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

Abstract

The invention discloses an intelligent water quality detection method and system for a marine water supply system, and relates to the technical field of water quality detection. The method comprises the following steps: detecting to obtain first water quality information of a target water sample of marine water supply, detecting to obtain second water quality information in combination with a water quality database of the marine water supply, and forming target water quality information; calling a water quality total alkalinity analysis strategy to analyze the target water quality information to obtain a water quality total alkalinity coefficient; dynamically detecting 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; the water quality total alkalinity coefficient and the real-time water quality parameter set are combined to obtain the real-time total alkalinity of the marine water supply; if the real-time total alkalinity is not in the preset total alkalinity threshold value, water quality abnormity early warning is conducted on the marine water supply. The technical problems that in the prior art, water quality monitoring in a marine water supply system is not intelligent enough, and it is difficult to early warn water quality abnormity in time are solved, and the technical effect of improving the water quality management efficiency and the water quality safety of the marine water supply system is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quality detection, and particularly relates to a water quality intelligent detection method and system for a ship water supply system. BACKGROUND

[0002] In the ship water supply system, the safety and stability of water quality directly affect the health of the crew and the normal operation of the ship. The ship water supply not only needs to meet the daily drinking, washing and other living needs of the crew, but also may be related to the cooling, cleaning and other industrial uses of part of the equipment. However, due to the particularity of the ship navigation environment, such as the complexity and variability of the marine environment, the limited and closed internal space of the ship, and other factors, the water quality of the ship water supply is easily affected by various factors, such as microbial breeding and pipeline corrosion product mixing. The traditional water quality monitoring method mainly relies on manual sampling and laboratory analysis, which not only consumes a long time, but also cannot reflect the changes of water quality in real time, which may lead to the failure to discover water quality abnormalities in time and affect the safety of drinking water for the crew. In addition, the detection of key parameters such as total alkalinity in the water quality in the prior art usually adopts a static detection method, which is difficult to cope with the complex dynamic environmental changes of the ship, and lacks efficient and intelligent water quality monitoring means. SUMMARY

[0003] The present application provides a water quality intelligent detection method and system for a ship water supply system, which solves the technical problem that the water quality monitoring in the ship water supply system in the prior art is not intelligent enough and it is difficult to timely warn water quality abnormalities.

[0004] In a first aspect, the present application provides a water quality intelligent detection method for a ship water supply system, which comprises: detecting first water quality information of a target water sample of the ship water supply, and detecting second water quality information in combination with a water quality database of the ship water supply to form target water quality information; calling a total alkalinity analysis strategy to analyze the target water quality information to obtain a total alkalinity coefficient of water quality; dynamically detecting the ship water supply based on a predetermined detection frequency to obtain a real-time water quality parameter set of a real-time water sample; obtaining real-time total alkalinity of the ship water supply in combination with the total alkalinity coefficient of water quality and the real-time water quality parameter set; and if the real-time total alkalinity is not within a predetermined total alkalinity threshold, warning water quality abnormalities of the ship water supply.

[0005] In a second aspect, the present application provides a water quality intelligent detection system for a ship water supply system, which comprises: The water quality information acquisition module is configured to detect first water quality information of a target water sample of the ship water supply, and detect second water quality information in combination with a water quality database of the ship water supply to form target water quality information; the analysis module is configured to analyze the target water quality information by calling a water quality total alkalinity analysis strategy to obtain a water quality total alkalinity coefficient; the detection module is configured to dynamically detect the ship water supply based on a predetermined detection frequency to obtain a real-time water quality parameter set of a real-time water sample; the alkalinity calculation module is configured to obtain real-time total alkalinity of the ship water supply in combination with the water quality total alkalinity coefficient and the real-time water quality parameter set; and the early warning module is configured to perform water quality abnormality early warning on the ship water supply if the real-time total alkalinity is not within a predetermined total alkalinity threshold.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages: First, the first water quality information of the target water sample of the ship water supply is detected, and the second water quality information is detected in combination with the water quality database of the ship water supply to form the target water quality information. Then, the target water quality information is analyzed by calling the water quality total alkalinity analysis strategy to obtain the water quality total alkalinity coefficient. Then, the ship water supply is dynamically detected based on the predetermined detection frequency to obtain the real-time water quality parameter set of the real-time water sample. Further, the real-time total alkalinity of the ship water supply is obtained in combination with the water quality total alkalinity coefficient and the real-time water quality parameter set. Finally, if the real-time total alkalinity is not within the predetermined total alkalinity threshold, the ship water supply is subjected to water quality abnormality early warning. The technical problem of insufficient intelligentization of water quality monitoring in the ship water supply system and difficulty in timely early warning of water quality abnormality in the prior art is solved, and the technical effect of improving the water quality management efficiency and water quality safety of the ship water supply system is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0008] Figure 1 A water quality intelligent detection method flowchart for a ship water supply system provided by the embodiments of the present application; Figure 2 A water quality intelligent detection system structure diagram for a ship water supply system provided by the embodiments of the present application.

