A digital construction site supervision method, system, storage medium and electronic device

Abstract

A digital construction site supervision method, system, storage medium and electronic device, which relates to the technical field of construction site supervision. The method includes: obtaining planned energy consumption data of multiple areas of a target construction site, and a construction association diagram between the areas; obtaining energy consumption data corresponding to each of the areas collected by the preset energy consumption monitoring network; determining the energy consumption intensity corresponding to each energy source in each of the areas according to the energy consumption data and the planned energy consumption data; when there is an abnormal area where any of the energy consumption intensities exceeds the corresponding preset intensity threshold, determining an associated area affected by the abnormal area according to the construction association diagram; determining the warning levels of the abnormal area and the associated area, and generating a warning report for the target construction site based on the warning levels. Implementing the technical solution provided by the present application improves the effectiveness of construction site supervision.

Description

Technical Field

[0001] The present application relates to the technical field of construction site supervision, and particularly relates to a digital construction site supervision method, system, storage medium and electronic device. Background Art

[0002] With the continuous expansion of the scale of construction projects, construction site energy management has become an important part of project management. Construction site energy management is not only related to project cost control, but also an important measure for the construction industry to implement energy conservation and emission reduction policies.

[0003] Currently, construction site energy management mainly adopts the methods of energy consumption monitoring and regular statistical analysis. By deploying energy consumption monitoring devices in each construction area, energy consumption data such as electricity consumption and water consumption are collected, and an energy consumption statistical report is generated regularly. When it is found that the energy consumption in a certain area is abnormal, on-site managers will make judgments and handle it according to experience. Due to the construction process dependencies and resource sharing relationships among construction areas, it is difficult to timely discover and respond to the chain reactions that may be caused by abnormal energy consumption by relying only on the energy consumption data of a single area, resulting in low effectiveness of construction site supervision. Summary of the Invention

[0004] The present application provides a digital construction site supervision method, system, storage medium and electronic device, which improves the effectiveness of construction site supervision.

[0005] In a first aspect, the present application provides a digital construction site supervision method, and the method includes:

[0006] Obtain the planned energy consumption data of multiple areas of the target construction site, and the construction association diagram among the areas;

[0007] Obtain the energy consumption data corresponding to each area collected by the preset energy consumption monitoring network;

[0008] Determine the energy consumption intensity of each energy source in each area according to the energy consumption data and the planned energy consumption data;

[0009] When there is any abnormal area where the energy consumption intensity exceeds the corresponding preset intensity threshold, determine the associated areas affected by the abnormal area according to the construction association diagram;

[0010] Determine the warning levels of the abnormal area and the associated areas, and generate a warning report for the target construction site based on the warning levels.

[0011] By adopting the above technical solution, by obtaining the planned energy consumption data and construction correlation diagrams of multiple areas of the target construction site, and combining with the real-time energy consumption data collected by the energy consumption monitoring network, it is possible to determine the energy consumption intensity corresponding to each energy source in each area. When an abnormal area is found, the associated areas affected by the abnormal area can be determined according to the construction correlation diagram, and then the warning levels of the abnormal area and the associated areas can be determined and a warning report can be generated, thus realizing the systematic supervision of energy consumption anomalies in multiple areas of the construction site. It can not only timely detect the energy consumption abnormal areas, but also accurately identify the associated areas affected by the anomalies, effectively respond to the chain reaction brought by the energy consumption anomalies, and improve the effectiveness of construction site supervision.

[0012] In the second aspect of the present application, a digital construction site supervision system is provided, and the system includes:

[0013] A data acquisition module, configured to acquire the planned energy consumption data of multiple areas of the target construction site, as well as the construction correlation diagrams between the areas; acquire the energy consumption data corresponding to each area collected by the preset energy consumption monitoring network;

[0014] An energy consumption calculation module, configured to determine the energy consumption intensity corresponding to each energy source in each area according to the energy consumption data and the planned energy consumption data;

[0015] An abnormality determination module, configured to, when there is any abnormal area where any of the energy consumption intensities exceeds the corresponding preset intensity threshold, determine the associated areas affected by the abnormal area according to the construction correlation diagram;

[0016] A warning generation module, configured to determine the warning levels of the abnormal area and the associated areas, and generate a warning report for the target construction site based on the warning levels.

[0017] In the third aspect of the present application, a computer storage medium is provided. The computer storage medium stores multiple instructions, and the instructions are adapted to be loaded and executed by a processor to perform the above method steps.

[0018] In the fourth aspect of the present application, an electronic device is provided, including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the above method steps.

[0019] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] By obtaining the planned energy consumption data and construction correlation diagrams of multiple areas of the target construction site and combining with the real-time energy consumption data collected by the energy consumption monitoring network, this application can determine the energy consumption intensity corresponding to each energy source in each area. When an abnormal area is detected, it can determine the associated areas affected by the abnormal area according to the construction correlation diagram, and then determine the warning levels of the abnormal area and the associated areas and generate a warning report, thus realizing the systematic supervision of energy consumption anomalies in multiple areas of the construction site. It can not only detect energy consumption abnormal areas in a timely manner, but also accurately identify the associated areas affected by the anomalies, effectively coping with the chain reaction brought by energy consumption anomalies and improving the effectiveness of construction site supervision. Description of the Drawings

[0021] Figure 1 is a schematic flowchart of a digital construction site supervision method provided by an embodiment of this application;

[0022] Figure 2 is a schematic block diagram of a digital construction site supervision system provided by an embodiment of this application;

[0023] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of this application.

[0024] Description of the reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments.

[0026] In the description of the embodiments of this application, words such as "for example" or "for illustration" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0027] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0029] Please refer to Figure 1 , and a flowchart of a digital construction site supervision method is specifically proposed. This method can be implemented depending on a computer program, can be implemented depending on a single-chip microcomputer, or can run on a digital construction site supervision system. This computer program can be integrated in a computer device or can run as an independent tool-class application. Specifically, this method includes steps 10 to 50, and the above steps are as follows:

[0030] Step 10: Obtain the planned energy consumption data of multiple areas of the target construction site, as well as the construction association diagram between each area.

