Atmospheric pollution diffusion prediction system based on ecological big data analysis

Abstract

The invention discloses an atmospheric pollution diffusion prediction system based on ecological big data analysis, relates to the technical field of atmospheric pollution diffusion prediction, and solves the technical problem that in the prior art, gas emission type analysis cannot be performed according to characteristics of different activeness of exhaust gas corresponding to different atmospheric heights. The regional emission influence evaluation unit is used for carrying out emission influence evaluation on the emission region and inferring whether diffusion prediction needs to be carried out on the current emission region or not through evaluation; the pollution trend identification unit is used for carrying out trend identification on the exhaust gas in the exhaust area and carrying out type division on the exhaust gas according to the trend identification; and the sub-trend prediction unit is used for performing sub-trend prediction on different types of exhaust gases in the exhaust area.

Description

Technical Field

[0001] The present invention relates to the technical field of air pollution diffusion prediction, and specifically to an air pollution diffusion prediction system based on ecological big data analysis. Background Art

[0002] With the rapid development of industrialization and urbanization, the problem of air pollution has become increasingly serious, posing a great threat to the ecological environment; accurately predicting the diffusion trend of air pollution is crucial for taking preventive and control measures in advance.

[0003] The patent with the publication number CN115809728A discloses a method, device, and computer equipment for predicting air pollution diffusion conditions. The method includes: obtaining meteorological prediction data of a target range; using a first correction coefficient to correct the wind speed data in the meteorological prediction data of the target area to obtain the corrected meteorological prediction data of the target area; the target range includes the target area; determining the atmospheric stability prediction data of the target area based on the corrected meteorological prediction data; and determining the prediction result of the air pollution diffusion conditions of the target area based on the atmospheric stability prediction data and a second correction coefficient.

[0004] However, in the prior art, making decisions solely based on meteorological data is likely to cause deviations in prediction results, and it is unable to analyze the gas emission types according to the characteristics that the activity of the emitted gases varies with different atmospheric heights, and unable to collect targeted influence parameters according to the emission types, thus reducing the accuracy of the prediction of the emitted gases.

[0005] In view of the above technical deficiencies, a solution is now proposed. Summary of the Invention

[0006] The purpose of the present invention is to solve the above-mentioned problems and propose an air pollution diffusion prediction system based on ecological big data analysis.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An air pollution diffusion prediction system based on ecological big data analysis includes a pollution diffusion prediction platform, which is communicatively connected to a regional emission impact assessment unit, a pollution trend identification unit, and a sub-trend prediction unit;

[0009] The regional emission impact assessment unit assesses the emission impact of the emission area and infers whether diffusion prediction is required for the current emission area through the assessment;

[0010] The pollution trend identification unit identifies the trend of the emitted gases in the emission area and classifies the emitted gases according to the trend identification;

[0011] Sub - trend prediction unit, which conducts sub - trend prediction on different types of emission gases within the emission area.

[0012] As a preferred embodiment of the present invention, the process of the regional emission impact assessment unit is as follows:

[0013] Collect the components of the emission gas and label them as increasing components, collect the generated components after the emission of the emission gas, and label them as secondary product components; obtain the pollution components based on historical emission analysis; obtain the pollution - increasing components and pollution secondary - product components according to the comparison of pollution components;

[0014] Obtain the rising speed of the pollution - increasing components within the emission stage of the current emission area. At the same time, collect the average value of the time interval between the gas emission moment and the generation moment of the pollution secondary - product components within the emission stage, and analyze the data collected during the gas emission stage:

[0015] Since the start of gas emission, if the rising speed of the pollution - increasing components shows a continuous upward trend, or the average value of the time interval between the gas emission moment and the generation moment of the pollution secondary - product components shows a continuous downward trend, it is inferred that there is an impact on the gas emission within the emission area and the impact continues to increase. Then, mark the current emission stage as the pollution emission stage; conversely, if the rising speed of the pollution - increasing components shows a reciprocating floating trend, and the average value of the time interval between the gas emission moment and the generation moment of the pollution secondary - product components does not show a continuous downward trend, and when both the rising speed and the average value of the time interval are lower than the corresponding thresholds, mark the current emission stage as the safe emission stage.

