Rapid traceability method and system for pollutants

Abstract

The invention discloses a pollutant rapid tracing method and system, and belongs to the technical field of pollutant tracing, the pollutant rapid tracing method comprises the following specific steps: 1, reversely simulating a pollution trajectory: based on pollutant concentration data, meteorological data and topographic data of a real-time monitoring point, calculating a pollution trajectory according to the pollutant concentration data, the meteorological data and the topographic data of the real-time monitoring point; a pollutant migration space-time path is reconstructed through a particle filtering algorithm, and a probability thermodynamic diagram is generated by adopting a Gaussian diffusion correction model to lock a potential source region. Through multi-stage coupling analysis including reverse simulation, candidate source identification, evidence chain construction, traceability conclusion and a multi-dimensional evidence chain including a time sequence, space, path and responsibility fusion mechanism, traceability precision and speed are remarkably improved, pollution event response time is shortened from an hour level to a minute level, the false alarm rate is greatly reduced, and the method is suitable for large-scale popularization and application. And rapid locking and accurate positioning of the pollution source are realized.

Description

Technical Field

[0001] This invention belongs to the field of pollutant source tracing technology, specifically relating to a rapid pollutant source tracing method and system. Background Technology

[0002] Large AI models (or simply "large models") refer to a class of artificial intelligence models with a large number of parameters built from artificial neural networks. Large AI models are a relatively new concept that has emerged in the last decade. They are typically pre-trained on massive amounts of data using self-supervised or semi-supervised learning, and then their performance and capabilities are further optimized through fine-tuning based on instructions and human alignment. Large models are characterized by a large number of parameters, large amounts of training data, and large computational resources, and possess the ability to solve general tasks, follow human instructions, and perform complex reasoning.

[0003] Current pollutant source tracing technologies mainly rely on single data sources, making it difficult to effectively integrate the dynamic behavior of pollution sources and information on responsible parties. Existing methods generally suffer from problems such as disconnect between responsibility tracing and actual control, high computational complexity, and slow response speed. This results in inaccurate location and poor timeliness in sudden pollution incidents, failing to meet the urgent need to quickly identify pollution sources and carry out precise remediation.

[0004] Traditional methods have significant shortcomings in integrating physical models with multi-dimensional evidence chains. They lack mechanisms for automatically linking corporate behavior with environmental regulatory data, making it difficult to achieve closed-loop management of pollution sources from identification to responsible parties, thus hindering the efficiency of source tracing and the effectiveness of governance. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method and system for rapid source tracing of pollutants.

[0006] The technical solution adopted to solve the above-mentioned technical problems is: a rapid pollutant source tracing method, including the following specific steps:

[0007] Step 1: Reverse simulation of pollution trajectory: Based on pollutant concentration data, meteorological data and topographic data from real-time monitoring points, the spatiotemporal path of pollutant migration is reconstructed using a particle filter algorithm, and a Gaussian diffusion correction model is used to generate a probability heat map to identify potential source areas;

[0008] Step 2: Candidate pollution source identification: Based on the high-probability source areas output in Step 1, and combined with the enterprise's GIS database and traffic monitoring data, a list of candidate pollution sources that meet the spatiotemporal constraints is selected, including stationary emission sources, mobile sources, and area sources.

[0009] Step 3: Constructing a multi-dimensional chain of evidence: Performing four-dimensional correlation verification on candidate pollution sources;

[0010] Step 4: Source Tracing Conclusion Generation: By fusing the weights of the four-dimensional evidence chain through a Bayesian network, the probability of pollution sources is calculated and a list of dominant pollution sources, contribution rates, and legally responsible entities is output.

[0011] Through the aforementioned technical solution, and via multi-stage coupled analysis—including reverse simulation, candidate source identification, evidence chain construction, source tracing conclusions, and a multi-dimensional evidence chain fusion mechanism encompassing temporal, spatial, path, and responsibility dimensions—the accuracy and speed of source tracing have been significantly improved. This reduces pollution incident response time from hours to minutes, drastically lowers the false alarm rate, and enables rapid and precise location of pollution sources. This invention effectively links pollution source identification with the tracing of responsible parties. By directly connecting to the environmental compliance database and automatically generating a responsibility list, it significantly reduces the cost of ineffective investigations and provides a strong basis for subsequent targeted governance, law enforcement accountability, and pollution prevention and control, thereby significantly improving environmental emergency response capabilities and governance efficiency.

