Carbon emission monitoring method and system based on five-base collaborative integration

Abstract

The invention provides a carbon emission monitoring method and system based on five-base cooperative integration, and belongs to the technical field of carbon emission monitoring, and the method comprises the steps: determining a target region based on the monitoring data of a space-based satellite; the target area is monitored based on air-based remote sensing, a first area is determined, and the first area is an area in the target area; determining inspection features of the first area; in response to the condition that the inspection features meet different inspection conditions, controlling the target equipment to inspect the first area according to the corresponding inspection frequency to obtain target carbon source data; the target equipment comprises a ground monitoring station, an aviation unmanned aerial vehicle or a mobile monitoring vehicle; and performing carbon emission monitoring on the target area based on the target carbon source data. According to the carbon emission monitoring method and system based on five-base cooperative integration, the accuracy and effectiveness of carbon emission monitoring can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of carbon emission monitoring, and more specifically, to a carbon emission monitoring method and system based on five-base coordinated integration. Background Art

[0002] As the global climate change problem becomes increasingly serious, carbon emission monitoring has become a key link in responding to climate change and formulating emission reduction strategies. Traditional carbon emission monitoring methods often rely on a single monitoring method. At the same time, there is a lack of effective coordination between different monitoring methods, which leads to a decrease in the accuracy of carbon emission monitoring results. Therefore, there is an urgent need for an accurate and effective carbon emission monitoring method. Summary of the invention

[0003] The purpose of the present invention is to provide a carbon emission monitoring method and system based on the coordinated integration of five bases to improve the accuracy and effectiveness of carbon emission monitoring.

[0004] In a first aspect of the embodiments of the present disclosure, a carbon emission monitoring method based on five-base coordinated integration is provided, comprising: Determine target areas based on space-based satellite monitoring data; Monitoring the target area based on air-based remote sensing to determine a first area, where the first area is an area within the target area; determining inspection characteristics of the first area; In response to the inspection characteristics satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency to obtain target carbon source data; the target device includes a ground monitoring station, an aerial drone or a mobile monitoring vehicle; Carbon emissions in the target area are monitored based on the target carbon source data.

[0005] A second aspect of the embodiment of the present disclosure provides a carbon emission monitoring system based on five-base coordinated integration, including: A target area determination module, used to determine the target area based on the monitoring data of the space-based satellite; A first region determination module is used to monitor the target region based on air-based remote sensing and determine a first region, where the first region is a region within the target region; An inspection feature determination module, used to determine the inspection features of the first area; A regional inspection module, for controlling the target device to inspect the first area at a corresponding inspection frequency in response to the inspection characteristics satisfying different inspection conditions, and obtaining target carbon source data; the target device includes a ground monitoring station, an aerial drone or a mobile monitoring vehicle; A carbon emission monitoring module is used to monitor carbon emissions in the target area based on the target carbon source data.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned carbon emission monitoring method based on the coordinated integration of the five bases when executing the computer program.

[0007] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned carbon emission monitoring method based on the coordinated integration of the five bases are implemented.

[0008] The beneficial effects of the carbon emission monitoring method and system based on the five-base coordinated integration provided by the embodiments of the present disclosure are: The disclosure determines the target area through the monitoring data of space-based satellites. Based on the advantages of global coverage and continuous observation of space-based satellites, it can quickly and accurately lock in the abnormal atmospheric greenhouse gas concentration and high-carbon emission industrial clusters, efficiently coordinate resources, and save a lot of ground manpower investigation costs; then, air-based remote sensing is further refined to determine the first area, accurately locate the places where emission sources are concentrated and the situation is complicated, so that monitoring is more targeted; then, the inspection characteristics are clarified and arranged accordingly, and ground monitoring stations, aerial drones or mobile monitoring vehicles are flexibly called to meet the needs of different scenarios; finally, the disclosure can fully grasp the carbon emission information, provide detailed basis for policy formulation and emission reduction action planning, and effectively promote low-carbon development and the process of responding to climate change. Therefore, the disclosure can improve the accuracy and effectiveness of carbon emission monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a carbon emission monitoring method based on five-base coordinated integration provided in one embodiment of the present disclosure; Figure 2 A structural block diagram of a carbon emission monitoring system based on five-base coordinated integration provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0012] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0013] Please refer to Figure 1 , Figure 1 A flow chart of a carbon emission monitoring method based on five-base coordinated integration provided in one embodiment of the present disclosure, the method comprising: S101: Determine a target area based on monitoring data from a space-based satellite.

[0014] In this embodiment, space-based satellites refer to various artificial satellites operating outside the Earth's atmosphere. They orbit the Earth and are equipped with a variety of sensors and detection equipment. They can capture electromagnetic waves of different bands, covering visible light, infrared, microwaves, etc., and thus achieve all-round observation of the Earth's surface and atmospheric environment. Space-based satellites can use their own sensors to collect various types of information, and the data after digitization and encoding processing is monitoring data. The present disclosure can download the monitoring data of space-based satellites through websites (such as EarthData). For carbon emission monitoring, atmospheric composition data can be monitored, for example, the concentration distribution of greenhouse gases such as carbon dioxide and methane; land use data can also be monitored, and different landform types such as cities, forests, farmlands or deserts can be distinguished; vegetation coverage data can also be monitored, which can reflect the growth status of vegetation and indirectly relate to carbon sink capacity.