[0009] Legend: water quality information acquisition module 11, analysis module 12, detection module 13, alkalinity calculation module 14, early warning module 15. DETAILED DESCRIPTION

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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: 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.

[0014] 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.

[0015] Further, the second water quality information is detected in combination with the water quality database of the ship water supply, including: The preset water quality concentration threshold is obtained by analyzing the historical water quality concentration records in the water quality database, wherein the preset water quality concentration threshold includes the first water quality concentration; the 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 by the micro spectrometer; the second water quality information is formed based on the corresponding relationship between the first water quality concentration and the first transmission spectrum data.

[0016] Preferably, the water quality database of the ship water supply system is accessed, and the database stores detection information of a plurality of historical water samples, including a water quality concentration label, a detection time, corresponding water environment parameters (such as temperature, pressure), and historical spectrum measurement data, etc.; by statistically analyzing the historical water quality concentration data in the water quality database, the distribution state of the water quality concentration can be modeled and classified by using methods such as K-means clustering analysis, histogram density estimation, or quantile division, a plurality of typical water quality concentration values are determined, and their fluctuation ranges are further determined, thereby forming a plurality of preset water quality concentration thresholds. The preset water quality concentration thresholds cover high, medium, and low concentration levels, and are used for subsequent comparison and modeling, wherein at least one first water quality concentration is selected as a representative.

[0017] According to the requirement of the first water quality concentration, the experimental grade pure water and the standard solution are used for artificial preparation to form the first standard water sample with accurate concentration. The preparation of the standard water sample is performed in accordance with the national laboratory water standard (such as GB / T 6682-2008), so as to ensure that the water quality composition in the preparation process is not polluted by other impurities, and the accuracy is guaranteed by using clean containers, constant volume operation, and other methods.

[0018] The first standard water sample is detected by using an integrated micro-spectrometer device. The micro-spectrometer is a visible-near infrared transmission spectrometer based on a MEMS structure, the measurement wavelength range is 400-1100 nm, the spectral sampling interval is not higher than 5 nm, and the automatic integration time adjustment function is used to adapt to the changes of light transmission intensity of samples with different concentrations. After the obtained raw spectral data is preprocessed by Savitzky-Golay smoothing, baseline drift removal, standardization and the like, stable first transmission spectral data is output, which is used as a representative reference sample for subsequent analysis. The above-mentioned first water quality concentration and the obtained first transmission spectral data are in one-to-one correspondence, and the function model (such as polynomial fitting, support vector regression SVR or BP neural network) is constructed by taking the sample points and combining similar data of other concentration points in the database, to form a complete concentration-spectrum mapping model. Based on the concentration-spectrum mapping model, a basic information structure for water quality concentration inversion can be formed, which is denoted as second water quality information.

[0019] The water quality total alkalinity analysis strategy is called to analyze the target water quality information to obtain a water quality total alkalinity coefficient.

[0020] In the embodiments of the present application, the system calls the water quality total alkalinity analysis strategy to process and analyze the target water quality information to obtain a water quality total alkalinity coefficient. Specifically, the target water quality information includes first water quality information obtained by real-time detection and second water quality information obtained by water quality database analysis; the system extracts a plurality of alkalinity-related parameter variables from the target water quality information, including but not limited to pH value, conductivity, total dissolved solids concentration (TDS) and multi-dimensional transmission spectral characteristic value, etc.; the system performs normalization processing on the above-mentioned parameters, and performs fitting analysis based on a pre-trained regression model or machine learning model (such as a multiple linear regression model, a support vector regression SVR or a shallow neural network model), wherein the water quality total alkalinity analysis strategy model takes a water quality parameter set as input and takes a total alkalinity value in a historical sample as a training label, thereby establishing a mapping relationship between the water quality parameters and the total alkalinity, and finally outputting a water quality total alkalinity coefficient of the current target water sample. The water quality total alkalinity coefficient is used to characterize the relative contribution degree of each characteristic variable to the total alkalinity under different water quality conditions.

