Water quality monitoring method and system for industrial sewage

Abstract

The invention discloses a water quality monitoring method and system for industrial sewage, and belongs to the technical field of sewage treatment.The method comprises the steps that real-time water quality data of the industrial sewage in a target area is collected; performing feature extraction on the water surface image, and performing pollutant component analysis on a feature extraction result through the water quality detection spectrum to obtain a pollution range of the industrial sewage and a current water quality feature; deducing the water quality space change of the industrial sewage through geographic features and meteorological change conditions of a target area based on the pollution range, and generating a pollution diffusion network; performing pollution tracing according to the pollution diffusion network and the current water quality characteristics, and determining a main pollution source of the industrial sewage; according to the main pollution sources, sewage treatment measures conforming to the geographic features are generated for the target area. Therefore, by implementing the method and the device, the problem that the pollution source of the industrial sewage is difficult to quickly and accurately identify in the prior art can be solved.

Description

Technical Field

[0001] This application belongs to the field of wastewater treatment technology, specifically relating to a method and system for monitoring the water quality of industrial wastewater. Background Technology

[0002] With the continuous development of industrial wastewater treatment projects, more and more wastewater treatment measures are being implemented. Accurate identification of pollution sources is crucial in the industrial wastewater treatment process, as it determines which wastewater treatment measures can achieve rapid and effective treatment. The main sources of industrial wastewater pollution are wastewater, exhaust gas, and solid waste discharged from industrial production, including organic pollutants, inorganic pollutants, heavy metals, and oily substances. Due to the numerous types of pollution sources, accurately identifying them based on real-time water quality data collected from industrial wastewater is a pressing problem that needs to be solved.

[0003] Previously, pollution sources were primarily identified through reports submitted by discharging companies and manual judgment, followed by the selection of corresponding wastewater treatment measures. However, this method is inefficient and inaccurate, and the selection of incorrect treatment measures can lead to resource waste and high wastewater treatment costs. Furthermore, existing methods rarely consider the impact of different geographical environments on wastewater diffusion, resulting in low accuracy in pollution source tracing. Summary of the Invention

[0004] This application proposes a method and system for monitoring the water quality of industrial wastewater, which can solve the problem of difficulty in quickly and accurately identifying the pollution sources of industrial wastewater in the prior art.

[0005] The first aspect of this application provides a method for monitoring the water quality of industrial wastewater, the method comprising: Real-time water quality data of industrial wastewater in the target area is collected; wherein, the real-time water quality data includes water quality detection spectra and water surface images; Feature extraction is performed on the water surface image, and pollutant composition analysis is conducted on the feature extraction results using the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater; Based on the pollution range, the spatial changes in the water quality of the industrial wastewater are extrapolated by taking into account the geographical features and meteorological changes of the target area, thereby generating a pollution diffusion network; The process of extrapolating the spatial changes in the water quality of industrial wastewater based on the geographical features and meteorological changes of the target area includes: analyzing the impact of the geographical features and meteorological changes on the diffusion degree of various pollutants, generating external influencing factors; constructing a pollution simulation scenario based on the geographical features and meteorological changes; wherein the pollution simulation scenario needs to set wind speed, temperature and humidity, precipitation, topographic conditions, water body characteristics, and climate of the target area; based on the pollution simulation scenario and the topographic conditions in the target area, using the meteorological changes in the target area as control variables, simulating the movement rate and direction of various pollutants in the current water quality characteristics, analyzing the effect of geographical features and meteorological changes on pollutant diffusion, and obtaining the external influencing factors of the target area when the change process of the control variables is completed; Based on the pollution diffusion network and the current water quality characteristics, pollution source tracing is performed to determine the main pollution sources of the industrial wastewater. Based on the main pollution sources, wastewater treatment measures that conform to the geographical characteristics of the target area are generated.

[0006] The aforementioned scheme combines spectral and surface images of industrial wastewater for joint analysis. Visual features from the images are matched with the spectra to determine the wastewater's quality, identifying the corresponding pollution range and current water quality characteristics. Then, the scheme considers the geographical features and meteorological changes of the target area on wastewater movement, achieving a more precise determination of the pollution spread range and providing sufficient evidence for subsequent pollution source identification. Pollution source tracing is then performed based on the pollution spread network. Current water quality characteristics are used to quickly locate discharge points, precisely pinpointing the source of leaking industrial wastewater and identifying the corresponding primary pollution source. Finally, based on the pollutant composition of the primary sources and the geographical features of the target area, wastewater treatment measures for the current ecological environment are generated, contributing to rational and efficient wastewater management.

[0007] In one possible implementation of the first aspect, feature extraction is performed on the water surface image, and pollutant composition analysis is conducted on the feature extraction results using the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater, specifically: Visual features are extracted from the water surface image, and the extraction results are matched with a preset wastewater image database to determine the pollution type corresponding to the industrial wastewater; Partial least squares regression is performed on the water quality detection spectrum corresponding to the pollution type, and the current water quality characteristics are obtained by analyzing the pollutant composition in the industrial wastewater. The wastewater concentration in the current water quality characteristics is compared with a preset wastewater concentration range, and the pollution range is determined based on the detection location of the water quality detection spectrum through the comparison results.

[0008] The above scheme extracts visual features related to the surface of industrial wastewater from images and determines possible pollution types by matching these visual features. Then, water quality detection spectroscopy is used to verify these possible pollution types, more accurately determining the types of pollutants contained in the current industrial wastewater. Finally, the location of the pollution is determined by combining the detection location information, thus defining the pollution range. Furthermore, because the complex composition of wastewater can lead to excessive spectral interference, partial least squares regression is used to process the spectra, achieving accurate classification of pollutants.

[0009] In one possible implementation of the first aspect, visual features are extracted from the water surface image, and the extracted results are matched with a preset wastewater image database to determine the pollution type corresponding to the industrial wastewater, specifically: Based on preset visual labels, anomaly detection is performed on the water surface images to extract visual features of each water surface image; wherein, the visual labels include water color, water turbidity, water surface debris, and water surface gloss. Based on the visual features, all water surface images belonging to the same shooting point are uniformly described in a scene to obtain pollution scene description text; Based on a pre-defined wastewater image library, the pollution scene description text is matched for content, and the wastewater is classified according to the matching results to determine the pollution type.

[0010] The above-mentioned scheme analyzes water surface images using a variety of visual tags, identifies and extracts visual features such as water color, presence of foam and debris, and turbidity, and further obtains a description of the pollution scene to make a rough judgment on industrial wastewater and determine the corresponding pollution type.

