Carbon emission reduction data evaluation method and system for green building

Abstract

The invention discloses a carbon emission reduction data assessment method and system for a green building, and relates to the technical field of carbon emission reduction assessment, and the method comprises the steps: obtaining Q construction nodes of a target building, carrying out the node basic demand information collection, and obtaining a Q node basic demand information set; performing construction condition extraction on the Q construction nodes to obtain a construction condition index set of the Q nodes; association mapping is carried out, construction scheme screening matching is carried out based on an association mapping result, and Q node construction schemes are obtained; and performing data evaluation based on the Q node construction schemes to obtain a target carbon emission data evaluation result. According to the invention, the technical problem that the accuracy and reliability of the evaluation result of the carbon emission data of the green building are insufficient in the prior art is solved, and the evaluation of the carbon emission data of the green building is realized through construction node information collection, association mapping and scheme screening and matching. And the accuracy and reliability of the target carbon emission data evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction assessment, and in particular to a carbon emission reduction data assessment method and system for green buildings. Background Art

[0002] In the current global context of actively responding to climate change and vigorously advocating sustainable development, the development of green buildings as an important way to reduce building energy consumption and carbon emissions has received widespread attention. Traditional buildings often consume a lot of energy and produce huge carbon emissions during construction and use, which has placed a heavy burden on the environment. There are many problems with the existing green building carbon emission data assessment technology. On the one hand, in the data collection stage, it is often impossible to fully and accurately obtain the basic demand information and construction condition indicators of each node in the building construction process. For example, for some complex building structures or special construction environments, it is difficult to accurately collect relevant key data such as energy consumption and material use, which leads to a lack of reliable data basis for subsequent assessments. On the other hand, in terms of assessment methods, there is a lack of effective correlation mapping mechanisms and scientific construction plan screening and matching strategies. Many assessment methods simply apply general models without fully considering the uniqueness of different buildings and the inherent connections between various construction nodes, making the assessment results unable to truly reflect the actual carbon emissions of green buildings.

[0003] Existing technologies have technical problems such as insufficient accuracy and reliability of green building carbon emission data assessment results. Summary of the invention

[0004] The present application provides a carbon emission reduction data assessment method and system for green buildings, which is used to solve the technical problem of insufficient accuracy and reliability of green building carbon emission data assessment results in the prior art.

[0005] In view of the above problems, the present application provides a carbon emission reduction data assessment method and system for green buildings.

[0006] In a first aspect of the present application, a carbon emission reduction data assessment method for green buildings is provided, the method comprising:

[0007] Acquire Q construction nodes of the target building, traverse the Q construction nodes to collect basic demand information of the nodes, and obtain a set of basic demand information of Q nodes, wherein Q is an integer greater than or equal to 1; extract construction conditions for the Q construction nodes according to preset construction condition indicators, and obtain a set of construction condition indicators of Q nodes; perform association mapping on the set of basic demand information of the Q nodes and the set of construction condition indicators of the Q nodes, perform construction plan screening and matching based on the association mapping results, and obtain construction plans of Q nodes; perform data evaluation based on the Q node construction plans, and obtain target carbon emission data evaluation results.

[0008] A second aspect of the present application provides a carbon emission reduction data assessment system for green buildings, the system comprising:

[0009] A basic demand information set acquisition module is used to acquire Q construction nodes of the target building, traverse the Q construction nodes to collect node basic demand information, and obtain Q node basic demand information sets, wherein Q is an integer greater than or equal to 1; a construction condition indicator set acquisition module is used to extract construction conditions for the Q construction nodes according to preset construction condition indicators, and obtain Q node construction condition indicator sets; a construction plan acquisition module is used to associate and map the Q node basic demand information sets and the Q node construction condition indicator sets, and screen and match construction plans based on the association mapping results to obtain Q node construction plans; an evaluation result acquisition module is used to perform data evaluation based on the Q node construction plans to obtain target carbon emission data evaluation results.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Get Q construction nodes of the target building, traverse the Q construction nodes to collect node basic demand information, and obtain Q node basic demand information sets; extract construction conditions for the Q construction nodes, and obtain Q node construction condition indicator sets; perform association mapping, screen and match construction plans based on the association mapping results, and obtain Q node construction plans; perform data evaluation based on the Q node construction plans, and obtain target carbon emission data evaluation results. The technical effect of evaluating green building carbon emission data through construction node information collection, association mapping, and plan screening and matching has been achieved, and the accuracy and reliability of the target carbon emission data evaluation results have been improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] 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.

[0013] Figure 1 A schematic diagram of a process flow of a carbon emission reduction data assessment method for green buildings provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a carbon emission reduction data assessment system for green buildings provided in an embodiment of the present application.

[0015] Explanation of reference numerals: basic demand information set acquisition module 10, construction condition indicator set acquisition module 20, construction plan acquisition module 30, evaluation result acquisition module 40. DETAILED DESCRIPTION

[0016] The present application provides a carbon emission reduction data assessment method and system for green buildings, which is used to solve the technical problem of insufficient accuracy and reliability of green building carbon emission data assessment results in the prior art.

[0017] 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.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a carbon emission reduction data assessment method for green buildings, the method comprising:

[0019] Step S100: Acquire Q construction nodes of the target building, traverse the Q construction nodes to collect node basic demand information, and obtain Q node basic demand information sets, where Q is an integer greater than or equal to 1.

[0020] Specifically, for target buildings, such as the State Grid Dispatching Building and the Rural Power Supply Station, they are divided into multiple logical areas as construction nodes based on the functional layout drawings and actual usage of the buildings. For the State Grid Dispatching Building, the dispatching center, equipment room, office area, etc. can be divided into different construction nodes. For the Rural Power Supply Station, the business hall, distribution room, employee rest area, etc. can be determined as construction nodes. After determining the Q construction nodes, the basic demand information of the nodes is collected. Through field measurement tools, the spatial dimensions of each node, including area, height and other data, are accurately measured to determine the space requirements. Professional energy consumption monitoring equipment is used to record the power, operating time and other information of the existing equipment in the node to obtain energy demand data. At the same time, the historical maintenance records and equipment lists of the building are consulted to collect basic information about the building structure, equipment model, etc. After a comprehensive and detailed investigation of each node, the various types of information collected are classified and sorted, and finally a set of basic demand information of Q nodes is formed, providing a detailed data basis for subsequent evaluation work.

[0021] Step S200: extracting construction conditions for the Q construction nodes according to preset construction condition indicators to obtain a set of Q node construction condition indicators.

