Digital construction site supervision method and system, storage medium and electronic equipment

Abstract

The invention discloses a digital construction site supervision method and system, a storage medium and electronic equipment, and relates to the technical field of construction site supervision. The method comprises the following steps: acquiring planned energy consumption data of a plurality of areas of a target construction site and a construction association diagram between the areas; acquiring energy consumption data corresponding to each area acquired by the preset energy consumption monitoring network; determining energy consumption intensity corresponding to each energy source in each region according to the energy consumption data and the planned energy consumption data; when any abnormal area of which the energy consumption intensity exceeds a corresponding preset intensity threshold exists, determining an association area influenced by the abnormal area according to the construction association diagram; and determining early warning levels of the abnormal region and the associated region, and generating an early warning report of the target construction site based on the early warning levels. By implementing the technical scheme provided by the invention, the effectiveness of construction site supervision is improved.

Description

Technical Field

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

[0002] As the scale of construction projects continues to expand, 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] At present, construction site energy management mainly adopts the methods of energy consumption monitoring and regular statistical analysis. By deploying energy consumption monitoring equipment in each construction area, energy consumption data such as electricity consumption and water consumption are collected, and energy consumption statistical reports are generated regularly. When abnormal energy consumption is found in a certain area, the on-site management personnel will deal with it based on experience. Due to the dependence of construction processes and resource sharing between construction areas, it is difficult to timely discover and respond to the chain reaction that may be caused by abnormal energy consumption if only the energy consumption data of a single area is relied on for management, resulting in low effectiveness of supervision on the construction site. Summary of the invention

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

[0005] In a first aspect, the present application provides a digital construction site supervision method, the method comprising: Obtaining planned energy consumption data for multiple areas of a target construction site, and a construction correlation diagram between the areas; Obtaining energy consumption data corresponding to each of the areas collected by the preset energy consumption monitoring network; Determine 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 any abnormal area whose energy consumption intensity exceeds the corresponding preset intensity threshold, determining the associated area affected by the abnormal area according to the construction association diagram; Determine the warning levels of the abnormal area and the associated area, and generate a warning report for the target construction site based on the warning levels.

[0006] By adopting the above technical solution, by obtaining the planned energy consumption data and construction association diagram of multiple areas of the target construction site, combined with the real-time energy consumption data collected by the energy consumption monitoring network, the energy consumption intensity corresponding to each energy in each area can be determined, and when an abnormal area is found, the associated area affected by the abnormal area is determined according to the construction association diagram, and then the warning level of the abnormal area and the associated area is determined and a warning report is generated, thereby realizing systematic supervision of energy consumption anomalies in multiple areas of the construction site, which can not only timely discover energy consumption abnormal areas, but also accurately identify the associated areas affected by the anomalies, effectively respond to the chain reaction caused by energy consumption anomalies, and improve the effectiveness of construction site supervision.

[0007] In a second aspect of the present application, a digital construction site supervision system is provided, the system comprising: A data acquisition module is used to acquire planned energy consumption data of multiple areas of the target construction site, as well as a construction association diagram between the areas; and to acquire energy consumption data corresponding to the areas collected by the preset energy consumption monitoring network; An energy consumption calculation module, used to determine 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; An abnormality determination module, used for determining, when there is any abnormal area whose energy consumption intensity exceeds the corresponding preset intensity threshold, the associated area affected by the abnormal area according to the construction association diagram; The warning generation module is used to determine the warning levels of the abnormal area and the associated area, and generate a warning report for the target construction site based on the warning levels.

[0008] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: This application obtains the planned energy consumption data and construction correlation diagrams of multiple areas in the target construction site, and combines them with the real-time energy consumption data collected by the energy consumption monitoring network to determine the energy consumption intensity corresponding to each energy in each area. When an abnormal area is found, the associated area affected by the abnormal area is determined according to the construction correlation diagram, and then the warning level of the abnormal area and the associated area is determined and a warning report is generated, thereby realizing systematic supervision of energy consumption anomalies in multiple areas of the construction site. It can not only detect abnormal energy consumption areas in a timely manner, but also accurately identify the associated areas affected by the anomalies, effectively respond to the chain reaction caused by abnormal energy consumption, and improve the effectiveness of construction site supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flowchart of a digital construction site supervision method provided in an embodiment of the present application; Figure 2 It is a module schematic diagram of a digital construction site supervision system provided by an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0012] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0013] In order to enable technicians in this field 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 drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0015] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0016] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0017] Please refer to Figure 1 , a flowchart of a digital construction site supervision method is proposed. The method can be implemented by a computer program, a single-chip microcomputer, or a digital construction site supervision system. The computer program can be integrated into a computer device or run as an independent tool application. Specifically, the method includes steps 10 to 50, and the steps are as follows: Step 10: Obtain planned energy consumption data for multiple areas of the target construction site, as well as a construction correlation diagram between the areas.

[0018] Among them, the target construction site in this embodiment of the application refers to a construction site of a building project that requires 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.

[0019] In the embodiment of the present application, the planned energy consumption data refers to the expected energy consumption index obtained after allocating energy consumption to each area based on 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 benchmark data for evaluating the energy consumption status of each area.

