Construction waste resource utilization and energy consumption calculation based on Internet of things

With the development of urbanization, the city has entered the stage of improving the quality of the stock, and the number of urban demolition has increased, resulting in the generation of construction waste and great pressure on the environment. The discharge of construction waste threatens the sustainable development of urban environment. And because large public buildings have high energy consumption and high emissions, it is urgent to establish an effective energy consumption measurement system. In the past, the calculation method for energy consumption was relatively single, which could not effectively manage energy consumption. This paper discusses how to use construction waste scientifically based on the Internet of things, and using advanced measurement technology to monitor and analyze building energy consumption is an effective way to solve this problem. Based on this, this paper constructs a building energy consumption measurement system through the Internet of things technology. By using BP linear neural network model to predict, the system effectively improves the prediction accuracy and makes it close to the actual value. The system is mainly based on the collection terminal, the centralized data terminal and the data management terminal and then builds a communication bridge between the terminals through the Internet of things and the Internet. The response time of energy consumption monitoring and management is relatively short, and the response speed of power control management and energy consumption management analysis is relatively fast, which can effectively measure the energy consumption of buildings. In addition, this paper also makes a brief analysis of the recycling of construction waste and puts forward the corresponding strategic analysis of resource utilization. This paper designs an effective energy consumption measurement system by introducing the Internet of things technology into the building field.


Introduction
At present, China's annual output of construction waste is huge, but the recycling level of construction waste is not high, and the recycling process is mainly aimed at resource-based treatment and high value-added waste recycling (Wang et al. 2010). The recovery rate of construction waste in large-and medium-sized cities in China is only about 30%, while that in developed countries has reached more than 90%. In addition, nearly 2 / 3 of China's construction waste is not harmlessly treated, but directly stored or transported to the landfill for accumulation. On the one hand, it increases transportation costs and occupies land resources. On the other hand, construction waste will produce harmful substances during transportation, which will have a negative impact on the urban environment (Wu et al. 2021). Through research, it is concluded that construction waste can reach a relatively stable state after decades of accumulation, but it will still cause environmental problems after stabilization (Yuan 2013). Energy is an important resource for social development. With China's economic and social development, the energy problem has become increasingly prominent, and the form of energy conservation and emission reduction is grim (Ma and Zhang 2020). In recent years, the growth rate of energy consumption has gradually increased to 8.9%, and the two major energy consuming fields-Industry and construction industry account for 66% and 26%, respectively (Yuan et al. 2011). As China's current energy utilization rate is low and energy consumption is increasing, it is more necessary to focus on and conduct in-depth research on energy sustainable development and environmental pollution. With the continuous improvement of people's living standards, building energy consumption has become a major obstacle to China's sustainable development (Ma et al. 2019). In view of this problem, the state has vigorously promoted the promulgation of relevant bills to promote the development of sustainable green building technology, and strictly implemented the energy consumption detection system, so as to promote energy conservation and emission reduction and achieve low energy consumption development (Cai et al. 2009). The State Council implements clear consumption measurement methods for different types of energy consumption, measures energy consumption by household, category and substance, and orders correction of waste. Therefore, this paper establishes a building energy consumption measurement system based on the Internet of things technology. The system develops the software platform of building energy consumption measurement system through the Internet of things, and reduces the resources occupied by the system operation by using its advantages of free and open source, thus providing platform support for energy management based on BP neural network, and combining BP neural network with energy consumption data prediction. By setting reasonable parameters, the accuracy of data prediction can be improved and energy consumption can be effectively reduced. With the development of urbanization, many cities are facing the dilemma of ''surrounded by construction waste''. Compared with developed countries, China's construction waste management started late. At present, it is faced with problems such as large accumulation of construction waste, low recycling level, lack of relevant laws, regulations and regulatory policies, which hinder sustainable development. Therefore, based on the concept of resource utilization in development, this paper makes a strategic analysis of this phenomenon.

