Water ecological environment fine supervision and early warning traceability system

Abstract

The invention belongs to the technical field of water environment protection, and particularly relates to a water ecological environment fine supervision and early warning traceability system which integrates various water ecological environment data resources. Various index data of a water environment are accurately generated by utilizing resources such as automatic water quality monitoring data, fingerprint early warning traceability data, video monitoring, unmanned aerial vehicle thermal imaging monitoring, online monitoring of a sewage treatment plant, online monitoring of a wastewater-related enterprise, basic information of a sewage draining exit in a river and the like; visual dynamic monitoring of dynamic monitoring, abnormal warning, risk early warning and the like of the water pollution condition and the environment risk condition and real-time display of one graph are achieved, the early warning traceability function is achieved, the early warning categories include enterprise online monitoring data counterfeiting, monitoring data exceeding and monitoring data trend change, whether water flow exists at a discharge port or not is recognized in combination with meteorological data, and the real-time display of the water pollution condition and the environment risk condition is achieved. And for pollution events, multiple data analysis can be carried out, and finally, the pollution source is locked.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water environment protection, and particularly relates to a water ecological environment fine supervision and early warning traceability system. BACKGROUND

[0002] Ecological environment monitoring is an important aspect of ecological environment protection work. At present, in the aspect of water environment monitoring system, there are problems such as single supervision means, insufficient diversification and intelligentization supervision, insufficient coverage, difficult manual investigation, unclear local responsibility division, and low intelligent integration degree, which cannot perform high-quality monitoring work.

[0003] For example, for some industrial developed areas, various water-related enterprises distributed along rivers directly affect the advantages and disadvantages of the water quality of the section. These enterprises are mostly located in the upstream area of the section, and the industrial wastewater generated in the production process flows into the section monitoring water area along the pipe network and tributaries, which is a key factor for the stability of the water quality of the section. However, the intelligent supervision and early warning capability of key risk sources (industrial parks and sewage outlets) is insufficient, which results in the lack of water environment monitoring and early warning and risk prevention capability according to the characteristics of the water environment, the inability to achieve early warning, the whole process supervision of the flow of polluted water to the river, and the inability to discover and effectively trace the pollution event in time. For example, an industrial cluster area with an area of 6 square kilometers is dominated by down and its product processing, supported by petroleum chemical industry and modern logistics industry, and is an important down processing distribution center and petroleum chemical base in China. The industrial cluster area has a large number of enterprise sewage outlets, and the supervision is difficult. In addition, some enterprises also have the behaviors of online monitoring data falsification, dark pipe secret discharge, rainwater outlet pollution, or using vehicles to fill sewage for secret discharge during the rain period. In view of the above situations, it is necessary to establish an effective supervision, early warning and traceability capability system. SUMMARY

[0004] The application aims to provide a water ecological environment fine supervision and early warning traceability system to solve the technical problem that the existing water environment monitoring, early warning and traceability are difficult, which is not conducive to realizing efficient and accurate water environment supervision and early warning traceability.

[0005] To solve the above technical problem, the application adopts the following technical scheme: A water ecological environment fine supervision and early warning traceability system is provided, which comprises an Internet of Things perception layer, an infrastructure layer, a data resource layer, a business application layer and a user layer. The Internet of Things perception layer is used for data collection of the water ecological environment, and comprises a water quality monitoring station, a water quality fingerprint early warning traceability instrument, video monitoring, unmanned aerial vehicle thermal imaging monitoring and enterprise online monitoring. The infrastructure layer includes computing, storage, network and security resources required for the operation of other layers, and is used to provide unified computing power for other layers, and the Internet of Things sensing layer is connected with the infrastructure layer; The data resource layer is used for gathering various types of water ecological environment related data, including obtaining real-time monitoring information of the Internet of Things sensing layer, obtaining business data generated by various business departments related to water environment, and obtaining ecological environment related data generated by other external agencies, and integrating the gathered water ecological environment related data into a water ecological environment monitoring resource library through unified technical specifications, a unified information classification coding system, a unified data exchange platform, a unified information resource directory system, and a unified object-oriented data organization requirement, for use by the business application layer; The business application layer performs accurate and intelligent water environment monitoring through the construction of a water environment intelligent monitoring platform, including unified dynamic monitoring, abnormal alarm and risk early warning of water environment quality, water pollution and water environment risk, water quality simulation, pollution tracing and data fraud analysis to analyze the changes of surface water quality in the target area and whether the water quality of the key river basin examination section is stable and up to standard; The user layer is used for information interaction between the data resource layer and the business application layer by PC or mobile terminal for staff of various business departments including water ecological environment department, monitoring station, law enforcement team and operation and maintenance team.

[0006] Further, the water quality monitoring station in the Internet of Things sensing layer is constructed according to the following arrangement: for water bodies not crossing regions, water quality monitoring stations are set up at both ends; for water bodies crossing regions, water quality monitoring stations are set up at both ends and at the section of the boundary, and the water quality monitoring station set up at the section of the boundary is in the downstream region of the section of the boundary; for rivers with industrial wastewater, water quality monitoring stations are added downstream of wastewater discharge according to the length of the river; The water quality monitoring station monitors and analyzes water temperature, pH value, conductivity, turbidity, dissolved oxygen, ammonia nitrogen, total phosphorus, COD and permanganate; The water quality fingerprint early warning and tracing instrument includes an online water quality fingerprint early warning and tracing instrument and a mobile water quality fingerprint early warning and tracing instrument, the online water quality fingerprint early warning and tracing instrument is arranged downstream of an industrial cluster, and the mobile water quality fingerprint early warning and tracing instrument is used for daily inspection and emergency investigation of sudden pollution events; and enterprise water quality fingerprint databases and industry fingerprint databases are constructed in the data resource layer through actual investigation; The unmanned aerial vehicle thermal imaging monitoring is used for night or rainy season auxiliary patrol and evidence collection; The video monitoring is installed at the enterprise wastewater discharge outlet and at the enterprise online monitoring sampling site, the video monitoring installed at the enterprise wastewater discharge outlet can clearly see the water discharge condition, and the video monitoring installed at the enterprise online monitoring sampling site can clearly see the sampling condition.

[0007] Furthermore, the infrastructure layer is implemented based on the government cloud resources allocated by the Big Data Bureau.

[0008] Furthermore, the data resource layer includes a data aggregation module, a data cleaning module, a data processing module, and a data service module; The data aggregation module is equipped with a water environment comprehensive supervision raw database, an IoT sensing raw database, and other business system raw databases. It also uses data acquisition tools, including a data exchange and integration platform, a streaming data access engine, and a government data sharing and exchange platform, to access and store various ecological and environmental related data resources. The data cleaning module is used to transform, denoise, normalize, and desensitize the aggregated data. The data cleaning module is equipped with a national standard library, an industry standard library, and an ecological and environmental department standard library. Based on the standard library, the data is further extracted, compared, standardized, correlated, and fused before being classified and stored. The data processing module is equipped with a basic database, which includes an enterprise basic database, a water environment quality database, a pollution source online monitoring database, a sewage discharge supervision database, an environmental risk database, and a comprehensive decision analysis database. This database is used to classify and store the data processed by the data cleaning module. At the same time, it relies on data tag extraction to perform secondary extraction and processing on the data to support data sharing and application. The data service module is used to provide services such as data exchange, interface calls, file acquisition, batch data output, and real-time data output to support the construction of water environment business scenario applications, including water environment information supervision, water environment source tracing analysis, water environment task collaboration, water environment emergency response, and a comprehensive water environment APP.

[0009] Furthermore, the acquisition of business data generated by various business departments related to the water environment, as well as ecological and environmental data generated by other outsourced offices and bureaus, includes: acquiring online monitoring data of sewage treatment plants, basic information on sewage discharge outlets into rivers, industry water quality data, and enterprise water quality data.

