Intelligent construction digital monitoring system and method

Abstract

The invention relates to the field of intelligent construction, in particular to an intelligent construction digital monitoring system and method, and the system comprises a data obtaining module which obtains environment data, equipment states and personnel states in a construction process, and generates a current data set and a historical data set; the data analysis module reads the current data set and the historical data set generated by the data acquisition module for analysis processing, identifies trends and abnormal conditions in the current data set and the historical data set, and generates a real-time monitoring report; the prediction and evaluation module analyzes the real-time monitoring report generated by the data analysis module to obtain a project progress prediction result, a project cost prediction result and a safety risk evaluation result; and the coping strategy generation module correspondingly generates a coping strategy according to the project progress prediction result, the project cost prediction result and the personnel safety risk assessment result obtained by the prediction and assessment module. The system collects data, analyzes the data, predicts the result, formulates a targeted coping strategy, carries out decision support for a manager, assists the manager in making a decision more quickly and accurately, and improves the management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction, and particularly to a system and method for digital monitoring of intelligent construction. Background Art

[0002] There are a series of problems in traditional engineering construction. The monitoring and management of construction often rely on manual monitoring, paper records, and regular progress reports. In the process of monitoring and managing building construction, the project progress and construction strategies are usually planned manually, with high labor intensity and low work efficiency.

[0003] With the rapid development of information technology, especially the continuous maturity of technologies such as the Internet of Things, big data, artificial intelligence, and machine learning, the construction management of nuclear power construction is developing towards intelligent construction. Intelligent construction refers to using advanced information technology, automation technology, artificial intelligence and other means to improve the efficiency, quality and safety of the construction industry. It involves multiple fields, including building design, construction management, operation and maintenance, etc. The core concept of intelligent construction is to optimize the entire life cycle of buildings through digital and intelligent means. In the prior art, during the construction process of nuclear power, there is still a lack of a system that can comprehensively monitor and manage construction, predict the project progress, and propose countermeasures. Summary of the Invention

[0004] The present invention provides a system and method for digital monitoring of intelligent construction, which is used to solve the problem that there is a lack of a system in the prior art that can comprehensively monitor and manage construction, predict the project progress, and propose countermeasures.

[0005] The technical solution of the present invention is as follows:

[0006] The present invention proposes an intelligent construction digital monitoring system, which includes a data acquisition module, a data analysis module, a prediction and evaluation module, and a countermeasure generation module. The data acquisition module acquires environmental data, equipment status, and personnel status during the construction process, and generates a current data set and a historical data set; the data analysis module reads the current data set and the historical data set generated by the data acquisition module for analysis and processing, identifies trends and abnormal situations in the current data set and the historical data set, and generates a real-time monitoring report; the prediction and evaluation module analyzes the real-time monitoring report generated by the data analysis module to obtain a project progress prediction result, a project cost prediction result, and a safety risk assessment result; the countermeasure generation module correspondingly generates countermeasures according to the project progress prediction result, the project cost prediction result, and the personnel safety risk assessment result obtained by the prediction and evaluation module.

[0007] In some embodiments, the environmental data, device status, and personnel status obtained by the data acquisition module specifically include: the environmental data specifically includes at least one of temperature, humidity, air quality, noise level, and light intensity; the device status includes at least one of device ID, device type, operating status, energy consumption data, and maintenance status; the personnel status includes at least one of personnel ID, personnel name, location information, and timestamp; the data acquisition module includes sensors and edge computing devices, the sensors are used to detect the environmental data, device status, and personnel status during the construction process, and the edge computing device is used to filter and compress the sensor data using an edge computing framework.

[0008] In some embodiments, the data analysis module reads the current data set and the historical data set generated by the data acquisition module for analysis and processing, identifies trends and anomalies in the current data set and the historical data set, and generates a real-time monitoring report, which specifically includes: the data analysis module uses linear interpolation to fill in the data in the missing data set for the current data set and the historical data set, and uses the Z-Score method to detect and remove outliers, so as to obtain cleaned data; the data analysis module aggregates the cleaned data of the monitoring points in different spatial regions by minute, hour, or day, and calculates the average value, maximum value, and minimum value of the data set after data aggregation to obtain aggregated data; the data analysis module uses a linear regression model to identify the long-term trend of the aggregated data and uses the Z-score method to detect outliers in the aggregated data; the data analysis module generates a visual real-time monitoring report based on the aggregated data, long-term trend, and outliers.

