Carbon emission evaluation method and system for cold region city based on big data fusion

Abstract

The application relates to the technical field of data management, in particular to a cold region city carbon emission evaluation method and system based on big data fusion. The technical scheme comprehensively considers weather changes, personnel activities and plant growth conditions of each region of the cold region city, comprehensively and systematically evaluates carbon emission matching conditions, quickly and accurately identifies growth abnormalities by quantifying plant growth parameter deviation from a standard range, weather factor deviation from a safety range and personnel activity influence when analyzing future plant growth conditions, accurately evaluates future carbon absorption effects by comprehensively considering various factors such as plant canopy and carbon emission height matching in carbon absorption effect analysis, predicts future regional carbon emission by combining personnel activities and weather changes, guarantees prediction accuracy by training and verifying a model through historical data, comprehensively considers carbon absorption and carbon emission abnormalities when performing regional carbon emission matching analysis, and takes targeted measures according to quantitative indexes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, in particular to a cold region city carbon emission evaluation method and system based on big data fusion. BACKGROUND

[0002] With the increasingly serious global warming problem, reducing carbon emissions and achieving carbon balance have become the focus of attention of countries around the world. Due to its unique geographical location and climate conditions, cold region cities face different challenges and characteristics in carbon emissions and carbon absorption. In terms of carbon emissions, cold region cities have long and cold winters, and to meet the heating needs of residents, energy consumption is huge, mainly relying on fossil fuels such as coal and natural gas, which leads to a large amount of carbon emissions. At the same time, the carbon emissions in the fields of transportation and industry in cold region cities cannot be ignored. In addition, the personnel activity pattern in cold region cities is significantly affected by the climate, and outdoor activities decrease in winter, while indoor activities increase, further increasing building energy consumption and carbon emissions. At present, most of the evaluation methods for urban carbon emissions do not fully consider the special circumstances of cold region cities. Some traditional evaluation methods only focus on the statistics of carbon emissions in energy consumption and industrial production processes, ignoring the comprehensive influence of weather changes, personnel activities, and plant growth on carbon emissions and carbon absorption. In addition, the existing evaluation methods lack accurate prediction of future plant growth and carbon absorption effects, making it difficult to achieve dynamic management and precise control of regional carbon emissions. In terms of plant growth management, the existing technology cannot accurately identify plant growth abnormalities in a timely manner, and cannot make targeted adjustments according to the influence of weather changes and personnel activities on plant growth. In terms of carbon absorption effect evaluation, traditional methods do not fully consider the matching relationship between plant canopy and carbon emission height and the influence of plant community spatial structure on carbon absorption, resulting in inaccurate evaluation results. In terms of regional carbon emission prediction, there is a lack of comprehensive analysis of factors such as personnel activities and weather changes, and the accuracy and reliability of the prediction model need to be improved.

[0003] Due to the lack of comprehensive, systematic, and accurate cold region city carbon emission evaluation methods, city managers are difficult to develop scientific and reasonable carbon emission reduction policies and measures, and cannot effectively improve the carbon balance management level of cold region cities. Therefore, it is of great practical significance and application value to develop a cold region city carbon emission evaluation method based on big data fusion.

[0004] In view of this, the present application proposes a cold region city carbon emission evaluation method and system based on big data fusion. SUMMARY

[0005] In order to overcome the defects and deficiencies proposed in the background art, the present application provides a cold region city carbon emission evaluation method and system based on big data fusion.

[0006] In order to achieve the above object, the following technical solutions are adopted in the present application.

[0007] In a first aspect, the present application provides a cold city carbon emission evaluation method based on big data fusion, comprising the following steps:

[0008] Step 1, obtaining the weather change situation of each region of the cold city, the personnel activity situation of each region, and the plant growth situation of the corresponding region;

[0009] Step 2, based on the influence of personnel activity situation and the influence of future weather change situation, analyzing the future growth situation of the plants in the corresponding region, and based on the future growth situation of the plants in each position of the carbon source of the corresponding region, analyzing the carbon absorption effect;

[0010] Step 3, based on the personnel activity situation and the future weather change situation, estimating the carbon emission of the future region;

[0011] Step 4, based on the carbon absorption effect analysis result of the corresponding region and the carbon emission estimation result of the future region, performing regional carbon emission matching analysis;

[0012] Step 5, based on the regional carbon emission matching analysis result, performing regional carbon emission early warning.

[0013] In an implementation manner of the present application, the weather change situation is the future situation of weather factors affecting plant growth and the future situation of weather factors affecting plant photosynthesis in weather forecast, wherein the weather factors affecting plant growth and the weather factors affecting plant photosynthesis are different, wherein the weather factors affecting plant growth in cold regions mainly include temperature change, snow and frozen soil change, and light and photoperiod condition; and the weather factors affecting plant photosynthesis in cold regions mainly include temperature and enzyme activity, and carbon dioxide concentration; the personnel activity situation includes the number of personnel in the gathering area, the carbon emission change situation, and the land replacement situation of plants, wherein the land replacement situation of plants is obtained through land planning data, and the plant growth situation of the corresponding region includes the type of plants, the growth characteristic situation of plants, and the coverage rate situation of plants.

