Energy-saving design optimization method based on intelligent construction green construction

Abstract

The application belongs to the technical field of building construction management, and is specifically an energy-saving design optimization method based on intelligent construction green construction, comprising construction process multi-source data acquisition processing, energy consumption characteristic analysis, energy-saving strategy generation, design scheme optimization screening and construction monitoring feedback; the application integrates various data source information and deeply mines energy consumption characteristic rules, rapidly generates various feasible energy-saving strategies according to different energy consumption characteristics and construction conditions and preliminarily evaluates the effects, comprehensively considers various factors to determine a feasible and economic optimal energy-saving design scheme, monitors the construction process to timely find and solve energy consumption abnormal problems, realizes comprehensive optimization of building engineering green construction energy-saving design, effectively reduces energy consumption in the construction process, improves energy utilization efficiency, effectively promotes the development of building engineering green construction, and has high intelligent degree.

Description

Technical Field

[0001] This invention relates to the field of building construction management technology, specifically to an energy-saving design optimization method based on intelligent construction and green construction. Background Technology

[0002] In the field of construction engineering, energy consumption is enormous. Various operations during the construction process, such as earthwork excavation, concrete pouring, and building material hoisting, require a large amount of machinery and equipment to operate, resulting in extremely high energy consumption. However, traditional construction methods lack systematic planning and consideration in energy utilization, leading to extremely serious energy waste, with much energy being lost in unnecessary processes.

[0003] Most existing energy-saving design methods focus on the building operation phase, while paying far too little attention to energy-saving issues during the construction phase. This makes it difficult to efficiently and accurately collect and integrate massive amounts of multi-source heterogeneous construction data, mine construction energy consumption characteristics, and output precise energy-saving optimization strategies. Furthermore, it is impossible to reasonably analyze and provide timely warnings about the performance of construction energy-saving management and the potential risks of construction adjustments. This is not conducive to improving the energy utilization efficiency of building construction and promoting the development of green construction.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an energy-saving design optimization method based on intelligent construction and green construction. It solves the problems of existing technologies, which are unable to efficiently and accurately collect and integrate massive amounts of multi-source heterogeneous construction data, effectively mine construction energy consumption characteristics and output precise energy-saving optimization strategies, and cannot reasonably analyze and promptly warn of construction energy-saving control performance and construction adjustment execution risks, which are not conducive to improving energy utilization efficiency and promoting the development of green construction in building engineering.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The energy-saving design optimization method based on intelligent construction and green construction includes the following steps:

[0008] Step 1: The multi-source heterogeneous data fusion module collects various data related to energy consumption during the construction process of building engineering and fuses data from different sources and in different formats.

[0009] Step 2: The intelligent energy consumption characteristic analysis module performs in-depth analysis on the data provided by the multi-source heterogeneous data fusion module to uncover the characteristics and patterns of construction energy consumption;

[0010] Step 3: The dynamic generation module for energy-saving strategies dynamically generates a variety of feasible energy-saving strategies based on the energy consumption characteristics analysis results, combined with the actual situation of the building project and the requirements of green construction, and conducts a preliminary evaluation of the energy-saving effects of different energy-saving strategies.

[0011] Step 4: The design scheme comprehensive optimization module comprehensively considers multiple energy-saving strategies and combines several factors, including construction cost, construction schedule and construction quality, to comprehensively optimize the energy-saving design scheme and generate the optimal energy-saving design scheme.

[0012] Step 5: The real-time monitoring and feedback module monitors the actual energy consumption in real time during construction and feeds back the energy consumption monitoring information to the energy consumption characteristic intelligent analysis module and the construction supervision center.

[0013] Furthermore, in step one, before the construction of the building project begins, the multi-source heterogeneous data fusion module establishes a connection with various data source devices according to the preset data collection list, and continuously collects various types of data through real-time monitoring and periodic reading.

[0014] For data in different formats, data conversion and standardization techniques are used to convert them into a format that the system can recognize. Furthermore, data fusion algorithms are used to associate and integrate relevant data from multiple channels, eliminating data redundancy and conflicts, and generating a complete and consistent construction dataset.

[0015] Furthermore, in step two, the specific operation process of the energy consumption characteristic intelligent analysis module is as follows:

[0016] After receiving the data processed by the multi-source heterogeneous data fusion module, the system uses data mining and machine learning algorithms to perform time series analysis and correlation analysis on the construction energy consumption data. By analyzing the trend of energy consumption data over time, the system determines the peak and trough periods of energy consumption during the construction process and further analyzes the reasons for the formation of peak periods.

