15results about How to "Guaranteed enforceability" patented technology

The application provides a micro-grid light storage capacity configuration method and system, which comprises the following steps: obtaining the power consumption load data of the micro-grid for abnormal identification and reconstruction; taking the photovoltaic installed capacity, the energy storage configuration capacity and the energy storage converter rated power as the decision variables, and constructing a capacity configuration optimization model with the minimum equivalent annual comprehensive cost of the system life cycle as the target; dividing the annual time axis into several fixed-length planning declaration blocks, constructing a dynamic tie-line constraint with the tie-line planned active power in each block as the endogenous decision variable; constructing a virtual island checking time window at each grid-connected step of the full-time sequence operation simulation, and constructing a virtual new energy island output constraint, a virtual charging and discharging power constraint, an energy storage energy state constraint and a dynamic power supply integral hard constraint; and iteratively solving to obtain an optimal configuration scheme. The application realizes the collaborative improvement of the grid-connected friendliness and the off-grid power supply capability of the micro-grid.

The application relates to the technical field of textile automatic control, and discloses a multi-device cooperative scheduling electric control system of a textile manufacturing digital workshop, which comprises a token generation module, a diffusion intensity module, a linkage control module, a closed-loop control module and a gradual recovery module. The token generation module is used for collecting quality parameters output by upstream processing devices and generating quality diffusion tokens when a comprehensive quality risk value meets a triggering condition. The diffusion intensity module is used for calculating risk diffusion intensities of each candidate downstream receiving device in the quality diffusion tokens according to the comprehensive quality risk value. The linkage control module is used for generating linkage control actions for each downstream receiving device. The closed-loop control module is used for distributing the linkage control actions to corresponding execution ends and monitoring execution states. The gradual recovery module is used for controlling a recovery process of the downstream receiving device. The application can enable the downstream receiving device to implement targeted linkage control according to a risk influence degree, improve cooperative scheduling among multiple devices, and inhibit further diffusion of quality abnormalities among multiple processes and multiple devices.

The application discloses a flood risk area personnel emergency transfer planning method and device, and belongs to the technical field of emergency transfer path planning, which comprises the following steps: constructing a road network map of a target area based on three-dimensional geographic data, and obtaining the relative elevation index of the roadbed of each path in the road network map; determining a sensitive path set based on the road network map and historical disaster data, wherein the sensitive path set comprises a river-crossing bridge and a wading path; performing hydrological simulation on the target area by using a hydrological model; calculating the passing cost of each path in the road network map by using a cost function; and determining the optimal transfer path of a first user to be transferred at time t according to the passing cost of each path in the road network map; wherein if a path in the sensitive path set is flooded at time t, the passing cost of the flooded sensitive path at time t is set to infinity. The method makes the planned personnel transfer route executable under extreme rainstorm working conditions.

The present application relates to the technical field of fastener assembly, in particular to a simulation distribution method for fastener assembly process, comprising: obtaining position coordinates of ideal assembly points and actual assembly points respectively through CAD model and three-coordinate measuring machine, and calculating deviation vectors of actual assembly points and ideal assembly points, taking the deviation vectors as sample points, fitting deviation function to output error tensor field; correcting the position of ideal assembly point of each fastener to generate a corrected point position, generating a process path sequence based on the corrected point position, constructing a node set and an edge set, calculating the scheduling priority weight of each node, and constructing a task cooperation graph based on the node set and the edge set; defining an assembly total scheduling cost function under the constraints of the task cooperation graph, and outputting a task distribution sequence by using a heuristic search strategy, converting each assembly point position and torque setting value of the task distribution sequence into control coordinates and equipment control format under the reference system of the work station respectively to generate complete action instructions.

The invention discloses a test case reduction method based on precursor state reasoning, and belongs to the technical field of software testing. The problem that in a deep reinforcement learning agent test of an existing adversarial attack framework, due to the fact that state disturbance cannot be executed and test cases are redundant, efficiency is low is solved. According to the method, reverse reasoning is carried out to generate the fault test case set based on the fault occurrence state and action, and the state and action which greatly influence the target fault occurrence are focused on to ensure that the generated fault test case can accurately reflect the root cause of the fault occurrence. After a test case set is generated, node reduction is carried out on each test case in the test case set, key states and actions relative to a target fault are reserved, and redundancy or states and actions with small triggering influence on the target fault are removed; then verifying whether each test case can reproduce a target fault one by one, and removing invalid test cases to obtain a simplified test case set; the method is mainly used for test case reduction.

