A method and system for multi-region coordinated management of greenhouse gas emissions

Abstract

The application discloses a kind of multi-region greenhouse gas emission collaborative management method and system, it is related to gas emission management technical field, the method includes: for each target area, determine emission main line and carry out based on emission factor Branch decoupling and multistage empowerment, construct multidimensional quadrant diagram;With emission right as the first management element, with the emphasis on the basis of emission cycle as the second management element, execute element directional decision, determine management scheme;Identify the management scheme, distributed deployment in upper collaborative end and regional edge, execute regional emission collaborative management under the end collaboration.The application solves the technical problems that the multi-region greenhouse gas emission management lacks collaborative mechanism in the prior art, leading to low emission management efficiency, achieves the collaborative management and accurate control of multi-region greenhouse gas emission, improves the technical effect of emission management efficiency.

Description

Technical Field

[0001] This invention relates to the field of gas emission management technology, specifically to a multi-regional greenhouse gas emission collaborative management method and system. Background Technology

[0002] With the global climate change problem becoming increasingly severe, effectively controlling greenhouse gas emissions has become a focal point. In the practice of multi-regional coordinated emission reduction, existing technologies have significant shortcomings. Traditional management models often isolate different regions, lacking a unified coordination mechanism, resulting in the inability to share emission data between regions and hindering the formation of synergistic effects. Analysis of emission sources remains at a broad level, failing to finely decouple and weight emission branches based on emission factors, leading to a lack of precision in control. Simultaneously, management decisions rely on experience-based judgment, lacking scientific decision-making models and dynamic adjustment mechanisms, resulting in low management efficiency, difficulty in adapting to complex and ever-changing emission scenarios, and inability to meet the actual needs of multi-regional coordinated greenhouse gas emission reduction.

[0003] Existing technologies suffer from a lack of coordination mechanisms in managing greenhouse gas emissions across multiple regions, leading to inefficient emissions management. Summary of the Invention

[0004] This application provides a method and system for coordinated management of greenhouse gas emissions in multiple regions, which addresses the technical problem of low efficiency in emission management due to the lack of a coordinated mechanism in the existing multi-region greenhouse gas emission management.

[0005] In view of the above problems, this application provides a method and system for coordinated management of greenhouse gas emissions in multiple regions.

[0006] A first aspect of this application provides a method for coordinated management of greenhouse gas emissions in multiple regions, the method comprising:

[0007] For each target region, the main emission line is determined and branch line decoupling and multi-level weighting based on emission factors are performed to construct a multi-dimensional quadrant map. A unified management platform is established based on the multi-dimensional quadrant map, with emission quotas as the first management element and the emphasis based on the emission cycle as the second management element. Based on the management decision-maker embedded in the platform, element-oriented decision-making is performed to determine the management plan. The management plan is identified and distributed across the upper-level coordination terminal and the regional edge terminal to implement regional emission collaborative management under the coordination of the execution terminal. The upper-level coordination terminal and the regional edge terminal are interconnected.

[0008] A second aspect of this application provides a multi-regional greenhouse gas emission collaborative management system, the system comprising:

[0009] The multi-dimensional quadrant map construction module is used to determine the main emission line for each target area and perform branch decoupling and multi-level weighting based on emission factors to construct a multi-dimensional quadrant map. The management scheme determination module is used to establish a unified management platform based on the multi-dimensional quadrant map, with emission quotas as the first management element and the emphasis based on the emission cycle as the second management element. Based on the management decision-maker embedded in the platform, it performs element-oriented decision-making to determine the management scheme. The emission collaborative management module is used to identify the management scheme, distribute it at the upper-level collaborative end and the regional edge, and perform regional emission collaborative management under the collaboration of the execution end. The upper-level collaborative end and the regional edge are interconnected.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] For each target region, an emission main line is determined, and branch lines are decoupled and weighted at multiple levels based on emission factors to construct a multi-dimensional quadrant map. A unified management platform is established based on this multi-dimensional quadrant map, with emission quotas as the first management element and emission cycle-based emphasis as the second management element. Based on the platform's embedded management decision-maker, element-oriented decisions are executed to determine management schemes. These management schemes are identified and distributed across higher-level coordination terminals and regional edges, enabling coordinated regional emission management under execution-level coordination. This achieves the technical effect of realizing coordinated management and precise control of greenhouse gas emissions across multiple regions, improving emission management efficiency. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A schematic diagram of a multi-regional greenhouse gas emission collaborative management method provided in this application embodiment;

[0014] Figure 2 This is a schematic diagram of a multi-regional greenhouse gas emission collaborative management system provided in an embodiment of this application.

[0015] Figure labeling: Multidimensional quadrant diagram construction module 10, management scheme determination module 20, emission collaborative management module 30. Detailed Implementation

[0016] This application provides a multi-regional greenhouse gas emission collaborative management method and system to address the technical problem of low emission management efficiency caused by the lack of a collaborative mechanism in the existing multi-regional greenhouse gas emission management.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, this application provides a method for coordinated management of greenhouse gas emissions in multiple regions, the method comprising:

[0019] Step S100: For each target region, determine the main emission line and perform branch decoupling and multi-level weighting based on emission factors to construct a multi-dimensional quadrant map.