[0015] Considering that space-based satellites are not restricted by geographical and weather conditions, they can effectively monitor the concentrations of multiple greenhouse gases, and after summarizing them, the total value of greenhouse gas concentrations can be obtained.

[0016] Determine the target area based on space-based satellite monitoring data, including: Based on space-based satellite monitoring of multiple regions, the total concentration of greenhouse gases in multiple regions is obtained; If the total greenhouse gas concentration in each area is greater than or equal to the first preset concentration value, the area is taken as the target area; If the total greenhouse gas concentration in each area is less than the first preset concentration value, the area is regarded as a non-target area; The first preset concentration value may be a concentration pre-set according to actual conditions, and is related to the degree of impact on human health.

[0017] Therefore, the target area is a key monitoring area.

[0018] When the total greenhouse gas concentration in a certain area reaches or exceeds the first preset concentration value, it means that the greenhouse gas emissions in the area are serious or the carbon cycle is out of balance. At this time, it is necessary to focus on carbon emissions monitoring in the area; when the total greenhouse gas concentration in a certain area is less than the first preset concentration value, it means that the area is in a relatively reasonable state or does not need to be focused on, but it is not completely ignored, but resources are focused on monitoring the target area. At the same time, space-based satellites will continue to monitor multiple areas, so the target area or non-target area changes dynamically.

[0019] S102: Monitor the target area based on air-based remote sensing and determine a first area, where the first area is an area within the target area.

[0020] In this embodiment, air-based remote sensing refers to a technical means of carrying remote sensing equipment on various types of aircraft, taking advantage of the aircraft flying in the atmosphere to observe, detect and collect data on ground targets from the air. These aircraft can fly according to predetermined routes and altitudes, and the sensors they carry can obtain multi-band images, spectral information, etc. of the target area.

[0021] The target area is a monitoring area predetermined by S101, which can be a city, an industrial park, a forest or a river basin, etc.

[0022] The first area can be the area with the most concentrated carbon emission sources in the target area, or the area with the highest carbon emission intensity, etc.

[0023] In this embodiment, monitoring the target area based on air-based remote sensing to determine the first area includes: Based on air-based remote sensing, the target area is monitored to obtain various carbon emission sources; If the type of carbon emission source is greater than or equal to the first type threshold, the area where it is located is regarded as the first area; If the types of carbon emission sources are less than the first type threshold, the area where they are located is regarded as a non-first area.

[0024] The first category threshold can be a pre-set value used to measure the limit standard of the number of carbon emission sources. The determination of this threshold is set after comprehensive consideration of many factors, such as the functional nature of the target area, the size of the area, and the distribution pattern of carbon emission sources in similar areas in the past.

[0025] The first area or non-first area is a sub-area in the target area. When it is analyzed through air-based remote sensing monitoring that the number of types of carbon emission sources in a sub-area reaches or exceeds the preset first category threshold, this sub-area is designated as the first area. For example, assuming that the first category threshold is set to 5 types, after air-based remote sensing monitoring, it is found that a certain factory concentration area has 5 or more carbon emission sources such as factory chimney emissions, automobile exhaust emissions, garbage incineration emissions, biomass combustion emissions, and chemical production process emissions. Then the sub-area where this factory concentration area is located is determined as the first area.

[0026] S103: Determine inspection characteristics of the first area.

[0027] In this embodiment, the inspection characteristics may be some iconic and representative characteristics or attributes that can reflect the characteristics, status and differences of the area with other areas during the inspection of the first area; they may also be reflected through monitoring data, such as the characteristics of changes in carbon emission concentration, the distribution density characteristics of different carbon emission sources, etc.

[0028] This embodiment collects, organizes and analyzes various relevant information of the first area that has been determined, including but not limited to its geographical location, topography, land use type, types and distribution of carbon emission sources, surrounding environment, etc., to find out a series of iconic features that can reflect the carbon emissions and related aspects of the area.

[0029] S104: In response to the inspection characteristics satisfying different inspection conditions, control the target device to inspect the first area according to the corresponding inspection frequency to obtain target carbon source data; the target device includes a ground monitoring station, an aerial drone or a mobile monitoring vehicle.

[0030] In this embodiment, the inspection conditions are some pre-set measurement standards or rules used to judge whether the inspection characteristics have reached a level that requires a specific inspection method.

[0031] In response to the inspection feature satisfying that the carbon emission sources are concentrated and fixed, controlling the ground monitoring station to inspect the first area to obtain first carbon source data, where the first carbon source data is target carbon source data; In response to the inspection feature that the carbon emission sources are dispersed and changing, the mobile monitoring vehicle or the aerial drone is controlled to inspect the first area to obtain second carbon source data, which is the target carbon source data.