Relevant work
In the literature, linear regression and exponential smoothing are used for the first prediction, and the results of the first prediction are used for the second prediction of the trained BP neural network (Cui and Jing 2019). The simulation results show that it is more reasonable to choose the influence factor value between 0 and 1 in the process of linear regression prediction. The smaller the value is, the closer the prediction result is to the actual value; in exponential smoothing prediction, according to the time-series law of energy consumption data, it is reasonable to take a value between 0.1 and 0.5 as the weight factor (Goia et al. 2010;Song et al. 2005). The smaller the value, the closer the predicted result is to the actual value. The second prediction based on BP neural network in the literature improves the prediction accuracy and reduces the error between the prediction accuracy and the actual data by 1.56 * 2.1%. Compared with the first prediction result, the second prediction result is closer to the actual value. Therefore, more accurate energy conservation planning can be carried out. With Wamp as the development environment and ThinkPHP framework as the development framework, the literature has designed and developed a software platform for building energy management system (Hannan et al. 2018). This platform has many functions, such as retrieving energy consumption data, querying historical energy consumption data information, analyzing energy consumption trend and providing energy saving reports (Missaoui et al. 2014). The system integrates security and extensibility functions, and can effectively complete the target tasks. In the literature, a building energy management system is developed using PHP combined with Internet of things technology and Apache server, which can create a good, safe and reliable environment (Minoli et al. 2017). Due to the portability and extensibility of PHP, when the energy consumption monitoring is in progress, the functions can be extended or transferred to other operating systems for maintenance and management according to specific needs. Literature development based on net building energy consumption management system, using M-BUS and industrial Ethernet as the transmission media of energy consumption data, finally realizes the efficient management of energy consumption data (Marinakis and Doukas 2018).
3 Internet of things energy consumption measurement technology

Internet of things technology
The Internet of things has a three-tier architecture, which is the perception layer responsible for perception work, the network layer responsible for transmitting information, and the application layer for processing data transmitted from the network layer. Each layer of the system architecture has corresponding responsibilities and technical fields. Cskip (d) can be calculated from formula (1), that is, the offset when the parent node allocates an address to a node with a network depth of d: If the newly added child node Ak is a router node, the parent node Ap assigns a network address to it by formula (2): If the nth newly added child node an is a terminal node, the parent node Ap allocates a network address to it according to formula (3): Make one-step decision by formula (4): If Eq. (4) is satisfied, it indicates that the destination node is a descendant node of the router node, and the data packet will be sent to its child node N through Eq. (5): If Eq. (4) is not satisfied, it indicates that the destination node is not a descendant node of the router node and sends the packet to its parent node.
If D is the destination node address, Ak (k = 1, 2…) is the neighbor node address, and A' is the nearest address from d to Ak, use Eq. (6) to calculate a': This can save hops and maximize local efficiency. At the same time, when performing the above steps, it is necessary to avoid selecting the nodes that have been selected, so as to save the calculation cost. Calculate the remaining route hops routecost using Eq. (7): The algorithm selects NS-2 as the network simulation platform and adopts IEEE 802.15.4 physical layer and media access control layer protocols. The simulation results are shown in Fig. 1.
As can be seen from Fig. 1, with the increase of the number of nodes, the delay of the original algorithm increases sharply, while the growth trend of the improved algorithm is relatively gentle. Taking node 200 as an example, the delay of the improved algorithm is about 25 ms, while the original algorithm reaches 45 ms.

Energy consumption calculation
In China's construction industry, the measurement of carbon emissions has not yet achieved statistics, and there is no impact scale to evaluate low carbon emissions at different stages of the whole life cycle of buildings. According to the difficulty of obtaining data, carbon footprint calculation methods are generally divided into three categories: one is the actual measurement method, the other is the material balance method, and the third is the emission coefficient method. In the current statistical literature, the emission coefficient method is the most commonly used calculation method.

Measurement method
Measure flow rate, flow rate, concentration, etc. After confirming the data validity of the emission of the target gas through nationally recognized measurement means or other monitoring equipment, the total emission of the target gas is calculated.

Material balance algorithm
According to the law of conservation of mass, estimate the materials used in the construction process. According to the principle that the quality of input materials equals the quality of output materials, it integrates industrial emission source emission, production process and management, comprehensive utilization of resources and raw materials, and environmental management. It is scientific to calculate carbon emissions in the production process.

