Carbon emission optimization method and device based on high-precision carbon emission calculation

Abstract

The present application discloses a carbon emission optimization method, device, equipment and medium based on high-precision carbon emission calculation, which can optimize and iterate the project to be optimized according to the optimization restriction information and design reference information of the project to be optimized, so as to fully consider the purpose and effect of the design requirements of the project to be optimized during the optimization process, so that the target optimization information after the optimization iteration can not only meet the purpose of reducing carbon emissions relative to the basic information, but also meet the optimization restriction information and design reference information of the current design requirements of the project to be optimized, so as to meet the personalized needs of reducing carbon emissions. Moreover, the embodiment of the present application also uses a high-precision carbon emission algorithm to calculate carbon emissions during the iteration process, which greatly improves the accuracy of the carbon emission calculation of the optimization information during the optimization iteration process, and provides a reliable guarantee for the optimization effect of the optimization iteration.

Description

Technical Field

[0001] The present disclosure generally relates to the field of carbon emission optimization technology, and more particularly to a carbon emission optimization method, device, equipment and medium based on high-precision carbon emission calculation. Background Art

[0002] At a time when environmental protection and sustainable development are increasingly attracting global attention, the assessment and reduction of project carbon emissions has become an important issue. However, existing carbon emission calculation methods have some significant shortcomings. For example, CN202410454202.7 discloses a method and device for implementing a dynamic knowledge graph based on a graph database. The method is based on the graph database NebulaGraph and proposes a way to record the entire life cycle of data generation, change, deletion, etc. to display the evolution process of community topology or individual attribute information on the graph. CN202410444187.8 discloses a method, device, computer equipment and storage medium for large-model multi-round dialogue, and its construction method is: using historical question and answer pairs and the latest user questions as nodes to construct a graph of the target conversation; calculating the correlation scores between historical question and answer pairs and between the latest user questions and historical question and answer pairs; constructing directed connections in the graph; marking and filtering the directed relationships in the graph according to the correlation scores, and obtaining the connected sub-relationships corresponding to the latest user questions. Figure 2 Delete low-correlation nodes from the connected subgraph to obtain related historical question-answer pairs that are closely related to the question node and meet the token processing restrictions of the large language model; input the related historical question-answer pairs and the latest user questions into the large language model to obtain the output results of the latest user questions. CN202410444328.6 discloses an intelligent question-answering method and system, medium, and device for the field of information security. The intelligent question-answering method comprises: obtaining the question input by the user end as the question text, vectorizing the question text, and obtaining the question vector; vectorizing the knowledge text in the knowledge base to obtain a knowledge vector data set composed of multiple knowledge vectors; according to the question vector, vector retrieval is performed on the knowledge vector data set to obtain the N knowledge vectors with the highest correlation with the question vector in the knowledge vector data set; according to the question text and the knowledge text corresponding to the N knowledge vectors, an answer text set is obtained; vectorizing the answer text set to obtain multiple answer vectors; performing similarity analysis on the multiple answer vectors with the question vector respectively; selecting the answer text corresponding to the answer vector with the highest similarity as the output answer.

[0003] It can be seen that although these patents use artificial intelligence models to input carbon emission information, there are still problems with limited input information, such as the need to follow the problem chain for input, etc. Therefore, there is a lack of accurate assessment capabilities for the entire life cycle of complex projects. This results in the calculation results being unable to fully and accurately reflect the actual carbon emissions of the project, which in turn affects decision makers in formulating emission reduction strategies. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desired to provide a carbon emission optimization method and device based on high-precision carbon emission calculation, which improves the reliability and accuracy of carbon emission optimization and makes the final target optimization information more reasonable and accurate.

[0005] In a first aspect, an embodiment of the present application provides a carbon emission optimization method based on high-precision carbon emission calculation, including:

[0006] Get the text and image information of the project to be optimized;

[0007] Based on the text information and the image information, extract basic information, optimization restriction information and design reference information of the project to be optimized;

[0008] Based on the basic information, using a high-precision carbon emission algorithm to calculate basic carbon emissions;

[0009] The basic information is optimized iteratively based on the optimization restriction information and the design reference information, and the optimized carbon emissions corresponding to the optimization information obtained in each iteration are calculated using the high-precision carbon emission algorithm, until the target optimization information that meets the optimization restriction information and the design reference information is obtained, and the optimized carbon emissions corresponding to the target are the lowest.

[0010] In some embodiments, the calculating basic carbon emissions based on the basic information using a high-precision carbon emissions algorithm includes:

[0011] Based on the basic information, determining each implementation stage corresponding to the project to be optimized and characteristic data corresponding to each implementation stage;

[0012] Obtaining carbon emission calculation characteristics and carbon emission factors corresponding to each of the implementation stages;

[0013] Based on the carbon emission calculation characteristics corresponding to each of the implementation stages, the carbon emission factors and the characteristic data corresponding to the project to be optimized, the corresponding carbon emission factors of each of the implementation stages of the project to be optimized are calculated.

[0014] The basic carbon emissions of the corresponding stage;

[0015] The basic carbon emissions are obtained based on the stage basic carbon emissions corresponding to each of the implementation stages.

[0016] In some embodiments, the optimizing iteratively performing optimization on the basic information based on the optimization restriction information and the design reference information, and calculating the optimized carbon emissions corresponding to the optimized information obtained in each iteration using the high-precision carbon emission algorithm, includes:

[0017] Inputting the basic information, the optimization restriction information and the design reference information into a prediction model to obtain optimized prediction data; the optimized prediction data includes at least one element to be optimized;

[0018] For each of the elements to be optimized, within the element constraint range corresponding to the element to be optimized in the optimization restriction information, the element to be optimized is optimized and adjusted, and optimization information corresponding to the element to be optimized is generated;

[0019] The optimized carbon emissions corresponding to the element to be optimized are calculated based on the high-precision carbon emissions algorithm.

[0020] In some embodiments, the optimizing adjustment of the element to be optimized includes optimizing the area of ​​the element to be optimized; the element constraint range corresponding to the element to be optimized includes an element area greater than or equal to 0 and an area ratio with at least one associated element remains unchanged.