[0010] Furthermore, the intelligent water environment supervision platform is constructed with the following functional modules: a water system overview module, a water environment monitoring and supervision module, a video surveillance module, and a river discharge outlet module. The "Water System Overview" module displays the overall situation of the water system in the monitored area in the form of a "single map". Based on spatial GIS technology, it displays the geographical location of rivers, drinking water sources, and reservoirs in the monitored area, as well as the river flow direction. It also displays the spatial and quantitative components of the water environment, including drinking water sources, reservoirs, sewage treatment plants, wastewater enterprises, sewage outlets into rivers, and water quality monitoring stations. The water environment monitoring and supervision unified map module displays the water ecological environment data resources of the supervised area in the form of a "single map". It publishes the overall supervision status of the watershed on the GIS map, performs statistical analysis on various water environment-related elements according to spatial layers, and realizes the linkage of data statistical analysis with the spatial control system and layers through the control of the spatial control system and layers. The specific content displayed includes: the overall water quality status of the watershed, the surface water quality status, the supervision status of water-related enterprises, the distribution of sewage outlets into rivers, the early warning and source tracing status, and real-time updates of online monitoring data, critical warnings, and alarms for exceeding standards. The video surveillance map module acquires video surveillance data and forms a video location map based on a GIS geographic information system. It extracts suspicious video segments from the video surveillance and records them separately, including the recording date, time, relevant information for the preliminary judgment of the suspicious situation, and allows users to set the maximum extraction time for each video. The videos are then automatically stored in a designated folder. The "One Map of Sewage Outlets into Rivers" module provides a comprehensive overview of the geographical distribution and sewage discharge information of sewage outlets in a single map format. It screens sewage outlet information based on the outlet number, the river it belongs to, the nature of the outlet, on-site photos, and the method of discharge into the river. The "One Map of Sewage Outlets into Rivers" module also provides an APP interface for sewage outlet investigation, enabling on-site personnel to fill in information and monitor data, locate, photograph, and fill in information about sewage outlets, and achieve collaborative work between the on-site personnel and the platform.

[0011] Furthermore, the intelligent water environment monitoring platform also includes a big data intelligent early warning engine module, which comprises the following functional units: Water Environment Early Warning Rule Base Unit: The water environment early warning rule base is a rule base obtained by combining policy documents and specific business analysis. It is used to provide the basis and rules for judging various early warning situations, including rules for exceeding standards, rules for trends, rules for meeting standards, and rules for data anomalies. Early warning standard management unit: used to set different alarm standards according to national standards. The alarm standard setting adopts the threshold range method, which is used to generate corresponding alarms when the monitored data meets the corresponding standards; Equipment anomaly alarm unit: used to extend the communication protocol according to the requirements of the Ecological and Environmental Protection Bureau, realize the real-time alarm function for various situations such as on-site faults, shutdowns, and anomalies, and push alarm information to relevant responsible persons; Data fluctuation early warning unit: used for early warning of water environment quality monitoring. When the monitoring data remains unchanged or the data change range is very small, an early warning information can be sent. Data anomaly early warning unit: When the monitored data shows a maximum value, a minimum value, or an invalid value, it sends an early warning message; Data Exceedance Early Warning Unit: Used for early warning of water environment quality monitoring. When the data exceeds the preset standard value, an alarm is triggered and the relevant responsible persons are notified. Water quality change early warning unit: It provides early warnings on changes in water quality categories in the water environment through a month-on-month comparison. When the water quality declines by one category or more, an early warning is issued; if the water quality decline does not improve, the previous day's early warning is maintained. Video surveillance early warning unit: It identifies abnormal online monitoring data and meteorological data of related enterprises to determine the status of water flow at discharge outlets, and issues early warnings for abnormal events at enterprise discharge outlets and sewage treatment facilities; Historical Early Warning Query Unit: Allows users to query various historical alarm information from water quality monitoring stations, analyze the causes of data anomalies, and export query results.

[0012] Furthermore, the intelligent water environment monitoring platform also includes a task tracking, scheduling, and closed-loop processing module. This module, based on the early warning information provided by the big data intelligent early warning engine module, pushes the information to relevant management departments using built-in task types and processing templates. Relevant personnel then conduct targeted on-site investigations, handle the issues, and provide feedback based on this information, forming a closed-loop task processing mechanism and archiving the data. The task tracking, scheduling, and closed-loop processing module includes the following functional units: Task assignment unit: Used to communicate with mobile devices to distribute and schedule events or tasks, and provide work guidance; Task entry unit: This is used by personnel to upload images and documents involved in the process to the system for review by auditors; Task review unit: Used to review the completion progress of tasks in the list; Task Query Unit: Used to view and query personal pending events and completed events in a list format, as well as query details of pending and completed events, including event number, event status, event location, event level, event description, initiating unit, initiation time, and processing time.

[0013] Furthermore, the intelligent water environment supervision platform also includes the following functional modules: pollution source logical relationship analysis module, water pollution source tracing analysis module, and water quality simulation analysis module; The pollution source logical relationship analysis module is used to integrate all elements of water environment quality monitoring data in the monitoring area, collect and analyze basic information of water environment quality monitoring sections in the basin, basic hydrological information of major watersheds, and basic information of wastewater pollution source discharge in major watersheds. It determines the upstream and downstream relationship of each monitoring section according to the water flow direction, determines the inflow and outflow relationship between the main stream of the water system and each major tributary, and sorts out the wastewater discharge destination and outlet location of each pollution source. It also compares and displays the online monitoring data of water quality monitoring stations, sewage treatment plants, and wastewater enterprises on a single map. It randomly selects monitoring stations and automatically or manually dynamically generates comparison curves based on logical relationships to show the relationship between various pollution sources, thereby assisting managers in incident investigation. The water pollution source tracing and analysis module includes a conventional source tracing and analysis unit and a water pollution fingerprint early warning source tracing and analysis unit. The conventional source tracing analysis unit uses the pollutant characteristic information and attributes of pollution source enterprises, combined with the basic data of tributaries, main streams, and cross sections, to establish a list of relationships between the basin, tributaries, main streams, cross sections, discharge outlets, and enterprises. Then, by using the relationship between major pollutants, wastewater discharge areas, pollution water quality diffusion characteristics, and characteristic pollutants of enterprises, the scope of suspected polluting enterprises is narrowed down, providing a basis for law enforcement decisions. The water pollution fingerprint early warning and source tracing analysis unit, upon detecting data exceeding standards, connects to the water quality fingerprint early warning and source tracing instrument for fingerprint database comparison to determine whether the pollution originates from an industrial enterprise. If the data matches that of an enterprise that has established a fingerprint database, the pollution source is directly identified. Otherwise, the conventional source tracing analysis unit performs routine source tracing analysis, comparing similar enterprises based on fluctuations in online monitoring data, discharge characteristics, and spatial relationships. If a comparison result is provided, targeted investigations are conducted. If not, the data from upstream stations of the exceeding-standard site is analyzed. Based on the monitoring station data, a "grid-based, segmented" area is identified, and the mobile water quality fingerprint early warning and source tracing instrument is used to investigate upstream from the exceeding-standard site segment by segment within the identified segmented area. The water quality simulation and analysis unit is used to construct various water quality simulation and analysis models to intelligently simulate and analyze the trend of water quality changes in rivers within the regulatory area. For water pollution in receiving water bodies, the water quality model uses cross-sectional monitoring data, rainfall data, hydrological data, water quality data, meteorological data, and pollution source data to simulate the situation of water pollution, thereby enabling the prediction of water pollution change trends and the planning of the layout of sewage outlets in important water systems and key areas within the basin.

