply; and issuing an early warning for water quality anomalies if the real-time total alkalinity is not at a predetermined total alkalinity threshold. This invention solves the technical problem of insufficient intelligence in water quality monitoring and difficulty in timely early warning of water quality anomalies in existing marine water supply systems, achieving the technical effect of improving the efficiency of water quality management and water quality safety in marine water supply systems.

Description

Technical Field

[0001] This invention relates to the field of water quality testing technology, and specifically to an intelligent water quality testing method and system for marine water supply systems. Background Technology

[0002] In marine water supply systems, the safety and stability of water quality directly impact the health of crew members and the normal operation of the vessel. Marine water supply not only needs to meet the daily drinking and washing needs of crew members but may also be used for industrial purposes such as cooling and cleaning of equipment. However, due to the unique characteristics of the marine environment, such as its complexity and variability, and the limited and enclosed space within the vessel, the quality of marine water supply is easily affected by various factors, such as microbial growth and the contamination of pipeline corrosion products. Traditional water quality monitoring methods rely heavily on manual sampling and laboratory analysis, which are time-consuming and cannot reflect changes in water quality in real time, easily leading to the failure to detect water quality anomalies in a timely manner, thus affecting the safety of drinking water for crew members. Furthermore, current technologies for detecting key parameters such as total alkalinity in water typically employ static detection methods, which are insufficient to cope with the complex dynamic environmental changes on board ships, lacking efficient and intelligent water quality monitoring methods. Summary of the Invention

[0003] This application provides a method and system for intelligent water quality detection in marine water supply systems, which solves the technical problem that water quality monitoring in existing marine water supply systems is not intelligent enough and it is difficult to provide timely early warning of water quality anomalies.

[0004] The first aspect of this application provides a method for intelligent water quality detection in a marine water supply system, the method comprising:

[0005] The system detects first water quality information of the target water sample for marine water supply, and combines this with the marine water supply water quality database to detect second water quality information, thus forming the target water quality information. It then retrieves a total alkalinity analysis strategy to analyze the target water quality information, obtaining a total alkalinity coefficient. Based on a predetermined detection frequency, it dynamically monitors the marine water supply to obtain a real-time water quality parameter set for the real-time water sample. Combining the total alkalinity coefficient with the real-time water quality parameter set, it obtains the real-time total alkalinity of the marine water supply. If the real-time total alkalinity is not at a predetermined total alkalinity threshold, it issues a water quality anomaly warning for the marine water supply.

[0006] A second aspect of this application provides a smart water quality detection system for marine water supply systems, the system comprising:

[0007] The system includes a water quality information acquisition module for detecting the first water quality information of a target water sample for marine water supply, and combining this information with the marine water supply water quality database to detect the second water quality information, thus forming the target water quality information. An analysis module is used to retrieve a total alkalinity analysis strategy to analyze the target water quality information and obtain the total alkalinity coefficient. A detection module is used to dynamically detect the marine water supply based on a predetermined detection frequency to obtain a real-time water quality parameter set for the real-time water sample. An alkalinity calculation module is used to combine the total alkalinity coefficient and the real-time water quality parameter set to obtain the real-time total alkalinity of the marine water supply. An early warning module is used to issue an early warning for abnormal water quality in the marine water supply if the real-time total alkalinity is not at a predetermined total alkalinity threshold.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the primary water quality information of the target water sample for marine water supply is obtained, and combined with the marine water supply water quality database, secondary water quality information is obtained, forming the target water quality information. Next, a total alkalinity analysis strategy is used to analyze the target water quality information to obtain the total alkalinity coefficient. Then, the marine water supply is dynamically monitored based on a predetermined detection frequency to obtain a real-time water quality parameter set for the real-time water sample. Further, the real-time total alkalinity of the marine water supply is obtained by combining the total alkalinity coefficient and the real-time water quality parameter set. Finally, if the real-time total alkalinity is not at a predetermined total alkalinity threshold, an early warning of water quality anomalies is issued for the marine water supply. This solves the technical problem of insufficient intelligence in water quality monitoring and difficulty in timely early warning of water quality anomalies in existing marine water supply systems, achieving the technical effect of improving the efficiency of water quality management and water quality safety in marine water supply systems. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of the intelligent water quality detection method for marine water supply systems provided in this application embodiment;

[0012] Figure 2 This is a schematic diagram of the intelligent water quality detection system for marine water supply systems provided in an embodiment of this application.

[0013] Figure labeling: Water quality information acquisition module 11, analysis module 12, detection module 13, alkalinity calculation module 14, early warning module 15. Detailed Implementation

[0014] This application provides a method and system for intelligent water quality detection in marine water supply systems, which solves the technical problem that water quality monitoring in existing marine water supply systems is not intelligent enough and it is difficult to provide timely early warning of water quality anomalies.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides a method for intelligent water quality detection in marine water supply systems, wherein the method includes:

[0018] The first water quality information of the target water sample for marine water supply is obtained by detection, and the second water quality information is obtained by combining the water quality database of marine water supply, thus forming the target water quality information.