[0032] Considering the fixed nature of the ground monitoring station, when the carbon emission sources are concentrated and fixed, the ground monitoring station can well cover the monitoring range of the first area, thereby obtaining the carbon source data of the first area, i.e., the first carbon source data. For example, the carbon emission sources in the industrial park are concentrated and fixed.

[0033] Considering the flexibility and mobility of mobile monitoring vehicles or aerial drones, when the carbon emission sources are dispersed and changing, mobile monitoring vehicles or aerial drones can well track the distribution or movement of carbon sources in the first area, thereby obtaining the carbon source data of the first area, i.e., the second carbon source data. For example, the carbon emission sources of highways are dispersed and changing.

[0034] Different inspection conditions correspond to different inspection strategies and equipment usage methods.

[0035] The target equipment is a specific equipment that can be used to inspect the first area, including ground monitoring stations, aerial drones or mobile monitoring vehicles. Ground monitoring stations are established at fixed locations, equipped with professional monitoring instruments, and can measure carbon emissions and other related indicators of the surrounding environment for a long time and continuously. Aerial drones are unmanned aerial vehicles that fly in the air and can flexibly observe the target area according to the set route and carry relevant sensors to collect data. Mobile monitoring vehicles are mobile and can travel between different locations. The vehicle is equipped with monitoring equipment and can stop at any time to carry out monitoring work. It is suitable for vehicles that can flexibly monitor different locations.

[0036] The inspection frequency is the time interval for inspecting and monitoring the first area using the target device. The target carbon source data is the data information related to the carbon emission source collected by the target device after inspecting the first area according to the corresponding inspection frequency, such as the emission concentration, emission amount, emission time node, and specific location of greenhouse gases such as carbon dioxide and methane of each carbon emission source.

[0037] This embodiment first determines whether the inspection characteristics presented by the first area match the pre-set different inspection conditions. Once it is found that these inspection characteristics meet a certain inspection condition, the corresponding target device is controlled accordingly to carry out inspection work on the first area according to the inspection frequency adapted thereto.

[0038] S105: Monitoring carbon emissions in the target area based on the target carbon source data.

[0039] In this embodiment, carbon emission monitoring refers to the process of using certain technical means and methods to continuously and systematically observe, measure, record and analyze the emission of greenhouse gases such as carbon dioxide in the target area. Carbon emission monitoring may include assessing the degree or level of carbon emission risk.

[0040] Carbon emissions monitoring of target areas based on target carbon source data, including: Determine the carbon emission risk level based on target carbon source data; Determine carbon emission monitoring results based on carbon emission risk level.

[0041] Taking into account the actual conditions of different regions and industries and the different benchmark values ​​set for carbon emission targets, the risk levels are determined as follows: The first level is the low risk level, ranging from ; The second level is the medium-low risk level, ranging from ; The third level is the medium risk level, ranging from ; The fourth level is medium-high risk level, ranging from ; The fifth level is a high-risk level, ranging from .

[0042] Target carbon source data can include multi-dimensional information, which can be used to quantify the carbon emissions corresponding to unit output. Accordingly, different carbon emissions correspond to different risk levels.

[0043] From the above, it can be concluded that the disclosure determines the target area through the monitoring data of space-based satellites. Based on the advantages of global coverage and continuous observation of space-based satellites, it can quickly and accurately lock in the abnormal atmospheric greenhouse gas concentration and high-carbon emission industrial clusters, efficiently coordinate resources, and save a lot of ground manpower investigation costs; then, air-based remote sensing is further refined to determine the first area, accurately locate the places where emission sources are concentrated and the situation is complicated, so that monitoring is more targeted; then, the inspection characteristics are clarified and arranged accordingly, and ground monitoring stations, aerial drones or mobile monitoring vehicles are flexibly called to meet the needs of different scenarios; finally, the disclosure can fully grasp the carbon emission information, provide detailed basis for policy formulation and emission reduction action planning, and effectively promote low-carbon development and the process of responding to climate change. Therefore, the disclosure can improve the accuracy and effectiveness of carbon emission monitoring.

[0044] In one embodiment of the present disclosure, monitoring a target area based on air-based remote sensing to determine a first area includes: Monitor the target area based on airborne remote sensing to obtain the amount of greenhouse gases; If the amount of greenhouse gas is greater than or equal to the first threshold, the area where the greenhouse gas is located is regarded as the first area; If the amount of greenhouse gases is less than the first threshold, the area where the greenhouse gases are located is regarded as a non-first area.

[0045] In this embodiment, the amount of greenhouse gases refers to the total content of greenhouse gases within a certain spatial range in the target area calculated by using gas detection equipment carried by air-based remote sensing, such as the total content of greenhouse gases per cubic meter of air. It can be presented in quantitative forms such as volume concentration, mass or quantity. Common greenhouse gases include carbon dioxide, methane, nitrous oxide, etc.

[0046] The first threshold value may be a numerical value pre-set after comprehensive consideration, which serves as a critical line for judging whether the amount of greenhouse gases has reached a level of focus. The threshold values ​​may be different in different regions and for different monitoring targets.