[0014] Furthermore, the intelligent water environment monitoring platform also includes the following functional modules: intelligent decision-making module, system management module, and water environment management APP module; The intelligent decision-making module is used to integrate and analyze multi-source data from water quality monitoring stations and monitoring data from water-related enterprises and sewage treatment plants. Through data analysis and visualization tools, it extracts key information, identifies trends, and predicts results, thereby providing data support and intelligent decision-making assistance. The intelligent decision-making module includes an intelligent extraction unit, a distance extraction unit, and a self-selected enterprise unit. The intelligent extraction unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it automatically extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period and analyzes the emission ratio. The distance extraction unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period by distance and analyzes the emission ratio. The self-selected enterprise unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period by self-selected enterprises and analyzes the emission ratio. The system management module includes an organization management unit, a system resource management unit, and a permission configuration management unit. The organization management unit is used to manage personnel involved in water pollution control, including organization management, role management, and user management. The system resource management unit is used to allocate permissions and display the framework for various functions and data dictionary information of the system. The permission configuration management unit is used to allocate corresponding resources to each role based on a role-based permission allocation system and to perform security authentication. The water environment management APP module is used to provide client services for viewing basic watershed information and monitoring information on mobile devices, including the distribution of monitoring stations, water quality information, analysis results of the current status of water environment quality, and analysis of the current status of pollution discharge. Based on the mobile monitoring and alarm system, it processes water quality monitoring and pollution source monitoring alarm information, queries information related to sudden water environment pollution accidents, and simultaneously views the simulation prediction results of pollutant migration and diffusion and the results of emergency monitoring, as well as locates, photographs, and fills in information about sewage outlets into rivers.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: This refined water ecological environment monitoring and early warning tracing system integrates various water ecological environment data resources, utilizing automatic water quality monitoring data, fingerprint early warning tracing data, video surveillance, drone thermal imaging monitoring, online monitoring of sewage treatment plants, online monitoring of wastewater-related enterprises, and basic information on sewage discharge outlets into rivers. It accurately generates various water environment indicator data, achieving dynamic monitoring, abnormal alarms, and risk warnings of water pollution and environmental risks, along with visualized dynamic monitoring and real-time display on a single map. This provides technical support for ensuring stable water quality compliance in the Yellow River basin. It has early warning tracing functions, with early warning categories including falsification of online monitoring data by enterprises, exceeding monitoring data standards, changes in monitoring data trends, identification of water flow at discharge outlets based on meteorological data, and changes in cross-sectional water quality. Furthermore, it can perform multiple data analyses for pollution events, ultimately pinpointing pollution sources or narrowing the scope of pollution investigation, providing crucial evidence for precise pollution control and rapid law enforcement. Attached Figure Description

[0016] The present invention will be explained in detail below with reference to the accompanying drawings. It should be noted that the drawings are used to provide a further understanding of the present invention and form part of the specification. They are used together with the embodiments of the present invention to explain the present invention, but should not impose any limitation on the implementability of the present invention.

[0017] In the attached diagram: Figure 1 This is an overall architecture diagram of an embodiment of the refined monitoring, early warning and source tracing system for aquatic ecological environment of the present invention.

[0018] Figure 2 This is a site distribution map of some water quality monitoring stations in one embodiment of the refined water ecological environment monitoring and early warning tracing system of the present invention.

[0019] Figure 3 This is monitoring data from station 6 located in Dongzhangzhuang, as described in one embodiment of the refined water ecological environment monitoring, early warning and source tracing system of the present invention.

[0020] Figure 4 This is a network architecture diagram of the Internet of Things (IoT) sensing layer, infrastructure layer, and user layer in one embodiment of the refined water ecological environment monitoring, early warning, and source tracing system of the present invention.

[0021] Figure 5 This is an architecture diagram of the data resource layer in one embodiment of the refined monitoring, early warning and source tracing system for the aquatic ecological environment of the present invention.

[0022] Figure 6 This is a flowchart of water quality early warning and source tracing analysis in one embodiment of the refined water ecological environment monitoring and early warning tracing system of the present invention.

[0023] Figure 7This is a schematic diagram of the analytical principle of the water pollution fingerprint early warning and tracing instrument in one embodiment of the water ecological environment fine monitoring and early warning tracing system of the present invention.

[0024] Figure 8 This is a flowchart illustrating the user-side early warning and source tracing process via a water environment management APP in one embodiment of the refined water ecological environment monitoring and early warning tracing system of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0026] In one embodiment, a refined water ecological environment monitoring and early warning system is provided, which is used for water environment monitoring in Pingyi County. Figure 1 As shown, the refined water ecological environment monitoring and early warning tracing system includes an IoT sensing layer, an infrastructure layer, a data resource layer, a business application layer, and a user layer. Among them, the IoT sensing layer and the infrastructure layer are hardware layers, while the data resource layer, the business application layer, and the user layer are software layers.

[0027] The IoT sensing layer is responsible for data collection of the aquatic ecological environment. The IoT sensing layer includes the construction of water quality monitoring stations, water quality fingerprint early warning and tracing instruments, video surveillance, drone thermal imaging monitoring, and enterprise online monitoring.

[0028] The setup and functions of the relevant devices in the IoT sensing layer are as follows: Water quality monitoring stations are constructed according to the following layout: For water bodies that do not cross regions, water quality monitoring stations are set up at both the inlet and outlet; for water bodies that cross regions, water quality monitoring stations are set up at both the inlet and outlet plus the boundary section, with the boundary section located downstream of the boundary section; for rivers with industrial wastewater, water quality monitoring stations are added downstream of the wastewater discharge point, depending on the river length.

[0029] The following section further illustrates the deployment of water quality monitoring stations using examples of their locations. Figure 2 As shown, Figure 2This is a map showing the distribution of some water quality monitoring stations. Station 1 is located on Shidu Avenue, Station 2 is located at Xiapo Village Bridge, Station 3 is located at Tongshi Town Bridge, Station 4 is located at Dongweigou, Station 5 is located at Guanjiazhuang, Station 6 is located at Dongzhangzhuang, Station 7 is located at Tangcun Reservoir, and Station 8 is located at Qiandonggu. As can be seen from the map, the Pingyi County Industrial Cluster Area is a key area for supervision. Considering the current situation where enterprises are directly discharging wastewater into Bailatonggou and Limingtiangou, one water quality monitoring station (Station 1 and Station 2) has been constructed at each of the Bailatonggou and Limingtiangou (Fengxiang Avenue) locations within the industrial park. This is to monitor the water quality of upstream water and serve as the first layer of judgment in the entire supervision system. The water quality monitoring station network uses 4G. The water flow at this location is uniform, indicating good water quality representativeness, and the surrounding environment is stable and reliable.

[0030] Furthermore, as shown in the above figure, Liangmiaogou, a major tributary of the Junhe River, is a long river without monitoring stations. When data anomalies occur, manual investigation upstream is required, which is difficult and time-consuming. Based on the "grid-based and segmented" working model, two new water quality monitoring stations (Station 3 and Station 4) were built upstream of the confluence of Liangmiaogou and Bailatonggou, and at the confluence of Bailinggou and Liangmiaogou. The network uses 4G, and the cross-section at this location has uniform water flow, good water quality representativeness, and a stable and reliable surrounding environment. Through the deployment of these stations, "grid-based and segmented" management was achieved, providing data support for source tracing capabilities, improving timeliness, and resolving the issue of environmental protection departments' responsibility for pollution incidents.

[0031] The water quality monitoring station monitors and analyzes five conventional water quality parameters: water temperature, pH value, conductivity, turbidity, and dissolved oxygen, as well as ammonia nitrogen, total phosphorus, COD, and permanganate. Combined with... Figure 3 As shown, Figure 3 This is the monitoring data from station 6 located in Dongzhangzhuang.

[0032] The water quality fingerprint early warning and tracing device includes an online water quality fingerprint early warning and tracing device and a mobile water quality fingerprint early warning and tracing device. The online water quality fingerprint early warning and tracing device is deployed downstream of the industrial cluster area, while the mobile water quality fingerprint early warning and tracing device is used for routine inspections and emergency investigations of sudden pollution incidents. Furthermore, through actual research, enterprise water quality fingerprint databases and industry fingerprint databases are built at the data resource layer.