[0019] In this embodiment, a target water sample is collected from the marine water supply system. The target water sample is then preliminarily tested using an online water quality detection device equipped with a micro-sensor module to obtain first water quality information. This first water quality information includes multiple parameters such as total alkalinity, nitrite concentration, water temperature, and pH value. These parameters can be obtained in real time by corresponding electrode-type sensors or optical sensors. The detection frequency can be set to once every 10 minutes based on the operating status of the marine water supply system. The system calls a preset marine water supply water quality database, which stores historical water quality data of the marine water supply system under different operating cycles and environmental conditions, including the concentration composition, environmental parameters, and transmission spectrum data of each historical sample. By comparing the currently detected first water quality information with the historical records in the database, using a least Euclidean distance or correlation matching algorithm, several historical sample information most closely related to the current water sample are retrieved from the database. The spectral data features of these samples and their corresponding water quality concentration labels are extracted to form second water quality information. The second water quality information includes the typical transmission spectrum of the historical sample, the corresponding water quality concentration threshold, and its statistical fluctuation range. The system integrates the first water quality information obtained from the above detection with the second water quality information obtained from database reasoning to generate target water quality information, which is used for subsequent total alkalinity analysis and dynamic early warning judgment.

[0020] Furthermore, by combining the aforementioned marine water supply water quality database, second water quality information is obtained, including:

[0021] A preset water quality concentration threshold is obtained by analyzing historical water quality concentration records in the water quality database, wherein the preset water quality concentration threshold includes a first water quality concentration; a first standard water sample is prepared with the first water quality concentration as a constraint, and the first standard water sample is detected by a micro spectrometer to obtain first transmission spectrum data; and second water quality information is formed based on the correspondence between the first water quality concentration and the first transmission spectrum data.

[0022] Preferably, the system accesses the water quality database of the marine water supply system. This database stores detection information for multiple historical water samples, including water quality concentration labels, detection time, corresponding water environment parameters (such as temperature and pressure), and historical spectral measurement data. Statistical analysis of the historical water quality concentration data in the database is performed, using methods such as K-means clustering, histogram density estimation, or quantile partitioning, to model and classify the distribution of water quality concentrations. This identifies multiple typical water quality concentration values ​​and further determines their fluctuation range, thereby forming multiple preset water quality concentration thresholds. These preset water quality concentration thresholds cover high, medium, and low concentration levels for subsequent comparison and modeling, and at least one is selected as a representative first water quality concentration.

[0023] To meet the first water quality concentration requirements, laboratory-grade pure water and standard solutions were artificially prepared to form a first standard water sample with accurate concentration. The preparation of the standard water sample was carried out in accordance with national laboratory water standards (such as GB / T 6682-2008) to ensure that the water components were not contaminated by other impurities during the preparation process. At the same time, clean containers and volume adjustment operations were used to ensure accuracy.

[0024] A first standard water sample was subjected to spectral analysis using an integrated micro-spectrometer device. This micro-spectrometer, a MEMS-based visible-near-infrared transmission spectrometer, measured wavelengths from 400 to 1100 nm with a sampling interval of no more than 5 nm. It features automatic integration time adjustment to accommodate variations in transmittance at different concentrations. The raw spectral data underwent preprocessing operations such as Savitzky-Golay smoothing, baseline drift removal, and standardization to output stable first transmission spectral data, which served as a representative reference sample for subsequent analysis. A one-to-one correspondence was established between the first water quality concentration and the obtained first transmission spectral data. Using this as a sample point, a function model (such as multinomial fitting, support vector regression (SVR), or backpropagation neural network) was constructed by combining similar data from other concentration points in the database, forming a complete concentration-spectrum mapping model. Based on this concentration-spectrum mapping model, a fundamental information structure for water quality concentration inversion was formed, denoted as the second water quality information.

[0025] The total alkalinity analysis strategy is used to analyze the target water quality information to obtain the total alkalinity coefficient.

[0026] In this embodiment, the system retrieves a total alkalinity analysis strategy to process and analyze the target water quality information to obtain the total alkalinity coefficient. Specifically, the target water quality information includes first water quality information obtained from real-time detection and second water quality information obtained from water quality database analysis. The system extracts multiple alkalinity-related parameter variables from the target water quality information, including but not limited to pH value, conductivity, total dissolved solids (TDS), and multidimensional transmission spectral characteristic values. The system normalizes the above parameters and performs fitting analysis based on a pre-trained regression model or machine learning model (e.g., multiple linear regression model, support vector regression (SVR), or shallow neural network model). The total alkalinity analysis strategy model takes a set of water quality parameters as input and uses the total alkalinity value in historical samples as training labels to establish a mapping relationship between water quality parameters and total alkalinity. Finally, it outputs the total alkalinity coefficient of the current target water sample. The total alkalinity coefficient is used to characterize the relative contribution of each characteristic variable to the total alkalinity under different water quality conditions.

