1001 results about "Collaborative management" patented technology

The invention relates to the technical field of management and control systems, in particular to a digital intelligence system applied to collaborative management and control of water-power engineering construction participating units, which comprises a project data management module covering water-power engineering construction full life cycle management, extracting multi-source information data from various subsystems, and constructing a project view and a project management information base; the project risk analysis module is used for constructing an intelligent risk analysis model for risk analysis and generating a dynamic evaluation result, a graded early warning notification and an auxiliary decision scheme; the project collaborative management module is used for processing graded early warning notification and auxiliary decision-making schemes by using a multi-layer perceptron model, and generating finalizing service achievements and management process records; and the project document management module is used for performing compliance automatic checking and processing on the electronic documents needing to be archived, and dynamically updating and optimizing the project management information base. Through the closed-loop management and control system, the cooperative management and control efficiency of water-power engineering construction participation units is improved.

The invention discloses an unmanned aerial vehicle low-altitude intelligent collaborative management system based on dynamic airspace gridding, and relates to the technical field of unmanned aerial vehicle management. Comprising the following steps: acquiring real-time environment information of a low-altitude airspace and operation state information of an unmanned aerial vehicle group; performing dynamic airspace gridding processing according to the real-time environment information to obtain a plurality of airspace grid units and airspace attribute information; according to the airspace grid units, the airspace attribute information and the operation state information of the unmanned aerial vehicle group, a cooperative flight path planning scheme is generated, and the technical key points are as follows: the information acquisition link provides comprehensive and accurate basic data, the dynamic airspace gridding processing realizes flexible adaptation of the airspace, and the cooperative path planning ensures efficient and ordered group flight. Flight control and dynamic adjustment guarantee smooth propulsion of tasks; all links support and promote each other, for example, a prediction result of the airspace state advanced judgment model can provide prospective guidance for path planning, and a deviation monitoring and adjusting mechanism can correct flight deviation in time.

The invention discloses a power distribution network collaborative management method and system based on artificial intelligence, and relates to the technical field of electrochemical detection, and the method comprises the steps: constructing a distributed edge computing node network, deploying nodes at key positions of a power distribution network, achieving the collection and preprocessing of local power data, and reducing the cross-regional data transmission pressure; an AI real-time communication scheduling model is established based on the preprocessed data, communication resources are dynamically allocated according to the operation state of the power distribution network, and fault data transmission is guaranteed preferentially; seamless interaction of multi-protocol equipment is realized through a self-adaptive protocol conversion mechanism containing protocol identification, format conversion and data verification; training a fault diagnosis model by using a federated learning framework, and enabling edge nodes to only upload parameters to a coordination center for aggregation and updating, so as to balance model precision and data privacy; when a fault is detected, a millisecond response mechanism is started, and a processing strategy is generated and executed in combination with edge local decision and central global optimization.

The invention discloses a hierarchical collaborative management method and system for a virtual power plant based on multi-modal deep learning, and the method comprises the steps: constructing a four-dimensional data collection system, and achieving privacy enhancement preprocessing through federated learning and a differential privacy technology; a Bi-LSTM and a heterogeneous graph neural network are adopted to construct a three-mode deep fusion model, the weight is dynamically adjusted in combination with an environment-user dual-drive attention mechanism, and the load prediction precision and the space resource utilization rate are improved; a multi-target scheduling strategy is generated based on a five-dimensional target function and an improved DDPG algorithm, and physical feasibility is ensured through digital twinborn pre-verification; efficient execution and excitation transparency are realized through edge layer FPGA + NPU hardware acceleration and block chain evidence storage; and constructing a user participation ecology by using a natural language interaction strategy engine and a stepped incentive mechanism. The power grid economy, the equipment reliability and the user participation degree are remarkably improved, and intelligent upgrading of the virtual power plant is promoted.

The invention relates to the technical field of engineering collaborative management, and discloses a BIM (Building Information Modeling)-based engineering full-life-cycle collaborative management method. The method comprises the following steps: acquiring building information model data and associated progress monitoring data of a full life cycle of a project, and providing a data basis for collaborative management. Based on a stage decomposition strategy, a single-stage dependency graph is constructed according to building information model data, and association logic of elements such as tasks and resources in each stage is clearly presented. Through a cross-stage dependency integration method, the single-stage dependency graph is integrated into a full-life-cycle dependency graph, and a cross-stage task connection and influence relationship is defined. In combination with data consistency verification and resource constraint review, conflict detection is performed according to a full life cycle dependency graph, and potential data conflicts and resource conflicts are identified in advance. And based on a progress fluctuation analysis mechanism, correcting the dynamic parameters according to a conflict detection result, and finally generating a full-life-cycle collaborative management report.

