Environment risk early warning service system based on multi-source data fusion

Abstract

This invention relates to the field of environmental monitoring and early warning technology, specifically disclosing an environmental risk early warning service system based on multi-source data fusion. The system simultaneously collects real-time environmental monitoring data sequences and dynamic trajectory simulation data sets of pollutant release in the target area; after anomaly identification and correction of the monitoring data, it extracts diffusion characteristics representing the intensity and direction of pollutant migration; based on the simulation data and real-time meteorological parameters, it performs inverse probability inference to generate diffusion path characteristics representing the spatial probability of potential pollution sources; after normalizing and aligning the two types of features, it performs multi-level confidence calibration and nonlinear fusion to generate a quantitative assessment value of the regional risk situation; based on the interval of the assessment value, it initiates an early warning level and automatically generates structured early warning instructions. This invention solves the technical problem of abnormal observation error propagation in multi-source heterogeneous data fusion through dynamic adaptive error processing and robust fusion mechanisms, improving the accuracy, reliability, and timeliness of early warning.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and early warning technology, specifically to an environmental risk early warning service system based on multi-source data fusion. Background Technology

[0002] With the acceleration of industrialization and urbanization, the frequency and potential harm of sudden environmental pollution incidents (such as leaks of toxic and harmful gases and water pollution) are increasing, posing a serious threat to the ecological environment and public safety. Traditional environmental risk early warning mainly relies on real-time data from fixed monitoring stations or single numerical model simulations, which has obvious limitations. Although current big data analysis technology has made it possible to integrate multi-source environmental information, in practical applications, there are still problems such as insufficient fusion of heterogeneous multi-source data, transmission of abnormal observation errors, and disconnect between early warning and response.

[0003] The existing technology has the following shortcomings:

[0004] In the process of real-time fusion and assimilation of multi-source heterogeneous data, the problem of "abnormal observation error transmission and pollution" caused by static and simplified error processing mechanisms, and the risk of fundamental misjudgment in emergency response to sudden environmental events. Summary of the Invention

[0005] The purpose of this invention is to provide an environmental risk early warning service system based on multi-source data fusion to solve the problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] An environmental risk early warning service system based on multi-source data fusion includes:

[0008] The multi-source data synchronous acquisition module is used to synchronously acquire real-time environmental monitoring data sequences of the target area and dynamic trajectory simulation data sets of pollutant release within the target area;

[0009] The anomaly identification and feature extraction module performs anomaly detection and multi-source data consistency correction on real-time environmental monitoring data sequences based on dynamic thresholds and correlations with adjacent monitoring points. Subsequently, it extracts change patterns that characterize the intensity and direction of pollutant migration as diffusion features through spatiotemporal coupling analysis.

[0010] The reverse probability extrapolation module, based on a dynamic trajectory simulation dataset and incorporating real-time meteorological field parameters, performs multi-path probability reverse extrapolation from each monitoring location to the potential release area, calculates the likelihood estimate for each backtracking path, and generates diffusion path characteristics represented by spatial probability density.

[0011] The multi-feature fusion and risk assessment module normalizes and aligns diffusion features and diffusion path features in the spatiotemporal dimension. It constructs an adaptive weight allocation strategy based on the real-time reliability of monitoring data and the spatial coverage of the inferred path, performs multi-level confidence calibration and coupling calculation on diffusion features and diffusion path features, and outputs a quantitative assessment value of the regional risk situation.

[0012] The dynamic early warning response and solution generation module activates the corresponding early warning response level based on the continuous numerical range of the quantitative assessment value. When the quantitative assessment value enters the preset high-risk range, it immediately triggers the generation of early warning instructions and the release of dynamic risk avoidance solutions.

[0013] As a further aspect of the present invention: the extraction of change patterns characterizing the intensity and direction of pollutant migration as diffusion features specifically includes:

[0014] Based on the real-time environmental monitoring data sequence after anomaly detection and consistency correction, spatial interpolation calculation of concentration distribution is performed in the spatial grid between each adjacent monitoring point to generate a continuous concentration spatial distribution surface.

[0015] The concentration spatial distribution surface is subjected to time series difference calculation to obtain the concentration gradient change of each grid cell at each adjacent time point, and the direction of most significant gradient change is extracted.

[0016] The stable movement direction of each grid cell within a continuous time window is correlated and integrated with the average concentration gradient intensity in the movement direction. The output is a change pattern characterizing the dominant direction and relative intensity of pollutant migration, i.e., diffusion characteristics.

[0017] As a further aspect of the present invention: the extraction of the most significant gradient change direction specifically includes:

[0018] For each grid cell, calculate its concentration gradient vector at adjacent time points. The concentration gradient vector consists of the concentration change rate and the concentration difference between spatially adjacent grid cells.

[0019] The gradient vector is decomposed into eight predefined main directions, and the vector projection length in each direction is calculated to form a set of directional projection intensities.

[0020] From the set of azimuth projection intensities, select azimuths with positive projection intensities that exceed the dynamic threshold, and determine the azimuth with the highest projection intensity as the most significant movement direction of the grid cell at adjacent time points.

