2175 results about "Management efficiency" patented technology

The invention discloses a health monitoring method and system for an intelligent building, and belongs to the field of building construction health monitoring, and the method comprises the steps: obtaining internal and external multi-dimensional data of the intelligent building in real time through a plurality of sensors, the multi-dimensional data comprising environment data, structure data of the intelligent building, equipment state data and personnel activity data; preprocessing the multi-dimensional data to obtain preprocessed multi-dimensional data; according to the preprocessed multi-dimensional data, a health condition analysis result of the intelligent building is output by utilizing a pre-trained health monitoring model, and the health condition analysis result comprises the suitability degree of environmental conditions, the stability degree of a building structure, the equipment health degree and the risk degree of personnel activities; when the health condition analysis result indicates that the intelligent building is abnormal in health, an early warning signal is sent out; and determining a repair scheme of the intelligent building according to the type of the health abnormality. According to the method, the safety, comfort and management efficiency of the building can be improved.

The invention provides a building equipment operation data intelligent analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: extracting historical positioning data in a space calibration interval, and calculating a position offset vector set between a predicted trajectory point and an actual trajectory point according to the trajectory point on a predicted motion trajectory; taking the weighted sum of squares of minimizing the position offset vector as an optimization target to obtain a three-dimensional space transformation parameter optimization model; according to the optimization model, a quasi-Newton optimization algorithm is adopted to solve the optimization model, and a final calibration parameter set is generated; and based on the final calibration parameter set, correcting the predicted motion trajectory, performing spatial relationship analysis on the corrected trajectory and a spatial risk field, calculating a collision risk probability and an accident severity level, generating a comprehensive risk score, and triggering a response action according to a preset threshold value. The construction safety level and the management efficiency are improved.

The invention discloses a data management method based on a distributed storage system, relates to the technical field of data management, and is used for solving the problem of storage management behavior strategy mismatch. On the basis of dynamic perception of data access behaviors in a distributed storage system, a behavior feature vector fusing an access mutation rate, periodicity and an access span is constructed, behavior pattern recognition and strategy structure generation are completed, optimal deployment and hierarchical storage of copies are achieved through node resource state perception and construction of a cost function, and the strategy structure generation efficiency is improved. And after compression and consistency configuration are executed and the strategy falls to the ground, the compressibility and the offset trend of the behavior path are analyzed, scheduling management information is extracted, and a stable execution or strategy adjustment signal is generated, so that the problem of strategy execution mismatching caused by behavior perception deficiency in the distributed storage system is reduced, and the strategy execution efficiency is improved. The strategy closed-loop control and the resource scheduling optimization under behavior driving are realized, so that the data management efficiency and the strategy adaptation capability of the distributed storage system are improved.

The invention discloses an intelligent water quality monitoring device based on Internet of Things, relates to the technical field of environmental monitoring, and aims to analyze a complex nonlinear relationship in water quality data, accurately predict a water quality change trend and identify potential water quality problems in advance through a deep learning model and a system. The dynamic weight calculation module can dynamically adjust the importance of water quality parameters according to real-time data and environmental changes, the monitoring precision and adaptability are improved, an intelligent feedback mechanism automatically adjusts the sampling frequency and the sensor position when an early warning signal is triggered, accurate monitoring of the water quality changes is ensured, and the monitoring accuracy is improved. The Internet of Things technology supports real-time transmission of water quality data and analysis results to the cloud, managers can check water quality states, historical trends and early warning records through a remote monitoring interface, the response capability and management efficiency of the system are improved, and the intelligent water quality monitoring device has the advantages of being high in precision, high in intelligence and high in adaptability.

