82 results about "Planning process" patented technology

The invention relates to the technical field of energy planning, and particularly discloses a source-storage-transmission combined planning method considering carbon emission responsibility allocation, and the method comprises the steps: building a carbon emission responsibility double-layer allocation strategy; based on a carbon emission responsibility double-layer allocation strategy, establishing a source-storage-transmission combined planning model considering carbon emission responsibility allocation; and solving the source-storage-transmission combined planning model considering the carbon responsibility allocation to obtain a system source-storage-network optimal combined planning scheme. According to the method, carbon emission responsibilities are scientifically shared according to the principle of'who causes, who undertakes, who governs and who benefits', the effect of the carbon emission responsibilities in the planning process is effectively considered, an energy storage system is additionally arranged, the system flexibility is improved, and renewable energy consumption is promoted.

The invention relates to a resource allocation and process planning optimization method under complex constraint conditions, which comprises the following steps of: representing various constraint conditions of a process planning process by utilizing a double-layer directed acyclic graph, and generating and constructing an initial feasible solution set meeting all the constraint conditions; initializing a state space, wherein the state space comprises state variables of a process number, an average process length, reward value fluctuation and fitness; noise is collected and coupled to a Q network, action selection is carried out, and low-level heuristic algorithm options are output; generating a new taboo sequence by using the selected low-level heuristic algorithm, and optimizing the current feasible solution; switching a low-level heuristic algorithm according to the improvement amplitude and the execution duration; and outputting an optimal processing feature sequence and process division combination after multiple times of training. Compared with the prior art, the method has the advantages that multiple process decisions can be optimized at the same time, sub-optimal results caused by separate optimization processes are avoided, processing time and resource consumption are remarkably reduced, and production efficiency and scheme adaptability are improved.

The present invention relates to systems and methods for intelligently optimizing supply chain is provided. In particular, the systems and methods provide the capability to configure supply chain systems so as to: balance between cost and service is optimised and profitability maximised; configure system parameters to respond to both current and future risks; ensure that variability is built into plans enabling maximised efficiency; and human error and bias are eliminated from the planning process such that pro-active rather than reactive behaviour becomes the norm.

The embodiment of the invention provides an enterprise information architecture (4A)-based AI Agent planning enhancement system, a control device and equipment, and the system comprises a Prompt construction module which is used for analyzing an unstructured demand to obtain a structured demand, and cooperating with other modules to generate a standardized Prompt comprising a business process, a data entity and a tool strategy; the knowledge matching module is used for matching related knowledge based on a knowledge graph employment number fusion knowledge base and sorting the related knowledge into a knowledge list containing business processes, practical experience, a data list and a tool list; and the context reasoning module is used for generating a reasoning result based on the historical data and the knowledge list and feeding back the reasoning result to the Prompt construction module so as to assist in generating the normalized Prompt. According to the system, the integration capability of the AI Agent on business logic and data resources in the task planning process is remarkably improved, so that the AI Agent can more accurately understand enterprise-level business scenes, and standardized and intelligent planning support is provided for practical application of the AI technology in enterprise digital transformation.

The invention discloses a joint planning method based on shared energy storage suppliers and a local integrated energy system, and relates to the technical field of energy optimization. The invention aims to solve the problems of cost distribution and efficiency in the planning process of a regional integrated energy system and a shared energy storage supplier. The method comprises the steps of establishing a joint planning framework of a two-stage multi-cooperative game, improving a Nash bargaining method by using a boundary benefit contribution rate to quantify a bargaining level, and proposing an improved alternating direction multiplier A-M-ADMMD based on a double-update acceleration iteration strategy to improve solving efficiency. A post-event shared energy storage cost distribution model is constructed to realize reasonable cost allocation; according to the invention, by optimizing energy storage planning and service pricing, the overall operation cost is reduced, and the overall planning benefit maximization of the regional integrated energy system and the shared energy storage supplier is realized; the method effectively improves the economical efficiency and reliability of the energy system, and is suitable for various comprehensive energy system scenes.

