Environment parameter analysis method and system based on data processing

Abstract

The application discloses an environmental parameter analysis method and system based on data processing, and belongs to the technical field of water pollution tracing. The system comprises an environmental perception module, an abnormality analysis module, a tracing processing module and a visualization module. The environmental perception module is used for collecting a GIS map, collecting environmental parameters through a monitoring device, building a virtual river scene and dynamically mapping. The abnormality analysis module performs abnormality analysis on the environmental parameters in the virtual river scene and marks corresponding monitoring devices. A propagation route is planned for the marked monitoring devices, a suspected index is calculated according to intersections and suspicious points are set. The tracing processing module analyzes the upstream and downstream relationships between the suspicious points, deletes downstream suspicious points with equivalent substitutes from bottom to top in sequence and retains corresponding upstream suspicious points, divides key areas according to the remaining suspicious points and calculates abnormality indexes. The visualization module displays each key area and the abnormality indexes in the virtual river scene through a visualization screen, and simultaneously gives an early warning to on-site staff for investigation and treatment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water pollution tracing, in particular to an environmental parameter analysis method and system based on data processing. BACKGROUND

[0002] With the increasing demand for global water resource protection, real-time perception of river water environmental state and rapid response to pollution events have become the core issue of ecological governance. Water pollution diffusion has significant spatio-temporal dynamic characteristics. The migration behavior of pollutants under the action of water dynamics is closely related to the propagation properties of pollutants, river morphology, and hydrological fluctuations.

[0003] At present, the current mainstream water quality anomaly tracing model has certain limitations. On the one hand, the diffusion dynamics separation phenomenon caused by the difference in pollution propagation properties is not fully considered. The abnormal signal triggering of pollution events at the monitoring terminal has significant time dispersion characteristics. For example, oil pollutants form a film on the surface of water due to surface tension effect, and their flow rate is significantly different from that of dissolved heavy metal ions. At the same time, due to the spatial non-uniformity of river hydrodynamic conditions, such as the sharp increase in turbulence intensity in steep slope sections, the same pollution source in different river sections presents abnormal monitoring timing. On the other hand, the existing method mechanically relies on the threshold alarm mechanism of dense monitoring equipment, ignoring the objective law that the migration rate of pollution groups in high flow rate rivers exceeds the conventional diffusion model: the near distance sensor is in the vortex buffer zone and thus lags behind the downstream sensor in capturing the sudden pollution signal. This asynchrony diagnosis blind area formed by the interaction of diffusion mechanism and hydrodynamic dynamics makes the existing tracing model unable to construct an accurate pollution backward propagation path, which weakens the physical rationality and practical guidance value of pollution source positioning. SUMMARY

[0004] The present application aims to provide an environmental parameter analysis method and system based on data processing to solve the problems raised in the background.

[0005] To solve the above technical problems, the present application provides an environmental parameter analysis method based on data processing, comprising:

[0006] S100, collect GIS maps and collect environmental parameters of rivers at different positions in the specified area through monitoring equipment. A virtual river scene is built according to the GIS map using digital twinning technology, and dynamic mapping is performed in combination with real-time environmental parameters.

[0007] The GIS map is used to describe the topographic structure and distribution of the river channel in the specified area.

[0008] The specified area refers to a spatial range pre-defined according to the hydrological unit boundary of the river basin and the administrative management demand.

[0009] The environmental parameters include monitoring indicators and hydrological indicators.

[0010] The monitoring indicators refer to physical, chemical and biological parameters for quantifying the pollution characteristics and ecological risks of the water body.

[0011] Specifically, the pollution concentration indicators, biological toxicity indicators and sensory indicators are included.

[0012] By analyzing the concentration of pollutant components, the spatio-temporal distribution and biological effects, the pollution type is identified, the pollution source characteristics are determined, and the water ecological health status is evaluated.

[0013] The hydrological indicators refer to physical quantities describing the dynamic characteristics and transport capacity of the water body.

[0014] Specifically, the flow indicators, flow characteristics indicators, hydraulic form indicators and transport parameters are included.

[0015] By describing the convection-diffusion process of pollutants through hydrological indicators, the water environmental capacity is calculated, and the pollution diffusion path is predicted.

[0016] Building a virtual river scene and performing dynamic mapping specifically includes:

[0017] S101, analyzing the vector layer data of the GIS map, extracting the river center line coordinate set, river bank boundary line elevation points and river bed terrain raster data, and generating a river network skeleton structure model based on the Delaunay triangulation algorithm.

[0018] The Delaunay triangulation algorithm is used to generate a topologically stable river network skeleton from GIS data, solving the mathematical description problem of complex river structure.

[0019] S102, optimize the river surface by Laplace smoothing algorithm, interpolate the hydraulic radius along the center line, derive the river roughness coefficient combined with Manning formula, and build a virtual river scene using digital twinning technology and construct a three-dimensional river network model in it.

[0020] Based on the Laplace smoothing algorithm, the river surface curvature error is corrected, and the roughness coefficient is dynamically derived combined with the Manning formula, improving the physical reality of fluid simulation.

[0021] S103, map the deployment positions of all monitoring devices to the virtual river scene and calibrate the coordinate offset. Access the monitoring indicator data stream at different positions and map them as multi-color column rendering layers in the three-dimensional river network model.

[0022] S104, solve the Navier-Stokes equation based on the fluid dynamics model, use the real-time collected hydrological indicators as boundary conditions to drive the dynamic update of the water movement vector field in the virtual river scene, and realize visual mapping.

[0023] Using the Navier-Stokes equations as the core, real-time hydrological indicators are used as boundary conditions to drive water movement, achieving physical-level simulation of pollutant diffusion processes.

[0024] Construct a high-precision digital river model to achieve dynamic integration of environmental parameters and geographic information, providing a spatialized real-time data base for pollution analysis.

[0025] S200. In a virtual river scenario, perform anomaly analysis and label the environmental parameters collected by each monitoring device, plan propagation routes for the labeled monitoring devices, calculate the suspicion index based on the intersection of different propagation routes, and set suspicious points. Specifically, this includes:

[0026] S201. Obtain monitoring equipment in the virtual river scene. The latest monitoring indicators And calculate the past duration. Average values ​​of various monitoring indicators Different fluctuation thresholds are preset for each monitoring indicator.

[0027] S202, According to the formula: Calculate the fluctuation coefficient of each monitoring indicator. Monitoring indicators with fluctuation coefficients greater than the corresponding fluctuation thresholds are set as abnormal indicators, and monitoring devices with abnormal indicators are marked.

[0028] The intensity of parameter fluctuations is quantitatively characterized by calculating the relative difference between the instantaneous value and the historical average value of the monitoring indicator.

[0029] It identifies abnormal indicators exceeding preset thresholds, providing a mathematical basis for pollution event alarms. By normalizing the absolute value difference with the benchmark value, it eliminates the sensitivity differences of indicators with different dimensions, enabling the assessment of comparability anomalies in multivariate monitoring data.

