Residential land carbon emission analysis method and device and storage medium

Abstract

The invention discloses a residential land carbon emission analysis method and device and a storage medium, and relates to the technical field of carbon emission analysis, and the method comprises the steps: obtaining land feature index data of a to-be-analyzed residential land, and inputting the land feature index data into a predetermined carbon emission prediction model, and obtaining a prediction result output by the carbon emission prediction model. According to the residential land carbon emission analysis method, the carbon emission analysis of the residential land can be realized only according to the land feature indexes, and the analyzed residential land can be the planned residential land, so that the carbon emission analysis of the residential land can be carried out without depending on the energy consumption data of the residential land, and the carbon emission analysis efficiency of the residential land is improved. And carbon emission analysis can be carried out on the planned residential land. According to the improved carbon emission prediction model provided by the embodiment of the invention, the carbon emission of the residential land can be analyzed more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission analysis, and in particular to a method and device for analyzing carbon emissions from residential land, and a storage medium. Background Art

[0002] As the concentration of human economic activities and resource and energy consumption, cities are the main areas that cause global warming and climate change. Some people predict that urban carbon emissions will reach 73% of the world's total in 2030, and the proportion of carbon dioxide emitted by urban areas will exceed 80% of the world's total, becoming the main source of carbon emissions. Residential land is the basic unit of urban carbon emissions, which is mainly composed of countless households. Relevant studies have shown that 30% to 40% of urban carbon emissions come from household carbon emissions. Therefore, residential land carbon emissions occupy an important position in the total urban carbon emissions. Low-carbon residential areas are important spatial carriers and basic units for building low-carbon cities. Analyzing residential land carbon emissions is of great significance for building low-carbon cities.

[0003] The existing technology for carbon emission analysis of residential land is mainly aimed at built residential land, and analyzes its carbon emissions based on the energy consumption of water, electricity, gas and other energy sources in the residential land. When the above data cannot be obtained or the residential land is in the planning stage, it is difficult to realize the carbon emission analysis of residential land. Therefore, how to analyze the carbon emission of residential land without relying on the energy consumption data of residential land, and how to analyze the carbon emission of residential land in planning, has become a technical problem that needs to be solved. Summary of the invention

[0004] The present invention provides a residential land carbon emission analysis method and device, and a storage medium, which are used to perform carbon emission analysis on residential land without relying on energy consumption data of the residential land, and can perform carbon emission analysis on planned residential land.

[0005] In order to solve the above technical problems, the present invention discloses a method for analyzing carbon emissions from residential land in a first aspect, the method comprising: Obtaining land use characteristic index data of the residential land to be analyzed, wherein the land use characteristic index data includes one or more of the following: community land area, total building area, volume ratio, green space ratio, building density, resident population density, job density, land use mix, road network density, intersection density, bus line network density, number of public service facilities, and parking space ratio per household; Inputting the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model, wherein the prediction result is used to represent the predicted amount of carbon emissions of the residential land to be analyzed; The carbon emission prediction model is obtained using multiple sets of training data sets, each set of training data sets includes land use characteristic index training data and carbon emissions corresponding to the characteristic index data, and the carbon emission prediction model is used to determine the corresponding carbon emissions according to the land use characteristic index data; The carbon emission prediction function expression of the carbon emission prediction model is:

[0006] in: is the predicted value of carbon emissions at the tth iteration; is the characteristic variable of carbon emissions; For about The prediction function of .

[0007] As an optional implementation, in the first aspect of the present invention, before inputting the land use characteristic index into the pre-trained carbon emission prediction model, the method further includes: The land use characteristic index data is normalized according to the following formula:

[0008] Among them, X is the data after normalization; x is the data before normalization; is the minimum value of the data; is the maximum value of the data.

[0009] As an optional embodiment, in the first aspect of the present invention, the method further comprises: For each of the land use characteristic indicators in the land use characteristic indicator data, the influence degree parameter corresponding to the land use characteristic indicator is calculated according to the following formula:

[0010] Among them, x is the land use characteristic index, is the influence degree parameter corresponding to the land use characteristic index x, and the influence degree parameter is used to measure the influence degree of the corresponding land use characteristic index on the carbon emission of the residential land to be analyzed, S is the set of all land use characteristic indicators, f is the prediction function, T is the characteristic indicator subset in the set S, n is the number of all land use characteristic indicators, It is the land use characteristic indicator other than T.

[0011] As an optional embodiment, in the first aspect of the present invention, the method further comprises: Determine whether the predicted carbon emission of the residential land to be analyzed is greater than a preset carbon emission standard value, and if it is determined that the predicted carbon emission is greater than the carbon emission standard value, calculate the carbon emission difference between the predicted carbon emission and the carbon emission standard value; According to the carbon emission difference and the impact degree parameter corresponding to each of the land use characteristic indicators, a planning modification plan for the residential land to be analyzed is determined, and the planning modification plan includes the data adjustment amount of each of the land use characteristic indicators.

