A multi-stage fingerprint spectrum construction method for river section

Abstract

The application discloses a kind of river section multistage fingerprint spectrum construction method, device, electronic equipment and storage medium and computer program product, method specifically includes: obtaining the water environment information of river section, then using online timing water sampling equipment carries out the water sample collection of river section, according to the information obtained, filters out characteristic pollutant, constructs the multistage fingerprint spectrum database of river section based on characteristic pollutant by multiple instruments, finally according to multistage fingerprint spectrum database, river section is monitored early warning traceability.This application adopts online automatic water sample collection scheme, solves the problem of large workload and high labor cost, low efficiency when collecting water sample, reduces cost and improves efficiency;The application also uses the method of constructing multistage fingerprint spectrum, the early warning traceability workload of river section is greatly reduced, can obtain the real-time data of river section in time, is more accurate and timely, can be widely applied in environmental pollution monitoring technical field.

Description

Technical Field

[0001] This invention relates to the field of environmental pollution monitoring technology, and in particular to a method for constructing multi-level fingerprint maps of river cross sections. Background Technology

[0002] Rivers are the origin of life and the cradle of all human civilization. Human development is inseparable from rivers; they not only provide water resources but also house numerous aquatic organisms. However, economic development, population growth, and the advancement of industry and agriculture have led to significant water waste. Furthermore, the increasing pollution of water bodies by various harmful substances has resulted in the continuous deterioration of river conditions and frequent damage to their ecosystems. The sources of pollutants in water bodies are widespread and complex, making the rapid identification of these sources a key challenge in current research on water environmental safety and a crucial aspect of ensuring water safety. Finding the source and quickly analyzing the pollution origin has become a focus of research for experts both domestically and internationally.

[0003] Existing online river water quality monitoring and early warning technologies mainly suffer from the following problems:

[0004] 1. Existing technologies require too much work for screening pollutants and cannot quickly and accurately select and analyze key pollutants.

[0005] 2. Existing technologies are unable to quickly and accurately select and analyze key pollutants, or to provide timely control, early warning, and source tracing for toxic and harmful pollutants. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide an efficient method for constructing multi-level fingerprint maps of river cross sections.

[0007] On one hand, embodiments of the present invention provide a method for constructing multi-level fingerprint maps of river cross sections, including:

[0008] Obtain water environment information from river cross-sections;

[0009] Based on the water environment information of the river cross-section, water samples were collected from the river cross-section.

[0010] Characteristic pollutants were screened based on water samples from the river cross-section and the water environment information.

[0011] A multi-level fingerprint database of river cross sections was established based on the characteristic pollutants described.

[0012] The source tracing results are obtained by screening and calculating water samples from the river section based on the multi-level fingerprint database.

[0013] Optionally, obtaining the water environment information of the river cross-section includes:

[0014] To obtain information on pollution sources, hydrology, and basic water quality conditions in the river water environment.

[0015] Optionally, the step of screening characteristic pollutants based on water samples from river sections and the water environment information includes:

[0016] Water quality indicators were obtained by performing a full scan of the river cross-section using high-resolution mass spectrometry. These indicators were then matched and compared with a mass spectrometry database to screen for characteristic pollutants.

[0017] Optionally, establishing a multi-level fingerprint database of river sections based on the characteristic pollutants includes:

[0018] Multiple databases will be established based on the classification of the characteristic pollutants.

[0019] A multi-level fingerprint database of the river cross-section is constructed based on each of the aforementioned databases.

[0020] Optionally, the establishment of multiple databases based on the characteristic pollutant classification includes:

[0021] The primary fingerprint spectrum was determined by establishing a basic water quality parameter database through conventional water quality parameter instrument analysis.

[0022] Secondary fingerprint spectra were determined by analyzing the established fluorescence spectrum library using a three-dimensional fluorescence tracer.

[0023] The tertiary fingerprint spectrum was determined by analyzing the established fingerprint database of VOCs in water using a portable gas chromatography-mass spectrometry system.

[0024] The fourth-level fingerprint spectrum was determined by analyzing the heavy metal spectrum and the spectrum of toxic organic pollutants established by inductively coupled plasma mass spectrometry and liquid chromatography-mass spectrometry.

[0025] Optionally, the step of screening and calculating the water samples from the river section based on the multi-level fingerprint database to obtain the source tracing results includes:

[0026] Upload the data from the multi-level fingerprint database to the cloud platform;

[0027] The data is analyzed using a water quality model on the cloud platform to determine whether an early warning is needed and to obtain the source tracing results.

[0028] On the other hand, embodiments of the present invention also provide a multi-level fingerprint map construction device for river cross sections, comprising:

[0029] The information acquisition module is used to acquire water environment information of the river cross section and to collect water samples from the river cross section based on the water environment information of the river cross section.

[0030] The fingerprint spectrum construction module is used to screen characteristic pollutants based on water samples from the river section and the water environment information, and to establish a multi-level fingerprint spectrum database of the river section based on the characteristic pollutants.

[0031] The execution and monitoring module is used to screen and calculate the source tracing results of water samples from the river section based on the multi-level fingerprint database.