[0030] S203. Obtain the flow direction and velocity of the river channel where the marked monitoring equipment is located in the three-dimensional river network model. Use a fluid dynamics model to invert the propagation path for the marked monitoring equipment based on anomaly indicators. The propagation path is an evolutionary trajectory with both spatiotemporal dimensions. Specifically, this includes:

[0031] S2031. Identify all the bifurcations of the river in the three-dimensional river network model, divide the river into different channels according to the bifurcations, and each channel has only one inlet and one outlet. Analyze the channel in which each monitoring device is located.

[0032] S2032, Analytical Marker Monitoring Equipment River channel The flow direction, retrieve the river channel data within the preset historical time period. and the flow velocity data of all its upstream channels, for the river channel and all upstream river channels respectively establish historical flow rate time series.

[0033] S2033、Acquire monitoring device Lower abnormal index Set time , and abnormal index Pre-set diffusion speed , using fluid dynamics model for monitoring device Lower abnormal index Inversion propagation route. Specifically includes:

[0034] S2033-1, the location of the monitoring device As the starting point of inversion, set the time As the inversion time, the reverse flow direction of the river channel As the inversion direction, the combination of the above is the initial condition of inversion.

[0035] S2033-2, fluid dynamics model according to the initial condition, and the length and historical flow rate time series of the river channel, inverse analysis within the preset historical period, abnormal index According to the historical flow rate of the river channel combined with the diffusion position change of the diffusion speed .

[0036] Because the flow rate of the river channel is real-time change, so the flow rate used in the inversion of different river channels is not the flow rate at the same historical moment, and the inversion has different time differences. The specific difference time is related to the flow rate, diffusion speed and river length.

[0037] S2033-3, according to the diffusion position change of abnormal index , combined with the specified area boundary as the end point of inversion, generate the propagation path containing spatial position and time dimension, the propagation path has an inversion starting point and at least one inversion end point.

[0038] The inversion end point is the position of the specified area boundary, the source of the river or the position of the monitoring device. Because of the existence of the upstream branch, the propagation path will branch according to the number of upstream river channels during the inversion process, so that the propagation path may have multiple inversion end points.

[0039] S2033-4, the monitoring device through which the propagation path is taken as the ruling object, respectively calculate the river distance between each ruling object and the inversion starting point, arrange all the ruling objects in ascending order according to the river distance, and pre-set error time .

[0040] S2033-5, analyze the historical moment of the first ruling object The location of the propagation path , judge the ruling object Downstream monitoring indicators At historical time Before and after the length of time Whether the inside is set as an abnormal indicator.

[0041] S2033-6, if the result is yes, continue to judge the next decision object; if the result is no, delete the decision object The location is taken as the inversion endpoint, and the decision object is deleted Afterwards, all the propagation paths in the upstream river channel. Continue to judge the next decision object until all decision objects are completed, and then take the remaining propagation path as the propagation route.

[0042] The reliability of the propagation path is verified through the "decision object". If a monitoring device does not trigger an exception within the prediction time window, the path is truncated and the endpoint is reset.

[0043] In the process of fluid dynamics inversion, by comparing the matching of the time window of the predicted abnormal time of the propagation path and the actual abnormal record, the topology structure of the pollution diffusion path is dynamically corrected.

[0044] The spatiotemporal verification mechanism of the decision object functions to suppress the accumulation of inversion errors. When the measured abnormal state of the decision object does not match the model prediction, the current branch path is automatically truncated and the endpoint is reset, improving the physical credibility of the tracing path.

[0045] In view of the real-time change characteristics of river flow velocity, a historical flow velocity time series is independently established for each branch river, and the diffusion velocity Calculate the heterochronous deviation.

[0046] The set time refers to the historical time when the fluctuation coefficient of the abnormal indicator is greater than the corresponding fluctuation threshold.

[0047] S2034, continue to mark the monitoring device Other abnormal indicators, and each abnormal indicator of other marked monitoring devices respectively inversed the propagation route, and each abnormal indicator under the same marked monitoring device only has one propagation route.

[0048] S204, analyze the overlapping of the propagation routes under the same abnormal indicator and divide the intersection section, set the prediction point in the intersection section and calculate the suspicious index, and the different index thresholds are preset for various abnormal indicators, and the suspicious points are set after screening all prediction points. Specifically includes:

[0049] S2041, get the propagation routes of the abnormal indicators under all marked monitoring devices , all mapped in the three-dimensional river network model, preset the intersection volume , at least The section where the at least one propagation route overlaps is taken as the abnormal indicator The intersection of roads.

[0050] S2042, Analyze the intersection sections of various propagation routes. Within a given time interval, calculate the standard deviation of the time for all propagation paths at the same location as the deviation index. This is an intersection section. Plot a line graph showing how the deviation index changes with location.

[0051] Calculate the time standard deviation of multiple propagation routes at the intersection point to locate the point with the highest spatiotemporal consistency.

[0052] S2043. Select the point corresponding to the smallest deviation index in the line chart at the intersection section. The positions within are set as prediction points, representing abnormal indicators. Draw line graphs for each intersection section and set prediction points for the corresponding intersection section based on each line graph.

[0053] S2044. Evenly distribute in each line graph For each data point, calculate the standard deviation of the deviation from the index for that data point. and average Obtain the deviation index for each prediction point. Substitute into the formula to calculate the suspected index :

[0054] ;

[0055] In the formula, , and The preset weighting coefficients, To predict the length of the intersection segment corresponding to the point, The average length of all intersecting road segments. It is a constant greater than 1.

[0056] It is a constant. To predict the number of overlapping propagation paths corresponding to the intersection points, This represents the average number of overlapping propagation routes across all intersecting road segments.

[0057] High-probability pollution source locations are screened through multi-dimensional feature coupling. In the suspected index calculation formula, the numerator term emphasizes the weight of long intersection road sections and high propagation rates. The denominator term incorporates the deviation from the standard deviation and mean of the index to suppress false alarms in spatiotemporally discrete regions.

[0058] S2045 is an abnormal indicator. A preset index threshold is set, and all prediction points with a suspected index greater than the index threshold are screened out as suspicious points. The prediction points and index threshold are set for each abnormal index, and the suspicious points are obtained after screening.

[0059] The traditional threshold alarm limit is broken, the space-time source prediction of pollution events is realized, and the false alarm rate is reduced.

[0060] S300, analyze the upstream and downstream relationships between suspicious points, delete downstream suspicious points with equivalent substitutes in turn from bottom to top according to the relationship and retain the corresponding upstream suspicious points, and divide the key area according to the remaining suspicious points and calculate the abnormal index. Specifically, it includes:

[0061] S301, according to the abnormal index The flow direction of the river where each suspicious point is located is analyzed, and the upstream and downstream relationships between suspicious points are analyzed. According to the upstream and downstream relationships, the downstream suspicious points with equivalent substitutes are deleted in turn and the corresponding upstream suspicious points are retained. Specifically, it includes:

[0062] S3011, a tree relationship diagram is established according to the upstream and downstream relationships between suspicious points, and whether there are equivalent substitutes of other upstream suspicious points for each downstream suspicious point is analyzed in turn from downstream to upstream. Specifically, it includes:

[0063] S3011-1, analyze all upstream suspicious points of the downstream suspicious point, calculate the river distance between each upstream suspicious point and the downstream suspicious point , and sort all upstream suspicious points in ascending order of river distance.