[0031] Among them, the target construction site in the embodiments of the present application refers to the construction site of a building project that needs to be subject to energy consumption supervision. The construction site is divided into multiple areas with different construction tasks, and there are construction process dependencies and resource sharing relationships between these areas.

[0032] The planned energy consumption data in the embodiments of the present application refers to the expected energy consumption index obtained by allocating energy consumption to each area according to the overall energy consumption limit of the construction site, combined with factors such as the construction task volume and construction difficulty coefficient of each area. This index serves as the reference data for evaluating the energy consumption status of each area.

[0033] The construction association diagram in the embodiments of the present application refers to a topological structure diagram that describes the construction dependencies between each area of the construction site. This diagram reflects the construction time sequence relationship and resource allocation relationship between each area by analyzing the construction process combination of each area and considering the parallelism and mutual exclusivity of the processes.

[0034] Specifically, due to the different construction tasks and complexities in each area of the construction site, it is necessary to reasonably allocate energy consumption indicators and consider the relevance of the construction links. Therefore, first, obtain the overall energy consumption limit of the target construction site and the construction task volume of each area. Among them, the construction task volume includes the project quantity and the construction difficulty coefficient. The project quantity can be specific construction indicators such as the volume of concrete and the amount of steel bars used. The construction difficulty coefficient reflects the complexity of the construction process. Then, use the weighted calculation method to calculate the energy consumption weights of each area. Next, divide the weighted construction task volume of each area by the sum of the weighted construction task volumes of all areas to obtain the energy consumption allocation ratio of each area. Then, multiply the overall energy consumption limit by the energy consumption allocation ratio of each area to obtain the preliminary planned energy consumption data for each area. Obtain the construction process combinations of each area, and generate a construction correlation diagram by analyzing the parallelism and mutual exclusivity between the processes. The parallelism represents the number of processes that can be carried out simultaneously, and the mutual exclusivity means that some processes cannot be carried out simultaneously. According to these process characteristics, construct a construction correlation diagram that reflects the construction dependence relationship between areas. After obtaining the construction correlation diagram, further analyze the construction time sequence relationship of each area, identify and mark the critical path. By calculating the process overlap degree (i.e., the number of processes under construction simultaneously) of each area on the critical path, obtain the energy consumption allocation coefficient for each time sequence node. Finally, correct the preliminary planned energy consumption data for each area according to the energy consumption allocation coefficient to obtain the final planned energy consumption data.

[0035] Based on the above embodiments, as an alternative embodiment, the step of obtaining the planned energy consumption data of multiple areas of the target construction site and the construction correlation diagram between the areas may further include the following steps:

[0036] Step 101: Obtain the overall energy consumption limit of the target construction site and the construction task volume of each area, where the construction task volume includes the project quantity and the construction difficulty coefficient.

[0037] Specifically, obtain the overall energy consumption limit through the construction project management system. This limit is the maximum energy consumption indicator determined according to the construction scale, construction period requirements, and energy conservation goals of the project. For example, a 100,000-square-meter commercial complex project is set at 2 million kWh. At the same time, obtain the specific project quantity data of each area from the project quantity list management module. For example, the foundation construction area includes 5,000 cubic meters of earth excavation volume, 200 tons of steel bar construction volume, and 2,000 cubic meters of concrete pouring volume, etc. And determine the construction difficulty coefficient of each area through the construction difficulty assessment model. For example, the coefficient of the high-altitude operation area is 1.5, the coefficient of the underground construction area is 2.0, and the coefficient of the ordinary area is 1.0.

[0038] Step 102: Calculate the energy consumption weights of each area according to the construction task volume to obtain the energy consumption allocation ratio of each area.

[0039] Specifically, calculate the energy consumption weight for each region by multiplying the standardized project quantity of each region by its corresponding construction difficulty coefficient to obtain the weighted construction task quantity. For example, if the standardized project quantity of a certain region is 1000 units and the construction difficulty coefficient is 1.5, then the weighted construction task quantity of this region is 1500 units. Then, calculate the proportion of the weighted construction task quantity of each region in the total construction task quantity, which is the energy consumption allocation ratio of each region. The specific calculation formula is: Energy consumption allocation ratio of a certain region = Weighted construction task quantity of this region ÷ Sum of weighted construction task quantities of all regions × 100%. For example, if the weighted construction task quantity of a certain region is 1500 units and the sum of weighted construction task quantities of all regions is 10000 units, then the energy consumption allocation ratio of this region is 15%.

[0040] Step 103: Determine the preliminary planned energy consumption data for each region based on the overall energy consumption limit and the energy consumption allocation ratio.

[0041] Specifically, after obtaining the energy consumption allocation ratio of each region, multiply the overall energy consumption limit by the energy consumption allocation ratio of each region to obtain the preliminary planned energy consumption data for each region. For example, if the overall energy consumption limit is 2 million kWh and the energy consumption allocation ratio of a certain region is 15%, then the preliminary planned energy consumption data for this region is 300,000 kWh. Through this calculation method, the system ensures that each region obtains an energy consumption index that matches its construction task quantity and construction difficulty, which not only guarantees the realization of the overall energy consumption control goal of the construction site but also meets the actual construction needs of each region.

[0042] Step 104: Obtain the construction process combinations of each region, and generate a construction correlation diagram based on the parallelism and mutual exclusivity of the processes in the construction process combinations.