[0033] In this embodiment, one online water quality fingerprint early warning and tracing device and one desktop water quality fingerprint early warning and tracing device were constructed: One online water quality fingerprint early warning device was deployed at the confluence of Liming Tunnel and Liangmiao Tunnel, creating a customized fingerprint database for wastewater-related enterprises and sewage discharge outlets into the river within the industrial cluster area. Pollution events were traced through fluorescence spectral feature analysis. The desktop water quality fingerprint early warning and tracing device was used to respond to sudden pollution events and for manual investigation and tracing in areas not covered by the online device. Through actual surveys, a total of 34 enterprise water quality fingerprint databases and 2 industry fingerprint databases were established.

[0034] The principle of water quality fingerprint pollution early warning and source tracing technology and instruments is to quickly diagnose pollution sources by comparing water quality fingerprints. By projecting the fluorescence intensity of a water sample onto a plane with excitation and emission wavelengths as the horizontal and vertical axes, a three-dimensional fluorescence spectrum can be obtained. Each fluorescent substance has its unique three-dimensional fluorescence spectrum. Water contains various fluorescent substances, such as oils, humic acids, proteins, surfactants, vitamins, aromatic compounds such as phenols, and pesticide residues. The fluorescence spectrum varies with the type and content of pollutants and has a one-to-one correspondence with the pollution source, known as a water quality fingerprint. The water quality fingerprint of wastewater differs significantly from that of unpolluted water samples, and can be used to determine whether a water body is polluted. There are significant differences in the water quality fingerprints of wastewater from different industries; even within the same industry, the water quality fingerprints of different companies often differ significantly. Therefore, water quality fingerprint comparison can be used to determine the pollution source.

[0035] Drone thermal imaging monitoring is used to assist in patrols and evidence collection at night or during the rainy season. Drone thermal imaging monitoring utilizes infrared thermal imaging technology to detect the infrared radiation of target objects and converts the temperature distribution image of the target object into a video image through photoelectric conversion and signal processing. This technology can effectively identify the status of wastewater discharge from enterprises, allowing for clear observation of the situation ahead even in complete darkness or in adverse cloud cover, further improving work efficiency and reducing safety risks for law enforcement personnel. Drones can take off at any time, offering advantages such as high timeliness, good maneuverability, and wide patrol range. They can fly directly over enterprises to collect evidence, unrestricted by space or terrain conditions. Drones equipped with infrared thermal imagers are an important supplement to daily environmental protection, leaving no place for environmental violations to hide. This embodiment deploys one set of drone thermal imaging monitoring equipment to assist in patrols and evidence collection.

[0036] Video surveillance cameras are installed at the enterprise's sewage outlets and online monitoring sampling points. The video surveillance cameras installed at the enterprise's sewage outlets can clearly see the sewage discharge situation, while the video surveillance cameras installed at the enterprise's online monitoring sampling points can see the sampling situation.

[0037] In this embodiment, addressing the difficulty of tracing the source of down products from similar enterprises in the industrial cluster, video surveillance systems are installed at 20 existing enterprises and 4 under construction within the cluster. Each enterprise has two sets of video surveillance cameras, totaling 48 sets. These cameras are installed at the enterprises' wastewater discharge outlets and online monitoring sampling points. The systems at wastewater discharge outlets are required to clearly show the discharge process, while those at online monitoring sampling points are required to clearly show the sampling process. This monitoring of wastewater discharge and the enterprises' dilution of water samples will replace the direct observation of environmental personnel, enabling timely detection, prevention, and handling of environmental violations, effectively deterring illegal activities. The video surveillance system uses solar power and 4G network transmission, making deployment convenient and flexible, with locations adjustable as needed. Since the installation locations are within the enterprise areas, the specific locations will need to be determined in consultation with the enterprises during implementation.

[0038] The infrastructure layer includes the computing, storage, networking, and security resources required for the operation of other layers, providing unified computing power to them. The IoT sensing layer connects to the infrastructure layer. In this embodiment, the infrastructure layer is implemented based on government cloud resources allocated by the Big Data Bureau, combined with... Figure 4 As shown, all data from the planned front-end sensing devices in the entire system is transmitted back to the center via 4G data cards. Since the environmental protection personnel's APP is connected to the internet, the cloud resources allocated to this project by the Big Data Bureau are mapped to the internet zone to meet the needs of data uploading and APP usage. The Pingyi Branch of the Ecological Environment Bureau's PC accesses data and information in the data resource layer and business application layer of the infrastructure layer through the government network. Regarding the security requirements of the software deployment environment in this embodiment, the government cloud has already been built and does not need to be rebuilt.

[0039] Combination Figure 1 As shown, the data resource layer is used to aggregate various types of water ecological environment related data, including real-time monitoring information from the Internet of Things sensing layer, business data generated by various business departments related to the water environment, and ecological environment related data generated by other outsourced offices and bureaus. Through unified technical specifications, unified information classification and coding system, unified data exchange platform, unified information resource catalog system, and unified requirements for object-oriented data organization, the aggregated water ecological environment related data is integrated into a water ecological environment monitoring resource library for use by the business application layer.

[0040] This layer collects business data from various water environment-related departments, as well as ecological and environmental data from other outsourced offices and bureaus. This includes online monitoring data from wastewater treatment plants, basic information on sewage discharge outlets into rivers, industry water quality data, and enterprise water quality data. Other business data from various water environment-related departments, and ecological and environmental data from other outsourced offices and bureaus, can be aggregated into the data resource layer as needed.

[0041] Combination Figure 1 and Figure 5 As shown, the data resource layer includes a data aggregation module, a data cleaning module, a data processing module, and a data service module.

[0042] The data aggregation module collects various types of water environment data from different sources to achieve data resource aggregation and storage. The module includes a raw data repository for comprehensive water environment monitoring, an IoT sensing raw data repository, and raw data repositories for other business systems. It utilizes data acquisition tools, including a data exchange and integration platform, a streaming data access engine, and a government data sharing and exchange platform, to access and store various ecological and environmental related data resources.

[0043] The specific data collected includes, but is not limited to, the following (determined according to actual needs): online data from automatic river section monitoring (monitored by water quality monitoring stations), online automatic monitoring data from water-related enterprises, manual monitoring data from sewage discharge outlets into rivers, manual monitoring data from important urban domestic sewage, video surveillance data, and data from water quality fingerprint early warning and source tracing instruments; the appropriate collection method is selected based on the data source, data type, and the provision and openness of the data source; various types of environmental data are specifically sorted out, and ecological environment data from different sources are collected centrally and dynamically to solve the problem of data silos.

[0044] The data cleaning module is used to transform, denoise, normalize, and de-identify the aggregated data. The data cleaning module has a national standard library, an industry standard library, and an ecological and environmental department standard library. Based on the standard library, the data is further extracted, compared, standardized, correlated, and integrated before being classified and stored.

[0045] The data cleaning module transforms data from different data sources into a unified format, supplements missing records, removes erroneous or duplicate records, and improves data quality to meet analytical needs. During the data cleaning process, it can also provide early warnings for abnormal data, visually displaying data anomalies generated under different task monitoring. Based on the visualization, users can identify the specific data objects causing the anomalies, the causes, and the maintenance objects for the abnormal data, facilitating timely resolution of data anomalies and ensuring data integrity and stability.

[0046] The data processing module includes a basic database, comprising enterprise databases, water environment quality databases, pollution source online monitoring databases, sewage discharge supervision databases, environmental risk databases, and comprehensive decision analysis databases. This database is used to classify and store the data processed by the data cleaning module. It also utilizes data tag extraction for secondary data processing to support data sharing and application. The data processing module performs extraction, comparison, standardization, correlation, and fusion of the cleaned data before storing it in different types of databases to support data sharing and application.

