An industrial process carbon emission evaluation method and device based on big data and a storage medium

Abstract

The application discloses an industrial process carbon emission evaluation method and device based on big data and a storage medium, and belongs to the technical field of carbon emission evaluation. The method comprises the following steps: acquiring meteorological data, biological environment data, historical carbon emission processing data, estimated emission and emission data of each unit region in a region to be evaluated; obtaining a carbon emission diffusion value according to the meteorological data, and obtaining a diffusion influence value according to the meteorological data and the estimated emission; calculating a diffusion index according to the carbon emission diffusion value and the diffusion influence value, and calculating a retention index according to the estimated emission and the emission data; calculating a bearing index according to the estimated emission, the retention index, the emission data, the historical carbon emission processing data and the biological environment data; and obtaining a carbon emission evaluation result after obtaining a carbon emission optimization index of each unit region according to the normalization calculation of the diffusion index, the retention index and the bearing index. Through implementation of the application, the problem that the prior art does not consider the influence of a region and leads to inaccurate evaluation can be solved.

Description

Technical Field

[0001] This invention relates to the field of carbon emission assessment technology, and in particular to a method, apparatus and storage medium for assessing carbon emissions from industrial processes based on big data. Background Technology

[0002] Currently, industrial processes are a major source of energy consumption and carbon emissions. Intelligent carbon management of these processes is crucial for promoting low-carbon transformation in the industrial sector, and scientific carbon emission assessment is a vital foundation for intelligent management. Existing carbon emission assessment technologies for industrial processes typically focus on the overall emissions of entities such as enterprises or industrial parks, conducting macro-level emission statistics and real-time monitoring. Specifically, this can be achieved by collecting carbon emission data related to industrial processes through sensor information systems or by using machine learning models to predict carbon emission trends, providing data support for the macro-level control of industrial carbon emissions.

[0003] However, existing carbon emission assessment technologies often treat carbon emission behavior and data from different geographical locations as a homogeneous set, failing to incorporate geographical location as a key spatial attribute into the consideration of carbon emission assessment. They lack coupling analysis of geographical location spatial attributes and carbon emission assessment, and ignore the effect of carbon emission location on carbon emission processing. As a result, existing assessment results cannot reflect the differences in the adaptability of different regions to carbon emissions, and cannot meet the needs of industrial low-carbon transformation for accurate carbon emission assessment. Summary of the Invention

[0004] This invention provides a method, apparatus, and storage medium for assessing carbon emissions in industrial processes based on big data. It can effectively solve the problem that existing technologies cannot reflect the differences in the adaptability of carbon emissions to different regions and cannot meet the needs of accurate carbon emission assessment for industrial low-carbon transformation.

[0005] One embodiment of the present invention provides a method for assessing carbon emissions from industrial processes based on big data, comprising: Acquire meteorological data, biological environment data, historical carbon emission treatment data, estimated emissions of industrial process carbon emissions, and emission data for each unit area in the region to be evaluated; the unit areas are divided according to the geographical location of the region to be evaluated. The impact of meteorology on carbon emissions is assessed based on the meteorological data to obtain a carbon emission diffusion value. The degree of diffusion impact of carbon emissions on a unit area is assessed based on the meteorological data and the estimated emissions to obtain a diffusion impact value. The diffusion index is calculated based on the carbon emission diffusion value and the diffusion impact value. The retention index, which characterizes the impact of carbon emission accumulation within a unit area, is calculated based on the diffusion index, the estimated emissions, and the emission data. The carrying capacity index, which characterizes the comprehensive carbon emission carrying capacity of a unit area, is calculated based on the estimated emissions, the retention index, the emissions data, the historical carbon emission treatment data, and the biological environment data. The carbon emission optimization index for each unit region is obtained by normalizing the diffusion index, retention index and absorption index of each unit region. The carbon emission optimization index is used to evaluate the area to be evaluated, and the carbon emission assessment results are obtained.

[0006] Furthermore, the meteorological data includes: temperature inversion data, wind data, and topographic data; the temperature inversion data includes average temperature inversion frequency, average temperature inversion intensity, and average temperature inversion duration; the wind data includes wind speed and turbulence intensity; the topographic data includes average mixed layer height and topographic openness value. The impact of meteorology on carbon emissions is assessed based on the aforementioned meteorological data, resulting in carbon emission diffusion values, including: Linear regression calculations are performed based on the average inversion frequency, the average inversion intensity, and the average inversion duration to obtain the inversion diffusion value for each unit area. The wind diffusion value of each unit area is obtained by linear regression calculation based on the wind speed and the turbulence intensity. Linear regression calculations were performed based on the average mixing layer height and the terrain openness value to obtain the terrain diffusion value for each unit area. The carbon emission diffusion value is obtained by normalizing the inversion diffusion value, the wind diffusion value, and the terrain diffusion value.

[0007] Furthermore, the wind data also includes wind direction; Based on the meteorological data and the estimated emissions, the extent of carbon emission diffusion impact on a unit area is assessed to obtain a diffusion impact value, including: Based on the wind direction, wind speed and turbulence intensity in the wind data, the carbon emission process is simulated based on the preset atmospheric diffusion model to obtain the diffusion coverage area and diffusion path. Obtain pathway material information of the diffusion path; wherein, the pathway material information includes the types of reactants that react chemically with carbon emission substances and the amount of each reactant; Based on the types and amounts of reactants, obtain the synthetic substances generated after each reactant reacts with carbon emission substances, assess the degree of synthetic hazard of the synthetic substances and the current carbon hazard of the carbon emission substances, and calculate the hazard difference between the degree of synthetic hazard and the degree of carbon hazard. For each reactant type, the hazard value is calculated based on the hazard difference and the corresponding reactant amount, and the sum of the hazard values ​​of all reactant types is used as the path pollution value of the diffusion path. Obtain regional information of the diffusion coverage area; wherein, the regional information includes regional area, population density, and ecological sensitivity; The regional importance value of the diffusion coverage area is obtained by weighting the area, population density, and ecological sensitivity. The path pollution values ​​of all diffusion paths are summed and then normalized with the regional importance value to obtain the diffusion impact value.

[0008] Furthermore, the emission data includes emission height, emission temperature, and emission velocity; Based on the diffusion index, the estimated emissions, and the emission data, a retention index is calculated to characterize the impact of carbon emission accumulation within a unit area, including: The emission impact index of industrial process carbon emissions is obtained by linear regression calculation based on the emission height, emission temperature and emission rate. The emission impact index and the diffusion index are normalized, and the difference between the normalized emission impact index and the normalized diffusion index is used as the emission retention index. The retention index, which characterizes the impact of carbon emission accumulation within a unit area, is calculated by multiplying the emission retention index and the estimated emissions.

[0009] Furthermore, the biological environmental data includes: biological data and soil data; the biological data includes biological species, food chain diagram, carbon uptake rate, carbon storage capacity, carbon turnover time, and biological tolerance for each biological species; the soil data includes soil organic carbon content, soil organic carbon density, and the proportion of carbon pool components; the emission data also includes raw carbon emissions. Based on the estimated emissions, the retention index, the emission data, the historical carbon emission treatment data, and the biological environment data, a carrying capacity index is calculated to characterize the comprehensive carbon emission carrying capacity of a unit area, including: Based on biological species, linear regression calculations were performed according to carbon absorption rate, carbon storage capacity and carbon turnover time to obtain the biological carbon sequestration value per unit area. The similarity between soil organic carbon content, soil organic carbon density, and carbon pool component ratio and the preset carbon sequestration soil threshold is calculated to obtain the carbon sequestration similarity. Linear regression calculations were performed based on the biological carbon sequestration value, the carbon sequestration similarity, and the original carbon emissions to obtain the carbon sequestration capacity value of each unit area for carbon emissions. Based on the estimated emissions, the retention index, and the biological tolerance, the predicted changes in the number of each biological species are predicted to obtain the predicted number of biological changes. The biological impact value calculated from the aforementioned food chain diagram and the predicted amount of biological change is used as the ecological impact value of carbon emissions on a unit area. The historical adjustment value is obtained by weighting the historical processing frequency and the historical carbon emission ratio in the historical carbon emission processing data. Linear regression analysis was performed based on the historical adjustment values ​​and the historical carbon emission utilization rate in the historical carbon emission treatment data to obtain the adjustment capacity value per unit area. The carbon sequestration capacity value, the ecological impact value, and the regulation capacity value are weighted and calculated to obtain the carrying capacity index, which characterizes the comprehensive carbon emission carrying capacity of each unit area.

[0010] Furthermore, the biological impact value calculated from the aforementioned food chain diagram and the predicted biological change is used as the ecological impact value of carbon emissions on a unit area, including: For each biological species within each unit area, determine the number of connecting chains corresponding to it in the biological chain diagram; The single-category impact value for each biological species is calculated by multiplying the absolute value of the predicted number of biological changes with the number of connecting chains. The individual impact values ​​of all biological species within each unit area are summed to obtain the biological impact value, which is then used as the ecological impact value of carbon emissions on the unit area.

[0011] Furthermore, based on the diffusion index, retention index, and absorption index of each unit region, a normalized calculation is performed to obtain the carbon emission optimization index for each unit region, including: The diffusion index, the retention index, and the acceptance index are subjected to extreme value normalization. Based on the preset weights for diffusion, retention, and acceptance dimensions, the diffusion index, retention index, and acceptance index after extreme value normalization are weighted and summed to obtain the carbon emission optimization index for each unit region. The retention index is a reverse indicator, and the preset retention dimension weight is negative.

