Low-carbon regulation control method and system based on intelligent construction site comprehensive evaluation

Abstract

The invention discloses a low-carbon adjustment control method and system based on intelligent construction site comprehensive evaluation, and relates to the technical field of intelligent construction sites, and the method comprises the steps: monitoring the real-time engineering data of an intelligent construction site, and extracting the real-time resource consumption characteristics of energy consumption data and material use data; extracting real-time carbon emission characteristics of the environmental parameter data; extracting real-time efficiency reference features of the construction equipment state data and the construction progress data; according to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time efficiency reference characteristics, evaluating the real-time carbon emission condition of the intelligent construction site by using the low-carbon evaluation model after training optimization to obtain a low-carbon performance score of the intelligent construction site; generating an adjustment control instruction according to the low-carbon performance score of the intelligent construction site; and sending the adjustment control instruction to construction equipment of the corresponding intelligent construction site or a management system of the intelligent construction site.

Description

Technical Field

[0001] The present application relates to the technical field of smart construction sites, and more specifically, to a low-carbon regulation and control method and system based on comprehensive evaluation of smart construction sites. Background Art

[0002] In the modern construction industry, with the rapid development of emerging technologies such as the Internet of Things, artificial intelligence and big data, the concept of smart construction sites has emerged. Smart construction sites transform construction sites through digitalization, informatization and intelligent means, and realize real-time monitoring, data analysis, intelligent early warning and optimized scheduling of all aspects of the construction site. This new construction site management model aims to improve management efficiency, reduce safety risks and environmental pollution, and improve construction quality and economic benefits. However, although the construction of smart construction sites has made some progress, it still faces challenges in achieving low-carbon development.

[0003] In the actual promotion process, due to the lack of effective low-carbon regulation and control methods and systems, multiple entities are still insufficient in controlling energy consumption and carbon emissions, and are not efficient in coordinating low-carbon development. The existence of these problems not only limits the potential of smart construction sites in reducing energy consumption and carbon emissions, but also affects the progress of green and low-carbon transformation in the construction industry.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a low-carbon regulation and control method and system based on comprehensive evaluation of smart construction sites to solve the above-mentioned technical problems.

[0006] The present application provides a low-carbon regulation and control method based on comprehensive evaluation of smart construction sites, comprising: monitoring real-time engineering data of smart construction sites; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on a carbon dioxide concentration sensor and a gas flow meter;

[0007] extracting real-time resource consumption characteristics of the energy consumption data and the material usage data;

[0008] Extracting real-time carbon emission characteristics of the environmental parameter data;

[0009] Extracting real-time performance reference features of the construction equipment status data and the construction progress data;

[0010] According to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time performance reference characteristics, the real-time carbon emission situation of the smart construction site is evaluated by using the low-carbon evaluation model that has been trained and optimized to obtain a low-carbon performance score of the smart construction site;

[0011] generating an adjustment control instruction according to the low-carbon performance score of the smart construction site;

[0012] The adjustment control instruction is sent to the corresponding construction equipment of the smart construction site or the management system of the smart construction site.

[0013] Furthermore, the low-carbon evaluation model is trained and optimized based on the following steps:

[0014] Collecting historical energy consumption data, historical material usage data, historical construction equipment status data, historical construction progress data, and historical environmental parameter data of construction sites of the same construction type as the smart construction site; wherein the construction type is a building project, a railway project, a highway project, a water conservancy project, or a municipal project;

[0015] extracting historical resource consumption characteristics of the historical energy consumption data and the historical material usage data;

[0016] Extracting historical carbon emission characteristics of the historical environmental parameter data;

[0017] Extracting historical performance reference features of the historical construction equipment status data and the historical construction progress data;

[0018] Determine an evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site according to the historical resource consumption characteristics, the historical carbon emission characteristics, and the historical performance reference characteristics; wherein the evaluation correlation coefficient is used to determine the evaluation index;

[0019] The low-carbon evaluation model based on the long short-term memory neural network is trained and optimized according to the evaluation correlation coefficient, the historical resource consumption characteristics, the historical carbon emission characteristics and the historical performance reference characteristics.