[0027] Furthermore, the total alkalinity analysis strategy is used to analyze the target water quality information to obtain the total alkalinity coefficient, including:

[0028] Obtain any water quality concentration and match it with any transmission spectrum data corresponding to the arbitrary water quality concentration in the second water quality information; analyze the multi-domain spectral feature information of the arbitrary transmission spectrum data to obtain arbitrary transmission spectrum feature values; extract the first total alkalinity from the first water quality information and use the first total alkalinity as the dependent variable; the first water quality parameter set in the first water quality information and the arbitrary transmission spectrum feature values ​​constitute independent variables; perform correlation analysis on the independent variables and the dependent variables according to the water quality total alkalinity analysis strategy to obtain the arbitrary total alkalinity coefficient corresponding to the arbitrary water quality concentration; form the water quality total alkalinity coefficient based on the correspondence between the arbitrary water quality concentration and the arbitrary total alkalinity coefficient.

[0029] First, an arbitrary water quality concentration is selected from the constructed target water quality information as the representative concentration value for the current analysis. Then, transmission spectrum data corresponding to this arbitrary water quality concentration is retrieved and matched from the second water quality information, ensuring a one-to-one correspondence between the acquired spectral data and the concentration level in the water quality database. Next, the matched arbitrary transmission spectrum data is analyzed in depth to extract its multi-domain spectral feature information. This multi-domain spectral feature information includes, but is not limited to, time-domain features (such as transmittance variation trends and extreme points), frequency-domain features (such as dominant frequency distribution and concentrated spectral energy regions), and time-frequency fusion domain features (such as time-frequency distribution patterns after wavelet decomposition). Through this feature extraction process, a complete set of arbitrary transmission spectrum feature values ​​is formed.

[0030] After obtaining complete spectral feature information, the first total alkalinity value of the current water sample is further extracted from the first water quality information, and this value serves as the dependent variable in the analytical modeling. Simultaneously, a first set of water quality parameters (e.g., pH, conductivity, dissolved solids content, etc.) is extracted from the first water quality information and combined with the arbitrary transmission spectral feature values ​​to form a set of independent variables. Next, the total alkalinity analysis strategy model is invoked to perform correlation modeling analysis on the relationship between the above dependent and independent variables. This strategy can be a statistical analysis or machine learning model such as multiple linear regression, principal component regression, support vector machine regression, or neural networks. The mapping relationship between the total alkalinity influencing factors and feature values ​​under the current water quality concentration conditions is obtained through analysis, and an arbitrary total alkalinity coefficient corresponding to that arbitrary water quality concentration is generated accordingly. Finally, the total alkalinity coefficients corresponding to multiple different water quality concentrations are mapped and summarized to construct a complete concentration-alkalinity coefficient relationship table to obtain the total alkalinity coefficient of the water quality.

[0031] Furthermore, analyzing the multi-domain spectral feature information of the arbitrary transmission spectral data to obtain arbitrary transmission spectral feature values ​​includes:

[0032] Read preset multi-domain features, wherein the preset multi-domain features include preset time-domain features, preset frequency-domain features, and preset time-frequency fusion domain features; sequentially collect features from the preset time-domain features, preset frequency-domain features, and preset time-frequency fusion domain features from the arbitrary transmission spectral data to obtain arbitrary time-domain feature parameters, arbitrary frequency-domain feature parameters, and arbitrary time-frequency fusion domain feature parameters, respectively; the arbitrary time-domain feature parameters, the arbitrary frequency-domain feature parameters, and the arbitrary time-frequency fusion domain feature parameters constitute the multi-domain spectral feature information.

[0033] The system calls a preset multi-domain feature parameter set, which includes three typical feature domains: time-domain features, frequency-domain features, and time-frequency fusion domain features. Preset time-domain features capture the original variation trend of the spectral signal along the time or wavelength axis, often including average transmittance, maximum / minimum transmittance, rate of change, slope distribution, and peak / trough positions. Preset frequency-domain features reveal the spectral structure information of the spectral signal after Fourier transform, with typical indicators including dominant frequency components, concentrated spectral energy ranges, bandwidth, and spectral dispersion. Preset time-frequency fusion domain features use wavelet packet decomposition and short-time Fourier transform to localize the spectral signal, extracting its frequency domain behavior and structural distribution within a specific time window, obtaining parameters such as wavelet energy distribution maps, principal component wavelet coefficients, and sub-band feature mean values.