The application relates to a smart factory robot scheduling method and system, a terminal and a storage medium, and relates to the technical field of smart factories. The method comprises the following steps: receiving a task instruction from a production management system, and adding the task instruction to a task pool; obtaining running state information of robots in the smart factory; selecting at least one task instruction from the task pool, and filtering candidate robots for executing the task instruction and generating a candidate robot set according to a matching relationship between a task type of the task instruction and a robot state; calculating a task completion time of a to-be-scheduled task for the to-be-scheduled task; comparing and analyzing each candidate robot in the candidate robot set based on the task completion time, and determining a target robot for executing the to-be-scheduled task; and distributing the to-be-scheduled task to a task queue of the target robot. The application has the effect of improving the problem of low task completion efficiency caused by unreasonable robot scheduling.

The invention discloses a building group virtual power plant real-time regulation and control method and system based on unmanned aerial vehicle cooperation. Comprising the steps that an unmanned aerial vehicle cluster flies to a preset position outside each building, and real-time temperature data collected by an indoor temperature sensor of the building are acquired through a wireless network; the virtual power plant control center takes the temperature data as representative values of the indoor temperatures of the buildings, obtains power grid dispatching requirements in combination with the real-time adjustable potential of the air conditioning systems of the buildings, and calculates and generates the optimal power regulating variables of the buildings by solving a preset optimization model; and according to the optimal power regulation quantity and the data acquisition time interval of each building, calculating a perception priority score of each building, and based on the score, by taking the minimization of the total task execution cost as a target, allocating a perception target building of the next regulation and control period to the unmanned aerial vehicle cluster. According to the invention, flexible acquisition of the building group temperature data and accurate regulation and control of the air conditioner load are realized, the real-time performance and reliability of the virtual power plant in response to power grid dispatching are improved, and the resource utilization rate is improved.

The invention discloses a dynamic optimization method and device for medical treatment resource reservation, equipment and a medium. The method comprises the following steps: acquiring and preprocessing multi-dimensional panoramic data; constructing a bimodal patient model of static clinical portrait and dynamic state evolution, and combining a dynamic weight clinical index evaluation and clinical priority dynamic quantification method to obtain clinical features of the patient; obtaining an inspection time length prediction result, and constructing an inspection operation constraint matrix to obtain an inspection operation constraint rule; an MADDPG multi-agent resource scheduling model under the CTDE framework is constructed, and a resource scheduling joint optimization strategy is obtained through multi-agent joint training; and generating a reservation scheme based on the resource scheduling joint optimization strategy, and performing rolling updating on the reservation scheme based on a look-ahead rolling rescheduling mechanism. The invention belongs to the field of diagnosis strategy optimization. According to the invention, a more reasonable doctor-seeing appointment strategy can be provided for the doctor-seeing personnel.

The application provides a high-pressure rotary jet pile construction optimization control method, and relates to the technical field of rock foundation construction control.The application obtains soil types and permeability coefficients layer by layer along the depth, constructs a slurry diffusion model based on fluid-solid coupling theory, calculates a pressure field, and outputs a slurry saturation space distribution matrix.A strength mapping program is used to obtain a pile body compressive strength prediction value, a gradient descent correction mechanism is activated after comparing the prediction value with a target strength, the correction amplitude is adaptively adjusted according to the soil types, and the boundary initial value is updated.After convergence, the optimal slurry flow and jetting pressure are used as the boundary initial value of the next depth layer, and the optimal construction parameter sequence of the whole pile length is generated by layer-by-layer recursion to the pile bottom.The interlayer transfer of the slurry flow and the jetting pressure guarantees the physical continuity of the construction state, and the gradient correction amplitude is adaptively matched with the stratum condition, so that the optimization result directly corresponds to the equipment operation freedom, and the control accuracy and the executability are improved.

