An abnormal carbon emission early warning method, early warning system, medium and program product

Abstract

In order to improve the timeliness and accuracy of monitoring abnormal carbon emission, the application provides an abnormal carbon emission early warning method, an early warning system, a medium and a program product. The method comprises: acquiring multi-dimensional environmental data of a to-be-monitored area in a preset time period; identifying data characteristics of each group of multi-dimensional environmental data, and determining a carbon emission source type corresponding to each group of multi-dimensional environmental data based on the data characteristics of each group of multi-dimensional environmental data; determining a carbon emission amount corresponding to each group of multi-dimensional environmental data based on each group of multi-dimensional environmental data, determining an abnormal carbon emission area from the to-be-monitored area based on each carbon emission amount and the corresponding carbon emission source type; and generating an abnormal carbon emission early warning based on the abnormal carbon emission area and the abnormal carbon emission amount corresponding to the abnormal carbon emission area. The application facilitates improving the timeliness and accuracy of monitoring abnormal carbon emission.

Description

Technical Field

[0001] The present application relates to the technical field of abnormal carbon emission monitoring, and in particular to an abnormal carbon emission early warning method, early warning system, medium and program product. Background Art

[0002] Against the backdrop of growing attention to global climate change and environmental protection, the monitoring and management of carbon emissions has become a focus of attention for businesses and governments, particularly in areas such as transportation, industrial production, and energy consumption. Carbon emissions primarily come from fossil fuels like coal, oil, and natural gas, which release large amounts of carbon dioxide during combustion or use. Industrial production, transportation, and agricultural activities are also significant sources of carbon emissions. These different sources of carbon emissions have distinct characteristics and patterns, increasing the complexity of the carbon emission process.

[0003] Traditional methods for monitoring abnormal carbon emissions mostly rely on manual meter reading and regular inspections. These traditional monitoring methods are not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in low-quality monitoring data and making it difficult to reflect the actual situation of carbon emissions in a timely and accurate manner. Summary of the Invention

[0004] In order to improve the timeliness and accuracy of monitoring abnormal carbon emissions, the present application provides an abnormal carbon emissions early warning method, early warning system, medium and program product.

[0005] In the first aspect, the present application provides a method for early warning of abnormal carbon emissions, which adopts the following technical solutions:

[0006] An abnormal carbon emission early warning method, comprising:

[0007] Obtain multi-dimensional environmental data of the area to be monitored within a preset time period;

[0008] Identifying data characteristics of each set of multi-dimensional environmental data, and determining the carbon emission source type corresponding to each set of multi-dimensional environmental data based on the data characteristics of each set of multi-dimensional environmental data;

[0009] Determining the carbon emissions corresponding to each set of multi-dimensional environmental data based on each set of multi-dimensional environmental data, and determining an abnormal carbon emission area from the area to be monitored based on each carbon emission amount and the corresponding carbon emission source type;

[0010] An abnormal carbon emission warning is generated based on the abnormal carbon emission area and the abnormal carbon emission amount corresponding to the abnormal carbon emission area.

[0011] By adopting the above technical solution, the multi-dimensional environmental data that the area to be monitored may face in the future is analyzed, and after distinguishing the multi-dimensional environmental data of different carbon emission source types, the carbon emission quantitative analysis is carried out according to their characteristics, which is convenient for predicting and understanding the carbon emission status of the area to be monitored in the future. Based on the predicted carbon emissions and source types, it is convenient to accurately screen out abnormal carbon emission areas from the area to be monitored, and by issuing early warning signals in a timely manner, it is helpful to discover and deal with the abnormal carbon emission situations that the area to be monitored may face in advance, and take corresponding measures to deal with them.

[0012] In one possible implementation, determining the abnormal carbon emission area from the area to be monitored based on each carbon emission amount and the corresponding carbon emission source type includes:

[0013] Acquire weather data of the monitored area within the preset time period, and determine retention parameters corresponding to each set of multi-dimensional environmental data based on the weather data and the carbon emission source type corresponding to each set of multi-dimensional environmental data;

[0014] Determine the carbon emission retention layer corresponding to each set of multi-dimensional environmental data based on the carbon emission and retention parameters corresponding to each set of multi-dimensional environmental data;

[0015] Overlaying all carbon emission retention layers to obtain a carbon emission image corresponding to the area to be monitored during the preset time period;

[0016] A region in the carbon emission image where the pixel depth is higher than a preset depth limit and the area is higher than a preset area limit is determined as an abnormal carbon emission region.

[0017] By adopting the above technical solution, by introducing weather data and combining it with the type of carbon emission source, it is convenient to more accurately determine the retention parameters corresponding to each group of multidimensional environmental data. These retention parameters are convenient to reflect the retention characteristics corresponding to different multidimensional environmental data, and are quantified based on the retention characteristics. The quantification results are displayed using the carbon emission retention layer, which is convenient for intuitively displaying the carbon emission distribution and intensity caused by different multidimensional environmental data. Finally, by superimposing all the carbon emission retention layers, the final carbon emission image of the monitored area is obtained, which is convenient for intuitively viewing the carbon emissions that the monitored area may face in the future under the influence of multidimensional environmental data. By identifying the regional pixel depth and regional area of ​​each region in the carbon emission image, it is convenient to timely discover and deal with abnormal emissions.

