A risk supervision method and system based on gas data monitoring

Abstract

The application relates to the field of urban pollution risk monitoring, and discloses a risk supervision method and system based on gas data monitoring, which realizes the gridization supervision purpose of urban pollution gas risks and random pollution source emissions by monitoring pollution gas and simulating atmospheric flow of the environment atmosphere of a supervised city, especially efficient enterprise pollution supervision can be achieved, the range is limited through atmospheric pollution monitoring, so that the uncontrolled pollution discharge points of enterprises are conveniently investigated, and related urban pollution problems can be found and fed back in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of urban pollution risk monitoring, and particularly relates to a risk supervision method and system based on gas data monitoring. BACKGROUND

[0002] For pollution supervision and pollution control of cities, the relevant environmental departments and municipal departments usually jointly supervise and control, supervise and control the environmental protection of related enterprises, and timely handle and trace the pollution control of the pollution of the city.

[0003] However, the artificial investigation method is one-sided and cannot achieve full-time efficient coverage. Usually, pollution is discovered only after a long time, at which time the influence has spread seriously, and there are hidden cases of some enterprises secretly discharging. From the time of secretly discharging to the time of discovering the pollution source, the cumulative amount of pollutants is already large, and serious diffusion behavior may have occurred. Therefore, the method in the prior art has the problem of serious lag in pollution discovery. SUMMARY

[0004] The purpose of the present application is to provide a risk supervision method and system based on gas data monitoring to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A risk supervision method based on gas data monitoring, comprising:

[0007] Obtain the environmental structure distribution of the supervised city to establish a twin supervision model, the environmental structure distribution is used to build a fluid restriction boundary, and the twin supervision model includes environmental atmosphere filling and river water filling;

[0008] Obtain the atmospheric environment data of the supervised city through a meteorological interface, and perform motion fitting on the twin supervision model based on fluid motion simulation to obtain fluid motion data within the supervised city range within a period of time;

[0009] Obtain the gas backhaul data of the distributed sensing node, map based on the location information in the twin supervision model, and perform risk gas type judgment and marking, the sensing node is distributed in a grid shape;

[0010] If the corresponding sensing node is marked, motion fitting of gas diffusion is performed based on the fluid motion data to obtain the risk influence range in the future period of time;

[0011] The risk influence range is verified based on continuously updated gas backtracking data, and the risk influence range is excluded and verified based on unmarked and marked sensing nodes to obtain the actual influence range.

[0012] As a further scheme of the present application, the method further comprises the steps of:

[0013] Meteorological record data at the marked sensing node and several sensing nodes adjacent to the marked sensing node are obtained, fluid motion simulation is performed based on the meteorological record data, and fluid motion record data in the current range is obtained;

[0014] Retroactive simulation of the flow coverage area is performed according to the maximum tangential curve of the fluid flow at the marked node, and continuous retroactive simulation is performed based on the interval duration between the marking time of the marked sensing node and the initial marking time of the risk gas type to obtain the fluid retroactive coverage area;

[0015] When the fluid retroactive coverage area overlaps with unmarked sensing nodes, the fluid retroactive area is segmented based on the fluid retroactive curve connecting the unmarked sensing nodes, and the part of the segmented fluid retroactive area that does not cover the marked sensing node is eliminated to limit the fluid retroactive coverage area;

[0016] When the sensing node is the initial marking of the risk gas type, the interval duration is zero, at which time the interval duration is replaced by a range interval limit, and the range interval limit is determined by the fluid retroactive coverage area and unmarked sensing nodes.

[0017] As a further scheme of the present application, the method further comprises the steps of:

[0018] Water channel distribution data of the monitored city are obtained, and are mapped in the twin monitoring model to perform fluid motion simulation to obtain the water flow state at each water channel, and the water channel distribution data includes water channel position flow direction data, water channel fall data, and unit water flow data;

[0019] If the end region of the fluid retroactive coverage area in reverse order of time overlaps with the region where the water channel is located, additional retroactive simulation of water flow is performed to obtain the water source pollution diffusion region;

[0020] Gas diffusion motion fitting is performed on the associated region based on the water source pollution coverage area to update the fluid retroactive coverage area.