[0034] After loading the feature domain, the system sequentially applies various feature extraction algorithms to arbitrary transmission spectral data. Specifically, first, the spectral signal is processed in the time domain to obtain arbitrary time-domain feature parameters; then, frequency domain analysis is performed to extract arbitrary frequency-domain feature parameters; subsequently, time-frequency analysis operations such as wavelet transform are performed to obtain arbitrary time-frequency fusion domain feature parameters. Finally, the parameter sets from the above three feature dimensions are structurally integrated to form complete multi-domain spectral feature information. This information constitutes the arbitrary transmission spectral feature value, which can be used as a high-dimensional input variable for the subsequent alkalinity prediction model, exhibiting good discriminative power and representativeness.

[0035] Furthermore, the arbitrary transmission spectral data is sequentially subjected to feature collection using the preset time-domain features, the preset frequency-domain features, and the preset time-frequency fusion domain features to obtain arbitrary time-domain feature parameters, arbitrary frequency-domain feature parameters, and arbitrary time-frequency fusion domain feature parameters, including:

[0036] The arbitrary transmission spectral data is segmented to obtain a segmentation result; the first transmission spectral component is extracted from the segmentation result, and the ratio of the first information value of the first transmission spectral component to the arbitrary information value of the arbitrary transmission spectral data is analyzed and denoted as the first component value coefficient; the first component value coefficient is sorted in descending order to obtain a transmission spectral component sequence, and the first transmission spectral component in the transmission spectral component sequence is extracted; the characteristic parameters of the first transmission spectral component are used as the arbitrary time-frequency fusion domain characteristic parameters.

[0037] Preferably, the arbitrary transmission spectral data is preprocessed and segmented. Segmentation can be based on wavelength range, equally spaced slices, or local extrema to create windows, resulting in a segmentation result composed of multiple independent spectral components. Subsequently, each transmission spectral component is extracted from the segmentation result, and its primary information value is analyzed using metrics such as information entropy, mutual information, and variance contribution rate. Simultaneously, the overall arbitrary transmission spectral data undergoes the same value assessment to obtain its arbitrary information value. Furthermore, the ratio of the information value of each component to the overall information value is used as the evaluation index for that component, defined as the primary component value coefficient. This coefficient characterizes the relative information importance of that spectral component within the overall data.

[0038] After calculating the first component value coefficients of all spectral components, these coefficients are sorted in descending order to obtain a sequence of transmission spectral components arranged according to their information contribution. In this sequence, the first transmission spectral component is the sub-segment with the highest information value in the current data, possessing the most significant distinguishing features. The system further extracts key feature parameters from this first transmission spectral component, such as wavelet coefficients, local spectral density, and principal component values ​​within the window, using them as representative information and defining them as feature parameters in the arbitrary time-frequency fusion domain.

[0039] The ship's water supply is dynamically monitored based on a predetermined detection frequency to obtain a real-time water quality parameter set for the real-time water sample.

[0040] In this embodiment, the system performs continuous dynamic monitoring of the ship's water supply based on a predetermined monitoring frequency, thereby acquiring various water quality parameters of the current water sample in real time and forming a real-time water quality parameter set. The predetermined monitoring frequency is preset by the system and is usually set according to the ship's navigation cycle, water quality stability assessment results, and the operating characteristics of the water supply system. For example, it can be once per hour, once every thirty minutes, or triggered for immediate monitoring at key nodes (such as after water source replacement or system maintenance).

[0041] For example, the system automatically collects target water samples through sensing and sampling components installed in the main water supply pipeline or water storage device. The sampled water samples are then simultaneously analyzed from multiple dimensions by configured multi-parameter water quality sensing devices, such as a spectral detection module, conductivity sensor, pH probe, and residual chlorine detector. The detected parameters may include, but are not limited to, the water sample's temperature, conductivity, pH value, turbidity, total dissolved solids (TDS), oxidation-reduction potential (ORP), residual chlorine content, and the concentration of various ions (such as Ca). 2+ Mg 2+ Cl - (etc.) and spectral response characteristics, etc.

[0042] The real-time total alkalinity of the marine water supply is obtained by combining the total alkalinity coefficient of the water with the real-time water quality parameter set.

[0043] The system extracts the real-time water quality concentration information corresponding to the currently sampled water sample from the real-time water quality parameter set, serving as a key index for matching with the total alkalinity coefficient. The system retrieves the total alkalinity coefficient entry that is closest to or best matches the current real-time water quality concentration from a pre-built set of total alkalinity coefficients; this is the real-time total alkalinity coefficient. Based on a weighted calculation using the real-time total alkalinity coefficient and the real-time water quality parameters, the real-time total alkalinity of the marine water supply is obtained.

[0044] Furthermore, the real-time total alkalinity of the marine water supply is obtained by combining the total alkalinity coefficient of the water with the real-time water quality parameter set, including:

[0045] Extract the real-time water quality concentration from the real-time water quality parameter set; match the real-time total alkalinity coefficient corresponding to the real-time water quality concentration in the total alkalinity coefficient; combine the real-time total alkalinity coefficient with the real-time water quality parameter set to obtain the real-time total alkalinity.