[0017] Furthermore, we conducted comparative analysis of energy consumption data for different construction equipment, identified high-energy-consuming equipment, analyzed the reasons for its high energy consumption, and combined this with construction progress information to analyze the energy consumption characteristics of different construction stages and identify key high-energy-consuming construction links.

[0018] Furthermore, in step three, the energy-saving strategy dynamic generation module generates a variety of targeted energy-saving strategies based on the energy consumption characteristic analysis results, combined with the pre-set energy-saving strategy library and green construction standards, using intelligent algorithms; after generating the energy-saving strategies, the energy-saving effect of each strategy is preliminarily evaluated using historical data and simulation models.

[0019] Furthermore, in step four, the specific operation process of the design scheme comprehensive optimization module includes:

[0020] The system receives various energy-saving strategies and their preliminary evaluation results generated by the dynamic energy-saving strategy generation module, and simultaneously acquires construction cost data, construction schedule plans, and construction quality requirements. Using a multi-objective optimization algorithm, it comprehensively weighs and optimizes various energy-saving strategies with the goals of reducing energy consumption, controlling costs, and ensuring schedule and quality. Through this multi-objective optimization algorithm, the system identifies the energy-saving strategy combination that maximizes energy consumption reduction while meeting construction cost, schedule, and quality requirements, thus forming the optimal energy-saving design scheme.

[0021] Furthermore, the real-time monitoring and feedback module acquires the actual energy consumption data during the construction process in real time, compares the actual energy consumption data with the expected energy consumption data for the corresponding time period and construction stage in the optimal energy-saving design scheme, and calculates the deviation value. When the deviation value exceeds the corresponding preset threshold, it is determined that an abnormal situation has occurred. When an abnormal situation occurs, the cause of the deviation is analyzed, including equipment failure, changes in construction process and changes in environmental factors.

[0022] Furthermore, the real-time monitoring feedback module is connected to the construction energy conservation management and assessment module. The real-time monitoring feedback module sends energy consumption monitoring information to the construction energy conservation management and assessment module. The construction energy conservation management and assessment module is used to set the energy consumption management period and analyze the construction energy conservation management and assessment performance during the energy consumption management period.

[0023] The system analyzes and generates either an energy-saving control compliance signal or an energy-saving control alarm signal, and sends the signal to the construction supervision center. When the construction supervision center receives the energy-saving control alarm signal, it issues a corresponding warning.

[0024] Furthermore, the specific analysis process of the construction energy conservation management and assessment module is as follows:

[0025] All energy consumption monitoring information sent by the real-time monitoring feedback module during the energy consumption management period is obtained. The number of times the deviation value of the actual energy consumption data from the expected energy consumption data exceeds the corresponding preset threshold during the energy consumption management period is marked as the construction energy consumption deviation alarm value. The construction energy consumption deviation alarm value is compared with the preset construction energy consumption deviation alarm threshold. If the construction energy consumption deviation alarm value exceeds the preset construction energy consumption deviation alarm threshold, an energy-saving control alarm signal is generated.

[0026] If the construction energy consumption warning value does not exceed the preset construction energy consumption warning threshold, the deviation value is calculated by comparing the deviation value with the corresponding preset threshold to obtain the deviation status value, and the average value of all deviation status values ​​during the pipe consumption period is calculated to obtain the deviation pipe meter value, and the deviation status value with the largest value during the pipe consumption period is marked as the deviation amplitude value.

[0027] The energy-saving management evaluation coefficient is calculated by weighting and summing the construction energy consumption warning value, the deviation meter value, and the deviation amplitude value. The energy-saving management evaluation coefficient is then compared with the preset energy-saving management evaluation coefficient threshold. If the energy-saving management evaluation coefficient exceeds the preset energy-saving management evaluation coefficient threshold, an energy-saving management alarm signal is generated; if the energy-saving management evaluation coefficient does not exceed the preset energy-saving management evaluation coefficient threshold, an energy-saving management qualified signal is generated.