[0016] As a preferred embodiment of the present invention, collect the increase frequency of the peak value of the gas emission volume within the emission area during the pollution emission stage; at the same time, collect the continuous duration during which the gas emission speed of the corresponding emission area at adjacent moments shows no decreasing trend within the pollution emission stage; conduct a comparison with the corresponding thresholds for the data collected during the pollution emission stage;

[0017] If the increase frequency of the gas emission volume peak exceeds the peak increase frequency, or the continuous duration during which the gas emission speed shows no decreasing trend exceeds the continuous duration threshold, then mark the current pollution emission stage as the pollution impact stage;

[0018] If the increase frequency of the gas emission volume peak does not exceed the peak increase frequency, and the continuous duration during which the gas emission speed shows no decreasing trend does not exceed the continuous duration threshold, then mark the current pollution emission stage as the pollution stable stage.

[0019] As a preferred embodiment of the present invention, the process of the pollution trend identification unit is as follows:

[0020] Collect the heights of gas emission points in the emission area, where the gas emission points are represented as emission points such as factory chimneys and emission pipes; collect the heights of the emitted gases according to the heights of the gas emission points, and divide them into ascending gases and descending gases according to the densities of the emitted gases; and obtain the height range of the emitted gases according to the gas emission trajectory starting from the gas emission point, that is, the height range formed by the height peak of the ascending gas and the height valley of the descending gas in the emitted gas.

[0021] As a preferred embodiment of the present invention, identify the air adiabatic cooling rate according to the height range, take 100 meters as the height threshold, and collect the temperature drop span corresponding to each 100-meter interval within the height range. If the temperature drop span exceeds 1 degree Celsius every 100 meters, mark the corresponding height as the stratified height value;

[0022] Mark the height range above the stratified height value as the super-adiabatic cooling rate; conversely, mark the height range above the stratified height value as the sub-adiabatic cooling rate; and mark the height interval corresponding to the super-adiabatic cooling rate as the gas diffusion interval; mark the height interval corresponding to the sub-adiabatic cooling rate as the gas accumulation interval;

[0023] According to the distribution range of the emitted gases in the emission area, combine the interval type to divide the emission stage type of the gas emission stage, that is, if the distribution range of the emitted gases is in the gas diffusion interval, mark the corresponding emitted gas type as the diffusion pollution type; conversely, if the distribution range of the emitted gases is in the gas accumulation interval, mark the corresponding emitted gas type as the accumulation pollution type.

[0024] As a preferred embodiment of the present invention, the process of the sub-trend prediction unit is as follows:

[0025] When the gas of the diffusion pollution type is emitted in the emission area, mark the gas distribution area corresponding to the diffusion pollution type as the diffusion trajectory area, and at the same time collect the environmental parameters of the diffusion trajectory area, obtain the duration of the wind direction in each direction within the diffusion trajectory area, and obtain the peak duration according to the comparison of the durations, and mark the risk in the direction corresponding to the peak duration as the trend direction; after determining the trend direction, mark the risks in the remaining directions as non-trend directions.

[0026] As a preferred embodiment of the present invention, when the wind blows in the trend direction and the non-trend direction, collect the excess amount of the average wind force in the trend direction compared to the non-trend direction, and at the same time collect the increase span of the area involved in the components contained in the emitted gas corresponding to the trend direction and the increase span of the area involved in the same type of components in the non-trend direction, and calculate the span numerical ratio according to the span ratio;

[0027] If the excess amount of the wind power mean value exceeds the excess amount threshold, or the span value ratio exceeds the value ratio threshold, the trend direction is taken as the current diffusion direction; if the excess amount of the wind power mean value does not exceed the excess amount threshold and the span value ratio does not exceed the value ratio threshold, the non-trend direction is taken as the current diffusion direction.

[0028] As a preferred embodiment of the present invention, according to the diffusion direction, the span of the increase in the component content of the diffused area and the span of the increase in the area of the diffused area are collected. If any value in the span of the increase in the component content of the diffused area and the span of the increase in the area of the diffused area exceeds the set threshold, it is predicted that the diffused gas in the emission area is in the continuous increase stage;

[0029] If the values in the span of the increase in the component content of the diffused area and the span of the increase in the area of the diffused area do not exceed the set threshold, it is predicted that the diffused gas in the emission area is in the initial stage of diffusion.