The invention discloses a power grid planning method, system and equipment and a computer readable storage medium, and aims to greatly reduce manual operation and improve data processing accuracy and efficiency by automatically collecting and processing GIS map data, a power grid operation state and load prediction data. And path planning is performed by combining an A algorithm with a GIS technology, so that the path is optimal and feasible, and the planning period is remarkably shortened. The system comprises a data acquisition module, a problem definition module, a path planning module, a cost calculation module, a path optimization module, a scheme generation module, a visual display module and the like, all the modules work cooperatively, and systematization and standardization of the planning process are achieved. Through an intelligent optimization algorithm, the planning scheme quality is improved, multiple factors are comprehensively considered, and it is ensured that the scheme is scientific, reasonable and economical. The modular design enables the system to be easy to expand and optimize, adapts to power grid planning problems of different scales and complexities, improves the adaptability and flexibility of planning, and provides a comprehensive and efficient solution for power grid planning.

The invention discloses a multi-robot path planning algorithm based on priority game and A3C. According to the algorithm, the states and priorities of all robots are initialized firstly, and then path planning is carried out according to the priorities. In the planning process, the robot collects experience by interacting with the environment, and learning and optimization are carried out by using an A3C model. The algorithm employs a specific reward function to assess the performance of the robot to guide its learning behavior. Through continuous iteration and optimization, the robot gradually learns an optimal path planning strategy. The method has the advantage of being high in practicability and can be widely applied to scenes needing cooperative work of multiple robots, such as the fields of warehouse logistics, intelligent manufacturing and the like. Compared with a traditional path planning method, the method has the advantages that the efficiency and quality of path planning are improved, the collision rate is reduced, and better adaptability and robustness are achieved.

The invention discloses an assembly process autonomous planning method based on a knowledge graph, and relates to the field of intelligent assembly.The method comprises the steps that a product ontology model, an assembly process library and an assembly equipment library are constructed through the knowledge graph, a knowledge graph reasoning query method is adopted, and the assembly process library is constructed by analyzing the geometric feature structure of the assembly process; independent selection of an assembly process is completed; and importing the assembly equipment as an example into the knowledge graph, selecting the assembly equipment capable of completing the assembly task in combination with related parameters of the assembly process, and finally realizing autonomous planning of the assembly process. The method can solve the assembly planning problem of long time consumption in the complex product assembly process planning process, reduces the dependence of the assembly planning process on personnel experience, and greatly improves the assembly planning efficiency.

The invention relates to the technical field of computers, and discloses a model training method, electronic equipment, a readable storage medium and a program product, which can realize the planning of an autonomous generation task of a task model and improve the performance and generalization ability of the model. The method comprises the steps of obtaining task demand information of a first task model; and according to the task demand information, controlling an intelligent agent represented by the task model to execute a plan corresponding to the user interaction task, and generating a process reward and a result reward of each planning process in the plan. And then, according to the process reward and the result reward, obtaining a plurality of adjusted planning processes. And furthermore, according to the task demand information, the first task model is controlled to execute the adjusted planning processes, and a plurality of new process rewards and new result rewards are generated. Then, according to the new process reward and the result reward, it is judged that the multiple adjusted planning processes take effect; and training the first task model according to the adjusted planning processes.

The invention discloses a multi-AGV task allocation method in an intelligent picking warehouse based on a Hungary algorithm. The method is fused with path planning framework rolling time domain conflict resolution (RHCR) for use. According to one method, a dynamic allocation mechanism is adopted, and the one-to-one correspondence relationship between the AGV and the task is not fixed before the AGV actually arrives at a pickup point; and 2, constructing a periodic redistribution strategy, and calling a Hungary algorithm to carry out redistribution after each RHCR planning cycle is ended. According to the method, a task allocation process is decomposed into a multi-round assignment problem, information brought by a planning process is fully utilized for allocation through a Hungary algorithm, an AGV task execution sequence is dynamically adjusted according to a staged allocation result, and deep collaboration of task allocation and path planning is realized. Compared with a traditional allocation method in which task allocation is regarded as an independent process, the method provided by the invention effectively improves the operation efficiency of the AGV system on the premise of ensuring that the calculation complexity is only the sum of multiple independent Hungary algorithms.