[0018] In one possible implementation, the method further includes:

[0019] Determine a region where the pixel depth of the region is within a preset depth range, or where the area of ​​the region is within a preset area range as an observation region;

[0020] When the number of observations in the observation area is higher than a preset threshold, determining whether there is an associated observation area group based on the weather data, the associated observation area group includes at least two observation areas with the same weather characteristics, and the area spacing between at least two observation areas is less than a preset spacing threshold;

[0021] If so, at least two observation areas contained in the associated observation area group domain are merged to obtain a merged area, and based on the preset depth limit and the preset area limit, it is determined whether the merged area is an abnormal carbon emission area.

[0022] By adopting the above technical solution, the observation area is determined by setting a preset depth range and a preset area range, which helps to screen out carbon emission areas with specific spatial characteristics. By merging observation areas with the same weather characteristics and smaller spacing to form a larger merged area, the preset depth limit and the preset area limit are used to judge whether there are abnormal carbon emissions in the merged area, which facilitates the disclosure of potential abnormal carbon emission areas. By introducing weather data and regional spacing, it is convenient to screen observation areas that can be merged, rather than merging all observation areas. It is convenient to merge and analyze observation areas that meet the relevant merging conditions without ignoring observation areas with potential carbon emission anomalies, thereby avoiding unnecessary merging processing and improving processing efficiency.

[0023] In one possible implementation, when there is no abnormal carbon emission area, the method further includes:

[0024] Determining a critical layer corresponding to the carbon emission image based on the preset depth limit and the preset area limit;

[0025] Determining critical concern data features and critical concern carbon emissions corresponding to each critical concern data feature based on the critical layer, and determining a control feature corresponding to each critical concern data feature from the data features corresponding to the multi-dimensional environmental data;

[0026] Critical concern information is generated based on the critical concern carbon emissions corresponding to each critical concern data feature and the carbon emissions corresponding to each corresponding control feature.

[0027] By adopting the technical scheme, when there is no abnormal carbon emission area, the critical layer that may cause abnormal carbon emission is determined based on the preset depth limit value and the preset area limit value, the corresponding critical attention data feature and the corresponding critical attention carbon emission amount are determined based on the critical layer, and finally the related staff is reminded to supervise the data feature that may cause abnormal carbon emission in a targeted manner through the generation of the critical attention information, so that the probability of occurrence of abnormal carbon emission is reduced.

[0028] In a possible implementation manner, the method further includes:

[0029] acquiring a field image corresponding to the to-be-monitored area, and identifying whether the field image contains a preset carbon emission activity feature;

[0030] If yes, acquiring actual multi-dimensional environmental data of the to-be-monitored area, and generating a dynamic carbon emission image based on the field image and the actual multi-dimensional environmental data;

[0031] tracking the preset carbon emission activity feature based on the dynamic carbon emission image.

[0032] By adopting the technical scheme, when the to-be-monitored area contains the preset carbon emission activity feature, a dynamic carbon emission image is generated in combination with the actual environmental data, so that the actual situation of the carbon emission activity can be accurately reflected, and the monitoring accuracy is improved. Tracking the preset carbon emission activity feature based on the dynamic carbon emission image can realize continuous and detailed monitoring of the carbon emission activity, and help to more accurately analyze and evaluate the immediate impact of the preset sudden behavior on the to-be-monitored area.

[0033] In a possible implementation manner, the generating of the dynamic carbon emission image based on the field image and the actual multi-dimensional environmental data includes:

[0034] generating an initial AR scene image based on the field image, and determining an AR carbon emission position corresponding to each actual multi-dimensional environmental data from the initial AR scene image according to an actual data feature corresponding to each actual multi-dimensional environmental data;

[0035] acquiring a carbon emission factor corresponding to each actual multi-dimensional environmental data, and determining a dynamic carbon emission amount corresponding to each AR carbon emission position based on each actual multi-dimensional environmental data;

[0036] superimposing each dynamic carbon emission amount to the AR carbon emission position in the initial AR scene image to obtain the dynamic carbon emission image.

[0037] By adopting the above technical solution, by combining the actual multi-dimensional environmental data with the on-site image, an initial AR scene image is generated, and the AR carbon emission position corresponding to each actual multi-dimensional environmental data is determined in the initial AR scene image, which helps to clearly display the spatial distribution and source of carbon emissions. By superimposing each dynamic carbon emission amount on each corresponding AR carbon emission position, it is convenient to generate a more accurate dynamic carbon emission image.

[0038] In a second aspect, the present application provides an early warning system, which adopts the following technical solutions:

[0039] An early warning system, comprising:

[0040] at least one processor;

[0041] Memory;

[0042] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned abnormal carbon emission early warning method.

[0043] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0044] A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the above-mentioned abnormal carbon emission early warning method.

[0045] In a fourth aspect, the present application provides a computer program product that adopts the following technical solution:

[0046] A computer program product includes a computer program, which implements the above-mentioned abnormal carbon emission early warning method when executed by a processor.