[0032] On the other hand, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory is used to store a program, and the processor executes the program to implement the above-mentioned method for constructing multi-level fingerprint maps of river cross sections.

[0033] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a program that is executed by a processor to implement the above-described method for constructing multi-level fingerprint maps of river cross sections.

[0034] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the above-mentioned method for constructing multi-level fingerprint maps of river cross sections.

[0035] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0036] The embodiments of the present invention include at least the following beneficial results: The method adopts the method of constructing a multi-level fingerprint spectrum, which greatly reduces the workload of early warning and source tracing of river sections, can obtain real-time data of river sections in a timely manner, can quickly and accurately select and analyze key pollutants, and can also carry out timely control and early warning of toxic and harmful pollutants. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of the steps in the method for constructing a multi-level fingerprint map of a river cross section provided in this embodiment of the invention;

[0039] Figure 2This is a schematic diagram of the online automatic water sampling device provided in an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of the structure for constructing a multi-level fingerprint map of a river cross section provided in an embodiment of the present invention;

[0041] Figure 4 This is a flowchart illustrating the specific steps of implementing the present invention;

[0042] Figure 5 This is a schematic diagram of a multi-level fingerprint map construction device for river cross sections provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] To address the problems existing in the prior art, embodiments of the present invention provide a method for constructing multi-level fingerprint maps of river cross sections, such as... Figure 1 As shown, the method includes steps 101 to 105:

[0045] Step 101: Obtain water environment information of the river cross section.

[0046] Among them, water environment information includes pollution sources, hydrology, and basic water quality conditions in the river water environment.

[0047] Reference Figure 3 The main method of information gathering is through on-site investigation, historical data collection, and consultation with relevant personnel. The investigation of pollution sources in river sections and watersheds is divided into point source and non-point source investigations. Point source investigations require investigating the location of the discharge outlets, whether the pollution is centralized or decentralized, the main water quality parameters of the discharged wastewater, and the subsequent treatment of the discharged wastewater by each point source. Non-point source investigations require investigating the area and location of the non-point source pollution sources, and the main pollutants. The investigation of river hydrological parameters mainly involves collecting historical river hydrological data, such as river level, runoff, sediment content, and flow velocity. The investigation of basic water quality parameters mainly involves measuring water temperature, pH, suspended solids, dissolved oxygen, conductivity, chlorophyll, chemical oxygen demand, total organic carbon, and suspended solids concentration.

[0048] Step 102: Collect water samples from the river section based on the water environment information of the river section.

[0049] The water samples collected from the river cross-section were obtained using an online timed water sampling device. The structural diagram of the sampling device is shown in [reference needed]. Figure 2 The water sampling equipment collects water samples at set intervals daily, which can be customized, such as every 12 hours, a suitable interval. When the online timer control panel of the water sampling equipment receives a collection signal, it activates the water pump to collect water samples from the river section into a sedimentation tank. After settling in the sedimentation tank for half an hour, the samples are transferred to various storage tanks, and then pumped to different devices for testing. There can be one or more sedimentation tanks and storage tanks. Figure 2 The quantities shown are for illustrative purposes only, and the actual quantities can be set according to the actual situation. This invention does not impose any restrictions.

[0050] Step 103: Screen characteristic pollutants based on the water samples from the river section and the water environment information.

[0051] Among them, high-resolution mass spectrometry was used to perform a full scan of water samples from a river section to obtain data such as the elution time, chromatographic peaks, and the size of the parent and daughter ions of various pollutants. Then, the obtained data was matched and compared with data in the mass spectrometry library to screen out characteristic pollutants. The screening principle for characteristic pollutants was to select pollutants whose peak area was greater than 1000 after deducting 5 times the area of ​​the blank peak, with a confidence value greater than or equal to 75% and an exact mass number error of less than 1 part per million. These characteristic pollutants have high resolution, high detection frequency, high stability, and high sensitivity.

[0052] Step 104: Establish a multi-level fingerprint database of river sections based on the characteristic pollutants.

[0053] First, multiple databases are established based on the classification of characteristic pollutants. This step includes: using online timed water sampling equipment to send collected river cross-section water samples to conventional index detection instruments to detect basic water quality parameters such as pH, temperature, suspended solids, dissolved oxygen, conductivity, chemical oxygen demand, total organic carbon, suspended solids concentration, chlorophyll, ammonia nitrogen, and phosphorus to determine the primary fingerprint spectrum; then, the water samples are pumped to sedimentation tanks, left to stand for half an hour, and then pumped to various storage tanks. The water samples in the storage tanks are then transferred to a three-dimensional fluorescence tracer to generate a three-dimensional fluorescence spectrum to determine the secondary fingerprint spectrum, and finally, a portable gas chromatography-mass spectrometry (GC-MS) is used to analyze the secondary fingerprint spectrum. VOCs in the water were detected using a multi-stage fingerprinting system to determine a tertiary fingerprint spectrum. Water samples from some storage tanks were then taken to the laboratory for analysis. First, the water samples were filtered. Then, one portion of the samples was directly sent to an inductively coupled plasma mass spectrometer (ICP-MS) to generate a heavy metal spectrum, while the other portion underwent solid-phase extraction and vacuum concentration before being sent to a liquid chromatography-mass spectrometry (LC-MS) system to generate a toxic organic pollutant spectrum. The heavy metal and toxic organic pollutant spectra determined a quaternary fingerprint spectrum. These five spectrum databases together constitute a multi-stage fingerprint spectrum of pollution sources at a river section. The multi-stage fingerprint spectra of all pollution sources in a river basin constitute a multi-stage fingerprint spectrum database for the target river.