[0021] As a further aspect of the present invention: the generation of diffusion path features represented by spatial probability density specifically includes:

[0022] Based on the fused dynamic trajectory simulation data set and real-time meteorological field parameters, a preset number of reverse random walk algorithms are launched at each monitoring location to generate multiple candidate paths that trace back from the monitoring point to each grid cell in the upstream space.

[0023] For each candidate path, a co-occurrence frequency weight is assigned to each grid cell traversed by the path, taking into account the frequency of path occurrence under similar meteorological conditions in historical pollutant release events. The product of all weights on the path is then used as the initial likelihood estimate for the corresponding path.

[0024] The initial likelihood estimates of all candidate paths are mapped onto a unified spatial grid. Spatial kernel density smoothing and superposition are performed on the initial likelihood estimates of all traversed paths within each grid cell to generate a spatial probability density in grid units, i.e., diffusion path features.

[0025] As a further aspect of the present invention: the generation of multiple candidate paths tracing back from the monitoring point to each grid cell in the upstream space specifically includes:

[0026] For each monitoring location, the initial reverse movement direction is determined based on the wind speed and direction in the real-time meteorological field parameters, and the range of random disturbance of direction and movement step size for each step are set according to the historical statistical distribution of wind direction and turbulence intensity.

[0027] Starting from the monitoring position, based on the initial reverse movement azimuth, the range of random azimuth disturbance and the movement step size, multiple independent step-by-step backtracking processes are executed. Each process is continuously stepped until the preset maximum number of backtracking steps is reached or the calculation area boundary is left, forming a candidate path.

[0028] Record the grid cell sequence traversed by each candidate path, and summarize the grid cell sequences of all candidate paths generated at all monitoring locations as input for subsequent calculation of the initial likelihood estimate of the path.

[0029] As a further aspect of the present invention: the multi-level confidence calibration and coupling calculation performed on the diffusion characteristics and diffusion path characteristics to output a quantitative assessment value of the regional risk situation specifically includes:

[0030] The normalized and aligned diffusion features and diffusion path features are input into the first-layer calibration process, and the spatial probability density of the diffusion path features is locally enhanced or suppressed based on the temporal stability of the diffusion features.

[0031] The features after the first layer of calibration are input into the second layer of calibration process. Based on the spatial coverage of the inference path, the values ​​of the insufficiently covered areas in the features are completed by confidence diffusion based on spatial adjacency.

[0032] After completing the second-level calibration, the two types of features are coupled grid by grid. The migration intensity represented by the diffusion feature on each grid is nonlinearly superimposed with the spatial probability represented by the diffusion path feature to generate the risk contribution value of the grid.

[0033] The risk contribution values ​​of all grids within the calculation area are aggregated and, after global normalization, output as a quantitative assessment value of the regional risk situation.

[0034] As a further aspect of the present invention: the nonlinear superposition of the migration intensity represented by the diffusion feature and the spatial probability represented by the diffusion path feature on each grid to generate the risk contribution value of the grid specifically includes:

[0035] Based on historical case data statistics, the contribution threshold between migration intensity and spatial probability is determined, and independent mapping and transformation rules are defined for the numerical intervals on both sides of the threshold.

[0036] The migration intensity value of each grid is converted into the first contribution component according to the mapping transformation rule, and the spatial probability value of the same grid is converted into the second contribution component.

[0037] Based on the contribution threshold, a preset nonlinear superposition rule is applied to the first contribution component and the second contribution component. The nonlinear superposition rule uses a weighted geometric average method when either component is below the threshold, and uses a power-weighted method when both are above the threshold.

[0038] The superimposed calculation results are used as the risk contribution value of the corresponding grid.

[0039] As a further aspect of the present invention: the generation of the real-time triggered warning command specifically includes:

[0040] Analyze the quantitative assessment value and the continuous numerical range in which the quantitative assessment value is located, and extract the key quantitative parameters of the current risk situation. The key quantitative parameters include the estimated scope of impact, the level of impact, and the direction of the diffusion trend.

[0041] Based on the extracted key quantitative parameters, the matching contingency plan elements are retrieved and called from the multi-dimensional contingency plan library. The contingency plan elements include at least target area information, a list of responding departments, and preliminary control measures.

[0042] Key quantitative parameters and the elements of the invoked contingency plan are structurally assembled to generate early warning instructions that include specific risk identification, dynamic impact assessment and standardized action guidelines;

[0043] Based on the pre-defined distribution rules according to the responsibilities of different response departments, the generated early warning instructions are pushed out in a targeted manner to complete the immediate triggering of the early warning process.

[0044] As a further aspect of the present invention: the structured assembly of key quantification parameters and invoked pre-plan elements specifically includes:

[0045] Establish a dynamic mapping relationship between key quantitative parameters and contingency plan elements, and fill parameter values ​​into preset logical slots of contingency plan elements according to parameter type;

[0046] Based on the pre-plan elements filled with parameter values, an element connection diagram is generated according to the subordinate and collaborative relationships between the pre-plan elements. The organizational order and hierarchical structure of each element in the early warning instruction are determined according to the connection diagram.

[0047] Following the organizational order and hierarchical structure, the filled-in contingency plan elements are serialized and spliced ​​together, and pre-set guiding explanatory text is inserted between adjacent elements to form a complete and coherent early warning instruction text.