[0047] In summary, this application includes at least one of the following beneficial technical effects:

[0048] By analyzing the multi-dimensional environmental data that the area to be monitored may face in the future, and after distinguishing the multi-dimensional environmental data of different carbon emission source types, quantitative carbon emission analysis is carried out according to their characteristics, which makes it easier to predict and understand the carbon emission status of the area to be monitored in the future. Based on the predicted carbon emissions and source types, it is easy to accurately screen out abnormal carbon emission areas from the area to be monitored, and by issuing early warning signals in a timely manner, it helps to discover and deal with the abnormal carbon emissions that the area to be monitored may face in advance, and take corresponding measures to deal with them.

[0049] By generating dynamic carbon emission images in combination with actual environmental data when the preset carbon emission activity characteristics are included in the monitored area, it is convenient to accurately reflect the actual situation of carbon emission activities, thereby improving monitoring accuracy. Tracking the preset carbon emission activity characteristics based on the dynamic carbon emission images can achieve continuous and detailed monitoring of carbon emission activities, which helps to more accurately analyze and evaluate the immediate impact of preset sudden behaviors on the monitored area. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of an abnormal carbon emission early warning method in an embodiment of the present application;

[0051] Figure 2 This is a schematic diagram of a carbon emission tracking process in an embodiment of the present application;

[0052] Figure 3 It is a structural diagram of an early warning system in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following is combined with Figures 1 to 3 This application is described in further detail.

[0054] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0055] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0056] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0057] Specifically, embodiments of the present application provide a method for early warning of abnormal carbon emissions, which is executed by an early warning system. The early warning system can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet computer, laptop computer, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present application.

[0058] refer to Figure 1 , Figure 1 : is a flow chart of an abnormal carbon emission early warning method in an embodiment of the present application, the method includes steps S110 to S140, wherein:

[0059] Step S110: Acquire multi-dimensional environmental data of the area to be monitored within a preset time period.

[0060] Specifically, the monitored area can be areas along transport routes within a transport chain, areas surrounding transport hubs, and so on. The specific monitored area is not specifically limited in this embodiment of the application, as long as the area meets the abnormal carbon emissions warning requirements. The preset time period is a period of time after the current time. The preset time period can be 3 hours or 4 hours. The specific length is not limited in this embodiment of the application.

[0061] In the transportation chain, there are multiple reasons that may cause abnormal carbon emissions, that is, there are multiple sources of carbon emissions, and each source of carbon emissions corresponds to a set of multi-dimensional environmental data. Among them, the source of carbon emissions may be transportation, for example, in the process of transporting goods in the monitored area, diesel trucks, aviation fuel, marine fuel and other transportation equipment generate carbon emissions, and the corresponding multi-dimensional environmental data can be carbon emission data of different historical time periods, carbon emission data of different transportation modes and transportation equipment, etc.; energy industry, for example, the mining or processing of coal, oil and natural gas in the monitored area; industrial production, for example, the combustion of fossil fuels in the production process of heavy industries such as steel, cement, and chemicals in the monitored area, and the corresponding multi-dimensional environmental data. The data can be carbon emission data of different mining or processing areas during operations, the correspondence between carbon emission data and output energy during energy mining or processing, etc.; agricultural activities, such as greenhouse gases produced by animal husbandry and crop breeding in the monitored area, the corresponding multi-dimensional environmental data can be carbon emission data of different growth cycles, carbon emission data of different agricultural areas, carbon emission data of different agricultural types, etc.; indirect emissions of infrastructure, such as carbon emissions generated by the construction and maintenance of roads, railways, ports, and airports, the manufacture and maintenance of transportation vehicles, etc., the corresponding multi-dimensional environmental data can be carbon emission data of different construction or maintenance stages, carbon emission data of different infrastructure projects, etc.

[0062] Since the preset time period is a period of time after the current moment, when obtaining multidimensional environmental data within the preset time period, the historical multidimensional environmental data uploaded to the early warning system within the historical time period can be analyzed to identify the trends and patterns of different carbon emission sources, and then screen from the historical multidimensional environmental data based on the identified trends and patterns. Among them, preset statistical algorithms, preset machine algorithms, etc. can be used to conduct in-depth analysis of historical multidimensional environmental data, and then a prediction model is established based on the identification results to predict the carbon emissions of different carbon emission sources in the future, that is, screening from historical multidimensional environmental data. Among them, the preset statistical algorithm can be an autoregressive sliding average model, a differential autoregressive sliding average model, etc., and the preset machine algorithm can be a linear regression algorithm, a logistic regression algorithm, a support vector machine algorithm, etc. The specific preset statistical algorithms, preset machine algorithms, etc. are not limited in the embodiments of this application, as long as the trends and patterns of each carbon emission source can be analyzed based on the historical multidimensional environmental data of each carbon emission source.

[0063] Historical multi-dimensional environmental data can be collected by sensors or monitoring equipment installed in the monitored area and uploaded to the early warning system. It can also be obtained from data sources such as environmental monitoring stations and traffic management systems. The specific acquisition method is not specifically limited in the embodiments of this application.