[0012] As an optional embodiment, in the first aspect of the present invention, the method further comprises: Obtaining an interaction parameter between any two land use characteristic indicators, wherein the positive and negative signs of the interaction parameter indicate whether the two land use characteristic indicators have a positive or negative impact, and the magnitude of the interaction parameter indicates the degree of mutual influence between the two land use characteristic indicators; And, determining the planning modification scheme of the residential land to be analyzed according to the carbon emission difference and the impact degree parameter corresponding to each of the land characteristic indicators, comprises: Determining the initial data adjustment amount of each of the land use characteristic indicators according to the carbon emission difference and the impact degree parameter corresponding to each of the land use characteristic indicators; According to the initial data adjustment amount of all the land use characteristic indicators and the interaction parameter between any two land use characteristic indicators, the initial data adjustment amount of each land use characteristic indicator is corrected to obtain the data adjustment amount of each land use characteristic indicator, thereby determining the planning modification plan for the residential land to be analyzed.

[0013] As an optional implementation, in the first aspect of the present invention, before inputting the land use characteristic index data into the pre-trained carbon emission prediction model, the method further includes: Acquire location information and climate information of the residential land to be analyzed, and determine model parameters corresponding to the residential land to be analyzed according to the location information and climate information; wherein the location information includes longitude and latitude information and altitude information, and the climate information includes one or more of temperature information, humidity information, air pressure information, light information, wind direction information, and wind speed information; Based on the model parameters, a carbon emission prediction model is determined.

[0014] As an optional implementation manner, in the first aspect of the present invention, determining the model parameters corresponding to the residential land to be analyzed according to the location information and climate information includes: Analyze the periodic changes of the climate information within one year and determine the climate change cycle corresponding to the climate information; Determining a periodic model parameter corresponding to each of the climate change cycles according to the location information and the periodic climate information within each of the climate change cycles; And, determining a carbon emission prediction model based on the model parameters includes: For each of the climate change cycles, determining a carbon emission cycle prediction model corresponding to the climate change cycle according to the cycle model parameters corresponding to the climate change cycle; And, inputting the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model includes: Inputting the land use characteristic index data into each of the carbon emission cycle prediction models to obtain the cycle prediction results output by each of the carbon emission cycle prediction models; A prediction result is determined according to all the period prediction results and the time length of the climate change cycle corresponding to each of the period prediction results.

[0015] The second aspect of the present invention discloses a residential land carbon emission analysis device, the device comprising: An index acquisition module is used to acquire land use characteristic index data of the residential land to be analyzed, wherein the land use characteristic index data includes one or more of the following: community land area, total building area, volume ratio, green space ratio, building density, resident population density, job density, land use mix, road network density, intersection density, bus line network density, number of public service facilities and average parking space ratio per household; A carbon emission analysis module, used to input the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model, wherein the prediction result is used to represent the predicted amount of carbon emissions of the residential land to be analyzed; The carbon emission prediction model is obtained using multiple sets of training data sets, each set of training data sets includes land use characteristic index training data and carbon emissions corresponding to the characteristic index data, and the carbon emission prediction model is used to determine the corresponding carbon emissions according to the land use characteristic index data; The carbon emission prediction function expression of the carbon emission prediction model is:

[0016] in: is the predicted value of carbon emissions at the tth iteration; is the characteristic variable of carbon emissions; For about The prediction function of .

[0017] As an optional implementation, in the second aspect of the present invention, the device further includes: The normalization module is used to normalize the land use characteristic index data according to the following formula before inputting the land use characteristic index into the pre-trained carbon emission prediction model:

[0018] Among them, X is the data after normalization; x is the data before normalization; is the minimum value of the data; is the maximum value of the data.

[0019] As an optional implementation, in the second aspect of the present invention, the device further includes: The influence degree module is used to calculate the influence degree parameter corresponding to each of the land use characteristic indicators in the land use characteristic indicator data according to the following formula:

[0020] Among them, x is the land use characteristic index, is the influence degree parameter corresponding to the land use characteristic index x, and the influence degree parameter is used to measure the influence degree of the corresponding land use characteristic index on the carbon emission of the residential land to be analyzed, S is the set of all land use characteristic indicators, f is the prediction function, T is the characteristic indicator subset in the set S, n is the number of all land use characteristic indicators, It is the land use characteristic indicator other than T.

[0021] As an optional implementation, in the second aspect of the present invention, the device further includes: A judgment module, used to judge whether the predicted carbon emission of the residential land to be analyzed is greater than a preset carbon emission standard value, and if it is judged that the predicted carbon emission is greater than the carbon emission standard value, then calculate the carbon emission difference between the predicted carbon emission and the carbon emission standard value; The planning modification module determines the planning modification scheme of the residential land to be analyzed according to the carbon emission difference and the impact degree parameter corresponding to each of the land use characteristic indicators, and the planning modification scheme includes the data adjustment amount of each of the land use characteristic indicators.