[0048] The beneficial effects of this invention are:

[0049] (1) This invention effectively overcomes the problems of data error propagation and abnormal observation interference by deeply integrating multi-source heterogeneous data such as real-time monitoring, simulated trajectories, meteorological parameters, and historical cases, and by employing inverse probability inference and multi-level confidence calibration techniques. This method can more accurately identify the migration paths and potential sources of pollutants and quantitatively assess the risk situation, thereby reducing the probability of false alarms and missed alarms and providing decision-makers with more credible early warning information.

[0050] (2) By constructing a multi-dimensional contingency plan library and establishing a parameterized and structured instruction assembly engine, this invention can automatically match contingency plan elements, generate early warning instructions containing specific action guidelines, and accurately push them according to departmental responsibilities based on real-time generated quantitative assessment results. This achieves seamless linkage between risk assessment and emergency response, upgrading the traditional "monitoring-alarm-manual decision-making" process to an intelligent closed loop of "perception-assessment-decision-release," shortening emergency response time and improving the overall efficiency of responding to sudden environmental events. Attached Figure Description

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Please see Figure 1 As shown, the present invention is an environmental risk early warning service system based on multi-source data fusion, comprising:

[0055] The multi-source data synchronous acquisition module is used to synchronously acquire real-time environmental monitoring data sequences of the target area and dynamic trajectory simulation data sets of pollutant release within the target area;

[0056] The anomaly identification and feature extraction module performs anomaly detection and multi-source data consistency correction on real-time environmental monitoring data sequences based on dynamic thresholds and correlations with adjacent monitoring points. Subsequently, it extracts change patterns that characterize the intensity and direction of pollutant migration as diffusion features through spatiotemporal coupling analysis.

[0057] The reverse probability extrapolation module, based on a dynamic trajectory simulation dataset and incorporating real-time meteorological field parameters, performs multi-path probability reverse extrapolation from each monitoring location to the potential release area, calculates the likelihood estimate for each backtracking path, and generates diffusion path characteristics represented by spatial probability density.

[0058] The multi-feature fusion and risk assessment module normalizes and aligns diffusion features and diffusion path features in the spatiotemporal dimension. It constructs an adaptive weight allocation strategy based on the real-time reliability of monitoring data and the spatial coverage of the inferred path, performs multi-level confidence calibration and coupling calculation on diffusion features and diffusion path features, and outputs a quantitative assessment value of the regional risk situation.

[0059] The dynamic early warning response and solution generation module activates the corresponding early warning response level based on the continuous numerical range of the quantitative assessment value. When the quantitative assessment value enters the preset high-risk range, it immediately triggers the generation of early warning instructions and the release of dynamic risk avoidance solutions.

[0060] In the multi-source data synchronous acquisition module, real-time environmental monitoring data sequences of the target area and dynamic trajectory simulation data sets of pollutant release within the target area are acquired synchronously, specifically including:

[0061] The acquisition of real-time environmental monitoring data sequences is achieved by accessing the data streams of various environmental monitoring devices deployed within the target area. These devices typically include fixed-site air quality monitors, online water quality analyzers, and mobile monitoring equipment such as portable sensors mounted on mobile monitoring vehicles or drones. These devices continuously measure and generate raw monitoring records containing pollutant concentrations, geographical locations, and timestamps at preset frequencies. Data from all monitoring points is transmitted in real-time to a central data processing node via a dedicated communication network, and then aggregated and arranged according to a unified spatiotemporal reference to form a time-series monitoring data covering the target area.

[0062] The acquisition of a dynamic trajectory simulation dataset of pollutant release within the target area is based on a selected numerical simulation method. First, refined geographic information data, historical meteorological data, and a list of potential pollutant release sources for the target area are collected as basic inputs. Then, numerical simulation methods using computational fluid dynamics or Lagrange particle diffusion are employed to set different simulation scenarios. Each scenario initiates an independent simulation by changing key parameters (such as the location and intensity of the release source and background wind field conditions). Each calculation outputs the transport and diffusion trajectory data of pollutants in three-dimensional space and time under that specific parameter combination. By running a large number of such scenario simulations in batches, a simulation dataset containing multiple sets of different possible trajectories is ultimately generated.

[0063] To achieve synchronous acquisition of the two types of data, the system establishes a unified data scheduling command. This command triggers both the collection request for monitoring data and the retrieval request for the simulation data set simultaneously. Monitoring data collection is a real-time, continuous data stream access process. The retrieval of the simulation data set involves retrieving and extracting one or more sets of simulated trajectory data that best match the current actual weather forecast background from a pre-generated scenario simulation results database stored on the high-performance computing server. The system aligns and marks the reception times of the two data streams through a time server, ensuring consistency in time reference for data processed in subsequent steps, thereby completing the synchronous acquisition of the two types of data and providing a foundation for subsequent fusion analysis.