[0014] In an implementation manner of the present application, the step 2 of analyzing the future growth situation of the plants in the corresponding region comprises the following specific steps:

[0015] Step 21, obtain the growth characteristics of the corresponding plant, analyze the plant growth anomaly through the growth characteristics of the corresponding plant, and the plant growth anomaly analysis method is: obtaining the height, diameter and color situation growth parameters of the plant in the current growth cycle and the standard deviation of the corresponding growth parameter cycle standard growth range, weighting the standard deviation of each growth parameter to obtain the plant growth anomaly, and through the deviation of the height, diameter, color and other parameters of the plant from the standard range, the growth anomaly can be quickly identified; the change of the growth parameter directly reflects the health status, and the standard deviation can objectively measure the abnormality degree, the weighting weight is obtained through the corresponding experiment, and the specific experimental process is: obtaining different individuals of the same plant, growing in the same environment, sorting based on the growth situation, and analyzing the weight based on the sorting result;

[0016] Step 22, obtain the future situation of the weather factor affecting plant growth in the weather forecast, obtain the standard deviation of each weather factor and the range of the safe weather factor of the corresponding growth cycle plant, and obtain the weather influence anomaly through the weighted sum result of the standard deviation of each weather factor, quantitatively analyze the influence of future weather on plant growth, and evaluate the influence of future weather on plant growth, and the weight is also obtained through the corresponding experiment;

[0017] Step 23, obtain the land replacement of the plant by the personnel expansion, obtain the land replacement speed, and set the ratio of the land replacement speed to the land replacement standard speed as the personnel influence anomaly, monitor the occupation speed of human activities on the plant habitat, analyze the damage of human activities to the plant, and when the land replacement speed exceeds the natural recovery capacity, the biodiversity will decrease;

[0018] Step 24, obtain the plant growth anomaly, weather influence anomaly and personnel influence anomaly, and obtain the future plant growth anomaly through weighted sum, in this step, the risk is comprehensively predicted through the plant state, weather and human influence, and the weighted fusion method can quantitatively measure the contribution of each factor.

[0019] In an implementation manner of the present application, the carbon absorption effect analysis in step 2 includes the following specific contents:

[0020] Step 25, simulating the carbon source diffusion process under the influence of future environment through the carbon source diffusion simulation software, obtaining the carbon source diffusion proportion of the standard concentration carbon emission in each direction and the carbon emission height of each position, and the carbon source diffusion process under the influence of future environment is simulated through the carbon source diffusion simulation software, which has the advantages that the diffusion proportion of the standard concentration carbon emission in each direction and the carbon emission height of each position can be clearly mastered;

[0021] Step 26, obtain the plant canopy height situation of each angle, the plant leaf surface situation, the leaf shedding situation, and the chlorophyll content in the leaf, and obtain the weather factors affecting the photosynthesis of the plant and the abnormal growth of the future plant, wherein the plant canopy is the height range of the plant leaves, the plant photosynthetic rate is analyzed based on the plant leaf surface situation, the leaf shedding situation, the chlorophyll content in the leaf, and the weather factors affecting the photosynthesis of the plant, the photosynthetic rate of the plant is closely related to the leaf surface situation, the leaf shedding, the chlorophyll content, and the weather factors, the historical data reflects the internal relationship between them, and the relationship can be mined through the construction of a model and used for future prediction.

[0022] Step 27, obtain the height anomaly of the corresponding position by the difference degree of the plant canopy height situation of each position at each angle and the carbon emission height range of each position, wherein the difference degree is a standard value 1 minus the intersection of the height ranges divided by the carbon emission height range, the distance of each position relative to the carbon emission source is obtained, the importance weight of each position is obtained based on the reciprocal of the distance, the future carbon absorption anomaly of the corresponding position is obtained by weighted sum of the height anomaly of the corresponding position and the future photosynthetic anomaly of the plant of the corresponding position, the future carbon absorption effect anomaly of the corresponding position is obtained by multiplying the future carbon absorption anomaly of the corresponding position and the importance weight, the future carbon absorption effect anomaly results of the corresponding angle are obtained by adding the future carbon absorption effect anomaly of each position at the corresponding angle, the height anomaly is obtained by the difference degree of the plant canopy height and the carbon emission height range, the future carbon absorption anomaly and effect anomaly are calculated by combining the importance weight of each position, and the advantages are that the spatial relationship between the plant canopy and the carbon emission and the photosynthetic capacity of the plant are comprehensively considered, and the future carbon absorption effect of each angle can be more comprehensively and accurately evaluated.

[0023] In an implementation manner of the present application, the carbon emission prediction of the future area in step 3 includes the following specific contents:

[0024] The personnel activity situation and the future weather change situation of the corresponding area are obtained, the personnel activity situation and the future weather change situation of the corresponding area are input into the constructed carbon emission neural network prediction model to predict the carbon emission amount of the future period, and the carbon emission anomaly of the future period is obtained by dividing the carbon emission amount of the future period by the corresponding carbon emission amount safety.

