Building full life cycle carbon emission tracking method and platform
By building a deep neighborhood tree to predict and deploy monitoring devices, the problem of insufficient carbon emissions in the existing technology is solved, and accurate tracking of carbon emissions at each node of the building's entire life cycle is achieved, and the accuracy of carbon emissions tracking is improved.
Patent Information
- Application Number
- CN202411666623.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-21
- Publication Date
- 2025-03-04
AI Technical Summary
The existing carbon emission monitoring technology mainly focuses on excessive carbon emissions, it is difficult to effectively monitor insufficient carbon emissions, and it is difficult to accurately identify and track carbon emissions at all nodes throughout the building's life cycle.
By obtaining the first life cycle node and the first carbon emission influencing elements of the entire life cycle node sequence of the building, a deep neighborhood tree is constructed to predict carbon emissions, set the target value of carbon emissions tracking, and deploy a carbon emission monitoring device to monitor carbon emissions in real time. When the monitoring value is inconsistent with the target value, an abnormal signal of carbon emission tracking is generated.
Two-way abnormal monitoring of carbon emissions at all nodes throughout the building life cycle is achieved, and the accuracy of carbon emission tracking is improved, ensuring that carbon emissions are neither exceeding the standard nor low, and balancing environmental protection and engineering efficiency.
Smart Images
Figure CN119558716A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of carbon emission tracking technology, and in particular to a method and platform for tracking carbon emissions throughout the life cycle of a building. Background Art
[0002] In the management process of the whole life cycle of a building, the monitoring of carbon emissions is a key step in achieving green buildings and reducing environmental impacts. Traditional carbon emissions monitoring methods usually adopt a simple monitoring device layout method, which measures carbon emissions and compares them with the set emission standards. When the monitoring value exceeds the emission standard, it is regarded as an abnormal situation and corresponding treatment measures are taken. However, this method has certain limitations. In actual operations, the standard carbon emissions at each stage of the building should be controlled within a limited reasonable range. Excessive emissions may cause environmental pollution, while too low carbon emissions may mean abnormalities in construction equipment or operation processes, thus affecting the quality and progress of the construction project. However, existing monitoring technologies mainly focus on excessive carbon emissions monitoring and lack effective monitoring methods for insufficient carbon emissions. Such monitoring methods are difficult to accurately identify and track the carbon emissions of each node when faced with complex dynamic changes in carbon emissions throughout the life cycle of a building, and are unable to respond to and adjust abnormal emissions in a timely manner. Summary of the invention
[0003] The present application provides a method and platform for tracking carbon emissions throughout the entire life cycle of a building, which is used to solve the technical problem that traditional carbon emission monitoring devices can only monitor carbon emissions exceeding the standard but cannot monitor carbon emissions below the standard.
[0004] In view of the above problems, this application provides a method and platform for tracking carbon emissions throughout the life cycle of a building.
[0005] The first aspect of the present application provides a method for tracking carbon emissions throughout the life cycle of a building, the method comprising: obtaining a first life cycle node and a first carbon emission influencing factor in a node sequence throughout the life cycle of the building; taking the first carbon emission influencing factor as the root node, constructing a deep neighborhood tree to predict carbon emissions and obtain a first carbon emission prediction value; setting the first carbon emission prediction value as a carbon emission tracking target value for the first life cycle node; when the building enters the first life cycle node, deploying a carbon emission monitoring device to obtain a carbon emission monitoring value; when the carbon emission monitoring value is inconsistent with the carbon emission tracking target value, generating a carbon emission tracking abnormality signal and sending it to the building carbon emission management client.
[0006] The second aspect of the present application provides a building life cycle carbon emission tracking platform, the platform comprising: a node element acquisition module, the node element acquisition module is used to obtain the first life cycle node and the first carbon emission influencing factor of the building life cycle node sequence; a carbon emission prediction module, the carbon emission prediction module is used to take the first carbon emission influencing factor as the root node, construct a deep neighborhood tree for carbon emission prediction, and obtain a first carbon emission prediction value; a tracking target value setting module, the tracking target value setting module is used to set the first carbon emission prediction value as the carbon emission tracking target value of the first life cycle node; a monitoring value acquisition module, the monitoring value acquisition module is used to deploy a carbon emission monitoring device and obtain a carbon emission monitoring value when the building enters the first life cycle node; a tracking abnormality signal generation module, the tracking abnormality signal generation module is used to generate a carbon emission tracking abnormality signal when the carbon emission monitoring value is inconsistent with the carbon emission tracking target value, and send it to the building carbon emission management client.
