895 results about "Project management" patented technology

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

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 a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

The invention belongs to the technical field of resource scheduling, and particularly discloses a resource scheduling method and system based on reinforcement learning, and the method comprises the steps: describing the dependency and conflict relation between tasks through constructing a task causal relation graph which can be dynamically updated; constructing a state space and an action space based on a current task state and a causal relationship, training an intelligent agent by adopting a reinforcement learning model in combination with a multi-target reward function, and generating a scheduling and resource allocation strategy; linear programming and heuristic joint resource allocation are carried out under strategy guidance, and task priorities and causal relationships are dynamically adjusted in combination with task states; by collecting execution data and analyzing strategy deviation, a model structure and parameters are further adjusted, and a complete closed-loop optimization process is formed. According to the method, task priority dynamic adjustment, resource allocation strategy self-adaptive updating and scheduling process closed-loop optimization are realized, and the method is suitable for complex project management scenes under multi-task and multi-resource constraints.

The invention provides a prefabricated pipe pile construction total factor intelligent management method and system based on the digital twinborn technology, and relates to the technical field of construction management, a three-dimensional model is established through the BIM technology, pipe pile attributes are bound, construction data are collected in real time through the Internet of Things, and a dynamic digital twinborn body is formed. Through integration of an API interface and a project management platform, an AI algorithm is used for analyzing data, resource allocation and construction plans are dynamically adjusted, instructions are optimized and pushed to the site, construction efficiency and installation precision are improved, and the problems that traditional management is low in efficiency and large in error are solved.

The invention discloses a construction project whole-process management system and method with a BIM3D-6D technology as a base, and relates to the technical field of construction project management, and the method comprises the steps: loading a 3D building information model through a BIM lightweight calculation server, extracting the spatial topological relation and IFC semantic attributes of geometric components, and carrying out the calculation of the 3D building information model through a BIM lightweight calculation server; spatial topology analysis and semantic attribute mapping are carried out on the spatial topology relation of the geometric components and the IFC semantic attributes, and a three-dimensional data topology network packet based on a graph structure is generated; performing multi-dimensional deduction operation on the three-dimensional data topology network packet based on the graph structure to obtain a deviation compensation strategy containing an optimal cost control path; and carrying out dimension expansion and data fusion on the 3D building information model to generate a 5D cost dimension data set. Discrete decisions are converted into a continuous field model through mathematical transformation, so that a resource flow path has spatial relevance and time continuity, and the beneficial effect of dynamically optimizing a construction process is achieved.

The invention provides a project management software document uploading method based on OCR and a large language model, and relates to the technical field of large language models, and the method comprises the steps: receiving a project document picture, and carrying out OCR recognition through a multi-scale feature pyramid and an attention-enhanced convolutional neural network; inputting the text information into a pre-trained large language model for classification and key field extraction; determining a data partitioning strategy according to the item type to create a storage space; establishing a multi-dimensional index structure to store project information and analyzing a content logic relationship; and generating and displaying a project information relation graph. According to the invention, intelligent identification, classification and correlation analysis of the project documents are realized, and the project management efficiency and the data utilization value are improved.

The invention relates to the technical field of project management, in particular to a large-model batch reasoning and data flow optimization system oriented to an MOE architecture. According to the method, a collaborative architecture of the request access module, the environment sensing module, the expert routing engine, the resource scheduling module and the dynamic optimization control module is set, the text length and the subject type are extracted by using the request access module, a basis is provided for accurate routing, and the GPU video memory, the I / O bandwidth and the request queue depth are acquired in real time through the environment sensing module, so that the real-time routing is realized. The system load is comprehensively monitored, meanwhile, an expert sub-network is activated through an expert routing engine according to request features, invalid calculation is avoided, weight loading and resource allocation are managed through a resource scheduling module, the I / O bottleneck is reduced, and finally an optimization strategy is intelligently triggered through a dynamic optimization control module based on routing conflict factors. The problems of large reasoning delay fluctuation and unbalanced resource utilization rate mentioned in the background technology are solved, and stable low-delay response and resource collaborative optimization in a high-concurrency scene is realized.