[0011] In one possible implementation of the first aspect, based on the pollution range, the spatial changes in the water quality of the industrial wastewater are extrapolated through the geographical features and meteorological changes of the target area to generate a pollution diffusion network, specifically: Mark the pollution points related to the pollution range on the map corresponding to the target area, and mark the water flow direction of the industrial wastewater to obtain a pollution area map; Based on the historical water quality information and external influencing factors of the target area at the previous moment, the spatial variation of the industrial wastewater quality is analyzed according to the pollution area map to obtain the pollution diffusion network of the target area.

[0012] The above scheme marks nodes containing industrial wastewater on a map of the target area, then considers the impact of recent meteorological changes and the geographical features of the target area on wastewater diffusion, quantifies these impacts, and obtains the corresponding external influencing factors. By quantifying the influence of meteorology and geographical location on the physical migration, chemical transformation, and biodegradation processes of pollutants, the scheme achieves accurate prediction of the pollution diffusion network in the target area.

[0013] In one possible implementation of the first aspect, based on historical water quality information and external influencing factors of the target area at the previous time step, the spatial variation of the industrial wastewater quality is analyzed according to the pollution area map to obtain the pollution diffusion network of the target area, specifically: Based on the differences in pollutant composition between the historical water quality information and the current water quality characteristics, water quality change parameters for the industrial wastewater in each wastewater segment are generated; Based on the degree of change of the water quality parameters, the pollution accumulation area is marked on the pollution area map; Based on external influencing factors, taking the pollution accumulation area as the origin and the water flow direction as the diffusion direction, the diffusion trajectory and range of the industrial wastewater within the wastewater section are predicted, and the pollution diffusion network is generated; wherein, the weight of each node in the pollution diffusion network is determined by the distance between the wastewater section and the pollution accumulation area.

[0014] The above scheme first obtains the changes in water quality over a period of time, and then marks the pollution clusters with more severe pollution on the map. Based on the pollution clusters, the direction of pollution diffusion is predicted, and influence weights representing the degree of pollution diffusion are assigned according to distance, resulting in a pollution diffusion network from the pollution source to the pollution source transfer, providing data support for subsequent investigation of pollution sources.

[0015] In one possible implementation of the first aspect, pollution source tracing is performed based on the pollution diffusion network and the current water quality characteristics to determine the main pollution source of the industrial wastewater, specifically as follows: The wastewater segment to be detected with a wastewater concentration exceeding a first threshold is extracted from the pollution diffusion network; Within the industrial zones of the target area, zones whose pollutants conform to the current water quality characteristics are selected as potential pollution zones. The diffusion path between the wastewater section to be tested and the potentially polluted area is found on the pollution diffusion network. The pollution source is traced by detecting the trend of wastewater concentration change along the diffusion path, and the corresponding key discharge outlets are identified. The main pollution sources are determined based on the industrial buildings corresponding to the key sewage outlets.

[0016] The above scheme calculates the diffusion path between the wastewater section to be tested with excessive wastewater content and industrial areas with similar discharge substances and water quality characteristics. Then, based on the diffusion path, it finds the sewage outlets where the wastewater concentration increases to trace the source of pollution, and identifies the industrial buildings that discharge pollutants as the main pollution source, thereby further determining the main pollutant components contained in industrial wastewater.

[0017] In one possible implementation of the first aspect, pollution source tracing is performed by detecting the wastewater concentration change trend along the diffusion path to identify the corresponding key discharge outlets, specifically: Based on the real-time water quality data, construct a wastewater concentration change trend curve along the diffusion path; The first pollutant exhibiting an abnormal upward trend was identified by analyzing the wastewater concentration change trend curve. The concentration of the first pollutant at each discharge outlet along the diffusion path is investigated, and isotope detection is performed on the wastewater at the discharge outlets to identify the key discharge outlets that meet the current water quality characteristics; wherein, if there is rainfall during the pollution source tracing process, concentration detection and isotope detection are performed in conjunction with the collected rainfall data.

[0018] In one possible implementation of the first aspect, wastewater treatment measures conforming to the geographical characteristics of the target area are generated based on the main pollution source, specifically as follows: The main pollution sources were analyzed to obtain information on their complex components. Based on the information on the composite components and the degree of pollution of the industrial wastewater, preliminary treatment measures were determined; Based on the topography and climate conditions of the target area, the preliminary treatment measures were optimized to obtain the aforementioned wastewater treatment measures.

[0019] The above-mentioned scheme obtains various components from major pollution sources, providing detailed data for taking appropriate treatment measures. Furthermore, by combining this data with the topography and climate conditions of the target area, the rationality of the treatment measures is optimized, resulting in wastewater treatment measures that can be tailored to local conditions.

[0020] The second aspect of this application provides a water quality monitoring system for industrial wastewater, the system comprising: a data acquisition module, a feature extraction module, a diffusion network construction module, a pollution source identification module, and a treatment measure generation module; The data acquisition module is used to collect real-time water quality data of industrial wastewater in the target area; the real-time water quality data includes water quality detection spectra and water surface images. The feature extraction module is used to extract features from the water surface image and analyze the pollutant composition of the feature extraction results through the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater. The diffusion network construction module is used to extrapolate the spatial changes in the water quality of the industrial wastewater based on the pollution range and the geographical features and meteorological changes of the target area, thereby generating a pollution diffusion network. The pollution source determination module is used to trace pollution sources based on the pollution diffusion network and the current water quality characteristics, and to determine the main pollution sources of the industrial wastewater. The treatment measures generation module is used to generate wastewater treatment measures that conform to the geographical characteristics of the target area based on the main pollution sources.

[0021] The aforementioned scheme combines spectral and surface images of industrial wastewater for joint analysis. Visual features from the images are matched with the spectra to determine the wastewater's quality, identifying the corresponding pollution range and current water quality characteristics. Then, the scheme considers the geographical features and meteorological changes of the target area on wastewater movement, achieving a more precise determination of the pollution spread range and providing sufficient evidence for subsequent pollution source identification. Pollution source tracing is then performed based on the pollution spread network. Current water quality characteristics are used to quickly locate discharge points, precisely pinpointing the source of leaking industrial wastewater and identifying the corresponding primary pollution source. Finally, based on the pollutant composition of the primary sources and the geographical features of the target area, wastewater treatment measures for the current ecological environment are generated, contributing to rational and efficient wastewater management. Attached Figure Description

[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the specific process of a water quality monitoring method for industrial wastewater provided in the first embodiment of this application; Figure 2 This is a structural diagram of a water quality monitoring system for industrial wastewater provided in the second embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0026] First Embodiment Existing wastewater treatment measures primarily rely on identifying pollution sources. However, the sources of industrial wastewater are closely related to local weather patterns and geographical location. These external factors influence the diffusion, migration, and degradation of pollutants through various pathways. Therefore, it is necessary to combine the climate and geographical characteristics of the industrial wastewater location to quickly and accurately locate pollution sources and customize more suitable wastewater treatment measures.