[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: Obtain the first life cycle node and the first carbon emission influencing factor of the building's full life cycle node sequence; take the first carbon emission influencing factor as the root node, construct a deep neighborhood tree to predict carbon emissions, and obtain the first carbon emission prediction value; set the first carbon emission prediction value as the carbon emission tracking target value of the first life cycle node; when the building enters the first life cycle node, deploy a carbon emission monitoring device to obtain a carbon emission monitoring value; when the carbon emission monitoring value is inconsistent with the carbon emission tracking target value, generate a carbon emission tracking abnormality signal and send it to the building carbon emission management client. The technical effect of predicting carbon emissions through a deep neighborhood tree and combining the monitoring device to perform two-way abnormal monitoring of carbon emissions at each life cycle node to improve the accuracy of carbon emission tracking is achieved. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] 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.
[0009] Figure 1 A schematic diagram of a method for tracking carbon emissions throughout the life cycle of a building provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a building life cycle carbon emission tracking platform provided in an embodiment of the present application.
[0010] Explanation of the reference numerals: node element acquisition module 1, carbon emission prediction module 2, tracking target value setting module 3, monitoring value acquisition module 4, tracking abnormal signal generation module 5. DETAILED DESCRIPTION
[0011] This application provides a method and platform for tracking carbon emissions throughout the entire life cycle of a building, which is used to solve the problem of predicting carbon emissions through a deep neighborhood tree, and combines a monitoring device to perform two-way abnormal monitoring of carbon emissions at each life cycle node, thereby improving the accuracy of carbon emissions tracking.
[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0013] Embodiment 1, as Figure 1 As shown, the present application provides a method for tracking carbon emissions throughout the life cycle of a building, the method comprising: Obtain the first life cycle node and the first carbon emission influencing factor of the building's entire life cycle node sequence.
[0014] In one embodiment, in the carbon emission tracking of the whole life cycle of a building, the system terminal first determines the key nodes of the building at different stages, and the main factors that affect carbon emissions at each node. Specifically, the whole life cycle of a building can be divided into multiple stages, such as material production, construction, operation, demolition and recycling, and each stage has its own unique carbon emission characteristics. The first life cycle node refers to a specific time point or link of the building in these stages, such as a key process in the material production stage, or a specific task in the construction stage. The first carbon emission influencing factor associated with the node refers to those factors that directly affect the carbon emissions at that stage. For example, in the material production stage, the factors affecting carbon emissions include but are not limited to the types of materials used, the mining and transportation methods of materials, and the transportation distance; in the construction stage, the influencing factors include but are not limited to the building structure design, the types of equipment used, energy consumption, etc. These factors will play a decisive role in the carbon emissions of each node, so they need to be identified and tracked.
[0015] In a possible implementation, obtaining the first life cycle node and the first carbon emission impact factor of the building life cycle node sequence includes: When the first life cycle node belongs to the material production stage, the first carbon emission influencing factor includes material type, material mining volume, material mining equipment model, material transportation vehicle model, and material transportation distance; when the first life cycle node belongs to the construction stage, the first carbon emission influencing factor includes building structure topology, energy layout topology, construction equipment model, and construction equipment working hours; when the first life cycle node belongs to the operation stage, the first carbon emission influencing factor includes building orientation, household type, number of households, and energy layout topology; when the first life cycle node belongs to the demolition stage, the first carbon emission influencing factor includes demolition equipment model and demolition equipment working hours; when the first life cycle node belongs to the recycling stage, the first carbon emission influencing factor includes recycling equipment model and recycling equipment working hours.
[0016] Optionally, the factors affecting carbon emissions will vary at different stages of the building's life cycle, and each stage has its own unique factors to consider. When the first life cycle node is in the material production stage, carbon emissions are mainly determined by the material selection and acquisition process. Influencing factors include, but are not limited to, the type of materials used, the amount of materials mined, the type of mining equipment, the type of tools used to transport materials, and the distance of transportation. These factors directly affect the total carbon emissions during the material production process. When the first life cycle node is in the construction stage, carbon emissions are affected by the building design and construction methods, including but not limited to the structural topology of the building, the distribution topology of energy, the type of construction equipment, and the working hours of the equipment, which are all important factors in determining carbon emissions during construction. When the first life cycle node is in the operation stage, the factors affecting carbon emissions are mainly related to the use of the building, including but not limited to the orientation of the building, the type and number of residents, and the distribution topology of energy, which will jointly affect the energy consumption and carbon emission levels during operation. When the first life cycle node is in the demolition stage, carbon emissions are mainly determined by the type of equipment and its working time during the demolition process. Different types of demolition equipment and their working hours will affect the carbon emissions during the demolition process. When the first life cycle node is in the recycling stage, carbon emissions are closely related to the type of equipment used for recycling and its working hours. These factors determine the amount of carbon emissions generated during the recycling process. By analyzing the different influencing factors in these stages, it is possible to more effectively predict and track carbon emissions at each stage, thereby achieving accurate management of carbon emissions throughout the building life cycle.
