Carbon Emission Reduction Data Evaluation Method and System for Green Buildings

Abstract

The present invention discloses a carbon emission reduction data evaluation method and system for green buildings, which relates to the technical field of carbon emission reduction evaluation. The method includes: obtaining Q construction nodes of a target building, collecting node basic requirement information, and obtaining a set of Q node basic requirement information; extracting construction conditions for the Q construction nodes to obtain a set of Q node construction condition indicators; performing association mapping, screening and matching construction plans based on the association mapping results to obtain Q node construction plans; and performing data evaluation based on the Q node construction plans to obtain an evaluation result of target carbon emission data. The present invention solves the technical problem of insufficient accuracy and reliability of the evaluation result of green building carbon emission data in the prior art, and achieves the technical effect of evaluating the green building carbon emission data through construction node information collection, association mapping, and plan screening and matching, and improving the accuracy and reliability of the evaluation result of target carbon emission data.

Description

Technical Field

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

[0002] In the context of the current global efforts to address climate change and advocate sustainable development, green buildings, as an important way to reduce building energy consumption and carbon emissions, have received extensive attention. Traditional buildings often consume a large amount of energy during construction and use, generating huge amounts of carbon emissions and imposing a heavy burden on the environment. There are many problems with existing green building carbon emission data assessment technologies. On the one hand, during the data collection stage, it is often impossible to comprehensively and accurately obtain the basic requirement information of each node and the construction condition indicators during the building construction process. For example, for some complex building structures or special construction environments, it is difficult to accurately collect key data such as energy consumption and material use, which leads to a lack of a 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 internal connections between construction nodes, making the assessment results unable to truly reflect the actual carbon emissions of green buildings.

[0003] The prior art has the technical problem of insufficient accuracy and reliability of the assessment results of green building carbon emission data. Summary of the Invention

[0004] The present application provides a carbon emission reduction data assessment method and system for green buildings, which are used to solve the technical problem of insufficient accuracy and reliability of the assessment results of green building carbon emission data 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 the first aspect of the present application, a carbon emission reduction data assessment method for green buildings is provided. The method includes:

[0007] Obtain Q construction nodes of the target building, traverse the Q construction nodes to collect node basic requirement information, and obtain Q sets of node basic requirement information, where 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 Q sets of node construction condition indicators; perform correlation mapping on the Q sets of node basic requirement information and the Q sets of node construction condition indicators, and perform construction plan screening and matching based on the correlation mapping results to obtain Q node construction plans; perform data assessment based on the Q node construction plans to obtain the target carbon emission data assessment result.

[0008] In a second aspect of the present application, a carbon emission reduction data evaluation system for green buildings is provided. The system includes:

[0009] A basic requirement information set acquisition module, configured to acquire Q construction nodes of a target building, traverse the Q construction nodes to collect node basic requirement information, and obtain Q node basic requirement information sets, where Q is an integer greater than or equal to 1; a construction condition index set acquisition module, configured to extract construction conditions for the Q construction nodes according to preset construction condition indexes, and obtain Q node construction condition index sets; a construction plan acquisition module, configured to perform an associated mapping on the Q node basic requirement information sets and the Q node construction condition index sets, and perform a construction plan screening and matching based on the associated mapping result to obtain Q node construction plans; an evaluation result acquisition module, configured to perform data evaluation based on the Q node construction plans to obtain a target carbon emission data evaluation result.

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

[0011] Acquire Q construction nodes of a target building, traverse the Q construction nodes to collect node basic requirement information, and obtain Q node basic requirement information sets; extract construction conditions for the Q construction nodes to obtain Q node construction condition index sets; perform an associated mapping, and perform a construction plan screening and matching based on the associated mapping result to obtain Q node construction plans; perform data evaluation based on the Q node construction plans to obtain a target carbon emission data evaluation result. It achieves the technical effect of evaluating the carbon emission data of green buildings through construction node information collection, associated mapping, and plan screening and matching, and improves the accuracy and reliability of the target carbon emission data evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0013] Figure 1 It is a schematic flow chart of a carbon emission reduction data evaluation method for green buildings provided by an embodiment of the present application;

[0014] Figure 2 It is a schematic structural diagram of a carbon emission reduction data evaluation system for green buildings provided by an embodiment of the present application.

[0015] Description of the accompanying drawing reference numerals: The basic requirement information set acquisition module 10, the construction condition index set acquisition module 20, the construction plan acquisition module 30, and the evaluation result acquisition module 40. Specific implementation manners

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

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0018] Embodiment 1, as Figure 1 shown, the present application provides a carbon emission reduction data evaluation method for green buildings, and the method includes:

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

[0020] Specifically, for the target building, such as the State Grid dispatching building and the rural power supply station, according to the functional layout drawings and actual usage conditions of the building, it is divided into multiple logical areas as construction nodes. 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, power distribution room, employee rest area, etc. can be determined as construction nodes. After determining the Q construction nodes, node basic requirement information is collected. Through on-site measurement tools, the spatial dimensions of each node are accurately measured, including data such as area and height, to determine the spatial requirements. Professional energy consumption monitoring equipment is used to record information such as the power and operation duration of the existing equipment in the node to obtain energy demand data. At the same time, consult the historical maintenance records and equipment lists of the building to collect basic information about the building structure and equipment models. After a comprehensive and detailed investigation of each node, the various types of information collected are classified and sorted, and finally Q node basic requirement information sets are formed, providing a detailed data basis for subsequent evaluation work.

[0021] Step S200: Extract construction conditions for the Q construction nodes according to preset construction condition indicators, and obtain Q node construction condition indicator sets.