[0020] Furthermore, determining an evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site according to the historical resource consumption characteristics, the historical carbon emission characteristics, and the historical performance reference characteristics includes:

[0021] Define the first observation value X1 of the historical resource consumption characteristic = (x11, x12, ..., x1 n ) and the first tag value T1=(t11,t12,....,t1 n );

[0022] Define the second observation value X2 of the historical carbon emission characteristic = (x21, x22, ...., x2 n ) and the second tag value T2 = (t21, t22, ...., t2 n );

[0023] Define the third observation value X3 of the historical performance reference feature = (x31, x32, ..., x3 n ) and the third tag value T3=(t31,t32,....,t3 n );

[0024] Among them, the sample data volume corresponding to X1, the sample data volume corresponding to T1, the sample data volume corresponding to X2, the sample data volume corresponding to T2, the sample data volume corresponding to X3, and the sample data volume corresponding to T3 are all defined as n;

[0025] Determine an evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site based on the following formula;

[0026]

[0027] Wherein, i is the cumulative index; Relev is the evaluation correlation coefficient; Represents x1 i rank; Indicates t1 i rank; Represents x2 i rank; Indicates t2 i rank; Represents x3 i rank; Indicates t3 i rank.

[0028] Furthermore, the low-carbon evaluation model based on the long short-term memory neural network is trained and optimized according to the evaluation correlation coefficient, the historical resource consumption characteristics, the historical carbon emission characteristics and the historical performance reference characteristics, including:

[0029] Inputting the evaluation correlation coefficient into the fully connected layer of the low-carbon evaluation model to extract the corresponding embedded features;

[0030] According to the embedded features, the historical resource consumption features, the historical carbon emission features, and the historical performance reference features, a low-carbon evaluation model based on a long short-term memory neural network is trained and optimized until a target loss function converges;

[0031]

[0032] Wherein, L is the target loss function; j is an index variable used to traverse the training data set, the training data set includes the embedded features, the historical resource consumption features, the historical carbon emission features and the historical performance reference features; N represents the total number of sample data in the training data set; is the mean square error, which is used to measure the carbon emission prediction value of the low-carbon evaluation model for time step j and the true value y j The difference between j ) is the regularization term of the hidden state, R(h j )=||h j || 2 ,h j represents the hidden state at time step j; S(ω, V) is the L2 regularization term of the model parameters of the low-carbon evaluation model, and ω and V are weight parameters in the model parameters; is the loss item of the adjustment measure; is a loss function that is robust to outliers; α1, α2, α3, α4, and α5 are all weight coefficients;

[0033] δ is used to control The point at which the switch occurs between the square and linear terms.

[0034] Furthermore, generating an adjustment control instruction according to the low-carbon performance score of the smart construction site includes:

[0035] One or more of the following regulatory control instructions are generated based on the low-carbon performance score of the smart construction site: instructions for adjusting operating parameters of construction equipment, instructions for optimizing construction work processes, instructions for improving material usage strategies, or instructions for adjusting a construction schedule.

[0036] Furthermore, the carbon dioxide concentration sensor is set at multiple different locations of the construction site of the smart construction site; wherein the multiple different locations include locations where multiple construction equipment are located and multiple randomly selected locations.

[0037] Furthermore, the energy consumption data includes temporary water consumption data and temporary electricity consumption data of the construction site of the smart construction site.