Emission coefficient method
Coefficient method is the most widely used method to calculate carbon emissions at present. It refers to the calculation method based on the average of carbon dioxide emissions per production unit under normal technical, economic and management conditions. Divide carbon emissions into standard coal method and other energy types, and calculate and summarize the final carbon emissions according to appropriate emission factors. Literature research shows that through various inspections, the calculation results of the standard coal method are more deviated than those of the energy type method, so the energy type method shall prevail.
The calculation of carbon emissions in the whole life cycle of buildings is shown in formula (8): where C L is the total life cycle carbon emission of the building.
The coefficient method used in the construction field is actually a cost budgeting method, which measures carbon emissions according to the determined coefficient.
According to the established carbon emission coefficient, the corresponding activity is obtained, and the carbon emission generated by this part of activities is determined, that is, the basic principle of the carbon emission coefficient method. The calculation formula is shown in formula (9): where C is carbon emission; X is the consumption of carbon emission activities in each period; A is the carbon emission coefficient. According to the model of coefficient method and the above analysis of carbon emission composition, the carbon emission calculation model of high-rise residential buildings is established, as shown in formula (10): where s is I, II, III and IV which are life cycle stages, respectively; X is a, b, d, e and f, which are carbon emission categories; A is the carbon emission coefficient of different carbon emission sources; t is the project t of such carbon emissions in this period. H is the number of floors, 18, 20, 23 and 33, respectively, and the floor height h is 2.8 m, 2.9 m and 3.0 m, respectively. The shape coefficient calculated by formula (11) is shown in Table 1: According to the data, draw the change diagram of shape coefficient on building storey height, as shown in Fig. 2.

Evaluation system for utilization of construction waste resources
To analyze various factors affecting the utilization of construction waste resources, first use various literature to search, first formulate evaluation standards for different resources, and then use Delphi method to solicit opinions on the published index selection. A set of comprehensive evaluation index system was established by the research experts. In this paper, a total of 20 questionnaires were issued (the main respondents were identified as universities, affiliated researchers and enterprise employees), and some main experts were interviewed by telephone or on the spot to collect and analyze secondary indicators to assess the impact factors. Based on the previous comprehensive, scientific, independent and representative research results, including the structure of the paper and the index system for evaluating the utilization of waste resources. According to the questionnaire survey results, the experts are arranged to score, and the weight of each item in the waste resource utilization evaluation index system is calculated and determined by AHP, and the consistency is tested. The comprehensive model of evaluation structure is constructed based on the steps of AHP, as shown in Fig. 3:

Result analysis
As we all know, there are many types of buildings in cities, such as commercial buildings, industrial buildings and campus buildings. The energy consumption proportion of each building is different, and the specific situation is as follows: in commercial buildings, the proportion of air conditioning power consumption is high, accounting for 25 * 40% of the total energy consumption. The main reason is that commercial buildings usually work in the daytime, and generally do not use lighting equipment, so the electricity consumption for lighting is relatively small; in industrial buildings, the proportion of electricity consumption is higher than that of air conditioning and lighting. The main reason is that industrial buildings mainly rely on machinery, generators and other mechanical equipment, and energy consumption belongs to the category of electricity consumption, usually accounting for about 45 * 50%; The campus buildings mainly include the consumption of power, air conditioning, power supply and lighting sockets, which are mainly lighting sockets. Compared with lighting sockets, the air conditioning equipment in the campus office building is relatively small. The proportion of power consumption of lamp holder, air conditioner and power is about 5:3:1.5. Table 2 shows the actual energy consumption data of three sub items of an office building of a university in a province in a week: The experimental prediction of air conditioning power consumption and the comparison between simulation results and actual energy consumption data are shown in Fig. 4.
The blue solid line in the figure represents the actual air conditioning energy consumption data, and the other three solid lines represent the simulation results with impact factors of 0.5, 1.0 and 1.5, respectively. The dotted line represents the prediction simulation results of the linear BP model. Taking the time-series result equal to 3 as an example, the predicted results with impact factors of 0.5, 1.0 and 1.5 are 220.94, 221.40 and 221.91, respectively, and the actual data result is 218.59. Therefore, the smaller the value of the influence factor, the closer the linear regression prediction result is to the actual value. The BP   4 Design of IOT building energy consumption measurement system