[0022] Specifically, a set of complete preset construction condition indicators that meet green building standards needs to be established first, covering key indicators in multiple aspects such as energy efficiency, environmental impact, and resource utilization. For example, the heat transfer coefficient of the building envelope, the proportion of renewable energy utilization, and the indoor air quality standard. For the determined Q construction nodes, taking the State Grid dispatching building as an example, for the extraction of the construction conditions of its building envelope, devices such as heat flow meters and infrared thermal imagers are used to measure the heat transfer coefficients of parts such as walls, roofs, and windows, and to judge whether their heat insulation performance meets the requirements of the preset indicators. These data are recorded as the construction condition indicators of the building envelope at this node. In terms of energy utilization, through the power monitoring system, the energy consumption data of various devices within the node are analyzed, and combined with the local energy supply structure and the situation of renewable energy resources, such as the available degree of solar energy and wind energy, the construction condition indicators of the node in terms of energy supply and utilization efficiency are determined, such as whether it is suitable to install solar photovoltaic panels or small wind turbines, etc. The same method is adopted for rural power supply stations. In terms of the indoor environment, an air quality monitor is used to detect the concentrations of pollutants such as formaldehyde and carbon dioxide, as well as the temperature and humidity conditions in the business hall and office areas, and the construction condition indicators are determined according to the relevant indoor environmental quality standards. Through the comprehensive analysis and data collection of each construction node, the obtained various construction condition indicators are classified and sorted, and finally a set of construction condition indicators for Q nodes is formed, providing an accurate data basis for subsequent correlation mapping and scheme screening.

[0023] Step S300: Perform correlation mapping on the set of Q node basic requirement information and the set of Q node construction condition indicators, and based on the correlation mapping results, perform construction scheme screening and matching to obtain Q node construction schemes.

[0024] Specifically, an algorithm based on eigenvector and similarity calculation is used to achieve correlation mapping and scheme screening and matching, and the set of each node's basic requirement information and the set of node construction condition indicators are transformed into eigenvectors. For example, for the node basic requirement information, if it includes factors such as space area requirements, equipment power requirements, and personnel density, these factors are quantified respectively and combined into an eigenvector. Similarly, for the node construction condition indicators, such as daylighting coefficient, heat insulation performance indicators, and energy supply types, they are also transformed into corresponding eigenvectors. The cosine similarity algorithm is used to calculate the similarity between these eigenvectors. By calculating the similarity, the correlation degree between each node's basic requirement information and the construction condition indicators is obtained. Then, in the construction scheme database, the schemes are sorted according to these correlation degrees. For combinations with higher similarities, the corresponding construction schemes are preferentially screened out. A similarity threshold is set. When the similarity is greater than this threshold, the corresponding construction scheme is included in the candidate set, and finally the most suitable construction scheme for each node is determined, thus obtaining Q node construction schemes.

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

[0026] Specifically, guided by the Q node construction plans, deeply explore the vast amount of big data resources related to the construction industry. For target buildings such as the State Grid dispatching building and rural power supply stations, accurately screen out the detailed data after the implementation of these specific construction plans in similar scenarios from the database, including the energy consumption of various equipment, the carbon emission data during the production, transportation, and construction of building materials, etc., so as to construct a set of carbon emission data for the implementation of the Q plans. At the same time, comprehensively evaluate according to various factors such as the actual operation effect, stability, and durability, and generate a set of reliability for the implementation of the Q plans. Then, conduct a centralized screening of the set of carbon emission data for the implementation of the Q plans. First, calculate the mean value of each set, and circle the adjacent data range according to the preset data tolerance bandwidth centered on this mean value to form a mean adjacent set, and extract the edge data and classify it into the left and right edge plan implementation carbon emission data. Further construct adjacent sets around these edge data, and then perform iterative calculations based on the quantitative relationship between different adjacent sets. For example, if the number of adjacent data on one side meets specific conditions, iterate the mean value based on the edge data to that side until the preset iteration stop standard is reached, and finally obtain the centralized carbon emission data for the implementation of the Q plans.

[0027] Meanwhile, conduct a centralized screening of the set of reliability for the implementation of the Q plans to obtain the optimized reliability values of each set. Finally, use the method of weighted summation to combine the centralized carbon emission data for the implementation of the Q plans with the corresponding centralized reliability for the implementation of the plans, and accurately calculate the comprehensive and scientific evaluation results of the target carbon emission data of the target buildings, providing solid data support and decision-making basis for the determination of the carbon emission reduction effect of green buildings and the optimization of subsequent strategies.

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

[0029] Step S310: Perform an association mapping on the Q node basic requirement information sets and the Q node construction condition index sets to obtain the Q node requirement - node construction condition index mapping relationship.

[0030] Step S320: Use the Q node requirement - node construction condition index mapping relationship as an index to perform a retrieval and matching in the construction plan database to obtain the Q node construction plans.

[0031] Specifically, the elements in each set of node basic requirement information and node construction condition indicators are transformed into the form of feature vectors. Among them, the requirement information can construct vectors according to dimensions such as functional requirements, spatial requirements, and energy consumption requirements, while the construction condition indicators form vectors around aspects such as geographical environment, climate conditions, and policy and regulation requirements. Then, the cosine similarity algorithm is used to calculate the similarity between these feature vectors, and the element pairs with high similarity constitute an important part of the node requirement - node construction condition indicator mapping relationship. At the same time, the Analytic Hierarchy Process (AHP) is combined to assign weights to the importance of different dimensions to further optimize the accuracy of the mapping relationship. After multiple iterative calculations and adjustments, finally Q comprehensive and accurate node requirement - node construction condition indicator mapping relationships are obtained, providing a reliable basis for subsequent retrieval and matching.

[0032] Adopt a combination of hash table and greedy algorithm. According to the Q node requirement - node construction condition indicator mapping relationships, construct a hash table, use the key features in the mapping relationships as hash keys, and store the corresponding possible solution indexes as values in the hash table. Then, traverse the hash table. For the mapping relationship of each node, use the greedy algorithm to screen in its corresponding set of possible solutions. The greedy algorithm compares each solution in turn according to a pre - set evaluation criterion, such as the lowest carbon emission, the optimal cost - benefit, the highest resource utilization rate, etc., and selects the local optimal solution. During the comparison process, quantify and evaluate each index of each solution. For example, calculate its carbon emission through a carbon emission calculation model, and evaluate its construction and operation costs through a cost estimation model. After retrieving the mapping relationship of each node in the hash table and screening with the greedy algorithm, finally Q node construction solutions that meet the requirements are obtained, ensuring that each solution can better adapt to the requirements and construction conditions of the corresponding node, providing a reasonable basis for subsequent carbon emission reduction data evaluation.

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

[0034] Step S410: Using the Q node construction solutions as indexes, perform data mining on the data of the execution results of the solutions based on big data to obtain a set of Q carbon emissions of solution executions and a set of Q reliability degrees of solution executions.

[0035] Step S420: Conduct centralized screening on the set of Q carbon emissions of solution executions to obtain Q centralized carbon emissions of solution executions.