[0022] Specifically, it is necessary to first establish a set of perfect preset construction condition indicators that meet the green building standards, covering key indicators in energy efficiency, environmental impact, resource utilization, etc., such as the heat transfer coefficient of the building envelope, the proportion of renewable energy utilization, indoor air quality standards, etc. For the Q construction nodes determined, taking the State Grid dispatching building as an example, the construction conditions of its building envelope are extracted, and the heat transfer coefficients of walls, roofs, windows and other parts are measured using equipment such as heat flow meters and infrared thermal imagers to determine whether their thermal insulation performance meets the preset indicator requirements. These data are recorded as the construction condition indicators of the envelope structure of the node. In terms of energy utilization, the energy consumption data of various equipment in the node are analyzed through the power monitoring system, combined with the local energy supply structure and renewable energy resources, such as the availability of solar energy and wind energy, to determine the construction condition indicators of the node in terms of energy supply and utilization efficiency, such as whether it is suitable for installing solar photovoltaic panels or small wind turbines. For rural power supply stations, a similar method is also used. In terms of indoor environment, air quality monitors are used to detect the concentration of pollutants such as formaldehyde and carbon dioxide, as well as temperature and humidity in business halls and office areas, and construction condition indicators are determined based on relevant indoor environmental quality standards. Through comprehensive analysis and data collection of each construction node, the various types of construction condition indicators obtained are classified and sorted, and finally a set of Q node construction condition indicators is formed, providing accurate data basis for subsequent association mapping and solution screening.

[0023] Step S300: performing association mapping on the basic demand information sets of the Q nodes and the construction condition indicator sets of the Q nodes, and performing construction plan screening and matching based on the association mapping results to obtain construction plans for the Q nodes.

[0024] Specifically, an algorithm based on feature vector and similarity calculation is used to realize association mapping and scheme screening and matching, and each node basic demand information set and node construction condition indicator set are converted into feature vectors. For example, for the node basic demand information, if it contains factors such as space area demand, equipment power demand, and personnel density, these factors are quantified and form a feature vector. Similarly, for node construction condition indicators, such as daylighting factor, thermal insulation performance index, energy supply type, etc., they are also converted into corresponding feature vectors. The cosine similarity algorithm is used to calculate the similarity between these feature vectors. By calculating the similarity, the degree of association between the basic demand information and the construction condition indicators of each node is obtained. Then, in the construction scheme database, the schemes are sorted according to these association degrees, and the corresponding construction schemes are preferentially screened for combinations with higher similarity. A similarity threshold is set. When the similarity is greater than the threshold, the corresponding construction scheme is included in the candidate set, and the most suitable construction scheme for each node is finally determined, thereby obtaining Q node construction schemes.

[0025] Step S400: Perform data evaluation based on the Q node construction plans to obtain target carbon emission data evaluation results.

[0026] Specifically, guided by the Q node construction plans, the massive big data resources related to the construction industry are deeply mined. For target buildings such as the State Grid dispatching building and rural power supply stations, detailed data after the implementation of these specific construction plans in similar scenarios are accurately screened from the database, including the energy consumption of various equipment, the production and transportation of building materials, and the carbon emission data during the construction process, so as to construct a set of Q scheme execution carbon emission data. At the same time, based on the comprehensive evaluation of multiple factors such as actual operation effect, stability and durability, a set of Q scheme execution reliability is generated. Then, a centralized screening work is carried out for the Q scheme execution carbon emission data set. The mean of each set is first calculated, and the neighbor data range is circled according to the preset data tolerance bandwidth as the center to form a mean neighbor set, and the edge data is extracted to distinguish the left and right edge scheme execution carbon emission data. Further, a neighbor set is constructed around these edge data, and then iterative operations are performed based on the quantitative relationship between different neighbor sets. For example, if the number of neighbors on one side meets a specific condition, the mean is iterated based on the edge data to that side until the preset iteration stop standard is reached, and finally the centralized carbon emission data of the Q scheme execution is obtained.

[0027] At the same time, the Q scheme execution reliability sets are also centrally screened to obtain the optimized reliability values ​​of each set. Finally, the Q scheme execution centralized carbon emission data are combined with the corresponding scheme execution centralized reliability by weighted summation to accurately calculate the comprehensive and scientific target carbon emission data evaluation results of the target building, providing solid data support and decision-making basis for the determination of carbon emission reduction effectiveness of green buildings and subsequent strategy optimization.

[0028] In a possible implementation, step S300 further includes:

[0029] Step S310: Associatively mapping the Q node basic demand information sets and the Q node construction condition indicator sets to obtain a mapping relationship between Q node demands and node construction condition indicators.

[0030] Step S320: Using the mapping relationship between the Q node requirements and the node construction condition indicators as an index, search and match are performed in the construction plan database to obtain Q node construction plans.

[0031] Specifically, the elements in each node basic demand information set and node construction condition index set are converted into feature vectors, where demand information can be constructed based on dimensions such as functional requirements, spatial requirements, and energy consumption requirements, and construction condition indicators are formed around geographical environment, climate conditions, policy and regulatory requirements. Then, the cosine similarity algorithm is used to calculate the similarity between these feature vectors. The element pairs with high similarity constitute an important part of the node demand-node construction condition index mapping relationship. At the same time, the importance of different dimensions is weighted according to the analytic hierarchy process (AHP) to further optimize the accuracy of the mapping relationship. After multiple iterative calculations and adjustments, Q comprehensive and accurate node demand-node construction condition index mapping relationships are finally obtained, providing a reliable basis for subsequent retrieval and matching.

[0032] A hash table is constructed based on the mapping relationship between Q node requirements and node construction condition indicators by combining hash tables and greedy algorithms. The key features in the mapping relationship are used as hash keys, and the corresponding possible solution indexes are stored in the hash table as values. Then, the hash table is traversed, and the mapping relationship of each node is screened in the corresponding possible solution set using the greedy algorithm. The greedy algorithm compares each solution in turn according to the pre-set evaluation criteria, such as the lowest carbon emission, the best cost-effectiveness, and the highest resource utilization, and selects the local optimal solution. During the comparison process, each indicator of each solution is quantitatively evaluated, such as calculating its carbon emissions through a carbon emission calculation model, and evaluating its construction and operation costs through a cost estimation model. After searching the mapping relationship of each node in the hash table and screening it with the greedy algorithm, Q node construction solutions that meet the requirements are finally obtained, ensuring that each solution can better adapt to the needs and construction conditions of the corresponding node, providing a reasonable basis for the subsequent carbon emission reduction data evaluation.

[0033] In a possible implementation, step S400 further includes:

[0034] Step S410: Using the Q node construction schemes as indexes, data mining of scheme execution results is performed based on big data to obtain Q scheme execution carbon emission data sets and Q scheme execution reliability sets.

[0035] Step S420: centrally screening the carbon emission data sets for the Q schemes to obtain centralized carbon emission data for the Q schemes.

[0036] Step S430: centrally screening the Q solution execution reliability sets to obtain the Q solution execution central reliability.