[0028] Furthermore, the construction energy conservation management and control assessment module is connected to the construction adjustment execution analysis module. The construction energy conservation management and control assessment module sends the energy conservation management and control qualified signal to the construction adjustment execution analysis module. When the construction adjustment execution analysis module receives the energy conservation management and control qualified signal, it analyzes the performance of the adjustment measures at the construction site during the energy conservation and control period. Through analysis, it determines whether an adjustment execution unqualified signal or adjustment execution hidden danger signal is generated, and sends the adjustment execution unqualified signal or adjustment execution hidden danger signal to the construction supervision center. When the construction supervision center receives the adjustment execution unqualified signal or adjustment execution hidden danger signal, it issues a corresponding warning.

[0029] Furthermore, the specific analysis process of the construction adjustment execution analysis module is as follows:

[0030] All adjustment measures required during the consumption period are obtained. The task issuance time of the corresponding adjustment measures is marked as the first time, and the completion time of the corresponding adjustment measures is marked as the second time. The time difference between the first time and the second time is calculated to obtain the adjustment detection value. The adjustment detection value is compared with the preset adjustment detection threshold. If the adjustment detection value exceeds the preset adjustment detection threshold, the corresponding adjustment detection value is marked as the adjustment time value.

[0031] The system obtains the number of time-varying adjustment values ​​during the consumption period and calculates the ratio of this number to the total number of time-varying adjustment values ​​to obtain the time-varying adjustment characteristic value. It also calculates the time-varying adjustment characteristic value by comparing the time-varying adjustment value with the corresponding preset time-varying adjustment threshold and the time-varying adjustment characteristic value. The system then compares the time-varying adjustment characteristic value with the preset time-varying adjustment characteristic threshold and the preset time-varying adjustment characteristic threshold, respectively. If either the time-varying adjustment characteristic value or the time-varying adjustment characteristic value exceeds the preset threshold, an adjustment execution alarm signal is generated.

[0032] If neither the adjustment characteristic value nor the adjustment time characteristic value exceeds the corresponding preset threshold, then the supervisory personnel distributed in the construction site are obtained, and the construction site is divided into several supervisory areas. If there are no supervisory personnel in the corresponding supervisory area, then the corresponding supervisory area is judged to be in a state of potential efficiency risks in the implementation of measures. The total duration of the corresponding supervisory area in the state of potential efficiency risks in the implementation of measures during the consumption period is obtained and marked as the efficiency risk time measurement value. The efficiency risk time measurement value is compared with the preset efficiency risk time measurement threshold. If the efficiency risk time measurement value exceeds the preset efficiency risk time measurement threshold, then the corresponding supervisory area is marked as an area of ​​untimely implementation.

[0033] The system obtains the number of non-timely implementation areas in the construction site and calculates the ratio of these areas to the total number of monitored areas to obtain the high-risk area occupancy value. It also calculates the efficiency risk performance value by averaging the time-measured efficiency risk values ​​of all monitored areas. The high-risk area occupancy value and efficiency risk performance value are compared with the preset high-risk area occupancy threshold and preset efficiency risk performance threshold, respectively. If the high-risk area occupancy value or efficiency risk performance value exceeds the corresponding preset threshold, an adjustment execution risk signal is generated.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] 1. In this invention, by integrating information from multiple data sources and deeply mining the energy consumption characteristics and patterns, multiple feasible energy-saving strategies are quickly generated based on different energy consumption characteristics and construction conditions, and the effects are initially evaluated. A comprehensive consideration of multiple factors is taken into account to determine the feasible and economical optimal energy-saving design scheme. Furthermore, the construction process is monitored to promptly detect and resolve abnormal energy consumption problems, thereby achieving comprehensive optimization of green construction energy-saving design for building projects, significantly improving energy utilization efficiency, and powerfully promoting the development of green construction in building projects. It also has a high degree of intelligence.

[0036] 2. In this invention, the construction energy conservation management and control assessment module analyzes the performance of construction energy conservation management and control during the energy consumption management period. When an energy conservation management and control alarm signal is generated, the supervision of construction energy consumption is strengthened and corresponding targeted improvement measures are taken in a timely manner. When an energy conservation management and control qualified signal is generated, the performance of the adjustment measures implemented at the construction site during the energy consumption management period is analyzed. When an adjustment implementation unqualified signal or adjustment implementation hidden danger signal is generated, the subsequent implementation management and on-site supervision are strengthened to ensure the timeliness of subsequent measures, which is conducive to achieving green construction of building projects and improving energy conservation effects. Attached Figure Description

[0037] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0038] Figure 1 This is a flowchart of the method of the present invention;