The invention relates to the technical field of multi-project collaboration, in particular to an enterprise multi-project collaboration management method and system based on data security analysis, and the method comprises the following steps: based on data security requirements, identifying key task nodes, evaluating resource composition and execution deviations, forming task tension indexes, and analyzing path propulsion fluctuation and interruption conditions according to the task tension indexes; identifying a calling aggregation structure of a key resource; judging a task time sequence, resource overlapping and dependency difference between paths; generating a conflict characteristic quantity; constructing a sorting rule by synthesizing a propelling state and a conflict relationship; according to the method, data security requirements are included in scheduling judgment, a cross-project state recognition mechanism is established, the task tension degree is evaluated in combination with task output field integrity and scheduling stage offset, so that structure sensitive tasks are captured, propulsion fluctuation and interruption frequency are superposed in a path, the stability of a task chain is measured, and an uneven scheduling area is positioned.

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention provides a supply chain collaborative management method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and quantifies collaborative, substitution and complementary relationship strength among commodities by constructing a commodity association graph network; utilizing a graph attention mechanism to identify a commodity group and calculate a demand influence coefficient, and performing time sequence analysis on historical order data to obtain basic demand prediction; meanwhile, a scene recognition model is constructed by adopting reinforcement learning, a basic prediction result is adjusted according to a current market scene, the demand conduction quantity in the commodity group is calculated in combination with a demand influence coefficient, and final demand prediction is obtained; and finally, based on a prediction result, determining a collaborative decision-making scheme of each participant by applying a cooperative game method, and generating an inventory allocation instruction. According to the method, the complex incidence relation between the commodities and the market dynamic change are accurately captured, the supply chain prediction accuracy and the cooperation efficiency are remarkably improved, and inventory optimization and cost reduction are realized.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is improved.

The invention discloses a road-oriented full-process digital collaborative management system and method, particularly relates to the technical field of road data management, and is used for solving the problem of abnormal multi-role collaborative conflict recognition. According to the method, dynamic linkage management of task states and role behaviors in the whole highway design process is achieved by building the task-driven atlas and the multi-role scheduling model, and the dynamic linkage management of the task states and the role behaviors in the whole highway design process is achieved by extracting access behaviors, task records and data version states, calculating role collaborative offset and recognizing and controlling the write-in permission of task conflict nodes. A priority factor is constructed based on a stage index, data dependence and a time urgency degree, a nonlinear model is adopted to generate scores, automatic reconstruction of a scheduling sequence and data ownership is driven, role permission and a collaborative view are synchronously updated, a data access control closed loop based on task state evolution is formed, and a three-dimensional responsibility chain is constructed by whole-process behavior traces. The traceable management of collaborative operation is realized, and the intelligence of collaborative scheduling and the accuracy of data management are improved.

The invention belongs to the field of fault monitoring, and particularly relates to a fault self-healing method and system under an intelligent operation and maintenance global P2P architecture, and the method comprises the steps: constructing a global operation and maintenance monitoring double-layer node network which comprises a basic monitoring subnet and a collaborative management and control subnet; the basic monitoring subnet collects platform operation data in real time according to a preset inspection strategy, determines an abnormal root node and an association node through causal association analysis, generates an early warning signal and uploads the early warning signal to the collaborative management and control subnet; after the collaborative management and control subnet receives the early warning, a fault self-recovery strategy is started, resources are dynamically allocated, abnormal node data batch migration and recovery are executed, and a self-recovery result is fed back to the basic monitoring subnet to trigger loop check until abnormity is eliminated; according to the method, real-time monitoring and intelligent analysis of operation and maintenance data are realized through cooperative work of the double-layer node network, and disaster tolerance and load adaptive adjustment capability of the system are effectively improved in combination with an automatic fault self-healing mechanism and data difference migration.