[0025] In an implementation manner of the present application, the carbon emission matching analysis of the area in step 4 includes the following specific contents:

[0026] The future carbon absorption effect anomaly results of each angle of the area and the carbon emission anomaly of the future period are obtained, and the carbon emission matching anomaly of each angle of the future area is obtained after weighted sum;

[0027] The carbon emission matching abnormality of the future region in each angle is compared with the set carbon emission matching abnormality threshold value, if the carbon emission matching abnormality of the corresponding angle is greater than or equal to the set carbon emission matching abnormality threshold value, the corresponding angle needs to be supplemented with plants, or the propaganda of reducing carbon emission is needed, if the carbon emission matching abnormality of the corresponding angle is less than the set carbon emission matching abnormality threshold value, the corresponding angle does not need to be supplemented with plants, the future carbon absorption effect abnormality result of each angle of the region and the carbon emission abnormality of the future period are obtained, and the carbon emission matching abnormality of each angle of the future region is obtained by weighted summation.

[0028] In an implementation form of the present application, the step 5 comprises the following specific contents:

[0029] The angle needing to be supplemented with plants is sent to the management department as an early warning signal, the management department plants trees in the corresponding angle according to the angle needing to be supplemented with plants, and then the steps 1 to 4 are performed again, so that the carbon emission matching abnormality of the corresponding angle is less than the set carbon emission matching abnormality threshold value, the angles needing to be supplemented with plants are sent to the management department, the management department plants trees according to these angles, and then the steps 1 to 4 are performed again, so that the carbon emission matching abnormality of the corresponding angle is less than the set carbon emission matching abnormality threshold value.

[0030] In a second aspect, the present application also provides a cold region city carbon emission evaluation system based on big data fusion, comprising:

[0031] A data acquisition module acquires weather changes of each region of the cold region city, personnel activity conditions of each region, and plant growth conditions of the corresponding region;

[0032] A carbon absorption effect analysis module analyzes future growth conditions of plants in the corresponding region based on the influence of personnel activity conditions and the influence of future weather changes, and analyzes carbon absorption effect based on future growth conditions of plants in each position of the carbon source of the corresponding region;

[0033] A carbon emission estimation module estimates carbon emission of the future region based on personnel activity conditions and future weather changes;

[0034] A carbon emission matching analysis module performs regional carbon emission matching analysis based on carbon absorption effect analysis results of the corresponding region and carbon emission estimation results of the future region;

[0035] A warning module performs regional carbon emission warning based on regional carbon emission matching analysis results.

[0036] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be invoked by the processor, and the processor executes the method for evaluating carbon emission of a cold region city based on big data fusion by invoking the computer program stored in the memory.

[0037] In a fourth aspect, the present application provides a computer readable storage medium storing instructions, which, when executed on a computer, cause the computer to execute the method for evaluating carbon emission of a cold region city based on big data fusion.

[0038] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0039] The present application obtains the weather change of each region of the cold region city, the personnel activity of each region, and the plant growth of the corresponding region, analyzes the future growth of the plants in the corresponding region based on the influence of the personnel activity and the future weather change, analyzes the carbon absorption effect of the plants in the corresponding region based on the future growth of the plants in each position of the carbon source, estimates the carbon emission of the future region based on the personnel activity and the future weather change, and analyzes the carbon emission matching of the region based on the analysis result of the carbon absorption effect of the corresponding region and the estimation result of the carbon emission of the future region. The weather change, personnel activity, and plant growth of each region of the cold region city are comprehensively considered to comprehensively and systematically evaluate the carbon emission matching condition. In the analysis of the future growth of the plants, the growth abnormality can be quickly and accurately identified by quantifying the plant growth parameter deviation from the standard range, the weather factor deviation from the safety range, and the influence of the personnel activity. In the carbon absorption effect analysis, the future carbon absorption effect is accurately evaluated by comprehensively considering various factors such as the matching of the plant canopy and the carbon emission height. In the prediction of the carbon emission of the future region, the personnel activity and the weather change are combined, the model is trained and verified based on the historical data to ensure the prediction accuracy. In the analysis of the carbon emission matching of the region, the carbon absorption and the carbon emission abnormality are comprehensively considered, the targeted measures are taken according to the quantitative indexes, and the carbon balance management efficiency and accuracy are improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments, made with reference to the accompanying drawings:

[0041] Fig. 1 FIG. 1 is a schematic diagram of the overall process of the method embodiment 1 of the present application;

[0042] Fig. 2 FIG. 2 is a schematic diagram of the process of analyzing the future growth of the plants in the corresponding region in step 2 of the method embodiment 1 of the present application;

[0043] Fig. 3A structural schematic diagram of system embodiment 2 of the present application. DETAILED DESCRIPTION

[0044] In order to make the above objectives, features and advantages of the present application more apparent, a detailed description of the specific embodiments of the present application will be given below with reference to the accompanying drawings.

[0045] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0046] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is separate from or mutually exclusive with other embodiments.

