Atmospheric pollutant prediction method based on big data

Abstract

The invention relates to the field of atmospheric pollutant prediction, and discloses an atmospheric pollutant prediction method based on big data, and the method comprises the steps: obtaining the region position information and holding activity information of a target region, and determining a plurality of pollution sources in a preset range of the target region according to the region position information; determining an activity pollution influence value according to the activity type and the activity scale degree; determining a target historical time period according to the activity holding time period; screening the plurality of alternative prediction models according to the prediction precision of the alternative prediction models to obtain a target prediction model; training the target prediction model, and taking the trained target prediction model as an atmospheric pollutant prediction model; predicting the pollutant concentration of the target area based on the atmospheric pollutant prediction model to obtain an initial prediction result; and adjusting the initial prediction result according to the activity pollution influence value to obtain a standard prediction result. Therefore, the prediction of the atmospheric pollutants is realized.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric pollutant prediction, and in particular to an atmospheric pollutant prediction method based on big data. Background Art

[0002] When holding large-scale events (such as fireworks shows), air quality is one of the important factors to ensure the smooth progress of the event. Through big data prediction technology, the air quality conditions during the event can be evaluated in advance and corresponding safeguards can be formulated.

[0003] In the existing atmospheric pollutant prediction process, the following technical problems often occur:

[0004] First, the conventional atmospheric pollutant prediction methods have a large prediction area and lack local atmospheric pollutant prediction;

[0005] Second, since the time of large-scale events is fixed and cannot be changed, if pollutants exceed the standard before the event, it will affect the smooth progress of the event. In order to ensure that the air quality at the time of the event meets the standard, an effective emission restriction plan needs to be formulated. The existing technology lacks a targeted emission restriction plan formulation method and often adopts a "one-size-fits-all" emission restriction;

[0006] Third, when there are multiple faulty air quality monitoring points, the staff lacks a clear maintenance policy, resulting in low maintenance efficiency. As a result, some monitoring points that urgently need repair cannot be handled in a timely manner, affecting the smooth progress of activities. Summary of the invention

[0007] The summary of the invention is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The summary of the invention is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0008] The present invention proposes an atmospheric pollutant prediction method based on big data to solve one or more of the technical problems mentioned in the above background technology part.

[0009] The present invention provides an atmospheric pollutant prediction method based on big data, comprising: obtaining regional location information and event information of a target area, and determining multiple pollution sources within a preset range of the target area according to the regional location information; wherein the event information includes the time period of the event, the type of activity, and the scale of the event; determining the activity pollution impact value according to the activity type and the scale of the activity; determining the target historical time period according to the time period of the event and obtaining the pollutant concentration information and meteorological information of each of the multiple pollution sources in the target historical time period; obtaining multiple alternative prediction models and the prediction accuracy of each of the multiple alternative prediction models; screening the multiple alternative prediction models according to the prediction accuracy of the alternative prediction models to obtain a target prediction model; training the target prediction model through the pollutant concentration information and meteorological information, and using the trained target prediction model as the atmospheric pollutant prediction model; predicting the pollutant concentration of the target area in the event time period based on the atmospheric pollutant prediction model to obtain an initial prediction result; adjusting the initial prediction result according to the activity pollution impact value to obtain a standard prediction result.

[0010] Optionally, the activity pollution impact value is determined by the following steps:

[0011] Acquire multiple historical activity data, the historical activity data including historical activity types and historical activity scales; match the multiple historical activity data according to the activity types and activity scales to obtain target historical activity data; obtain historical activity pollution emissions of the target historical activity data;

[0012] Obtaining the environmental capacity of the target area and a pre-established environmental quality standard table, and obtaining the standard allowable emission of the target area by querying the environmental capacity in the environmental quality standard table, wherein the environmental quality standard table includes different environmental capacities and the standard allowable emission corresponding to each environmental capacity;

[0013] The ratio of historical activity pollution emissions to standard allowable emissions is calculated to obtain the activity pollution impact value.

[0014] Optionally, standard prediction results are determined by the following steps:

[0015] According to the pollution impact value of the activity, the adjustment value of the initial prediction result is determined, and the adjustment value is calculated with the initial prediction result to obtain the standard prediction result.

[0016] Optionally, each of the multiple pollution sources has a corresponding pollution source type, and the pollution source type corresponding to each pollution source is determined by the following steps:

[0017] Obtain the emission frequency of each pollution source in the target historical time period;

[0018] The pollution source type of the pollution source with fixed emission frequency among multiple pollution sources is determined as a fixed pollution source to obtain a fixed pollution source group; the pollution source type of the pollution source with non-fixed emission frequency among multiple pollution sources is determined as a temporary pollution source.