[0009] In some embodiments, the linear interpolation method estimates the linear relationship between data points to calculate a reasonable value for the missing data points; the Z-Score method calculates the ratio of the standard deviation of each data point to the average value of the data set, and identifies outliers by comparing the standard deviation ratios; the linear regression model fits the long-term trend of the aggregated data through a formula, and the specific fitting formula is as formula (1):

[0010] y = β 0 + β 1 x (1)

[0011] where y is the dependent variable, i.e., the predicted value, x is the independent variable, i.e., time, β 0 is the intercept, and β 1 is the slope.

[0012] In some embodiments, the Z-score method uses formula (2) to detect outliers in the aggregated data, and the specific formula (2) is:

[0013]

[0014] where X is the data point, σ is the mean of the aggregated data, and μ is the standard deviation of the aggregated data;

[0015] In some embodiments, the real-time monitoring report is sent to the user in a visual form, and the visual form includes at least one of a chart, a curve graph, a heat map, a line graph, a bar graph, and a scatter plot.

[0016] In some embodiments, an intelligent construction digital monitoring system is characterized in that the prediction and evaluation module uses the ARIMA model to obtain the project progress prediction result, specifically as shown in formula (3):

[0017]

[0018] where y t is the predicted value at time t, representing the project progress or the completion degree of the task; c is the constant term, which is the bias term in the ARIMA model; is the autoregressive coefficient, which is related to the autocorrelation of the historical progress data; p is the order of the autoregressive term, representing the memory degree of the model; θ is the moving average coefficient, representing the influence of historical errors on the current prediction; q is the order of the moving average term, representing the memory of the model for past errors; ∈ t is the error term, representing the gap between the actual value and the predicted value;

[0019] The prediction and evaluation module uses the COCOMO II model to obtain the project cost prediction result, specifically as shown in formula (4):

[0020]

[0021] where E is the workload; A is a constant, the constant in the COCOMO II model; KLOC is the project scale, representing the number of tasks; B is the scale index, representing the complexity, scale, and experience level of the project; EM i is the cost driver factor, representing the influence of different aspects of the project on the cost;

[0022] The prediction and evaluation module uses the FAIR model to obtain the safety risk assessment result, specifically as shown in formula (5):

[0023] RisK=Loss Event Frequency×Probable Loss Magnitude (5)

[0024] where Loss Event Frequency is the loss event probability, representing the probability of the loss event occurring, and Probable Loss Magnitude is the probable loss amplitude, representing the maximum loss that may be caused when the event occurs.

[0025] In some embodiments, the response strategies generated by the response strategy generation module include environmental anomaly response, equipment failure response, and personnel safety response. The environmental anomaly response takes the response measures in the historical dataset as a reference and uses a decision function to generate a response strategy in combination with the data in the real-time monitoring report; the equipment failure response takes the equipment maintenance records in the historical data as a reference and uses a decision function to generate a response strategy in combination with the data in the real-time monitoring report; the personnel safety response takes the safety incident records in the historical data as a reference and uses a decision function to generate a response strategy in combination with the data in the real-time monitoring report. The decision function used by the strategy generation module is as follows in formula (6):

[0026] Strategy=f(Environment,Equipment,Personnel,Historical Data) (6)

[0027] Where Strategy is the generated response strategy, f is the decision function, Environment, Equipment, and Personnel are the data in the real-time monitoring report, and Historical Data is the data in the historical dataset.

[0028] The present invention proposes an intelligent construction digital monitoring method, which includes:

[0029] Step 1: The data acquisition module acquires environmental data, equipment status, and personnel status during the construction process, and generates a current dataset and a historical dataset;

[0030] Step 2: The data analysis module analyzes and processes the current dataset and the historical dataset, identifies the trends and anomalies in the current dataset and the historical dataset, and generates a real-time monitoring report;

[0031] Step 3: The prediction and evaluation module analyzes the real-time monitoring report to obtain the project progress prediction result, the project cost prediction result, and the safety risk assessment result;

[0032] Step 4: The response strategy generation module correspondingly generates response strategies according to the project progress prediction result, the project cost prediction result, and the personnel safety risk assessment result.