[0021] Further, the water quality total alkalinity analysis strategy is called to analyze the target water quality information to obtain a water quality total alkalinity coefficient, including: acquire an arbitrary water quality concentration, and match arbitrary transmittance spectrum data corresponding to the arbitrary water quality concentration in the second water quality information; analyze multi-domain spectrum feature information of the arbitrary transmittance spectrum data to obtain an arbitrary transmittance spectrum feature value; extract a first total alkalinity in the first water quality information, and take the first total alkalinity as a dependent variable; a first water quality parameter set in the first water quality information and the arbitrary transmittance spectrum feature value form an independent variable; perform correlation analysis on the independent variable and the dependent variable according to the water quality total alkalinity analysis strategy to obtain an arbitrary total alkalinity coefficient corresponding to the arbitrary water quality concentration; and form the water quality total alkalinity coefficient based on a corresponding relationship between the arbitrary water quality concentration and the arbitrary total alkalinity coefficient.

[0022] First, an arbitrary water quality concentration is selected from the target water quality information constructed as a representative concentration value for current analysis. Then, transmittance spectrum data corresponding to the arbitrary water quality concentration is searched and matched in the second water quality information, to ensure that the acquired spectrum data and the concentration level have a one-to-one corresponding relationship in the water quality database. Next, the arbitrary transmittance spectrum data matched is analyzed in depth to extract its multi-domain spectrum feature information. The multi-domain spectrum feature information includes but is not limited to time domain features (such as transmittance trend, extreme points, etc.), frequency domain features (such as main frequency distribution, spectrum energy concentration area, etc.), and time-frequency fusion domain features (such as time-frequency distribution pattern after wavelet decomposition, etc.), and through the above feature extraction process, a complete set of arbitrary transmittance spectrum feature values is formed.

[0023] After acquiring the complete spectrum feature information, the first total alkalinity value of the current water sample is further extracted from the first water quality information, which is used as a dependent variable in the analysis modeling. At the same time, a first water quality parameter set (such as pH, conductivity, dissolved solid content, etc.) is extracted from the first water quality information, and combined with the arbitrary transmittance spectrum feature value to form an independent variable set. Next, a water quality total alkalinity analysis strategy model is called to perform correlation modeling analysis on the relationship between the above dependent variable and independent variable. The strategy can be a statistical analysis or machine learning model such as multiple linear regression, principal component regression, support vector machine regression, neural network, etc. Through the analysis, the mapping relationship between the total alkalinity influence factor and the feature value under the current water quality concentration condition is obtained, and the arbitrary total alkalinity coefficient corresponding to the arbitrary water quality concentration is generated accordingly. Finally, the total alkalinity coefficients corresponding to multiple different water quality concentrations are mapped and induced to construct a complete concentration-alkalinity coefficient relationship table to obtain the water quality total alkalinity coefficient.

[0024] Further, analyzing the multi-domain spectrum feature information of the arbitrary transmittance spectrum data to obtain an arbitrary transmittance spectrum feature value includes: reading 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 performing feature collection of the preset time-domain features, the preset frequency-domain features and the preset time-frequency fusion domain features on the arbitrary transmission spectrum data to obtain arbitrary time-domain feature parameters, arbitrary frequency-domain feature parameters and arbitrary time-frequency fusion domain feature parameters respectively; and 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 spectrum feature information.

[0025] reading a preset multi-domain feature parameter set through a system call, wherein the multi-domain feature parameter set includes three typical feature domains: time-domain features, frequency-domain features and time-frequency fusion domain features. The preset time-domain features are used to capture the original change trend of the spectrum signal on the time axis or the wavelength axis, and commonly include average transmittance, maximum / minimum transmittance, change rate, slope distribution, wave peak / trough position and the like; the preset frequency-domain features are used to reveal the frequency spectrum structure information of the spectrum signal after Fourier transform, and typical indexes include main frequency component, frequency spectrum energy concentration interval, frequency band width, frequency spectrum dispersion and the like; and the preset time-frequency fusion domain features use wavelet packet decomposition, short-time Fourier transform and the like to perform localization processing on the spectrum signal to extract its frequency domain behavior and structure distribution in a specific time window to obtain parameters such as wavelet energy distribution diagram, principal component wavelet coefficient and sub-band feature mean value.

[0026] After completing the feature domain loading, the system sequentially applies various feature extraction algorithms to the arbitrary transmission spectrum data. Specifically, first, time-domain processing is performed on the spectrum signal to obtain arbitrary time-domain feature parameters; then, frequency-domain analysis is performed to extract arbitrary frequency-domain feature parameters; subsequently, wavelet transform and the like time-frequency analysis operation is performed to obtain arbitrary time-frequency fusion domain feature parameters. Finally, the parameter sets in the above three feature dimensions are structured and integrated to constitute complete multi-domain spectrum feature information, which constitutes the arbitrary transmission spectrum feature value and can be used as a high-dimensional input variable of a subsequent alkalinity prediction model, and has good distinguishability and representativeness.

