A pollution and carbon reduction strategy determination method, device, equipment and medium

Abstract

The application provides a pollution reduction and carbon reduction strategy determination method and device, equipment and medium, wherein, space emission data of a target space is acquired, and a grid emission inventory of the target space is constructed based on the space emission data of the target space; a collaborative control effect index of each grid is determined based on a space emission data table of each grid, and a collaborative control effect weight of each grid is determined according to the collaborative control effect index of each grid; industry emission data of different industries in each grid is acquired, and key grids are determined based on the industry emission data of different industries in each grid; optimal technologies of each key grid are determined from a preconfigured pollution reduction and carbon reduction collaborative technology library based on the collaborative control effect weight of each key grid; and a pollution reduction and carbon reduction strategy of the target space is determined based on the optimal technologies of each key grid. The above method can improve the adaptability, execution efficiency and implementation effect of the pollution reduction and carbon reduction strategy.

Description

Technical Field

[0001] This invention relates to the field of geographic information science, and more specifically, to a method, apparatus, equipment, and medium for determining pollution reduction and carbon reduction strategies. Background Technology

[0002] The coordinated governance of global climate change and air pollution has become a core issue for the international community, with countries introducing policies to promote synergistic effects in pollution and carbon reduction. Against this backdrop, scientifically formulating regional pollution and carbon reduction strategies requires breaking through the limitations of traditional governance models to address the complex challenges of differentiated regional emission characteristics and diversified technological needs.

[0003] Currently, the determination of pollution and carbon reduction strategies mainly relies on two traditional models. The first is experience-driven strategy formulation: in most scenarios, strategy selection is based on the qualitative understanding of regional industries and energy structures by managers or technical personnel, lacking quantitative data support. For example, directly borrowing technical solutions from other regions, or prioritizing widely used technologies that have not been locally adapted (such as uniformly promoting a certain type of energy-saving equipment). The second is a regionally homogenized governance model: applying indiscriminate technology configurations to different regional units within the target space, ignoring differences in industry composition, emission intensity, geographical environment, etc. For example, using the same emission reduction measures in mixed industrial and residential areas, or failing to distinguish the pollution and carbon emission characteristics of different regions when selecting technologies.

[0004] However, the study found that, on the one hand, when strategies are formulated based on experience, the lack of quantitative data to support qualitative understanding can easily lead to a disconnect between the strategies and actual needs, resulting in poor adaptability and limited pollution and carbon reduction effects, as well as resource waste. On the other hand, the regional homogeneous governance model uses indiscriminate technology configuration, ignoring differences in industry composition and emission intensity between regions, making it impossible to accurately identify key control areas. This results in a lack of targeted governance and reduced efficiency and effectiveness of pollution and carbon reduction strategies. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method, apparatus, equipment and medium for determining pollution reduction and carbon reduction strategies, so as to improve the adaptability, execution efficiency and effectiveness of pollution reduction and carbon reduction strategies.

[0006] In a first aspect, embodiments of this application provide a method for determining pollution reduction and carbon reduction strategies, the method comprising: Acquire spatial emission data of the target space, and construct a gridded emission list of the target space based on the spatial emission data of the target space, wherein the gridded emission list contains spatial emission data tables of each grid in the target space; Based on the spatial emission data tables of each grid, the collaborative control effect index of each grid is determined, and the collaborative control effect weight of each grid is determined based on the collaborative control effect index of each grid. Acquire industry emission data for different industries within each grid, and identify key grids based on the industry emission data for different industries within each grid. The optimal technology for each key grid is determined from a pre-configured pool of pollution reduction and carbon reduction technologies based on the weights of the collaborative control effects of each key grid. The pollution reduction and carbon reduction strategies for the target space are determined based on the optimal technology of each key grid.

[0007] Optionally, constructing a gridded emission inventory of the target space based on the spatial emission data of the target space includes: Construct a spatial emission database based on the spatial emission data; Based on the spatial emission database, the gridded emission inventory is established using a point source spatial mapping method.

[0008] Optionally, the determination of the collaborative control effect index for each grid based on the spatial emission data table of each grid includes: For each grid, obtain the carbon dioxide emission reduction data and air pollutant emission reduction data for that grid from the spatial emission data table for that grid; The synergistic control effect index of each grid is determined based on the carbon dioxide emission reduction data and air pollutant emission reduction data of that grid.

[0009] Optionally, determining the cooperative control effect weight of each grid based on the cooperative control effect index of each grid includes: The level of collaborative control effect for each grid is determined based on the collaborative control effect index of each grid. The weight of the collaborative control effect of each grid is determined based on the collaborative control effect level of each grid.

[0010] Optionally, the step of identifying key grids based on industry emission data from different industries within each grid includes: For each grid, the emission share score, emission intensity score, and technology substitution potential score of each industry in the grid area are determined based on the industry emission data of each industry in that grid. The industry score for each industry within the grid is determined based on the emission share score, emission intensity score, technological substitution potential score, and pre-configured weight values ​​of each industry within the grid area. The key grids are selected from each grid based on the industry scores of each industry within each grid.

[0011] Optionally, the weighting of the collaborative control effect based on each key grid determines the optimal technology for each key grid from a pre-configured library of collaborative pollution reduction and carbon reduction technologies, including: For each grid, determine whether there is an applicable technology suitable for that grid in the pollution reduction and carbon reduction synergistic technology library; If there are applicable technologies in the pollution reduction and carbon reduction synergistic technology library that are suitable for the grid, then the matching degree between the grid and each applicable technology is calculated based on the synergistic control effect weight of the grid and the technical parameters of each applicable technology. The optimal technology for this grid is determined from among the applicable technologies based on the degree of matching between the grid and each applicable technology.