[0019] Optionally, the stationary pollution source has a corresponding pollutant emission limit plan, which is generated by the following steps:

[0020] Obtain the pollutant emission time period of each fixed pollution source in the fixed pollution source group; determine the fixed pollution sources in the fixed pollution source group whose pollutant emission time period coincides with the event holding time period as key pollution sources, and obtain the key pollution source group;

[0021] Obtain the emission of each key pollution source in the key pollution source group, calculate the emission of each key pollution source, and obtain the total amount of pollutant emissions corresponding to the key pollution source group;

[0022] Determine the degree of pollutant excess based on the total amount of pollutant emissions; compare the degree of pollutant excess with the preset standard degree, and if the degree of pollutant excess is greater than or equal to the preset standard degree, generate a pollutant emission restriction plan, which includes a restriction time and a standard emission amount; send the pollutant emission restriction plan to the terminal equipment corresponding to each key pollution source in the key pollution source group, so that the management personnel corresponding to the terminal equipment corresponding to each key pollution source can perform pollutant emission restriction operations;

[0023] The standard emission levels included in the pollutant emission limit plan are determined through the following steps:

[0024] The geographical location information of each key pollution source in the key pollution source group is obtained, and the straight-line distance value between each key pollution source and the target area is determined according to the geographical location information of each key pollution source; the first standard emission amount is generated for the key pollution sources in the key pollution source group whose straight-line distance value is greater than or equal to the preset distance value; the second standard emission amount is generated for the key pollution sources in the key pollution source group whose straight-line distance value is less than the preset distance value, and the first standard emission amount is less than the second standard emission amount.

[0025] The present invention has the following beneficial effects:

[0026] 1. By obtaining the target area and time of large-scale events, and using the pollutant concentration information and meteorological information of the target area in the target historical time period corresponding to the time period of the event as training samples, the target prediction model is trained. Therefore, the trained target prediction model is an atmospheric pollutant prediction model for the target area during the time period of the event, so that the atmospheric pollutant prediction model has local and targeted predictions; holding large-scale events itself will also have an impact on the prediction of atmospheric pollutants. The type of activity and its scale are the main factors affecting atmospheric pollution. Environmental capacity is the environment's ability to accommodate pollutants, and plays a vital role in determining the impact of large-scale events on atmospheric pollution. The initial prediction results are adjusted by the activity pollution impact value determined by the activity type and scale combined with the environmental capacity of the activity area, which can more comprehensively consider the impact of the activity on atmospheric pollutants and be more consistent with the actual situation, thereby reducing the error of the initial prediction results and improving the reliability and accuracy of the prediction results;

[0027] 2. Different types of pollution sources often have different emission frequencies due to their different mechanisms and conditions for producing pollutants. Therefore, emission frequency is an important basis for distinguishing different types of pollution sources. The type of pollution source can be determined by whether the emission frequency of the pollution source is fixed, which improves the accuracy of pollution source identification and helps to formulate targeted pollution control plans; the decline in environmental quality may affect the smooth progress of activities. By limiting the emissions of fixed pollution sources during the activities in advance, the smooth progress of activities can be ensured; formulating different pollutant emission limits for different key pollution sources can more accurately control the emissions of each pollution source and avoid "one-size-fits-all" emission limits;

[0028] 3. According to the characteristics of normal distribution, data points that fall outside three standard deviations of the mean are considered to be extremely rare. Therefore, the triple standard deviation method is used to identify abnormal monitoring points in the pollutant concentration data group, which can improve the accuracy of determining abnormal monitoring points; turn on indicator light reminders and generate abnormal information for abnormal monitoring points. The indicator light can remind staff and reduce response time, allowing them to discover and respond to abnormal monitoring points in the first place, and there is no need for staff to actively judge, thereby simplifying the investigation process and saving investigation manpower; key areas of activities often gather a large number of people. The maintenance priority is determined by the location attributes of each abnormal monitoring point, which improves the timeliness of maintenance of abnormal monitoring points and ensures the smooth progress of activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0030] Figure 1 It is a flow chart of an atmospheric pollutant prediction method based on big data of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0032] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0033] It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0035] The names of the messages or information exchanged between multiple devices of the present invention are only for illustrative purposes, and are not used to limit the scope of these messages or information.

[0036] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0037] like Figure 1 Shown is a flow chart of an atmospheric pollutant prediction method based on big data of the present invention.