The invention belongs to the technical field of building project management, and particularly relates to a building curtain wall assembly management technology, in particular to a building engineering project management intelligent system and method. A three-dimensional matrix including component identification, material consumption, a planned entering time window and a material sensitivity label is constructed based on a component entering plan; meanwhile, a construction site space model is established, the occupation state of a three-dimensional matrix simulation material storage yard is loaded in the model according to a component entering plan, and component entering is dynamically optimized by comprehensively considering storage yard space overlapping and meteorological adaptability factors in combination with meteorological prediction data in an assembly time window; prejudgment and adjustment of the assembly plan before implementation are realized, and the change frequency in the actual execution process is effectively reduced, so that the waste of manpower and material resources is remarkably reduced, and the stability and efficiency of a construction organization are improved.

The invention discloses an EPC project resource dynamic scheduling method based on multi-objective optimization, and the method comprises the steps: extracting project starting time and activity attributes from an EPC project, constructing a project activity list and a project resource list, constructing a multi-objective function, a decision variable and a constraint condition, and carrying out the dynamic scheduling of the EPC project resource. And processing the project activity list, the project resource list and the project starting time by using a multi-objective optimization algorithm containing crossover and mutation operation to obtain an optimal resource scheduling scheme containing the project construction period, the cost and the resource utilization rate, and after the scheme is issued and executed, obtaining the progress, the cost and the resource use condition in real time, thereby realizing the resource scheduling. A progress deviation rate, a cost performance index and a resource load rate are calculated and normalized, a comprehensive evaluation value is obtained through a linear weighting model, and whether a multi-objective optimization algorithm is optimized or not is judged according to the comprehensive evaluation value, namely, the weight of each objective function is adjusted according to the highest index after normalization, and a scheme is regenerated. According to the invention, resource dynamic optimization scheduling is realized, and project management efficiency is improved.

The invention, which relates to the technical field of mineral product screening, discloses an intelligent construction management platform for city updating, comprising the following steps: establishing a unified data standard and a data interface protocol to ensure that data of different systems, departments and enterprises in the platform can be seamlessly jointed; according to the invention, through unifying the data standard and the interface protocol, data sharing of different systems is realized, and project management efficiency and decision accuracy are improved. The real-time data acquisition and intelligent analysis technology enhances the safety monitoring and intelligent early warning functions, and ensures that potential safety hazards in the construction process are recognized and processed in time. Meanwhile, the flexibility and the emergency response capability of the project are improved by a dynamic adjustment and intelligent early warning mechanism, resources and construction plans can be automatically adjusted in the progress of the project, emergency problems can be dealt with, progress delay and resource waste are reduced, and smooth progress of the project is guaranteed.

The invention discloses a large model cue word intelligent design and verification system supporting multi-person cooperation, which designs cue words in a manner of cue word project management and multi-person cooperation to realize specialized design of the cue words. The difficulty of cue word design is simplified by using AI aided design; the operation stability and accuracy of cue words in different models and different computing power are verified through single-point testing and batch testing, so that cue word verification and iteration form a closed loop, and the cue word quality is improved; after the cue word is published, directly outputting an interface with an access key for external calling, and storing the interface as a cue word case into a cue word knowledge base; compared with an existing system and technology, the cue word version management difficulty is effectively reduced, the workload of cue word designers is reduced, the cue word quality is improved, and the risk of cue word asset loss is reduced.

The invention discloses a project progress, quality and safety collaborative management and control system and method based on BIM and Internet of Things, and belongs to the technical field of constructional engineering informatization management. In order to improve engineering management efficiency and reliability, the system comprises a data acquisition layer, a data processing layer, an application layer and a security module. The data acquisition layer is connected with the data processing layer, the data processing layer is connected with the application layer, and the security module is respectively connected with the data acquisition layer, the data processing layer and the application layer; the data acquisition layer comprises IoT equipment, an external data interface, data middleware and a BIM model, the IoT equipment acquires data through the external data interface, the IoT equipment is connected with the BIM model through the data middleware, and the data processing layer comprises a multi-source data fusion engine and an intelligent algorithm module; the application layer comprises a model management module, a model display module, an early warning management module, a data management module, a three-dimensional visual monitoring platform, an intelligent decision center and a mobile terminal module. The project management efficiency is improved.

The invention relates to the technical field of performance evaluation, and provides an intelligent project performance evaluation and optimization platform which comprises a multi-source heterogeneous data fusion module, an association rule deep mining engine, a self-adaptive dynamic performance evaluation model, a closed-loop optimization decision module and an intelligent interaction system. According to the invention, an intelligent, dynamic and closed-loop project performance evaluation and optimization platform is constructed by fusing an advanced artificial intelligence technology and a project management theory, and the scientificity, refinement and intelligent level of project management is greatly improved.