[0022] As an optional implementation, in the second aspect of the present invention, the device further includes: The interaction impact analysis module is used to obtain the interaction impact parameter between any two land use characteristic indicators. The positive and negative signs of the interaction impact parameter indicate whether the two land use characteristic indicators have a positive impact or a negative impact. The magnitude of the interaction impact parameter indicates the degree of mutual influence between the two land use characteristic indicators. Furthermore, the planning modification module determines the planning modification scheme of the residential land to be analyzed according to the carbon emission difference and the impact degree parameter corresponding to each of the land characteristic indicators, including: Determining the initial data adjustment amount of each of the land use characteristic indicators according to the carbon emission difference and the impact degree parameter corresponding to each of the land use characteristic indicators; According to the initial data adjustment amount of all the land use characteristic indicators and the interaction parameter between any two land use characteristic indicators, the initial data adjustment amount of each land use characteristic indicator is corrected to obtain the data adjustment amount of each land use characteristic indicator, thereby determining the planning modification plan for the residential land to be analyzed.

[0023] As an optional implementation, in the second aspect of the present invention, the device further includes: A location acquisition module, used for acquiring the location information and climate information of the residential land to be analyzed before inputting the land characteristic index data into the pre-trained carbon emission prediction model, and determining the model parameters corresponding to the residential land to be analyzed according to the location information and climate information; wherein the location information includes longitude and latitude information and altitude information, and the climate information includes one or more of temperature information, humidity information, air pressure information, light information, wind direction information, and wind speed information; The model building module is used to determine a carbon emission prediction model based on the model parameters.

[0024] As an optional implementation, in the second aspect of the present invention, the location acquisition module determines the model parameters corresponding to the residential land to be analyzed according to the location information and climate information, including: Analyze the periodic changes of the climate information within one year and determine the climate change cycle corresponding to the climate information; Determining a periodic model parameter corresponding to each of the climate change cycles according to the location information and the periodic climate information within each of the climate change cycles; And, the model building module determines a carbon emission prediction model based on the model parameters, including: For each of the climate change cycles, determining a carbon emission cycle prediction model corresponding to the climate change cycle according to the cycle model parameters corresponding to the climate change cycle; Furthermore, the carbon emission analysis module inputs the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model, including: Inputting the land use characteristic index data into each of the carbon emission cycle prediction models to obtain the cycle prediction results output by each of the carbon emission cycle prediction models; A prediction result is determined according to all the period prediction results and the time length of the climate change cycle corresponding to each of the period prediction results.

[0025] The third aspect of the present invention discloses a residential land carbon emission analysis system, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the residential land carbon emission analysis method disclosed in the first aspect of the present invention.

[0026] A fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the residential land carbon emission analysis method disclosed in the first aspect of the present invention.

[0027] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains land use characteristic index data of the residential land to be analyzed, and then inputs the land use characteristic index data into a predetermined carbon emission prediction model to obtain the prediction result output by the carbon emission prediction model. The residential land carbon emission analysis method of the present invention can realize the carbon emission analysis of the residential land only based on the land use characteristic index, and the residential land to be analyzed can be the planned residential land. Therefore, the embodiment of the present invention can perform carbon emission analysis on the residential land without relying on the energy consumption data of the residential land, and can also perform carbon emission analysis on the planned residential land. And the improved carbon emission prediction model proposed in the embodiment of the present invention can more accurately analyze the carbon emissions of residential land. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 It is a flow chart of a residential land carbon emission analysis method disclosed in an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a residential land carbon emission analysis device disclosed in an embodiment of the present invention; Figure 3 It is a structural schematic diagram of another residential land carbon emission analysis device disclosed in an embodiment of the present invention; Figure 4It is a structural schematic diagram of a residential land carbon emission analysis system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] The present invention discloses a residential land carbon emission analysis method, device and storage medium, which are used to perform carbon emission analysis on residential land without relying on energy consumption data of the residential land, and can perform carbon emission analysis on planned residential land.

[0034] Embodiment 1 See also Figure 1 , Figure 1 This is a flow chart of a method for analyzing carbon emissions from residential land disclosed in an embodiment of the present invention. Figure 1 The residential land carbon emission analysis method described above can be applied to a residential land carbon emission analysis device, and the residential land carbon emission analysis device can be integrated in a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the residential land carbon emission analysis method may include the following operations: Step 101: Obtain land use characteristic index data of the residential land to be analyzed.

[0035] In an embodiment of the present invention, the land use characteristic index data may include one or more of the community land area, total building area, volume ratio, green space ratio, building density, permanent population density, job density, land use mix, road network density, intersection density, bus network density, the number of public service facilities and the average parking space ratio per household. The above-mentioned land use characteristic index may be the characteristics of the residential land that has been built, or the characteristics of the residential land that is being planned. For the residential land that has been built and put into use, the above-mentioned land use characteristic index may be obtained by actual measurement or by obtaining data from relevant data sources. For the residential land that is being planned, it may be analyzed from the planning map.