[0036] Step S430: Conduct centralized screening on the set of Q reliability degrees of solution executions to obtain Q centralized reliability degrees of solution executions.

[0037] Step S440: Based on the Q centralized reliability degrees of solution executions, perform a summation calculation on the Q centralized carbon emissions of solution executions to obtain the evaluation result of the target carbon emission data.

[0038] Specifically, first, the distributed storage technology is utilized to store a vast amount of green building-related data, ensuring the high availability and scalability of the data. Indexed by the Q-node construction plans, the MapReduce programming model is adopted for data mining. In the Map stage, the data is classified according to the node construction plans, and the features that may be related to carbon emissions and reliability are extracted, such as building material types, energy supply system parameters, construction process details, etc., and corresponding weights are assigned to each feature. For carbon emission data mining, in the Reduce stage, the decision tree regression algorithm is used to train the model based on the classified data and feature weights, and the model parameters are optimized through continuous iteration to predict the carbon emission data of each node construction plan under different working conditions, thereby constructing a set of carbon emission data for Q plans. In terms of reliability data mining, an inference algorithm based on Bayesian network is adopted. First, the Bayesian network structure is constructed according to historical data and expert knowledge to determine the probability relationship between various factors (such as construction team experience, equipment brand reliability, etc.) in the node construction plan, and then the conditional probability formula is used for inference and calculation in the Bayesian network, comprehensively considering the influence of various factors on the reliability of the plan, and finally generating a set of reliability for Q plans, providing solid data support for subsequent evaluation.

[0039] Operations are carried out on the set of carbon emission data for Q plans. 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. Centered on this average value, the data within this bandwidth range is selected from each set to construct a new subset. In this subset, the data points located at the edge positions, namely the minimum and maximum values, are identified again. Then, the tolerance bandwidth is further narrowed, and the data is screened and analyzed again around the new central reference value (determined by adjusting based on the previous average value and edge values). This process is repeated multiple times to continuously optimize the data screening range. Through this step-by-step iteration and refined screening method, finally, a most representative data that can effectively reflect the actual carbon emissions of the plan execution and excludes the interference of outliers is determined from each set, thus successfully obtaining the centralized carbon emission data for Q plans, laying a solid foundation for subsequent accurate evaluation.

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

[0041] Take the Q centralized reliabilities of the scheme executions obtained as the key coefficients, and correspond them one by one to the Q centralized carbon emission data of the scheme executions. For the carbon emission data of each node, multiply it by its corresponding reliability coefficient, so that the carbon emission data of the scheme with high reliability will have a greater influence when finally summing up, and vice versa. By successively performing the cumulative summation operation on the product results of each node, the carbon emission performance and its reliability weight of each node construction scheme are comprehensively considered, so as to accurately obtain the evaluation result of the target carbon emission data of the target building, providing a quantitative basis for the formulation and implementation of the carbon emission reduction strategy of green buildings.

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

[0043] Step S421: Calculate the means of the Q sets of carbon emission data of the scheme executions respectively, and obtain Q means of the carbon emission data of the scheme executions.

[0044] Step S422: In the Q sets of carbon emission data of the scheme executions, construct Q neighbor sets of the means of the carbon emission data of the scheme executions according to the preset data tolerance bandwidth.

[0045] Step S423: Extract the edge data in the Q neighbor sets of the means of the carbon emission data of the scheme executions to obtain Q left-edge carbon emission data of the scheme executions and Q right-edge carbon emission data of the scheme executions, where the Q left-edge carbon emission data of the scheme executions are less than the Q means of the carbon emission data of the scheme executions, and the Q right-edge carbon emission data of the scheme executions are greater than the Q means of the carbon emission data of the scheme executions.

[0046] Step S424: Combine with the preset data tolerance bandwidth to construct Q neighbor sets of the Q left-edge carbon emission data of the scheme executions and Q neighbor sets of the Q right-edge carbon emission data of the scheme executions.

[0047] Step S425: Execute the Q left-edge schemes for carbon emission data to perform the nearest neighbor sets of carbon emission data for the Q left-edge schemes and the Q right-edge schemes for carbon emission data to perform the nearest neighbor sets of carbon emission data. Iterate on the mean carbon emission data for the Q schemes to obtain the centralized carbon emission data for the Q schemes.

[0048] Specifically, for each set of carbon emission data for a scheme execution, it is processed in a loop-through manner. First, initialize an accumulative variable to 0, then read each carbon emission data element in the set one by one and accumulate it into the accumulative variable. After traversing all the data in a set, divide the value of the accumulative variable by the total number of data in the set. Through this arithmetic mean calculation method, the mean carbon emission data for the scheme execution of this set is obtained. Repeat this process for all Q sets, thus successfully obtaining the mean carbon emission data for the Q scheme executions. These means will serve as key reference points for subsequent data processing and analysis, helping to accurately grasp the approximate level of carbon emissions for each scheme execution.

[0049] For each set of carbon emission data for a scheme execution for which the mean has been obtained, determine a value range according to a 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 set of carbon emission data for the scheme execution, and select the data within this value range to form a new subset. This subset is the nearest neighbor set of the mean carbon emission data for the scheme execution of this set. Repeat this process for all Q sets of carbon emission data for the scheme executions, and finally successfully construct the Q nearest neighbor sets of the mean carbon emission data for the scheme executions. These nearest neighbor sets can filter out the data that is relatively close to the mean, which helps to further analyze the distribution characteristics and stability of the data, and ensures the accuracy and reliability of the entire carbon emission data assessment process.

[0050] For the Q constructed nearest neighbor sets of the mean carbon emission data for the scheme executions, analyze and process them one by one. For each nearest neighbor set, sort the data in it in ascending order through a sorting algorithm. After sorting, the data at the leftmost position is the minimum value in this set, which is determined as the carbon emission data for the left-edge scheme execution; the data at the rightmost position is the maximum value, which is determined as the carbon emission data for the right-edge scheme execution. Operate on each nearest neighbor set in this way, and finally successfully extract the Q carbon emission data for the left-edge scheme executions and the Q carbon emission data for the right-edge scheme executions. The acquisition of these edge data is crucial for subsequently understanding the data dispersion degree in depth, judging the rationality of the data, and further optimizing the data screening process, providing an important reference basis for accurately evaluating the carbon emission data, helping to identify possible outliers or extreme situations, and thus ensuring the reliability of the entire assessment system.

