Abnormal carbon emission early warning method, early warning system, medium and program product

Abstract

In order to improve the timeliness and accuracy of monitoring the abnormal carbon emission condition, the invention provides an abnormal carbon emission early warning method and system, a medium and a program product, and the method comprises the steps: obtaining multi-dimensional environment data of a to-be-monitored region within a preset time period; identifying data features 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 features of each group of multi-dimensional environmental data; determining the carbon emission corresponding to each group of multi-dimensional environmental data based on each group of multi-dimensional environmental data, and determining an abnormal carbon emission area from the to-be-monitored area based on each carbon emission and the corresponding carbon emission source type; and generating an abnormal carbon emission early warning based on the abnormal carbon emission region and the abnormal carbon emission corresponding to the abnormal carbon emission region. According to the application, the timeliness and accuracy of monitoring the abnormal carbon emission condition can be improved.

Description

Technical Field

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

[0002] In the context of increasing global attention to climate change and environmental protection, the monitoring and management of carbon emissions have become the focus of attention for enterprises and governments. Especially in areas such as transportation, industrial production, and energy consumption, the monitoring and management of carbon emissions are particularly important. Since carbon emissions mainly come from fossil fuels such as coal, oil, and natural gas, these energy sources will release a large amount of carbon dioxide during combustion or use. In addition, industrial production, transportation, agricultural activities, etc. are also important sources of carbon emissions. These carbon emissions from different sources have different characteristics and laws, increasing the complexity of the carbon emission process.

[0003] Most traditional abnormal carbon emission monitoring methods rely on manual meter reading, regular inspections, etc. These traditional monitoring methods are not only time-consuming and laborious, but also easily affected by human factors, resulting in low-quality monitoring data and difficulty in accurately reflecting the actual situation of carbon emissions in a timely manner. Summary of the Invention

[0004] In order to improve the timeliness and accuracy of monitoring abnormal carbon emission situations, this application provides an abnormal carbon emission warning method, a warning system, a medium, and a program product.

[0005] In the first aspect, this application provides an abnormal carbon emission warning method, adopting the following technical solution: An abnormal carbon emission warning method, comprising: Obtaining multi-dimensional environmental data of a to-be-monitored area within a preset time period; Identifying the data characteristics of each group of multi-dimensional environmental data, and determining the corresponding carbon emission source type of each group of multi-dimensional environmental data based on the data characteristics of each group of multi-dimensional environmental data; Determining the corresponding carbon emission amount of each group of multi-dimensional environmental data based on each group of multi-dimensional environmental data, and 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; 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.

[0006] By adopting the above technical solution, by analyzing the multi-dimensional environmental data that the area to be detected may face in a future period of time, and after distinguishing the multi-dimensional environmental data of different carbon emission source types, performing carbon emission quantification analysis according to their characteristics, it is convenient to predict and understand the carbon emission situation of the area to be monitored in a future period of time. Based on the predicted carbon emission amount and source type, it is convenient to accurately screen out abnormal carbon emission areas from the area to be monitored, and by sending out early warning signals in time, it helps to detect and handle the possible carbon emission anomalies in the area to be monitored in advance, and take corresponding measures for handling.

[0007] In a possible implementation manner, 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: Obtain the weather data of the area to be monitored within the 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 type corresponding to each group of multi-dimensional environmental data; Based on the carbon emission amount and retention parameters corresponding to each group of multi-dimensional environmental data, determine the carbon emission retention layer corresponding to each group of multi-dimensional environmental data; Perform superposition processing on all the carbon emission retention layers to obtain the carbon emission image corresponding to the area to be monitored within the preset time period; Determine the area where the regional pixel depth in the carbon emission image is higher than the preset depth limit value and the regional area is higher than the preset area limit value as the abnormal carbon emission area.

[0008] By adopting the above technical solution, by introducing weather data and combining with the carbon emission source type, it is convenient to more accurately determine the retention parameters corresponding to each group of multi-dimensional environmental data. These retention parameters are convenient for reflecting the retention characteristics corresponding to different multi-dimensional environmental data, quantifying based on the retention characteristics, and using the carbon emission retention layer to display the quantification result, which is convenient for intuitively displaying the carbon emission distribution and intensity caused by different multi-dimensional environmental data. Finally, by superimposing all the carbon emission retention layers, the final carbon emission image of the area to be monitored is obtained, which is convenient for intuitively viewing the possible carbon emission situation of the area to be monitored in a future period of time under the influence of multi-dimensional environmental data. By identifying the regional pixel depth and regional area of each area in the carbon emission image, it is convenient to detect and handle abnormal emission situations in time.