[0033] The present invention proposes an intelligent construction digital monitoring method, and the specific content of step 2 includes:

[0034] Step 2.1: The data analysis module fills in the data in the missing dataset using the linear interpolation method, and uses the Z-Score method to detect and remove outliers to obtain cleaned data;

[0035] Step 2.2: The data analysis module aggregates the cleaned data of the monitoring points in different spatial regions by minute, hour, or day, and calculates the average, maximum, and minimum values of the dataset after data aggregation to obtain the aggregated data;

[0036] Step 2.3: The data analysis module uses a linear regression model to identify the long-term trend of the aggregated data;

[0037] Step 2.4: The data analysis module uses the Z-score method to detect outliers in the aggregated data;

[0038] Step 2.5: The data analysis module generates a visual real-time monitoring report based on the aggregated data, long-term trend, and outliers.

[0039] Implementing the present invention has the following beneficial effects:

[0040] 1. The present invention proposes a system and method for intelligent construction digital monitoring. The present invention obtains the current dataset and historical dataset through the data acquisition module, providing a basis for subsequent prediction and evaluation of the system; the present invention adopts the data analysis module to analyze the dataset, determines the long-term change direction of the data, helps predict future change trends, and is used for long-term planning and decision-making support of the project; at the same time, the data analysis module detects outliers in the data to help discover potential problems or abnormal events.

[0041] 2. The present invention provides a basis for budget control by predicting and evaluating the future completion situation of the project through the prediction and evaluation module, and combines the predicted project cost; it evaluates the safety risks at the construction site to provide a reference for formulating safety measures. The present invention formulates targeted countermeasures through the countermeasure generation module according to the analysis and prediction results, provides decision-making support for managers, and assists managers to make decisions more quickly and accurately, improving management efficiency. Brief Description of the Drawings

[0042] Figure 1 It is a flowchart of an intelligent construction digital monitoring method proposed by an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of an intelligent construction digital monitoring system proposed by an embodiment of the present invention. Detailed Embodiments

[0044] The technical solutions of the present invention will be clearly and completely described below with reference to the drawings and specific embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] As From Figure 1 to Figure 2As shown in the figure, the present invention proposes an intelligent construction digital monitoring system, which includes a data acquisition module, a data analysis module, a prediction and evaluation module, and a response strategy generation module. The data acquisition module acquires environmental data, equipment status, and personnel status during the construction process, and generates a current data set and a historical data set. The data analysis module reads the current data set and the historical data set generated by the data acquisition module for analysis and processing, identifies trends and anomalies in the current data set and the historical data set, and generates a real-time monitoring report. The prediction and evaluation module analyzes the real-time monitoring report generated by the data analysis module to obtain the project progress prediction result, the project cost prediction result, and the safety risk assessment result. The response strategy generation module correspondingly generates response strategies according to the project progress prediction result, the project cost prediction result, and the personnel safety risk assessment result obtained by the prediction and evaluation module.

[0046] The data acquisition module includes sensors and edge computing devices. The sensors are used to detect environmental data, equipment status, and personnel status during the construction process, and the edge computing devices are used to filter and compress sensor data using an edge computing framework. The environmental data, equipment status, and personnel status acquired by the data acquisition module specifically include: the environmental data specifically includes at least one of temperature, humidity, air quality, noise level, and light intensity. Among them, the air quality includes PM2.5, PM10, and CO2 concentration.

[0047] The equipment status includes at least one of equipment ID, equipment type, operating status, energy consumption data, and maintenance status. Among them, the operating status includes normal, faulty, and standby; the maintenance status includes no maintenance required, under maintenance, and to be maintained.

[0048] The personnel status includes at least one of personnel ID, personnel name, location information, and timestamp. Among them, the location information includes longitude, latitude, and elevation.