The invention discloses an intelligent building safety management system based on big data, and belongs to the technical field of intelligent buildings. Comprising the following modules: a multi-source data fusion and preprocessing module, which adopts intelligent data standardization and feature extraction technologies to realize efficient integration and semantic conversion of cross-source heterogeneous data and ensure data quality and consistency; the security risk dynamic knowledge graph construction module is used for constructing a semantic association network of building security elements through a graph neural network so as to continuously self-learn and dynamically update a risk feature relationship; the intelligent risk assessment and early warning module is used for carrying out comprehensive modeling, accurate positioning, layered assessment and intelligent early warning on building safety risks; the safety decision support module is used for providing an executable risk assessment report and an emergency decision suggestion; and the safety management visualization module adopts an interactive multi-dimensional visualization technology to intuitively present the dynamic evolution process of the building safety risk, so that the risk perception and management efficiency is remarkably improved.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The invention relates to the technical field of the Internet of Things and artificial intelligence, and discloses an equipment maintenance fault intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: representing an entity and a relation as a time function, and constructing a time sequence knowledge graph; monitoring vector change in the knowledge embedding space, detecting a concept drift phenomenon and mining an equipment performance evolution mode; constructing an equipment state transition model by using a recurrent neural network, predicting the future state of the equipment and evaluating the fault risk; collecting feedback processing knowledge conflicts and updating the model; generating multi-time-scale fault risk early warning and maintenance suggestions; according to the method, the knowledge acquisition efficiency is improved, dynamic knowledge support of the full life cycle of the equipment is provided, early recognition of gradient faults is realized, the method has adaptive knowledge updating capability, multi-dimensional fault analysis is provided, maintenance resource configuration is optimized, and the equipment management efficiency is improved.

The invention provides a continuous casting quality control method based on a meta-cognitive coordination architecture agent cluster, and relates to the technical field of ferrous metallurgy intelligent manufacturing. The continuous casting quality control method comprises the steps of data input and standardization, center coordination and intelligent agent cluster operation and maintenance. In the central coordination process, the meta-cognitive coordination agent serves as a core to coordinate six kinds of functional agents including a semantic analysis agent, a data perception agent, a defect prediction agent, a root cause analysis agent, a process optimization agent and a digital twinborn agent, and task scheduling and state monitoring of the whole system are achieved. Three key functions of task decomposition, data scheduling and closed-loop management and control are completed; in the closed-loop management and control step, risk early warning, defect prediction, root cause analysis, process optimization and digital twinborn verification and feedback are carried out for continuous casting quality. Compared with a traditional scheme, optimization is carried out in the aspects of whole process, multiple modes, intelligence, collaboration and the like, the management efficiency is improved, and economic benefits can also be increased.

The invention relates to a navigation equipment health management method based on an SAITS algorithm and a digital twin platform. The method comprises the following steps: acquiring navigation equipment operation data; adopting an improved SAITS algorithm to carry out interpolation on missing data, and predicting to obtain future navigation equipment operation data; identifying current and future abnormal states of the navigation equipment by adopting a self-adaptive anomaly detection algorithm; constructing an equipment fault mode knowledge graph according to the identified equipment exception; simulating the operation of the navigation system by using a digital twin platform, verifying the confidence coefficient of equipment abnormity, and generating a health assessment report; if the confidence coefficient is not smaller than a confidence coefficient threshold value, generating an exception coping strategy in combination with an equipment fault mode knowledge graph; incremental learning or retraining is carried out on an adaptive anomaly detection algorithm to improve the detection accuracy. The method has remarkable advantages in the aspects of improving the navigation equipment management efficiency, reducing the fault risk, optimizing the maintenance cost and the like, and is particularly suitable for water transportation scenes with strict requirements on reliability and real-time performance.

The invention provides a construction dynamic twinning method based on the Internet of Things and BIM, and relates to the technical field of digital twinning, and the method comprises the steps: obtaining heterogeneous data streams collected by a sensor, and carrying out the analysis and classification of the data streams, and generating a standardized data set; performing semantic mapping on the standardized data set according to association rules of BIM component identifiers and data attributes to generate a component association data set; decrypting and decompressing the encrypted data packet at the cloud, analyzing the construction progress time sequence data by adopting a distributed computing framework, and generating a deviation detection result; dynamically adjusting a routing rule of a data link according to a deviation detection result, and redistributing a processing priority to optimize link configuration; updating a BIM component state based on the optimized link configuration, and generating a real-time updated digital twin model by adopting cloud computing resources; three-dimensional visual data are extracted from the digital twin model and mapped to a BIM model interface by adopting a rendering technology, so that the monitoring precision and the management efficiency of the construction process are effectively improved.

