Pollution source information display method and device, electronic equipment and computer readable medium

Abstract

This disclosure provides embodiments of a method, apparatus, electronic device, and computer-readable medium for displaying pollution source information. One specific implementation of the method includes: acquiring a predicted meteorological field information sequence and grid area information; downscaling the predicted meteorological field information sequence to obtain a first meteorological information sequence; inputting the first meteorological information sequence and the predicted meteorological field information sequence into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence; generating various backward trajectory information based on the second meteorological information sequence; generating a pollutant contribution rate set corresponding to each grid identifier based on each backward trajectory information; acquiring a set of information about the entities to which the pollution sources belong; generating a target pollution source information set based on the set of information about the entities to which the pollution sources belong and the pollutant contribution rate set; and controlling an associated display device to display the target pollution source information set. This implementation can improve the accuracy of pollution control and reduce the degree of air pollution.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of air pollution control technology, specifically to pollution source information display methods, devices, electronic devices, and computer-readable media. Background Technology

[0002] In recent years, severe air pollution events have occurred frequently, increasingly impacting people's daily lives. Timely identification of pollution sources for pollution control is crucial for reducing severe air pollution. Currently, the common method for displaying pollution source information is to generate pollution source information based on atmospheric data and a backward trajectory model, and then display this information for users to view.

[0003] However, the inventors discovered that when displaying pollution source information using the above method, the following technical problems often exist: the scale of the directly obtained atmospheric information is relatively high, resulting in a low resolution of the backward trajectory generated by combining the backward trajectory model, which in turn leads to a low accuracy of the pollution source information, resulting in a low accuracy of pollution control and causing serious air pollution.

[0004] In the process of adopting technical solutions to address the technical problems in the background art, the following technical problem often arises: how to improve the accuracy of pollutant alarm information detected by monitoring stations to save alarm resources. A conventional solution to this problem is to determine the pollutant alarm threshold using historical alarm data from the monitoring stations, and then compare the detected pollutant concentration with the threshold to improve the accuracy of the pollutant alarm information detected by the monitoring stations. However, this conventional solution still suffers from the following second problem: the detection of pollutant concentrations is affected by external environmental factors (such as weather and detection equipment), leading to lower accuracy in the detected pollutant concentrations, resulting in lower accuracy of pollutant alarm information and ultimately wasting alarm resources.

[0005] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of this disclosure provide methods, apparatus, electronic devices, and computer-readable media for displaying pollution source information to address one or more of the technical problems mentioned in the background section above.

[0008] In a first aspect, some embodiments of this disclosure provide a method for displaying pollution source information. The method includes: in response to detecting at least one pollutant alarm information corresponding to a target environmental monitoring station, acquiring a predicted meteorological field information sequence and grid area information for a corresponding target environmental monitoring area, wherein the target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes each grid identifier; downscaling the predicted meteorological field information sequence to obtain a first meteorological information sequence; inputting the first meteorological information sequence and the predicted meteorological field information sequence into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence, wherein the preset downscaling meteorological information generation model includes a data fusion layer, a first downscaling layer, a second downscaling layer, and an output layer; generating backward trajectory information corresponding to each pollutant identifier based on the second meteorological information sequence; generating a pollutant contribution rate set corresponding to each grid identifier based on the backward trajectory information; acquiring a set of pollution source ownership information corresponding to the target environmental monitoring area; generating a target pollution source information set based on the pollution source ownership information set and the pollutant contribution rate set; and controlling an associated display device to display the target pollution source information set.

[0009] Secondly, some embodiments of this disclosure provide a pollution source information display device, the device comprising: a first acquisition unit configured to acquire a predicted meteorological field information sequence and grid area information of a corresponding target environmental monitoring area in response to detecting at least one pollutant alarm information of a corresponding target environmental monitoring station, wherein the target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes each grid identifier; a downscaling unit configured to downscale the predicted meteorological field information sequence to obtain a first meteorological information sequence; and an input unit configured to input the first meteorological information sequence and the predicted meteorological field information sequence into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence, wherein the first meteorological information sequence and the predicted meteorological field information sequence are input into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence. The preset downscaling meteorological information generation model includes a data fusion layer, a first downscaling layer, a second downscaling layer, and an output layer; a first generation unit is configured to generate backward trajectory information corresponding to each pollutant identifier based on the second meteorological information sequence; a second generation unit is configured to generate a pollutant contribution rate set corresponding to each grid identifier based on the backward trajectory information; a second acquisition unit is configured to acquire a set of pollution source ownership information corresponding to the target environmental monitoring area; a third generation unit is configured to generate a target pollution source information set based on the pollution source ownership information set and the pollutant contribution rate set; and a display unit is configured to control associated display devices to display the target pollution source information set.

[0010] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0012] The above-described embodiments of this disclosure have the following beneficial effects: The pollution source information display method of some embodiments of this disclosure can improve the accuracy of pollution control and reduce the degree of air pollution. Specifically, the reason for the low accuracy of pollution control and severe air pollution is that the directly obtained atmospheric information has a high scale, resulting in a low resolution of the backward trajectory generated by combining the backward trajectory model, leading to low accuracy of pollution source information, and thus low accuracy of pollution control and severe air pollution. Based on this, the pollution source information display method of some embodiments of this disclosure firstly, in response to detecting at least one pollutant alarm information of a corresponding target environmental monitoring station, acquires the predicted meteorological field information sequence and grid area information of the corresponding target environmental monitoring area. The target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes the identifiers of each grid. Therefore, when the environmental monitoring station detects a pollutant alarm, it can obtain meteorological information and information of each sub-area for a future period of time in the monitoring area, which can be used to trace the pollution source. Secondly, the predicted meteorological field information sequence is downscaled to obtain a first meteorological information sequence. This yields highly refined meteorological information, which can be used to improve the accuracy of pollution sources. Then, the first meteorological information sequence and the predicted meteorological field information sequence are input into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence. This preset downscaling meteorological information generation model includes a data fusion layer, a first downscaling layer, a second downscaling layer, and an output layer. This allows for further downscaling of the meteorological information, resulting in more refined meteorological information. Next, based on the second meteorological information sequence, backward trajectory information corresponding to each pollutant identifier is generated. This yields highly accurate backward trajectory information, which can be used to trace pollution sources. Then, based on the backward trajectory information, a set of pollutant contribution rates corresponding to each grid identifier is generated. This yields the contribution rates of each pollutant in each sub-region, which can be used to locate pollution sources. Following this, a set of information on the entities to which pollution sources belong in the target environmental monitoring area is obtained. This yields the entities to which all pollution sources belong in the monitoring area, which can be used to locate pollution sources. Based on the set of information on the entities to which pollution sources belong and the set of pollutant contribution rates, a target pollution source information set is generated. This allows for tracing each pollution source, obtaining information about each pollution source, which can then be viewed by the user. Finally, the associated display devices are controlled to display the aforementioned set of target pollution source information. This allows for the display of highly accurate information on each pollution source, facilitating user access for pollution control. Furthermore, because meteorological information can be downscaled multiple times during pollution source tracing to obtain more refined meteorological data, the accuracy of the traced pollution source information is improved. This, in turn, enhances the precision of pollution control and reduces the degree of air pollution. Attached Figure Description

[0013] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0014] Figure 1 This is a flowchart of some embodiments of the pollution source information display method according to the present disclosure;

[0015] Figure 2 These are schematic diagrams illustrating the structure of some embodiments of the pollution source information display device according to this disclosure;

[0016] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0020] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0021] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0022] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Figure 1A flow 100 of some embodiments of a pollution source information display method according to the present disclosure is shown. The pollution source information display method includes the following steps:

[0024] Step 101: In response to detecting at least one pollutant alarm information of the corresponding target environmental monitoring station, obtain the predicted meteorological field information sequence and grid area information of the corresponding target environmental monitoring area.