[0064] S3011-2, get the first upstream suspicious point , count the number of abnormal indexes , the number of all transmission routes , and the number of transmission routes that pass through the downstream suspicious point and the upstream suspicious point in the same direction.

[0065] The transmission route is in the form of a tree diagram, and in the actual calculation process, each transmission route is first disassembled into a transmission single chain in the form of a starting point to an end point, and each transmission single chain only has a starting point and an end point without branching.

[0066] After all transmission routes are disassembled into transmission single chains, remove duplicate transmission single chains. Count the number of transmission routes that pass through the downstream suspicious point and the upstream suspicious point in the same direction as , and the number of all transmission single chains as .

[0067] S3011-3, if divide by as the upstream suspicious point of the trust index, when the trust index is greater than the preset threshold, set the upstream suspicious point as the downstream suspicious point There is an equivalent alternative relationship.

[0068] S3011-4, if the trust index is not greater than the preset threshold, then according to the order to extract the next upstream suspicious point analysis and calculation of trust index, according to the order of all upstream suspicious points in turn to calculate the trust index, meet the conditions set equivalent alternative relationship.

[0069] After setting the equivalent alternative relationship, delete the downstream suspicious point and keep the upstream suspicious point, and do not continue to calculate the trust index.

[0070] S3012, when the downstream suspicious point There is an equivalent alternative upstream suspicious point , the operation deletes the downstream suspicious point And keep the upstream suspicious point .

[0071] S3013, after all upstream suspicious point analysis and judgment, downstream suspicious point Still no equivalent alternative upstream suspicious point, then the operation keeps the downstream suspicious point .

[0072] S3014, all upstream suspicious points of the downstream suspicious point Respectively as the analysis object, further analyze whether there is an equivalent alternative upstream suspicious point in each analysis object.

[0073] S3015, if the conditions are met, delete and keep, if not, according to the direction from downstream to upstream in turn to promote analysis, until all downstream suspicious points are completed analysis, judgment and operation.

[0074] S302, preset basic distance , according to the suspicious index of the remaining suspicious point Calculate the reference distance . In the virtual river scene, a circular reference area is established with the remaining suspicious point position as the center and the reference distance As the radius.

[0075] The reference distance The calculation formula is as follows:

[0076] ;

[0077] In the formula, The preset attenuation index is Abnormal index The maximum suspicious index among all the remaining suspicious points.

[0078] The higher the suspicious index, the closer the pollution source is to the suspicious point location, and the more quickly the pollution source location exclusion range can be narrowed by narrowing the reference area range, improving the subsequent investigation efficiency.

[0079] S303, analyze the reference area coverage of each remaining suspicious point, take the overlapping area between the reference areas as the key area, and calculate the abnormal index of the key area. Divide the key area for each abnormal index and calculate the abnormal index.

[0080] Abnormal index The calculation formula is as follows:

[0081] ;

[0082] In the formula, is the number of overlapping reference areas in the key area, is the area of the key area, is the area of the first overlapping reference area in the key area, is the suspicious index of the remaining suspicious point corresponding to the first overlapping reference area in the key area.

[0083] The abnormal index calculation formula spatially weights and fuses the abnormal contributions of each reference area covered by the key area. By multiplying the area of the key area with the abnormal representation degree of the unit area of the reference area, and accumulating the suspicious index of the remaining suspicious point corresponding to all reference areas in the overlapping area.

[0084] The abnormal index is used to quantify the comprehensive pollution risk level of the key area: when the reference areas of multiple high-suspicious-index suspicious points overlap closely, the index will increase significantly, intuitively reflecting the aggregation effect of the pollution core area, and providing a quantitative basis for emergency response priority determination.

[0085] Eliminate redundant suspicious points and quantify the pollution core area to improve the efficiency of source tracing.

[0086] S400, through the monitoring center visual screen, real-time display the location of each key area in the virtual river scene and the abnormal index, and pre-warning prompt the on-site staff to investigate and handle each key area in reverse order according to the abnormal index.

[0087] Arrange the key areas in reverse order according to the abnormal index to guide the staff to prioritize high-risk areas.

[0088] Map the monitoring index data stream to the three-dimensional river network model, and intuitively display the pollution degree through the change of column height / color.

[0089] The present invention also provides an environmental parameter analysis system based on data processing, including an environmental perception module, an anomaly analysis module, a source tracing module, and a visualization module.

[0090] The environmental perception module is used to collect GIS maps, gather environmental parameters through monitoring equipment, build virtual river scenes, and dynamically map them.

[0091] The process involves acquiring GIS maps, collecting environmental parameters through monitoring equipment, and then constructing a virtual river scene based on digital twin technology. Specifically, this includes:

[0092] The coordinates of the river centerline and the topographic data of the riverbed are extracted by parsing the GIS vector layer, and the Deloni triangulation algorithm is applied to generate the river network skeleton model.

[0093] The Laplace smoothing algorithm was used to optimize the river channel surface. The hydraulic radius was calculated by interpolation along the centerline, and the roughness coefficient of the Manning formula was derived to construct a three-dimensional river network model.

[0094] The system receives real-time monitoring data streams, maps them to multi-chromatographic column rendering layers, and solves the Navier-Stokes equations based on a fluid dynamics model. It then uses real-time hydrological indicators as boundary conditions to drive dynamic updates of the water body.

[0095] It enables real-time dynamic mapping of high-precision virtual scenes, provides visualization of river topography and parameters, improves the data integrity and real-time performance of environmental monitoring, and lays the foundation for subsequent analysis.

[0096] The anomaly analysis module performs anomaly analysis on environmental parameters in a virtual river scene and marks the corresponding monitoring devices. It plans propagation routes for the marked monitoring devices, calculates the suspicion index based on the confluence points, and sets suspicious points.

[0097] Anomaly detection and suspicious point setting are performed in a virtual river scene. Specifically, this includes:

[0098] Calculate the fluctuation coefficient of the monitoring indicators, set thresholds based on historical averages to identify abnormal indicators and mark monitoring equipment.

[0099] The propagation path of each abnormal indicator under the monitoring equipment was inverted using a fluid dynamics model.

[0100] Then, the overlapping propagation routes of the same abnormal indicators are analyzed, the intersection segments are divided and a deviation index line chart is established, prediction points are set and suspected indices are calculated, and suspicious points are screened in combination with index thresholds.

[0101] The system automatically identifies pollution anomalies and accurately predicts the location of pollution sources using a propagation path model, thereby improving the efficiency and reliability of anomaly detection.

[0102] The traceability processing module analyzes the upstream and downstream relationship between suspicious points, deletes downstream suspicious points with equivalent substitutes from bottom to top in turn and retains corresponding upstream suspicious points, divides key areas according to the remaining suspicious points and calculates an abnormal index.

[0103] The upstream and downstream relationship of suspicious points is analyzed to optimize positioning, specifically including:

[0104] A tree-shaped relationship diagram is established to compare upstream and downstream suspicious points, a credibility index is calculated to identify equivalent substitute relationship, downstream suspicious points that can be replaced are deleted and upstream suspicious points are retained.

[0105] Then, a reference area is set according to the suspicious index of the remaining suspicious points, the space is divided into circular reference areas, the overlapping area of the reference areas is analyzed as a key area and an abnormal index is calculated.