[0038] Step 101, obtaining regional location information and event information of a target area, and determining multiple pollution sources within a preset range of the target area according to the regional location information; wherein the event information includes the time period, type, and scale of the event;

[0039] In some embodiments, the execution subject of a method for predicting atmospheric pollutants based on big data of the present invention may be a background server. The target area is a designated research area, which may be the entire activity area of ​​a large-scale fireworks show. The regional location information of the target area is obtained from the open source electronic map interface, and the regional location information of the target area may be No. 103, XX Street, XX District. The regional location information of the target area is used to obtain multiple pollution sources within a preset range of the target area. The preset range of the target area covers all pollution sources that may affect the air quality of the target area. The preset range of the target area may be multiple pollution sources within 10 kilometers around the target area as the center, and the multiple pollution sources may be factory emission sources, construction site dust, etc. The event information of the target area is obtained from the activity terminal, and the event information includes the time period of the event, the type of activity, and the scale of the event. For example, the time period of the event in the event information may be 18:00-21:00 on May 10, 2023, the type of activity in the event information may be a fireworks show, and the scale of the event in the event information may be a large-scale event.

[0040] Step 102, determining the pollution impact value of the activity according to the activity type and activity scale;

[0041] Optionally, the activity pollution impact value is determined by the following steps:

[0042] Acquire multiple historical activity data, the historical activity data including historical activity types and historical activity scales; match the multiple historical activity data according to the activity types and activity scales to obtain target historical activity data; obtain historical activity pollution emissions of the target historical activity data;

[0043] Obtaining the environmental capacity of the target area and a pre-established environmental quality standard table, and obtaining the standard allowable emission of the target area by querying the environmental capacity in the environmental quality standard table, wherein the environmental quality standard table includes different environmental capacities and the standard allowable emission corresponding to each environmental capacity;

[0044] The ratio of historical activity pollution emissions to standard allowable emissions is calculated to obtain the activity pollution impact value.

[0045] In some embodiments, multiple historical activity data of the past year are obtained from the terminal, and the historical activity data include historical activity types and historical activity scales. The historical activity types included in the historical activity data may be fireworks shows or music festivals, and the activity scales included in the historical activity data may be large-scale activities or medium-sized activities. The multiple historical activity data may include historical activity data 1 and historical activity data 2. The historical activity data with the same activity types and activity scales as the target area's event information are matched from the multiple historical activity data as the target historical activity data. If the activity types of historical activity data 2 and the target area's events are both fireworks shows and the activity scales are both large-scale events, then historical activity data 2 is used as the target historical activity data. Since the target historical activity is configured with a pollutant concentration monitoring point, the pollutant concentration monitoring point is used to monitor the pollutant concentration data of the event. The pollutant concentration data obtained is multiplied by the total activity duration of the target historical activity to obtain the historical activity pollution emission corresponding to the target historical activity. The historical activity pollution emission is uploaded to the terminal, and the historical activity pollution emission of the target historical activity data is obtained from the terminal, which may be 0.5 tons. Environmental capacity refers to the maximum load value of pollutants that an environment can accommodate under the premise of ensuring that human survival and development are not harmed and the natural ecological balance is not destroyed. The environmental capacity of the target area and the pre-established environmental quality standard table are obtained from the comprehensive database of the target area. The environmental capacity of the target area may be 400 tons. By querying the environmental capacity in the environmental quality standard table, the standard allowable emission of the target area may be 0.7 tons, wherein the environmental quality standard table includes different environmental capacities and the standard allowable emission corresponding to each environmental capacity. The historical activity pollution emission is calculated by ratio with the standard allowable emission to obtain the activity pollution impact value. Specifically, taking historical activity data 2 as an example, the historical activity pollution emission of 0.5 tons in historical activity data 2 is calculated by ratio with the standard allowable emission of 0.7 tons in the target area to obtain the activity pollution impact value of 0.714. The calculation process is 0.5 tons divided by 0.7 tons equals 0.714. If the activity pollution impact value is less than 1, it indicates that the pollution impact of the activity is within an acceptable range. If the activity pollution impact value is greater than or equal to 1, it indicates that the activity causes a greater pollution impact.

[0046] Step 103, according to the time period of the event, determine the target historical time period and obtain the pollutant concentration information and meteorological information of each of the multiple pollution sources in the target historical time period; obtain the forecast demand information of the target area and obtain multiple alternative forecast models and the forecast accuracy of each of the multiple alternative forecast models; according to the forecast accuracy of the alternative forecast models, screen the multiple alternative forecast models to obtain the target forecast model;