[0054] Among them, the fingerprint refers to the water quality fingerprint. Regardless of the industry, each company's raw materials, intermediate products, characteristic pollutants, processes, and management levels are different, resulting in different residual pollutants discharged into the water. Therefore, each company's wastewater contains its own unique information, which is the water pattern of the wastewater. For example, using the three-dimensional fluorescence spectrum of a water pollution early warning and tracing instrument, it can be seen that each substance emits different fluorescence, and the higher the concentration of a substance, the more prominent it is in the spectrum.

[0055] Among them, the basic water quality parameters are characteristic indicators of various substances in water used to represent the quality and trend of water environment. In the embodiments of this invention, they mainly include data such as cross-sectional water sample temperature, pH, temperature, suspended solids, dissolved oxygen, conductivity, chemical oxygen demand, total organic carbon, suspended solids concentration, chlorophyll, ammonia nitrogen, and phosphorus.

[0056] The three-dimensional fluorescence spectrum mainly consists of fluorescence peaks, fluorescence patterns, and fluorescence indices. The fluorescence index includes: the fluorescence index (FI), defined as the ratio of the fluorescence emission spectrum intensity at 470 nm to 520 nm when the laser wavelength is 370 nm (FI = F470 / F520). This ratio reflects the relative contribution of aromatic amino acids and non-aromatic substances to the fluorescence intensity of dissolved organic matter, and thus can serve as an indicator of the source of substances and the degree of degradation of dissolved organic matter; the humification index (HIX), defined as the quotient of the integral values ​​of the fluorescence peak values ​​between 435~480 nm and 300~345 nm at a 254 nm laser wavelength (F435~480 / F300~345), with a higher HIX index indicating a higher degree of humification of dissolved organic matter; and the biogenic index (BIX), defined as the index of the biogenic source at an excitation wavelength of 310 nm. The ratio of fluorescence intensity at 380 nm and 430 nm (BIX=F380 / F430) is used to estimate the relative contribution of endogenous substances to dissolved organic matter. The freshness index (β:α) is defined as the ratio of fluorescence intensity at 380 nm to fluorescence intensity in the 420-435 nm range when the excitation wavelength is 310 nm. It reflects the proportion of newly generated dissolved organic matter in the total dissolved organic matter and is an important basis for assessing the biological activity of aquatic bodies.

[0057] Among them, VOCs in water, namely volatile organic compounds, are mainly divided into eight types: aromatic hydrocarbons, halogenated hydrocarbons, alkanes, alkenes, alcohols, aldehydes, ketones, esters, and some other compounds.

[0058] Based on the information of various pollution sources and the sources of pollutants investigated, characteristic pollutants are further subdivided into industry-specific pollutants from industrial pollution sources, agricultural pollution sources, and urban pollution sources. Industry-specific pollutants refer to the representative portion of pollutants emitted by a certain industry, which can show the pollution level of the industry. Generally, they can be understood in terms of quantity as pollutants emitted in larger quantities.

[0059] For example, industrial pollution sources are classified into key pollution sources and general pollution sources according to their scale, emission characteristics, and discharge volume. Key pollution sources include: 1. All industrial activity units that generate heavy metals, hazardous waste, and radioactive substances, such as food manufacturing, petroleum processing, chemical manufacturing, metal smelting, agricultural and sideline product processing, textiles, leather products, papermaking, metal mineral manufacturing, and power and heat production and supply; 2. All industrial activity units involved in heavily polluting industries, such as coal mining and washing, medical manufacturing, chemical fiber manufacturing, oil and gas extraction, beverage manufacturing, wood processing manufacturing, and electronic equipment manufacturing; 3. All industrial activity units above a certain scale in key industries, such as industries involving electroplating, smelting, and painting processes. Agricultural pollution sources mainly include: 1. Planting industry pollution sources, mainly targeting major production areas of grain crops, cash crops, and vegetable crops, including pollutants such as fertilizers, pesticides, agricultural films, and straw; 2. Livestock farming pollution sources, targeting pollutants such as manure produced by farmers raising pigs, dairy cows, laying hens, and broilers; 3. Aquaculture pollution sources, mainly pollutants such as feed, fish medicine, and fertilizers from fish, shrimp, and crabs raised on a large scale.