[0030] As a preferred embodiment of the present invention, when the gas of the accumulated pollution type is emitted in the emission area, the reduction rate of the deviation of the gas component content at each position in the corresponding distribution area of the accumulated pollution type emission gas is collected, and at the same time, the span of the increase in the corresponding gas component content at the surrounding positions of the distribution area is collected;

[0031] If the reduction rate of the deviation of the gas component content at each position in the corresponding distribution area of the accumulated pollution type emission gas exceeds the deviation reduction rate threshold, or the span of the increase in the corresponding gas component content at the surrounding positions of the distribution area exceeds the increase span threshold, it is predicted that the accumulated pollution type emission gas is in the stage of aggravated pollution;

[0032] If the reduction rate of the deviation of the gas component content at each position in the corresponding distribution area of the accumulated pollution type emission gas does not exceed the deviation reduction rate threshold and the span of the increase in the corresponding gas component content at the surrounding positions of the distribution area does not exceed the increase span threshold, it is predicted that the accumulated pollution type emission gas is in the stage of stable emission.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] 1. In the present invention, by evaluating the impact of industrial product emissions in the emission area, it is inferred whether there is an impact on the real-time atmospheric emission state of the current emission area, so as to predict the pollution diffusion according to the analysis of the atmospheric emission product state in the emission area, improve the accuracy of pollution diffusion prediction, and also be able to plan emissions according to the real-time atmospheric pollution state, reasonably plan the atmospheric product emissions, and reduce the concentrated impact of atmospheric pollution.

[0035] 2. In the present invention, the trend of the exhaust gas in the emission area is identified, and the influence of the atmospheric environment at the current emission height on the polluting gas is analyzed and inferred through the emission height where the exhaust gas is located, so as to infer the diffusion trend of the polluting gas after emission according to the influence analysis, effectively and specifically collect data for further prediction according to the diffusion trend, and also be able to carry out targeted pollution prevention and control according to the diffusion trend, effectively reducing the working intensity of the prevention and control of polluting gases, and the targeted control can also improve the prevention and control efficiency of polluting gases.

[0036] 3. In the present invention, the sub-trend prediction of different types of exhaust gases in the emission area is carried out, and the targeted trend prediction is carried out according to different exhaust gas types, and the prediction accuracy is improved through targeted data collection, so as to improve the monitoring efficiency of pollution diffusion in the emission area, avoid one-sided pollution monitoring, lack of pertinence, generate a large amount of monitoring data, reduce the monitoring efficiency of pollution diffusion while increasing the data collection cost, and the large amount of collected data also affects the accuracy of pollution diffusion control. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0038] Figure 1 is the principle block diagram of the present invention;

[0039] Figure 2 is the method flow chart of the regional emission impact assessment unit in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The phrase occurring in various places in the specification is not necessarily referring to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0042] Please refer to Figure 1As shown in the figure, an air pollution diffusion prediction system based on ecological big data analysis includes a pollution diffusion prediction platform, where the pollution diffusion prediction platform is communicatively connected to a regional emission impact assessment unit, a pollution trend identification unit, and a sub-trend prediction unit;

[0043] The pollution diffusion prediction platform generates a regional emission impact assessment signal and sends the regional emission impact assessment signal to the regional emission impact assessment unit;

[0044] The regional emission impact assessment unit is used to receive the regional emission impact assessment signal and, after receiving it, conduct an emission impact assessment on the emission area. By assessing the impact through the emissions of industrial products in the emission area, it infers whether there is an impact on the real-time atmospheric emission status of the current emission area, so as to conduct pollution diffusion prediction based on the analysis of the atmospheric emission product status in the emission area, improving the accuracy of pollution diffusion prediction. It can also conduct emission planning according to the real-time air pollution status, reasonably plan the emissions of atmospheric products, and reduce the concentrated impact of air pollution; where the atmospheric products refer to products such as emission gases generated by industrial processing in industrial areas, and the emission area refers to the area where industrial construction equipment such as factories is located;

[0045] Please refer to Figure 2 As shown in the figure, conduct gas emission monitoring on the emission area, collect the components of the emission gas and mark them as increased components, and according to the chemical reactions of the emission gas in the atmospheric environment after emission, collect the generated components after the emission gas is emitted and mark them as secondary product components;

[0046] Based on the components included in the historical pollution gases generated in the current emission area and the components included in the existing air pollution gases, and uniformly mark them as pollution components; based on the comparison of the pollution components, obtain the pollution increased components and the pollution secondary product components;

