Intelligent building construction operation management method based on big data

Abstract

The invention discloses an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

Description

Technical Field

[0001] The present application relates to the technical field of construction management, and more specifically, to an intelligent construction operation management method based on big data. Background Art

[0002] With the continuous expansion of construction projects and the increasing complexity of construction environments, traditional construction management methods are no longer able to meet the safety, quality, and schedule requirements of modern construction projects. During the construction process, factors such as site safety risks, structural risks, environmental risks, and personnel and equipment risks often intertwine, leading to numerous safety incidents and project delays. Therefore, safety supervision and risk management of construction projects have become crucial components to ensure the smooth progress of projects.

[0003] Currently, most construction projects rely on manual inspections, record-keeping, and traditional risk assessment methods for risk management. While these traditional methods can identify issues to a certain extent, their inability to monitor construction site risks in real time and the lack of a scientific, systematic risk prediction and assessment mechanism result in many potential safety hazards going undetected and unaddressed, increasing uncertainty and risk during the construction process. Furthermore, when faced with complex construction environments and changing conditions, these traditional methods often suffer from information lags and untimely responses, making it difficult to effectively prevent and address emergencies.

[0004] In recent years, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent management and monitoring methods have gradually been introduced into the construction industry. Real-time data collection from construction sites using sensors and monitoring equipment, combined with data analysis, risk prediction, and dynamic assessment, is expected to improve safety management during construction. However, existing construction supervision systems often suffer from limited monitoring content, incomplete risk identification, and insufficient early warning and decision-making support, failing to fully implement comprehensive risk analysis and real-time response.

[0005] To sum up, how to achieve real-time monitoring and accurate risk assessment of the entire construction process of construction projects, and provide effective early warning and emergency response strategies based on this, has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, the present application aims to provide an intelligent construction management method based on big data, which includes the following steps:

[0007] Step 1: Realize real-time data collection of construction site personnel, equipment, and environment, and establish a dynamic information database;

[0008] Step 2: Preprocess and extract features from the collected multi-source heterogeneous data to achieve real-time data fusion and intelligent analysis;

[0009] Step 3: Accurately identify risk factors and implement refined early warning and intelligent intervention for individual risks;

[0010] Step 4: Dynamically optimize construction resource allocation to improve resource utilization efficiency while reducing costs and safety risks;

[0011] Step 5: Build a blockchain data sharing mechanism to achieve data traceability and intelligent management throughout the construction life cycle;

[0012] Step 6: Form an adaptive closed-loop feedback mechanism to promote the intelligent transformation of construction management from passive response to active prediction.

[0013] Furthermore, step 1 includes the following steps:

[0014] Deploy various sensor devices at key locations on the construction site, including cameras, RFID readers, and environmental monitors, to form a complete IoT perception layer;

[0015] By equipping workers with smart helmets or positioning cards and combining them with image recognition technology, we can obtain real-time information on the location, working status, and safety protection of on-site personnel;

[0016] Install condition monitoring sensors on construction machinery and equipment to collect data on equipment operating parameters, working hours, and location information;

[0017] Continuously collect environmental parameters at the construction site through temperature and humidity sensors, noise sensors, and dust concentration detectors;

[0018] Build a unified data collection and transmission network to aggregate and store data collected by various sensing devices;

[0019] Build a dynamic information database to classify, store and update the collected multi-dimensional data in real time.

[0020] Furthermore, step 2 includes the following steps:

[0021] Build a distributed edge computing architecture and deploy multiple edge computing nodes at the construction site;

[0022] Develop specialized data pre-processing modules for different types of data sources to perform data cleaning, noise reduction, and standardization operations to ensure consistent data quality;

[0023] Extract features required for target detection and tracking from image data, extract status feature parameters from equipment operation data, and extract trend features from environmental monitoring data;

[0024] Design a data fusion engine to fuse feature information from different data sources at multiple levels;

[0025] Establish a general data analysis framework, conduct in-depth analysis of the fused data based on machine learning algorithms, and build an anomaly detection model.

[0026] Furthermore, step 3 includes the following steps:

[0027] Conduct in-depth research on the collected data on construction accidents, hidden dangers, and risk events to identify the typical characteristics and development patterns of various risk events and form a risk knowledge base;

[0028] Match and compare currently collected multi-source data with historical risk patterns in real time to quickly detect potential risk factors and conduct dynamic assessments of risk levels for single risk types;

[0029] Set different warning thresholds and response strategies based on risk levels, and promptly notify relevant personnel through various means;

[0030] Automatically generate intervention recommendations based on the urgency and impact of the risk, and provide multiple optional response plans for decision makers to refer to;

[0031] Conduct real-time tracking and effectiveness evaluation of implemented intervention measures, and continuously optimize single risk intervention strategies based on the evaluation results.

[0032] Furthermore, step 4 includes the following steps:

[0033] Establish a digital twin environment for construction resource allocation, digitally model various resources on the construction site, and collect construction site data in real time to provide a training environment for reinforcement learning algorithms;

[0034] Design a deep reinforcement learning reward function that integrates resource utilization efficiency, construction cost, and safety risk objectives into the reward calculation;

[0035] Train a deep reinforcement learning agent to enable it to autonomously decide on the optimal resource allocation plan based on the current state of the construction scene;

[0036] Dynamically adjust resource allocation decisions based on real-time data, and dynamically adjust resource allocation plans according to current construction progress, weather conditions, and personnel availability;

[0037] Collect execution data on resource allocation decisions to continuously update and improve reinforcement learning models.

[0038] Furthermore, resource allocation decisions are dynamically adjusted based on real-time data. According to the current construction progress, weather conditions, and personnel availability, resource allocation plans are dynamically adjusted, including the following steps:

[0039] Analyze collected real-time data to assess construction progress and plan deviations, predict the potential impact of weather changes on construction, and examine personnel attendance and work efficiency indicators to identify problems and potential risks in current resource allocation;

[0040] Based on data analysis results and project management experience, make resource allocation recommendations, including personnel additions or transfers, equipment additions or replacements, and material reserves or scheduling measures to ensure the feasibility and timeliness of the plan;

[0041] Simulate and measure different adjustment plans, evaluate implementation effectiveness and cost impact, select the optimal plan, and develop contingency plans to ensure flexibility in responding to emergencies;

[0042] Quickly carry out resource allocation work according to the confirmed adjustment plan, and continuously monitor the construction progress and resource utilization effect, promptly identify problems in the implementation process, and ensure that the adjustment plan can achieve the expected results.