[0060] For example, electroplating and thermal power plants are both sources of industrial pollution. Wastewater from electroplating mainly contains heavy metals such as chromium, cadmium, nickel, and copper ions, acids and alkalis, cyanides, and various electroplating additives. The flue gas emitted by thermal power plants during coal combustion contains pollutants such as carbon monoxide, sulfur dioxide, and dust. Agricultural pollution sources are mainly caused by the use of fertilizers and pesticides in agricultural production. For example, nitrogen and phosphorus compounds from fertilizers can flow into water bodies, causing eutrophication. Pesticide pollution mainly includes organochlorine pesticide pollution, organophosphorus pesticide pollution, and organonitrogen pesticide pollution. Domestic pollution sources are mainly caused by domestic wastewater discharged from urban and rural areas, which mainly contains organic matter, synthetic detergents and chlorides, as well as pathogens, viruses, and parasite eggs.

[0061] Taking the electroplating industry, a source of industrial pollution, as an example, the electroplating process includes pretreatment, electroplating, and post-treatment. Taking zinc plating as an example, the first step, pretreatment, generates organic pollutants in the degreasing and pickling / activation stages. Pollutants that may be present during degreasing include: 1. Surfactants, with solvents containing gasoline, trichloroethylene, tetrachloroethylene, etc.; 2. Water softeners containing organic carboxylates, organic polyphosphonates, etc. Pollutants that may be present during pickling / activation include corrosion inhibitors, such as pyridine, hexamethylenetetramine, di-o-tolyl thiourea, propylene sulfonate, and cationic surfactants. The second step, electroplating, may generate organic pollutants including: 1. Cyanide, with main brighteners containing formaldehyde, fenaldehyde, coumarin, salicylic acid, furfural, benzyl acetone, o-chlorobenzaldehyde, etc.; 2. Carrier brighteners containing dextrin, triethylenetetramine, and epoxy amine condensates, etc.; 3. Auxiliary brighteners containing pyridine, quinoline, chloromethylbenzene, and ethylene oxide, etc. The third step of the post-treatment process involves the generation of organic pollutants during passivation, anti-discoloration, and stripping. Organic pollutants generated during passivation include: 1. Chromate passivating agents containing sodium formate and glacial acetic acid, while chromium-free organic passivating agents contain diaminotriazine, tannic acid, acrylic resin, benzotriazine, and citric acid; 2. Pollutants that may be generated during the anti-discoloration stage include ethyl acetate, epoxy resin, melamine, phenolic resin, vinyl resin, silicone resin, fluoropolymer, and paraffin; 3. Organic pollutants that may be generated during the stripping stage include surfactants, sodium m-nitrobenzenesulfonate, aminotriacetic acid, glycerol, triethanolamine, hexamethylenetetramine, and citric acid. The heavy metals that may be generated during electroplating depend on the company's business operations; commonly found heavy metals in electroplating include zinc, copper, nickel, and chromium.

[0062] Heavy metals are mainly divided into three categories: the first category is traditional heavy metals, such as lead, cadmium, arsenic, mercury and other metals; the second category is metals commonly found in industries such as electroplating and smelting, such as zinc, copper, chromium, nickel and other metals; and the third category is heavy metals commonly found in soil, such as aluminum, iron, titanium, thallium, manganese and other metals.

[0063] Toxic organic pollutants refer to organic substances that can cause human poisoning or environmental pollution. Although their concentration in water is not high, they have a long residual time in water bodies and are cumulative. For example, organochlorine pesticides are characterized by high toxicity, stable chemical properties, long residual time, easy solubility in fats, and strong accumulation in aquatic organisms. Their concentration can reach hundreds of thousands of times that in water, affecting not only the reproduction of aquatic organisms but also harming human health through the food chain. Polychlorinated biphenyls (PCBs) are highly toxic, highly fat-soluble, easily absorbed by organisms, and chemically stable. They are used to make insulating oils, lubricants, additives, etc., and mainly come from the plastics, resins, and rubber industries. Polycyclic aromatic hydrocarbons (PAHs) mainly come from environmental pollution caused by crude oil and petroleum, such as waste oil discharged into the ocean through rivers, ship discharges and accidental oil spills, and offshore oil field leaks and blowouts. There are also nitrogen- and phosphorus-containing organic compounds mainly from domestic and agricultural pollution, which often lead to eutrophication and river pollution.

[0064] Step 105: Screen and calculate the water samples from the river section based on the multi-level fingerprint database to obtain the source tracing results.

[0065] After constructing a multi-level fingerprint database, water quality monitoring is conducted. Water samples are collected from river sections using an online automatic water sampler. First, a water pump transports the samples to a conventional water quality parameter instrument for testing. After testing, the data is transmitted to a cloud platform. The cloud platform processes the data and compares it with the "Surface Water Environmental Quality Standard." If all data items meet the standard, no warning is needed. If any data item does not meet the standard, a warning is issued. For example, according to the "Surface Water Environmental Quality Standard," the pH value standard for surface water is 6 to 9. If the detected pH value is less than 6 or greater than 9, a warning is required.

[0066] The water sample, after being tested by conventional water quality parameters, is then pumped to a three-dimensional fluorescence tracer to analyze the industry characteristics of the water sample. Based on these industry characteristics, industry-specific pollutants are identified, and the presence of suspected pollution sources is determined. If no suspected pollution source is found, the sample is returned to the river section for further analysis. If a suspected pollution source is found, the analysis proceeds to the next step.