[0039] Figure 2 This is a system block diagram of Embodiment 1 of the present invention;

[0040] Figure 3 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example 1: As Figure 1-2 As shown, the energy-saving design optimization method based on intelligent construction and green construction proposed in this invention includes the following steps:

[0043] Step 1: Before the construction of a building project begins, the multi-source heterogeneous data fusion module collects various data related to energy consumption during the construction process and integrates data from different sources and in different formats. Through multi-source data fusion, a comprehensive range of energy consumption-related information during the construction process can be obtained, avoiding the limitations of a single data source. This provides a reliable basis for accurately analyzing energy consumption and formulating energy-saving strategies. Furthermore, data fusion processing improves data quality and usability, reducing the workload of data processing in subsequent analysis processes.

[0044] Specifically, the multi-source heterogeneous data fusion module establishes connections with various data source devices, such as sensors on construction equipment, energy metering instruments, and project management software, based on a preset data collection list, and continuously collects various types of data through real-time monitoring and periodic reading.

[0045] For data in different formats, such as digital signals, text information, and image data, data conversion and standardization techniques are used to convert them into a format that the system can recognize. At the same time, data fusion algorithms are used to associate and integrate relevant data from multiple channels, eliminate data redundancy and conflicts, and generate a complete and consistent construction dataset. For example, the running time data of construction equipment is fused with energy consumption data to obtain the energy consumption of the equipment under different operating conditions, providing rich information for subsequent analysis.

[0046] Step Two: The intelligent energy consumption characteristic analysis module conducts in-depth analysis of the data provided by the multi-source heterogeneous data fusion module to uncover the characteristics and patterns of construction energy consumption. It can accurately grasp the dynamic changes and inherent laws of construction energy consumption, providing precise targets and directions for energy-saving design. By identifying high-energy-consuming equipment and processes, targeted energy-saving measures can be formulated to improve energy-saving effects. The specific operation process of the intelligent energy consumption characteristic analysis module is as follows:

[0047] After receiving the data processed by the multi-source heterogeneous data fusion module, the system uses data mining and machine learning algorithms to perform time series analysis and correlation analysis on the construction energy consumption data. By analyzing the trend of energy consumption data over time, the system determines the peak and trough periods of energy consumption during the construction process and further analyzes the reasons for the formation of peak periods, such as whether they are related to specific construction links or concentrated operation of equipment.

[0048] Meanwhile, energy consumption data of different construction equipment were compared and analyzed to identify high-energy-consuming equipment and analyze the reasons for its high energy consumption, such as equipment aging and unreasonable operating parameters. In addition, combined with construction progress information, the energy consumption characteristics of different construction stages were analyzed to identify key construction links with high energy consumption. For example, the analysis found that concrete mixing equipment accounted for a large proportion of energy consumption during the foundation construction stage, and the peak energy consumption period was between 10:00 am and 12:00 pm every day. Further analysis showed that the equipment was under heavy load during this period and some equipment was aging, resulting in excessive energy consumption.

[0049] Step 3: The dynamic generation module for energy-saving strategies dynamically generates a variety of feasible energy-saving strategies based on the energy consumption characteristics analysis results, combined with the actual situation of the building project and the requirements of green construction. It also conducts a preliminary evaluation of the energy-saving effects of different energy-saving strategies, which helps to formulate the optimal energy-saving design.

[0050] Specifically, the energy-saving strategy dynamic generation module generates various targeted energy-saving strategies based on the results of energy consumption characteristic analysis, combined with a pre-set energy-saving strategy library and green construction standards, using intelligent algorithms. For example, for high-energy-consuming equipment, it can generate equipment update strategies, optimized operating parameter strategies, and reasonable equipment operation time scheduling strategies; for peak energy consumption periods, it can generate off-peak construction strategies and adjusted construction sequence strategies.

[0051] After generating energy-saving strategies, historical data and simulation models are used to conduct a preliminary assessment of the energy-saving effect of each strategy, predicting the possible reduction in energy consumption and the energy cost savings after adopting the strategy. For example, for the problem of excessive energy consumption caused by the aging of concrete mixing equipment, an energy-saving strategy for upgrading the equipment is generated, and a simulation model is used to predict the daily reduction in energy consumption and the amount of cost savings after upgrading the equipment.