The invention relates to the technical field of distributed energy storage and intelligent power grid control, in particular to an intelligent distributed energy storage cluster collaborative management system. Comprising a state acquisition module used for acquiring local power grid state parameters of an edge computing node in real time; the power grid state parameters comprise power grid frequency, power grid voltage and a heartbeat communication state with the cloud management platform; the risk calculation module is used for calculating quantified island risk indexes; the mode judgment module is used for determining a current operation mode decision; the mode switching module is used for switching the system from a vertical cooperative mode to a horizontal autonomous mode when the island risk index is greater than an island switching threshold value; and when the island risk index is smaller than or equal to the island switching threshold value, the system is maintained to operate in the vertical cooperation mode. According to the system, misjudgment and missed judgment of a traditional single threshold value mode are overcome, it is ensured that the mode switching module can be switched to the horizontal autonomous mode from the vertical cooperative mode in time under the real risk, and decision making is accurate and reliable.

The invention provides an intelligent laboratory full-process collaborative management system and method based on multi-dimensional data fusion, and the method comprises the steps: obtaining an original data stream from laboratory instrument equipment, carrying out the format recognition and conversion of heterogeneous instrument data through a standard protocol adapter, and generating a first data set in a unified format; aiming at the first data set, executing automatic analysis by adopting data middleware, and integrating into a structured second data set based on a metadata rule of field mapping and unit conversion; according to the second data set, a multi-source data fusion model is constructed, data are classified according to instrument types through batch processing and aggregated according to timestamps, and a fused third data set is generated; aiming at the initial process configuration, adjusting a node sequence and a parameter threshold value by using a visual design tool, and generating an optimized process configuration adaptive to the diversified scene; according to the optimized process configuration, process scheduling is executed through a dynamic process engine, execution time and resource occupation are monitored in real time, and a process execution log is generated.

The invention discloses a digital twinning-based heterogeneous body-equipped intelligent equipment collaborative management system and method, and belongs to the technical field of digital twinning and body-equipped intelligent systems. The system comprises a digital twin platform, a communication module, a task scheduling module, a cooperative control module and an edge computing node. The digital twinborn platform constructs a high-fidelity three-dimensional model of a physical environment and digital twinborn bodies of the intelligent devices with the bodies; the communication module is used for realizing bidirectional data interaction between the platform and the intelligent equipment; the task scheduling module performs task allocation and path planning by adopting a reinforcement learning algorithm based on the global dynamic situation map; the cooperative control module models an equipment space-time relationship through a graph neural network, predicts conflicts in real time and generates a dynamic avoidance strategy; and the edge computing node carries out real-time preprocessing on the equipment sensing data. According to the method, global optimization scheduling and multi-device intelligent cooperation are realized, and the overall efficiency, robustness and adaptability of the system are remarkably improved.

The invention discloses a supply chain collaborative management method and system for a whole-industry link platform, and the method comprises the steps: constructing a distributed supply chain data warehouse through employing a block chain and lightweight encryption; generating a multi-dimensional matching degree score table to recommend suppliers; decrypting and storing the encrypted bidding file, and calculating a comprehensive score according to a quantitative index and an expert qualitative index; constructing a hierarchical prediction model, and outputting a three-dimensional prediction result; calculating a supply-demand gap according to a supply-demand elastic coefficient coupling algorithm, automatically generating an order, and synchronizing the order to each end in real time; building a supply chain digital twinborn model, and dynamically planning an optimal transportation route through an intelligent fusion road condition algorithm; early warning information is automatically sent to generate a coping strategy, and full-link operation data are integrated for visual display; the objective of the invention is to solve the problems of existing supply chain data splitting, low supplier matching efficiency, low demand prediction precision, rigid logistics planning, risk monitoring lagging and poor collaboration of all links.

The invention discloses a digital twinning-based flow machine operation intelligent collaborative management system, and the system comprises an operation data collection module which is used for obtaining multi-source data in a target scene and carrying out the preprocessing of the multi-source data; the twinning modeling module is used for constructing a flow machine operation digital twinning model and generating a twinning operation initial situation; the space-time path analysis module is used for constructing a time hierarchical path network and generating a path conflict cone set and a feasible space-time path set; the operation load calculation and scheduling module is used for establishing task link representation, generating a storage yard congestion potential field and forming a multi-stream-machine cooperative scheduling candidate scheme; and the virtual-real correction module is used for issuing execution and collecting operation feedback, correcting the twin model and generating an optimized target collaborative scheduling scheme. According to the method, the space-time-behavior-link coupled digital twinborn model is constructed to realize flow machine operation predictive collaborative scheduling, and the method has the advantages of conflict recognition in advance, congestion trend suppression and remarkable improvement of scheduling efficiency.