[0047] The data service module provides services such as data exchange, API calls, file retrieval, batch data output, and real-time data output to support the construction of water environment business application scenarios, including water environment information monitoring, water environment source tracing analysis, water environment task collaboration, water environment emergency response, and a comprehensive water environment APP. The data service module analyzes data, and the analyzed data can be shared through resource directories and data interfaces. It can also support business applications by developing data analysis products.

[0048] The business application layer conducts precise and intelligent supervision of the water environment by building a smart water environment supervision platform. This includes unified dynamic monitoring, abnormal alarms, and risk warnings of water environment quality, water pollution, and water environment risks. It also enables water quality simulation, pollution source tracing, data fraud detection, analysis of surface water quality changes in target areas, and whether the water quality of key river basin assessment sections is stable and meets standards.

[0049] Specifically, in this embodiment, the water environment intelligent supervision platform of the business application layer is constructed with the following functional modules: a water system overview map module, a water environment monitoring and supervision map module, a video surveillance map module, and a river discharge outlet map module.

[0050] The "Water System Overview" module presents the overall situation of the water system in the monitored area in a "single map" format. Based on spatial GIS technology, it displays the geographical location of rivers, drinking water sources, and reservoirs in the monitored area, as well as the direction of river flow. It also displays the spatial and quantitative components of the water environment, including drinking water sources, reservoirs, sewage treatment plants, wastewater enterprises, sewage outlets into rivers, and water quality monitoring stations.

[0051] The "One Map for Water Environment Monitoring and Supervision" module displays the water ecological environment data resources of the monitored area in a "single map" format. It publishes the overall monitoring status of the watershed on a GIS map, and performs statistical analysis on various water environment-related elements based on spatial layers. Through the control of the spatial management system and layers, the data statistical analysis is linked with the spatial management system and layers. The specific content displayed includes: the overall water quality status of the watershed, the surface water quality status, the supervision status of water-related enterprises, the distribution of sewage outlets into rivers, and the status of early warning and source tracing. It also provides real-time updates of online monitoring data, critical warnings, and alarms for exceeding standards.

[0052] The video surveillance map module acquires video surveillance data and forms a video location map based on a GIS geographic information system. It then extracts suspicious video clips from the video surveillance and records them separately, including the date, time, preliminary judgment information on the suspicious situation, and allows users to set the maximum extraction time for each video. The videos are then automatically stored in a designated folder.

[0053] The "One Map of Sewage Outlets into Rivers" module provides a comprehensive overview of the geographical distribution and sewage discharge information of sewage outlets in a single map format. It allows for screening of sewage outlet information based on outlet number, river, outlet type, on-site photos, and discharge method. The "One Map of Sewage Outlets into Rivers" module also provides an APP interface for sewage outlet investigation, enabling on-site personnel to fill in information and monitor data, locate, photograph, and report sewage outlets, and achieve collaborative work between the on-site and platform terminals.

[0054] The intelligent water environment supervision platform is constructed through modules such as a water system overview map, a water environment monitoring and supervision map, a video surveillance map, and a river discharge outlet map. It enables dynamic monitoring, abnormal alarms, and risk warnings of the entire county's unified water environment quality, water pollution, and environmental risks, and provides visualized dynamic monitoring and real-time display of these data on a single map. It can also intuitively analyze changes in surface water quality in the target area as a whole.

[0055] In addition, the intelligent water environment monitoring platform also includes a big data intelligent early warning engine module. Based on the collected data, this module provides business early warning solutions for various operations. Through the writing of business rules, environmental quality and business early warning solutions can be configured quickly, establishing rapid relationships, standard relationships, and rule relationships between various data points. This enables timely early warnings of data anomalies and exceedances, forming a powerful data processing and utilization capability that drives the task generation process for related operations.

[0056] The big data intelligent early warning engine module includes the following functional units: Water Environment Early Warning Rule Base Unit: The water environment early warning rule base is a rule base obtained by combining policy documents and specific business analysis. It is used to provide the basis and rules for judging various early warning situations, including rules for exceeding standards, rules for trends, rules for meeting standards, and rules for data anomalies.

[0057] Early warning standard management unit: used to set different alarm standards according to national standards. The alarm standard setting adopts the threshold range method, which is used to generate corresponding alarms when the monitored data meets the corresponding standards.

[0058] Equipment anomaly alarm unit: Used to extend the communication protocol according to the requirements of the Ecological and Environmental Protection Bureau, realize the real-time alarm function for various situations such as on-site failure, shutdown and abnormality, and push the alarm information to the relevant person in charge.

[0059] Data fluctuation early warning unit: used for early warning of water environment quality monitoring. When the monitoring data remains unchanged or the data change range is very small, an early warning information can be sent.

[0060] Data anomaly early warning unit: When the monitored data shows a maximum value, a minimum value, or an invalid value, an early warning message is sent.

[0061] Data Exceedance Early Warning Unit: Used for early warning of water environment quality monitoring. When the data exceeds the preset standard value, an alarm is triggered and the relevant responsible persons are notified.

[0062] Water quality change early warning unit: It provides early warning of changes in water quality categories in the water environment by comparing the previous day's data. When the water quality deteriorates by one category or more, an early warning is issued; if the water quality deteriorates and does not improve, the previous day's warning is maintained.

[0063] Video surveillance early warning unit: It identifies abnormalities in online monitoring data and meteorological data of related enterprises to determine the status of water flow at discharge outlets and issues early warnings for abnormal events at enterprise discharge outlets and sewage treatment facilities.

[0064] Historical Early Warning Query Unit: Allows users to query various historical alarm information from water quality monitoring stations, analyze the causes of data anomalies, and export query results.

[0065] Furthermore, the intelligent water environment supervision platform also includes a task tracking and scheduling closed-loop handling module. Based on the early warning information provided by the big data intelligent early warning engine module, the module pushes the information to relevant management departments through built-in task types and handling templates. Relevant personnel then conduct targeted on-site investigations, handling, and feedback based on the information, forming a closed-loop task processing and archiving management.

[0066] The closed-loop processing of task tracking and scheduling includes the following functional units: Task assignment unit: Used to communicate with mobile devices to distribute and schedule events or tasks, and provide work guidance; Task entry unit: This is used by personnel to upload images and documents involved in the process to the system for review by auditors; Task review unit: Used to review the completion progress of tasks in the list; Task Query Unit: Used to view and query personal pending events and completed events in a list format, as well as query details of pending and completed events, including event number, event status, event location, event level, event description, initiating unit, initiation time, and processing time.

[0067] Furthermore, the intelligent water environment supervision platform also includes the following functional modules: pollution source logical relationship analysis module, water pollution source tracing analysis module, and water quality simulation analysis module.

[0068] The pollution source logical relationship analysis module is used to integrate all elements of water environment quality monitoring data in the monitoring area, collect and analyze basic information of water environment quality monitoring sections in the basin, basic hydrological information of major watersheds, and basic information of wastewater pollution source discharge in major watersheds. It determines the upstream and downstream relationships of each monitoring section based on the water flow direction, determines the inflow and outflow relationships of the main stream of the water system and each major tributary, and sorts out the wastewater discharge destination and outlet location of each pollution source. It also compares and displays the online monitoring data of water quality monitoring stations, sewage treatment plants, and wastewater enterprises on a single map. By randomly selecting monitoring stations and dynamically generating comparison curves automatically or manually based on logical relationships, it displays the relationships between various pollution sources to assist managers in incident investigation.

[0069] The water pollution source tracing and analysis module can comprehensively analyze all relevant data. Through correlation analysis and cross-verification, it can implement a "grid-based and segmented" management model. For routine early warnings and pollution events, it can identify suspected pollution sources in the first instance. For pollution sources outside the existing monitoring system, it can also use the elimination method to save a lot of ineffective investigation time and greatly improve work efficiency.

[0070] Specifically, the water pollution source tracing and analysis module includes a conventional source tracing and analysis unit and a water pollution fingerprint early warning source tracing and analysis unit.