[0012] Optionally, the step of calculating the matching degree between the grid and each applicable technology based on the collaborative control effect weights of the grid and the technical parameters of each applicable technology includes: The pollution reduction and carbon reduction requirements of the grid are determined based on the weight of the collaborative control effect of the grid. Based on the pollution reduction and carbon reduction demand value of the grid and the technical parameters of each applicable technology, the numerator dot product value of the grid and each applicable technology, the grid demand weighted value, and the technology emission reduction capacity value are determined. The matching degree between the grid and each applicable technology is determined based on the molecular dot product value, grid demand weighting value, and technology emission reduction capacity value.

[0013] Secondly, embodiments of this application provide a device for determining pollution reduction and carbon reduction strategies, the device comprising: A gridded emission inventory construction module is used to acquire spatial emission data of a target space and construct a gridded emission inventory of the target space based on the spatial emission data of the target space, wherein the gridded emission inventory includes a spatial emission data table of each grid in the target space; The collaborative control effect weight determination module is used to determine the collaborative control effect index of each grid based on the spatial emission data table of each grid, and to determine the collaborative control effect weight of each grid based on the collaborative control effect index of each grid. The key grid screening module is used to obtain industry emission data of different industries within each grid and determine key grids based on the industry emission data of different industries within each grid. The optimal technology screening module is used to determine the optimal technology for each key grid from a pre-configured pollution reduction and carbon reduction collaborative technology library based on the collaborative control effect weights of each key grid. The pollution reduction and carbon reduction strategy determination module is used to determine the pollution reduction and carbon reduction strategy for the target space based on the optimal technology of each key grid.

[0014] Optionally, constructing a gridded emission inventory of the target space based on the spatial emission data of the target space includes: Construct a spatial emission database based on the spatial emission data; Based on the spatial emission database, the gridded emission inventory is established using a point source spatial mapping method.

[0015] Optionally, the determination of the collaborative control effect index for each grid based on the spatial emission data table of each grid includes: For each grid, obtain the carbon dioxide emission reduction data and air pollutant emission reduction data for that grid from the spatial emission data table for that grid; The synergistic control effect index of each grid is determined based on the carbon dioxide emission reduction data and air pollutant emission reduction data of that grid.

[0016] Optionally, determining the cooperative control effect weight of each grid based on the cooperative control effect index of each grid includes: The level of collaborative control effect for each grid is determined based on the collaborative control effect index of each grid. The weight of the collaborative control effect of each grid is determined based on the collaborative control effect level of each grid.

[0017] Optionally, the step of identifying key grids based on industry emission data from different industries within each grid includes: For each grid, the emission share score, emission intensity score, and technology substitution potential score of each industry in the grid area are determined based on the industry emission data of each industry in that grid. The industry score for each industry within the grid is determined based on the emission share score, emission intensity score, technological substitution potential score, and pre-configured weight values ​​of each industry within the grid area. The key grids are selected from each grid based on the industry scores of each industry within each grid.

[0018] Optionally, the weighting of the collaborative control effect based on each key grid determines the optimal technology for each key grid from a pre-configured library of collaborative pollution reduction and carbon reduction technologies, including: For each grid, determine whether there is an applicable technology suitable for that grid in the pollution reduction and carbon reduction synergistic technology library; If there are applicable technologies in the pollution reduction and carbon reduction synergistic technology library that are suitable for the grid, then the matching degree between the grid and each applicable technology is calculated based on the synergistic control effect weight of the grid and the technical parameters of each applicable technology. The optimal technology for this grid is determined from among the applicable technologies based on the degree of matching between the grid and each applicable technology.

[0019] Optionally, the step of calculating the matching degree between the grid and each applicable technology based on the collaborative control effect weights of the grid and the technical parameters of each applicable technology includes: The pollution reduction and carbon reduction requirements of the grid are determined based on the weight of the collaborative control effect of the grid. Based on the pollution reduction and carbon reduction demand value of the grid and the technical parameters of each applicable technology, the numerator dot product value of the grid and each applicable technology, the grid demand weighted value, and the technology emission reduction capacity value are determined. The matching degree between the grid and each applicable technology is determined based on the molecular dot product value, grid demand weighting value, and technology emission reduction capacity value.

[0020] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the pollution reduction and carbon reduction strategy determination method described in any of the optional embodiments of the first aspect are executed.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the pollution reduction and carbon reduction strategy determination method described in any of the optional embodiments of the first aspect.

[0022] The technical solution provided in this application includes, but is not limited to, the following beneficial effects: This application first acquires spatial emission data for the target space, and constructs a gridded emission inventory based on this data. This transforms traditional "general area-based statistics" of emission data into precise grid-level units, enabling visualized location of pollution source distribution. By integrating multi-source emission data, high-emission hotspots can be accurately identified, providing a data foundation for subsequent differentiated governance. Next, based on the spatial emission data tables of each grid, a synergistic control effect index is determined for each grid. Based on this index, the synergistic control effect weights for each grid are determined, quantifying the synergistic emission reduction potential of pollutants and greenhouse gases within each grid. A weighted model dynamically allocates priorities, avoiding resource waste caused by a "one-size-fits-all" approach. Finally, industry emission data from different sectors within each grid is acquired. Based on this data, key grids are dynamically matched with optimal technologies, allowing for the selection of key grids that contribute significantly to regional emission reduction targets. These technologies are then combined with adaptation technologies from an industry emission characteristic matching technology library. Then, based on the weights of the collaborative control effects of each key grid, the optimal technology for each key grid is determined from a pre-configured pool of collaborative pollution reduction and carbon reduction technologies. This allows for multi-objective optimization matching of technical parameters from the pool with grid requirements. Finally, based on the optimal technologies of each key grid, a pollution reduction and carbon reduction strategy for the target space is determined. This enables the integration of multi-grid technology combinations to form a collaborative strategy covering the entire domain.