[0064] Step S120: identifying data features of each set of multi-dimensional environmental data, and determining the carbon emission source type corresponding to each set of multi-dimensional environmental data based on the data features of each set of multi-dimensional environmental data.

[0065] Specifically, a preset feature recognition algorithm can be used to identify the data features of each group of multi-dimensional environmental data. The specific preset feature recognition algorithm is not specifically limited in the embodiments of this application, as long as it can identify the data features contained in each group of multi-dimensional environmental data. Different data features correspond to different carbon emission source types. Carbon emission source types can include fixed source emissions, mobile source emissions, and indirect source emissions. The carbon emission source types corresponding to different data features can be determined through a preset type mapping relationship. The preset type mapping relationship is the correspondence between different data features and carbon emission source types. The specific content of the preset type mapping relationship is not specified in the embodiments of this application. It can be determined by relevant staff based on historical experimental data and uploaded to the early warning system.

[0066] Step S130: determining the carbon emissions corresponding to each set of multi-dimensional environmental data based on each set of multi-dimensional environmental data, and determining abnormal carbon emission areas from the area to be monitored based on each carbon emission and the corresponding carbon emission source type.

[0067] Specifically, different multi-dimensional environmental data correspond to different carbon emissions calculation methods. For example, when the source of carbon emissions is transportation, the vehicle type, fuel consumption, emission factor and other parameters should be identified from the corresponding multi-dimensional environmental data first, and then the carbon emissions should be calculated based on the identified parameters. The early warning system contains carbon emissions calculation methods corresponding to different carbon emission sources, which can be retrieved from the early warning system when the relevant carbon emissions calculation formula needs to be called or used.

[0068] The calculated carbon emissions can be directly summed to obtain the total carbon emissions, and then the total carbon emissions can be compared with the preset carbon emission threshold to determine whether there is an abnormal carbon emission area in the monitored area. The preset carbon emission threshold can be determined by relevant staff based on historical experimental data and uploaded to the early warning system. However, since carbon emission gases have a certain degree of mobility, when analyzing the total amount or concentration of carbon emission gases in the monitored area, the retention of carbon emission gases can be analyzed according to the type of carbon emission source. Specifically, based on each carbon emission amount and the corresponding carbon emission source type, the abnormal carbon emission area can be determined from the monitored area, which can include:

[0069] Obtain weather data for the area to be monitored within a preset time period, and determine the retention parameters corresponding to each group of multi-dimensional environmental data based on the weather data and the carbon emission source types corresponding to each group of multi-dimensional environmental data; determine the carbon emission retention layers corresponding to each group of multi-dimensional environmental data based on the carbon emissions and retention parameters corresponding to each group of multi-dimensional environmental data; superimpose all carbon emission retention layers to obtain a carbon emission image corresponding to the area to be monitored during the preset time period; determine the areas in the carbon emission image where the pixel depth is higher than the preset depth limit and the area is higher than the preset area limit as abnormal carbon emission areas.

[0070] Specifically, since weather data such as wind speed, wind direction, temperature, and humidity in the monitored area are important factors affecting carbon emission gases, for example, the greater the wind speed, the faster the carbon emission gases diffuse, the higher the temperature, the farther the carbon emission gases move, and the stronger the diffusion ability, etc., it is necessary to consider the impact of weather data on the retention results of carbon emission gases when analyzing the retention of carbon emission gases in the monitored area. Weather data can be obtained by accessing the meteorological system, or obtained by relevant staff and uploaded to the early warning system. The method of obtaining weather data is not specifically limited in the embodiments of this application.

[0071] There are two approaches to determining retention parameters based on weather data and carbon emission source types:

[0072] Method 1: Based on training weather data and training multi-dimensional environmental data, a simulation model for predicting the retention of carbon emission gases is established. Finally, the weather data corresponding to the monitored area and each group of multi-dimensional environmental data are imported into the simulation model to obtain the retention parameters corresponding to each group of multi-dimensional environmental data. Among them, machine learning algorithms such as regression models and neural networks can be used to establish the simulation model.

[0073] Method two, first, based on a preset first mapping relationship, determine the first score corresponding to different carbon emission source types, then based on a preset second mapping relationship, determine the second score corresponding to different weather data, and finally, sum the first score and the second score to obtain a total score, and finally, determine the retention level based on a preset third mapping relationship. Different retention levels correspond to different retention parameters, and the retention parameters include but are not limited to diffusion range and diffusion rate. Among them, the preset first mapping relationship is the correspondence between the carbon emission source type and the first score, the preset second mapping relationship is the correspondence between the weather data and the second score, and the preset third mapping relationship is the correspondence between the total score and the retention level. The specific content is not specifically limited in the embodiment of this application and can be determined by relevant staff based on historical experimental data.