[0071] The conventional source tracing analysis unit uses the pollutant characteristics and attributes of pollutant source enterprises, combined with the basic data of tributaries, main streams, and cross sections, to establish a list of relationships between the basin, tributaries, main streams, cross sections, discharge outlets, and enterprises. Then, by analyzing the relationship between major pollutants, wastewater discharge areas, polluted water diffusion characteristics, and characteristic pollutants of enterprises, the scope of suspected polluting enterprises is narrowed down, providing a basis for law enforcement decisions.

[0072] The water pollution fingerprint early warning and source tracing analysis unit, upon receiving alarms for exceeding monitoring data (data from water quality monitoring stations), connects to the water quality fingerprint early warning and source tracing instrument for fingerprint database comparison to determine whether industrial pollution is the cause. Combined with... Figure 6 and Figure 7As shown, the early warning principle of the water quality fingerprint source tracing instrument is as follows: The water quality fingerprint source tracing instrument projects the fluorescence intensity of the water sample onto a plane with the excitation wavelength and emission wavelength as the horizontal and vertical axes to obtain a three-dimensional fluorescence spectrum. The fluorescence spectrum varies with the type and content of pollutants and has characteristics corresponding to the pollution source. The water quality fingerprint of wastewater is significantly different from that of unpolluted water samples, which can be used to determine whether the water body is polluted; there are obvious differences in the water quality fingerprints of wastewater from different industries; the water quality fingerprints of different companies in the same industry often also have obvious differences, so the pollution discharge source can be determined by comparing water quality fingerprints. The water quality fingerprint database includes the system's built-in general industry fingerprint database, 3 newly created local industry fingerprint databases, and 7 newly created enterprise water quality fingerprint databases, which can quickly diagnose pollution discharge sources through water quality fingerprint comparison. The water quality fingerprint source tracing instrument first compares data by setting up a control group, comparing the instrument's recognition spectrum with monitoring data from automatic water quality monitoring stations. Then, it collects water samples from the discharging unit to obtain water quality fingerprint information. By filtering out water body marker information and comparing it with a fingerprint database, the correlation between markers and fingerprint information is analyzed, ultimately generating and pushing risk warning information. When water quality anomalies are detected, it can quickly identify pollution sources, completing a pollution source tracing within 30 minutes, which is faster than automatic water quality monitoring stations.

[0073] Based on the analysis results of the water quality fingerprint early warning and source tracing instrument, if the data is from a company that has built a fingerprint database, the pollution source is directly identified. If not, conventional source tracing analysis is performed using a conventional source tracing analysis unit. By analyzing the fluctuations in the company's online monitoring data, the characteristics of its discharge, and spatial relationships, similar companies are compared. If a comparison result is provided, targeted investigations are conducted. If not, the data from upstream stations of the exceeding-standard sites are analyzed. Based on the monitoring station data, the area is identified in a "grid-like and segmented" manner. The mobile water quality fingerprint early warning and source tracing instrument is then used to investigate upstream from the exceeding-standard site to the identified segmented area segment by segment.

[0074] In this embodiment, an overall fingerprint database is generated by using the industry fingerprint database built into the water quality fingerprint early warning and tracing instrument, as well as two newly created industry databases and 34 enterprise databases based on the industry and enterprise characteristics of Pingyi County. Through the risk early warning model, risk warnings are issued for abnormal water quality, and fingerprint database comparison and tracing are performed simultaneously. The tracing cycle is within 15 minutes. Since the data analysis cycle of the monitoring station is 1-4 hours, early warnings can be issued and risk prevention can be carried out in advance.

[0075] The water quality simulation and analysis unit is used to construct various water quality simulation and analysis models to intelligently simulate and analyze the trend of water quality changes in rivers within the regulatory area. For water pollution in receiving water bodies, the water quality model uses cross-sectional monitoring data, rainfall data, hydrological data, water quality data, meteorological data, and pollution source data to simulate the situation of water pollution, thereby enabling the prediction of water pollution change trends and the planning of the layout of sewage outlets in important water systems and key areas within the basin.

[0076] Furthermore, the intelligent water environment supervision platform also includes the following functional modules: intelligent decision-making module, system management module, and water environment management APP module.

[0077] The intelligent decision-making module integrates and analyzes diverse data from water quality monitoring stations, water-related enterprises, and sewage treatment plants. Through data analysis and visualization tools, it extracts key information, identifies trends, and predicts results, thereby providing data support and intelligent decision-making assistance.

[0078] The intelligent decision-making module includes an intelligent extraction unit, a distance extraction unit, and a self-selected enterprise unit. The intelligent extraction unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it automatically extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period and analyzes the emission ratio. The distance extraction unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period by distance and analyzes the emission ratio. The self-selected enterprise unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period by self-selected enterprises and analyzes the emission ratio.

[0079] The system management module includes an organization management unit, a system resource management unit, and a permission configuration management unit. The organization management unit is used to manage personnel involved in water pollution control, including organization management, role management, and user management. The system resource management unit is used to allocate permissions and display the framework for various functions and data dictionary information of the system. The permission configuration management unit is used to allocate corresponding resources to each role based on a role-based permission allocation system and to perform security authentication.

[0080] The water environment management APP module provides client services for mobile viewing of basic watershed information and monitoring data, including monitoring station distribution, water quality information, analysis results of current water environment quality, and analysis of current pollution discharge. Based on the mobile monitoring and alarm system, it processes water quality monitoring and pollution source monitoring alarm information, queries information related to sudden water pollution accidents, and simultaneously views the simulation prediction results of pollutant migration and diffusion and the results of emergency monitoring. It also allows for locating, photographing, and reporting information on sewage outlets into rivers.

[0081] Combination Figure 1 and Figure 4 As shown, the user layer is used by staff from various departments, including the Water Supply and Ecological Environment Department, Monitoring Station, Enforcement Team, and Operation and Maintenance Team, to interact with the data resource layer and business application layer via PC or mobile devices. Taking user-initiated early warning and source tracing as an example, combined with... Figure 8 As shown, the water environment management APP module can connect with the water environment smart supervision platform to conduct early warning and source tracing analysis. The platform generates early warning tasks for abnormal water quality events through a risk prevention and control early warning model and pushes them to law enforcement personnel. The task includes the results of suspected pollution sources obtained from fingerprint comparison. Law enforcement personnel conduct on-site investigations and handle the issues based on the results through the APP, and fill in the investigation and handling results and upload them to the platform. After the platform approves the task, it archives it. If it fails, it is returned to the law enforcement personnel for reprocessing.

[0082] Based on the above embodiments, it can be seen that the refined water ecological environment supervision and early warning tracing system has the following advantages: (1) Multi-dimensional perception and meeting the needs of real-time monitoring. By integrating various water ecological environment data resources, using water quality automatic monitoring data, fingerprint early warning tracing data, video monitoring, UAV thermal imaging monitoring, sewage treatment plant online monitoring, wastewater-related enterprise online monitoring, and basic information of sewage outlets into rivers, the system accurately generates various water environment indicator data, realizes dynamic monitoring of water pollution and environmental risk, abnormal alarms, risk warnings, etc., and visualizes dynamic monitoring and real-time display of "one map", providing technical support for the stable compliance of water quality in the Yellow River Basin. (2) Intelligent early warning and tracing analysis. By adopting intelligent algorithms, the system realizes automatic identification and early warning of anomalies. The early warning categories include falsification of enterprise online monitoring data, exceeding of monitoring data standards, changes in monitoring data trends, identification of whether there is water flow at the outlet, changes in cross-sectional water quality, etc., combined with meteorological data. The system can perform multiple data analysis for pollution events, and finally realize the locking of pollution sources or narrowing of pollution investigation scope, providing important basis for precise pollution control and rapid law enforcement. (3) Big data decision support. By fully integrating and analyzing multidimensional data, the system establishes a correlation between "pollution source-outlet-path-section". Through data analysis and visualization tools, it provides powerful data support and intelligent decision-making assistance, extracting key information, gaining insights into trends, and predicting results from massive amounts of data, thereby making more accurate and effective decisions and providing stronger technical support for the sustainable development of the water environment.