System demand analysis
Chinese cities have entered a period of rapid development.
The construction industry and real estate industry are growing at an alarming rate, and the number of urban construction is increasing. Therefore, it is urgent to solve the problem of construction waste management. In recent years, China has attached great importance to the comprehensive utilization of construction waste, formulated laws and regulations on the comprehensive utilization of construction waste, strengthened the strict enforcement of the comprehensive utilization of construction waste, improved the recycling level of construction waste, and constantly improved the comprehensive utilization level of construction waste. This paper analyzes the system requirements of building energy consumption monitoring from the perspective of building waste recycling. The home page of this system requires to display building energy consumption data. In the process of display, combined with the application needs of energy consumption data at the present stage, users can more accurately and clearly see all aspects of energy consumption through energy calculation and consumption data management strategies and then achieve the effect of energy conservation through comparative analysis.
The visualization platform of building energy consumption monitoring system displays statistics of buildings, floors, lighting, air conditioning, electricity, etc. Each data item in the system can set a maximum value. If the collected data exceed the maximum value, the system alarm function will be triggered. The data to be displayed on this page includes: the total power consumption and cost of the project, including the total power consumption and cost of the current day and the current month.
On the power circuit data page, on the one hand, the power consumption data can be refined into power components through the power circuit diagram, so as to monitor the power consumption of the whole building and floor. On the other hand, combining the advantages of traditional power configuration tools, a web configuration software is added, through which the power circuit can be edited according to different items, so that design changes can be easily made.

System structure design
This study takes the energy consumption of large public buildings as the research object, and uses integrated sensors and DSP controllers to collect environmental and energy consumption data such as temperature, lighting, electricity and water in different spaces of facilities. The collected data is sent to the centralized data terminal through GSM and other wireless communication modules, and then the centralized terminal uploads the data to the Internet through the gateway through TCP / IP, and stores it in the database brought to the cloud server. With the browser software on mobile phones and personal computers, you can log in and access dynamic web pages anytime and anywhere and view the information stored in the database in real time. At the same time, managers and operation and maintenance personnel can also adjust various parameter options through the Web terminal. The overall structure of the system is shown in Fig. 5.
The overall structure of the system can be summarized as ''three ends and two bridges''. The former refers to the data collection, concentration and processing end, and the latter refers to the connection between terminals through the Internet of things and the Internet. The system is composed of multiple energy consumption data collection nodes. Each node integrates sensors and wireless devices through a controller to achieve effective collection and upload of energy consumption data. The communication between the data acquisition terminal and the centralized data terminal adopts wireless transmission, so that each node can immediately send the collected data to the centralized data terminal. Using processors with higher processing capacity and cache capacity, the data management terminal and the centralized data terminal use TCP / IP transmission protocol combined with CGI extended program interface to complete reliable communication, receive and store the data uploaded to the centralized terminal through the cloud server, thus saving the cost and space cost required to set up the local server for operation and maintenance.

System function design
The data center receives the point data collected by the data collector and stores it in the database of the energy consumption monitoring system. The first layer of the system is the TCP monitoring layer, which is the entrance of the program into the data center. Each data collector request gets a thread from the thread pool in the data center for processing. The second layer is the basic class layer, which is the foundation of the data center implementation and defines all functions used in the TCP monitoring implementation. The third layer is the data storage layer, which is divided into a real-time data storage area and a backup data storage area to realize data storage and backup.
The data center software is developed using C # programming language, using multi-threading technology, and realizes multi-client thread naming and spatial communication through the reference system. The data center must know the type of each data packet from the collector and give different responses to different data packets. When it receives a heartbeat packet from the data collector, it will respond with a synchronization packet; if receiving a data packet requesting authentication from the collector, responding to a random sequence of data packets; when receiving the MD5 calculated in the data packet and the collector value, compare the received value with the locally calculated value. If the verification is successful, notify the collector to find out whether all points are synchronized. Otherwise, continue to introduce to the next point. After the synchronization is completed, wait for the next data packet to arrive; if the breakpoint recovery is received from the collector, it is also necessary to judge whether the data packet is the last packet. If so, the response value received by all packets will be returned when the breakpoint is resumed.