[0025] In some embodiments, the executing entity (e.g., a computing device) of the pollution source information display method may, in response to detecting at least one pollutant alarm message from a corresponding target environmental monitoring station, acquire a predicted meteorological field information sequence and grid area information for the corresponding target environmental monitoring area. The target environmental monitoring area corresponds to the target environmental monitoring station. The target environmental monitoring area can be an area requiring environmental monitoring. For example, the target environmental monitoring area can be an administrative region of a city. The target environmental monitoring station can be an atmospheric environmental monitoring station monitoring the target environmental monitoring area. The pollutant alarm message can indicate that the pollutant concentration in the corresponding target environmental monitoring area is greater than a preset pollutant concentration threshold. The pollutant concentration can be the concentration of the corresponding pollutant. The pollutant can be, but is not limited to, one of the following: ozone, PM10, PM2.5, sulfur dioxide, nitrogen dioxide, and carbon monoxide. The preset pollutant concentration threshold can be a pre-set minimum pollutant concentration that indicates the pollutant concentration is harmful to human health. The predicted meteorological field information sequence can be a sequence of predicted meteorological field information over a future period arranged in ascending chronological order. The future period can be the next 16 days. The predicted meteorological field information can be Global Forecast System (GFS) meteorological data. The aforementioned grid area information can be information about the target environmental monitoring area. The aforementioned grid area information can include, but is not limited to, individual grid identifiers. The grid identifier can be a unique identifier for the grid. Each grid corresponds to a sub-region within the target environmental monitoring area. The sub-region can be an area that is evenly divided into the target environmental monitoring area according to a preset number of divisions. The preset number of divisions can be a pre-defined number of divisions required. The sub-region can also be an administrative sub-region included in the target environmental monitoring area. For example, if the target environmental monitoring area can be the area corresponding to city xx, then the sub-region can be the area corresponding to county yy within city xx. "xx" and "yy" can be any characters. In practice, the aforementioned implementing entity can, in response to detecting at least one pollutant alarm information from the corresponding target environmental monitoring station, obtain the predicted meteorological field information sequence and grid area information of the corresponding target environmental monitoring area from the database via a wired or wireless connection.

[0026] Optionally, prior to step 101, the aforementioned implementing entity may also perform the following steps for each pollutant identifier included in each pollutant identifier:

[0027] The first step is to obtain the first and second current pollutant concentrations corresponding to the aforementioned pollutant identifiers at the target environmental monitoring station. Here, the pollutant identifier can be a unique identifier for the pollutant. The first current pollutant concentration can be the pollutant concentration corresponding to the aforementioned pollutant identifier at the current time. The second current pollutant concentration can be the pollutant concentration corresponding to the aforementioned pollutant identifier at a time adjacent to the current time. The time adjacent to the current time can be a time point with an interval of a preset interval. The preset interval can be a pre-set interval close to zero. In practice, the executing entity can obtain the first and second current pollutant concentrations of the target environmental monitoring station from a database via wired or wireless connection.

[0028] The second step is to determine the time corresponding to the current concentration of the first pollutant as the first monitoring time.

[0029] The third step is to determine the time corresponding to the current concentration of the second pollutant as the second monitoring time.

[0030] The fourth step is to determine the time difference as the difference between the second monitoring time and the first monitoring time.

[0031] The fifth step is to determine the difference between the first current pollutant concentration and the second current pollutant concentration as the pollutant concentration difference.

[0032] The sixth step is to determine the ratio of the above-mentioned pollutant concentration difference to the above-mentioned time difference as the concentration change rate.

[0033] Step 7: In response to determining that the concentration change rate meets a preset concentration change rate condition, generate pollutant alarm information corresponding to the pollutant identifier. The preset concentration change rate condition can be that the concentration change rate is greater than a preset concentration change rate. The preset concentration change rate can be a pre-set concentration change rate characterizing a change in pollutant concentration that warrants an alarm. In practice, the executing entity can fill the pollutant identifier into a preset pollutant alarm information template to generate pollutant alarm information corresponding to the pollutant identifier. The preset pollutant alarm information template can be "Pollutant concentration of A exceeds the standard," where A can represent the pollutant identifier to be filled in.

[0034] To address the second technical problem mentioned above, and leveraging our strengths in big data technology, the following solution can be adopted:

[0035] Optionally, prior to step 101, the aforementioned implementing entity may also perform the following steps for each pollutant identifier included in each pollutant identifier:

[0036] The first step is to obtain the historical pollutant concentration sequences and historical site alarm information sequences corresponding to the aforementioned pollutant identifiers for the target environmental monitoring stations. The historical pollutant concentration sequences can be sequences of historical pollutant concentrations arranged in ascending chronological order. Historical pollutant concentrations can be the pollutant concentrations at corresponding historical time points. The historical site alarm information sequences can be sequences of historical site alarm information arranged in ascending chronological order. Historical site alarm information can be warnings issued at historical time points indicating that the pollutant concentrations at the target environmental monitoring stations exceed the standards. Historical site alarm information may include, but is not limited to, pollutant identifiers and historical time points. In practice, the implementing entity can obtain the historical pollutant concentration sequences and historical site alarm information sequences corresponding to the aforementioned pollutant identifiers for the target environmental monitoring stations from the database via wired or wireless connections.

[0037] The second step involves inputting the aforementioned historical pollutant concentration sequence and the aforementioned historical site alarm information sequence into a pre-trained pollutant alarm threshold generation model to obtain the pollutant alarm threshold. The pollutant alarm threshold generation model can be a linear function that takes the historical pollutant concentration sequence and the historical site alarm information sequence as input and outputs the pollutant alarm threshold. The aforementioned pollutant alarm threshold can be the minimum pollutant concentration at which a user needs to be alerted to exceed the standard.