[0027] Further, sequentially performing feature collection of the preset time-domain features, the preset frequency-domain features and the preset time-frequency fusion domain features on the arbitrary transmission spectrum data to obtain arbitrary time-domain feature parameters, arbitrary frequency-domain feature parameters and arbitrary time-frequency fusion domain feature parameters includes: segmenting the arbitrary transmission spectrum data to obtain a segmentation result; extracting a first transmission spectrum component in the segmentation result, and analyzing a ratio of a first information value of the first transmission spectrum component to an arbitrary information value of the arbitrary transmission spectrum data, denoted as a first component value coefficient; descending the first component value coefficient to obtain a transmission spectrum component sequence, and extracting a first transmission spectrum component in the transmission spectrum component sequence; and taking a characteristic parameter of the first transmission spectrum component as the arbitrary time-frequency fusion domain characteristic parameter.

[0028] Preferably, the arbitrary transmission spectrum data is preprocessed and segmented, and the segmentation manner can be window division according to a wavelength range, equal-interval slicing or local extreme points to obtain a segmentation result composed of multiple independent spectrum components. Subsequently, each transmission spectrum component is extracted from the segmentation result one by one, and a first information value of each spectrum component is analyzed by using a metric method such as information entropy, mutual information or variance contribution rate. At the same time, the overall arbitrary transmission spectrum data is also subjected to the same value evaluation to obtain an arbitrary information value thereof. Further, a ratio between the information value of each component and the overall information value is taken as an evaluation index of the component, defined as a first component value coefficient, which represents the relative information importance of the spectrum component in the overall data.

[0029] After the first component value coefficients of all the spectrum components are calculated, the coefficients are sorted in descending order to obtain a transmission spectrum component sequence arranged according to information contribution degrees. In the sequence, the first transmission spectrum component is a sub-section with the highest information value in the current data and has the most significant distinguishing feature. The system further extracts a key characteristic parameter such as a wavelet coefficient, a local spectral density or a window principal component value from the first transmission spectrum component as a representative information, defined as an arbitrary time-frequency fusion domain characteristic parameter.

[0030] Based on a predetermined detection frequency, the marine water supply is dynamically detected to obtain a real-time water quality parameter set of a real-time water sample.

[0031] In the embodiments of the present application, the system performs a continuous dynamic detection operation on the marine water supply based on a predetermined detection frequency, thereby obtaining each water quality parameter of a current water sample in real time to form a real-time water quality parameter set. The predetermined detection frequency is preset by the system, and is usually set according to a ship navigation cycle, a water quality stability evaluation result and a water supply system operation characteristic, for example, once every hour, once every thirty minutes, or triggering an immediate detection at a key node (such as water source replacement or system maintenance).

[0032] For example, the system automatically collects target water samples through a sensing sampling component arranged in a water supply main pipe or a water storage device, and performs multi-dimensional synchronous detection on the sampled water samples by a configured spectral detection module, a conductivity sensor, a pH probe, a residual chlorine detector, and other multi-parameter water quality sensing equipment. The detection content can include but is not limited to the temperature, conductivity, pH value, turbidity, total dissolved solids (TDS), oxidation-reduction potential (ORP), residual chlorine content, ion concentration (such as Ca 2 ⁺, Mg 2 ⁺, Cl⁻, etc.), and spectral response characteristics of the water sample.

[0033] The real-time total alkalinity of the marine water supply is obtained by combining the water quality total alkalinity coefficient with the real-time water quality parameter set.

[0034] The real-time water quality concentration information corresponding to the current sampled water sample is extracted from the real-time water quality parameter set as a key index for matching the water quality total alkalinity coefficient. The system retrieves the total alkalinity coefficient item that is closest to or most matched with the real-time water quality concentration from the pre-constructed water quality total alkalinity coefficient, i.e., the real-time total alkalinity coefficient. Based on the real-time total alkalinity coefficient and the real-time water quality parameter, a weighted calculation is performed to obtain the real-time total alkalinity of the marine water supply.

[0035] Further, the real-time total alkalinity of the marine water supply is obtained by combining the water quality total alkalinity coefficient with the real-time water quality parameter set, including: The real-time water quality concentration in the real-time water quality parameter set is extracted; the real-time total alkalinity coefficient corresponding to the real-time water quality concentration is matched in the water quality total alkalinity coefficient; and the real-time total alkalinity is obtained by combining the real-time total alkalinity coefficient with the real-time water quality parameter set.