[0020] In the embodiment of the present application, the construction association diagram refers to a topological structure diagram that describes the construction dependency relationship between various areas of the construction site. The diagram analyzes the construction process combination of each area, considers the parallelism and mutual exclusivity of the processes, and reflects the construction timing relationship and resource allocation relationship between various areas.

[0021] Specifically, due to the different construction tasks and construction complexity of each area of ​​the construction site, it is necessary to reasonably allocate energy consumption indicators and consider the correlation of construction links. Therefore, the overall energy consumption limit of the target construction site and the construction task volume of each area are first obtained. Among them, the construction task volume includes the engineering volume and the construction difficulty coefficient. The engineering volume can be a specific construction indicator such as the concrete volume and the steel bar usage. The construction difficulty coefficient reflects the complexity of the construction process. Then, the energy consumption weight of each area is calculated using a weighted calculation method. Then, the weighted construction task volume of each area is divided by the sum of the weighted construction task volumes of all areas to obtain the energy consumption allocation ratio of each area. Then, the overall energy consumption limit is multiplied by the energy consumption allocation ratio of each area to obtain the preliminary planned energy consumption data of each area. The construction process combination of each area is obtained, and the construction association diagram is generated by analyzing the parallelism and mutual exclusivity between the processes. Parallelism indicates the number of processes that can be carried out simultaneously, and mutual exclusivity indicates that some processes cannot be carried out at the same time. According to these process characteristics, a construction association diagram reflecting the construction dependency relationship between regions is constructed. After obtaining the construction association diagram, the construction timing relationship of each area is further analyzed to identify and mark the critical path. By calculating the process overlap of each area on the critical path (i.e. the number of processes constructed at the same time), the energy consumption allocation coefficient of each time series node is obtained. Finally, the preliminary planned energy consumption data of each area is corrected according to the energy consumption allocation coefficient to obtain the final planned energy consumption data.

[0022] Based on the above embodiment, as an optional embodiment, the step of obtaining the planned energy consumption data of multiple areas of the target construction site and the construction association diagram between the areas may further include the following steps: Step 101: Obtain the overall energy consumption limit of the target construction site and the construction task volume of each area, wherein the construction task volume includes the engineering volume and the construction difficulty coefficient.

[0023] Specifically, the overall energy consumption limit is obtained through the construction site project management system. The limit is the maximum energy consumption index determined according to the construction scale, construction period requirements and energy-saving targets of the project. For example, a commercial complex project of 100,000 square meters is set at 2 million kWh. At the same time, the specific engineering quantity data of each area is obtained from the bill of quantities management module, such as the foundation construction area includes 5,000 cubic meters of earth excavation, 200 tons of steel bar construction and 2,000 cubic meters of concrete pouring, etc., and the construction difficulty coefficient of each area is determined through the construction difficulty assessment model, such as 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.

[0024] Step 102: Calculate the energy consumption weight of each area according to the construction task volume to obtain the energy consumption allocation ratio of each area.

[0025] Specifically, the energy consumption weight of each area is calculated, and the standardized engineering volume of each area is multiplied by its corresponding construction difficulty coefficient to obtain the weighted construction task volume. For example, if the standardized engineering volume of a certain area is 1,000 units and the construction difficulty coefficient is 1.5, then its weighted construction task volume is 1,500 units. Then, calculate the proportion of the weighted construction task volume of each area to the total construction task volume, which is the energy consumption allocation ratio of each area. The specific calculation formula is: Energy consumption allocation ratio of a certain area = weighted construction task volume of the area ÷ the sum of weighted construction task volumes of all areas × 100%. For example, if the weighted construction task volume of a certain area is 1,500 units and the sum of weighted construction task volumes of all areas is 10,000 units, then the energy consumption allocation ratio of the area is 15%.

[0026] Step 103: Based on the overall energy consumption limit and the energy consumption allocation ratio, determine the preliminary planned energy consumption data for each area.

[0027] Specifically, after obtaining the energy consumption allocation ratio of each area, the overall energy consumption limit is multiplied by the energy consumption allocation ratio of each area to obtain the preliminary planned energy consumption data of each area. For example, if the overall energy consumption limit is 2 million kWh and the energy consumption allocation ratio of a certain area is 15%, then the preliminary planned energy consumption data of the area is 300,000 kWh. Through this calculation method, the system ensures that each area obtains energy consumption indicators that match its construction task volume and construction difficulty, which not only ensures the realization of the overall energy consumption control target of the construction site, but also meets the actual construction needs of each area.

[0028] Step 104: Obtain the construction process combination of each area, and generate a construction association diagram according to the parallelism and mutual exclusivity of the processes in the construction process combination.