[0064] In the anomaly identification and feature extraction module, anomaly detection and multi-source data consistency correction are performed on the real-time environmental monitoring data sequence based on dynamic thresholds and correlations with adjacent monitoring points. Subsequently, spatiotemporal coupling analysis is used to extract change patterns that characterize the intensity and direction of pollutant migration as diffusion features, specifically including:

[0065] First, based on the real-time environmental monitoring data sequence with completed anomaly detection and consistency correction, a spatial distribution surface for concentration is constructed. Specifically, a regular two-dimensional spatial grid is established within the geographical area covered by each adjacent monitoring point. For each grid node without direct monitoring data, its concentration value is obtained through a spatial interpolation method based on inverse distance weighting. This method calculates the distance from the grid node to a predetermined number of surrounding monitoring points, and uses a specified power of the inverse of the distance as the weight to perform a weighted average of the measured concentration values ​​of each monitoring point, thereby estimating the concentration of the node and ultimately generating a continuous spatial distribution surface for concentration covering the entire target area.

[0066] Secondly, a time-series difference analysis is performed on the generated concentration spatial distribution surface to quantify the spatiotemporal variation of the concentration gradient. For each identical grid cell, its concentration values ​​at two consecutive time points are obtained. The concentration value at the later time point is subtracted from the concentration value at the earlier time point to obtain the concentration change of that grid cell within this time interval. Simultaneously, the concentration differences between that grid cell and its four directly adjacent grid cells (east, south, west, and north) at the current time point are calculated. The concentration change and the concentration differences in the four directions together constitute the basic data of the gradient vector describing the spatiotemporal trend of the concentration of that grid cell.

[0067] Next, the most significant direction of movement is determined. The gradient vector data obtained in the previous step is mapped to a coordinate system of eight preset main geographic directions (East, Southeast, South, Southwest, West, Northwest, North, and Northeast). For each direction, the projection length of the gradient vector in that direction is calculated, specifically the dot product of each component of the vector with the corresponding directional unit vector. These eight projection lengths constitute the set of directional projection intensities for that grid cell within the current time interval. The dynamic threshold is a percentage value pre-calculated based on the historical concentration fluctuation statistics of the local area where the grid cell is located. From the set of directional projection intensities, all directions with positive projection intensities (indicating an increase in concentration or transport in that direction) and values ​​exceeding the dynamic threshold are selected. From these compliant directions, the direction with the highest projection intensity value is selected as the most significant direction of movement for that grid cell at this specific adjacent time point.

[0068] Finally, by judging the directional stability and integrating the intensity over a continuous time period, the final diffusion characteristics are formed. For a grid cell, the most significant movement direction identified within a set continuous time window (e.g., six consecutive time intervals) is observed. The dominant direction with the highest frequency among these directions is statistically analyzed, and its frequency is calculated. If this frequency exceeds a preset stability threshold (e.g., 67%), the migration direction of the grid cell within this time window is considered stable. For grid cells with stable movement directions, this stable direction is determined as their dominant migration direction. Simultaneously, the arithmetic mean of all projected intensities along this stable direction within the same time window is calculated as the average concentration gradient intensity of the grid cell. Finally, the dominant migration direction of each grid cell is correlated with its corresponding average concentration gradient intensity, outputting a spatial distribution map that simultaneously includes directional and intensity attributes. This represents the diffusion characteristics characterizing the spatiotemporal variation pattern of pollutant migration.

[0069] In the reverse probability extrapolation module, based on the dynamic trajectory simulation dataset and incorporating real-time meteorological field parameters, multi-path probability reverse extrapolation is performed from each monitoring location to the potential release area. The likelihood estimate for each retrospective path is calculated, generating diffusion path characteristics represented by spatial probability density, specifically including:

[0070] First, a reverse random walk is performed to generate a set of candidate paths starting from the monitoring point. For each monitoring location that reports anomalies in pollutant concentration, an independent reverse inversion process is initiated. The initial movement direction is determined by the real-time wind direction at that location at the current moment, specifically in the exact opposite direction. For example, if the real-time wind direction is easterly, the initial reverse movement azimuth is set to west. The random disturbance range for each movement step is set by querying a historical meteorological database and finding the standard deviation of wind direction under conditions similar to the current wind speed and atmospheric stability level, for example, ±22.5 degrees. The movement step size is determined based on the typical diffusion step size for the corresponding turbulence intensity level in historical statistics, for example, one kilometer. Starting from the grid cell where the monitoring location is located, based on the initial azimuth, disturbance range, and step size, a step is performed: first, an angle offset randomly generated within the disturbance range is superimposed on the initial azimuth, and then the distance along this direction is moved by one step to reach a new upstream grid cell. This new cell is used as the starting point for the next step, and the above step process with random disturbance is repeated. This backtracking process continues until the cumulative number of steps reaches a preset maximum (e.g., 200 steps), or the movement path leaves the pre-defined geographical boundary of the computational area. For the same monitoring location, the entire process is repeated independently, for example, 500 times. Each time, it starts from the original monitoring point and introduces a different sequence of random disturbances, generating 500 different candidate paths composed of a series of grid cell sequences. All such paths generated from all monitoring locations are then aggregated to form a total candidate path set.