[0036] The system extracts the real-time water quality concentration of the current water sample from the real-time water quality parameter set. This water quality concentration reflects the ion composition or dissolved substance level under the current water supply state as a key variable, and serves as a matching index for searching in the water quality total alkalinity coefficient database. In the matching process, the system preferentially selects the concentration item that is equal to or closest to the real-time water quality concentration value, and extracts the real-time total alkalinity coefficient corresponding thereto. The system jointly processes the real-time total alkalinity coefficient and the current real-time water quality parameter set, i.e., performs weighted calculation, so that each parameter participates in the operation under the 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 the corresponding weight coefficients, plus a constant term, to obtain the final prediction value.

[0037] Further, 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: 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 dynamically detected; the spectral deviation index is obtained by comparing the standard transmission spectrum data with the real-time transmission spectrum data; and the real-time total alkalinity is calibrated by taking the spectral deviation index as a weight.

[0038] 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 dynamically detected; the spectral deviation index is obtained by comparing the standard transmission spectrum data with the real-time transmission spectrum data; and the real-time total alkalinity is calibrated by taking the spectral deviation index as a weight.

[0039] Further, the spectral deviation index is obtained by comparing the standard transmission spectrum data with the real-time transmission spectrum data, comprising: The standard curve of the standard transmission spectrum data and the real-time curve of the real-time transmission spectrum data are respectively obtained by curve processing; the maximum curve distance is obtained by comparing the standard curve with the real-time curve; and the spectral deviation index is obtained by normalizing the maximum curve distance.

[0040] The system processes the two sets of original transmission spectrum data into corresponding spectral curve forms, respectively obtaining a standard curve of the standard transmission spectrum data and a real-time curve of the real-time transmission spectrum data. The curve processing usually includes smoothing, interpolation and normalization of the spectral data, etc. preprocessing steps to eliminate noise and data sampling differences, and to ensure the continuity and comparability of the spectral curve. The system calculates the maximum curve distance between the standard curve and the real-time curve by comparing the differences between them in the entire wavelength range, wherein 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 degree of the two curves, and is an important indicator for measuring spectral differences. The system normalizes the obtained maximum curve distance to convert the distance into a dimensionless spectral deviation index, usually using the maximum possible distance or a preset threshold for normalization to ensure the stability and consistency of the spectral deviation index value.

[0041] If the real-time total alkalinity is not within the predetermined total alkalinity threshold, an abnormal water quality warning is given for the marine water supply.

[0042] If the detected real-time total alkalinity value is not within the predetermined total alkalinity threshold range, the system will automatically trigger an abnormal water quality warning for the marine water supply. This warning mechanism is based on the pre-set total alkalinity normal range threshold, which is determined according to the process requirements and safety standards of the marine water supply system. When the real-time total alkalinity exceeds the upper and lower limits, it indicates that the water quality may have abnormal changes, such as scaling risk caused by high alkalinity, or corrosion problems caused by low alkalinity.

[0043] Once the abnormal warning is triggered, the system will timely remind the relevant management personnel through the predetermined alarm mode (such as audible and visual alarm, SMS notification, remote monitoring platform push, etc.) to prompt them to respond quickly and take appropriate measures. In addition, the warning information can also be used to start the automatic adjustment device to adjust the water quality parameters to ensure the safe operation of the system and the stability of the water quality. Through the real-time total alkalinity threshold monitoring and abnormal warning mechanism, the marine water supply system can realize intelligent risk prevention and control and efficient operation and maintenance management.

[0044] Further, before the abnormal water quality warning is given for the marine water supply if the real-time total alkalinity is not within the predetermined total alkalinity threshold, it further includes: If the real-time total alkalinity is not within the predetermined total alkalinity threshold, the system collects corrosion features of the system equipment of the marine water supply to obtain a corrosion feature set; analyzes the corrosion feature set to obtain a real-time corrosion index of the system equipment; if the real-time corrosion index reaches a predetermined corrosion threshold, the system equipment is subjected to emergency maintenance treatment.

[0045] Before detecting that the real-time total alkalinity is not at the predetermined total alkalinity threshold, thereby preparing to give a water quality abnormality early warning for the ship water supply, the system also monitors and analyzes the corrosion state of the ship water supply system equipment. Specifically, the system first collects corrosion-related feature data through the installed sensors or collection devices for the water supply pipeline, valves and related equipment, forms a corrosion feature set, which may include a number of indicators such as metal surface damage degree, electrochemical corrosion rate, and metal ion concentration change. Subsequently, the system uses a pre-set corrosion analysis model to comprehensively analyze the collected corrosion feature set, and calculates the real-time corrosion index of the system equipment. The corrosion index, as a quantitative indicator, reflects the current corrosion risk level and its development trend of the equipment. When the real-time corrosion index reaches or exceeds the predetermined corrosion threshold, it indicates that the equipment has serious corrosion hidden dangers. The system will automatically trigger an emergency maintenance processing program, and timely notify the maintenance personnel to maintain or replace the equipment, to prevent system failure or safety accidents caused by corrosion.