[0037] Step S440: summing up the carbon emission data of the Q scheme execution centers based on the reliability of the Q scheme execution centers to obtain the target carbon emission data evaluation result.

[0038] Specifically, firstly, distributed storage technology is used to store massive amounts of green building-related data to ensure high availability and scalability of data. With Q node construction plans as indexes, the MapReduce programming model is used for data mining. In the Map stage, the data is classified according to the node construction plan, and features that may be related to carbon emissions and reliability, such as building material type, energy supply system parameters, construction process details, etc., are extracted, and corresponding weights are assigned to each feature. For carbon emission data mining, the decision tree regression algorithm is used in the Reduce stage to train the model based on the classified data and feature weights. By continuously iteratively optimizing the model parameters, the carbon emission data of each node construction plan under different working conditions is predicted, thereby constructing a set of Q scheme execution carbon emission data. In terms of reliability data mining, an inference algorithm based on Bayesian networks is used. First, a Bayesian network structure is constructed based on historical data and expert knowledge to determine the probability relationship between various factors in the node construction plan (such as construction team experience, equipment brand reliability, etc.), and then the conditional probability formula is used to perform inference calculations in the Bayesian network, comprehensively considering the impact of multiple factors on the scheme reliability, and finally generating a set of Q scheme execution reliability, providing solid data support for subsequent evaluation.

[0039] The operation is carried out on the carbon emission data sets of Q schemes. For each set, the arithmetic mean of all its data is calculated as the preliminary central reference value. Then, a reasonable preset data tolerance bandwidth is set, and the data within this bandwidth range is screened out in each set with the average value as the center to construct a new subset. In this subset, the data points at the edge are identified again, that is, the minimum and maximum values. Then, the tolerance bandwidth is further narrowed, and the data is screened and analyzed again around the new central reference value (adjusted and determined according to the previous average and edge values), and this process is repeated many times to continuously optimize the data screening range. Through this step-by-step iteration and refined screening method, the most representative data that can effectively reflect the actual situation of carbon emissions from the implementation of the scheme and exclude the interference of outliers is finally determined from each set, thereby successfully obtaining the concentrated carbon emission data of the Q schemes, laying a solid foundation for subsequent accurate evaluation.

[0040] For each set of reliability of program execution, we first use statistical analysis methods to perform preliminary processing, calculate the mean and standard deviation statistical indicators of the set, so as to understand the central tendency and dispersion of the data. Then, we use box plot analysis technology to identify outliers in the data, and remove those values ​​that deviate too much from the distribution range of the main data, so as to obtain a relatively pure data subset that can better reflect the true reliability level. On this basis, we calculate the mean of the new subset again, and combine the threshold range set by expert experience to fine-tune and screen the unreasonable data that may still exist, and finally determine the centralized reliability of program execution for each set, forming Q sets of centralized reliability of program execution.

[0041] The obtained Q scheme execution centralized reliability is used as the key coefficient, and corresponds to the corresponding Q scheme execution centralized carbon emission data one by one. For the carbon emission data of each node, multiply it by its corresponding reliability coefficient, so that the scheme with high reliability will have a greater influence on the carbon emission data in the final sum, and vice versa. By cumulatively summing the product results of each node in turn, the carbon emission performance of each node construction plan and its reliability weight are comprehensively considered, so as to accurately obtain the target carbon emission data evaluation results of the target building, and provide a quantitative basis for the formulation and implementation of green building carbon emission reduction strategies.

[0042] In a possible implementation, step S420 further includes:

[0043] Step S421: Calculate the mean of the carbon emission data sets for the Q solutions respectively to obtain the mean of the carbon emission data for the Q solutions.

[0044] Step S422: constructing, in the Q scheme execution carbon emission data sets, Q scheme execution carbon emission data mean neighbor sets of the mean values ​​of the Q scheme execution carbon emission data according to a preset data tolerance bandwidth.

[0045] Step S423: Extract edge data from the neighboring set of the mean carbon emission data of the Q schemes, and obtain Q left edge carbon emission data of the schemes and Q right edge carbon emission data of the schemes, wherein the Q left edge carbon emission data of the schemes are smaller than the mean of the carbon emission data of the Q schemes, and the Q right edge carbon emission data of the schemes are larger than the mean of the carbon emission data of the Q schemes.

[0046] Step S424: In combination with the preset data tolerance bandwidth, construct a neighbor set of the Q left edge schemes executing the carbon emission data and a neighbor set of the Q right edge schemes executing the carbon emission data.

[0047] Step S425: According to the neighbor sets of the carbon emission data executed by the Q left edge schemes and the neighbor sets of the carbon emission data executed by the Q right edge schemes, iterate the mean of the carbon emission data executed by the Q schemes to obtain the concentrated carbon emission data executed by the Q schemes.

[0048] Specifically, for each set of carbon emission data from the execution of each plan, a loop traversal method is used to process it. First, an accumulation variable is initialized to 0, and then the carbon emission data elements in the set are read one by one and accumulated into the accumulation variable. After traversing all the data in a set, the value of the accumulation variable is divided by the total number of data in the set. Through this arithmetic average calculation method, the mean of the carbon emission data from the execution of the plan in the set is obtained. This process is repeated for all Q sets, so that the mean of the carbon emission data from the execution of Q plans is successfully obtained. These means will serve as key benchmarks for subsequent data processing and analysis, helping to accurately grasp the approximate level of carbon emissions from the execution of each plan.

[0049] For each scheme execution carbon emission data set that has obtained the mean, a value range is determined based on the preset data tolerance bandwidth. The lower limit of this range is the mean minus half of the tolerance bandwidth, and the upper limit is the mean plus half of the tolerance bandwidth. Then, traverse each data element in the corresponding scheme execution carbon emission data set, select the data within this value range, and form a new subset. This subset is the mean neighbor set of the scheme execution carbon emission data for this set. Repeat this process for all Q scheme execution carbon emission data sets, and finally successfully construct Q scheme execution carbon emission data mean neighbor sets. These neighbor sets can filter out data that are closer to the mean, which helps to further analyze the distribution characteristics and stability of the data and ensure the accuracy and reliability of the entire carbon emission data evaluation process.

[0050] The Q neighbor sets of mean carbon emission data for the constructed schemes are analyzed and processed one by one. For each neighbor set, the data in it is arranged in ascending order through a sorting algorithm. After the sorting is completed, the data on the far left is the minimum value in the set, and it is determined as the carbon emission data for the left edge scheme; the data on the far right is the maximum value, and it is determined as the carbon emission data for the right edge scheme. In this way, each neighbor set is operated, and finally Q carbon emission data for the left edge scheme and Q carbon emission data for the right edge scheme are successfully extracted. The acquisition of these edge data plays a key role in the subsequent in-depth understanding of the discrete degree of the data, judging the rationality of the data, and further optimizing the data screening process. It provides an important reference for the accurate evaluation of carbon emission data, helps to identify possible outliers or extreme situations, and thus ensures the reliability of the entire evaluation system.