[0074] For any set of multidimensional environmental data, after determining the retention parameters, the preset mathematical model, carbon emissions, and retention parameters can be used to simulate the diffusion process of carbon emission gases to obtain the carbon emission retention layer corresponding to the set of multidimensional environmental data, wherein the preset mathematical model can be a Gaussian diffusion model, a Lagrangian diffusion membrane, etc. The specific mathematical model is not specifically limited in the embodiment of this application, as long as the diffusion process of carbon emission gases can be simulated by means of a mathematical model. Finally, the simulation results are converted into a carbon emission retention layer with the help of a preset visualization tool. The specific preset visualization tool is not specifically limited in the embodiment of this application. Based on the above method, a carbon emission retention layer corresponding to each set of multidimensional environmental data can be obtained. The carbon emission retention layer can be obtained by dividing the monitored area into several small grids, each grid corresponding to a pixel in the layer, and calculating the carbon emission gas concentration value in each grid according to the simulated diffusion result. The calculated concentration value is mapped to the pixel value. Generally, the higher the concentration value, the larger the pixel value. The carbon emission retention layer can present complex carbon emission situations in an intuitive way, so that non-professionals can also quickly understand the retention and spatial distribution of carbon emission gases under the influence of the multidimensional environmental data.

[0075] Since the carbon emission retention layer can combine the carbon emission situation with the specific geographical location, the carbon emission retention layers corresponding to different multi-dimensional environmental data can be superimposed. After all the carbon emission retention layers are superimposed, the carbon emission image corresponding to the preset time period of the monitored area can be obtained. By analyzing the pixel depth corresponding to each pixel point in the carbon emission image, the carbon emission image is first divided into initial regions, and the regional pixel depth and regional area corresponding to each initial region are identified. The region in the carbon emission image with a regional pixel depth higher than the preset depth limit and a regional area higher than the preset area limit is determined as an abnormal carbon emission region, wherein the preset depth limit and the preset area limit are not specifically limited in the embodiment of the present application, and can be determined by relevant staff based on historical experimental data and uploaded to the early warning system. When performing the initial regional division of the carbon emission image, it is necessary to ensure that the pixel difference corresponding to different pixel points in the initial region after division is not higher than the preset difference threshold. By superimposing all carbon emission retention layers, the final carbon emission image of the monitored area is obtained, which facilitates intuitive viewing of the carbon emissions that the monitored area may face in the future under the influence of multi-dimensional environmental data. By identifying the regional pixel depth and regional area of ​​each region in the carbon emission image, it is convenient to promptly discover and deal with abnormal emissions.

[0076] Step S140: generating an abnormal carbon emission warning based on the abnormal carbon emission area and the abnormal carbon emission amount corresponding to the abnormal carbon emission area.

[0077] Specifically, the carbon emission image may contain multiple abnormal carbon emission areas, and the specific number is not specifically limited in the embodiment of this application. After the abnormal carbon emission area is determined, an abnormal carbon emission warning is generated in a timely manner based on the abnormal carbon emission area and the abnormal carbon emission amount corresponding to the abnormal carbon emission area, and the abnormal carbon emission warning is promptly fed back to the relevant management personnel so that the relevant management personnel can take timely response measures.

[0078] For the embodiments of the present application, by analyzing the multi-dimensional environmental data that the area to be monitored may face in the future, and after distinguishing the multi-dimensional environmental data of different carbon emission source types, carbon emission quantitative analysis is performed according to their characteristics, which facilitates the prediction and understanding of the carbon emission status of the area to be monitored in the future. Based on the predicted carbon emissions and source types, it is convenient to accurately screen out abnormal carbon emission areas from the area to be monitored, and by issuing early warning signals in a timely manner, it helps to discover and deal with the abnormal carbon emission situations that the area to be monitored may face in advance, and take corresponding measures to deal with them.

[0079] Furthermore, in order to facilitate the disclosure of potential abnormal carbon emission areas, the method provided in the embodiment of the present application further includes:

[0080] An area whose pixel depth is within a preset depth range, or an area whose area is within a preset area range, is determined as an observation area; when the number of observations in the observation area is higher than a preset threshold, it is determined based on the weather data whether there is an associated observation area group, and the associated observation area group contains at least two observation areas with the same weather characteristics, and the area spacing between at least two observation areas is less than the preset spacing threshold; if so, at least two observation areas contained in the associated observation area group are merged to obtain a merged area, and based on the preset depth limit and the preset area limit, it is determined whether the merged area is an abnormal carbon emission area.

[0081] Specifically, since abnormal carbon emission regions are regions where the pixel depth is higher than the preset depth limit and the area is higher than the preset area limit, that is, regions where the pixel depth is not higher than the preset depth limit or the area is not higher than the preset area limit are all non-abnormal carbon emission regions. However, potential abnormal carbon emission regions may exist within non-abnormal carbon emission regions. In this case, by introducing a preset depth range and a preset area range, a portion of regions where the pixel depth is within the preset depth range or the area is within the preset area range can be selected from the non-abnormal carbon emission regions as observation regions. By analyzing whether there is a correlation between the observation regions, it is determined whether the observation regions can be merged.

