An intelligent alarm system for monitoring plant networks and rivers in industrial parks for sudden pollution accidents

Abstract

The present invention discloses an intelligent alarm system for monitoring plant networks and rivers in the event of sudden pollution accidents in industrial parks. The system relates to the field of river monitoring technology and includes a topology module, an equipment monitoring module, a water quality monitoring module, and a pollution analysis module. The topology module obtains a GIS map based on GIS technology and divides the watershed into different levels of grid units step by step to form a topology map; the equipment detection module is used to monitor the relevant equipment of each enterprise in the industrial park to obtain the water condition data of the enterprise; and multiple water quality monitoring modules are respectively arranged between the rivers between each enterprise to obtain water quality data of the corresponding river area. The present invention can accurately track the actual diffusion path of pollutants by dynamically constructing flow vectors and combining them with river topology correction. In the case of multiple enterprises coexisting and complex pollution sources, the system can use the concentration change rate and flow vector to quickly identify "suspected pollution sources" and quickly provide reliable tracing basis.

Description

Technical Field

[0001] The present invention relates to the technical field of river monitoring, and in particular to an intelligent alarm system for monitoring river networks of industrial parks and sudden pollution accidents. Background Art

[0002] With the rapid development of industrialization, sudden pollution incidents in industrial parks are increasing. These incidents typically manifest as a rapid increase in pollutant concentrations within a short period of time, which then spreads along river channels or pipe networks, severely impacting the surrounding environment and ecosystem. Furthermore, the diffusion of pollutants in river channels is influenced by multiple factors, including flow direction, velocity, topography, and river structure. The diffusion path is often not linear, but rather nonlinear along the river channel or its branches.

[0003] After searching, a Chinese patent (publication number: CN114814135B) discloses a river water pollution source tracing method and system based on multi-element monitoring. The patent includes: real-time collection of river water quality information and construction of suspected water-polluted river sections based on the river water quality information; collection and abnormality judgment of water quality data of suspected water-polluted river sections through distributed monitoring instruments to obtain abnormal data alarm river sections; point-by-point tracking and processing of abnormal data alarm river sections to determine water pollution areas; based on the water pollution areas, iterative inversion calculations are performed in combination with differential evolution algorithms and river models to obtain water pollution river section information.

[0004] In the existing technology, current pollution monitoring systems are mainly based on fixed-point monitoring, which cannot capture the diffusion path of pollutants in real time and dynamically. Especially in the case of complex rivers and the coexistence of multiple enterprise sewage outlets, it is easy to make it difficult to trace the source of pollution and delay governance. Therefore, the present invention proposes an intelligent alarm system for factory network river monitoring for sudden pollution accidents in industrial parks. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent alarm system for monitoring plant networks and rivers in industrial parks for sudden pollution accidents, so as to solve the problems mentioned in the above background technology.

[0006] The present invention can be implemented through the following technical solutions: an intelligent alarm system for monitoring plant networks and rivers in industrial parks for sudden pollution accidents, including a topology module, an equipment monitoring module, a water quality monitoring module, and a pollution analysis module;

[0007] The topology module obtains a GIS map based on GIS technology and divides the watershed into different levels of grid cells step by step to form a topological map. The topological map includes the watershed level, regional level, block level, unit level and sub-catchment level, and establishes a topological association structure between elements such as enterprise sewage discharge, pipe network, outlet, river network and water system;

[0008] The equipment detection module is used to test the relevant equipment of each enterprise in the industrial park to obtain the enterprise's water condition data. The relevant equipment includes the enterprise's internal rainwater collection pool, accident emergency pool, internal rainwater transmission pipe, internal sewage transmission pipe and enterprise river outlet;

[0009] Among them, the company's internal rainwater collection pool and accident emergency pool are monitored by installing liquid level sensors;

[0010] The internal transmission pipes for rainwater and sewage are monitored by installing conductivity sensors;

[0011] The company's outlet into the river is monitored by installing turbidity sensors;

[0012] Multiple water quality monitoring modules are respectively set up between the rivers between various enterprises, and monitor the water quality of the river through the chemical reduction potential sensor and the dissolved oxygen sensor to obtain water quality data of the corresponding river area;

[0013] The pollution analysis module establishes a pollution analysis model based on big data, and uses the pollution analysis model to judge the water condition data and water quality data of each enterprise and identify abnormal values. At the same time, the pollution analysis module presets alarm thresholds for the water condition data and water quality data of each enterprise, and triggers an alarm when the abnormal value exceeds the corresponding alarm threshold;

[0014] In addition, the pollution analysis module will locate the water condition data or water quality data of the enterprise that triggers the alarm through the topology module to identify the source of pollution.