[0009] In a possible implementation manner, the method further includes: Determine the area where the regional pixel depth is within the preset depth range or the regional area is within the preset area range as the observation area; When the number of observations in the observation area is higher than a preset threshold, it is determined 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 distance between at least two observation areas is less than a preset distance threshold; If so, at least two observation areas included in the associated observation area group are merged to obtain a merged area, and it is determined whether the merged area is an abnormal carbon emission area based on the preset depth limit and the preset area limit.

[0010] By adopting the above technical solution, determining the observation area by setting a preset depth range and a preset area range helps to screen out carbon emission areas with specific spatial characteristics. By merging observation areas with the same weather characteristics and a small distance to form a larger merged area, and using the preset depth limit and the preset area limit to judge whether there is abnormal carbon emission in the merged area, it is convenient to reveal potential abnormal carbon emission areas. By introducing weather data and regional distance, it is convenient to screen observation areas that can be merged, rather than merging all observation areas, which is convenient to conduct merged analysis on observation areas that meet relevant merger conditions without ignoring observation areas with potential carbon emission abnormalities, thus avoiding unnecessary merger processing and improving processing efficiency.

[0011] In a possible implementation manner, when there is no abnormal carbon emission area, the method further includes: Determining a critical layer corresponding to the carbon emission image based on the preset depth limit and the preset area limit; Determining critical attention data features and corresponding critical attention carbon emissions for each critical attention data feature based on the critical layer, and determining corresponding reference features for each critical attention data feature from the data features corresponding to the multi-dimensional environmental data; Generating critical attention information based on the critical attention carbon emissions corresponding to each critical attention data feature and the carbon emissions corresponding to each corresponding reference feature.

[0012] By adopting the above technical solution, when there is no abnormal carbon emission area, a critical layer that may cause abnormal carbon emission is determined based on the preset depth limit and the preset area limit, and then the corresponding critical attention data features and corresponding critical attention carbon emissions are determined based on the critical layer. Finally, by generating critical attention information, relevant staff are reminded to conduct targeted supervision on data features that may cause abnormal carbon emission, which is convenient for early prevention and taking corresponding measures, thus reducing the probability of abnormal carbon emission.

[0013] In a possible implementation manner, the method further includes: Obtain the on-site image corresponding to the area to be monitored, and identify whether the on-site image contains preset carbon emission activity characteristics; If so, obtain the 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; Perform carbon emission tracking on the preset carbon emission activity characteristics based on the dynamic carbon emission image.

[0014] By adopting the above technical solution, when the preset carbon emission activity characteristics are included in the area to be monitored, a dynamic carbon emission image is generated by combining the actual environmental data, which is convenient for accurately reflecting the actual situation of carbon emission activities, thereby facilitating the improvement of monitoring accuracy. Tracking the preset carbon emission activity characteristics based on the dynamic carbon emission image can realize continuous and detailed monitoring of carbon emission activities, and contribute to more accurately analyzing and evaluating the immediate impact of preset sudden behaviors on the area to be monitored.

[0015] In a possible implementation manner, the generating a dynamic carbon emission image based on the on-site image and the actual multi-dimensional environmental data includes: Generate an initial AR scene image based on the on-site image, and determine the AR carbon emission position corresponding to each actual multi-dimensional environmental data from the initial AR scene image according to the actual data characteristics 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 amount corresponding to each AR carbon emission position based on each actual multi-dimensional environmental data; Superimpose each dynamic carbon emission amount corresponding to the AR carbon emission position in the initial AR scene image to obtain a dynamic carbon emission image.

[0016] 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.

[0017] In a second aspect, the present application provides an early warning system, adopting the following technical solution: An early warning system, the early warning system includes: At least one processor; A memory; At least one application program, where the at least one application program is stored in a memory and configured to be executed by at least one processor, and the at least one application program is configured to: execute the above-mentioned abnormal carbon emission warning method.

[0018] In a third aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: A computer-readable storage medium, including: a computer program capable of being loaded and executed by a processor to execute the above-mentioned abnormal carbon emission warning method.