[0038] The third step involves normalizing the concentrations of each historical pollutant in the aforementioned historical pollutant concentration sequence to obtain a normalized historical pollutant concentration sequence. In practice, firstly, the implementing entity can determine the maximum value of each historical pollutant concentration in the aforementioned historical pollutant concentration sequence as the first historical pollutant concentration. Secondly, the minimum value of each historical pollutant concentration in the aforementioned historical pollutant concentration sequence is determined as the second historical pollutant concentration. Then, the difference between the first historical pollutant concentration and the second historical pollutant concentration is determined as the first historical pollutant concentration difference. Next, for each historical pollutant concentration included in the aforementioned historical pollutant concentration sequence, the difference between the first historical pollutant concentration and the second historical pollutant concentration is determined as the second historical pollutant concentration difference. Then, for each determined second historical pollutant concentration difference, the ratio of the second historical pollutant concentration difference to the first historical pollutant concentration difference is determined as the normalized historical pollutant concentration. Finally, the determined normalized historical pollutant concentrations are arranged in ascending order according to their position in the aforementioned historical pollutant concentration sequence to obtain the normalized historical pollutant concentration sequence.

[0039] The fourth step involves generating a set of historical pollutant concentration correlation coefficients based on the aforementioned normalized historical pollutant concentration sequence and the historical pollutant concentration series. In practice, for each normalized historical pollutant concentration in the aforementioned normalized historical pollutant concentration sequence, the executing entity can perform a correlation analysis on the aforementioned normalized historical pollutant concentration and the aforementioned historical pollutant concentration sequence using the Canonical Correlation Analysis (CCA) algorithm to obtain historical pollutant concentration correlation coefficients. Then, the obtained historical pollutant concentration correlation coefficients are used to determine the set of historical pollutant concentration correlation coefficients.

[0040] Fifth, for each historical pollutant concentration correlation coefficient included in the aforementioned set of historical pollutant concentration correlation coefficients, in response to determining that the aforementioned historical pollutant concentration correlation coefficient satisfies a preset correlation coefficient condition, the historical pollutant concentration in the aforementioned historical pollutant concentration sequence corresponding to the aforementioned historical pollutant concentration correlation coefficient is determined as the target historical pollutant concentration. Wherein, the aforementioned preset correlation coefficient condition can be that the aforementioned historical pollutant concentration correlation coefficient is greater than or equal to a preset historical pollutant concentration correlation coefficient. The aforementioned preset historical pollutant concentration correlation coefficient can be a pre-defined historical pollutant concentration correlation coefficient.

[0041] Step 6: Input the determined historical pollutant concentrations of each target, the aforementioned pollutant identifiers, and the current time into the pre-trained pollutant concentration generation model for the prediction site to obtain the predicted pollutant concentrations at the site. The pollutant concentration generation model for the prediction site can be a time series prediction model that takes the historical pollutant concentrations of each target, the aforementioned pollutant identifiers, and the current time as input, and the predicted pollutant concentrations at the site as output. The predicted pollutant concentrations at the site can be the pollutant concentrations monitored by the corresponding atmospheric environmental monitoring stations. The pollutant concentration generation model for the prediction site can include an input layer (first prediction layer), a second prediction layer, a third prediction layer, and an output layer. The input layer can be used to extract feature vectors from the input data. The first, second, and third prediction layers can be different types of time series prediction models used to predict the pollutant concentrations at the site. As an example, the first prediction layer can be a model that is good at capturing long-term time series features. For example, the first prediction layer can be a Transformer model. The second prediction layer can be a model that is good at capturing short-term time series features. For example, the second prediction layer can be an ARMA (Auto-Regressive Moving Average) model. The third prediction layer can be a time series prediction model that includes seasonal patterns. For example, the third prediction layer mentioned above can be a Seasonal Decomposition of Time Series (STL) model. The output layer can be used to determine the first preset seasonal model coefficient as the third model coefficient in response to the current time and pollutant identification meeting preset seasonal conditions; and to determine the second preset seasonal model coefficient as the third model coefficient in response to the current time and pollutant identification not meeting the preset seasonal conditions. The sum of the products of the predicted site pollutant concentration output by the first prediction layer and the preset first model coefficient, the predicted site pollutant concentration output by the second prediction layer and the preset second model coefficient, and the predicted site pollutant concentration output by the third prediction layer and the third model coefficient is determined as the predicted site pollutant concentration. The sum of the preset first model coefficient, the preset second model coefficient, and the third model coefficient can be 1. The preset first model coefficient and the preset second model coefficient can be coefficients of a pre-defined corresponding time series prediction model. The preset first model coefficient and the preset second model coefficient can be the same. The preset seasonal condition can be that the pollutant identification is a pollutant susceptible to seasonal influences, and the current time is in the season in which the pollutant corresponding to the pollutant identification is susceptible to influences. As an example, if the pollutant is identified as PM2.5 and the current time is winter, then the preset seasonality condition is met. Both the preset first seasonality model coefficient and the preset second seasonality model coefficient can be pre-set coefficients corresponding to the second prediction layer.It should be noted that the preset first seasonality model coefficient is greater than the preset second seasonality model coefficient. For example, the preset first seasonality model coefficient can be 0.6, and the preset second seasonality model coefficient can be 0.4. The input layer is connected to the first prediction layer, the second prediction layer, and the third prediction layer, respectively. The first prediction layer, the second prediction layer, and the third prediction layer are connected to the output layer.

[0042] Step 7: Obtain the current site monitoring pollutant concentration corresponding to the aforementioned pollutant identifier. The current site monitoring pollutant concentration can be the pollutant concentration detected by the target environmental monitoring station at the current time. In practice, the implementing entity can obtain the current site monitoring pollutant concentration corresponding to the aforementioned pollutant identifier from the database via wired or wireless connection.

[0043] Step 8: Based on the predicted pollutant concentrations at the above-mentioned sites and the pollutant concentrations at the current sites, determine the pollutant concentration at the target site. The pollutant concentration at the target site can be the actual pollutant concentration for user reference. In practice, the executing entity can set the preset initial sampling number as the initial sampling number and perform the following determination steps:

[0044] The first determining step involves defining the difference between the predicted pollutant concentration at the aforementioned site and the currently monitored pollutant concentration at the aforementioned site as the concentration prediction difference. The preset initial sampling number can be a pre-defined number of times pollutant concentrations will be sampled and detected. For example, the preset initial sampling number can be 1.

[0045] The second determining step involves, in response to determining that the aforementioned concentration prediction difference is less than a preset concentration prediction difference, determining the current site's monitored pollutant concentration as the target site's monitored pollutant concentration. The preset concentration prediction difference can be a pre-defined concentration prediction difference.

[0046] The third determination step, in response to determining that the above-mentioned concentration prediction difference is greater than or equal to a preset concentration prediction difference, and the initial sampling number is less than the preset sampling number, controls the associated air quality detector to perform a detection operation, obtains the detected pollutant concentration corresponding to the above-mentioned pollutant label as the current station's monitored pollutant concentration, and determines the sum of the initial sampling number and the preset increment step as the update sampling number, and continues to execute the determination step with the update sampling number as the initial sampling number. The preset sampling number can be a maximum value of the number of times the pollutant concentration is sampled and detected in advance. For example, the preset sampling number can be 3 or 5. The detection operation can be the detection of the pollutant concentration corresponding to the above-mentioned pollutant label. The preset increment step can be a preset value of the number of samplings increased each time. For example, the preset increment step can be 1.