[0021] In some embodiments, the optimization constraint information further includes a cost constraint, and the method further includes:

[0022] Based on each type of optimization information, calculate the cost information, cost optimization amount and carbon emission optimization amount corresponding to each type of optimization information;

[0023] Based on the carbon emission optimization amount and the cost optimization amount, determining a first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost information;

[0024] Obtain the second-order optimization weights corresponding to the optimized carbon emissions and costs;

[0025] Determining an evaluation index corresponding to the optimization information based on the first-order optimization evaluation and the second-order optimization weight;

[0026] The optimization information with the largest evaluation index is used as the target optimization information.

[0027] In some embodiments, determining the first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost information based on the carbon emission optimization amount and the cost optimization amount includes:

[0028] Determining the possibility of obtaining the carbon emission optimization amount and the cost optimization amount from a plurality of evaluation results based on the size relationships between the carbon emission optimization amount and the cost optimization amount and the corresponding plurality of reference coefficients respectively;

[0029] Based on the multiple acquisition possibilities, a first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost optimization amount is constructed.

[0030] In a second aspect, an embodiment of the present application provides a carbon emission optimization device based on high-precision carbon emission calculation, comprising:

[0031] An acquisition module is used to obtain text information and image information of the project to be optimized;

[0032] An extraction module, used for extracting basic information, optimization restriction information and design reference information of the project to be optimized based on the text information and the image information;

[0033] A calculation module, used to calculate basic carbon emissions based on the basic information using a high-precision carbon emission algorithm;

[0034] An iterative module is used to optimize and iterate the basic information based on the optimization restriction information and the design reference information, and use the high-precision carbon emission algorithm to calculate the optimized carbon emissions corresponding to the optimization information obtained in each iteration, until the target optimization information that meets the optimization restriction information and the design reference information is obtained, and the optimized carbon emissions corresponding to the target are the lowest.

[0035] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the embodiment of the present application when executing the program.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the embodiment of the present application.

[0037] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the method described in the embodiment of the present application.

[0038] In the optimization process, the embodiment of the present application can optimize and iterate the project to be optimized according to the optimization restriction information and design reference information of the project to be optimized, so as to fully consider the purpose and effect of the design requirements of the project to be optimized in the optimization process, so that the target optimization information after the optimization iteration can not only meet the purpose of reducing carbon emissions relative to the basic information, but also meet the optimization restriction information and design reference information of the current design requirements of the project to be optimized, so as to meet the personalized needs of reducing carbon emissions. Moreover, the embodiment of the present application also uses a high-precision carbon emission algorithm to calculate carbon emissions in the iteration process, which greatly improves the accuracy of the carbon emission calculation of the optimization information in the optimization iteration process, and provides a reliable guarantee for the optimization effect of the optimization iteration. At the same time, the high-precision carbon emission algorithm also fully considers the impact of data limitations on the accuracy of carbon emissions, and fully considers the extensiveness and accuracy of data in the process of data information collection and calculation. The application of a large language model to assist in calculation and iteration improves the generalization of data collection and optimization, and the richness of statistical information, thereby improving the reliability and accuracy of carbon emission optimization, so that the target optimization information finally obtained is more reasonable and accurate.

[0039] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0041] Figure 1 The process diagram of the carbon emission optimization method based on high-precision carbon emission calculation provided by an embodiment of the present application is shown Figure 2

[0042] Figure 2 The following is a schematic diagram of a process based on high-precision carbon emission calculation provided by an embodiment of the present application. Figure 2

[0043] Figure 3 The schematic diagram shows the principle of high-precision carbon emission calculation provided by an embodiment of the present application. Figure 2

[0044] Figure 4 A schematic diagram of the process of a carbon emission optimization method based on high-precision carbon emission calculation provided by another embodiment of the present application is shown Figure 2

[0045] Figure 5 The process diagram of the carbon emission optimization method based on high-precision carbon emission calculation provided by another embodiment of the present application is shown as follows Figure 2

[0046] Figure 6 The carbon emission calculation based on high-precision carbon emission calculation provided by an embodiment of the present application is shown.

[0047] Schematic diagram of the structure of the optimization device Figure 2

[0048] Figure 7 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0050] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] In order to further illustrate the technical solution provided by the embodiment of the present application, this is described in detail below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiment of the present application provides the method operation instruction steps shown in the following embodiments or drawings, more or less operation instruction steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiment of the present application. The method may be executed in the order of the method shown in the embodiment or drawings or in parallel during the actual processing process or when the device is executed.

[0052] Please refer to Figure 1 , Figure 1 FIG. 1 is a flow chart of a carbon emission optimization method based on high-precision carbon emission calculation provided by an embodiment of the present application. Figure 1 As shown, the method includes:

[0053] Step 101, obtaining text information and image information of the item to be optimized.

[0054] It should be noted that the text information and image information are relevant information of the project to be optimized provided by the user. Among them, the image information can be a site plan of the project to be optimized. Preferably, the image information is a site plan containing the initial design information of the project to be optimized, and the image information includes but is not limited to at least one element in the initial design, such as roads. The text information can be the user's description of the project to be optimized, or the parameter file information of the information to be optimized. In the embodiment of the present application, the text information includes but is not limited to the basic information of the project to be optimized and the element optimization restriction information.

[0055] Optionally, the text information and image of the project to be optimized can be information input by the user at one time, or can be information input by the user in batches based on the staged calculation results. For example, the user can input the text information and image information used to extract the basic information, optimization restriction information and design reference information of the project to be optimized at one time, or can further input the text information and image information used to extract the optimization restriction information and design reference information based on the basic carbon emissions.

[0056] Step 102: extract basic information, optimization restriction information and design reference information of the project to be optimized based on the text information and image information.

[0057] It should be noted that the basic information is the parameter information corresponding to the entire life cycle stage of the project to be optimized, including but not limited to the geographical location, maintenance time, the number of materials corresponding to the elements, the transportation distance of the materials, the equipment and equipment types used in the construction process, the use time, etc. The optimization restriction information is the constraint conditions for project optimization, including but not limited to element optimization constraints and cost constraints. Among them, the element optimization constraints are constraints that meet the needs of the project, such as restrictions on the change of element area. In other words, when optimizing the carbon emissions of the project, it is also necessary to ensure that the project meets the design requirements. The design reference information is another aspect of the constraints that the project optimization meets, including but not limited to the spatial layout of the project to be optimized, the objective laws between different elements, etc.