[0017] In a possible implementation, the step of constructing a node sequence for the entire life cycle of a building includes: The carbon emission tracking step is received through the building carbon emission management client; according to the carbon emission tracking step, the material production stage, the construction stage, the operation stage, the demolition stage and the recycling stage are traversed and divided respectively to obtain the material production stage life cycle node sequence, the construction stage life cycle node sequence, the operation stage life cycle node sequence, the demolition stage life cycle node sequence until the recycling stage life cycle node sequence; the material production stage life cycle node sequence, the construction stage life cycle node sequence, the operation stage life cycle node sequence, the demolition stage life cycle node sequence until the recycling stage life cycle node sequence are arranged in sequence to construct the building full life cycle node sequence.
[0018] Optionally, during the process of building carbon emission tracking, the system terminal receives the carbon emission tracking step set by the user through the building carbon emission management client. This carbon emission tracking step is the time interval set by the user for tracking carbon emissions, which determines the frequency of carbon emission monitoring. Subsequently, based on this carbon emission tracking step, the system terminal traverses different stages of the building life cycle, including the material production stage, the construction stage, the operation stage, the demolition stage, and the recycling stage. Each stage is subdivided into multiple nodes according to the step to form a life cycle node sequence for each stage. For example, in the material production stage, this stage is divided into several nodes, each node corresponds to a specific time point or operation process of the stage, so as to obtain the material production stage life cycle node sequence. The same operation is also applied to the construction, operation, demolition and recycling stages. After the node sequence of each stage is generated, the system terminal arranges the node sequences of these stages in sequence according to the chronological order of the building life cycle to form a complete building life cycle node sequence. This sequence covers all key nodes from material production to building recycling, providing a structured framework for carbon emission tracking and management.
[0019] Taking the first carbon emission influencing factor as the root node, construct a deep neighborhood tree to predict carbon emissions and obtain a first carbon emission prediction value; set the first carbon emission prediction value as the carbon emission tracking target value of the first life cycle node.
[0020] In one embodiment, the system terminal takes the first carbon emission influencing factor as the starting point to construct a deep neighborhood tree. This deep neighborhood tree is a hierarchical structure that helps the system terminal analyze the relationship between various factors related to carbon emissions. Taking the material production stage as an example, the system terminal uses the first carbon emission influencing factor in the material production stage, such as material type, material mining volume, material mining equipment model, etc. as the root node of the tree, representing the basic conditions for carbon emission prediction. Subsequently, according to this core element, more sample sets related to the first carbon emission influencing factor are added to this tree structure layer by layer to form a hierarchical tree structure, namely a deep neighborhood tree. Through the construction of the deep neighborhood tree, the system terminal can comprehensively consider multiple influencing factors, remove leaf nodes, and finally obtain the carbon emission prediction value of the first life cycle node. This carbon emission prediction value will be used as a standard for future monitoring and comparison to track and evaluate the actual carbon emissions. Afterwards, the system terminal sets the obtained first carbon emission prediction value as the carbon emission tracking target value of the building life cycle node, which represents the expected carbon emissions of the node. This carbon emissions tracking target value will serve as a benchmark for actual carbon emissions monitoring and will be compared with the actual carbon emissions monitored at the node to determine whether the carbon emissions are within a reasonable range in order to understand the current carbon emissions status.
[0021] In a possible implementation, taking the first carbon emission influencing factor as the root node, a deep neighborhood tree is constructed, including: Taking the first carbon emission influencing factor as the screening benchmark, a layer of sample sets is collected; the first layer of sample sets are traversed as the screening benchmark respectively, and a second layer of sample sets are collected; until the K-1 layer of sample sets are traversed as the screening benchmark respectively, a K layer of sample sets is constructed, K is an integer, 5≥K≥2; according to the root node, the first layer of sample sets, the second layer of sample sets until the K layer of sample sets, the deep neighborhood tree is constructed.