[0019] In a fourth aspect, the present application provides a computer program product, adopting the following technical solution: A computer program product, including a computer program, where when the computer program is executed by a processor, it implements the above-mentioned abnormal carbon emission warning method.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: By analyzing the multi-dimensional environmental data that the area to be detected may face in the future, and after distinguishing the multi-dimensional environmental data of different carbon emission source types, carbon emission quantification analysis is carried out according to their characteristics, which is convenient for predicting and understanding the carbon emission situation in the area to be monitored in the future. Based on the predicted carbon emission amount and source type, it is convenient to accurately screen out abnormal carbon emission areas from the area to be monitored, and by sending out early warning signals in a timely manner, it helps to detect and handle the possible carbon emission anomalies in the area to be monitored in advance, and take corresponding measures for processing.

[0021] When the preset carbon emission activity characteristics are included in the area to be monitored, by combining the actual environmental data to generate a dynamic carbon emission image, it is convenient to accurately reflect the actual situation of carbon emission activities, 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 realized, which helps to more accurately analyze and evaluate the immediate impact of preset sudden behaviors on the area to be monitored. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic flowchart of an abnormal carbon emission warning method in an embodiment of the present application; Figure 2 is a schematic flowchart of a carbon emission tracking process in an embodiment of the present application; Figure 3 is a schematic structural diagram of a warning system in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will Figures 1 to 3 further describe the present application in detail.

[0024] After reading this specification, those skilled in the art can make modifications to this embodiment that do not contribute creatively as needed, but as long as they are within the scope of the claims of this application, they are protected by the patent law.

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without making creative efforts belong to the scope of protection of this application.

[0026] It should be noted that in the alternative embodiments of this application, for relevant data such as object information, when the embodiments in this application are applied to specific products or technologies, object permission or consent needs to be obtained, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions. That is to say, if the embodiments in this application involve data related to an object, it needs to be obtained under the authorization and consent of the object, the authorization and consent of relevant departments, and 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 needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiments also need to be implemented under the authorization and consent of the object.

[0027] Specifically, the embodiments of this application provide an abnormal carbon emission warning method, which is executed by a warning system. The warning system can be a server or a terminal device. Among them, the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected through wired or wireless communication methods, and this application does not limit this here.

[0028] Refer to Figure 1 , Figure 1 is a schematic flowchart of an abnormal carbon emission warning method in the embodiments of this application. The method includes steps S110 - S140, where: Step S110: Obtain multi-dimensional environmental data of the area to be monitored within a preset time period.

[0029] Specifically, the area to be monitored can be the area along the transportation route in the transportation chain, the area around the transportation hub, etc. The specific area to be monitored is not specifically limited in the embodiments of the present application, as long as this area has the need for abnormal carbon emission warning. The preset time period is a period of time after the current moment. The duration corresponding to the preset time period can be 3 hours or 4 hours. The specific duration is not limited in the embodiments of the present application.

[0030] In the transportation chain, there are multiple reasons that may cause abnormal carbon emission situations, that is, there are multiple carbon emission sources, and each carbon emission source corresponds to a set of multi-dimensional environmental data. Among them, the carbon emission source may be transportation. For example, during the process of transporting goods in the area to be monitored, the carbon emissions generated by transportation equipment such as diesel trucks, aviation fuel, and marine fuel, and the corresponding multi-dimensional environmental data can be carbon emission data in different historical time periods, carbon emission data of different transportation modes and transportation equipment, etc.; the energy industry, such as the mining or processing of coal, oil, and natural gas in the area to be monitored; industrial production, such as the combustion of fossil fuels during the production process of heavy industries such as steel, cement, and chemical industries in the area to be monitored, and the corresponding multi-dimensional environmental data can be carbon emission data during the operation process in different mining or processing areas, the corresponding relationship between carbon emission data and the output energy volume during the energy mining or processing process, etc.; agricultural activities, such as the greenhouse gases generated by animal breeding, crop breeding, etc. in the area to be monitored, and the corresponding multi-dimensional environmental data can be carbon emission data in different growth cycles, carbon emission data in different agricultural areas, carbon emission data of different agricultural types, etc.; indirect emissions from infrastructure, such as the carbon emissions generated by the construction and maintenance of roads, railways, ports, airports, the manufacturing and maintenance of transportation tools, etc., and the corresponding multi-dimensional environmental data can be carbon emission data at different construction or maintenance stages, carbon emission data of different infrastructure projects, etc.