[0012] Furthermore, the four-dimensional association verification includes:

[0013] Time-series correlation verification: Compare the degree of consistency between the enterprise's production logs and the time window of the pollution event;

[0014] Spatial matching verification: Source apportionment is performed using a pollutant characteristic fingerprint database, and heavy metal components and isotope ratios are compared;

[0015] Path validation: Simulation of pollutant diffusion paths based on CFD fluid dynamics models;

[0016] Accountability verification: Environmental compliance records and historical violation data of related enterprises.

[0017] The above technical solutions improve the accuracy and speed of traceability through four-dimensional correlation verification.

[0018] Furthermore, the iterative formula for the particle filter algorithm in step one is:

[0019]

[0020] Where χ represents the candidate location of the pollution source, O represents the observation dataset, and O t Let T be the observed concentration vector at time t, and T be the length of the observation time window. This is the transition probability function based on particle filtering.

[0021] By employing the aforementioned technical solutions and utilizing particle transfer probability functions constrained by meteorological and topographical conditions, the accuracy of inverse trajectory simulation is improved, thus addressing the problem of diffusion path distortion in complex environments.

[0022] Furthermore, the weights of the four-dimensional evidence chain in step three are dynamically optimized through training with historical cases, using the following formula:

[0023]

[0024] Where, ω i For learnable evidence weights, E i This is the confidence evaluation function for the four-dimensional chain of evidence.

[0025] Through the above technical solution, the evidence weight ω i By self-optimizing based on historical cases, the false positive rate is greatly reduced, solving the problem of poor adaptability of static rules.

[0026] Furthermore, the probability calculation formula for the Bayesian network in step four is as follows:

[0027]

[0028] Among them, P(T) s P(L) represents the temporal correlation probability. s P(P) represents the spatial matching probability. s P(R) represents the probability that the path is reasonable. s ) represents the probability of liability association.

[0029] Through the above technical solutions, the probability quantification and fusion of four-dimensional evidence improves the accuracy of identifying responsible parties and solves the traceability bias caused by subjective judgment.

[0030] A system for rapid pollutant source tracing includes a reverse simulation engine, a candidate source identification module, an evidence chain construction module, and a source tracing conclusion generation module. The reverse simulation engine is responsible for performing particle filtering inversion and Gaussian diffusion modeling, outputting a pollutant probability heatmap. The candidate source identification module connects to an enterprise GIS database and a traffic monitoring system to generate a list of candidate sources with spatiotemporal constraints. The evidence chain construction module incorporates four-dimensional verification logic and integrates a pollutant feature fingerprint database, an enterprise compliance database, and a CFD path simulation unit. The source tracing conclusion generation module fuses evidence weights through a Bayesian network to output pollution source determination results and a list of responsible parties.

[0031] The above technical solutions enable the engine-based architecture to support second-level source tracing response, which significantly improves efficiency compared to traditional systems and solves the latency problem caused by computing resource bottlenecks.

[0032] Furthermore, the evidence chain construction module is directly connected to the environmental protection department's regulatory database, automatically retrieves the company's environmental compliance records and historical violation data, and generates a responsibility association matrix. The evidence chain construction module includes a dynamic weight learning unit.

[0033] The above technical solutions enable the system to have continuous evolution capabilities, thus solving the long-term performance degradation caused by model solidification.

[0034] The beneficial effects of this invention are as follows: Through multi-stage coupled analysis—reverse simulation, candidate source identification, evidence chain construction, source tracing conclusions, and a multi-dimensional evidence chain—including temporal, spatial, path, and responsibility fusion mechanisms—this invention significantly improves the accuracy and speed of source tracing, reducing pollution event response time from hours to minutes, drastically lowering the false alarm rate, and achieving rapid identification and precise location of pollution sources. This invention effectively links pollution source identification with the tracing of responsible parties, automatically generating a responsibility list by directly connecting to the environmental compliance database, significantly reducing the cost of ineffective investigations, and providing strong decision-making support for subsequent targeted governance, law enforcement accountability, and pollution prevention and control, thus significantly improving environmental emergency response capabilities and governance efficiency. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the traceability process of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0037] like Figure 1 As shown, this embodiment of a rapid pollutant source tracing method and system includes the following specific steps:

[0038] Step 1: Reverse simulation of pollution trajectory: Based on pollutant concentration data, meteorological data and topographic data from real-time monitoring points, the spatiotemporal path of pollutant migration is reconstructed using a particle filter algorithm, and a Gaussian diffusion correction model is used to generate a probability heat map to identify potential source areas;

[0039] Step 2: Candidate pollution source identification: Based on the high-probability source areas output in Step 1, and combined with the enterprise's GIS database and traffic monitoring data, a list of candidate pollution sources that meet the spatiotemporal constraints is selected, including stationary emission sources, mobile sources, and area sources.

[0040] Step 3: Constructing a multi-dimensional chain of evidence: Performing four-dimensional correlation verification on candidate pollution sources;

[0041] Step Four: Source Tracing Conclusion Generation: By fusing the weights of the four-dimensional evidence chain through a Bayesian network, the probability of pollution sources is calculated, and a list of dominant pollution sources, contribution rates, and legally responsible entities is output. Through multi-stage coupled analysis—reverse simulation, candidate source identification, evidence chain construction, source tracing conclusions, and a multi-dimensional evidence chain fusion mechanism encompassing temporal, spatial, path, and responsibility aspects—the accuracy and speed of source tracing are significantly improved, reducing pollution event response time from hours to minutes, drastically lowering the false alarm rate, and achieving rapid identification and precise location of pollution sources. This invention effectively links pollution source identification with the tracing of responsible entities. By directly connecting to the environmental compliance database and automatically generating a responsibility list, it significantly reduces the cost of ineffective investigations and provides a strong decision-making basis for subsequent targeted governance, law enforcement accountability, and pollution prevention and control, significantly improving environmental emergency response capabilities and governance efficiency.

[0042] The four-dimensional association verification includes:

[0043] Time-series correlation verification: Compare the degree of consistency between the enterprise's production logs and the time window of the pollution event;

[0044] Spatial matching verification: Source apportionment is performed using a pollutant characteristic fingerprint database, and heavy metal components and isotope ratios are compared;

[0045] Path validation: Simulation of pollutant diffusion paths based on CFD fluid dynamics models;

[0046] Responsibility traceability verification: Link the environmental compliance records and historical violation data of related enterprises, and improve the accuracy and speed of traceability through four-dimensional correlation verification.

[0047] The iterative formula for the particle filter algorithm in step one is:

[0048]

[0049] Where χ represents the candidate location of the pollution source, O represents the observation dataset, and O t Let T be the observed concentration vector at time t, and T be the length of the observation time window. The transition probability function is based on particle filtering. By using a particle transition probability function constrained by meteorological and terrain conditions, the accuracy of inverse trajectory simulation is improved, and the problem of diffusion path distortion in complex environments is solved.

[0050] In step three, the weights of the four-dimensional evidence chain are dynamically optimized through training with historical cases. The specific formula is as follows:

[0051]

[0052] Where, ω i For learnable evidence weights, E i Let ω be the confidence evaluation function for the four-dimensional chain of evidence, and let ω be the evidence weight. iBy self-optimizing based on historical cases, the false positive rate is greatly reduced, solving the problem of poor adaptability of static rules.

[0053] The probability calculation formula for the Bayesian network in step four is as follows:

[0054]

[0055] Among them, P(T) s P(L) represents the temporal correlation probability. s P(P) represents the spatial matching probability. s P(R) represents the probability that the path is reasonable. s The probability of liability association and the probability fusion of four-dimensional evidence improve the accuracy of identifying responsible parties and resolve the traceability bias caused by subjective judgment.

[0056] A system for rapid pollutant source tracing includes a reverse simulation engine, a candidate source identification module, an evidence chain construction module, and a source tracing conclusion generation module. The reverse simulation engine is responsible for performing particle filtering inversion and Gaussian diffusion modeling, outputting a pollutant probability heatmap. The candidate source identification module connects to an enterprise GIS database and a traffic monitoring system to generate a list of candidate sources with spatiotemporal constraints. The evidence chain construction module incorporates four-dimensional verification logic and integrates a pollutant feature fingerprint database, an enterprise compliance database, and a CFD path simulation unit. The source tracing conclusion generation module fuses evidence weights through a Bayesian network to output pollution source determination results and a list of responsible parties. This engine-based architecture supports second-level source tracing response, significantly improving efficiency compared to traditional systems and solving the latency problem caused by computational resource bottlenecks.