[0047] Step nine: In response to the pollutant concentration at the target site being greater than or equal to the pollutant alarm threshold, a pollutant alarm message is generated based on the pollutant identifier, and the corresponding alarm device is controlled to perform a pollutant alarm operation. The alarm device can be a device used to alert the user. For example, the alarm device can be an alarm or an indicator light. The pollutant alarm operation can be an operation to alert the user that the concentration of the corresponding pollutant exceeds the standard. As an example, when the alarm device is an alarm, the pollutant alarm operation can be an operation to make the alarm sound. As another example, when the alarm device is an indicator light, the pollutant alarm operation can be an operation to turn on the indicator light.

[0048] The above-described technical solution and its related content, as an inventive point of this disclosure, solve the technical problem that "the accuracy of the detected pollutant concentration is low due to the influence of the external environment (e.g., weather, detection equipment) during the detection of pollutant concentration, resulting in low accuracy of pollutant alarm information and thus wasting alarm resources." Factors leading to wasted alarm resources often include: the influence of the external environment (e.g., weather, detection equipment) during the detection of pollutant concentration, resulting in low accuracy of the detected pollutant concentration, thus causing low accuracy of pollutant alarm information and wasting alarm resources. Solving these factors can save alarm resources. To achieve this effect, some embodiments of the pollution source information display method of this disclosure, while determining the pollutant alarm threshold through historical alarm data from monitoring stations, also verifies the detected current pollutant concentration by comparing the predicted current pollutant concentration with the detected current pollutant concentration. When the difference between the predicted and detected current pollutant concentrations is small, the current pollutant concentration can be used as the actual value to determine whether the pollutant concentration exceeds the standard, thereby improving the accuracy of pollutant alarm information. Because the prediction of current pollutant concentrations takes into account both seasonal variations and long-term and short-term series variations, the accuracy of the predicted current pollutant concentrations can be improved, which in turn improves the accuracy of pollutant alarm information, thereby saving alarm resources.

[0049] Step 102: Downscale the predicted meteorological field information sequence to obtain the first meteorological information sequence.

[0050] In some embodiments, the executing entity can downscale the predicted meteorological field information sequence to obtain a first meteorological information sequence. This first meteorological information sequence can be a sequence of various first meteorological information items arranged in ascending chronological order. The first meteorological information may include, but is not limited to, atmospheric temperature, atmospheric humidity, air pressure, wind speed, wind direction, and precipitation. In practice, the executing entity can downscale the predicted meteorological field information sequence using a preset WRF (Weather Research and Forecasting) downscaling method to obtain the first meteorological information sequence. This preset WRF downscaling method can be a pre-defined dynamic downscaling method of the WRF model or a statistical downscaling method of the WRF model.

[0051] Step 103: Input the first meteorological information sequence and the predicted meteorological field information sequence into the preset downscaling meteorological information generation model to obtain the second meteorological information sequence.

[0052] In some embodiments, the executing entity can input the first meteorological information sequence and the predicted meteorological field information sequence into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence. The preset downscaling meteorological information generation model can be a neural network that takes the first meteorological information sequence and the predicted meteorological field information sequence as input and the second meteorological information sequence as output. The neural network can be a convolutional neural network. The preset downscaling meteorological information generation model can include a data fusion layer, a first downscaling layer, a second downscaling layer, and an output layer. The data fusion layer can be used to fuse the first meteorological information and the predicted meteorological field information at the same time. The first downscaling layer can perform a mixed downscaling process on the fused data to obtain a first mixed downscaling meteorological field information. The first downscaling layer can be a mixed downscaling model of the WRF model. The second downscaling layer can perform a further mixed downscaling process on the first mixed downscaling meteorological field information to obtain a second mixed downscaling meteorological field information. The second downscaling layer can also be a mixed downscaling model of the WRF model. The output layer can be used to arrange the obtained second mixed downscaling meteorological field information as individual second meteorological information sequences to obtain a second meteorological information sequence. The data fusion layer is connected to the first downscaling layer. The first downscaling layer is connected to the second downscaling layer. The second downscaling layer is connected to the output layer.

[0053] Step 104: Based on the second meteorological information sequence, generate the backward trajectory information corresponding to each pollutant identifier.

[0054] In some embodiments, the executing entity can generate backward trajectory information corresponding to each pollutant identifier based on the second meteorological information sequence. The pollutant identifier and the backward trajectory information can correspond one-to-one. The backward trajectory information can characterize the trajectory of pollutant transport and diffusion for the corresponding pollutant identifier. The backward trajectory information can include, but is not limited to, a sequence of trajectory point coordinates. The trajectory point coordinate sequence can be a sequence of trajectory point coordinates arranged in ascending chronological order. The trajectory point coordinates can be the latitude and longitude coordinates of the trajectory points. The trajectory points can be points on the trajectory formed during the pollutant diffusion process. In practice, the executing entity can input the second meteorological information sequence into a preset backward trajectory model to obtain the backward trajectory information corresponding to each pollutant identifier. The preset backward trajectory model can be a Hysplit (Hybrid single particle lagrangian integrated trajectory) backward trajectory model.

[0055] Step 105: Based on the information of each backward trajectory, generate a set of pollutant contribution rates corresponding to each grid identifier.

[0056] In some embodiments, a set of pollutant contribution rates corresponding to each of the aforementioned backward trajectory information is generated. Each grid identifier corresponds to one pollutant contribution rate. In practice, the executing entity can generate the set of pollutant contribution rates corresponding to each of the aforementioned grid identifiers using various methods based on the aforementioned backward trajectory information.

[0057] Optionally, the aforementioned grid area information may further include the total number of grids. The total number of grids can be the number of sub-regions included in the target environmental monitoring area. The predicted meteorological field information in the aforementioned predicted meteorological field information sequence may include the predicted pollutant concentrations corresponding to each pollutant identifier. There can be a one-to-one correspondence between pollutant identifiers and predicted pollutant concentrations. The predicted pollutant concentration can be the predicted pollutant concentration corresponding to the pollutant identifier.

[0058] In some optional implementations of certain embodiments, the aforementioned execution entity can generate a set of pollutant contribution rates corresponding to each of the aforementioned grid identifiers based on the aforementioned backward trajectory information through the following steps:

[0059] The first step is to perform the following sub-steps for each of the aforementioned backward trajectory information:

[0060] The first sub-step involves arranging the predicted pollutant concentrations corresponding to the aforementioned backward trajectory information within the predicted meteorological field information sequence to obtain a predicted pollutant concentration sequence. The predicted pollutant concentrations corresponding to the aforementioned backward trajectory information can be those whose corresponding pollutant identifier is the same as the pollutant identifier corresponding to the aforementioned backward trajectory information. In practice, the predicted pollutant concentrations corresponding to the aforementioned backward trajectory information within the predicted meteorological field information sequence can be arranged in ascending chronological order to obtain the predicted pollutant concentration sequence.