[0029] Specifically, in order to accurately reflect the construction dependency between different areas and reasonably adjust the energy consumption distribution, the system first obtains the construction process combination of each area from the project progress management system. For example, the process combination of a certain foundation construction area includes: earth excavation, foundation pit support, steel bar binding, formwork installation, concrete pouring and maintenance, while the process combination of the adjacent main construction area includes: steel bar binding, formwork installation, concrete pouring, maintenance, formwork removal, etc. The parallelism and mutual exclusivity between each process are analyzed according to the process characteristics. Parallelism refers to the degree to which the process can be carried out simultaneously, which is quantified by the time overlap rate. For example, the steel bar binding process in adjacent areas can be carried out simultaneously, and the parallelism is 100%; while mutual exclusivity indicates the constraint relationship that the processes cannot be constructed at the same time, such as the formwork installation and formwork removal processes in the same area are mutually exclusive, and the mutual exclusivity is 100%. The system stores the parallelism and mutual exclusivity data between each process in the relationship matrix, for example, setting the process relationship value 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, which adopts a directed graph structure, in which nodes represent each area, connecting lines represent the construction dependency relationship between areas, and the weight value of the connecting line is calculated by the parallelism and mutual exclusivity of the relevant processes. For example, when the construction processes of two areas have a high degree of parallelism, the weight value of the connecting line between them is large, indicating that the two areas may have a large demand for energy in the same time period; when there is mutual exclusivity between the processes, the weight value of the connecting line is negative, indicating that these processes need to stagger the construction time.

[0030] Step 105: Dynamically adjust the preliminary planned energy consumption data according to the construction association diagram to obtain the planned energy consumption data of each area.

[0031] Specifically, based on the constructed construction association diagram, the critical path analysis method is used to determine the construction timing relationship of each area. Specifically, by analyzing the connection relationship and weight between nodes in the construction association diagram, the system identifies the longest path from the beginning to the end of the project and marks it as the critical path. For example, in a certain engineering project, the system identifies the sequence of "foundation construction area → main construction area → equipment installation area → decoration construction area" as the critical path, and the construction progress of these areas directly affects the construction period of the entire project.

[0032] After obtaining the critical path, calculate the process overlap of each area on the critical path. When calculating, the system first divides the project duration into several time nodes, such as one time node per week or month. Then, for each area on the critical path, count the number of processes that are carried out simultaneously with other areas at each time node. For example, at a certain time node, the main construction area is carrying out three processes: steel bar binding, formwork installation, and concrete pouring. Among them, steel bar binding and formwork installation are carried out simultaneously with the pipeline pre-buried process in the adjacent equipment installation area. Then, the process overlap of this area at this time node is 2.

[0033] Next, the time series energy consumption is allocated based on the process overlap. First, the energy consumption allocation coefficient of each time series node is calculated. The calculation formula is: Energy consumption allocation coefficient = (1 + process overlap × adjustment factor) ÷ total number of time series nodes. The adjustment factor is a parameter determined based on historical construction experience, which is used to adjust the impact of process overlap on energy consumption. For example, it is set to 0.15. For example, if the process overlap of a certain area at a specific time series node is 2, the adjustment factor is 0.15, and the project is divided into 20 time series nodes, then the energy consumption allocation coefficient of the time series node is (1 + 2 × 0.15) ÷ 20 = 0.065.

[0034] Finally, the preliminary planned energy consumption data of each region is corrected according to the energy consumption allocation coefficient. During the correction process, the preliminary planned energy consumption data of each region is allocated according to the energy consumption allocation coefficient of the time series node to obtain the energy consumption index of each time series node. For example, the preliminary planned energy consumption of a region is 300,000 kWh, and the energy consumption allocation coefficient of a certain time series node is 0.065, then the energy consumption index allocated to the time series node is 19,500 kWh. The system summarizes the energy consumption indicators of all time series nodes to obtain the final planned energy consumption data of each region.

[0035] Step 20: Obtain energy consumption data corresponding to each area collected by a preset energy consumption monitoring network.

[0036] In the embodiment of the present application, the energy consumption monitoring network refers to a multi-level energy consumption data collection system deployed on the construction site, which is an Internet of Things network consisting of electricity metering devices, water meters, data collectors, communication modules and a central controller distributed in each construction area.

[0037] In the embodiment of the present application, energy consumption data refers to data indicators collected through an energy consumption monitoring network that reflect the consumption of electricity and water resources during the construction process.

[0038] Specifically, the system collects energy consumption data in real time through a pre-deployed energy consumption monitoring network. The monitoring network has installed metering devices such as smart electricity meters and smart water meters at key nodes in various areas of the construction site to realize the 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 tower crane operation process; a smart water meter is installed in the concrete pouring area to monitor the water consumption for concrete maintenance; and an independent electricity meter is set up in the living and office area to count the daily office and living electricity consumption.

[0039] Energy consumption data is obtained by layered collection. At the bottom layer, the smart meter records the power consumption data every 5 minutes, including parameters such as voltage, current, and power factor; the smart water meter records the water consumption data once an hour. The data collector collects the data of these metering devices through RS485 or wireless communication in a cycle of 15 minutes, and performs preliminary processing, such as data format conversion and outlier filtering. For example, the average power of a tower crane in a collection cycle is 50 kilowatts, and the running time is 0.25 hours. The power consumption of this cycle is calculated to be 12.5 kWh. The data after preliminary processing is uploaded to the central controller through the communication module. The communication module uses a 4G wireless network or a construction site LAN to ensure the real-time and reliability of data transmission. After receiving the data, the central controller first classifies and stores it according to the construction area, such as classifying the power consumption data of the tower crane into the main construction area and the maintenance water data into the concrete construction area. Then, the system performs time alignment and data completion on the data to deal with data missing or delay problems that may occur during the collection process. Through this layered collection and processing method, the system finally forms energy consumption data sets for each area. 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.