[0046] In summary, the embodiments of the present application have at least the following technical effects: First, the first water quality information of the target water sample of the ship water supply is detected, and the second water quality information is detected in combination with the water quality database of the ship water supply to form the target water quality information. Then, the water quality total alkalinity analysis strategy is called to analyze the target water quality information to obtain the water quality total alkalinity coefficient. Then, the ship water supply is dynamically detected based on the predetermined detection frequency to obtain the real-time water quality parameter set of the real-time water sample. Further, the real-time total alkalinity of the ship water supply is obtained in combination with the water quality total alkalinity coefficient and the real-time water quality parameter set. Finally, if the real-time total alkalinity is not at the predetermined total alkalinity threshold, the ship water supply is given a water quality abnormality early warning. The technical problem of insufficient intelligentization of water quality monitoring in the ship water supply system and difficulty in timely early warning of water quality abnormality in the prior art is solved, and the technical effects of improving the water quality management efficiency and water quality safety of the ship water supply system are achieved.

[0047] Embodiment two, based on the same inventive concept as the water quality intelligent detection method for the ship water supply system in the foregoing embodiments, as shown in Figure 2 The present application provides a water quality intelligent detection system for a ship water supply system, wherein the system comprises: The water quality information acquisition module 11 is configured to detect first water quality information of a target water sample of the ship water supply, and detect second water quality information in combination with a water quality database of the ship water supply to form target water quality information; the analysis module 12 is configured to analyze the target water quality information by calling a water quality total alkalinity analysis strategy to obtain a water quality total alkalinity coefficient; the detection module 13 is configured to perform dynamic detection on the ship water supply based on a predetermined detection frequency to obtain a real-time water quality parameter set of a real-time water sample; the alkalinity calculation module 14 is configured to obtain real-time total alkalinity of the ship water supply in combination with the water quality total alkalinity coefficient and the real-time water quality parameter set; and the early warning module 15 is configured to perform water quality abnormality early warning on the ship water supply if the real-time total alkalinity is not within a predetermined total alkalinity threshold.

[0048] Further, the water quality information acquisition module 11 is configured to perform the following method: A preset water quality concentration threshold is obtained by analyzing historical water quality concentration records in a 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 first transmission spectrum data of the first standard water sample is detected by a micro spectrometer; and the second water quality information is formed based on a corresponding relationship between the first water quality concentration and the first transmission spectrum data.

[0049] Further, the analysis module 12 is configured to perform the following method: An arbitrary water quality concentration is obtained, and arbitrary transmission spectrum data corresponding to the arbitrary water quality concentration in the second water quality information is matched; multi-domain spectral feature information of the arbitrary transmission spectrum data is analyzed to obtain an arbitrary transmission spectrum feature value; a first total alkalinity in the first water quality information is extracted, and the first total alkalinity is taken as a dependent variable; a first water quality parameter set in the first water quality information and the arbitrary transmission spectrum feature value form independent variables; a correlation analysis is performed on the independent variables and the dependent variable according to the water quality total alkalinity analysis strategy to obtain an arbitrary total alkalinity coefficient corresponding to the arbitrary water quality concentration; and the water quality total alkalinity coefficient is formed based on a corresponding relationship between the arbitrary water quality concentration and the arbitrary total alkalinity coefficient.

[0050] Further, the analysis module 12 is configured to perform the following method: A preset multi-domain feature is read, wherein the preset multi-domain feature includes a preset time domain feature, a preset frequency domain feature, and a preset time-frequency fusion domain feature; the arbitrary transmission spectrum data is sequentially subjected to feature collection of the preset time domain feature, the preset frequency domain feature, and the preset time-frequency fusion domain feature to respectively obtain an arbitrary time domain feature parameter, an arbitrary frequency domain feature parameter, and an arbitrary time-frequency fusion domain feature parameter; and the arbitrary time domain feature parameter, the arbitrary frequency domain feature parameter, and the arbitrary time-frequency fusion domain feature parameter form the multi-domain spectral feature information.

[0051] Further, the analysis module 12 is configured to execute the following method: Segmenting the arbitrary transmission spectrum data to obtain a segmentation result; extracting a first transmission spectrum component in the segmentation result, and analyzing to obtain a ratio of a first information value of the first transmission spectrum component to an arbitrary information value of the arbitrary transmission spectrum data, denoted as a first component value coefficient; descending the first component value coefficient to obtain a transmission spectrum component sequence, and extracting a first transmission spectrum component in the transmission spectrum component sequence; and taking a characteristic parameter of the first transmission spectrum component as the arbitrary time-frequency fusion domain characteristic parameter.