[0047] When the calculated greenhouse gas content is greater than or equal to the preset threshold, it means that the greenhouse gas emissions in this area are not good and need to be monitored. That is, as the first area, we can further concentrate resources to explore the sources of carbon emissions and study the laws of gas diffusion. When the calculated greenhouse gas content is less than the preset threshold, it means that the greenhouse gas problem in this area is not acute. As a non-first area, routine monitoring and regular reviews are sufficient to reasonably allocate limited resources.

[0048] From the above, it can be concluded that this embodiment can locate the high-value area of ​​greenhouse gases, so that monitoring manpower and material resources can be concentrated. This embodiment can improve monitoring efficiency, and air-based remote sensing can flexibly shuttle, obtain target area data in a short time, and efficiently identify the sub-area that needs to be focused on (i.e., the first area).

[0049] In one embodiment of the present disclosure, the inspection characteristics include weather characteristics; In response to the inspection feature satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency includes: In response to the inspection feature being a weather feature: If the weather characteristics meet the first weather condition, controlling the ground monitoring station to inspect the first area according to the first inspection frequency; If the weather characteristic satisfies the second weather condition, controlling at least one device in the target device to inspect the first area according to the second inspection frequency; The weather conditions of the first weather condition and the second weather condition are different, and the inspection frequencies of the first inspection frequency and the second inspection frequency are different.

[0050] In this embodiment, the weather characteristics are information about the real-time weather conditions in the first area, which may include meteorological characteristics such as temperature, precipitation, snowfall, wind speed, visibility or cloud cover. Changes in weather characteristics will affect the work efficiency of the target equipment inspection and the accuracy of data collection.

[0051] The first weather condition or the second weather condition may be two types of discrimination criteria set according to weather conditions, and are used to distinguish which weather is suitable for which inspection method.

[0052] The first weather condition is extreme weather, for example, extreme weather may include rainfall, snowfall, heavy rain, blizzard or heavy fog; the second weather condition is non-extreme weather, for example, non-extreme weather may include sunny, calm, cloudy, no precipitation or no blizzard.

[0053] The first inspection frequency and the second inspection frequency can correspond to the time interval regularity of the target device inspecting the first area under different weather conditions. The first inspection frequency is less than the second inspection frequency. There is a mapping relationship between weather characteristics and inspection frequencies.

[0054] In response to weather characteristics being extreme weather: If the extreme weather is rainy weather, the ground monitoring station is controlled to inspect the first area according to the inspection frequency a1; If the extreme weather is snowfall, the ground monitoring station is controlled to inspect the first area according to the inspection frequency a2; If the extreme weather is foggy, the ground monitoring station is controlled to inspect the first area according to the inspection frequency a3; The first inspection frequency includes an a1 inspection frequency, an a2 inspection frequency and an a3 inspection frequency, the a1 inspection frequency is greater than the a2 inspection frequency, and the a2 inspection frequency is greater than or equal to the a3 inspection frequency.

[0055] In response to weather characteristics being non-extreme: If the weather is not extreme but sunny or windless, control at least one of the target devices to inspect the first area according to the inspection frequency b1; If the weather is not extreme but cloudy, control at least one of the target devices to inspect the first area according to the inspection frequency b2; The second inspection frequency includes a b1 inspection frequency and a b2 inspection frequency, and the b1 inspection frequency is greater than the b2 inspection frequency.

[0056] Considering that when the weather characteristics meet the conditions of rain, snow or fog, the weather is bad and is not conducive to inspection by aerial drones or mobile monitoring vehicles, the ground monitoring station is controlled to obtain carbon emission data of the first area at a regular interval according to the first inspection frequency. When the weather characteristics meet the conditions of clear, windless, no precipitation or no blizzard, each device in the target device can complete the inspection, and the inspection is performed at the second frequency.

[0057] From the above, it can be concluded that this embodiment can flexibly schedule target equipment according to the weather, which not only enables effective acquisition of carbon emission information in different weather conditions, but also ensures the consistency of the data, which is beneficial for providing an important basis for subsequent analysis of the carbon emission patterns in the first area or formulation of emission reduction strategies.

[0058] In one embodiment of the present disclosure, the inspection features include geographic features; In response to the inspection feature satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency, further comprising: In response to the inspection feature being a geographic feature: If the geographical feature satisfies the first geographical condition, the aerial drone is controlled to inspect the first area according to the third inspection frequency; If the geographical feature satisfies the second geographical condition, controlling at least one device among the target devices to inspect the first area according to a fourth inspection frequency; Among them, the terrains of the first geographical condition and the second geographical condition are different, and the inspection frequencies of the third inspection frequency and the fourth inspection frequency are different.

[0059] In this embodiment, the geographical features may be information such as topography, altitude, slope, land cover type, water system distribution, etc. of the first area. Different geographical features may affect the accessibility and deployment mode of the target device and the accuracy of the monitoring data.