[0046] The system extracts the real-time water quality concentration of the current water sample from the real-time water quality parameter set. This concentration serves as a key variable reflecting the ionic composition or dissolved substance level under the current water supply conditions and is used as a matching index to search the total alkalinity coefficient database. During the matching process, the system prioritizes selecting the concentration item that is equal to or closest to the real-time water quality concentration value and extracts its corresponding real-time total alkalinity coefficient. The system then performs joint processing of the real-time total alkalinity coefficient and the current real-time water quality parameter set, i.e., performs weighted calculations, incorporating each parameter into the calculation under coefficient control, and outputs the current real-time total alkalinity value. For example, the real-time total alkalinity value can be represented as a linear combination of multiple water quality parameters multiplied by their corresponding weight coefficients, with a constant term added to obtain the final predicted value.

[0047] Furthermore, after obtaining the real-time total alkalinity by combining the real-time total alkalinity coefficient with the real-time water quality parameter set, the method further includes:

[0048] In the second water quality information, the standard transmission spectrum data corresponding to the real-time water quality concentration is matched; the real-time transmission spectrum data of the marine water supply is obtained by dynamic detection; the spectral deviation index is obtained by comparing the standard transmission spectrum data and the real-time transmission spectrum data; and the real-time total alkalinity is calibrated using the spectral deviation index as a weight.

[0049] In the second water quality information section, based on the currently extracted real-time water quality concentration, corresponding standard transmission spectrum data is matched and obtained. This standard spectrum data represents the ideal or reference spectral response characteristics under that concentration condition. A spectral detection device installed in the water supply system dynamically acquires the real-time transmission spectrum data of the current marine water supply to reflect the actual optical changes in water quality. The system compares the standard transmission spectrum data with the real-time transmission spectrum data and calculates the spectral deviation index between them. This index quantifies the degree of optical characteristic deviation caused by water quality changes by analyzing the differences between the two sets of spectral curves (e.g., changes in absorption peaks within the wavelength range, spectral intensity shifts, etc.). The spectral deviation index can be obtained by normalizing the maximum curve distance or other statistical measurement methods to ensure good representativeness and stability within its numerical range. The system uses this spectral deviation index as a weighting factor to dynamically calibrate the previously calculated real-time total alkalinity. By adjusting the real-time total alkalinity value, its sensitivity and accuracy in responding to actual water quality changes are improved, further enhancing the real-time performance and reliability of total alkalinity estimation, thereby strengthening the overall performance of intelligent water quality detection in the marine water supply system.

[0050] Furthermore, the spectral deviation index is obtained by comparing the standard transmission spectral data with the real-time transmission spectral data, including:

[0051] By curve processing, the standard curve of the standard transmission spectrum data and the real-time curve of the real-time transmission spectrum data are obtained respectively; the maximum curve distance is obtained by comparing the standard curve and the real-time curve; the maximum curve distance is normalized to obtain the spectral deviation index.

[0052] The system performs curve processing on two sets of raw transmission spectrum data, converting them into corresponding spectral curves to obtain a standard curve for the standard transmission spectrum data and a real-time curve for the real-time transmission spectrum data. Curve processing typically includes preprocessing steps such as smoothing, interpolation, and normalization of the spectral data to eliminate noise and data sampling differences, ensuring the continuity and comparability of the spectral curves. The system calculates the maximum curve distance between the standard curve and the real-time curve by comparing their differences across the entire wavelength range. The maximum curve distance refers to the maximum absolute difference between the corresponding values ​​of the standard curve and the real-time curve at all wavelength points, reflecting the maximum deviation between the two curves and serving as an important indicator of spectral difference. The system normalizes the obtained maximum curve distance, converting it into a dimensionless spectral deviation index. Normalization is typically performed using the maximum possible distance or a preset threshold to ensure the stability and consistency of the spectral deviation index value.

[0053] If the real-time total alkalinity is not at the predetermined total alkalinity threshold, an abnormal water quality warning will be issued for the marine water supply.

[0054] If the detected real-time total alkalinity value is not within the predetermined total alkalinity threshold range, the system will automatically trigger an alert for abnormal water quality in marine water supply. This alert mechanism is based on pre-set normal total alkalinity thresholds, which are determined according to the process requirements and safety standards of the marine water supply system. When the real-time total alkalinity exceeds the upper or lower limit, it indicates that there may be abnormal changes in water quality, such as excessively high alkalinity leading to scaling risk, or excessively low alkalinity causing corrosion problems.

[0055] Once an anomaly warning is triggered, the system will promptly alert relevant management personnel through pre-defined alarm methods (such as audible and visual alarms, SMS notifications, and push notifications from remote monitoring platforms), prompting them to respond quickly and take appropriate measures. Furthermore, the warning information can also be used to activate automatic adjustment devices to adjust water quality parameters, ensuring system operational safety and water quality stability. Through this real-time total alkalinity threshold monitoring and anomaly warning mechanism, marine water supply systems can achieve intelligent risk prevention and control and efficient operation and maintenance management.