[0047] Embodiment 1

[0048] As shown in Figs. 1-2 The present embodiment provides a cold city carbon emission evaluation method based on big data fusion, specifically comprising the following steps:

[0049] Step 1, obtaining the weather change of each region of the cold city, the personnel activity of each region and the plant growth of the corresponding region;

[0050] In the embodiment, it should be noted that the weather change condition is a future condition of a weather factor affecting plant growth and a future condition of a weather factor affecting plant photosynthesis in a weather forecast, wherein the weather factor affecting plant growth and the weather factor affecting plant photosynthesis are different, the weather factor affecting plant growth is analyzed to predict the growth of plants by comprehensively considering the influence of personnel activities and weather conditions on the growth of plants, and the weather factor affecting plant photosynthesis is analyzed to analyze the level of plant photosynthesis by combining the growth of plants, personnel activities, and weather change conditions, wherein the weather factors affecting plant growth in cold regions mainly include temperature change, snow and frozen soil change, and light and photoperiod conditions; and the weather factors affecting plant photosynthesis in cold regions mainly include temperature and enzyme activity, and carbon dioxide concentration; the personnel activity condition includes the number of personnel in the gathering area, the change of carbon emission, and the land replacement of plants, wherein the land replacement of plants is obtained through land planning data, the number of personnel in the gathering area is replaced by the current number of personnel, because the probability of sudden change of the number of personnel in a period of time, for example, three months or six months, is very small, so the number of personnel in the future period is replaced by the current number of personnel, and the growth condition of plants in the corresponding area includes the type of plants, the growth characteristic condition of plants, and the coverage condition of plants, wherein the growth characteristic condition of plants includes the height, diameter, color condition of plants, the leaf condition of plants, the leaf shedding condition, and the chlorophyll content condition of leaves, which are obtained through plant statistical data, and it should be noted that the data of the application are obtained by the corresponding data collection terminal and stored in the corresponding storage component.

[0051] Step 2, based on the influence of personnel activities and the influence of future weather change conditions, analyze the future growth condition of plants in the corresponding area, and analyze the carbon absorption effect based on the future growth condition of plants at each position of the carbon source in the corresponding area.

[0052] In the embodiment, the analysis of the future growth condition of plants in the corresponding area in step 2 includes the following specific steps:

[0053] Step 21, obtain the growth characteristics of the corresponding plant, analyze the plant growth anomaly through the growth characteristics of the corresponding plant, and the plant growth anomaly analysis method is: obtaining the height, diameter and color situation growth parameters of the plant in the current growth cycle and the standard deviation of the corresponding growth parameter cycle standard growth range, weighting the standard deviation of each growth parameter to obtain the plant growth anomaly, by quantifying the deviation of the height, diameter, color and other parameters of the plant from the standard range, the growth anomaly can be quickly identified; the change of growth parameters directly reflects the health status, and the standard deviation can objectively measure the abnormality degree, the weighting weight here is obtained through corresponding experiment, and the specific experimental process is: obtaining different individuals of the same plant, growing in the same environment, sorting based on the growth situation, and analyzing the weight based on the sorting result;

[0054] Step 22, obtain the future situation of weather factors affecting plant growth in weather forecast, obtain the standard deviation of each weather factor and the range of safe weather factors of the corresponding growth cycle plant, obtain the weather influence anomaly through the weighted sum of the standard deviation of each weather factor, quantitatively analyze the influence of future weather on plant growth, and evaluate the influence of future weather on plant growth, the weight here is also obtained through corresponding experiment;

[0055] Step 23, obtain the land replacement situation of the plant by personnel expansion, obtain the land replacement speed situation, and set the ratio of the land replacement speed situation to the land replacement standard speed as the personnel influence anomaly, monitor the occupation speed of human activities on the habitat of the plant, analyze the damage of human activities to the plant, and when the land replacement speed exceeds the natural recovery capacity, the biodiversity will decrease;

[0056] Step 24, obtain the growth anomaly of the future plant by weighting and summing the plant growth anomaly, weather influence anomaly and personnel influence anomaly, in this step, the risk is comprehensively predicted by comprehensively considering the plant state, weather and human influence, and the weighted fusion method can scientifically quantify the contribution of each factor;

[0057] The carbon absorption effect analysis in step 2 includes the following specific contents:

[0058] Step 25, simulate the carbon source diffusion process under the influence of future environment by carbon source diffusion simulation software, obtain the carbon source diffusion proportion of standard concentration carbon emission in each direction and the carbon emission height of each position, commonly used carbon source diffusion simulation tools include: AERMOD (EPA recommended atmospheric diffusion model), CALPUFF (suitable for long distance, complex terrain scene), ADMS (advanced atmospheric diffusion modeling system), FLEXPART / WRF (Lagrangian particle diffusion model) and open source tools (such as CFD diffusion module in OpenFOAM); input data includes: emission intensity, emission height and emission temperature / speed, meteorological data: wind speed, wind direction, temperature vertical profile, atmospheric stability (P-G classification), humidity (future climate scenario data can come from CMIP6 or RCP / SSP prediction); DEM elevation data (affecting local airflow and diffusion); surface features: roughness, vegetation cover (affecting near-surface turbulence); statistics of carbon source diffusion proportion of standard concentration carbon emission in different wind direction sectors (such as 4, 8 or 16 directions) and carbon emission height of each position, the more the directions, the smaller the division angle, if 16 directions are selected, the angle of each sector is 22.5 degrees, 22.5 degrees is divided into an angle area, simulate the carbon source diffusion process under the influence of future environment by carbon source diffusion simulation software, the advantage is that the diffusion proportion of standard concentration carbon emission in each direction and the carbon emission height of each position can be clearly mastered, atmospheric diffusion is affected by emission source characteristics, meteorological conditions, terrain and surface features and other factors, these factors differ greatly under different environmental scenarios, professional simulation tools can consider these factors comprehensively, accurately simulate the carbon source diffusion under different climate scenarios, and provide basic data for subsequent analysis;