[0106] The layout of suspicious points is refined to reduce redundancy, the core pollution area is efficiently positioned through upstream and downstream logic and spatial overlap analysis, and the traceability accuracy and pollution source identification capability are improved.

[0107] The visualization module displays each key area and abnormal index in the virtual river scene through a visual screen, and simultaneously pre-warns on-site staff to investigate and handle.

[0108] The final result is used for display and early warning, specifically including:

[0109] The virtual river scene is rendered in real time through the visualization screen of the monitoring center, and the position of the key area and the abnormal index are highlighted. According to the abnormal index in descending order, a pre-warning prompt is generated to guide on-site staff to investigate and handle.

[0110] Intuitive environmental anomaly visualization management is provided to assist decision-makers in quickly responding to pollution incidents and optimizing resource allocation, enhancing the practical applicability and efficiency of the overall system.

[0111] Compared with the prior art, the beneficial effects achieved by the present application are:

[0112] Pollution diffusion spatiotemporal heterogeneous modeling capability: The present scheme breaks through the shackles of traditional homogeneous diffusion assumptions, deeply integrates fluid dynamics mechanism and river geographical feature modeling. By building a digital twin environment based on the real topological structure of the river, the physical and chemical specificity of the mass transfer speed of different pollutants is accurately quantified, and a dynamic calibration mechanism is coupled with hydrological parameters, fundamentally solving the problem of asynchronous monitoring signals.

[0113] Pollution transmission path reverse verification mechanism: adopt space-time decision verification mechanism to eliminate mechanical threshold false alarm. In view of the nonlinear characteristics of the migration rate of pollutants in high flow rate river, the fluid inversion model is used to dynamically trace the migration trajectory of the pollution group, and the consistency comparison between the predicted arrival time window of the upstream sensor and the measured abnormal record is carried out, so as to automatically correct the topology structure of the pollution transmission path. The "abnormal signal inversion" phenomenon of the remote sensor is transformed into the judgment basis of the tracing logic instead of the interference noise.

[0114] Suspicious point cluster dynamic optimization architecture: establish a suspicious point equivalent replacement tree analysis model based on pollution contribution weight. By analyzing the coverage relationship of multi-stage river pollution transmission, the redundant downstream monitoring points (such as the case where downstream point anomaly is completely caused by upstream pollution diffusion) are intelligently identified and removed, which significantly reduces the number of tracing target points. At the same time, the reference area radius self-adaptive scaling mechanism driven by suspicious degree is adopted, so that the associated space of high threat pollution source can be fully contained. BRIEF DESCRIPTION OF DRAWINGS

[0115] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, which together with the embodiments of the application are used to explain the application, and do not constitute a limitation on the application. In the drawings:

[0116] Figure 1 is a flow diagram of the environment parameter analysis method based on data processing of the application;

[0117] Figure 2 is a structural diagram of the environment parameter analysis system based on data processing of the application. DETAILED DESCRIPTION

[0118] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0119] Embodiment one: please refer to Figure 1 The application provides an environment parameter analysis method based on data processing, comprising:

[0120] S100, collect GIS maps, and collect environmental parameters of rivers at different positions in the specified area through monitoring equipment. A virtual river scene is built according to the GIS map by using digital twinning technology, and dynamic mapping is carried out in combination with real-time environmental parameters.

[0121] In the specific implementation process, the GIS map is used to describe the topographic structure and distribution of the river channel in the specified area.

[0122] The designated area refers to a spatial range pre-defined according to the hydrological unit boundary of a river basin and administrative management needs.

[0123] The environmental parameters include monitoring indicators and hydrological indicators.

[0124] The monitoring indicators refer to physical, chemical, and biological parameters used to quantify the pollution characteristics and ecological risks of water bodies.

[0125] Specifically, it covers pollution concentration indicators (such as chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), heavy metal (Hg, As, Cd, etc.) concentration), biological toxicity indicators (such as luminous bacteria inhibition rate, algal growth inhibition rate), and sensory indicators (such as turbidity, color, oil film coverage).

[0126] By analyzing the concentration of pollutant components, spatio-temporal distribution, and biological effects, the pollution type (such as organic pollution, nutrient salt pollution, heavy metal pollution) is identified, the pollution source characteristics (such as industrial wastewater fingerprint, agricultural non-point source pollution pattern) are determined, and the water ecological health status (such as biological integrity evaluation based on the macrobenthic index BMWP) is evaluated.

[0127] The hydrological indicators refer to physical quantities that describe the dynamic characteristics and transport capacity of water bodies.

[0128] Specifically, it includes flow indicators (such as cross-section flow, runoff depth), flow characteristics indicators (such as flow velocity vector, turbulence intensity, water surface slope), hydraulic form indicators (such as river roughness, hydraulic radius, Froude number (Fr)), and transport parameters (such as diffusion coefficient, longitudinal dispersion coefficient).

[0129] In the specific implementation process, the hydrological indicators are used to describe the convection-diffusion process of pollutants (such as simulating the expansion of pollutant plume through Taylor diffusion equation), calculate the water environmental capacity (such as back-calculation of pollution capacity based on one-dimensional water quality model), and predict the pollution diffusion path (such as determining the influence of laminar flow / turbulent flow on mixing efficiency by combining Reynolds number (Re)).

[0130] Building a virtual river scene and dynamic mapping specifically includes:

[0131] S101, analyze the vector layer data of the GIS map, extract the river center line coordinate set, river bank boundary line elevation points, and river bed terrain raster data, and generate a river network skeleton structure model based on the Delaunay triangulation algorithm.

[0132] The Delaunay triangulation algorithm is used to generate a topologically stable river network skeleton from GIS data, solving the mathematical description problem of complex river structures.

[0133] S102, optimize the river surface by Laplace smoothing algorithm, calculate the hydraulic radius by interpolation along the center line, derive the roughness coefficient of the river channel combining with the Manning formula, build a virtual river scene using digital twin technology and construct a three-dimensional river network model in it.

[0134] Based on the Laplace smoothing algorithm to correct the curvature error of the river surface, and combined with the Manning formula to dynamically derive the roughness coefficient, the physical reality of fluid simulation is improved.

[0135] S103, map the deployment positions of all monitoring devices to the virtual river scene and calibrate the coordinate offset. Access the monitoring index data stream at different locations and map them as multi-color column rendering layers in the three-dimensional river network model.

[0136] S104, based on the Navier-Stokes equation, use the real-time collected hydrological indicators as boundary conditions to drive the dynamic update of the water movement vector field in the virtual river scene, and realize visual mapping.

[0137] In the specific implementation process, taking the Navier-Stokes equation as the core, the real-time hydrological indicators (such as flow, flow velocity vector) are used as boundary conditions to drive water movement, and physical-level simulation of pollutant diffusion process is realized (such as determining the influence of water flow state on mixing efficiency through Froude number).

[0138] Construct a high-precision digital river model to realize the dynamic fusion of environmental parameters and geographic information, and provide spatialized real-time data foundation for pollution analysis.