[0031] Since the preset time period is a period of time after the current moment, when obtaining multi-dimensional environmental data within the preset time period, the historical multi-dimensional 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 screened from the historical multi-dimensional environmental data based on the identified trends and patterns. Among them, preset statistical algorithms, preset machine algorithms, etc. can be used to deeply analyze the historical multi-dimensional environmental data, and then a prediction model is established based on the identification results to predict the carbon emission situation of different carbon emission sources in the future for a period of time. That is, screening is performed from the historical multi-dimensional environmental data. Among them, the preset statistical algorithm can be an autoregressive moving average model, a differential autoregressive moving 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 algorithm, preset machine algorithm, etc. are not limited in the embodiments of the present application, as long as the trends and patterns of each carbon emission source can be analyzed based on the historical multi-dimensional environmental data of each carbon emission source.

[0032] The historical multi-dimensional environmental data can be collected by sensors or monitoring devices installed in the area to be monitored and then uploaded to the early warning system, or 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 the present application.

[0033] Step S120: Identify the data characteristics of each group of multi-dimensional environmental data, and determine the 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.

[0034] Specifically, a preset feature recognition algorithm can be used to identify the data characteristics of each group of multi-dimensional environmental data. The specific preset feature recognition algorithm is not specifically limited in the embodiments of the present application, as long as it can identify the data characteristics included in each group of multi-dimensional environmental data. Different data characteristics correspond to different carbon emission source types. The carbon emission source types can include fixed source emissions, mobile source emissions, and indirect source emissions. The carbon emission source type corresponding to different data characteristics can be determined through a preset type mapping relationship. The preset type mapping relationship is the corresponding relationship between different data characteristics and carbon emission source types. The specific content of this preset type mapping relationship is not specific in the embodiments of the present application and can be determined by relevant staff based on historical experimental data and then uploaded to the early warning system.

[0035] Step S130: Determine the carbon emissions corresponding to each group of multi-dimensional environmental data based on each group of multi-dimensional environmental data, and determine the abnormal carbon emission area from the area to be monitored based on each carbon emission and the corresponding carbon emission source type.

[0036] Specifically, the calculation methods of carbon emissions corresponding to different multi-dimensional environmental data are different. For example, when the carbon emission source is transportation, parameters such as vehicle type, fuel consumption, and emission factor should be identified from the corresponding multi-dimensional environmental data first, and then the carbon emissions should be calculated according to the identified parameters. The early warning system contains the calculation methods of carbon emissions corresponding to different carbon emission sources, and can be retrieved from the early warning system by itself when the relevant carbon emission calculation formulas need to be called or used.

[0037] The calculated carbon emissions can be directly summed to obtain the total carbon emissions, and then the total carbon emissions are compared with the preset carbon emission threshold to determine whether there are abnormal carbon emission areas in the area to be monitored. The preset carbon emission threshold can be determined by relevant staff according to historical experimental data and then uploaded to the early warning system. However, since carbon emission gases have a certain fluidity, when analyzing the total amount or concentration of carbon emission gases in the area to be monitored, the retention situation of carbon emission gases can be analyzed according to the type of carbon emission source. Among them, based on each carbon emission and the corresponding carbon emission source type, abnormal carbon emission areas are determined from the area to be monitored, which may specifically include: Obtain the weather data of the area to be monitored within the 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 type corresponding to each group of multi-dimensional environmental data; determine the carbon emission retention layer 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 the carbon emission retention layers to obtain the carbon emission image corresponding to the area to be monitored within the preset time period; determine the area where the pixel depth of the area in the carbon emission image is higher than the preset depth limit and the area area is higher than the preset area limit as the abnormal carbon emission area.

[0038] Specifically, since weather data such as wind speed, wind direction, temperature, and humidity in the area to be monitored are important factors affecting carbon emission gases. For example, the greater the wind speed, the faster the diffusion speed of carbon emission gases, and the higher the temperature, the farther the movement speed of carbon emission gases and the stronger the diffusion ability. Therefore, when analyzing the retention situation of carbon emission gases in the area to be monitored, the impact of weather data on the retention result of carbon emission gases needs to be considered. The weather data can be obtained by accessing the meteorological system or uploaded to the early warning system by relevant staff. The acquisition method of weather data is not specifically limited in the embodiments of the present application.