The invention relates to an aviation typical structure assembly sequence planning method and system and a storage medium, and the method comprises the steps: carrying out the feature recognition of a product three-dimensional mathematical model of an aviation typical structure, and obtaining a recognition result of the assembly features of the aviation typical structure; analyzing the recognition result into structured data, and constructing an assembly structure diagram model based on the structured data; and performing assembly sequence planning on the aviation typical structure by using the assembly structure diagram model to generate a final assembly sequence. The problems that the assembly sequence planning process depends on artificial experience and judgment, the design time is long, and the efficiency is low can be solved, and the efficiency and quality of assembly sequence planning are improved.

The invention discloses a flexible manufacturing system modularization intelligent programming method driven by a large language model, equipment and a medium. The method comprises a task decomposition step, a manufacturing subtask single-step description generation step and a process generation step. Wherein in the task decomposition step, step-by-step tasks are generated by using a large model according to user task description input by a user; in the manufacturing sub-task single-step description generation step, manufacturing sub-task single-step description is generated for each manufacturing sub-task in the step-by-step task by using a large model; in the process generation step, a complete description of a manufacturing process is generated by using a large model according to single-step descriptions of a plurality of manufacturing sub-tasks. According to the method, the powerful natural language understanding and generating capacity of the large language model is utilized, and automatic planning of the whole manufacturing process can be completed through the large language model only by designing appropriate task prompts. According to the method, detailed steps of the manufacturing process can be rapidly generated, interactive iteration with engineers can be carried out, and the interpretability, flexibility and adaptability of the planning process are improved.

The invention provides a heterogeneous satellite group dynamic planning method for multi-stage tasks, and belongs to the technical field of satellite task planning. The method comprises the following steps: firstly, constructing a satellite group for collaborative observation based on priori knowledge; subpackaging the observation task according to the received observation point type and observation point latitude and longitude information in the standardized task demand; secondly, quickly establishing a mapping relation model between a satellite group and a task package according to task and resource constraints; and finally, by constructing a satellite remote sensing grading task planning model, receiving earth observation tasks, and outputting a satellite task planning scheme. Satellite task planning is decomposed into a service line and a calculation line, the service line carries out satellite group construction and task subpackage, the calculation line carries out earth observation task arrangement, the two lines operate at the same time, the task planning time is effectively shortened by reducing the solution space of the satellite task planning process, and the task planning efficiency is improved. Therefore, more sufficient reaction time is reserved for subsequent tasks.

The invention discloses a green low-carbon distribution network planning method, and the method comprises the following steps: S1, determining planning targets and constraint conditions, and constructing a multi-target optimization model, the planning targets including investment cost minimization, power supply reliability maximization and carbon emission minimization; the constraint conditions comprise the voltage level, the line current-carrying capacity, the equipment capacity, the network topology limitation and the renewable energy source permeability; s2, solving the constructed optimization model by adopting a multi-target particle swarm optimization algorithm; according to the method, the multi-target particle swarm optimization algorithm is introduced, the calculation efficiency of green low-carbon distribution network planning is greatly improved, the optimal solution set of the multi-target problem can be found in a short time through cooperation between the multi-target particle swarm optimization algorithm and multi-population collaborative optimization, the planning process is accelerated, the time cost is reduced, and the planning efficiency is improved through multi-target optimization. On the premise of ensuring the power supply quality, the energy utilization rate is improved, and the energy waste is reduced.