[0051] The operations are carried out respectively for the obtained Q left edge scheme execution carbon emission data and Q right edge scheme execution carbon emission data. For each left edge scheme execution carbon emission data, a new value interval is determined according to the preset data tolerance bandwidth with it as the center. The lower limit of the interval is the left edge data minus half of the tolerance bandwidth, and the upper limit is the left edge data plus half of the tolerance bandwidth. In the original scheme execution carbon emission data set, all data in this interval are screened out, and these data constitute the corresponding left edge scheme execution carbon emission data neighbor set. Similarly, for each right edge scheme execution carbon emission data, the neighbor set is constructed according to the same method. Taking the right edge data as the benchmark, determine the value range, and then find the data falling within the range from the original data set to form the right edge scheme execution carbon emission data neighbor set. In this way, Q left edge scheme execution carbon emission data neighbor sets and Q right edge scheme execution carbon emission data neighbor sets are finally successfully constructed. The construction of these neighbor sets helps to further analyze the data distribution around the edge data, providing a more detailed information basis for subsequent data iteration and determination of centralized carbon emission data, thereby improving the accuracy and reliability of the entire carbon emission data assessment.

[0052] The neighbor set of each left edge scheme execution carbon emission data and the neighbor set of the right edge scheme execution carbon emission data are analyzed, the data number of the left edge neighbor set and the data number of the right edge neighbor set are calculated, and compared with the data number of the mean neighbor set of the scheme execution carbon emission data. If the data number of the left edge neighbor set is greater than or equal to the data number of the mean neighbor set, and the data number of the right edge neighbor set is less than the data number of the mean neighbor set, the mean iteration is performed based on the left edge scheme execution carbon emission data. The current scheme execution carbon emission data mean is updated to the weighted average of the left edge scheme execution carbon emission data and the original mean. The weight can be determined according to the compactness of the data distribution or other relevant factors. Repeat this process until the preset iteration stop condition is met, such as the mean difference obtained by two adjacent iterations is less than a certain threshold, and finally the left edge iterative execution carbon emission data of the set is obtained. Conversely, if the right edge neighbor set meets similar conditions, a similar iterative operation is performed on the right edge scheme execution carbon emission data to obtain the right edge iterative execution carbon emission data. If the number of data in the left and right edge neighbor sets is greater than or equal to the number of data in the mean neighbor set, the left and right edge data are considered at the same time. The carbon emission data of the left edge iteration execution and the carbon emission data of the right edge iteration execution are calculated respectively, and then the average of the two is taken as the new iteration mean. The iteration continues until the preset iteration stop condition is reached, and finally Q optimized scheme execution concentrated carbon emission data are obtained. These data can more accurately reflect the actual carbon emission situation and exclude the interference of outliers and noise.

[0053] In a possible implementation, step S425 further includes:

[0054] Step S4251: When only one of the number of Q left edge neighbors of the neighbor set of carbon emission data executed by the Q left edge schemes and the number of Q right edge neighbors of the neighbor set of carbon emission data executed by the Q right edge schemes is greater than or equal to the number of Q mean neighbors of the mean neighbor set of carbon emission data executed by the Q schemes, iterate the mean of the carbon emission data executed by the Q schemes to the left or right based on the carbon emission data executed by the Q left edge schemes or the carbon emission data executed by the Q right edge schemes until the preset iteration stop condition is met, and obtain Q left edge iterative execution carbon emission data or Q right edge iterative execution carbon emission data.

[0055] Step S4252: Calculate the mean of the scheme execution carbon emission data between the Q left edge iterative execution carbon emission data and the mean of the Q scheme execution carbon emission data, or calculate the mean of the scheme execution carbon emission data between the Q right edge iterative execution carbon emission data and the mean of the Q scheme execution carbon emission data, and obtain the concentrated carbon emission data of the Q scheme executions.

[0056] Specifically, the number of left edge neighbors of the Q left edge schemes executing carbon emission data neighbor set and the number of right edge neighbors of the Q right edge schemes executing carbon emission data neighbor set are compared and analyzed one by one with the number of mean neighbors of the mean neighbor set of the Q schemes executing carbon emission data. Once it is found that only one of the number of left edge neighbors or the number of right edge neighbors is greater than or equal to the number of mean neighbors, the iteration procedure is started. If the number of left edge neighbors meets the conditions, the iteration direction is determined to be left at this time, and the Q left edge schemes executing carbon emission data are fused and calculated with the current Q schemes executing carbon emission data mean. The weighted average method is used to assign reasonable weights to the two according to the distribution characteristics and importance of the data, and a new mean is calculated. Then, the new mean is used again with the left edge data for the next round of iterative calculation, and this process is repeated continuously. In each iteration process, the difference between the newly obtained mean and the previous mean is calculated and compared with the preset iteration stop threshold. Only when the difference is less than the threshold will the iteration stop, and finally the Q left edge iterative execution carbon emission data are successfully obtained. On the contrary, if the number of right-edge neighbors meets the conditions, the iteration direction is to the right, and according to a similar weighted average iterative algorithm, Q right-edge schemes are used to execute carbon emission data and the current mean for calculation and iteration until the preset iteration stop condition is met, thereby obtaining Q right-edge iterative execution carbon emission data, providing accurate basic data for subsequent data analysis and processing.

[0057] When the iteration is completed and Q left edge iteration execution carbon emission data or Q right edge iteration execution carbon emission data are obtained, the concentrated carbon emission data is calculated. If there are Q left edge iteration execution carbon emission data, these left edge iteration execution carbon emission data and the corresponding Q scheme execution carbon emission data means are extracted. Then, these two sets of data are merged into a new data set, covering all data points from the left edge iteration value to the mean. Then, using the standard mean calculation method, all data in this new set are summed and divided by the total number of data to obtain their average value. This average value is an important part of the scheme execution concentrated carbon emission data under the left edge iteration. After comprehensive consideration and calculation of all relevant data, the Q scheme execution concentrated carbon emission data finally obtained can effectively eliminate the interference of outliers and more accurately reflect the concentration trend and reasonable range of actual carbon emissions. Similarly, if the calculation is based on Q right edge iteration execution carbon emission data, the same process and method are followed to ensure that representative scheme execution concentrated carbon emission data can be accurately obtained under different circumstances.