[0061] The second sub-step involves determining the average value of each predicted pollutant concentration included in the above predicted pollutant concentration sequence as the average predicted pollutant concentration.

[0062] The third sub-step involves determining the number of grid trajectories for each grid identifier included in the aforementioned grid area information, based on the aforementioned backward trajectory information and the aforementioned grid identifier. In practice, firstly, the executing entity can obtain the grid area range corresponding to the aforementioned grid identifier from the database via a wired or wireless connection. The aforementioned grid area range can be the area covered by the sub-region corresponding to the grid identifier. The aforementioned grid area range can be represented by the latitude and longitude range corresponding to the sub-region. For example, the grid area range can be: "East longitude (112°42′, 114°14′), North latitude (34°16′, 34°58′)". Then, in response to determining that the coordinates of any trajectory point included in the aforementioned backward trajectory information are contained within the aforementioned grid area, a preset first trajectory number is determined as the number of grid trajectories. The preset first trajectory number can represent that the trajectory corresponding to the aforementioned backward trajectory information has passed through the sub-region of the corresponding grid identifier. For example, the preset first trajectory number can be 1. Finally, in response to determining that the coordinates of each trajectory point included in the aforementioned backward trajectory information are not contained within the aforementioned grid area, a preset second trajectory number is determined as the number of grid trajectories. The aforementioned preset second trajectory number can represent the sub-regions whose trajectories corresponding to the aforementioned backward trajectory information do not pass through the corresponding grid markers. For example, the aforementioned preset second trajectory number can be 0.

[0063] The fourth sub-step involves summing the number of each generated grid trajectory to determine the total number of pollutant trajectories.

[0064] The fifth sub-step involves determining the number of generated grid trajectories as a grid trajectory array.

[0065] The sixth sub-step involves, in response to determining that the average predicted pollutant concentration meets a preset pollutant concentration condition, defining the total number of pollutant trajectories as the first total number of pollutant trajectories and defining the grid trajectory array as the first grid trajectory array. The preset pollutant concentration condition can be: the average predicted pollutant concentration is greater than or equal to a preset pollutant concentration. The preset pollutant concentration can be a pre-defined minimum value characterizing a pollutant concentration exceeding the standard.

[0066] The seventh sub-step, in response to determining that the average predicted pollutant concentration does not meet the preset pollutant concentration condition, determines the total number of pollutant trajectories as the second total number of pollutant trajectories, and determines the grid trajectory array as the second grid trajectory array.

[0067] The second step is to determine the total number of trajectories by summing the total number of each pollutant trajectory generated.

[0068] The third step is to determine the average number of trajectories as the ratio of the total number of trajectories to the total number of grids.

[0069] Fourth, for each grid identifier included in the aforementioned grid area information, a pollutant contribution rate is generated corresponding to that grid identifier based on the aforementioned average trajectory number. This pollutant contribution rate represents the proportion of air pollution generated in the area corresponding to the grid identifier within the target environmental monitoring area.

[0070] The fifth step is to define the contribution rates of each pollutant as a set of pollutant contribution rates.

[0071] In some optional implementations of certain embodiments, the aforementioned execution entity can generate the pollutant contribution rate corresponding to the aforementioned grid identifier based on the aforementioned average trajectory number through the following steps:

[0072] The first step is to determine the number of first grid trajectories corresponding to the grid identifier in each of the generated first grid trajectory arrays as the number of first target grid trajectories.

[0073] The second step is to determine the total number of first grid trajectories by summing the number of each first target grid trajectory.

[0074] The third step is to determine the number of second grid trajectories corresponding to the grid identifier in each generated second grid trajectory array as the number of second target grid trajectories.

[0075] The fourth step is to determine the total number of second grid trajectories by summing the number of each second target grid trajectory.

[0076] The fifth step is to determine the total number of grid trajectories as the sum of the total number of the first grid trajectories and the total number of the second grid trajectories.

[0077] The sixth step is to determine the grid weight coefficient as the ratio of the total number of grid trajectories to the average number of trajectories.

[0078] Step 7: The ratio of the total number of the first grid trajectories to the total number of the grid trajectories is determined as the contribution rate of the first pollutant.

[0079] The eighth step is to determine the pollutant contribution rate by multiplying the contribution rate of the first pollutant by the grid weight coefficient.

[0080] Step 106: Obtain the set of information on the entities to which the pollution sources belong in the corresponding target environmental monitoring area.

[0081] In some embodiments, the executing entity may obtain a set of information on the entities to which pollution sources belong for the target environmental monitoring area. The pollution source entity information in this set may be information about the entities to which the pollution sources belong. The entities to which the pollution sources belong may be entities that generate pollutants (e.g., enterprises). The pollution source entity information may include, but is not limited to, pollution source entity identifiers. The pollution source entity identifier may be a unique identifier for the pollution source entity. In practice, the executing entity may obtain the set of information on the entities to which the pollution sources belong for the target environmental monitoring area from a database via wired or wireless connections.

[0082] Step 107: Generate a target pollution source information set based on the set of information about the entities to which the pollution sources belong and the set of pollutant contribution rates.

[0083] In some embodiments, the executing entity may generate a target pollution source information set based on the set of information about the pollution source's owner and the set of pollutant contribution rates. The target pollution source information in this set may be information about the owner of the entity that generates a significant proportion of the pollutants. In practice, the executing entity may generate the target pollution source information set using various methods based on the set of information about the pollution source's owner and the set of pollutant contribution rates.

[0084] Optionally, the pollution source ownership information in the aforementioned set of pollution source ownership information may also include the ownership coordinates. These ownership coordinates can be the latitude and longitude coordinates of the pollution source ownership entity.

[0085] In some optional implementations of certain embodiments, the aforementioned executing entity may generate a target pollution source information set based on the aforementioned pollution source ownership information set and the aforementioned pollutant contribution rate set through the following steps:

[0086] The first step is to perform the following sub-steps for each pollution source belonging to a specific entity in the aforementioned set of pollution source entity information:

[0087] The first sub-step involves determining the corresponding subject grid identifier based on the subject coordinates included in the pollution source ownership information and the various grid identifiers. In practice, the executing entity can determine the grid identifier that meets preset identifier conditions as the subject grid identifier corresponding to the pollution source ownership information. The preset identifier conditions can be: the grid area corresponding to the grid identifier contains the subject coordinates included in the pollution source ownership information.

[0088] The second sub-step is to determine the pollutant contribution rate corresponding to the main grid identifier in the above pollutant contribution rate set as the target pollutant contribution rate.

[0089] The third sub-step involves sorting the aforementioned pollutant contribution rate set to obtain a pollutant contribution rate sequence. In practice, the executing entity can sort the pollutant contribution rate set in descending order of numerical value to obtain the pollutant contribution rate sequence.