[0039] When determining the retention parameters based on the weather data and the carbon emission source type, there are two methods: Method 1: Based on the training weather data and training multi-dimensional environmental data, establish a simulation model for predicting the retention of carbon emission gases. Finally, import the weather data corresponding to the area to be monitored and each group of multi-dimensional environmental data 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.

[0040] Method 2: First, based on the preset first mapping relationship, determine the first scores corresponding to different carbon emission source types. Then, based on the preset second mapping relationship, determine the second scores corresponding to different weather data. Finally, perform a summation calculation on the first scores and the second scores to obtain the total score. Finally, based on the preset third mapping relationship, determine the retention level. Different retention levels correspond to different retention parameters. The retention parameters include, but are not limited to, the diffusion range and diffusion rate. Among them, the preset first mapping relationship is the corresponding relationship between the carbon emission source type and the first score, the preset second mapping relationship is the corresponding relationship between the weather data and the second score, and the preset third mapping relationship is the corresponding relationship between the total score and the retention level. The specific content is not specifically limited in the embodiments of the present application and can be determined by relevant staff according to historical experimental data.

[0041] For any group of multi-dimensional environmental data, after determining the retention parameters, a preset mathematical model, the carbon emission amount, and the retention parameters can be used to simulate the diffusion process of carbon emission gases to obtain the carbon emission retention layer corresponding to this group of multi-dimensional environmental data. Among them, the preset mathematical model can be a Gaussian diffusion model, a Lagrangian diffusion membrane model, etc. The specific mathematical model is not specifically limited in the embodiments of the present application, as long as it can simulate the diffusion process of carbon emission gases through a mathematical model. Finally, with the help of a preset visualization tool, the simulation result is converted into a carbon emission retention layer. The specific preset visualization tool is not specifically limited in the embodiments of the present application. Based on the above method, the carbon emission retention layer corresponding to each group of multi-dimensional environmental data can be obtained. The area to be monitored can be divided into several small grids, each grid corresponding to a pixel in the layer. According to the simulation diffusion result, the carbon emission gas concentration value in each grid is calculated, and the calculated concentration value is mapped to the pixel value. Usually, the higher the concentration value, the larger the pixel value. Through the carbon emission retention layer, the complex carbon emission situation can be presented in an intuitive way, so that it is convenient for relevant non-professionals to quickly understand the retention situation and spatial distribution of carbon emission gases under the influence of this multi-dimensional environmental data.

[0042] Since the carbon emission retention layer can combine the carbon emission situation with specific geographical locations, different carbon emission retention layers corresponding to multi-dimensional environmental data can be superimposed. After superimposing all the carbon emission retention layers, a carbon emission image corresponding to the area to be monitored during a preset time period can be obtained. By analyzing the pixel depths corresponding to each pixel point in the carbon emission image, the carbon emission image is initially divided into regions, and the regional pixel depth and regional area corresponding to each initial region are identified. Regions in the carbon emission image with a regional pixel depth higher than a preset depth limit and a regional area higher than a preset area limit are determined as abnormal carbon emission regions. Here, the preset depth limit and the preset area limit are not specifically defined in the embodiments of this application and can be determined by relevant staff based on historical experimental data and then uploaded to the early warning system. When initially dividing the carbon emission image into regions, it is necessary to ensure that the pixel difference between different pixel points within the divided initial regions does not exceed a preset difference threshold. By superimposing all the carbon emission retention layers, the final carbon emission image of the area to be monitored is obtained, which is convenient for intuitively viewing the possible carbon emission situation in the area to be monitored 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 handle abnormal emission situations.

[0043] Step S140: Generate an abnormal carbon emission warning based on the abnormal carbon emission region and the corresponding abnormal carbon emission amount.

[0044] Specifically, the carbon emission image may contain multiple abnormal carbon emission regions, and the specific number is not specifically defined in the embodiments of this application. After determining the abnormal carbon emission regions, an abnormal carbon emission warning is promptly generated based on the abnormal carbon emission regions and the corresponding abnormal carbon emission amounts, and the abnormal carbon emission warning is promptly fed back to relevant management personnel so that relevant management personnel can promptly take corresponding measures.

[0045] For the embodiments of this application, by analyzing the multi-dimensional environmental data that the area to be detected may face in the future, and after distinguishing the multi-dimensional environmental data of different carbon emission source types, carbon emission quantification analysis is carried out according to their characteristics, which is convenient for predicting and understanding the carbon emission situation in the area to be monitored in the future. Based on the predicted carbon emission amount and source type, it is convenient to accurately screen out abnormal carbon emission regions from the area to be monitored, and by promptly sending out a warning signal, it helps to discover and handle in advance the possible abnormal carbon emission situation in the area to be monitored and take corresponding measures for treatment.