[0040] 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 (such as tower cranes, elevators) and lighting electricity according to the purpose, and the water resource consumption is divided into construction water (such as concrete maintenance) and dust suppression water. At the same time, the system obtains basic data such as the construction area and current construction output value of each area from the project management database. For example, the construction area of ​​a main construction area is 2,000 square meters, the monthly construction output value 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 of various energy sources. For electricity, calculate the electricity intensity per unit area and the electricity intensity per unit output value. The calculation formula is: electricity intensity per unit area = total electricity consumption in the area ÷ construction area; electricity intensity per unit output value = total electricity consumption in the area ÷ construction output value. For example, the electricity intensity per unit area of ​​the above main construction area is 5 kWh / square meter, and the electricity intensity per unit output value is 100 kWh / 10,000 yuan. Similarly, by calculating the intensity of water resource use, we obtain that the water intensity per unit area is 0.1 cubic meters per square meter, and the water intensity per unit output value is 2 cubic meters per 10,000 yuan.

[0041] The system uses the same method to calculate the planned energy consumption intensity index based on the planned energy consumption data. For example, if the planned electricity consumption in the area is 9,000 kWh and the planned water consumption is 180 cubic meters, the planned electricity intensity per unit area is 4.5 kWh / m2, and the planned electricity intensity per unit output value is 90 kWh / 10,000 yuan; the planned water intensity per unit area is 0.09 cubic meters / m2, and the planned water intensity per unit output value is 1.8 cubic meters / 10,000 yuan.

[0042] Based on the above embodiment, as an optional embodiment, the step of 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 may further include the following steps: Step 301: acquiring real-time energy consumption data of each energy source in each area within a preset time window in the energy consumption data, wherein the length of the time window is determined according to the duration of the process.

[0043] Specifically, in order to more accurately reflect the dynamic changes in energy use during the construction process, the system needs to perform time series processing and analysis on the energy consumption data collected in real time. First, determine the appropriate time window length based on the characteristics of the processes currently being carried out in each area. 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 a longer duration, such as steel bar binding, a time window of 8 hours or longer may be set. This time window setting based on process characteristics can better capture the energy consumption changes of different construction activities. After determining the time window, the real-time energy consumption data of 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 meter every 5 minutes and water consumption data from the smart water meter.

[0044] Step 302: Perform sliding average processing on the real-time energy consumption data within the time window to obtain the average energy consumption value corresponding to each energy in each area.

[0045] Specifically, in order to eliminate the impact of data fluctuations on the analysis results, the system performs sliding average processing on the real-time data within the time window. Specifically, the processing process for the 6-hour time window 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.

[0046] 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.

[0047] Specifically, the average energy consumption value processed by sliding average and the planned energy consumption data for the corresponding time period are obtained from the database. Taking the main construction area as an example, within the 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 of 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 the 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 of electricity and water resources respectively. For electricity consumption, the calculation includes two dimensions: power intensity per unit area and power intensity per unit output value. Among them, the calculation formula for power intensity per unit area is: actual power intensity per unit area = (average power consumption of tower crane + average power consumption of lighting) ÷ construction area, for example, the actual power intensity per unit area is (300+50) ÷ 500 = 0.7 kWh / square meter. Similarly, the planned power intensity per unit area is (280+45)÷500=0.65 kWh / m2. The formula for calculating the power intensity per unit output value is: actual power intensity per unit output value = (average power consumption of tower crane + average power consumption of lighting)÷construction output value. The actual value is 17.5 kWh / 10,000 yuan, and the planned value is 16.25 kWh / 10,000 yuan. For water resource consumption, the same method is used to calculate the water intensity per unit area and the water intensity per unit output value.

[0048] Step 40: When there is any abnormal area whose energy consumption intensity exceeds the corresponding preset intensity threshold, the associated area affected by the abnormal area is determined according to the construction association diagram.

[0049] Specifically, when there is any abnormal area whose energy consumption intensity exceeds the corresponding preset intensity threshold, for example, the water intensity of concrete curing detected in the main construction area is 0.035 cubic meters / square meters, which exceeds the preset intensity threshold of 0.03 cubic meters / square meters, the system marks the area as an abnormal area. Subsequently, the pre-established construction association graph is read, which is stored in the form of an adjacency matrix, recording the process dependency and association strength between each area. For example, the concrete curing in the main construction area has a process close relationship with the waterproofing construction area, with an association strength of 0.8; there is a process connection relationship with the decoration construction area, with an association strength of 0.7; there is a resource sharing relationship with the material curing area, with an association strength of 0.6. Using the depth-first search algorithm, the construction association graph is traversed from the abnormal area to identify all possible affected association areas. During the search process, the system first examines the first-level association area directly connected to the abnormal area. When the association strength exceeds the preset threshold (such as 0.5), the area is included in the affected area set. For example, since the correlation strength between the waterproof construction area and the main construction area is 0.8, which exceeds the threshold of 0.5, and substandard concrete maintenance will directly affect the quality of waterproof layer construction, the system identifies the waterproof construction area as an affected area. For the identified first-level associated areas, the system continues to analyze their associations with other areas to identify the second-level affected areas. For example, the correlation strength between the waterproof construction area and the exterior wall construction area is 0.7, which exceeds 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 correlation strengths greater than the threshold are identified, and the associated areas affected by the abnormal area are obtained.