[0090] The fourth sub-step involves determining the subject information of the pollution source as target pollution source information based on the aforementioned pollutant contribution rate sequence and in response to determining that the target pollutant contribution rate meets a preset pollutant contribution rate condition. The preset pollutant contribution rate condition can be that the position of the target pollutant contribution rate in the aforementioned pollutant contribution rate sequence is less than or equal to a preset position. This preset position can be a pre-defined position. For example, the preset position can be 3.

[0091] The second step is to define the identified target pollution source information into a target pollution source information set.

[0092] Optionally, the pollutant alarm information in at least one of the above-mentioned pollutant alarm messages may also include a pollutant identifier.

[0093] Optionally, the aforementioned implementing entity may also perform the following steps:

[0094] The first step is to obtain the set of pollutant emission information for the entities to which the aforementioned pollution sources belong. There is a one-to-one correspondence between the pollution source entity information and the pollutant emission information. The pollutant emission information can be the information on pollutants emitted by the corresponding pollution source entity. This information may include, but is not limited to, the pollutant identifier and the current emission amount for each pollutant. There is a one-to-one correspondence between pollutants and their identifiers. There is also a one-to-one correspondence between pollutants and their current emission amounts. Pollutants can be, but are not limited to, one of the following: particulate matter, sulfur dioxide, nitrogen oxides (NOx), volatile organic compounds (VOCs), and carbon monoxide. The pollutant identifier can be a unique identifier for the corresponding pollutant. The current emission amount can be the emission amount of the pollutant at the current time.

[0095] The second step involves generating a set of identifiers for the target pollution source's owner based on the aforementioned pollutant emission information set and at least one pollutant alarm message. The identifiers for the target pollution source's owner in this set can be the identifiers of the owners of pollution sources that emit a significant number of pollutants. In practice, for each pollutant alarm message included in the aforementioned at least one pollutant alarm message, the executing entity can first perform the following sub-steps:

[0096] The first sub-step involves retrieving the associated emission identifier group corresponding to the pollutant identifier included in the aforementioned pollutant alarm information from the database via a wired or wireless connection. The associated emission identifier group can include identifiers of emitted pollutants that are related to or have a transformation relationship with the corresponding pollutant. For example, if the pollutant identifier is inhalable particulate matter (PM2.5), then the associated emission identifier can be particulate pollutants. As another example, if the pollutant identifier is ozone, then the associated emission identifier can be one of the following: nitrogen oxides (NOx) or volatile organic compounds (VOCs).

[0097] The second sub-step involves, for each associated emission identifier included in the aforementioned associated emission identifier group, determining the current pollutant emission amounts corresponding to the associated emission identifier in the aforementioned entity's pollutant emission information set as the target current pollutant emission amount group. Specifically, the current pollutant emission amount corresponding to the aforementioned associated emission identifier can be: the current pollutant emission amount corresponding to the same emission pollutant identifier as the aforementioned associated emission identifier.

[0098] The third sub-step involves sorting each group of current pollutant emissions for each target in descending order to obtain a sequence of current pollutant emissions for each target.

[0099] The fourth sub-step involves, for each determined sequence of current pollutant emissions, identifying the current pollutant emissions that meet a preset ranking condition within that sequence as a group of target pollutant emissions. The preset ranking condition allows the current pollutant emission to rank less than or equal to a preset ranking within the sequence.

[0100] The fifth sub-step involves determining the subject information of each pollution source corresponding to the determined target pollutant emission group as the target pollution source subject information group.

[0101] The sixth sub-step involves determining the entity identifiers of each pollution source included in the target pollution source entity information group as the target pollution source entity identifier group.

[0102] Then, the duplicate identifiers of each target pollution source belonging to the entity identifier group are deduplicated to obtain the target pollution source belonging to the entity identifier set.

[0103] The third step involves sending a pre-defined pollution emission warning message to the terminal of the entity to which the pollution source belongs, for each entity identifier included in the aforementioned target pollution source entity identifier set. This pre-defined pollution emission warning message can be a pre-set message indicating that pollutant emissions exceed standards, intended to alert the user. For example, the pre-defined pollution emission warning message could be "pollutant emissions exceed standards." The terminal of the entity to which the pollution source belongs can be the terminal of the entity to which the pollution source belongs.

[0104] Step 108: Control the associated display device to display the target pollution source information set.

[0105] In some embodiments, the aforementioned executing entity may control an associated display device to display the aforementioned target pollution source information set.

[0106] In some optional implementations of certain embodiments, the aforementioned execution entity may control an associated display device to display the aforementioned target pollution source information set through the following steps:

[0107] The first step is to control the associated display devices to display a regional map corresponding to the aforementioned target environmental monitoring area. In practice, the implementing entity can first use GIS (Geographic Information System) technology to create a regional map of the target environmental monitoring area. Then, it controls the associated display devices to display the regional map of the target environmental monitoring area.

[0108] The second step involves performing the following sub-steps for each target pollution source information included in the aforementioned target pollution source information set:

[0109] The first sub-step is to determine the contribution rate of the target pollutant corresponding to the above-mentioned target pollution source information as the contribution rate of the first pollutant.

[0110] The second sub-step involves determining the preset ownership display style information corresponding to the contribution rate of the first pollutant as the ownership display style information corresponding to the target pollution source information. The preset ownership display style information can be information about the display style of the pollution source ownership entity identifier that corresponds to the preset pollutant contribution rate. The preset pollutant contribution rate can be a preset pollutant contribution rate. The preset ownership display style information can include, but is not limited to, identifier size and identifier color. The identifier size can be the size of the pollution source ownership entity identifier. The identifier color can be the color of the pollution source ownership entity identifier.

[0111] The third sub-step involves determining the preset location information of the subject, corresponding to the coordinates of the subject included in the aforementioned target pollution source information, as the subject's display location information. This preset location information refers to the location of the pollution source's subject on the aforementioned regional map. The preset location information can include, but is not limited to, the subject's map coordinates. The subject's map coordinates can be the coordinates of the pollution source's subject on the aforementioned regional map.

[0112] The fourth sub-step involves displaying a control identifying the entity at the location corresponding to the entity's display information on the aforementioned area map, according to the entity's display style information. This entity identification control can be a control representing the entity to which the pollution source belongs.