[0083] This refined water ecological environment monitoring and early warning system has a driving effect on realizing the digitalization of water ecological environment monitoring and the intelligent transformation of automatic monitoring networks. It can promote the application of digital monitoring technologies and equipment, thereby supporting environmental decision-making, refined management, supervision and law enforcement, and realizing online water quality monitoring.

[0084] It should be noted that, unless otherwise defined, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains, and terms such as those defined in a common dictionary should be interpreted as having a meaning consistent with their meaning in the context of the relevant art. It should also be understood that the above is a description of the disclosure and should not be considered as a limitation thereof. Although several exemplary embodiments of the disclosure have been described, those skilled in the art will readily understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention. Therefore, all such modifications are intended to be included within the scope of the disclosure as defined in the claims, and will not be detailed here.

Claims

1. A refined monitoring, early warning, and source tracing system for aquatic ecological environment, characterized in that: It includes the Internet of Things (IoT) sensing layer, infrastructure layer, data resource layer, business application layer, and user layer; The IoT sensing layer is responsible for data collection of the aquatic ecological environment. The IoT sensing layer includes the constructed water quality monitoring station, water quality fingerprint early warning and tracing instrument, video surveillance, UAV thermal imaging monitoring, and enterprise online monitoring. The infrastructure layer includes the computing, storage, network, and security resources required for the operation of other layers, and is used to provide unified computing power for other layers. The IoT sensing layer is connected to the infrastructure layer. The data resource layer is used to aggregate various types of water ecological environment related data, including acquiring real-time monitoring information from the IoT sensing layer, acquiring business data generated by various business departments related to the water environment, and acquiring ecological environment related data generated by other outsourced offices and bureaus. Through unified technical specifications, a unified information classification and coding system, a unified data exchange platform, a unified information resource catalog system, and unified requirements for object-oriented data organization, the aggregated water ecological environment related data is integrated into a water ecological environment monitoring resource library for use by the business application layer. The business application layer conducts precise and intelligent supervision of the water environment by building a smart water environment supervision platform. This includes unified dynamic monitoring, abnormal alarms, and risk warnings of water environment quality, water pollution, and water environment risks. It also enables water quality simulation, pollution source tracing, data fraud detection, analysis of surface water quality changes in target areas, and whether the water quality of key river basin assessment sections is stable and meets the standards. The user layer is used by staff from various business departments, including the water supply and ecological environment department, monitoring station, law enforcement team, and operation and maintenance team, to interact with the data resource layer and business application layer via PC or mobile terminal.

2. The refined water ecological environment monitoring and early warning tracing system according to claim 1, characterized in that: The water quality monitoring stations in the IoT sensing layer are constructed according to the following deployment methods: For water bodies that do not cross regions, water quality monitoring stations are set up at both the inlet and outlet; for water bodies that cross regions, water quality monitoring stations are set up at both the inlet and outlet plus the boundary section, with the water quality monitoring station at the boundary section located downstream of the boundary section; for rivers with industrial wastewater, water quality monitoring stations are added downstream of the wastewater discharge point, depending on the length of the river. The water quality monitoring station monitors and analyzes the five conventional water quality parameters: water temperature, pH value, conductivity, turbidity, and dissolved oxygen, as well as ammonia nitrogen, total phosphorus, COD, and permanganate. The water quality fingerprint early warning and tracing device includes an online water quality fingerprint early warning and tracing device and a mobile water quality fingerprint early warning and tracing device. The online water quality fingerprint early warning and tracing device is deployed downstream of the industrial cluster area, and the mobile water quality fingerprint early warning and tracing device is used for routine inspections and emergency investigations of sudden pollution incidents. And through actual research, we will build enterprise water quality fingerprint databases and industry fingerprint databases at the data resource layer; The drone thermal imaging monitoring is used to assist in patrol and evidence collection at night or during the rainy season. The video surveillance cameras are installed at the enterprise's sewage outlet and at the enterprise's online monitoring sampling point. The video surveillance cameras installed at the enterprise's sewage outlet can clearly see the sewage discharge situation, and the video surveillance cameras installed at the enterprise's online monitoring sampling point can see the sampling situation.

3. The refined water ecological environment monitoring and early warning tracing system according to claim 1, characterized in that: The infrastructure layer is implemented based on the government cloud resources allocated by the Big Data Bureau.

4. The refined water ecological environment monitoring and early warning tracing system according to claim 1, characterized in that: The data resource layer includes a data aggregation module, a data cleaning module, a data processing module, and a data service module; The data aggregation module is equipped with a water environment comprehensive supervision raw database, an IoT sensing raw database, and other business system raw databases. It also uses data acquisition tools, including a data exchange and integration platform, a streaming data access engine, and a government data sharing and exchange platform, to access and store various ecological and environmental related data resources. The data cleaning module is used to transform, denoise, normalize, and desensitize the aggregated data. The data cleaning module is equipped with a national standard library, an industry standard library, and an ecological and environmental department standard library. Based on the standard library, the data is further extracted, compared, standardized, correlated, and fused before being classified and stored. The data processing module is equipped with a basic database, which includes an enterprise basic database, a water environment quality database, a pollution source online monitoring database, a sewage discharge supervision database, an environmental risk database, and a comprehensive decision analysis database. This database is used to classify and store the data processed by the data cleaning module. At the same time, it relies on data tag extraction to perform secondary extraction and processing on the data to support data sharing and application. The data service module is used to provide services such as data exchange, interface calls, file acquisition, batch data output, and real-time data output to support the construction of water environment business scenario applications, including water environment information supervision, water environment source tracing analysis, water environment task collaboration, water environment emergency response, and a comprehensive water environment APP.

5. The refined water ecological environment monitoring and early warning tracing system according to claim 1, characterized in that: The acquisition of business data generated by various departments related to the water environment, as well as ecological and environmental data generated by other outsourced offices and bureaus, includes: acquiring online monitoring data from sewage treatment plants, basic information on sewage outlets into rivers, industry water quality data, and enterprise water quality data.

6. The refined water ecological environment monitoring and early warning tracing system according to claim 1, characterized in that: The intelligent water environment supervision platform has the following functional modules: a water system overview module, a water environment monitoring and supervision module, a video surveillance module, and a river discharge outlet module. The "Water System Overview" module displays the overall situation of the water system in the monitored area in the form of a "single map". Based on spatial GIS technology, it displays the geographical location of rivers, drinking water sources, and reservoirs in the monitored area, as well as the river flow direction. It also displays the spatial and quantitative components of the water environment, including drinking water sources, reservoirs, sewage treatment plants, wastewater enterprises, sewage outlets into rivers, and water quality monitoring stations. The water environment monitoring and supervision unified map module displays the water ecological environment data resources of the supervised area in the form of a "single map". It publishes the overall supervision status of the watershed on the GIS map, performs statistical analysis on various water environment-related elements according to spatial layers, and realizes the linkage of data statistical analysis with the spatial control system and layers through the control of the spatial control system and layers. The specific content displayed includes: the overall water quality status of the watershed, the surface water quality status, the supervision status of water-related enterprises, the distribution of sewage outlets into rivers, the early warning and source tracing status, and real-time updates of online monitoring data, critical warnings, and alarms for exceeding standards. The video surveillance map module acquires video surveillance data and forms a video location map based on a GIS geographic information system. It extracts suspicious video segments from the video surveillance and records them separately, including the recording date, time, relevant information for the preliminary judgment of the suspicious situation, and allows users to set the maximum extraction time for each video. The videos are then automatically stored in a designated folder. The "One Map of Sewage Outlets into Rivers" module provides a comprehensive overview of the geographical distribution and sewage discharge information of sewage outlets in a single map format. It screens sewage outlet information based on the outlet number, the river to which it belongs, the nature of the outlet, on-site photos, and the method of discharge into the river. The "One Map of Sewage Outlets into Rivers" module also provides an APP interface for sewage outlet investigation, enabling on-site personnel to fill in information and monitor data, locate, photograph, and fill in information about sewage outlets, and achieve collaborative work between the on-site personnel and the platform.