[0047] In some embodiments, if the event time period is 18:00-21:00 on May 10, 2023, the target historical time period is 18:00-21:00 every day in the past year, and the pollutant concentration information and meteorological information of each pollution source in the target area from 18:00-21:00 every day in the past year are obtained from the meteorological website interface. Specifically, the pollutant concentration information can be the concentration data of pollutants such as PM2.5, PM10, SO2, and NO2, and the meteorological information can be temperature, humidity, wind speed, wind direction, etc. Meteorological information has an important influence on the diffusion, transmission and transformation process of pollutants, so meteorological conditions help to more accurately predict the change of pollutant concentration. Obtain multiple alternative prediction models from the model database, and the multiple alternative prediction models can be alternative prediction model A (long short-term memory network model), the prediction accuracy of alternative prediction model A is 95%, alternative prediction model B (decision tree model), the prediction accuracy of alternative prediction model B is 68%, and alternative prediction model C (support vector machine model), the prediction accuracy of alternative prediction model C is 57%. Multiple alternative prediction models are screened, and the screening condition is to select the alternative prediction model with the highest prediction accuracy among the multiple alternative prediction models. Alternative prediction model A is the alternative prediction model with the highest prediction accuracy among the multiple alternative prediction models, so alternative prediction model A is used as the target prediction model.

[0048] Step 104, training the target prediction model through pollutant concentration information and meteorological information, and using the trained target prediction model as an atmospheric pollutant prediction model;

[0049] In some embodiments, the target prediction model is trained, and the training process is as follows: initialize the cell state and hidden state of the neural unit in the long short-term memory network model, as well as other parameters in the long short-term memory network model; input the pollutant concentration information and meteorological information of the target area from 18:00 to 21:00 every day in the past year into the target prediction model (alternative prediction model A (long short-term memory network model)), and obtain the output result of the target prediction model, compare the output result with the true value (the obtained pollutant concentration information of the target area from 18:00 to 21:00 every day in the past year), calculate the loss value, and back-propagate the loss value from the output layer to the input layer layer by layer according to the gradient information of the loss function. The gradient value of each layer parameter is calculated by the chain rule, and in the parameter update process, the parameters of each layer are updated using optimization algorithms such as gradient descent to reduce the value of the loss function. This process needs to be iterated many times until the performance of the target prediction model meets the preset requirements or reaches the maximum number of iterations, that is, the training of the target prediction model is completed, and a trained atmospheric pollutant prediction model is obtained.

[0050] Step 105, predicting the pollutant concentration of the target area during the event period based on the atmospheric pollutant prediction model to obtain an initial prediction result; adjusting the initial prediction result according to the activity pollution impact value to obtain a standard prediction result.

[0051] In some embodiments, the trained atmospheric pollutant prediction model is used to predict the pollutant concentration in the target area during the event period (18:00-21:00 on May 10, 2023) to obtain an initial prediction result. Taking the concentrations of PM2.5, PM10, and NO2 as an example, the initial prediction result can be a PM2.5 concentration of 25 μg / m 3 、PM10 concentration is 23μg / m 3 , NO2 concentration is 27μg / m 3 .

[0052] Optionally, standard prediction results are determined by the following steps:

[0053] According to the pollution impact value of the activity, the adjustment value of the initial prediction result is determined, and the adjustment value is calculated with the initial prediction result to obtain the standard prediction result.

[0054] In some embodiments, if the activity pollution impact value is 0.714, the adjustment value of the initial prediction result is obtained by querying a pre-established pollution impact adjustment table, the pollution impact adjustment table includes multiple activity pollution impact intervals and adjustment values ​​corresponding to each activity pollution impact interval, the pollution impact adjustment table is obtained by converting the activity pollution impact value and the pollutant concentration, by matching the activity pollution impact value of 0.714 in the pollution impact adjustment table, the adjustment value corresponding to the activity pollution impact value of 0.714 is obtained as 1.1, the adjustment value is calculated with the initial prediction result, and the standard prediction result is obtained. For example, for the initial prediction result PM2.5 concentration of 25μg / m 3 、PM10 concentration is 23μg / m 3 , NO2 concentration is 27μg / m 3 Multiplying them by the adjustment values, the standard prediction result can be a PM2.5 concentration of 27.5 μg / m 3 、PM10 concentration is 25.3μg / m 3 , NO2 concentration is 29.7μg / m 3 .

[0055] In some embodiments, the target prediction model is trained by obtaining the target area and time for holding large-scale events, and using the pollutant concentration information and meteorological information of the target area in the target historical time period corresponding to the time period of the event as training samples. Therefore, the trained target prediction model is an atmospheric pollutant prediction model for the target area within the time period of the event, so that the atmospheric pollutant prediction model has local and targeted predictions; holding large-scale events themselves will also have an impact on the prediction of atmospheric pollutants. The type of activity and its scale are the main factors affecting atmospheric pollution. Environmental capacity is the environment's ability to accommodate pollutants, and plays a vital role in determining the impact of large-scale events on atmospheric pollution. The initial prediction results are adjusted by the activity pollution impact value determined by the activity type and scale combined with the environmental capacity of the activity area. This can more comprehensively consider the impact of the activity on atmospheric pollutants, which is more in line with the actual situation, thereby reducing the error of the initial prediction results and improving the reliability and accuracy of the prediction results.