[0058] In a feasible embodiment, a trained large language model (LLM) can be used to extract features from text information to obtain basic information, optimization restriction information, design reference information, etc. Specifically, the text information of the project to be optimized input by the user is input into the trained large language model, and the large language model segments the text information to obtain the geographical location, maintenance time, the number of materials corresponding to the element, the transportation distance of the materials, the equipment and equipment types used in the construction process, the use time, the element optimization restriction information, etc.

[0059] Optionally, the large language model implements feature extraction by vectorizing the features, that is, different types of information data are structured to obtain the feature values ​​corresponding to the features and store them in the corresponding positions of the features. For example, the data processed by the large language model is stored in the following form:

[0060] [{"name":"Oil","quantity":100},{"name":"Coal","quantity":50}]

[0061] [{"name":"Oil","emission_factor":0.7},{"name":"Coal","emission_factor":1.1}]

[0062] In another feasible embodiment, a trained image recognition model can be used to perform image recognition on the image information to obtain spatial layout information in the image information. In some embodiments, the image recognition model can be further used to obtain design information such as element proportions and element areas corresponding to the initial design in the project to be optimized, which can be used independently or in combination with text information as design reference information.

[0063] Step 103, based on the basic information, calculate the basic carbon emissions using a high-precision carbon emissions algorithm.

[0064] It should be noted that the high-precision carbon emission algorithm is a calculation method that can automatically collect project data and use advanced artificial intelligence algorithms for comprehensive analysis to address the shortcomings of existing technologies, such as carbon emission calculations being limited to specific input data and lacking the ability to accurately assess the entire life cycle of complex projects. It can greatly improve the accuracy and efficiency of carbon emission calculations.

[0065] Step 104, optimize and iterate the basic information based on the optimization restriction information and the design reference information, and use a high-precision carbon emission algorithm to calculate the optimized carbon emissions corresponding to the optimization information obtained in each iteration, until the target optimization information that meets the optimization restriction information and the design reference information is obtained, and the optimized carbon emissions corresponding to the target optimization information are the lowest.

[0066] It should be understood that, since the optimization restriction information and design reference information used for optimization restriction are obtained based on the text information and image information of the project to be optimized, the embodiment of the present application can optimize and iterate the project to be optimized according to the optimization restriction information and design reference information of the project to be optimized during the optimization process, so as to fully consider the purpose and effect of the design requirements of the project to be optimized during the optimization process, so that the target optimization information after the optimization iteration can not only meet the purpose of reducing carbon emissions relative to the basic information, but also meet the optimization restriction information and design reference information of the current design requirements of the project to be optimized, so as to meet the personalized needs of reducing carbon emissions. Moreover, the embodiment of the present application also uses a high-precision carbon emission algorithm to calculate carbon emissions during the iteration process, which greatly improves the accuracy of carbon emission calculation of optimization information during the optimization iteration process, and provides a reliable guarantee for the optimization effect of the optimization iteration. At the same time, the high-precision carbon emission algorithm also fully considers the impact of data limitations on the accuracy of carbon emissions, fully considers the extensiveness and accuracy of data in the process of data information collection and calculation, and uses a large language model to assist in calculation and iteration, so that the generalization of data collection and optimization and the richness of statistical information are improved, thereby improving the reliability and accuracy of carbon emission optimization, so that the target optimization information finally obtained is more reasonable and accurate.

[0067] In a possible embodiment, if Figure 2 and Figure 3 As shown, step 103, based on the basic information, using a high-precision carbon emission algorithm to calculate basic carbon emissions, includes:

[0068] Step 1031, based on the basic information, determine the various implementation stages corresponding to the project to be optimized and the characteristic data corresponding to each implementation stage.

[0069] It should be noted that project construction is completed in stages through multiple implementation phases, such as material production, material transportation, machinery construction, management and maintenance, and equipment use. By analyzing the basic information, it is possible to determine at least one implementation phase involved in the project to be optimized, and by calculating the carbon emissions in stages for at least one implementation phase, the basic carbon emissions of the entire project to be optimized can be obtained.

[0070] Specifically, in the embodiment of the present application, the trained large language model can be used to extract and classify the basic information to determine the feature data and the implementation stage to which the feature data belongs. For example, the type and quantity of the extracted materials are determined as the feature data corresponding to the material production stage, the extracted distance information is determined as the feature data corresponding to the material transportation stage, the name and usage time of the equipment are determined as the feature data corresponding to the equipment usage stage, and the geographical location and maintenance time are determined as the feature data of the maintenance stage.

[0071] Step 1032, obtaining carbon emission calculation characteristics and carbon emission factors corresponding to each implementation stage.

[0072] It should be noted that the carbon emission calculation characteristics and carbon emission factors corresponding to each implementation stage are determined and set in advance based on a large number of project analyses. Among them, the carbon emission factor is the carbon emission factor corresponding to each material. Specifically, the carbon emission factor can be obtained according to the accounting method in the 2006 IPCC National Greenhouse Gas Inventory Guidelines. The carbon emission calculation characteristics are calculation characteristics selected according to the actual situation of the implementation stage. For example, the carbon emission calculation characteristics corresponding to the material production stage include at least the name and quantity of the material, which are used to calculate the carbon emissions required to produce the corresponding quantity of the material; the carbon emission calculation characteristics corresponding to the material transportation stage include at least the distance, quantity, fuel consumption and carbon emission factor, which are used to calculate the carbon emissions required to transport the corresponding quantity of the material for the corresponding distance; the carbon emission calculation characteristics corresponding to the machinery construction stage include at least the material, quantity, machinery type, fuel consumption and carbon emission factor, which are used to calculate the carbon emissions required to complete the construction of the corresponding quantity of the material using the corresponding type of machinery; the carbon emission calculation characteristics corresponding to the management and maintenance stage include at least equipment, energy consumption, duration and carbon emission factor, which are used to calculate the carbon emissions required to maintain the corresponding equipment in the corresponding market, and the carbon emission calculation characteristics corresponding to the equipment use stage include at least the equipment name, energy consumption, duration, quantity and carbon emission factor, which are used to calculate the carbon emissions required to use the corresponding quantity of the corresponding equipment for the corresponding duration.