[0020] Specifically, for each target region, a comprehensive review of various activities within the region, including industrial production, transportation, and energy consumption, is conducted to assess their contribution to greenhouse gas emissions and determine the main emission pathway. This pathway represents the most significant greenhouse gas emission route in the region. Next, an emission factor database is connected, and the main emission pathway is decoupled based on these factors. Emission factors reflect the quantitative relationship of greenhouse gas production from specific activities. Using isomorphic factors, relevant data is selected from the emission factor database to subdivide the main emission pathway into multiple decoupled branches, clearly presenting the specific sources and composition of emissions. To further clarify the importance of each decoupled branch in overall emissions, multi-level weighting is applied. Starting from the branch type, different basic weights are assigned considering the differences between industrial, agricultural, and residential branches. Based on the quantification level, branches are classified and weighted according to data such as emission volume and frequency. Based on associated effects, the indirect impacts of branch emissions on the surrounding environment and ecosystems are analyzed and converted into weights. Finally, considering inter-regional emission rules and regulations, corresponding weights are assigned based on the emission correlations between regions. These factors are combined to form a weight distribution. The weight cascading process is then completed by traversing the decoupled branches to determine the first branch distribution. Finally, a multi-dimensional quadrant map is constructed. Taking the first target region as an example, based on the first branch distribution, multiple key dimensions such as emissions, emission change trends, and emission reduction difficulty are selected. The branch distribution data is visualized in the space formed by these dimensions to determine the first quadrant map. Quadrant representation is performed on the remaining target regions in the same way to obtain the Nth quadrant map (N is the number of target regions). These quadrant maps are then stitched together to form a multi-dimensional quadrant map. This map integrates diverse information on emissions from various regions, providing an intuitive and comprehensive data foundation for establishing a unified management platform and formulating management plans, thus facilitating the collaborative management of greenhouse gas emissions across multiple regions.

[0021] Step S200: Establish a unified management platform based on the multi-dimensional quadrant diagram, with emission quotas as the first management element and the emphasis based on the emission cycle as the second management element. Based on the management decision-maker embedded in the platform, perform element-oriented decision-making to determine the management plan.

[0022] Specifically, a unified management platform is built based on the generated multi-dimensional quadrant map. This platform uses emission quotas and emission cycle-based priorities as core management elements. First, emission quotas are determined as the primary management element. The platform analyzes historical emission data, industrial structure, and emission reduction commitments for each target region, and uses algorithms such as linear programming and analytic hierarchy process (AHP) to calculate and allocate greenhouse gas emission quotas for each region, thereby controlling the overall emission scale of the region. For the emission cycle-based priorities, the platform first constructs an emission cycle space for each target region using Geographic Information System (GIS) technology. By deploying sensor networks in each region, the location, type, and emission data of emission sources are collected, and circulation trajectory information is obtained by combining meteorological monitoring data. Within the emission cycle space, spatial analysis algorithms are used to spatially distribute emission sources, and digital modeling methods are employed to characterize the circulation trajectory, thereby determining the emission cycle. Then, based on information such as emission source density, circulation transport paths, and the distribution of environmentally sensitive areas, the emission cycle-based management priorities are determined. The platform incorporates a management decision-maker, which is built and trained using the Deep Q-Network (DQN) algorithm from reinforcement learning. First, emission quotas and emission cycle-based emphasis data are preprocessed, transforming them into feature vectors suitable for algorithm input. The state space is set as a combination of information such as emission quota usage and emission cycle characteristics of each region, while the action space contains different management decision options, such as adjusting emission quota allocation or implementing specific control measures for different regions. During training, the management decision-maker selects an action based on the current state, receiving a reward value and a new state after execution. The reward value is set based on whether it brings the target closer to the ideal emission management objective; for example, a positive reward is given if overall emissions are reduced while ensuring reasonable regional economic development, and a negative reward is given otherwise. Through continuous trial and error, the management decision-maker updates the parameters of the Q-network, gradually finding the optimal strategy. After multiple rounds of training, the management decision-maker executes element-oriented decisions based on these two management factors. Based on the multidimensional quadrant diagram, combined with emission weights and the emphasis based on the emission cycle, a series of decision-making results are output, forming a one-dimensional management scheme and a two-dimensional management scheme. Finally, a comprehensive and effective management scheme is determined, providing a precise decision-making basis for the coordinated management of greenhouse gas emissions in multiple regions.

[0023] Step S300: Identify the management scheme, deploy it in a distributed manner on the upper-level collaborative terminal and the regional edge terminal, and implement regional emission collaborative management under the collaboration of the execution terminal, wherein the upper-level collaborative terminal and the regional edge terminal establish an interactive cascade.