[0139] S200, perform anomaly analysis on the environmental parameters collected by each monitoring device in the virtual river scene and mark them, plan the propagation route for the marked monitoring devices, calculate the suspicious index at the intersection of different propagation routes and set suspicious points. Specifically includes:

[0140] S201, obtain the latest monitoring indicators of the monitoring devices in the virtual river scene , calculate the average value of each monitoring indicator in the past time , and set different fluctuation thresholds for each monitoring indicator.

[0141] S202, calculate the fluctuation coefficient of each monitoring indicator according to the formula: , set the monitoring indicators with fluctuation coefficients greater than the corresponding fluctuation thresholds as abnormal indicators, and mark the monitoring devices with abnormal indicators.

[0142] In the specific implementation process, the relative difference between the instantaneous value and the historical average value of the monitoring indicator is calculated to quantitatively represent the parameter fluctuation intensity. ​​​

[0143] It identifies abnormal indicators exceeding preset thresholds, providing a mathematical basis for pollution event alarms. By normalizing the absolute value difference with the benchmark value, it eliminates the sensitivity differences of indicators with different dimensions, enabling the assessment of comparability anomalies in multivariate monitoring data.

[0144] S203. Obtain the flow direction and velocity of the river channel where the marked monitoring equipment is located in the three-dimensional river network model. Use a fluid dynamics model to invert the propagation path for the marked monitoring equipment based on anomaly indicators. The propagation path is an evolutionary trajectory with both spatiotemporal dimensions. Specifically, this includes:

[0145] S2031. Identify all the bifurcations of the river in the three-dimensional river network model, divide the river into different channels according to the bifurcations, and each channel has only one inlet and one outlet. Analyze the channel in which each monitoring device is located.

[0146] S2032, Analytical Marker Monitoring Equipment River channel The flow direction, retrieve the river channel data within the preset historical time period. and the flow velocity data of all its upstream channels, for the river channel Historical flow velocity time series were established for all upstream channels.

[0147] S2033, Acquiring Monitoring Equipment Abnormal indicators Setting time and for abnormal indicators Preset diffusion speed A fluid dynamics model was used as the monitoring device. Abnormal indicators Inverting the propagation path. Specifically, this includes:

[0148] S2033-1, Monitoring equipment The location is used as the starting point for the inversion, and the time is set. As the inversion time, the river channel The reverse flow direction is used as the inversion direction, and the combination of these directions serves as the initial condition for the inversion.

[0149] S2033-2, the fluid dynamics model, based on initial conditions, river length, and historical flow velocity time series, performs inversion analysis on anomalous indicators within a preset historical period. Based on the historical flow velocity of the river channel and the diffusion rate The change in the diffusion location.

[0150] In practice, because the flow velocity of a river changes in real time, the flow velocities used to invert different rivers are not the same as those at the same historical moment, resulting in temporal differences in the inversion. The specific duration of these differences depends on the flow velocity, diffusion rate, and river length.

[0151] S2033-3、According to the abnormal index of the diffusion position change, the specified area boundary is combined as the inversion endpoint to generate a propagation path containing spatial position and time dimension, and the propagation path has one inversion starting point and at least one inversion endpoint.

[0152] The inversion endpoint is the specified area boundary position, the river source or the monitoring device position. Due to the existence of the upstream branch, the branch inversion will be carried out according to the number of upstream channels during the propagation path inversion at the branch, so that there may be multiple inversion endpoints in the propagation path.

[0153] S2033-4, the monitoring device through which the propagation path passes is taken as the adjudication object, the river distance between each adjudication object and the inversion starting point is calculated respectively, all adjudication objects are arranged in ascending order according to the river distance, and an error time length is preset .

[0154] S2033-5, analyze the historical time of the first adjudication object on the location of the propagation path , judge whether the monitoring index at the location of the adjudication object is set as an abnormal index within the time length before and after the historical time .

[0155] S2033-6, if the result is yes, continue to judge the next adjudication object; if the result is no, take the location of the adjudication object as the inversion endpoint, and delete the propagation path in all upstream channels after the adjudication object . Continue to judge the next adjudication object until all adjudication objects are judged, and then take the remaining propagation path as the propagation route.

[0156] The reliability of the propagation path is verified through the "adjudication object", if a monitoring device does not trigger an abnormality within the prediction time window (± ), the path is truncated and the endpoint is reset.

[0157] In the fluid dynamics inversion process, by comparing the time window matching of the propagation path prediction abnormal time and the actual abnormal record, the topology structure of the pollution diffusion path is dynamically corrected.

[0158] The spatiotemporal verification mechanism of the adjudication object functions to suppress the accumulation of inversion errors, when the measured abnormal state of the adjudication object does not match the model prediction, the current branch path is automatically truncated and the endpoint is reset, and the physical credibility of the tracing path is improved.

[0159] ​For the real-time change characteristics of river flow velocity, a historical flow velocity time series is established for each branch river. During inversion, the diffusion velocity is combined The asynchrony deviation is calculated (e.g., if the upstream flow velocity is fast, the pollution arrival time is shortened).

[0160] The setting time refers to the historical time when the fluctuation coefficient of the abnormal index is greater than the corresponding fluctuation threshold.

[0161] S2034, continue to monitor the equipment under the other abnormal indexes, and the abnormal indexes under the other marker monitoring equipment respectively invert the propagation route. Each abnormal index under the same marker monitoring equipment has only one propagation route.

[0162] S204, analyze the overlapping of the propagation routes under the same abnormal index and divide the intersection section, set the prediction point in the intersection section and calculate the suspicious index, and different index thresholds are preset for various abnormal indexes. After screening all prediction points, the suspicious points are set. Specifically, it includes:

[0163] S2041, obtain the propagation routes of the abnormal indexes under all marker monitoring equipment, all of which are mapped in the three-dimensional river network model, and the intersection volume is preset. At least the sections where the propagation routes overlap are set as the intersection sections of the abnormal indexes .

[0164] S2042, analyze the time interval of each propagation route in the intersection section , calculate the time standard deviation of all propagation routes at the same position as the deviation index . A line graph of the deviation index changing with the position is drawn for the intersection section .

[0165] In the specific implementation process, the time standard deviation of multiple propagation routes in the intersection section is calculated, and the point with the highest spatiotemporal consistency is located (e.g. the minimum value point predicts the pollution source).

[0166] S2043, select the point corresponding to the minimum deviation index in the line graph as the position in the intersection section , and draw a line graph for each intersection section of the abnormal indexes . According to each line graph, the prediction point of the corresponding intersection section is set.

[0167] S2044, uniformly set a point in each line graph, calculate the standard deviation and average value of the deviation index corresponding to these points. Obtain the deviation index of each prediction point , substitute into the formula to calculate the suspected index :

[0168] ;

[0169] wherein, , and are preset weight coefficients, is the length of the intersection road corresponding to the prediction point, is the average length of all intersection roads, is a constant greater than 1.

[0170] is a constant, is the number of overlapping propagation routes of the intersection road corresponding to the prediction point, is the average number of overlapping propagation routes of all intersection roads.

[0171] High-probability pollution source locations are screened through multi-dimensional feature coupling. In the suspected index calculation formula, the numerator term strengthens the weight of long intersection roads ( ) and high propagation volume ( ). The denominator term introduces the standard deviation of the deviation index ( ) and the average value ( ), to suppress false positives in the spatiotemporal discrete region.