[0036] Optionally, the land use characteristic indicator data can be displayed and described by grouping, as shown in the following table:

[0037] Step 102: input the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model, where the prediction result is used to represent the predicted amount of carbon emissions of the residential land to be analyzed.

[0038] In an embodiment of the present invention, a carbon emission prediction model is obtained using multiple sets of training data sets, each set of training data sets may include land use characteristic indicator training data and carbon emissions corresponding to the characteristic indicator data, and the carbon emission prediction model is used to determine the corresponding carbon emissions based on the land use characteristic indicator data.

[0039] In the embodiment of the present invention, there is a complex potential nonlinear relationship between carbon emissions from residential land and land characteristic indicators such as floor area ratio, green area ratio, building height, and building density, and there is also a mutual influence relationship between different land characteristic indicators. Therefore, it is necessary to propose a machine learning model that can match the above relationship. Commonly used machine learning models include support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision tree model (GBDT), artificial neural network (BP), etc. However, the above models cannot adapt well to the relationship between land characteristic indicators and carbon emissions. For this reason, the embodiment of the present invention is improved on the basis of the gradient boosting decision tree model (GBDT), and the following carbon emission prediction model is proposed: The carbon emission prediction function expression of the carbon emission prediction model is:

[0040] in: is the predicted value of carbon emissions at the tth iteration; is the characteristic variable of carbon emissions; For about The prediction function of During the training process, the carbon emission prediction model first iteratively generates many decision trees, and then merges the results of all trees to generate the final model. Each individual decision tree is built on the basis of minimizing a certain specified loss function, and each time a separate decision tree is built, a higher weight is given to the cases that are miscalculated in the previous tree. The carbon emission prediction model establishes multiple weak learners, uses the decision tree as the base learner, and adopts the gradient boosting algorithm to improve the generality or generalization ability of the model.

[0041] In a preferred embodiment, the model parameters of the carbon emission prediction model are set as follows: learning rate is 0.001, maximum tree depth is 10, minimum leaf node sample weight sum is 4, leaf node weight lambda is 0.1, leaf node weight alpha is 0.2, and maximum number of rounds is 10000.

[0042] In the embodiment of the present invention, the carbon emission prediction model reduces the model prediction error by sequentially integrating decision trees through an optimization algorithm. The model accuracy is usually better than models such as RF or neural networks. This is mainly reflected in the fact that when the carbon emission prediction model processes spatially heterogeneous data, it can handle the nonlinear and complex interaction effects between different features. It has the advantages of fast computing speed, high accuracy, strong robustness, etc. It can be used to construct regression and classification models, and the computing efficiency of GBDT is greatly improved through distributed computing.

[0043] In the embodiment of the present invention, the carbon emission analysis of residential land can be realized only based on the land characteristic index, and the residential land analyzed can be the residential land in the plan. Therefore, the embodiment of the present invention can perform carbon emission analysis on residential land without relying on the energy consumption data of the residential land, and can also perform carbon emission analysis on the residential land in the plan. And the improved carbon emission prediction model proposed in the embodiment of the present invention can analyze the carbon emission of residential land more accurately.

[0044] In an optional embodiment, in order to improve data reliability, the land use characteristic index data can be normalized. For example, due to the different areas of residential areas, in order to avoid the impact of residential area size on carbon emissions and solve the difference in variable scale caused by data of different dimensions, the rationality of the sample size in the feature space can be ensured through data normalization. Normalization aims to reduce the data scale, reduce data prediction errors, and accelerate algorithm convergence.

[0045] Therefore, in this optional embodiment, before inputting the land use characteristic index into the pre-trained carbon emission prediction model, the method may further include: The land use characteristic index data is normalized according to the following formula:

[0046] Among them, X is the data after normalization; x is the data before normalization; is the minimum value of the data; is the maximum value of the data.

[0047] In another optional embodiment, the method may further include: For each land use characteristic indicator in the land use characteristic indicator data, the impact degree parameter corresponding to the land use characteristic indicator is calculated according to the following formula:

[0048] Among them, x is the land use characteristic index, is the influence degree parameter corresponding to the land use characteristic index x, S is the set of all land use characteristic indicators, f is the prediction function, T is the characteristic indicator subset in the set S, n is the number of all land use characteristic indicators, It is the land use characteristic indicator other than T.

[0049] In this optional embodiment, due to the natural "black box" property of machine learning, it cannot well characterize the importance of each influencing factor. In response to the above problems, this optional embodiment proposes a method for calculating the degree of influence parameter based on the Shapley value principle in game theory. The degree of influence parameter is used to measure the influence of the corresponding land use characteristic index on the carbon emissions of the residential land to be analyzed. It can be understood as the importance of the contribution of individual input features to the model prediction value, and can be used to reveal the threshold and interactive synergy between the explanatory variable and the explained variable.

[0050] This optional embodiment can calculate the influence degree parameter of each land use characteristic indicator, so as to analyze the importance of different land use characteristic indicators.