[0082] Among them, since weather data will directly affect the distribution and diffusion of carbon emission gases in the area to be monitored, weather data can also be introduced to make a judgment when judging whether there is a correlation between the various observation areas. Specifically, when the number of observations in the observation area is higher than the preset threshold, the observation areas with the same weather characteristics and the regional spacing between the observation areas is less than the preset spacing threshold can be determined as an associated observation area group. The associated observation area group contains at least two observation areas. The same weather characteristics can be the same temperature, the same wind direction, the same wind speed, etc., which are not specifically limited in the embodiment of the present application. Among them, the preset threshold and the preset spacing threshold, etc. can be determined by relevant staff based on historical experimental data and uploaded to the early warning system, which are not specifically limited in the embodiment of the present application. When calculating the regional spacing between two observation areas, the regional center point of each observation area can be determined first, and the distance between the two regional center points can be determined as the regional spacing between the two observation areas.

[0083] After determining the associated observation area group, the regional edge information of each observation area in the associated observation area group can be identified based on a preset edge recognition algorithm. Based on the regional edge information of each observation area, all observation areas in the associated observation area group are merged. The specific preset edge recognition algorithm is not specifically limited in the embodiment of this application. Since there may be a distance between the observation areas included in the associated observation area group, the merged area of ​​the merged area is greater than the sum of the area of ​​all corresponding observation areas. After determining the merged area, whether the merged area is an abnormal carbon emission area is re-determined based on the preset depth limit and the preset area limit. That is, when the regional pixel depth of the merged area is higher than the preset depth limit, and the area of ​​the merged area is higher than the preset area limit, the merged area is determined to be an abnormal carbon emission area.

[0084] By merging observation areas with the same weather characteristics and smaller spacing to form a larger merged area, the preset depth limit and preset area limit are used to judge whether there is abnormal carbon emission in the merged area, so as to reveal potential abnormal carbon emission areas. In addition, not all observation areas are merged, so that the observation areas that meet the relevant merging conditions can be merged and analyzed without ignoring the observation areas with potential carbon emission anomalies, thereby avoiding unnecessary merging and improving processing efficiency.

[0085] Furthermore, when there is no abnormal carbon emission area, the method provided in the embodiment of the present application further includes:

[0086] The critical layer corresponding to the carbon emission image is determined based on the preset depth limit and the preset area limit; the critical concern data features and the critical concern carbon emissions corresponding to each critical concern data feature are determined based on the critical layer, and the control features corresponding to each critical concern data feature are determined from the data features corresponding to the multi-dimensional environmental data; critical concern information is generated based on the critical concern carbon emissions corresponding to each critical concern data feature and the carbon emissions corresponding to each corresponding control feature.

[0087] Specifically, after superimposing the carbon emission retention layers corresponding to each group of multi-dimensional environmental data, if there is no area in the obtained carbon emission image where the pixel depth is higher than the preset depth limit and the area is higher than the preset area limit, a critical layer can be determined based on the preset depth limit and the preset surface limit on the basis of the carbon emission image. The specific critical layer is not specifically limited in the embodiment of the present application, as long as it is ensured that after superimposing the critical layer on the basis of the current carbon emission image, at least one abnormal carbon emission area will appear in the updated carbon emission image.

[0088] Since the carbon emission retention layer is determined based on the corresponding carbon emissions and data features, after determining the critical layer, the critical concern data features corresponding to the carbon emission retention layer and the critical concern carbon emissions corresponding to each critical concern data feature can also be reversely deduced. The specific reverse deduction method is not specifically limited in the embodiment of this application. Based on the critical concern data features, the control features corresponding to each critical concern data feature can also be further determined from all multi-dimensional environmental data. Finally, based on the critical concern carbon emissions corresponding to each critical concern data feature and the carbon emissions corresponding to each corresponding control feature, critical concern information is generated. For example, the carbon emission image corresponds to data feature a, with a carbon emission of 80; data feature b, with a carbon emission of 60; and data feature c, with a carbon emission of 70. At this time, there is no abnormal carbon emission area in the carbon emission image. After determining the critical layer corresponding to the carbon emission image according to the preset depth limit and the preset area limit, the critical concern data features determined are critical concern data feature a, with a critical concern carbon emission of 10; critical concern data feature c, with a critical concern carbon emission of 15. The control features are data feature a and data feature c. The generated critical concern information is that when the carbon emission corresponding to data feature a is higher than 90, the carbon emission corresponding to data feature b is higher than 60, and the carbon emission corresponding to data feature c is higher than 85, the probability of abnormal carbon emissions in the monitored area will increase. At this time, by generating critical concern information, relevant staff are reminded to conduct targeted supervision of data features that may cause abnormal carbon emissions, so as to facilitate early prevention and take corresponding measures, thereby reducing the probability of abnormal carbon emissions.

[0089] Furthermore, the technical solution provided in the embodiment of the present application further includes steps S210 to S230, such as Figure 2 As shown, where:

[0090] Step S210: Acquire an on-site image corresponding to the area to be monitored, and identify whether the on-site image contains preset carbon emission activity features.

[0091] Specifically, an image acquisition device set in the area to be monitored can be used to capture the on-site image of the area to be monitored, and the shooting results can be uploaded to the early warning system. The specific method is not limited in the embodiment of this application. It is possible to identify and judge whether the on-site image contains preset carbon emission activity features based on a preset feature recognition algorithm. The specific preset feature recognition algorithm is not limited in the embodiment of this application. The preset carbon emission activity features can be temporary agricultural activities such as burning straw, using specific pesticides or fertilizers, or car accidents, traffic jams, etc., which lead to sudden increases in the amount of carbon emission gas in the area to be monitored.