[0023] In summary, this application constructs a gridded emission inventory of the target space, quantifies the effect of grid-based collaborative control and assigns weights, identifies key grids, dynamically matches the optimal technology in the technology library, and integrates them into a closed-loop decision-making mechanism for a comprehensive collaborative strategy. This mechanism can improve the adaptability, execution efficiency, and effectiveness of pollution reduction and carbon reduction strategies. To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart of a method for determining pollution reduction and carbon reduction strategies provided in Embodiment 1 of the present invention is shown; Figure 2 A flowchart of a gridded emission inventory construction method provided in Embodiment 1 of the present invention is shown; Figure 3 The flowchart of a method for determining a collaborative control effect index provided in Embodiment 1 of the present invention is shown; Figure 4 The flowchart of a method for determining the weight of collaborative control effect provided in Embodiment 1 of the present invention is shown; Figure 5 A flowchart of a method for determining key grids provided in Embodiment 1 of the present invention is shown; Figure 6 The flowchart of an optimal technique determination method provided in Embodiment 1 of the present invention is shown; Figure 7 A flowchart of a matching degree determination method provided in Embodiment 1 of the present invention is shown; Figure 8 This shows a schematic diagram of a pollution reduction and carbon reduction strategy determination device provided in Embodiment 2 of the present invention; Figure 9 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention is shown. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] Example 1 To facilitate understanding of this application, the following is combined with... Figure 1 The flowchart illustrating the method for determining pollution reduction and carbon reduction strategies provided in Embodiment 1 of the present invention will be described in detail for Embodiment 1 of this application.

[0028] See Figure 1 As shown, Figure 1 A flowchart of a method for determining pollution reduction and carbon reduction strategies provided in Embodiment 1 of the present invention is shown, wherein the method includes steps S101 to S105: S101: Obtain spatial emission data of the target space, and construct a gridded emission list of the target space based on the spatial emission data of the target space, wherein the gridded emission list contains spatial emission data tables of each grid in the target space.

[0029] Specifically, by collecting air pollutant emission inventories and related environmental statistics, statistical yearbooks, and energy statistical yearbooks for the target area, air pollution emission data is updated and improved. Simultaneously, carbon emission data is collected, including energy consumption data from different districts, counties, and enterprises. CO2 emission accounting is performed with reference to methods such as the IPCC Intergovernmental Panel on Climate Change and the "Provincial Greenhouse Gas Compilation Guidelines." Then, using GIS software and a point-source spatial mapping method, a 9km spatial gridded emission inventory is established, allocating emissions from industrial point sources and area sources (such as transportation). Each grid contains data on carbon dioxide emissions and emissions of different types of air pollutants emitted within that grid.

[0030] S102: Determine the collaborative control effect index of each grid based on the spatial emission data table of each grid, and determine the collaborative control effect weight of each grid based on the collaborative control effect index of each grid.

[0031] Specifically, firstly, the synergistic effect of each grid is evaluated, and the Synergistic Control Effect Index (CRI) is calculated. Then, the grids are classified according to the CRI, with different levels of grids corresponding to different carbon reduction weights and pollution reduction weights.

[0032] S103: Obtain industry emission data for different industries within each grid, and determine key grids based on the industry emission data for different industries within each grid.

[0033] Specifically, information such as pollutant and CO2 emission inventories and output value of enterprises in different industries within each grid is collected. Combined with industry pollution emission standards and regional industrial planning documents, the contribution of each polluting element by industry is quantified, including three parts: industry emission share score (weight 40%), emission intensity score (weight 30%), and technological substitution potential score (weight 30%). Through comprehensive scoring, key industries are identified, and grids containing industries with a comprehensive score below 70 are designated as key grids.

[0034] S104: Based on the weight of the collaborative control effect of each key grid, the optimal technology for each key grid is determined from the pre-configured pollution reduction and carbon reduction collaborative technology library.

[0035] Specifically, a collaborative technology library for pollution reduction and carbon reduction will be established. This library will contain the best technologies from various industries, categorized according to technology codes. It will also collect information on the adoption rate, emission reduction potential, and economic cost parameters of relevant technologies, and assess their Technology Readiness Level (TRL). Based on the collaborative control effect weights of key grids, the matching degree between each technology in the library and the grid requirements will be calculated to determine the optimal technology for each key grid.

[0036] S105: Determine the pollution reduction and carbon reduction strategy for the target space based on the optimal technology of each key grid.

[0037] Specifically, the optimal technologies from each key grid will be integrated to obtain a pollution reduction and carbon reduction strategy for the target space. This strategy will cover the technical implementation plans of different grids to achieve synergistic optimization of pollution reduction and carbon reduction in the target space, thereby improving the overall emission reduction effect and environmental quality.

[0038] After formulating pollution reduction and carbon reduction strategies, it is necessary to verify their effectiveness. The specific process includes: Based on a gridded emission inventory and optimal technology combinations, and combined with data such as emission reduction potential, emission reduction rates, and related planning after the implementation of pollution reduction and carbon reduction technologies, an emission reduction inventory under future scenarios is calculated. The emission reduction rates and amounts of pollutants and carbon dioxide for each grid after the implementation of different technologies are calculated. Simultaneously, three-dimensional gridded numerical models (such as the CMAQ air quality model for the target area, the WRF-Chem online coupled weather and chemical model, and the CAMx photochemical grid model) are established to simulate air quality changes, evaluate the improvement effect of emission reduction measures on atmospheric pollutant concentrations, and assess the synergistic optimization effect under different meteorological conditions and climate scenarios. This comprehensive and scientific evaluation of the implementation effect of synergistic pollution reduction and carbon reduction technologies ensures the feasibility and effectiveness of the technical solutions.

[0039] In an optional implementation, see Figure 2 As shown, Figure 2 The flowchart illustrates a gridded emission inventory construction method according to Embodiment 1 of the present invention, wherein the step of constructing a gridded emission inventory of the target space based on the spatial emission data of the target space includes steps S201-S202: S201: Construct a spatial emission database based on the spatial emission data.