[0060] The first geographical condition and the second geographical condition are two types of discrimination criteria set according to the differences in geographical factors such as topography, which are used to distinguish areas with different geographical forms. For example, the first geographical condition can be rugged and mountainous, and the second geographical condition can be flat and open. The third inspection frequency and the fourth inspection frequency both correspond to the time interval arrangement of the control target device to inspect the first area under different geographical conditions. The third inspection frequency is less than the fourth inspection frequency.

[0061] When the geographical characteristics of the first area meet the first geographical condition, such as steep terrain or undulating mountains, aerial drones are used to conduct regular inspections at the third inspection frequency to collect carbon emission information; when the geographical characteristics of the first area meet the second geographical condition, such as flat terrain and no tall obstacles, any device in the target setting can be inspected at the fourth inspection frequency to complete the carbon emission monitoring task.

[0062] Taking a mining city in a mountainous area as an example, the area around the city with concentrated mineral mining, serious ecological damage and high greenhouse gas emissions was designated as the target area, and a valley area was further subdivided as the first area. The first geographical condition was set as steep terrain, deep valleys, and an altitude difference of more than 200 meters; the second geographical condition was set as flat and open terrain with an altitude difference of less than 50 meters.

[0063] Deep in the valley, the terrain is rugged and there are many cliffs. The geographical features meet the second geographical condition. Aerial drones are dispatched for monitoring three times a week (the third inspection frequency). The gas sensors and high-definition cameras on board are used to accurately locate carbon emission sources such as mine ventilation holes and ore stockpiles, and to monitor the emissions of greenhouse gases such as carbon dioxide and methane in all directions.

[0064] In the relatively flat open area at the edge of the valley, the geographical features meet the second geographical condition. At this time, the mobile monitoring vehicle is arranged to move slowly along the simple mountain road, because the vehicle has strong mobility and can adapt to complex road conditions; the ground monitoring station is built on a relatively safe high ground with a wide field of vision in the valley. The two cooperate with each other and conduct inspections at a frequency of 5 times a week (the fourth inspection frequency) to fully collect carbon emission data deep in the valley.

[0065] From the above, it can be concluded that this embodiment controls the appropriate target equipment according to the terrain difference to perform accurate and efficient carbon emission monitoring. For example, when encountering the second geographical condition with complex terrain, the aerial drone can be controlled to monitor according to the third inspection frequency. When encountering the second geographical condition with flat and open terrain, any target equipment can be inspected according to the fourth inspection frequency, and the monitoring task can be completed well. At the same time, different inspection frequencies fit the actual geographical situation, avoid waste of resources, reduce unnecessary operations, and can capture carbon emission information in an all-round way.

[0066] In one embodiment of the present disclosure, the inspection characteristics include status characteristics of the carbon emission source; In response to the inspection feature satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency, further comprising: In response to the inspection feature being a status feature of a carbon emission source: If the state characteristic of the carbon emission source meets the first state condition, controlling the mobile monitoring vehicle and / or the aerial drone to inspect the first area according to the fifth inspection frequency; If the state characteristic of the carbon emission source satisfies the second state condition, controlling at least one device in the target device to inspect the first area according to the sixth inspection frequency; The first state condition and the second state condition have different states, and the fifth inspection frequency and the sixth inspection frequency have different inspection frequencies.

[0067] In this embodiment, the state characteristics of the carbon emission source refer to the working state, emission stability, and other conditions of the carbon emission source. The first state condition and the second state condition are two types of distinction criteria set according to the different emission states of the carbon emission source. The first state condition is a mobile carbon emission source, and the second state condition is a static carbon emission source. For example, a mobile carbon emission source may be a road vehicle, a ship, an airplane, or an excavator, and a static carbon emission source may be a thermal power plant, a steel plant, or some chemical plants. The fifth inspection frequency and the sixth inspection frequency correspond to the state conditions of different carbon emission sources, and control the time interval arrangement of the target equipment inspection of the first area. The fifth inspection frequency is greater than the sixth inspection frequency because the number of inspections needs to be increased for mobile carbon emission sources to keep up with emission dynamics.

[0068] Considering the high mobility of mobile carbon emission sources, mobile monitoring vehicles with high mobility can be arranged to get close to emission sources, and aerial drones can be used for high-altitude overlooking, and regular monitoring can be carried out according to the fifth inspection frequency to monitor carbon emission details. Considering that static mobile carbon emission sources are relatively stable, at least one type of equipment among ground monitoring stations, mobile monitoring vehicles, and drones can be arranged according to demand, and flexible inspections can be carried out according to the sixth inspection frequency.

[0069] It can be concluded from the above that the inspection flexibility of this embodiment is greatly improved. In the face of different carbon emission source states, it is possible to select appropriate target equipment and inspection frequency. When the state characteristic of the carbon emission source is mobile, mobile monitoring vehicles and / or aerial drones are arranged to cooperate with each other to efficiently monitor carbon emission data. At the same time, this embodiment makes carbon emission data monitoring more accurate, and is consistent with the inspection of the real-time state of the emission source, and can capture the details of concentration and composition changes.