[0024] Specifically, the management plan is identified, and through a coding parsing program, various instructions and strategies within the plan are converted into executable machine language format, ensuring that both the upper-level coordination terminal and the regional edge terminals can accurately understand and execute them. Subsequently, distributed deployment is implemented. The upper-level coordination terminal, as the central hub of the entire management system, receives the macro-control portion of the management plan, such as the allocation plan for overall emission quotas in each region and cross-regional emission coordination strategies. It integrates aggregated data from various regions, controlling the emission situation from a global perspective, and interacts with the regional edge terminals via network communication technology. The regional edge terminals obtain detailed execution strategies related to their respective regions from the management plan, including specific control measures for each emission branch within the region and refined management instructions based on the local emission cycle. An interactive cascading mechanism is established between the upper-level coordination terminal and the regional edge terminals, implemented based on technologies such as message queues and real-time communication protocols. The regional edge terminals provide real-time feedback to the upper-level coordination terminal on local emission data, the progress of management measures implementation, and any problems encountered; the upper-level coordination terminal adjusts its management strategies promptly based on feedback from each region and issues updated instructions to the regional edge terminals. Regional emission collaborative management is executed in end-to-end coordination mode. Regional endpoints implement specific emission management operations based on local conditions, such as real-time monitoring of key emission sources and controlling enterprise production activities according to emission quotas. Simultaneously, these regional endpoints upload monitoring and management performance data to the higher-level coordination platform in real time. Based on this data, the higher-level coordination platform analyzes the overall effectiveness of emission management in each region. If any regional emissions are found to be abnormal or have failed to meet expected targets, adjustment instructions are sent to the corresponding regional endpoints through an interactive cascading mechanism. This achieves coordinated management of greenhouse gas emissions across multiple regions, ensuring that each region effectively controls its total greenhouse gas emissions while meeting its own development needs.

[0025] In one possible implementation, step S100 further includes:

[0026] Step S110: For the first target area, determine the first emission main line, wherein the first target area is any target area.

[0027] Step S120: Connect to the emission factor library, decouple the first emission main line based on isomorphic factors, and determine multiple decoupling branches.

[0028] Step S130: Perform weight concatenation processing on the multiple decoupled branches to determine the distribution of the first branch.

[0029] Specifically, for the primary target area (which can represent any area participating in collaborative management, such as different factory areas, commercial areas, etc.), the primary emission baseline needs to be determined. Due to the differences in function and activities among different types of areas (factory areas, commercial areas, etc.), their emission environments are drastically different, and therefore their emission baselines also differ. For factory areas, emissions are mainly from combustion during industrial production processes and chemical reactions; for commercial areas, emissions primarily originate from energy consumption, transportation, and other activities. Accurately determining the emission baseline can clearly identify the sources of greenhouse gas emissions in that area.

[0030] After connecting to the emission factor database, the Apriori association rule mining algorithm is used to decouple the first emission mainline based on isomorphic factors. First, data related to the first emission mainline is extracted from the emission factor database, and isomorphic factors such as hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride in fluorinated gases are labeled as key elements. The Apriori algorithm generates frequent one-itemsets by scanning the dataset, representing the frequency of occurrence of a single isomorphic factor in the emission data. A support threshold is set, and low-frequency factors are filtered out. Next, candidate two-itemsets are generated using the frequent one-itemsets, and their support is calculated by scanning the dataset again. Frequent two-itemsets that meet the support threshold are retained, and so on, gradually generating higher-order frequent itemsets. During the generation process, the algorithm identifies strong correlations between isomorphic factors and with each stage of the first emission mainline. For example, it was found that in a certain factory area, under a specific production process, the emission of hydrofluorocarbons is closely related to the temperature control of the production equipment, while the emission of sulfur hexafluoride is strongly correlated with the operating time of electrical equipment. Based on these strong correlations, the primary emission line is broken down according to different combinations of isomorphic factors and related links, thereby identifying multiple decoupled branches and clearly showing the emission paths and sources corresponding to different isomorphic factors.

[0031] Multiple decoupled branches are weighted and cascaded to determine the distribution of the first branch. This process employs the Analytic Hierarchy Process (AHP) to construct a four-level weighting system: First, weighting is applied based on branch type (e.g., industrial, transportation, residential sources) to determine basic weight coefficients; second, weighting is applied based on the emission data quantification level (emission volume, emission intensity, growth trend), generating dynamic weights through standardization; third, a co-occurrence effect assessment model is introduced, considering indirect factors such as environmental sensitivity and ecological impact for third weighting; finally, fourth, weighting is applied in conjunction with cross-regional emission rules and collaborative control objectives, forming a complete weight matrix. By traversing all decoupled branches, the four levels of weights are cascaded (e.g., using a weighted geometric average) to obtain the comprehensive weight value for each decoupled branch. Based on the weight values, the decoupled branches are sorted in descending order, and combined with preset threshold parameters (e.g., cumulative weight coverage reaching 80%), they are automatically divided into three levels: core branches, important branches, and general branches, ultimately forming the first branch distribution, providing a quantitative basis for the subsequent construction of a multi-dimensional quadrant map.

[0032] In one possible implementation, step S130 further includes:

[0033] Step S131: Assign weights based on branch type, quantification level, associated effects, and cross-regional emission rules to determine the weight distribution.

[0034] Step S132: Traverse the decoupling branches, perform weight cascading processing according to the weight distribution, and determine the first branch distribution.