The invention provides an urban construction and traffic engineering investigation and design enterprise product quality management system and method. The system comprises a cross-project type quality data fusion module, a dynamic defect evolution prediction module, an intelligent layered spot check engine, a three-dimensional quality situation deduction module, a conference knowledge graph construction module, a result influence evaluation module and a self-adaptive file management system. All the modules are connected through a unified data bus to form a closed-loop management process of quality data acquisition, analysis, decision and feedback. According to the system, through the functions of multi-modal data fusion, defect prediction, intelligent spot check, situation deduction, knowledge precipitation, achievement evaluation, file management and the like, the whole-process intelligent management and control of the project quality are realized, and the management efficiency and the decision scientificity are improved.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention relates to the technical field of constructional engineering management, and discloses a multi-channel complex constructional engineering dynamic management system and method. The system comprises a multi-source data integration module, a dynamic progress prediction module, a resource optimization distribution module, a risk dynamic evaluation module, a cooperative control module and the like. The multi-source data integration module collects multi-dimensional real-time data to obtain structured engineering data; the dynamic progress prediction module generates a construction progress prediction result in combination with a specific algorithm; the resource optimization distribution module constructs a model to distribute resources; the risk dynamic evaluation module calculates the risk probability; and the cooperative control module adjusts construction process parameters. According to the invention, dynamic management of complex constructional engineering can be realized, the data processing accuracy, the construction progress prediction precision and the resource allocation rationality are improved, the risk is accurately evaluated, the construction process is optimized, the constructional engineering management efficiency and quality are effectively improved, and the engineering is ensured to be carried out smoothly.

The invention relates to the technical field of community security, in particular to an intelligent community security management system and method, and the method comprises the steps: collecting equipment data, verifying the daily operation of equipment, collecting personnel and event data, and carrying out the data association and data integration; when an abnormal event is identified, determining a basic level according to the integrated data, determining an associated risk probability through a Bayesian network model, inputting the basic level and the associated risk probability into a dynamic weight adaptive grading model, and determining a final level of the event; inputting the obtained event final grade into a trend prediction model to obtain a risk prediction value, determining a comprehensive risk value of each region according to the risk prediction value, and generating a real-time risk thermodynamic diagram; and according to the final grade of the event and the associated data, screening the processing personnel meeting the conditions to perform task assignment, and performing event processing by the processing personnel. According to the scheme, by constructing the equipment, event and personnel association chain, dynamic verification and intelligent grading are realized, and the management efficiency is improved.

The invention relates to the technical field of progress management, in particular to a whole-process project cost progress management method and system, equipment and a medium. According to the method, the engineering project is structurally decomposed, the mapping relation between the cost data and the progress data is established, and real-time monitoring of the cost execution deviation is achieved; the method comprises the following steps: firstly, obtaining divided item engineering data and dividing construction nodes, then calculating planned cost and actual cost of each node, further analyzing cost execution deviation through a mathematical model, and finally establishing a cost progress control curve based on deviation data and generating an adjustment scheme. The construction cost management and the progress management are organically combined, the limitation of a traditional independent management mode is broken through, the construction cost deviation can be found and corrected in time, and the management efficiency and the control precision of an engineering project are effectively improved.

The invention discloses a large model intelligent agent dynamic tool arrangement method and system, a medium and equipment, and relates to the technical field of large models. The method comprises the following steps: acquiring interface metadata of an open platform, generating a standardized large model tool description based on the interface metadata, and registering the standardized large model tool description to a tool library; receiving a query request of a user, and determining a target tool based on the query request and the semantic similarity of each tool description in the tool library; mapping service parameters in the query request into interface calling parameters of the target tool; executing interface calling corresponding to the target tool based on the interface calling parameter, and preprocessing a calling result; and displaying the pre-processed calling result in a visual form. By implementing the technical scheme provided by the invention, the dynamic arrangement of the management tool can be realized, the adaptability of the system to the business scene change is improved, and the management efficiency of an enterprise is further improved.