[0043] Furthermore, step 5 includes the following steps:

[0044] Build a unified blockchain network platform, use distributed ledger technology to ensure data is decentralized and tamper-proof, and combine smart contracts to set data access rights and sharing boundaries to ensure security and privacy;

[0045] Establish standards for uploading construction data to the blockchain, classify and encode all types of data generated during the construction process, and unify formats and metadata standards;

[0046] Convert various rules and processes in construction management into smart contract code to automatically execute project acceptance, payment settlement and quality assessment processes;

[0047] Build a data traceability analysis platform to realize information traceability of the entire construction process based on blockchain data.

[0048] Furthermore, step 6 includes the following steps:

[0049] Based on the analysis framework established in step 2, real-time analysis of construction quality scenarios is performed to automatically detect construction quality issues and automatically trigger corresponding warning and disposal processes based on the severity;

[0050] Comprehensively analyze construction site personnel attendance rates, equipment operating status, and material supply data, combined with historical project experience, to accurately predict potential deviations in project progress;

[0051] Based on the resource allocation data from step 4, analyze the cost structure and changing trends to identify the risk of cost overruns;

[0052] Form an intelligent closed-loop management mechanism, comprehensively integrate the analysis results of the three dimensions of quality, progress, and cost, automatically evaluate the effectiveness of various management measures, continuously learn and adjust management strategies, and gradually improve the accuracy of predictions and the level of intelligent management.

[0053] Compared with the prior art, this application has the following beneficial effects:

[0054] This application uses big data and artificial intelligence technologies, combined with real-time data collection, intelligent analysis, precise risk identification and dynamic resource optimization, to realize an intelligent construction operation management system, thereby improving construction efficiency, reducing safety risks and promoting the intelligent transformation of construction management towards active prediction and closed-loop feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of a method for intelligent construction operation management based on big data disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0057] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0058] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0059] like Figure 1 As shown, a method for intelligent construction operation management based on big data includes the following steps:

[0060] Step 1: Realize real-time data collection of construction site personnel, equipment, and environment, and establish a dynamic information database;

[0061] Step 2: Preprocess and extract features from the collected multi-source heterogeneous data to achieve real-time data fusion and intelligent analysis;

[0062] Step 3: Accurately identify risk factors and implement refined early warning and intelligent intervention for individual risks;

[0063] Step 4: Dynamically optimize construction resource allocation to improve resource utilization efficiency while reducing costs and safety risks;

[0064] Step 5: Build a blockchain data sharing mechanism to achieve data traceability and intelligent management throughout the construction life cycle;

[0065] Step 6: Form an adaptive closed-loop feedback mechanism to promote the intelligent transformation of construction management from passive response to active prediction.

[0066] In step 1, real-time data collection plays a fundamental role. Real-time data collection of construction site personnel, equipment, and the environment is achieved through a variety of sensors and devices, such as RFID, smart wearable devices, and environmental monitoring sensors. These devices accurately record personnel locations, equipment operating status, construction progress, and environmental parameters such as temperature, humidity, noise, and air quality. Using IoT technology, this data can be transmitted to a cloud platform in real time, forming a dynamic information database. This database not only provides real-time data from the construction site but also stores a large amount of historical data through big data technology, providing data support for subsequent analysis and decision-making. The establishment of a dynamic information database enables construction managers to maintain a constant understanding of the site's status, facilitates timely problem identification and decision-making, and greatly improves transparency and controllability during the construction process.

[0067] In step 2, the collected data often comes from different sensors and devices and is heterogeneous, including structured data (such as sensor values) and unstructured data (such as video, sound, etc.). Therefore, data preprocessing and feature extraction are very critical steps. Through data cleaning, denoising, standardization and other means, invalid information can be removed to ensure the accuracy and consistency of the data. At the same time, through feature extraction technology, the raw data is converted into features that can reflect the status of the construction site, such as changes in equipment load and personnel work efficiency. This process can not only process large-scale real-time data streams, but also extract valuable information to support intelligent analysis and decision-making. Through machine learning and artificial intelligence algorithms, these data can be subjected to deep learning analysis to help identify potential problems in construction, such as equipment failure, personnel fatigue, and delayed construction progress, providing a scientific basis for subsequent management decisions.

[0068] The key to step 3 lies in accurately identifying potential risks during the construction process, including safety risks, quality risks, and schedule risks. By analyzing data from multiple sources in real time, combined with historical data and on-site environmental factors, the intelligent system can identify potential risks and predict their probability of occurrence. For example, equipment status monitoring can predict the risk of equipment failure; personnel behavior data can assess employee safety operational risks; and environmental monitoring can identify risks posed by external factors such as hazardous weather. By establishing a risk-based early warning system, targeted and refined interventions can be implemented based on the type and level of risk. For example, if the system detects signs of equipment failure, it can automatically issue an early warning and notify maintenance personnel. If personnel operate improperly, the system can provide safety alerts through intelligent reminders. This intelligent risk identification and intervention mechanism can minimize unexpected risks during construction, ensuring safe and smooth construction.

[0069] In step 4, optimizing resource allocation is a core issue in construction management. Traditional resource allocation methods often rely on manual experience, resulting in low efficiency and prone to resource waste. Intelligent methods can dynamically optimize resource allocation based on real-time data. Leveraging artificial intelligence algorithms, the system automatically adjusts resource allocation based on real-time construction progress, personnel and equipment usage, and environmental factors. For example, if a particular section of work progresses slowly, the system can automatically deploy additional equipment or personnel; if weather conditions change at the construction site, the system can adjust the construction plan to avoid unnecessary risks. By dynamically adjusting resource usage, construction efficiency can be improved, equipment idleness can be reduced, and excessive resource consumption can be avoided, thereby reducing overall construction costs. Furthermore, intelligent resource allocation reduces human intervention, lowers the risk of operational errors and safety accidents, and improves the safety of the construction process.