[0057] The evidence chain construction module is directly connected to the environmental protection department's regulatory database, automatically retrieves the company's environmental compliance records and historical violation data, and generates a responsibility association matrix. The evidence chain construction module includes a dynamic weight learning unit, which enables the system to have continuous evolution capabilities and solves the long-term performance degradation caused by model solidification.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A method for rapid source tracing of pollutants, characterized in that, The specific steps include the following: Step 1: Reverse simulation of pollution trajectory: Based on pollutant concentration data, meteorological data and topographic data from real-time monitoring points, the spatiotemporal path of pollutant migration is reconstructed using a particle filter algorithm, and a Gaussian diffusion correction model is used to generate a probability heat map to identify potential source areas; Step 2: Candidate pollution source identification: Based on the high-probability source areas output in Step 1, and combined with the enterprise's GIS database and traffic monitoring data, a list of candidate pollution sources that meet the spatiotemporal constraints is selected, including stationary emission sources, mobile sources, and area sources. Step 3: Constructing a multi-dimensional chain of evidence: Performing four-dimensional correlation verification on candidate pollution sources; Step 4: Source Tracing Conclusion Generation: By fusing the weights of the four-dimensional evidence chain through a Bayesian network, the probability of pollution sources is calculated and a list of dominant pollution sources, contribution rates, and legally responsible entities is output.

2. The rapid pollutant source tracing method according to claim 1, characterized in that, The four-dimensional association verification includes: Time-series correlation verification: Compare the degree of consistency between the enterprise's production logs and the time window of the pollution event; Spatial matching verification: Source apportionment is performed using a pollutant characteristic fingerprint database, and heavy metal components and isotope ratios are compared; Path validation: Simulation of pollutant diffusion paths based on CFD fluid dynamics models; Accountability verification: Environmental compliance records and historical violation data of related enterprises.

3. The rapid pollutant source tracing method according to claim 2, characterized in that, The iterative formula for the particle filter algorithm in step one is: Where x represents the candidate location of the pollution source, O represents the observation dataset, and O t Let T be the observed concentration vector at time t, and T be the length of the observation time window. This is the transition probability function based on particle filtering.

4. The rapid pollutant source tracing method according to claim 3, characterized in that, In step three, the weights of the four-dimensional evidence chain are dynamically optimized through training with historical cases. The specific formula is as follows: Where, ω i For learnable evidence weights, E i This is the confidence evaluation function for the four-dimensional chain of evidence.

5. The rapid pollutant source tracing method according to claim 4, characterized in that, The probability calculation formula for the Bayesian network in step four is as follows: Among them, P(T) s P(L) represents the temporal correlation probability. s P(P) represents the spatial matching probability. s P(R) represents the probability that the path is reasonable. s ) represents the probability of liability association.

6. The system for a rapid pollutant source tracing method according to claim 5, characterized in that, The system includes a reverse simulation engine, a candidate source identification module, an evidence chain construction module, and a source tracing conclusion generation module. The reverse simulation engine is responsible for performing particle filter inversion and Gaussian diffusion modeling, and outputting a pollutant probability heatmap. The candidate source identification module is responsible for connecting the enterprise GIS database and traffic monitoring system to generate a list of candidate sources with spatiotemporal constraints. The evidence chain construction module is responsible for building a four-dimensional verification logic and integrating a pollutant feature fingerprint database, an enterprise compliance database, and a CFD path simulation unit. The source tracing conclusion generation module is responsible for fusing evidence weights through a Bayesian network to output the pollution source determination result and a list of responsible parties.

7. The system for a rapid pollutant source tracing method according to claim 6, characterized in that, The evidence chain construction module is directly connected to the environmental protection department's regulatory database, automatically retrieves the company's environmental compliance records and historical violation data, and generates a responsibility association matrix. The evidence chain construction module includes a dynamic weight learning unit.

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