[0051] Operations are carried out separately for the carbon emission data of the Q left-edge schemes obtained and the carbon emission data of the Q right-edge schemes. For the carbon emission data of each left-edge scheme, centered on it, a new value range is determined according to the preset data tolerance bandwidth. The lower limit of this range 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 set of carbon emission data for scheme execution, all the data within this range are filtered out. These data constitute the neighboring set of the carbon emission data for the corresponding left-edge scheme. Similarly, for the carbon emission data of each right-edge scheme, a neighboring set is constructed in the same way. Based on the right-edge data, the value range is determined, and then the data falling within this range are found from the original data set to form the neighboring set of the carbon emission data for the right-edge scheme. In this way, Q neighboring sets of the carbon emission data for the left-edge schemes and Q neighboring sets of the carbon emission data for the right-edge schemes are finally successfully constructed. The construction of these neighboring sets helps to further analyze the data distribution around the edge data, providing a more detailed information basis for subsequent data iteration and the determination of centralized carbon emission data, thereby improving the accuracy and reliability of the entire carbon emission data evaluation.

[0052] Analyze the neighboring sets of the carbon emission data for each left-edge scheme and the neighboring sets of the carbon emission data for the right-edge scheme. Calculate the number of data in the left-edge neighboring set and the number of data in the right-edge neighboring set, and compare them respectively with the number of data in the neighboring set of the mean carbon emission data for scheme execution. If the number of data in the left-edge neighboring set is greater than or equal to the number of data in the mean neighboring set, and the number of data in the right-edge neighboring set is less than the number of data in the mean neighboring set, then mean iteration is performed based on the carbon emission data of the left-edge scheme. Update the current mean carbon emission data for scheme execution to the weighted average of the carbon emission data of the left-edge scheme and the original mean, and the weight can be determined according to the tightness of the data distribution or other relevant factors. Repeat this process until the preset iteration stop condition is met, such as the difference between the means obtained in two adjacent iterations is less than a certain threshold, and finally obtain the left-edge iterative carbon emission data for this set. Conversely, if the right-edge neighboring set meets similar conditions, then similar iterative operations are performed based on the carbon emission data of the right-edge scheme to obtain the right-edge iterative carbon emission data. If the number of data in both the left-edge and right-edge neighboring sets is greater than or equal to the number of data in the mean neighboring set, then both the left-edge and right-edge data are considered. Calculate the left-edge iterative carbon emission data and the right-edge iterative carbon emission data respectively, and then take the average of the two as the new iterative mean. Continue to iterate until the preset iteration stop condition is reached, and finally obtain Q optimized centralized carbon emission data for scheme execution, which 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 Q left-edge neighbor quantities of the carbon emission data neighbor set executed by the Q left-edge schemes and the Q right-edge neighbor quantities of the carbon emission data neighbor set executed by the Q right-edge schemes is greater than or equal to the Q mean neighbor quantities of the carbon emission data mean neighbor set executed by the Q schemes, iterate the Q-scheme carbon emission data mean 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 to the left or right until a preset iteration stop condition is met, obtaining Q left-edge iteratively executed carbon emission data or Q right-edge iteratively executed carbon emission data.

[0055] Step S4252: Calculate the mean of the carbon emission data executed by the Q schemes between the Q left-edge iteratively executed carbon emission data and the Q-scheme carbon emission data mean, or calculate the mean of the carbon emission data executed by the Q schemes between the Q right-edge iteratively executed carbon emission data and the Q-scheme carbon emission data mean, obtaining the Q-scheme centralized carbon emission data.

[0056] Specifically, the left-edge neighbor quantity of the carbon emission data neighbor set executed by the Q left-edge schemes and the right-edge neighbor quantity of the carbon emission data neighbor set executed by the Q right-edge schemes are compared and analyzed one by one with the mean neighbor quantity of the carbon emission data mean neighbor set executed by the Q schemes. Once it is found that only one of the left-edge neighbor quantity or the right-edge neighbor quantity is greater than or equal to the mean neighbor quantity, the iterative program is started. If the left-edge neighbor quantity meets the condition, the iterative direction is determined to be to the left at this time. The carbon emission data executed by the Q left-edge schemes is fused with the current Q-scheme carbon emission data mean, and a weighted average method is used to allocate reasonable weights to the two according to the data distribution characteristics and importance, and a new mean is calculated. Then, the new mean is iteratively calculated with the left-edge data in the next round, 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, Q left-edge iteratively executed carbon emission data is successfully obtained. On the contrary, if the right-edge neighbor quantity meets the condition, the iterative direction is to the right, and according to a similar weighted average iterative algorithm, the carbon emission data executed by the Q right-edge schemes is used to calculate and iterate with the current mean until the preset iteration stop condition is met, so as to obtain Q right-edge iteratively executed carbon emission data, providing accurate basic data for subsequent data analysis and processing.

[0057] When the iteration is completed and Q left-edge iterative execution carbon emission data or Q right-edge iterative execution carbon emission data are obtained, the centralized carbon emission data is calculated. If there are Q left-edge iterative execution carbon emission data, extract these left-edge iterative execution carbon emission data and the average value of the corresponding Q scenario execution carbon emission data. Then, merge these two sets of data into a new data set, covering all data points from the left-edge iteration value to the average value. Next, use the standard mean calculation method to sum all the data in this new set and divide by the total number of data to obtain their average value. This average value is an important part of the scenario execution centralized carbon emission data in the case of left-edge iteration. After comprehensive consideration and calculation of all relevant data, the Q scenario execution centralized carbon emission data finally obtained can effectively exclude the interference of outliers and more accurately reflect the centralized trend and reasonable range of actual carbon emissions. Similarly, if the calculation is based on Q right-edge iterative execution carbon emission data, the same process and method are followed to ensure that representative scenario execution centralized carbon emission data can be accurately obtained in different situations.

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

[0059] Step S4253: When the Q left-edge neighbor quantities of the Q left-edge scenario execution carbon emission data neighbor set and the Q right-edge neighbor quantities of the Q right-edge scenario execution carbon emission data neighbor set are both greater than or equal to the Q mean neighbor quantities of the Q scenario execution carbon emission data mean neighbor set, iterate the Q scenario execution carbon emission data mean based on the Q left-edge scenario execution carbon emission data and the Q right-edge scenario execution carbon emission data simultaneously to the left and right until a preset iteration stop condition is reached, obtaining Q left-edge iterative execution carbon emission data and Q right-edge iterative execution carbon emission data.