[0073] In a feasible embodiment, for the parsed basic information, SQL query can be used to obtain the characteristic data, carbon emission calculation characteristics and carbon emission factors of each of the above implementation stages.

[0074] Specifically, first construct an SQL query statement to formulate the information and conditions to be retrieved, such as materials and quantities, and then execute this SQL query statement in the tableExecute tool to obtain the returned query results. Among them, the SQL query statement can be queried in the database corresponding to each implementation stage. For example, the carbon emission settlement characteristics and carbon emission factors corresponding to the material production stage can be queried and obtained from the material production carbon emission database, the carbon emission calculation characteristics and carbon emission factors corresponding to the material transportation stage can be queried and obtained from the database corresponding to the material transportation stage, the carbon emission calculation characteristics and carbon emission factors corresponding to the machinery construction stage can be queried and obtained from the database corresponding to the machinery construction stage, the carbon emission calculation characteristics and carbon emission factors corresponding to the management and maintenance stage can be queried and obtained from the database corresponding to the management and maintenance stage, and the carbon emission calculation characteristics and carbon emission factors corresponding to the equipment use stage can be queried and obtained from the database corresponding to the equipment use stage.

[0075] Optionally, the database corresponding to each implementation stage can be independently set up in the server, that is, a server is set up as the database corresponding to each implementation stage to store the carbon emission calculation characteristics, carbon emission factors and characteristic values ​​of each implementation stage obtained based on training or user input; it can also be distributed and stored in a server cluster according to preset rules, and this application does not make specific limitations.

[0076] Step 1033, based on the carbon emission calculation characteristics corresponding to each implementation stage, the carbon emission factors and the characteristic data corresponding to the project to be optimized, calculate the stage basic carbon emissions corresponding to each implementation stage of the project to be optimized.

[0077] Specifically, the stage-based carbon emissions corresponding to each implementation stage can be calculated according to the preset stage-based carbon emissions formula corresponding to each implementation stage to obtain the stage-based carbon emissions corresponding to each implementation stage.

[0078] For example, for the material production stage, the basic carbon emissions of the material production stage can be calculated according to the following formula:

[0079]

[0080] Among them, C p is the carbon emission generated by the material, Mi is the amount of the i-th material, Ni is the carbon emission factor of the i-th material, i = 1, 2..., n.

[0081] For the material transportation stage, the basic carbon emissions of the material transportation stage can be calculated according to the following formula:

[0082]

[0083] Among them, C b is the carbon emission in the material transportation process, Di is the transportation distance of the ith material, Ti is the energy consumption of the ith material transportation, Ei is the carbon emission factor of the ith material transportation equipment, i = 1, 2..., n.

[0084] For the mechanical construction phase, the basic carbon emissions of the mechanical construction phase can be calculated according to the following formula:

[0085]

[0086] Among them, C c is the carbon emission of the mechanical construction process, Fi is the energy consumption per unit shift of the i-th construction machinery, Ri is the number of shifts of the i-th construction machinery, Wi is the carbon emission factor of the i-th construction machinery, i = 1, 2..., n.

[0087] For the management and maintenance stage, the basic carbon emissions during the management and maintenance stage can be calculated according to the following formula:

[0088]

[0089] Among them, C u is the carbon emission of the management and maintenance process, Ki is the energy consumption of the ith maintenance content, Oi is the carbon emission factor of the ith plant maintenance, i = 1, 2..., n.

[0090] For the equipment use phase, the basic carbon emissions during the equipment use phase can be calculated according to the following formula:

[0091]

[0092] Among them, C m is the carbon emission during the use of the equipment, Qi is the number of the i-th lighting equipment, Hi is the annual energy consumption of the i-th lighting equipment, Ji is the carbon emission factor of the i-th lighting equipment, and i = 1, 2..., n.

[0093] Step 1034, obtaining basic carbon emissions based on the stage basic carbon emissions corresponding to each implementation stage.

[0094] Specifically, the carbon emissions of each implementation stage can be summed up to obtain the basic carbon emissions of the entire project to be optimized.

[0095] For example, the following formula may be used for summation:

[0096] CCO2=C p +C b +C c +C u +C m

[0097] Among them, C p is the carbon emission generated by the material, C b is the carbon emission during material transportation, C c is the carbon emission of the mechanical construction process, C u is the carbon emission during the maintenance process, C m Carbon emissions during the use of the equipment.

[0098] Therefore, the embodiment of the present application can divide the implementation stages of the optimized project, and then perform high-precision carbon emission calculations based on each implementation stage, thereby effectively avoiding the omission of features in carbon emission calculations, and realizing targeted carbon emission calculations of different features, thereby effectively improving the accuracy of carbon emission calculations. Based on the effective generalization of text information and image information based on the large language model to improve data richness, the reliability and accuracy of data calculations are further guaranteed.

[0099] In a possible embodiment, if Figure 4As shown, the basic information is optimized and iterated based on the optimization restriction information and the design reference information, and the optimized carbon emissions corresponding to the optimized information obtained in each iteration are calculated using a high-precision carbon emission algorithm, including:

[0100] Step 401, input basic information, optimization restriction information and design reference information into a prediction model to obtain optimized prediction data; the optimized prediction data includes at least one element to be optimized.

[0101] It should be noted that the prediction model is a trained model that can understand basic information, optimization restriction information and design reference information. Specifically, the feature values ​​of the basic information, optimization restriction information and design reference information extracted by the large language model are input into the prediction model to obtain at least one element to be optimized that can be selected from the basic information by the prediction model.

[0102] For example, for a building park, the prediction model can output "road" as the element to be optimized based on the basic information, optimization restriction information and design reference information, so as to reduce carbon emission materials by optimizing the "road" element, such as reducing the design area of ​​the "road" element, or replacing materials with lower carbon emissions, etc., thereby achieving the purpose of reducing carbon emissions.

[0103] Step 402 : For each element to be optimized, within the element constraint range corresponding to the element to be optimized in the optimization restriction information, the element to be optimized is optimized and adjusted, and optimization information corresponding to the element to be optimized is generated.

[0104] It should be noted that the factor constraint range can be the minimum design quantity of the factor, the maximum construction cost, etc., and can be at least one item set by the user according to the optimization requirements of the project to be optimized, usually provided by the design reference information. For example, the road area within the building park cannot be 0 to ensure that the final design information can meet the practicality requirements of the project.