[0059] Step 26, obtain the plant canopy height situation, plant leaf surface situation, leaf shedding situation, and chlorophyll content in the leaf of the carbon source at each angle, and obtain the weather factors affecting the photosynthesis of the plant and the future growth anomaly of the plant, wherein the plant canopy is the height range of the plant leaves, based on the plant leaf surface situation, leaf shedding situation, chlorophyll content in the leaf, and weather factors affecting the photosynthesis of the plant, the photosynthetic rate of the plant is analyzed, the specific steps are: obtaining the leaf surface situation, leaf shedding situation, chlorophyll content in the leaf, and weather factors affecting the photosynthesis of the corresponding plant, and obtaining the photosynthetic rate of the historical plant, constructing a deep learning neural network model with the input of the leaf surface situation, leaf shedding situation, chlorophyll content in the leaf, and weather factors affecting the photosynthesis of the corresponding plant, and the output of the photosynthetic rate of the plant, predicting the photosynthetic rate of the future plant through the constructed neural network model, dividing the photosynthetic rate anomaly analysis result by the corresponding photosynthetic rate safety value to obtain the future photosynthetic anomaly of the corresponding plant, obtaining the plant canopy and related situations at each angle of the carbon source and performing photosynthetic rate analysis, the advantages are that multiple factors affecting the photosynthesis of the plant are comprehensively considered, the future plant photosynthetic rate is predicted by using the deep learning neural network model and the anomaly is analyzed, and the photosynthetic capacity of the plant in the future environment can be more accurately evaluated; the photosynthetic rate of the plant is closely related to the leaf surface situation, leaf shedding, chlorophyll content, and weather factors, and the historical data reflects the internal relationship between them, by constructing a model, the relationship can be mined and used for future prediction, the specific steps are: first, through multiple channels of data sources such as historical meteorological records, plant research reports, and field monitoring archives, the leaf surface situation (such as leaf size, flatness, etc.), leaf shedding situation (number of shedding leaves, time regularity, etc.), chlorophyll content in the leaf (content value at different periods), and weather factors affecting the photosynthesis of the plant (light intensity, temperature, humidity, carbon dioxide concentration, etc.) of the corresponding plant are collected, and the photosynthetic rate data of the plant at the corresponding time period are obtained; then, the collected data is cleaned to remove incorrect, duplicate, or abnormal data, and standardized processing is performed to make each feature comparable; then, the processed data is divided into training set, validation set, and test set according to a certain proportion, then a suitable deep learning neural network architecture is selected, in this embodiment, a multilayer perceptron is selected, the number of input layer neurons is determined according to the number of input features, the photosynthetic rate is determined as the output to determine the number of output layer neurons, and a proper number of hidden layers and neurons are set in the middle; then, the training set data is used to train the constructed model, and the validation set data is used to adjust the hyperparameters of the model, such as learning rate, to optimize the performance of the model during the training process;Finally, the trained model is evaluated using the test set data by calculating the mean square error index to measure the accuracy and reliability of the model prediction, and the construction of the entire deep learning neural network model is completed.

[0060] Step 27, obtain the height anomaly of the corresponding position by the difference degree of the plant canopy height of each position at each angle and the carbon emission height range of each position, wherein the difference degree is the standard value 1 minus the intersection of the height range divided by the carbon emission height range, for example, the plant canopy height is 1-3 meters, the corresponding position carbon emission height range is 2-4 meters, the height range intersection is 1 meter (2-3 meter height range), the carbon emission height range is 2 meters (2-4 meter height range), so the difference degree is 1-1 / 2=1 / 2, obtain the distance of each position relative to the carbon emission source, based on the reciprocal of the distance to obtain the importance weight of each position, obtain the future carbon absorption anomaly of the corresponding position by weighting and summing the height anomaly of the corresponding position and the future photosynthesis anomaly of the plant of the corresponding position, obtain the future carbon absorption effect anomaly of the corresponding position by multiplying the future carbon absorption anomaly of the corresponding position and the importance weight, add the future carbon absorption effect anomaly of each position at the corresponding angle to obtain the future carbon absorption effect anomaly result at the corresponding angle, obtain the height anomaly by the difference degree of the plant canopy height and the carbon emission height range, calculate the future carbon absorption anomaly and effect anomaly based on the importance weight of each position, the advantage is that the spatial relationship between the plant canopy and the carbon emission and the photosynthetic capacity of the plant are considered comprehensively, which can more comprehensively and accurately evaluate the future carbon absorption effect at each angle, the matching degree of the plant canopy and the carbon emission height will affect the carbon absorption efficiency, the distance of the position and the carbon emission source will also affect its importance to carbon absorption, and the comprehensive consideration of these factors can more scientifically evaluate the carbon absorption effect;