[0052] Further, the alkalinity calculation module 14 is configured to execute the following method: Extracting a real-time water quality concentration in the real-time water quality parameter set; matching a real-time total alkalinity coefficient corresponding to the real-time water quality concentration in the water quality total alkalinity coefficient; and combining the real-time total alkalinity coefficient and the real-time water quality parameter set to obtain the real-time total alkalinity.

[0053] Further, the alkalinity calculation module 14 is configured to execute the following method: Matching standard transmission spectrum data corresponding to the real-time water quality concentration in the second water quality information; dynamically detecting real-time transmission spectrum data of the marine water supply; comparing the standard transmission spectrum data and the real-time transmission spectrum data to obtain a spectrum deviation index; and calibrating the real-time total alkalinity by taking the spectrum deviation index as a weight.

[0054] Further, the alkalinity calculation module 14 is configured to execute the following method: Obtaining a standard curve of the standard transmission spectrum data and a real-time curve of the real-time transmission spectrum data by curve processing, respectively; comparing the standard curve and the real-time curve to obtain a maximum curve distance; and normalizing the maximum curve distance to obtain the spectrum deviation index.

[0055] Further, the early warning module 15 is configured to execute the following method: If the real-time total alkalinity is not in the predetermined total alkalinity threshold, collecting corrosion features of system equipment of the marine water supply to obtain a corrosion feature set; analyzing the corrosion feature set to obtain a real-time corrosion index of the system equipment; and if the real-time corrosion index reaches a predetermined corrosion threshold, performing emergency maintenance processing on the system equipment.

[0056] It should be noted that the above-mentioned embodiment sequences of the present application are merely for description only, but not for representing the advantages and disadvantages of the embodiments. And the above-mentioned embodiment sequences of the present application are described in the specification. The processes depicted in the drawings do not necessarily require the particular sequence or continuous sequence shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0057] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0058] The specification and drawings are merely exemplary of the present application, and any and all modifications, variations, combinations or equivalents that are within the scope of the present application should be included. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for intelligent detection of water quality for a ship water supply system, characterized in that, The method comprises: detecting first water quality information of a target water sample of marine water supply, and detecting second water quality information in combination with a water quality database of the marine water supply to form target water quality information; calling a water quality total alkalinity analysis strategy to analyze the target water quality information to obtain a water quality total alkalinity coefficient; based on a predetermined detection frequency, dynamically detecting the marine water supply to obtain a real-time water quality parameter set of a real-time water sample; combining the water quality total alkalinity coefficient and the real-time water quality parameter set to obtain real-time total alkalinity of the marine water supply; if the real-time total alkalinity is not within a predetermined total alkalinity threshold, issuing a water quality abnormality warning for the marine water supply.

2. The water quality intelligent detection method for the water supply system of a ship according to claim 1, characterized in that, In combination with the water quality database of the marine water supply, the second water quality information is detected, comprising: obtaining a preset water quality concentration threshold by analyzing historical water quality concentration records in the water quality database, wherein the preset water quality concentration threshold comprises a first water quality concentration; preparing a first standard water sample with the first water quality concentration as a constraint, and detecting the first standard water sample by a micro spectrometer to obtain first transmission spectrum data; forming the second water quality information based on a corresponding relationship between the first water quality concentration and the first transmission spectrum data.

3. The water quality intelligent detection method for the water supply system of a ship according to claim 1, characterized in that, The water quality total alkalinity analysis strategy is called to analyze the target water quality information to obtain a water quality total alkalinity coefficient, comprising: obtaining an arbitrary water quality concentration and matching arbitrary transmission spectrum data corresponding to the arbitrary water quality concentration in the second water quality information; analyzing multi-domain spectral feature information of the arbitrary transmission spectrum data to obtain an arbitrary transmission spectrum feature value; extracting a first total alkalinity in the first water quality information, and taking the first total alkalinity as a dependent variable; a first water quality parameter set in the first water quality information and the arbitrary transmission spectrum feature value form an independent variable; according to the water quality total alkalinity analysis strategy, performing correlation analysis on the independent variable and the dependent variable to obtain an arbitrary total alkalinity coefficient corresponding to the arbitrary water quality concentration; forming the water quality total alkalinity coefficient based on a corresponding relationship between the arbitrary water quality concentration and the arbitrary total alkalinity coefficient.