[0105] In a feasible embodiment, the optimization adjustment of the elements to be optimized includes optimizing the area of ​​the elements to be optimized, that is, reducing the use of materials corresponding to the elements to be optimized and the carbon emissions in transportation and equipment use by reducing the design area of ​​the elements to be optimized, thereby achieving the purpose of reducing carbon emissions.

[0106] The element constraint range corresponding to the element to be optimized includes an element area greater than or equal to 0 and an area ratio with at least one associated element remains unchanged.

[0107] It should be noted that the area of ​​the element to be optimized before optimization and the area ratio of at least one associated element can be determined by performing image recognition on the image information. Specifically, the image information input by the user for representing the design scheme in the basic information can be input into the image recognition model, and the image recognition model performs image recognition on the image information based on the training results to determine the spatial layout corresponding to the basic information, and then performs image segmentation on the image information based on the spatial layout to obtain the area ratio between the various elements.

[0108] For example, the open source OpenCV (Open Source Computer Vision Library) can be used to customize image recognition and analyze image information to extract the spatial layout, for example: defanalyze_site_layout(image):

[0109] #Image recognition API call, return space layout

[0110] site_layout=image_recognition_api.analyze(image)

[0111] returnsite_layout

[0112] Then, we use the open source OpenCV custom image segmentation again to complete the image segmentation based on the previously extracted spatial layout and obtain the area ratio between the elements, for example: defsegment_elements(site_layout):

[0113] #Image segmentation, return the proportion of each element

[0114] element_ratios=image_segmentation_module.segment(site_layout)

[0115] returnelementratios

[0116] In a feasible embodiment, on the basis of optimizing the elements, it is also necessary to ensure the completion of the project as a whole. For example, optimizing the area of ​​a single element also requires ensuring the completion of the total area of ​​the project. Based on this, it is also necessary to determine the total area and the specific area of ​​each element based on the image information. For example:

[0117]

[0118] Therefore, the embodiment of the present application uses open source OpenCV to perform image recognition and segmentation on the image information of the project to be optimized provided by the user, so as to obtain the area of ​​each element in the initial design of the project to be optimized, the area ratio between each element and the total area of ​​the project to be optimized, thereby providing accurate and reliable data information for effective optimization while improving the efficiency of obtaining data information, thereby providing a guarantee for the reliability and optimization efficiency of carbon emission optimization.

[0119] In a feasible embodiment, the optimization of the element to be optimized can be performed directly based on the element constraint range, for example, when the element has a minimum restriction, the element is directly optimized to the corresponding minimum value. For example, the element constraint range corresponding to element 1 is an area of ​​not less than 2m 2 At this time, the area of ​​element 1 can be directly optimized to 2m 2 It should be understood that when the element 1 has an area ratio associated element 2, the element constraint range and area ratio corresponding to element 1 and element 2 can be determined first, and then the minimum value that can be achieved can be selected based on the element constraint range and area ratio for optimization and adjustment. For example, the element constraint range of element 1 is an area of ​​not less than 2m 2 , the element constraint range of element 2 is no less than 5m 2 , and the area ratio is element 1: element 2 = 1:2. At this time, if the area of ​​element 1 is optimized to 2m 2 , the area of ​​area ratio factor 2 is optimized to 4m 2 , which is smaller than the element constraint range corresponding to element 2. Therefore, it is necessary to optimize and adjust according to the element constraint range corresponding to element 2. The optimization adjustment result is 2.5m for element 1. 2 , element 2 is 5m 2 .

[0120] In another feasible embodiment, in order to ensure that the optimized and adjusted design solution fits the initial design as much as possible, the optimization elements may be gradually optimized and adjusted in an iterative manner.

[0121] Specifically, since the purpose of the optimization adjustment is to reduce the carbon emissions of the elements to be optimized, the embodiment of the present application associates the carbon emissions of the elements to be optimized with the unit area, for example:

[0122]

[0123] #Example

[0124] total_area = 10000 #Total area of ​​element 1, for example 10,000 square meters

[0125] total_emission = 1500 #Total carbon emissions of element 1, for example 1500 tons of CO2

[0126] unit_area_emission=calculate_unit_area_emission(total_area,total_emission)

[0127] print(f″Carbon emissions per unit area: {unit_area_emission} tons CO2 / square meter″)

[0128] Then, the prediction model is used to obtain a guess of the adjustment ratio, that is, the adjustment amount of each optimization iteration of the element to be optimized predicted by the prediction model, for example:

[0129] fori, elementinenumerate(elements):

[0130] #Suggested savings ratio adjustment initial guess

[0131] saving_percentage=llm_model.predict_saving_percentage(element)

[0132] initial_guess[i]*=(1-saving_percentage)

[0133] Therefore, the prediction model can be used to predict the proportion of iterative optimization adjustment and output the optimized area at the same time, so as to judge whether the optimization adjustment plan given by the prediction model meets the constraint range of the elements.

[0134] Optionally, the prediction model can be used to continuously iterate and output the optimization adjustment ratio of the elements to be optimized, generate optimization information for each optimization adjustment, and calculate the optimized carbon emissions corresponding to the optimization information, so as to reduce carbon emissions while being as close to the initial design as possible.

[0135] Step 403, calculating the optimized carbon emissions corresponding to the elements to be optimized based on a high-precision carbon emissions algorithm.

[0136] It should be understood that for each factor to be optimized, carbon emission calculations can be performed according to each implementation stage as in the aforementioned scheme to improve the reliability of the optimized carbon emission calculations and provide reliable data support for the final optimization information selection.

[0137] In a feasible embodiment, the final optimization information selection can also be implemented using a prediction model, for example:

[0138]

[0139] Among them, the scipy.optimize module provides a function called minimize for finding the best solution. When the minimize function is called, it will be traversed according to the defined objective function (objective_function), initial guess (initial_guess) and constraints (constraints), so as to find the optimization information that satisfies all constraints and makes the value of the objective function as small as possible. In an embodiment of the present application, the objective function can be set to the negative value of the reduction in carbon emissions, that is, the reduction in carbon emissions is as large as possible. The optimization starting value of the initial guess can be the initial design value of each element, so that each element or each element to be optimized can be iteratively optimized and adjusted during the optimization process. The constraint is the element constraint range, and multiple elements correspond to multiple element constraint ranges.