[0012] Furthermore, the carbon emission optimization index is used to assess the area to be evaluated, resulting in a carbon emission assessment result, including: The carbon emission optimization index of each unit area is ranked, and the unit area with the largest carbon emission optimization index is taken as the optimal matching area for industrial process carbon emissions. Carbon emission assessment reports are generated based on the diffusion index, retention index, absorption index, carbon emission optimization index, and optimal suitability area for each unit area, and these reports serve as the final carbon emission assessment results.

[0013] As an improvement to the above solution, another embodiment of the present invention provides a big data-based industrial process carbon emission assessment device, comprising: The regional data acquisition module is used to acquire meteorological data, biological environment data, historical carbon emission treatment data, estimated emissions of industrial process carbon emissions, and emission data for each unit area in the region to be evaluated; the unit areas are divided according to the geographical location of the region to be evaluated. The diffusion index calculation module is used to assess the impact of meteorology on carbon emissions based on the meteorological data to obtain a carbon emission diffusion value, assess the degree of diffusion impact of carbon emissions on a unit area based on the meteorological data and the estimated emissions to obtain a diffusion impact value, and calculate a diffusion index based on the carbon emission diffusion value and the diffusion impact value. The retention index calculation module is used to calculate, based on the diffusion index, the estimated emissions and the emissions data, a retention index that characterizes the impact of carbon emission accumulation within a unit area. The carrying capacity index calculation module is used to calculate, based on the estimated emissions, the retention index, the emissions data, the historical carbon emissions treatment data, and the biological environment data, to obtain a carrying capacity index that characterizes the comprehensive carbon emission carrying capacity of a unit area. The carbon emission optimization index calculation module is used to perform normalization calculations based on the diffusion index, retention index and acceptance index of each unit area to obtain the carbon emission optimization index of each unit area. The carbon emission assessment module is used to assess the area to be assessed based on the carbon emission optimization index and obtain the carbon emission assessment results.

[0014] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a big data-based industrial process carbon emission assessment method as described in the above embodiments.

[0015] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the big data-based industrial process carbon emission assessment method described in the above embodiment.

[0016] By implementing this invention, at least the following beneficial effects are achieved: This invention provides a method, apparatus, and storage medium for assessing carbon emissions from industrial processes based on big data. The method divides the area to be assessed into multiple unit regions based on their geographical location, fully incorporating geographical location—a key spatial attribute—into the core considerations of the entire carbon emission assessment process. Subsequently, it calculates carbon emission diffusion values ​​and diffusion impact values ​​based on meteorological data from each unit region, ultimately obtaining a diffusion index characterizing the atmospheric diffusion capacity of carbon emissions in the region. Then, it combines the diffusion index, estimated industrial process carbon emissions, and emission data to calculate a retention index characterizing the carbon emission accumulation impact within each unit region. Finally, it integrates estimated emissions, retention index, emission data, biological environmental data of each unit region, and historical carbon emission treatment data. According to the calculation, a carrying capacity index representing the comprehensive carbon emission carrying capacity of a unit area was obtained, realizing the deep coupling of geographical location spatial attributes with the whole chain assessment of carbon emission diffusion, retention, and carrying capacity. It fully considers the differentiated characteristics of the treatment effects of carbon emission diffusion reduction, accumulation control, and absorption and carrying capacity of different geographical locations. Finally, the carbon emission optimization index was obtained by normalizing the diffusion index, retention index, and carrying capacity index of each unit area, and the final assessment output was completed based on the optimization index. It accurately quantifies and reflects the differences in adaptability of different geographical regions to industrial process carbon emissions, realizing the spatial and refined accurate assessment of industrial process carbon emissions, and fully adapting to the core needs of industrial low-carbon transformation for refined carbon emission assessment. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of an industrial process carbon emission assessment method based on big data, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an industrial process carbon emission assessment device based on big data, provided in an embodiment of the present invention. Detailed Implementation

[0018] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 To address the problem that existing technologies cannot reflect the differences in carbon emission adaptability across different regions and thus cannot meet the demand for accurate carbon emission assessment during industrial low-carbon transformation, an embodiment of the present invention provides a flowchart illustrating a big data-based industrial process carbon emission assessment method, including: S1. Obtain meteorological data, biological environment data, historical carbon emission treatment data, estimated emissions of industrial process carbon emissions, and emission data for each unit area in the region to be evaluated; where the unit area is divided according to the geographical location of the region to be evaluated. Specifically, the area to be assessed represents the overall geographical scope of the proposed site selection and layout for industrial process carbon emissions, serving as the overall boundary for carbon emission assessment. This boundary can be defined based on industrial project planning, administrative boundaries, and watershed boundaries. A unit area represents the smallest assessment unit obtained by dividing the area to be assessed according to preset division rules based on its geographical location. This unit is the basic unit for analyzing the spatial attributes of carbon emissions. Division rules can employ methods such as geographical grid division, administrative village-level boundary division, or natural geographical unit division to ensure that each unit area possesses independent geographical, meteorological, and biological environmental attributes. Meteorological data comprises measured or historical statistical meteorological parameters related to atmospheric motion and diffusion conditions within the unit area, serving as the foundational data for assessing the atmospheric diffusion capacity of carbon emissions. Biological environmental data comprises biological and soil parameters related to the carbon sequestration capacity and ecological balance characteristics of the ecosystem within the unit area, forming the core foundational data for assessing the area's capacity to absorb carbon emissions. Historical carbon emission treatment data comprises statistical data related to the treatment, absorption, and resource utilization of carbon emissions within the unit area over a historical period, reflecting the region's capacity for artificial regulation and treatment of carbon emissions. The estimated carbon emissions from industrial processes are the total carbon emissions calculated in advance based on production processes, capacity planning, and energy consumption plans. This includes the total emissions of carbon dioxide and other carbon-containing greenhouse gases and pollutants associated with industrial production, and serves as the fundamental source strength data for assessment. Emission data consists of the process parameters of the emission sources of industrial carbon emissions, which are core emission characteristic parameters affecting the initial diffusion and uplift effects of carbon emissions.

[0020] To illustrate, the geographical boundaries of the area to be assessed are first delineated. Based on a Geographic Information System (GIS), the area is divided into several continuous unit areas according to a preset grid size or administrative and natural geographical boundaries, ensuring that each unit area has independent spatial attributes. For each unit area, meteorological data is collected through the National Meteorological Science Data Center, biological environmental data is collected through the ecological and environmental departments, and historical carbon emission treatment data is collected through the local ecological and environmental bureaus. Based on the feasibility study report, production process design documents, and energy consumption accounting report of the industrial project to be assessed, the estimated carbon emissions of the industrial process are calculated, and emission data is extracted from the project's exhaust gas emission system design documents.

[0021] To illustrate, in addition to grid division based on geographical location, the division of unit areas can also adopt existing administrative map boundaries, or users can set their own division criteria and grid size according to the accuracy requirements of the assessment.

[0022] S2. Assess the impact of meteorology on carbon emissions based on the meteorological data to obtain a carbon emission diffusion value. Assess the degree of diffusion impact of carbon emissions on a unit area based on the meteorological data and the estimated emissions to obtain a diffusion impact value. Calculate the diffusion index based on the carbon emission diffusion value and the diffusion impact value. Specifically, the carbon emission diffusion value is a quantitative indicator based on meteorological data of a unit area, quantifying the local diffusion and mitigation capacity of the regional atmospheric environment for carbon emissions. A higher value indicates a stronger local atmospheric diffusion capacity for carbon emissions, making it less likely for carbon emissions to accumulate locally. The diffusion impact value is a quantitative indicator based on meteorological data and estimated emissions, quantifying the comprehensive impact of carbon emissions from a unit area on surrounding areas. A higher value indicates a greater negative impact on surrounding areas after carbon emissions diffuse. The diffusion index, which integrates the carbon emission diffusion value and diffusion impact value, is a core quantitative indicator comprehensively characterizing the entire diffusion effect of carbon emissions within a unit area. It is a core parameter for assessing the diffusion adaptability of a region's carbon emissions; a higher value indicates a stronger local diffusion capacity for carbon emissions in the region, a smaller negative impact on surrounding areas after diffusion, and better diffusion adaptability.

[0023] Indicatively, based on meteorological data such as temperature inversion, wind field, and topography of a unit area, a pre-set quantitative model is used to assess the local diffusion capacity of the regional atmospheric environment for carbon emissions and calculate the carbon emission diffusion value. Then, combining wind direction and wind speed parameters from the meteorological data, with the estimated emissions as the source strength, an atmospheric diffusion model is used to simulate the diffusion trajectory and coverage of carbon emissions, assess the comprehensive impact of carbon emission diffusion on the surrounding area, and calculate the diffusion impact value. Next, the carbon emission diffusion value and the diffusion impact value are normalized to 0-1 to eliminate dimensional differences, and then weighted and summed according to pre-set weights to obtain the diffusion index of the unit area.

[0024] Preferably, the meteorological data includes: temperature inversion data, wind data, and topographic data; the temperature inversion data includes average temperature inversion frequency, average temperature inversion intensity, and average temperature inversion duration; the wind data includes wind speed and turbulence intensity; the topographic data includes average mixed layer height and topographic openness value. The impact of meteorology on carbon emissions is assessed based on the aforementioned meteorological data, resulting in carbon emission diffusion values, including: Linear regression calculations are performed based on the average inversion frequency, the average inversion intensity, and the average inversion duration to obtain the inversion diffusion value for each unit area. The wind diffusion value of each unit area is obtained by linear regression calculation based on the wind speed and the turbulence intensity. Linear regression calculations were performed based on the average mixing layer height and the terrain openness value to obtain the terrain diffusion value for each unit area. The carbon emission diffusion value is obtained by normalizing the inversion diffusion value, the wind diffusion value, and the terrain diffusion value.