[0060] Step S4254: Calculate the average value of the scenario 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 scenario execution centralized carbon emission data.

[0061] Specifically, when the number of Q left-edge neighbors in the Q left-edge scheme's carbon emission data neighbor set and the number of Q right-edge neighbors in the Q right-edge scheme's carbon emission data neighbor set are both greater than or equal to the number of Q mean neighbors in the Q schemes' carbon emission data mean neighbor set, iterative operations need to be carried out simultaneously to the left and right. Starting from the Q left-edge scheme's carbon emission data neighbor set, based on the Q left-edge scheme's carbon emission data, combined with the current Q schemes' carbon emission data mean, a new temporary mean is calculated through weighted primary fusion. Then, check the corresponding neighbor number situation, that is, count the number of data in the newly generated left-edge related neighbor set after this iteration, and compare it with the left-edge neighbor number in the previous iteration. If the new neighbor number is greater than or equal to the previous one, continue to perform the next round of calculation according to the iterative rule with the current new temporary mean and the Q left-edge scheme's carbon emission data, and repeat this process in a loop.

[0062] Starting from the Q right-edge scheme's carbon emission data neighbor set, similarly, based on the Q right-edge scheme's carbon emission data and the current Q schemes' carbon emission data mean, the primary fusion operation is carried out according to the corresponding iterative rule to obtain a new temporary mean. After that, count the number of data in the corresponding right-edge neighbor set of this iteration and compare it with the right-edge neighbor number in the previous iteration. If the new neighbor number is greater than or equal to the previous one, continue the next round of iterative operation based on the current new temporary mean and the Q right-edge scheme's carbon emission data. Keep such an iterative process on both sides until the preset iterative stop condition is met. This stop condition is the neighbor number. That is, when after a certain iteration, for either the left or the right side, the neighbor number obtained in the next iteration is less than the neighbor number obtained in the previous iteration, stop the iteration, and finally successfully obtain the Q left-edge iterative carbon emission data and the Q right-edge iterative carbon emission data.

[0063] After successfully obtaining the carbon emission data for Q left-edge iterative executions and the carbon emission data for Q right-edge iterative executions, traverse the entire dataset and filter out all the scenario execution carbon emission data that lies between the carbon emission data for Q left-edge iterative executions and the carbon emission data for Q right-edge iterative executions. Integrate them into a new data set. Next, perform a summation operation on all the data in this new set, that is, add up the numerical values of each data. After completing the summation, divide this total by the total number of data in the new set to obtain a value representing the central tendency of this part of the dataset. For Q different cases, repeat the above steps to obtain Q such means, and these means are the Q scenario execution centralized carbon emission data required. This process can effectively exclude the interference of abnormal data and extreme values, accurately reflect the core level of carbon emission for each scenario execution, and provide key data support for subsequent carbon emission assessments.

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

[0065] Step S42531: Use the carbon emission data for the Q left-edge scenario executions as the carbon emission data for Q left-edge one-time iterative executions, and at the same time use the carbon emission data for the Q right-edge scenario executions as the carbon emission data for Q right-edge one-time iterative executions.

[0066] Step S42532: Use the left-edge data in the neighbor set of the carbon emission data for the Q left-edge scenario executions as the carbon emission data for Q left-edge two-time iterative executions, and use the right-edge data in the neighbor set of the carbon emission data for the Q right-edge scenario executions as the carbon emission data for Q right-edge two-time iterative executions.

[0067] Step S42533: Combine the preset data tolerance bandwidth to construct the neighbor sets of the carbon emission data for Q left-edge two-time iterative executions of the carbon emission data for Q left-edge two-time iterative executions, and the neighbor sets of the carbon emission data for Q right-edge two-time iterative executions of the carbon emission data for Q right-edge two-time iterative executions.

[0068] Step S42534: Determine whether the number of Q left-edge two-time iterative neighbors in the neighbor set of the carbon emission data for Q left-edge two-time iterative executions is greater than or equal to the number of the Q left-edge neighbors corresponding to the carbon emission data for Q left-edge one-time iterative executions. If so, continue the iteration based on the carbon emission data for Q left-edge one-time iterative executions until the preset iteration stop condition is met to obtain the carbon emission data for Q left-edge iterative executions, where 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, operations are respectively carried out on the carbon emission data of Q left-edge secondary iterations and the carbon emission data of Q right-edge secondary iterations. For the carbon emission data of Q left-edge secondary iterations, for each carbon emission data of a left-edge secondary iteration, taking it as the center, the value range is determined according to the preset data tolerance bandwidth. The lower limit of this range is the carbon emission data of the left-edge secondary iteration minus half of the preset data tolerance bandwidth, and the upper limit is the carbon emission data of the left-edge secondary iteration plus half of the preset data tolerance bandwidth. Then, in the original carbon emission data set, all the data within this value range are carefully screened out, and these data are integrated to construct a new set, that is, the neighbor set corresponding to the carbon emission data of this left-edge secondary iteration. Repeat this process for all Q carbon emission data of left-edge secondary iterations, so as to obtain Q neighbor sets of carbon emission data of left-edge secondary iterations. Similarly, for the carbon emission data of Q right-edge secondary iterations, taking each carbon emission data of a right-edge secondary iteration as the benchmark, the value range is determined according to the preset data tolerance bandwidth, and then the data within this range are screened out from the original data set to construct the corresponding neighbor set of carbon emission data of the right-edge secondary iteration. Finally, Q neighbor sets of carbon emission data of right-edge secondary iterations are successfully constructed. The construction of these neighbor sets provides an important data basis for subsequent judgment of the iteration direction and further optimization of the calculation of carbon emission data.

