2014 results about "Project management" patented technology

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

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.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention provides a visual project management method and system based on task tracking, and relates to the technical field of project management, and the method comprises the steps: collecting project risk information through employing a context awareness technology, generating a risk propagation network, and calculating a risk diffusion coefficient; performing hierarchical analysis on project requirements based on the risk diffusion coefficient to obtain a task decomposition scheme, constructing a task transmission chain based on the task decomposition scheme and generating a visual task relation graph, and generating a task state transition rule and an alternative task path based on an early warning state of a task node, meanwhile, constructing a task elastic scheduling mechanism and storing the task elastic scheduling mechanism into a rule database; and identifying a preorder task node and a post-order task node on the task transmission chain, sending the task reconstruction instruction to the preorder task node and the post-order task node to realize dynamic task adjustment, updating the risk propagation network according to an adjustment result, and optimizing the risk diffusion coefficient.

The invention provides a digital project management method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. Constructing a multi-level resource constraint matrix; generating a resource collaborative optimization configuration model by using a deep neural network; calculating a real-time scheduling weight coefficient; constructing a resource scheduling objective function; solving an optimal resource scheduling scheme by adopting an improved ant colony algorithm; inputting the optimal scheme into an intelligent cooperative controller for dynamic adjustment; and intelligent cooperative scheduling of power grid engineering construction resources is realized. According to the invention, the resource scheduling efficiency and accuracy are improved.

The invention relates to the technical field of project management, in particular to an intelligent management method and system for a civil engineering project, and the method comprises the following steps: obtaining personnel distribution, equipment state and material inventory data, extracting a key task node and an execution state, recognizing a progress lagging task, screening a key path node, analyzing a time difference, and marking a progress trend. And identifying an equipment influence section, judging resource allocation imbalance, and generating a progress monitoring index. According to the invention, through multi-source data acquisition of personnel distribution, equipment state and material inventory, difference identification between a task state and a plan node is enhanced, progress lag early warning efficiency is improved, progress fluctuation dynamic classification and trend identification are realized, construction rhythm optimization is assisted, and through coincidence analysis of equipment operation and a task fluctuation area, the progress lag early warning efficiency is improved. The construction efficiency bottleneck is identified, the scheduling accuracy is improved, fine progress monitoring is realized through investment and period difference comparison, and the progress control capability and the collaboration of on-site resource allocation are comprehensively enhanced.

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 relates to the field of information management, and discloses a constructional engineering information management system, which comprises a data acquisition module, an intelligent analysis module, a risk prediction module, a resource optimization module and a feedback learning module, the data acquisition module is used for acquiring multi-source data related to constructional engineering; the intelligent analysis module carries out fusion, preprocessing and feature extraction on the collected data; the risk prediction module predicts possible risks in the future by using a statistical analysis model; the resource optimization module dynamically adjusts and optimizes manpower, equipment, materials and process arrangement of the construction site according to the risk prediction result; and the feedback learning module is used for collecting a system execution result, risk prediction accuracy and user feedback, and recording an optimized execution result. The method has the advantage of realizing efficient engineering management and decision support.

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 discloses an intelligent construction method based on data driving, and relates to the field of intelligent construction, and the method comprises the steps: collecting multi-source heterogeneous data of an intelligent construction project; performing feature selection on the acquired multi-source heterogeneous data by adopting a recursive feature elimination (RFE) algorithm to obtain a feature subset reflecting project progress, quality and safety; according to the feature subset, a multi-target model used for predicting project progress, quality and safety is constructed through a support vector machine (SVM) algorithm; a non-dominated sorting genetic algorithm NSGA-II is adopted to solve the multi-target model, a simulated annealing strategy is introduced in the iterative search process of the NSGA-II to jump out of a local optimal solution, and an optimized Pareto optimal solution set is output; and selecting an optimal parameter combination according to the Pareto optimal solution set, and adjusting the BIM model of the current intelligent construction project by using the optimal parameter combination. In view of low multi-objective optimization efficiency in intelligent construction project management in the prior art, the optimal model parameters considering different management objectives are obtained, and the efficiency is improved.

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, which relates to the technical field of supply chain information, discloses a comprehensive supply chain control system comprising a transaction and collaborative service module, an order management and decision support module and a data interaction and integration module. The transaction and cooperation service module is used for processing transaction information of supply chain participants, ensuring information safety and accuracy, managing the full life cycle of a contract and performing real-time monitoring, establishing a real-time communication and cooperation project management channel, and improving cooperation efficiency; according to the invention, the transaction and collaborative service module has a significant effect, encryption processing ensures the security of transaction information, leakage and tampering are prevented, contract management is efficient and flexible, a multi-parameter evaluation optimization template reduces the default risk, a collaborative mechanism improves the collaborative efficiency of all parties, and the response speed of a supply chain is enhanced.