[0015] A further technical improvement of the present invention is that the pollution analysis module calculates the water quality data difference between adjacent water quality data, and forms a vector map of water pollution based on the water quality data difference. The vectorized approach intuitively presents the flow direction and concentration changes of pollutants, which can help accurately locate pollution sources and pollution paths. The specific steps include:

[0016] S1. The pollution analysis module sets the sampling frequency of each water quality monitoring module and collects the water quality data of each water quality monitoring module to form a time series of each water quality monitoring module;

[0017] S2. Clean the water quality data of each time series, remove outliers or noise data, and normalize the cleaned water quality data to unify different indicators to a unified scale;

[0018] S3. Pair all adjacent water quality monitoring modules according to the river direction and spatial distance of the corresponding river channel;

[0019] S4, the pollution analysis module generates a direction vector according to the positions of two adjacent water quality monitoring modules, and maps the water quality data difference matrix to the direction vector to form a weighted vector;

[0020] S5. The pollution analysis module plots all weighted vectors on the GIS map to show the pollution flow direction and pollution intensity.

[0021] A further technical improvement of the present invention is that: in step S5, the pollution analysis module indicates the flow direction and concentration change of pollutants by using color coding and arrows;

[0022] In addition, the pollution analysis module obtains the pollutant diffusion trend through time series based on the flow direction and concentration changes of pollutants.

[0023] A further technical improvement of the present invention is that: the pollution analysis module tracks the pollution flow direction in the vector diagram along the arrow direction to identify the diffusion path of the corresponding pollutant;

[0024] If the pollution concentration in the vector diagram near the outlet of an enterprise into the river increases, and the pollution concentration difference between its upstream and downstream exceeds the preset pollution threshold, the corresponding enterprise will be marked as a "suspected pollution source".

[0025] A further technical improvement of the present invention is that the pollution analysis module corrects the direction vector based on the topological map, and the correction method includes the following steps:

[0026] Q1. Find the location of the river where the corresponding water quality monitoring module is located in the topology map;

[0027] Q2. Adjust the direction of the direction vector according to the river channel relationship in the topological map to make it consistent with the actual flow direction of the river channel;

[0028] Q3. If the monitoring module spans multiple river sections, the direction will be allocated based on the flow ratio of each river branch;

[0029] If pollutants spread to multiple branches of the river, the pollution intensity will be redistributed according to the flow proportion of each river branch.

[0030] A further technical improvement of the present invention is that the pollution analysis module makes corrections based on the direction vector, combining the actual flow direction of the river and the diffusion trend of pollutants to obtain a flow direction vector that is more consistent with the actual flow path of pollutants;

[0031] In addition, the pollution analysis module adds a correction mechanism when drawing flow vectors to ensure that the starting and ending points of each flow vector match the nodes or edges on the river path;

[0032] If the start and end points of the flow direction vector are outside the river channel, they are projected onto the nearest river path.

[0033] A further technical improvement of the present invention is that: the pollution analysis module presets a tolerance range;

[0034] When the pollution analysis module detects that the deviation between the flow direction vector and the river flow direction is within the tolerance range, the correction mechanism is automatically triggered.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] By dynamically constructing flow vectors and combining them with river topology correction, the present invention can accurately track the actual diffusion path of pollutants. In addition, when multiple enterprises coexist and the pollution sources are complex, the system can use the concentration change rate and flow vectors to quickly identify "suspected pollution sources" and quickly provide reliable tracing evidence.

[0037] At the same time, the present invention combines time series analysis and diffusion models. The system can update pollutant diffusion trends in real time and predict areas that may be affected in the future. It also uses flow vector and concentration change rate analysis to identify high-risk areas and key nodes, providing data support for optimizing the layout of monitoring points.

[0038] On the other hand, the present invention combines GIS maps to dynamically display the flow direction and concentration changes of pollutants, making the pollution diffusion path more intuitive and helping users quickly understand the pollution situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0040] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0041] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0042] Example 1

[0043] See also Figure 1 The present invention provides an intelligent alarm system for monitoring plant networks and rivers in industrial parks for sudden pollution accidents, including a topology module, an equipment monitoring module, a water quality monitoring module, and a pollution analysis module;

[0044] The topology module obtains GIS maps based on GIS technology and divides the watershed into different levels of grid units step by step to form a topological map. The topological map includes watershed level, regional level, block level, unit level and sub-catchment level, and establishes the topological relationship structure between elements such as enterprise sewage discharge, pipe network, outlet, river network and water system.