[0049] The data analysis module uses the linear interpolation method to fill in the data in the missing data set for the current data set and the historical data set, and uses the Z-Score method to detect and remove outliers, so as to obtain cleaned data. The linear interpolation method estimates the linear relationship between data points to calculate the reasonable value of the missing data points. The Z-Score method calculates the ratio of the standard deviation of each data point to the average value of the data set, and identifies outliers by comparing the standard deviation ratios. Then, the data analysis module aggregates the cleaned data of the monitoring points in different spatial regions by minute, hour, or day, and calculates the average value, maximum value, and minimum value of the data set after data aggregation to obtain aggregated data. Then, the data analysis module uses a linear regression model to identify the long-term trend of the aggregated data and uses the Z-score method to detect outliers in the aggregated data. Among them, the linear regression model identifies the long-term trend of the aggregated data, and the specific fitting formula is as formula (1):

[0050] y = β 0 + β 1 x (1)

[0051] where y is the dependent variable, i.e., the predicted value, x is the independent variable, i.e., time, and β 0 is the intercept, and β 1 is the slope;

[0052] The Z - score method uses formula (2) to detect outliers in the aggregated data. The specific formula (2) is:

[0053]

[0054] where X is the data point, σ is the mean of the aggregated data, and μ is the standard deviation of the aggregated data.

[0055] Finally, the data analysis module generates a visual real - time monitoring report from the aggregated data, long - term trend, and outliers. The real - time monitoring report is sent to the user in a visual form, and the visual form includes at least one of charts, line graphs, heatmaps, line charts, bar charts, and scatter plots.

[0056] The prediction evaluation module analyzes the real - time monitoring report generated by the data analysis module to obtain the project progress prediction result, project cost prediction result, and safety risk assessment result. Among them, the prediction evaluation module uses the ARIMA model to obtain the project progress prediction result, specifically as formula (3):

[0057]

[0058] where y t is the predicted value at time t, representing the progress of the project or the completion degree of the task; c is the constant term, which is the bias term in the ARIMA model; is the autoregressive coefficient, related to the autocorrelation of historical progress data; p is the order of the autoregressive term, representing the memory degree of the model; θ is the moving average coefficient, representing the impact of historical errors on the current prediction; q is the order of the moving average term, representing the memory of the model for past errors; ∈ t is the error term, representing the gap between the actual value and the predicted value. The above parameters are all set manually according to the actual situation and obtained from the real - time monitoring report.

[0059] The prediction evaluation module uses the COCOMO II model to obtain the project cost prediction result, specifically as formula (4):

[0060]

[0061] where E is the workload; A is a constant, the constant in the COCOMO II model; KLOC is the project size, representing the number of tasks; B is the size exponent, representing the complexity, size, and experience level of the project; EMi It is the cost driving factor, indicating the impact of different aspects of the project on the cost. The above parameters are all set manually according to the actual situation and obtained from the real-time monitoring report.

[0062] The prediction and evaluation module uses the FAIR model to obtain the safety risk assessment result, specifically as shown in formula (5):

[0063] RisK = Loss Event Frequency × Probable Loss Magnitude (5)

[0064] Among them, Loss Event Frequency is the probability of the loss event, indicating the probability of the loss event occurring, and Probable Loss Magnitude is the possible loss amplitude, indicating the maximum loss that may be caused when the event occurs. The above parameters are all set manually according to the actual situation and obtained from the real-time monitoring report.

[0065] The coping strategies generated by the coping strategy generation module include coping with environmental anomalies, coping with equipment failures, and coping with personnel safety. For coping with environmental anomalies, the coping measures in the historical data set are taken as a reference, and the decision function is used in combination with the data in the real-time monitoring report to generate coping strategies; for coping with equipment failures, the equipment maintenance records in the historical data are taken as a reference, and the decision function is used in combination with the data in the real-time monitoring report to generate coping strategies; for coping with personnel safety, the safety event records in the historical data are taken as a reference, and the decision function is used in combination with the data in the real-time monitoring report to generate coping strategies; the decision function used by the coping strategy generation module is as shown in formula (6):

[0066] Strategy = f(Environment, Equipment, Personnel, Historical Data) (6)

[0067] Among them, Strategy is the generated coping strategy, f is the decision function, Environment, Equipment, Personnel are the data in the real-time monitoring report, and Historical Data is the data in the historical data set.