[0071] Next, an initial likelihood estimate is calculated for each candidate path. This calculation relies on a pre-built historical path-meteorological association database. This database stores confirmed cases of pollutant release events, each case recording the main meteorological conditions at the time of the event (such as wind speed, prevailing wind direction, and stability) and the most likely source area of ​​pollution determined through backward analysis. For each candidate path to be evaluated, the system first matches the current real-time meteorological field parameters with the meteorological conditions of historical cases, filtering out all meteorologically similar historical cases. Then, it examines the degree of spatial overlap between the grid cell sequence traversed by the current candidate path and the most likely source area determined in each meteorologically similar historical case. A co-occurrence frequency weight is calculated for each grid cell on the current path, defined as the proportion of the number of cases in which the grid cell appears within the most likely source area of ​​the corresponding case out of all meteorologically similar historical cases. The initial likelihood estimate of a candidate path is obtained by sequentially multiplying the co-occurrence frequency weights of all grid cells traversed along the path.

[0072] Finally, the likelihood estimates of all paths are spatially integrated to generate diffusion path features in the form of spatial probability density. A blank grid, consistent with the grid used for the aforementioned spatial interpolation, is established, covering the entire computational domain. For each path in the candidate path set, its calculated initial likelihood estimate is accumulated to each grid cell traversed by that path. After accumulating the values ​​for all paths, each grid cell obtains an original accumulated probability value. Subsequently, this original probability distribution is subjected to spatial kernel density smoothing. For each grid cell, a local square window with a side length of several grid cells (e.g., 5 by 5) is defined. A predefined two-dimensional symmetric weight kernel (e.g., a weight of 1 at the center, linearly decreasing outwards to a weight of 0 at the edges) is used to perform a weighted average of the original accumulated probability values ​​of all grid cells within this window, and the result of the weighted average is assigned to the central grid cell. This smoothing process traverses all grid cells, ultimately generating a new, continuously varying probability density distribution field. This probability density distribution field is the diffusion path feature, where the probability density value of each grid cell comprehensively reflects the likelihood of that cell being a potential source of pollutants, as indicated by all backward inference paths.

[0073] In the multi-feature fusion and risk assessment module, diffusion features and diffusion path features are normalized and aligned in the spatiotemporal dimensions. An adaptive weight allocation strategy is constructed based on the real-time reliability of monitoring data and the spatial coverage of the inferred path. Multi-level confidence calibration and coupling calculations are performed on the diffusion features and diffusion path features to output a quantitative assessment value of the regional risk situation, specifically including:

[0074] First, a first-level confidence calibration is performed. This calibration, based on the temporal stability of diffusion characteristics, locally adjusts the spatial probability density of diffusion path characteristics. The temporal stability of diffusion characteristics is calculated for each grid cell by taking the standard deviation of the migration intensity values ​​extracted over several recent consecutive time points (e.g., six), and defining the reciprocal of this standard deviation as the stability coefficient of that cell. A higher coefficient indicates that the migration intensity of that cell fluctuates less and is more stable over a short period. For the spatial probability density value of each grid cell in the diffusion path characteristics, it is multiplied by the corresponding grid cell's stability coefficient. If the stability coefficient is greater than 1, it has a local enhancement effect; if it is less than 1, it has a local suppression effect. The purpose of this operation is to place greater trust in regions where pollutant concentration migration patterns show stability in the probabilistic inference of pollution sources.

[0075] Secondly, a second layer of confidence calibration is performed to address the data sparsity problem caused by uneven spatial coverage of the backpropagation paths. The "path spatial coverage" of each grid cell is calculated, defined as the ratio of the number of paths passing through that grid cell in all backpropagation random walks when generating the candidate path set, to the average number of paths passed through all cells within the local region to which that grid cell belongs (e.g., a rectangular region centered on that cell and consisting of five grids). Grid cells with a coverage ratio below a preset threshold (e.g., 0.5) are considered to have insufficient confidence in their diffusion path feature values. For such cells, their feature values ​​are completed using interpolation based on spatial adjacency: the feature values ​​of all directly adjacent grid cells with coverage ratios above the threshold are taken, the arithmetic mean of these feature values ​​is calculated, and this mean is used to replace the feature value of the cell with insufficient confidence. This process is performed on the feature data after the first layer of calibration.

[0076] Subsequently, a grid-by-grid nonlinear overlay of features is performed to generate the risk contribution value for each grid. The key to this process lies in determining the nonlinear joint effect of migration intensity and spatial probability on risk contribution. Based on statistical analysis of numerous historical environmental risk cases, a contribution threshold C is determined. The value of this threshold C is determined by statistically analyzing the distribution of the normalized logarithmic values ​​of migration intensity and spatial probability corresponding to actual high-risk events in historical cases; for example, the intensity or probability value corresponding to the 75th quantile of this joint distribution is taken as C. Independent mapping transformation rules are defined for both sides of the threshold: for input values ​​less than the threshold C, the corresponding contribution component is equal to the input value itself; for input values ​​greater than or equal to the threshold C, the corresponding contribution component is equal to the threshold C plus the square root of the difference between the input value and the threshold C. Let the migration intensity value of a certain grid cell after the aforementioned calibration be M, and the spatial probability density value be P. First, according to the above mapping transformation rules, M is converted into the first contribution component CM, and P is converted into the second contribution component CP.