[0140] In a feasible embodiment, the optimization restriction information also includes cost restrictions. Therefore, after the optimization adjustment, the cost information corresponding to each optimization information needs to be calculated. At this time, there may be at least one optimization information that achieves the expected reduction target of carbon emissions and does not exceed the cost restriction condition for selection, and each has its own advantages. For example, the cost corresponding to the optimization information with a higher carbon emission optimization amount is relatively high, or the carbon emission optimization amount corresponding to the optimization information with a lower cost is relatively low. At this time, how to select the target optimization information from the optimization information with mutual advantages becomes a problem to be solved.

[0141] In the related art, corresponding weight values ​​are usually set artificially for carbon emissions and costs, and the target optimization information is selected through direct weight calculation. However, such an operation is usually subjective because the weight values ​​are artificially set, and the selection of weight values ​​is usually not scientific enough, and the contribution of carbon emission optimization to the selection of optimization information is ignored, making the selection process not scientific and accurate enough. Based on this, the present application further proposes an evaluation scheme for optimization information, evaluates multiple optimized optimization information, and selects the optimal optimization information based on scientific and reliable evaluation results.

[0142] In a possible embodiment, if Figure 5 As shown, including:

[0143] Step 501, based on each type of optimization information, calculate the cost information, cost optimization amount and carbon emission optimization amount corresponding to each type of optimization information.

[0144] It should be understood that the cost information can be directly calculated according to the cost calculation rules based on the materials, consumption, etc. in each implementation stage of the optimization information to calculate the cost information corresponding to the optimization information. This application does not specifically limit the calculation of the cost information. The cost optimization amount is the ratio of the difference between the cost information and the cost limit to the cost limit, and the carbon emission optimization amount is the ratio of the difference between the optimized carbon emissions and the basic carbon emissions to the basic carbon emissions. Among them, the cost optimization amount can be positive or negative. When the cost optimization amount is a positive value, it means that the cost information has decreased relative to the cost limit. When the cost optimization amount is a negative value, it means that the cost information has increased relative to the cost limit.

[0145] Step 502: Based on the carbon emission optimization amount and the cost optimization amount, determine the first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost information.

[0146] It should be noted that the first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost optimization amount is the possibility of obtaining different evaluation results for the carbon emission optimization amount and the cost optimization amount, that is, the possibility of obtaining different evaluation results. Among them, the evaluation results can include three evaluation results: very good, average and poor.

[0147] Specifically, based on the size relationships between the carbon emission optimization amount and the cost optimization amount and the corresponding multiple reference coefficients, the possibility of obtaining the carbon emission optimization amount and the cost optimization amount and multiple evaluation results is determined; then based on the multiple acquisition possibilities, the first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost optimization amount is constructed.

[0148] It should be noted that the multiple evaluation results corresponding to the carbon emission optimization amount are the same as the multiple evaluation results corresponding to the cost optimization amount, that is, three evaluation results: very good, average and poor. The multiple reference coefficients corresponding to the carbon emission optimization amount are different from the multiple reference coefficients corresponding to the cost optimization amount.

[0149] Exemplarily, the carbon emission optimization amount can be obtained by obtaining the possibility of obtaining multiple evaluation results according to the following formula:

[0150]

[0151] Among them, L CO2 is the carbon emission optimization amount, and L1, L2 and L3 are multiple reference coefficients corresponding to the carbon emission optimization amount. Since there are restrictions in the application scenario of the embodiment of the present application, such as the need to keep the overall design area of ​​the project unchanged and the area ratio of the associated elements unchanged, the value range of the carbon emission optimization amount is generally between 0-10%. Therefore, in the embodiment of the present application, parameter L1 is selected as 0, parameter L2 is selected as 0.05, and parameter L3 is selected as 0.1.

[0152] Correspondingly, the cost optimization amount can be obtained by the following formula to obtain the possibility of obtaining multiple evaluation results:

[0153]

[0154] Among them, L C is the cost optimization amount, and L'1, L'2 and L'3 are multiple reference coefficients corresponding to the cost optimization amount. Since there are restrictions in the application scenario of the embodiment of the present application, such as the need to keep the overall design area of ​​the project unchanged and the area ratio of the associated elements unchanged, the effective value range of the cost optimization amount is basically consistent with the value range of the carbon emission optimization amount, generally between 0-10%. Therefore, in the embodiment of the present application, the parameter L'1 is selected as 0, the parameter L'2 is selected as 0.05, and the parameter L'3 is selected as 0.10.

[0155] Furthermore, the first-order optimization evaluation corresponding to the carbon emission optimization amount is M(M 较差 , M 一般 , M 很好 ), the first-order optimization evaluation corresponding to the cost optimization amount is N(N 较差 , N 一般 , N 很好 ).

[0156] Step 503, obtaining the second-order optimization weights corresponding to the optimized carbon emissions and costs.

[0157] It should be understood that in the embodiment of the present application, in order to better describe the importance relationship between the carbon emission optimization amount and the cost optimization amount, a 1-9 scale method is selected to assign a value to the relationship between the two. Among them, the 1-9 scale method is:

[0158] Scale meaning 1 Indicates that two elements have the same importance. 3 Indicates that compared with two elements, the former is slightly more important than the latter 5 Indicates that compared with two elements, the former is obviously more important than the latter 7 Indicates that compared with two elements, the former is extremely important than the latter 9 Indicates that compared with two elements, the former is more important than the latter

[0159] Thus, the following relationship can be obtained:

[0160] Carbon emissions optimization Cost Optimization Carbon emissions optimization 1 5 Cost Optimization 1 / 5 1

[0161] That is to say, when both the carbon emission optimization amount limit and the cost constraint conditions are met, the carbon emission optimization amount is obviously more important than the cost optimization amount, and the weights of the carbon emission optimization amount and the cost optimization amount are recorded as the second-order optimization weight W = (0.16, 0.84) respectively.

[0162] Step 504: Determine the evaluation index corresponding to the optimization information based on the first-order optimization evaluation and the second-order optimization weight.