The invention discloses a water conservancy project construction management informatization platform, and relates to the technical field of water conservancy project management, and the platform comprises an evaluation form generation module, a form filling verification module, an intelligent approval module, a classification filing module and a safety management module. According to the invention, a whole-user, full-service and full-life-cycle construction management standardization system is constructed; automatic filling and real-time verification are realized by means of a natural language processing technology and machine learning, and data entry experience and accuracy are improved; decision support is provided through big data analysis, a filling strategy and an approval path are recommended through a model, and process management is optimized; digitized upgrading of form full-life-cycle management is realized, and the method is suitable for a water conservancy project construction management scene with a complex approval process; the state evolution trend of hidden danger points or major hazard sources in a future period of time is simulated, and early warning and plan generation are carried out based on the state evolution trend.

The invention discloses an EOD project-oriented multi-level collaborative planning system and a resource allocation method, and relates to the technical field of project management and resource scheduling optimization. The method comprises the following steps: carrying out credible alignment and encrypted aggregation on heterogeneous data of a government chain, an enterprise chain and a community chain through a block chain, and generating a dynamic heterogeneous graph containing government task nodes and industrial development nodes; a government, enterprise and community three-party asymmetric game model is constructed, contribution degrees of all parties are calculated, and priority weights are determined. Through a gating time sequence attention mechanism, a node state is predicted, a coupling influence value of a governance efficiency attenuation factor and an industrial income fluctuation factor is calculated, a near-end strategy optimization algorithm is combined, and the investment proportion of funds, equipment and human resources is dynamically adjusted by taking comprehensive income maximization as a target. A disturbance sample is simulated by using a Monte Carlo method, the robustness of a priority weight is evaluated, a resource configuration strategy is formed, and the node state of the heterogeneous graph is iteratively updated in real time, so that resource configuration is optimized and the system cooperation efficiency is improved.

The invention provides a task risk early warning method and system based on project management data, and relates to the technical field of project management early warning. According to the method, the task risk index weight is automatically adjusted to adapt to process states and external environment changes in different stages of a project, the accuracy and flexibility of risk assessment are remarkably improved, and good dynamic adaptability is achieved; performing grading evaluation on tasks in each stage of the project by means of a quantitative comprehensive risk value, determining a management priority, and assisting a project team to focus on key risks; a task cooperation relationship is visually presented by constructing a task cooperation network, a center node and a fragile link are accurately identified, and a risk propagation path is effectively grasped; and real-time monitoring and graded early warning are realized in combination with a dynamic risk early warning rule, so that the risk can be found and dealt with in time.

The invention discloses an AI-based digital project performance evaluation data processing method and system, and relates to the technical field of artificial intelligence and digital project management, and the method comprises a multi-source data collection module which is used for obtaining data; the entity alignment module is used for establishing an association relationship among cross-system data entities; the dynamic index generation module is used for dynamically adjusting the evaluation index weight by utilizing a reinforcement learning framework; the efficiency prediction module is used for modeling a task dependency relationship according to the time sequence diagram convolutional network and outputting a delay risk probability; and the visual interface is used for displaying the data. According to the method, cross-system entity alignment, dynamic index weight adjustment, task delay risk prediction and root cause analysis are realized through AI technologies such as BERT semantic matching and a graph neural network, visual data display and interaction functions are provided by virtue of a visual interface, the problems of data dispersion, index static state, risk lag and the like in traditional project evaluation are solved, and the project evaluation efficiency is improved. And the decision support capability of project management is improved.

The invention relates to the technical field of data processing, in particular to a construction progress and resource collaborative prediction data processing method and related products, and the method comprises the steps: constructing a BIM knowledge graph; monitoring the real-time dynamic data in real time, and generating a multi-dimensional risk prediction vector if a risk event deviating from a construction plan is recognized; constructing a multi-objective optimization model, and solving to generate a collaborative optimization strategy; executing a collaborative optimization strategy, updating the construction progress plan and the resource allocation plan, and synchronously updating the BIM knowledge graph; according to the method, the BIM knowledge graph fused with the multi-source heterogeneous data is constructed, the information barrier between traditional BIM design data and field execution data is broken through, and the future time and cost deviation is predicted in a quantitative mode by performing linkage influence propagation simulation on risk events, so that the risk events are predicted more accurately. Project management is converted from a traditional'post remedy 'mode to a'pre-control' mode.