[0070] Step 5 emphasizes the mechanism for data sharing and traceability, where blockchain technology plays a crucial role. Blockchain provides a decentralized distributed ledger technology that ensures that all data during the construction process is traceable and tamper-proof. In construction, every aspect, from material procurement and equipment deployment to construction progress and quality monitoring, can be recorded via blockchain, ensuring data transparency and credibility. By establishing a blockchain data sharing mechanism, all parties involved in the construction process (such as contractors, suppliers, and regulatory authorities) can achieve real-time data sharing and collaboration, ensuring smooth information flow. Furthermore, the immutability of data ensures traceability throughout the construction process. In the event of quality issues or safety incidents, historical data can be queried through the blockchain to trace the responsible party, ensuring fairness, justice, and transparency.

[0071] In step 6, by establishing an adaptive closed-loop feedback mechanism, construction management can shift from its previous passive response model to a proactive prediction and prevention model. Based on real-time and historical data, this closed-loop feedback mechanism not only enables real-time adjustments to resource allocation and management strategies during the construction process, but also automatically responds to predicted risks and issues. For example, if the construction progress deviates from plan, the system can automatically adjust the work schedules of construction personnel or equipment to ensure timely completion. When risk factors reach a certain threshold, the system automatically issues an alert and takes intervention measures. Simultaneously, the system continuously learns and optimizes the prediction model based on feedback data, gradually improving prediction accuracy and decision-making rationality. This adaptive feedback mechanism makes construction management more intelligent and dynamic, significantly improving construction efficiency and safety, and promoting the development of intelligent and refined construction management.

[0072] In summary, a big data-based intelligent construction management approach forms a complete closed-loop construction management system, encompassing real-time data collection, data preprocessing, risk identification, resource optimization, data sharing, and adaptive feedback mechanisms. Each step utilizes technological means to ensure efficient, secure, and transparent construction management. By integrating advanced technologies such as big data, artificial intelligence, the Internet of Things, and blockchain, this approach not only improves construction efficiency but also effectively reduces costs and safety risks.

[0073] Furthermore, step 1 includes the following steps:

[0074] Deploy various sensor devices at key locations on the construction site, including cameras, RFID readers, and environmental monitors, to form a complete IoT perception layer;

[0075] By equipping workers with smart helmets or positioning cards and combining them with image recognition technology, we can obtain real-time information on the location, working status, and safety protection of on-site personnel;

[0076] Install condition monitoring sensors on construction machinery and equipment to collect data on equipment operating parameters, working hours, and location information;

[0077] Continuously collect environmental parameters at the construction site through temperature and humidity sensors, noise sensors, and dust concentration detectors;

[0078] Build a unified data collection and transmission network to aggregate and store data collected by various sensing devices;

[0079] Build a dynamic information database to classify, store and update the collected multi-dimensional data in real time.

[0080] Among them, the sensor equipment adopts a hierarchical mesh topology, with a data aggregation node set up every 25 to 30 meters in the construction area. Each aggregation node is connected to 6 to 10 sensor devices through a hybrid wired and wireless method, including industrial-grade cameras (resolution 1280×720 pixels, support for night vision function, protection level IP67), medium-frequency RFID readers (working frequency 433MHz, effective reading distance 3 to 5 meters, support for simultaneous identification of 50 tags), and integrated environmental monitoring devices (simultaneously monitoring temperature, humidity, noise, and dust concentration, with a data sampling interval of 60 seconds). Data transmission adopts a dual backup mechanism of industrial Ethernet and 4G network. In the event of network interruption, data can be locally cached for 24 hours, and automatically synchronized after the network is restored. It has automatic fault detection function. When a sensor node is offline for more than 10 minutes, a fault notification will be sent to maintenance personnel to ensure the continuous and stable operation of the monitoring network and timely maintenance response.

[0081] Among them, the smart safety helmet integrates a low-power sensor module with a built-in three-axis accelerometer and gyroscope sensor, which can detect the movement state and posture changes of the worker's head. When an abnormal static state of more than 5 seconds (acceleration change is less than 0.1g) or a violent impact (impact acceleration exceeds 8g) is detected, an early warning signal is sent to the nearest data collection node through the built-in Bluetooth 5.0 module. The safety helmet is equipped with an LED indicator and an 85-decibel buzzer for local alarm. The RTK-GPS module is used to achieve outdoor positioning with an accuracy of 3 to 5 meters, and the Bluetooth beacon network is used to achieve regional positioning with an accuracy of 5 to 10 meters in indoor environments. The device has a battery life of no less than 72 hours, supports USB-C fast charging, has an IP54 protection level, can adapt to the dust and light water splash environment of the construction site, and effectively improves the safety protection level and emergency response capability of the workers.

[0082] Among them, the construction machinery equipment status monitoring adopts a composite monitoring technology based on vibration and temperature, and industrial-grade vibration sensors and temperature sensors are installed on the bearing seats, reducers and engines of key equipment. The vibration sensor sampling frequency is 1kHz, and the measurement range is 0-200Hz. It can detect the equipment's rotation frequency characteristics and harmonic components. The temperature sensor monitors the working temperature of key components of the equipment with a sampling interval of 5 minutes. By establishing a baseline model for normal operation of the equipment, an early warning message is generated when the vibration intensity exceeds 150% of the baseline value or the temperature exceeds the normal range by 10 degrees Celsius. A trend analysis algorithm is used to predict equipment maintenance needs, and the prediction time window is 7 to 21 days, thereby improving equipment utilization efficiency and the overall safety level of the construction site.

[0083] Among them, environmental monitoring equipment includes industrial-grade temperature and humidity sensors (temperature measurement range -20℃ to 80℃, accuracy ±0.5℃, humidity measurement range 5% to 95%RH, accuracy ±3%RH), noise meter (measurement range 35-120dB, accuracy ±2dB, support A-weighting and C-weighting), laser dust sensor (PM2.5 and PM10 measurement range 0-500μg / m 3 , resolution 5μg / m 3 ), portable hazardous gas detectors (capable of detecting common gases such as CO, H2S, CH4, O2, etc.), all sensor equipment has a protection level of IP65 or above, can work normally under ambient temperatures of -10℃ to 60℃, and the data collection interval is 300 seconds. When the environmental parameters exceed the standard values ​​of construction safety regulations, a graded alarm will be triggered within 30 seconds, including on-site sound and light warnings and mobile message push, to ensure that construction personnel can understand the environmental conditions in a timely manner and take corresponding protective measures.