[0021] As a further scheme of the present application, the method further comprises the steps of:

[0022] A city municipal management website is accessed to obtain the underground pipeline distribution of the monitored city, and each discharge point and the corresponding discharge type distributed on the pipeline;

[0023] The underground pipeline is monitored by the distributed sensing node, and the type of the recorded discharge of the corresponding regional underground pipeline is judged, if there is a non-recorded risk gas type, the current part of the underground pipeline is marked.

[0024] As a further scheme of the present application: further comprising the steps of:

[0025] The personnel distribution of the city is obtained and mapped in the twin supervision model, and is superimposed and verified with the actual influence range, and the social influence result is obtained, the social influence result includes the total amount of influence range and the total amount of influence population;

[0026] The risk rating is based on the total amount of influence range and the total amount of influence population, and the risk rating is fed back, the risk rating is also associated with the risk gas type, used to represent the social influence degree of the current pollution.

[0027] The embodiment of the present application aims to provide a risk supervision system based on gas data monitoring, comprising:

[0028] The twin supervision module is used for obtaining the environmental structure distribution of the supervised city to establish a twin supervision model, the environmental structure distribution is used to build a fluid restriction boundary, and the twin supervision model includes environmental atmosphere filling and river water filling;

[0029] The atmospheric fitting module is used for obtaining the atmospheric environment data of the supervised city through the meteorological interface, and performing motion fitting on the twin supervision model based on fluid motion simulation, to obtain the fluid motion data within the supervised city range within a period of time;

[0030] The back mapping module is used for obtaining the gas back data of the distributed sensing node, mapping based on the location information in the twin supervision model, and judging and marking the risk gas type, the sensing node is distributed in a grid shape;

[0031] The risk fitting module is used for if the corresponding sensing node is marked, the motion fitting of the gas diffusion is based on the fluid motion data, to obtain the risk influence range in the future period of time;

[0032] The range limiting module is used for verifying the risk influence range based on the continuously updated gas back data, excluding and verifying the risk influence range based on the unmarked and marked sensing nodes, to limit the actual influence range.

[0033] As a further scheme of the present application: further comprising a backtracking simulation module, specifically comprising:

[0034] A meteorological synchronization unit is configured to acquire meteorological record data at the marked sensor node and a plurality of adjacent sensor nodes, simulate fluid motion based on the meteorological record data, and acquire fluid motion record data in a current range;

[0035] A backtracking fitting unit is configured to simulate a backtracking of a flow coverage area according to a maximum tangential curve of fluid flow at the marked node, simulate a continuous backtracking based on an interval duration between a marked time of the marked sensor node and a first marked time of the risk gas type, and acquire a fluid backtracking coverage area;

[0036] A backtracking limiting unit is configured to split the fluid backtracking area based on a fluid backtracking curve connecting the unmarked sensor nodes when the fluid backtracking coverage area overlaps the unmarked sensor nodes, eliminate a part of the split fluid backtracking area that does not cover the marked sensor nodes, and limit the fluid backtracking coverage area;

[0037] An interval limiting unit is configured to set the interval duration to zero when the sensor node is a first marked sensor node of the risk gas type, replace the interval duration with a range interval limit determined by the fluid backtracking coverage area and the unmarked sensor nodes.

[0038] As a further scheme of the present application, a waterway supervision module is further included, and specifically includes:

[0039] A waterway simulation unit is configured to acquire water channel distribution data of a supervised city, map the data in the twin supervision model to simulate fluid motion, and acquire water flow states at each water channel, wherein the water channel distribution data includes water channel position and flow direction data, water channel fall data, and unit water flow data;

[0040] A water pollution fitting unit is configured to additionally simulate backtracking of water flow if an end region of the fluid backtracking coverage area in a time reverse order overlaps a region where the water channel is located, and acquire a water source pollution diffusion region;

[0041] A carrying diffusion unit is configured to simulate gas diffusion motion of a related region based on the water source pollution coverage area, and update the fluid backtracking coverage area.

[0042] As a further scheme of the present application, a municipal supervision module is further included, and specifically includes:

[0043] A pipeline synchronization unit is configured to access a municipal management website of a city to acquire underground pipeline distribution of the supervised city, and each discharge point and a corresponding discharge type distributed on the pipeline;

[0044] The pipeline supervision unit is used for gas monitoring of the underground pipeline by the distributed sensing node, and judging the on-record discharge type of the underground pipeline in the corresponding area, and if there is a non-on-record risk gas type, marking the current part of the underground pipeline.