The invention discloses a dynamic equipment management service system and method for deep learning. The method comprises the following steps: S1, collecting real-time operation data of equipment and preprocessing the real-time operation data; s2, constructing a long-short-term memory network model, and outputting comprehensive early warning indexes of the equipment; s3, constructing an optimization problem based on the comprehensive early warning index of the equipment, and setting an objective function and constraint conditions; s4, performing global search on the optimization problem by adopting an improved dragon fly algorithm, and generating a plurality of resource scheduling and maintenance candidate schemes; s5, performing local optimization on the candidate scheme by using a simulated annealing algorithm, and determining an optimal resource scheduling and maintenance decision scheme; and S6, feeding back the real-time prediction result and the optimal decision result to form closed-loop control. Through fusion of deep learning prediction, the improved dragon fly algorithm and the simulated annealing algorithm, real-time closed-loop control of accurate early warning of the equipment state and dynamic resource scheduling decision is realized, so that the failure rate is effectively reduced, and the equipment management efficiency is improved.

The invention provides a potential information identification method and system in combination with a heterogeneous network environment, and the method comprises the steps: firstly obtaining a heterogeneous network environment data set of a target network environment, which comprises original information resources of various data formats, and carrying out the feature extraction of the heterogeneous network environment data set to obtain an associated feature set; performing hierarchical pattern matching processing on the associated feature set based on a pre-trained information pattern analysis model to generate a potential information identification result set containing significant value information resource attribute identifiers and distribution paths, and generating a dynamic resource integration strategy according to the potential information identification result set; and the resource management system is used for adjusting a resource scheduling rule and a storage path mapping relation, finally feeding back a dynamic resource integration strategy to the data management system, triggering a resource directory updating operation, and improving heterogeneous network resource utilization and management efficiency.

The invention belongs to the technical field of oil well production management, and discloses an electric submersible pump well group management system and method based on digital twinning. According to the method, operation data of an electric submersible pump unit and environmental data in a shaft are collected in real time, a multi-scale digital twinborn model is constructed for the received data, and the mechanical structure and electrical characteristics of the electric submersible pump unit, the multiphase flow dynamic state in the shaft and geological pressure field data are obtained; trend prediction is carried out on operation parameters, energy efficiency indexes and health states of the multi-scale digital twin model of each well; through a machine learning algorithm and an expert knowledge base, trend prediction data is associated with a historical health database, early warning and intelligent diagnosis of equipment faults are completed, and a maintenance strategy is generated. The method can assist managers to make decisions based on the historical health database. According to the invention, unified management and intelligent well patrol of multiple well sites can be realized, and the oil well production management efficiency is greatly improved.

The invention discloses a standard address pipeline data association management method and system based on multi-level collaborative coding, belongs to the technical field of government affair digitization, and aims to solve the technical problem of how to generate and manage standard address data in government affair business, realize automatic triggering and standardized circulation of cross-department business data and improve the management efficiency. According to the technical scheme, the method comprises the steps of cross-department business triggering: automatically triggering a road and courtyard address code generation process through construction project land pre-examination and construction project planning permission business events; a standard road name is generated based on road pre-examination data, a road unified identification code is synthesized according to a 4 + 6 + 9 segmentation rule, a house number is generated according to planning permission data, and a courtyard unified identification code is generated in combination with a naming system; the house prediction data triggers the generation of a house unified identification code; and data dynamic association: constructing a reverse index based on the address coding feature vector, and realizing millisecond-level accurate matching of the address and population information through a distributed query engine.

The invention belongs to the technical field of authority management, and particularly relates to a dynamic authority management system based on an integrated system, which is characterized in that on one hand, dynamic authority portraits of employees are described periodically by integrating multi-system data, and dynamic risk values of real-time operation behaviors of the employees are analyzed based on the latest dynamic authority portraits; on the other hand, through monitoring an organization event queue in real time and analyzing metadata of an event message, an organization event type is identified, a change rule corresponding to the organization event type is matched, an authority synchronization instruction is generated, and transactional operation is started; a behavior risk real-time assessment and event-driven cross-system collaboration mechanism is adopted, automatic and precise dynamic adjustment of the authority is realized, the access control security and management efficiency in an integrated network environment are improved, and in addition, a self-optimization capability based on machine learning is introduced, so that the authority management has the characteristics of continuous learning and evolution.