[0025] Specifically, inversion data refers to historical statistical parameters of atmospheric inversion phenomena within a unit area. Inversion refers to an abnormal atmospheric stratification where temperature increases with altitude within a certain range, inhibiting vertical convection and significantly weakening the vertical diffusion capacity of carbon emissions. It is a core meteorological parameter affecting local carbon emission diffusion. Average inversion frequency is the proportion of days / periods with inversion phenomena within a unit area to the total number of days / periods in the statistical period, reflecting the frequency of inversion occurrences. Average inversion intensity is the temperature increase per unit height within the inversion layer within a unit area within the statistical period, reflecting the strength of the inversion's inhibition of atmospheric convection; the greater the intensity, the weaker the vertical diffusion capacity of carbon emissions. Average inversion duration is the average duration (in hours) from formation to dissipation of each inversion phenomenon within a unit area within the statistical period, reflecting the duration of the inversion's lasting impact.

[0026] Specifically, the higher the wind speed, the stronger the horizontal diffusion capacity of carbon emissions. Turbulence intensity is the ratio of the standard deviation of wind speed fluctuations to the average wind speed, reflecting the strength of atmospheric turbulence. The stronger the turbulence intensity, the stronger the atmosphere's ability to dilute and diffuse carbon emissions.

[0027] Specifically, the average mixing layer height is the average height of the atmospheric mixing layer in a unit area within the statistical period. The mixing layer refers to the air layer above the Earth's surface where air undergoes strong vertical mixing. The higher the layer, the greater the vertical space available for diluting carbon emissions, and the stronger the carbon emission diffusion capacity. The terrain openness value is a dimensionless index that quantitatively characterizes the terrain openness of a unit area. The more open the terrain, the more sufficient the horizontal diffusion space for carbon emissions, and the smaller the diffusion obstacles. It can be assessed through regional terrain undulation, the proportion of obstacles, and valley terrain features.

[0028] Specifically, the inversion diffusion value is a quantitative indicator based on inversion data, assessing the impact of inversion phenomena on carbon emission diffusion capacity. A higher value indicates a weaker inhibitory effect of inversion on carbon emission diffusion and a stronger carbon emission diffusion capacity under inversion conditions. The wind diffusion value is a quantitative indicator based on wind data, assessing the impact of wind fields on carbon emission diffusion capacity. A higher value indicates a stronger ability of wind fields to transport and dilute carbon emissions, resulting in better horizontal diffusion of carbon emissions. The topographic diffusion value is a quantitative indicator based on topographic data, assessing the impact of topographic conditions on carbon emission diffusion capacity. A higher value indicates a smaller obstacle to carbon emission diffusion by topographic conditions and a stronger diffusion capacity adapted to the terrain.

[0029] Schematic, using average inversion frequency, average inversion intensity, and average inversion duration as independent variables, and inversion diffusion value as the dependent variable, a linear regression equation is constructed using historical meteorological data and diffusion experiment data: Inversion diffusion value = a1 + b1 × average inversion frequency + c1 × average inversion intensity + d1 × average inversion duration + e1, where a1 is the intercept term, b1, c1, and d1 are the regression coefficients of the corresponding independent variables, and b1, c1, and d1 are all negative, indicating that the higher the inversion frequency, the greater the intensity, and the longer the duration, the smaller the inversion diffusion value. e1 is the random error term. Substituting the inversion data of a unit area into the above equation, the inversion diffusion value of that unit area is calculated.

[0030] Schematic, using wind speed and turbulence intensity as independent variables and wind diffusion value as the dependent variable, a linear regression equation is constructed: Wind diffusion value = a² + b² × wind speed + c² × turbulence intensity + e², where a² is the intercept term, b² and c² are the regression coefficients of the corresponding independent variables, and both b² and c² are positive values, indicating that the greater the wind speed and the greater the turbulence intensity, the greater the wind diffusion value. e² is the random error term. Substituting the wind data of a unit area into the above equation, the wind diffusion value of that unit area is calculated.

[0031] Schematic, using the average mixing layer height and terrain openness as independent variables and terrain diffusion as the dependent variable, a linear regression equation is constructed: Terrain diffusion value = a3 + b3 × average mixing layer height + c3 × terrain openness value + e3, where a3 is the intercept term, b3 and c3 are the regression coefficients of the corresponding independent variables, and b3 and c3 are both positive values, representing that the higher the mixing layer height and the more open the terrain, the greater the terrain diffusion value, and e3 is the random error term. Substituting the terrain data of a unit area into the above equation, the terrain diffusion value of that unit area is calculated.

[0032] Indicatively, the inversion diffusion value, wind diffusion value, and topographic diffusion value are respectively subjected to 0-1 standardization extreme value normalization to eliminate dimensional differences. Then, according to the preset weighting coefficients: the weight of the inversion dimension is 0.4, the weight of the wind field dimension is 0.4, and the weight of the topographic dimension is 0.2, the carbon emission diffusion value of the unit area is obtained by weighted summation.

[0033] In a preferred embodiment of the present invention, a large amount of field tracer test data is collected, harmless tracer gas is released at a specific point, and the concentration distribution is measured downwind. The decay characteristics of concentration with downwind distance and diffusion effect indicators such as plume width are used as dependent variables, and corresponding meteorological parameters are used as independent variables. After verifying the linear trend by plotting a scatter plot, the regression coefficients are obtained through least squares fitting, thus completing the construction of the linear regression equation. The vertical diffusion parameter σ in the classic Gaussian diffusion model can be directly used. z As a terrain diffusion value, σ zIt describes the vertical diffusion range of pollutants, the size of which is directly affected by the average mixing layer height and the degree of terrain openness, and can intuitively reflect the diffusion capacity under terrain conditions.

[0034] By implementing this embodiment, a multi-dimensional quantitative system for carbon emission diffusion capacity was constructed, incorporating all three core influencing factors—temperature inversion, wind field, and topography—into the assessment. This comprehensively covers the meteorological and geographical conditions affecting atmospheric carbon emission diffusion, and the assessment results are more consistent with the actual diffusion capacity of the region. A linear regression model was used to quantify the impact of each factor separately. The calculation method is simple, highly reproducible, and can directly calculate the diffusion value. At the same time, the regression coefficients can be flexibly adjusted according to regional characteristics to adapt to the assessment needs of different geographical scenarios, further enhancing the accuracy of this method in assessing the spatial diffusion effect of carbon emissions.

[0035] Preferably, the wind data also includes wind direction; Based on the meteorological data and the estimated emissions, the extent of carbon emission diffusion impact on a unit area is assessed to obtain a diffusion impact value, including: Based on the wind direction, wind speed and turbulence intensity in the wind data, the carbon emission process is simulated based on the preset atmospheric diffusion model to obtain the diffusion coverage area and diffusion path. Obtain pathway material information of the diffusion path; wherein, the pathway material information includes the types of reactants that react chemically with carbon emission substances and the amount of each reactant; Based on the types and amounts of reactants, obtain the synthetic substances generated after each reactant reacts with carbon emission substances, assess the degree of synthetic hazard of the synthetic substances and the current carbon hazard of the carbon emission substances, and calculate the hazard difference between the degree of synthetic hazard and the degree of carbon hazard. For each reactant type, the hazard value is calculated based on the hazard difference and the corresponding reactant amount, and the sum of the hazard values ​​of all reactant types is used as the path pollution value of the diffusion path. Obtain regional information of the diffusion coverage area; wherein, the regional information includes regional area, population density, and ecological sensitivity; The regional importance value of the diffusion coverage area is obtained by weighting the area, population density, and ecological sensitivity. The path pollution values ​​of all diffusion paths are summed and then normalized with the regional importance value to obtain the diffusion impact value.

[0036] Specifically, the preset atmospheric diffusion model is a mathematical model used to simulate the diffusion, transport, and dilution of pollutants in the atmosphere. This embodiment preferentially adopts the classic Gaussian plume diffusion model, which is suitable for simulating the diffusion of industrial carbon emissions from point sources. The diffusion coverage area is the geographical space that carbon emissions can reach after being emitted from a unit area under the action of atmospheric diffusion; it is the spatial boundary of the impact of carbon emission diffusion. The diffusion path is the main transport trajectory of carbon emission substances under the action of wind fields; it is the core path through which carbon emissions react with atmospheric substances along the way and produce secondary effects.

[0037] Specifically, pathway material information refers to information about substances in the atmospheric environment along the diffusion path that can chemically react with carbon emissions, including the types and amounts of reactants. Synthetic hazard level refers to the degree of harm to the ecological environment and human health caused by the synthetic substances formed after the reaction of carbon emissions with reactants in the pathway, which can be quantitatively scored from 0 to 1 based on national environmental hazard classification standards and toxicological parameters. Carbon hazard level refers to the degree of harm to the ecological environment and human health caused by the carbon emissions themselves, which is quantitatively scored from 0 to 1 based on greenhouse potential and toxicity parameters. Hazard difference is the difference between synthetic hazard level and carbon hazard level; a positive difference indicates an increase in hazard after the reaction, and a negative difference indicates a decrease in hazard after the reaction. Pathway pollution value is an indicator that quantitatively characterizes the comprehensive degree of secondary hazards caused by chemical reactions during the transport of carbon emissions along the diffusion path; the higher the value, the more severe the secondary pollution hazard of the diffusion path.

[0038] Specifically, ecological sensitivity is a quantitative characterization of the response of ecosystems within the diffusion coverage area to carbon emissions and related pollutant disturbances, reflecting the ease with which ecological problems occur in the region. The higher the value, the more ecologically sensitive the region, and the higher the risk of being affected by carbon emissions. Regional importance is an indicator that quantitatively characterizes the social and ecological importance of the diffusion coverage area. The higher the value, the more severe the social and ecological consequences of carbon emissions on the region.