[0043] Specifically, in order to accurately reflect the construction dependencies between regions and reasonably adjust the energy consumption allocation, the system first obtains the construction process combinations of each region from the project schedule management system. For example, the process combination of a foundation construction area includes processes such as earth excavation, foundation pit support, steel bar binding, formwork installation, concrete pouring, and curing; while the process combination of an adjacent main structure construction area includes processes such as steel bar binding, formwork installation, concrete pouring, curing, and formwork removal. Analyze the parallelism and mutual exclusivity between processes according to the process characteristics. Parallelism refers to the degree to which processes can be carried out simultaneously, quantified by the time overlap rate. For example, the steel bar binding processes in adjacent areas can be carried out simultaneously, with a parallelism of 100%; while mutual exclusivity represents the constraint relationship that processes cannot be constructed simultaneously. For example, the formwork installation and formwork removal processes in the same area are mutually exclusive, with a mutual exclusivity of 100%. The system stores the parallelism and mutual exclusivity data between processes in a relationship matrix. For example, the process relationship value is set between -1 and 1, where -1 indicates complete mutual exclusivity, 1 indicates complete parallelism, and 0 indicates no direct association. Based on the process relationship matrix, the system constructs a construction association graph. This association graph adopts a directed graph structure, where nodes represent each region, and the connecting lines represent the construction dependencies between regions. The weight value of the connecting line is calculated comprehensively from the parallelism and mutual exclusivity of the relevant processes. For example, when the construction processes of two regions have a high degree of parallelism, the weight value of the connecting line between them is large, indicating that these two regions may have a large energy demand in the same time period; when there is mutual exclusivity between processes, the weight value of the connecting line is negative, indicating that these processes need to stagger the construction time.

[0044] Step 105: Dynamically adjust the preliminary planned energy consumption data according to the construction association graph to obtain the planned energy consumption data for each region.

[0045] Specifically, based on the constructed construction association graph, the critical path analysis method is used to determine the construction time sequence relationship of each region. Specifically, by analyzing the connection relationship and weight between nodes in the construction association graph, the system identifies the longest path from the start to the end of the project and labels it as the critical path. For example, in a certain engineering project, the system identifies the sequence of "foundation construction area → main structure construction area → equipment installation area → decoration construction area" as the critical path, and the construction progress of these areas directly affects the project duration.

[0046] After obtaining the critical path, calculate the process overlap degree of each area on the critical path. When calculating, the system first divides the project duration into several time sequence nodes, such as one week or one month as a time sequence node. Then, for each area on the critical path, count the number of processes that are carried out simultaneously with other areas at each time sequence node. For example, at a certain time sequence node, the main construction area is carrying out three processes: steel bar binding, formwork installation, and concrete pouring. Among them, the steel bar binding and formwork installation are carried out simultaneously with the pipeline embedding process in the adjacent equipment installation area. Then the process overlap degree of this area at this time sequence node is 2.

[0047] Next, based on the process overlap degree, conduct time sequence energy consumption allocation. First, calculate the energy consumption allocation coefficient of each time sequence node. The calculation formula is: Energy consumption allocation coefficient = (1 + process overlap degree × adjustment factor) ÷ total number of time sequence nodes. Among them, the adjustment factor is a parameter determined according to historical construction experience, which is used to adjust the influence degree of process overlap on energy consumption. For example, it is set to 0.15. If the process overlap degree of a certain area at a specific time sequence node is 2, the adjustment factor is 0.15, and the project is divided into 20 time sequence nodes, then the energy consumption allocation coefficient of this time sequence node is (1 + 2 × 0.15) ÷ 20 = 0.065.

[0048] Finally, correct the preliminary planned energy consumption data of each area according to the energy consumption allocation coefficient. During the correction process, allocate the preliminary planned energy consumption data of each area according to the energy consumption allocation coefficient of the time sequence node to obtain the energy consumption index of each time sequence node. For example, the preliminary planned energy consumption of a certain area is 3 million kWh, and the energy consumption allocation coefficient at a certain time sequence node is 0.065. Then the energy consumption index allocated to this time sequence node is 0.195 million kWh. The system summarizes the energy consumption indexes of all time sequence nodes to obtain the final planned energy consumption data of each area.

[0049] Step 20: Obtain the energy consumption data corresponding to each area collected by the preset energy consumption monitoring network.

[0050] The energy consumption monitoring network in this embodiment of the present application refers to a multi-level energy consumption data acquisition system deployed at the construction site. This system is an Internet of Things network composed of electric energy metering devices, water meters, data collectors, communication modules, and a central controller distributed in each construction area.

[0051] The energy consumption data in this embodiment of the present application refers to the data indicators reflecting the consumption of electric energy, water resources, etc. during the construction process collected through the energy consumption monitoring network.

[0052] Specifically, the system collects energy consumption data in real time through a pre-deployed energy consumption monitoring network. This monitoring network installs metering devices such as smart electricity meters and smart water meters at key nodes in each area of the construction site to achieve automatic collection and transmission of energy consumption data. For example, a smart electricity meter is installed in the tower crane distribution box in the main construction area to record the electricity consumption during the operation of the tower crane; a smart water meter is installed in the concrete pouring area to monitor the water consumption for concrete curing; an independent electricity meter is set up in the living and office area to count the daily electricity consumption for office and living.

[0053] The energy consumption data is obtained by using a hierarchical collection method. At the bottom layer, the smart electricity meter records the electricity consumption data every 5 minutes, including parameters such as voltage, current, and power factor; the smart water meter records the water consumption data every hour. The data collector collects the data of these metering devices every 15 minutes through RS485 or wireless communication methods and performs preliminary processing, such as data format conversion and outlier filtering. For example, if the average power of a certain tower crane in a collection cycle is 50 kilowatts and the operating time is 0.25 hours, the electricity consumption for this cycle is calculated to be 12.5 kilowatt-hours. The preliminarily processed data is uploaded to the central controller through the communication module. The communication module uses a 4G wireless network or the construction site local area network to ensure the real-time and reliable data transmission. After receiving the data, the central controller first classifies and stores it according to the construction area. For example, the tower crane electricity consumption data is classified into the main construction area, and the curing water consumption data is classified into the concrete construction area. Then, the system performs time alignment and data completion on the data to handle possible data missing or delay problems during the collection process. Through this hierarchical collection and processing method, the system finally forms the energy consumption data sets for each area.

[0054] Step 30: Determine the energy consumption intensity corresponding to each energy source in each area according to the energy consumption data and the planned energy consumption data.