[0056] Furthermore, before issuing a water quality anomaly warning for the marine water supply if the real-time total alkalinity is not at a predetermined total alkalinity threshold, the method further includes:

[0057] If the real-time total alkalinity is not at the predetermined total alkalinity threshold, corrosion characteristics are collected from the marine water supply system equipment to obtain a corrosion characteristic set; the corrosion characteristic set is analyzed to obtain the real-time corrosion index of the system equipment; if the real-time corrosion index reaches the predetermined corrosion threshold, emergency repairs are performed on the system equipment.

[0058] Before issuing an early warning for abnormal water quality in the marine water supply system, the system monitors and analyzes the corrosion status of the equipment. Specifically, the system first collects corrosion-related characteristic data from the water supply pipelines, valves, and related equipment using installed sensors or acquisition devices, forming a corrosion feature set. This feature set may include multiple indicators such as the degree of metal surface damage, electrochemical corrosion rate, and changes in metal ion concentration. Subsequently, the system uses a preset corrosion analysis model to comprehensively analyze the collected corrosion feature set, calculating the real-time corrosion index of the system equipment. This corrosion index serves as a quantitative indicator, reflecting the current corrosion risk level and its development trend. When the real-time corrosion index reaches or exceeds the predetermined corrosion threshold, it indicates a serious corrosion hazard. The system will automatically trigger an emergency maintenance procedure, promptly notifying maintenance personnel to perform equipment maintenance or replacement to prevent system failures or safety accidents caused by corrosion.

[0059] In summary, the embodiments of this application have at least the following technical effects:

[0060] First, the primary water quality information of the target water sample for marine water supply is obtained, and combined with the marine water supply water quality database, secondary water quality information is obtained, forming the target water quality information. Next, a total alkalinity analysis strategy is used to analyze the target water quality information to obtain the total alkalinity coefficient. Then, the marine water supply is dynamically monitored based on a predetermined detection frequency to obtain a real-time water quality parameter set for the real-time water sample. Further, the real-time total alkalinity of the marine water supply is obtained by combining the total alkalinity coefficient and the real-time water quality parameter set. Finally, if the real-time total alkalinity is not at a predetermined total alkalinity threshold, an early warning of water quality anomalies is issued for the marine water supply. This solves the technical problem of insufficient intelligence in water quality monitoring and difficulty in timely early warning of water quality anomalies in existing marine water supply systems, achieving the technical effect of improving the efficiency of water quality management and water quality safety in marine water supply systems.

[0061] Example 2, based on the same inventive concept as the intelligent water quality detection method for marine water supply systems in the foregoing examples, such as... Figure 2 As shown, this application provides an intelligent water quality detection system for marine water supply systems, wherein the system includes:

[0062] The water quality information acquisition module 11 is used to detect the first water quality information of the target water sample for marine water supply, and combine it with the water quality database of the marine water supply to detect the second water quality information, thus forming the target water quality information; the analysis module 12 is used to retrieve the total alkalinity analysis strategy to analyze the target water quality information and obtain the total alkalinity coefficient; the detection module 13 is used to dynamically detect the marine water supply based on a predetermined detection frequency to obtain the real-time water quality parameter set of the real-time water sample; the alkalinity calculation module 14 is used to combine the total alkalinity coefficient and the real-time water quality parameter set to obtain the real-time total alkalinity of the marine water supply; the early warning module 15 is used to issue an early warning of water quality anomalies to the marine water supply if the real-time total alkalinity is not at a predetermined total alkalinity threshold.

[0063] Furthermore, the water quality information acquisition module 11 is used to perform the following method:

[0064] A preset water quality concentration threshold is obtained by analyzing historical water quality concentration records in the water quality database, wherein the preset water quality concentration threshold includes a first water quality concentration; a first standard water sample is prepared with the first water quality concentration as a constraint, and the first standard water sample is detected by a micro spectrometer to obtain first transmission spectrum data; and second water quality information is formed based on the correspondence between the first water quality concentration and the first transmission spectrum data.

[0065] Furthermore, the analysis module 12 is used to perform the following methods:

[0066] Obtain any water quality concentration and match it with any transmission spectrum data corresponding to the arbitrary water quality concentration in the second water quality information; analyze the multi-domain spectral feature information of the arbitrary transmission spectrum data to obtain arbitrary transmission spectrum feature values; extract the first total alkalinity from the first water quality information and use the first total alkalinity as the dependent variable; the first water quality parameter set in the first water quality information and the arbitrary transmission spectrum feature values ​​constitute independent variables; perform correlation analysis on the independent variables and the dependent variables according to the water quality total alkalinity analysis strategy to obtain the arbitrary total alkalinity coefficient corresponding to the arbitrary water quality concentration; form the water quality total alkalinity coefficient based on the correspondence between the arbitrary water quality concentration and the arbitrary total alkalinity coefficient.