[0058] In a possible implementation, step S425 further includes:

[0059] Step S4253: When the number of Q left edge neighbors of the neighbor set of the Q left edge schemes executing carbon emission data and the number of Q right edge neighbors of the neighbor set of the Q right edge schemes executing carbon emission data are both greater than or equal to the number of Q mean neighbors of the mean neighbor set of the Q schemes executing carbon emission data, iterate the mean of the carbon emission data executed by the Q schemes to the left and right at the same time based on the carbon emission data executed by the Q left edge schemes and the carbon emission data executed by the Q right edge schemes until the preset iteration stop condition is reached, and obtain Q left edge iterative execution carbon emission data and Q right edge iterative execution carbon emission data.

[0060] Step S4254: Calculate the mean of the scheme execution carbon emission data between the Q left edge iterative execution carbon emission data and the Q right edge iterative execution carbon emission data to obtain the Q scheme execution concentrated carbon emission data.

[0061] Specifically, when the number of Q left edge neighbors of the Q left edge schemes executing carbon emission data neighbor set and the number of Q right edge neighbors of the Q right edge schemes executing carbon emission data neighbor set are both greater than or equal to the number of Q mean neighbors of the Q schemes executing carbon emission data mean neighbor set, iterative operations should be carried out to the left and right at the same time. Starting from the left edge scheme executing carbon emission data neighbor set, the first fusion calculation is performed based on the Q left edge schemes executing carbon emission data, combined with the current Q schemes executing carbon emission data mean, and a new temporary mean is obtained in a weighted manner. Next, check the corresponding number of neighbors, that is, count the number of data in the newly generated left edge related neighbor set after this iteration, and compare it with the number of left edge neighbors in the previous iteration. If the new number of neighbors is greater than or equal to the number of neighbors in the previous round, then continue to use the current new temporary mean and the Q left edge schemes to execute carbon emission data again according to the iteration rules for the next round of calculations, and repeat this process.

[0062] Starting from the neighbor set of carbon emission data executed by the right edge scheme, the Q right edge scheme carbon emission data and the current Q scheme carbon emission data are used as the mean. The initial fusion operation is performed according to the corresponding iteration rules. After a new temporary mean is obtained, the number of data in the right edge neighbor set corresponding to this iteration is counted and compared with the number of right edge neighbors in the previous round. If the new number of neighbors is greater than or equal to that of the previous round, the next round of iteration will be performed based on the current new temporary mean and the Q right edge scheme carbon emission data. Both sides continue this iteration process until the preset iteration stop condition is met. This stop condition is the number of neighbors, that is, after a certain iteration, whether it is the left or the right, when the number of neighbors obtained in the next round of iteration is less than the number of neighbors obtained in the previous round of iteration, the iteration is stopped, and finally Q left edge iteration carbon emission data and Q right edge iteration carbon emission data are successfully obtained.

[0063] After successfully obtaining Q left edge iteration execution carbon emission data and Q right edge iteration execution carbon emission data, traverse the entire data set, filter out all the scheme execution carbon emission data between the Q left edge iteration execution carbon emission data and the Q right edge iteration execution carbon emission data, and integrate them into a new data set. Next, perform a sum operation on all the data in this new set, that is, add the values ​​of each data, and after the sum is completed, divide this sum by the total number of data in the new set to obtain a value representing the trend of this part of the data set. For Q different situations, repeat the above steps to obtain Q such means, which are the required Q scheme execution concentrated carbon emission data. This process can effectively eliminate the interference of abnormal data and extreme values, accurately reflect the core level of carbon emissions for each scheme execution, and provide key data support for subsequent carbon emission assessment.

[0064] In a possible implementation, step S4253 further includes:

[0065] Step S42531: The carbon emission data of the Q left edge schemes are used as the carbon emission data of the Q left edge one-iteration execution, and the carbon emission data of the Q right edge schemes are used as the carbon emission data of the Q right edge one-iteration execution.

[0066] Step S42532: Use the left edge data in the neighbor set of Q left edge schemes to execute carbon emission data as the Q left edge secondary iteration execution carbon emission data, and use the right edge data in the neighbor set of Q right edge schemes to execute carbon emission data as the Q right edge secondary iteration execution carbon emission data.

[0067] Step S42533: In combination with the preset data tolerance bandwidth, construct the Q left edge quadratic execution carbon emission data neighbor sets of the Q left edge quadratic execution carbon emission data, and the Q right edge quadratic execution carbon emission data neighbor sets of the Q right edge quadratic execution carbon emission data.

[0068] Step S42534: Determine whether the number of Q left edge secondary iteration neighbors of the neighbor set of the Q left edge secondary iteration carbon emission data is greater than or equal to the number of Q left edge neighbors corresponding to the Q left edge single iteration carbon emission data, and if so, continue to iterate based on the Q left edge single iteration carbon emission data until the preset iteration stop condition is met to obtain Q left edge iterative execution carbon emission data, wherein the preset iteration stop condition is that the number of neighbors obtained in the next iteration is less than the number of neighbors obtained in the previous iteration.

[0069] Step S42535: Determine whether the number of Q right edge secondary iteration neighbors of the neighbor set of the Q right edge secondary iteration carbon emission data is greater than or equal to the number of Q right edge neighbors corresponding to the Q right edge single iteration carbon emission data; if so, continue to iterate based on the Q right edge single iteration carbon emission data until the preset iteration stop condition is met to obtain the Q right edge iterative execution carbon emission data.

[0070] Specifically, data initialization is performed to directly designate the carbon emission data of Q left edge scheme executions as the carbon emission data of Q left edge one-iteration executions, and at the same time, the carbon emission data of Q right edge scheme executions are determined as the carbon emission data of Q right edge one-iteration executions, to provide initial values ​​for subsequent iterative calculations.

[0071] Targeted operations are carried out on the Q neighbor sets of carbon emission data executed by the left edge scheme and the Q neighbor sets of carbon emission data executed by the right edge scheme. For each neighbor set of carbon emission data executed by the left edge scheme, data is retrieved in the set to find the data on the far left. These data represent relative minimum values ​​in their respective neighbor sets. The left edge data screened out from these Q sets are integrated as Q left edge secondary iterative execution carbon emission data for further subsequent iterative calculations to help more accurately explore the distribution of data. At the same time, similar operations are performed on the Q right edge scheme execution carbon emission data neighbor sets to locate the rightmost data in each set, which are the relative maximum values ​​in their respective sets. The right edge data of these Q sets are aggregated to form Q right edge secondary iterative execution carbon emission data. Through such operations, a more accurate edge data foundation is provided for subsequent iterative steps, which helps to better grasp the boundary characteristics of the data during data processing, thereby more accurately calculating the central trend of the scheme execution carbon emission data.