The invention relates to the technical field of supply chain management, in particular to a digital visual management system and method applied to a building material supply chain. The method comprises the following steps: acquiring real-time position data, material attribute data and time sequence node data; performing data preprocessing and integration on the real-time position data, the material attribute data and the time sequence node data to obtain standard building material supply chain data; obtaining a construction drawing; basic building materials of the standard building material supply chain data are extracted based on the construction drawing, wherein the basic building materials comprise concrete, reinforcing steel bars and building blocks; building pouring abnormity monitoring is carried out based on the concrete, the steel bars and the building blocks, and pouring abnormity monitoring data is generated. Through multi-source data integration, ternary coupling analysis, scheduling optimization and three-dimensional dynamic visualization, the problems of information island, scheduling conflict and low management efficiency of the building material supply chain are solved, and intelligent, efficient and transparent management of the supply chain is realized.

The invention relates to the technical field of production management in the injection molding industry, and discloses a method and system for automatically generating a production scheduling plan based on production in the injection molding industry, and the method comprises the steps: obtaining a production order, an equipment state and material inventory data, generating original production parameter data, and deploying a distributed production scheduling network; collecting and analyzing multi-stage equipment load data and equipment state data, and generating corresponding influence factors; order priority data are collected, and production task areas are divided; and optimizing and calculating a production scheduling plan based on the task area data, integrating the multi-factor data to generate comprehensive production scheduling data, and further generating and outputting a production scheduling instruction. The system comprises a production scheduling network deployment module, an equipment load analysis module, an equipment state analysis module, a production task area analysis module and a production scheduling instruction generation module. Through multi-data integration and scientific modeling, the intelligent precision of injection molding production scheduling is realized, the equipment management efficiency, the order delivery capability and the overall production efficiency are improved, and the system is suitable for automatic production scheduling management in the injection molding industry.

The invention relates to the technical field of project management, in particular to an enterprise project management system and method oriented to multi-project collaboration. A resource scheduling module; a progress control module; a cost quantification management module; and a risk management module. According to the scheme, through a micro-service architecture and a distributed data storage technology, multi-project full-period collaborative management is realized. And each module can operate and expand independently, so that the flexibility and expandability of the system are remarkably improved. The distributed data storage can ensure the real-time synchronization and quick access of the data even under the condition that a plurality of projects run in parallel. By constructing the incidence relation matrix, the system can clearly present the relation between each task and the resource of the project, and solid data support is provided for subsequent resource allocation and task management. The optimal configuration of resources and the smooth promotion of projects are realized, and the management efficiency of enterprises and the project success rate are remarkably improved.

The invention provides a logistics supply chain management method and system based on a block chain technology, and relates to the technical field of data processing, and the method comprises the steps that a storage center scans a traceability code and verifies goods information when goods are warehoused, and after verification is passed, the goods are encrypted and stored in a block chain to obtain warehouse-in data; a dynamic inventory database is established according to the goods category and the storage location number through the warehousing data, and an inventory state early warning index is generated. According to the logistics supply chain management method and system based on the block chain technology provided by the invention, dynamic and accurate management of inventory, real-time optimization of logistics paths and credible traceability of full-chain information are realized through distributed storage and non-tampering characteristics of the block chain in combination with an intelligent contract and the Internet of Things technology, the supply chain collaboration efficiency is improved, and the logistics supply chain management efficiency is improved. And the operation risk is reduced.

The invention relates to the technical field of warehouse management, and discloses a warehouse management system and method based on dynamic inventory prediction. According to the invention, market, sales, supply chain and warehouse internal data are collected in real time, a dynamic inventory prediction model is constructed in combination with a long short-term memory (LSTM) network, the future inventory demand can be accurately predicted, and the prediction precision is significantly improved; an inventory optimization strategy is generated based on a multi-objective optimization algorithm, so that the inventory holding cost, the stockout cost and the transportation cost are effectively reduced; and intelligent warehouse operation instruction generation and real-time monitoring are realized through the execution control module, and the response capability of an enterprise to market fluctuation and emergencies is enhanced. According to the system and the method, the warehouse management efficiency is improved, the operation risk is reduced, and an efficient and reliable solution is provided for modern logistics and supply chain management.