[0035] Specifically, the first weighting is based on the type of emission source. Different types of emission sources have different impacts on greenhouse gas emissions in different ways and to varying degrees. For example, industrial production sources, transportation sources, and residential sources have different activity characteristics and emission characteristics, so they are assigned different basic weights to initially distinguish the importance of each type of source in overall emissions. Next, the second weighting is based on quantitative levels. A deep analysis of the emission data of each decoupled source is conducted, considering quantitative indicators such as emission amount, emission frequency, and emission concentration. These indicators are converted into specific levels, and corresponding weights are assigned to each level to make the weight allocation more consistent with actual emission conditions. The third weighting focuses on associated effects. This step takes into account the differences in greenhouse effects caused by different greenhouse gases. In real-world scenarios, although the quantitative level of carbon dioxide content may be high, substances like fluorinated gases may cause a higher greenhouse intensity even at the same level. Therefore, by assessing the greenhouse effect of each greenhouse gas in a specific scenario and standardizing the measurement, the third weighting is applied to each decoupled source to ensure that the weights accurately reflect the potential impact of different gas emissions. Finally, a fourth weighting is applied based on cross-regional emission rules, taking into account factors such as emission linkages between regions, relevant policy regulations, and the sharing of emission reduction responsibilities, to assign corresponding weights to each decoupling branch. The weighting results from these four dimensions are then combined to determine the weight distribution, forming a weighting system that comprehensively reflects the importance of each decoupling branch.

[0036] After determining the weight distribution, the first branch distribution is determined by traversing the decoupling branches and performing weight cascading processing. Each decoupling branch is visited sequentially, and weights determined from four dimensions—branch type, quantification level, associated effects, and inter-regional emission rules—are applied to the corresponding branch. For example, for a decoupling branch originating from industrial production, the basic weight assigned based on the branch type is first combined with the weights assigned based on the quantification level, associated effects, and inter-regional emission rules, and then calculated using a pre-defined weight cascading algorithm, such as weighted multiplication or weighted summation, to obtain the comprehensive weight value for that branch. This process is repeated for all decoupling branches to obtain the comprehensive weight value for each branch. Then, the decoupling branches are sorted according to these comprehensive weight values, with branches having higher comprehensive weights identified as key branches with a greater impact on overall greenhouse gas emissions, while branches with lower comprehensive weights have a relatively smaller impact. Based on the ranking results, combined with management needs and actual conditions, different branch categories are divided, and the distribution of the first branch is finally determined. This clearly presents the importance and distribution of each decoupled branch in the overall emission system, providing an accurate basis for the subsequent construction of a multi-dimensional quadrant map and the formulation of targeted management strategies.

[0037] In one possible implementation, step S100 further includes:

[0038] Step S140: Determine the first quadrant of the first target region, and perform multidimensional characterization of the first quadrant based on the distribution of the first branch to determine the first quadrant map.

[0039] Step S150: Perform quadrant representation on each target region until the Nth quadrant map is determined, where N is the number of target regions.

[0040] Step S160: The first quadrant map up to the Nth quadrant map are spliced ​​together to determine the multidimensional quadrant map.

[0041] Specifically, the first step is to define the first quadrant, starting with the primary target area. This quadrant serves as the basic framework for displaying emission information for that area, using key emission-related dimensions as coordinate axes, such as emission volume, emission trends, and the difficulty of emission reduction. Then, based on the determined distribution data of the first branch, the information of each decoupled branch in these dimensions is mapped. For example, for branches with large emissions, a clear growth trend, and high emission reduction difficulty, specific data points are marked on the corresponding coordinate axes. These data points are then visualized within the first quadrant using methods such as connecting lines and filling in, providing a multi-dimensional representation and clearly presenting the emission characteristics of each branch, thus defining the first quadrant map. This first quadrant map visually displays the comprehensive situation of different emission branches within the primary target area across multiple key dimensions.

[0042] The same quadrant representation process is repeated for the remaining target regions. For each target region, its quadrant is determined using the same method as for the first target region, and multidimensional representation is performed based on the branch distribution data of that region to generate a corresponding quadrant map. Throughout this process, it is ensured that the coordinate axis dimensions and data representation methods of each quadrant map remain consistent for unified analysis and comparison later. As each target region is processed, the second quadrant map, the third quadrant map, and so on, up to the Nth quadrant map (N being the number of target regions), are determined sequentially. Each quadrant map displays the characteristics and distribution of greenhouse gas emission branches within that region from a different regional perspective.

[0043] After completing the quadrant maps for each target region—from the first quadrant to the Nth quadrant (where N is the number of target regions)—the stitching operation begins. First, all quadrant maps are preprocessed according to a unified standard to ensure consistency in axis scales, data units, graphic scale, and the meaning of the emission dimensions they represent, laying the foundation for accuracy and coherence in the stitching. Then, the quadrant maps are systematically combined according to pre-defined stitching rules. These rules can be determined based on actual needs and analytical objectives, such as arranging them according to geographical location or emission scale. For example, adjacent quadrant maps can be stitched together sequentially, visually reflecting the differences and connections in emissions across different geographical locations. During the stitching process, the coordinate axes of each quadrant map are precisely aligned, ensuring seamless integration of emission data throughout the stitched graph, forming a continuous and unified visualization. After stitching, the resulting multidimensional quadrant map integrates greenhouse gas emission information from all target regions into a single graphic. This multi-dimensional quadrant chart allows for simultaneous observation of different regions' performance across multiple dimensions, including emissions, emission trends, and the difficulty of emission reduction. It quickly identifies regions and sub-regions with high emissions, rapid growth, and significant reduction challenges, while also revealing the similarities and differences in emission characteristics among regions. This comprehensive and intuitive presentation provides managers with a macro-level perspective, helping them develop more targeted and collaborative greenhouse gas emission management strategies, and achieving effective control and coordinated management of greenhouse gas emissions across multiple regions.