[0067] When a suspected source of pollution is found, the water sample that has passed through the three-dimensional fluorescence tracer is transferred to a portable gas chromatography-mass spectrometry (GC-MS) instrument via a water pump. The GC-MS instrument detects industry-specific pollutants and then transmits the data to the cloud platform. This is the on-site operational stage.

[0068] The water sampler stores some of the collected water samples in a storage tank. When the operator takes the water samples from the tank to the laboratory, the water samples are first filtered. A portion of the water samples are sent to an inductively coupled plasma mass spectrometer to detect the heavy metal index. Another portion of the water samples undergoes solid-phase extraction and vacuum concentration before being sent to a liquid chromatography-mass spectrometry (LC-MS) instrument for detection based on industry-specific pollutants. Finally, the obtained fingerprint index values ​​are transmitted to a cloud platform. Combined with the surveyed water quality and hydrological data, the water quality early warning model calculates the VOCs, heavy metals, and toxic organic pollutants data obtained earlier to determine whether the results exceed the set threshold. If the results do not exceed the threshold, river water quality monitoring continues. If the results exceed the threshold, an early warning and source tracing are initiated. The multi-level fingerprint database is constructed to quickly trace and locate the pollution source.

[0069] The setting of thresholds involves two aspects. Firstly, thresholds are determined by relevant standards and regulations, such as the "Surface Water Environmental Quality Standard," the "Urban Wastewater Treatment Plant Pollutant Discharge Standard," and the "Textile Dyeing and Finishing Industry Water Pollutant Discharge Standard." Secondly, for indicators not covered by these standards and regulations, thresholds are set using historical data of river section water quality indicators and monitoring data over a longer period. Large amounts of data will form certain structures and patterns, allowing for the creation of corresponding data models, such as standard constraint models, statistical constraint models, trend change models, and probability density models, to derive the thresholds. For example, assuming the river section water quality is Class IV, according to the "Surface Water Environmental Quality Standard," the zinc threshold should be set at 2.0 ng / L. If this standard is exceeded, an early warning and source tracing will be initiated. For indicators not covered by the standards, such as atrazine, based on long-term river monitoring data and model calculations, the threshold is set at 200 ng / L. If this threshold is exceeded, an early warning and source tracing will be initiated.

[0070] This analysis incorporates collected water quality and hydrological data. Regarding water quality, conventional water quality parameters respond to different pollutants, exhibiting varying response characteristics and correlations. For example, temperature is a physical indicator of water quality, affecting oxygen solubility and aquatic biological activity. pH is a crucial indicator of acid, alkali, and heavy metal pollution; sudden pollution incidents are often accompanied by abnormal pH changes. Conductivity reflects the concentration of dissociated ions in water; abnormal conductivity may be related to industrial wastewater containing heavy metal ions. Hydrological parameters include background river water quality (concentrations of pollutants in the river), the river's water standard classification, flow rate, and rainfall data from the city.

[0071] The water quality early warning models are divided into mutation-type early warning models and gradual change-type early warning models. Mutation-type water quality early warning models include fixed threshold early warning models, dynamic threshold early warning models, multi-factor collaborative mutation early warning models, and rare data combination early warning models; gradual change-type water quality early warning models include data continuous deterioration early warning models, data relationship change early warning models, water quality trend prediction early warning models, and prediction deviation early warning models. The water quality early warning model is selected according to the actual situation, and the selection of water quality early warning models is not limited in this embodiment of the invention.

[0072] The data from routine water quality instrument testing include cross-sectional water sample temperature, pH, temperature, suspended solids, dissolved oxygen, conductivity, chemical oxygen demand, total organic carbon, suspended solids concentration, chlorophyll, ammonia nitrogen, and phosphorus.

[0073] Source tracing, specifically water pollution source tracing, aims to quickly identify the source of pollutants when water quality exceeds standards, using model systems engineering to take timely measures. By densely deploying monitoring sections and combining them with water quality models, and leveraging the different types and concentrations of pollutants, a pollution source fingerprint database is constructed using water quality spectrometry analysis technology. This database includes sewage outlets, wastewater treatment plants, and related enterprises within the watershed. By mapping the water quality spectrometry characteristics of the monitoring sections one-to-one with the fingerprint characteristics in the database, pollution sources can be accurately and quickly identified. Water quality fingerprint comparison allows for rapid pollution early warning and source location.

[0074] Among them, cloud platforms refer to cloud platforms that can store and process data, including water quality models that can be calculated, such as the existing smart water quality environment monitoring cloud platforms in China and the online monitoring and early warning system of the Pearl River Basin.

[0075] Solid-phase extraction is a physical extraction process that involves both liquid and solid phases. It is based on liquid-solid chromatography and uses selective adsorption and selective elution to enrich, separate, and purify samples. Vacuum concentration refers to the process where, under the induction of secondary steam and the suction of the high vacuum of the separator, the concentrated material and secondary steam enter the separator at a relatively high speed along the tangential direction.

[0076] To improve accuracy and reduce false alarms, it is usually necessary to test and judge several consecutive sets of collected water samples. The specific number of water samples is determined by the actual situation. For example, it can be three sets, five sets, or ten sets. This embodiment of the invention does not limit the number of sets.