4. The water quality intelligent detection method for the water supply system of a ship according to claim 3, characterized in that, The multi-domain spectral feature information of the arbitrary transmission spectrum data is analyzed to obtain an arbitrary transmission spectrum feature value, comprising: reading a preset multi-domain feature, wherein the preset multi-domain feature comprises a preset time domain feature, a preset frequency domain feature and a preset time-frequency fusion domain feature; sequentially performing feature collection of the preset time domain feature, the preset frequency domain feature and the preset time-frequency fusion domain feature on the arbitrary transmission spectrum data to respectively obtain an arbitrary time domain feature parameter, an arbitrary frequency domain feature parameter and an arbitrary time-frequency fusion domain feature parameter; the arbitrary time domain feature parameter, the arbitrary frequency domain feature parameter and the arbitrary time-frequency fusion domain feature parameter form the multi-domain spectral feature information.

5. The water quality intelligent detection method for the water supply system of a ship according to claim 4, characterized in that, The multi-domain spectral feature information of the arbitrary transmission spectrum data is analyzed to obtain an arbitrary transmission spectrum feature value, comprising: segmenting the arbitrary transmission spectrum data to obtain a segmentation result; extracting a first transmittance spectral component in the segmentation result, and analyzing a ratio of a first information value of the first transmittance spectral component to an arbitrary information value of the arbitrary transmittance spectral data, denoted as a first component value coefficient; descending the first component value coefficient to obtain a transmittance spectral component sequence, and extracting a first transmittance spectral component in the transmittance spectral component sequence; taking a characteristic parameter of the first transmittance spectral component as the arbitrary time-frequency fusion domain characteristic parameter.

6. The water quality intelligent detection method for the water supply system of a ship according to claim 1, characterized in that, combining the water quality total alkalinity coefficient and the real-time water quality parameter set to obtain the real-time total alkalinity of the marine water supply, including: extracting a real-time water quality concentration in the real-time water quality parameter set; matching a real-time total alkalinity coefficient corresponding to the real-time water quality concentration in the water quality total alkalinity coefficient; combining the real-time total alkalinity coefficient and the real-time water quality parameter set to obtain the real-time total alkalinity.

7. The water quality intelligent detection method for the water supply system of a ship according to claim 6, characterized in that, After combining the real-time total alkalinity coefficient and the real-time water quality parameter set to obtain the real-time total alkalinity, further including: matching standard transmittance spectral data corresponding to the real-time water quality concentration in the second water quality information; dynamically detecting the real-time transmittance spectral data of the marine water supply; comparing the standard transmittance spectral data and the real-time transmittance spectral data to obtain a spectral deviation index; calibrating the real-time total alkalinity by taking the spectral deviation index as a weight.

8. The water quality intelligent detection method for the water supply system of a ship according to claim 7, characterized in that, comparing the standard transmittance spectral data and the real-time transmittance spectral data to obtain a spectral deviation index, including: respectively obtaining a standard curve of the standard transmittance spectral data and a real-time curve of the real-time transmittance spectral data through curve processing; comparing the standard curve and the real-time curve to obtain a maximum curve distance; normalizing the maximum curve distance to obtain the spectral deviation index.

9. The water quality intelligent detection method for the water supply system of a ship according to claim 1, characterized in that, If the real-time total alkalinity is not in a predetermined total alkalinity threshold, before the water quality abnormality early warning of the marine water supply, further including: If the real-time total alkalinity is not in the predetermined total alkalinity threshold, collecting corrosion features of a system device of the marine water supply to obtain a corrosion feature set; analyzing the corrosion feature set to obtain a real-time corrosion index of the system device; if the real-time corrosion index reaches a predetermined corrosion threshold, performing emergency maintenance processing on the system device.

10. A water quality intelligent detection system for a ship water supply system, characterized in that, The water quality intelligent detection method for a marine water supply system of any one of claims 1-9, the system comprising: a water quality information acquisition module, configured to detect first water quality information of a target water sample of a marine water supply, and combine a water quality database of the marine water supply to detect second water quality information, to form target water quality information; an analysis module, configured to call a water quality total alkalinity analysis strategy to analyze the target water quality information to obtain a water quality total alkalinity coefficient; a detection module, configured to dynamically detect the marine water supply based on a predetermined detection frequency to obtain a real-time water quality parameter set of a real-time water sample; an alkalinity calculation module, configured to combine the water quality total alkalinity coefficient and the real-time water quality parameter set to obtain a real-time total alkalinity of the marine water supply; an early warning module, configured to perform water quality abnormality early warning on the marine water supply if the real-time total alkalinity is not in a predetermined total alkalinity threshold.

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