[0044] In one possible implementation, step S200 further includes:

[0045] Step S210: Based on the multi-dimensional quadrant map, and with the emission quota of the zone and the emission quota of the branch line within the zone as the decision-making objectives, determine the one-dimensional management scheme.

[0046] Step S220: Based on the emission cycle of each target area, determine a two-dimensional management plan with location-based and management-based priorities as decision objectives.

[0047] Step S230: Determine the management scheme based on the one-dimensional management scheme and the two-dimensional management scheme.

[0048] Specifically, a one-dimensional management scheme is constructed based on the data characteristics of a multi-dimensional quadrant map. First, quantitative data on core indicators such as emissions and emission growth rates are extracted for each target region. Combined with pre-set regional total emission control targets, a linear programming algorithm is used to solve for the optimal allocation of emission quotas. For example, for industrial zones with high emission intensity but significant emission reduction potential, their initial emission quota ceiling is dynamically lowered; for ecological protection zones, the quota is appropriately relaxed. At the sub-branch level within the region, the emission contribution of each decoupled branch is calculated using the analytic hierarchy process (AHP), and the total regional quota is then redistributed to specific industries or equipment according to the contribution ratio. This one-dimensional scheme achieves macro-control of total emissions through rigid quota constraints, but it does not consider the spatial correlation and dynamic changes between regions.

[0049] A two-dimensional management plan is developed, focusing on both location-based and management-based approaches, to address the emission cycles of each target region. First, for each target region, the emission cycle is determined through on-site monitoring, meteorological data collection, and atmospheric transport model analysis. This includes the specific location of emission sources, the transport paths of emissions within and between regions, and circulation trajectories. Based on this emission cycle data, the location-based approach is determined. For regions upstream in emission transmission with large emissions, given their significant impact on downstream regions, higher management attention is given, with stricter emission limits and regulatory standards. For regions downstream in emission transmission, significantly affected by emissions from other regions, the focus is on enhancing their ability to cope with external emission inputs, such as strengthening environmental monitoring and early warning systems. Simultaneously, the management-based approach is determined based on factors such as the industrial structure and emission reduction potential of each region. For regions with rapid economic development but a heavy industrial structure and significant emission reduction pressure, the management-based approach is to promote industrial upgrading and technological innovation to achieve emission reduction targets. For ecologically fragile regions sensitive to greenhouse gas emissions, the management-based approach prioritizes ecological protection and strictly restricts the introduction of high-emission projects. Taking into account both location-based and management-based approaches, a two-dimensional management plan was developed, encompassing measures such as emission control, technical support, and regional collaboration. This plan provides more targeted and differentiated management strategies for each target region, thereby enhancing the effectiveness of multi-regional greenhouse gas emission collaborative management.

[0050] A multi-objective genetic algorithm is used to integrate one-dimensional and two-dimensional management schemes to determine the final management scheme. The multi-objective genetic algorithm simulates the natural evolutionary process, searching for the optimal solution in a complex solution space through selection, crossover, and mutation operations. The emission quota data for zones and branch emission quotas within zones from the one-dimensional management scheme, and the location-based and management-based information from the two-dimensional management scheme, are encoded to form an initial population. Each individual represents a possible combination of management schemes. Next, multiple fitness functions are set to evaluate the quality of each individual. These fitness functions comprehensively consider factors such as total emission control targets, regional economic development needs, and ecological environmental protection requirements. For example, one fitness function measures whether the emission quota allocation meets the overall emission reduction target, another assesses the impact of management measures on regional economic growth, and yet another considers the degree of ecological environmental protection. In the selection operation, based on the values ​​of the fitness functions, better individuals are selected for the next generation, while poorer individuals are eliminated, giving better management scheme combinations a greater chance of being inherited by the next generation. Crossover simulates the exchange of genes in organisms, randomly selecting two individuals and exchanging parts of their gene segments to generate new individuals, thus exploring a broader solution space. Mutation randomly alters the genes of individuals with a certain probability, preventing the algorithm from getting trapped in local optima. After multiple rounds of selection, crossover, and mutation operations, the algorithm gradually converges to a set of Pareto optimal solutions. These solutions represent management schemes that achieve a balance between different objectives. Finally, from the set of Pareto optimal solutions, the most suitable management scheme is selected as the final determined management scheme, achieving effective and coordinated management of greenhouse gas emissions across multiple regions.

[0051] In one possible implementation, step S220 further includes:

[0052] Step S221: Construct a loop space for each target area;

[0053] Step S222: Through local data acquisition, determine the emission sources and circulation trajectories of each target area, perform spatial hashing of the emission sources in the circulation space, perform digital state characterization of the circulation trajectory, and determine the emission cycle.

[0054] Specifically, a circular space is constructed for each target area. This is a virtual, digital spatial model built upon Geographic Information System (GIS) technology and regional foundational data. First, topographical data of the target area, such as mountains, rivers, and urban layout, is collected. A three-dimensional terrain model is then created using GIS technology, providing a geographic framework for emission cycle simulation. Simultaneously, by incorporating information such as regional administrative boundaries and the distribution of meteorological monitoring stations, suitable areas for emission cycle analysis are delineated. Based on this, the space is functionally zoned according to different functional areas, such as industrial zones, commercial zones, and residential zones, to facilitate subsequent analysis of emission characteristics for different area types, thus completing the construction of the circular space.