[0092] Step S220: If yes, then obtain actual multi-dimensional environmental data of the area to be monitored, and generate a dynamic carbon emission image based on the on-site image and the actual multi-dimensional environmental data.

[0093] Specifically, the actual multi-dimensional environmental data can be collected by sensors or monitoring equipment installed in the area to be monitored and uploaded to the early warning system. It can also be obtained from data sources such as environmental monitoring stations and traffic management systems. The specific acquisition method is not specifically limited in the embodiments of this application. Based on the actual multi-dimensional environmental data, the actual carbon emissions corresponding to each group of multi-dimensional environmental data can be determined, and each actual carbon emission can be superimposed on the corresponding position of the on-site image. Since the actual multi-dimensional environmental data will change over time, a dynamic carbon emission image can be obtained by superimposing each actual carbon emission on the corresponding position of the on-site image. Among them, in order to facilitate the generation of a more accurate dynamic carbon emission image, when generating a dynamic carbon emission image based on the on-site image and the actual multi-dimensional environmental data, it can specifically include:

[0094] An initial AR scene image is generated based on the on-site image, and the AR carbon emission position corresponding to each actual multi-dimensional environmental data is determined from the initial AR scene image according to the actual data characteristics corresponding to each actual multi-dimensional environmental data; the carbon emission factor corresponding to each actual multi-dimensional environmental data is obtained, and the dynamic carbon emissions corresponding to each AR carbon emission position are determined based on each actual multi-dimensional environmental data; each dynamic carbon emission amount is superimposed on the AR carbon emission position in the initial AR scene image to obtain a dynamic carbon emission image.

[0095] Specifically, the collected on-site image can be first imported into a preset AR system, or the preset AR technology can be used to superimpose virtual elements such as coordinate axes, grids, and road signs in the on-site image to generate an initial AR scene image for subsequent data positioning. The preset AR technology can be ARKit technology, ARCore technology, etc. The specific preset AR system or preset AR technology is not specifically limited in the embodiments of this application, as long as it can enhance the on-site image.

[0096] Since the initial AR scene image is generated based on the on-site image of the area to be monitored, the virtual elements contained in the initial AR scene are consistent with the actual elements contained in the area to be monitored. The actual positions of the actual elements that will generate carbon emission gases can be first identified from the on-site image according to the preset feature recognition algorithm, and then the corresponding virtual elements, i.e., AR carbon emission positions, are located from the initial AR scene image based on the actual positions. For example, when the actual multi-dimensional environmental data is indirect carbon emissions caused by the construction or maintenance of infrastructure such as ports and airports, the AR carbon emission position can be the AR port or AR airport in the initial AR scene image. When the actual multi-dimensional environmental data is greenhouse gas emission data generated by animal breeding, crop breeding, etc., the AR carbon emission position can be the AR animal breeding area or AR crop breeding area in the initial AR scene. The preset feature recognition algorithm is not specifically limited in the embodiment of the present application, as long as it can perform feature recognition. Each dynamic carbon emission amount is superimposed on each corresponding AR carbon emission position to generate a more accurate dynamic carbon emission image.

[0097] Step S230: Tracking carbon emissions based on the dynamic carbon emission image and the preset carbon emission activity characteristics.

[0098] Specifically, carbon emissions tracking is performed based on pre-set carbon emission activity characteristics. That is, carbon emissions tracking is performed on sudden behaviors that contain pre-set carbon emission activity characteristics. By recording the location and amount of carbon emissions caused by sudden behaviors in real time, the carbon emissions of sudden behaviors can be monitored in real time. Because carbon emissions tracking requires a large amount of manpower, material and financial resources, if no sudden behaviors occur in the monitored area, additional carbon emissions tracking is not required. Only monitoring and generating abnormal carbon emissions warnings are required.

[0099] For the embodiments of the present application, by generating a dynamic carbon emission image in combination with actual environmental data when the preset carbon emission activity characteristics are included in the monitored area, it is convenient to accurately reflect the actual situation of carbon emission activities in the monitored area, thereby facilitating the improvement of monitoring accuracy. By tracking the preset carbon emission activity characteristics based on the dynamic carbon emission image, continuous and detailed monitoring of carbon emission activities can be achieved, which helps to more accurately analyze and evaluate the immediate impact of preset sudden behaviors on the monitored area.

[0100] The present application provides an early warning system. Figure 3 As shown, Figure 3 The illustrated early warning system 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the early warning system 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the early warning system 300 does not constitute a limitation on the embodiments of the present application.

[0101] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0102] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 The fact that only one line is used does not mean that there is only one bus or one type of bus.