[0172] S2045、is the anomaly index A preset index threshold is set to screen all prediction points with a suspected index greater than the index threshold as suspicious points. The prediction point and index threshold are set for each anomaly index, and the suspicious points are obtained after screening.

[0173] The traditional threshold alarm limitation is broken through, the spatiotemporal source prediction of pollution events is realized, and the false positive rate is reduced.

[0174] S300、analyze the upstream and downstream relationships between suspicious points, sequentially delete downstream suspicious points that exist in equivalent substitution according to the relationship from bottom to top and retain the corresponding upstream suspicious points, and divide the key area according to the remaining suspicious points and calculate the anomaly index. Specifically, it includes:

[0175] S301、according to the flow direction of the river where each suspicious point is located, analyze the upstream and downstream relationships between suspicious points. Sequentially delete downstream suspicious points that exist in equivalent substitution according to the upstream and downstream relationships and retain the corresponding upstream suspicious points. Specifically, it includes: S3011、according to the upstream and downstream relationships between suspicious points, establish a tree relationship diagram, and sequentially analyze whether each downstream suspicious point exists in equivalent substitution of other upstream suspicious points according to the direction from downstream to upstream. Specifically, it includes:

[0176] ​

[0177] S3011-1, analyze all upstream suspicious points of the downstream suspicious point, respectively calculate the river distance between each upstream suspicious point and the downstream suspicious point, and sort all upstream suspicious points in ascending order of river distance. S3011-2, obtain the first upstream suspicious point , count the abnormal index

[0178] , count the number of all propagation routes , and the number of propagation routes passing through the downstream suspicious point and the upstream suspicious point in the same direction .

[0179] In the specific implementation process, the propagation route is in a tree shape, and in the actual calculation process, each propagation route is first disassembled into a propagation single chain in the form of a starting point to an end point, and each propagation single chain only has a starting point and an end point and does not branch.

[0180] After all the propagation routes are disassembled into propagation single chains, remove the duplicate propagation single chains. Count the number of propagation routes passing through the downstream suspicious point and the upstream suspicious point in the same direction as , and the number of all propagation single chains as .

[0181] S3011-3, divide by as the credibility index of the upstream suspicious point , and when the credibility index is greater than the preset threshold, set the upstream suspicious point as having an equivalent replacement relationship with the downstream suspicious point .

[0182] S3011-4, when the credibility index is not greater than the preset threshold, then extract the next upstream suspicious point according to the sorting and calculate the credibility index, and calculate the credibility index according to the sorting of all upstream suspicious points in turn, and set the equivalent replacement relationship when the condition is met.

[0183] In the specific implementation process, after setting the equivalent replacement relationship, delete the downstream suspicious point and retain the upstream suspicious point, and do not continue to calculate the credibility index.

[0184] S3012, when the downstream suspicious point has an equivalent replacement upstream suspicious point , the operation deletes the downstream suspicious point and retains the upstream suspicious point .

[0185] ​​S3013, after all upstream suspicious points analysis and judgment are completed, downstream suspicious points When there is still no equivalent alternative upstream suspicious point, the operation retains the downstream suspicious point .

[0186] S3014, all upstream suspicious points of the downstream suspicious point are taken as analysis objects respectively, and further analysis is performed on whether there is an equivalent alternative upstream suspicious point for each analysis object.

[0187] S3015, if the condition is met, the operation is deleted and retained, otherwise, analysis is sequentially promoted in the direction from downstream to upstream until all downstream suspicious points are analyzed, judged and operated.

[0188] S302, preset basic distance , reference distance is calculated according to the suspicious index of the remaining suspicious point . A circular reference area is established in the virtual river scene with the remaining suspicious point position as the center and the reference distance as the radius.

[0189] The reference distance is calculated according to the following formula:

[0190] ;

[0191] In the formula, is the preset attenuation index, is the maximum suspicious index of all remaining suspicious points below the abnormal index .

[0192] In the specific implementation process, the higher the suspicious index, the closer the pollution source to the suspicious point position, and by narrowing the reference area range, the pollution source position exclusion range can be quickly narrowed, and the subsequent investigation efficiency can be improved.

[0193] S303, analyze the coverage of the reference area of each remaining suspicious point, take the overlapping area of the reference areas as the key area, and calculate the abnormal index of the key area. Divide the key area for each abnormal index and calculate the abnormal index.

[0194] The abnormal index is calculated according to the following formula:

[0195] ;

[0196] In the formula, is the number of overlapping reference areas of the key area, is the area of the key area, is the area of the first overlapping reference area of the key area, The suspected index of the remaining suspicious points corresponding to the first reference area overlapping the focus area.

[0197] The abnormal index calculation formula fuses the abnormal contributions of each reference area covered by the focus area by spatial weighting. By multiplying the area of the focus area with the abnormal representation degree of the unit area of the reference area, and accumulating the suspected indexes of the remaining suspicious points corresponding to all reference areas in the overlapping area.

[0198] The abnormal index is used to quantify the comprehensive pollution risk level of the focus area: when multiple reference areas of high suspected points overlap closely, the index will increase significantly, intuitively reflecting the aggregation effect of the pollution core area, and providing a quantitative basis for the priority judgment of emergency response.

[0199] Eliminate redundant suspicious points and quantify pollution core areas to improve traceability efficiency.

[0200] S400, through the monitoring center visual screen, the location of each focus area in the virtual river scene and the abnormal index are displayed in real time, and the on-site workers are prompted to investigate and handle each focus area in reverse order of the abnormal index.

[0201] In the specific implementation process, the focus areas are arranged in reverse order of the abnormal index, guiding the workers to preferentially handle high-risk areas (such as areas with doubled abnormal index requiring immediate response).

[0202] Map the monitoring index data stream to the three-dimensional river network model, and intuitively display the pollution degree through the change of the chromatographic column height / color (such as red column highlighting when COD exceeds the standard).

[0203] Example two: please refer to Figure 2 , the present application also provides an environmental parameter analysis system based on data processing, which comprises an environmental perception module, an abnormal analysis module, a traceability processing module and a visualization module.

[0204] The environmental perception module is used to collect GIS maps, collect environmental parameters through monitoring devices, build a virtual river scene and dynamically map.

[0205] Collect GIS maps (describe the distribution of river terrain), and collect environmental parameters (including monitoring indicators and hydrological indicators) through monitoring devices, and then build a virtual river scene based on digital twinning technology. Specifically, it includes:

[0206] Extract the river centerline coordinates and riverbed terrain data by analyzing the GIS vector layer, and generate the river network skeleton model by applying the Delaunay triangulation algorithm.

[0207] Optimize the river curve surface using the Laplace smoothing algorithm, interpolate the water radius along the centerline, and derive the roughness coefficient of the Manning formula to construct the three-dimensional river network model.

[0208] In the specific implementation process, the monitoring index data stream is accessed in real time, mapped into a multi-chromatographic column rendering layer, and the Navier-Stokes equation is solved based on the fluid dynamics model. The dynamic update of the water body is driven by real-time hydrological indicators as boundary conditions.

[0209] It enables real-time dynamic mapping of high-precision virtual scenes, provides visualization of river topography and parameters, improves the data integrity and real-time performance of environmental monitoring, and lays the foundation for subsequent analysis.