[0045] As a further scheme of the present application: further comprising a social assessment module, specifically comprising:

[0046] The distribution mapping unit is used for obtaining the personnel distribution of the managed urban area and mapping in the twin supervision model, and superimposing and verifying with the actual influence range to obtain the social influence result, the social influence result including the total amount of influence range and the total amount of influenced population.

[0047] The risk assessment unit is used for risk rating based on the total amount of influence range and the total amount of influenced population, and feedback of the risk rating, the risk rating being further associated with the risk gas type, for representing the social influence degree of the current pollution.

[0048] Compared with the prior art, the present application has the beneficial effects that: by monitoring the pollution gas of the supervised urban environment atmosphere and simulating the atmospheric flow, the purpose of grid supervision of the urban pollution gas risk and the random pollution source discharge is achieved, especially efficient enterprise pollution discharge supervision can be achieved, the scope is limited by atmospheric pollution monitoring, to facilitate the investigation of uncontrolled enterprise pollution discharge points, and timely discovery and feedback processing of related urban pollution problems. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 It is a flowchart of a risk supervision method based on gas data monitoring.

[0050] Figure 2 It is a flowchart of the traceability step in a risk supervision method based on gas data monitoring.

[0051] Figure 3 It is a composition diagram of a risk supervision system based on gas data monitoring. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0053] The specific implementation mode of the present application is described in detail below in combination with specific examples.

[0054] As Figure 1The risk supervision method based on gas data monitoring provided by one embodiment of the application comprises the following steps:

[0055] S10, acquire the environmental structure distribution of the supervised city to establish a twin supervision model, the environmental structure distribution is used to build a fluid restriction boundary, and the twin supervision model comprises environmental atmosphere filling and river water body filling;

[0056] S20, acquire the atmospheric environment data of the supervised city through a meteorological interface, and perform motion fitting on the twin supervision model based on fluid motion simulation to acquire the fluid motion data within the range of the supervised city within a period of time;

[0057] S30, acquire the gas backhaul data of the distributed sensing nodes, map based on the location information in the twin supervision model, and perform risk gas type judgment and marking, the sensing nodes are distributed in a grid shape;

[0058] S40, if the corresponding sensing node is marked, perform motion fitting of gas diffusion based on the fluid motion data to acquire the risk influence range in the future period of time;

[0059] S50, verify the risk influence range based on the continuously updated gas backhaul data, exclude and verify the risk influence range based on the unmarked and marked sensing nodes to narrow down to acquire the actual influence range.

[0060] In this embodiment, a risk supervision method based on meteorological data monitoring is given. By monitoring the pollution gas in the environment of the supervised city and simulating the atmospheric flow, the purpose of grid supervision of the risk of pollution gas in the city and the emission of random pollution sources is achieved. Especially, efficient enterprise pollution supervision can be achieved. Through the way of atmospheric pollution monitoring, the scope is limited to facilitate the investigation of uncontrolled enterprise pollution points, and the related urban pollution problems are discovered and fed back in time. In the existing actual scene, for the environmental pollution management of the city, there is a perfect management and supervision process. For local enterprises, their pollution needs to be supervised by the relevant environmental protection part, and the relevant department personnel need to conduct regular patrol to ensure that the emission of each enterprise meets the standard and is controlled, so as to avoid the pollution caused by non-compliant emission and random emission to the city and affect the health of personnel. However, the artificial investigation method is one-sided and cannot achieve full-time efficient coverage. Usually, it is not until a long time after the pollution occurs that it is discovered, and the influence has already spread seriously. Specifically, in this embodiment, the scheme adopted to achieve supervision is to perform data twinning on the supervised city, combine the diffusion model of the city and the distributed gas sensing monitoring node to monitor and judge the pollution source and pollution interval. That is, a plurality of sensing nodes are arranged in a grid shape in the city for monitoring the type of gas at the node position, and after the monitoring is completed, the data is uniformly returned to the management. After the return, the server can judge the type of gas at the node according to the data, and judge the risk gas according to the risk list. At the same time, according to the structure type of the supervised city, the city model is proportionally twinned. In the twinned model, the atmospheric flow simulation of the supervised city is performed to obtain the air flow state data at different positions at different time nodes. Then, when the gas data of the sensing node is mapped in the twinned model, the time can be traced back and advanced according to the air flow state data (the air flow state data in the future period of time is obtained through meteorological environment prediction), so as to obtain the diffusion source area of the harmful pollution gas and the area to be diffused and covered, so as to delimit the risk influence range, facilitate the relevant departments to discover and manage in time, and the step S50 functions to limit the range during the simulation of the risk influence range, that is, the maximum diffusion influence boundary is set by the sensing node not reported as a risk.