[0047] Obtain the rising speed of the pollution increased components during the emission stage in the current emission area, and at the same time collect the average interval duration between the gas emission moment and the generation moment of the pollution secondary product components during the emission stage, and analyze the data collected during the gas emission stage:

[0048] Since the start of gas emissions, if the rising speed of the pollution-increasing components shows a continuous upward trend, or the average time interval between the gas emission moment and the generation moment of the pollution secondary product components shows a continuous downward trend, it is inferred that there is an impact on the gas emissions in the emission area and the impact continues to increase. Then, the current emission stage is marked as the pollution emission stage; conversely, if the rising speed of the pollution-increasing components shows a reciprocating floating trend and the average time interval between the gas emission moment and the generation moment of the pollution secondary product components does not show a continuous downward trend, and both the rising speed and the average time interval are lower than the corresponding thresholds, the current emission stage is marked as the safe emission stage; it should be noted that the atmospheric environment can consume some gas components. Although there is pollution, when the content is below a certain threshold, there is no need to carry out emission-targeted control.

[0049] During the pollution emission stage, evaluate the impact of pollution emissions to infer whether it can be designated as a pollution risk area and be able to decide whether to identify the trend of the pollution risk area. Since the impact of pollution emissions changes constantly, under low impact, the pollution risk can be reduced through the treatment reaction of the atmospheric environment. In this scenario, the pollution parameters collected in real time have no practical significance for monitoring pollution diffusion, so there is no need to identify the trend.

[0050] Collect the increasing frequency of the peak gas emissions in the emission area during the pollution emission stage, where the peak increasing frequency is expressed as the emission peak at any emission moment in the pollution emission stage of the emission area and the corresponding increasing frequency. Through the peak increasing frequency, the emission demand of the current emission stage can be reflected; at the same time, collect the continuous duration during which the gas emission speed in the corresponding emission area at adjacent moments in the pollution emission stage shows no decreasing trend; compare the data collected during the pollution emission stage with the corresponding thresholds. The thresholds set in this application are all threshold parameters artificially set by professionals in the actual working scenario according to the historical values of the collected data for data detection.

[0051] If the increasing frequency of the peak gas emissions in the emission area during the pollution emission stage exceeds the peak increasing frequency, or the continuous duration during which the gas emission speed in the corresponding emission area at adjacent moments in the pollution emission stage shows no decreasing trend exceeds the duration threshold, then the current pollution emission stage is marked as the pollution impact stage, and the pollution impact stage is sent to the pollution diffusion prediction platform for pollution trend identification and prediction.

[0052] If the increasing frequency of the peak gas emissions in the emission area during the pollution emission stage does not exceed the peak increasing frequency, and the continuous duration during which the gas emission speed in the corresponding emission area at adjacent moments in the pollution emission stage shows no decreasing trend does not exceed the duration threshold, then the current pollution emission stage is marked as the pollution stable stage, and the pollution stable stage is sent to the pollution diffusion prediction platform for real-time monitoring of pollution components.

[0053] Generate a pollution trend recognition signal and send the pollution trend recognition signal to the pollution trend recognition unit simultaneously;

[0054] The pollution trend recognition unit is used to, after receiving the pollution trend recognition signal, conduct trend recognition on the discharged gas in the emission area, analyze and infer the impact of the atmospheric environment at the current emission height on the polluted gas through the emission height where the discharged gas is located, so as to infer the diffusion trend of the polluted gas after emission according to the impact analysis, effectively collect data targeted according to the diffusion trend for further prediction, and can also conduct targeted pollution prevention and control according to the diffusion trend, effectively reducing the work intensity of polluted gas prevention and control, and targeted control can also improve the efficiency of polluted gas prevention and control;

[0055] Collect the height of the gas emission points in the emission area, where the gas emission points are represented as emission points such as factory chimneys and emission pipes; collect the emission gas height according to the height of the gas emission points, and divide it into floating gas and descending gas according to the density of the emission gas; and obtain the height range of the emission gas according to the gas emission trajectory starting from the gas emission point, that is, the height range formed by the height peak of the floating gas and the height valley of the descending gas in the emission gas; it should be noted that the common form of gas emission is chimney emission, and the chimney has a certain height;