[0068] The present invention proposes an intelligent construction digital monitoring method, which includes:

[0069] Step 1: The data acquisition module acquires environmental data, equipment status, and personnel status during the construction process to generate the current dataset and the historical dataset. The environmental data includes at least one of temperature, humidity, air quality, noise level, and light intensity; the air quality includes PM2.5, PM10, and CO2 concentration. The equipment status includes at least one of equipment ID, equipment type, operating status, energy consumption data, and maintenance status; the operating status includes normal, faulty, standby; the maintenance status includes no maintenance required, under maintenance, to be maintained. The personnel status includes at least one of personnel ID, personnel name, location information, and timestamp; the location information includes longitude, latitude, and elevation.

[0070] Step 2: The data analysis module analyzes and processes the current dataset and the historical dataset to identify trends and anomalies in the current dataset and the historical dataset, and generates a real-time monitoring report.

[0071] Step 2.1: The data analysis module fills in the data in the missing dataset using the linear interpolation method and uses the Z-Score method to detect and remove outliers to obtain cleaned data. The linear interpolation method estimates the reasonable value of the missing data point through the linear relationship between data points. The Z-Score method calculates the ratio of the standard deviation of each data point to the average value of the dataset, and identifies and removes outliers by comparing the standard deviation ratios to ensure the accuracy and reliability of the dataset.

[0072] Step 2.2: The data analysis module aggregates the cleaned data of the monitoring points in different spatial regions by minute, hour, or day, and calculates the average value, maximum value, and minimum value of the aggregated dataset to obtain aggregated data. The data analysis module calculates the average value, maximum value, and minimum value of the dataset within each time unit to reflect the changing trend and fluctuation range of the data over time. By aggregating the cleaned data and calculating the average value of each region, the overall level of the environment, equipment, or personnel status in different regions can be evaluated, which helps managers make more informed decisions and improve construction efficiency and safety.

[0073] Step 2.3: The data analysis module uses a linear regression model to identify the long-term trend of the aggregated data. The linear regression model fits the long-term trend of the aggregated data through the formula, and the specific fitting formula is as formula (1):

[0074] y = β 0 + β 1 x (1)

[0075] where y is the dependent variable, i.e., the predicted value, x is the independent variable, i.e., time, β 0 is the intercept, and β 1 is the slope;

[0076] Step 2.4: The data analysis module uses the Z - score method to detect outliers in the aggregated data. Among them, the Z - score method is specifically as shown in formula (2):

[0077]

[0078] where X is the data point, σ is the mean of the aggregated data, and μ is the standard deviation of the aggregated data.

[0079] Step 2.5: The data analysis module generates a visual real - time monitoring report based on the aggregated data, long - term trend, and outliers. The real - time monitoring report is sent to the user terminal in a visual form, and the visual form includes at least one of charts, curve graphs, heat maps, line graphs, bar graphs, and scatter plots.

[0080] Step Three: The prediction and evaluation module analyzes the real - time monitoring report to obtain the project progress prediction result, project cost prediction result, and safety risk assessment result. Among them, the prediction and evaluation module uses the ARIMA model to obtain the project progress prediction result, specifically as shown in formula (3):

[0081]

[0082] where y t is the predicted value at time t, representing the progress of the project or the completion degree of the task; c is a constant term, which is the bias term in the ARIMA model; is the autoregressive coefficient, related to the autocorrelation of historical progress data; p is the order of the autoregressive term, representing the memory degree of the model; θ is the moving average coefficient, representing the influence of historical errors on the current prediction; q is the order of the moving average term, representing the memory of the model for past errors; ε t is the error term, representing the gap between the actual value and the predicted value.

[0083] The prediction and evaluation module uses the COCOMO II model to obtain the project cost prediction result, specifically as shown in formula (4):

[0084]

[0085] where E is the workload; A is a constant, the constant in the COCOMO II model; KLOC is the project size, representing the number of tasks; B is the size index, representing the complexity, size, and experience level of the project; EM i is the cost driver factor, representing the impact of different aspects of the project on the cost.

[0086] The prediction and evaluation module uses the FAIR model to obtain the safety risk assessment result, specifically as shown in formula (5):

[0087] Risk = Loss Event Frequency × Probable Loss Magnitude (5)

[0088] Among them, Loss Event Frequency is the probability of loss events, indicating the probability of loss events occurring, and Probable Loss Magnitude is the possible loss magnitude, indicating the maximum loss that may be caused when the event occurs.