[0052] Step 4: The comprehensive optimization module of the design scheme takes into account multiple energy-saving strategies and combines factors such as construction cost, construction schedule, and construction quality to comprehensively optimize the energy-saving design scheme and generate the optimal energy-saving design scheme. This module comprehensively considers various factors and systematically optimizes the energy-saving design scheme, avoiding the drawbacks of solely pursuing energy saving while neglecting other important factors, and ensuring that the generated energy-saving design scheme is feasible and economical. The specific operation process is as follows:

[0053] The system receives various energy-saving strategies and their preliminary evaluation results generated by the dynamic energy-saving strategy generation module, and simultaneously acquires information such as construction cost data, construction schedule, and construction quality requirements. Using a multi-objective optimization algorithm, with the goals of reducing energy consumption, controlling costs, and ensuring schedule and quality, the system comprehensively weighs and optimizes various energy-saving strategies. For example, when considering the replacement of high-energy-consuming equipment, it not only considers the energy-saving effect after the equipment replacement, but also the equipment procurement cost, installation and commissioning time, and the impact on the construction schedule.

[0054] By using multi-objective optimization algorithms, the combination of energy-saving strategies that maximizes energy consumption reduction while meeting the requirements of construction cost, schedule, and quality is identified, forming the optimal energy-saving design scheme. For example, after comprehensive optimization, a combination strategy of updating some high-energy-consuming equipment, optimizing equipment operating parameters, and rationally arranging construction time is determined. Under the premise of ensuring construction progress and quality, it is expected to reduce the total construction energy consumption by 15%, while keeping the equipment update cost within a reasonable range.

[0055] Step 5: The real-time monitoring and feedback module monitors the actual energy consumption in real time during construction and feeds the energy consumption monitoring information back to the intelligent energy consumption characteristic analysis module and the construction supervision center. This enables dynamic management of the construction process, timely detection and resolution of abnormal energy consumption issues, and ensures the effective implementation of energy-saving design schemes. Furthermore, by continuously adjusting and optimizing the design schemes, it can adapt to changes in various uncertain factors during construction and further improve energy-saving effects.

[0056] Specifically, the real-time monitoring and feedback module obtains the actual energy consumption data during the construction process from the multi-source heterogeneous data fusion module in real time, compares the actual energy consumption data with the expected energy consumption data for the corresponding time period and construction stage in the optimal energy-saving design scheme, and calculates the deviation value. When the deviation value exceeds the corresponding preset threshold, it is determined that an abnormal situation has occurred. When an abnormal situation occurs, the cause of the deviation is analyzed, such as equipment failure, changes in construction process and changes in environmental factors.

[0057] Based on the deviation analysis results, the intelligent energy consumption characteristic analysis module is triggered to re-analyze the data for further optimization. For example, if the actual energy consumption of a certain construction stage is found to be 20% higher than expected, and analysis shows that this is due to a sudden equipment failure leading to a decrease in operating efficiency, this information is fed back. The intelligent energy consumption characteristic analysis module re-analyzes the energy consumption characteristics of that stage, the energy-saving strategy dynamic generation module generates energy-saving strategies for equipment failure repair and operation optimization, and the design scheme comprehensive optimization module adjusts and optimizes the original energy-saving design scheme to ensure that energy consumption during construction is always controlled within a reasonable range.

[0058] Example 2: Figure 3 As shown, the difference between this embodiment and Embodiment 1 is that the real-time monitoring feedback module is connected to the construction energy-saving management and evaluation module. The real-time monitoring feedback module sends energy consumption monitoring information to the construction energy-saving management and evaluation module. The construction energy-saving management and evaluation module is used to set the energy consumption management period, preferably seven days; and to analyze the construction energy-saving management and evaluation performance during the energy consumption management period.

[0059] The system analyzes and generates energy-saving control compliance signals or energy-saving control alarm signals, which are then sent to the construction supervision center. Upon receiving an energy-saving control alarm signal, the construction supervision center issues a corresponding warning to remind back-end management personnel to strengthen the monitoring of construction energy consumption and take timely and targeted improvement measures to ensure the subsequent energy-saving effects of construction. The specific analysis process of the construction energy-saving control assessment module is as follows:

[0060] All energy consumption monitoring information sent by the real-time monitoring feedback module during the energy consumption management period is obtained. The number of times the deviation value of the actual energy consumption data from the expected energy consumption data exceeds the corresponding preset threshold during the energy consumption management period is marked as the construction energy consumption deviation alarm value. The construction energy consumption deviation alarm value is compared with the preset construction energy consumption deviation alarm threshold. If the construction energy consumption deviation alarm value exceeds the preset construction energy consumption deviation alarm threshold, it indicates that the energy-saving management performance during the energy consumption management period is significantly poor, and an energy-saving management alarm signal is generated.