7. The refined water ecological environment monitoring and early warning tracing system according to claim 6, characterized in that: The intelligent water environment monitoring platform also includes a big data intelligent early warning engine module, which comprises the following functional units: Water Environment Early Warning Rule Base Unit: The water environment early warning rule base is a rule base obtained by combining policy documents and specific business analysis. It is used to provide the basis and rules for judging various early warning situations, including rules for exceeding standards, rules for trends, rules for meeting standards, and rules for data anomalies. Early warning standard management unit: used to set different alarm standards according to national standards. The alarm standard setting adopts the threshold range method, which is used to generate corresponding alarms when the monitored data meets the corresponding standards; Equipment anomaly alarm unit: used to extend the communication protocol according to the requirements of the Ecological and Environmental Protection Bureau, realize the real-time alarm function for various situations such as on-site faults, shutdowns, and anomalies, and push alarm information to relevant responsible persons; Data fluctuation early warning unit: used for early warning of water environment quality monitoring. When the monitoring data remains unchanged or the data change range is very small, an early warning information can be sent. Data anomaly early warning unit: When the monitored data shows a maximum value, a minimum value, or an invalid value, it sends an early warning message; Data Exceedance Early Warning Unit: Used for early warning of water environment quality monitoring. When the data exceeds the preset standard value, an alarm is triggered and the relevant responsible persons are notified. Water quality change early warning unit: It provides early warning of changes in water quality categories in the water environment through a month-on-month comparison. When the water quality deteriorates by one category or more, an early warning is issued. If the water quality decline does not improve, the previous day's warning will remain in effect; Video surveillance early warning unit: It identifies abnormal online monitoring data and meteorological data of related enterprises to determine the status of water flow at discharge outlets, and issues early warnings for abnormal events at enterprise discharge outlets and sewage treatment facilities; Historical Early Warning Query Unit: Allows users to query various historical alarm information from water quality monitoring stations, analyze the causes of data anomalies, and export query results.

8. The refined water ecological environment monitoring and early warning tracing system according to claim 7, characterized in that: The intelligent water environment monitoring platform also includes a task tracking, scheduling, and closed-loop processing module, which comprises the following functional units: Task assignment unit: Used to communicate with mobile devices to distribute and schedule events or tasks, and provide work guidance; Task entry unit: This is used by personnel to upload images and documents involved in the process to the system for review by auditors; Task review unit: Used to review the completion progress of tasks in the list; Task Query Unit: Used to view and query personal pending events and completed events in a list format, as well as query details of pending and completed events, including event number, event status, event location, event level, event description, initiating unit, initiation time, and processing time.

9. The refined water ecological environment monitoring and early warning tracing system according to claim 8, characterized in that: The intelligent water environment supervision platform also includes the following functional modules: pollution source logical relationship analysis module, water pollution source tracing analysis module, and water quality simulation analysis module. The pollution source logical relationship analysis module is used to integrate all elements of water environment quality monitoring data in the monitoring area, collect and analyze basic information of water environment quality monitoring sections in the basin, basic hydrological information of major watersheds, and basic information of wastewater pollution source discharge in major watersheds. It determines the upstream and downstream relationship of each monitoring section according to the water flow direction, determines the inflow and outflow relationship between the main stream of the water system and each major tributary, and sorts out the wastewater discharge destination and outlet location of each pollution source. It also compares and displays the online monitoring data of water quality monitoring stations, sewage treatment plants, and wastewater enterprises on a single map. It randomly selects monitoring stations and automatically or manually dynamically generates comparison curves based on logical relationships to show the relationship between various pollution sources, thereby assisting managers in incident investigation. The water pollution source tracing and analysis module includes a conventional source tracing and analysis unit and a water pollution fingerprint early warning source tracing and analysis unit. The conventional source tracing analysis unit uses the pollutant characteristic information and attributes of pollution source enterprises, combined with the basic data of tributaries, main streams, and cross sections, to establish a list of relationships between the basin, tributaries, main streams, cross sections, discharge outlets, and enterprises. Then, by using the relationship between major pollutants, wastewater discharge areas, pollution water quality diffusion characteristics, and characteristic pollutants of enterprises, the scope of suspected polluting enterprises is narrowed down, providing a basis for law enforcement decisions. The water pollution fingerprint early warning and source tracing analysis unit, upon detecting data exceeding standards, connects to the water quality fingerprint early warning and source tracing instrument for fingerprint database comparison to determine whether the pollution originates from an industrial enterprise. If the data matches that of an enterprise that has established a fingerprint database, the pollution source is directly identified. Otherwise, the conventional source tracing analysis unit performs routine source tracing analysis, comparing similar enterprises based on fluctuations in online monitoring data, discharge characteristics, and spatial relationships. If a comparison result is provided, targeted investigations are conducted. If not, the data from upstream stations of the exceeding-standard site is analyzed. Based on the monitoring station data, a "grid-based, segmented" area is identified, and the mobile water quality fingerprint early warning and source tracing instrument is used to investigate upstream from the exceeding-standard site segment by segment within the identified segmented area. The water quality simulation and analysis unit is used to construct various water quality simulation and analysis models to intelligently simulate and analyze the trend of water quality changes in rivers within the regulatory area. For water pollution in receiving water bodies, the water quality model uses cross-sectional monitoring data, rainfall data, hydrological data, water quality data, meteorological data, and pollution source data to simulate the situation of water pollution, thereby enabling the prediction of water pollution change trends and the planning of the layout of sewage outlets in important water systems and key areas within the basin.

10. The refined water ecological environment monitoring and early warning tracing system according to claim 9, characterized in that: The intelligent water environment monitoring platform also includes the following functional modules: intelligent decision-making module, system management module, and water environment management APP module; The intelligent decision-making module is used to integrate and analyze multi-source data from water quality monitoring stations and monitoring data from water-related enterprises and sewage treatment plants. Through data analysis and visualization tools, it extracts key information, identifies trends, and predicts results, thereby providing data support and intelligent decision-making assistance. The intelligent decision-making module includes an intelligent extraction unit, a distance extraction unit, and a self-selected enterprise unit. The intelligent extraction unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it automatically extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period and analyzes the emission ratio. The distance extraction unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period by distance and analyzes the emission ratio. The self-selected enterprise unit combines monitoring data from water quality monitoring stations, real-time monitoring data from water-related enterprises, and real-time monitoring data from wastewater treatment plants. Based on the emission values ​​of pollutants, it extracts monitoring data from water-related enterprises associated with the water quality monitoring station and wastewater treatment plants for the same time period by self-selected enterprises and analyzes the emission ratio. The system management module includes an organization management unit, a system resource management unit, and a permission configuration management unit; the organization management unit is used to manage personnel involved in water pollution control, including organization management, role management, and user management. The system resource management unit is used to allocate permissions and display the framework for various functions and data dictionary information of the system. The permission configuration management unit is used to allocate corresponding resources to each role and perform security authentication based on a role-based permission allocation system. The water environment management APP module is used to provide client services for viewing basic watershed information and monitoring information on mobile devices, including the distribution of monitoring stations, water quality information, analysis results of the current status of water environment quality, and analysis of the current status of pollution discharge. Based on the mobile monitoring and alarm system, it processes water quality monitoring and pollution source monitoring alarm information, queries information related to sudden water environment pollution accidents, and simultaneously views the simulation prediction results of pollutant migration and diffusion and the results of emergency monitoring, as well as locates, photographs, and fills in information about sewage outlets into rivers.

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