The invention relates to the technical field of constructional engineering informatization management, and discloses a constructional engineering full-period collaborative management and risk prediction system, which comprises a resource token definition module, a resource token generation module, a resource management module and a risk prediction module, and is characterized in that physical elements are mapped into discrete resource tokens configured with unit time delay charge rates; the process transactional modeling module is used for packaging the process into an atomic transaction unit containing a request and a release instruction; the discrete event rehearsal simulation engine executes circulation in the virtual time axis and records the hanging duration; the deadlock detection module monitors the occupancy topology in real time to identify a loop waiting closed loop; according to the dynamic priority arbitration logic, an accumulated lag weight value is calculated according to the product of the rate and the duration, resources are forcibly allocated accordingly to eliminate deadlock, the dynamic arbitration algorithm based on the time value gradient is constructed, the physical lag cost is converted into the calculation weight, and the capacity of the system for automatically converging to the optimal solution under complex constraints is improved.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention provides a multi-agent collaborative management method and system for whole-process engineering consultation. The method comprises the following steps: S1, obtaining a whole-process engineering consultation task; s2, task decomposition is conducted on the obtained whole-process engineering consultation task based on an LLM large language model, task decomposition characteristics of the whole-process engineering consultation task are obtained, and the task decomposition characteristics comprise one or more subtasks; s3, task classification feature extraction is carried out on each subtask of the task decomposition features, and task classification labels corresponding to the subtasks are obtained; s4, according to the task classification label corresponding to each sub-task, matching a corresponding agent from the candidate agents to process the sub-task, and obtaining a sub-task solution output by the agent; and S5, performing optimization processing according to the obtained sub-task solution to obtain a whole-process engineering consultation task solution. According to the invention, the effect of providing the whole-process engineering consultation task solution can be improved.

The invention relates to the technical field of education collaborative management, and discloses a higher vocational education collaborative management system based on a multi-source knowledge graph and an intelligent agent, and the system comprises an ontology graph module which is used for obtaining data from all business systems and generating versioned knowledge graph snapshots; the rule compiling module is used for compiling laws and regulations and standards to form a discrete constraint set; the event influence module is used for determining an influence sub-graph and screening a sub-constraint set; the task decomposition optimization module is used for decomposing tasks and selecting bid-winning execution plans; the course arrangement solving module is used for solving a course arrangement result based on the constraint model; the post matching module is used for comparing the student ability with the post demand to obtain a post qualification set; and the evidence write-back module is used for constructing an evidence directed acyclic graph, generating a fingerprint hash value and forming a new version knowledge graph snapshot. According to the invention, data unified modeling, rule structured expression, task automatic execution and whole-process traceability in higher vocational education management are realized.

The invention relates to an agricultural microbial preparation production line multi-device collaborative control method and system, and the method comprises the steps: calculating a device health score based on operation state information, combining the device health score with a biological risk index to generate a task release score, and generating a production task instruction when the task release score meets a preset release threshold value, constructing a cooperative interlocking matrix between the multiple types of process equipment and the material handling equipment based on the production task instruction, distributing a control execution instruction to the multiple types of process equipment and the material handling equipment, respectively obtaining equipment feedback state information and material circulation information, executing consistency verification on the equipment feedback state information and the material circulation information, and sending a verification result to the material handling equipment; a consistency verification result is obtained, during manual modification, a revision audit chain corresponding to the control event information is generated, and when the running state information or the control event information triggers a preset abnormal threshold value, a disposal control instruction is generated. The method has the effect of improving the production safety.

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 discloses a cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, and relates to the technical field of data processing, and the system comprises a data processing module which obtains real-time data of purchase cost, exchange rate fluctuation, storage cost and logistics cost, cleans and standardizes multi-source heterogeneous data, obtains a cost data set of a unified format, and stores the cost data set of the unified format; the trend analysis module is used for calculating the change trend of the purchase cost, the exchange rate fluctuation, the storage cost and the logistics cost in the time dimension according to the cost data set in the unified format to obtain the dynamic weight of each cost factor, and the inventory pricing module is used for calculating the real-time comprehensive cost of each commodity by adopting a weighted average algorithm according to the dynamic weight of each cost factor and calculating the real-time comprehensive cost of each commodity. A dynamic inventory pricing result is obtained; according to the cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, the problems of pricing lag and profit loss caused by the fact that a traditional inventory pricing method cannot respond to cost fluctuation in real time are solved.