[0056] Conduct air adiabatic cooling rate recognition according to the height range, take one hundred meters as the height threshold, and collect the temperature drop span corresponding to each one hundred meters within the height range. If the temperature drop span exceeds one degree Celsius every one hundred meters, mark the corresponding height as the stratified height value;

[0057] Mark the height range above the stratified height value as the super-adiabatic cooling rate; conversely, mark the height range above the stratified height value as the sub-adiabatic cooling rate; and mark the height interval corresponding to the super-adiabatic cooling rate as the gas diffusion interval; mark the height interval corresponding to the sub-adiabatic cooling rate as the gas accumulation interval;

[0058] According to the distribution range of the discharged gas in the emission area, combine the interval type to divide the emission stage type of the gas emission stage, that is, if the distribution range of the discharged gas is in the gas diffusion interval, mark the corresponding discharged gas type as the diffusion pollution type; conversely, if the distribution range of the discharged gas is in the gas accumulation interval, mark the corresponding discharged gas type as the accumulation pollution type;

[0059] It should be noted that the collection of the distribution range of the discharged gas needs to confirm the height after the discharged gas exists at the current height for a set threshold duration. For example, if the gas is in the initial stage of emission or there are occasional environmental impacts, its height is continuously monitored and the height range is not determined temporarily;

[0060] Send both the diffusion pollution type and the accumulation pollution type to the pollution diffusion prediction platform, and send and store the height distribution range of the corresponding type together;

[0061] At the same time, generate a sub-trend prediction signal and send the sub-trend prediction signal to the sub-trend prediction unit;

[0062] The sub-trend prediction unit is used to perform sub-trend prediction on different types of emission gases in the emission area after receiving the sub-trend prediction signal, conduct targeted trend prediction according to different emission gas types, improve the prediction accuracy through targeted data collection, so as to improve the pollution diffusion monitoring efficiency in the emission area, avoid one-sided pollution monitoring and lack of pertinence, generate a large amount of monitoring data, reduce the pollution diffusion monitoring efficiency while increasing the data collection cost, and the large amount of collected data also affects the accuracy of pollution diffusion control;

[0063] When the gas of the diffusion pollution type is emitted in the emission area, mark the gas distribution area corresponding to the diffusion pollution type as the diffusion trajectory area. At the same time, collect the environmental parameters of the diffusion trajectory area, obtain the duration of the wind direction in each direction within the diffusion trajectory area, and obtain the peak duration according to the comparison of the durations. And mark the risk of the direction corresponding to the peak duration as the trend direction; after determining the trend direction, mark the risks of the remaining directions as non-trend directions;

[0064] When the wind blows in the trend direction and the non-trend direction, collect the excess amount of the average wind force in the trend direction compared to the non-trend direction, and at the same time collect the increase span of the area involved in the components contained in the emission gas corresponding to the trend direction and the increase span of the area involved in the same type of components in the non-trend direction, and calculate the span numerical ratio according to the span ratio;

[0065] If the excess amount of the average wind force exceeds the excess amount threshold, or the span numerical ratio exceeds the numerical ratio threshold, then use the trend direction as the current diffusion direction; if the excess amount of the average wind force does not exceed the excess amount threshold and the span numerical ratio does not exceed the numerical ratio threshold, then use the non-trend direction as the current diffusion direction;

[0066] And collect the increase span of the component content and the increase span of the area of the diffused area according to the diffusion direction. If any value in the increase span of the component content and the increase span of the area of the diffused area exceeds the set threshold, it is predicted that the diffused gas in the emission area is in the continuous increase stage; and send the prediction result to the pollution diffusion prediction platform and conduct pollution control of the diffused gas; if the values of both the increase span of the component content and the increase span of the area of the diffused area do not exceed the set threshold, it is predicted that the diffused gas in the emission area is in the initial diffusion stage, and send the prediction result to the pollution diffusion prediction platform. After receiving the prediction result, the pollution diffusion prediction platform conducts peak-shifting regulation on the emissions in the emission area and continuously monitors;

[0067] When accumulating pollution type gas emissions in the emission area, collect the reduction rate of the gas component content deviation at each position within the corresponding distribution area of the accumulated pollution type emission gas, and at the same time collect the rising span of the corresponding gas component content at the surrounding positions of the distribution area;