[0050] Based on the above embodiment, as an optional embodiment, the step of determining the associated area affected by the abnormal area according to the construction association diagram may further include the following steps: Step 401: construct a regional association matrix based on the construction association diagram, wherein each matrix element in the association matrix represents the degree of association between regions.

[0051] Specifically, the system constructs an n×n regional association matrix R based on the construction association graph, where n is the total number of construction areas. ij Represents the degree of association between area i and area j, and its value range is [0,1]. The determination of the degree of association takes into account factors such as process connection relationship, resource sharing degree and spatial position relationship. For example, for a construction site including the main construction area, waterproofing construction area, decoration construction area and material maintenance area, the constructed 4×4 association matrix is ​​as follows: The degree of association between area 1 (main construction) and area 2 (waterproofing construction) is r 12 =0.8, indicating that the processes in the two areas are closely connected; the correlation degree between area 1 and area 3 (decoration construction) is r 13=0.6, indicating that there is process dependence but the time span is large; the correlation degree between area 1 and area 4 (material maintenance) is r 14 =0.4, indicating that there is mainly a resource sharing relationship.

[0052] Step 402: Starting from the abnormal area, the regional association matrix is ​​traversed using a breadth-first search method to determine the first-level associated area directly associated with the abnormal area.

[0053] Specifically, when the system detects that the water intensity in the main construction area is abnormal, it uses the breadth-first search algorithm to traverse the regional association matrix and identify the first-level association area directly connected to the abnormal area. Specifically, the system checks all non-zero elements in the row of the abnormal area in the association matrix and records the corresponding column index as the first-level association area. For example, by checking the first row of the matrix, it can be found that the areas directly associated with the main construction area include: waterproof construction area (r 12 =0.8), decoration construction area (r 13 =0.6) and material curing area (r 14 =0.4).

[0054] Step 403: Calculate the influence transmission coefficient of the primary correlation area, wherein the influence transmission coefficient increases as the correlation degree increases.

[0055] Specifically, for the identified first-level associated area, the system calculates its influence conduction coefficient λ. The calculation formula for the influence conduction coefficient is: λ=k×r, where k is the base value of the conduction coefficient (the value is 1.2) and r is the degree of association. This calculation method ensures that the greater the degree of association, the stronger the influence conduction effect. For example, the influence conduction coefficient of the waterproof construction area is λ1=1.2×0.8=0.96, the influence conduction coefficient of the decoration construction area is λ2=1.2×0.6=0.72, and the influence conduction coefficient of the material maintenance area is λ3=1.2×0.4=0.48.

[0056] Step 404: Determine the first-level associated area corresponding to the influence transmission coefficient greater than the preset threshold as the affected associated area.

[0057] Specifically, the calculated influence conduction coefficient is compared with the preset threshold (such as 0.6) to determine the final affected associated area. When the influence conduction coefficient is greater than the preset threshold, the area is included in the affected area set. In this example, the waterproof construction area (λ1=0.96>0.6) and the decoration construction area (λ2=0.72>0.6) are determined as affected associated areas, while the material maintenance area (λ3=0.48<0.6) is not included in the affected associated area set.

[0058] Step 50: Determine the warning levels of the abnormal area and the associated area, and generate a warning report for the target construction site based on the warning levels.

[0059] Specifically, the system first calculates the energy intensity deviation rate δ of the abnormal area, and the calculation formula is: δ=(actual energy intensity-preset threshold) / preset threshold×100%. Based on the size of the deviation rate, the warning level of the abnormal area is divided into three levels: when the deviation rate δ≤10%, it is a third-level warning (yellow warning), when 10%<δ≤20%, it is a second-level warning (orange warning), and when δ>20%, it is a first-level warning (red warning). In this example, the deviation rate of the main construction area is 16.7%, so it is determined to be a second-level warning. For the affected associated areas, the system determines its warning level based on the impact transmission coefficient λ calculated in the previous period. The judgment criteria for the warning level are: when 0.6≤λ<0.8, it is a third-level warning, when 0.8≤λ<1.0, it is a second-level warning, and when λ≥1.0, it is a first-level warning. After determining the warning level of each area, the system automatically generates a warning report. The early warning report contains the following main contents: first, the basic information of the warning time and warning area, such as "at 10:00 on December 30, 2024, water use intensity was abnormal in the main construction area"; second, the specific situation of the abnormal area, including actual water use intensity (0.035 cubic meters / square meter), preset threshold (0.03 cubic meters / square meter), deviation rate (16.7%) and warning level (secondary warning); then the analysis results of the affected related areas, such as "waterproofing construction area (influencing conduction coefficient 0.96, second-level warning) may cause delays in waterproofing construction period" and "decoration construction area (influencing conduction coefficient 0.72, third-level warning) needs to adjust the construction plan appropriately".