[0027] like Figure 1 As shown, to address the problem of difficulty in quickly and accurately identifying pollution sources of industrial wastewater in existing technologies, the first embodiment of this application provides a detailed flowchart of a method for monitoring the water quality of industrial wastewater. This embodiment's method for monitoring the water quality of industrial wastewater includes steps S1 to S5, detailed below: Step S1: Collect real-time water quality data of industrial wastewater in the target area.

[0028] In this embodiment, real-time water quality data of industrial wastewater within the target area is obtained primarily by photographing the wastewater surface and collecting spectral data of the wastewater. The water surface images are mainly obtained under natural light conditions, combining multi-angle shots of the water surface and the shoreline. Furthermore, to analyze the dynamic changes in the water surface, continuous video recording of the industrial wastewater surface is also used. Compared to static images, dynamic video is more effective at capturing the diffusion patterns of foam and flowing water. The captured video is then decomposed frame by frame into multiple water surface images.

[0029] Water quality detection spectra are primarily obtained by analyzing samples of industrial wastewater using a water quality analyzer. The type of water quality detection spectrum is related to the type of pollutant. For example, ultraviolet-visible spectroscopy is mainly used to detect organic suspended solids in water; laser-induced breakdown spectroscopy is mainly used to detect various heavy metals; ultraviolet absorption spectroscopy is used for the detection of organic matter; and near-infrared spectroscopy is used to detect oil pollutants.

[0030] Step S2: Extract features from the water surface image and analyze the pollutant composition of the feature extraction results using the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater.

[0031] In this embodiment, visual features in the water surface image are first identified to distinguish industrial wastewater from normal water quality, thus roughly determining the type of wastewater. Then, for each wastewater type, a corresponding water quality detection spectrum is used to analyze the pollutant composition, determining the various pollutants contained in the industrial wastewater and their concentrations. Furthermore, based on the pollutant concentrations, the water sections exceeding pollution standards are identified, thereby determining the pollution range of the industrial wastewater.

[0032] First, based on normal water body images and preset visual labels, anomaly identification is performed on the surface images of industrial wastewater. Anomalies that deviate from normal water body conditions are marked on the surface images, and visual features of each surface image are extracted accordingly. The anomaly identification process mainly involves detecting whether the industrial wastewater surface contains foam / bubbles or other debris, water color, turbidity, surface gloss, and light transmittance. This application converts these parameters into visual labels for visual feature extraction.

[0033] These visual features are then integrated to obtain a pollution scene description text that can fully describe the overall visual situation of a water surface image.

[0034] Specifically, the visual features of all water surface images belonging to the same shooting point are input into a preset image analysis model. The water surface scene at the shooting point is described using a preset description template to obtain a pollution scene description text for that shooting point.

[0035] For example, considering several water surface images belonging to the same downstream channel, the visual features of image A include: the water is yellow, there are few bubbles on the surface, and there is an oil film on the surface; the visual features of image B include: the water is yellow, there are impurities in the water, there are colored bubbles on the surface, and there is abnormal reflection on the surface. Integrating the visual features of these two images, the pollution scene description of this downstream channel can be obtained as follows: the water surface is generally yellow, with some continuous colored bubbles, an oil film on the surface, and chemical sediment beneath the oil film.

[0036] By integrating the visual features of multiple water surface images from the same shooting point, a comprehensive scene description of a sewage section can be achieved, capturing more complete sewage conditions that cannot be fully displayed due to limited shooting angles, and providing more data support for subsequent pollution type identification.

[0037] Then, based on a pre-set wastewater image database, the content of the pollution scene description text is matched to identify the pollution type corresponding to the industrial wastewater. For example, for industrial wastewater that is yellow, contains impurities, has colored bubbles on the surface, or exhibits abnormal reflectivity, the wastewater image database determines that the water is contaminated with organic dyes.

[0038] Then, the water quality detection spectrum used to identify the pollution type is determined, and the spectrum is processed by partial least squares regression. By considering the overlap of these pollutant components in the spectrum, the spectrum is analyzed to fully determine the types and concentrations of various pollutants in industrial wastewater, thereby obtaining the corresponding current water quality characteristics. Among them, partial least squares regression can solve the multiple correlations between multiple independent variables and is suitable for situations with few samples, enabling the decomposition of complex wastewater.

[0039] For example, for industrial wastewater containing oil pollutants, near-infrared spectroscopy is used to analyze the pollutant composition. A characteristic peak is detected near 320 nm in the spectrum, indicating that the industrial wastewater contains petroleum hydrocarbons, and the concentration of petroleum hydrocarbons is determined. For industrial wastewater contaminated with heavy metals, atomic absorption spectroscopy is used to analyze the pollutant composition, and it is determined that the industrial wastewater contains chromium particles and mercury ions.

[0040] Then, the wastewater concentration characterized by the current water quality features is compared with the preset wastewater concentration range to determine the wastewater segment with excessive pollution. Then, the pollution range of industrial wastewater is determined by combining the detection location corresponding to the water quality detection spectrum.

[0041] Step S3: Based on the pollution range, the spatial changes in the water quality of the industrial wastewater are extrapolated by taking into account the geographical features and meteorological changes of the target area, and a pollution diffusion network is generated.

[0042] Based on the obtained pollution range of industrial wastewater, the relevant pollution points are marked on the map of the target area, and the flow direction of the industrial wastewater is also marked on the map to obtain a map containing the pollution area.

[0043] The system acquires meteorological changes in the target area over a previous period and then constructs a pollution simulation scenario based on the geographical features of the target area. Specifically, based on the meteorological changes, the pollution simulation scenario sets the target area's wind speed, temperature and humidity, precipitation, topography, water features, and climate (e.g., whether it is an arid or humid region). Based on the geographical features, the pollution simulation scenario sets the topography (e.g., whether it is a plain or a mountainous area, and its location within a watershed) and water features (e.g., river flow velocity and groundwater infiltration).

[0044] Based on the pollution simulation scenario and the terrain conditions within the target area, and using meteorological changes within the target area as control variables, the simulation examines the movement rate and direction of various pollutants in the current water quality characteristics from the starting point of the control variables. During the simulation, the combined effects of geographical features and meteorological changes on pollutant diffusion are analyzed, thereby deriving the degree of influence of these external factors on pollutant diffusion and migration. When the change process of the control variables is completed, the external influencing factors of the target area are obtained. The starting point of the meteorological changes is the starting point of the data acquisition period; completing the change process of the control variables means simulating the entire meteorological change process within the data acquisition time.