[0061] If the construction energy consumption warning value does not exceed the preset construction energy consumption warning threshold, the deviation value is calculated by comparing the deviation value with the corresponding preset threshold to obtain the deviation status value, and the average value of all deviation status values ​​during the pipe consumption period is calculated to obtain the deviation pipe meter value, and the deviation status value with the largest value during the pipe consumption period is marked as the deviation amplitude value.

[0062] The energy conservation management and control evaluation coefficient is obtained by weighted summation of the construction energy consumption warning value, the deviation of the pipe meter value, and the deviation of the amplitude. Specifically, the construction energy consumption warning value, the deviation of the pipe meter value, and the deviation of the amplitude are each assigned a corresponding preset weight coefficient, and the construction energy consumption warning value, the deviation of the pipe meter value, and the deviation of the amplitude are each multiplied by the corresponding preset weight coefficient. The sum of the three sets of product results is marked as the energy conservation management and control evaluation coefficient.

[0063] It should be noted that the higher the value of the energy-saving management evaluation coefficient, the worse the overall energy-saving management performance during the consumption management period. The energy-saving management evaluation coefficient is compared with the preset energy-saving management evaluation coefficient threshold. If the energy-saving management evaluation coefficient exceeds the preset energy-saving management evaluation coefficient threshold, it indicates that the overall energy-saving management performance during the consumption management period is poor, and an energy-saving management alarm signal is generated. If the energy-saving management evaluation coefficient does not exceed the preset energy-saving management evaluation coefficient threshold, it indicates that the overall energy-saving management performance during the consumption management period is good, and an energy-saving management qualified signal is generated.

[0064] Example 3: Figure 3 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the construction energy-saving management and control assessment module is connected to the construction adjustment execution analysis module. The construction energy-saving management and control assessment module sends the energy-saving management and control qualified signal to the construction adjustment execution analysis module. When the construction adjustment execution analysis module receives the energy-saving management and control qualified signal, it analyzes the performance of the adjustment measures at the construction site of the building project during the energy consumption period.

[0065] The analysis determines whether an adjustment execution non-compliance signal or adjustment execution potential risk signal has been generated. This signal is then sent to the construction supervision center. Upon receiving this signal, the construction supervision center issues a corresponding warning to remind back-end management personnel to strengthen subsequent measure implementation management and on-site supervision, ensuring the timeliness of subsequent measures. This is beneficial for achieving green construction and improving energy-saving effects in building projects. The specific analysis process of the construction adjustment execution analysis module is as follows:

[0066] All adjustment measures required during the consumption period are obtained. The task issuance time of the corresponding adjustment measures is marked as the first time, and the completion time of the corresponding adjustment measures is marked as the second time. The time difference between the first time and the second time is calculated to obtain the adjustment detection value. The adjustment detection value is compared with the preset adjustment detection threshold. If the adjustment detection value exceeds the preset adjustment detection threshold, it indicates that the execution efficiency of the corresponding adjustment measures is slow. Then, the corresponding adjustment detection value is marked as the adjustment time value.

[0067] The system obtains the number of time-varying adjustment values ​​during the consumption management period and calculates the ratio of these values ​​to the total number of time-varying adjustment values ​​to obtain the time-varying adjustment characteristic value. It also calculates the adjustment detection value by comparing these values ​​with the corresponding preset time-varying adjustment threshold and calculates the time-varying adjustment characteristic value by averaging all the time-varying adjustment values ​​during the consumption management period. The system then compares the time-varying adjustment characteristic value with the preset time-varying adjustment characteristic threshold and the preset time-varying adjustment characteristic threshold, respectively. If either the time-varying adjustment characteristic value or the adjustment characteristic value exceeds the preset threshold, it indicates that the overall efficiency of the measures adjustment during the consumption management period is poor, and an adjustment execution alarm signal is generated.