[0045] The equipment detection module is used to test the relevant equipment of each enterprise in the industrial park to obtain the enterprise's water condition data. The relevant equipment includes the enterprise's internal rainwater collection pool, accident emergency pool, internal rainwater transmission pipe, internal sewage transmission pipe and enterprise river outlet;

[0046] Among them, the company's internal rainwater collection pool and accident emergency pool are monitored by installing liquid level sensors;

[0047] The internal transmission pipes for rainwater and sewage are monitored by installing conductivity sensors;

[0048] The company's outlet into the river is monitored by installing turbidity sensors;

[0049] Multiple water quality monitoring modules are installed in the rivers between various enterprises, and the river water quality is monitored through chemical reduction potential sensors and dissolved oxygen sensors to obtain water quality data for the corresponding river area;

[0050] The pollution analysis module establishes a pollution analysis model based on big data, and uses the pollution analysis model to judge the water condition data and water quality data of each enterprise and identify abnormal values. At the same time, the pollution analysis module presets the alarm thresholds for the water condition data and water quality data of each enterprise, and triggers an alarm when the abnormal value exceeds the corresponding alarm threshold;

[0051] In addition, the pollution analysis module will locate the water condition data or water quality data of the enterprise that triggers the alarm through the topology module to identify the source of pollution.

[0052] The pollution analysis module calculates the water quality data difference between adjacent water quality data and forms a vector map of water quality pollution based on the water quality data difference. It uses vectorization to intuitively present the flow direction and concentration changes of pollutants, which can help accurately locate the pollution source and pollution path. The specific steps include:

[0053] S1. The pollution analysis module sets the sampling frequency of each water quality monitoring module and collects the water quality data of each water quality monitoring module to form a time series of each water quality monitoring module;

[0054] S2. Clean the water quality data of each time series, remove outliers or noise data, and normalize the cleaned water quality data to unify different indicators to a unified scale;

[0055] S3, according to the river direction and spatial distance of the corresponding river, all adjacent water quality monitoring modules are paired. In this embodiment, the upstream water quality monitoring module is marked as M i , the downstream water quality monitoring module is marked as M j ;

[0056] The water quality data differences between adjacent water quality monitoring modules are calculated respectively. The indicators in the water quality data include dissolved oxygen (DO), oxidation-reduction potential (ORP), turbidity, liquid level (L) and electrical conductivity (EC);

[0057] Therefore, the pollution analysis module calculates the difference in water quality indicators between adjacent water quality monitoring modules including:

[0058] Dissolved oxygen index difference: ΔDO i.j =DO j -DO i ;

[0059] Oxidation-reduction potential difference: ΔORP i.j =ORP j -ORP i ;

[0060] Turbidity index difference: ΔTurbidity i.j =Turbidity j -Turbidity i ;

[0061] Liquid level index difference: ΔL i.j =L j -L i ;

[0062] Conductivity index difference: ΔEC i.j =EC j -EC i ;

[0063] And the water quality monitoring module generates the corresponding water quality data difference matrix based on the calculated water quality data difference:

[0064] ΔM i.j =[ΔDO i,j ,ΔORP i,j ,ΔTurbidity i,j ,ΔL i,j ,ΔEC i,j ];

[0065] S4, the pollution analysis module is based on the position of two adjacent water quality monitoring modules (X i ,Y i ) and (X j ,Y j), generating a direction vector:

[0066] And the water quality data difference matrix ΔM i.j Mapping to direction vector Form a weighted vector:

[0067]

[0068] S5, the pollution analysis module will transform all weighted vectors Plotted on GIS map to show pollution flow and intensity;

[0069] The pollution analysis module uses color coding and arrows to indicate the flow direction and concentration changes of pollutants;

[0070] In this embodiment, the magnitude of contamination changes is indicated by setting different arrow lengths, and the arrows indicate the flow direction;

[0071] And by setting red to indicate an increase in pollution concentration, and blue to indicate a decrease in pollution concentration;

[0072] The pollution analysis module obtains the pollutant diffusion trend through time series based on the flow direction and concentration changes of pollutants. The specific steps are as follows:

[0073] a1. Use the pollutant concentration time series data of the corresponding water quality monitoring module to calculate the corresponding change rate and trend. The formula is:

[0074] Where ΔC is the concentration change rate; C(t) and C(t+Δt) are the concentrations of pollutants at the current moment and the next moment respectively;

[0075] a2. Combine the time series of pollutants with the direction vector Combined, the diffusion trend of pollutants is dynamically displayed, and the formula is:

[0076] Where, To correspond to the diffusion trend of pollutants, is the direction vector of the corresponding pollutant, and ΔC is the concentration change rate of the corresponding pollutant.