[0045] Specifically, the simulation system constructs an initial geographic model based on the geographical features of the target area, representing its slope and aspect, water flow direction, and catchment areas. Furthermore, it optimizes the geographic model using acquired soil type, porosity, and surface runoff data to obtain a geographic simulation model of the target area. Historical meteorological change data for the target area, including temperature fluctuations, wind speed and direction changes, and rainfall, are input into the geographic simulation model. The simulation begins at the start of the historical time period, observing the changes in water flow velocity and runoff volume along various path segments within the geographic simulation model, as well as the area changes and confluence times of each catchment area. This yields simulation results characterizing how wastewater flows, where it collects, whether its flow direction changes, and the degree of runoff volume change during the historical time period. To improve simulation accuracy, the impact of rainfall on wastewater movement is adjusted by considering porosity and evaporation rates over the historical time period.

[0046] For example, in a coastal plain of a monsoon region during summer, the recent weather patterns have primarily consisted of heavy rainfall and typhoons. Based on the pollution simulation scenario described above, the movement rate and direction of pollutants are simulated by adjusting rainfall and wind speed. During the simulation, the flow of water in the plain changes from slow to rapid due to rainfall, leading to sewage overflow and backflow into farmland, as well as sudden overflows. Additionally, strong winds cause fine particulate matter from industrial exhaust gases to settle into water bodies, indirectly polluting water quality. This allows for the precise identification of external influencing factors. Furthermore, the risk of industrial equipment pipeline leaks can be analyzed when environmental humidity rises sharply due to heavy rain, as pipeline leaks can lead to the accidental release of pollutants.

[0047] Compared to existing technologies that only consider pollutant diffusion caused by river flow, the embodiments of this application also take into account the impact of meteorological changes and more geographical features on pollutant diffusion, achieving more accurate prediction of pollutant diffusion paths and enabling rapid and accurate identification of pollution sources.

[0048] For example, mountains can block pollutants, making it difficult for them to disperse and leading to higher levels of pollution in waterways over time. Sandy soils allow pollutants to infiltrate more easily, increasing the likelihood of groundwater contamination.

[0049] Historical water quality information for the target area at the previous time point is obtained; this historical water quality information is data collected prior to the real-time water quality data. The differences in pollutant composition between the historical water quality information and the current water quality characteristics are analyzed, such as whether there are any new types of pollutants and whether pollutant concentrations have increased or decreased. Water quality change parameters for industrial wastewater in each wastewater segment are generated; wherein, the wastewater segment is determined based on artificially divided river segments on the pollution area map.

[0050] Based on the water quality change parameters, the pollution degree change curves of each sewage segment are plotted. The change degree of the water quality change parameters is taken as the change trend of the pollution degree. Sewage segments with higher pollution degree or higher pollution degree increase trend are identified as pollution accumulation areas and marked on the pollution area map.

[0051] This process involves collecting historical water quality information from multiple historical time points, calculating the corresponding water quality change parameters for these time points, and then plotting the historical time points on the x-axis and the water quality change parameters on the y-axis to obtain pollution degree change curves for each wastewater segment by plotting the fluctuations of the water quality change parameters over time. These pollution degree change curves reveal the increase / decrease in pollutant concentration within each wastewater segment over time, providing insight into the changing trends of pollution levels in each segment.

[0052] Using the pollution accumulation area as the origin and the water flow direction as the diffusion direction, the diffusion trajectory and range of industrial wastewater in each wastewater section are predicted under the consideration of external influencing factors. A pollution diffusion network is generated by tracking the flow trajectory of industrial wastewater.

[0053] Specifically, the origin representing the pollution accumulation area is superimposed on the river network of the target area, and the pollution accumulation area is marked on the river network based on the superposition result. Then, using fluid dynamics and diffusion direction, the flow direction of sewage is tracked from each pollution accumulation area within the superimposed river network to determine the downstream impact path containing the sewage flow path. During the tracking process, a dynamic buffer is constructed to dynamically adjust the diffusion coefficient, which changes as the distance between the pollutant and the pollution accumulation area increases, thereby modeling the diffusion path. The diffusion coefficient describes the attenuation of pollutants during the diffusion process and is related to the drift distance of the pollutants; the lower the diffusion coefficient, the farther the pollutants are from the pollution accumulation area, the lower the pollutant content in the water flow, and the weaker its pollution diffusion capacity. When the diffusion coefficient is below a certain threshold, it can be considered that the corresponding downstream impact path hardly contains pollutants entering another area, thus determining the endpoint of the pollutant flow and the midpoint of the diffusion path.

[0054] Furthermore, the modeling process divides the various regions according to their pollution levels, including the pollution accumulation zone, the dynamic buffer zone, and the background zone. The dynamic buffer zone is an area with significant fluctuations in pollution levels, which can be determined by the pollution level change curve. The background zone is an area with relatively flat pollution levels and low pollution levels.

[0055] When modeling based on the above-mentioned regional division, the diffusion trajectory and range of industrial wastewater are tracked according to different regions and external influencing factors. Starting from the pollution accumulation area and ending at the background area (i.e., the area with a low diffusion coefficient), a pollution diffusion network is constructed using the direction of pollutant flow as the connection between the starting and ending points. The nodes of this pollution diffusion network are artificially planned wastewater segments, and the current pollution level is marked at each node.

[0056] As an improvement to the above scheme, node weights of the pollution diffusion network are constructed based on the distance between each sewage segment and the pollution accumulation area. The closer a sewage segment is to the pollution accumulation area, the higher its weight. The node weights are used to describe the radiation impact of the pollution accumulation area on the surrounding area; the higher the node weight, the greater the pollution impact on the surrounding environment.

[0057] Step S4: Based on the pollution diffusion network and the current water quality characteristics, trace the pollution source to determine the main pollution source of the industrial wastewater.

[0058] The wastewater segment to be tested is extracted from the pollution diffusion network where the wastewater concentration exceeds a first threshold, and the area where the industrial pollutants generated in the target area match the current water quality characteristics is obtained, and such area is called the potential pollution area.

[0059] Optionally, in this embodiment of the application, the definition of "current water quality characteristics conforming" is that the types of industrial pollutants generated in the area highly match the types of pollutants in the current water quality characteristics.

[0060] The diffusion path between the wastewater segment to be tested and the potentially polluted area is located on the aforementioned pollution diffusion network. Then, based on the difference between real-time water quality data and historical water quality information, a wastewater concentration change trend curve for the diffusion path is constructed to characterize the concentration changes of all pollutants in industrial wastewater. By searching for the peak value of the wastewater concentration change trend curve within a preset range, the first pollutant exhibiting an abnormal upward trend is identified.