[0038] The present application provides a low-carbon regulation and control system based on comprehensive evaluation of smart construction sites, including a real-time engineering data monitoring unit, a classification feature extraction unit, a low-carbon performance scoring unit, and a regulation control unit; wherein,

[0039] The real-time engineering data monitoring unit is used to monitor the real-time engineering data of the smart construction site; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on a carbon dioxide concentration sensor and a gas flow meter;

[0040] The classification feature extraction unit is used to extract the real-time resource consumption features of the energy consumption data and the material usage data; extract the real-time carbon emission features of the environmental parameter data; extract the real-time performance reference features of the construction equipment status data and the construction progress data;

[0041] The low-carbon performance scoring unit is used to evaluate the real-time carbon emission of the smart construction site using the low-carbon evaluation model trained and optimized according to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time performance reference characteristics, so as to obtain a low-carbon performance score of the smart construction site;

[0042] The regulation control unit is used to generate regulation control instructions according to the low-carbon performance score of the smart construction site; and send the regulation control instructions to the corresponding construction equipment of the smart construction site or the management system of the smart construction site.

[0043] Furthermore, the regulation control unit generates a regulation control instruction according to the low-carbon performance score of the smart construction site, and is configured as follows:

[0044] One or more of the following regulatory control instructions are generated based on the low-carbon performance score of the smart construction site: instructions for adjusting operating parameters of construction equipment, instructions for optimizing construction work processes, instructions for improving material usage strategies, or instructions for adjusting a construction schedule.

[0045] Furthermore, the carbon dioxide concentration sensor is set at multiple different locations of the construction site of the smart construction site; wherein the multiple different locations include locations where multiple construction equipment are located and multiple randomly selected locations.

[0046] Based on the embodiments provided by the present application, the real-time engineering data of the smart construction site is monitored; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on the carbon dioxide concentration sensor and the gas flow meter; the real-time resource consumption characteristics of the energy consumption data and the material usage data are extracted; the real-time carbon emission characteristics of the environmental parameter data are extracted; the real-time performance reference characteristics of the construction equipment status data and the construction progress data are extracted; according to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time performance reference characteristics, the real-time carbon emission situation of the smart construction site is evaluated by using the low-carbon evaluation model completed by training and optimization to obtain the low-carbon performance score of the smart construction site; the adjustment control instruction is generated according to the low-carbon performance score of the smart construction site; the adjustment control instruction is sent to the construction equipment of the corresponding smart construction site or the management system of the smart construction site. Thus, the energy consumption and carbon emission of the construction site are effectively monitored, analyzed and controlled, thereby improving the low-carbon performance of the smart construction site and promoting the construction industry to develop in a more environmentally friendly and sustainable direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0048] Figure 1 is a flow chart of an optional low-carbon regulation control method based on comprehensive evaluation of smart construction sites according to an embodiment of the present application;

[0049] Figure 2 This is a structural diagram of an optional low-carbon regulation and control system based on comprehensive evaluation of smart construction sites according to an embodiment of the present application.

[0050] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0052] Alternatively, if Figure 1 As shown, the present application provides a low-carbon regulation control method based on comprehensive evaluation of smart construction sites, including:

[0053] S101, monitoring the real-time engineering data of the smart construction site; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on a carbon dioxide concentration sensor and a gas flow meter;

[0054] S102, extracting real-time resource consumption characteristics of energy consumption data and material usage data;

[0055] S103, extracting real-time carbon emission characteristics of environmental parameter data;

[0056] S104, extracting real-time performance reference features of construction equipment status data and construction progress data;

[0057] S105, evaluating the real-time carbon emission of the smart construction site using the trained and optimized low-carbon evaluation model according to the real-time resource consumption characteristics, the real-time carbon emission characteristics, and the real-time performance reference characteristics, to obtain a low-carbon performance score of the smart construction site;

[0058] S106, generating an adjustment control instruction according to the low-carbon performance score of the smart construction site;

[0059] S107, sending the adjustment control instruction to the corresponding smart construction site construction equipment or the smart construction site management system.