[0061] As shown in Figure 2 , as another preferred embodiment of the present application, it further includes the steps of:

[0062] S61, obtaining the meteorological record data of the marked sensing node and a plurality of sensing nodes adjacent thereto, performing fluid motion simulation based on the meteorological record data to obtain the fluid motion record data in the current range;

[0063] S62, backtracking simulation of the flow coverage area is performed according to the maximum tangential curve of the fluid flow at the marked node, and continuous backtracking simulation is performed based on the interval duration of the marking time of the marked sensing node and the initial marking time of the risk gas type, so as to obtain a fluid backtracking coverage area;

[0064] S63, when the fluid backtracking coverage area overlaps with the unmarked sensing node, the fluid backtracking area is segmented based on the fluid backtracking curve connecting the unmarked sensing node, and the part not covering the marked sensing node after segmentation is eliminated, so as to limit the fluid backtracking coverage area;

[0065] S64, when the sensing node is the initial marking of the risk gas type, the interval duration is zero, at this time, the interval duration is replaced by the range interval limit, and the range interval limit is determined by the fluid backtracking coverage area and the unmarked sensing node.

[0066] In this embodiment, the step of obtaining the fluid backtracking coverage area by backtracking simulation for determining the pollution area is supplemented. Specifically, when the data of a sensing node marks the risk, it is assumed that the maximum range on both sides of the sensing node along the air flow direction has been polluted (the maximum range refers to the air flow vertical position involved by the adjacent sensing node), at this time, a curve-shaped air flow front (i.e. the maximum tangential curve) can be obtained (borrowing the cold front and warm front expression in meteorology), the time backtracking of the air flow front is performed, the area swept by the air flow front is obtained according to the air flow motion state at different positions of the continuous time node, that is, the backtracking coverage area, and in this process, the backtracking coverage area may overlap with other sensing nodes, at this time, the range of the backtracking coverage area is reduced through whether the overlapped sensing node is risk marked (if not marked, it means that the air flow flowing curve on the other side of the corresponding point of the sensing node does not exist the diffusion situation of the current judged risk gas), through such a way, the position information of a possible pollution emission source is determined, which facilitates the relevant part to quickly trace the source management.

[0067] As another preferred embodiment of the present application, it further includes the steps of:

[0068] obtaining water channel distribution data of the supervised city, and mapping in the twin supervision model to perform fluid motion simulation to obtain the water flow state at each water channel, wherein the water channel distribution data includes water channel position flow direction data, water channel fall data and unit water flow data;

[0069] if the end region of the fluid backtracking coverage area in time reverse order overlaps with the area where the water channel is located, additional backtracking simulation of the water flow is performed to obtain a water source pollution diffusion area;

[0070] Perform fitting of gas diffusion movement of the associated area based on the water pollution coverage area to update the fluid backtracking coverage area.

[0071] Further comprising the steps of:

[0072] Accessing a municipal management website to obtain the distribution of underground pipes in the city, and various discharge points and corresponding discharge types distributed on the pipes;

[0073] Monitoring the underground pipes by the distributed sensing nodes, and judging the discharge types of the underground pipes in the corresponding area, if there is a non-recorded risk gas type, marking the current part of the underground pipes.

[0074] In this embodiment, a pollution simulation supervision method based on city water distribution is supplemented, mainly including two distributions, one based on city water channels, and the other based on municipal underground pipes. In practice, both of them can become random pollution sites of related irregular enterprises, and usually because the pollution sites are hidden, it is difficult to be discovered in time, especially for the former, the water channel penetrates in the open air in the city, and the pollution will be further carried to a larger range of the city, diffused with the atmosphere, and even affect the city drinking water source. Here, the twin motion simulation of fluid is performed by mapping the water channel structure and the pipe structure, so that the corresponding pollution discharge point interval can be judged according to the detection data of the sensing nodes.

