11 results about "Critical path method" patented technology

The invention discloses a deployment optimization method and system for multi-type collaborative construction, and relates to the technical field of intelligent scheduling, and the method comprises the steps: collecting the pre-construction data of each type of work, and carrying out the hierarchical splitting of a construction task according to the type of work and a working stage, and obtaining construction sub-tasks; constructing a dependency relationship of construction sub-tasks by utilizing topological sorting and a critical path method, and defining a sub-task execution constraint and a work type scheduling constraint by using a constraint planning method; generating a preliminary scheduling scheme by using a heuristic algorithm, and optimizing sub-task execution sorting according to the sub-task execution constraint and the work type scheduling constraint; and dynamically adjusting the execution sorting of the subtasks according to the dependency relationship of the subtasks by using a parallel scheduling algorithm to obtain a scheduling scheme of collaborative construction. According to the method, resource conflicts and delay risks are effectively avoided, high parallelism and load balancing of multiple work types of tasks are achieved, and the construction efficiency and reliability can be remarkably improved in a complex construction environment.

The invention relates to a tunnel construction progress model generation method based on artificial intelligence. The method comprises the following steps: acquiring multi-source heterogeneous data such as tunnel field equipment operation and geological change, and performing multi-modal fusion to form a unified data set; based on this, a particle filter algorithm is used to correct equipment positioning deviation, and a real-time position optimization result is generated. Comparing the time and space with the progress plan, and identifying and quantifying deviation points through a critical path method and a deviation analysis algorithm to form deviation data. And constructing a construction progress model by combining construction basic parameters and adopting a genetic algorithm, generating an adjustment scheme by taking a construction period and resources as targets, and obtaining an optimal scheme through compliance screening and fitness calculation iterative optimization. By the adoption of the method, efficient integration of heterogeneous data can be achieved, the equipment positioning precision is improved to the centimeter level, multi-dimensional quantification of progress deviation is achieved, progress deviation recognition and resource allocation efficiency is improved, and a technical scheme is provided for tunnel intelligent construction.

The invention provides a discrete manufacturing intelligent scheduling method based on a graph theory. The discrete manufacturing intelligent scheduling method comprises the steps of 1, constructing a heterogeneous graph model of scheduling elements; step 2, static scheduling optimization based on a critical path method; step 3, resource allocation optimization based on a multi-resource bipartite graph matching method; and 4, dynamic response and rescheduling are carried out. Based on a graph theory method, a complex'process-resource-constraint 'relationship is converted into a visual heterogeneous graph model, and a systematic scheduling solution based on the graph theory is constructed, so that the scheduling solution is suitable for a discrete manufacturing scene with multi-process, multi-equipment, multi-constraint and dynamic disturbance characteristics; the method is used for realizing static planning of production plan scheduling, resource optimization distribution and dynamic adjustment full-process optimization.

The invention discloses a construction engineering project progress management system and an optimization method, and belongs to the technical field of crossing of a construction engineering technology and an information technology. The system comprises a data acquisition layer, a processing analysis layer, a progress management core layer and a user interaction layer, wherein the acquisition layer acquires multi-source data through an Internet of Things sensor, an intelligent monitoring device and image recognition; the processing layer processes data through a sliding window, K-means and the like; the core layer comprises a progress planning (three-point estimation method and critical path method) module, a tracking monitoring (earned value analysis and graded early warning) module, a risk assessment (analytic hierarchy process, R = P * I * W) module and a resource optimization (simplex and genetic algorithm) module; the optimization method comprises the steps of multi-dimensional data fusion, LSTM dynamic early warning, Markov + Q-learning adaptive resource optimization and intelligent decision support. The early warning accuracy can be improved by 35% +, the resource utilization rate can be improved by 20% +, the construction period can be shortened by 5-10%, and efficient project promotion is guaranteed.

This invention discloses an intelligent project scheduling method and system based on multi-constraint optimization, relating to the field of smart project management. It constructs a multi-dimensional constraint model for the project, including task sequence relationships, resource skill matrices, construction space grids and capacity, and personnel risk coefficients. An initial feasible plan is generated based on the critical path method and heuristic resource balancing. The initial plan is then iteratively optimized using a multi-objective optimization algorithm to obtain the Pareto optimal plan. This invention improves the rationality and feasibility of project scheduling by constructing a multi-dimensional constraint model that comprehensively integrates various constraints such as tasks, resources, space, and personnel risks. By combining the critical path method and multi-objective optimization algorithms, a globally optimal plan is generated, achieving synergistic optimization of schedule, cost, resource balance, and personnel risk, thereby improving project construction efficiency.

This invention discloses a project portfolio optimization method based on heterogeneous networks, relating to the fields of project management and intelligent decision-making. The method first acquires project objectives, capability requirements, resource inputs, task dependencies, and risk information, and then standardizes and encodes projects, capabilities, tasks, resources, and strategies. Based on RDF triples, it constructs project association networks, capability association networks, and capability-project fusion heterogeneous networks. It uses grey relational analysis and entropy weighting to determine capability importance, and combines project support, project contribution, data envelopment analysis, CRITIC, critical path method, and TOPSIS to complete project value, core projects, critical path, and risk assessment. Under budget, capability thresholds, project mutual exclusion, and schedule constraints, a multi-objective portfolio optimization model is established, and NSGA-II is used to generate a Pareto optimal solution set. Finally, a recommended solution is output through weighted scoring. This invention can improve the systematicity, accuracy, and risk controllability of resource allocation.