[0040] Specifically, in constructing the grid database, this application employs a method combining data collection and processing with emission factor calculation to build a basic database covering pollutants, CO2 emissions, and key meteorological elements in the target area. Specifically, the air pollution emission data is based on the research team's existing air pollutant emission inventory for the target area (including SO2, NOx, and PM2.5 emissions). 2.5 PM 10 The data includes VOCs, CO2, etc., and is further updated and improved by integrating environmental statistics data, statistical yearbooks, and energy statistical yearbooks of the target region. Carbon emission data mainly comes from the statistical yearbooks, energy statistical yearbooks, and energy consumption data (including coal, oil, natural gas, electricity, etc.) of various districts, counties, and enterprises in the environmental statistics database of the target region. CO2 emissions of the target region are calculated according to the IPCC (Intergovernmental Panel on Climate Change) and the "Guidelines for the Compilation of Provincial Greenhouse Gases". At the same time, the ammonia nitrogen and total phosphorus emissions of water pollution sources at the major watershed scale in the target region are also collected.

[0041] S202: Based on the spatial emission database, establish the gridded emission inventory using the point source spatial mapping method.

[0042] Specifically, using GIS software, a gridded emission inventory with a 9km spatial resolution was established through a point source spatial mapping method. Emission allocation followed these rules: emissions from industrial point sources were 100% assigned to their respective grids; area source emissions (e.g., traffic sources) were allocated to surrounding grids proportionally based on road length. Each grid was uniquely numbered and contained emission data for carbon dioxide and various air pollutants (SO2, NOx, PM, VOCs, etc.) emitted within that grid. Table 1 shows the spatial emission data for grid i, where i is the grid number, j is the pollutant type, and k is the department code.

[0043] Table 1 In an optional implementation, see Figure 3 As shown, Figure 3 The flowchart illustrates a method for determining a cooperative control effect index according to Embodiment 1 of the present invention, wherein determining the cooperative control effect index of each grid based on the spatial emission data table of each grid includes steps S301-S302: S301: For each grid, obtain the carbon dioxide emission reduction data and air pollutant emission reduction data of that grid from the spatial emission data table of that grid.

[0044] Specifically, the CO2 emission reduction data is the CO2 emission reduction rate of the grid over the past five years, denoted as RE(CO2), and the air pollutant emission reduction data is the comprehensive air pollutant emission reduction rate of the grid over the past five years, denoted as RE(AP). The specific data is extracted from the spatial emission data table of the grid, which records the emissions and changing trends of CO2 and air pollutants (SO2, NOx, PM2.5, VOCs, etc.) within each grid. For example, the spatial emission data table of grid i contains annual change data for CO2 and pollutant emissions from sector k (such as cement manufacturing and steel). By calculating the rate of change of emissions over the past five years, RE(CO2) and RE(AP) are obtained.

[0045] S302: Determine the synergistic control effect index of each grid based on the carbon dioxide emission reduction data and air pollutant emission reduction data of this grid.

[0046] Specifically, first, the pollutant weights are configured, with SO2 having a weight of [missing value]. NOx is PM2.5 weighting VOCs weights are The weights can be calculated using either the pollution equivalent conversion factor or the environmental standard weighting method. Pollution equivalent method: weights , , , Referring to the pollution equivalent values ​​stipulated in the Environmental Protection Tax Law of the People's Republic of China. Environmental standard weighted method: Taking the Ambient Air Quality Standard (GB3095-2012) as an example, the weights are calculated by normalizing the annual average concentration limits of pollutants. Table 2 shows the allocation weights (normalized weights) corresponding to different pollutants:

[0047] Table 2 Then, the Collaborative Control Effectiveness Index (CRI) is calculated using the following formula:

[0048]

[0049] Among them, RE(CO2) is the CO2 emission reduction rate over the past five years, and RE(AP) is the comprehensive emission reduction rate of air pollutants, which can be calculated by weighted average of the emission reduction rates of each pollutant and their respective weights. This represents the SO2 emission reduction rate over the past five years. For the past five years Emission reduction rate For the past five years Emission reduction rate For the past five years Emission reduction rate.

[0050] The above formula quantifies the degree of synergy between pollution reduction and carbon reduction in each grid, providing a basis for subsequent weight allocation and technology matching.

[0051] In an optional implementation, see Figure 4 As shown, Figure 4 The flowchart illustrates a method for determining the cooperative control effect weights according to Embodiment 1 of the present invention, wherein determining the cooperative control effect weights of each grid based on the cooperative control effect index of each grid includes steps S401-S402: S401: Determine the level of collaborative control effect for each grid based on the collaborative control effect index of each grid.

[0052] Specifically, the criteria for classifying the synergistic control effectiveness level, or CRI level, are as follows: Level 0: CRI ≤ 0, judged as "no synergistic effect", indicating that pollution reduction and carbon reduction measures within the grid have no synergy or conflict; Level 1: 0 < CRI ≤ 1, judged as "the degree of emission reduction of air pollutants is higher than that of CO2", indicating that the pollution reduction effect dominates the synergistic effect; Level 2: 1 < CRI ≤ 2, judged as "the measures have the same degree of emission reduction for air pollutants and CO2", indicating that the two are synergistically balanced; Level 3: CRI > 2, judged as "the degree of emission reduction of CO2 by the measures is higher than that of air pollutants", indicating that the carbon reduction effect dominates the synergistic effect. This level classification quantifies the synergistic characteristics of the grid through CRI value ranges, providing a basis for weight allocation.

[0053] S402: Determine the weight of the collaborative control effect of each grid based on the collaborative control effect level of each grid.