[0061] Step 3, based on the personnel activity and the future weather change, estimate the carbon emission of the future area;

[0062] In this embodiment, the carbon emission estimation of the future area in step 3 includes the following specific contents:

[0063] The personnel activity in the corresponding area and the future weather change are obtained, and the personnel activity in the corresponding area and the future weather change are input into the constructed carbon emission neural network prediction model to predict the carbon emission in the future period. The carbon emission in the future period is divided by the corresponding carbon emission safety amount to obtain the carbon emission anomaly in the future period. The construction method of the carbon emission neural network prediction model is as follows: the number of personnel in the historical area, the carbon emission change, and the land replacement of expansion to plants are obtained, and the weather change of the historical area is obtained. A deep learning neural network model is constructed, in which the input is the number of personnel in the area, the land replacement of expansion to plants, and the weather change of the area, and the output is the carbon emission change. The historical data is divided into a 75% data training set and a 25% data verification set. The model is trained by the 75% data training set to obtain an initial deep learning neural network model. The initial deep learning neural network model is verified by the 25% data verification set. The initial deep learning neural network model with the highest carbon emission change data accuracy is output as the carbon emission neural network prediction model. In the future area carbon emission estimation, the carbon emission neural network prediction model is used to predict the carbon emission in the future period and analyze the anomaly by combining the personnel activity and the future weather change. The advantages are that the multiple key factors affecting carbon emission can be considered, the accuracy of the prediction is ensured by training and verifying the model with historical data, the number of personnel in the area, the land replacement, and the weather change all affect carbon emission, the historical data reflects the rules between them and carbon emission change, and the rules can be used to predict future carbon emission by constructing the model.

[0064] Step 4, based on the carbon absorption effect analysis result of the corresponding area and the carbon emission estimation result of the future area, performing regional carbon emission matching analysis;

[0065] In this embodiment, the regional carbon emission matching analysis in step 4 includes the following specific contents:

[0066] The future carbon absorption effect anomaly result of each angle of the area and the carbon emission anomaly in the future period are obtained, and the carbon emission matching anomaly of each angle of the future area is obtained after weighted summation;

[0067] The carbon emission matching abnormality of each angle in the future region is compared with the set carbon emission matching abnormality threshold value. If the carbon emission matching abnormality of the corresponding angle is greater than or equal to the set carbon emission matching abnormality threshold value, the corresponding angle needs to be supplemented with plants or needs to be promoted to reduce carbon emission. If the carbon emission matching abnormality of the corresponding angle is less than the set carbon emission matching abnormality threshold value, the corresponding angle does not need to be supplemented with plants. The future carbon absorption effect abnormality results of each angle in the region and the carbon emission abnormality of the future period are obtained. The carbon emission matching abnormality of each angle in the future region is obtained by weighted summation. The advantage is that the abnormal conditions of carbon absorption and carbon emission are comprehensively considered, and the carbon emission matching conditions of each angle in the region can be comprehensively and systematically evaluated. Carbon absorption and carbon emission are two key factors of regional carbon balance. Considering only one aspect cannot accurately reflect the overall carbon emission matching condition of the region. By weighted summation, the two can be combined to obtain a more representative index. Then, the carbon emission matching abnormality of each angle in the future region is compared with the set carbon emission matching abnormality threshold value, and whether to supplement plants or promote carbon emission reduction is determined according to the comparison result. The advantage is that targeted measures can be taken according to the quantitative index, and the efficiency and accuracy of carbon balance management can be improved. The basis is that a reasonable threshold value can clearly define the normal and abnormal limits of regional carbon emission matching. When the abnormal value exceeds the threshold value, it indicates that the carbon balance of the angle is problematic and needs to be adjusted accordingly.

[0068] Step 5: Regional carbon emission early warning based on regional carbon emission matching analysis results;

[0069] In this embodiment, step 5 includes the following specific contents:

[0070] The angle that needs to be supplemented with plants is obtained and sent to the management department. The management department plants trees in the corresponding angle and then performs steps 1 to 4 again. Finally, the carbon emission matching abnormality of the corresponding angle is less than the set carbon emission matching abnormality threshold value. The angle that needs to be supplemented with plants is obtained and sent to the management department. The management department plants trees according to these angles and then performs steps 1 to 4 again. Finally, the carbon emission matching abnormality of the corresponding angle is less than the set carbon emission matching abnormality threshold value. The advantage is that a closed-loop carbon balance management mechanism is formed. Through continuous adjustment and optimization, the carbon emission matching condition of the region is gradually improved. Planting trees can increase the carbon absorption capacity of plants and help alleviate the carbon emission matching abnormality problem. Repeating the previous steps can continuously monitor and evaluate the adjustment effect to ensure that the expected carbon balance target is achieved.