[0163] Specifically, the second-order optimization weights are fused to obtain:

[0164]

[0165] Then, the second-order optimization weight is multiplied by the first-order optimization evaluation to obtain the complete evaluation index, that is, P = W × R, and then the last column of P corresponding to the "very good" evaluation is used as the evaluation index.

[0166] Step 505: Use the optimization information with the largest evaluation index as the target optimization information.

[0167] Therefore, the embodiment of the present application scientifically evaluates and assigns weights to the carbon emission optimization amount and the cost optimization amount, so that the final evaluation index can fully consider the advantages and disadvantages of the carbon emission optimization amount and the cost optimization amount, while also fully considering the importance of the carbon emission optimization amount and the cost optimization amount in decision-making, effectively avoiding human subjective influence and greatly improving the reliability of target optimization information selection.

[0168] In summary, the embodiment of the present application can optimize and iterate the project to be optimized according to the optimization restriction information and design reference information of the project to be optimized during the optimization process, so as to achieve the purpose and effect of fully considering the design requirements of the project to be optimized during the optimization process, so that the target optimization information after the optimization iteration can not only meet the purpose of reducing carbon emissions relative to the basic information, but also meet the optimization restriction information and design reference information of the current design requirements of the project to be optimized, so as to achieve the personalized needs of reducing carbon emissions. Moreover, the embodiment of the present application also uses a high-precision carbon emission algorithm to calculate carbon emissions during the iteration process, which greatly improves the accuracy of the carbon emission calculation of the optimization information during the optimization iteration process, and provides a reliable guarantee for the optimization effect of the optimization iteration. At the same time, the high-precision carbon emission algorithm also fully considers the impact of data limitations on the accuracy of carbon emissions, and fully considers the extensiveness and accuracy of data during data information collection and calculation. The application of a large language model to assist in calculation and iteration improves the generalization of data collection and optimization, and the richness of statistical information, thereby improving the reliability and accuracy of carbon emission optimization, so that the target optimization information finally obtained is more reasonable and accurate.

[0169] It should be noted that although the operations of the method of the present invention are described in a particular order in the drawings, this does not require or imply that the operations must be performed in this particular order or that all illustrated operations must be performed to achieve desired results.

[0170] Figure 6 FIG. 1 shows a schematic diagram of the structure of a carbon emission optimization device based on high-precision carbon emission calculation provided by an embodiment of the present application. Figure 6 As shown, the carbon emission optimization device 10 based on high-precision carbon emission calculation includes:

[0171] An acquisition module 11 is used to acquire text information and image information of the item to be optimized;

[0172] An extraction module 12, used to extract basic information, optimization restriction information and design reference information of the project to be optimized based on the text information and the image information;

[0173] A calculation module 13, configured to calculate basic carbon emissions based on the basic information using a high-precision carbon emissions algorithm;

[0174] The iteration module 14 is used to optimize and iterate the basic information based on the optimization restriction information and the design reference information, and use the high-precision carbon emission algorithm to calculate the optimized carbon emissions corresponding to the optimization information obtained in each iteration, until the target optimization information that meets the optimization restriction information and the design reference information is obtained, and the optimized carbon emissions corresponding to the target are the lowest.

[0175] In some embodiments, the computing module 13 is further configured to:

[0176] Based on the basic information, determining each implementation stage corresponding to the project to be optimized and characteristic data corresponding to each implementation stage;

[0177] Obtaining carbon emission calculation characteristics and carbon emission factors corresponding to each of the implementation stages;

[0178] Calculate the stage-based carbon emissions corresponding to each of the implementation stages of the project to be optimized based on the carbon emission calculation characteristics corresponding to each of the implementation stages, the carbon emission factors and the characteristic data corresponding to the project to be optimized;

[0179] The basic carbon emissions are obtained based on the stage basic carbon emissions corresponding to each of the implementation stages.

[0180] In some embodiments, the iteration module 14 is further configured to:

[0181] Inputting the basic information, the optimization restriction information and the design reference information into a prediction model to obtain optimized prediction data; the optimized prediction data includes at least one element to be optimized;

[0182] For each of the elements to be optimized, within the element constraint range corresponding to the element to be optimized in the optimization restriction information, the element to be optimized is optimized and adjusted, and optimization information corresponding to the element to be optimized is generated;

[0183] The optimized carbon emissions corresponding to the element to be optimized are calculated based on the high-precision carbon emissions algorithm.

[0184] In some embodiments, the optimizing adjustment of the element to be optimized includes optimizing the area of ​​the element to be optimized; the element constraint range corresponding to the element to be optimized includes an element area greater than or equal to 0 and an area ratio with at least one associated element remains unchanged.

[0185] In some embodiments, the iteration module 14 is further configured to:

[0186] Based on each type of optimization information, calculate the cost information, cost optimization amount and carbon emission optimization amount corresponding to each type of optimization information;

[0187] Based on the carbon emission optimization amount and the cost optimization amount, determining a first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost information;

[0188] Obtain the second-order optimization weights corresponding to the optimized carbon emissions and costs;

[0189] Determining an evaluation index corresponding to the optimization information based on the first-order optimization evaluation and the second-order optimization weight;

[0190] The optimization information with the largest evaluation index is used as the target optimization information.

[0191] In some embodiments, the iteration module 14 is further configured to:

[0192] Determining the possibility of obtaining the carbon emission optimization amount and the cost optimization amount from a plurality of evaluation results based on the size relationships between the carbon emission optimization amount and the cost optimization amount and the corresponding plurality of reference coefficients respectively;

[0193] Based on the multiple acquisition possibilities, a first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost optimization amount is constructed.

[0194] It should be understood that the units or modules described in the carbon emission optimization device 10 based on high-precision carbon emission calculation are similar to those described in the reference Figure 1 The various steps in the method described above correspond to each other. Therefore, the operations and features described above for the method are also applicable to the carbon emission optimization device 10 based on high-precision carbon emission calculation and the units contained therein, and will not be repeated here. The carbon emission optimization device 10 based on high-precision carbon emission calculation can be pre-implemented in a browser or other security application of an electronic device, or can be loaded into a browser or its security application of an electronic device by downloading or the like. The corresponding units in the carbon emission optimization device 10 based on high-precision carbon emission calculation can cooperate with the units in the electronic device to implement the solution of the embodiment of the present application.