[0046] Furthermore, to facilitate revealing potential abnormal carbon emission regions, the method provided in the embodiments of this application further includes: Determine the area where the regional pixel depth is within a preset depth range or the regional area is within a preset area range as the observation area; when the number of observations in the observation area is higher than a preset threshold, determine whether there is an associated observation area group based on weather data. The associated observation area group includes at least two observation areas with the same weather characteristics, and the regional distance between at least two observation areas is less than a preset distance threshold; if so, merge at least two observation areas included in the associated observation area group to obtain a merged area, and determine whether the merged area is an abnormal carbon emission area based on a preset depth limit and the preset area limit.

[0047] Specifically, since the abnormal carbon emission area is an area where the regional pixel depth is higher than the preset depth limit and the regional area is higher than the preset area limit, that is, the area where the regional pixel depth is not higher than the preset depth limit or the regional area is not higher than the preset area limit is a non-abnormal carbon emission area. However, there may be potential abnormal carbon emission areas in the non-abnormal carbon emission areas. At this time, by introducing a preset depth range and a preset area range, select some areas where the regional pixel depth is within the preset depth range or the regional area is within the preset area range from the non-abnormal carbon emission areas as the observation areas, and judge whether the observation areas can be merged by analyzing whether there is an association relationship between the observation areas.

[0048] Among them, since the weather data will directly affect the distribution and diffusion of carbon emission gases in the area to be monitored, therefore, when judging whether there is an association relationship between the observation areas, weather data can also be introduced for judgment. Specifically, when the number of observations in the observation area is higher than a preset threshold, the observation areas with the same weather characteristics and the regional distance between the observation areas less than the preset distance threshold can be determined as the associated observation area group. The associated observation area group includes 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 embodiments of the present application. Among them, the preset threshold, the preset distance threshold, etc. can be determined by relevant staff according to historical experimental data and then uploaded to the early warning system, which are not specifically limited in the embodiments of the present application. When calculating the regional distance 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 is determined as the regional distance between the two observation areas.

[0049] After determining the associated observation area group, the area edge information of each observation area in the associated observation area group can be identified based on a preset edge recognition algorithm. All the observation areas in the associated observation area group are merged based on the area edge information of each observation area. The specific preset edge recognition algorithm is not specifically limited in the embodiments of the present application. Since there may be an interval distance between the observation areas included in the associated observation area group, the area of the merged area is greater than the sum of the area of all the corresponding observation areas. After determining the merged area, it is determined again based on the preset depth limit and the preset area limit whether the merged area is an abnormal carbon emission area, that is, when the area 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 as an abnormal carbon emission area.

[0050] By merging the observation areas with the same weather characteristics and a small spacing to form a larger merged area, and using the preset depth limit and the preset area limit to determine whether there is an abnormal carbon emission situation in the merged area, it is convenient to reveal potential abnormal carbon emission areas. In addition, not all the observation areas are merged, which is convenient for merging and analyzing the observation areas that meet the relevant merging conditions on the premise of not ignoring the observation areas with potential carbon emission anomalies, so as to avoid unnecessary merging processes and improve the processing efficiency.

[0051] Further, when there is no abnormal carbon emission area, the method provided in the embodiments of the present application further includes: Determining a critical layer corresponding to the carbon emission image based on the preset depth limit and the preset area limit; determining critical attention data features and the corresponding critical attention carbon emissions for each critical attention data feature based on the critical layer, and determining the corresponding reference features for each critical attention data feature from the data features corresponding to the multi-dimensional environmental data; generating critical attention information based on the critical attention carbon emissions corresponding to each critical attention data feature and the carbon emissions corresponding to each corresponding reference feature.

[0052] Specifically, when there is no area in the carbon emission image obtained after superimposing the carbon emission retention layers corresponding to each group of multi-dimensional environmental data where the area 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 area limit on the basis of the carbon emission image. The specific critical layer is not specifically limited in the embodiments of the present application, as long as it is ensured that after superimposing the critical layer on the current carbon emission image, at least one abnormal carbon emission area will appear in the updated carbon emission image.