[0068] If the reduction rate of the gas component content deviation at each position within the corresponding distribution area of the accumulated pollution type emission gas exceeds the deviation reduction rate threshold, or the rising span of the corresponding gas component content at the surrounding positions of the distribution area exceeds the rising span threshold, then predict that the accumulated pollution type emission gas is in the stage of pollution aggravation, and send the prediction result to the pollution diffusion prediction platform. After receiving the prediction result, the pollution diffusion prediction platform controls the accumulated pollution type emission gas in the distribution area, and the control method is as in the prior art, such as accelerating the gas dissipation speed, etc.;

[0069] If the reduction rate of the gas component content deviation at each position within the corresponding distribution area of the accumulated pollution type emission gas does not exceed the deviation reduction rate threshold, and the rising span of the corresponding gas component content at the surrounding positions of the distribution area does not exceed the rising span threshold, then predict that the accumulated pollution type emission gas is in the emission stable stage, and send the prediction result to the pollution diffusion prediction platform.

[0070] When the present invention is in use, the regional emission impact assessment unit assesses the emission impact of the emission area, and infers whether diffusion prediction needs to be carried out on the current emission area through the assessment; the pollution trend identification unit identifies the trend of the emission gas in the emission area, and classifies the emission gas according to the trend identification; the sub-trend prediction unit predicts the sub-trends of different types of emission gases in the emission area.

[0071] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An air pollution diffusion prediction system based on ecological big data analysis, characterized in that, It includes a pollution diffusion prediction platform, which is communicatively connected to a regional emission impact assessment unit, a pollution trend identification unit, and a sub-trend prediction unit; The regional emission impact assessment unit assesses the emission impact of the emission area and infers whether pollution diffusion prediction is required for the current emission area through the assessment; The pollution trend identification unit identifies the trend of the emitted gas in the emission area and classifies the emitted gas according to the trend identification; The sub-trend prediction unit conducts sub-trend predictions on different types of emitted gases in the emission area.

2. The air pollution diffusion prediction system based on ecological big data analysis according to claim 1, characterized in that The process of the regional emission impact assessment unit is as follows: Collect the components of the emitted gas and label them as increasing components, collect the generated components after the emission of the emitted gas, and label them as secondary product components; obtain the pollution components based on historical emission analysis; obtain the pollution increasing components and pollution secondary product components through comparison of the pollution components; Obtain the rising speed of the pollution increasing components during the emission stage of the current emission area, and at the same time collect the average interval duration between the gas emission moment and the generation moment of the pollution secondary product components during the emission stage, and analyze the data collected during the gas emission stage: Since the start of gas emission, if the rising speed of the pollution increasing components shows a continuous upward trend, or the average interval duration between the gas emission moment and the generation moment of the pollution secondary product components shows a continuous downward trend, it is inferred that there is an impact on the gas emission in the emission area and the impact continues to increase, then mark the current emission stage as the pollution emission stage; otherwise, if the rising speed of the pollution increasing components shows a reciprocating floating trend and the average interval duration between the gas emission moment and the generation moment of the pollution secondary product components does not show a continuous downward trend, and when both the rising speed and the average interval duration are lower than the corresponding thresholds, mark the current emission stage as the safe emission stage.

3. The air pollution diffusion prediction system based on ecological big data analysis according to claim 2, characterized in that, Collect the increase frequency of the peak gas emission volume in the emission area during the pollution emission stage; at the same time, collect the continuous duration during which the gas emission speed in the corresponding emission area at adjacent moments does not show a decreasing trend during the pollution emission stage; Compare the data collected during the pollution emission stage with the corresponding thresholds; If the increase frequency of the peak gas emission volume exceeds the peak increase frequency, or the continuous duration during which the gas emission speed does not show a decreasing trend exceeds the continuous duration threshold, then mark the current pollution emission stage as the pollution impact stage; If the increase frequency of the peak gas emission volume does not exceed the peak increase frequency and the continuous duration during which the gas emission speed does not show a decreasing trend does not exceed the continuous duration threshold, then mark the current pollution emission stage as the pollution stable stage.