An AI-and IoT-driven real-time construction management system continuously acquires and analyzes high-frequency sensor data, including temperature, GPS, and RFID inputs. Utilizing advanced predictive machine learning and reinforcement learning algorithms, the system forecasts schedule deviations and resource conflicts with an accuracy exceeding 90%. Upon identifying deviations, it autonomously triggers corrective actions—such as reallocating resources or adjusting task sequences—typically within five seconds. This integrated, closed-loop management approach seamlessly interfaces with external project management tools, achieving approximately 15-20% improved schedule adherence and notable cost reductions based on preliminary data. The system's modular architecture supports diverse sensor technologies and AI frameworks, ensuring adaptability and sustained performance in large-scale, dynamic construction environments.

The present application relates to the technical field of project management, and discloses an integrated management system and method for a large-scale project. The system comprises: a budget subsystem, used for obtaining target project information, and carrying out multi-level classification processing and coding processing on the target project information to obtain budget codes; a contract management subsystem, used for decomposing the target project information, generating a plurality of project contracts and associating the plurality of project contracts with the budget codes; a construction management and control subsystem, used for splitting the project contracts to obtain unit projects, encoding the unit projects to obtain unit codes, and associating quantity data corresponding to the unit projects with a preset construction drawing list; and a joint control subsystem, used for carrying out joint control on the project cost, the project progress and the project quality on the basis of a measurement visa so as to carry out integrated management on a large-scale project. The present application realizes integrated management of large-scale project construction.

The invention provides a method, a device, equipment and a medium for generating an engineering project management digital model, and the method comprises the steps: collecting original data related to engineering project features, carrying out the preprocessing, forming a standardized project data set, constructing a space-time reference frame based on the data set, fusing a multi-dimensional feature tensor of multiple engineering features, and carrying out the construction of a space-time reference frame. The method comprises the steps of generating a project progress node and an integrated feature tensor, fusing the project progress node and the integrated feature tensor to generate a multi-source association graph containing entity relationships and feature semantics, generating a verified engineering project management digital model according to the project progress node, the integrated feature tensor and the association graph, and dynamically updating and optimizing the model in response to change data in project execution. According to the method, the problems of data dispersion, lack of association and static stiffness of the model in a traditional method are solved, automatic construction and dynamic evolution from multi-source heterogeneous data to an intelligent decision-making model are realized, and the accuracy, real-time performance and intelligent level of project management are remarkably improved.

A system and method for collaborative creation and management of intellectual property (IP) projects is disclosed. The system includes a server configured to dynamically assign roles and permissions to participants based on predefined criteria. A graphical user interface (GUI) facilitates real-time collaboration, editing, and version control, while a semantic analysis engine ensures compliance with intellectual property standards and assesses content novelty. Predictive analytics generate metrics, such as patentability scores and inventorship overlap, to guide project development. Contributions are electronically tracked and stored in a secure, non-transitory computer-readable medium alongside generated metrics. The system supports role-specific recommendations, automated notifications, and integration with external data sources for enhanced functionality. Participants can visualize contributions, resolve conflicts, and simulate commercialization outcomes, enabling efficient project management and innovation. This invention improves the collaborative development process, ensuring compliance, enhancing productivity, and maximizing the value of intellectual property assets.

The invention belongs to the technical field of project management, and particularly relates to a scientific research project full-process management platform. Comprising a scientific research project management cockpit module, a scientific research project library management module, a scientific research project execution management module, a scientific research project document management module, a scientific research project budget management module, an intellectual property library management module, a scientific and technological achievement library management module, a scientific research project report management module and a scientific research project billboard module. The beneficial effects of the invention are that the system achieves the higher refined management requirements of scientific and technological innovation through an informatization means, and solves the problems of project dispersion, repeated research and development, and project and talent cultivation. And the obvious defects of thin base, late starting and insufficient professional degree in scientific and technological innovation management of production enterprises and research and development institutions for energy power generation are overcome.