[0071] It should be noted that the set parameters (such as set weight parameters, safety values, and set threshold values) of the present application are obtained through historical data experiments. The specific acquisition steps can be,

[0072] Obtain the weather change of each region in the cold city, the personnel activity of each region, and the plant growth of the corresponding region, and input the angle of plant replenishment in each step of the embodiment to output, and obtain the judgment result of whether the carbon emission of each angle in the future period meets the demand; and import the calculation result and the judgment result into the matlab fitting software to fit the data, and output the set parameter value that meets the maximum judgment accuracy.

[0073] Embodiment 2

[0074] As Fig. 3 shown, the embodiment provides a cold city carbon emission evaluation system based on big data fusion, which is used to implement the cold city carbon emission evaluation method based on big data fusion in embodiment 1, and specifically includes: a data acquisition module that acquires the weather change of each region in the cold city, the personnel activity of each region, and the plant growth of the corresponding region; a carbon absorption effect analysis module that analyzes the future growth of plants in the corresponding region based on the influence of personnel activity and the influence of future weather change, and analyzes the carbon absorption effect of plants in the corresponding region based on the future growth of plants in each position of the carbon source; a carbon emission estimation module that estimates the carbon emission of the future region based on the personnel activity and the future weather change; a carbon emission matching analysis module that performs regional carbon emission matching analysis based on the carbon absorption effect analysis result of the corresponding region and the carbon emission estimation result of the future region; and a warning module that performs regional carbon emission warning based on the regional carbon emission matching analysis result. The specific steps of each module of the system embodiment are the same as the specific steps of the method embodiment of embodiment 1, and will not be repeated here.

[0075] Embodiment 3

[0076] An electronic device of an embodiment of the present application includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a cold city carbon emission evaluation method based on big data fusion by calling the computer program stored in the memory. It should be noted that all computer programs of the cold city carbon emission evaluation method based on big data fusion are implemented using C language.

[0077] Embodiment 4

[0078] The embodiment provides a computer readable storage medium having an erasable computer program stored thereon.

[0079] When the computer program runs on the computer device, the computer device executes the above-mentioned cold city carbon emission evaluation method based on big data fusion.

[0080] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, and the like, which includes one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0081] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device, and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0083] In several embodiments provided in the present application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of units is only one, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices, or units, which can be electrical, mechanical, or other forms.

[0084] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0085] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit.

[0086] In the description of the specification, the description referring to the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are contained in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0087] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements of the present application can be made, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A cold region city carbon emission evaluation method based on big data fusion, characterized in that, The method comprises the following steps: Step 1, obtaining the weather change of each region in the cold city, the personnel activity of each region, and the plant growth of the corresponding region; Step 2, based on the influence of personnel activity and the influence of future weather change, analyzing the future growth of plants in the corresponding region, and based on the future growth of plants in each position of the carbon source in the corresponding region, analyzing the carbon absorption effect; Step 3, based on the personnel activity and the future weather change, estimating the carbon emission of the future region; Step 4, based on the carbon absorption effect analysis result of the corresponding region and the carbon emission estimation result of the future region, performing regional carbon emission matching analysis; Step 5, based on the regional carbon emission matching analysis result, performing regional carbon emission early warning; wherein, the step 2 of analyzing the future growth of plants in the corresponding region comprises the following specific steps: Step 21, obtaining the growth characteristics of the corresponding plant, and performing plant growth anomaly analysis based on the growth characteristics of the corresponding plant, the plant growth anomaly analysis method being: obtaining the standard deviation of the height, diameter and color condition growth parameters of the plant in the current growth period and the standard growth range of the corresponding growth parameter period, and obtaining the plant growth anomaly by weighted sum of the standard deviation of each growth parameter; Step 22, obtaining the future situation of weather factors affecting plant growth in the weather forecast, obtaining the standard deviation of each weather factor and the range of safe weather factors of the corresponding growth period plant, and obtaining the weather influence anomaly by weighted sum of the standard deviation of each weather factor; Step 23, obtaining the land replacement of personnel expansion to plants, obtaining the land replacement speed, and setting the ratio of the land replacement speed to the standard land replacement speed as the personnel influence anomaly; Step 24, obtaining the plant growth anomaly, weather influence anomaly and personnel influence anomaly, and obtaining the future plant growth anomaly by weighted sum.

2. The big data fusion-based cold city carbon emission evaluation method according to claim 1, characterized in that, The weather change is the future situation of weather factors affecting plant growth in the weather forecast and the future situation of weather factors affecting plant photosynthesis, the personnel activity includes the number of personnel in the gathering area, the carbon emission change and the land replacement of expansion to plants, and the plant growth of the corresponding region includes the type of plant, the growth characteristics of plant and the coverage rate of plant, wherein the growth characteristics of plant include the height, diameter, color condition, leaf surface, leaf shedding and chlorophyll content of plant, which are obtained by plant statistical data.