[0077] To ensure the accuracy and reliability of the data, it is necessary to identify outliers in the uploaded data, promptly detect and clean the data, and provide an accurate and effective data foundation for water quality early warning. Data anomalies are determined through the following aspects: First, the operating status of the monitoring instrument; data anomalies usually occur when the monitoring instrument experiences power outages, pump malfunctions, or other abnormalities. Second, the monitoring data itself; data anomalies are usually indicated when the data exceeds the instrument's range or when pollutant concentrations are negative. Third, the monitoring data remains unchanged for an extended period; while water quality fluctuates normally, data anomalies are usually indicated if the water quality values ​​remain unchanged over multiple periods. Fourth, the monitoring data exhibits drastic fluctuations. The fluctuation range of the monitoring data can be set based on the river section water quality data; this range should be significantly larger than the daily fluctuation range of the river section, and the specific setting is determined according to the river section conditions. This embodiment of the invention does not impose any limitations on this. The valid data after anomaly detection and data cleaning will be used in the calculation and judgment of the water quality model.

[0078] The following detailed examples illustrate the implementation and application of the multi-level fingerprint map construction method for river cross-sections provided in this invention in a specific river basin. The specific implementation steps are as follows: Figure 4 :

[0079] 1. First, collect information on the river's water environment, such as conducting on-site surveys and interviewing relevant personnel. Then, collect information on the polluting enterprises. Before conducting on-site investigations of pollution sources, collect data to understand the basic situation of the pollution sources, such as their location, discharge methods, and subsequent wastewater treatment, to determine the discharge of each pollution source and its specific impact on the surrounding environment. During on-site surveys, you can directly visit and record monitoring data, or you can use drones to obtain specific information about pollution sources from the air, to gain a more comprehensive and clear understanding of the impact of pollution sources on the environment.

[0080] 2. Then, a multi-level fingerprint spectrum is constructed. Water samples are collected daily at set times using an online timed water sampling device, with a sampling interval of 12 hours. Samples are collected from various pollution sources along the river cross-section. When the online timed control panel of the sampling device receives a collection signal, the water pump is activated to collect the river cross-section water sample into a sedimentation tank. After settling for half an hour, the sample is transferred to various storage tanks. The water sample is then pumped to conventional index testing instruments to detect basic water quality parameters such as pH, temperature, suspended solids, dissolved oxygen, conductivity, chemical oxygen demand, total organic carbon, suspended solids concentration, chlorophyll, ammonia nitrogen, and phosphorus to determine the primary fingerprint spectrum. Finally, the water sample is pumped to a three-dimensional fluorescence tracer to generate a three-dimensional fluorescence spectrum to determine the secondary fingerprint spectrum. The fingerprint spectrum was generated, and then the VOCs in the water were detected by portable gas chromatography-mass spectrometry to determine the tertiary fingerprint spectrum. Then, water samples from some storage tanks were taken to the laboratory for analysis. After filtration, a portion of the water samples were directly sent to inductively coupled plasma mass spectrometry to generate a heavy metal spectrum, while the other portion of the water samples were subjected to solid-phase extraction and vacuum concentration before being sent to liquid chromatography-mass spectrometry to generate a toxic organic pollutant spectrum. The heavy metal spectrum and the toxic organic pollutant spectrum determined the quaternary fingerprint spectrum. The five spectrum databases together constitute the multi-level fingerprint spectrum of a river section of a pollution source. The multi-level fingerprint spectra of all pollution sources in a river basin constitute the multi-level fingerprint spectrum database of the target river.

[0081] 3. Finally, after the multi-level fingerprint database is completed, the monitoring of water quality at river sections is initiated. In rivers requiring water quality monitoring, online timed water sampling equipment is used to collect water samples daily, set to collect samples every 12 hours. When river pollution is detected, the online water sampling device can collect water samples from the river section at the pollution site in real time. First, the water pump transports the water sample to a conventional water quality parameter instrument for testing. After testing, the data is transmitted to a cloud platform, where a water quality model is used to process the data. The obtained data is compared with the "Surface Water Environmental Quality Standard," and an early warning is issued for any data items that do not meet the standard. Then, the water sample is pumped to a three-dimensional fluorescence tracer to analyze the industry characteristics of the water sample. Based on the industry characteristics, industry-specific pollutants are screened out, and the presence of suspected pollution sources is determined. If no pollution source is found, the water sample is returned to the river section for collection. If a pollution source is found, the next step of analysis is performed. When a suspected pollution source is found, the water sample is pumped to a portable gas chromatography-mass spectrometry (GC-MS) instrument. The GC-MS instrument detects industry-specific pollutants, and the data is then transmitted to the cloud platform. The above is the on-site operational stage. The water sampler stores a portion of the collected water in a tank. When the operator takes the water sample from the tank to the laboratory, the sample is first filtered. A portion of the sample is sent to an inductively coupled plasma mass spectrometer (ICP-MS) to detect heavy metal indices. Another portion of the sample undergoes solid-phase extraction and vacuum concentration before being sent to a liquid chromatography-mass spectrometry (LC-MS) system for detection based on industry-specific pollutants. Finally, the resulting fingerprint index values ​​are transmitted to a cloud platform. Combined with the surveyed water quality and hydrological data, the water quality early warning model calculates the VOCs, heavy metals, and toxic organic pollutants data to determine if the results exceed set thresholds. If the results do not exceed the set thresholds, river water quality monitoring continues. If the results exceed the set thresholds, an early warning and source tracing mechanism is activated. The multi-level fingerprint database is used to quickly locate the pollution source.