[0084] Among them, the dynamic information database adopts a hybrid storage architecture, combining the relational database MySQL to store structured data and the time series database InfluxDB to store sensor time series data. The database deployment adopts a master-slave replication configuration. The master node is responsible for data writing, and the slave node provides query services. The data redundancy is set to double copies. Each node is configured with 16GB of memory and 1TB of SSD storage space. The database supports processing 100,000 data writes and 50,000 query operations per second. Through data index optimization, the response time of a single query is controlled within 500 milliseconds. The data retention policy is set to keep detailed data for 3 months and aggregated statistical data for 2 years. Expired data is migrated to low-cost network storage through data compression technology.

[0085] In summary, Step 1 builds a highly integrated IoT perception layer by comprehensively deploying various sensor devices and collecting and storing data. This mechanism not only enables real-time monitoring of personnel, equipment, and the environment at the construction site, but also ensures efficient, safe, and standardized construction through intelligent data analysis, risk identification, and resource optimization. Through precise sensor data collection and management, every detail of the construction site can be perceived and managed in real time, significantly enhancing the level of intelligent construction management and providing strong data support for subsequent construction decision-making and optimization.

[0086] Furthermore, step 2 includes the following steps:

[0087] Build a distributed edge computing architecture and deploy multiple edge computing nodes at the construction site;

[0088] Develop specialized data pre-processing modules for different types of data sources to perform data cleaning, noise reduction, and standardization operations to ensure consistent data quality;

[0089] Extract features required for target detection and tracking from image data, extract status feature parameters from equipment operation data, and extract trend features from environmental monitoring data;

[0090] Design a data fusion engine to fuse feature information from different data sources at multiple levels;

[0091] Establish a general data analysis framework, conduct in-depth analysis of the fused data based on machine learning algorithms, and build an anomaly detection model.

[0092] Among them, the distributed edge computing architecture adopts an industrial-grade edge computing device deployment solution, setting up 2 to 4 edge computing nodes at the construction site. Each node uses an Intel Core i7 processor, 16GB of memory and 256GB of SSD storage configuration, and supports Docker containerized deployment. The edge node is connected to the cloud server via Gigabit Ethernet or 4G / 5G network, and the network delay is controlled within 100 milliseconds. Each node can process data streams from 50 to 80 sensor devices at the same time, and distributes computing tasks through load balancing strategies. When the CPU utilization rate of a single node exceeds 85%, the new task will be assigned to other nodes for processing. The edge node has a local data caching function and can store 72 hours of historical data. It ensures the normal operation of local analysis functions during network interruptions, and realizes automatic deployment and updating of applications through container orchestration technology to ensure the stable and reliable operation of the edge computing system.

[0093] Among them, the data preprocessing module builds a real-time data processing pipeline based on the Apache Kafka stream processing platform, and uses traditional computer vision algorithms for preprocessing image data, including Gaussian filtering for denoising, histogram equalization for contrast enhancement, edge detection and other operations; a sliding window median filter algorithm is used to eliminate outliers for sensor data, and the window size is set to 10 data points, which can effectively identify and correct more than 95% of obvious abnormal data. The maximum and minimum value normalization method is used for data standardization to unify the numerical range of different types of sensor data into the [0,1] interval. The average processing delay of data preprocessing is controlled within 2 seconds, and 1 million records can be processed per hour in batch mode.

[0094] Among them, the feature extraction process adopts a multi-level feature extraction strategy. For image data, the traditional feature extraction method based on HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns) is used, combined with the SVM classifier to identify 12 common targets such as safety helmets, reflective vests, construction workers, and vehicles; for equipment operation data, time domain features are extracted, including 8 statistical feature parameters such as mean, variance, peak, and effective value, and frequency domain features are extracted through FFT transformation to extract the main frequency components and spectral energy distribution; for environmental monitoring data, change trend features are extracted, including 6 feature parameters such as linear trend coefficient, coefficient of variation, and periodic detection. The 20 to 30 most discriminative features are screened out from high-dimensional features through a feature selection algorithm, and the feature importance is evaluated using correlation analysis and information gain methods.

[0095] Among them, the data fusion engine adopts a fusion method based on rules and statistics to establish a weighted fusion model for multi-source data. The weight coefficient is dynamically adjusted according to the reliability and importance of each data source. The weight coefficient of image data is 0.3, the weight coefficient of equipment status data is 0.4, and the weight coefficient of environmental data is 0.3. The fusion process is divided into two stages: data layer fusion and decision layer fusion. The data layer fusion adopts a method combining weighted average and voting mechanism to integrate multi-source observation results of the same target. The decision layer fusion adopts Bayesian reasoning method to calculate the posterior probability based on prior probability and observation evidence. The confidence assessment of the fusion result adopts uncertainty quantification method.

[0096] Among them, the anomaly detection model adopts a hybrid method based on the combination of statistical analysis and machine learning. The statistical analysis part uses the 3σ criterion and box plot method to detect outliers of numerical data. The machine learning part uses the Isolation Forest algorithm to detect abnormal patterns in multidimensional data. The isolation forest model contains 100 decision trees, the maximum depth of each tree is set to 8 layers, and the anomaly detection threshold is set to the 85% quantile of the anomaly score. The model is trained on a training set containing 100,000 normal data and 10,000 abnormal data. The model parameters are optimized using the grid search method. The anomaly detection accuracy on the test set reaches 75% to 80%, the recall rate reaches 70% to 75%, and the F1 score reaches 72% to 77%. The single detection time is controlled within 100 milliseconds. The model is incrementally updated based on new data every week to maintain the timeliness of detection performance, which can meet the performance requirements and accuracy requirements of real-time anomaly monitoring at construction sites.

[0097] In summary, step 2 plays a crucial role in intelligent construction management. By building a distributed edge computing architecture and data preprocessing modules, we achieve effective fusion and in-depth analysis of multi-source heterogeneous data. This process not only improves the efficiency and accuracy of data processing but also enables the construction site to respond to various changes and emergencies in real time. The introduction of data fusion and anomaly detection models enables the construction management system to more intelligently identify risks, optimize resource allocation, and effectively improve the safety and efficiency of the construction process. Through these technical means, construction site data can be comprehensively integrated and deeply mined, driving the development of more efficient and intelligent construction management.