[0022] Optionally, in order to predict carbon emissions, the system terminal needs to construct a deep neighborhood tree for predicting carbon emissions by pruning. First, the system terminal uses the first carbon emission influencing factor as the basis for screening, and collects the first layer of sample sets from the carbon emission management logs of various channels through the set constraints. This layer of sample sets contains all data related to the influencing factor and meeting the corresponding constraints, forming the preliminary data basis for carbon emission prediction, that is, the first layer of sample sets. Subsequently, the first layer of sample sets is traversed, and each sample is used as a new screening benchmark to collect the second layer of sample sets in the same way as above. In this way, the system terminal will continue to traverse the sample sets of the previous layer layer by layer until the Kth layer of sample sets is collected. K represents the number of layers, which is an integer between 2 and 5. The specific number of layers depends on the complexity of the required carbon emission influencing factors. Afterwards, a multi-level deep neighborhood tree is constructed by combining the root node and the sample sets of each layer (from the first layer to the Kth layer). This deep neighborhood tree can comprehensively and deeply reflect the relationship between multiple factors affecting carbon emissions, thereby providing a basis for accurate prediction of carbon emissions.
[0023] In a possible implementation, taking the first carbon emission influencing factor as a screening basis, a layer of sample sets is collected, including: The first carbon emission influencing factor includes a type carbon emission influencing factor and a quantitative carbon emission influencing factor; the type carbon emission influencing factor is logically and relationally configured to obtain a first-level constraint condition; through the building carbon emission management client, a quantitative carbon emission influencing factor threshold is configured for the quantitative carbon emission influencing factor; according to the quantitative carbon emission influencing factor threshold, the quantitative carbon emission influencing factor is logically and relationally configured to obtain a second-level constraint condition; a first-level sample set that meets the first-level constraint condition is collected online; and the first-layer sample set that meets the second-level constraint condition is sorted from the first-level sample set.
[0024] Optionally, during the sample collection process, the system terminal divides the first carbon emission impact factor into two categories: type carbon emission impact factor and quantitative carbon emission impact factor. Type carbon emission impact factor refers to classification or category information related to carbon emission, which is usually descriptive or qualitative, and is used to define the basic conditions or attributes that affect carbon emission in the construction process. Quantitative carbon emission impact factor refers to specific numerical values or quantifiable indicators related to carbon emission, which is usually quantitative and reflects the numerical parameters in actual operation. For type carbon emission impact factors, the system terminal identifies all type factors related to carbon emission in the current stage. For example, in the material production stage, the type carbon emission impact factors include material type, material mining equipment model, and material transportation tool model. Among these type factors, they are combined together through a "logical and" (AND) relationship, which means that each factor must be satisfied at the same time to generate a primary constraint condition. For example, in the material production stage, only when the material type is known (such as steel), the material mining equipment model meets specific standards (such as a model of a specific brand of excavator), and the material transportation tool model also meets specific requirements (such as using trucks for transportation), only these three factors must be satisfied at the same time to constitute a primary constraint condition. This logic and relationship ensures that carbon emission tracking and prediction are only carried out under specific combinations, forming preliminary screening conditions. Once these type elements are configured with logic and relationships, the system terminal combines these elements into a first-level constraint. This constraint defines the type screening criteria under a specific scenario. For example, in the material production stage, the first-level constraint may be "using steel, excavator model A1, and truck model B2". In this way, further data screening will be carried out only when the material production process meets these conditions. For the quantitative carbon emission influencing factors, the system terminal obtains the range of each over-quantified carbon emission influencing factor set by the user through the building carbon emission management client, and sets specific numerical thresholds for the quantitative carbon emission influencing factors based on these ranges. For example, there will be a clear quantitative standard for material mining volume, material transportation distance, etc. According to these thresholds, the logic and relationship configuration is performed again to generate secondary constraints, which further limit the specific range of carbon emissions. For example, in the material production stage, it can be set that the material mining volume is between 50 and 100 tons and the transportation distance is between 100 and 500 kilometers. Only when all these conditions are met can it be included in the sample set. After obtaining the primary and secondary constraints, the system terminal connects to multiple data sources according to the reserved ports, and uses the primary constraints to filter the carbon emission management logs in each data source to determine the primary sample set. The data sources include but are not limited to the construction industry platform, energy management platform, transportation logistics platform, etc. On this basis, samples that meet the secondary constraints are further filtered out from the primary sample set, and finally a layer of sample sets that meets all conditions is obtained, providing accurate data support for subsequent carbon emission forecasts.