[0072] According to the preset data tolerance bandwidth, the Q left edge second iteration carbon emission data and the Q right edge second iteration carbon emission data are operated respectively. For the Q left edge second iteration carbon emission data, for each left edge second iteration carbon emission data, the value range is determined according to the preset data tolerance bandwidth with it as the center. The lower limit of the range is the left edge second iteration carbon emission data minus half of the preset data tolerance bandwidth, and the upper limit is the left edge second iteration carbon emission data plus half of the preset data tolerance bandwidth. Then, in the original carbon emission data set, carefully select all data within this value range, integrate these data, and construct a new set, that is, the neighbor set corresponding to this left edge second iteration carbon emission data. Repeat this process for all Q left edge second iteration carbon emission data, so as to obtain Q left edge second iteration carbon emission data neighbor sets. Similarly, for the Q right edge second iteration carbon emission data, each right edge second iteration carbon emission data is used as a benchmark, the value range is determined according to the preset data tolerance bandwidth, and then the data within the range is screened out from the original data set to construct the corresponding right edge second iteration carbon emission data neighbor set. Finally, Q right edge second iteration carbon emission data neighbor sets are successfully constructed, and the construction of these neighbor sets provides important data basis for subsequent judgment of iteration direction and further optimization of carbon emission data calculation.

[0073] The iteration in the left edge direction is critically judged and operated, and the number of data in the neighbor set of Q left edge second iteration carbon emission data is counted one by one to obtain the number of Q left edge second iteration neighbors. At the same time, the number of Q left edge neighbors corresponding to the Q left edge first iteration carbon emission data is obtained. Subsequently, the two sets of numbers are carefully compared, and for each corresponding left edge data situation, it is determined whether the number of left edge second iteration neighbors is greater than or equal to the number of left edge first iteration neighbors. If this condition is met, it means that the distribution of data in the left edge direction has further mining value, and the iterative calculation is continued according to the established iterative algorithm based on the Q left edge first iteration carbon emission data. In each iteration process, a new neighbor set is reconstructed, and the number of data in it is counted, that is, the number of neighbors obtained in the next iteration is obtained. Next, the number of neighbors newly obtained this time is compared with the number of neighbors obtained in the previous iteration. As long as the number of neighbors obtained in the next iteration is not less than the number of neighbors obtained in the previous iteration, the next round of iteration will continue. Until a certain iteration, the number of neighbors obtained in the next iteration is less than the number of neighbors obtained in the previous iteration. At this time, the preset iteration stop condition is met, the iteration is stopped, and Q left edge iteration execution carbon emission data are successfully obtained. This process ensures that the data iteration in the left edge direction can be carried out reasonably and effectively, providing a reliable left edge data foundation for the final accurate calculation of carbon emission data.

[0074] Data statistics are performed on the neighbor sets of Q right edge second iteration carbon emission data, and the number of data in each set is calculated one by one to obtain the number of Q right edge second iteration neighbors. At the same time, the number of Q right edge neighbors corresponding to the previously recorded Q right edge first iteration carbon emission data is called out. Next, a detailed comparison is carried out for each group of corresponding right edge data, and it is determined one by one whether the number of right edge second iteration neighbors is greater than or equal to the number of right edge first iteration neighbors. If this condition is met, it indicates that from the perspective of the right edge, the distribution of data has the value of further mining. Then, the Q right edge first iteration carbon emission data is used as the starting point, and a new round of iterative calculation is started according to the established iterative algorithm. In the iterative process, each iteration will construct a corresponding neighbor set based on the new result, and count the number of data in the set, that is, obtain the number of neighbors for the next iteration, and compare the number of neighbors newly obtained this time with the number of neighbors generated by the previous iteration. As long as the number of neighbors in the next iteration is greater than or equal to the previous one, the next round of iteration will continue. Until a certain iteration, the number of neighbors of the next iteration is less than that of the previous one, and the preset iteration stop condition is met at this time, and the iteration is terminated, thereby successfully obtaining Q right edge iteration execution carbon emission data. Through this rigorous operation process, it is ensured that the data processing in the right edge direction can be carried out scientifically and reasonably, providing effective right edge data support for the accurate calculation of carbon emission data.

[0075] Embodiment 2, based on the same inventive concept as the carbon emission reduction data assessment method for green buildings in the above embodiment, Figure 2 As shown, the present application provides a carbon reduction data assessment system for green buildings. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0076] The basic demand information set acquisition module 10 is used to acquire Q construction nodes of the target building, traverse the Q construction nodes to collect node basic demand information, and obtain Q node basic demand information sets, where Q is an integer greater than or equal to 1.

[0077] The construction condition indicator set acquisition module 20 is used to extract construction conditions for the Q construction nodes according to preset construction condition indicators to obtain Q node construction condition indicator sets.

[0078] The construction plan acquisition module 30 is used to associate and map the Q node basic demand information sets and the Q node construction condition indicator sets, and screen and match the construction plans based on the association mapping results to obtain the Q node construction plans.

[0079] The evaluation result acquisition module 40 is used to perform data evaluation based on the Q node construction plans to obtain target carbon emission data evaluation results.

[0080] Furthermore, the construction plan acquisition module 30 also includes:

[0081] The indicator mapping relationship acquisition unit is used to associate and map the Q node basic demand information sets with the Q node construction condition indicator sets to obtain the Q node demand-node construction condition indicator mapping relationships.

[0082] The retrieval and matching unit is used to perform retrieval and matching in the construction plan database based on the mapping relationship between the Q node requirements and the node construction condition indicators as an index to obtain the Q node construction plans.

[0083] Furthermore, the evaluation result acquisition module 40 further includes:

[0084] The data mining unit is used to perform data mining of scheme execution results based on big data using the Q node construction schemes as indexes, and obtain Q scheme execution carbon emission data sets and Q scheme execution reliability sets.

[0085] The carbon emission data acquisition unit is used to centrally screen the carbon emission data sets of the Q schemes to obtain the centralized carbon emission data of the Q schemes.

[0086] The centralized reliability acquisition unit is used to centrally screen the Q scheme execution reliability sets to obtain the centralized execution reliability of the Q schemes.

[0087] A summing calculation unit is used to sum the concentrated carbon emission data of the Q schemes based on the concentrated reliability of the Q schemes to obtain the target carbon emission data evaluation result.

[0088] Furthermore, the carbon emission data acquisition unit also includes:

[0089] The carbon emission data set mean calculation unit is used to calculate the mean of the carbon emission data sets executed by the Q schemes respectively, and obtain the mean of the carbon emission data executed by the Q schemes.

[0090] The mean nearest neighbor set construction unit is used to construct, in the Q scheme execution carbon emission data sets, the mean nearest neighbor sets of the Q scheme execution carbon emission data of the mean of the Q scheme execution carbon emission data according to the preset data tolerance bandwidth.

[0091] An edge data extraction unit is used to extract edge data from the neighbor set of the mean carbon emission data of the Q schemes, and obtain Q left edge scheme execution carbon emission data and Q right edge scheme execution carbon emission data, wherein the Q left edge scheme execution carbon emission data are less than the mean of the Q scheme execution carbon emission data, and the Q right edge scheme execution carbon emission data are greater than the mean of the Q scheme execution carbon emission data.