[0061] Then, the concentration of the first pollutant in the wastewater discharged from each discharge outlet is investigated along the diffusion path. At discharge outlets where the concentration exceeds a set range, isotopic tracing of the first pollutant in the wastewater is performed to identify the key discharge outlets discharging industrial wastewater.

[0062] Furthermore, during pollution source tracing, if there are instances of rainfall, strong winds, or a sharp rise in temperature, it is necessary to combine the collected meteorological change data with concentration and isotope analysis. For example, during rainfall, concentration and isotope analysis can be performed in conjunction with collected rainfall data.

[0063] Finally, based on the industrial buildings corresponding to the key discharge outlets, the main sources of industrial wastewater pollution were identified.

[0064] For example, if a high concentration of sulfate is found in a sewage outlet, the isotope ratio range of sulfur in the wastewater near the sewage outlet is detected and compared with the sulfur ratio range of the industrial wastewater at the sewage outlet to pinpoint the main source of pollution.

[0065] Step S5: Based on the main pollution sources, generate wastewater treatment measures that conform to the geographical characteristics of the target area.

[0066] The main pollution sources were analyzed to obtain information on their complex components. Based on this information and the degree of pollution in the industrial wastewater, preliminary treatment measures were determined.

[0067] However, considering that wastewater treatment needs to be optimized in combination with local geographical features to achieve the desired effect, this application's embodiments optimize the preliminary treatment measures based on the topography and climate conditions of the target area to obtain wastewater treatment measures.

[0068] For example, in the case of sewage overflow caused by heavy rain in plain areas with high temperature and heavy rainfall, it is necessary to increase the number of screens and sedimentation tanks to prevent the impact of suspended matter during the rainy season. Moreover, due to the characteristics of plains being prone to water accumulation, more sedimentation tanks are needed to filter sewage.

[0069] In another example, for water-scarce regions, it is recommended to use reclaimed water reuse technology to treat industrial wastewater, thereby achieving efficient use of water resources.

[0070] Implementing the embodiments of this application has the following beneficial effects: This application embodiment uses joint analysis of spectral and surface images of industrial wastewater to match visual features in the images with pollutant components in the spectrum, determining the water quality of the industrial wastewater and obtaining its pollution range and current water quality characteristics. Then, it considers the impact of geographical features and meteorological changes in the target area on the movement of pollutants in the wastewater, achieving a more accurate determination of the pollution diffusion range and providing sufficient evidence for subsequent pollution source identification. Next, pollution source tracing is performed based on the pollution diffusion network, using current water quality characteristics to quickly investigate and accurately locate the discharge outlet of leaking industrial wastewater, identifying the corresponding main pollution source. Finally, based on the pollutant components of the main pollution source and the geographical features of the target area, wastewater treatment measures for the current ecological environment are generated, contributing to reasonable and efficient wastewater treatment.

[0071] Second Embodiment Furthermore, in order to implement the industrial wastewater water quality monitoring system corresponding to the above method embodiments and achieve the corresponding functions and technical effects, Figure 2A structural diagram of an industrial wastewater quality monitoring system is provided. For ease of explanation, only the parts relevant to this embodiment are shown. The industrial wastewater quality monitoring system provided in this application embodiment includes: The data acquisition module 201 is used to collect real-time water quality data of industrial wastewater in the target area; wherein, the real-time water quality data includes water quality detection spectrum and water surface image.

[0072] In this embodiment, real-time water quality data of industrial wastewater within the target area is primarily obtained by photographing the wastewater surface and collecting spectral data of the wastewater. The water surface images are mainly obtained under natural light conditions, combining multi-angle shots of the water surface and the shoreline. Furthermore, to analyze the dynamic changes in the water surface, continuous video recording of the industrial wastewater surface is also used. Compared to static images, dynamic video is more effective at capturing the diffusion patterns of foam and flowing water. The captured video is then decomposed frame by frame into multiple water surface images.

[0073] Water quality detection spectra are primarily obtained by analyzing samples of industrial wastewater using a water quality analyzer. The type of water quality detection spectrum is related to the type of pollutant. For example, ultraviolet-visible spectroscopy is mainly used to detect organic suspended solids in water; laser-induced breakdown spectroscopy is mainly used to detect various heavy metals; ultraviolet absorption spectroscopy is used for the detection of organic matter; and near-infrared spectroscopy is used to detect oil pollutants.

[0074] The feature extraction module 202 is used to extract features from the water surface image and verify the feature extraction results through the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater.

[0075] In this embodiment of the application, visual features are extracted from the water surface image, and the extraction results are matched with a preset wastewater image database to determine the pollution type corresponding to the industrial wastewater; Partial least squares regression is performed on the water quality detection spectrum corresponding to the pollution type, and the current water quality characteristics are obtained by analyzing the pollutant composition in the industrial wastewater. The wastewater concentration in the water quality characteristics is compared with a preset wastewater concentration range, and the pollution range is determined based on the detection location of the water quality detection spectrum.

[0076] The diffusion network construction module 203 is used to generate a pollution diffusion network by extrapolating the spatial changes in the water quality of the industrial wastewater based on the pollution range and the geographical features and meteorological changes of the target area.

[0077] In this embodiment of the application, pollution points related to the pollution range are marked on the map corresponding to the target area, and the water flow direction of the industrial wastewater is also marked to obtain a pollution area map; Based on the historical water quality information and external influencing factors of the target area at the previous moment, the spatial variation of the industrial wastewater quality is analyzed according to the pollution area map to obtain the pollution diffusion network of the target area.

[0078] The pollution source determination module 204 is used to trace the pollution source based on the pollution diffusion network and the current water quality characteristics, and to determine the main pollution source of the industrial wastewater.

[0079] In this embodiment of the application, the wastewater segment to be detected with a wastewater concentration exceeding a first threshold is extracted from the pollution diffusion network; Within the industrial zones of the target area, zones whose pollutants conform to the current water quality characteristics are selected as potential pollution zones. The diffusion path between the wastewater section to be tested and the polluted area is found on the pollution diffusion network. The pollution source is traced by detecting the trend of wastewater concentration change along the diffusion path, and the corresponding key discharge outlets are identified. The main pollution sources are determined based on the industrial buildings corresponding to the key sewage outlets.

[0080] The treatment measures generation module 205 is used to generate wastewater treatment measures that conform to the geographical characteristics of the target area based on the main pollution sources.

[0081] In this embodiment, the main pollution sources are analyzed to obtain information on their composite components. Based on this composite component information and the degree of pollution in the industrial wastewater, preliminary treatment measures are determined.