[0098] Furthermore, step 3 includes the following steps:

[0099] Conduct in-depth research on the collected data on construction accidents, hidden dangers, and risk events to identify the typical characteristics and development patterns of various risk events and form a risk knowledge base;

[0100] Match and compare currently collected multi-source data with historical risk patterns in real time to quickly detect potential risk factors and conduct dynamic assessments of risk levels for single risk types;

[0101] Set different warning thresholds and response strategies based on risk levels, and promptly notify relevant personnel through various means;

[0102] Automatically generate intervention recommendations based on the urgency and impact of the risk, and provide multiple optional response plans for decision makers to refer to;

[0103] Conduct real-time tracking and effectiveness evaluation of implemented intervention measures, and continuously optimize single risk intervention strategies based on the evaluation results.

[0104] Among them, the intervention recommendations are constructed based on the case-based reasoning (CBR) method, and an intervention measures library containing 500 typical cases is established. Each case contains information such as problem description, solution, implementation cost, and expected effect. When a risk is identified, the 3 to 5 most similar historical cases are found through case similarity matching, and the case similarity is calculated using the nearest neighbor algorithm. The similarity evaluation considers 8 matching factors such as risk type, severity, environmental conditions, and resource status. Each factor is assigned a different weight coefficient. Intervention recommendations are divided into three levels: immediate measures, short-term measures, and long-term measures. The response time for immediate measures is within 5 minutes, the response time for short-term measures is within 1 hour, and the response time for long-term measures is within 24 hours. 2 to 3 optional options are provided for each level. The solution evaluation considers four dimensions such as feasibility, effectiveness, cost, and risk, and uses a simple scoring method to rank and recommend solutions. The generation time of intervention recommendations is controlled within 10 seconds.

[0105] Among them, the intervention effect evaluation adopts a before-and-after statistical analysis method, and evaluates the intervention effect by changing key indicators before and after the implementation of the intervention measures. The evaluation indicators include six quantitative indicators such as the number of risk events, safety inspection pass rate, operation efficiency, and cost changes. The data collection period is set at three time nodes: 7 days before the intervention, 7 days after the intervention, and 30 days after the intervention. The t-test method is used to judge the significance of the indicator changes. When the p-value is less than 0.05, the intervention effect is considered significant. The effect evaluation report includes an indicator change trend chart, statistical analysis results, and improvement suggestions. The evaluation results are used to update the case library and optimize the intervention strategy. By establishing a feedback mechanism for the intervention effect, the methods and strategies of risk intervention are continuously improved. The effectiveness of the intervention strategy is evaluated using the success rate statistical method, and the target success rate is set at more than 70%. The actual success rate is evaluated regularly and the strategy is adjusted to gradually improve the overall effect of risk intervention.

[0106] In summary, through the implementation of the above steps, risk management capabilities at construction sites have been significantly enhanced. Based on big data analysis and artificial intelligence technologies, construction sites are able to identify and assess potential safety risks in real time, shifting from traditional post-event response to proactive prediction. Leveraging technologies such as risk knowledge bases, real-time data matching, dynamic assessments, automated intervention recommendations, and effectiveness evaluations, construction site safety has been enhanced, not only improving construction efficiency but also reducing losses caused by the failure to promptly identify and address risks. This intelligent risk management model not only plays a vital role in ensuring construction safety but also drives the construction industry towards a more digital and automated direction.

[0107] Furthermore, we conduct in-depth research on the collected data on construction accidents, hidden dangers, and risk events, identify the typical characteristics and development patterns of various risk events, and form a risk knowledge base, which includes the following steps:

[0108] Calculate the similarity between risk events, use clustering algorithms to classify similar events into different categories, and identify typical risk types, which can be expressed as: Among them, d(X i ,X j ) represents the similarity between the i-th and j-th risk events; n represents the number of feature dimensions; x ik represents the kth feature of the i-th risk event; x jk represents the kth feature of the jth risk event;

[0109] Based on the cluster analysis results, a risk knowledge base is constructed. Each risk event category will include its typical characteristics, occurrence patterns and response strategies, which can be expressed as follows: Risk Knowledge Record k ={C k ,Fk ,P k ,S k}, where C k Indicates the risk event category, F k is a typical feature set, P k is the occurrence law of this category, and S k Is a response strategy or early warning rule; RiskKnowledge Record k represents a record in the risk knowledge base, where k is the category of the risk event;

[0110] Based on the risk knowledge base and historical data, a machine learning algorithm is used to predict the probability of risk events and automatically select intervention strategies to reduce potential risks, which can be expressed as: Among them, P(Risk Event|X) represents the probability of a risk event occurring given the current data X; β0, β1, β2, ..., β n are the parameters obtained through training, x1, x2, ..., x n is the eigenvalue in the data;

[0111] By tracking the effects of intervention measures and providing feedback, we can continuously optimize risk prediction and intervention strategies, which can be expressed as: Among them, A t R represents the intervention strategy at time t; i (A) represents the benefit or effect of intervention strategy A on risk management under the i-th event; N represents the total number of risk events.

[0112] In summary, by deeply mining data on construction accidents, hidden dangers, and risk events, combined with technologies such as cluster analysis, machine learning, and impact evaluation, a dynamically updated risk knowledge base can be established at the construction site. This knowledge base not only helps managers identify and classify different risk types but also provides accurate decision support for the real-time assessment, prediction, and intervention of various risks during the construction process. By continuously tracking and optimizing intervention measures, the system continuously improves prediction accuracy and intervention effectiveness, thereby effectively reducing safety risks during the construction process and improving construction site safety and management efficiency.

[0113] Furthermore, the currently collected multi-source data is matched and compared with historical risk patterns in real time to quickly detect potential risk factors and conduct a dynamic assessment of the risk level of a single risk type, including the following steps:

[0114] The weighted cosine similarity is used to calculate the matching degree S(X,P) between real-time data and historical risk patterns, and the similarity between the current construction status and historical risks is obtained, which can be expressed as follows: Among them, Xp and P p are the pth features in the current data and historical patterns respectively; M is the total number of data items considered in the risk assessment process; m represents the number of risk types in the constructed historical risk pattern library; w p Indicates the contribution of each data item to the risk assessment results;

[0115] Based on the matching degree S(X,P) and characteristic data, the risk level R of a single risk type is calculated for preliminary judgment of the risk level, which is expressed as follows: R = α0 + α1·S(X,P) + α2·f(X) + α3·f(P), where α0 is a constant term representing the basic risk level; α1, α2, and α3 are used to adjust the weights of the matching degree S(X,P), the characteristic f(X), and f(P) in the risk level assessment, respectively; f(X) is the characteristic function of real-time data, representing the features extracted from real-time collected data; f(P) is the characteristic function of historical risk patterns, representing the features extracted from historical risk data;

[0116] Using the risk level R(t) of the previous moment of the real-time matching degree S(X,P), the formula R(t+1)=R(t)+β·(S(X,P)-S(X prev ,P prev )) is dynamically adjusted, where R(t+1) is the risk level at time t+1, which is the risk status assessed based on current data and historical patterns and is used to dynamically update the risk assessment; β is the adjustment factor used to control the magnitude of the risk level update to ensure that real-time data updates can smoothly reflect risk changes; S(X prev ,P prev ) is the real-time data X at time t pre and historical mode P prev The matching degree between them is used as the historical status to dynamically update the risk level.