[0060] Based on the above embodiment, as an optional embodiment, the step of 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 may further include the following steps: Step 501: Calculate energy consumption exceeding standard indexes of the abnormal area and the associated area respectively, wherein the energy consumption exceeding standard index is determined based on the deviation between the energy consumption intensity and the corresponding intensity threshold.

[0061] Specifically, the energy consumption excess index σ of the abnormal area and the associated area is calculated using the following formula: 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 intensity of the main construction area is 0.035 cubic meters / square meters, the preset threshold is 0.03 cubic meters / square meters, and the water weight coefficient is 1.2, its energy consumption excess index σ1=1.2×(0.035-0.03) / 0.03=0.2. For the associated area, the attenuation effect of the influence conduction coefficient λ on the excess index also needs to be considered, that is, σ2=σ1×λ. For example, if the influence conduction coefficient of the waterproof construction area is 0.96, then its energy consumption excess index is 0.2×0.96=0.192.

[0062] Step 502: Based on the positions of the abnormal area and the associated area in the construction association graph, the regional importance of the abnormal area and the associated area is determined respectively, and the regional importance increases as the number of paths connecting other areas increases.

[0063] Specifically, the importance α of each area is analyzed based on the construction association graph. The importance is calculated using the improved PageRank algorithm, taking into account the number of connection paths and path strength with other areas. The calculation formula is: α=(1-d)+d×Σ(αj×rji / Σrjk), where d is the damping coefficient (value 0.85), rji is the degree of association from area j to area i, and αj is the importance of the associated area j. For example, the main construction area connects multiple areas such as waterproofing construction and decoration construction, with a total of 5 paths, a weighted average of association strength of 0.75, and a calculated importance α1 of 0.82; while the waterproofing construction area has fewer connection paths, and the calculated importance α2 is 0.65.

[0064] Step 503: Determine corresponding warning scores according to the energy consumption exceeding standard index and the regional importance of the abnormal area and the associated area respectively, and map the warning scores to corresponding warning levels based on a preset score range.

[0065] Specifically, the warning score S is calculated based on the energy consumption excess index and the regional importance. The calculation formula is: S=β1×σ+β2×α, where β1 and β2 are the weight coefficients of the excess index and importance respectively (both are 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 range: when S≥0.5, it is a first-level warning, when 0.3≤S<0.5, it is a second-level warning, and when S<0.3, it is a third-level warning. Based on this, 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.

[0066] Step 504: Generate a warning report including risk assessment and improvement suggestions based on the warning levels corresponding to the abnormal area and the associated area and the historical warning data.

[0067] Specifically, an early warning report is generated by combining the early warning level and historical early warning data. The report first lists the specific early warning information of each area, including quantitative indicators such as early warning level, energy consumption exceeding standard index, and regional importance. For example, "Main construction area: Level 1 early warning (red), energy consumption exceeding standard index 0.2, regional importance 0.82, early warning score 0.51". Then, the system generates risk assessment results by analyzing the handling experience of similar cases in the historical early warning database. Based on the risk assessment results, the system further generates improvement suggestions, such as "Suggestions: 1. Immediately check the water supply system in the main construction area and investigate the causes of abnormalities; 2. Optimize the process arrangement of the waterproof construction area and adjust the construction plan appropriately; 3. Strengthen on-site water use monitoring and establish a water use efficiency evaluation mechanism." These suggestions include both immediate response measures and long-term improvement strategies.

[0068] Based on the above embodiment, as an optional embodiment, a digital construction site supervision method may further include the following process: Specifically, during the abnormality handling process, the system collects and processes data in real time, including abnormality occurrence time, abnormal area, abnormality type, initial energy consumption intensity, treatment measures taken, energy consumption intensity after treatment, and other information. For example, when water intensity abnormality occurs in the main construction area, the abnormality type is recorded as "exceeding maintenance water use", the initial water intensity is 0.035 cubic meters / square meters, and the treatment measures taken include "optimizing the sprinkler system parameter settings" and "adjusting the maintenance time allocation". After treatment, the water intensity is reduced to 0.028 cubic meters / square meters. At the same time, the system records auxiliary information such as cost input and time consumption during the processing process to form a complete processing record.

[0069] The effect coefficient μ of the treatment measure is calculated according to the change in energy consumption intensity before and after treatment. The calculation formula is: μ=k×(E1-E2) / E1×(T0 / T), where E1 is the energy consumption intensity before treatment, E2 is the energy consumption intensity after treatment, T0 is the standard treatment time, T is the actual treatment time, and k is the cost adjustment coefficient. For example, for the treatment measure using the optimized spray system, E1=0.035, E2=0.028, T0=4 hours, T=5 hours, k=1.2, then the effect coefficient μ1=1.2×(0.035-0.028) / 0.035×(4 / 5)=0.192. For the measure of adjusting the curing time, the effect coefficient μ2=0.168 is calculated in the same way.