[0077] Next, apply the preset non - linear superposition rule to calculate the risk contribution value R of this grid. This rule is divided into two cases according to the relationship between CM and CP relative to the critical point C:

[0078] Case 1: When CM < C or CP < C, use the weighted geometric mean method for calculation. The specific calculation formula is as follows: ;

[0079] where w is the weight coefficient, whose value is determined according to the historical statistics of the dominant risk type in the area where this grid cell is located, and the value range is between zero and one. If the historical statistics show that the risk in this area is more inclined to be driven by rapid pollutant migration, then w takes a value close to 1; if it is more inclined to be indicated by the probability of a clear pollution source, then w takes a value close to 0.

[0080] Case 2: When CM ≥ C and CP ≥ C, use the power - weighted method for calculation. The specific calculation formula is as follows: ;

[0081] where, is a balance coefficient between 0 and 1, and its value is 0.5. and are power coefficients greater than 1, used to amplify the contributions of high - intensity and high - probability values, and their specific values are obtained by fitting historical high - risk case data. For example, is equal to 1.5, is equal to 1.3.

[0082] Finally, sum up the risk contribution values of all grids in the calculation area , and after global normalization, obtain the quantitative evaluation value Q of the regional risk situation. Specifically, first calculate the arithmetic mean of the risk contribution values of all N grid cells in the entire target area, denoted as ; then calculate the standard deviation of these risk contribution values, denoted as . The quantitative evaluation value Q of the regional risk situation is calculated in the following way: Q is equal to (the maximum value of the risk contribution values of all grids in the whole area minus μ_R), then divided by , then multiply the result by 10, and finally add 50 to obtain an evaluation value within the range of 0 to 100. This final evaluation value Q is the quantitative index representing the overall environmental risk level of the current target area.

[0083] In the dynamic early - warning response and plan generation module, start the corresponding early - warning response level according to the continuous numerical interval where the quantitative evaluation value is located. When the quantitative evaluation value enters the preset high - risk interval, immediately trigger the generation of early - warning instructions and the release of dynamic risk - avoidance plans, specifically including:

[0084] First, the quantitative assessment values ​​and their corresponding intervals are analyzed to extract key quantitative parameters. Continuous numerical intervals are pre-defined; for example, the assessment range of 0 to 100 is divided into four continuous intervals: 0 to 30 for low risk, 30 to 60 for medium risk, 60 to 85 for relatively high risk, and 85 to 100 for high risk. When a quantitative assessment value falls into a certain interval, analysis of that interval is triggered. The impact range is estimated as follows: grid cells whose risk contribution value exceeds twice the standard deviation of the overall regional average risk contribution value are identified. The geographical locations of these cells are connected, and the area of ​​their circumscribed rectangles is calculated as the preliminary impact range. The level of impact is directly mapped to the interval in which the assessment value is located; for example, a high-risk interval corresponds to level four. The determination of the diffusion trend direction is based on the migration dominance direction that appears most frequently within the identified high-risk grid cell set, as identified in the diffusion characteristics.

[0085] Secondly, based on the extracted key quantitative parameters, matching retrieval and retrieval are performed from a multi-dimensional contingency plan database. This database is a structured database where each plan record contains multiple fields, such as the applicable risk type, the threshold for triggering the impact level, characteristics of the main affected area, a list of relevant response departments, and a series of preliminary control measures. During the retrieval, the pollution type of the current risk (e.g., gas leak), the calculated impact level (e.g., level four), and the size category of the estimated impact area (e.g., greater than 5 square kilometers) are used as joint query conditions. All plan records that fully match the above joint conditions are retrieved from the database. From these records, the target area name, a list of response departments sorted by responsibility (e.g., environmental monitoring department, emergency management department, fire and rescue department, traffic control department), and the specific preliminary control measures text matching the current impact area (e.g., establishing a warning line within three kilometers downwind of the affected area) are extracted.

[0086] Subsequently, key quantitative parameters and invoked contingency plan elements are structurally assembled to generate a complete early warning instruction text. This process first establishes a dynamic mapping relationship: the numerical results of the "impact range estimation" are filled into all slots marked "impact range" in the contingency plan measures text; specific "diffusion trend directions" (such as "northeast direction") are filled into all slots marked "diffusion direction". Next, the logical relationships between the filled contingency plan elements are analyzed: for example, the "road closure" measure implemented by the "traffic control department" depends on the "delineation of core control area" decision issued by the "emergency management department". Based on such subordinate and collaborative relationships, an element connection diagram is generated, where nodes are contingency plan elements and directed edges represent execution order dependencies. Based on this diagram, the narrative order of the early warning instruction is determined as follows: first, explain the risk positioning (integrating target area information and impact range), then conduct a dynamic impact assessment (combining the degree of impact and diffusion trend), and finally list standardized action guidelines according to departmental responsibilities. Following this order, the filled-in contingency plan element text blocks are spliced ​​together, and preset coherent guiding statements (such as "Based on the above risk assessment, all relevant departments are requested to immediately implement the following measures:") are automatically inserted between the blocks, ultimately forming a logically coherent and clearly defined early warning instruction text.