[0117] In summary, real-time matching and comparison of currently collected multi-source data with historical risk patterns can effectively identify potential risk factors. This, through techniques such as weighted cosine similarity calculation, risk level assessment, and dynamic adjustment, provides a scientific basis for construction site safety management. Real-time risk assessment not only improves the sensitivity of risk detection but also allows for flexible adjustment of risk levels based on actual construction status and historical data, ensuring that construction managers can take timely measures to reduce the probability of accidents. This multi-level, dynamic risk assessment mechanism significantly improves construction site safety and provides strong data support for subsequent risk interventions.

[0118] Furthermore, step 4 includes the following steps:

[0119] Establish a digital twin environment for construction resource allocation, digitally model various resources on the construction site, and collect construction site data in real time to provide a training environment for reinforcement learning algorithms;

[0120] Design a deep reinforcement learning reward function that integrates resource utilization efficiency, construction cost, and safety risk objectives into the reward calculation;

[0121] Train a deep reinforcement learning agent to enable it to autonomously decide on the optimal resource allocation plan based on the current state of the construction scene;

[0122] Dynamically adjust resource allocation decisions based on real-time data, and dynamically adjust resource allocation plans according to current construction progress, weather conditions, and personnel availability;

[0123] Collect execution data on resource allocation decisions to continuously update and improve reinforcement learning models.

[0124] Among them, the digital twin environment adopts a three-dimensional visualization platform based on CAD models and real-time data, and uses the open source three-dimensional engine Three.js to build a digital model of the construction site on the Web. The model includes key elements such as the main building structure, large-scale construction equipment, material stacking areas, temporary facilities, etc. The model texture uses photos taken on the spot. The digital twin model obtains real-time sensor data through the RESTful API interface, and the data is updated once a minute. The equipment status, environmental parameters, personnel location and other information in the model are synchronized with the actual situation, and the delay time is controlled within 5 minutes. The three-dimensional model has basic interactive functions such as zooming, rotating, and roaming. Detailed status information and historical data can be viewed by clicking on model elements, providing construction management personnel with an intuitive visualization of the on-site conditions.

[0125] Among them, the resource allocation optimization adopts genetic algorithm to solve the multi-objective optimization problem. The optimization objectives include maximizing resource utilization, minimizing total cost and minimizing safety risk. Resource utilization is calculated as the ratio of equipment usage time to total available time, and the target value is set at 75% to 85%; total cost includes labor cost, equipment rental cost, material transportation cost, etc., and cost control is carried out based on the project budget; safety risk is calculated as the weighted average probability of risk event occurrence, and the target value is 20% lower than the historical average level. The genetic algorithm parameters are set as population size 100, crossover probability 0.7, mutation probability 0.1, and number of iterations 200 generations. The fitness function adopts the weighted sum method, and the weight ratio of the three objectives is 0.4:0.4:0.2.

[0126] In summary, by building a digital twin environment for construction resource allocation and integrating it with deep reinforcement learning algorithms, construction resource management will not only become more intelligent and precise, but will also significantly improve construction efficiency, reduce costs, and ensure safety. Digital twin technology provides real-time monitoring and data feedback of the construction site, while deep reinforcement learning enables intelligent agents to autonomously learn and optimize resource allocation decisions. Based on dynamic adjustments and real-time feedback, construction projects can flexibly respond to site changes, improve resource utilization, and effectively reduce construction risks. With the continuous optimization of reinforcement learning models, construction resource allocation will become more efficient and refined, providing a solid technical foundation for the smooth implementation of construction projects.

[0127] Furthermore, resource allocation decisions are dynamically adjusted based on real-time data. According to the current construction progress, weather conditions, and personnel availability, resource allocation plans are dynamically adjusted, including the following steps:

[0128] Analyze collected real-time data to assess construction progress and plan deviations, predict the potential impact of weather changes on construction, and examine personnel attendance and work efficiency indicators to identify problems and potential risks in current resource allocation;

[0129] Based on data analysis results and project management experience, make resource allocation recommendations, including personnel additions or transfers, equipment additions or replacements, and material reserves or scheduling measures to ensure the feasibility and timeliness of the plan;

[0130] Simulate and measure different adjustment plans, evaluate implementation effectiveness and cost impact, select the optimal plan, and develop contingency plans to ensure flexibility in responding to emergencies;

[0131] Quickly carry out resource allocation work according to the confirmed adjustment plan, and continuously monitor the construction progress and resource utilization effect, promptly identify problems in the implementation process, and ensure that the adjustment plan can achieve the expected results.

[0132] Through real-time data analysis and dynamic resource allocation adjustments, construction sites can flexibly respond to the ever-changing construction environment and external factors. The effective execution of each step provides crucial guarantees for efficient resource utilization, continued progress in construction, and cost and risk control. Data analysis provides a scientific basis for decision-making, resource allocation recommendations ensure the feasibility and timeliness of adjustment plans, simulations and measurements optimize solution selection, and real-time monitoring and adjustments during implementation ensure efficient execution. Through the integration of this series of measures, construction projects can be efficiently managed in complex and changing environments, ensuring on-time and high-quality completion.

[0133] Furthermore, step 5 includes the following steps:

[0134] Build a unified blockchain network platform, use distributed ledger technology to ensure data is decentralized and tamper-proof, and combine smart contracts to set data access rights and sharing boundaries to ensure security and privacy;

[0135] Establish standards for uploading construction data to the blockchain, classify and encode all types of data generated during the construction process, and unify formats and metadata standards;

[0136] Convert various rules and processes in construction management into smart contract code to automatically execute project acceptance, payment settlement and quality assessment processes;

[0137] Build a data traceability analysis platform to realize information traceability of the entire construction process based on blockchain data.