[0055] Emission sources and circulation trajectories are determined through local data collection. Multiple monitoring methods are employed, such as installing pollutant monitoring equipment at fixed sites to conduct real-time monitoring of key emission areas like industrial chimneys and major transportation routes, acquiring information on the location of emission sources, the types of pollutants emitted, and the emission volume. Simultaneously, meteorological monitoring equipment collects meteorological data such as wind direction, wind speed, temperature, and air pressure. Atmospheric diffusion models, combined with meteorological data and emission source information, simulate the diffusion path of pollutants in the atmosphere, thereby determining the circulation trajectory. After determining the emission sources and circulation trajectories, processing is performed within a pre-constructed circulation space. For emission sources, spatial scattering is performed based on their geographical coordinates within the circulation space, accurately marking the emission sources on their corresponding geographical locations in a visual form, allowing managers to intuitively see the distribution of emission sources. For circulation trajectories, state characterization is achieved through digital means, converting the simulated circulation trajectory into a series of digital data points, recording information such as the trajectory's direction and speed changes, and displaying them in the circulation space as dynamic lines or color gradients, clearly presenting the diffusion path and range of pollutants. By combining the spatial distribution of emission sources and the digital state characterization of circulation trajectories, the emission cycle of each target area can be accurately determined, providing data support for the subsequent development of two-dimensional management schemes that shift from a location-based to a management-based approach.

[0056] In one possible implementation, step S230 further includes:

[0057] Step S231: Deploy the one-dimensional management scheme on the upper-level collaborative terminal, and deploy the two-dimensional management scheme on the regional edge terminal. Through terminal collaboration, execute emission management for each target region.

[0058] Specifically, firstly, a cloud-based management hub system is built at the upper-level coordination end. A distributed database stores data such as zonal emission quotas and intra-zone branch emission quota allocations from the one-dimensional management scheme. A microservice architecture enables dynamic monitoring of emission quotas and cross-regional quota allocation. For example, when an industrial branch emission quota in a certain region is detected to be depleted too quickly, an early warning is automatically triggered, and a cross-regional quota transfer instruction is generated by calculating the available redundant quotas in other regions. At the regional edge, edge computing nodes and IoT monitoring terminals are deployed to transform the location-oriented and management-oriented strategies in the two-dimensional management scheme into executable rules. In areas with concentrated emissions, such as industrial parks, smart meters and gas sensors are installed to collect emission data in real time. Edge computing nodes preprocess the data according to the emission reduction standards in the two-dimensional scheme, automatically generating rectification work orders for enterprises exceeding emission standards and pushing them to the enterprise management system. Simultaneously, a two-way communication channel is established between the upper-level coordination terminal and the regional edge terminals via a 5G network. The regional edge terminals upload local emission data and management execution status to the upper-level coordination terminal every 15 minutes. The upper-level coordination terminal, based on overall emission trends, uses a deep learning model to optimize the weight allocation strategy and distributes the adjusted one-dimensional scheme parameters to each regional edge terminal. Furthermore, both ends establish a data-sharing ledger using blockchain technology to ensure the immutability and transparency of emission weight trading and cross-regional collaborative governance. Ultimately, through end-to-end real-time data interaction and intelligent decision-making linkage, precise coordination of multi-regional emission management is achieved.

[0059] In one possible implementation, step S300 further includes:

[0060] Step S310: Introduce limit alarm conditions and deploy over-limit alarm devices, which are installed at the edge of the area. The limit alarm conditions include weight over-limit alarms and instantaneous variable over-limit alarms.

[0061] Step S320: Local management is performed at the edge of each area. If the over-limit alarm is triggered by the management data alarm, an over-limit vector is generated, wherein the over-limit vector includes the over-limit scale and trend.

[0062] Step S330: The over-limit vector is transmitted back to the unified management platform for emission cycle tracing and feedback management.

[0063] Specifically, to achieve refined control over emissions in various regions, limit alarm conditions are introduced and over-limit alarms are deployed at the regional boundaries. Limit alarm conditions set thresholds in two dimensions: first, a quota over-limit alarm, based on the emission quotas allocated in the one-dimensional management scheme for each zone and its branch lines. An alarm is triggered when the usage of emission quotas in a certain zone or specific branch line reaches a warning threshold (e.g., the remaining amount is less than 20% of the initial allocation), ensuring the achievement of total emission control targets; second, an instantaneous variable over-limit alarm, focusing on real-time data during the emission process, setting standard limits for variables such as emission concentration and instantaneous emission volume. For example, when the instantaneous carbon dioxide emission concentration of an industrial enterprise exceeds the national environmental quality standards, an alarm mechanism is immediately activated. The over-limit alarms are connected to monitoring equipment and data processing systems at the regional boundaries via IoT technology, collecting and analyzing emission data in real time. Once the monitoring data triggers the limit alarm conditions, an alarm signal is quickly issued, providing crucial evidence for timely intervention and adjustment of emission management strategies, ensuring that regional emissions remain within a controllable range.

[0064] Each region implements local emission control according to the two-dimensional management scheme, while over-limit alarms continuously screen management data. Once emission data reaches the limit alarm condition, the alarm mechanism is immediately triggered, and an over-limit vector is automatically generated. This vector not only records the specific value of the over-limit, i.e., the over-limit scale (e.g., exceeding the emission quota by 20 tons, or exceeding the emission concentration by 50%), but also predicts emission trends through time series analysis to determine whether the over-limit situation will continue to worsen or improve. For example, when the fluoride gas emission concentration of a chemical plant continues to rise and triggers an alarm, the over-limit vector will predict the emission trend in the next 12 hours based on the concentration change data of the past 72 hours, providing a quantitative basis for subsequent decision-making.