The present invention relates to the technical field of energy planning, and specifically discloses a source-storage-transmission joint planning method considering carbon emission responsibility sharing. The method includes: establishing a two-layer carbon emission responsibility sharing strategy; based on the two-layer carbon emission responsibility sharing strategy, establishing a source-storage-transmission joint planning model considering carbon emission responsibility sharing; solving the source-storage-transmission joint planning model considering carbon responsibility sharing to obtain an optimal joint planning scheme for the system's source-storage-network. According to the principle of "who causes, who bears, who governs, and who benefits", this method scientifically shares carbon emission responsibilities, effectively considers the role of carbon emission responsibilities in the planning process, adds energy storage systems, improves system flexibility, and promotes the consumption of renewable energy.

A system and method for operating a robotic system to use two or more tools to manipulate objects. The robotic system may coordinate planning processes and / or plan implementations based on grouping the objects.

The invention discloses a planning process generation method based on a block chain. Comprising the following steps of full-link marketing data collection and block chain evidence storage, planning process template creation, planning demand and multi-source data chaining, intelligent planning scheme generation, task decomposition and intelligent contract driving, planning process full-link tracing, on-chain data intelligent analysis and feedback and automatic settlement and optimization iteration. The invention discloses a planning process generation system based on a block chain. The system comprises a data acquisition and evidence storage module, a template management module, a demand and data processing module, an intelligent planning engine module, a task execution and cooperation module, a full-link data acquisition module, a data analysis and feedback module and a tracing and redisk module. The invention belongs to the technical field of planning process management, and particularly provides a block chain-based planning process generation method and system of an intelligent marketing ecosystem, which integrate intelligent planning, block chain credible cooperation and full-link data credible analysis.

The application relates to the technical field of hydrology and water resources simulation, and discloses a SWAT watershed hydrology model task planning system, method and product, which comprises a knowledge graph construction module, an embedded index construction module, a query coding and two-stage retrieval module, a context construction module and a large language model task planning module. Through the complete planning process of knowledge graph modeling, vector index construction, two-stage retrieval, context generation and structured task output, the whole-process intelligentization from a user's natural language demand to SWAT watershed hydrology model executable task planning is realized, the SWAT file / parameter structure can be accurately matched, the professionalism, accuracy and executability of task planning are improved, and the problems of high operation threshold, large artificial workload, unstable and unexecutable output of a general large model of a traditional SWAT watershed hydrology model are solved.

The embodiment of the invention provides an agent asynchronous planning method which can be applied to the technical field of artificial intelligence. The agent asynchronous planning method comprises the steps of executing an asynchronous planning process according to a current execution state of an agent; in response to execution of the asynchronous planning process, acquiring a planning path set matched with an expected execution state corresponding to a current execution state; and obtaining an effective planned path according to the planned path set. The embodiment of the invention further provides an agent asynchronous planning device and equipment, a storage medium and a program product.

The application provides a man-machine cooperative cognitive decision space construction and on-the-spot decision method, that is, in the action planning process, on the basis of the main line action plan designed based on the static task purpose, a person estimates possible variables in action execution in advance, refines situation cognitive characteristics of key decision points, and proposes countermeasures, the system explores diversified possible action processes, analyzes and excavates key variables which have greater influence on action results, explores the best action options under the guidance of countermeasures and trains a dynamic planning intelligent agent, finally, the cognitive characteristics and countermeasure options, the planning intelligent agent are added to the main line action plan, and a cognitive-decision space is constructed; in the action execution process, the cognitive-decision space is used to guide the real-time monitoring of the change of situation characteristics and the dynamic triggering of key decision points, the online optimization of the best countermeasure and the dynamic generation of subsequent action planning.