[0138] Among them, the data sharing mechanism adopts a distributed data management platform based on digital signatures. The platform includes three types of participant nodes: construction units, supervision units, and construction units. Each type of participant is equipped with one master node and one backup node, totaling six network nodes. Data transmission is encrypted using the HTTPS protocol, and the digital signature uses the RSA-2048 algorithm to ensure the integrity and non-tamperability of the data. Data access control adopts role-based authority management (RBAC), defining eight roles such as project manager, technical leader, quality inspector, and safety officer. Each role has different data access rights. The platform supports writing and querying 1,000 data records per hour. Data synchronization adopts a master-slave replication mechanism. When the master node fails, it automatically switches to the backup node. The platform provides two access methods: Web interface and mobile APP, and supports online viewing, downloading and statistical analysis of data.

[0139] Among them, the construction data standardization is based on the unified data format specification formulated based on the construction engineering industry standard. The data classification adopts five first-level classifications: construction process, quality management, safety management, schedule management, and cost management. Each first-level classification has 6 to 10 second-level classifications, totaling 38 data categories. Each data record contains 15 basic fields such as data ID, creation time, data source, person in charge, and data content. The data encoding adopts a layered encoding method with the format of "project code-classification code-serial number" and a total length of 16 bits. The data format adopts the JSON standard and supports multiple data types such as text, numbers, dates, and files. The data quality check includes 4 categories of 32 inspection rules, including required field check, data type check, numerical range check, and logical consistency check. All inspection rules must be passed before data is uploaded. Data that fails the inspection will return detailed error information. The data standard is reviewed and updated once a year to ensure the applicability and advancement of the standard.

[0140] Among them, the construction of the data traceability analysis platform specifically includes: developing a visual traceability interface, which intuitively presents the data flow and correlation relationships of each link in the construction process through various display methods such as timeline, flow chart and relationship network, and supports users to quickly locate detailed information of a specific time period or process; establishing a multi-dimensional query engine to support data retrieval and correlation analysis according to multiple dimensions such as time, space, personnel, equipment, materials, etc., to achieve cross-system and cross-stage data integration and correlation query; building an abnormal event traceability mechanism, when quality problems or safety accidents are discovered, it can quickly trace back to the relevant construction personnel, materials used, operating equipment and environmental conditions, and provide complete data support for accident investigation and responsibility determination; designing a data analysis algorithm module, through statistical analysis, trend prediction and correlation mining methods, etc., to discover the weak links and improvement space of construction management from the traceability data, provide data-driven suggestions and optimization solutions for management decisions, and support deep mining and knowledge extraction of historical data.

[0141] In summary, the application of blockchain technology can improve the efficiency of various management processes while ensuring data security, transparency, and traceability. A unified blockchain platform provides decentralized, tamper-proof storage for construction data, while the introduction of smart contracts automates project acceptance, payment settlement, and quality assessment processes, reducing the risk of manual intervention. The development of standards for uploading construction data to the blockchain ensures data consistency and standardization, providing a reliable foundation for subsequent data analysis and decision-making. Furthermore, the establishment of a data traceability and analysis platform enables real-time tracking of every aspect of the construction process, providing a transparent source of information for all parties. In summary, the application of blockchain technology not only enhances the intelligence of construction management but also provides strong guarantees for the safe, transparent, and compliant implementation of projects.

[0142] Furthermore, step 6 includes the following steps:

[0143] Based on the analysis framework established in step 2, real-time analysis of construction quality scenarios is performed to automatically detect construction quality issues and automatically trigger corresponding warning and disposal processes based on the severity;

[0144] Comprehensively analyze construction site personnel attendance rates, equipment operating status, and material supply data, combined with historical project experience, to accurately predict potential deviations in project progress;

[0145] Based on the resource allocation data from step 4, analyze the cost structure and changing trends to identify the risk of cost overruns;

[0146] Form an intelligent closed-loop management mechanism, comprehensively integrate the analysis results of the three dimensions of quality, progress, and cost, automatically evaluate the effectiveness of various management measures, continuously learn and adjust management strategies, and gradually improve the accuracy of predictions and the level of intelligent management.

[0147] Among them, the comprehensive analysis and prediction of project progress deviation specifically includes: comprehensively considering key factors such as personnel attendance rate, equipment availability rate, material supply timeliness rate, weather impact coefficient and completion quality of previous processes, and predicting the actual completion time of each process through time series analysis and regression algorithm; based on the critical path method and resource-constrained project scheduling theory, automatically generating multiple adjustment plans when progress deviation is detected, including strategies such as resource reallocation, process sequence optimization and parallel operation arrangement; analyzing the impact of various uncertain factors on the overall progress of the project through Monte Carlo simulation method, identifying progress sensitive nodes and key risk sources; dynamically adjusting the warning threshold of progress deviation according to the historical data of project execution and the characteristics of the current stage, avoiding the problem of excessive warning or untimely warning, and at the same time establishing a progress correction effect evaluation system to continuously optimize the accuracy and practicality of the progress prediction model.

[0148] Among them, the formation of an intelligent closed-loop management mechanism specifically includes: weighted integration of quality indicators, progress indicators and cost indicators, establishing a comprehensive project performance evaluation system, and realizing a quantitative evaluation of the overall effect of construction management; continuously optimizing management strategies through deep reinforcement learning technology, automatically adjusting prediction model parameters and decision rules according to the actual effects of management measures, and improving the intelligence level of the management system; regularly collecting and analyzing the execution results of various management measures, identifying successful experiences and lessons from failure, forming a management knowledge base and using it to guide subsequent management decisions; through regular data mining and pattern recognition, discovering new problems and improvement opportunities in the management process, automatically updating management rules and optimization algorithms, realizing the self-evolution and continuous improvement of the management system, and at the same time establishing a cross-project experience sharing mechanism to promote and apply successful management experience and best practices to other similar projects, and realizing the accumulation and inheritance of management wisdom.

[0149] In summary, through real-time data analysis, intelligent algorithms, and comprehensive integration, construction management can achieve precise control and optimization across the three dimensions of quality, schedule, and cost. Real-time analysis and early warning mechanisms promptly identify quality issues during construction and automatically trigger remediation processes, ensuring project quality remains under control. Comprehensive analysis of key construction site data, combined with historical experience, accurately predicts schedule deviations and avoids delays. Analysis of resource allocation data provides a precise basis for cost control, identifying potential overruns and ensuring project budgets are reasonable. The intelligent closed-loop management mechanism continuously learns and adjusts, optimizing decisions during the construction process and enhancing the level of intelligent management. This fully integrated intelligent management approach provides more efficient, precise, and reliable control for construction projects, driving construction management towards digitalization, intelligence, and efficiency.