[0025] In a possible implementation, constructing a deep neighborhood tree to predict carbon emissions and obtaining a first carbon emissions prediction value includes: Step 1: According to the deep neighborhood tree, obtain a first leaf node set, wherein the first leaf node set belongs to a first parent node together; Step 2: When each leaf node of the first leaf node set has no child nodes, count the concentrated intervals of the carbon emission record values of the first leaf node set, store them in the first parent node, and prune the first leaf node set at the same time; Step 3: When the first leaf node of the first leaf node set has a second child node set, update the first parent node according to the first leaf node, update the first leaf node set according to the second child node set, and return to step 1; Repeat the analysis until there is only one root node left in the deep neighborhood tree, and the root node has the first carbon emission prediction value.
[0026] Optionally, in the process of carbon emission prediction, the system terminal gradually processes and analyzes data through a deep neighborhood tree. First, a layer of leaf nodes is randomly extracted from the deep neighborhood tree to construct a first leaf node set. These leaf nodes all belong to the same upper node in the tree, that is, the first parent node. Subsequently, each leaf node in the first leaf node set is checked to see if it has a child node. If each leaf node in the current first leaf node set no longer has a child node, that is, they are the bottom layer of the tree and cannot be further decomposed downward. Then the system terminal will count the carbon emission records of these leaf nodes, and obtain the concentrated interval of the carbon emission record values of the first leaf node set by setting the interval with the average value as the center, setting the interval according to the minimum and maximum values of the data, and using the median and quartiles to set the interval. Among them, setting the interval with the average value as the center is suitable for data close to normal distribution, setting the interval according to the minimum and maximum values of the data is suitable for data with a wide range and no need to exclude outliers, and using the median and quartiles to set the interval is suitable for data with many outliers or asymmetric distribution. Taking the average value as the center setting interval as an example, the system terminal calculates the average value of the carbon emission record value of the first leaf node set, and then calculates the standard deviation of the carbon emission record value of the first leaf node set. After that, the calculated average value is added or subtracted by n standard deviations to obtain the concentrated interval of the carbon emission record value of the first leaf node set, where n is usually 1 or 2. The statistically obtained concentrated interval is then stored in the first parent node, and these leaf nodes are pruned, that is, the leaf nodes without child nodes are deleted to simplify the tree structure. If some leaf nodes in the first leaf node set have child nodes, the system terminal uses these child nodes as the second child node set, and uses the leaf node to update the first parent node, that is, the information in the leaf node replaces the information in the first parent node. Then update the second child node set obtained from the current leaf node to the new first leaf node set. After completing the above update, the system terminal will return to step 1 and continue to process the new first leaf node set. If these new leaf nodes also have child nodes, repeat this process until the leaf node without child nodes is finally reached, and perform relevant statistics and pruning operations. Then, the system terminal repeatedly executes these steps to process the leaf nodes in the tree layer by layer until only one root node remains in the entire tree structure. This root node is the final result of the aggregation of all carbon emission data, and the root node contains the first carbon emission prediction value, that is, the carbon emission prediction value of the building life cycle node. Through this layer-by-layer traversal, statistics and pruning method, the system terminal can effectively extract the carbon emission prediction results from complex multi-layer data, gradually summarize and optimize the accuracy of the prediction. Ultimately, the entire deep neighborhood tree will be simplified to a root node, providing an accurate carbon emission prediction value.
[0027] When the building enters the first life cycle node, a carbon emission monitoring device is deployed to obtain a carbon emission monitoring value.
[0028] In one embodiment, when a construction project enters the first life cycle node, the system terminal will automatically deploy carbon emission monitoring devices to monitor the actual carbon emissions of this stage in real time. These monitoring devices are installed in key locations and are responsible for collecting and recording the carbon emission data of the building at this node. Through these monitoring devices, the system terminal can obtain specific carbon emission monitoring values, that is, the actual carbon emissions generated in the current stage. These monitoring values will be used to compare with the obtained carbon emission tracking target values to ensure that the carbon emissions are within a reasonable range.