A charging station planning method based on charging requirements and power distribution network development comprises the following steps: on the basis of a target node, determining a to-be-planned network frame model, establishing a double-layer planning model, when a network frame is planned on the upper layer, generating a power distribution network frame planning scheme for the network frame by using a network structure rapid generation method based on a broken circle method, and improving the solving speed; the line bearing capacity of the power distribution network is comprehensively considered during site selection and sizing of the electric vehicle charging station, so that the income of the charging station and the benefit of an EV user are balanced on the premise that the feeder line bearing capacity of the power distribution network is not exceeded, and a win-win situation is realized. And finally, solving the model based on an improved particle swarm algorithm of a single-dimensional search volume probability variation method, and finally obtaining a charging station and net rack planning scheme. According to the method, the reasonability of locating and sizing of the charging station can be ensured in the planning process, the development of a power distribution network frame can be comprehensively considered, and the solving efficiency is improved by using the mutation particle swarm optimization.

The invention provides a land space planning scheme processing method, device and system and electronic equipment, and the method comprises the steps: responding to a received planning scheme, calling a space analysis tool and / or a planning knowledge base by at least two agents, and obtaining a sensing result; generating a judgment result according to the perception result and predetermined target preference information; calling a pre-configured structured expression template, and generating negotiation messages respectively corresponding to the at least two agents; information interaction is carried out between the at least two agents based on an agent interaction protocol A2A; evaluating the planning scheme and the like to obtain correction suggestion information; and processing the correction suggestion information proposed by the plurality of second intelligent agents, selecting one instruction as a processing result and issuing the processing result to the corresponding second intelligent agent, ending negotiation if the current state meets the convergence condition, and starting the next round if the current state does not meet the convergence condition. The method can improve the information interaction efficiency in the land planning process.

The invention discloses an unmanned ship integrated path planning method based on space-time state search and optimization. The method comprises the following steps: constructing a unified space-time state space and dynamic model; performing forward search based on the space-time state space to generate a preliminary feasible trajectory; performing continuous space-time optimization on the preliminary trajectory to generate a smooth executable trajectory; performing online rolling execution and local re-planning; according to the method, a search space is lifted from a traditional two-dimensional geometric position to a four-dimensional space-time state space containing speed, course and time, and geometric accessibility, dynamic feasibility and space-time collision-free performance are synchronously considered in a unified framework. Different from an existing separated architecture that the path is planned first and then the speed is adjusted, an integrated track containing position, course, speed and time information is directly generated in the planning process, the problem that planning and execution are disjointed is fundamentally solved, and it is ensured that the planning result can be directly executed.

The embodiment of the invention discloses a path planning method and device, equipment and a storage medium, and relates to the technical field of path planning. The method comprises the following steps: acquiring an ordered binary decision diagram corresponding to the robot based on a path planning instruction; the ordered binary decision diagram is used for representing all planned routes in a target scene where the robot is located; obtaining a constraint condition and a task target corresponding to the target scene; determining a target planning path which simultaneously meets the constraint condition and the task target in the ordered binary decision diagram; by determining the target planning path which simultaneously meets the constraint condition and the task target in the ordered binary decision diagram, the state space of the planning process is reduced, and the robot path planning efficiency is improved.

The invention provides an on-orbit spacecraft sensitive shortcut operation control planning mode design method and system. The on-orbit spacecraft sensitive shortcut operation control planning mode design method comprises the following steps: determining a spacecraft operation control planning process design strategy; designing an operation and control planning process; establishing an information interaction model between operation and control planning implementation systems; and determining an implementation method of the operation and control planning process. According to the technical scheme, the operation and control planning process adopts a design strategy of'two-layer planning and staged iteration ', a step-by-step progressive information interaction model is established, and efficient and agile implementation of the operation and control planning process is ensured.

The application provides a new line railway transmission and data network port intelligent planning method and system, relates to the railway communication technical field, and solves the technical problems of low efficiency, easy error and difficulty in meeting complex design specifications caused by the dependence of the existing railway communication port planning on manual experience. The method comprises the following steps: obtaining a business requirement list and a device resource list; constructing a constraint optimization model based on a preset port planning rule library; based on the business requirement list and the device resource list, the constraint optimization model is optimized and solved by a constraint optimization algorithm to obtain a port allocation result; based on the port location in the port allocation result and the physical distance between the device cabinet and the ODF frame, the tail fiber length is calculated; the port allocation result and the tail fiber length are structured and coded and integrated to obtain a port configuration account. The application is used in the new line railway transmission and data network port planning process.