[0056] In some embodiments, in order to further solve the second technical problem described in the background technology section, that is, "since the time of a large-scale event is fixed and cannot be changed, if the pollutants exceed the standard before the event starts, it will affect the smooth progress of the event. In order to ensure that the air quality at the time of the event meets the standard, an effective emission restriction plan needs to be formulated. The existing technology lacks a targeted emission restriction plan formulation method and often adopts a "one-size-fits-all" emission restriction", each of the multiple pollution sources has a corresponding pollution source type, and the pollution source type corresponding to each pollution source is determined by the following steps:

[0057] Obtain the emission frequency of each pollution source in the target historical time period;

[0058] The pollution source type of the pollution source with fixed emission frequency among multiple pollution sources is determined as a fixed pollution source to obtain a fixed pollution source group; the pollution source type of the pollution source with non-fixed emission frequency among multiple pollution sources is determined as a temporary pollution source.

[0059] In some embodiments, the emission frequency of each pollution source in the target historical time period (18:00-21:00 every day in the past year) is obtained from the terminal of each pollution source. Assuming that the multiple pollution sources include a first pollution source and a second pollution source, if the emission frequency of the first pollution source can be fixed once every day at 8 am, or twice a day, then the first pollution source is a fixed pollution source, such as factory emissions, power plant emissions, etc. If the emission frequency of the second pollution source is not fixed, it can be once at 8 am on Monday and once at 9 am on Monday, then the second pollution source is determined to be a temporary pollution source, such as dust from construction sites, agricultural burning, etc.

[0060] Stationary pollution sources have corresponding pollutant emission limit plans, which are generated through the following steps:

[0061] Step 1: Obtain the pollutant emission time period of each fixed pollution source in the fixed pollution source group; determine the fixed pollution sources in the fixed pollution source group whose pollutant emission time period coincides with the event time period as key pollution sources, and obtain the key pollution source group;

[0062] In some embodiments, the pollutant emission time period is recorded from the terminal of the fixed pollution source, so the pollutant emission time period of each fixed pollution source is obtained from the terminal of the fixed pollution source. Assume that the fixed pollution source group includes fixed pollution source one, fixed pollution source two, and fixed pollution source three, wherein fixed pollution source one can be a sewage treatment plant, and the pollutant emission time period is from 19:00 to 20:00 every afternoon. Fixed pollution source two can be a waste incineration plant, and the pollutant emission time period is from 20:00 to 22:00 every afternoon. Fixed pollution source three can be an industrial furnace plant, and the pollutant emission time period is from 6:00 to 7:00 every morning. When the time period for holding an event is 18:00-21:00 on May 10, 2023, fixed pollution source one and fixed pollution source two overlap with the time period for holding an event, and fixed pollution source one and fixed pollution source two are determined as key pollution sources, and the key pollution source group includes key pollution source one (fixed pollution source one) and key pollution source two (fixed pollution source two).

[0063] Step 2: Obtain the emission of each key pollution source in the key pollution source group, calculate the emission of each key pollution source, and obtain the total amount of pollutant emissions corresponding to the key pollution source group;

[0064] Step 3: Determine the degree of pollutant exceeding the standard based on the total amount of pollutant emissions; compare the degree of pollutant exceeding the standard with the preset standard degree. If the degree of pollutant exceeding the standard is greater than or equal to the preset standard degree, generate a pollutant emission restriction plan, which includes a restriction time and a standard emission amount; send the pollutant emission restriction plan to the terminal equipment corresponding to each key pollution source in the key pollution source group, so that the management personnel corresponding to the terminal equipment corresponding to each key pollution source can perform the pollutant emission restriction operation;

[0065] The standard emission levels included in the pollutant emission limit plan are determined through the following steps:

[0066] The geographical location information of each key pollution source in the key pollution source group is obtained, and the straight-line distance value between each key pollution source and the target area is determined according to the geographical location information of each key pollution source; the first standard emission amount is generated for the key pollution sources in the key pollution source group whose straight-line distance value is greater than or equal to the preset distance value; the second standard emission amount is generated for the key pollution sources in the key pollution source group whose straight-line distance value is less than the preset distance value, and the first standard emission amount is less than the second standard emission amount.