[0055] Specifically, first, the collected energy consumption data is classified and summarized. Taking the main construction area as an example, the electricity consumption is divided into equipment electricity consumption (such as tower cranes, elevators) and lighting electricity consumption according to usage, and the water resource consumption is divided into construction water consumption (such as concrete curing) and dust suppression water consumption. At the same time, the system obtains basic data such as the construction area and the current construction output value of each area from the project management database. For example, the construction area of a certain main construction area is 2,000 square meters, the construction output value in that month is 1 million yuan, the actual electricity consumption is 10,000 kWh, and the actual water consumption is 200 cubic meters. Calculate the energy consumption intensity indicators for various types of energy. For electricity, calculate the electricity consumption intensity per unit area and the electricity consumption intensity per unit output value. The calculation formulas are: Electricity consumption intensity per unit area = total electricity consumption in the area ÷ construction area; Electricity consumption intensity per unit output value = total electricity consumption in the area ÷ construction output value. For example, the electricity consumption intensity per unit area of the above-mentioned main construction area is 5 kWh / square meter, and the electricity consumption intensity per unit output value is 100 kWh / 10,000 yuan. Similarly, calculate the water usage intensity of water resources, and obtain the water consumption intensity per unit area of 0.1 cubic meter / square meter and the water consumption intensity per unit output value of 2 cubic meters / 10,000 yuan.

[0056] Based on the planned energy consumption data, the system calculates the planned energy consumption intensity indicators using the same method. For example, if the planned electricity consumption in this area is 9,000 kWh and the planned water consumption is 180 cubic meters, then the planned electricity consumption intensity per unit area is 4.5 kWh / square meter, and the planned electricity consumption intensity per unit output value is 90 kWh / 10,000 yuan; the planned water consumption intensity per unit area is 0.09 cubic meter / square meter, and the planned water consumption intensity per unit output value is 1.8 cubic meters / 10,000 yuan.

[0057] On the basis of the above embodiments, as an optional embodiment, the step of determining the energy consumption intensity corresponding to each energy in each area according to the energy consumption data and the planned energy consumption data may further include the following steps:

[0058] Step 301: Obtain the real-time energy consumption data of each energy in each area within a preset time window in the energy consumption data, where the length of the time window is determined according to the duration of the process.

[0059] Specifically, in order to more accurately reflect the dynamic change characteristics of energy use during the construction process, the system needs to perform time series processing and analysis on the real-time collected energy consumption data. First, according to the characteristics of the ongoing processes in each area, the appropriate time window length is determined. For example, for the concrete pouring process, considering that its duration is usually 4 - 6 hours, the system sets a 6-hour time window; for processes with longer durations such as steel bar binding, an 8-hour or longer time window may be set. This time window setting based on process characteristics can better capture the energy consumption change laws of different construction activities. After determining the time window, the real-time energy consumption data for the corresponding period is extracted from the energy consumption database. Taking the main construction area as an example, the system collects electricity consumption data from the smart electricity meter and water consumption data from the smart water meter every 5 minutes.

[0060] Step 302: Perform a moving average process on the real-time energy consumption data within the time window to obtain the average energy consumption value corresponding to each energy source in each area.

[0061] Specifically, in order to eliminate the influence of data fluctuations on the analysis results, the system performs a moving average process on the real-time data within the time window. Specifically, for the 6-hour time window, the processing process is as follows: First, the time window is divided into 72 sampling points (6 hours × 12 sampling points / hour); then, the average value of each sampling point and the 5 sampling points before and after it (a total of 11 points) is calculated as the average energy consumption value at that moment.

[0062] Step 303: Based on the average energy consumption value corresponding to each energy source and the planned energy consumption data for the corresponding time period, determine the energy consumption intensity corresponding to each energy source in each area.

[0063] Specifically, obtain the average energy consumption values processed by moving average and the planned energy consumption data for the corresponding time periods from the database. Taking the main construction area as an example, within a 6-hour time window, the average power consumption of the tower crane is 300 kWh, the average power consumption of lighting is 50 kWh, and the average water consumption for construction is 15 cubic meters; the corresponding planned data are: the planned power consumption of the tower crane is 280 kWh, the planned power consumption of lighting is 45 kWh, and the planned water consumption is 14 cubic meters. At the same time, the system obtains the real-time construction parameters of this area, such as the construction area of 500 square meters and the current construction output value of 200,000 yuan. Then calculate the energy consumption intensity indicators for electric energy and water resources respectively. For electric energy consumption, the calculation includes two dimensions: the electricity intensity per unit area and the electricity intensity per unit output value. Among them, the formula for the electricity intensity per unit area is: actual electricity intensity per unit area = (average power consumption of tower crane + average power consumption of lighting) ÷ construction area. For example, the actual electricity intensity per unit area is (300 + 50) ÷ 500 = 0.7 kWh / square meter. Similarly, the planned electricity intensity per unit area is (280 + 45) ÷ 500 = 0.65 kWh / square meter. The formula for the electricity intensity per unit output value is: actual electricity intensity per unit output value = (average power consumption of tower crane + average power consumption of lighting) ÷ construction output value, and the calculated actual value is 17.5 kWh / 10,000 yuan, and the planned value is 16.25 kWh / 10,000 yuan. For water resource consumption, use the same method to calculate the water intensity per unit area and the water intensity per unit output value.

[0064] Step 40: When there is any abnormal area where any energy consumption intensity exceeds the corresponding preset intensity threshold, determine the associated areas affected by the abnormal area according to the construction association diagram.

[0065] Specifically, when there is any abnormal area where the energy consumption intensity exceeds the corresponding preset intensity threshold. For example, when the concrete curing water intensity is detected as 0.035 cubic meters per square meter in the main construction area, exceeding the preset intensity threshold of 0.03 cubic meters per square meter, the system marks this area as an abnormal area. Subsequently, the pre-established construction correlation graph is read. This correlation graph is stored in the form of an adjacency matrix, recording the process dependency relationships and correlation intensities between regions. For example, there is a process adjacency relationship between the concrete curing in the main construction area and the waterproof construction area, with a correlation intensity of 0.8; there is a process connection relationship with the decoration construction area, with a correlation intensity of 0.7; and there is a resource sharing relationship with the material curing area, with a correlation intensity of 0.6. Using the depth-first search algorithm, starting from the abnormal area, the construction correlation graph is traversed to identify all possible affected associated areas. During the search process, the system first examines the first-level associated areas directly connected to the abnormal area. When the correlation intensity exceeds the preset threshold (such as 0.5), this area is included in the set of affected areas. For example, since the correlation intensity between the waterproof construction area and the main construction area is 0.8, exceeding the threshold of 0.5, and the non-compliance of concrete curing will directly affect the construction quality of the waterproof layer, the system identifies the waterproof construction area as an affected area. For the identified first-level associated areas, the system continues to analyze their correlation relationships with other areas to identify second-level affected areas. For example, the correlation intensity between the waterproof construction area and the exterior wall construction area is 0.7, exceeding the threshold, so the exterior wall construction area is identified as a second-level affected area. This recursive search process continues until all areas with a correlation intensity greater than the threshold are identified, obtaining the associated areas affected by the abnormal area.