[0073] Perform key judgments and operations on the iterations in the left-edge direction. Count the number of data in the Q nearest-neighbor sets of the carbon emission data for the second left-edge iteration one by one to obtain the Q numbers of nearest neighbors for the second left-edge iteration. At the same time, obtain the Q numbers of left-edge nearest neighbors corresponding to the carbon emission data for the first left-edge iteration. Subsequently, make a detailed comparison between these two sets of quantities. For each corresponding left-edge data situation, determine whether the number of nearest neighbors for the second left-edge iteration is greater than or equal to the number of nearest neighbors for the first left-edge iteration. If this condition is met, it means that there is value in further exploring the distribution of data in the left-edge direction. Then, based on the Q carbon emission data for the first left-edge iteration, continue the iterative calculation according to the established iterative algorithm. In each iteration process, a new nearest-neighbor set is reconstructed, and the number of data in it is counted, that is, the number of nearest neighbors obtained in the next iteration is obtained. Immediately afterwards, compare the newly obtained number of nearest neighbors with the number of nearest neighbors obtained in the previous iteration. As long as the number of nearest neighbors obtained in the next iteration is not less than the number of nearest neighbors obtained in the previous iteration, the next round of iteration will continue. Until a certain iteration when the number of nearest neighbors obtained in the next iteration is less than the number of nearest neighbors obtained in the previous iteration, at this time, the preset iteration stop condition is satisfied, and the iteration is stopped, and the Q carbon emission data for the left-edge iteration is 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 basis for the final accurate calculation of carbon emission data.

[0074] Perform data statistics on the nearest neighbor sets of carbon emission data for Q right-edge secondary iterations, calculate the number of data in each set one by one, and thus obtain the number of nearest neighbors for Q right-edge secondary iterations. At the same time, retrieve the Q right-edge nearest neighbor quantities corresponding to the carbon emission data of Q right-edge primary iterations previously recorded. Next, conduct a detailed comparison for each set of corresponding right-edge data, and judge one by one whether the number of right-edge secondary iteration nearest neighbors is greater than or equal to the number of right-edge primary iteration nearest neighbors. If this condition holds, it indicates that from the perspective of the right edge, the data distribution has value for further exploration. Then, starting from the carbon emission data of Q right-edge primary iterations, a new round of iterative calculation is initiated according to the established iterative algorithm. During the iterative process, for each iteration, a corresponding nearest neighbor set is constructed based on the new result, and the number of data in this set is counted, that is, the number of nearest neighbors for the next iteration is obtained, and the newly obtained number of nearest neighbors for this time is compared with the number of nearest neighbors generated in the previous iteration. As long as the number of nearest neighbors for the next iteration is greater than or equal to that of the previous iteration, the next round of iteration will continue. Until a certain iteration, the number of nearest neighbors for the next iteration is less than that of the previous iteration. At this time, the preset iteration stop condition is satisfied, and the iteration is terminated, thus successfully obtaining the carbon emission data of Q right-edge iterative executions. Through this rigorous operation process, it is ensured that the data processing in the right-edge direction can be advanced scientifically and reasonably, providing effective right-edge data support for accurately calculating carbon emission data.

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

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

[0077] A construction condition index set acquisition module 20, configured to extract construction conditions for the Q construction nodes according to preset construction condition indexes, and obtain Q node construction condition index sets.

[0078] A construction plan acquisition module 30, configured to perform association mapping on the Q node basic requirement information sets and the Q node construction condition index sets, and perform construction plan screening and matching based on the association mapping result to obtain Q node construction plans.

[0079] An evaluation result acquisition module 40, configured to perform data evaluation based on the Q node construction plans to obtain a target carbon emission data evaluation result.

[0080] Further, the construction plan acquisition module 30 further includes:

[0081] An index mapping relationship acquisition unit, configured to perform associated mapping on the Q node basic requirement information sets and the Q node construction condition index sets to obtain Q node requirement - node construction condition index mapping relationships.

[0082] A retrieval and matching unit, configured to perform retrieval and matching in the construction plan database with the Q node requirement - node construction condition index mapping relationships as indexes to obtain Q node construction plans.

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

[0084] A data mining unit, configured to perform data mining on the scheme execution result data based on big data with the Q node construction plans as indexes to obtain a Q - scheme execution carbon emission data set and a Q - scheme execution reliability set.

[0085] A carbon emission data acquisition unit, configured to perform centralized screening on the Q - scheme execution carbon emission data set to obtain Q - scheme execution centralized carbon emission data.

[0086] A centralized reliability acquisition unit, configured to perform centralized screening on the Q - scheme execution reliability set to obtain Q - scheme execution centralized reliability.

[0087] A summation calculation unit, configured to perform summation calculation on the Q - scheme execution centralized carbon emission data based on the Q - scheme execution centralized reliability to obtain the target carbon emission data evaluation result.

[0088] Further, the carbon emission data acquisition unit further includes:

[0089] A carbon emission data set mean value calculation unit, configured to calculate the mean values of the Q - scheme execution carbon emission data sets respectively to obtain Q - scheme execution carbon emission data mean values.

[0090] A mean - value near - neighbor set construction unit, configured to construct Q - scheme execution carbon emission data mean - value near - neighbor sets of the Q - scheme execution carbon emission data mean values in the Q - scheme execution carbon emission data sets according to a preset data tolerance bandwidth.

[0091] An edge data extraction unit is used to extract edge data from the Q - scheme - executed carbon emission data mean - neighbor set, obtaining Q left - edge - scheme - executed carbon emission data and Q right - edge - scheme - executed carbon emission data, where the Q left - edge - scheme - executed carbon emission data is less than the Q - scheme - executed carbon emission data mean, and the Q right - edge - scheme - executed carbon emission data is greater than the Q - scheme - executed carbon emission data mean.

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

[0093] A carbon emission data mean iteration unit is used to iterate the Q - scheme - executed carbon emission data mean according to the Q - left - edge - scheme - executed carbon emission data neighbor set of the Q left - edge - scheme - executed carbon emission data and the Q - right - edge - scheme - executed carbon emission data neighbor set of the Q right - edge - scheme - executed carbon emission data, obtaining the Q - scheme - executed centralized carbon emission data.

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

[0095] An iteration execution unit is used to, when only one of the Q left - edge neighbor numbers of the Q - left - edge - scheme - executed carbon emission data neighbor set and the Q right - edge neighbor numbers of the Q - right - edge - scheme - executed carbon emission data neighbor set is greater than or equal to the Q mean neighbor numbers of the Q - scheme - executed carbon emission data mean neighbor set, iterate the Q - scheme - executed carbon emission data mean to the left or right based on the Q left - edge - scheme - executed carbon emission data or the Q right - edge - scheme - executed carbon emission data until a preset iteration stop condition is met, obtaining Q left - edge iteratively - executed carbon emission data or Q right - edge iteratively - executed carbon emission data.