[0039] Schematic, using a unit area as the carbon emission point source, and combining wind direction, wind speed, turbulence intensity, and topographic diffusion values ​​to set the boundary conditions of the atmospheric diffusion model, the estimated emissions from industrial processes are used as the source strength input to the pre-set Gaussian plume diffusion model to simulate the spatiotemporal diffusion range, diffusion trajectory, and concentration distribution data of carbon emissions. The diffusion coverage area is delineated based on the spatiotemporal diffusion range, and the corresponding primary and secondary diffusion paths are extracted based on the diffusion trajectory. Furthermore, during the simulation, the vertical diffusion parameters of the model need to be set in conjunction with the inversion data and mixing layer height of the unit area, and the topographic correction coefficient of the model needs to be set in conjunction with the terrain openness value to ensure that the simulation results closely match the actual diffusion characteristics of the region.

[0040] Schematic, for each diffusion path, the types and amounts of reactants that can react with carbon emission substances in the path are obtained through atmospheric environmental monitoring data; based on chemical reaction equations, the synthesized substances after each reactant reacts with carbon emission substances are determined, and the degree of synthesis hazard of the synthesized substances and the degree of carbon hazard of the carbon emission substances are assessed respectively, and the hazard difference between the two is calculated; then, according to the formula: Hazard value of a single type of reactant = Hazard difference × Reactant amount, the hazard value of a single type of reactant is calculated, and the hazard values ​​of all reactant types are summed to obtain the path pollution value of a single diffusion path; if there are multiple diffusion paths, the path pollution values ​​of all paths are summed to obtain the total path pollution value.

[0041] Schematic, the area, population density, and ecological sensitivity of the diffusion coverage area are normalized to 0-1, and then weighted and summed according to preset weights: area weight 0.2, population density weight 0.4, and ecological sensitivity weight 0.4, to obtain the regional importance value of the diffusion coverage area. Then, the total path pollution value and the regional importance value are normalized to 0-1, and then weighted and summed according to preset weights: path pollution value weight 0.5 and regional importance value weight 0.5, to obtain the diffusion impact value of the unit area.

[0042] In a preferred embodiment of the present invention, the assessment of the degree of hazard of synthesis is taken as an example. First, carbon dioxide reacts with calcium magnesium silicate in basalt to produce calcium carbonate and magnesium carbonate. The synthesized substances are stable and have extremely low ecotoxicity, with a synthesis hazard level of 0.05, which is far lower than the carbon hazard level of carbon dioxide (0.3), resulting in a hazard difference of -0.25. Nitrogen oxides and sulfur oxides from industrial carbon emissions react in the atmosphere to produce ammonium nitrate and ammonium sulfate, which are core components of PM2.5. The synthesis hazard level is 0.8, which is far higher than the carbon hazard level of the original substances (0.3), resulting in a hazard difference of 0.5.

[0043] In a preferred embodiment of this invention, the diffusion impact value and the carbon emission diffusion value are normalized and then summed to obtain the diffusion index. When carbon is emitted, the stronger the diffusion capacity of a region, the smaller the impact on the local population. However, carbon dioxide does not disappear after diffusion; it simply shifts to other areas. If the affected area is more significantly impacted, then carbon emission in that location is not optimized. For example, carbon emission in location A, due to its strong diffusion capacity, will not cause significant impact on location A. However, if carbon emission spreads from location A to location B, which has a higher population density and more residents, the safety risks associated with carbon emission diffusion will increase. While the diffusion capacity of location C is not as strong as that of location A, its diffusion area is in the suburbs, and it will not significantly affect human life and the ecosystem. Therefore, emission in location C is clearly more optimal. Thus, when emitting carbon, it is necessary to consider not only the local emission location but also the impact of diffusion to other areas. This will result in a more comprehensive intelligent analysis of industrial process carbon emissions.

[0044] By implementing this embodiment, a comprehensive quantitative system for the diffusion impact of carbon emissions was constructed. This system not only considers the spatial range of carbon emission diffusion but also covers the secondary pollution hazards during the diffusion process, providing a more comprehensive assessment. The introduction of hazard difference calculations can distinguish between different scenarios where the degree of hazard increases or decreases during the diffusion process, and is particularly suitable for assessing benign reactions such as mineralization and carbon sequestration, making the calculation of diffusion impact values ​​more consistent with actual environmental effects. By combining population density and ecological sensitivity to assess the importance of the region, the dual impact of carbon emission diffusion on people's livelihoods and the environment is fully considered, making the assessment results more in line with the actual needs of environmental impact assessments for industrial projects and improving the practicality of the method.

[0045] S3. Calculate the retention index based on the diffusion index, the estimated emissions, and the emissions data to characterize the impact of carbon emission accumulation within a unit area. Specifically, the retention index is an indicator that quantifies the degree of carbon emission accumulation and its cumulative effect within a unit area. The higher the value, the easier it is for carbon emissions to accumulate in that unit area, and the more serious the local cumulative impact. It is a reverse assessment indicator.

[0046] Indicatively, based on emission height, temperature, and velocity in emission data, the impact of the emission source's own characteristics on carbon emission retention is assessed to obtain the emission impact index; then, by combining the emission impact index and the diffusion index, the basic retention effect of carbon emissions is calculated to obtain the emission retention index; finally, by combining the estimated emissions from industrial processes, the retention index, which characterizes the degree of carbon emission accumulation in a unit area, is finally calculated.

[0047] Preferably, the emission data includes emission height, emission temperature, and emission velocity; Based on the diffusion index, the estimated emissions, and the emission data, a retention index is calculated to characterize the impact of carbon emission accumulation within a unit area, including: The emission impact index of industrial process carbon emissions is obtained by linear regression calculation based on the emission height, emission temperature and emission rate. The emission impact index and the diffusion index are normalized, and the difference between the normalized emission impact index and the normalized diffusion index is used as the emission retention index. The retention index, which characterizes the impact of carbon emission accumulation within a unit area, is calculated by multiplying the emission retention index and the estimated emissions.

[0048] Specifically, emission height refers to the vertical height of the industrial carbon emission outlet (chimney) above the ground. It is a core parameter determining the initial lift height of carbon emissions and the near-ground concentration. The higher the emission height, the less likely carbon emissions are to accumulate near the ground. Emission temperature is the temperature of the industrial carbon emission flue gas when it exits the outlet. The higher the emission temperature, the stronger the buoyancy lift effect of the flue gas, the higher the effective source height of carbon emissions, and the stronger the diffusion capacity. Emission velocity is the initial flow velocity of the industrial carbon emission flue gas when it exits the outlet. The faster the emission velocity, the stronger the momentum lift effect of the flue gas, the higher the effective source height of carbon emissions, and the stronger the diffusion capacity.

[0049] Specifically, the emission impact index is an indicator that quantifies the degree to which emission characteristics themselves affect the retention and accumulation of carbon emissions. The higher the value, the more unfavorable the emission characteristics are to carbon emission diffusion, and the more likely they are to cause local retention. The emission retention index is an indicator that quantifies the basic retention effect of carbon emissions per unit area by integrating emission characteristics and regional diffusion capacity. The higher the value, the more likely carbon emissions are to remain and accumulate locally.

[0050] Schematic, a linear regression equation is constructed using emission height, emission temperature, and emission velocity as independent variables, and the emission impact index as the dependent variable: Emission Impact Index = a4 + b4 × Emission Height + c4 × Emission Temperature + d4 × Emission Velocity + e4, where a4 is the intercept term; b4 is negative, indicating that the higher the emission height, the smaller the emission impact index; c4 and d4 are positive, indicating that the higher the emission temperature and velocity, the stronger the emission lift effect and the larger the emission impact index; and e4 is the random error term. Substituting emission data into the equation, the emission impact index is calculated. The retention index is inversely proportional to the square of the effective source height. Doubling the chimney height can reduce the maximum ground-level carbon concentration to about 1 / 4. Therefore, the absolute value of the regression coefficient for emission height is usually much higher than that for temperature and velocity. Emission temperature and velocity, through buoyancy and momentum lifting effects, can cause flue gas to continue rising for hundreds of meters after leaving the emission outlet, significantly increasing the effective source height of carbon emissions. Therefore, high temperature and high velocity emissions will increase the initial lifting height, but will also increase the emission impact index. Ultimately, the combined effect is reflected through the difference between the emission temperature and velocity and the diffusion index.

[0051] Schematic, the emission impact index and diffusion index are both normalized to their extreme values ​​using a 0-1 standardization process. The emission retention index is calculated using the formula: Emission Retention Index = Normalized Emission Impact Index - Normalized Diffusion Index. A higher diffusion index and a lower emission retention index indicate stronger regional diffusion capacity and weaker carbon emission retention effect. Finally, the retention index is calculated using the formula: Retention Index = Emission Retention Index × Estimated Emissions. A higher estimated emission volume indicates higher total carbon emissions and a more significant local accumulation effect, resulting in a higher retention index. When the retention index is negative, it means that the regional diffusion capacity is far stronger than the retention effect caused by emission characteristics, and carbon emissions will hardly accumulate locally. When the index is positive, a higher value indicates a more significant local accumulation effect and a higher probability of local carbon dioxide concentration exceeding standards and suffocation risk.

[0052] By implementing this embodiment, the final retention index is calculated by combining the estimated emissions, which fully considers the impact of total emissions on the cumulative effect. This allows the assessment results to be adapted to industrial projects with different capacities and emission scales, making them more versatile. It provides accurate basic data for the subsequent calculation of the carrying-over index and the optimization index, and at the same time, it can provide an optimization basis for the process design of industrial project emission outlets, further enhancing the practical value of the method.

[0053] S4. Calculate the carrying capacity index based on the estimated emissions, the retention index, the emissions data, the historical carbon emissions treatment data, and the biological environment data to obtain the carrying capacity index for characterizing the comprehensive carbon emissions carrying capacity of a unit area. Specifically, the carrying capacity index is a core quantitative indicator that comprehensively characterizes a region's capacity to carry, absorb, regulate, and adapt to carbon emissions. The higher the value, the stronger the region's comprehensive capacity to carry carbon emissions.