[0082] In summary, the multi-level fingerprint map construction method for river cross-sections according to embodiments of the present invention has the following advantages:

[0083] 1. This method uses an online automatic water sampling device, which solves the problems of large workload, high labor costs and low efficiency when collecting water samples, thus reducing costs and increasing efficiency.

[0084] 2. This method employs a multi-level fingerprint mapping approach, which significantly reduces the workload of early warning and source tracing for river sections. It enables timely acquisition of real-time data on river sections, rapid and accurate selection and analysis of key pollutants, and timely control and early warning of toxic and harmful pollutants. This provides ecological risk assessment for the water environment of river sections and data support and suggestions for relevant departments to make corresponding decisions regarding rivers.

[0085] Reference Figure 5This invention also provides a multi-level fingerprint map construction device for river cross sections, comprising:

[0086] The information acquisition module 501 is used to acquire water environment information of the river cross section and to collect water samples from the river cross section based on the water environment information of the river cross section.

[0087] The fingerprint spectrum construction module 502 is used to screen characteristic pollutants based on the water samples of the river section and the water environment information, and to establish a multi-level fingerprint spectrum database of the river section based on the characteristic pollutants.

[0088] The execution and monitoring module 503 is used to screen and calculate the source tracing results of the water samples of the river section based on the multi-level fingerprint database.

[0089] This invention also provides an electronic device capable of constructing multi-level fingerprint maps for river cross-sections. First, it acquires the water environment information of the river cross-section. Based on this information, it uses an online, timed water sampling device to collect water samples from the river cross-section. Then, it filters characteristic pollutants based on the water samples and the water environment information. A multi-level fingerprint map database for the river cross-section is established based on these characteristic pollutants. Finally, it uses the multi-level fingerprint map database to screen and calculate the source tracing results from the river cross-section water samples. This invention employs an online automatic water sampling device, solving the problems of high workload, high labor costs, and low efficiency in water sample collection, thus reducing costs and increasing efficiency. The method of constructing multi-level fingerprint maps significantly reduces the workload for early warning and source tracing of river cross-sections, enabling timely acquisition of real-time data, rapid and accurate selection and analysis of key pollutants, and timely control and early warning of toxic and harmful pollutants. This provides ecological risk assessment for the river cross-section water environment and data support and suggestions for relevant departments to make corresponding decisions regarding rivers.

[0090] This invention also provides a readable storage medium storing a program that is executed by a processor to implement the above-described multi-level fingerprint map construction of a river cross section.

[0091] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-mentioned method for constructing multi-level fingerprint maps of river cross sections.

[0092] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform... Figure 1The method shown.

[0093] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0094] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0095] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0097] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0098] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0099] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0100] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0101] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for constructing multi-level fingerprint maps of river cross sections, characterized in that, include: Obtain water environment information from river cross-sections; Based on the water environment information of the river cross-section, water samples were collected from the river cross-section. Characteristic pollutants were screened based on water samples from the river cross-section and the water environment information. A multi-level fingerprint database of river cross sections was established based on the characteristic pollutants described. The establishment of a multi-level fingerprint database for river sections based on the characteristic pollutants includes: Multiple databases will be established based on the classification of the characteristic pollutants. A multi-level fingerprint database of the river cross-section is constructed based on each of the aforementioned databases; The establishment of multiple databases based on the classification of the characteristic pollutants includes: The primary fingerprint spectrum was determined by establishing basic water quality parameters through conventional water quality parameter instrument analysis. The secondary fingerprint spectrum was determined by analyzing the three-dimensional fluorescence spectrum established using a three-dimensional fluorescence tracer. The tertiary fingerprint spectrum was determined by analyzing the established fingerprint database of VOCs in water using a portable gas chromatography-mass spectrometry system. The fourth-level fingerprint spectrum was determined by analyzing the heavy metal spectrum and toxic organic pollutant spectrum established by inductively coupled plasma mass spectrometry and liquid chromatography-mass spectrometry. The source tracing results are obtained by screening and calculating water samples from the river section based on the multi-level fingerprint database. The step of screening and calculating water samples from the river section based on the multi-level fingerprint database to obtain the source tracing results includes: On-site operational phase: The water pump delivers the water sample to a conventional water quality parameter instrument for testing, and an early warning is issued when any data item does not meet the standard; Water samples that have undergone conventional water quality parameter testing are transported to a three-dimensional fluorescence tracer via a water pump. The industry characteristics of the water samples are analyzed, and industry-specific pollutants are screened out based on these characteristics. The presence of suspected pollution sources is then determined based on these pollutants. If no suspected pollution sources are found, the process returns to collect water samples from the river section. If a suspected pollution source is found, the process proceeds to the next step of analysis. When a suspected source of pollution is found, the water sample that has passed through the three-dimensional fluorescence tracer is transferred to a portable gas chromatography-mass spectrometry (GC-MS) instrument via a water pump. The GC-MS instrument detects industry-specific pollutants and then transmits the data to the cloud platform. Laboratory stage: Some of the collected water samples are stored in a storage tank. When the operator takes the water samples from the storage tank to the laboratory, the water samples are first filtered. A portion of the water samples are sent to an inductively coupled plasma mass spectrometer to detect the heavy metal index in the water samples. Another portion of the water samples are then subjected to solid phase extraction and vacuum concentration before being sent to a liquid chromatography-mass spectrometry system. Based on the characteristics of pollutants in the industry, the resulting fingerprint index values ​​are transmitted to the cloud platform. Combined with the water quality and hydrological data collected, the water quality early warning model calculates the data of VOCs, heavy metals, and toxic organic pollutants to determine whether the results exceed the set threshold. If the results do not exceed the threshold, river water quality monitoring continues. If the results exceed the threshold, an early warning and source tracing are initiated. The pollution source is quickly located by constructing a multi-level fingerprint database.