[0054] Specifically, the correspondence between the weights of the collaborative control effect and the levels of the collaborative control effect is shown in Table 3. Table 3 shows the weights of the collaborative control effect (including the carbon reduction weight) of the grid when the grid CRI level is level 1, level 2, and level 3. and pollution reduction weight ):

[0055] Table 3 When the CRI level is Level 1 (carbon reduction demand-driven): When the grid CRI shows that CO2 emission reduction lags behind air pollutants, the carbon reduction weight is increased to strengthen the demand for carbon emission reduction; Level 2 (balanced demand): When the effects of pollution reduction and carbon reduction are balanced, equal weighting is used to maintain a coordinated balance; Level 3 (pollution reduction demand-driven): When the effect of CO2 emission reduction is significantly higher than that of air pollutants, the pollution reduction weight is increased to make up for the shortcomings in pollution control. Through dynamic weighting, a refined response to the coordinated needs of different grids can be achieved.

[0056] In an optional implementation, see Figure 5 As shown, Figure 5 The flowchart of a method for determining key grids according to Embodiment 1 of the present invention is shown, wherein determining key grids based on industry emission data of different industries within each grid includes steps S501 to S503: S501: For each grid, the emission share score, emission intensity score, and technology substitution potential score of each industry in the grid area are determined based on the industry emission data of each industry in that grid.

[0057] Specifically, the emissions percentage score is calculated as follows: First, the emissions percentage of a particular industry is calculated, and then the emissions percentage score for that industry is determined based on that emissions percentage. The formula for calculating the emissions percentage of a particular industry is: Here, we take an industry whose emissions account for 33.3% of the total as the maximum score, and then standardize this score to obtain the emissions percentage score: Substituting the data, the emission share score for the steel industry is calculated as 30 ÷ 33.3% = 90 points. Industry-specific benchmarks can be adjusted here. For example, a full score can be awarded if the steel and cement industries account for 40% of the total emissions in the grid, while a full score can be awarded if other industries account for 33.3% of the total emissions in the grid.

[0058] Emission intensity score: First, calculate the emission intensity value for a specific industry, and then determine the emission intensity score for that industry based on that value. The formula for calculating the emission intensity value for a specific industry is as follows: Then, the emission intensity score is calculated (the higher the ratio, the lower the score), and the formula is: , where e is the natural constant.

[0059] Technology Substitution Potential Score: Obtain a list of feasible technologies for the industry from the technology database, identify the potential technology with the best emission reduction rate from the list, and then use the emission reduction rate of the potential technology as the optimal emission reduction rate in the following formula to calculate the potential score: After obtaining the potential score, dynamic industry adjustments need to be made based on the technology maturity level (TRL) of different technologies. This involves determining the corresponding bonus value based on the TRL level of the potential technology (obtainable from a table). (For TRL levels 1-3, the bonus value is 0; for TRL levels 4-6, the bonus value is +5 to +10, which can be randomly selected within this range; for TRL levels 7-9, the bonus value is +15 to +25, which can also be randomly selected within this range). This bonus value is then summed with the potential score to obtain the adjusted technology substitution potential score. Table 4 shows the correspondence between the technology maturity level (TRL) and the bonus value.

[0060]

[0061] Table 4 For example, the optimal emission reduction rate for the steel industry is 48% (corresponding to potential technology A), and the potential score is 48. Then, by looking up the table, the technology maturity of potential technology A is found to be 8 (taking TRL ≥ 7 as an example, +20 points). The technology substitution potential score after TRL level bonus is 48 + 20 = 68 points.

[0062] S502: Determine the industry score for each industry within the grid based on the emission share score, emission intensity score, technological substitution potential score, and pre-configured weight values ​​of each industry within the grid area.

[0063] Specifically, weights are assigned to the emission share score, emission intensity score, and technological substitution potential score for each industry, such as a weight of 40% for the emission share score, 30% for the emission intensity score, and 30% for the technological substitution potential score. Based on the above three indicators and their corresponding weights for each industry, the comprehensive score for each industry is calculated using the following expression.

[0064] The overall score is calculated as follows: Emissions percentage score × Emissions percentage score weight + Emission intensity score × Emission intensity score weight + Technology substitution potential score × Technology substitution potential score weight.

[0065] S503: Select the key grids from each grid based on the industry scores of each industry within each grid.

[0066] Specifically, a scoring threshold, such as 70 points, is set. Grids containing industries with a comprehensive score below this threshold (referred to as key industries) are designated as key grids. For example, if the cement manufacturing industry has a comprehensive score of 82 points and the steel industry has a comprehensive score of 67 points in a certain grid, then that grid is designated as a key grid because it contains the steel industry (a key industry) with a comprehensive score below 70 points. Grids containing industries with an intensity exceeding 1.5 times the provincial average intensity of the same industry or exceeding the annual average concentration limit (referred to as key industries) can also be prioritized and designated as key grids.

[0067] In an optional implementation, see Figure 6 As shown, Figure 6 The flowchart illustrates an optimal technology determination method provided in Embodiment 1 of the present invention, wherein the optimal technology for each key grid is determined from a pre-configured pollution reduction and carbon reduction collaborative technology library based on the collaborative control effect weights of each key grid, including steps S601-S603: S601: For each grid, determine whether there is an applicable technology suitable for that grid in the pollution reduction and carbon reduction collaborative technology library.

[0068] Specifically, before executing step S601, a pollution reduction and carbon reduction synergistic technology library is constructed. The feasible technologies in this library are compiled according to the following technology codes: T - {Industry Code K} - {Technology Type Code (1 digit)} - ​​{Serial Number (3 digits)}. Example: T-32-A-015 (Technology No. 15 in the Combustion Optimization Category for the Iron and Steel Industry). The industry codes for different technologies are used to identify the industries to which the technology is applicable.

[0069] When constructing a collaborative technology library for pollution reduction and carbon reduction, the following steps and technical dimensions are followed: Technical parameters for each feasible technology are collected in three aspects: technical performance, economic efficiency, and applicability. Specifically, technical performance includes emission reduction efficiency, energy consumption, and byproducts; economic efficiency includes investment cost, operation and maintenance costs, and payback period; and applicability includes industry codes and scale requirements. Based on this, the technical parameters are collected and cleaned, with multi-source data primarily derived from actual engineering cases and national technology databases.