[0054] Indicatively, based on biological environmental data, the carbon sequestration capacity of a unit area's natural ecosystem is assessed to obtain a carbon sequestration capacity value. Then, combined with estimated emissions and retention index, the impact of carbon emissions on the ecological balance of the unit area is assessed to obtain an ecological impact value. Based on historical carbon emission treatment data, the unit area's capacity for artificial treatment, regulation, and resource utilization of carbon emissions is assessed to obtain a regulation capacity value. Finally, the carbon sequestration capacity value, ecological impact value, and regulation capacity value are integrated to comprehensively calculate a carrying capacity index that characterizes the region's overall capacity to absorb carbon emissions.

[0055] Preferably, the biological environmental data includes: biological data and soil data; the biological data includes biological species, food chain diagram, carbon uptake rate, carbon storage capacity, carbon turnover time, and biological tolerance for each biological species; the soil data includes soil organic carbon content, soil organic carbon density, and carbon pool component ratio; the emission data also includes raw carbon emissions. Based on the estimated emissions, the retention index, the emission data, the historical carbon emission treatment data, and the biological environment data, a carrying capacity index is calculated to characterize the comprehensive carbon emission carrying capacity of a unit area, including: Based on biological species, linear regression calculations were performed according to carbon absorption rate, carbon storage capacity and carbon turnover time to obtain the biological carbon sequestration value per unit area. The similarity between soil organic carbon content, soil organic carbon density, and carbon pool component ratio and the preset carbon sequestration soil threshold is calculated to obtain the carbon sequestration similarity. Linear regression calculations were performed based on the biological carbon sequestration value, the carbon sequestration similarity, and the original carbon emissions to obtain the carbon sequestration capacity value of each unit area for carbon emissions. Based on the estimated emissions, the retention index, and the biological tolerance, the predicted changes in the number of each biological species are predicted to obtain the predicted number of biological changes. The biological impact value calculated from the aforementioned food chain diagram and the predicted amount of biological change is used as the ecological impact value of carbon emissions on a unit area. The historical adjustment value is obtained by weighting the historical processing frequency and the historical carbon emission ratio in the historical carbon emission processing data. Linear regression analysis was performed based on the historical adjustment values ​​and the historical carbon emission utilization rate in the historical carbon emission treatment data to obtain the adjustment capacity value per unit area. The carbon sequestration capacity value, the ecological impact value, and the regulation capacity value are weighted and calculated to obtain the carrying capacity index, which characterizes the comprehensive carbon emission carrying capacity of each unit area.

[0056] Specifically, biological data refers to biological characteristic parameters related to biological carbon sequestration and ecological balance within an ecosystem per unit area, and is the core data for assessing the carbon emission carrying capacity of natural ecosystems. Soil data refers to soil physicochemical parameters related to soil carbon sequestration capacity within a unit area. Soil is the largest carbon pool in terrestrial ecosystems, and its carbon sequestration capacity is the core foundation for regional carbon emission carrying capacity. Carbon absorption rate is the amount of carbon dioxide absorbed and fixed from the atmosphere by organisms through photosynthesis per unit area and per unit time, determining the speed of biological carbon sequestration. Monitoring the carbon absorption rate can be achieved through long-term monitoring of the net carbon exchange of a regional ecosystem using the eddy covariance method, obtaining accurate carbon absorption rate data. Carbon storage capacity is the total amount of carbon accumulated and stored in an ecosystem per unit area at a certain point in time, reflecting the size of the ecosystem's carbon pool. Carbon turnover time is the average residence time of carbon atoms after entering the ecosystem before being released back into the atmosphere, measuring the persistence of carbon locked in the ecosystem, measured in years. Carbon turnover time can be obtained through isotope tracing to determine the residence time of carbon atoms in the ecosystem, or through existing technology surveys to obtain the default carbon turnover time for different vegetation types. Biological tolerance refers to the ability of individual organisms or populations to survive, grow, and reproduce without significant harm when exposed to carbon emission-related substances, reflecting the organisms' adaptability to carbon emissions. A food chain diagram is a topological structure representing the predator-prey relationships among species within a unit of area, reflecting the trophic structure and species relationships of a regional ecosystem.

[0057] Specifically, soil organic carbon content, the percentage of organic carbon per unit mass of soil, is a core indicator for measuring soil carbon sequestration capacity. Soil organic carbon density, the total amount of organic carbon stored in a soil layer at a certain depth per unit area, comprehensively reflects the soil's carbon sequestration potential. The carbon pool component ratio refers to the proportion of active, slow-release, and inert organic carbon pools in the soil; the higher the proportion of inert carbon pools, the stronger the stability of soil carbon sequestration.

[0058] Specifically, raw carbon emissions refer to the original carbon emissions generated by the existing ecosystem and human activities within a unit area under natural conditions, reflecting the original utilization of the area's carbon sequestration capacity. The carbon sequestration soil threshold is a preset standard value for various indicators of the optimal carbon sequestration soil, which can be set with reference to the physicochemical parameters of high-quality carbon sequestration soils such as black soil and forest soil. Carbon sequestration similarity is the degree of similarity between the actual soil data of a unit area and the preset carbon sequestration soil threshold, calculated using a cosine similarity algorithm, with a value range of 0-1. The higher the value, the closer the soil's carbon sequestration capacity is to the optimal standard.

[0059] Specifically, biological carbon sequestration value is an indicator that quantifies the ability of vegetation and other organisms to absorb and fix carbon emissions based on the characteristics of regional biological communities. The higher the value, the stronger the biological carbon sequestration capacity. Carbon sequestration capacity value is a core indicator that comprehensively characterizes the ability of a unit area's natural ecosystem to sequester and absorb new carbon emissions.

[0060] Specifically, historical processing frequency refers to the frequency of carbon emission control and mitigation measures implemented within a unit area during the statistical period, reflecting the normalization of regional carbon emission control. Historical carbon emission ratio is the ratio of treated carbon emissions to total carbon emissions within a unit area during the statistical period, reflecting the actual effectiveness of carbon emission control. Historical adjustment value is an indicator that quantitatively characterizes a region's historical capacity for carbon emission control. Historical carbon emission utilization rate is the ratio of carbon emissions utilized for resource recovery within a unit area during the statistical period, reflecting the region's capacity for resource utilization of carbon emissions. Adjustment capacity value is an indicator that comprehensively characterizes a region's capacity for artificial adjustment, control, and utilization of carbon emissions.

[0061] Schematably, a linear regression equation is constructed with carbon uptake rate, carbon storage capacity, and carbon turnover time corresponding to biological species as independent variables, and biological carbon sequestration value as the dependent variable: Biological carbon sequestration value = a5 + b5 × carbon uptake rate + c5 × carbon storage capacity + d5 × carbon turnover time + e5, where a5 is the intercept term, b5, c5, and d5 are all positive values, and e5 is the random error term. The biological data per unit area are substituted into the equation to calculate the biological carbon sequestration value. Simultaneously, the biological carbon sequestration value can be calibrated by measuring the biomass increase of plants and algae per unit area, combined with a carbon content coefficient (usually 0.5) to calculate the actual carbon sequestration amount, thus calibrating the results of the linear regression equation.

[0062] Schematic, using a preset carbon sequestration soil threshold as the standard vector, and the actual soil organic carbon content, soil organic carbon density, and carbon pool component proportions per unit area as comparison vectors, the cosine similarity algorithm is used to calculate the similarity between the two, obtaining the carbon sequestration similarity within the range of 0-1. Using biological carbon sequestration value, carbon sequestration similarity, and original carbon emissions as independent variables, and carbon sequestration capacity as the dependent variable, a linear regression equation is constructed: Carbon sequestration capacity = a6 + b6 × biological carbon sequestration value + c6 × carbon sequestration similarity - d6 × original carbon emissions + e6, where a6 is the intercept term, b6 and c6 are positive values; d6 is positive, indicating that the higher the original carbon emissions, the greater the original carbon occupation, and the weaker the newly added carbon sequestration capacity; e6 is the random error term; substituting the data, the carbon sequestration capacity value is calculated.

[0063] Indicatively, by combining the estimated emissions and retention index, the actual exposure concentration of carbon emissions in a unit area is determined. By combining the biological tolerance of each species, the rate of change in the number of each species is predicted. Multiplying this rate by the base population size yields the predicted biological change. Then, based on the food chain diagram and the predicted biological change, the biological impact value is calculated and used as the ecological impact value of carbon emissions on a unit area.

[0064] To illustrate, after normalizing the historical treatment frequency and the historical carbon emission ratio, the historical adjustment value is obtained by weighting and summing them according to a 50% weight. Then, a linear regression equation is constructed with the historical adjustment value and the historical carbon emission utilization rate as independent variables and the adjustment capacity value as the dependent variable. The adjustment capacity value is calculated by substituting the data into the equation.

[0065] Finally, the carbon sequestration capacity value, ecological impact value, and regulation capacity value were normalized by 0-1 standardization. The ecological impact value was a reverse indicator and was reverse normalized. The carbon sequestration capacity value was weighted at 0.5, the ecological impact value at 0.3, and the regulation capacity value at 0.2, and then weighted and summed to obtain the carrying capacity index.