[0210] The anomaly analysis module performs anomaly analysis on environmental parameters in a virtual river scene and marks the corresponding monitoring devices. It plans propagation routes for the marked monitoring devices, calculates the suspicion index based on the confluence points, and sets suspicious points.

[0211] Anomaly detection and suspicious point setting are performed in a virtual river scene. Specifically, this includes:

[0212] Calculate the fluctuation coefficient of the monitoring indicators, set thresholds based on historical averages to identify abnormal indicators and mark monitoring equipment.

[0213] In the specific implementation process, the propagation path of each abnormal indicator under the marker monitoring equipment is inverted using a fluid dynamics model (taking the river counterflow direction as the initial condition, and combining historical flow velocity time series and diffusion velocity to simulate the diffusion trajectory of abnormal indicators in the spatiotemporal dimension).

[0214] Then, the overlapping propagation routes of the same abnormal indicators are analyzed, the intersecting segments are divided and a deviation index line chart is established, prediction points are set and suspected indices are calculated, and suspicious points are screened in combination with index thresholds.

[0215] The system automatically identifies pollution anomalies and accurately predicts the location of pollution sources using a propagation path model, thereby improving the efficiency and reliability of anomaly detection.

[0216] The source tracing module analyzes the upstream and downstream relationships between suspicious points, deletes downstream suspicious points with equivalent substitutes from bottom to top and retains the corresponding upstream suspicious points, divides key areas based on the remaining suspicious points and calculates the anomaly index.

[0217] Analyzing the upstream and downstream relationships of suspicious points to optimize localization, specifically including:

[0218] A tree-like relationship diagram is constructed to compare upstream and downstream suspicious points, and a confidence index is calculated to identify equivalent substitution relationships. Suspicious downstream points that can be substituted are deleted, while suspicious upstream points are retained.

[0219] Then, a reference area is set based on the suspected index of the remaining suspicious points. The space is divided into circular reference areas, and the overlapping areas of the reference areas are analyzed as key areas and anomaly indices are calculated.

[0220] In the specific implementation process, the suspicious point layout refining reduces redundancy, efficiently locates the core pollution area through upstream and downstream logic and spatial overlap analysis, and improves the accuracy of traceability and the ability of pollution source identification.

[0221] The visualization module displays the key areas and anomaly indexes in the virtual river scene through the visualization screen, and simultaneously provides early warning prompts for on-site staff to investigate and handle.

[0222] The final results are used for display and early warning, specifically including:

[0223] In the specific implementation process, the virtual river scene is rendered in real time through the visualization screen of the monitoring center, and the key area position and anomaly index are highlighted. The early warning prompts are generated in descending order of anomaly index, guiding on-site staff to investigate and handle.

[0224] The intuitive environmental anomaly visualization management is provided to assist decision makers in quickly responding to pollution incidents and optimizing resource allocation, thereby enhancing the practical applicability and efficiency of the overall system.

[0225] It should be noted that, in this article, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0226] Finally, it should be noted that: the above only describes the preferred embodiments of the present application, and does not limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An environmental parameter analysis method based on data processing, characterized in that: The method includes: S100: Collect GIS maps and monitor environmental parameters of rivers at different locations within a designated area using monitoring equipment; use digital twin technology to build a virtual river scene based on the GIS map and dynamically map it in conjunction with real-time environmental parameters; GIS maps are used to describe the topographic structure and distribution of rivers and waterways within a specified area; The designated area refers to the spatial range pre-delineated based on the boundaries of hydrological units in a river basin and administrative management needs; Environmental parameters include various monitoring indicators and hydrological indicators; Monitoring indicators refer to physical, chemical, and biological parameters used to quantify the characteristics of water pollution and ecological risks; Hydrological indicators are physical quantities that describe the dynamic characteristics and transport capacity of water bodies. The construction and dynamic mapping of a virtual river scene specifically includes: S101. Analyze the vector layer data of the GIS map, extract the coordinate set of the river centerline, the elevation points of the riverbank boundary line and the raster data of the riverbed topography, and generate a river network skeleton structure model based on the Deloni triangulation algorithm. S102. Optimize the river channel surface using the Laplace smoothing algorithm, calculate the hydraulic radius by interpolation along the centerline, derive the river channel roughness coefficient by combining the Manning formula, build a virtual river scene using digital twin technology, and construct a three-dimensional river network model within it. S103. Map the deployment locations of all monitoring devices to the virtual river scene and calibrate the coordinate offset; access the data streams of various monitoring indicators at different locations and map them to multi-chromatographic column rendering layers in the three-dimensional river network model respectively; S104. Based on the fluid dynamics model, solve the Navier-Stokes equations and use real-time collected hydrological indicators as boundary conditions to drive the dynamic update of the water motion vector field in the virtual river scene and realize visualization mapping. S200. In a virtual river scenario, perform anomaly analysis and label the environmental parameters collected by each monitoring device, plan propagation routes for the labeled monitoring devices, calculate the suspicion index based on the intersection of different propagation routes, and set suspicious points; specifically including: S201. Obtain monitoring equipment in the virtual river scene. The latest monitoring indicators And calculate the past duration. Average values ​​of various monitoring indicators Different fluctuation thresholds are preset for each monitoring indicator; S202, According to the formula: Calculate the fluctuation coefficient of each monitoring indicator. Monitoring indicators with fluctuation coefficients greater than the corresponding fluctuation thresholds are set as abnormal indicators, and monitoring devices with abnormal indicators are marked. S203. Obtain the flow direction and velocity of the river channel where the marked monitoring equipment is located in the three-dimensional river network model. Use the fluid dynamics model to invert the propagation path of the marked monitoring equipment based on the anomaly index. The propagation path is an evolution trajectory with both spatiotemporal dimensions. S204. Analyze the overlapping propagation routes under the same abnormal indicators and divide the intersection segments. Set prediction points in the intersection segments and calculate the suspected index. Different index thresholds are preset for various abnormal indicators. After screening all prediction points, suspicious points are set. S300. Analyze the upstream and downstream relationships between suspicious points, delete downstream suspicious points with equivalent substitutions from bottom to top according to the relationship, and retain the corresponding upstream suspicious points. Divide key areas based on the remaining suspicious points and calculate the anomaly index; specifically including: S301, Based on abnormal indicators Analyze the flow direction of the river channels where each suspicious point is located, and analyze the upstream and downstream relationships between the suspicious points; delete downstream suspicious points with equivalent substitutes in order of upstream and downstream relationships, and retain the corresponding upstream suspicious points; S302, Preset Base Distance Based on the suspected index of the remaining suspicious points Calculate reference distance Using the remaining suspicious points as the center, and the reference distance... Establish a circular reference area with a radius in the virtual river scene; Reference distance The calculation formula is as follows: ； In the formula, The preset attenuation index, Abnormal indicators The highest suspected index among all remaining suspicious points; S303. Analyze the reference area coverage of each remaining suspicious point, take the overlapping areas between reference areas as key areas, and calculate the anomaly index of the key areas; divide the key areas for each anomaly indicator and calculate the anomaly index respectively. The S400 system displays the location and anomaly index of each key area in the virtual river scene in real time through the monitoring center's visualization screen. At the same time, it provides early warnings to on-site staff to investigate and handle each key area in reverse order of the anomaly index.