[0029] In a possible implementation, when the building enters the first life cycle node, a carbon emission monitoring device is deployed to obtain a carbon emission monitoring value, including: When the building enters the first life cycle node, a number of carbon emission monitoring devices are deployed to obtain a number of carbon emission monitoring values; the average of the several carbon emission monitoring values is calculated to obtain the carbon emission monitoring value.
[0030] Optionally, when the building enters the first life cycle node, the system terminal will deploy multiple carbon emission monitoring devices at the node to more comprehensively monitor the carbon emissions at this stage. Each monitoring device will record the carbon emission data of its location or equipment respectively, and these recorded data will be used as carbon emission monitoring values. Subsequently, the system terminal processes all carbon emission monitoring values obtained through the monitoring device and calculates their mean to obtain a comprehensive carbon emission data, that is, the carbon emission monitoring value of the node. This mean represents the actual carbon emissions of the building node as a whole, which is used for subsequent comparison and analysis with the target value.
[0031] When the carbon emission monitoring value is inconsistent with the carbon emission tracking target value, a carbon emission tracking abnormality signal is generated and sent to the building carbon emission management client.
[0032] In one embodiment, when the comparison result of the obtained carbon emission monitoring value and the carbon emission tracking target value is inconsistent, the system terminal will perform corresponding steps according to the comparison result. Specifically, when the carbon emission monitoring value is greater than the carbon emission tracking target value, it means that the carbon emission of the building node exceeds the expected range, which may cause pollution to the environment or violate carbon emission regulations. At this time, the system terminal generates an excessive emission abnormal signal, and adds it to the carbon emission tracking abnormal signal and sends it to the building carbon emission management client, reminding the user to take timely action, such as adjusting the operating parameters of the equipment, reducing energy consumption, or checking whether the equipment has abnormal conditions that cause excessive emissions. When the carbon emission monitoring value is less than the carbon emission tracking target value, it means that the construction or operation efficiency is low, or the equipment does not work as expected. At this time, the system terminal generates an excessively low emission abnormal signal, adds it to the carbon emission tracking abnormal signal and sends it to the building carbon emission management client, prompting the user to check whether the equipment is faulty, whether the energy supply is insufficient, or whether there are problems in the construction process, so as to avoid affecting the quality and progress of the project. Through the above process, two-way monitoring of carbon emissions can be achieved to ensure that carbon emissions are neither excessive nor too low, and balance environmental protection and project efficiency.
[0033] Embodiment 2 is based on the same inventive concept as the method for tracking carbon emissions throughout the life cycle of a building in the aforementioned embodiment. Figure 2 As shown, the present application provides a building life cycle carbon emission tracking platform, and the platform and method embodiments in the embodiments of the present application are based on the same inventive concept. Among them, the platform includes: node element acquisition module 1: the node element acquisition module 1 is used to obtain the first life cycle node and the first carbon emission influencing factor of the building life cycle node sequence; carbon emission prediction module 2: the carbon emission prediction module 2 is used to take the first carbon emission influencing factor as the root node, construct a deep neighborhood tree for carbon emission prediction, and obtain the first carbon emission prediction value; tracking target value setting module 3: the tracking target value setting module 3 is used to set the first carbon emission prediction value as the carbon emission tracking target value of the first life cycle node; monitoring value acquisition module 4: the monitoring value acquisition module 4 is used to deploy a carbon emission monitoring device and obtain a carbon emission monitoring value when the building enters the first life cycle node; tracking abnormal signal generation module 5: the tracking abnormal signal generation module 5 is used to generate a carbon emission tracking abnormal signal when the carbon emission monitoring value is inconsistent with the carbon emission tracking target value, and send it to the building carbon emission management client.
[0034] Furthermore, the node element acquisition module 1 also includes: When the first life cycle node belongs to the material production stage, the first carbon emission influencing factor includes material type, material mining volume, material mining equipment model, material transportation vehicle model, and material transportation distance; when the first life cycle node belongs to the construction stage, the first carbon emission influencing factor includes building structure topology, energy layout topology, construction equipment model, and construction equipment working hours; when the first life cycle node belongs to the operation stage, the first carbon emission influencing factor includes building orientation, household type, number of households, and energy layout topology; when the first life cycle node belongs to the demolition stage, the first carbon emission influencing factor includes demolition equipment model and demolition equipment working hours; when the first life cycle node belongs to the recycling stage, the first carbon emission influencing factor includes recycling equipment model and recycling equipment working hours.