[0067] In some embodiments, the emission of each key pollution source is obtained from the terminal of each pollution source. The emission of key pollution source one and key pollution source two included in the key pollution source group is added together to obtain the total amount of pollutant emissions corresponding to the key pollution source group, which can be 800 kilograms. A pre-configured pollutant emission comparison table is obtained. The pollutant emission comparison table includes multiple pollutant emissions and the degree of pollutant excess corresponding to each pollutant emission. By querying the total amount of pollutant emissions corresponding to the key pollution source group in the pollutant emission comparison table, the degree of pollutant excess is obtained to be 60%. The degree of pollutant excess is compared with the preset standard degree (50%). If the degree of pollutant excess is greater than or equal to the preset standard degree, a pollutant restriction emission plan is generated. The pollutant restriction emission plan includes a restriction time and a standard emission amount. Specifically, the restriction time included in the pollutant restriction emission plan can be 18:00-21:00 on May 10, 2023. The standard emission amount included in the pollutant restriction emission plan is determined by the following steps:

[0068] The geographical location information of each key pollution source in the key pollution source group is obtained through an open source electronic map interface, and the straight-line distance value between each key pollution source and the target area is obtained through the open source electronic map interface, which can be five kilometers; the key pollution sources in the key pollution source group whose straight-line distance value is greater than or equal to the preset distance value (three kilometers) generate the first standard emission amount (the first standard emission amount is 100 kilograms obtained by querying the pre-established pollutant emission limit table); the key pollution sources in the key pollution source group whose straight-line distance value is less than the preset distance value generate the second standard emission amount (the second standard emission amount is 200 kilograms obtained by querying the pre-established pollutant emission limit table), and the first standard emission amount is less than the second standard emission amount. The pollutant emission restriction scheme is sent to the terminal device corresponding to each key pollution source in the key pollution source group, so that the manager corresponding to the terminal device corresponding to each key pollution source performs the pollutant emission restriction operation, and the terminal device can be the work computer of the manager corresponding to each key pollution source.

[0069] In some embodiments, different types of pollution sources often have different emission frequencies due to their different mechanisms and conditions for producing pollutants. Therefore, emission frequency is an important basis for distinguishing different types of pollution sources. The type of pollution source is determined by whether the emission frequency of the pollution source is fixed, which improves the accuracy of pollution source identification and helps to formulate targeted pollution control plans; the decline in environmental quality may affect the smooth progress of activities. By limiting the emissions of fixed pollution sources during the activities in advance, the smooth progress of the activities can be ensured; formulating different pollutant emission limits for different key pollution sources can more accurately control the emissions of each pollution source and avoid "one-size-fits-all" emission limits.

[0070] In some embodiments, in order to further solve the technical problem three described in the background technology part, that is, "when there are multiple faulty air quality monitoring points, the staff lacks a clear maintenance policy, resulting in low maintenance efficiency, so that some monitoring points that urgently need to be repaired cannot be handled in time, affecting the smooth progress of activities", in some embodiments of the present invention, the target area is configured with multiple air quality monitoring points, each of the multiple air quality monitoring points is configured with an indicator light, each air quality monitoring point has a corresponding maintenance priority, and the maintenance priority corresponding to each air quality monitoring point is determined by the following steps:

[0071] Step 1: Obtain equipment identification and equipment location information of each air quality monitoring point among multiple air quality monitoring points in the target area; obtain a pollutant concentration data group of each air quality monitoring point;

[0072] Step 2: Calculate the pollutant concentration data group of each air quality monitoring point to obtain the standard deviation of the pollutant concentration data group of each air quality monitoring point; for multiple air quality monitoring points, determine the air quality monitoring points corresponding to the pollutant concentration data exceeding three times the standard deviation in the pollutant concentration data group of each air quality monitoring point as abnormal monitoring points to obtain an abnormal monitoring point group, and generate abnormal information corresponding to each abnormal monitoring point in the abnormal monitoring point group and control the corresponding indicator light to turn on to obtain an abnormal monitoring point group, wherein the abnormal information includes equipment identification and equipment location information;

[0073] Step 3: Obtain a key location point group of the target area, determine whether there is an abnormal monitoring point within the preset range of the key location point group, and if so, determine the location attribute of the corresponding abnormal monitoring point as level one; if not, determine the location attribute of the corresponding abnormal monitoring point as level two; score each abnormal monitoring point in the abnormal monitoring point group according to the location attribute of the abnormal monitoring point, and obtain the importance score of each abnormal monitoring point;

[0074] Step 4: Obtain the maintenance station location information of the maintenance station, and determine the maintenance distance from the maintenance station to each abnormal monitoring point in the abnormal monitoring point group according to the maintenance station location information; determine the maintenance distance score corresponding to each abnormal monitoring point according to the maintenance distance of each abnormal monitoring point; configure weights for the importance score and maintenance distance score corresponding to each abnormal monitoring point respectively, and perform weighted summation of the importance score and maintenance distance score corresponding to each abnormal monitoring point through the weights to obtain the maintenance score of each abnormal monitoring point;

[0075] Step 5: Determine the maintenance priority of each abnormal monitoring point according to the maintenance score of each abnormal monitoring point.