[0087] Finally, the targeted delivery of warning instructions is completed according to preset distribution rules. The distribution rules are a predefined list that specifies the detailed versions of the instructions that different response departments should receive, along with the corresponding communication protocols and addresses (such as internal office system interfaces and SMS gateways). After generating the main text of the warning instruction, the parts directly related to the responsibilities of specific departments are extracted or trimmed according to the rules. For example, a sub-text containing only traffic management and control measures is generated for the traffic control department. Subsequently, the corresponding instruction texts are sent to all units listed in the response department list through various protocol channels, thus completing the entire process from risk quantification assessment to emergency instruction triggering.

[0088] The working principle of this invention is as follows: First, real-time environmental monitoring data sequences and dynamic trajectory simulation data sets of pollutant release in the target area are collected simultaneously. Then, anomaly detection and correction are performed on the monitoring data. By constructing a spatial distribution surface of concentration, analyzing spatiotemporal gradient changes, and extracting the most significant stable movement direction, diffusion characteristics characterizing the intensity and direction of pollutant migration are generated. Simultaneously, based on the simulation data and real-time meteorological parameters, a reverse random walk algorithm is used to generate multiple candidate paths tracing back from the monitoring points. The path likelihood values ​​are calculated using historical cases, and spatial kernel density smoothing is used to generate diffusion path characteristics characterizing the probability of potential pollution sources. Next, the two types of features are normalized and aligned. Based on the temporal stability of diffusion features and the spatial coverage of paths, a two-level confidence calibration is performed. Then, through the set nonlinear superposition rules, grid-by-grid fusion is carried out to generate risk contribution values ​​and summarize and calculate the quantitative assessment value of regional risk status. Finally, the corresponding early warning level is activated according to the continuous interval in which the assessment value is located. When entering a high-risk interval, the elements in the contingency plan library are automatically matched to assemble and generate an early warning instruction containing specific risk location, impact assessment and action guidelines, and push it to relevant departments, thereby realizing the whole process from multi-source data fusion perception to intelligent judgment and accurate early warning response.

[0089] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An environmental risk early warning service system based on multi-source data fusion, characterized in that, include: The multi-source data synchronous acquisition module is used to synchronously acquire real-time environmental monitoring data sequences of the target area and dynamic trajectory simulation data sets of pollutant release within the target area; The anomaly identification and feature extraction module performs anomaly detection and multi-source data consistency correction on real-time environmental monitoring data sequences based on dynamic thresholds and correlations with adjacent monitoring points. Subsequently, it extracts change patterns that characterize the intensity and direction of pollutant migration as diffusion features through spatiotemporal coupling analysis. The reverse probability extrapolation module, based on a dynamic trajectory simulation dataset and incorporating real-time meteorological field parameters, performs multi-path probability reverse extrapolation from each monitoring location to the potential release area, calculates the likelihood estimate for each backtracking path, and generates diffusion path characteristics represented by spatial probability density. The multi-feature fusion and risk assessment module normalizes and aligns diffusion features and diffusion path features in the spatiotemporal dimension. It constructs an adaptive weight allocation strategy based on the real-time reliability of monitoring data and the spatial coverage of the inferred path, performs multi-level confidence calibration and coupling calculation on diffusion features and diffusion path features, and outputs a quantitative assessment value of the regional risk situation. The dynamic early warning response and solution generation module activates the corresponding early warning response level based on the continuous numerical range of the quantitative assessment value. When the quantitative assessment value enters the preset high-risk range, it immediately triggers the generation of early warning instructions and the release of dynamic risk avoidance solutions.

2. The environmental risk early warning service system based on multi-source data fusion according to claim 1, characterized in that, The extraction of patterns that characterize the intensity and direction of pollutant migration as diffusion features specifically includes: Based on the real-time environmental monitoring data sequence after anomaly detection and consistency correction, spatial interpolation calculation of concentration distribution is performed in the spatial grid between each adjacent monitoring point to generate a continuous concentration spatial distribution surface. The concentration spatial distribution surface is subjected to time series difference calculation to obtain the concentration gradient change of each grid cell at each adjacent time point, and the direction of most significant gradient change is extracted. The stable movement direction of each grid cell within a continuous time window is correlated and integrated with the average concentration gradient intensity in the movement direction. The output is a change pattern characterizing the dominant direction and relative intensity of pollutant migration, i.e., diffusion characteristics.

3. The environmental risk early warning service system based on multi-source data fusion according to claim 2, characterized in that, The extraction of the most significant gradient change direction specifically includes: For each grid cell, calculate its concentration gradient vector at adjacent time points. The concentration gradient vector consists of the concentration change rate and the concentration difference between spatially adjacent grid cells. The gradient vector is decomposed into eight predefined main directions, and the vector projection length in each direction is calculated to form a set of directional projection intensities. From the set of azimuth projection intensities, select azimuths with positive projection intensities that exceed the dynamic threshold, and determine the azimuth with the highest projection intensity as the most significant movement direction of the grid cell at adjacent time points.