[0070] Based on the calculated effect coefficient, the system sorts the measures in the processing record library. First, classify by abnormal type, such as classifying all treatment measures for the "exceeding maintenance water" type, and then in each category, sort the treatment measures in descending order according to the size of the effect coefficient to generate the optimal treatment solution set. For example, for the type of maintenance water exceeding the standard, the sorted treatment solutions are: optimizing the sprinkler system (effect coefficient 0.192), adjusting the maintenance time (effect coefficient 0.168), replacing water-saving equipment (effect coefficient 0.145), etc. When the system detects a new abnormal area, it first identifies its abnormal type, and then selects the treatment measure with the highest effect coefficient from the corresponding optimal treatment solution set. After implementing the treatment measures, the system continues to collect treatment effect data and update the treatment record library. If the new treatment effect is better than the historical record, the ranking of the measure in the optimal treatment solution set is improved; if the effect is not as expected, its ranking position is lowered.

[0071] See also Figure 2 , is a module diagram of a digital construction site supervision system provided in an embodiment of the present application, the system comprising: A data acquisition module is used to acquire planned energy consumption data of multiple areas of the target construction site, as well as a construction association diagram between the areas; and to acquire energy consumption data corresponding to the areas collected by the preset energy consumption monitoring network; An energy consumption calculation module, used to determine 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; An abnormality determination module, used for determining, when there is any abnormal area whose energy consumption intensity exceeds the corresponding preset intensity threshold, the associated area affected by the abnormal area according to the construction association diagram; The warning generation module is used to determine the warning levels of the abnormal area and the associated area, and generate a warning report for the target construction site based on the warning levels.

[0072] Optionally, the data acquisition module is further used to acquire the overall energy consumption limit of the target construction site and the construction task volume of each area, wherein the construction task volume includes the engineering volume and the construction difficulty coefficient; Calculate 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 preliminary planned energy consumption data for each of the areas; Acquire a construction process combination of each of the areas, and generate the construction association diagram according to the parallelism and mutual exclusivity of the processes in the construction process combination; The preliminary planned energy consumption data is dynamically adjusted according to the construction association diagram to obtain the planned energy consumption data for each of the areas.

[0073] Optionally, the data acquisition module is further used to determine the construction timing relationship of each of the areas according to the construction association diagram, and identify the critical path in the construction timing relationship; Calculating the process overlap of each of the areas on the critical path, wherein the process overlap represents the number of processes being constructed simultaneously; Based on the process overlap, the preliminary planned energy consumption data is time-series allocated to obtain the energy consumption allocation coefficient of each time-series node; The preliminary planned energy consumption data of each of the areas is corrected according to the energy consumption allocation coefficient to obtain the planned energy consumption data.

[0074] Optionally, the energy consumption calculation module is further used to obtain real-time energy consumption data of each energy source in each of the areas within a preset time window in the energy consumption data, wherein the length of the time window is determined according to the duration of the process; Performing sliding average processing on the real-time energy consumption data within the time window to obtain an average energy consumption value corresponding to each energy source in each of the areas; Based on the average energy consumption value corresponding to each energy source and the planned energy consumption data for the corresponding time period, the energy consumption intensity corresponding to each energy source in each of the areas is determined.

[0075] Optionally, the abnormality determination module is further used to construct a regional association matrix based on the construction association diagram, wherein 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 a primary association area directly associated with the abnormal area; Calculating the influence transmission coefficient of the primary correlation area, wherein the influence transmission coefficient increases as the correlation degree increases; The first-level associated area corresponding to the influence transmission coefficient being greater than a preset threshold is determined as the affected associated area.

[0076] Optionally, the warning generation module is further used to calculate the energy consumption exceeding standard index of the abnormal area and the associated area respectively, wherein the energy consumption exceeding 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 map, respectively determine the regional importance of the abnormal area and the associated area, wherein the regional importance increases as the number of paths connecting other areas increases; Determine corresponding warning scores according to the energy consumption exceeding standard index and the regional importance of the abnormal area and the associated area respectively, and map the warning scores to corresponding warning levels based on a preset score range; A warning report including risk assessment and improvement suggestions is generated according to the warning levels and historical warning data corresponding to the abnormal area and the associated area.

[0077] Optionally, the warning generation module is further used to collect the energy consumption abnormality treatment measures and treatment effect data of the abnormal area, and establish a treatment record library including abnormality type, treatment measures and effect score; Calculating the effect coefficients of various treatment measures based on the treatment record library, wherein the effect coefficients increase as the degree of reduction in energy consumption intensity after treatment increases; Sorting the processing measures in the processing record library according to the effect coefficient to generate an optimal processing solution set for different abnormality types; When a new abnormal area appears, a treatment measure is selected from the optimal treatment solution set according to its abnormal type, and the treatment record library is updated.

[0078] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, 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 embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0079] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a digital construction site supervision method of the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0080] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 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 .