The invention discloses a software development project progress prediction management method based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: extracting a causal relation from entity relation graph data, and constructing a causal knowledge graph; a graph structure analysis method and a time sequence prediction method are combined, predicted completion time and delay probability are calculated for the causal knowledge graph and the associated time sequence data, and a progress risk assessment result is generated; performing causal chain identification on the progress risk assessment result and the causal knowledge graph by adopting a causal reasoning method to generate delay interpretation information; according to the delay interpretation information, a preset resource priority and a task weight, a rule driving and priority scheduling strategy is adopted to construct a compensation scheduling scheme; and implementing a compensation scheduling scheme, collecting implementation effect data and analyzing a scheduling effect through a closed-loop feedback and dynamic adjustment mechanism, and generating an optimized scheduling management scheme. The intelligent level and the practical value of project progress management are greatly improved.

The invention discloses an AI-driven building engineering intelligent management system, and relates to the technical field of data processing, and the system comprises a process data set obtaining module which is used for obtaining K engineering project process data sets; the engineering project risk degree obtaining module is used for obtaining K engineering project risk degrees; the callable configuration information obtaining module is used for obtaining the callable configuration information of the project; the configuration shortage probability obtaining module is used for obtaining K configuration shortage probabilities; the collaborative management scheme obtaining module is used for obtaining a collaborative management scheme; and the resource calling management module is used for carrying out resource calling management on the engineering callable configuration information. The technical problems that in the prior art, in the building engineering construction process, the fitting degree between management and the actual situation is low, and the management reliability is low are solved, and the technical effect of improving the building engineering management quality based on AI driving is achieved.

The invention relates to the technical field of engineering project management, in particular to an engineering progress risk early warning method and system based on adaptive threshold learning. Training the preprocessed data by using a C-L algorithm to obtain a classification model and a classification threshold; using the classification model to classify project progress data, constructing a completion time prediction model and a prediction threshold, and predicting and classifying cases lacking completion time; self-adaptively updating the threshold value on the test set through a C-L algorithm until a satisfactory test effect is achieved, and outputting a final prediction threshold value; the self-adaptive threshold mechanism can automatically adjust the early warning threshold according to the characteristics of different engineering projects, and compared with a traditional fixed threshold method, the early warning accuracy is improved by 20-30%.

The invention provides a BIM-combined construction process digital management method and system, and the method comprises the steps: firstly collecting a real-time three-dimensional model data set of a construction site, including geometric attributes and spatial topology association information of construction components, then analyzing the logic execution relation between different construction components, generating a progress scheduling rule set, and carrying out the construction process digital management. The actual construction progress data is dynamically matched with the set to obtain a construction process anomaly detection strategy and a resource allocation optimization strategy, then component parameter configuration of the real-time three-dimensional model data set is adjusted according to the anomaly detection strategy, updated three-dimensional model verification data is generated, and the updated three-dimensional model verification data is stored in a database. And finally, a construction progress adjustment instruction is generated according to the resource allocation optimization strategy and the updated three-dimensional model verification data, and is synchronized to the construction management terminal, so that digital and intelligent management of the construction process is realized, and the construction efficiency and the project management level are effectively improved.

The invention provides a building engineering project scheduling method based on multi-objective dynamic microphone optimization, and the method comprises the steps: firstly, building an optimization model with the minimization of the construction period, the minimization of the cost and the minimization of the resource fluctuation as targets, and enabling an objective function to cover the total construction period, the total cost and the resource fluctuation variance; secondly, designing an improved multi-target dynamic microphone optimization algorithm which ensures the feasibility and diversity of solutions through a dynamic elite retention mechanism, a mixed position updating rule, a three-layer constraint processing mechanism and a dual-threshold convergence monitoring system, so as to improve the robustness of the dynamic microphone optimization algorithm; and finally, solving the problem of the constructional engineering project by adopting a multi-target dynamic microphone optimization algorithm to obtain a construction project scheduling scheme. According to the method, on the premise of meeting resource limitation and task logic relations, the project construction period, the total cost and the stability of resource use are collaboratively optimized, automatic and intelligent optimization of the project scheduling process is realized, and the project management efficiency and scientificity are improved.

A multi-agent and human project management and collaboration tool can provide the ability to create and manage projects and software agents in a cohesive framework. The collaboration tool can provide a multi-agent, multi-player channel where agents and humans work together to achieve an outcome. The collaboration tool can initiate a new project, where the objective is defined, and a team is dynamically built to achieve this objective. The team roles are parsed, and an agent is created for each role. Each agent is equipped with a personalized skillset and a working memory, allowing them to effectively collaborate with humans and each other to progress autonomously through the project stages. The agents can complete tasks based on the project plan and the results are stored in the agent's working memory. The project status is updated, and a report can be generated after each stage, providing a record of the project's progress.