[0051] In another optional embodiment, the above-mentioned carbon emission analysis results can be used to measure the carbon emission level of residential land. Furthermore, the planning and construction of residential land can be guided based on the feedback of the analysis results. For example, for residential land that has been built, improvements can be made based on the carbon emission analysis results; for residential land that is still under planning, the plan can be modified based on the analysis results, thereby providing support for low-carbon city construction.

[0052] Therefore, in this optional embodiment, the method may further include: Determine whether the predicted carbon emission of the residential land to be analyzed is greater than the preset carbon emission standard value. If it is determined that the predicted carbon emission is greater than the carbon emission standard value, calculate the carbon emission difference between the predicted carbon emission and the carbon emission standard value. According to the carbon emission difference and the impact degree parameters corresponding to each land use characteristic indicator, the planning modification plan for the residential land to be analyzed is determined, and the planning modification plan may include the data adjustment amount for each land use characteristic indicator.

[0053] In this optional embodiment, if the carbon emissions of residential land exceed the standard value, it means that the residential land can be improved accordingly to meet the requirements of low-carbon city construction. To this end, it is necessary to optimize each land use characteristic index. During the optimization process, the data adjustment amount of different land use characteristic indicators is reasonably allocated based on the impact degree parameters corresponding to each land use characteristic index. For example, the impact degree parameter is equivalent to a data adjustment weight value, so that each land use characteristic index parameter is allocated a corresponding data adjustment ratio or range.

[0054] In this optional embodiment, it is further optional to preset the adjustable range for each land use characteristic indicator. For example, the residential land area and the total building area are generally fixed and difficult to adjust, so even if the impact is large, it is difficult to adjust them. For green space ratio and the number of public service facilities, the adjustment range is large, and a larger adjustable range can be set for them. According to the adjustable range and the impact parameter, the data adjustment amount of each land use characteristic indicator is calculated.

[0055] In an optional embodiment, different land use characteristic indicators have mutual influences. For example, the optional embodiment finds that multiple characteristic indicators have interactive effects with building density, among which the land use mix and building density have opposite effects on carbon emissions in residential areas, and when the floor area ratio is lower than about 2.0 and the building density is greater than about 31%, there is a negative interactive relationship between the two. Therefore, in order to reduce carbon emissions from residential land, the two should be matched within a reasonable range. For another example, there is a positive interactive relationship between the total building area and the building density, and the two interact in the same direction. A higher total building area and building density will produce a positive interaction and aggravate the generation of carbon emissions. When the floor area ratio is less than about 2.0, as the land use mix increases, carbon emissions remain basically unchanged or even slightly decrease, which suggests that if you want to reduce carbon emissions from residential land, reducing the floor area ratio can be an effective option. The bus network density and the average parking space ratio per household are mutually exclusive. When both values ​​are at a low level, they have a negative effect on carbon emissions.

[0056] Therefore, the method may further include: Obtain the interaction parameter between any two land use characteristic indicators. The positive or negative sign of the interaction parameter indicates whether the two land use characteristic indicators have a positive or negative impact. The magnitude of the interaction parameter indicates the degree of mutual influence between the two land use characteristic indicators. And, according to the carbon emission difference and the impact degree parameter corresponding to each land use characteristic indicator, the planning modification plan of the residential land to be analyzed is determined, which may include: Determine the initial data adjustment amount for each land use characteristic indicator based on the carbon emission difference and the impact degree parameter corresponding to each land use characteristic indicator; According to the initial data adjustment amount of all land use characteristic indicators and the interaction parameter between any two land use characteristic indicators, the initial data adjustment amount of each land use characteristic indicator is corrected to obtain the data adjustment amount of each land use characteristic indicator, thereby determining the planning modification plan for the residential land to be analyzed.

[0057] In this optional embodiment, when determining the data adjustment amount of each land use characteristic indicator, the initial data adjustment amount of each land use characteristic indicator is first determined based on the carbon emission difference and the influence degree parameter corresponding to each land use characteristic indicator. The initial data adjustment amount only considers the influence degree parameter of each land use characteristic indicator, and does not consider the influence between different land use characteristic indicators. Therefore, the initial data adjustment amount can be corrected according to the influence of different land use characteristic indicators. For example: the influence parameter between indicator A and indicator B is -1.2, that is, if the influence of indicator A on carbon emissions increases by one unit, the influence of indicator B on carbon emissions decreases by 1.2 units. Therefore, it is necessary to correct the initial data adjustment amount of each land use characteristic indicator based on the initial data adjustment amount of all land use characteristic indicators and the interactive influence parameter between any two land use characteristic indicators to obtain the data adjustment amount of each land use characteristic indicator. Thereby providing a more reasonable adjustment scheme for the feedback design of residential land.

[0058] In another optional embodiment, the carbon emissions of places with different geographical locations and different climatic conditions are actually different. For example, the north is colder in winter and there is a need for heating, which increases carbon emissions; while some places with pleasant climates have little temperature difference throughout the year and less carbon emissions. Therefore, only one carbon emission prediction model is used to analyze the carbon emissions of residential land, which may result in errors due to the above reasons.