[0082] However, considering that wastewater treatment needs to be optimized in combination with local geographical features to achieve the desired effect, this application's embodiments optimize the preliminary treatment measures based on the topography and climate conditions of the target area to obtain wastewater treatment measures.

[0083] For example, in the case of sewage overflow caused by heavy rain in plain areas with high temperature and heavy rainfall, it is necessary to increase the number of screens and sedimentation tanks to prevent the impact of suspended matter during the rainy season. Moreover, due to the characteristics of plains being prone to water accumulation, more sedimentation tanks are needed to filter sewage.

[0084] In another example, for water-scarce regions, it is recommended to use reclaimed water reuse technology to treat industrial wastewater, thereby achieving efficient use of water resources.

[0085] In some embodiments, the feature extraction module 202 specifically comprises: First, identify visual features in the water surface image to distinguish industrial wastewater from normal water quality, thus roughly determining the type of wastewater. Then, for each wastewater type, find the corresponding water quality detection spectrum to analyze the pollutant composition, determine the various pollutants contained in the industrial wastewater and their concentrations, and further determine the sections of water exceeding pollution standards based on the pollutant concentrations, thereby determining the scope of industrial wastewater pollution.

[0086] First, based on normal water body images and preset visual labels, anomaly identification is performed on the surface images of industrial wastewater. Anomalies that deviate from normal water body conditions are marked on the surface images, and visual features of each surface image are extracted accordingly. The anomaly identification process mainly involves detecting whether the industrial wastewater surface contains foam / bubbles or other debris, water color, turbidity, surface gloss, and light transmittance. This application converts these parameters into visual labels for visual feature extraction.

[0087] These visual features are then integrated to obtain a pollution scene description text that can fully describe the overall visual situation of a water surface image.

[0088] Specifically, the visual features of all water surface images belonging to the same shooting point are input into a preset image analysis model. The water surface scene at the shooting point is described using a preset description template to obtain a pollution scene description text for that shooting point.

[0089] For example, considering several water surface images belonging to the same downstream channel, the visual features of image A include: the water is yellow, there are few bubbles on the surface, and there is an oil film on the surface; the visual features of image B include: the water is yellow, there are impurities in the water, there are colored bubbles on the surface, and there is abnormal reflection on the surface. Integrating the visual features of these two images, the pollution scene description of this downstream channel can be obtained as follows: the water surface is generally yellow, with some continuous colored bubbles, an oil film on the surface, and chemical sediment beneath the oil film.

[0090] By integrating the visual features of multiple water surface images from the same shooting point, a comprehensive scene description of a sewage section can be achieved, capturing more complete sewage conditions that cannot be fully displayed due to limited shooting angles, and providing more data support for subsequent pollution type identification.

[0091] Then, based on a pre-set wastewater image database, the content of the pollution scene description text is matched to identify the pollution type corresponding to the industrial wastewater. For example, for industrial wastewater that is yellow, contains impurities, has colored bubbles on the surface, or exhibits abnormal reflectivity, the wastewater image database determines that the water is contaminated with organic dyes.

[0092] Then, the water quality detection spectrum used to identify the pollution type is determined, and the spectrum is processed by partial least squares regression. By considering the overlap of these pollutant components in the spectrum, the spectrum is analyzed to fully determine the types and concentrations of various pollutants in industrial wastewater, thereby obtaining the corresponding current water quality characteristics. Among them, partial least squares regression can solve the multiple correlations between multiple independent variables and is suitable for situations with few samples, enabling the decomposition of complex wastewater.

[0093] For example, for industrial wastewater containing oil pollutants, near-infrared spectroscopy is used to analyze the pollutant composition. A characteristic peak is detected near 320 nm in the spectrum, indicating that the industrial wastewater contains petroleum hydrocarbons, and the concentration of petroleum hydrocarbons is determined. For industrial wastewater contaminated with heavy metals, atomic absorption spectroscopy is used to analyze the pollutant composition, and it is determined that the industrial wastewater contains chromium particles and mercury ions.

[0094] Then, the wastewater concentration characterized by the current water quality features is compared with the preset wastewater concentration range to determine the wastewater segment with excessive pollution. Then, the pollution range of industrial wastewater is determined by combining the detection location corresponding to the water quality detection spectrum.

[0095] In some embodiments, the diffusion network construction module 203 specifically comprises: Based on the obtained pollution range of industrial wastewater, the relevant pollution points are marked on the map of the target area, and the flow direction of the industrial wastewater is also marked on the map to obtain a map containing the pollution area.

[0096] The system acquires meteorological changes in the target area over a previous period and then constructs a pollution simulation scenario based on the geographical features of the target area. Specifically, based on the meteorological changes, the pollution simulation scenario sets the target area's wind speed, temperature and humidity, precipitation, topography, water features, and climate (e.g., whether it's an arid or humid region). Based on the geographical features, the pollution simulation scenario sets the topography (e.g., whether it's a plain or mountainous area, and its location within a watershed) and water features (e.g., river flow velocity and groundwater infiltration).

[0097] Based on the pollution simulation scenario and the terrain conditions within the target area, and using meteorological changes within the target area as control variables, the simulation examines the movement rate and direction of various pollutants in the current water quality characteristics from the starting point of the control variables. During the simulation, the combined effects of geographical features and meteorological changes on pollutant diffusion are analyzed, thereby deriving the degree of influence of these external factors on pollutant diffusion and migration. When the change process of the control variables is completed, the external influencing factors of the target area are obtained. The starting point of the meteorological changes is the starting point of the data acquisition period; completing the change process of the control variables means simulating the entire meteorological change process within the data acquisition time.

[0098] For example, in a coastal plain of a monsoon region during summer, the recent weather patterns have primarily consisted of heavy rainfall and typhoons. Based on the pollution simulation scenario described above, the movement rate and direction of pollutants are simulated by adjusting rainfall and wind speed. During the simulation, the flow of water in the plain changes from slow to rapid due to rainfall, leading to sewage overflow and backflow into farmland, as well as sudden flooding. Additionally, strong winds cause particulate matter from industrial exhaust gases to settle into water bodies, indirectly polluting water quality. This allows for the precise identification of external influencing factors. Furthermore, the risk of industrial equipment pipeline leaks can be analyzed when environmental humidity rises sharply due to heavy rain, as pipeline leaks can lead to the accidental release of pollutants.

[0099] Compared to existing technologies that only consider pollutant diffusion caused by river flow, the embodiments of this application also take into account the impact of meteorological changes and more geographical features on pollutant diffusion, achieving more accurate prediction of pollutant diffusion paths and enabling rapid and accurate identification of pollution sources.