[0065] Once an exceedance vector is generated at the edge of a region, it is transmitted back to the unified management platform in real time via a high-speed and stable communication network. Upon receiving the exceedance vector, the platform immediately initiates the emission cycle source tracing procedure. Using a Geographic Information System (GIS), the platform accurately locates the geographical position of the exceedance emission source. Combined with atmospheric diffusion models, meteorological data (such as wind direction and speed), and emission data from surrounding areas, it simulates the pollutant transport path to trace whether the exceedance is due to local emission sources or the influence of emission transport from surrounding areas. For example, if a region experiences a momentary exceedance of carbon dioxide emission concentration, the platform can analyze emission sources such as surrounding chemical industrial parks and transportation hubs, combined with wind direction data, to determine whether it is due to illegal emissions from local industrial enterprises or pollutant drift from upstream areas. After completing the source tracing, the unified management platform conducts feedback management based on one-dimensional and two-dimensional management schemes. If the violation is determined to be caused by a local emission source, the platform will issue instructions to the corresponding regional border based on the scale and trend of the violation, such as requiring rectification within a specified period or adjusting the emission quotas for that region and its branches. If the violation is due to cross-regional transmission, the platform will coordinate with upstream and downstream regional borders to initiate a collaborative emission reduction mechanism between regions. This will be achieved through optimizing the allocation of emission quotas and strengthening joint monitoring to jointly resolve the violation issue. Simultaneously, the platform will feed back the processing results and adjusted management strategies to each regional border, forming a closed-loop management system to ensure that greenhouse gas emissions in multiple regions remain under control and effectively achieve collaborative management goals.

[0066] Example 2, based on the same inventive concept as the multi-regional greenhouse gas emission collaborative management method in the foregoing examples, such as... Figure 2 As shown, this application provides a multi-regional greenhouse gas emission collaborative management system. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0067] The multidimensional quadrant map construction module 10 is used to determine the emission main line for each target area and perform branch decoupling and multi-level weighting based on emission factors to construct a multidimensional quadrant map.

[0068] The management scheme determination module 20 is used to establish a unified management platform based on the multi-dimensional quadrant diagram, with emission quotas as the first management element and the emphasis based on the emission cycle as the second management element. Based on the management decision-maker embedded in the platform, it performs element-oriented decision-making to determine the management scheme.

[0069] The emission collaborative management module 30 is used to identify the management scheme and is distributed across the upper-level collaborative terminal and the regional edge terminal to implement regional emission collaborative management under the collaboration of the execution terminal. The upper-level collaborative terminal and the regional edge terminal are interconnected.

[0070] Furthermore, the system is also used for the following functions:

[0071] For a first target region, a first emission main line is determined, wherein the first target region can be any target region; by connecting the emission factor library, the first emission main line is decoupled based on isomorphic factors to determine multiple decoupled branches; for the multiple decoupled branches, a weighted concatenation process is performed to determine the distribution of the first branch.

[0072] Furthermore, the system is also used for the following functions:

[0073] The weight distribution is determined by assigning weights based on branch type, quantification level, associated effects, and cross-regional emission rules. The decoupled branches are traversed, and weight cascading processing is performed according to the weight distribution to determine the first branch distribution.

[0074] Furthermore, the system is also used for the following functions:

[0075] The first quadrant of the first target region is determined. Based on the distribution of the first branch, the first quadrant is represented in multiple dimensions to determine the first quadrant map. Quadrant representation is performed on each target region until the Nth quadrant map is determined, where N is the number of target regions. The first quadrant map up to the Nth quadrant map are spliced ​​together to determine the multidimensional quadrant map.

[0076] Furthermore, the system is also used for the following functions:

[0077] Based on the multidimensional quadrant map, and with the emission quotas of the zones and the emission quotas of the branch lines within the zones as decision-making objectives, a one-dimensional management scheme is determined; based on the emission cycles of each target area, and with the location-based emphasis and management-based emphasis as decision-making objectives, a two-dimensional management scheme is determined; and based on the one-dimensional management scheme and the two-dimensional management scheme, the management scheme is determined.

[0078] Furthermore, the system is also used for the following functions:

[0079] For each target area, a circulation space is constructed; through local data collection, the emission sources and circulation trajectories of each target area are determined; the spatial hashing of emission sources is performed in the circulation space; the digital state representation of the circulation trajectory is performed; and the emission cycle is determined.

[0080] Furthermore, the system is also used for the following functions:

[0081] The one-dimensional management solution is deployed on the upper-level collaborative terminal, and the two-dimensional management solution is deployed on the regional edge terminal. Through terminal collaboration, emission management of each target region is executed.

[0082] Furthermore, the system is also used for the following functions:

[0083] Limit alarm conditions are introduced and over-limit alarms are deployed at the edge of the area. The limit alarm conditions include weighted over-limit alarms and instantaneous variable over-limit alarms. Each edge of the area performs local management. If the over-limit alarm triggers a management data alarm, an over-limit vector is generated. The over-limit vector includes the over-limit scale and trend. The over-limit vector is then transmitted back to the unified management platform for emission cycle tracing and feedback management.