[0096] A centralized carbon emission data execution unit is used to calculate the mean of the scheme - executed carbon emission data between the Q left - edge iteratively - executed carbon emission data and the Q - scheme - executed carbon emission data mean or calculate the mean of the scheme - executed carbon emission data between the Q right - edge iteratively - executed carbon emission data and the Q - scheme - executed carbon emission data mean, obtaining the Q - scheme - executed centralized carbon emission data.

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

[0098] An edge iterative execution unit is used to, when the Q left-edge neighbor numbers of the carbon emission data neighbor sets executed by the Q left-edge schemes and the Q right-edge neighbor numbers of the carbon emission data neighbor sets executed by the Q right-edge schemes are both greater than or equal to the Q mean neighbor numbers of the carbon emission data mean neighbor sets executed by the Q schemes, perform iteration on the Q mean carbon emission data executed by the Q schemes simultaneously to the left and right 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 a preset iteration stop condition is reached, obtaining Q left-edge iterative execution carbon emission data and Q right-edge iterative execution carbon emission data.

[0099] A centralized carbon emission data calculation unit is used to calculate the mean of the carbon emission data executed by the schemes between the Q left-edge iterative execution carbon emission data and the Q right-edge iterative execution carbon emission data, obtaining the Q centralized carbon emission data executed by the schemes.

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

[0101] An edge first-iteration execution carbon emission data acquisition unit is used to take the carbon emission data executed by the Q left-edge schemes as the Q left-edge first-iteration execution carbon emission data, and at the same time take the carbon emission data executed by the Q right-edge schemes as the Q right-edge first-iteration execution carbon emission data.

[0102] An edge second-iteration execution carbon emission data acquisition unit is used to take the left-edge data in the neighbor sets of the carbon emission data executed by the Q left-edge schemes as the Q left-edge second-iteration execution carbon emission data, and take the right-edge data in the neighbor sets of the carbon emission data executed by the Q right-edge schemes as the Q right-edge second-iteration execution carbon emission data.

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

[0104] A left-edge second-iteration judgment unit is used to judge whether the Q left-edge second-iteration neighbor numbers of the Q left-edge second-iteration execution carbon emission data neighbor sets are greater than or equal to the Q left-edge neighbor numbers corresponding to the Q left-edge first-iteration execution carbon emission data. If so, continue to perform iteration based on the Q left-edge first-iteration execution carbon emission data until a preset iteration stop condition is met, obtaining the Q left-edge iterative execution carbon emission data, where the preset iteration stop condition is that the neighbor number obtained in the next iteration is less than the neighbor number obtained in the previous iteration.

[0105] A right-edge secondary iteration judgment unit is configured to determine whether the Q right-edge secondary iteration neighbor numbers of the Q right-edge secondary iteration execution carbon emission data neighbor sets are greater than or equal to the Q right-edge neighbor numbers corresponding to the Q right-edge primary iteration execution carbon emission data. If so, continue the iteration based on the Q right-edge primary iteration execution carbon emission data until a preset iteration stop condition is met, and obtain the Q right-edge iteration execution carbon emission data.

[0106] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the 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 are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0108] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications 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 equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A carbon emission reduction data evaluation method for green buildings, characterized in that, The method includes: Obtain Q construction nodes of the target building, traverse the Q construction nodes to collect node basic requirement information, and obtain a set of Q node basic requirement information, where 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 Q node construction condition indicators; Perform association mapping on the set of Q node basic requirement information and the set of Q node construction condition indicators, and perform construction plan screening and matching based on the association mapping result to obtain Q node construction plans; Perform data evaluation based on the Q node construction plans to obtain the target carbon emission data evaluation result, including: Using the Q node construction plans as indexes, perform data mining on the plan execution result data based on big data to obtain a set of Q plan execution carbon emission data and a set of Q plan execution reliability; Perform centralized screening on the set of Q plan execution carbon emission data to obtain Q plan execution centralized carbon emission data; Perform centralized screening on the set of Q plan execution reliability to obtain Q plan execution centralized reliability; Perform summation calculation on the Q plan execution centralized carbon emission data based on the Q plan execution centralized reliability to obtain the target carbon emission data evaluation result.

2. The carbon emission reduction data evaluation method for green buildings according to claim 1, characterized in that Perform association mapping on the set of Q node basic requirement information and the set of Q node construction condition indicators, and perform construction plan screening and matching based on the association mapping result to obtain Q node construction plans, including: Perform association mapping on the set of Q node basic requirement information and the set of Q node construction condition indicators to obtain a Q node requirement - node construction condition indicator mapping relationship; Using the Q node requirement - node construction condition indicator mapping relationship as an index, perform retrieval and matching in the construction plan database to obtain Q node construction plans.

3. The carbon emission reduction data evaluation method for green buildings according to claim 1, wherein Perform centralized screening on the set of Q plan execution carbon emission data to obtain Q plan execution centralized carbon emission data, including: Calculate the mean values of the set of Q plan execution carbon emission data respectively to obtain Q plan execution carbon emission data mean values; In the set of Q plan execution carbon emission data, construct Q plan execution carbon emission data mean value neighborhood sets of the Q plan execution carbon emission data mean values according to a preset data tolerance bandwidth; Extract the edge data in the Q plan execution carbon emission data mean value neighborhood sets to obtain Q left - edge plan execution carbon emission data and Q right - edge plan execution carbon emission data, where the Q left - edge plan execution carbon emission data is less than the Q plan execution carbon emission data mean values, and the Q right - edge plan execution carbon emission data is greater than the Q plan execution carbon emission data mean values; Combine the preset data tolerance bandwidth to construct Q left - edge plan execution carbon emission data neighborhood sets of the Q left - edge plan execution carbon emission data and Q right - edge plan execution carbon emission data neighborhood sets of the Q right - edge plan execution carbon emission data; Execute the Q left-edge schemes for carbon emission data to obtain the Q left-edge nearest neighbor sets of carbon emission data and the Q right-edge schemes for carbon emission data to obtain the Q right-edge nearest neighbor sets of carbon emission data. Iterate the mean of the carbon emission data for the Q schemes to obtain the centralized carbon emission data for the Q schemes.