The invention relates to the technical field of building construction, in particular to a BIM and unmanned aerial vehicle integrated building construction monitoring method, which comprises the following steps of S1, deploying an unmanned aerial vehicle at a construction site; s2, the unmanned aerial vehicle is used for collecting construction data when flying on a preset path; s3, performing primary processing and compression on the collected data through an edge calculation unit built in the unmanned aerial vehicle; s4, automatically matching the data transmitted by the unmanned aerial vehicle with a pre-imported BIM model; s5, if it is found that a deviation exists between the construction progress and the BIM model preset plan, an adjustment suggestion is generated according to an analysis result; and S6, correspondingly adjusting the flight path and the data acquisition plan of the unmanned aerial vehicle according to the adjustment suggestion. According to the invention, by integrating the unmanned aerial vehicle and the BIM model and adopting an advanced data synchronization technology, the real-time performance and accuracy of building construction monitoring are remarkably improved, so that project management is more efficient and flexible, and errors and cost are reduced.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a generative interface panel having multiple automated assistant services. Each assistant service may access a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention provides a project management method, system and device and a medium, and belongs to the field of project management. The method comprises the following steps: collecting project related data, and sorting and cleaning the project related data to obtain a structured data set; adopting an entity identification and relation extraction algorithm to carry out entity labeling and relation extraction processing on the structured data set so as to construct a knowledge graph related to the project; the method comprises the following steps: performing task decomposition by adopting a graph traversal algorithm on the basis of a knowledge graph, generating a task list and a priority sequence thereof, and monitoring task progress and resource utilization rate in real time on the basis of a dynamic optimization algorithm so as to dynamically adjust task priority and a resource configuration strategy. The task module is automatically decomposed based on the knowledge graph, the task decomposition efficiency and accuracy are improved, intelligent resource allocation and task priority adjustment are achieved in combination with dynamic planning and an optimization algorithm, the team cooperation efficiency is improved, and the flexibility and adaptability of a project are improved.

The invention relates to the technical field of project management, in particular to a BIM-based building project resource scheduling optimization method, which comprises the following steps: based on the current progress state of a construction task, collecting the consumption of constructors, mechanical equipment and materials, comparing the task plan progress with the real-time progress, screening out construction links with the backward task progress, and establishing a construction project resource scheduling optimization model; and judging a resource type, and establishing a task progress offset. According to the method, the construction progress is accurately monitored, real-time evaluation of resource consumption is achieved, waste and scheduling lag are avoided, the construction path is optimized by means of the BIM model, the key path monitoring precision is improved, progress management and control effectiveness is ensured, key path progress deviation is analyzed, the resource shortage risk is recognized, stable supply of construction resources is guaranteed, and on the basis of the influence of resource shortage, the construction efficiency is improved. The resource distribution sequence is optimized, the resource utilization efficiency is improved, the supply chain delivery cycle is matched, the resource scheduling is optimized, the influence of resource supply and demand imbalance on the construction progress is reduced, and the continuous and stable construction progress is ensured.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention relates to the technical field of project management, in particular to a building engineering project construction quality management method and system, and the method comprises the following steps: obtaining a drawing node sequence index and comparing a process stage number, screening compliant nodes, judging the reasonability of a task configuration time sequence, analyzing the deviation of an included angle between a crack angle and a load direction, and recognizing structural abnormality. And aggregating the partition abnormal nodes to form a construction project quality risk distribution section mapping result. According to the method, the structure drawing node sequence is compared with the construction process, it is guaranteed that input data logic is unified, the task configuration reasonability is improved through input time and stage sequence cross check, a stress deviation judgment mechanism is established in the crack direction through vector included angle and load comparison, and structural logic verification of inspection data is achieved; abnormal nodes are identified according to double standards of time compliance and stress deviation, a spatial density model is constructed by combining partition aggregation with an area ratio, building quality risk distribution is accurately presented, and the problem focusing and processing priority judgment capability is enhanced.

The invention relates to the technical field of project management, and discloses an engineering project risk management method and system, and the method comprises the steps: carrying out the feature extraction and dimension division of engineering project monitoring data, and obtaining a risk feature data set; performing cross influence analysis of quality risk-cost risk and progress risk-resource risk on the risk feature data set by adopting a bidirectional correlation calculation mechanism to obtain a risk correlation intensity matrix; based on the risk association intensity matrix and the historical risk evolution data, comprehensively evaluating the risk occurrence probability, the influence degree and the duration to obtain a risk level evaluation vector; performing hierarchical disassembly on the risk level evaluation vector to generate a risk decomposition hierarchical tree; performing multi-level risk classification on the risk decomposition hierarchical tree, and constructing a multi-level risk classification matrix; and establishing a risk response decision table according to the multi-level risk classification matrix, and generating a risk management control strategy, thereby improving the accuracy of engineering project risk management.

This disclosure provides a method for the standardized production, financing and delivery of housing using distributed ledger technology and a decentralized artificially intelligent cyber-physical system (CPS or invention) for compiling data and executing human and deep learning sequences for the purposes of standardizing the planning and execution of real estate development, regulatory compliance, financing, supply chain procurement, industrial manufacturing processes, logistical processes as well as project management tasks, and other various construction, manufacturing and delivery tasks and procedures to standardize development, production, and delivery of standardizes building modules and standardized building components in a standardized smart factory that are used to assemble structures.