[0067] Furthermore, the analysis module 12 is used to perform the following methods:

[0068] Read preset multi-domain features, wherein the preset multi-domain features include preset time-domain features, preset frequency-domain features, and preset time-frequency fusion domain features; sequentially collect features from the preset time-domain features, preset frequency-domain features, and preset time-frequency fusion domain features from the arbitrary transmission spectral data to obtain arbitrary time-domain feature parameters, arbitrary frequency-domain feature parameters, and arbitrary time-frequency fusion domain feature parameters, respectively; the arbitrary time-domain feature parameters, the arbitrary frequency-domain feature parameters, and the arbitrary time-frequency fusion domain feature parameters constitute the multi-domain spectral feature information.

[0069] Furthermore, the analysis module 12 is used to perform the following methods:

[0070] The arbitrary transmission spectral data is segmented to obtain a segmentation result; the first transmission spectral component is extracted from the segmentation result, and the ratio of the first information value of the first transmission spectral component to the arbitrary information value of the arbitrary transmission spectral data is analyzed and denoted as the first component value coefficient; the first component value coefficient is sorted in descending order to obtain a transmission spectral component sequence, and the first transmission spectral component in the transmission spectral component sequence is extracted; the characteristic parameters of the first transmission spectral component are used as the arbitrary time-frequency fusion domain characteristic parameters.

[0071] Furthermore, the alkalinity calculation module 14 is used to perform the following method:

[0072] Extract the real-time water quality concentration from the real-time water quality parameter set; match the real-time total alkalinity coefficient corresponding to the real-time water quality concentration in the total alkalinity coefficient; combine the real-time total alkalinity coefficient with the real-time water quality parameter set to obtain the real-time total alkalinity.

[0073] Furthermore, the alkalinity calculation module 14 is used to perform the following method:

[0074] In the second water quality information, the standard transmission spectrum data corresponding to the real-time water quality concentration is matched; the real-time transmission spectrum data of the marine water supply is obtained by dynamic detection; the spectral deviation index is obtained by comparing the standard transmission spectrum data and the real-time transmission spectrum data; and the real-time total alkalinity is calibrated using the spectral deviation index as a weight.

[0075] Furthermore, the alkalinity calculation module 14 is used to perform the following method:

[0076] By curve processing, the standard curve of the standard transmission spectrum data and the real-time curve of the real-time transmission spectrum data are obtained respectively; the maximum curve distance is obtained by comparing the standard curve and the real-time curve; the maximum curve distance is normalized to obtain the spectral deviation index.

[0077] Furthermore, the early warning module 15 is used to perform the following methods:

[0078] If the real-time total alkalinity is not at the predetermined total alkalinity threshold, corrosion characteristics are collected from the marine water supply system equipment to obtain a corrosion characteristic set; the corrosion characteristic set is analyzed to obtain the real-time corrosion index of the system equipment; if the real-time corrosion index reaches the predetermined corrosion threshold, emergency repairs are performed on the system equipment.

[0079] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0080] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0081] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for intelligent water quality detection in marine water supply systems, characterized in that, The method includes: The first water quality information of the target water sample for marine water supply is obtained by detection, and the second water quality information is obtained by combining the water quality database of marine water supply, thus forming the target water quality information; The target water quality information is analyzed by retrieving the total alkalinity analysis strategy to obtain the total alkalinity coefficient. The total alkalinity coefficient is used to characterize the relative contribution of each characteristic variable to the total alkalinity under different water quality conditions. The ship's water supply is dynamically monitored based on a predetermined detection frequency to obtain a real-time water quality parameter set for the real-time water sample. The real-time total alkalinity of the marine water supply is obtained by combining the total alkalinity coefficient of the water with the real-time water quality parameter set. If the real-time total alkalinity is not at the predetermined total alkalinity threshold, an abnormal water quality warning will be issued for the marine water supply. The second water quality information obtained from the detection includes: A preset water quality concentration threshold is obtained by analyzing historical water quality concentration records in the water quality database, wherein the preset water quality concentration threshold includes a first water quality concentration; A first standard water sample is prepared with the first water quality concentration as a constraint, and the first transmission spectrum data is obtained by detecting the first standard water sample using a micro spectrometer; The second water quality information is formed based on the correspondence between the first water quality concentration and the first transmission spectral data; The total alkalinity coefficient of the water includes: Obtain any water quality concentration and match any transmission spectrum data corresponding to the arbitrary water quality concentration in the second water quality information; Analyze the multi-domain spectral feature information of the arbitrary transmission spectral data to obtain arbitrary transmission spectral feature values; Extract the first total alkalinity from the first water quality information, and use the first total alkalinity as the dependent variable; The first set of water quality parameters in the first water quality information and the arbitrary transmission spectrum feature value constitute the independent variable; Based on the water quality total alkalinity analysis strategy, a correlation analysis is performed on the independent variable and the dependent variable to obtain the arbitrary total alkalinity coefficient corresponding to any water quality concentration; The total alkalinity coefficient of water quality is formed based on the correspondence between the arbitrary water quality concentration and the arbitrary total alkalinity coefficient; The real-time total alkalinity of the marine water supply is obtained by: Extract the real-time water quality concentration from the set of real-time water quality parameters; The total alkalinity coefficient of the water quality is matched with the real-time total alkalinity coefficient corresponding to the real-time water quality concentration; The real-time total alkalinity is obtained by combining the real-time total alkalinity coefficient with the real-time water quality parameter set.