[0113] The above-described embodiments of this disclosure have the following beneficial effects: The pollution source information display method of some embodiments of this disclosure can improve the accuracy of pollution control and reduce the degree of air pollution. Specifically, the reason for the low accuracy of pollution control and severe air pollution is that the directly obtained atmospheric information has a high scale, resulting in a low resolution of the backward trajectory generated by combining the backward trajectory model, leading to low accuracy of pollution source information, and thus low accuracy of pollution control and severe air pollution. Based on this, the pollution source information display method of some embodiments of this disclosure firstly, in response to detecting at least one pollutant alarm information of a corresponding target environmental monitoring station, acquires the predicted meteorological field information sequence and grid area information of the corresponding target environmental monitoring area. The target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes the identifiers of each grid. Therefore, when the environmental monitoring station detects a pollutant alarm, it can obtain meteorological information and information of each sub-area for a future period of time in the monitoring area, which can be used to trace the pollution source. Secondly, the predicted meteorological field information sequence is downscaled to obtain a first meteorological information sequence. This yields highly refined meteorological information, which can be used to improve the accuracy of pollution sources. Then, the first meteorological information sequence and the predicted meteorological field information sequence are input into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence. This preset downscaling meteorological information generation model includes a data fusion layer, a first downscaling layer, a second downscaling layer, and an output layer. This allows for further downscaling of the meteorological information, resulting in more refined meteorological information. Next, based on the second meteorological information sequence, backward trajectory information corresponding to each pollutant identifier is generated. This yields highly accurate backward trajectory information, which can be used to trace pollution sources. Then, based on the backward trajectory information, a set of pollutant contribution rates corresponding to each grid identifier is generated. This yields the contribution rates of each pollutant in each sub-region, which can be used to locate pollution sources. Following this, a set of information on the entities to which pollution sources belong in the target environmental monitoring area is obtained. This yields the entities to which all pollution sources belong in the monitoring area, which can be used to locate pollution sources. Based on the set of information on the entities to which pollution sources belong and the set of pollutant contribution rates, a target pollution source information set is generated. This allows for tracing each pollution source, obtaining information about each pollution source, which can then be viewed by the user. Finally, the associated display devices are controlled to display the aforementioned set of target pollution source information. This allows for the display of highly accurate information on each pollution source, facilitating user access for pollution control. Furthermore, because meteorological information can be downscaled multiple times during pollution source tracing to obtain more refined meteorological data, the accuracy of the traced pollution source information is improved. This, in turn, enhances the precision of pollution control and reduces the degree of air pollution.

[0114] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a pollution source information display device, which are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0115] like Figure 2 As shown, a pollution source information display device 200 in some embodiments includes: a first acquisition unit 201, a downscaling unit 202, an input unit 203, a first generation unit 204, a second generation unit 205, a second acquisition unit 206, a third generation unit 207, and a display unit 208. The first acquisition unit 201 is configured to acquire a predicted meteorological field information sequence and grid area information for a corresponding target environmental monitoring area in response to detecting at least one pollutant alarm information for a corresponding target environmental monitoring station. The target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes grid identifiers. The downscaling unit 202 is configured to downscale the predicted meteorological field information sequence to obtain a first meteorological information sequence. The input unit 203 is configured to input the first meteorological information sequence and the predicted meteorological field information sequence into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence. The preset downscaling meteorological information generation model includes a number of... The system comprises a fusion layer, a first downscaling layer, a second downscaling layer, and an output layer. A first generation unit 204 is configured to generate backward trajectory information corresponding to each pollutant identifier based on the aforementioned second meteorological information sequence. A second generation unit 205 is configured to generate a pollutant contribution rate set corresponding to each grid identifier based on the aforementioned backward trajectory information. A second acquisition unit 206 is configured to acquire a set of pollution source ownership information corresponding to the aforementioned target environmental monitoring area. A third generation unit 207 is configured to generate a target pollution source information set based on the aforementioned pollution source ownership information set and the aforementioned pollutant contribution rate set. A display unit 208 is configured to control an associated display device to display the target pollution source information set.

[0116] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0117] The following is for reference. Figure 3This document illustrates a structural schematic of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0118] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0119] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0120] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0121] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0122] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0123] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs. When the electronic device executes the aforementioned one or more programs, the electronic device causes the following actions: In response to detecting at least one pollutant alarm information corresponding to a target environmental monitoring station, the electronic device acquires a predicted meteorological field information sequence and grid area information for the corresponding target environmental monitoring area, wherein the target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes each grid identifier; the electronic device downscales the predicted meteorological field information sequence to obtain a first meteorological information sequence; the electronic device inputs the first meteorological information sequence and the predicted meteorological field information sequence into a preset downscaled meteorological information generation model to obtain a second meteorological information sequence, wherein the preset downscaled meteorological information generation model includes a data fusion layer, a first downscaled layer, a second downscaled layer, and an output layer; based on the second meteorological information sequence, the electronic device generates each backward trajectory information corresponding to each pollutant identifier; based on the backward trajectory information, the electronic device generates a pollutant contribution rate set corresponding to each grid identifier; the electronic device acquires a set of pollution source ownership information for the target environmental monitoring area; based on the set of pollution source ownership information and the set of pollutant contribution rates, the electronic device generates a target pollution source information set; and controls an associated display device to display the target pollution source information set.

[0124] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0126] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a first acquisition unit, a downscaling unit, an input unit, a first generation unit, a second generation unit, a second acquisition unit, a third generation unit, and a display unit. The names of these units do not necessarily limit the specific unit; for example, the first acquisition unit may also be described as "a unit that, in response to detecting at least one pollutant alarm information corresponding to a target environmental monitoring station, acquires a predicted meteorological field information sequence and grid area information for the corresponding target environmental monitoring area."

[0127] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0128] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for displaying pollution source information, comprising: In response to detecting at least one pollutant alarm information corresponding to a target environmental monitoring station, a predicted meteorological field information sequence and grid area information of the corresponding target environmental monitoring area are obtained, wherein the target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes each grid identifier; The predicted meteorological field information sequence is downscaled to obtain the first meteorological information sequence; The first meteorological information sequence and the predicted meteorological field information sequence are input into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence. The preset downscaling meteorological information generation model includes a data fusion layer, a first downscaling layer, a second downscaling layer, and an output layer. Based on the second meteorological information sequence, generate each backward trajectory information corresponding to each pollutant identifier; Based on the aforementioned backward trajectory information, a pollutant contribution rate set corresponding to each grid identifier is generated, including: For each backward trajectory information included in the aforementioned backward trajectory information, the following steps are performed: Arrange the predicted pollutant concentrations corresponding to the backward trajectory information in the predicted meteorological field information sequence to obtain the predicted pollutant concentration sequence; The average value of each predicted pollutant concentration included in the predicted pollutant concentration sequence is determined as the average predicted pollutant concentration. For each grid identifier included in the grid region information, the number of grid trajectories is determined based on the backward trajectory information and the grid identifier; The sum of the number of each generated grid trajectory is determined as the total number of pollutant trajectories; The number of each generated grid trajectory is defined as a grid trajectory array; In response to determining that the predicted average pollutant concentration meets the preset pollutant concentration condition, the total number of pollutant trajectories is determined as the first total number of pollutant trajectories, and the grid trajectory array is determined as the first grid trajectory array; In response to determining that the predicted average pollutant concentration does not meet the preset pollutant concentration condition, the total number of pollutant trajectories is determined as the second total number of pollutant trajectories, and the grid trajectory array is determined as the second grid trajectory array; The total number of trajectories is determined by summing the total number of each pollutant trajectory generated. The ratio of the total number of trajectories to the total number of grid cells is determined as the average number of trajectories; For each grid identifier included in the grid area information, a pollutant contribution rate corresponding to the grid identifier is generated based on the average number of trajectories. The determined contribution rates of each pollutant are defined as a set of pollutant contribution rates. Obtain the set of information on the entities to which the pollution sources belong in the target environmental monitoring area; Based on the set of information about the entities to which the pollution sources belong and the set of pollutant contribution rates, a set of target pollution source information is generated. Control the associated display device to display the target pollution source information set.