[0092] An edge scheme construction unit is used to construct, in combination with the preset data tolerance bandwidth, Q left edge scheme execution carbon emission data neighbor sets of the Q left edge scheme execution carbon emission data, and Q right edge scheme execution carbon emission data neighbor sets of the Q right edge scheme execution carbon emission data.

[0093] A carbon emission data mean iteration unit is used to iterate the mean of the carbon emission data executed by the Q left edge schemes according to the Q left edge schemes executing the carbon emission data neighbor sets and the Q right edge schemes executing the carbon emission data neighbor sets, so as to obtain the concentrated carbon emission data executed by the Q schemes.

[0094] Furthermore, the carbon emission data mean iteration unit further includes:

[0095] An iterative execution unit is used to iterate the mean of the carbon emission data executed by the Q schemes to the left or right based on the carbon emission data executed by the Q left edge schemes or the carbon emission data executed by the Q right edge schemes, when only one of the Q left edge neighbors of the neighbor set of the carbon emission data executed by the Q left edge schemes and the Q right edge neighbors of the neighbor set of the carbon emission data executed by the Q right edge schemes is greater than or equal to the Q mean neighbors of the mean neighbor set of the carbon emission data executed by the Q schemes, until a preset iteration stop condition is met, so as to obtain Q left edge iterative execution carbon emission data or Q right edge iterative execution carbon emission data.

[0096] The centralized carbon emission data execution unit is used to calculate the mean of the scheme execution carbon emission data between the Q left edge iterative execution carbon emission data and the mean of the scheme execution carbon emission data, or to calculate the mean of the scheme execution carbon emission data between the Q right edge iterative execution carbon emission data and the mean of the Q scheme execution carbon emission data, so as to obtain the centralized carbon emission data of the Q scheme executions.

[0097] Furthermore, the carbon emission data mean iteration unit further includes:

[0098] An edge iteration execution unit is used to iterate the mean of the carbon emission data executed by the Q schemes to the left and right at the same time based on the Q left edge schemes executing the carbon emission data and the Q right edge schemes executing the carbon emission data, when the number of Q left edge neighbors of the neighbor set of the Q left edge schemes executing the carbon emission data and the number of Q right edge neighbors of the neighbor set of the Q right edge schemes executing the carbon emission data are both greater than or equal to the number of Q mean neighbors of the neighbor set of the Q schemes executing the carbon emission data, until a preset iteration stop condition is met, to obtain Q left edge iterative execution carbon emission data and Q right edge iterative execution carbon emission data.

[0099] The concentrated carbon emission data calculation unit is used to calculate the mean of the scheme execution carbon emission data between the Q left edge iterative execution carbon emission data and the Q right edge iterative execution carbon emission data, and obtain the concentrated carbon emission data of the Q scheme executions.

[0100] Furthermore, the edge iteration execution unit further includes:

[0101] The edge one-iteration execution carbon emission data acquisition unit is used to use the Q left edge scheme execution carbon emission data as the Q left edge one-iteration execution carbon emission data, and at the same time use the Q right edge scheme execution carbon emission data as the Q right edge one-iteration execution carbon emission data.

[0102] The edge secondary iteration execution carbon emission data acquisition unit is used to use the left edge data in the Q left edge scheme execution carbon emission data neighbor set as the Q left edge secondary iteration execution carbon emission data, and use the right edge data in the Q right edge scheme execution carbon emission data neighbor set as the Q right edge secondary iteration execution carbon emission data.

[0103] The edge secondary iteration carbon emission data neighbor set construction unit is used to construct the Q left edge secondary iteration carbon emission data neighbor sets of the Q left edge secondary iteration carbon emission data, and the Q right edge secondary iteration carbon emission data neighbor sets of the Q right edge secondary iteration carbon emission data, in combination with the preset data tolerance bandwidth.

[0104] The left edge secondary iteration judgment unit is used to determine whether the number of Q left edge secondary iteration neighbors of the neighbor set of the Q left edge secondary iteration carbon emission data is greater than or equal to the number of Q left edge neighbors corresponding to the Q left edge single iteration carbon emission data; if so, continue to iterate based on the Q left edge single iteration carbon emission data until the preset iteration stop condition is met to obtain Q left edge iterative execution carbon emission data, wherein the preset iteration stop condition is that the number of neighbors obtained in the next iteration is less than the number of neighbors obtained in the previous iteration.

[0105] The right edge secondary iteration judgment unit is used to determine whether the number of Q right edge secondary iteration neighbors of the neighbor set of the Q right edge secondary iteration carbon emission data is greater than or equal to the number of Q right edge neighbors corresponding to the Q right edge single iteration carbon emission data. If so, the iteration continues based on the Q right edge single iteration carbon emission data until the preset iteration stop condition is met to obtain the Q right edge iterative execution carbon emission data.

[0106] 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.

[0107] 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.

[0108] 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 carbon emission reduction data assessment method for green buildings, characterized in that: The method comprises: Acquire Q construction nodes of the target building, traverse the Q construction nodes to collect node basic demand information, and obtain a set of Q node basic demand information, where Q is an integer greater than or equal to 1; Extracting construction conditions for the Q construction nodes according to preset construction condition indicators to obtain a set of Q node construction condition indicators; Performing association mapping on the Q node basic demand information sets and the Q node construction condition indicator sets, and performing construction plan screening and matching based on the association mapping results to obtain Q node construction plans; Data evaluation is performed based on the Q node construction plans to obtain target carbon emission data evaluation results.

2. The carbon emission reduction data evaluation method for green buildings according to claim 1, characterized in that: The Q node basic demand information sets and the Q node construction condition indicator sets are associated and mapped, and construction schemes are screened and matched based on the associated mapping results to obtain Q node construction schemes, including: Associatively mapping the Q node basic demand information sets and the Q node construction condition indicator sets to obtain a mapping relationship between Q node demand and node construction condition indicator; Using the mapping relationship between the Q node requirements and the node construction condition indicators as an index, a search and match is performed in the construction plan database to obtain the Q node construction plans.

3. The carbon emission reduction data assessment method for green buildings according to claim 1, characterized in that: Based on the Q node construction plans, data evaluation is performed to obtain target carbon emission data evaluation results, including: Using the Q node construction plans as indexes, data mining of plan execution results is performed based on big data to obtain a set of Q plan execution carbon emission data and a set of Q plan execution reliability data; Centrally screening the carbon emission data sets for the Q schemes to obtain centralized carbon emission data for the Q schemes; Centrally screening the Q scheme execution reliability sets to obtain the Q scheme execution centralized reliability; The concentrated carbon emission data of the Q schemes are summed up based on the concentrated reliability of the Q schemes to obtain the target carbon emission data evaluation result.