4. The air pollution diffusion prediction system based on ecological big data analysis according to claim 3, characterized in that, The process of the pollution trend identification unit is as follows: Collect the height of the gas emission point in the emission area; collect the height of the emitted gas according to the height of the gas emission point, and classify the emitted gas into floating gas and descending gas according to the density of the emitted gas; and obtain the height range of the emitted gas based on the gas emission trajectory starting from the gas emission point, that is, the height range formed by the height peak of the floating gas and the height valley of the descending gas in the emitted gas.

5. The air pollution diffusion prediction system based on ecological big data analysis according to claim 4, characterized in that Identify the air adiabatic cooling rate according to the height range, with 100 meters as the height threshold, and collect the temperature drop span corresponding to each 100-meter interval within the height range. If the temperature drop span exceeds 1 degree Celsius for each 100-meter interval, mark the corresponding height as the stratified height value; Mark the height range above the stratified height value as the super-adiabatic cooling rate; conversely, mark the height range above the stratified height value as the sub-adiabatic cooling rate; and mark the height interval corresponding to the super-adiabatic cooling rate as the gas diffusion interval; mark the height interval corresponding to the sub-adiabatic cooling rate as the gas accumulation interval; According to the distribution range of the emitted gas in the emission area, combine the interval type to divide the emission stage type of the gas emission stage. That is, if the distribution range of the emitted gas is in the gas diffusion interval, mark the corresponding emitted gas type as the diffusion pollution type; conversely, if the distribution range of the emitted gas is in the gas accumulation interval, mark the corresponding emitted gas type as the accumulation pollution type.

6. The air pollution diffusion prediction system based on ecological big data analysis according to claim 5, wherein, The process of the sub-trend prediction unit is as follows: When the gas of the diffusion pollution type is emitted in the emission area, mark the gas distribution area corresponding to the diffusion pollution type as the diffusion trajectory area. At the same time, collect the environmental parameters of the diffusion trajectory area, obtain the duration of the wind direction in each direction within the diffusion trajectory area, and obtain the peak value of the duration according to the comparison of the durations. And mark the risk of the direction corresponding to the peak value of the duration as the trend direction; After determining the trend direction, mark the risks of the remaining directions as non-trend directions.

7. The air pollution diffusion prediction system based on ecological big data analysis according to claim 6, characterized in that When the wind blows in the trend direction and the non-trend direction, collect the excess amount of the average wind force in the trend direction compared to the non-trend direction. At the same time, collect the increase span of the area involved in the components contained in the emitted gas corresponding to the trend direction and the increase span of the area involved in the same type of components in the non-trend direction. Calculate the span numerical ratio according to the ratio of the spans; If the excess amount of the average wind force exceeds the excess amount threshold, or the span numerical ratio exceeds the numerical ratio threshold, then take the trend direction as the current diffusion direction; If the excess amount of the average wind force does not exceed the excess amount threshold, and the span numerical ratio does not exceed the numerical ratio threshold, then take the non-trend direction as the current diffusion direction.

8. The air pollution diffusion prediction system based on ecological big data analysis according to claim 7, characterized in that And collect the increase span of the component content and the increase span of the area of the diffused area according to the diffusion direction. If any value in the increase span of the component content and the increase span of the area of the diffused area exceeds the set threshold, it is predicted that the diffused gas in the emission area is in the continuous increase stage; If neither the increase span of the component content nor the increase span of the area of the diffused area exceeds the set threshold, it is predicted that the diffused gas in the emission area is in the initial diffusion stage.

9. The air pollution diffusion prediction system based on ecological big data analysis according to claim 8, characterized in that When the gas of the accumulation pollution type is emitted in the emission area, collect the reduction speed of the gas component content deviation at each position within the distribution area corresponding to the emitted gas of the accumulation pollution type. At the same time, collect the increase span of the corresponding gas component content at the surrounding positions of the distribution area; If the reduction speed of the gas component content deviation at each position within the distribution area corresponding to the emitted gas of the accumulation pollution type exceeds the deviation reduction speed threshold, or the increase span of the corresponding gas component content at the surrounding positions of the distribution area exceeds the increase span threshold, it is predicted that the emitted gas of the accumulation pollution type is in the stage of aggravated pollution; If the reduction rate of the gas component content deviation at each position within the corresponding distribution area of the cumulative pollution type emission gas does not exceed the deviation reduction rate threshold, and the increase span of the corresponding gas component content at the positions around the distribution area does not exceed the increase span threshold, it is predicted that the cumulative pollution type emission gas is in a stable emission stage.

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