2. The environmental parameter analysis method based on data processing according to claim 1, characterized in that: S203 includes: S2031. Identify all the bifurcations of the river in the three-dimensional river network model, divide the river into different channels according to the bifurcations, and each channel has only one inlet and one outlet. Analyze the channel where each monitoring device is located. S2032, Analytical Marker Monitoring Equipment River channel The flow direction, retrieve the river channel data within the preset historical time period. and the flow velocity data of all its upstream channels, for the river channel Historical flow velocity time series were established for all its upstream channels; S2033, Acquiring Monitoring Equipment Abnormal indicators Setting time and for abnormal indicators Preset diffusion speed A fluid dynamics model was used as the monitoring device. Abnormal indicators Invert the propagation route; S2034, Continue to provide marker monitoring equipment The propagation paths of each other abnormal indicator and each abnormal indicator under other marker monitoring devices are inverted. Under the same marker monitoring device, each abnormal indicator has only one propagation path.

3. The environmental parameter analysis method based on data processing according to claim 2, characterized in that: S2033 includes: S2033-1, Monitoring equipment The location is used as the starting point for the inversion, and the time is set. As the inversion time, the river channel The reverse direction is used as the inversion direction, and the combination of these directions serves as the initial condition for the inversion. S2033-2, the fluid dynamics model, based on initial conditions, river length, and historical flow velocity time series, performs inversion analysis on anomalous indicators within a preset historical period. Based on the historical flow velocity of the river channel and the diffusion rate Changes in the diffusion location; S2033-3, Based on abnormal indicators The changes in the diffusion location, combined with the specified region boundary as the inversion endpoint, generate a propagation path that includes spatial location and time dimensions. The propagation path has one inversion starting point and at least one inversion endpoint. S2033-4. Taking the monitoring equipment traversed by the propagation path as the adjudication objects, calculate the river distance between each adjudication object and the inversion starting point, arrange all adjudication objects in ascending order of river distance, and preset the error duration. ; S2033-5, Analyzing the propagation path in the first adjudicating object Historical moments at the location Determine the subject of the ruling monitoring indicators At a historical moment Duration before and after Is the internal indicator set as an abnormal indicator? S2033-6: If the result is yes, continue to judge the next judgment object; otherwise, the judgment object will be... The location is used as the endpoint of the inversion, and the adjudication object is deleted. Then, determine the propagation path within all upstream river channels; continue to determine the next adjudication object until all adjudication objects have been determined, and then use the remaining propagation path as the propagation route.

4. The environmental parameter analysis method based on data processing according to claim 1, characterized in that: S204 includes: S2041. Obtain abnormal indicators from all marked monitoring devices. The propagation routes are all mapped onto the three-dimensional river network model, with preset convergence rates. , will at least Road segments with overlapping propagation routes are used as anomaly indicators. The intersection of roads; S2042, Analyze the intersection sections of various propagation routes. Within a given time interval, calculate the standard deviation of the time for all propagation paths at the same location as the deviation index. ; is an intersection section Plot a line graph showing how the deviation index changes with location; S2043. Select the point corresponding to the smallest deviation index in the line chart at the intersection section. The positions within are set as prediction points, representing abnormal indicators. Draw line graphs for each intersection section and set prediction points for the corresponding intersection section based on each line graph; S2044. Evenly distribute in each line graph For each data point, calculate the standard deviation of the deviation from the index for that data point. and average Obtain the deviation index for each prediction point. Substitute into the formula to calculate the suspected index : ； In the formula, , and The preset weighting coefficients, To predict the length of the intersection segment corresponding to the point, The average length of all intersecting road segments. It is a constant greater than 1; It is a constant. To predict the number of overlapping propagation paths corresponding to the intersection points, This represents the average number of overlapping propagation routes across all intersecting road segments. S2045 is an abnormal indicator. A preset index threshold is used to filter out all predicted points with an index greater than the threshold as suspicious points; prediction points and index thresholds are set for each of the other abnormal indicators, and suspicious points are obtained after filtering.

5. The environmental parameter analysis method based on data processing according to claim 1, characterized in that: S301 includes: S3011. Establish a tree diagram based on the upstream and downstream relationships between suspicious points, and analyze whether each downstream suspicious point has other upstream suspicious points that can be equivalently substituted, following the direction from downstream to upstream; specifically including: S3011-1, Analysis of Suspicious Downstream Points For all upstream suspicious points, calculate the relationship between each upstream suspicious point and its downstream suspicious point. The distance between the river channels is used to sort all upstream suspicious points in ascending order of river distance; S3011-2, Obtain the first suspicious upstream point Statistical abnormal indicators Number of all propagation routes and simultaneously passing through suspected downstream points and upstream suspicious points And the number of propagation routes in the same direction ; S3011-3, will Divide by As an upstream suspicious point The credibility index is used to identify suspicious upstream points. Set as a suspected downstream point There is an equivalent substitution relationship; S3011-4. When the credibility index is not greater than the preset threshold, the next upstream suspicious point is extracted according to the sorting and the credibility index is calculated. The credibility index is calculated in turn according to the sorting of all upstream suspicious points. If the condition is met, the equivalent substitution relationship is set. S3012, When downstream suspicious points There are upstream suspicious points with equivalent substitutions. At that time, the operation deletes suspicious downstream points. And retain upstream suspicious points ; S3013. After all upstream suspicious points have been analyzed and judged, downstream suspicious points... If no equivalent alternative upstream suspicious point exists, the operation retains the downstream suspicious point. ; S3014, downstream suspicious points All upstream suspicious points are taken as analysis objects, and further analysis is conducted to determine whether there are equivalent substitute upstream suspicious points for each analysis object; S3015. If the conditions are met, delete and retain the operation; otherwise, proceed with the analysis from downstream to upstream until all suspicious downstream points have been analyzed, judged, and operated.

6. The environmental parameter analysis method based on data processing according to claim 1, characterized in that: In S303, the anomaly index The calculation formula is as follows: ； In the formula, The number of reference areas that overlap with the key areas. The area of ​​the key area, The first overlapping key areas The area of ​​each reference region The first overlapping key areas Each reference area corresponds to the suspected index of the remaining suspicious points.

7. An environmental parameter analysis system based on data processing, applied to the environmental parameter analysis method based on data processing as described in claim 1, characterized in that: The system includes an environmental perception module, an anomaly analysis module, a source tracing and processing module, and a visualization module; The environmental perception module is used to collect GIS maps, gather environmental parameters through monitoring equipment, build virtual river scenes, and dynamically map them. The anomaly analysis module performs anomaly analysis on environmental parameters in a virtual river scene and marks the corresponding monitoring devices; it plans propagation routes for the marked monitoring devices, calculates the suspicion index based on the confluence points, and sets suspicious points; The source tracing module analyzes the upstream and downstream relationships between suspicious points, deletes downstream suspicious points with equivalent substitutes from bottom to top and retains the corresponding upstream suspicious points, divides key areas based on the remaining suspicious points and calculates the anomaly index; The visualization module displays key areas and abnormal indices in the virtual river scene on a visual screen, while also providing early warnings to on-site staff to investigate and handle the situation.

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