[0070] In one embodiment of the present disclosure, the carbon emission monitoring method based on the five-base coordinated integration further includes: Determine the target influencing factors of the first area; the target influencing factors include population density, vegetation coverage and energy utilization rate; determining concentrations of various greenhouse gases in the first region; Determine the comprehensive correlation based on the target influencing factors and the concentrations of various greenhouse gases; Based on the comprehensive correlation, the changing trends of the concentrations of various greenhouse gases are determined.

[0071] In this embodiment, the target influencing factor is closely related to the carbon emissions in the first region and is a key factor that can affect the trend of greenhouse gas concentration. If the energy consumption in a densely populated area is high, the carbon emissions generated in all aspects of food, clothing, housing and transportation will increase accordingly; regarding vegetation coverage, vegetation is a natural carbon sink, and a high coverage rate indicates a strong ability to absorb carbon dioxide, which is conducive to neutralizing carbon emissions; high energy utilization rate means less carbon emissions generated by consuming the same amount of energy, otherwise carbon emissions will increase.

[0072] The concentrations of various greenhouse gases may include the values ​​of carbon dioxide, methane and nitrous oxide in the first region. The accumulation of the above greenhouse gases is a key indicator for measuring the carbon emission level in the first region. The comprehensive correlation can quantify the value of the degree of internal connection between the target influencing factors and the concentrations of various greenhouse gases. The changing trend is the dynamic trend of the concentrations of various greenhouse gases over time, whether it is rising, falling or fluctuating, which is conducive to estimating the future trend of regional carbon emissions and planning response strategies in advance.

[0073] With the help of data analysis models and statistical methods, population density, vegetation coverage, energy utilization rate and greenhouse gas concentration are comprehensively considered, and the correlation values ​​of various factors and greenhouse gas concentration under the synergistic effect are estimated. Combined with past monitoring data, regional development plans, etc., it is predicted whether the concentrations of various greenhouse gases will rise, decrease or remain stable in the future, providing early warning and decision-making support for emission reduction and ecological protection.

[0074] Assume that the target influencing factor set is , represents population density PD, Indicates vegetation coverage VC, represents the energy utilization rate EU; the greenhouse gas concentration set is , common as Indicates the concentration of carbon dioxide, Indicates methane concentration, etc. Indicates the number of greenhouse gas types considered.

[0075] First, all data are standardized to eliminate the impact of dimensions and make different indicators comparable. , which can be PD, VC, EC or a greenhouse gas concentration , the standardized value The calculation formula is:

[0076] in, Representation variables The mean value of variable The standard deviation of .

[0077] After standardization, represents population density, represents the vegetation coverage rate, represents the energy utilization rate, and the greenhouse gas concentration becomes .

[0078] Secondly, the pairwise correlation between each target influencing factor and each greenhouse gas concentration is calculated, denoted as , which represents the correlation coefficient between the target influencing factor and the greenhouse gas concentration:

[0079] in, represents the number of samples, Indicates the sample number, Indicates the standardized target influencing factors The mean of Indicates The factors affecting the target, , Represents the normalized greenhouse gas concentration The mean of Indicates Greenhouse gas concentrations, .

[0080] Then, determine the weight .

[0081] Finally, the calculation formula of comprehensive correlation degree CD is as follows:

[0082] in, Represents the adjustment factor, which is used to further refine the correlation weights between different factors and greenhouse gas combinations. The adjustment factor value range is set between 0 and 1, and can be set comprehensively based on historical monitoring data, regional development patterns, etc. Initially, it can be set to 0.5 for calculation, and then optimized according to actual conditions.

[0083] From the above, it can be concluded that this embodiment clarifies the intrinsic connection between human activities, natural carbon sinks, energy use and greenhouse gases by considering the target influencing factors such as population density, vegetation coverage, and energy utilization. This embodiment quantifies the influence of various factors through comprehensive correlation, making the cause of carbon emissions no longer vague. Finally, according to the comprehensive correlation, the trend of greenhouse gas concentration changes can be predicted, which can help regions to plan emission reduction strategies in advance, enterprises to optimize production processes, and regulatory authorities to accurately control and promote low-carbon and green development in all directions.

[0084] In one embodiment of the present disclosure, carbon emission monitoring of a target area is performed based on target carbon source data, including: Determine the carbon emission coefficient based on the target carbon source data; The risk level is determined based on the carbon emission coefficient, and the risk level is the result of carbon emission monitoring.

[0085] In this embodiment, the carbon emission coefficient can quantify the carbon emission value corresponding to a unit activity or product output. For example, the carbon dioxide emissions corresponding to the production of each ton of steel, or the amount of greenhouse gases emitted by automobile exhaust per kilometer traveled. The carbon emission coefficient reflects the efficiency and intensity of greenhouse gas emissions from different production and living activities. It is used to measure the carbon emission level and is a key indicator that links specific activities with carbon emissions.