[0075] As another preferred embodiment of the present application, it further comprises:

[0076] Obtaining the personnel distribution in the managed urban area and mapping in the twin supervision model, and superimposing and verifying with the actual influence range to obtain a social influence result, the social influence result including an influence range total amount and an influence population total amount;

[0077] Performing risk rating based on the influence range total amount and the influence population total amount, and feeding back the risk rating, the risk rating being further associated with a risk gas type, for representing the social influence degree of the current pollution.

[0078] In this embodiment, the step of mapping the personnel in the supervised city is supplemented, the population distribution density of different grid areas is used to judge how many personnel the current pollution diffusion may affect the life and safety of, so as to correspondingly set the risk level, so that the relevant departments can take different emergency management measures according to different risk ratings.

[0079] As shown in Figure 3 The present application also provides a risk supervision system based on gas data monitoring, which comprises:

[0080] The twin supervision module 100 is used to acquire an environmental structure distribution of a supervised city to establish a twin supervision model, the environmental structure distribution is used to build a fluid restriction boundary, and the twin supervision model comprises an environmental atmospheric filling and a river water body filling.

[0081] The atmospheric fitting module 200 is used to acquire atmospheric environment data of the supervised city through a meteorological interface, and perform motion fitting on the twin supervision model based on fluid motion simulation to acquire fluid motion data within the range of the supervised city within a period of time.

[0082] The back mapping module 300 is used to acquire gas back data of a distributed sensing node, map based on location information in the twin supervision model, and perform risk gas type judgment and marking, and the sensing node is distributed in a grid shape.

[0083] The risk fitting module 400 is used to perform motion fitting of gas diffusion based on fluid motion data if the corresponding sensing node is marked to acquire a risk influence range in a future period of time.

[0084] The range limiting module 500 is used to verify the risk influence range based on continuously updated gas back data, and exclude and verify the risk influence range based on unmarked and marked sensing nodes to limit the actual influence range.

[0085] As another preferred embodiment of the present application, a backtracking simulation module is further included, specifically comprising:

[0086] The meteorological synchronization unit is used to acquire meteorological record data at the marked sensing node and a plurality of adjacent sensing nodes thereof, perform fluid motion simulation based on the meteorological record data, and acquire fluid motion record data within the current range.

[0087] The backtracking fitting unit is used to perform backtracking simulation of a flow coverage area according to a maximum tangential curve of fluid flow at the marked node, and perform continuous backtracking simulation based on an interval duration between a marking time of the marked sensing node and a first marking time of the risk gas type to acquire a fluid backtracking coverage area.

[0088] The backtracking limiting unit is used to split the fluid backtracking area based on a fluid backtracking curve connecting the unmarked sensing nodes when the fluid backtracking coverage area overlaps with the unmarked sensing nodes, eliminate a part of the split fluid backtracking area which does not cover the marked sensing nodes, and limit the fluid backtracking coverage area.

[0089] Interval restriction unit, when the sensing node is the first labeling for the risk gas type, the interval duration is zero, and the range interval restriction is used to replace the interval duration, which is determined by the fluid backtracking coverage area and the unlabeled sensing node.

[0090] As another preferred embodiment of the present application, it further includes a waterway supervision module, specifically comprising:

[0091] Waterway simulation unit, for obtaining water channel distribution data of the supervised city and mapping in the twin supervision model for fluid motion simulation to obtain water flow state at each water channel, wherein the water channel distribution data includes water channel position flow direction data, water channel fall data and unit water flow data;

[0092] Water pollution fitting unit, for additional backtracking simulation of water flow if the time-reversed end region of the fluid backtracking coverage area overlaps with the water channel region, to obtain the water source pollution diffusion region;

[0093] Carrying diffusion unit, for gas diffusion motion fitting of the associated region based on the water source pollution coverage area to update the fluid backtracking coverage area.

[0094] As another preferred embodiment of the present application, it further includes a municipal supervision module, comprising:

[0095] Pipeline synchronization unit, for accessing the city municipal management website to obtain the underground pipeline distribution of the supervised city and each discharge point and corresponding discharge type distributed on the pipeline;

[0096] Pipeline supervision unit, for gas monitoring of the underground pipeline by the distributed sensing node and judgment according to the recorded discharge type of the corresponding region underground pipeline, if there is a non-recorded risk gas type, the current part of the underground pipeline is marked.