According to the industrial solid waste multi-target collaborative management method and system based on the digital twinborn and hybrid optimization algorithm, industrial solid waste data are collected in real time through an Internet of Things sensor, a dynamic digital twinborn model is constructed to simulate a physical process, and the problems of data dispersion and lag in a traditional method are solved; establishing a multi-objective optimization function by combining three major objectives of economy, environment and society, and generating a Pareto optimal strategy set by adopting an improved NSGA-II and particle swarm optimization (PSO) hybrid algorithm; the method combines historical and real-time data through a twin model, employs an LSTM algorithm to synchronously predict the type and quantity of solid wastes, the equipment operation state and the environment change, achieves the dynamic simulation and prediction of the whole process of solid waste generation, transportation and processing, remarkably improves the data integration efficiency, provides precise real-time support for decision making, and improves the real-time performance of the system. The treatment cost is reduced, the carbon emission is reduced, and the public satisfaction is improved; the strategy response time is shortened through dynamic model correction, an intelligent solution is provided for industrial solid waste management, and the function of multi-dimensional collaborative optimization of industrial solid waste management is achieved.

The invention provides a low-altitude flight accident intelligent identification method based on multi-source data fusion, and the method comprises the steps: carrying out the encryption processing of flight control data, dynamically mapping environment data through an airspace grid mapping model, carrying out the time-space correlation of the two kinds of data, and obtaining multi-source data; constructing a five-dimensional evidence evaluation system through the information entropy of the multi-source data, and carrying out dynamic weight adjustment on the evaluation system according to a real-time data triggering condition to obtain a causal contribution degree; an accident liability contribution degree is obtained through a weight modeling algorithm; then evidence conflict detection is carried out to obtain recombination credibility; and finally, training a dynamic weight distribution model by recombining credibility, and identifying a low-altitude flight accident to obtain a responsibility division result. According to the method, multi-dimensional data are integrated in real time, a complete flight accident evidence chain is constructed, and a dynamic evaluation and multi-party collaborative management mechanism is introduced, so that a scientific basis is provided for responsibility division, and the responsibility affirmation efficiency and credibility are improved.

The invention discloses a municipal road intelligent construction and collaborative management method based on digital twinning, and belongs to the field of digital twinning, and the method comprises the steps: S1, building a unified data twinning body composed of BIM, construction monitoring, environment and equipment data; s2, real-time synchronization of the virtual model and the entity is realized through time sequence mapping; s3, performing full-period control by adopting a multi-agent collaborative optimization algorithm; s4, embedding a risk evolution prediction model to carry out structure and material risk assessment; s5, multi-work-type tasks are distributed based on scene hierarchy; s6, progress self-checking and closed-loop control are achieved through two-way communication; s7, performing multi-stage data fusion to realize model consistency updating; s8, establishing a full life cycle carbon emission and energy consumption evaluation system; and S9, outputting a comprehensive decision report, and realizing integrated management of planning, construction and operation and maintenance. The method has the beneficial effects of improving construction efficiency and decision-making precision.

The invention discloses a BIM application method and system in cost management. The method comprises the steps of BIM model construction, cost data integration, engineering quantity list and cost report generation, collaborative management, AI real-time optimization, digital twinborn early warning management, design change and contract text analysis, edge calculation cost management, block chain data guarantee and the like. The system comprises a plurality of modules such as a BIM model construction module and a cost data integration module. According to the method, software is utilized to generate a BIM model, cost data is integrated, data quality is evaluated before a report is generated, multi-specialty and multi-participant authority is cooperatively managed, AI real-time optimization, digital twinborn early warning, a natural language processing engine analyzes a change document, cost is processed through edge calculation, safety protection is achieved, and data is guaranteed in combination with a block chain. Compared with the prior art, the construction cost calculation accuracy is improved, personal errors are reduced, the association between the component and the construction cost data is enhanced, the change response efficiency is improved, and the data transmission delay is reduced.

The invention discloses a construction site material RFID tracking and BIM collaborative management system, particularly relates to the field of constructional engineering digital management, and is used for solving the problems of evidence chain breakage, progress labeling forking and acceptance certificate deviation caused by tag and component corresponding failure due to replacement and splitting operations. Time mark correction events are unified through an identity mapping module, tag keys are established to correspond to model keys in a one-to-one mode, an evolution sealing module detects change to generate a new instance and constructs a father-son pedigree redirection event, and a causal chaining module organizes read-write events into a monotonic sequence and promotes a state. And the mutual identification judgment module fuses the identity chain and the space path evidence to calculate a decision coefficient to trigger backtracking correction, and the iterative archiving module summarizes a check result to optimize rules and solidifies snapshots, so that full-link traceability and collaborative consistency of materials from entrance to installation are realized.