[0086] The risk level is a level divided according to the carbon emission coefficient and other relevant considerations, which is used to intuitively represent the potential risk level brought by carbon emissions in the target area. The higher the level, the more serious the carbon emission problem is, and the greater the risk of climate change, environmental degradation, policy restrictions, etc.; conversely, the risk is relatively low. It is a key result obtained from carbon emission monitoring and provides a basis for subsequent decision-making.

[0087] The formula for the carbon emission factor is as follows:

[0088]

[0089] in, represents the carbon emission factor, It refers to the greenhouse gas emissions generated during the production of a certain activity or product within a certain time and scope. It indicates the quantitative indicators such as the activity level or product output corresponding to the greenhouse gas emissions. Indicates The carbon emission factor of a type of energy, that is, the greenhouse gas emissions generated by unit energy consumption, Indicates The consumption of energy, the unit depends on the type of energy, Indicates the number of types of energy used.

[0090] From the above, it can be concluded that this embodiment makes carbon emissions concrete by accurately determining the carbon emission coefficient, and analyzes the quantitative relationship between each production link, energy use and carbon emissions, which is conducive to enterprises to find the crux of high emissions, optimize processes and upgrade technologies in a targeted manner, and reduce energy consumption and emissions. By determining the risk level based on the carbon emission coefficient, regulators can use this to strengthen environmental supervision and accurately screen key control objects; enterprises can clarify their own risk level and stimulate the internal driving force of emission reduction; the public can also intuitively know the regional carbon emission situation, and multi-party collaboration can effectively promote regional green, low-carbon and sustainable development.

[0091] Corresponding to the carbon emission monitoring method based on the five-base coordinated integration in the above embodiment, Figure 2 This is a structural block diagram of a carbon emission monitoring system based on five-base coordinated integration provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The carbon emission monitoring system 20 based on the coordinated integration of five bases includes: a target area determination module 21, a first area determination module 22, an inspection feature determination module 23, a regional inspection module 24 and a carbon emission monitoring module 25.

[0092] Wherein, the target area determination module 21 is used to determine the target area based on the monitoring data of the space-based satellite; A first region determination module 22 is used to monitor the target region based on air-based remote sensing and determine a first region, where the first region is a region within the target region; An inspection feature determination module 23, used to determine the inspection feature of the first area; The regional inspection module 24 is used to control the target device to inspect the first area according to the corresponding inspection frequency in response to the inspection characteristics satisfying different inspection conditions, and obtain the target carbon source data; the target device includes a ground monitoring station, an aerial drone or a mobile monitoring vehicle; The carbon emission monitoring module 25 is used to monitor carbon emissions in a target area based on target carbon source data.

[0093] In one embodiment of the present disclosure, the first region determination module 22 is further configured to: Monitor the target area based on airborne remote sensing to obtain the amount of greenhouse gases; If the amount of greenhouse gas is greater than or equal to the first threshold, the area where the greenhouse gas is located is regarded as the first area; If the amount of greenhouse gases is less than the first threshold, the area where the greenhouse gases are located is regarded as a non-first area.

[0094] In one embodiment of the present disclosure, the inspection characteristics include weather characteristics; The regional inspection module 24 is also specifically used for: In response to the inspection feature being a weather feature: If the weather characteristics meet the first weather condition, controlling the ground monitoring station to inspect the first area according to the first inspection frequency; If the weather characteristic satisfies the second weather condition, controlling at least one device in the target device to inspect the first area according to the second inspection frequency; The weather conditions of the first weather condition and the second weather condition are different, and the inspection frequencies of the first inspection frequency and the second inspection frequency are different.

[0095] In one embodiment of the present disclosure, the inspection features include geographic features; The regional inspection module 24 is also specifically used for: In response to the inspection feature being a geographic feature: If the geographical feature satisfies the first geographical condition, the aerial drone is controlled to inspect the first area according to the third inspection frequency; If the geographical feature satisfies the second geographical condition, controlling at least one device among the target devices to inspect the first area according to a fourth inspection frequency; Among them, the terrains of the first geographical condition and the second geographical condition are different, and the inspection frequencies of the third inspection frequency and the fourth inspection frequency are different.

[0096] In one embodiment of the present disclosure, the inspection characteristics include status characteristics of the carbon emission source; The regional inspection module 24 is also specifically used for: In response to the inspection feature being a status feature of a carbon emission source: If the state characteristic of the carbon emission source meets the first state condition, controlling the mobile monitoring vehicle and / or the aerial drone to inspect the first area according to the fifth inspection frequency; If the state characteristic of the carbon emission source satisfies the second state condition, controlling at least one device in the target device to inspect the first area according to the sixth inspection frequency; The first state condition and the second state condition have different states, and the fifth inspection frequency and the sixth inspection frequency have different inspection frequencies.

[0097] In one embodiment of the present disclosure, the carbon emission monitoring system 20 based on the coordinated integration of five bases further includes: a change trend determination module; A change trend determination module is used to determine target influencing factors of the first area; the target influencing factors include population density, vegetation coverage and energy utilization rate; determining concentrations of various greenhouse gases in the first region; Determine the comprehensive correlation based on the target influencing factors and the concentrations of multiple greenhouse gases; Based on the comprehensive correlation, the changing trends of the concentrations of various greenhouse gases are determined.