[0100] For example, mountains can block pollutants, making it difficult for them to disperse and leading to higher levels of pollution in waterways over time. Sandy soils allow pollutants to infiltrate more easily, increasing the likelihood of groundwater contamination.

[0101] Historical water quality information for the target area at the previous time point is obtained; this historical water quality information is data collected prior to the real-time water quality data. The differences in pollutant composition between the historical water quality information and the current water quality characteristics are analyzed, such as whether there are any new types of pollutants and whether pollutant concentrations have increased or decreased. Water quality change parameters for industrial wastewater in each wastewater segment are generated; wherein, the wastewater segment is determined based on artificially divided river segments on the pollution area map.

[0102] Based on the water quality change parameters, the pollution degree change curves of each sewage segment are plotted. The change degree of the water quality change parameters is taken as the change trend of the pollution degree. Sewage segments with higher pollution degree or higher pollution degree increase trend are identified as pollution accumulation areas and marked on the pollution area map.

[0103] Using the pollution accumulation area as the origin and the water flow direction as the diffusion direction, the diffusion trajectory and range of industrial wastewater in each wastewater section are predicted under the consideration of external influencing factors. A pollution diffusion network is generated by tracking the flow trajectory of industrial wastewater.

[0104] Specifically, the origin representing the pollution accumulation area is superimposed with the river network of the target area. Downstream impact paths are tracked using hydrodynamics and diffusion direction. During the tracking process, a dynamic buffer is constructed to dynamically adjust the diffusion coefficient, which changes with the increasing distance between the pollutant and the pollution accumulation area, thus modeling the diffusion path. The diffusion coefficient describes the attenuation of pollutants during the diffusion process.

[0105] Furthermore, the modeling process divides the various regions according to their pollution levels, including the pollution accumulation zone, the dynamic buffer zone, and the background zone. The dynamic buffer zone is an area with significant fluctuations in pollution levels, which can be determined by the pollution level change curve. The background zone is an area with relatively flat pollution levels and low pollution levels.

[0106] When modeling based on the above-mentioned regional division, the diffusion trajectory and range of industrial wastewater are tracked according to different regions and external influencing factors, generating a pollution diffusion network with the background area as the endpoint. The nodes of the pollution diffusion network are related to the artificially planned wastewater sections and pollution levels.

[0107] As an improvement to the above scheme, node weights of the pollution diffusion network are constructed based on the distances between each wastewater segment and the pollution accumulation area. These node weights describe the radiative impact of the pollution accumulation area on the surrounding area and are also related to the diffusion coefficient.

[0108] In some embodiments, the pollution source determination module 204 specifically comprises: The wastewater segment to be tested is extracted from the pollution diffusion network where the wastewater concentration exceeds a first threshold, and the area where the industrial pollutants generated in the target area match the current water quality characteristics is obtained, and such area is called the potential pollution area.

[0109] Optionally, in this embodiment of the application, the definition of "current water quality characteristics conforming" is that the types of industrial pollutants generated in the area highly match the types of pollutants in the current water quality characteristics.

[0110] The diffusion path between the wastewater segment to be tested and the potentially polluted area is located on the aforementioned pollution diffusion network. Then, based on the difference between real-time water quality data and historical water quality information, a wastewater concentration change trend curve for the diffusion path is constructed to characterize the concentration changes of all pollutants in industrial wastewater. By searching for the peak value of the wastewater concentration change trend curve within a preset range, the first pollutant exhibiting an abnormal upward trend is identified.

[0111] Then, the concentration of the first pollutant in the wastewater discharged from each discharge outlet is investigated along the diffusion path. At discharge outlets where the concentration exceeds a set range, isotopic tracing of the first pollutant in the wastewater is performed to identify the key discharge outlets discharging industrial wastewater.

[0112] Furthermore, during pollution source tracing, if there are instances of rainfall, strong winds, or a sharp rise in temperature, it is necessary to combine the collected meteorological change data with concentration and isotope analysis. For example, during rainfall, concentration and isotope analysis can be performed in conjunction with collected rainfall data.

[0113] Finally, based on the industrial buildings corresponding to the key discharge outlets, the main sources of industrial wastewater pollution were identified.

[0114] For example, if a high concentration of sulfate is found in a sewage outlet, the isotope ratio range of sulfur in the wastewater near the sewage outlet is detected and compared with the sulfur ratio range of the industrial wastewater at the sewage outlet to pinpoint the main source of pollution.

[0115] Implementing the embodiments of this application has the following beneficial effects: This application embodiment uses joint analysis of spectral and surface images of industrial wastewater to match visual features in the images with pollutant components in the spectrum, determining the water quality of the industrial wastewater and obtaining its pollution range and current water quality characteristics. Then, it considers the impact of geographical features and meteorological changes in the target area on the movement of pollutants in the wastewater, achieving a more accurate determination of the pollution diffusion range and providing sufficient evidence for subsequent pollution source identification. Next, pollution source tracing is performed based on the pollution diffusion network, using current water quality characteristics to quickly investigate and accurately locate the discharge outlet of leaking industrial wastewater, identifying the corresponding main pollution source. Finally, based on the pollutant components of the main pollution source and the geographical features of the target area, wastewater treatment measures for the current ecological environment are generated, contributing to reasonable and efficient wastewater treatment.

[0116] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring the water quality of industrial wastewater, characterized in that, include: Real-time water quality data of industrial wastewater in the target area is collected; wherein, the real-time water quality data includes water quality detection spectra and water surface images; Feature extraction is performed on the water surface image, and pollutant composition analysis is conducted on the feature extraction results using the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater; Based on the pollution range, the spatial changes in the water quality of the industrial wastewater are extrapolated by taking into account the geographical features and meteorological changes of the target area, thereby generating a pollution diffusion network; The process of extrapolating the spatial changes in the water quality of industrial wastewater based on the geographical features and meteorological changes of the target area includes: analyzing the impact of the geographical features and meteorological changes on the diffusion degree of various pollutants, generating external influencing factors; constructing a pollution simulation scenario based on the geographical features and meteorological changes; wherein the pollution simulation scenario needs to set wind speed, temperature and humidity, precipitation, topographic conditions, water body characteristics, and climate of the target area; based on the pollution simulation scenario and the topographic conditions in the target area, using the meteorological changes in the target area as control variables, simulating the movement rate and direction of various pollutants in the current water quality characteristics, analyzing the effect of geographical features and meteorological changes on pollutant diffusion, and obtaining the external influencing factors of the target area when the change process of the control variables is completed; Based on the pollution diffusion network and the current water quality characteristics, pollution source tracing is performed to determine the main pollution sources of the industrial wastewater. Based on the main pollution sources, wastewater treatment measures that conform to the geographical characteristics of the target area are generated.