4. The carbon emission reduction data evaluation method for green buildings according to claim 3, characterized in that, Execute the Q left-edge schemes for carbon emission data to obtain the Q left-edge nearest neighbor sets of carbon emission data and the Q right-edge schemes for carbon emission data to obtain the Q right-edge nearest neighbor sets of carbon emission data. Iterate the mean of the carbon emission data for the Q schemes to obtain the centralized carbon emission data for the Q schemes, including: When only one of the Q left-edge neighbor numbers of the Q left-edge nearest neighbor sets of carbon emission data executed by the Q left-edge schemes and the Q right-edge neighbor numbers of the Q right-edge nearest neighbor sets of carbon emission data executed by the Q right-edge schemes is greater than or equal to the Q mean neighbor numbers of the Q mean nearest neighbor sets of carbon emission data executed by the Q schemes, iterate the mean of the carbon emission data for 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 a preset iteration stop condition is met, obtaining the Q left-edge iteratively executed carbon emission data or the Q right-edge iteratively executed carbon emission data; Calculate the mean of the carbon emission data for the schemes between the Q left-edge iteratively executed carbon emission data and the mean of the carbon emission data for the Q schemes, or calculate the mean of the carbon emission data for the schemes between the Q right-edge iteratively executed carbon emission data and the mean of the carbon emission data for the Q schemes, to obtain the centralized carbon emission data for the Q schemes.

5. The carbon emission reduction data evaluation method for green buildings according to claim 4, wherein, Execute the Q left-edge schemes for carbon emission data to obtain the Q left-edge nearest neighbor sets of carbon emission data and the Q right-edge schemes for carbon emission data to obtain the Q right-edge nearest neighbor sets of carbon emission data. Iterate the mean of the carbon emission data for the Q schemes to obtain the centralized carbon emission data for the Q schemes, including: When both the Q left-edge neighbor numbers of the Q left-edge nearest neighbor sets of carbon emission data executed by the Q left-edge schemes and the Q right-edge neighbor numbers of the Q right-edge nearest neighbor sets of carbon emission data executed by the Q right-edge schemes are greater than or equal to the Q mean neighbor numbers of the Q mean nearest neighbor sets of carbon emission data executed by the Q schemes, iterate the mean of the carbon emission data for the Q schemes to both the left and right 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 a preset iteration stop condition is met, obtaining the Q left-edge iteratively executed carbon emission data and the Q right-edge iteratively executed carbon emission data; Calculate the mean of the carbon emission data for the schemes between the Q left-edge iteratively executed carbon emission data and the Q right-edge iteratively executed carbon emission data to obtain the centralized carbon emission data for the Q schemes.

6. The carbon emission reduction data evaluation method for green buildings according to claim 5, wherein, Simultaneously to the left and right, iterate the carbon emission data execution of the Q left-edge scenarios and the carbon emission data execution of the Q right-edge scenarios to calculate the mean carbon emission data execution of the Q scenarios until a preset iteration stop condition is reached, obtaining the Q left-edge iterative carbon emission data executions and the Q right-edge iterative carbon emission data executions, including: Use the carbon emission data executions of the Q left-edge scenarios as the Q left-edge first-iteration carbon emission data executions, and simultaneously use the carbon emission data executions of the Q right-edge scenarios as the Q right-edge first-iteration carbon emission data executions; Use the left-edge data in the neighbor sets of the carbon emission data executions of the Q left-edge scenarios as the Q left-edge second-iteration carbon emission data executions, and use the right-edge data in the neighbor sets of the carbon emission data executions of the Q right-edge scenarios as the Q right-edge second-iteration carbon emission data executions; Combined with the preset data tolerance bandwidth, construct the neighbor sets of the Q left-edge second-iteration carbon emission data executions of the Q left-edge second-iteration carbon emission data executions and the neighbor sets of the Q right-edge second-iteration carbon emission data executions of the Q right-edge second-iteration carbon emission data executions; Judge whether the number of Q left-edge second-iteration neighbors in the neighbor set of the Q left-edge second-iteration carbon emission data executions is greater than or equal to the number of Q left-edge neighbors corresponding to the Q left-edge first-iteration carbon emission data executions. If so, continue to iterate based on the Q left-edge first-iteration carbon emission data executions until the preset iteration stop condition is met, obtaining the Q left-edge iterative carbon emission data executions, where 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; Judge whether the number of Q right-edge second-iteration neighbors in the neighbor set of the Q right-edge second-iteration carbon emission data executions is greater than or equal to the number of Q right-edge neighbors corresponding to the Q right-edge first-iteration carbon emission data executions. If so, continue to iterate based on the Q right-edge first-iteration carbon emission data executions until the preset iteration stop condition is met, obtaining the Q right-edge iterative carbon emission data executions.

7. Carbon emission reduction data evaluation system for green buildings, characterized in that, The system is used to implement the carbon emission reduction data evaluation method for green buildings according to any one of claims 1-6. The system includes: A basic requirement information set acquisition module, used to obtain Q construction nodes of the target building, traverse the Q construction nodes to collect node basic requirement information, and obtain a set of Q node basic requirement information, where Q is an integer greater than or equal to 1; A construction condition index set acquisition module, used to extract construction conditions for the Q construction nodes according to preset construction condition indicators, and obtain a set of Q node construction condition indicators; A construction plan acquisition module, used to perform an association mapping on the set of Q node basic requirement information and the set of Q node construction condition indicators, and perform construction plan screening and matching based on the association mapping result to obtain Q node construction plans; An evaluation result acquisition module, used to perform data evaluation based on the Q node construction plans to obtain a target carbon emission data evaluation result; The evaluation result acquisition module further includes: A data mining unit, which is used to index with the Q node construction plans, perform data mining on the data of the execution results of the plans based on big data, and obtain a set of Q execution carbon emission data of the plans and a set of Q execution reliability of the plans; A carbon emission data acquisition unit, which is used to centrally screen the set of Q execution carbon emission data of the plans to obtain Q centralized carbon emission data of the plan executions; A centralized reliability acquisition unit, which is used to centrally screen the set of Q execution reliability of the plans to obtain Q centralized reliabilities of the plan executions; A summation calculation unit, which is used to perform summation calculation on the Q centralized carbon emission data of the plans based on the Q centralized reliabilities of the plan executions to obtain the evaluation result of the target carbon emission data.

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