[0066] In a preferred embodiment of the present invention, different regions have different ecological environments and varying carbon sequestration capacities. For example, the northeastern Amazon rainforest, with its denser wood, has twice the carbon sequestration capacity of the southwest. Similarly, wetlands can enhance their regulatory capacity through vegetation photosynthesis and soil carbon accumulation. Furthermore, different regions have different biodiversity and varying adaptability to carbon emissions, leading to changes in biodiversity structure and impacting regional ecological balance, further exacerbating the negative effects of carbon emissions. In addition, different regions have different levels of economic development and attitudes towards carbon emissions. When a region's economic capacity is insufficient to handle carbon emissions, emitting carbon there undoubtedly increases its burden, resulting in extreme imbalances in carbon emissions. Regions with the capacity to handle more carbon emissions can minimize their negative effects. For example, if region A has no capacity to handle carbon emissions, its residents will have to endure the widespread impacts. Region B, on the other hand, has the capacity to handle carbon emissions, extracting and converting them for utilization. Therefore, region B is less directly affected by carbon emissions because some of the emissions have been processed. Secondly, it can generate other benefits from carbon emissions and is also conducive to the development of the area, so area B obviously has a stronger ability to regulate carbon emissions.

[0067] By implementing this embodiment, the two core dimensions of carbon sequestration—biological and soil—are covered, the boundaries of the assessment data are refined, and the assessment of natural carbon sequestration capacity has a complete and unified data standard. The inclusion of raw carbon emissions fully considers the impact of regional background carbon emissions on carbon sequestration capacity, avoiding overestimation of carbon sequestration capacity in areas with high background emissions, and making the calculation of carbon sequestration capacity values ​​more accurate. Combining biological tolerance and food chain diagrams to assess ecological impact values ​​not only considers the impact of carbon emissions on individual species but also covers the chain effects on the entire ecosystem's food chain, making the assessment of ecological impact more comprehensive and scientific. At the same time, the assessment results can provide precise data support for improving regional ecological carbon sequestration capacity and the layout of carbon emission control facilities.

[0068] Preferably, the biological impact value calculated from the predicted biological change quantity and the biological chain diagram are used as the ecological impact value of carbon emissions on a unit area, including: For each biological species within each unit area, determine the number of connecting chains corresponding to it in the biological chain diagram; The single-category impact value for each biological species is calculated by multiplying the absolute value of the predicted number of biological changes with the number of connecting chains. The individual impact values ​​of all biological species within each unit area are summed to obtain the biological impact value, which is then used as the ecological impact value of carbon emissions on the unit area.

[0069] Specifically, the number of links in a food chain diagram refers to the number of direct associations between a species and other species, including the total number of upstream and downstream predator-prey relationships. This reflects the species' criticality in the ecosystem; the more links, the greater the impact of species change on the entire ecosystem. Only directly related links are counted, not indirect links. If a species is a apex predator in the ecosystem and has no downstream predator-prey relationships, only the number of its upstream predator-prey relationships is counted. The single-species impact value is an indicator that quantifies the degree of impact of a single species' population change on the ecosystem; the higher the value, the more significant the ecological cascading effects of the species change.

[0070] Schematic, for each species in the food chain diagram of a unit area, the number of direct predation and predation associations with other species is counted, i.e., the number of links for that species; for example, a certain herbaceous plant is a food source for 3 herbivores, so its number of links is 3. The single-species impact value is calculated according to the formula: Single-species impact value = |Predicted biodiversity change| × Number of links; where a positive predicted biodiversity change represents an increase in species population, and a negative value represents a decrease in species population. The absolute value is taken to ensure that regardless of population increase or decrease, the degree of impact on the ecosystem can be quantified. Finally, the single-species impact values ​​of all species in the unit area are summed to obtain the total biodiversity impact value for that area, which is directly used as the ecological impact value of carbon emissions on the unit area. After calculating the biodiversity impact value, extreme value normalization is performed on the biodiversity impact values ​​of all unit areas before being substituted into the calculation of the carrying capacity index to ensure the comparability of ecological impact values ​​across different areas. If the predicted biodiversity change is 0, it means that the species population is not affected by carbon emissions, its single-species impact value is 0, and it is not included in the summation calculation.

[0071] In a preferred embodiment of the present invention, a biochain diagram of the unit area is constructed, comprising five core organisms: herbaceous plants, insects, hares, foxes, and eagles. The number of connecting chains for each organism is counted as 2, 2, 2, 2, and 1, respectively. Combining the estimated emissions, retention index, and biological tolerance, the predicted changes in the number of each organism are as follows: herbaceous plants - 800 plants, insects - 1200 individuals, hares - 150 individuals, foxes - 20 individuals, and eagles - 2 individuals. Then, the single-category impact value of each organism is calculated: herbaceous plants: 800 × 2 = 1600, insects: 1200 × 2 = 2400, hares: 150 × 2 = 300, foxes: 20 × 2 = 40, and eagles: 2 × 1 = 2. Finally, all single-category impact values ​​are summed to obtain the total biological impact value = 1600 + 2400 + 300 + 40 + 2 = 4342, which is taken as the ecological impact value of the unit area.

[0072] By implementing this embodiment, the ecological criticality of species is quantified by the number of food chain links, which fully considers the chain reaction of carbon emissions on the ecosystem, avoids underestimating the impact of changes in key species, and makes the assessment of ecological impact more in line with the actual operating rules of the ecosystem. The impact of species quantity changes is calculated using absolute values, which can accurately quantify the disturbance to the ecological balance whether the species quantity increases or decreases, covering all ecological change scenarios caused by carbon emissions.

[0073] S5. Normalize the carbon emission optimization index of each unit area based on the diffusion index, retention index and acceptance index of each unit area; Specifically, the carbon emission optimization index is a final quantitative indicator that comprehensively characterizes the overall adaptability of a unit area to industrial process carbon emissions. The higher the value, the more suitable the unit area is as a layout area for industrial process carbon emissions.

[0074] Indicatively, the extreme value normalization method is used to standardize the diffusion index, retention index, and absorption index of all unit areas by 0-1, with the retention index being the inverse indicator and undergoing inverse normalization. Then, corresponding weight coefficients are preset for the diffusion index, retention index, and absorption index, and the three normalized indices are weighted and summed to obtain the carbon emission optimization index for each unit area.

[0075] Preferably, the carbon emission optimization index for each unit area is obtained by normalizing the diffusion index, retention index, and absorption index, including: The diffusion index, the retention index, and the acceptance index are subjected to extreme value normalization. Based on the preset weights for diffusion, retention, and acceptance dimensions, the diffusion index, retention index, and acceptance index after extreme value normalization are weighted and summed to obtain the carbon emission optimization index for each unit region. The retention index is a reverse indicator, and the preset retention dimension weight is negative.

[0076] Specifically, the diffusion dimension weight is the proportion of the diffusion index in the calculation of the carbon emission optimization index, reflecting the importance of diffusion adaptability in the overall regional assessment. The retention dimension weight is the proportion of the retention index in the calculation of the carbon emission optimization index. Since the retention index is a negative indicator, its weight is negative, reflecting the negative contribution of the cumulative effect of carbon emissions in the overall assessment. The carrying capacity dimension weight is the proportion of the carrying capacity index in the calculation of the carbon emission optimization index, reflecting the importance of the region's comprehensive carrying capacity in the overall assessment.

[0077] In a preferred embodiment of the present invention, the diffusion index, retention index, and absorption index of all unit areas within the area to be evaluated are extracted, and the maximum and minimum values ​​of each index are determined. The diffusion index and absorption index (positive indicators) are processed according to the positive extreme value normalization formula; the higher the value, the closer the normalized value is to 1. The retention index (negative indicator) is processed according to the negative extreme value normalization formula; the higher the value, the closer the normalized value is to 0. Finally, the carbon emission optimization index for each unit area is calculated according to the formula: Carbon emission optimization index = Normalized diffusion index × Diffusion dimension weight + Normalized retention index × Retention dimension weight + Normalized absorption index × Absorption dimension weight; where the diffusion dimension weight and absorption dimension weight are positive values, the retention dimension weight is negative values, and the sum of all weights is 1.

[0078] Indicatively, the weights are set as follows: diffusion dimension weight 0.3, retention dimension weight -0.3, and carrying capacity dimension weight 0.4. If the evaluation project focuses on the impact of carbon emission diffusion, the diffusion dimension weight can be increased; if the focus is on the regional ecological carrying capacity, the carrying capacity dimension weight can be increased. If the value of a certain index is exactly the same in all units within the evaluation area, with the maximum and minimum values ​​equal, the normalized value of that index is uniformly set to 0.5 to avoid calculation errors with a denominator of 0. Since the sum of the weights is 1, the normalized value ranges from 0 to 1, and the final carbon emission optimization index ranges from -1 to 1. The higher the value, the better the regional suitability. For high-emission projects such as chemical and thermal power plants, the absolute value of the retention dimension weight can be increased to prioritize areas where carbon emissions are less likely to accumulate; for projects near ecologically sensitive areas, the carrying capacity dimension weight can be increased to prioritize areas with strong ecological carrying capacity.

[0079] By implementing this embodiment, extreme value normalization perfectly solves the problems of inconsistent dimensions and large differences in numerical ranges among the three core indices, making the weighted summation calculation logic reasonable. For the inverse indicator attribute of the retention index, negative weights are set to accurately reflect the negative impact of carbon emission agglomeration and accumulation effects on regional adaptability, ensuring that the calculation logic of the optimization index fully aligns with the physical meaning of the three indices. Adjustable weight settings allow the method to adapt to different types of industrial projects and scenarios with different assessment focuses, significantly improving its versatility and flexibility. The carbon emission optimization index, as the final comprehensive adaptability indicator, can be directly used for regional ranking and optimal region selection, providing an intuitive and accurate decision-making basis for industrial carbon emission layout.

[0080] S6. Evaluate the area to be evaluated based on the carbon emission optimization index to obtain the carbon emission assessment results.

[0081] Specifically, the carbon emission assessment results are the final conclusions output after the assessment of the region to be assessed is completed based on the carbon emission optimization index. These conclusions include the suitability ranking of each unit region, the optimal suitable region, details of assessment parameters for each dimension, and the assessment report.