[0195] For the several modules or units mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0196] Reference below Figure 7 , Figure 7 An electronic device or a

[0197] The structural diagram of the server's computer system,

[0198] like Figure 7 As shown, the computer system includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage part 708 to the random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation instructions of the system are also stored. The CPU 701, the ROM 702 and the RAM 703 are connected to each other through a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0199] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0200] In particular, according to an embodiment of the present application, the above reference flow chart Figure 2 The described process can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present application are executed.

[0201] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium such as a computer-readable storage medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0202] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operating instructions of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the aforementioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operating instruction, or can be implemented with a combination of dedicated hardware and computer instructions.

[0203] The units or modules involved in the embodiments described in the present application may be implemented by software or hardware. The units or modules described may also be arranged in a processor, for example, they may be described as: a processor includes an acquisition module, an extraction module, a calculation module, and an iteration module. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves, for example, the acquisition module may also be described as "acquiring text information and image information of the project to be optimized".

[0204] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are used by one or more processors to execute the carbon emission optimization method based on high-precision carbon emission calculation described in the present application.

[0205] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A carbon emission optimization method based on high-precision carbon emission calculation, characterized in that: include: Get the text and image information of the project to be optimized; Based on the text information and the image information, extract basic information, optimization restriction information and design reference information of the project to be optimized; Based on the basic information, using a high-precision carbon emission algorithm to calculate basic carbon emissions; Iteratively optimizing the basic information based on the optimization restriction information and the design reference information, and using the high-precision carbon emission algorithm to calculate the optimized carbon emission corresponding to the optimization information obtained in each iteration, until the target optimization information satisfying the optimization restriction information and the design reference information is obtained, and the optimized carbon emission corresponding to the target optimization information is the lowest; The optimizing and iterating the basic information based on the optimization restriction information and the design reference information, and calculating the optimized carbon emissions corresponding to the optimized information obtained in each iteration by using the high-precision carbon emission algorithm, includes: Inputting the basic information, the optimization restriction information and the design reference information into a prediction model to obtain optimized prediction data; the optimized prediction data includes at least one element to be optimized; For each of the elements to be optimized, within the element constraint range corresponding to the element to be optimized in the optimization restriction information, the element to be optimized is optimized and adjusted, and optimization information corresponding to the element to be optimized is generated; The optimized carbon emissions corresponding to the element to be optimized are calculated based on the high-precision carbon emissions algorithm.

2. The carbon emission optimization method based on high-precision carbon emission calculation according to claim 1 is characterized in that: The method of calculating basic carbon emissions based on the basic information using a high-precision carbon emissions algorithm includes: Based on the basic information, determining each implementation stage corresponding to the project to be optimized and characteristic data corresponding to each implementation stage; Obtaining carbon emission calculation characteristics and carbon emission factors corresponding to each of the implementation stages; Calculate the stage-based carbon emissions corresponding to each of the implementation stages of the project to be optimized based on the carbon emission calculation characteristics corresponding to each of the implementation stages, the carbon emission factors and the characteristic data corresponding to the project to be optimized; The basic carbon emissions are obtained based on the stage basic carbon emissions corresponding to each of the implementation stages.

3. The carbon emission optimization method based on high-precision carbon emission calculation according to claim 1 is characterized in that: The optimizing and adjusting the element to be optimized includes optimizing the area of ​​the element to be optimized; the element constraint range corresponding to the element to be optimized includes an element area greater than or equal to 0 and an area ratio with at least one associated element remains unchanged.

4. The carbon emission optimization method based on high-precision carbon emission calculation according to claim 1 is characterized in that: The optimization restriction information further includes a cost restriction, and the method further includes: Based on each type of optimization information, calculate the cost information, cost optimization amount and carbon emission optimization amount corresponding to each type of optimization information; Based on the carbon emission optimization amount and the cost optimization amount, determining a first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost information; Obtain the second-order optimization weights corresponding to the optimized carbon emissions and costs; Determining an evaluation index corresponding to the optimization information based on the first-order optimization evaluation and the second-order optimization weight; The optimization information with the largest evaluation index is used as the target optimization information.

5. The carbon emission optimization method based on high-precision carbon emission calculation according to claim 4 is characterized in that: The determining, based on the carbon emission optimization amount and the cost optimization amount, a first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost information includes: Determining the possibility of obtaining the carbon emission optimization amount and the cost optimization amount from a plurality of evaluation results based on the size relationships between the carbon emission optimization amount and the cost optimization amount and the corresponding plurality of reference coefficients respectively; Based on the multiple acquisition possibilities, a first-order optimization evaluation corresponding to the carbon emission optimization amount and the cost optimization amount is constructed.

6. A carbon emission optimization device based on high-precision carbon emission calculation, characterized in that: include: An acquisition module is used to obtain text information and image information of the project to be optimized; An extraction module, used for extracting basic information, optimization restriction information and design reference information of the project to be optimized based on the text information and the image information; A calculation module, used to calculate basic carbon emissions based on the basic information using a high-precision carbon emission algorithm; An iteration module, configured to iteratively optimize the basic information based on the optimization restriction information and the design reference information, and use the high-precision carbon emission algorithm to calculate the optimized carbon emission corresponding to the optimization information obtained in each iteration, until the target optimization information satisfying the optimization restriction information and the design reference information is obtained, and the optimized carbon emission corresponding to the target optimization information is the lowest; The optimizing and iterating the basic information based on the optimization restriction information and the design reference information, and calculating the optimized carbon emissions corresponding to the optimized information obtained in each iteration by using the high-precision carbon emission algorithm, includes: Inputting the basic information, the optimization restriction information and the design reference information into a prediction model to obtain optimized prediction data; the optimized prediction data includes at least one element to be optimized; For each of the elements to be optimized, within the element constraint range corresponding to the element to be optimized in the optimization restriction information, the element to be optimized is optimized and adjusted, and optimization information corresponding to the element to be optimized is generated; The optimized carbon emissions corresponding to the element to be optimized are calculated based on the high-precision carbon emissions algorithm.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the carbon emission optimization method based on high-precision carbon emission calculation as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the carbon emission optimization method based on high-precision carbon emission calculation as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the carbon emission optimization method based on high-precision carbon emission calculation described in any one of claims 1 to 5 is implemented.

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