[0089] Step 4: The response strategy generation module generates corresponding response strategies based on the project progress prediction result, the project cost prediction result, and the personnel safety risk assessment result. The response to environmental anomalies includes, according to the abnormal patterns of environmental data, the response strategy generation module taking the response measures in the historical dataset as a reference and using the decision function to generate response strategies in combination with the data in the real-time monitoring report; for example, when the temperature is too high or too low, generate strategies to increase or decrease the operating time of ventilation equipment, where the response measures referring to historical data are, for example, increasing air purification equipment.

[0090] The response to equipment failures includes, according to the abnormal patterns of equipment status, using the equipment maintenance records in historical data as a reference and using the decision function to generate strategies for equipment repair or replacement in combination with the data in the real-time monitoring report.

[0091] The response to personnel safety includes, according to the abnormal patterns of personnel status, using the safety event records in historical data as a reference and using the decision function to generate response strategies in combination with the data in the real-time monitoring report. For example, when personnel are in a high-risk area, generate strategies for personnel evacuation or safety warnings.

[0092] The decision function adopted by the response strategy generation module is as follows in formula (6):

[0093] Strategy = f(Environment, Equipment, Personnel, Historical Data) (6)

[0094] Among them, Strategy is the generated response strategy, f is the decision function, Environment, Equipment, Personnel are the data in the real-time monitoring report, and Historical Data is the data in the historical dataset.

[0095] The above embodiments only illustrate several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. An intelligent construction digital monitoring system, characterized in that: The system includes a data acquisition module, a data analysis module, a prediction and evaluation module and a response strategy generation module. The data acquisition module acquires environmental data, equipment status and personnel status during the construction process to generate a current data set and a historical data set; the data analysis module reads the current data set and the historical data set generated by the data acquisition module for analysis and processing, identifies trends and abnormal conditions in the current data set and the historical data set, and generates a real-time monitoring report; the prediction and evaluation module analyzes the real-time monitoring report generated by the data analysis module to obtain project schedule prediction results, project cost prediction results and safety risk assessment results; the response strategy generation module generates a response strategy according to the project schedule prediction results, project cost prediction results and personnel safety risk assessment results obtained by the prediction and evaluation module.

2. According to claim 1, the intelligent construction digital monitoring system is characterized in that: The environmental data, equipment status and personnel status acquired by the data acquisition module specifically include: environmental data specifically includes at least one of temperature, humidity, air quality, noise level and light intensity; equipment status includes at least one of equipment ID, equipment type, operating status, energy consumption data and maintenance status; personnel status includes at least one of personnel ID, personnel name, location information and timestamp; the data acquisition module includes sensors and edge computing devices, the sensors are used to detect environmental data, equipment status and personnel status during the construction process, and the edge computing devices are used to filter and compress sensor data using the edge computing framework.

3. The intelligent construction digital monitoring system according to claim 1 is characterized in that: The data analysis module reads the current data set and the historical data set generated by the data acquisition module for analysis and processing, identifies the trends and anomalies in the current data set and the historical data set, and generates a real-time monitoring report, specifically including: the data analysis module uses linear interpolation method to fill the data in the missing data set for the current data set and the historical data set, and uses the Z-Score method to detect and remove outliers, thereby obtaining cleaned data; the data analysis module aggregates the cleaned data of the monitoring points in different spatial areas by minutes, hours or days, and calculates the average, maximum and minimum values ​​of the data set after data aggregation to obtain aggregated data; the data analysis module uses a linear regression model to identify the long-term trend of the aggregated data, and uses the Z-score method to detect outliers in the aggregated data; the data analysis module generates a visualized real-time monitoring report based on the aggregated data, long-term trends and outliers.

4. The intelligent construction digital monitoring system according to claim 3 is characterized in that: The linear interpolation method estimates the linear relationship between data points, thereby calculating the reasonable value of the missing data points; the Z-Score method calculates the standard deviation ratio of each data point to the mean value of the data set, and identifies outliers by comparing the standard deviation ratios; The linear regression model fits the long-term trend of the aggregated data through a formula, and the specific fitting formula is as follows: y=β0+β1x (1) Where y is the dependent variable, i.e. the predicted value, x is the independent variable, i.e. time, β0 is the intercept, and β1 is the slope.