System test analysis
This kind of statistical analysis mainly shows the status of construction waste in a city for a period of time, focusing on its changes and trends. Intuitively feel the increase and decrease of construction waste inventory in the past six months through the line chart to provide decision support for relevant decision makers. Compared with the bar chart, the line chart shows the trend more intuitively, and the two statistical expression methods are often used in combination. On the one hand, it accurately represents the stock; on the other hand, it also shows the trend. The process of realizing such statistics is calculated at the front end. The database queries the construction waste inventory data of each month during this period. The monthly data and inventory of construction waste are stored in the system. The x-axis represents the month, and the y-axis represents the stock of construction waste. The statistics of construction waste stock in a city are shown in Fig. 6: The system convenience test focuses on power consumption monitoring and management, power consumption control and management, power consumption analysis and management and other modules. The system convenience test is shown in Fig. 7.
When the system has 300 users at the same time, the response time of power consumption monitoring and management is 1620 ms, the response time of power consumption control and management is 1200 ms, and the response time of power consumption monitoring and analysis is 220 ms.

Energy consumption prediction and optimization model
After continuous development, multi-objective optimization problem has gradually become a very important Fig. 5 Overall structure of IOT building energy consumption measurement system problem in the field of mathematics. It is widely used by researchers in various fields to solve various multi-objective optimization problems, usually in the following forms: where F(x) = (f1(x),f2(x),…,fm(x)) is expressed as the objective function corresponding to the M optimization objectives, respectively, and G G(x) = (g1(x), g2 (x),…, gt (x)) is expressed as finally, H(x) = (h1(x),h2(x),…,hs(x)) represents s equality constraints on X.
The establishment of the fuzzy model first needs to obtain the sampling interval of environmental data. Here, a single interval is composed of the maximum and minimum values of a series of data collected on each recording day, which is used to represent the thermal comfort of each recording day, that is, the interval on the i-th day can be expressed by formula (14).
After that, the following statistical quantities of the target interval can be written: Construction waste resource utilization and energy consumption calculation based on Internet… 7575 where x m l , x m r represent the sample mean values of i data intervals of all left and right end points, respectively; sl, sr are the standard deviations of all left and right end points, respectively; tiltir is the value of left and right endpoints in all I data intervals. According to data and literature, the uncertainty of all I data intervals is shown in formula (19), where Dx = Dx(a, c) = ks.
After the uncertainty is obtained, the parameters of the fuzzy model can be obtained by solving the parameter equation, as shown in the following formula.
where m is the average value of the fixed parameters of the fuzzy model. To sum up, the upper and lower membership functions of the fuzzy model are l k (x) and l k (x), respectively, as shown in the following formula: The temperature and humidity data are substituted into the obtained fuzzy model, and the thermal comfort fuzzy model is shown in the following formula.
where t is temperature data and h is humidity data, and then the final optimization target is the global average of the fuzzy model, as shown in formula (26).
After the establishment of the thermal comfort fuzzy model, the initially difficult to quantify data such as temperature and humidity data and energy consumption data can be fully solved by the multi-objective optimization strategy. The set of optimization objective functions is as follows, where f (x) is the power consumption data substituted into the model.
After completing the data processing and establishing the model, the simulation of multi-objective optimization method and fuzzy model is started, and MATLAB is still selected for simulation. Here, the gamultiobj function of Matlab tool is used to simulate the model. And the optimal solution set is obtained by training the model, as shown in Table 3.
It can be concluded from Table 3 that 22.7 * 26.1°C is in a relatively comfortable temperature range and 44.5% * 48.2% is in a relatively comfortable humidity range on the premise of maintaining energy conservation. By analogy with the annual data, the energy consumption value is also at a low level, and the comfort value is positive and high, indicating that the indoor temperature and humidity are in a comfortable and pleasant state.