[0035] Furthermore, the node element acquisition module 1 also includes: The carbon emission tracking step is received through the building carbon emission management client; according to the carbon emission tracking step, the material production stage, the construction stage, the operation stage, the demolition stage and the recycling stage are traversed and divided respectively to obtain the material production stage life cycle node sequence, the construction stage life cycle node sequence, the operation stage life cycle node sequence, the demolition stage life cycle node sequence until the recycling stage life cycle node sequence; the material production stage life cycle node sequence, the construction stage life cycle node sequence, the operation stage life cycle node sequence, the demolition stage life cycle node sequence until the recycling stage life cycle node sequence are arranged in sequence to construct the building full life cycle node sequence.
[0036] Furthermore, the carbon emission prediction module 2 also includes: Taking the first carbon emission influencing factor as the screening benchmark, a layer of sample sets is collected; the first layer of sample sets are traversed as the screening benchmark respectively, and a second layer of sample sets are collected; until the K-1 layer of sample sets are traversed as the screening benchmark respectively, a K layer of sample sets is constructed, K is an integer, 5≥K≥2; according to the root node, the first layer of sample sets, the second layer of sample sets until the K layer of sample sets, the deep neighborhood tree is constructed.
[0037] Furthermore, the carbon emission prediction module 2 also includes: The first carbon emission influencing factor includes a type carbon emission influencing factor and a quantitative carbon emission influencing factor; the type carbon emission influencing factor is logically and relationally configured to obtain a first-level constraint condition; through the building carbon emission management client, a quantitative carbon emission influencing factor threshold is configured for the quantitative carbon emission influencing factor; according to the quantitative carbon emission influencing factor threshold, the quantitative carbon emission influencing factor is logically and relationally configured to obtain a second-level constraint condition; a first-level sample set that meets the first-level constraint condition is collected online; and the first-layer sample set that meets the second-level constraint condition is sorted from the first-level sample set.
[0038] Furthermore, the carbon emission prediction module 2 also includes: Step 1: According to the deep neighborhood tree, obtain a first leaf node set, wherein the first leaf node set belongs to a first parent node together; Step 2: When each leaf node of the first leaf node set has no child nodes, count the concentrated intervals of the carbon emission record values of the first leaf node set, store them in the first parent node, and prune the first leaf node set at the same time; Step 3: When the first leaf node of the first leaf node set has a second child node set, update the first parent node according to the first leaf node, update the first leaf node set according to the second child node set, and return to step 1; Repeat the analysis until there is only one root node left in the deep neighborhood tree, and the root node has the first carbon emission prediction value.
[0039] Furthermore, the monitoring value acquisition module 4 also includes: When the building enters the first life cycle node, a number of carbon emission monitoring devices are deployed to obtain a number of carbon emission monitoring values; the average of the several carbon emission monitoring values is calculated to obtain the carbon emission monitoring value.
[0040] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0041] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
[0042] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.
Claims
1. A method for tracking carbon emissions during the entire life cycle of a building, characterized in that: include: Obtain the first life cycle node and the first carbon emission impact factor of the building's full life cycle node sequence; Taking the first carbon emission influencing factor as a root node, constructing a deep neighborhood tree to predict carbon emissions, and obtaining a first carbon emission prediction value; Setting the first carbon emission prediction value as the carbon emission tracking target value at the first life cycle node; When the building enters the first life cycle node, a carbon emission monitoring device is deployed to obtain a carbon emission monitoring value; When the carbon emission monitoring value is inconsistent with the carbon emission tracking target value, a carbon emission tracking abnormality signal is generated and sent to the building carbon emission management client.
2. A method for tracking carbon emissions during the entire life cycle of a building as claimed in claim 1, characterized in that: Obtain the first life cycle node and the first carbon emission impact factor of the building's full life cycle node sequence, including: When the first life cycle node belongs to the material production stage, the first carbon emission influencing factor includes material type, material mining volume, material mining equipment model, material transportation tool model, and material transportation distance; When the first life cycle node belongs to the construction stage, the first carbon emission influencing factor includes building structure topology, energy layout topology, construction equipment model, and construction equipment working hours; When the first life cycle node belongs to the operation stage, the first carbon emission influencing factor includes building orientation, household type, number of households, and energy layout topology; When the first life cycle node belongs to the demolition stage, the first carbon emission influencing factor includes the demolition equipment model and the working time of the demolition equipment; When the first life cycle node belongs to the recycling stage, the first carbon emission influencing factor includes the recycling equipment model and the working time of the recycling equipment.