[0053] Since the carbon emission retention layer is determined based on the corresponding carbon emissions and data characteristics, after determining the critical layer, the critical attention data characteristics corresponding to the carbon emission retention layer and the critical attention carbon emissions corresponding to each critical attention data characteristic can also be deduced reversely. The specific reverse deduction method is not specifically limited in the embodiments of the present application. According to the critical attention data characteristics, the corresponding reference characteristics for each critical attention data characteristic can be further determined from all multi-dimensional environmental data. Finally, based on the critical attention carbon emissions corresponding to each critical attention data characteristic and the carbon emissions corresponding to each corresponding reference characteristic, critical attention information is generated. For example, the carbon emission image corresponds to data characteristic a with a carbon emission of 80; data characteristic b with a carbon emission of 60; data characteristic 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 preset area limit, the determined critical attention data characteristics are critical attention data characteristic a with a critical attention carbon emission of 10; critical attention data characteristic c with a critical attention carbon emission of 15, and the reference characteristics are data characteristic a and data characteristic c. The generated critical attention information is that when the carbon emission corresponding to data characteristic a is higher than 90, the carbon emission corresponding to data characteristic b is higher than 60, and the carbon emission corresponding to data characteristic c is higher than 85, the probability of abnormal carbon emission in the area to be monitored will increase. At this time, by generating critical attention information, relevant staff are reminded to conduct targeted supervision on the data characteristics that may cause abnormal carbon emission situations, which is convenient for early prevention and taking corresponding measures, so as to reduce the probability of abnormal carbon emission situations.

[0054] Further, the technical solution provided in the embodiments of the present application further includes steps S210-S230, as Figure 2 shown, where: Step S210: Obtain the on-site image corresponding to the area to be monitored, and identify whether the on-site image contains preset carbon emission activity characteristics.

[0055] Specifically, the on-site image of the area to be monitored can be captured by an image acquisition device set in the area to be monitored and the capture result is uploaded to the early warning system. The specific method is not specifically limited in the embodiments of the present application. Whether the on-site image contains preset carbon emission activity characteristics can be identified and judged from the on-site image according to a preset feature recognition algorithm. The specific preset feature recognition algorithm is not specifically limited in the embodiments of the present application. The preset carbon emission activity characteristics can be characteristics corresponding to temporary agricultural activities such as burning straw, using specific pesticides or fertilizers, or sudden behaviors such as car accidents and traffic jams that cause a sudden increase in the amount of carbon emission gas in the area to be monitored.

[0056] Step S220: If so, obtain the 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.

[0057] Specifically, the actual multi-dimensional environmental data can be collected by sensors or monitoring devices installed in the area to be monitored and then uploaded to the early warning system, or can 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 the present 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 is superimposed on the corresponding position of the on-site image. Since the actual multi-dimensional environmental data will change over time, after superimposing each actual carbon emission on the corresponding position of the on-site image, a dynamic carbon emission image can be obtained. 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 may specifically include: Generate an initial AR scene image based on the on-site image, and determine the AR carbon emission position corresponding to each actual multi-dimensional environmental data from the initial AR scene image according to the actual data characteristics 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 position based on each actual multi-dimensional environmental data; superimpose each dynamic carbon emission on the AR carbon emission position in the initial AR scene image to obtain a dynamic carbon emission image.

[0058] Specifically, the collected on-site image can be imported into a preset AR system first, or virtual elements such as coordinate axes, grids, and road signs can be superimposed on the on-site image using a preset AR technology 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 the present application, as long as it can perform enhancement processing on the on-site image.

[0059] Since the initial AR scene image is generated based on the on-site image of the area to be monitored, the virtual elements included in the initial AR scene are consistent with the actual elements included in the area to be monitored. The actual positions of the actual elements that generate carbon emission gases can be identified from the on-site image according to the preset feature recognition algorithm first, and then the corresponding virtual elements, that is, the AR carbon emission positions, can be located from the initial AR scene image based on the actual positions. For example, when the actual multi-dimensional environmental data is the 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 the greenhouse gas emission data generated by animal farming, crop farming, etc., the AR carbon emission position can be the AR animal farming area or AR crop farming area in the initial AR scene. Among them, the preset feature recognition algorithm is not specifically limited in the embodiments of the present application, as long as it can perform feature recognition. The dynamic carbon emission amount of each item is superimposed on each corresponding AR carbon emission position to generate a more accurate dynamic carbon emission image.