The invention relates to the technical field of engineering project management, in particular to an engineering project construction quality management method and system, and the method comprises the following steps: extracting multiple items of design information of a target building based on project construction drawing information, and simulating and analyzing the stress distribution of a building structure under multiple loads through building a building information model. And evaluating the stability of the target building design, identifying and marking design defects, and obtaining a simulation analysis result. According to the method, by extracting the design information, constructing the building information model, evaluating the stability of building design, recognizing the design problem in the early stage, calculating the actual solidification rate of concrete and detecting the concrete quality, reworking and overhigh cost caused by the concrete quality problem are reduced, and defects in prefabricated parts are detected through ultrasonic waves; the construction safety and quality are improved, the construction deviation is quantified through measurement of an actual construction image and comparative analysis of the actual construction image and a design drawing, and efficient management of the construction quality of a target project is achieved.

The invention discloses an intelligent science and technology project matching and pre-scoring system based on a large model, and relates to the technical field of project matching evaluation, and the system comprises a data collection module which collects multi-source science and technology project information; the data preprocessing module is used for cleaning, converting and standardizing the data; the large model construction module comprises a feature coding layer, a matching decision layer and a pre-scoring prediction layer; the intelligent matching module outputs a matching degree and similar items; a pre-scoring module; and a user interaction module, a model updating module, a security module and a privacy protection module. The large model fuses multiple technical training, each module has a specific function and algorithm, accurate and efficient operation of the system is guaranteed, and data security privacy is protected. According to the method, a large number of science and technology projects can be efficiently processed, the evaluation period is shortened, the matching accuracy and the pre-scoring reliability are improved, multiple factors are considered, the subjective influence is reduced, the system can be dynamically updated, the knowledge graph is updated, the management monitoring function is perfected, intelligent development of science and technology project management is promoted, and resource configuration is optimized.

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

A prefabricated building component stability monitoring and early warning method and system, relating to the technical field of engineering project management. The method comprises: on the basis of a target building engineering drawing, constructing a construction node sequence; generating a plurality of engineering construction simulation models; acquiring a sample data set of a plurality of construction nodes, performing stability-associated factor analysis, and constructing a node factor mapping sequence; by using the node factor mapping sequence as monitoring data, performing construction monitoring, and determining construction monitoring data of a first construction node; on the basis of the construction monitoring data, performing mapping and acquiring a first engineering construction simulation model, performing error analysis by means of a twin comparison channel, and determining a first construction error; and on the basis of the first construction error, generating a construction early warning signal, and, on the basis of the construction early warning signal, acquiring a first construction compensation parameter and performing subsequent construction compensation for target building engineering, thereby achieving the technical effects of improving the safety and reliability of a prefabricated building structure and reducing potential risks and losses.

The present invention provides a multi-project scheduling optimization algorithm and system based on resource constraints. By loading test tasks, personnel information and equipment status, a task dependency graph is constructed to determine the execution order. Through the priority calculation module, each task is assigned a priority according to the deadline, resource requirements and task urgency, ensuring that high-priority tasks are executed in a timely manner. The scheduling execution module uses the shortest job first (SJF) or backtracking algorithm to optimize task scheduling. In addition, the real-time monitoring module monitors the task progress and resource status, and the conflict detection and processing module can adjust the personnel and task allocation in real time to cope with resource contention. Finally, the evaluation and optimization module generates an evaluation report after the project is completed, records the resource utilization efficiency, and provides improvement suggestions for subsequent projects. The present invention effectively improves the efficiency of multi-project management in a resource-constrained environment, and is suitable for scientific research, engineering and other multi-task collaborative fields.

The invention relates to the technical field of engineering management, and discloses an engineering management resource optimization configuration platform, which comprises the following modules: a data acquisition and management module used for acquiring real-time state data of manpower, equipment and materials in an engineering project through Internet of Things equipment and sensors; the resource pooling and sharing module is used for carrying out centralized management on resources in the engineering projects, constructing a cross-project resource pool and carrying out resource sharing according to project requirements and resource states; and the intelligent scheduling and optimizing module is used for dynamically scheduling resources based on project requirements, task priorities and task dependency relationships. By adopting an intelligent scheduling method combining a hybrid genetic algorithm and a particle swarm optimization algorithm and a deep reinforcement learning dynamic scheduling module, resources can be dynamically allocated according to project requirements and task priorities, the utilization rate of the resources can be maximized, the phenomenon that the resources are idle or reused is reduced, and efficient configuration of the resources is ensured.

The invention provides a Web-based MBSE collaborative research and development management system, which belongs to the field of model-based system engineering and comprises a demand center used for managing demands generated in a system development process and supporting system models, simulation data and demand entries to trace; the model center is used for managing model data generated in a system development process taking a demand as an input; the simulation center is used for calling and executing each model and recording simulation configuration, simulation tasks and simulation result data; the document center is used for extracting data and automatically generating and managing various documents; the digital clue is used for managing digital clue data of the MBSE and displaying an association tracing relation of data in a forward design process based on a model; and the project management module is used for managing the information of the MBSE research and development project. According to the method, a model-based system digital research and development process is realized, team cooperation efficiency and product design quality are improved, and tool support is provided for complex product MBSE activities.