[0103] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0104] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0105] The early warning system includes, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers are also possible. Figure 3 The warning system shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0106] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0107] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0108] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0109] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for early warning of abnormal carbon emissions, characterized in that: include: Obtain multi-dimensional environmental data of the area to be monitored within a preset time period; Identifying data characteristics of each set of multi-dimensional environmental data, and determining the carbon emission source type corresponding to each set of multi-dimensional environmental data based on the data characteristics of each set of multi-dimensional environmental data; Determining the carbon emissions corresponding to each set of multi-dimensional environmental data based on each set of multi-dimensional environmental data, and determining an abnormal carbon emission area from the area to be monitored based on each carbon emission amount and the corresponding carbon emission source type; generating an abnormal carbon emission warning based on the abnormal carbon emission area and the abnormal carbon emission amount corresponding to the abnormal carbon emission area; The step of determining an abnormal carbon emission area from the area to be monitored based on each carbon emission amount and the corresponding carbon emission source type includes: Obtain weather data of the area to be monitored within the preset time period, and determine retention parameters corresponding to each group of multi-dimensional environmental data based on the weather data and the carbon emission source types corresponding to each group of multi-dimensional environmental data, wherein first scores corresponding to different carbon emission source types are determined based on a preset first mapping relationship; second scores corresponding to different weather data are determined based on a preset second mapping relationship; the first score and the second score are summed to obtain a total score, and a retention level is determined based on a preset third mapping relationship, where different retention levels correspond to different retention parameters, and the retention parameters include diffusion range and diffusion rate, wherein the preset first mapping relationship is a correspondence between the carbon emission source type and the first score, the preset second mapping relationship is a correspondence between the weather data and the second score, and the preset third mapping relationship is a correspondence between the total score and the retention level; Based on the carbon emissions and retention parameters corresponding to each set of multi-dimensional environmental data, a carbon emission retention layer corresponding to each set of multi-dimensional environmental data is determined, wherein the diffusion process of carbon emission gas is simulated according to a preset mathematical model, carbon emissions, and retention parameters to obtain the carbon emission retention layer corresponding to each set of multi-dimensional environmental data; Overlaying all carbon emission retention layers to obtain a carbon emission image corresponding to the area to be monitored during the preset time period; Determine an area in the carbon emission image where the pixel depth is higher than a preset depth limit and the area is higher than a preset area limit as an abnormal carbon emission area; Among them, also include: Determine a region where the pixel depth of the region is within a preset depth range, or where the area of ​​the region is within a preset area range as an observation region; When the number of observations in the observation area is higher than a preset threshold, determining whether there is an associated observation area group based on the weather data, the associated observation area group includes at least two observation areas with the same weather characteristics, and the area spacing between at least two observation areas is less than a preset spacing threshold; If so, at least two observation areas contained in the associated observation area group domain are merged to obtain a merged area, and based on the preset depth limit and the preset area limit, it is determined whether the merged area is an abnormal carbon emission area.

2. The abnormal carbon emission early warning method according to claim 1, characterized in that: When there are no abnormal carbon emission areas, it also includes: Determining a critical layer corresponding to the carbon emission image based on the preset depth limit and the preset area limit, wherein, after superimposing the carbon emission retention layers corresponding to the respective sets of multi-dimensional environmental data, when no region in the obtained carbon emission image has a pixel depth higher than the preset depth limit and an area higher than the preset area limit, determining a critical layer based on the carbon emission image based on the preset depth limit and the preset surface limit, and after superimposing the critical layer on the current carbon emission image, the updated carbon emission image contains at least one abnormal carbon emission region; Determining critical concern data features and critical concern carbon emissions corresponding to each critical concern data feature based on the critical layer, and determining a control feature corresponding to each critical concern data feature from the data features corresponding to the multi-dimensional environmental data; Critical concern information is generated based on the critical concern carbon emissions corresponding to each critical concern data feature and the carbon emissions corresponding to each corresponding control feature.

3. The abnormal carbon emission early warning method according to claim 1 is characterized in that: Also includes: Acquire an on-site image corresponding to the area to be monitored, and identify whether the on-site image contains a preset carbon emission activity feature; If so, obtaining actual multi-dimensional environmental data of the area to be monitored, and generating a dynamic carbon emission image based on the on-site image and the actual multi-dimensional environmental data; Carbon emissions are tracked based on the dynamic carbon emissions image for the preset carbon emissions activity characteristics.

4. The abnormal carbon emission early warning method according to claim 3 is characterized in that: Generating a dynamic carbon emission image based on the on-site image and the actual multi-dimensional environmental data includes: generating an initial AR scene image based on the scene image, and determining an AR carbon emission position corresponding to each actual multi-dimensional environmental data from the initial AR scene image according to actual data features corresponding to each actual multi-dimensional environmental data; Obtain the carbon emission factor corresponding to each actual multi-dimensional environmental data, and determine the dynamic carbon emission corresponding to each AR carbon emission location based on each actual multi-dimensional environmental data; Each dynamic carbon emission amount is superimposed on the AR carbon emission position in the initial AR scene image to obtain a dynamic carbon emission image.

5. An early warning system, characterized in that: The early warning system includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute an abnormal carbon emission early warning method according to any one of claims 1-4.

6. A computer-readable storage medium, characterized in that include: A computer program is stored which can be loaded by a processor and executes an abnormal carbon emission early warning method according to any one of claims 1 to 4.

7. A computer program product, characterized in that The method comprises a computer program, which, when executed by a processor, implements the steps of the abnormal carbon emission early warning method according to any one of claims 1 to 4.

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