[0097] As another preferred embodiment of the present application, it further includes a social assessment module, specifically comprising:

[0098] Distribution mapping unit, for obtaining personnel distribution of the managed urban area and mapping in the twin supervision model, and superimposing verification with the actual impact range to obtain social impact results, wherein the social impact results include impact range total and impact population total;

[0099] Risk assessment unit, for risk rating based on the impact range total and the impact population total, and feedback of the risk rating, wherein the risk rating is also associated with the risk gas type, used to represent the social impact degree of the current pollution.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0101] Other embodiments of the present disclosure will be apparent to those skilled in the art with the accomplishment of the present disclosure as reflected in the specification and embodiments. The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure following the general principles of the present disclosure and including common knowledge or conventional technical means in the art not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0102] It should be understood that the present disclosure is not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A risk monitoring method based on gas data monitoring, characterized in that, Include: The environmental structure distribution of the monitored city is obtained to establish a twin regulatory model. The environmental structure distribution is used to construct fluid constraint boundaries. The twin regulatory model includes environmental atmospheric filling and river water body filling. Atmospheric environmental data of the monitored city is obtained through a meteorological interface, and the twin monitoring model is motion-fitted based on fluid motion simulation to obtain fluid motion data within the monitored city area over a period of time. Gas feedback data from distributed sensor nodes is acquired, mapped onto a twin regulatory model based on location information, and the types of hazardous gases are identified and marked. The sensor nodes are distributed in a grid pattern. If the corresponding sensor node is marked, the motion of gas diffusion is fitted based on fluid motion data to obtain the risk impact range over a future period of time. The risk impact range is verified based on continuously updated gas feedback data, and the risk impact range is excluded and verified based on unmarked and marked sensor nodes in order to narrow down the actual impact range. It also includes the following steps: Acquire meteorological record data of the marked sensor node and several adjacent sensor nodes, perform fluid motion simulation based on the meteorological record data, and acquire fluid motion record data within the current range; The backtracking simulation of the flow coverage area is performed based on the maximum tangential curve of the fluid flow at the marked node, and the backtracking simulation is performed continuously based on the duration of the interval between the marking time of the marked sensor node and the initial marking time of the represented risk gas type to obtain the fluid backtracking coverage area. When the fluid backtracking coverage area overlaps with an unmarked sensor node, the fluid backtracking area is segmented based on the fluid backtracking curve connecting the unmarked sensor node, and the segmented part that does not cover the marked sensor node is eliminated to narrow the fluid backtracking coverage area. When the sensing node is the first to mark the type of hazardous gas, the interval duration is zero. In this case, the interval duration is replaced by a range limit, which is determined by the fluid backtracking coverage area and the unmarked sensing node.

2. The risk monitoring method based on gas data monitoring according to claim 1, characterized in that, It also includes the following steps: The distribution data of water canals in the monitored city is obtained and mapped in the twin monitoring model to simulate fluid motion. The water flow status at each water canal is obtained. The water canal distribution data includes water canal location flow direction data, water canal drop data, and unit water flow rate data. If the end region of the fluid backtracking coverage area in reverse time overlaps with the area where the canal is located, then additional backtracking simulations are performed on the water flow to obtain the water source pollution diffusion area; Based on the water pollution coverage area, the gas diffusion motion of the associated area is fitted to update the fluid backtracking coverage area.

3. The risk monitoring method based on gas data monitoring according to claim 2, characterized in that, It also includes the following steps: Access the city's municipal management website to obtain information on the distribution of underground pipelines in the city, as well as the various discharge points and corresponding discharge types along the pipelines; The system monitors underground pipelines using distributed sensor nodes and determines the type of gas entering the pipeline based on the recorded gas types in the corresponding area. If a non-recorded hazardous gas type is present, the current section of the underground pipeline is marked.

4. The risk monitoring method based on gas data monitoring according to claim 1, characterized in that, It also includes the following steps: The distribution of personnel in the managed urban area is obtained and mapped in the twin supervision model, and then superimposed with the actual impact range for verification to obtain the social impact results, which include the total impact range and the total number of people affected. Risk ratings are conducted based on the total area affected and the total number of people affected, and feedback is provided on these risk ratings. These risk ratings are also associated with the type of hazardous gas and are used to characterize the degree of social impact of the current pollution.