[0084] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0085] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0086] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for coordinated management of greenhouse gas emissions in multiple regions, characterized in that, The method includes: For each target region, the main emission line is determined and branch line decoupling and multi-level weighting are carried out based on emission factors to construct a multi-dimensional quadrant map; A unified management platform is established based on the aforementioned multi-dimensional quadrant diagram. Emission quotas are the first management element, and the focus based on the emission cycle is the second management element. Based on the management decision-maker embedded in the platform, element-oriented decisions are executed to determine the management plan. The management scheme is identified and deployed in a distributed manner on the upper-level coordination terminal and the regional edge terminal, and regional emission collaborative management is implemented under the coordination of the execution terminal, wherein the upper-level coordination terminal and the regional edge terminal establish an interactive cascading connection; Execution factor-oriented decision-making, determining management plans, including: Based on the aforementioned multi-dimensional quadrant diagram, and with the emission quotas of different zones and the emission quotas of branch lines within the zones as the decision-making objectives, a one-dimensional management scheme is determined. Based on the emission cycles of each target area, and taking location-based and management-based priorities as decision-making objectives, a two-dimensional management plan is determined. The management scheme is determined based on the one-dimensional management scheme and the two-dimensional management scheme; The emission cycles for each target area include: For each target area, construct a circular space; By collecting data locally, the emission sources and circulation trajectories of each target area are determined. The spatial hashing of the emission sources is performed in the circulation space, and the digital state characterization of the circulation trajectory is performed to determine the emission cycle.

2. The multi-regional greenhouse gas emission collaborative management method as described in claim 1, characterized in that, Determine the main emission line and perform branch decoupling and multi-level weighting based on emission factors, including: For the first target area, a first emission line is determined, wherein the first target area can be any target area; By connecting to the emission factor library, the first emission main line is decoupled based on isomorphic factors to determine multiple decoupling branches; For the multiple decoupled branches, a weighted concatenation process is performed to determine the distribution of the first branch.

3. The multi-regional greenhouse gas emission collaborative management method as described in claim 2, characterized in that, For the multiple decoupled branches, a weighted cascade process is performed, including: The weight distribution is determined by first assigning weights based on branch line type, secondly based on quantification level, thirdly based on associated effects, and fourthly based on cross-regional emission rules. Traverse the decoupled branches and perform weight cascading processing according to the weight distribution to determine the first branch distribution.

4. The multi-regional greenhouse gas emission collaborative management method as described in claim 3, characterized in that, Constructing a multidimensional quadrant diagram includes: The first quadrant of the first target region is determined, and the first quadrant is characterized in multiple dimensions based on the distribution of the first branch line to determine the first quadrant map; Perform quadrant representation on each target region until the Nth quadrant map is determined, where N is the number of target regions; The first quadrant map up to the Nth quadrant map are stitched together to determine the multidimensional quadrant map.

5. The multi-regional greenhouse gas emission collaborative management method as described in claim 1, characterized in that, The one-dimensional management solution is deployed on the upper-level collaborative terminal, and the two-dimensional management solution is deployed on the regional edge terminal. Through terminal collaboration, emission management of each target region is executed.

6. The multi-regional greenhouse gas emission collaborative management method as described in claim 1, characterized in that, Following regional emission collaborative management under the execution-end collaboration, the following are included: Limit alarm conditions are introduced and over-limit alarms are deployed and installed at the edge of the area. The limit alarm conditions include weight over-limit alarms and instantaneous variable over-limit alarms. Each area edge performs local management. If the over-limit alarm triggers a management data alarm, an over-limit vector is generated, wherein the over-limit vector includes the over-limit scale and trend. The over-limit vector is transmitted back to the unified management platform for emission cycle tracing and feedback management.

7. A multi-regional greenhouse gas emission collaborative management system, characterized in that, The system is used to implement the multi-regional greenhouse gas emission coordinated management method according to any one of claims 1-6, the system comprising: The multi-dimensional quadrant map construction module is used to determine the emission main line for each target area and perform branch decoupling and multi-level weighting based on emission factors to construct a multi-dimensional quadrant map. The management scheme determination module is used to establish a unified management platform based on the multi-dimensional quadrant diagram, with emission quotas as the first management element and emission cycle-based emphasis as the second management element. Based on the management decision-maker embedded in the platform, it performs element-oriented decision-making to determine the management scheme. The emission coordination management module is used to identify the management scheme and is distributed across the upper-level coordination terminal and the regional edge terminal to implement regional emission coordination management under the coordination of the execution terminal. The upper-level coordination terminal and the regional edge terminal are interconnected. Based on the multidimensional quadrant map, and with the emission quotas of the zones and the emission quotas of the branch lines within the zones as decision-making objectives, a one-dimensional management scheme is determined; based on the emission cycles of each target area, and with the location-based emphasis and management-based emphasis as decision-making objectives, a two-dimensional management scheme is determined; and based on the one-dimensional management scheme and the two-dimensional management scheme, the management scheme is determined. For each target area, a circulation space is constructed; through local data collection, the emission sources and circulation trajectories of each target area are determined; the spatial hashing of emission sources is performed in the circulation space; the digital state representation of the circulation trajectory is performed; and the emission cycle is determined.

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