4. The carbon emission reduction data assessment method for green buildings according to claim 3, characterized in that: The Q scheme execution carbon emission data sets are centrally screened to obtain the Q scheme execution centralized carbon emission data, including: Calculate the mean of the carbon emission data sets of the Q schemes respectively to obtain the mean of the carbon emission data of the Q schemes; In the Q scheme execution carbon emission data sets, according to the preset data tolerance bandwidth, construct the Q scheme execution carbon emission data mean neighbor sets of the Q scheme execution carbon emission data mean; Extract edge data from the neighbor set of the mean carbon emission data of the Q schemes, and obtain Q left edge scheme execution carbon emission data and Q right edge scheme execution carbon emission data, wherein the Q left edge scheme execution carbon emission data are less than the mean of the Q scheme execution carbon emission data, and the Q right edge scheme execution carbon emission data are greater than the mean of the Q scheme execution carbon emission data; In combination with the preset data tolerance bandwidth, constructing a neighbor set of Q left edge schemes executing carbon emission data and a neighbor set of Q right edge schemes executing carbon emission data; According to the neighbor sets of carbon emission data executed by the Q left edge schemes and the neighbor sets of carbon emission data executed by the Q right edge schemes, the means of the carbon emission data executed by the Q schemes are iterated to obtain the concentrated carbon emission data executed by the Q schemes.

5. The carbon emission reduction data assessment method for green buildings according to claim 4, characterized in that: According to the Q left edge scheme execution carbon emission data neighbor sets and the Q right edge scheme execution carbon emission data neighbor sets, iterating the mean of the Q scheme execution carbon emission data to obtain the concentrated carbon emission data of the Q scheme executions, including: When only one of the Q left edge neighbors of the Q left edge scheme execution carbon emission data neighbor set and the Q right edge neighbors of the Q right edge scheme execution carbon emission data neighbor set is greater than or equal to the Q mean neighbors of the Q scheme execution carbon emission data mean neighbor set, iterate the mean of the Q scheme execution carbon emission data to the left or right based on the Q left edge scheme execution carbon emission data or the Q right edge scheme execution carbon emission data until a preset iteration stop condition is met, and obtain Q left edge iterative execution carbon emission data or Q right edge iterative execution carbon emission data; Calculate the mean of the scheme execution carbon emission data between the Q left edge iterative execution carbon emission data and the mean of the Q scheme execution carbon emission data, or calculate the mean of the scheme execution carbon emission data between the Q right edge iterative execution carbon emission data and the mean of the Q scheme execution carbon emission data, and obtain the concentrated carbon emission data of the Q scheme executions.

6. The carbon emission reduction data assessment method for green buildings according to claim 5, characterized in that: According to the Q left edge scheme execution carbon emission data neighbor sets and the Q right edge scheme execution carbon emission data neighbor sets, iterating the mean of the Q scheme execution carbon emission data to obtain the concentrated carbon emission data of the Q scheme executions, including: When the number of Q left edge neighbors of the neighbor set of carbon emission data executed by the Q left edge schemes and the number of Q right edge neighbors of the neighbor set of carbon emission data executed by the Q right edge schemes are both greater than or equal to the number of Q mean neighbors of the mean neighbor set of carbon emission data executed by the Q schemes, iterate the mean of the carbon emission data executed by the Q schemes based on the carbon emission data executed by the Q left edge schemes and the carbon emission data executed by the Q right edge schemes to the left and right at the same time until a preset iteration stop condition is met, to obtain Q left edge iterative execution carbon emission data and Q right edge iterative execution carbon emission data; The mean of the scheme execution carbon emission data between the Q left edge iterative execution carbon emission data and the Q right edge iterative execution carbon emission data is calculated to obtain the Q scheme execution concentrated carbon emission data.

7. The carbon emission reduction data assessment method for green buildings according to claim 6, characterized in that: At the same time, to the left and right, based on the Q left edge scheme execution carbon emission data and the Q right edge scheme execution carbon emission data, the mean of the Q scheme execution carbon emission data is iterated until a preset iteration stop condition is met, and Q left edge iterative execution carbon emission data and Q right edge iterative execution carbon emission data are obtained, including: The carbon emission data of the Q left edge solutions are used as the carbon emission data of the Q left edge one-iteration execution, and the carbon emission data of the Q right edge solutions are used as the carbon emission data of the Q right edge one-iteration execution; The left edge data in the neighbor set of Q left edge schemes executing carbon emission data are used as the Q left edge secondary iterative execution carbon emission data, and the right edge data in the neighbor set of Q right edge schemes executing carbon emission data are used as the Q right edge secondary iterative execution carbon emission data; In combination with the preset data tolerance bandwidth, constructing Q left edge second-time iteratively executed carbon emission data neighbor sets of the Q left edge second-time iteratively executed carbon emission data, and Q right edge second-time iteratively executed carbon emission data neighbor sets of the Q right edge second-time iteratively executed carbon emission data; Determine whether the number of Q left edge secondary iteration neighbors of the Q left edge secondary iteration carbon emission data neighbor set is greater than or equal to the number of Q left edge neighbors corresponding to the Q left edge single iteration carbon emission data, and if so, continue to iterate based on the Q left edge single iteration carbon emission data until a preset iteration stop condition is met to obtain Q left edge iterative execution carbon emission data, wherein the preset iteration stop condition is that the number of neighbors obtained in the next iteration is less than the number of neighbors obtained in the previous iteration; Determine whether the number of Q right edge secondary iterative neighbors of the neighbor set of the Q right edge secondary iterative carbon emission data is greater than or equal to the number of Q right edge neighbors corresponding to the Q right edge single iterative carbon emission data; if so, continue to iterate based on the Q right edge single iterative carbon emission data until the preset iteration stop condition is met to obtain the Q right edge iterative carbon emission data.

8. A carbon emission reduction data evaluation system for green buildings, characterized in that: The system is used to implement the carbon emission reduction data assessment method for green buildings according to any one of claims 1 to 7, and the system comprises: A basic demand information set acquisition module is used to acquire Q construction nodes of a target building, traverse the Q construction nodes to collect node basic demand information, and obtain Q node basic demand information sets, where Q is an integer greater than or equal to 1; A construction condition indicator set acquisition module is used to extract construction conditions for the Q construction nodes according to preset construction condition indicators to obtain a set of construction condition indicators for the Q nodes; A construction plan acquisition module is used to associate and map the Q node basic demand information sets with the Q node construction condition indicator sets, and screen and match the construction plans based on the association mapping results to obtain the Q node construction plans; The evaluation result acquisition module is used to perform data evaluation based on the Q node construction plans to obtain target carbon emission data evaluation results.

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