Resource utilization and development strategy analysis
The resource-based management and supervision of construction waste should pay close attention to updating the management concept, take planning as the guide, strengthen macro-planning control and guidance, and improve the ability to properly control the source of construction waste based on the construction requirements of ecological cities, green cities, sponge cities and waste free cities, improve the collaborative management ability of construction waste, establish the consciousness of multibody cooperation, establish the comprehensive management process and control system of construction waste, and form a reasonable control of construction waste by strengthening the information sharing and communication with the Ministry of housing and urban rural development.
In combination with the rural construction, transportation, environmental protection bureau, Planning Bureau, municipal administration and other departments, we will improve the coordinated management ability in standardizing transportation and comprehensive governance, and continue to develop the comprehensive law enforcement system in cities.
Recycling and efficient treatment of construction waste is one of the main ways to improve the utilization level of construction waste. From the perspective of the impact of construction waste on the environment, the impact of construction waste on the urban environment in the whole life cycle of a construction project is more or less; from the perspective of economic development, the development of comprehensive utilization rate of construction waste will not be achieved overnight. Sufficient preliminary research and planning work shall be carried out, including the source control of construction waste, the formulation of construction waste recycling plan, the promotion of construction waste recycling and the improvement of the quality of construction waste recycling products; from the perspective of administrative management, building an intelligent platform from the generation of construction waste to the control of disposal process not only improves the management ability of waste, effectively controls the occurrence of illegal acts, but also greatly improves the office efficiency of administrative departments.
Further strengthen the connection between waste recycling and full utilization of construction waste. Before the generation of construction waste, analyze the components of existing buildings, clarify the source and final destination of construction waste of different components, and effectively control and reduce the occurrence of illegal collection and disposal of construction waste; Promote the construction of an orderly, safe, sanitary, complete and controllable construction waste collection and transportation system that is classified and standardized at the source; Improve the management level of comprehensive utilization of construction waste, gradually form a municipal level construction waste treatment system with comprehensive planning, reasonable layout, advanced technology and efficient utilization of resources, and form a green and sustainable construction waste recycling industry model with a full treatment chain. We will strive to establish an environmental protection, safety and health control mechanism for the whole process of construction waste generation, and achieve intelligent and information-based control of the whole process of construction waste treatment. Complete resource recovery, and finally form a scientific and reasonable system of urban construction waste utilization and disposal through scientific planning and construction system, realize the scientific comprehensive utilization and disposal of urban construction waste, and greatly improve the level of safe utilization and disposal of urban construction waste. We will improve the quality of urban living environment in an all-round way, promote the construction of pilot cities for comprehensive utilization of construction waste, and promote the construction of ''waste free cities''.
Pre-evaluation and analysis of existing buildings in cities or regions, that is, analysis and evaluation of the life cycle of buildings. Analyze the maintenance, change of use or continued use of the building and the possible construction waste, classify the construction waste according to the recycling possibility, clarify its specific location, classify and register in the electronic file. During demolition, the construction waste shall be recycled according to the intended destination, so as to improve the recycling rate of construction waste and reduce resource waste and environmental pollution caused by unclear and irregular use.

Conclusion
With the development of all fields in today's society and the improvement of people's quality of life, energy consumption is increasing, resulting in a large amount of energy consumption, which is particularly prominent in commercial buildings, campus buildings and other large buildings. In order to implement the national ''13th fiveyear plan'' green building concept, on the basis of summarizing the previous research work, this paper carried out the demand analysis and research of building energy consumption measurement system, and formulated a set of system development and design based on the current Internet of things technology. Build an energy consumption measurement data query and energy consumption trend analysis system, predict energy consumption data by adding BP neural network algorithm, formulate energy conservation measures over time, accelerate energy conservation and emission reduction, and provide technical support for energy conservation and emission reduction. On the one hand, this work can provide a supporting platform for building energy conservation and emission reduction, and on the other hand, it can also respond to the important work of the 13th five-year plan and contribute to the social construction of a green friendly country.
Funding The authors have not disclosed any funding.
Data Availability Data will be made available on request.

Declarations
Conflict of interest The authors declare that they have no conflict of interests.
Ethical approval This article does not contain any studies with human participants performed by any of the authors.