[0068] Furthermore, if neither the adjustment characteristic value nor the adjustment time characteristic value exceeds the corresponding preset threshold, it indicates that the overall performance of the measures adjustment and implementation efficiency during the consumption period is relatively good. Then, the supervisory personnel distributed in the construction site are obtained, and the construction site is divided into several supervisory areas. If there are no supervisory personnel in the corresponding supervisory area, it is judged that the corresponding supervisory area is in a state of potential risks in the implementation efficiency of the measures.

[0069] The total duration during which the corresponding regulatory area is in a state of potential efficiency risks in the implementation of measures during the consumption period is obtained and marked as the efficiency risk time measurement value. The efficiency risk time measurement value is compared with the preset efficiency risk time measurement threshold. If the efficiency risk time measurement value exceeds the preset efficiency risk time measurement threshold, it indicates that the personnel supervision status of the corresponding regulatory area is poor, and the corresponding regulatory area is marked as an area of ​​untimely implementation.

[0070] The system obtains the number of areas in the construction site that are not implemented in a timely manner and calculates the high-risk area occupancy value by comparing it with the total number of monitored areas. It also calculates the efficiency risk performance value by averaging the time-measured efficiency risk values ​​of all monitored areas. The high-risk area occupancy value and efficiency risk performance value are compared with the preset high-risk area occupancy threshold and preset efficiency risk performance threshold, respectively. If the high-risk area occupancy value or efficiency risk performance value exceeds the corresponding preset threshold, it indicates that there are monitoring risks at the construction site during the monitoring period, which is not conducive to the rapid response to the implementation of various measures. In this case, an adjustment execution risk signal is generated.

[0071] The working principle of this invention is as follows: During use, a multi-source heterogeneous data fusion module integrates information from various data sources, providing comprehensive and accurate construction energy consumption data as a reliable basis for analysis. An intelligent energy consumption characteristic analysis module delves into the patterns of energy consumption characteristics, providing precise direction for energy-saving design. A dynamic energy-saving strategy generation module quickly generates multiple feasible energy-saving strategies based on different energy consumption characteristics and construction conditions, and preliminarily evaluates their effectiveness, providing rich options for decision-making. A comprehensive design optimization module fully considers factors such as energy saving, cost, schedule, and quality, generating feasible and economical optimal energy-saving design schemes. A real-time monitoring and feedback module monitors the construction process to promptly detect and resolve abnormal energy consumption issues, achieving comprehensive optimization of green construction energy-saving design for building projects. This effectively reduces energy consumption during construction, improves energy utilization efficiency, and powerfully promotes the development of green construction in building projects.

[0072] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0073] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An energy-saving design optimization method based on intelligent construction and green construction, characterized in that, Includes the following steps: Step 1: The multi-source heterogeneous data fusion module collects various data related to energy consumption during the construction process of building engineering and fuses data from different sources and in different formats. Step 2: The intelligent energy consumption characteristic analysis module performs in-depth analysis on the data provided by the multi-source heterogeneous data fusion module to uncover the characteristics and patterns of construction energy consumption; Step 3: The dynamic generation module for energy-saving strategies dynamically generates a variety of feasible energy-saving strategies based on the energy consumption characteristics analysis results, combined with the actual situation of the building project and the requirements of green construction, and conducts a preliminary evaluation of the energy-saving effects of different energy-saving strategies. Step 4: The design scheme comprehensive optimization module comprehensively considers multiple energy-saving strategies and combines several factors to comprehensively optimize the energy-saving design scheme and generate the optimal energy-saving design scheme. Step 5: The real-time monitoring and feedback module monitors the actual energy consumption in real time during construction and feeds back the energy consumption monitoring information to the energy consumption characteristic intelligent analysis module and the construction supervision center.

2. The energy-saving design optimization method based on intelligent construction and green construction according to claim 1, characterized in that, In step one, before the construction of the building project begins, the multi-source heterogeneous data fusion module establishes connections with various data source devices according to the preset data collection list, and continuously collects various types of data through real-time monitoring and periodic reading. For data of different formats, data conversion and standardization technologies are used to convert them into a unified format that the system can recognize, and data fusion algorithms are used to associate and integrate relevant data from multiple channels.