[0059] Therefore, in this optional embodiment, before inputting the land use characteristic index data into the pre-trained carbon emission prediction model, the method may further include: Acquire location information and climate information of the residential land to be analyzed, and determine model parameters corresponding to the residential land to be analyzed according to the location information and climate information; wherein the location information may include longitude and latitude information and altitude information, and the climate information may include one or more of temperature information, humidity information, air pressure information, light information, wind direction information, and wind speed information; Based on the model parameters, a carbon emission prediction model is determined.

[0060] In this optional embodiment, a series of models can be trained in advance based on some typical locations and residential land under climatic conditions, and the model parameters that determine the models can be saved. When carbon emission analysis is required for the residential land to be analyzed, the location information and climate information of the residential land to be analyzed are obtained, and the saved model parameters that match the location information and climate information are selected based on the location information and climate information, thereby constructing a carbon emission prediction model. Through the above operation, a more accurate carbon emission prediction model can be provided.

[0061] In another optional embodiment, the carbon emissions of the same residential land also change throughout the year. For example, heating in winter results in more carbon emissions, while the temperature in spring and autumn is suitable and the carbon emissions are lower. However, not all places have distinct four seasons. Some areas often have only two or three change cycles in a year. Therefore, it is necessary to first determine the climate change cycle based on the climate information of the residential land, and then analyze the carbon emissions in each cycle separately, so as to further improve the accuracy of the model analysis.

[0062] Therefore, in this optional embodiment, determining the model parameters corresponding to the residential land to be analyzed based on the location information and climate information may include: Analyze the periodic changes of climate information within a year and determine the climate change cycle corresponding to the climate information; Determine the periodic model parameters corresponding to each climate change cycle based on the location information and the periodic climate information within each climate change cycle; And, based on the model parameters, determining a carbon emission prediction model may include: For each climate change cycle, a carbon emission cycle prediction model corresponding to the climate change cycle is determined based on the cycle model parameters corresponding to the climate change cycle; And, inputting the land use characteristic index data into a predetermined carbon emission prediction model to obtain the prediction results output by the carbon emission prediction model may include: Input the land use characteristic index data into each carbon emission cycle prediction model to obtain the cycle prediction results output by each carbon emission cycle prediction model; The prediction results are determined based on all period prediction results and the time length of the climate change cycle corresponding to each period prediction result.

[0063] In this optional embodiment, different climate change cycles also correspond to different prediction models. For example, climate information includes spring climate information, summer climate information, autumn climate information and spring climate information; model parameters include spring model parameters, summer model parameters, autumn model parameters and spring model parameters. At this time, the entire prediction model is actually composed of several period prediction models, and each period prediction model determines the weight according to the time length of its corresponding climate change cycle. Finally, the prediction results of each period prediction model are synthesized to obtain the entire prediction result. This optional embodiment can take into account the factors of the climate change cycle, so as to determine the prediction results more reasonably.

[0064] Embodiment 2 See also Figure 2 , Figure 2 Schematic diagram of a carbon emission analysis device for residential land disclosed in an embodiment of the present invention. Figure 2 As shown, the residential land carbon emission analysis device may include: The index acquisition module 201 is used to acquire the land use characteristic index data of the residential land to be analyzed, and the land use characteristic index data may include one or more of the following: the residential land area, the total building area, the volume ratio, the green area ratio, the building density, the resident population density, the job density, the land use mix, the road network density, the intersection density, the bus line network density, the number of public service facilities and the parking space ratio per household; The carbon emission analysis module 202 is used to input the land use characteristic index data into a predetermined carbon emission prediction model to obtain the prediction result output by the carbon emission prediction model, and the prediction result is used to represent the carbon emission prediction amount of the residential land to be analyzed; The carbon emission prediction model is obtained using multiple sets of training data sets, each set of training data sets may include land use characteristic index training data and carbon emissions corresponding to the characteristic index data, and the carbon emission prediction model is used to determine the corresponding carbon emissions according to the land use characteristic index data; Among them, the carbon emission prediction function expression of the carbon emission prediction model is:

[0065] in: is the predicted value of carbon emissions at the tth iteration; is the characteristic variable of carbon emissions; For about The prediction function of .

[0066] In an optional embodiment, if Figure 3 As shown, the device may also include: The normalization module 203 is used to normalize the land use characteristic index data according to the following formula before inputting the land use characteristic index into the pre-trained carbon emission prediction model:

[0067] Among them, X is the normalized data; x is the data before normalization; is the minimum value of the data; is the maximum value of the data.

[0068] In another optional embodiment, Figure 3 As shown, the device may also include: The influence degree module 204 is used to calculate the influence degree parameter corresponding to each land use characteristic indicator in the land use characteristic indicator data according to the following formula:

[0069] Among them, x is the land use characteristic index, is the impact degree parameter corresponding to the land use characteristic index x, which is used to measure the impact degree of the corresponding land use characteristic index on the carbon emissions of the residential land to be analyzed. S is the set of all land use characteristic indicators, f is the prediction function, T is the characteristic indicator subset in the set S, n is the number of all land use characteristic indicators, It is the land use characteristic indicator other than T.