2. The method according to claim 1, wherein, Before acquiring the predicted meteorological field information sequence and grid area information of the corresponding target environmental monitoring area in response to detecting at least one pollutant alarm information of the corresponding target environmental monitoring station, the method further includes: For each pollutant label included in each pollutant label, perform the following steps: Obtain the first and second current pollutant concentrations corresponding to the pollutant identifiers at the target environmental monitoring station; The time corresponding to the current concentration of the first pollutant is determined as the first monitoring time; The time corresponding to the second current pollutant concentration is determined as the second monitoring time; The difference between the second monitoring time and the first monitoring time is determined as the time difference; The difference between the first current pollutant concentration and the second current pollutant concentration is determined as the pollutant concentration difference; The ratio of the pollutant concentration difference to the time difference is determined as the concentration change rate; In response to determining that the concentration change rate meets a preset concentration change rate condition, a pollutant alarm message corresponding to the pollutant identifier is generated based on the pollutant identifier.

3. The method according to claim 1, wherein, The step of generating the pollutant contribution rate corresponding to the grid identifier based on the average trajectory number includes: For each generated first grid trajectory array, the number of first grid trajectories corresponding to the grid identifier in the first grid trajectory array is determined as the number of first target grid trajectories; The sum of the number of each first target grid trajectory is determined as the total number of first grid trajectories; For each generated second grid trajectory array, the number of second grid trajectories corresponding to the grid identifier in the second grid trajectory array is determined as the number of second target grid trajectories; The sum of the number of each determined second target grid trajectory is taken as the total number of second grid trajectories; The sum of the total number of the first grid trajectories and the total number of the second grid trajectories is determined as the total number of grid trajectories; The ratio of the total number of grid trajectories to the average number of trajectories is determined as the grid weight coefficient; The ratio of the total number of the first grid trajectories to the total number of grid trajectories is determined as the contribution rate of the first pollutant; The product of the first pollutant contribution rate and the grid weight coefficient is determined as the pollutant contribution rate.

4. The method according to claim 1, wherein, The pollution source ownership information set includes the coordinates of the ownership entity; The step of generating a target pollution source information set based on the set of information about the pollution source's subject and the set of pollutant contribution rates includes: For each entity to which a pollution source belongs in the set of entity information, the following steps are performed: Based on the entity coordinates and grid identifiers included in the entity information to which the pollution source belongs, determine the entity grid identifier corresponding to the entity information to which the pollution source belongs; The pollutant contribution rate corresponding to the main grid identifier in the pollutant contribution rate set is determined as the target pollutant contribution rate; The pollutant contribution rate set is sorted to obtain a pollutant contribution rate sequence; Based on the pollutant contribution rate sequence, in response to determining that the target pollutant contribution rate meets the preset pollutant contribution rate condition, the subject information to which the pollution source belongs is determined as the target pollution source information; The identified target pollution source information is defined as a target pollution source information set.

5. The method according to claim 1, wherein, The method further includes: Obtain the pollutant emission information set of the entity to which the pollution source belongs; Based on the pollutant emission information set of the entity to which the target pollution source belongs and the at least one pollutant alarm information, generate an entity identifier set to which the target pollution source belongs; For each target pollution source entity identifier included in the target pollution source entity identifier set, a preset pollution emission warning information is sent to the pollution source entity terminal corresponding to the target pollution source entity identifier.

6. The method according to claim 4, wherein, The control-associated display device displays the target pollution source information set, including: Control the associated display device to display a regional map corresponding to the target environmental monitoring area; For each target pollution source information included in the target pollution source information set, the following steps are performed: The contribution rate of the target pollutant corresponding to the target pollution source information is determined as the first pollutant contribution rate. The preset subject display style information corresponding to the contribution rate of the first pollutant is determined as the subject display style information corresponding to the target pollution source information; The preset location information of the subject to which the target pollution source information belongs is determined as the display location information of the subject, wherein the preset location information of the subject to which the pollution source belongs is the location information of the subject to which the pollution source belongs on the regional map; On the area map, at the location corresponding to the entity's display location information, display the entity identifier control according to the entity identifier display style information.

7. A pollution source information display device, comprising: The first acquisition unit is configured to acquire a predicted meteorological field information sequence and grid area information of the corresponding target environmental monitoring area in response to detecting at least one pollutant alarm information of the corresponding target environmental monitoring station, wherein the target environmental monitoring area corresponds to the target environmental monitoring station, and the grid area information includes each grid identifier; A downscaling unit is configured to downscale the predicted meteorological field information sequence to obtain a first meteorological information sequence. The input unit is configured to input the first meteorological information sequence and the predicted meteorological field information sequence into a preset downscaling meteorological information generation model to obtain a second meteorological information sequence. The preset downscaling meteorological information generation model includes a data fusion layer, a first downscaling layer, a second downscaling layer, and an output layer. The first generation unit is configured to generate backward trajectory information corresponding to each pollutant identifier based on the second meteorological information sequence. The second generation unit is configured to generate a set of pollutant contribution rates corresponding to each grid identifier based on the various backward trajectory information, including: For each backward trajectory information included in the aforementioned backward trajectory information, the following steps are performed: Arrange the predicted pollutant concentrations corresponding to the backward trajectory information in the predicted meteorological field information sequence to obtain the predicted pollutant concentration sequence; The average value of each predicted pollutant concentration included in the predicted pollutant concentration sequence is determined as the average predicted pollutant concentration. For each grid identifier included in the grid region information, the number of grid trajectories is determined based on the backward trajectory information and the grid identifier; The sum of the number of each generated grid trajectory is determined as the total number of pollutant trajectories; The number of each generated grid trajectory is defined as a grid trajectory array; In response to determining that the predicted average pollutant concentration meets the preset pollutant concentration condition, the total number of pollutant trajectories is determined as the first total number of pollutant trajectories, and the grid trajectory array is determined as the first grid trajectory array; In response to determining that the predicted average pollutant concentration does not meet the preset pollutant concentration condition, the total number of pollutant trajectories is determined as the second total number of pollutant trajectories, and the grid trajectory array is determined as the second grid trajectory array; The total number of trajectories is determined by summing the total number of each pollutant trajectory generated. The ratio of the total number of trajectories to the total number of grid cells is determined as the average number of trajectories; For each grid identifier included in the grid area information, a pollutant contribution rate corresponding to the grid identifier is generated based on the average number of trajectories. The determined contribution rates of each pollutant are defined as a set of pollutant contribution rates. The second acquisition unit is configured to acquire a set of information on the entities to which the pollution sources belong in the target environmental monitoring area. The third generation unit is configured to generate a target pollution source information set based on the pollution source subject information set and the pollutant contribution rate set; The display unit is configured to control an associated display device to display the target pollution source information set.

8. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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