3. The energy-saving design optimization method based on intelligent construction and green construction according to claim 1, characterized in that, In step two, the specific operation process of the energy consumption characteristic intelligent analysis module is as follows: After receiving data processed by the multi-source heterogeneous data fusion module, the system uses data mining and machine learning algorithms to perform time series analysis and correlation analysis on construction energy consumption data. By analyzing the trend of energy consumption data over time, the system identifies peak and trough periods of energy consumption during construction and further analyzes the causes of peak periods. The system also compares and analyzes the energy consumption data of different construction equipment to identify high-energy-consuming equipment and analyze the reasons for its high energy consumption. In addition, combined with construction progress information, the system analyzes the energy consumption characteristics of different construction stages and identifies key construction links with high energy consumption.

4. The energy-saving design optimization method based on intelligent construction and green construction according to claim 1, characterized in that, In step three, the energy-saving strategy dynamic generation module generates a variety of targeted energy-saving strategies based on the energy consumption characteristic analysis results, combined with the pre-set energy-saving strategy library and green construction standards, using intelligent algorithms. After generating the energy-saving strategies, the energy-saving effect of each strategy is initially evaluated using historical data and simulation models.

5. The energy-saving design optimization method based on intelligent construction and green construction according to claim 1, characterized in that, In step four, the specific operation process of the design scheme comprehensive optimization module includes: It receives various energy-saving strategies and their preliminary evaluation results generated by the dynamic energy-saving strategy generation module, and simultaneously acquires construction cost data, construction schedule plans, and construction quality requirements information; it uses a multi-objective optimization algorithm to comprehensively weigh and optimize various energy-saving strategies with the goals of reducing energy consumption, controlling costs, and ensuring schedule and quality; and it forms the optimal energy-saving design scheme through the multi-objective optimization algorithm.

6. The energy-saving design optimization method based on intelligent construction and green construction according to claim 1, characterized in that, The real-time monitoring and feedback module acquires actual energy consumption data during the construction process in real time, compares the actual energy consumption data with the expected energy consumption data for the corresponding time period and construction stage in the optimal energy-saving design scheme, and calculates the deviation value; when the deviation value exceeds the corresponding preset threshold, it is determined that an abnormal situation has occurred.

7. The energy-saving design optimization method based on intelligent construction and green construction according to claim 6, characterized in that, The real-time monitoring feedback module is connected to the construction energy-saving management and assessment module. The construction energy-saving management and assessment module analyzes the construction energy-saving management performance during the energy consumption period and sends the energy-saving management qualified signal or energy-saving management alarm signal to the construction supervision center.

8. The energy-saving design optimization method based on intelligent construction and green construction according to claim 7, characterized in that, The specific analysis process of the construction energy conservation management and control assessment module is as follows: If the construction energy consumption deviation alarm value exceeds the preset construction energy consumption deviation alarm threshold, an energy-saving control alarm signal is generated; if the construction energy consumption deviation alarm value does not exceed the preset construction energy consumption deviation alarm threshold, an energy-saving control evaluation coefficient is calculated by weighting and summing the construction energy consumption deviation alarm value, deviation meter value, and deviation amplitude value. If the energy-saving control evaluation coefficient exceeds the preset energy-saving control evaluation coefficient threshold, an energy-saving control alarm signal is generated; if the energy-saving control evaluation coefficient does not exceed the preset energy-saving control evaluation coefficient threshold, an energy-saving control qualified signal is generated.

9. The energy-saving design optimization method based on intelligent construction and green construction according to claim 7, characterized in that, The construction energy conservation management and control assessment module is connected to the construction adjustment execution analysis module. When the construction adjustment execution analysis module receives the energy conservation management qualified signal, it analyzes the performance of the adjustment measures at the construction site during the energy consumption management period. Based on this, it determines whether to generate an adjustment execution unqualified signal or an adjustment execution hidden danger signal, and sends the adjustment execution unqualified signal or adjustment execution hidden danger signal to the construction supervision center.

10. The energy-saving design optimization method based on intelligent construction and green construction according to claim 9, characterized in that, The specific analysis process of the construction adjustment execution analysis module is as follows: If the adjustment feature value or adjustment time feature value exceeds the preset threshold, an adjustment execution alarm signal is generated; if neither the adjustment feature value nor the adjustment time feature value exceeds the corresponding preset threshold, the number of non-timely implementation areas in the construction site is obtained and its ratio with the total number of monitored areas is calculated to obtain the high-risk area occupancy value, and the efficiency risk performance value is calculated by averaging the efficiency risk time measurement values ​​of all monitored areas; if the high-risk area occupancy value or efficiency risk performance value exceeds the corresponding preset threshold, an adjustment execution risk signal is generated.

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