2. The method for monitoring the water quality of industrial wastewater according to claim 1, characterized in that, The process involves extracting features from the water surface image and analyzing the pollutant composition of the extracted features using the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater. Specifically: Visual features are extracted from the water surface image, and the extraction results are matched with a preset wastewater image database to determine the pollution type corresponding to the industrial wastewater; Partial least squares regression is performed on the water quality detection spectrum corresponding to the pollution type, and the current water quality characteristics are obtained by analyzing the pollutant composition in the industrial wastewater. The wastewater concentration in the current water quality characteristics is compared with a preset wastewater concentration range, and the pollution range is determined based on the detection location of the water quality detection spectrum through the comparison results.

3. The method for monitoring the water quality of industrial wastewater according to claim 2, characterized in that, The step of extracting visual features from the water surface image and matching the extracted results with a preset wastewater image database to determine the pollution type corresponding to the industrial wastewater specifically involves: Based on preset visual labels, anomaly detection is performed on the water surface images to extract visual features of each water surface image; wherein, the visual labels include water color, water turbidity, water surface debris, and water surface gloss. Based on the visual features, all water surface images belonging to the same shooting point are uniformly described in a scene to obtain pollution scene description text; Based on a pre-defined wastewater image library, the pollution scene description text is matched for content, and the wastewater is classified according to the matching results to determine the pollution type.

4. The method for monitoring the water quality of industrial wastewater according to claim 1, characterized in that, Based on the pollution range, the spatial changes in the water quality of the industrial wastewater are extrapolated through the geographical features and meteorological changes of the target area to generate a pollution diffusion network, specifically: Mark the pollution points related to the pollution range on the map corresponding to the target area, and mark the water flow direction of the industrial wastewater to obtain a pollution area map; Based on the historical water quality information and external influencing factors of the target area at the previous moment, the spatial variation of the industrial wastewater quality is analyzed according to the pollution area map to obtain the pollution diffusion network of the target area.

5. The method for monitoring the water quality of industrial wastewater according to claim 4, characterized in that, Based on the historical water quality information and external influencing factors of the target area at the previous time, the spatial variation of the industrial wastewater quality is analyzed according to the pollution area map to obtain the pollution diffusion network of the target area, specifically: Based on the differences in pollutant composition between the historical water quality information and the current water quality characteristics, water quality change parameters for the industrial wastewater in each wastewater segment are generated; Based on the degree of change of the water quality parameters, the pollution accumulation area is marked on the pollution area map; Based on external influencing factors, taking the pollution accumulation area as the origin and the water flow direction as the diffusion direction, the diffusion trajectory and range of the industrial wastewater within the wastewater section are predicted, and the pollution diffusion network is generated; wherein, the weight of each node in the pollution diffusion network is determined by the distance between the wastewater section and the pollution accumulation area.

6. The method for monitoring the water quality of industrial wastewater according to claim 1, characterized in that, The step of tracing the pollution source based on the pollution diffusion network and the current water quality characteristics to determine the main pollution source of the industrial wastewater specifically involves: The wastewater segment to be detected with a wastewater concentration exceeding a first threshold is extracted from the pollution diffusion network; Within the industrial zones of the target area, zones whose pollutants conform to the current water quality characteristics are selected as potential pollution zones. The diffusion path between the wastewater section to be tested and the potentially polluted area is found on the pollution diffusion network. The pollution source is traced by detecting the trend of wastewater concentration change along the diffusion path, and the corresponding key discharge outlets are identified. The main pollution sources are determined based on the industrial buildings corresponding to the key sewage outlets.

7. The method for monitoring the water quality of industrial wastewater according to claim 6, characterized in that, The method of tracing pollution sources by detecting the trend of wastewater concentration changes along the diffusion path and identifying corresponding key discharge outlets specifically involves: Based on the real-time water quality data, construct a wastewater concentration change trend curve along the diffusion path; The first pollutant exhibiting an abnormal upward trend was identified by analyzing the wastewater concentration change trend curve. The concentration of the first pollutant at each discharge outlet along the diffusion path is investigated, and isotope detection is performed on the wastewater at the discharge outlets to identify the key discharge outlets that meet the current water quality characteristics; wherein, if there is rainfall during the pollution source tracing process, concentration detection and isotope detection are performed in conjunction with the collected rainfall data.

8. The method for monitoring the water quality of industrial wastewater according to claim 1, characterized in that, The step of generating wastewater treatment measures that conform to the geographical characteristics of the target area based on the main pollution sources specifically includes: The main pollution sources were analyzed to obtain information on their complex components. Based on the information on the composite components and the degree of pollution of the industrial wastewater, preliminary treatment measures were determined; Based on the topography and climate conditions of the target area, the preliminary treatment measures were optimized to obtain the aforementioned wastewater treatment measures.

9. A water quality monitoring system for industrial wastewater, characterized in that, include: The module includes a data acquisition module, a feature extraction module, a diffusion network construction module, a pollution source identification module, and a treatment measure generation module. The data acquisition module is used to collect real-time water quality data of industrial wastewater in the target area; the real-time water quality data includes water quality detection spectra and water surface images. The feature extraction module is used to extract features from the water surface image and analyze the pollutant composition of the feature extraction results through the water quality detection spectrum to obtain the pollution range and current water quality characteristics of the industrial wastewater. The diffusion network construction module is used to extrapolate the spatial changes in the water quality of the industrial wastewater based on the pollution range and the geographical features and meteorological changes of the target area, thereby generating a pollution diffusion network. The process of extrapolating the spatial changes in the water quality of industrial wastewater based on the geographical features and meteorological changes of the target area includes: analyzing the impact of the geographical features and meteorological changes on the diffusion degree of various pollutants, generating external influencing factors; constructing a pollution simulation scenario based on the geographical features and meteorological changes; wherein the pollution simulation scenario needs to set wind speed, temperature and humidity, precipitation, topographic conditions, water body characteristics, and climate of the target area; based on the pollution simulation scenario and the topographic conditions in the target area, using the meteorological changes in the target area as control variables, simulating the movement rate and direction of various pollutants in the current water quality characteristics, analyzing the effect of geographical features and meteorological changes on pollutant diffusion, and obtaining the external influencing factors of the target area when the change process of the control variables is completed; The pollution source determination module is used to trace pollution sources based on the pollution diffusion network and the current water quality characteristics, and to determine the main pollution sources of the industrial wastewater. The treatment measures generation module is used to generate wastewater treatment measures that conform to the geographical characteristics of the target area based on the main pollution sources.

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