[0060] Step S230: Perform carbon emission tracking on the preset carbon emission activity features based on the dynamic carbon emission image.

[0061] Specifically, performing carbon emission tracking on the preset carbon emission activity features, that is, performing carbon emission tracking on the sudden behaviors including the preset carbon emission activity features, and recording the carbon emission positions and carbon emission amounts caused by the sudden behaviors in real time, so as to master the carbon emission situation of the sudden behaviors in real time. Since a large amount of manpower, material resources and financial resources are required for carbon emission tracking, when there is no sudden behavior in the area to be monitored, there is no need to perform additional carbon emission tracking processing, and only the monitoring and generation of abnormal carbon emission warnings are required.

[0062] For the embodiments of the present application, by generating a dynamic carbon emission image in combination with the actual environmental data when the preset carbon emission activity features are included in the area to be monitored, it is convenient to accurately reflect the actual situation of the carbon emission activities in the area to be monitored, thereby facilitating the improvement of the monitoring accuracy. Tracking the preset carbon emission activity features based on the dynamic carbon emission image can realize continuous and detailed monitoring of the carbon emission activities, and contribute to more accurately analyzing and evaluating the immediate impact of the preset sudden behaviors on the area to be monitored.

[0063] An early warning system is provided in the embodiments of the present application, as Figure 3 shown Figure 3The warning system 300 shown includes: a processor 301 and a memory 303. Among them, the processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the warning system 300 may further include a transceiver 304. It should be noted that in practical applications, the number of transceivers 304 is not limited to one, and the structure of the warning system 300 does not constitute a limitation on the embodiments of the present application.

[0064] The 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 devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present application. The 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.

[0065] The bus 302 may include a path for transmitting information between the above components. The bus 302 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 only one line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0066] The memory 303 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0067] The memory 303 is used to store the application program code for executing the solution of this application, and is controlled by the processor 301 for execution. The processor 301 is used to execute the application program code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0068] Among them, the 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), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The shown warning system is only an example, and should not bring any restrictions to the functions and usage scopes of the embodiments of this application.

[0069] The embodiments of this application provide a computer-readable storage medium, on which a computer program is stored. When it runs on a computer, it enables the computer to execute the corresponding content in the foregoing method embodiments.

[0070] The embodiments of this application provide a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method in any of the above embodiments.

[0071] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially according to the indication of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this document, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. 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 alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0072] The above are only some embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. An abnormal carbon emission early warning method, characterized in that: include: Obtain multi-dimensional environmental data of the area to be monitored within a preset time period; Identify data features of each set of multi-dimensional environmental data, and determine 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; Determine the carbon emissions corresponding to each group of multi-dimensional environmental data based on each group of multi-dimensional environmental data, and determine the abnormal carbon emission area from the area to be monitored based on each carbon emission and the corresponding carbon emission source type; 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.

2. The abnormal carbon emission early warning method according to claim 1 is characterized in that: The determining of 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: Acquire weather data of the monitored area 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 type corresponding to each group of multi-dimensional environmental data; Based on the carbon emissions and retention parameters corresponding to each set of multi-dimensional environmental data, determine the carbon emission retention layer corresponding to each set of multi-dimensional environmental data; All carbon emission retention layers are superimposed to obtain a carbon emission image corresponding to the area to be monitored in the preset time period; 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.

3. The abnormal carbon emission early warning method according to claim 2 is characterized in that: Also includes: Determine a region whose pixel depth is within a preset depth range, or whose area 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.

4. The abnormal carbon emission early warning method according to claim 2 is 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; Determine the critical concern data feature and the critical concern carbon emissions corresponding to each critical concern data feature based on the critical layer, and determine the 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.

5. The abnormal carbon emission early warning method according to claim 1 is characterized in that: Also includes: Acquire a site image corresponding to the area to be monitored, and identify whether the site image contains a preset carbon emission activity feature; If yes, then 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.

6. The abnormal carbon emission early warning method according to claim 5 is characterized in that: The generating a dynamic carbon emission image based on the on-site image and the actual multi-dimensional environmental data includes: Generate an initial AR scene image based on the scene image, and determine the AR carbon emission position corresponding to each actual multi-dimensional environmental data from the initial AR scene image according to the actual data feature 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.

7. 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 1-6.

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

9. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the steps of an abnormal carbon emission early warning method according to any one of claims 1 to 6.

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