[0082] To illustrate, all unit areas within the assessment area are sorted from high to low according to their carbon emission optimization index. The unit area with the highest carbon emission optimization index is selected as the optimal matching area for industrial process carbon emissions. The diffusion index, retention index, absorption index, carbon emission optimization index, ranking results, and suitability analysis of each unit area are compiled to generate a standardized carbon emission assessment report, which serves as the final output of the carbon emission assessment result.

[0083] To illustrate, in addition to global climate impacts, carbon dioxide emissions primarily have safety, ecological, and health effects on local areas. This embodiment uses spatial assessment to prioritize areas with the lowest local impacts, thereby reducing the immediate disruption of carbon emissions to residents' lives and the ecological environment.

[0084] Preferably, the carbon emission assessment result is obtained by assessing the area to be assessed based on the carbon emission optimization index, including: The carbon emission optimization index of each unit area is ranked, and the unit area with the largest carbon emission optimization index is taken as the optimal matching area for industrial process carbon emissions. Carbon emission assessment reports are generated based on the diffusion index, retention index, absorption index, carbon emission optimization index, and optimal suitability area for each unit area, and these reports serve as the final carbon emission assessment results.

[0085] Specifically, the optimal fit area is the unit area with the largest carbon emission optimization index within the area to be evaluated. It is the geographical area most suitable for the layout of industrial carbon emission sources after considering the overall diffusion capacity, retention effect, and carrying capacity.

[0086] In a preferred embodiment of the present invention, all unit areas within the assessment area are sorted in descending order of carbon emission optimization index. The unit area ranked first is the one with the highest carbon emission optimization index and is determined as the optimal suitable area for industrial process carbon emissions. Simultaneously, the top 5% of unit areas can be selected as candidate suitable areas. If two unit areas have the same carbon emission optimization index, the unit area with the higher index is prioritized as the optimal suitable area. In addition to the assessment report, a spatial distribution map of carbon emission adaptability for each unit area can be generated based on a GIS system, visually presenting the adaptability differences within the assessment area and providing visual support for project planning.

[0087] By implementing this embodiment, the generated assessment report meets industry standards and can be directly used as a supporting document for environmental impact assessment, carbon emission verification, and project approval for industrial projects, greatly improving the practicality and engineering application value of the method.

[0088] In a preferred embodiment of the present invention, a new production unit is planned to be built in the G Chemical Industrial Park. A carbon emission assessment needs to be conducted on the park and a surrounding 100km × 100km area. The specific implementation steps are as follows: First, the assessment area is defined as a 100km × 100km geographical area of ​​the park and its surroundings. Based on a GIS system, the assessment area is divided into 400 unit areas using a 5km × 5km grid. Then, for each unit area, daily routine meteorological data for the past three years is collected from the local meteorological station, along with biological environmental data and historical carbon emission treatment data. Based on the process design documents of the production unit, the estimated annual carbon emission of the unit is calculated to be 1.2 million tons of CO2 equivalent. Emission data for an emission height of 80m, an emission temperature of 120℃, and an emission velocity of 15m / s are extracted from the unit's exhaust gas emission system design documents. For each unit area, the carbon emission diffusion value is calculated based on the meteorological data, combined with the meteorological data... The diffusion impact value was calculated using the estimated emissions of 1.2 million tons. After normalization, the two values ​​were weighted and summed to obtain the diffusion index. Then, the retention index for each unit area was calculated by combining the diffusion index, estimated emissions, and emission data. The carrying capacity index for each unit area was calculated by combining the estimated emissions, retention index, emission data, historical carbon emission treatment data, and biological environment data. Next, the diffusion index, retention index, and carrying capacity index of all unit areas were normalized for extreme values. They were then weighted and summed with a weight of 0.3 for the diffusion dimension, 0.3 for the retention dimension, and 0.4 for the carrying capacity dimension to obtain the carbon emission optimization index for each unit area. Finally, the 400 unit areas were sorted from high to low according to the carbon emission optimization index. The unit area with the highest optimization index was selected as the optimal fit area. At the same time, a carbon emission assessment report containing assessment parameters, fit ranking, and site selection recommendations for each unit area was generated as the final assessment result.

[0089] By implementing this embodiment, multiple unit areas are divided according to the geographical location of the area to be assessed, fully incorporating the key spatial attribute of geographical location into the core considerations of the entire carbon emission assessment process. Subsequently, carbon emission diffusion values ​​and diffusion impact values ​​are calculated sequentially based on meteorological data from each unit area, ultimately obtaining a diffusion index characterizing the atmospheric diffusion capacity of carbon emissions in the region. Then, combining the diffusion index, estimated industrial carbon emissions, and emission data, a retention index characterizing the carbon emission accumulation impact within the unit area is calculated. Finally, by integrating estimated emissions, retention index, emission data, unit area biological environment data, and historical carbon emission treatment data, a value characterizing the unit area's impact on carbon emissions is calculated. The comprehensive absorption capacity index achieves deep coupling between geographical location spatial attributes and the entire chain assessment of carbon emission diffusion, retention, and absorption. It fully considers the differentiated characteristics of different geographical locations in terms of the effects of carbon emission diffusion reduction, accumulation control, and absorption. Finally, by normalizing the diffusion index, retention index, and absorption index of each unit area, a carbon emission optimization index is obtained, and the final assessment output is completed based on the optimization index. This accurately quantifies and reflects the differences in adaptability of different geographical regions to industrial process carbon emissions, realizing spatial and refined accurate assessment of industrial process carbon emissions, and fully meeting the core needs of industrial low-carbon transformation for refined carbon emission assessment.

[0090] See Figure 2 This is a schematic diagram of the structure of an industrial process carbon emission assessment device based on big data according to an embodiment of the present invention, comprising: The regional data acquisition module is used to acquire meteorological data, biological environment data, historical carbon emission treatment data, estimated emissions of industrial process carbon emissions, and emission data for each unit area in the region to be evaluated; the unit areas are divided according to the geographical location of the region to be evaluated. The diffusion index calculation module is used to assess the impact of meteorology on carbon emissions based on the meteorological data to obtain a carbon emission diffusion value, assess the degree of diffusion impact of carbon emissions on a unit area based on the meteorological data and the estimated emissions to obtain a diffusion impact value, and calculate a diffusion index based on the carbon emission diffusion value and the diffusion impact value. The retention index calculation module is used to calculate, based on the diffusion index, the estimated emissions and the emissions data, a retention index that characterizes the impact of carbon emission accumulation within a unit area. The carrying capacity index calculation module is used to calculate, based on the estimated emissions, the retention index, the emissions data, the historical carbon emissions treatment data, and the biological environment data, to obtain a carrying capacity index that characterizes the comprehensive carbon emission carrying capacity of a unit area. The carbon emission optimization index calculation module is used to perform normalization calculations based on the diffusion index, retention index and acceptance index of each unit area to obtain the carbon emission optimization index of each unit area. The carbon emission assessment module is used to assess the area to be assessed based on the carbon emission optimization index and obtain the carbon emission assessment results.

[0091] This invention provides a big data-based industrial process carbon emission assessment device. The device acquires meteorological data, biological environment data, historical carbon emission treatment data, estimated industrial process carbon emissions, and emission data for each unit area within the assessment region using a regional data acquisition module. The unit areas are defined based on the geographical location of the assessment region. In the diffusion index calculation module, the impact of meteorological conditions on carbon emissions is assessed based on the meteorological data to obtain a carbon emission diffusion value. The degree of diffusion impact of carbon emissions on the unit area is assessed based on the meteorological data and the estimated emissions to obtain a diffusion impact value. A diffusion index is calculated based on the carbon emission diffusion value and the diffusion impact value. In the retention index calculation module, the diffusion index is calculated based on the diffusion data... The system calculates a retention index, characterizing the impact of carbon emission accumulation within a unit area, based on the estimated emissions, the retention index, the emission data, historical carbon emission treatment data, and the biological environment data. In the carbon emission optimization index calculation module, a carrying capacity index, characterizing the comprehensive carbon emission carrying capacity of a unit area, is calculated based on the estimated emissions, the retention index, the emission data, historical carbon emission treatment data, and the biological environment data. In the carbon emission optimization index calculation module, a normalized calculation is performed based on the diffusion index, the retention index, and the carrying capacity index of each unit area to obtain the carbon emission optimization index for each unit area. Finally, in the carbon emission assessment module, the area to be assessed is evaluated based on the carbon emission optimization index to obtain the carbon emission assessment result.

[0092] By dividing the area to be assessed into multiple unit areas based on its geographical location, the key spatial attribute of geographical location is fully incorporated into the core considerations of the entire carbon emission assessment process. Subsequently, carbon emission diffusion values ​​and diffusion impact values ​​are calculated sequentially based on meteorological data from each unit area, ultimately yielding a diffusion index characterizing the atmospheric diffusion capacity of carbon emissions in the region. Then, combining the diffusion index, estimated industrial carbon emissions, and emission data, a retention index characterizing the carbon emission accumulation impact within the unit area is calculated. Finally, by integrating estimated emissions, retention index, emission data, biological environmental data of the unit area, and historical carbon emission treatment data, a comprehensive carbon emission carrying capacity index characterizing the unit area is calculated. The absorption capacity index achieves deep coupling between geographical location spatial attributes and the entire chain assessment of carbon emission diffusion, retention, and absorption. It fully considers the differentiated characteristics of different geographical locations in terms of the effects of carbon emission diffusion reduction, accumulation control, and absorption. Finally, by normalizing the diffusion index, retention index, and absorption index of each unit area, a carbon emission optimization index is obtained, and the final assessment output is completed based on the optimization index. This accurately quantifies and reflects the differences in adaptability of different geographical regions to industrial process carbon emissions, realizing spatial and refined accurate assessment of industrial process carbon emissions, and fully meeting the core needs of industrial low-carbon transformation for refined carbon emission assessment.