[0077] a3. By introducing ARIMA and LSTM models, the dynamic diffusion trend of pollutants is calculated;

[0078] And the pollution analysis module tracks the pollution flow in the direction of the arrow according to the pollution flow in the vector diagram and identifies the diffusion path of the corresponding pollutant;

[0079] If the pollution concentration in the vector diagram near the outlet of an enterprise into the river increases, and the pollution concentration difference between its upstream and downstream exceeds the preset pollution threshold, the corresponding enterprise will be marked as a "suspected pollution source".

[0080] Example 2

[0081] An intelligent alarm system for monitoring plant networks and rivers in industrial parks for sudden pollution accidents, including a topology module, an equipment monitoring module, a water quality monitoring module, and a pollution analysis module;

[0082] The topology module obtains GIS maps based on GIS technology and divides the watershed into different levels of grid units step by step to form a topological map. The topological map includes watershed level, regional level, block level, unit level and sub-catchment level, and establishes the topological relationship structure between elements such as enterprise sewage discharge, pipe network, outlet, river network and water system.

[0083] The equipment detection module is used to test the relevant equipment of each enterprise in the industrial park to obtain the enterprise's water condition data. The relevant equipment includes the enterprise's internal rainwater collection pool, accident emergency pool, internal rainwater transmission pipe, internal sewage transmission pipe and enterprise river outlet;

[0084] Among them, the company's internal rainwater collection pool and accident emergency pool are monitored by installing liquid level sensors;

[0085] The internal transmission pipes for rainwater and sewage are monitored by installing conductivity sensors;

[0086] The company's outlet into the river is monitored by installing turbidity sensors;

[0087] Multiple water quality monitoring modules are installed in the rivers between various enterprises, and the river water quality is monitored through chemical reduction potential sensors and dissolved oxygen sensors to obtain water quality data for the corresponding river area;

[0088] The pollution analysis module establishes a pollution analysis model based on big data, and uses the pollution analysis model to judge the water condition data and water quality data of each enterprise and identify abnormal values. At the same time, the pollution analysis module presets the alarm thresholds for the water condition data and water quality data of each enterprise, and triggers an alarm when the abnormal value exceeds the corresponding alarm threshold;

[0089] In addition, the pollution analysis module will locate the water condition data or water quality data of the enterprise that triggers the alarm through the topology module to identify the source of pollution.

[0090] The pollution analysis module calculates the water quality data difference between adjacent water quality data and forms a vector map of water quality pollution based on the water quality data difference. It uses vectorization to intuitively present the flow direction and concentration changes of pollutants, which can help accurately locate the pollution source and pollution path. The specific steps include:

[0091] S1. The pollution analysis module sets the sampling frequency of each water quality monitoring module and collects the water quality data of each water quality monitoring module to form a time series of each water quality monitoring module;

[0092] S2. Clean the water quality data of each time series, remove outliers or noise data, and normalize the cleaned water quality data to unify different indicators to a unified scale;

[0093] S3, according to the river direction and spatial distance of the corresponding river, all adjacent water quality monitoring modules are paired. In this embodiment, the upstream water quality monitoring module is marked as M i , the downstream water quality monitoring module is marked as M j ;

[0094] And the water quality monitoring module is based on the calculated water quality data difference ΔM i.j , generate the corresponding water quality data difference matrix;

[0095] S4, the pollution analysis module is based on the position of two adjacent water quality monitoring modules (X i ,Y i ) and (X j ,Y j ), generating a direction vector:

[0096] And the water quality data difference matrix ΔM i.j Mapping to direction vector Form a weighted vector:

[0097]

[0098] Compared with Example 1, the pollution analysis module is based on the topological map and the direction vector The calibration method comprises the following steps:

[0099] Q1. Find the location of the river where the corresponding water quality monitoring module is located in the topology map;

[0100] Q2. Adjust the direction vector according to the river relationship in the topological map The vector direction is consistent with the actual flow direction of the river;

[0101] Q3. If the monitoring module spans multiple river sections, the direction will be allocated based on the flow ratio of each river branch;