The application relates to a shared energy storage power distribution method and system based on AGC scheduling instructions, and the distribution method comprises the following steps: according to the AGC scheduling instructions, the abandoned power of each new energy power station participating in the shared energy storage transaction is calculated; according to the abandoned power and the market transaction power of each new energy power station, the total energy storage charging power of all new energy power stations participating in the shared energy storage transaction at different moments is calculated; and the energy storage charging power of each new energy power station participating in the shared energy storage transaction at different moments is calculated according to the total energy storage charging power, so that the conversion and distribution of the shared energy storage transaction result from power to power are realized.

The present application relates to the technical field of mobile edge computing, and provides a task offloading method and device, electronic equipment and storage medium, the method comprising: based on user preference and system multi-source observation features, selecting an initial score obtained by expert reasoning based on a large language model through preference gating, and then filtering with an engineering constraint mask to determine the final offloading action. The method provided by the present application calculates the selection probability by the user preference and system multi-source observation features of the target task to activate the matching target expert, generates the initial score of each candidate offloading action by the target expert reasoning based on the large language model, calculates the action feasibility mask in combination with the engineering constraint function, and performs mask processing on the initial score to generate the real evaluation score, and finally determines the final offloading action based on the evaluation score, which realizes the flexible trade-off of user preference in the high dynamic and unknown edge cloud environment, and effectively ensures the executability of the final output decision in the real business system.

The application discloses an energy management method for a new energy three-electricity system of an engineering vehicle, and relates to the technical field of new energy three-electricity systems.The method comprises the following steps: S100, analyzing the driving requirements of the engineering vehicle; S200, intelligently deciding the power; and S300, executing in real time based on feedforward.The application constructs a three-layer closed-loop control architecture of accurate requirement perception, intelligent power decision and safe feedforward execution, and constructs an energy management method with intelligent decision capability and strong execution force.The method not only eliminates the risk of engine stalling due to the superposition of electric-hydraulic load from the root by feedforward constraint based on real-time hydraulic power, but also realizes intelligent scheduling and fine management of battery and fuel energy by means of fixed electric quantity following and regenerative braking recovery strategy.Meanwhile, the safety execution is ensured by battery power boundary protection and anti-integration fan mechanism, so as to comprehensively guarantee the stability and service life of the three-electricity system.

The present application provides a kind of action planning device, action planning method and computer readable storage medium, for guaranteeing the executable of actual environment, and even if complex task, also make the load of memory relative light and generate action planning at high speed.This aspect of action planning device of the present application uses symbolic planner, generates the abstract behavior sequence containing more than one abstract behavior arranged in execution order.Action planning device uses motion planner, generates action sequence from each abstract behavior in execution order, and determines whether the generated action sequence can be physically executed by robotic device in actual environment.

The present application relates to the technical field of intelligent conference collaboration, and especially relates to a task adaptive allocation method and system in intelligent conference collaboration. A knowledge matching network is constructed based on task knowledge dependency and participant knowledge graph, and a knowledge propagation path is deduced, necessary knowledge nodes and gap nodes are identified, and a knowledge complementarity degree is calculated. According to the analysis result and the collaboration granularity characteristics, a task set is reconstructed through a bidirectional adjustment mechanism, tasks are decomposed and a knowledge transmission interface is established, or tasks are aggregated and a knowledge sharing mechanism is established. According to the reconstructed task structure and the cognitive switching cost, a cognitive state transition graph is constructed, a task execution sequence with minimum cognitive switching cost is planned, a time window is aligned, and a task allocation instruction is generated. The present application improves the adaptability and execution efficiency of task allocation, and reduces the cognitive switching cost.

The invention provides a robot dynamic path planning method and system based on deep learning, and the method comprises the steps: building a motion response model which can reflect the dynamic characteristics of a robot, and carrying out the global sensitivity analysis of key motion parameters including the track smoothness, the obstacle avoidance safety distance and the motion speed; determining a feasible interval of each key motion parameter according to an analysis result; a path point sequence and a system control instruction are taken as decision variables, path length and motion time minimization is taken as a target, numerical optimization solution is carried out under the constraint of a motion response model, and an optimal path point sequence and optimal control instruction combination and a corresponding optimal motion track are obtained through forward simulation; and determining an optimal trajectory tracking control parameter of each motion stage according to the feasible interval of each key motion parameter, the optimal path point sequence and the optimal control instruction. By adopting the scheme of the invention, accurate track tracking control of the multi-target optimization driven robot can be realized, so that the operation efficiency under a complex working condition is improved.