[0060] Based on the embodiments provided by the present application, the real-time engineering data of the smart construction site is monitored; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on the carbon dioxide concentration sensor and the gas flow meter; the real-time resource consumption characteristics of the energy consumption data and the material usage data are extracted; the real-time carbon emission characteristics of the environmental parameter data are extracted; the real-time performance reference characteristics of the construction equipment status data and the construction progress data are extracted; according to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time performance reference characteristics, the real-time carbon emission situation of the smart construction site is evaluated by using the low-carbon evaluation model completed by training and optimization to obtain the low-carbon performance score of the smart construction site; the adjustment control instruction is generated according to the low-carbon performance score of the smart construction site; the adjustment control instruction is sent to the construction equipment of the corresponding smart construction site or the management system of the smart construction site. Thus, the energy consumption and carbon emission of the construction site are effectively monitored, analyzed and controlled, thereby improving the low-carbon performance of the smart construction site and promoting the construction industry to develop in a more environmentally friendly and sustainable direction.

[0061] Furthermore, the low-carbon evaluation model is trained and optimized based on the following steps:

[0062] Collect historical energy consumption data, historical material usage data, historical construction equipment status data, historical construction progress data, and historical environmental parameter data of construction sites with the same construction type as the smart construction site; where the construction type is building engineering, railway engineering, highway engineering, water conservancy engineering, or municipal engineering;

[0063] Extract historical energy consumption data and historical resource consumption characteristics of historical material usage data;

[0064] Extract historical carbon emission characteristics from historical environmental parameter data;

[0065] Extract historical performance reference features of historical construction equipment status data and historical construction progress data;

[0066] According to the historical resource consumption characteristics, the historical carbon emission characteristics and the historical performance reference characteristics, an evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site is determined; wherein the evaluation correlation coefficient is used to determine the evaluation index;

[0067] According to the evaluation correlation coefficient, historical resource consumption characteristics, historical carbon emission characteristics and historical performance reference characteristics, the low-carbon evaluation model based on the long short-term memory neural network is trained and optimized.

[0068] Based on the embodiments provided in this application, the evaluation correlation coefficient is used to determine the evaluation index. Each smart construction site can obtain personalized evaluation indicators according to its own construction characteristics and environmental conditions, so as to more accurately evaluate and optimize its own low-carbon performance. The evaluation correlation coefficient plays a key role in the training process of the low-carbon evaluation model. They help the model learn which features are more important for predicting carbon emissions, thereby improving the training efficiency and prediction accuracy of the model; through the evaluation correlation coefficient, the model can more effectively use historical data, convert the information in the data into practical insights, and provide data support for the low-carbon operation of the smart construction site. The evaluation correlation coefficient enables the model to provide more accurate carbon emission assessments for construction management, helping decision makers make more informed low-carbon decisions during the construction process; the introduction of the evaluation correlation coefficient enables the model to adapt to construction sites of different types and sizes, as well as different construction conditions and environments, improving the adaptability and flexibility of the model; the evaluation correlation coefficient can be continuously updated with new data and construction practices, providing a basis for the continuous improvement of low-carbon performance for smart construction sites.

[0069] Furthermore, based on the historical resource consumption characteristics, historical carbon emission characteristics and historical performance reference characteristics, the evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site is determined, including:

[0070] Define the first observation value of historical resource consumption characteristics X1 = (x11, x12, ..., x1 n) and the first tag value T1=(t11,t12,....,t1 n );

[0071] Define the second observation value of historical carbon emission characteristics X2 = (x21, x22, ...., x2 n ) and the second tag value T2 = (t21, t22, ...., t2 n );

[0072] Define the third observation value X3 of the historical performance reference feature = (x31, x32, ..., x3 n ) and the third tag value T3=(t31,t32,....,t3 n );

[0073] Among them, the sample data volume corresponding to X1, the sample data volume corresponding to T1, the sample data volume corresponding to X2, the sample data volume corresponding to T2, the sample data volume corresponding to X3, and the sample data volume corresponding to T3 are all defined as n;

[0074] The evaluation correlation coefficient for evaluating the real-time carbon emissions of the smart construction site is determined based on the following formula;

[0075]

[0076] Among them, i is the cumulative index; Relev is the evaluation correlation coefficient; Represents x1 i rank; Indicates t1 i rank; Represents x2 i rank; Indicates t2 i rank; Represents x3 i rank; Indicates t3 i rank.