[0070] In addition, the Technology Maturity Level (TRL Level) is assessed based on the feasibility (known information) of each feasible technology. The assessment rules are shown in Table 5. You can find the Technology Maturity Level (TRL Level) corresponding to the feasibility of each feasible technology from Table 5.

[0071]

[0072] Table 5 Determining whether there are applicable technologies for the grid in the pollution reduction and carbon reduction synergistic technology library includes: determining whether there are feasible technologies in the pollution reduction and carbon reduction synergistic technology library that correspond to the key industries in the grid (by judging by the industry code in the technical code of the feasible technology; if there is an industry code that is the same as the key industries in the grid, then there are applicable technologies for the grid in the pollution reduction and carbon reduction synergistic technology library).

[0073] S602: If there are applicable technologies for the grid in the pollution reduction and carbon reduction synergistic technology library, then calculate the matching degree between the grid and each applicable technology based on the synergistic control effect weight of the grid and the technical parameters of each applicable technology.

[0074] Specifically, the matching degree calculation is based on the weights of the collaborative control effects (such as carbon reduction weights) in the grid. =0.7, pollution reduction weight =0.3) and technical parameters (such as CO2 emission reduction rate of 40% and pollutant emission reduction rate of 15%) are used to calculate the matching degree (numerator dot product divided by the product of the magnitudes of the two vectors). The higher the matching degree, the better the technology matches the grid requirements, and the higher the matching degree is, the more likely it is to be considered the optimal technology candidate.

[0075] S603: Determine the optimal technology for the grid from among the applicable technologies based on the matching degree between the grid and each applicable technology.

[0076] Specifically, all applicable technologies are sorted from highest to lowest matching degree (e.g., T-32-C-008 has a matching degree of 1.0 and T-32-D-102 has a matching degree of 0.92 in the example); the technology with the highest matching degree is selected as the optimal technology (if multiple technologies have the same matching degree, further screening is carried out by combining economic cost parameters (such as investment cost, payback period) or technology maturity (TRL level), such as prioritizing the selection of technologies with TRL ≥ 7 as the optimal technology).

[0077] In an optional implementation, see Figure 7 As shown, Figure 7 The flowchart of a matching degree determination method provided in Embodiment 1 of the present invention is shown, wherein the step of calculating the matching degree between the grid and each applicable technology based on the collaborative control effect weight of the grid and the technical parameters of each applicable technology includes steps S701 to S703: S701: Determine the pollution reduction and carbon reduction requirements of the grid based on the weight of the collaborative control effect of the grid.

[0078] Specifically, the pollution reduction and carbon reduction demand value includes the pollution reduction demand value Q. 碳 And carbon reduction demand value Q 污 Based on the collaborative control effect weights of this grid (carbon reduction weights) and pollution reduction weight The pollution reduction requirement value Q for this grid is calculated using the following expression. 碳 And carbon reduction demand value Q 污 :

[0079] S702: Based on the pollution reduction and carbon reduction demand value of the grid and the technical parameters of each applicable technology, determine the numerator dot product value of the grid and each applicable technology, the grid demand weighted value, and the technology emission reduction capacity value.

[0080] Specifically, the technical parameters include CO2 emission reduction rate and pollutant emission reduction rate.

[0081] For each applicable technology, the pollution reduction and carbon reduction demand value (pollution reduction demand value Q) is determined according to the grid. 碳 And carbon reduction demand value Q 污 The molecular dot product of the grid and the applicable technology, the grid demand weighting value, and the technology emission reduction capacity value are determined based on the technical parameters of the applicable technology (CO2 emission reduction rate and pollutant emission reduction rate).

[0082] S703: Determine the matching degree between the grid and each applicable technology based on the molecular dot product value, grid demand weighting value, and technology emission reduction capacity value of the grid.

[0083] Specifically, for each applicable technology, the matching degree between the mesh and the applicable technology is calculated according to the following formula (in practical applications, the maximum matching degree is 1.0, that is, when the calculated matching degree exceeds 1.0, 1.0 is taken as the value of the matching degree): .

[0084] In practical applications, when verifying and evaluating emission reduction effects, the process begins with generating an emission reduction inventory under future scenarios. This is based on a gridded emission inventory and the optimal technology combination. This is done in conjunction with the emission reduction potential and rate after the implementation of pollution reduction and carbon reduction technologies, relevant planning, and data such as emission factors. The inventory details the emission reduction rates and amounts of pollutants (SO2, NOx, PM2.5, VOCs, etc.) and CO2 for each grid after the implementation of different technologies. When evaluating the pollution reduction and carbon reduction effects of different grids, the following formula can be used to calculate the contribution rate of each emission reduction measure to air pollutants. and the contribution rate of various emission reduction measures to CO2 emission reduction This allows us to assess the synergistic benefits.

[0085]

[0086] in, , This represents the equivalent values ​​of air pollutants and carbon dioxide changes resulting from a specific emission reduction measure matched within a certain grid. , This represents the sum of the equivalent values ​​of air pollutants and the equivalent values ​​of carbon dioxide changes resulting from all grid-matched emission reduction measures, primarily targeting the same industry. For each grid, the equivalent values ​​of air pollutant reductions and CO2 emission reductions resulting from each emission reduction measure are first calculated separately. Then, these values ​​are substituted into the formula to calculate the corresponding contribution rate. In this way, the contribution of different measures to the synergy of pollution reduction and carbon reduction can be quantitatively assessed, providing a basis for subsequent optimization and adjustment.