4. The environmental risk early warning service system based on multi-source data fusion according to claim 1, characterized in that, The generation of diffusion path features represented by spatial probability density specifically includes: Based on the fused dynamic trajectory simulation data set and real-time meteorological field parameters, a preset number of reverse random walk algorithms are launched at each monitoring location to generate multiple candidate paths that trace back from the monitoring point to each grid cell in the upstream space. For each candidate path, a co-occurrence frequency weight is assigned to each grid cell traversed by the path, taking into account the frequency of path occurrence under similar meteorological conditions in historical pollutant release events. The product of all weights on the path is then used as the initial likelihood estimate for the corresponding path. The initial likelihood estimates of all candidate paths are mapped onto a unified spatial grid. Spatial kernel density smoothing and superposition are performed on the initial likelihood estimates of all traversed paths within each grid cell to generate a spatial probability density in grid units, i.e., diffusion path features.

5. The environmental risk early warning service system based on multi-source data fusion according to claim 4, characterized in that, The generation of multiple candidate paths tracing back from the monitoring points to various grid cells in the upstream space specifically includes: For each monitoring location, the initial reverse movement direction is determined based on the wind speed and direction in the real-time meteorological field parameters, and the range of random disturbance of direction and movement step size for each step are set according to the historical statistical distribution of wind direction and turbulence intensity. Starting from the monitoring position, based on the initial reverse movement azimuth, the range of random azimuth disturbance and the movement step size, multiple independent step-by-step backtracking processes are executed. Each process is continuously stepped until the preset maximum number of backtracking steps is reached or the calculation area boundary is left, forming a candidate path. Record the grid cell sequence traversed by each candidate path, and summarize the grid cell sequences of all candidate paths generated at all monitoring locations as input for subsequent calculation of the initial likelihood estimate of the path.

6. The environmental risk early warning service system based on multi-source data fusion according to claim 1, characterized in that, The process of performing multi-level confidence calibration and coupling calculations on diffusion characteristics and diffusion path characteristics to output a quantitative assessment value of the regional risk situation specifically includes: The normalized and aligned diffusion features and diffusion path features are input into the first-layer calibration process, and the spatial probability density of the diffusion path features is locally enhanced or suppressed based on the temporal stability of the diffusion features. The features after the first layer of calibration are input into the second layer of calibration process. Based on the spatial coverage of the inference path, the values ​​of the insufficiently covered areas in the features are completed by confidence diffusion based on spatial adjacency. After completing the second-level calibration, the two types of features are coupled grid by grid. The migration intensity represented by the diffusion feature on each grid is nonlinearly superimposed with the spatial probability represented by the diffusion path feature to generate the risk contribution value of the grid. The risk contribution values ​​of all grids within the calculation area are aggregated and, after global normalization, output as a quantitative assessment value of the regional risk situation.

7. The environmental risk early warning service system based on multi-source data fusion according to claim 6, characterized in that, The step of nonlinearly superimposing the migration intensity represented by the diffusion features on each grid with the spatial probability represented by the diffusion path features to generate the grid's risk contribution value specifically includes: Based on historical case data statistics, the contribution threshold between migration intensity and spatial probability is determined, and independent mapping and transformation rules are defined for the numerical intervals on both sides of the threshold. The migration intensity value of each grid is converted into the first contribution component according to the mapping transformation rule, and the spatial probability value of the same grid is converted into the second contribution component. Based on the contribution threshold, a preset nonlinear superposition rule is applied to the first contribution component and the second contribution component. The nonlinear superposition rule uses a weighted geometric average method when either component is below the threshold, and uses a power-weighted method when both are above the threshold. The superimposed calculation results are used as the risk contribution value of the corresponding grid.

8. The environmental risk early warning service system based on multi-source data fusion according to claim 1, characterized in that, The generation of the real-time trigger warning command specifically includes: Analyze the quantitative assessment value and the continuous numerical range in which the quantitative assessment value is located, and extract the key quantitative parameters of the current risk situation. The key quantitative parameters include the estimated scope of impact, the level of impact, and the direction of the diffusion trend. Based on the extracted key quantitative parameters, the matching contingency plan elements are retrieved and called from the multi-dimensional contingency plan library. The contingency plan elements include at least target area information, a list of responding departments, and preliminary control measures. Key quantitative parameters and the elements of the invoked contingency plan are structurally assembled to generate early warning instructions that include specific risk identification, dynamic impact assessment and standardized action guidelines; Based on the pre-defined distribution rules according to the responsibilities of different response departments, the generated early warning instructions are pushed out in a targeted manner to complete the immediate triggering of the early warning process.

9. The environmental risk early warning service system based on multi-source data fusion according to claim 8, characterized in that, The structured assembly of key quantitative parameters and invoked pre-plan elements specifically includes: Establish a dynamic mapping relationship between key quantitative parameters and contingency plan elements, and fill parameter values ​​into preset logical slots of contingency plan elements according to parameter type; Based on the pre-plan elements filled with parameter values, an element connection diagram is generated according to the subordinate and collaborative relationships between the pre-plan elements. The organizational order and hierarchical structure of each element in the early warning instruction are determined according to the connection diagram. Following the organizational order and hierarchical structure, the filled-in contingency plan elements are serialized and spliced ​​together, and pre-set guiding explanatory text is inserted between adjacent elements to form a complete and coherent early warning instruction text.

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