The invention discloses a large ship reconstruction time estimation method based on a spatial constraint critical path, relates to the technical field of shipbuilding, and proposes the following scheme: obtaining large ship reconstruction demand data including a subtask set composed of a plurality of subtasks, the subtasks including time consumption, three-dimensional space coordinates and resource demand vectors, and the time consumption, the three-dimensional space coordinates and the resource demand vectors being equal to the time consumption, the three-dimensional space coordinates and the resource demand vectors; a dependency relationship set, a spatial conflict rule base and a global resource pool among all the sub-tasks in the sub-task set are obtained, the dependency relationship is a time sequence constraint edge among the sub-tasks, and three-dimensional space coordinate conflict detection is carried out based on the sub-task set and the dependency relationship set in combination with the spatial conflict rule base. According to the method, the directed acyclic graph is constructed, and the key path is judged based on the double-time equality criterion, so that the problem of key path distortion caused by neglecting space and resource constraints in a traditional key path method is solved, time, space and resource triple constraint coupled key path accurate identification is realized, and the theoretical value and actual deviation of a construction period are reduced.

The invention relates to the technical field of safety evaluation, in particular to a building construction safety quality evaluation method and system, and the method comprises the following steps: carrying out the real-time analysis of the behaviors of a worker based on a construction site monitoring image through employing a decision tree classification algorithm, analyzing the posture and motion track of the worker through combining with a monitoring image feature extraction technology, and recognizing the abnormal behaviors. Comprising the steps of not wearing a safety helmet and entering a forbidden zone, carrying out safety risk marking, and generating a safety risk behavior classification. According to the method, risk labeling and prevention measures are taken in time through real-time analysis based on monitoring pictures and a decision tree classification algorithm. The application of a neural network image recognition technology improves the recognition precision and real-time performance of a dangerous area, and through the application of a variance analysis statistical method, the quantitative analysis of safety risks, the recognition of key risk factors, the use of a key path method project management method and the use of a Monte Carlo simulation risk management tool, the effect of safety measures can be predicted and evaluated, and the risk management efficiency is improved. And comprehensive improvement of construction safety is ensured.

The invention belongs to the technical field of intelligent management methods, and particularly relates to an intelligent matching system based on business operation characteristics and key processes, which comprises a data acquisition module used for acquiring data including historical work order data and business plan multi-source data; the business feature construction module is used for extracting business feature vectors by using a semantic analysis method based on the business plan multi-source data; the process map generation module is used for constructing a process directed graph based on historical work order data by using a critical path method, and inputting the business feature vector into the process directed graph to identify a key process; and the operation plan generation module is used for generating a multi-target operation plan based on the key process by using time sequence constraint planning and a genetic algorithm. Through semantic analysis, a critical path method and an intelligent optimization algorithm, a full-process intelligent system from business feature extraction to dynamic operation plan adjustment is constructed, and the multi-process cooperation efficiency is improved.

The application relates to the technical field of municipal engineering construction progress management, and discloses a construction progress management method and system fusing an underground pipeline digital model, which is suitable for a city water supply network reconstruction project. The method comprises the following steps: constructing an underground pipeline digital model fusing pipeline, geology, pipe material and construction constraint parameters and verifying the precision; generating an initial progress plan based on the model in combination with an improved critical path method and Monte Carlo simulation; collecting data through a multi-source sensing terminal and dynamically updating the model by using an improved Kalman filtering algorithm; quantifying progress deviation and locating causes by using an entropy weight-TOPSIS combination algorithm; adjusting progress and resources and formulating an emergency plan based on a particle swarm optimization algorithm; and constructing a progress-quality-pipeline safety correlation database in an acceptance stage to realize closed-loop management. Six function modules are correspondingly arranged in the system. The application improves the digitization and scientific level of progress management, solves problems such as poor adaptability and inaccurate deviation positioning of traditional management, and is suitable for complex working conditions.

The invention provides a distributed account book asynchronous communication optimization method and equipment based on a Nash equilibrium game and topological sorting. The method comprises the following steps: in response to a resource allocation request, solving Nash equilibrium based on a distributed optimal reaction iterative algorithm under a chain so as to obtain equilibrium bid and expected utility of each broker in a specific strategy space; determining a winning agent and locking a target resource according to a four-in-one rule including bid standard reaching, utility maximization, utility difference standard reaching and qualification compliance; constructing a business process directed acyclic graph by using a topological sorting algorithm, and calculating a process priority of each task node based on a critical path method; and calculating a dynamic comprehensive priority in combination with a Nash priority generated by a game and a process priority, and further driving a workflow engine to execute task asynchronous scheduling. According to the scheme, the problem of data consistency in a high-concurrency scene is effectively solved, and the resource utilization rate and the system throughput are remarkably improved.