2. The intelligent water quality detection method for marine water supply systems according to claim 1, characterized in that, Analyzing the multi-domain spectral feature information of the arbitrary transmission spectral data yields arbitrary transmission spectral feature values, including: Read preset multi-domain features, wherein the preset multi-domain features include preset time-domain features, preset frequency-domain features, and preset time-frequency fusion domain features; The preset time-domain features, preset frequency-domain features, and preset time-frequency fusion domain features are sequentially collected from the arbitrary transmission spectral data to obtain arbitrary time-domain feature parameters, arbitrary frequency-domain feature parameters, and arbitrary time-frequency fusion domain feature parameters, respectively. The arbitrary time-domain feature parameters, the arbitrary frequency-domain feature parameters, and the arbitrary time-frequency fusion domain feature parameters constitute the multi-domain spectral feature information.

3. The intelligent water quality detection method for marine water supply systems according to claim 2, characterized in that, The arbitrary transmission spectral data is sequentially subjected to feature collection using the preset time-domain features, the preset frequency-domain features, and the preset time-frequency fusion domain features to obtain arbitrary time-domain feature parameters, arbitrary frequency-domain feature parameters, and arbitrary time-frequency fusion domain feature parameters, including: The arbitrary transmission spectrum data is segmented to obtain the segmentation result; Extract the first transmission spectral component from the segmentation result, and analyze the ratio of the first information value of the first transmission spectral component to the arbitrary information value of the arbitrary transmission spectral data, which is denoted as the first component value coefficient. The first component value coefficient is sorted in descending order to obtain the transmission spectrum component sequence, and the first transmission spectrum component in the transmission spectrum component sequence is extracted; The characteristic parameters of the first transmission spectral component are used as the characteristic parameters of the arbitrary time-frequency fusion domain.

4. The intelligent water quality detection method for marine water supply systems according to claim 1, characterized in that, After obtaining the real-time total alkalinity by combining the real-time total alkalinity coefficient with the real-time water quality parameter set, the method further includes: Match the standard transmission spectrum data corresponding to the real-time water quality concentration in the second water quality information; The real-time transmission spectrum data of the marine water supply was obtained through dynamic detection; The spectral deviation index is obtained by comparing the standard transmission spectral data with the real-time transmission spectral data. The real-time total alkalinity is calibrated using the spectral deviation index as a weight.

5. The intelligent water quality detection method for marine water supply systems according to claim 4, characterized in that, The spectral deviation index is obtained by comparing the standard transmission spectral data with the real-time transmission spectral data, including: By curve processing, the standard curve of the standard transmission spectrum data and the real-time curve of the real-time transmission spectrum data are obtained respectively. The maximum curve distance is obtained by comparing the standard curve with the real-time curve; The maximum curve distance is normalized to obtain the spectral deviation index.

6. The intelligent water quality detection method for marine water supply systems according to claim 1, characterized in that, Before issuing a water quality anomaly warning for the marine water supply if the real-time total alkalinity is not at a predetermined total alkalinity threshold, the method further includes: If the real-time total alkalinity is not at the predetermined total alkalinity threshold, corrosion characteristics are collected from the marine water supply system equipment to obtain a corrosion characteristic set. The real-time corrosion index of the system equipment is obtained by analyzing the corrosion feature set; If the real-time corrosion index reaches the predetermined corrosion threshold, emergency repairs will be carried out on the system equipment.

7. A smart water quality detection system for marine water supply systems, characterized in that, The system is used to implement the intelligent water quality detection method for a marine water supply system according to any one of claims 1-6, the system comprising: The water quality information acquisition module is used to detect and obtain the first water quality information of the target water sample for marine water supply, and combine it with the water quality database of marine water supply to detect and obtain the second water quality information, thus forming the target water quality information. The analysis module is used to retrieve the total alkalinity analysis strategy to analyze the target water quality information and obtain the total alkalinity coefficient. The detection module is used to dynamically detect the marine water supply based on a predetermined detection frequency to obtain a real-time water quality parameter set of the real-time water sample. An alkalinity calculation module is used to combine the total alkalinity coefficient of the water quality with the real-time water quality parameter set to obtain the real-time total alkalinity of the marine water supply. The early warning module is used to issue an early warning of abnormal water quality for the marine water supply if the real-time total alkalinity is not at a predetermined total alkalinity threshold.

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