[0077] Furthermore, according to the evaluation correlation coefficient, historical resource consumption characteristics, historical carbon emission characteristics and historical performance reference characteristics, the low-carbon evaluation model based on the long short-term memory neural network model is trained and optimized, including:

[0078] Input the evaluation correlation coefficient into the fully connected layer of the low-carbon evaluation model to extract the corresponding embedding features;

[0079] According to the embedded features, historical resource consumption features, historical carbon emission features and historical performance reference features, the low-carbon evaluation model based on the long short-term memory neural network is trained and optimized until the target loss function converges;

[0080]

[0081] Where L is the target loss function; j is an index variable used to traverse the training data set, which includes embedded features, historical resource consumption features, historical carbon emission features, and historical performance reference features; N represents the total number of sample data in the training data set; is the mean square error, which is used to measure the carbon emission prediction value of the low-carbon evaluation model for time step j and the true value y j The difference between j ) is the regularization term of the hidden state, R(h j )=||h j || 2 ,h j represents the hidden state at time step j; S(ω, V) is the L2 regularization term of the model parameters of the low-carbon evaluation model, and ω and V are the weight parameters in the model parameters; is the loss item of the adjustment measure; is a loss function that is robust to outliers; α1, α2, α3, α4, and α5 are all weight coefficients;

[0082] δ is used to control The point at which the switch occurs between the square and linear terms.

[0083] Furthermore, adjustment control instructions are generated according to the low-carbon performance score of the smart construction site, including:

[0084] One or more of the following regulatory control instructions are generated according to the low-carbon performance score of the smart construction site: instructions for adjusting operating parameters of construction equipment, instructions for optimizing construction work processes, instructions for improving material usage strategies, or instructions for adjusting construction schedules.

[0085] Furthermore, carbon dioxide concentration sensors are set at multiple different locations of the construction site of the smart construction site; wherein the multiple different locations include locations where multiple construction equipment are located and multiple randomly selected locations.

[0086] Furthermore, the energy consumption data includes temporary water consumption data and temporary electricity consumption data at the construction site of the smart construction site.

[0087] Alternatively, if Figure 2 As shown, the present application provides a low-carbon regulation and control system based on comprehensive evaluation of smart construction sites, including a real-time engineering data monitoring unit 201, a classification feature extraction unit 202, a low-carbon performance scoring unit 203 and a regulation control unit 204; wherein,

[0088] The real-time engineering data monitoring unit 201 is used to monitor the real-time engineering data of the smart construction site; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on the carbon dioxide concentration sensor and the gas flow meter;

[0089] The classification feature extraction unit 202 is used to extract the real-time resource consumption features of energy consumption data and material usage data; extract the real-time carbon emission features of environmental parameter data; extract the real-time performance reference features of construction equipment status data and construction progress data;

[0090] The low-carbon performance scoring unit 203 is used to evaluate the real-time carbon emission of the smart construction site using the trained and optimized low-carbon evaluation model according to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time performance reference characteristics, so as to obtain a low-carbon performance score of the smart construction site;

[0091] The adjustment control unit 204 is used to generate an adjustment control instruction according to the low-carbon performance score of the smart construction site; and send the adjustment control instruction to the construction equipment of the corresponding smart construction site or the management system of the smart construction site.

[0092] Furthermore, the regulation control unit generates regulation control instructions according to the low-carbon performance score of the smart construction site, and is configured as follows:

[0093] One or more of the following regulatory control instructions are generated according to the low-carbon performance score of the smart construction site: instructions for adjusting operating parameters of construction equipment, instructions for optimizing construction work processes, instructions for improving material usage strategies, or instructions for adjusting construction schedules.

[0094] Furthermore, carbon dioxide concentration sensors are set at multiple different locations of the construction site of the smart construction site; wherein the multiple different locations include locations where multiple construction equipment are located and multiple randomly selected locations.