The application discloses a master-slave satellite task allocation method based on a double-layer backtracking mechanism, constructs a master-slave satellite cooperative task planning model with a target function of maximizing the efficiency generated by the master-slave satellite in executing tasks according to obtained master-slave satellite task planning requirements and constraint conditions, performs task planning conversion based on a Markov decision process (MDP), defines a state space, an action space and a reward function, solves the master-slave satellite cooperative task planning model by using a deep reinforcement learning (DQN) algorithm, performs task planning optimization through a double-layer backtracking mechanism, and obtains a task allocation strategy of the master-slave satellite based on the solved master-slave satellite cooperative task planning model. The double-layer backtracking mechanism is designed, a high-cost and low-yield planning decision is dynamically adjusted in a task planning process in a manner of combining global backtracking with local backtracking, resource waste and task conflicts are avoided, and the rationality and robustness of task allocation are improved.

The invention discloses an engineering task dynamic re-planning method, which relates to the technical field of engineering task dynamic re-planning, and comprises the following steps: collecting equipment space coordinates, operation surface point cloud data and task completion amount in real time through multiple sensors, and calculating a progress deviation value; generating a progress thermodynamic diagram covering the construction area based on a preset engineering task digital twinborn body; performing grading response judgment according to the progress deviation value, and triggering an early warning process or a re-planning process according to a response grade; and calling the multi-objective optimization model and solving to generate a re-planning scheme when the re-planning process is triggered, performing deduction simulation based on the real-time state data of the equipment in the digital twin sand table, and outputting the predicted construction period completion degree, the cost change rate and the risk triggering probability after the re-planning scheme is executed. According to the invention, differential response of early warning and re-planning is realized by adopting a hierarchical trigger mechanism; and the precision and timeliness of construction progress monitoring and the scientificity and feasibility of scheduling decisions are improved.

This disclosure relates to a multi-objective, two-layer optimization method and apparatus for a multi-regional integrated energy system. The method includes: establishing an upper-layer multi-objective programming model and a lower-layer optimization scheduling model; using the unit capacity of various types of energy supply units as initial values, generating velocity and position expressions for upper-layer particles based on a particle swarm optimization algorithm; solving the expression using a Gurobi solver to generate the daily power output state of various types of energy supply units in the multi-regional integrated energy system; updating the velocity and position expressions of the upper-layer particles based on the upper-layer multi-objective programming model and iteratively calculating; stopping the iteration when preset conditions are met, and calculating and generating a capacity configuration scheme for the multi-regional integrated energy system based on the velocity and position expressions of the upper-layer particles. This disclosure's two-layer optimization strategy incorporates the system's operational characteristics into the planning process, improving the feasibility of the planning scheme; regional energy mutual assistance can optimize the system's operation mode and improve overall efficiency.

The present invention relates to the technical field of aircraft manufacturing, and more specifically, to a fuselage skin AO planning method, system, device, and medium based on typical features. The method first obtains fuselage skin data based on acquired three-dimensional model data and constructs a fuselage skin library. Secondly, based on the aircraft three-dimensional model, a fuselage skin typical planning feature library is extracted and constructed. Then, the fuselage skin typical planning feature library is analyzed, and combined with knowledge graph technology, the relationship between the features and the skin is clarified, the fuselage skin AO is inferred, and a full-aircraft fuselage skin assembly content set is formed. Finally, a graph traversal algorithm is called to check the completeness of the full-aircraft fuselage skin assembly content set. This improves the structured expression of the fuselage skin AO planning process and effectively enhances the planning efficiency and accuracy.