[0070] In yet another optional embodiment, the device may further include: The judgment module 205 is used to judge whether the predicted carbon emission of the residential land to be analyzed is greater than the preset carbon emission standard value. If it is judged that the predicted carbon emission is greater than the carbon emission standard value, the carbon emission difference between the predicted carbon emission and the carbon emission standard value is calculated; The planning modification module 206 determines the planning modification scheme of the residential land to be analyzed according to the carbon emission difference and the impact degree parameter corresponding to each land use characteristic indicator. The planning modification scheme may include the data adjustment amount of each land use characteristic indicator.

[0071] In yet another optional embodiment, the device may further include: The interactive impact analysis module 207 is used to obtain the interactive impact parameter between any two land use characteristic indicators. The positive and negative signs of the interactive impact parameter indicate whether the two land use characteristic indicators have a positive impact or a negative impact. The magnitude of the interactive impact parameter indicates the degree of mutual impact between the two land use characteristic indicators. Furthermore, the planning modification module 206 determines the planning modification scheme of the residential land to be analyzed according to the carbon emission difference and the impact degree parameter corresponding to each land characteristic indicator, which may include: Determine the initial data adjustment amount for each land use characteristic indicator based on the carbon emission difference and the impact degree parameter corresponding to each land use characteristic indicator; According to the initial data adjustment amount of all land use characteristic indicators and the interaction parameter between any two land use characteristic indicators, the initial data adjustment amount of each land use characteristic indicator is corrected to obtain the data adjustment amount of each land use characteristic indicator, thereby determining the planning modification plan for the residential land to be analyzed.

[0072] In yet another optional embodiment, the device may further include: The location acquisition module 208 is used to obtain the location information and climate information of the residential land to be analyzed before inputting the land characteristic index data into the pre-trained carbon emission prediction model, and determine the model parameters corresponding to the residential land to be analyzed according to the location information and climate information; wherein the location information may include longitude and latitude information and altitude information, and the climate information may include one or more of temperature information, humidity information, air pressure information, light information, wind direction information, and wind speed information; The model building module 209 is used to determine a carbon emission prediction model based on model parameters.

[0073] In yet another optional embodiment, the location acquisition module 208 determines the model parameters corresponding to the residential land to be analyzed according to the location information and the climate information, which may include: Analyze the periodic changes of climate information within a year and determine the climate change cycle corresponding to the climate information; Determine the periodic model parameters corresponding to each climate change cycle based on the location information and the periodic climate information within each climate change cycle; Furthermore, the model building module 209 determines the carbon emission prediction model based on the model parameters, which may include: For each climate change cycle, a carbon emission cycle prediction model corresponding to the climate change cycle is determined based on the cycle model parameters corresponding to the climate change cycle; Furthermore, the carbon emission analysis module 202 inputs the land use characteristic index data into a predetermined carbon emission prediction model to obtain the prediction result output by the carbon emission prediction model, which may include: Input the land use characteristic index data into each carbon emission cycle prediction model to obtain the cycle prediction results output by each carbon emission cycle prediction model; The prediction results are determined based on all period prediction results and the time length of the climate change cycle corresponding to each period prediction result.

[0074] Embodiment 3 See also Figure 4 , Figure 4Schematic diagram of a carbon emission analysis system for residential land disclosed in an embodiment of the present invention. Figure 4 As shown, the residential land carbon emission analysis system may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the residential land carbon emission analysis method described in the first embodiment of the present invention.

[0075] Embodiment 4 An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the residential land carbon emission analysis method described in the first embodiment of the present invention.

[0076] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the residential land carbon emission analysis method described in Example 1.

[0077] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0078] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0079] Finally, it should be noted that the residential land carbon emission analysis method, device, and storage medium disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing carbon emissions from residential land, characterized in that: The method comprises: Obtaining land use characteristic index data of the residential land to be analyzed, wherein the land use characteristic index data includes one or more of the following: community land area, total building area, volume ratio, green space ratio, building density, resident population density, job density, land use mix, road network density, intersection density, bus line network density, number of public service facilities, and parking space ratio per household; Inputting the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model, wherein the prediction result is used to represent the predicted amount of carbon emissions of the residential land to be analyzed; The carbon emission prediction model is obtained using multiple sets of training data sets, each set of training data sets includes land use characteristic index training data and carbon emissions corresponding to the characteristic index data, and the carbon emission prediction model is used to determine the corresponding carbon emissions according to the land use characteristic index data; The carbon emission prediction function expression of the carbon emission prediction model is: in: is the predicted value of carbon emissions at the tth iteration; is the characteristic variable of carbon emissions; About The prediction function of .

2. The residential land carbon emission analysis method according to claim 1, characterized in that: Before inputting the land use characteristic index into the pre-trained carbon emission prediction model, the method further includes: The land use characteristic index data is normalized according to the following formula: Among them, X is the data after normalization; x is the data before normalization; is the minimum value of the data; is the maximum value of the data.