3. A method for tracking carbon emissions during the entire life cycle of a building as claimed in claim 2, characterized in that: The steps of constructing the node sequence of the entire life cycle of the building include: Receiving a carbon emission tracking step length through the building carbon emission management client; According to the carbon emission tracking step, the material production stage, the construction stage, the operation stage, the demolition stage and the recycling stage are traversed and divided respectively to obtain a material production stage life cycle node sequence, a construction stage life cycle node sequence, an operation stage life cycle node sequence, a demolition stage life cycle node sequence and finally a recycling stage life cycle node sequence; The material production phase life cycle node sequence, the construction phase life cycle node sequence, the operation phase life cycle node sequence, the demolition phase life cycle node sequence, and the recycling phase life cycle node sequence are arranged in sequence to construct the building full life cycle node sequence.
4. A method for tracking carbon emissions during the entire life cycle of a building as claimed in claim 1, characterized in that: Taking the first carbon emission influencing factor as the root node, a deep neighborhood tree is constructed, including: Taking the first carbon emission influencing factor as the screening basis, a layer of sample set is collected; Traversing the first layer sample set to respectively screen the benchmarks and collect the second layer sample set; Until the K-1 layer sample sets are traversed and the screening benchmarks are respectively formed, and the K layer sample sets are constructed, where K is an integer, 5≥K≥2; The deep neighborhood tree is constructed according to the root node, the first-layer sample set, the second-layer sample set, and finally the K-layer sample set.
5. A method for tracking carbon emissions during the entire life cycle of a building as claimed in claim 4, characterized in that: Taking the first carbon emission influencing factor as the screening basis, a layer of sample set is collected, including: The first carbon emission impact factor includes a type carbon emission impact factor and a quantitative carbon emission impact factor; Perform logic and relationship configuration on the carbon emission influencing factors of the type to obtain primary constraint conditions; By means of the building carbon emission management client, a quantitative carbon emission impact factor threshold is configured for the quantitative carbon emission impact factor; Performing logic and relationship configuration on the quantitative carbon emission influencing factors according to the quantitative carbon emission influencing factor thresholds to obtain secondary constraint conditions; Collecting a set of first-level samples that meet the first-level constraint conditions through an Internet connection; The first-layer sample set satisfying the second-level constraint condition is selected from the first-level sample set.
6. A method for tracking carbon emissions during the entire life cycle of a building as claimed in claim 1, characterized in that: Construct a deep neighborhood tree to predict carbon emissions and obtain the first carbon emissions prediction value, including: Step 1: obtaining a first leaf node set according to the deep neighborhood tree, wherein the first leaf node set belongs to a first parent node; Step 2: when each leaf node of the first leaf node set has no child node, count the concentrated interval of the carbon emission record values of the first leaf node set, store them in the first parent node, and prune the first leaf node set at the same time; Step 3: When the first leaf node of the first leaf node set has a second child node set, update the first parent node according to the first leaf node, update the first leaf node set according to the second child node set, and return to step 1; The analysis is repeated until only one root node is left in the deep neighborhood tree, and the root node has the first carbon emission prediction value.
7. A method for tracking carbon emissions during the entire life cycle of a building as claimed in claim 1, characterized in that: When the building enters the first life cycle node, a carbon emission monitoring device is deployed to obtain carbon emission monitoring values, including: When the building enters the first life cycle node, deploying a number of carbon emission monitoring devices to obtain a number of carbon emission monitoring values; An average of the plurality of carbon emission monitoring values is calculated to obtain the carbon emission monitoring value.
8. A building life cycle carbon emission tracking platform, characterized by: The platform is used to execute a method for tracking carbon emissions throughout the life cycle of a building as described in any one of claims 1 to 7, comprising: Node element acquisition module: obtain the first life cycle node and the first carbon emission impact element of the building's full life cycle node sequence; Carbon emission prediction module: taking the first carbon emission influencing factor as the root node, constructing a deep neighborhood tree to predict carbon emissions, and obtaining a first carbon emission prediction value; A tracking target value setting module: sets the first carbon emission prediction value as the carbon emission tracking target value of the first life cycle node; Monitoring value acquisition module: when the building enters the first life cycle node, a carbon emission monitoring device is deployed to obtain a carbon emission monitoring value; Tracking abnormality signal generation module: When the carbon emission monitoring value is inconsistent with the carbon emission tracking target value, a carbon emission tracking abnormality signal is generated and sent to the building carbon emission management client.