[0066] Based on the above embodiments, as an alternative embodiment, the step of determining the associated areas affected by the abnormal area according to the construction correlation graph may further include the following steps:

[0067] Step 401: Construct a regional correlation matrix based on the construction correlation graph, where each matrix element in the correlation matrix represents the degree of association between regions.

[0068] Specifically, the system constructs an n×n regional correlation matrix R based on the construction correlation graph, where n is the total number of construction areas. The element r ij in the matrix represents the degree of association between region i and region j, and its value range is [0,1]. The determination of the degree of association comprehensively considers factors such as process connection relationships, resource sharing degrees, and spatial position relationships. For example, for a construction site including a main construction area, a waterproof construction area, a decoration construction area, and a material curing area, the constructed 4×4 correlation matrix is as follows: The degree of association r 12 between region 1 (main construction) and region 2 (waterproof construction) = 0.8, indicating that the two regions have a close process connection; the degree of association r between region 1 and region 3 (decoration construction)13 = 0.6, indicating the existence of process dependencies but with a relatively large time span; the degree of association r between Region 1 and Region 4 (material curing) 14 = 0.4, indicating the existence of mainly resource sharing relationships.

[0069] Step 402: Starting from the abnormal region, traverse the region association matrix using the breadth - first search method to determine the primary associated regions directly associated with the abnormal region.

[0070] Specifically, when the system detects an abnormal water usage intensity in the main construction region, it traverses the region association matrix using the breadth - first search algorithm to identify the primary associated regions directly connected to the abnormal region. Specifically, the system checks all non - zero elements in the row of the abnormal region in the association matrix and records the corresponding column indices as the primary associated regions. For example, by checking the first row of the matrix, the regions directly associated with the main construction region include: the waterproof construction region (r 12 = 0.8), the decoration construction region (r 13 = 0.6), and the material curing region (r 14 = 0.4).

[0071] Step 403: Calculate the impact conduction coefficient of the primary associated regions, where the impact conduction coefficient increases with the increase in the degree of association.

[0072] Specifically, for the identified primary associated regions, the system calculates their impact conduction coefficient λ. The calculation formula for the impact conduction coefficient is: λ = k×r, where k is the conduction coefficient reference value (with a value of 1.2) and r is the degree of association. This calculation method ensures that the greater the degree of association, the stronger the impact conduction effect. For example, the impact conduction coefficient of the waterproof construction region λ1 = 1.2×0.8 = 0.96, the impact conduction coefficient of the decoration construction region λ2 = 1.2×0.6 = 0.72, and the impact conduction coefficient of the material curing region λ3 = 1.2×0.4 = 0.48.

[0073] Step 404: Determine the affected associated regions as those primary associated regions whose impact conduction coefficients are greater than the preset threshold.

[0074] Specifically, compare the calculated impact conduction coefficient with the preset threshold (such as 0.6) to determine the final affected associated regions. When the impact conduction coefficient is greater than the preset threshold, include this region in the set of affected regions. In this example, the waterproof construction region (λ1 = 0.96 > 0.6) and the decoration construction region (λ2 = 0.72 > 0.6) are determined as the affected associated regions, while the material curing region (λ3 = 0.48 < 0.6) is not included in the set of affected associated regions.

[0075] Step 50: Determine the warning levels of the abnormal area and the associated areas, and generate a warning report for the target construction site based on the warning levels.

[0076] Specifically, the system first calculates the energy consumption intensity deviation rate δ of the abnormal area. The calculation formula is: δ = (actual energy consumption intensity - preset threshold) / preset threshold × 100%. Based on the magnitude of the deviation rate, the warning levels of the abnormal area are divided into three levels: when the deviation rate δ ≤ 10%, it is a level-three warning (yellow warning); when 10% < δ ≤ 20%, it is a level-two warning (orange warning); when δ > 20%, it is a level-one warning (red warning). In this example, the deviation rate of the main construction area is 16.7%, so it is determined as a level-two warning. For the affected associated areas, the system determines their warning levels based on the impact conduction coefficient λ calculated previously. The criteria for determining the warning levels are: when 0.6 ≤ λ < 0.8, it is a level-three warning; when 0.8 ≤ λ < 1.0, it is a level-two warning; when λ ≥ 1.0, it is a level-one warning. After determining the warning levels of each area, the system automatically generates a warning report. The warning report includes the following main contents: First, it is the basic information of the warning time and the warning area, such as "At 10:00 on December 30, 2024, there was an abnormal water consumption intensity in the main construction area"; second, it is the specific situation of the abnormal area, including the actual water consumption intensity (0.035 cubic meters per square meter), the preset threshold (0.03 cubic meters per square meter), the deviation rate (16.7%), and the warning level (level-two warning); then, it is the analysis results of the affected associated areas, such as "The waterproof construction area (impact conduction coefficient 0.96, level-two warning) may cause a delay in the waterproof construction period", "The decoration construction area (impact conduction coefficient 0.72, level-three warning) needs to appropriately adjust the construction plan".

[0077] Based on the above embodiments, as an alternative embodiment, the step of determining the warning levels of the abnormal area and the associated areas and generating a warning report for the target construction site may further include the following steps:

[0078] Step 501: Calculate the energy consumption overrun indices of the abnormal area and the associated areas respectively, where the energy consumption overrun index is determined based on the deviation between the energy consumption intensity and the corresponding intensity threshold.