3. The residential land carbon emission analysis method according to claim 1, characterized in that: The method further comprises: For each of the land use characteristic indicators in the land use characteristic indicator data, the influence degree parameter corresponding to the land use characteristic indicator is calculated according to the following formula: Among them, x is the land use characteristic index, is the influence degree parameter corresponding to the land use characteristic index x, and the influence degree parameter is used to measure the influence degree of the corresponding land use characteristic index on the carbon emission of the residential land to be analyzed, S is the set of all land use characteristic indicators, f is the prediction function, T is the characteristic indicator subset in the set S, n is the number of all land use characteristic indicators, It is the land use characteristic indicator other than T.

4. The method for analyzing carbon emissions from residential land according to claim 3, characterized in that: The method further comprises: Determine whether the predicted carbon emission of the residential land to be analyzed is greater than a preset carbon emission standard value, and if it is determined that the predicted carbon emission is greater than the carbon emission standard value, calculate the carbon emission difference between the predicted carbon emission and the carbon emission standard value; According to the carbon emission difference and the impact degree parameter corresponding to each of the land use characteristic indicators, a planning modification plan for the residential land to be analyzed is determined, and the planning modification plan includes the data adjustment amount of each of the land use characteristic indicators.

5. The residential land carbon emission analysis method according to claim 4, characterized in that: The method further comprises: Obtaining an interaction parameter between any two land use characteristic indicators, wherein the positive and negative signs of the interaction parameter indicate whether the two land use characteristic indicators have a positive or negative impact, and the magnitude of the interaction parameter indicates the degree of mutual influence between the two land use characteristic indicators; And, determining the planning modification scheme of the residential land to be analyzed according to the carbon emission difference and the impact degree parameter corresponding to each of the land characteristic indicators, comprises: Determining the initial data adjustment amount of each of the land use characteristic indicators according to the carbon emission difference and the impact degree parameter corresponding to each of the land use characteristic indicators; According to the initial data adjustment amount of all the land use characteristic indicators and the interaction parameter between any two land use characteristic indicators, the initial data adjustment amount of each land use characteristic indicator is corrected to obtain the data adjustment amount of each land use characteristic indicator, thereby determining the planning modification plan for the residential land to be analyzed.

6. The residential land carbon emission analysis method according to claim 1, characterized in that: Before inputting the land use characteristic index data into the pre-trained carbon emission prediction model, the method further includes: Acquire location information and climate information of the residential land to be analyzed, and determine model parameters corresponding to the residential land to be analyzed according to the location information and climate information; wherein the location information includes longitude and latitude information and altitude information, and the climate information includes one or more of temperature information, humidity information, air pressure information, light information, wind direction information, and wind speed information; Based on the model parameters, a carbon emission prediction model is determined.

7. The method for analyzing carbon emissions from residential land according to claim 6, characterized in that: Determining the model parameters corresponding to the residential land to be analyzed according to the location information and climate information includes: Analyze the periodic changes of the climate information within one year and determine the climate change cycle corresponding to the climate information; Determining a periodic model parameter corresponding to each of the climate change cycles according to the location information and the periodic climate information within each of the climate change cycles; And, determining a carbon emission prediction model based on the model parameters includes: For each of the climate change cycles, determining a carbon emission cycle prediction model corresponding to the climate change cycle according to the cycle model parameters corresponding to the climate change cycle; And, inputting the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model includes: Inputting the land use characteristic index data into each of the carbon emission cycle prediction models to obtain the cycle prediction results output by each of the carbon emission cycle prediction models; A prediction result is determined according to all the period prediction results and the time length of the climate change cycle corresponding to each of the period prediction results.

8. A residential land carbon emission analysis device, characterized in that: The device comprises: An index acquisition module is used to acquire land use characteristic index data of the residential land to be analyzed, wherein the land use characteristic index data includes one or more of the following: community land area, total building area, volume ratio, green space ratio, building density, resident population density, job density, land use mix, road network density, intersection density, bus line network density, number of public service facilities and average parking space ratio per household; A carbon emission analysis module, used to input the land use characteristic index data into a predetermined carbon emission prediction model to obtain a prediction result output by the carbon emission prediction model, wherein the prediction result is used to represent the predicted amount of carbon emissions of the residential land to be analyzed; The carbon emission prediction model is obtained using multiple sets of training data sets, each set of training data sets includes land use characteristic index training data and carbon emissions corresponding to the characteristic index data, and the carbon emission prediction model is used to determine the corresponding carbon emissions according to the land use characteristic index data; The carbon emission prediction function expression of the carbon emission prediction model is: in: is the predicted value of carbon emissions at the tth iteration; is the characteristic variable of carbon emissions; About The prediction function of .

9. A residential land carbon emission analysis system, characterized in that: The system includes: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the residential land carbon emission analysis method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the residential land carbon emission analysis method as described in any one of claims 1-7.

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