5. The intelligent construction digital monitoring system according to claim 4 is characterized in that: The Z-score method uses formula (2) to detect outliers in the aggregated data. The specific formula (2) is: Where X is the data point, σ is the mean of the aggregated data, and μ is the standard deviation of the aggregated data.

6. The intelligent construction digital monitoring system according to claim 5 is characterized in that: The real-time monitoring report is sent to the user in a visual form, and the visual form includes at least one of a chart, a curve chart, a heat map, a line chart, a bar chart and a scatter plot.

7. The intelligent construction digital monitoring system according to claim 6, characterized in that: The intelligent construction digital monitoring system is characterized in that the prediction and evaluation module uses the ARIMA model to obtain the project progress prediction result, as shown in formula (3): where y t is the predicted value at time t, indicating the progress of the project or the degree of completion of the task; c is a constant term, which is a bias term in the ARIMA model; is the autoregressive coefficient, which is related to the autocorrelation of the historical progress data; p is the order of the autoregressive term, which indicates the memory degree of the model; θ is the moving average coefficient, which indicates the impact of historical errors on the current forecast; q is the order of the moving average term, which indicates the model's memory of past errors; ∈ t is the error term, which represents the difference between the actual value and the predicted value; The prediction and evaluation module uses the COCOMO II model to obtain the project cost prediction result, as shown in formula (4): Where E is the workload; A is a constant, a constant in the COCOMO II model; KLOC is the project size, which indicates the number of tasks; B is the size index, which indicates the complexity, scale and experience level of the project; EM i It is the cost driver, which indicates the impact of different aspects of the project on the cost; The prediction and evaluation module uses the FAIR model to obtain the safety risk evaluation result, as shown in formula (5): RisK=Loss Event Frequency×Probable Loss Magnitude (5) Among them, Loss Event Frequency is the probability of loss event, which indicates the probability of loss event occurrence, and ProbableLoss Magnitude is the possible loss magnitude, which indicates the maximum loss that may be caused when the event occurs.

8. The intelligent construction digital monitoring system according to claim 1 is characterized in that: The response strategies generated by the response strategy generation module include environmental anomaly response, equipment failure response and personnel safety response. The environmental anomaly response takes the response measures in the historical data set as a reference, and combines the data in the real-time monitoring report to generate the response strategy using a decision function; The equipment failure response uses the equipment maintenance records in the historical data as a reference, and combines the data in the real-time monitoring report with a decision function to generate a response strategy; The personnel safety response uses the safety event records in the historical data as a reference, and uses a decision function to generate a response strategy in combination with the data in the real-time monitoring report; the decision function used by the response strategy generation module is as follows: Strategy=f(Environment,Equipment,Personnel,Historical Data) (6) Strategy is the generated response strategy, f is the decision function, Environment, Equipment, Personnel are the data in the real-time monitoring report, and HistoricalData is the data in the historical data set.

9. A method for digital monitoring of intelligent construction according to any one of claims 1 to 8, characterized in that: The method comprises: Step 1: The data acquisition module acquires environmental data, equipment status, and personnel status during the construction process, and generates current data sets and historical data sets; Step 2: The data analysis module analyzes and processes the current data set and the historical data set, identifies trends and anomalies in the current data set and the historical data set, and generates a real-time monitoring report; Step 3: The prediction and evaluation module analyzes the real-time monitoring report to obtain the project schedule prediction results, project cost prediction results and safety risk assessment results; Step 4: The response strategy generation module generates response strategies according to the project schedule forecast results, project cost forecast results and personnel safety risk assessment results.

10. The method for digital monitoring of intelligent construction according to claim 9, characterized in that: The step 2 specifically includes: Step 2.1: The data analysis module uses linear interpolation to fill in the data in the missing data set, and uses the Z-Score method to detect and remove outliers to obtain cleaned data; Step 2.2: The data analysis module aggregates the cleaned data of monitoring points in different spatial areas by minute, hour or day, and calculates the average value, maximum value and minimum value of the data set after data aggregation to obtain aggregated data; Step 2.3: The data analysis module uses a linear regression model to identify the long-term trend of the aggregated data; Step 2.4: The data analysis module uses the Z-score method to detect outliers in the aggregated data; Step 2.5: The data analysis module generates a real-time monitoring report that visualizes the aggregated data, long-term trends and outliers.

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