[0150] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that modifications may be made to the technical solutions described in the above embodiments, or that some of the technical features may be replaced with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent construction operation management method based on big data, characterized in that: The following steps are involved: Step 1: Realize real-time data collection of construction site personnel, equipment, and environment, and establish a dynamic information database; Step 2: Preprocess and extract features from the collected multi-source heterogeneous data to achieve real-time data fusion and intelligent analysis; Step 3: Accurately identify risk factors and implement refined early warning and intelligent intervention for individual risks; Step 4: Dynamically optimize construction resource allocation to improve resource utilization efficiency while reducing costs and safety risks; Step 5: Build a blockchain data sharing mechanism to achieve data traceability and intelligent management throughout the construction life cycle; Step 6: Form an adaptive closed-loop feedback mechanism to promote the intelligent transformation of construction management from passive response to active prediction.

2. The intelligent construction operation management method based on big data according to claim 1 is characterized in that: Step 1 includes the following steps: Deploy various sensor devices at key locations on the construction site, including cameras, RFID readers, and environmental monitors, to form a complete IoT perception layer; By equipping workers with smart helmets or positioning cards and combining them with image recognition technology, we can obtain real-time information on the location, working status, and safety protection of on-site personnel; Install condition monitoring sensors on construction machinery and equipment to collect data on equipment operating parameters, working hours, and location information; Continuously collect environmental parameters at the construction site through temperature and humidity sensors, noise sensors, and dust concentration detectors; Build a unified data collection and transmission network to aggregate and store data collected by various sensing devices; Build a dynamic information database to classify, store and update the collected multi-dimensional data in real time.

3. The intelligent construction operation management method based on big data according to claim 1 is characterized in that: Step 2 includes the following steps: Build a distributed edge computing architecture and deploy multiple edge computing nodes at the construction site; Develop specialized data pre-processing modules for different types of data sources to perform data cleaning, noise reduction, and standardization operations to ensure consistent data quality; Extract features required for target detection and tracking from image data, extract status feature parameters from equipment operation data, and extract trend features from environmental monitoring data; Design a data fusion engine to fuse feature information from different data sources at multiple levels; Establish a general data analysis framework, conduct in-depth analysis of the fused data based on machine learning algorithms, and build an anomaly detection model.

4. The intelligent construction operation management method based on big data according to claim 1 is characterized in that: Step 3 includes the following steps: Conduct in-depth research on the collected data on construction accidents, hidden dangers, and risk events to identify the typical characteristics and development patterns of various risk events and form a risk knowledge base; Match and compare currently collected multi-source data with historical risk patterns in real time to quickly detect potential risk factors and conduct dynamic assessments of risk levels for single risk types; Set different warning thresholds and response strategies based on risk levels, and promptly notify relevant personnel through various means; Automatically generate intervention recommendations based on the urgency and impact of the risk, and provide multiple optional response plans for decision makers to refer to; Conduct real-time tracking and effectiveness evaluation of implemented intervention measures, and continuously optimize single risk intervention strategies based on the evaluation results.

5. The intelligent construction operation management method based on big data according to claim 1 is characterized in that: Step 4 includes the following steps: Establish a digital twin environment for construction resource allocation, digitally model various resources on the construction site, and collect construction site data in real time to provide a training environment for reinforcement learning algorithms; Design a deep reinforcement learning reward function that integrates resource utilization efficiency, construction cost, and safety risk objectives into the reward calculation; Train a deep reinforcement learning agent to enable it to autonomously decide on the optimal resource allocation plan based on the current state of the construction scene; Dynamically adjust resource allocation decisions based on real-time data, and dynamically adjust resource allocation plans according to current construction progress, weather conditions, and personnel availability; Collect execution data on resource allocation decisions to continuously update and improve reinforcement learning models.

6. The intelligent construction operation management method based on big data according to claim 5 is characterized in that: Dynamically adjust resource allocation decisions based on real-time data. This includes the following steps: Analyze collected real-time data to assess construction progress and plan deviations, predict the potential impact of weather changes on construction, and examine personnel attendance and work efficiency indicators to identify problems and potential risks in current resource allocation; Based on data analysis results and project management experience, make resource allocation recommendations, including personnel additions or transfers, equipment additions or replacements, and material reserves or scheduling measures to ensure the feasibility and timeliness of the plan; Simulate and measure different adjustment plans, evaluate implementation effectiveness and cost impact, select the optimal plan, and develop contingency plans to ensure flexibility in responding to emergencies; Quickly carry out resource allocation work according to the confirmed adjustment plan, and continuously monitor the construction progress and resource utilization effect, promptly identify problems in the implementation process, and ensure that the adjustment plan can achieve the expected results.

7. The intelligent construction operation management method based on big data according to claim 1 is characterized in that: Step 5 includes the following steps: Build a unified blockchain network platform, use distributed ledger technology to ensure data is decentralized and tamper-proof, and combine smart contracts to set data access rights and sharing boundaries to ensure security and privacy; Establish standards for uploading construction data to the blockchain, classify and encode all types of data generated during the construction process, and unify formats and metadata standards; Convert various rules and processes in construction management into smart contract code to automatically execute project acceptance, payment settlement and quality assessment processes; Build a data traceability analysis platform to realize information traceability of the entire construction process based on blockchain data.

8. The intelligent construction operation management method based on big data according to claim 1 is characterized in that: Step 6 includes the following steps: Based on the analysis framework established in step 2, real-time analysis of construction quality scenarios is performed to automatically detect construction quality issues and automatically trigger corresponding warning and disposal processes based on the severity; Comprehensively analyze construction site personnel attendance rates, equipment operating status, and material supply data, combined with historical project experience, to accurately predict potential deviations in project progress; Based on the resource allocation data from step 4, analyze the cost structure and changing trends to identify the risk of cost overruns; Form an intelligent closed-loop management mechanism, comprehensively integrate the analysis results of the three dimensions of quality, progress, and cost, automatically evaluate the effectiveness of various management measures, continuously learn and adjust management strategies, and gradually improve the accuracy of predictions and the level of intelligent management.

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