[0102] If the pollutants spread to multiple branches of the river, the pollution intensity will be redistributed according to the flow proportion of each branch of the river;

[0103] S5, the pollution analysis module will transform all weighted vectors Plotted on GIS map to show pollution flow and intensity;

[0104] The pollution analysis module uses color coding and arrows to indicate the flow direction and concentration changes of pollutants;

[0105] In this embodiment, the magnitude of contamination changes is indicated by setting different arrow lengths, and the arrows indicate the flow direction;

[0106] And by setting red to indicate an increase in pollution concentration, and blue to indicate a decrease in pollution concentration;

[0107] The pollution analysis module obtains the pollutant diffusion trend through time series based on the flow direction and concentration changes of pollutants. The specific steps are as follows:

[0108] a1. Use the pollutant concentration time series data of the corresponding water quality monitoring module to calculate the corresponding change rate and trend. The formula is:

[0109] Where ΔC is the concentration change rate; C(t) and C(t+Δt) are the concentrations of pollutants at the current moment and the next moment respectively;

[0110] a2. Combine the time series of pollutants with the direction vector Combined, the diffusion trend of pollutants is dynamically displayed, and the formula is:

[0111] Where, To correspond to the diffusion trend of pollutants, is the direction vector of the corresponding pollutant, and ΔC is the concentration change rate of the corresponding pollutant.

[0112] a3. By introducing ARIMA and LSTM models, the dynamic diffusion trend of pollutants is calculated;

[0113] And the pollution analysis module tracks the pollution flow in the direction of the arrow according to the pollution flow in the vector diagram and identifies the diffusion path of the corresponding pollutant;

[0114] The pollution analysis module is in the direction vector On this basis, the actual flow direction of the river and the diffusion trend of pollutants are combined to make corrections and obtain the flow direction vector, which is more consistent with the actual flow path of pollutants.

[0115] In this embodiment, flow monitoring units are arranged at the confluence points of the river branches to monitor the water flow of each river branch and Get the traffic proportion of each branch;

[0116] Where w iis the flow rate proportion of the i-th branch, Q i is the water flow of the i-th branch, ∑Q i is the total water flow of all branches;

[0117] The pollution analysis module obtains the pollutant concentration and flow direction vector of the corresponding confluence point as the initial intensity and direction of pollutant distribution in the branch. At the same time, the pollution analysis module determines the spatial geometric information of the river branch, including length, direction, and slope, which is used to subsequently calculate the direction vector of the pollutant diffusion path;

[0118] The pollution analysis module then generates branch flow direction vectors based on the upstream flow direction vector of the river confluence point and the geometric direction of each branch at the bifurcation point. It also weights the direction vectors according to the flow proportion of each branch to obtain the final branch flow direction vector.

[0119] The pollution analysis module is based on the flow ratio of each branch w i Assign pollution concentration to obtain the initial pollution concentration of each branch;

[0120] And in the process of branch pollutant diffusion, combined with the branch length L i , water flow velocity V i And the pollutant diffusion coefficient D, dynamically adjust the pollution concentration, the formula is:

[0121] Where C b,i (t) is the pollution concentration of the ith tributary at time t, C b,i is the initial pollution concentration of the ith tributary, e -k·t is the natural attenuation factor of the pollutant over time, is the supplementary concentration term in the pollutant diffusion process, indicating the contribution of the diffusion effect in the river branch. The shorter the branch and the larger the diffusion coefficient, the more significant the supplementary concentration of the pollutant diffusion.

[0122] Finally, the pollution analysis module combines the direction vector and the pollution concentration to generate the pollution diffusion vector of the branch, and dynamically updates the diffusion vector according to the diffusion of pollutants in each branch at each time t;

[0123] In addition, the pollution analysis module adds a correction mechanism when drawing flow vectors to ensure that the starting and ending points of each flow vector match the nodes or edges on the river path;

[0124] If the starting and ending points of the flow direction vector are outside the river channel, they are projected onto the nearest river path;

[0125] The pollution analysis module has a preset tolerance range;

[0126] When the pollution analysis module detects that the deviation between the flow direction vector and the river flow direction is within the tolerance range, the correction mechanism is automatically triggered.

[0127] If the pollution concentration in the vector diagram near the outlet of an enterprise into the river increases, and the pollution concentration difference between its upstream and downstream exceeds the preset pollution threshold, the corresponding enterprise will be marked as a "suspected pollution source".