[0087] Based on the above, a three-dimensional gridded numerical model is established to simulate air quality changes and evaluate the improvement effects of emission reduction measures on atmospheric pollutant concentrations (such as PM2.5 and O3), especially the synergistic optimization effects under different meteorological conditions. Furthermore, medium- and long-term air quality changes and CO2 concentration changes under the background of climate change are simulated. Different shared socio-economic pathways and climate scenarios, such as SSP245 (a climate scenario), are selected to conduct multi-scenario comparative analysis of the synergistic effects of key control grid units, assessing the impact on air quality. Through the above verification steps, this application can comprehensively and scientifically evaluate the implementation effects of pollution reduction and carbon reduction synergistic technologies, ensuring the feasibility and effectiveness of the technical solutions in practical applications, and providing strong technical support for achieving carbon peaking and carbon neutrality goals in target areas.

[0088] Example 2 See Figure 8 As shown, Figure 8 A schematic diagram of a pollution reduction and carbon reduction strategy determination device provided in Embodiment 2 of the present invention is shown, wherein the device includes: The gridded emission inventory construction module 801 is used to acquire spatial emission data of the target space and construct a gridded emission inventory of the target space based on the spatial emission data of the target space, wherein the gridded emission inventory includes a spatial emission data table of each grid in the target space; The collaborative control effect weight determination module 802 is used to determine the collaborative control effect index of each grid based on the spatial emission data table of each grid, and to determine the collaborative control effect weight of each grid based on the collaborative control effect index of each grid. The key grid screening module 803 is used to obtain industry emission data of different industries within each grid and determine key grids based on the industry emission data of different industries within each grid. The optimal technology screening module 804 is used to determine the optimal technology for each key grid from a pre-configured pollution reduction and carbon reduction collaborative technology library based on the collaborative control effect weight of each key grid. The pollution reduction and carbon reduction strategy determination module 805 is used to determine the pollution reduction and carbon reduction strategy for the target space based on the optimal technology of each key grid.

[0089] In an optional implementation, constructing a gridded emissions inventory of the target space based on the spatial emissions data of the target space includes: Construct a spatial emission database based on the spatial emission data; Based on the spatial emission database, the gridded emission inventory is established using a point source spatial mapping method.

[0090] In an optional implementation, the determination of the collaborative control effect index for each grid based on the spatial emission data table for each grid includes: For each grid, obtain the carbon dioxide emission reduction data and air pollutant emission reduction data for that grid from the spatial emission data table for that grid; The synergistic control effect index of each grid is determined based on the carbon dioxide emission reduction data and air pollutant emission reduction data of that grid.

[0091] In an optional implementation, determining the cooperative control effect weight of each grid based on the cooperative control effect index of each grid includes: The level of collaborative control effect for each grid is determined based on the collaborative control effect index of each grid. The weight of the collaborative control effect of each grid is determined based on the collaborative control effect level of each grid.

[0092] In an optional implementation, the determination of key grids based on industry emission data from different industries within each grid includes: For each grid, the emission share score, emission intensity score, and technology substitution potential score of each industry in the grid area are determined based on the industry emission data of each industry in that grid. The industry score for each industry within the grid is determined based on the emission share score, emission intensity score, technological substitution potential score, and pre-configured weight values ​​of each industry within the grid area. The key grids are selected from each grid based on the industry scores of each industry within each grid.

[0093] In an optional implementation, the optimal technology for each key grid is determined from a pre-configured library of pollution reduction and carbon reduction collaborative technologies based on the weights of the collaborative control effects for each key grid, including: For each grid, determine whether there is an applicable technology suitable for that grid in the pollution reduction and carbon reduction synergistic technology library; If there are applicable technologies in the pollution reduction and carbon reduction synergistic technology library that are suitable for the grid, then the matching degree between the grid and each applicable technology is calculated based on the synergistic control effect weight of the grid and the technical parameters of each applicable technology. The optimal technology for this grid is determined from among the applicable technologies based on the degree of matching between the grid and each applicable technology.

[0094] In an optional implementation, the step of calculating the matching degree between the grid and each applicable technology based on the collaborative control effect weights of the grid and the technical parameters of each applicable technology includes: The pollution reduction and carbon reduction requirements of the grid are determined based on the weight of the collaborative control effect of the grid. Based on the pollution reduction and carbon reduction demand value of the grid and the technical parameters of each applicable technology, the numerator dot product value of the grid and each applicable technology, the grid demand weighted value, and the technology emission reduction capacity value are determined. The matching degree between the grid and each applicable technology is determined based on the molecular dot product value, grid demand weighting value, and technology emission reduction capacity value.

[0095] Example 3 Based on the same application concept, see [link / reference] Figure 9 As shown, Figure 9 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention is shown, wherein, as Figure 9 As shown, the computer device 900 provided in Embodiment 3 of this application includes: The computer device 900 includes a processor 901, a memory 902, and a bus 903. The memory 902 stores machine-readable instructions that can be executed by the processor 901. When the computer device 900 is running, the processor 901 communicates with the memory 902 through the bus 903. When the machine-readable instructions are executed by the processor 901, the steps of the pollution reduction and carbon reduction strategy determination method shown in Embodiment 1 are performed.