[0079] Specifically, calculate the energy consumption over - standard index σ of the abnormal area and the associated area. The calculation formula is: σ = w×(E - E0) / E0, where E is the actual energy consumption intensity, E0 is the preset intensity threshold, and w is the energy consumption type weight coefficient. For example, when the actual water consumption intensity in the main construction area is 0.035 cubic meters per square meter, the preset threshold is 0.03 cubic meters per square meter, and the water consumption weight coefficient is 1.2, its energy consumption over - standard index σ1 = 1.2×(0.035 - 0.03) / 0.03 = 0.2. For the associated area, it is also necessary to consider the attenuation effect of the influence conduction coefficient λ on the over - standard index, that is, σ2 = σ1×λ. For example, the influence conduction coefficient of the waterproof construction area is 0.96, then its energy consumption over - standard index is 0.2×0.96 = 0.192.

[0080] Step 502: Based on the positions of the abnormal area and the associated area in the construction association diagram, determine the area importance degrees of the abnormal area and the associated area respectively. The area importance degree increases as the number of paths connecting other areas increases.

[0081] Specifically, analyze the importance degree α of each area based on the construction association diagram. The importance degree calculation uses an improved PageRank algorithm, which considers the number of connection paths with other areas and the path strength. The calculation formula is: α=(1 - d)+d×Σ(αj×rji / Σrjk), where d is the damping coefficient (with a value of 0.85), rji is the association degree from area j to area i, and αj is the importance degree of the associated area j. For example, the main construction area is connected to multiple areas such as waterproof construction and decoration construction. The total number of its paths is 5, and the weighted average of the association strength is 0.75. The calculated importance degree α1 is 0.82; while the waterproof construction area has fewer connection paths, and the calculated importance degree α2 is 0.65.

[0082] Step 503: Determine the corresponding warning scores according to the energy consumption over - standard indexes and area importance degrees of the abnormal area and the associated area respectively, and map the warning scores to the corresponding warning levels based on the preset score intervals.

[0083] Specifically, calculate the warning score S according to the energy consumption over - standard index and the area importance degree. The calculation formula is: S = β1×σ+β2×α, where β1 and β2 are the weight coefficients of the over - standard index and the importance degree respectively (both take 0.5). For example, the warning score S1 of the main construction area is 0.5×0.2 + 0.5×0.82 = 0.51; the warning score S2 of the waterproof construction area is 0.5×0.192+0.5×0.65 = 0.421. The system compares the calculated warning score with the preset score interval: when S≥0.5, it is a first - level warning; when 0.3≤S<0.5, it is a second - level warning; when S<0.3, it is a third - level warning. Accordingly, the main construction area is determined to be a first - level warning, and the waterproof construction area is determined to be a second - level warning.

[0084] Step 504: Generate a warning report including risk assessment and improvement suggestions based on the warning levels and historical warning data corresponding to the abnormal area and the associated area.

[0085] Specifically, combining the warning levels and historical warning data, generate a warning report. The report first lists the specific warning information for each area, including quantitative indicators such as warning levels, energy consumption over - standard indices, and area importance. For example, "Main construction area: Level - 1 warning (red), energy consumption over - standard index 0.2, area importance 0.82, warning score 0.51". Then, the system generates a risk assessment result by analyzing the disposal experience of similar cases in the historical warning database. Based on the risk assessment result, the system further generates improvement suggestions, such as "Suggestions: 1. Immediately check the water supply system in the main construction area and investigate the abnormal reasons; 2. Optimize the process arrangement in the waterproof construction area and appropriately adjust the construction plan; 3. Strengthen on - site water use monitoring and establish a water use efficiency assessment mechanism". These suggestions include both immediate response measures and long - term improvement strategies.

[0086] Based on the above - mentioned embodiments, as an alternative embodiment, a digital construction site supervision method may further include the following process:

[0087] Specifically, during the abnormal handling process, the system collects real - time processing data, including information such as the abnormal occurrence time, abnormal area, abnormal type, initial energy consumption intensity, taken handling measures, and energy consumption intensity after handling. For example, when there is an abnormal water use intensity in the main construction area, record the abnormal type as "excessive curing water", the initial water use intensity as 0.035 cubic meters per square meter, and the taken handling measures include "optimize the parameter settings of the spraying system" and "adjust the curing time allocation". After handling, the water use intensity drops to 0.028 cubic meters per square meter. At the same time, the system records auxiliary information such as the cost input and time consumption during the handling process to form a complete handling record.

[0088] Calculate the effectiveness coefficient μ of the handling measure according to the change in energy consumption intensity before and after handling. The calculation formula is: μ = k×(E1 - E2) / E1×(T0 / T), where E1 is the energy consumption intensity before handling, E2 is the energy consumption intensity after handling, T0 is the standard handling time, T is the actual handling time, and k is the cost adjustment coefficient. For example, for the handling measure of optimizing the spraying system, E1 = 0.035, E2 = 0.028, T0 = 4 hours, T = 5 hours, k = 1.2, then the effectiveness coefficient μ1 = 1.2×(0.035 - 0.028) / 0.035×(4 / 5)=0.192. For the measure of adjusting the curing time, calculate the effectiveness coefficient μ2 = 0.168 in the same way.

[0089] Based on the calculated effectiveness coefficients, the system sorts the measures in the processing record library. First, it classifies them by exception type. For example, all the processing measures for the type of "excessive conservation water use" are grouped together. Then, within each category, the processing measures are sorted in descending order according to the size of the effectiveness coefficients to generate an optimal set of processing solutions. For instance, for the type of excessive conservation water use, the sorted processing solutions are: optimizing the sprinkler system (effectiveness coefficient 0.192), adjusting the conservation time (effectiveness coefficient 0.168), replacing water-saving equipment (effectiveness coefficient 0.145), etc. When the system detects a new abnormal area, it first identifies its abnormal type, and then selects the processing measure with the highest effectiveness coefficient from the corresponding optimal set of processing solutions. After implementing the processing measure, the system continues to collect data on the processing effect and updates the processing record library. If the new processing effect is better than the historical record, the ranking of this measure in the optimal set of processing solutions is improved; if the effect fails to meet expectations, its ranking position is lowered.