3. The big data fusion-based cold city carbon emission evaluation method according to claim 2, characterized in that, The carbon absorption effect analysis in the step 2 comprises the following specific contents: Step 25, simulating the carbon source diffusion process under the influence of future environment by carbon source diffusion simulation software, obtaining the carbon source diffusion proportion of standard concentration carbon emission in each direction and the carbon emission height of each position; Step 26, obtain the plant canopy height situation of each angle, the plant leaf surface situation, the leaf shedding situation, and the chlorophyll content in the leaf, and obtain the weather factors affecting the photosynthesis of the plant and the growth anomaly of the future plant, analyze the photosynthetic rate of the plant based on the leaf surface situation, the leaf shedding situation, the chlorophyll content in the leaf, and the weather factors affecting the photosynthesis of the plant, obtain the photosynthetic rate anomaly analysis result by dividing the corresponding photosynthetic rate safety value by the photosynthetic rate of the future plant, and obtain the future photosynthetic anomaly of the corresponding plant by multiplying the photosynthetic rate anomaly analysis result and the growth anomaly of the future plant; Step 27, obtain the height anomaly of the corresponding position by the difference degree of the plant canopy height situation of each position in each angle and the carbon emission height range of each position, wherein the difference degree is a standard value 1 minus the intersection of the height ranges divided by the carbon emission height range, obtain the distance of each position relative to the carbon emission source, obtain the importance weight of each position based on the reciprocal of the distance, obtain the future carbon absorption anomaly of the corresponding position by weighted sum of the height anomaly of the corresponding position and the future photosynthetic anomaly of the plant of the corresponding position, obtain the future carbon absorption effect anomaly of the corresponding position by multiplying the future carbon absorption effect anomaly of the corresponding position and the importance weight, and obtain the future carbon absorption effect anomaly result of the corresponding angle by adding the future carbon absorption effect anomaly of each position in the corresponding angle.

4. The big data fusion-based cold city carbon emission evaluation method according to claim 3, characterized in that, The carbon emission prediction of the future area in the step 3 includes the following specific contents: Obtain the personnel activity situation and the future weather change situation of the corresponding area, input the personnel activity situation and the future weather change situation of the corresponding area into the constructed carbon emission neural network prediction model to predict the carbon emission amount of the future period, and obtain the carbon emission anomaly of the future period by dividing the carbon emission amount of the future period by the corresponding carbon emission safety amount, wherein the construction method of the carbon emission neural network prediction model is: obtaining the personnel quantity situation, the carbon emission change situation, and the land replacement situation of the plant caused by expansion in the historical area, and obtaining the weather change situation of the historical area, constructing a deep learning neural network model with the input of the personnel quantity situation, the land replacement situation of the plant caused by expansion, and the weather change situation of the area, and the output of the carbon emission change situation, dividing the historical data into a 75% data training set and a 25% data verification set, training the model by using the 75% data training set to obtain an initial deep learning neural network model, verifying the initial deep learning neural network model by using the 25% data verification set, and outputting the initial deep learning neural network model with the maximum carbon emission change data accuracy as the carbon emission neural network prediction model.

5. The big data fusion-based cold city carbon emission evaluation method according to claim 4, characterized in that, The regional carbon emission matching analysis in the step 4 includes the following specific contents: Obtain the future carbon absorption effect anomaly result of each angle of the region and the carbon emission anomaly of the future period, and obtain the carbon emission matching anomaly of each angle of the future region by weighted sum; The future regional carbon emission matching anomaly in each angle is compared with the set carbon emission matching anomaly threshold value. If the carbon emission matching anomaly in the corresponding angle is greater than or equal to the set carbon emission matching anomaly threshold value, the corresponding angle needs to be supplemented with plants, or the propaganda of reducing carbon emission needs to be carried out. If the carbon emission matching anomaly in the corresponding angle is less than the set carbon emission matching anomaly threshold value, the corresponding angle does not need to be supplemented with plants.

6. The big data fusion-based cold city carbon emission evaluation method according to claim 5, characterized in that, The step 5 includes the following specific contents: The angle needing to be supplemented with plants sends an early warning signal to the management department. The management department plants trees in the corresponding angle according to the angle needing to be supplemented with plants, and then performs steps 1 to 4 again. Finally, the carbon emission matching anomaly in the corresponding angle is less than the set carbon emission matching anomaly threshold value. The angle needing to be supplemented with plants sends an early warning signal to the management department. The management department plants trees in the corresponding angle according to the angle needing to be supplemented with plants, and then performs steps 1 to 4 again. Finally, the carbon emission matching anomaly in the corresponding angle is less than the set carbon emission matching anomaly threshold value.

7. The cold region city carbon emission evaluation system based on big data fusion is used to realize the cold region city carbon emission evaluation method based on big data fusion in any one of claims 1-6, characterized in that, The system comprises: A data acquisition module acquires weather changes in each region of the cold city, personnel activity conditions in each region, and plant growth conditions in the corresponding region. A carbon absorption effect analysis module analyzes future growth conditions of plants in the corresponding region based on the influence of personnel activity conditions and the influence of future weather changes, and analyzes carbon absorption effects based on future growth conditions of plants in each position of the carbon source in the corresponding region. A carbon emission estimation module estimates carbon emissions in the future region based on personnel activity conditions and future weather changes. A carbon emission matching analysis module performs regional carbon emission matching analysis based on carbon absorption effect analysis results of the corresponding region and carbon emission estimation results of the future region. An early warning module performs regional carbon emission early warning based on regional carbon emission matching analysis results.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the cold city carbon emission evaluation method based on big data fusion according to any one of claims 1-6 by calling the computer program stored in the memory.

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