[0098] In one embodiment of the present disclosure, the carbon emission monitoring module 25 is further configured to: Determine the carbon emission coefficient based on the target carbon source data; The risk level is determined based on the carbon emission coefficient, and the risk level is the result of carbon emission monitoring.

[0099] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned system embodiments, such as Figure 2 The functions of modules 21 to 25 are shown.

[0100] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0101] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0102] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0103] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the carbon emission monitoring method based on the coordinated integration of five bases provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0104] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0105] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0106] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0107] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0110] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A carbon emission monitoring method based on five-base coordinated integration, characterized in that: include: Determine target areas based on space-based satellite monitoring data; Monitoring the target area based on air-based remote sensing to determine a first area, where the first area is an area within the target area; determining inspection characteristics of the first area; In response to the inspection characteristics satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency to obtain target carbon source data; the target device includes a ground monitoring station, an aerial drone or a mobile monitoring vehicle; Carbon emissions in the target area are monitored based on the target carbon source data.

2. The carbon emission monitoring method based on five-base coordinated integration as claimed in claim 1 is characterized in that: The step of monitoring the target area based on air-based remote sensing to determine the first area includes: Monitor the target area based on airborne remote sensing to obtain the amount of greenhouse gases; If the amount of the greenhouse gas is greater than or equal to a first threshold, the area where the greenhouse gas is located is taken as the first area; If the amount of the greenhouse gas is less than the first threshold, the area where the greenhouse gas is located is regarded as a non-first area.

3. The carbon emission monitoring method based on five-base coordinated integration as claimed in claim 1 is characterized in that: The inspection characteristics include weather characteristics; In response to the inspection feature satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency includes: In response to the inspection feature being the weather feature: If the weather characteristic satisfies a first weather condition, controlling the ground monitoring station to inspect the first area at a first inspection frequency; If the weather characteristic satisfies the second weather condition, controlling at least one device among the target devices to inspect the first area according to a second inspection frequency; Among them, the weather conditions of the first weather condition and the second weather condition are different, and the inspection frequencies of the first inspection frequency and the second inspection frequency are different.

4. The carbon emission monitoring method based on five-base coordinated integration as claimed in claim 1 is characterized in that: The inspection features include geographical features; In response to the inspection feature satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency also includes: In response to the inspection feature being the geographical feature: If the geographical feature satisfies the first geographical condition, controlling the aerial drone to inspect the first area at a third inspection frequency; If the geographical feature satisfies a second geographical condition, controlling at least one of the target devices to inspect the first area at a fourth inspection frequency; Among them, the terrain of the first geographical condition is different from that of the second geographical condition, and the inspection frequencies of the third inspection frequency and the fourth inspection frequency are different.

5. The carbon emission monitoring method based on five-base coordinated integration as claimed in claim 1 is characterized in that: The inspection characteristics include state characteristics of carbon emission sources; In response to the inspection feature satisfying different inspection conditions, controlling the target device to inspect the first area according to the corresponding inspection frequency also includes: In response to the inspection feature being a status feature of the carbon emission source: If the state characteristic of the carbon emission source meets the first state condition, controlling the mobile monitoring vehicle and / or the aerial drone to inspect the first area at a fifth inspection frequency; If the state characteristic of the carbon emission source satisfies the second state condition, controlling at least one device among the target devices to inspect the first area at a sixth inspection frequency; The first status condition and the second status condition have different states, and the fifth inspection frequency and the sixth inspection frequency have different inspection frequencies.

6. The carbon emission monitoring method based on five-base coordinated integration as claimed in claim 1 is characterized in that: Also includes: Determining target influencing factors of the first area; the target influencing factors include population density, vegetation coverage and energy utilization rate; determining a plurality of greenhouse gas concentrations in the first region; Determining a comprehensive correlation based on the target influencing factors and the concentrations of the plurality of greenhouse gases; Based on the comprehensive correlation, the variation trends of the concentrations of the multiple greenhouse gases are determined.

7. The carbon emission monitoring method based on five-base coordinated integration as claimed in claim 1 is characterized in that: The carbon emission monitoring of the target area based on the target carbon source data includes: Determining a carbon emission coefficient based on the target carbon source data; A risk level is determined based on the carbon emission coefficient, and the risk level is the carbon emission monitoring result.

8. A carbon emission monitoring system based on five-base coordinated integration, characterized in that: include: A target area determination module, used to determine the target area based on the monitoring data of the space-based satellite; A first region determination module is used to monitor the target region based on air-based remote sensing and determine a first region, where the first region is a region within the target region; An inspection feature determination module, used to determine the inspection features of the first area; A regional inspection module, for controlling the target device to inspect the first area at a corresponding inspection frequency in response to the inspection characteristics satisfying different inspection conditions, and obtaining target carbon source data; the target device includes a ground monitoring station, an aerial drone or a mobile monitoring vehicle; A carbon emission monitoring module is used to monitor carbon emissions in the target area based on the target carbon source data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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