[0090] Please refer to Figure 2 , which is a schematic diagram of the modules of a digital construction site supervision system provided by an embodiment of the present application. The system includes:

[0091] A data acquisition module, configured to acquire the planned energy consumption data of multiple areas of the target construction site, as well as the construction association diagram between the areas; acquire the energy consumption data corresponding to each area collected by the preset energy consumption monitoring network;

[0092] An energy consumption calculation module, configured to determine the energy consumption intensity corresponding to each energy source in each area according to the energy consumption data and the planned energy consumption data;

[0093] An abnormality determination module, configured to, when there is any abnormal area where the energy consumption intensity exceeds the corresponding preset intensity threshold, determine the associated areas affected by the abnormal area according to the construction association diagram;

[0094] An early warning generation module, configured to determine the early warning levels of the abnormal area and the associated areas, and generate an early warning report for the target construction site based on the early warning levels.

[0095] Optionally, the data acquisition module is further configured to acquire the overall energy consumption limit of the target construction site and the construction task amounts of each area, where the construction task amount includes the engineering quantity and the construction difficulty coefficient;

[0096] Calculate the energy consumption weight of each area according to the construction task amount to obtain the energy consumption allocation ratio of each area;

[0097] Based on the overall energy consumption limit and the energy consumption allocation ratio, determine the preliminary planned energy consumption data of each area;

[0098] Obtain the construction process combinations of each of the said regions, and generate the construction correlation graph according to the parallelism and mutual exclusivity of the processes in the construction process combinations;

[0099] Dynamically adjust the preliminary planned energy consumption data according to the construction correlation graph to obtain the planned energy consumption data of each of the said regions.

[0100] Optionally, the data acquisition module is further configured to determine the construction time sequence relationship of each of the said regions according to the construction correlation graph, and identify the critical path in the construction time sequence relationship;

[0101] Calculate the process overlap degree of each of the said regions on the critical path, where the process overlap degree represents the number of processes under construction simultaneously;

[0102] Perform time sequence allocation on the preliminary planned energy consumption data based on the process overlap degree to obtain the energy consumption allocation coefficients of each time sequence node;

[0103] Correct the preliminary planned energy consumption data of each of the said regions according to the energy consumption allocation coefficients to obtain the planned energy consumption data.

[0104] Optionally, the energy consumption calculation module is further configured to obtain the real-time energy consumption data of each energy in each of the said regions within a preset time window in the energy consumption data, where the length of the time window is determined according to the process duration;

[0105] Perform a moving average process on the real-time energy consumption data within the time window to obtain the average energy consumption value corresponding to each energy in each of the said regions;

[0106] Determine the energy consumption intensity corresponding to each energy in each of the said regions based on the average energy consumption value corresponding to each energy and the planned energy consumption data of the corresponding time period.

[0107] Optionally, the anomaly determination module is further configured to construct a regional correlation matrix based on the construction correlation graph, where each matrix element in the correlation matrix represents the degree of association between regions;

[0108] Starting from the abnormal region, traverse the regional correlation matrix in a breadth-first search manner to determine the first-level associated regions directly associated with the abnormal region;

[0109] Calculate the influence conduction coefficient of the first-level associated regions, where the influence conduction coefficient increases as the degree of association increases;

[0110] Determine the affected associated regions as the first-level associated regions corresponding to the influence conduction coefficient greater than a preset threshold.

[0111] Optionally, the early warning generation module is further configured to calculate the energy consumption over - standard indexes of the abnormal area and the associated area respectively, where the energy consumption over - standard index is determined based on the deviation between the energy consumption intensity and the corresponding intensity threshold;

[0112] Based on the positions of the abnormal area and the associated area in the construction association diagram, determine the area importance degrees of the abnormal area and the associated area respectively, where the area importance degree increases as the number of paths connecting other areas increases;

[0113] Determine the corresponding early warning scores according to the energy consumption over - standard indexes and area importance degrees of the abnormal area and the associated area respectively, and map the early warning scores to the corresponding early warning levels based on a preset score interval;

[0114] Generate an early warning report including risk assessment and improvement suggestions according to the early warning levels corresponding to the abnormal area and the associated area and historical early warning data.

[0115] Optionally, the early warning generation module is further configured to collect the energy consumption abnormal treatment measures and treatment effect data of the abnormal area, and establish a treatment record library including abnormal types, treatment measures and effect scores;

[0116] Calculate the effect coefficients of various treatment measures based on the treatment record library, where the effect coefficient increases as the reduction degree of the energy consumption intensity after treatment increases;

[0117] Sort the treatment measures in the treatment record library according to the effect coefficients, and generate an optimal treatment plan set for different abnormal types;

[0118] When a new abnormal area appears, select treatment measures from the optimal treatment plan set according to its abnormal type, and update the treatment record library.

[0119] It should be noted that: when the system provided in the above - mentioned embodiment realizes its functions, only the division of the above - mentioned functional modules is used for illustration. In actual application, the above - mentioned functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above - mentioned embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0120] The embodiment of the present application also provides a computer storage medium. The computer storage medium can store multiple instructions, and the instructions are suitable for being loaded and executed by a processor to perform a digital construction site supervision method in the above - mentioned embodiment. The specific execution process can refer to the specific description in the above - mentioned embodiment, and will not be elaborated here.

[0121] Please refer toFigure 3 The present application also discloses an electronic device. Figure 3 Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0122] Among them, the communication bus 302 is used to implement connection communication between these components.

[0123] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0124] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0125] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305, the processor 301 executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one of the following hardware forms: Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 301 may integrate one or several combinations of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0126] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a digital construction site supervision method.

[0127] In Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user to obtain the data input by the user; and the processor 301 can be used to call the application program of a digital construction site supervision method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 is caused to execute the method described in one or more of the above embodiments. It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0128] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] In several implementation manners provided in this application, it should be understood that the disclosed device can be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division manners. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0130] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, each functional unit in various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of this application. And the aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0133] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, all equivalent changes and modifications made according to the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and the practice of the present disclosure.