2. The method for constructing a multi-level fingerprint map of a river cross section according to claim 1, characterized in that, The acquisition of water environment information from river cross-sections includes: To obtain information on pollution sources, hydrology, and basic water quality conditions in the river water environment.

3. The method for constructing a multi-level fingerprint map of a river cross section according to claim 2, characterized in that, The process of screening characteristic pollutants based on water samples from river sections and the water environment information includes: The water quality index was obtained by performing a full scan of the water sample from the river section using high-resolution mass spectrometry. The water quality index was then matched and compared with a mass spectrometry database to screen for characteristic pollutants.

4. A multi-level fingerprint mapping device for river cross-sections, comprising: The information acquisition module is used to acquire water environment information of the river cross section and to collect water samples from the river cross section based on the water environment information of the river cross section. The fingerprint spectrum construction module is used to screen characteristic pollutants based on water samples from the river section and the water environment information, and to establish a multi-level fingerprint spectrum database of the river section based on the characteristic pollutants. The establishment of a multi-level fingerprint database for river sections based on the characteristic pollutants includes: Multiple databases will be established based on the classification of the characteristic pollutants. A multi-level fingerprint database of the river cross-section is constructed based on each of the aforementioned databases; The establishment of multiple databases based on the classification of the characteristic pollutants includes: The primary fingerprint spectrum was determined by establishing basic water quality parameters through conventional water quality parameter instrument analysis. The secondary fingerprint spectrum was determined by analyzing the three-dimensional fluorescence spectrum established using a three-dimensional fluorescence tracer. The tertiary fingerprint spectrum was determined by analyzing the established fingerprint database of VOCs in water using a portable gas chromatography-mass spectrometry system. The fourth-level fingerprint spectrum was determined by analyzing the heavy metal spectrum and toxic organic pollutant spectrum established by inductively coupled plasma mass spectrometry and liquid chromatography-mass spectrometry. The execution and monitoring module is used to screen and calculate the source tracing results of water samples from the river section based on the multi-level fingerprint database. The step of screening and calculating water samples from the river section based on the multi-level fingerprint database to obtain the source tracing results includes: On-site operational phase: The water pump delivers the water sample to a conventional water quality parameter instrument for testing, and an early warning is issued when any data item does not meet the standard; Water samples that have undergone conventional water quality parameter testing are transported to a three-dimensional fluorescence tracer via a water pump. The industry characteristics of the water samples are analyzed, and industry-specific pollutants are screened out based on these characteristics. The presence of suspected pollution sources is then determined based on these pollutants. If no suspected pollution sources are found, the process returns to collect water samples from the river section. If a suspected pollution source is found, the process proceeds to the next step of analysis. When a suspected source of pollution is found, the water sample that has passed through the three-dimensional fluorescence tracer is transferred to a portable gas chromatography-mass spectrometry (GC-MS) instrument via a water pump. The GC-MS instrument detects industry-specific pollutants and then transmits the data to the cloud platform. Laboratory stage: Some of the collected water samples are stored in a storage tank. When the operator takes the water samples from the storage tank to the laboratory, the water samples are first filtered. A portion of the water samples are sent to an inductively coupled plasma mass spectrometer to detect the heavy metal index in the water samples. Another portion of the water samples are then subjected to solid phase extraction and vacuum concentration before being sent to a liquid chromatography-mass spectrometry system. Based on the characteristics of pollutants in the industry, the resulting fingerprint index values ​​are transmitted to the cloud platform. Combined with the water quality and hydrological data collected, the water quality early warning model calculates the data of VOCs, heavy metals, and toxic organic pollutants to determine whether the results exceed the set threshold. If the results do not exceed the threshold, river water quality monitoring continues. If the results exceed the threshold, an early warning and source tracing are initiated. The pollution source is quickly located by constructing a multi-level fingerprint database.

5. An electronic device, characterized in that, It includes a processor and a memory, the memory being used to store a program, and the processor executing the program to implement the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the method as described in any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 3.

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