The invention discloses a medical insurance intelligent auditing and path recommending method and system based on a large language model, and particularly relates to the technical field of medical insurance auditing, and the method comprises the steps: S1, analyzing massive medical insurance policy documents through a first LLM, and generating a comprehensive confidence coefficient in combination with triple confidence coefficients of semantics, context support degree and timeliness, after grading processing, constructing a dynamically updated medical insurance rule knowledge base; s2, converting free text medical advices of doctors into structured data by means of a second LLM, quantifying the integrity of the medical advices through field weights, and dynamically asking and complementing information in combination with constraint urgency; s3, associating the diagnosis and treatment scheme with the medical insurance information of the patient, matching rules in real time, and generating a color identifier for early warning violation; s4, recommending compliance alternative schemes in combination with the medical knowledge graph, and sorting according to multi-dimensional scores such as medical insurance economy and the like; and S5, automatically extracting multi-system data, and generating a standardized medical insurance declaration report. The medical insurance management efficiency is improved, the burden of medical care and medical insurance departments is relieved, and reasonable use of medical insurance funds is guaranteed.

The invention discloses an intelligent text report automatic generation method and system based on safety production data, and belongs to the technical field of big data and artificial intelligence, and the method comprises the steps: multi-source heterogeneous data collection and structured processing: collecting safety production event data including violation operation, violation command and violation of labor discipline, carrying out structured processing on the data; semantic enhancement prompt engineering and large language model reasoning: constructing prompt words, inputting structured data into a large language model, and performing semantic understanding and text generation on the data by utilizing a prompt engineering technology guide model; and dynamic report template adaptation and multi-format output: realizing structured report output through template matching, outputting a structured text report by the model, filling the structured text report into a preset template, and exporting the structured text report. The enterprise safety management efficiency is remarkably improved, the manual writing burden is reduced, informatization, standardization and intelligent processing of safety production data is achieved, and the safety management digitization and automation level of enterprises is improved.

The invention relates to the technical field of numerical control machining, in particular to an automatic input system and method for geometric parameters of a numerical control tool. The method comprises the following steps of obtaining point cloud data and material characteristics of a tool, removing abnormal points to clean the data, extracting the edge contour of the tool, performing geometric parameter mapping through the edge contour, determining geometric characteristics of the tool, reconstructing a tool framework, obtaining a numerical control machining task, and performing cutting load simulation on the reconstructed tool. Analyzing the initial wear data, performing heat effect prediction based on cutting load, calculating the thermal deformation degree of the cutter, performing deformation compensation on the initial wear data, generating geometric deformation data, drawing a deformation-time curve, performing time sequence tracking parameter compensation according to the change process of the cutter, and realizing real-time recording and updating. According to the method, the management efficiency and the machining precision of the numerical control tool are integrally improved, and the stability and predictability of the production process are enhanced.

The invention discloses an attention mechanism-based operation area construction behavior intelligent identification method, which comprises the following steps of S1, collecting and preprocessing multi-modal data, and generating a standardized construction action time sequence data set; s2, constructing a Transform network model, and extracting spatial features and time features; s3, the Transform network model is optimized based on a parrot optimization algorithm, and optimal hyper-parameter configuration is obtained; s4, carrying out training by utilizing the optimized Transform network model, and carrying out supervised learning by adopting a cross entropy loss function; s5, performing construction behavior intelligent identification, and outputting a construction action category and an identification confidence coefficient; and S6, triggering a real-time early warning mechanism, and pushing early warning information to a construction safety management platform. According to the method, efficient and accurate construction behavior recognition and high-risk action detection are realized by optimizing the Transform network model and dynamically adjusting the hyper-parameters, and the safety and management efficiency of a construction site are improved.