[0095] It should be noted that in the present application, the embodiments implemented on the low-carbon regulation and control system side based on the comprehensive evaluation of smart construction sites can be cross-referenced with the embodiments implemented on the low-carbon regulation and control method side based on the comprehensive evaluation of smart construction sites, and this application will not go into details one by one.

[0096] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A low-carbon regulation and control method based on comprehensive evaluation of smart construction sites, characterized in that: include: Monitor the real-time engineering data of the smart construction site; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on the carbon dioxide concentration sensor and the gas flow meter; extracting real-time resource consumption characteristics of the energy consumption data and the material usage data; Extracting real-time carbon emission characteristics of the environmental parameter data; Extracting real-time performance reference features of the construction equipment status data and the construction progress data; According to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time performance reference characteristics, the real-time carbon emission situation of the smart construction site is evaluated by using the low-carbon evaluation model that has been trained and optimized to obtain a low-carbon performance score of the smart construction site; generating an adjustment control instruction according to the low-carbon performance score of the smart construction site; The adjustment control instruction is sent to the corresponding construction equipment of the smart construction site or the management system of the smart construction site.

2. The low-carbon regulation and control method based on comprehensive evaluation of smart construction sites according to claim 1 is characterized in that: The low-carbon evaluation model is trained and optimized based on the following steps: Collecting historical energy consumption data, historical material usage data, historical construction equipment status data, historical construction progress data, and historical environmental parameter data of construction sites of the same construction type as the smart construction site; wherein the construction type is a building project, a railway project, a highway project, a water conservancy project, or a municipal project; extracting historical resource consumption characteristics of the historical energy consumption data and the historical material usage data; Extracting historical carbon emission characteristics of the historical environmental parameter data; Extracting historical performance reference features of the historical construction equipment status data and the historical construction progress data; Determine an evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site according to the historical resource consumption characteristics, the historical carbon emission characteristics, and the historical performance reference characteristics; wherein the evaluation correlation coefficient is used to determine the evaluation index; The low-carbon evaluation model based on the long short-term memory neural network is trained and optimized according to the evaluation correlation coefficient, the historical resource consumption characteristics, the historical carbon emission characteristics and the historical performance reference characteristics.

3. The low-carbon regulation and control method based on comprehensive evaluation of smart construction sites according to claim 2 is characterized in that: The step of determining an evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site according to the historical resource consumption characteristics, the historical carbon emission characteristics, and the historical performance reference characteristics includes: Define the first observation value X1 of the historical resource consumption characteristic = (x11, x12, ..., x1 n ) and the first tag value T1=(t11,t12,…,t1 n ); Define the second observation value X2 of the historical carbon emission characteristic = (x21, x22, ..., x2 n ) and the second tag value T2=(t21, t22,…, t2 n ); Define the third observation value X3 of the historical performance reference feature = (x31, x32, ..., x3 n ) and the third tag value T3=(t31, t32,…, t3 n ); Among them, the sample data volume corresponding to X1, the sample data volume corresponding to T1, the sample data volume corresponding to X2, the sample data volume corresponding to T2, the sample data volume corresponding to X3, and the sample data volume corresponding to T3 are all defined as n; Determine an evaluation correlation coefficient for evaluating the real-time carbon emission situation of the smart construction site based on the following formula; Wherein, i is the cumulative index; Relev is the evaluation correlation coefficient; Represents x1 i rank; Indicates t1 i rank; Represents x2 i rank; Indicates t2 i rank; Represents x3 i rank; Indicates t3 i rank.