[0128] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An intelligent alarm system for monitoring plant networks and rivers in industrial parks for sudden pollution accidents, including a topology module, an equipment monitoring module, a water quality monitoring module, and a pollution analysis module, characterized by: The topology module obtains a GIS map based on GIS technology and divides the watershed into different levels of grid units step by step to form a topology map; The equipment detection module is used to monitor the relevant equipment of each enterprise in the industrial park to obtain the water condition data of the enterprise; A plurality of water quality monitoring modules are respectively arranged between the rivers between the enterprises to obtain water quality data of the corresponding river areas; The pollution analysis module establishes a pollution analysis model based on big data, and uses the pollution analysis model to judge the water condition data and water quality data of each enterprise and identify abnormal values. At the same time, the pollution analysis module presets alarm thresholds for the water condition data and water quality data of each enterprise, and triggers an alarm when the abnormal value exceeds the corresponding alarm threshold; The pollution analysis module will locate the water condition data or water quality data of the enterprise that triggered the alarm through the topology module to identify the pollution source; The pollution analysis module calculates the water quality data difference between adjacent water quality data, and forms a vector diagram of water quality pollution according to the water quality data difference, including: S1. The pollution analysis module sets the sampling frequency of each water quality monitoring module and collects the water quality data of each water quality monitoring module to form a time series of each water quality monitoring module; S2. Clean the water quality data of each time series, remove outliers or noise data, and normalize the cleaned water quality data to unify different indicators to a unified scale; S3. Pair all adjacent water quality monitoring modules according to the river direction and spatial distance of the corresponding river channel, and calculate the water quality data difference of the adjacent water quality monitoring modules respectively; And the water quality monitoring module generates the corresponding water quality data difference matrix based on the water quality data difference: S4. The pollution analysis module generates a direction vector based on the positions of two adjacent water quality monitoring modules, and maps the water quality data difference matrix to the direction vector to form a weighted vector: S5, the pollution analysis module will transform all weighted vectors Draw it on a GIS map to show the direction and intensity of pollution.

2. The intelligent alarm system for monitoring sudden pollution accidents in industrial parks according to claim 1 is characterized in that: The topological map includes basin level, regional level, block level, unit level and sub-catchment level, and is used to establish a topological association structure between elements such as enterprise sewage discharge, pipeline network, outlet, river network and water system.

3. The intelligent alarm system for monitoring sudden pollution accidents in industrial parks according to claim 1 is characterized in that: In step S5, the pollution analysis module indicates the flow direction and concentration changes of pollutants by using color coding and arrows; In addition, the pollution analysis module obtains the pollutant diffusion trend through time series based on the flow direction and concentration changes of pollutants.

4. The intelligent alarm system for monitoring plant network and river for sudden pollution accidents in industrial parks according to claim 3 is characterized in that: The pollution analysis module tracks the pollution flow in the direction of the arrow in the vector diagram to identify the diffusion path of the corresponding pollutant; If the pollution concentration in the vector diagram near an enterprise's river outlet increases, and the pollution concentration difference between its upstream and downstream exceeds the preset pollution threshold, the corresponding enterprise will be marked as a "suspected pollution source." 5. The intelligent alarm system for monitoring plant network and river for sudden pollution accidents in industrial parks according to claim 4 is characterized in that: The pollution analysis module corrects the direction vector based on the topological map, and the correction method includes the following steps: Q1. Find the location of the river where the corresponding water quality monitoring module is located in the topology map; Q2. Adjust the direction of the direction vector according to the river channel relationship in the topological map to make it consistent with the actual flow direction of the river channel; Q3. If the monitoring module spans multiple river sections, the direction will be allocated based on the flow ratio of each river branch; If pollutants spread to multiple branches of the river, the pollution intensity will be redistributed according to the flow proportion of each river branch.

6. The intelligent alarm system for monitoring plant networks and rivers in industrial parks for sudden pollution accidents according to claim 3 is characterized in that: The pollution analysis module makes corrections based on the direction vector and combines the actual flow direction of the river and the diffusion trend of pollutants to obtain the flow direction vector; In addition, the pollution analysis module adds a correction mechanism when drawing flow vectors to ensure that the starting and ending points of each flow vector match the nodes or edges on the river path; If the start and end points of the flow direction vector are outside the river channel, they are projected onto the nearest river path.

7. The intelligent alarm system for monitoring plant network and river for sudden pollution accidents in industrial parks according to claim 6 is characterized in that: The pollution analysis module presets a tolerance range; When the pollution analysis module detects that the deviation between the flow direction vector and the river flow direction is within the tolerance range, the correction mechanism is automatically triggered.

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