[0081] The communication bus 302 is used to realize the connection and communication between these components.

[0082] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0083] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0084] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0085] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (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, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 305 may optionally be at least one storage device located away 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.

[0086] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a digital construction site supervision method in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders 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 required for the present application.

[0087] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0091] 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 this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0092] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0093] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, 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 comprises: Obtaining planned energy consumption data for multiple areas of a target construction site, and a construction correlation diagram between the areas; Obtaining energy consumption data corresponding to each of the areas collected by the preset energy consumption monitoring network; Determine 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 any abnormal area whose energy consumption intensity exceeds the corresponding preset intensity threshold, determining the associated area affected by the abnormal area according to the construction association diagram; Determine the warning levels of the abnormal area and the associated area, and generate 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 obtaining of planned energy consumption data of multiple areas of the target construction site and a construction association diagram between the areas includes: Obtaining the overall energy consumption limit of the target construction site and the construction task volume of each area, wherein the construction task volume includes the engineering volume and the construction difficulty coefficient; Calculate 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 preliminary planned energy consumption data for each of the areas; Acquire a construction process combination of each of the areas, and generate the construction association diagram according to the parallelism and mutual exclusivity of the processes in the construction process combination; The preliminary planned energy consumption data is dynamically adjusted according to the construction association diagram to obtain the planned energy consumption data for each of the areas.

3. The digital construction site supervision method according to claim 2, characterized in that: The dynamically adjusting the preliminary planned energy consumption data according to the construction association diagram to obtain the planned energy consumption data of each area includes: Determine the construction timing relationship of each of the areas according to the construction association diagram, and identify the critical path in the construction timing relationship; Calculating the process overlap of each of the areas on the critical path, wherein the process overlap represents the number of processes being constructed simultaneously; Based on the process overlap, the preliminary planned energy consumption data is time-series allocated to obtain the energy consumption allocation coefficient of each time-series node; The preliminary planned energy consumption data of each of the areas is corrected according to the energy consumption allocation coefficient to obtain the planned energy consumption data.

4. 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: Acquire the real-time energy consumption data of each energy source in each of the areas within a preset time window from the energy consumption data, wherein the length of the time window is determined according to the duration of the process; Performing sliding average processing on the real-time energy consumption data within the time window to obtain an average energy consumption value corresponding to each energy source in each of the areas; Based on the average energy consumption value corresponding to each energy source and the planned energy consumption data for the corresponding time period, the energy consumption intensity corresponding to each energy source in each of the areas is determined.

5. The digital construction site supervision method according to claim 1, characterized in that: The determining, according to the construction association diagram, the associated area affected by the abnormal area comprises: Constructing a regional association matrix based on the construction association diagram, wherein 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 a primary association area directly associated with the abnormal area; Calculating the influence transmission coefficient of the primary correlation area, wherein the influence transmission coefficient increases as the correlation degree increases; The first-level associated area corresponding to the influence transmission coefficient being greater than a preset threshold is determined as the affected associated area.

6. The digital construction site supervision method according to claim 1, characterized in that: The determining of the warning levels of the abnormal area and the associated area, and generating a warning report of the target construction site based on the warning levels, includes: Calculating energy consumption exceeding standard indexes of the abnormal area and the associated area respectively, wherein the energy consumption exceeding standard index is determined based on the deviation of energy consumption intensity and a corresponding intensity threshold; Based on the positions of the abnormal area and the associated area in the construction association map, respectively determine the regional importance of the abnormal area and the associated area, wherein the regional importance increases as the number of paths connecting other areas increases; Determine corresponding warning scores according to the energy consumption exceeding standard index and the regional importance of the abnormal area and the associated area respectively, and map the warning scores to corresponding warning levels based on a preset score range; A warning report including risk assessment and improvement suggestions is generated according to the warning levels and historical warning data corresponding to the abnormal area and the associated area.

7. The digital construction site supervision method according to claim 1, characterized in that: The method further comprises: Collecting the energy consumption abnormality treatment measures and treatment effect data of the abnormal area, and establishing a treatment record library including abnormality type, treatment measures and effect score; Calculating the effect coefficients of various treatment measures based on the treatment record library, wherein the effect coefficients increase as the degree of reduction in energy consumption intensity after treatment increases; Sorting the processing measures in the processing record library according to the effect coefficient to generate an optimal processing solution set for different abnormality types; When a new abnormal area appears, a treatment measure is selected from the optimal treatment solution set according to its abnormal type, and the treatment record library is updated.

8. A digital construction site supervision system, characterized in that: The system comprises: A data acquisition module is used to acquire planned energy consumption data of multiple areas of the target construction site, as well as a construction association diagram between the areas; and to acquire energy consumption data corresponding to the areas collected by the preset energy consumption monitoring network; An energy consumption calculation module, used to determine 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; An abnormality determination module, used for determining, when there is any abnormal area whose energy consumption intensity exceeds the corresponding preset intensity threshold, the associated area affected by the abnormal area according to the construction association diagram; The warning generation module is used to determine the warning levels of the abnormal area and the associated area, and generate a warning report for the target construction site based on the warning levels.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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