[0076] In some embodiments, the device identification and device location information of each air quality monitoring point are obtained from the terminal. The air quality monitoring points are used to monitor the pollutant concentration data of the activity, wherein the multiple air quality monitoring points may include air quality monitoring point 1, air quality monitoring point 2, and air quality monitoring point 3. For example, the device identification of air quality monitoring point 1 is "one" and the device location information is audience area 1, and the device identification of air quality monitoring point 2 is "two" and the device location information is work area 1. The corresponding pollutant concentration data is obtained from each air quality monitoring point to obtain a pollutant concentration data group for each air quality monitoring point. For example, the pollutant concentration data group of air quality monitoring point 1 may be the pollutant concentration data at 18:30, the pollutant concentration data at 18:40, and the pollutant concentration data at 18:50.

[0077] In some embodiments, the pollutant concentration data of the air quality monitoring point 1 includes PM10, PM2.5, SO2, NO2, O3, etc. Taking the PM2.5 concentration data of the air quality monitoring point 1 as an example, the PM2.5 concentration data group of the air quality monitoring point 1 is {50, 55, 60, 65, 70, 250}, (unit: μg / m 3 ), first calculate the mean and standard deviation of the data group: the mean is: 83.33, then calculate the standard deviation, and get a standard deviation of 77.11, of which only 250 in the PM2.5 concentration data group exceeds three times the standard deviation, for example, 250 is greater than 3 times 77.11, then the air quality monitoring point one is determined as an abnormal monitoring point, and an abnormal monitoring point group is obtained, and the abnormal information corresponding to each abnormal monitoring point in the abnormal monitoring point group is generated and the corresponding indicator light is turned on to obtain an abnormal monitoring point group, where the abnormal information includes equipment identification and equipment location information. Since the pollutant concentration data of air quality monitoring point one includes PM10, PM2.5, SO2, NO2, O3, etc., as long as any pollutant in the pollutant concentration data exceeds three times the standard deviation, the corresponding air quality monitoring point is determined as an abnormal monitoring point.

[0078] In some embodiments, a key position point group of the target area is obtained from the terminal, and each key position point in the key position point group includes the main channel or activity center of the activity, which often gathers a large number of people. Assume that the key position point group includes key position point 1 and key position point 2. The abnormal monitoring point group includes abnormal monitoring point 1 and abnormal monitoring point 2. When the abnormal monitoring point 1 is within the preset range (5 meters) of the key position point 1 in the key position point group, the position attribute of the abnormal monitoring point 1 is determined as the first level. If the abnormal monitoring point 2 is outside the preset range of the key position point 1 and the key position point 2 in the key position point group, the position attribute of the abnormal monitoring point 2 is determined as the second level. The maintenance station is in the target area, and the maintenance station location information of the maintenance station is obtained from the electronic map interface of the target area. Through the maintenance station location information, the maintenance distance from each abnormal monitoring point in the abnormal monitoring point group to the maintenance station is determined, which can be 28 meters from the maintenance station to the abnormal monitoring point 1, and 25 meters from the maintenance station to the abnormal monitoring point 2. The maintenance comparison table is obtained from the database, and the maintenance comparison table includes multiple location attributes, scores corresponding to each location attribute, multiple maintenance distances, and scores corresponding to each maintenance distance. By querying the location attribute and maintenance distance of each abnormal monitoring point in the maintenance comparison table, the importance score and maintenance distance score of each abnormal monitoring point are obtained. Specifically, the importance score of abnormal monitoring point 1 is 95 points and the maintenance distance score is 90 points; the importance score of abnormal monitoring point 2 is 85 points and the maintenance distance score is 92 points. A 60% weight is configured for the importance score corresponding to each abnormal monitoring point, and a 40% weight is configured for the maintenance distance score. The importance score and maintenance distance score corresponding to each abnormal monitoring point are weighted and summed by weight to obtain the maintenance score of each abnormal monitoring point. The maintenance score of abnormal monitoring point 1 is 93 points (95×60%+90×40%=93), and the maintenance score of abnormal monitoring point 2 is 87.8 points (85×60%+92×40%=87.8). The maintenance score of abnormal monitoring point 1 is greater than that of abnormal monitoring point 2. Therefore, the maintenance priority of abnormal monitoring point 1 is high, and abnormal monitoring point 1 is repaired first.

[0079] In these embodiments, according to the characteristics of the normal distribution, data points that fall outside three standard deviations of the mean are considered to be extremely rare. Therefore, the triple standard deviation method is used to identify abnormal monitoring points in the pollutant concentration data group, which can improve the accuracy of determining abnormal monitoring points; indicator light reminders are turned on for abnormal monitoring points and abnormal information is generated. The indicator light can remind staff and reduce response time, so that they can find and respond to abnormal monitoring points in the first place, and there is no need for staff to actively judge, thereby simplifying the troubleshooting process and saving troubleshooting manpower; key areas of activities often gather a large number of people, and the maintenance priority is determined by the location attributes of each abnormal monitoring point, so as to improve the timeliness of maintenance of abnormal monitoring points and ensure the smooth progress of activities.