5. A risk monitoring system based on gas data monitoring, characterized in that, Include: The twin regulatory module is used to acquire the environmental structure distribution of the monitored city to establish a twin regulatory model. The environmental structure distribution is used to construct fluid constraint boundaries. The twin regulatory model includes environmental atmospheric filling and river water body filling. The atmospheric fitting module is used to acquire atmospheric environmental data of the monitored city through the meteorological interface, and to perform motion fitting on the twin monitoring model based on fluid motion simulation to obtain fluid motion data within the monitored city area over a period of time. The backhaul mapping module is used to acquire gas backhaul data from distributed sensor nodes, map it in the twin monitoring model based on location information, and determine and mark the types of hazardous gases. The sensor nodes are distributed in a grid pattern. The risk fitting module is used to perform motion fitting of gas diffusion based on fluid motion data if the corresponding sensor node is marked, so as to obtain the risk impact range over a future period of time. The range narrowing module is used to verify the risk impact range based on continuously updated gas feedback data, and to exclude and verify the risk impact range based on unmarked and marked sensor nodes, so as to narrow down and obtain the actual impact range. It also includes a backtracking simulation module, specifically including: The meteorological synchronization unit is used to acquire meteorological record data of the marked sensor node and several adjacent sensor nodes, perform fluid motion simulation based on the meteorological record data, and acquire fluid motion record data within the current range. The backtracking fitting unit is used to perform backtracking simulation of the flow coverage area based on the maximum tangential curve of the fluid flow at the marked node, and to perform continuous backtracking simulation based on the duration of the interval between the marking time of the marked sensor node and the initial marking time of the represented risk gas type to obtain the fluid backtracking coverage area. The backtracking restriction unit is used to segment the fluid backtracking area based on the fluid backtracking curve connecting the unmarked sensor nodes when the fluid backtracking coverage area overlaps with the unmarked sensor nodes, and to eliminate the segmented part that does not cover the marked sensor nodes, so as to limit the fluid backtracking coverage area. An interval restriction unit is used when the sensing node is initially marked for the type of hazardous gas, and the interval duration is zero. In this case, the interval duration is replaced by a range interval restriction, which is determined by the fluid backtracking coverage area and the unmarked sensing node.

6. A risk monitoring system based on gas data monitoring according to claim 5, characterized in that, It also includes a waterway monitoring module, specifically including: The waterway simulation unit is used to acquire waterway distribution data of the monitored city and map it in the twin monitoring model to simulate fluid motion, and to acquire the water flow status at each waterway. The waterway distribution data includes waterway location and flow direction data, waterway drop data, and unit water flow data. The water pollution fitting unit is used to perform additional backtracking simulations on the water flow if the end region of the fluid backtracking coverage area in reverse time overlaps with the area where the water channel is located, in order to obtain the water source pollution diffusion area. The device carries a diffusion unit for fitting gas diffusion motion in the associated area based on the water source pollution coverage area, in order to update the fluid backtracking coverage area.

7. A risk monitoring system based on gas data monitoring according to claim 6, characterized in that, It also includes a municipal oversight module, which includes: The pipeline synchronization unit is used to connect to the city's municipal management website to obtain information on the distribution of underground pipelines in the city, as well as the various discharge points and their corresponding discharge types. The pipeline monitoring unit is used to monitor underground pipelines for gas through distributed sensing nodes and make judgments based on the recorded emission types of underground pipelines in the corresponding area. If there are non-recorded risk gas types, the current section of the underground pipeline is marked.

8. A risk monitoring system based on gas data monitoring according to claim 5, characterized in that, It also includes a social assessment module, which specifically includes: The distribution mapping unit is used to obtain the distribution of personnel in the managed urban area and map it in the twin supervision model, and to verify it by superimposing it with the actual impact range to obtain the social impact result, which includes the total impact range and the total number of people affected. The risk assessment unit is used to conduct risk rating based on the total scope of impact and the total number of people affected, and to provide feedback on the risk rating. The risk rating is also associated with the type of hazardous gas and is used to characterize the degree of social impact of the current pollution.

Images