[0134] This application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A digital construction site supervision method, characterized in that, The method includes: Obtaining the overall energy consumption limit of the target construction site and the construction task volumes of each area, where the construction task volume includes the engineering quantity and the construction difficulty coefficient; Calculating the energy consumption weight of each area according to the construction task volume to obtain the energy consumption allocation ratio of each area; Based on the overall energy consumption limit and the energy consumption allocation ratio, determining the preliminary planned energy consumption data of each area; Obtaining the construction process combinations of each area, and generating a construction correlation graph according to the parallelism and mutual exclusivity of the processes in the construction process combination; Dynamically adjusting the preliminary planned energy consumption data according to the construction correlation graph to obtain the planned energy consumption data of each area; Obtaining the energy consumption data corresponding to each area collected by a preset energy consumption monitoring network; Determining the energy consumption intensity corresponding to each energy source in each area according to the energy consumption data and the planned energy consumption data; When there is an abnormal area where any of the energy consumption intensities exceeds the corresponding preset intensity threshold, determining the associated areas affected by the abnormal area according to the construction correlation graph; Determining the warning levels of the abnormal area and the associated areas, and generating a warning report for the target construction site based on the warning levels.

2. The digital construction site supervision method according to claim 1, characterized in that The dynamically adjusting the preliminary planned energy consumption data according to the construction correlation graph to obtain the planned energy consumption data of each area includes: Determining the construction time sequence relationship of each area according to the construction correlation graph, and marking the critical path in the construction time sequence relationship; Calculating the process overlap degree of each area on the critical path, where the process overlap degree represents the number of processes under simultaneous construction; Performing time sequence allocation on the preliminary planned energy consumption data based on the process overlap degree to obtain the energy consumption allocation coefficient of each time sequence node; Correcting the preliminary planned energy consumption data of each area according to the energy consumption allocation coefficient to obtain the planned energy consumption data.

3. The digital construction site supervision method according to claim 1, characterized in that The determining the energy consumption intensity corresponding to each energy source in each area according to the energy consumption data and the planned energy consumption data includes: Obtaining the real-time energy consumption data of each energy source in each area within a preset time window in the energy consumption data, where the length of the time window is determined according to the process duration; Performing a moving average process on the real-time energy consumption data within the time window to obtain the average energy consumption value corresponding to each energy source in each area; Based on the average energy consumption value corresponding to each energy source and the planned energy consumption data for the corresponding time period, determining the energy consumption intensity corresponding to each energy source in each area.

4. The digital construction site supervision method according to claim 1, characterized in that, The determining the associated areas affected by the abnormal area according to the construction correlation graph includes: Constructing a regional association matrix based on the construction correlation graph, where each matrix element in the association matrix represents the degree of association between regions; Starting from the abnormal area, traversing the regional association matrix in a breadth-first search manner to determine the first-level associated areas directly associated with the abnormal area; Calculating the influence conduction coefficient of the first-level associated areas, where the influence conduction coefficient increases as the degree of association increases; Determining the first-level associated areas corresponding to the influence conduction coefficient greater than the preset threshold as the affected associated areas.

5. The digital construction site supervision method according to claim 1, characterized in that Determining the early warning levels of the abnormal area and the associated area, and generating an early warning report for the target construction site based on the early warning levels, including: Calculating the energy consumption over - standard indexes of the abnormal area and the associated area respectively, where the energy consumption over - standard index is determined based on the deviation between the energy consumption intensity and the corresponding intensity threshold; Based on the positions of the abnormal area and the associated area in the construction association diagram, determining the regional importance of the abnormal area and the associated area respectively, where the regional importance increases as the number of paths connecting other areas increases; Determining the corresponding early warning scores according to the energy consumption over - standard indexes and regional importance of the abnormal area and the associated area respectively, and mapping the early warning scores to the corresponding early warning levels based on a preset score interval; Generating an early warning report including risk assessment and improvement suggestions according to the early warning levels corresponding to the abnormal area and the associated area and historical early warning data.

6. The digital construction site supervision method according to claim 1, characterized in that The method further includes: Collecting the energy consumption abnormal treatment measures and treatment effect data of the abnormal area, and establishing a treatment record library including abnormal types, treatment measures and effect scores; Calculating the effect coefficients of various treatment measures based on the treatment record library, where the effect coefficient increases as the reduction degree of the energy consumption intensity after treatment increases; Sorting the treatment measures in the treatment record library according to the effect coefficients, and generating an optimal treatment plan set for different abnormal types; When a new abnormal area appears, selecting treatment measures from the optimal treatment plan set according to its abnormal type, and updating the treatment record library.

7. A digital construction site supervision system, characterized in that, The system includes: A data acquisition module, configured to obtain the overall energy consumption limit of the target construction site and the construction task amounts of each area, where the construction task amount includes the engineering quantity and the construction difficulty coefficient; calculating the energy consumption weight of each area according to the construction task amount to obtain the energy consumption allocation ratio of each area; determining the preliminary planned energy consumption data of each area based on the overall energy consumption limit and the energy consumption allocation ratio; obtaining the construction process combinations of each area, and generating a construction association diagram according to the parallelism and mutual exclusivity of the processes in the construction process combination; dynamically adjusting the preliminary planned energy consumption data according to the construction association diagram to obtain the planned energy consumption data of each area; obtaining the energy consumption data corresponding to each area collected by a preset energy consumption monitoring network; An energy consumption calculation module, configured to determine the energy consumption intensity of each energy source in each area according to the energy consumption data and the planned energy consumption data; An abnormality determination module, configured to, when there is an abnormal area where any of the energy consumption intensities exceeds the corresponding preset intensity threshold, determine the associated area affected by the abnormal area according to the construction association diagram; An early warning generation module, configured to determine the early warning levels of the abnormal area and the associated area, and generate an early warning report for the target construction site based on the early warning levels.

8. A computer-readable storage medium, characterized in that, The computer - readable storage medium stores multiple instructions, and the instructions are suitable for being loaded and executed by a processor to perform the method according to any one of claims 1 - 6.

9. An electronic device, characterized in that, It includes a processor, a memory, a user interface and a network interface. The memory is used for storing instructions. The user interface and the network interface are used for communicating with other devices. The processor is used for executing the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1-6.

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