4. The low-carbon regulation and control method based on comprehensive evaluation of smart construction sites according to claim 3 is characterized in that: The training and optimization of the low-carbon evaluation model based on the long short-term memory neural network model according to the evaluation correlation coefficient, the historical resource consumption characteristics, the historical carbon emission characteristics and the historical performance reference characteristics includes: Inputting the evaluation correlation coefficient into the fully connected layer of the low-carbon evaluation model to extract corresponding embedding features; According to the embedded features, the historical resource consumption features, the historical carbon emission features, and the historical performance reference features, a low-carbon evaluation model based on a long short-term memory neural network is trained and optimized until a target loss function converges; Wherein, L is the target loss function; j is an index variable used to traverse the training data set, the training data set includes the embedded features, the historical resource consumption features, the historical carbon emission features and the historical performance reference features; N represents the total number of sample data in the training data set; is the mean square error, which is used to measure the carbon emission prediction value of the low-carbon evaluation model for time step j and the true value y j The difference between j ) is the regularization term of the hidden state, R(h j )=‖h j ‖ 2 ,h j represents the hidden state at time step j; S(ω,V) is the L2 regularization term of the model parameters of the low-carbon evaluation model, and ω and V are weight parameters in the model parameters; is the loss item of the adjustment measure; It is a loss function that is robust to outliers; α1, α2, α3, α4, and α5 are all weight coefficients; δ is used to control The point at which the switch occurs between the square and linear terms.

5. The low-carbon regulation and control method based on comprehensive evaluation of smart construction sites according to claim 1 is characterized in that: The generating of the adjustment control instruction according to the low-carbon performance score of the smart construction site includes: One or more of the following regulatory control instructions are generated based on the low-carbon performance score of the smart construction site: instructions for adjusting operating parameters of construction equipment, instructions for optimizing construction work processes, instructions for improving material usage strategies, or instructions for adjusting a construction schedule.

6. The low-carbon regulation and control method based on comprehensive evaluation of smart construction sites according to claim 1 is characterized in that: The carbon dioxide concentration sensor is set at multiple different locations of the construction site of the smart construction site; wherein the multiple different locations include locations where multiple construction equipment are located and multiple randomly selected locations.

7. The low-carbon regulation and control method based on comprehensive evaluation of smart construction sites according to claim 1 is characterized in that: The energy consumption data includes temporary water consumption data and temporary electricity consumption data at the construction site of the smart construction site.

8. A low-carbon regulation and control system based on comprehensive evaluation of smart construction sites, the system implements the method as claimed in claim 1, characterized in that: It includes a real-time engineering data monitoring unit, a classification feature extraction unit, a low-carbon performance scoring unit and an adjustment control unit; among which, The real-time engineering data monitoring unit is used to monitor the real-time engineering data of the smart construction site; wherein the real-time engineering data includes energy consumption data, material usage data, construction equipment status data, construction progress data and environmental parameter data; the environmental parameter data is measured based on a carbon dioxide concentration sensor and a gas flow meter; The classification feature extraction unit is used to extract the real-time resource consumption features of the energy consumption data and the material usage data; extract the real-time carbon emission features of the environmental parameter data; extract the real-time performance reference features of the construction equipment status data and the construction progress data; The low-carbon performance scoring unit is used to evaluate the real-time carbon emission of the smart construction site using the low-carbon evaluation model trained and optimized according to the real-time resource consumption characteristics, the real-time carbon emission characteristics and the real-time performance reference characteristics, so as to obtain a low-carbon performance score of the smart construction site; The regulation control unit is used to generate regulation control instructions according to the low-carbon performance score of the smart construction site; and send the regulation control instructions to the corresponding construction equipment of the smart construction site or the management system of the smart construction site.

9. The low-carbon regulation and control system based on comprehensive evaluation of smart construction sites according to claim 8 is characterized in that: The regulation control unit generates a regulation control instruction according to the low-carbon performance score of the smart construction site, and is configured to: One or more of the following regulatory control instructions are generated based on the low-carbon performance score of the smart construction site: instructions for adjusting operating parameters of construction equipment, instructions for optimizing construction work processes, instructions for improving material usage strategies, or instructions for adjusting a construction schedule.

10. The low-carbon regulation and control system based on comprehensive evaluation of smart construction sites according to claim 8 is characterized in that: The carbon dioxide concentration sensor is set at multiple different locations of the construction site of the smart construction site; wherein the multiple different locations include locations where multiple construction equipment are located and multiple randomly selected locations.

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