[0080] The above descriptions are only some preferred embodiments of the present invention and the explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but also should cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) to form a technical solution.

Claims

1. A method for predicting atmospheric pollutants based on big data, characterized in that: include: Acquire regional location information and event information of a target area, and determine multiple pollution sources within a preset range of the target area according to the regional location information; wherein the event information includes the time period, type, and scale of the event; Determine the pollution impact value of the activity based on the type of activity and the scale of the activity; According to the time period of the event, determine the target historical time period and obtain the pollutant concentration information and meteorological information of each of the multiple pollution sources in the target historical time period; obtain multiple alternative prediction models and the prediction accuracy of each of the multiple alternative prediction models; according to the prediction accuracy of the alternative prediction models, screen the multiple alternative prediction models to obtain the target prediction model; The target prediction model is trained by using the pollutant concentration information and the meteorological information, and the trained target prediction model is used as an atmospheric pollutant prediction model; Based on the atmospheric pollutant prediction model, the pollutant concentration in the target area during the event time period is predicted to obtain an initial prediction result; the initial prediction result is adjusted according to the activity pollution impact value to obtain a standard prediction result.

2. The method for predicting atmospheric pollutants based on big data according to claim 1, characterized in that: The activity pollution impact value is determined by the following steps: Acquire multiple historical activity data, the historical activity data including historical activity types and historical activity scales; match the multiple historical activity data according to the activity types and activity scales to obtain target historical activity data; and acquire historical activity pollution emissions of the target historical activity data; Obtaining the environmental capacity of the target area and a pre-established environmental quality standard table, and obtaining the standard allowable emission amount of the target area by querying the environmental capacity in the environmental quality standard table, wherein the environmental quality standard table includes different environmental capacities and the standard allowable emission amount corresponding to each environmental capacity; The pollution impact value of the activity is obtained by calculating the ratio of the historical activity pollution emissions to the standard permitted emissions.

3. The method for predicting atmospheric pollutants based on big data according to claim 2, characterized in that: The standard prediction results are determined by the following steps: An adjustment value of the initial prediction result is determined according to the activity pollution impact value, and the adjustment value and the initial prediction result are calculated to obtain the standard prediction result.

4. The method for predicting atmospheric pollutants based on big data according to claim 3, characterized in that: Each of the multiple pollution sources has a corresponding pollution source type, and the pollution source type corresponding to each pollution source is determined by the following steps: Obtain the emission frequency of each pollution source within the target historical time period; The pollution source type of the pollution sources with fixed emission frequency among the multiple pollution sources is determined as a fixed pollution source to obtain a fixed pollution source group; the pollution source type of the pollution sources with non-fixed emission frequency among the multiple pollution sources is determined as a temporary pollution source.

5. The method for predicting atmospheric pollutants based on big data according to claim 4, characterized in that: The stationary pollution source has a corresponding pollutant emission restriction plan, and the pollutant emission restriction plan is generated by the following steps: Obtaining the pollutant emission time period of each fixed pollution source in the fixed pollution source group; determining the fixed pollution sources in the fixed pollution source group whose pollutant emission time period coincides with the event holding time period as key pollution sources, and obtaining a key pollution source group; Obtaining the emission of each key pollution source in the key pollution source group, calculating the emission of each key pollution source, and obtaining the total amount of pollutant emissions corresponding to the key pollution source group; According to the total amount of pollutant emissions, determine the degree of pollutant exceeding the standard; compare the degree of pollutant exceeding the standard with the preset standard degree, and if the degree of pollutant exceeding the standard is greater than or equal to the preset standard degree, generate a pollutant emission restriction plan, the pollutant emission restriction plan includes a restriction time and a standard emission amount; send the pollutant emission restriction plan to the terminal device corresponding to each key pollution source in the key pollution source group, so that the management personnel corresponding to the terminal device corresponding to each key pollution source execute the pollutant emission restriction operation; The standard emission amount included in the pollutant emission restriction scheme is determined by the following steps: Obtain the geographical location information of each key pollution source in the key pollution source group, and determine the straight-line distance value between each key pollution source and the target area based on the geographical location information of each key pollution source; generate a first standard emission amount for the key pollution sources in the key pollution source group whose straight-line distance value is greater than or equal to a preset distance value; generate a second standard emission amount for the key pollution sources in the key pollution source group whose straight-line distance value is less than the preset distance value, and the first standard emission amount is less than the second standard emission amount.

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