[0093] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0094] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a big data-based industrial process carbon emission assessment method as described in the above embodiments. The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0096] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0097] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device or other volatile solid-state storage device.

[0098] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the big data-based industrial process carbon emission assessment method described in the above embodiment.

[0099] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0100] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for assessing carbon emissions from industrial processes based on big data, characterized in that, include: Acquire meteorological data, biological environment data, historical carbon emission treatment data, estimated emissions of industrial process carbon emissions, and emission data for each unit area in the region to be evaluated; the unit areas are divided according to the geographical location of the region to be evaluated. The impact of meteorology on carbon emissions is assessed based on the meteorological data to obtain a carbon emission diffusion value. The degree of diffusion impact of carbon emissions on a unit area is assessed based on the meteorological data and the estimated emissions to obtain a diffusion impact value. The diffusion index is calculated based on the carbon emission diffusion value and the diffusion impact value. The retention index, which characterizes the impact of carbon emission accumulation within a unit area, is calculated based on the diffusion index, the estimated emissions, and the emission data. The carrying capacity index, which characterizes the comprehensive carbon emission carrying capacity of a unit area, is calculated based on the estimated emissions, the retention index, the emissions data, the historical carbon emission treatment data, and the biological environment data. The carbon emission optimization index for each unit region is obtained by normalizing the diffusion index, retention index and absorption index of each unit region. The carbon emission optimization index is used to evaluate the area to be evaluated, and the carbon emission assessment results are obtained.

2. The method for assessing carbon emissions from industrial processes based on big data as described in claim 1, characterized in that, The meteorological data includes: temperature inversion data, wind data, and topographic data; the temperature inversion data includes average temperature inversion frequency, average temperature inversion intensity, and average temperature inversion duration; the wind data includes wind speed and turbulence intensity; the topographic data includes average mixed layer height and topographic openness value. The impact of meteorology on carbon emissions is assessed based on the aforementioned meteorological data, resulting in carbon emission diffusion values, including: Linear regression calculations are performed based on the average inversion frequency, the average inversion intensity, and the average inversion duration to obtain the inversion diffusion value for each unit area. The wind diffusion value of each unit area is obtained by linear regression calculation based on the wind speed and the turbulence intensity. Linear regression calculations were performed based on the average mixing layer height and the terrain openness value to obtain the terrain diffusion value for each unit area. The carbon emission diffusion value is obtained by normalizing the inversion diffusion value, the wind diffusion value, and the terrain diffusion value.

3. The method for assessing carbon emissions from industrial processes based on big data as described in claim 2, characterized in that, The wind data also includes wind direction; Based on the meteorological data and the estimated emissions, the extent of carbon emission diffusion impact on a unit area is assessed to obtain a diffusion impact value, including: Based on the wind direction, wind speed and turbulence intensity in the wind data, the carbon emission process is simulated based on the preset atmospheric diffusion model to obtain the diffusion coverage area and diffusion path. Obtain pathway material information of the diffusion path; wherein, the pathway material information includes the types of reactants that react chemically with carbon emission substances and the amount of each reactant; Based on the types and amounts of reactants, obtain the synthetic substances generated after each reactant reacts with carbon emission substances, assess the degree of synthetic hazard of the synthetic substances and the current carbon hazard of the carbon emission substances, and calculate the hazard difference between the degree of synthetic hazard and the degree of carbon hazard. For each reactant type, the hazard value is calculated based on the hazard difference and the corresponding reactant amount, and the sum of the hazard values ​​of all reactant types is used as the path pollution value of the diffusion path. Obtain regional information of the diffusion coverage area; wherein, the regional information includes regional area, population density, and ecological sensitivity; The regional importance value of the diffusion coverage area is obtained by weighting the area, population density, and ecological sensitivity. The path pollution values ​​of all diffusion paths are summed and then normalized with the regional importance value to obtain the diffusion impact value.

4. The method for assessing carbon emissions from industrial processes based on big data as described in claim 1, characterized in that, The emission data includes emission height, emission temperature, and emission velocity; Based on the diffusion index, the estimated emissions, and the emission data, a retention index is calculated to characterize the impact of carbon emission accumulation within a unit area, including: The emission impact index of industrial process carbon emissions is obtained by linear regression calculation based on the emission height, emission temperature and emission rate. The emission impact index and the diffusion index are normalized, and the difference between the normalized emission impact index and the normalized diffusion index is used as the emission retention index. The retention index, which characterizes the impact of carbon emission accumulation within a unit area, is calculated by multiplying the emission retention index and the estimated emissions.

5. The method for assessing carbon emissions from industrial processes based on big data as described in claim 1, characterized in that, The biological environmental data includes biological data and soil data; the biological data includes biological species, food chain diagram, carbon uptake rate, carbon storage capacity, carbon turnover time, and biological tolerance for each biological species; the soil data includes soil organic carbon content, soil organic carbon density, and the proportion of carbon pool components; the emission data also includes raw carbon emissions. Based on the estimated emissions, the retention index, the emission data, the historical carbon emission treatment data, and the biological environment data, a carrying capacity index is calculated to characterize the comprehensive carbon emission carrying capacity of a unit area, including: Based on biological species, linear regression calculations were performed according to carbon absorption rate, carbon storage capacity and carbon turnover time to obtain the biological carbon sequestration value per unit area. The similarity between soil organic carbon content, soil organic carbon density, and carbon pool component ratio and the preset carbon sequestration soil threshold is calculated to obtain the carbon sequestration similarity. Linear regression calculations were performed based on the biological carbon sequestration value, the carbon sequestration similarity, and the original carbon emissions to obtain the carbon sequestration capacity value of each unit area for carbon emissions. Based on the estimated emissions, the retention index, and the biological tolerance, the predicted changes in the number of each biological species are predicted to obtain the predicted number of biological changes. The biological impact value calculated from the aforementioned food chain diagram and the predicted amount of biological change is used as the ecological impact value of carbon emissions on a unit area. The historical adjustment value is obtained by weighting the historical processing frequency and the historical carbon emission ratio in the historical carbon emission processing data. Linear regression analysis was performed based on the historical adjustment values ​​and the historical carbon emission utilization rate in the historical carbon emission treatment data to obtain the adjustment capacity value per unit area. The carbon sequestration capacity value, the ecological impact value, and the regulation capacity value are weighted and calculated to obtain the carrying capacity index, which characterizes the comprehensive carbon emission carrying capacity of each unit area.

6. The method for assessing carbon emissions from industrial processes based on big data as described in claim 5, characterized in that, The biological impact value calculated from the aforementioned food chain diagram and the predicted amount of biological change is used as the ecological impact value of carbon emissions per unit area, including: For each biological species within each unit area, determine the number of connecting chains corresponding to it in the biological chain diagram; The single-category impact value for each biological species is calculated by multiplying the absolute value of the predicted number of biological changes with the number of connecting chains. The individual impact values ​​of all biological species within each unit area are summed to obtain the biological impact value, which is then used as the ecological impact value of carbon emissions on the unit area.

7. The method for assessing carbon emissions from industrial processes based on big data as described in claim 1, characterized in that, Based on the normalized calculations of the diffusion index, retention index, and carrying capacity index for each unit area, the carbon emission optimization index for each unit area is obtained, including: The diffusion index, the retention index, and the acceptance index are subjected to extreme value normalization. Based on the preset weights for diffusion, retention, and acceptance dimensions, the diffusion index, retention index, and acceptance index after extreme value normalization are weighted and summed to obtain the carbon emission optimization index for each unit region. The retention index is a reverse indicator, and the preset retention dimension weight is negative.

8. The method for assessing carbon emissions from industrial processes based on big data as described in claim 1, characterized in that, The carbon emission assessment results are obtained by evaluating the area to be assessed based on the carbon emission optimization index, including: The carbon emission optimization index of each unit area is ranked, and the unit area with the largest carbon emission optimization index is taken as the optimal matching area for industrial process carbon emissions. Carbon emission assessment reports are generated based on the diffusion index, retention index, absorption index, carbon emission optimization index, and optimal suitability area for each unit area, and these reports serve as the final carbon emission assessment results.

9. A big data-based industrial process carbon emission assessment device, characterized in that, include: The regional data acquisition module is used to acquire meteorological data, biological environment data, historical carbon emission treatment data, estimated emissions of industrial process carbon emissions, and emission data for each unit area in the region to be evaluated; the unit areas are divided according to the geographical location of the region to be evaluated. The diffusion index calculation module is used to assess the impact of meteorology on carbon emissions based on the meteorological data to obtain a carbon emission diffusion value, assess the degree of diffusion impact of carbon emissions on a unit area based on the meteorological data and the estimated emissions to obtain a diffusion impact value, and calculate a diffusion index based on the carbon emission diffusion value and the diffusion impact value. The retention index calculation module is used to calculate, based on the diffusion index, the estimated emissions and the emissions data, a retention index that characterizes the impact of carbon emission accumulation within a unit area. The carrying capacity index calculation module is used to calculate, based on the estimated emissions, the retention index, the emissions data, the historical carbon emissions treatment data, and the biological environment data, to obtain a carrying capacity index that characterizes the comprehensive carbon emission carrying capacity of a unit area. The carbon emission optimization index calculation module is used to perform normalization calculations based on the diffusion index, retention index and acceptance index of each unit area to obtain the carbon emission optimization index of each unit area. The carbon emission assessment module is used to assess the area to be assessed based on the carbon emission optimization index and obtain the carbon emission assessment results.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a big data-based industrial process carbon emission assessment method as described in any one of claims 1 to 8.

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