[0096] Example 4 Based on the same concept, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the pollution reduction and carbon reduction strategy determination method described in any of the above embodiments.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] The computer program product for determining pollution reduction and carbon reduction strategies provided in this embodiment of the invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0099] The pollution reduction and carbon reduction strategy determination device provided in this embodiment of the invention can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this embodiment of the invention are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiments can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0100] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0101] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] In addition, the functional units in the embodiments provided by the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0103] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0105] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining pollution reduction and carbon reduction strategies, characterized in that, The method includes: Acquire spatial emission data of the target space, and construct a gridded emission list of the target space based on the spatial emission data of the target space, wherein the gridded emission list contains spatial emission data tables of each grid in the target space; Based on the spatial emission data tables for each grid, the collaborative control effectiveness index (CRI) for each grid is determined. Then, the collaborative control effectiveness weight for each grid is determined based on its CRI. The formula for calculating the CRI is as follows: ; RE(CO2) represents the CO2 emission reduction rate over the past five years, while RE(AP) represents the overall emission reduction rate of air pollutants, which can be calculated by weighted averaging of the emission reduction rates of each pollutant and their respective weights. This represents the SO2 emission reduction rate over the past five years. For the past five years Emission reduction rate For the past five years Emission reduction rate For the past five years Emission reduction rate; Acquire industry emission data for different industries within each grid, and identify key grids based on the industry emission data for different industries within each grid. The optimal technology for each key grid is determined from a pre-configured pool of pollution reduction and carbon reduction technologies based on the weights of the collaborative control effects of each key grid. The pollution reduction and carbon reduction strategies for the target space are determined based on the optimal technology of each key grid. The weighting of the collaborative control effect based on each key grid determines the optimal technology for each key grid from a pre-configured library of collaborative pollution reduction and carbon reduction technologies, including: For each grid, determine whether there is an applicable technology suitable for that grid in the pollution reduction and carbon reduction synergistic technology library; If there are applicable technologies in the pollution reduction and carbon reduction synergistic technology library that are suitable for the grid, then the matching degree between the grid and each applicable technology is calculated based on the synergistic control effect weight of the grid and the technical parameters of each applicable technology. The optimal technology for this grid is determined from among the applicable technologies based on the degree of matching between the grid and each applicable technology. The calculation of the matching degree between the grid and each applicable technology based on the collaborative control effect weights of the grid and the technical parameters of each applicable technology includes: The pollution reduction and carbon reduction requirements of the grid are determined based on the weight of the collaborative control effect of the grid. Based on the pollution reduction and carbon reduction demand value of the grid and the technical parameters of each applicable technology, the numerator dot product value of the grid and each applicable technology, the grid demand weighted value, and the technology emission reduction capacity value are determined. The matching degree between the grid and each applicable technology is determined based on the molecular dot product value, grid demand weighting value, and technology emission reduction capacity value.

2. The method according to claim 1, characterized in that, The construction of the gridded emission inventory of the target space based on the spatial emission data of the target space includes: Construct a spatial emission database based on the spatial emission data; Based on the spatial emission database, the gridded emission inventory is established using a point source spatial mapping method.

3. The method according to claim 1, characterized in that, The coordinated control effect index of each grid is determined based on the spatial emission data table of each grid, including: For each grid, obtain the carbon dioxide emission reduction data and air pollutant emission reduction data for that grid from the spatial emission data table for that grid; The synergistic control effect index of each grid is determined based on the carbon dioxide emission reduction data and air pollutant emission reduction data of that grid.

4. The method according to claim 1, characterized in that, The determination of the collaborative control effect weight of each grid based on the collaborative control effect index of each grid includes: The level of collaborative control effect for each grid is determined based on the collaborative control effect index of each grid. The weight of the collaborative control effect of each grid is determined based on the collaborative control effect level of each grid.

5. The method according to claim 1, characterized in that, The identification of key grids based on industry emission data from different industries within each grid includes: For each grid, the emission share score, emission intensity score, and technology substitution potential score of each industry in the grid area are determined based on the industry emission data of each industry in that grid. The industry score for each industry within the grid is determined based on the emission share score, emission intensity score, technological substitution potential score, and pre-configured weight values ​​of each industry within the grid area. The key grids are selected from each grid based on the industry scores of each industry within each grid.

6. A device for determining pollution reduction and carbon reduction strategies, characterized in that, The device includes: A gridded emission inventory construction module is used to acquire spatial emission data of a target space and construct a gridded emission inventory of the target space based on the spatial emission data of the target space, wherein the gridded emission inventory includes a spatial emission data table of each grid in the target space; The collaborative control effect weight determination module is used to determine the collaborative control effect index of each grid based on the spatial emission data table of each grid, and to determine the collaborative control effect weight of each grid based on the collaborative control effect index of each grid; wherein, the formula for calculating the collaborative control effect index (CRI) is: ; RE(CO2) represents the CO2 emission reduction rate over the past five years, while RE(AP) represents the overall emission reduction rate of air pollutants, which can be calculated by weighted averaging of the emission reduction rates of each pollutant and their respective weights. This represents the SO2 emission reduction rate over the past five years. For the past five years Emission reduction rate For the past five years Emission reduction rate For the past five years Emission reduction rate; The key grid screening module is used to obtain industry emission data of different industries within each grid and determine key grids based on the industry emission data of different industries within each grid. The optimal technology screening module is used to determine the optimal technology for each key grid from a pre-configured pollution reduction and carbon reduction collaborative technology library based on the collaborative control effect weights of each key grid. The pollution reduction and carbon reduction strategy determination module is used to determine the pollution reduction and carbon reduction strategy for the target space based on the optimal technology of each key grid. The weighting of the collaborative control effect based on each key grid determines the optimal technology for each key grid from a pre-configured library of collaborative pollution reduction and carbon reduction technologies, including: For each grid, determine whether there is an applicable technology suitable for that grid in the pollution reduction and carbon reduction synergistic technology library; If there are applicable technologies in the pollution reduction and carbon reduction synergistic technology library that are suitable for the grid, then the matching degree between the grid and each applicable technology is calculated based on the synergistic control effect weight of the grid and the technical parameters of each applicable technology. The optimal technology for this grid is determined from among the applicable technologies based on the degree of matching between the grid and each applicable technology. The calculation of the matching degree between the grid and each applicable technology based on the collaborative control effect weights of the grid and the technical parameters of each applicable technology includes: The pollution reduction and carbon reduction requirements of the grid are determined based on the weight of the collaborative control effect of the grid. Based on the pollution reduction and carbon reduction demand value of the grid and the technical parameters of each applicable technology, the numerator dot product value of the grid and each applicable technology, the grid demand weighted value, and the technology emission reduction capacity value are determined. The matching degree between the grid and each applicable technology is determined based on the molecular dot product value, grid demand weighting value, and technology emission reduction capacity value.

7. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the pollution reduction and carbon reduction strategy determination method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for determining pollution reduction and carbon reduction strategies as described in any one of claims 1 to 5.

Images