2741 results about "Optimal scheduling" patented technology

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

The invention discloses a PCFarm resource scheduling method and system based on dynamic load prediction, and relates to the technical field of resource scheduling, and the method comprises the steps: constructing a feature extractor based on multi-modal feature fusion, mapping original data into a high-dimensional feature vector, and obtaining a dependency relationship between tasks; modeling a cluster topology, predicting a load propagation effect between nodes, constructing a spatio-temporal joint prediction framework, and fusing a time sequence and spatial topology information; proposing a multi-objective optimization function; training a scheduling strategy generator, and generating an optimal scheduling scheme based on the current cluster state; designing the elastic expansion and contraction of the prediction drive; constructing a migration cost model, and quantifying the influence of task migration on the performance; the overall load balance degree of the cluster is calculated through the global controller, and a coarse-grained migration instruction is generated. By arranging the load prediction module and the resource scheduling module, resource allocation is dynamically adjusted according to the real-time state and task requirements of the cluster, and a better resource scheduling effect is achieved.

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The embodiment of the invention provides an industrial real-time data transmission guarantee method and system based on a TSN. The method is applied to the field of industrial communication and intelligent scheduling, and comprises the steps of obtaining industrial field network node information, completing TSN topology deployment and time synchronization, extracting communication characteristics and historical load data of various task flows, constructing a unified time sequence, and inputting a prediction model to generate a load prediction result. And in combination with task parameters, a scheduling strategy list and a prediction result, inputting a TSN digital twin network model to carry out analogue simulation, evaluating performance indexes under multiple strategies, selecting an optimal scheduling strategy and issuing the optimal scheduling strategy to TSN switching equipment, thereby realizing accurate scheduling control configuration of various task flows. According to the scheme, intelligent evaluation and automatic optimization issuing of the scheduling strategy are realized, the real-time performance and the deterministic communication capability of an industrial network are remarkably improved, the resource utilization efficiency and the adaptability of the scheduling decision are enhanced, and the key task flow is ensured to be reliably transmitted on time.

The present invention relates to the field of resource scheduling, and in particular to a resource scheduling method and apparatus based on computing cluster twin modeling. The method mainly comprises: constructing a time sequence matrix on the basis of available resource information of a computing cluster, and constructing a digital twin model for resource state on the basis of the time sequence matrix; extracting a time sequence from the digital twin model for resource state to construct a state space, solving a probability transfer matrix of the state space, and constructing a digital twin model for resource probability transfer on the basis of the probability transfer matrix; for the resource requirements, constructing a feasible solution set for resource allocation on the basis of the digital twin model for resource probability transfer, obtaining the feasible solution set for resource allocation on the basis of the feasible solution set, and forming a scheduling policy library on the basis of the feasible solution set; and receiving resource requests in parallel, extracting a corresponding feasible solution from the scheduling policy library, and allocating resources to each resource request by taking the feasible solution as an allocation policy. The present invention can realize unified quantitative description of real-time states of various resources, and can also realize dynamic intelligent optimal scheduling of the resources.

The invention relates to the technical field of server scheduling, and discloses a large model reasoning scheduling method based on an off-network computing power server, which comprises the following steps of: collecting hardware information of the off-network computing power server, and constructing a server resource vector; decomposing a large model reasoning task into a plurality of sub-tasks, and constructing task modeling; according to the hardware information of the off-network server and the task dependency relationship, performing task division by using an optimization method; performing dynamic task scheduling on the sub-tasks based on the current load state of the server; according to the real-time calculation load of the server, dynamically migrating part of tasks to other servers to ensure the load balance among the servers; and executing the distributed task, monitoring the running state of the server and the task completion condition, and dynamically adjusting a task distribution strategy according to feedback data. Through modeling task requirements and server resources, in combination with real-time monitoring and optimal scheduling, efficient calculation and resource utilization maximization of large model reasoning in an off-network environment are realized.

The invention relates to the technical field of computers, and discloses a distributed task scheduling method and system for heterogeneous tasks based on cloud native, and a medium, and the method comprises the steps: a user submits a task through a task management module, and the task carries description information; the system obtains hardware states and task load information of all nodes in a cluster in real time through a resource monitoring module, a scheduling optimization module analyzes task characteristics according to task types and selects an optimal scheduling strategy in combination with a current resource state, and a task distribution module distributes tasks to optimal computing nodes and starts task execution. In the task execution process, the system continuously monitors the task state, and dynamically adjusts resource allocation or executes preemptive scheduling when necessary. According to the method, efficient management and scheduling of heterogeneous computing resources are realized by optimizing a scheduling strategy and a resource allocation mechanism, and the overall performance and the resource utilization rate of the system are improved.

The invention discloses an industrial automation control method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting the operation data of production equipment, constructing a digital twin model, and forming a virtual production network; obtaining resource state data of each factory in the virtual production network in real time, dynamically generating an optimal cross-factory scheduling scheme, and simulating the scheduling scheme in the virtual production network for simulation verification and scheme adjustment; and deploying the scheduling scheme to a physical factory, controlling equipment to execute tasks through an automatic control system, and summarizing production data and simulation results for storage and backup. According to the method, the accuracy and efficiency of industrial automation control are remarkably improved, equipment operation data can be processed more efficiently, equipment dynamic behaviors are accurately simulated, a cross-factory optimal scheduling scheme is generated in real time, a closed-loop optimization mechanism is formed through simulation verification and feedback adjustment, the dynamic adaptability and global optimality of the scheduling scheme are achieved, and the scheduling efficiency is improved. Therefore, the resource utilization efficiency is greatly improved.

The invention discloses an optimal scheduling method and system for photovoltaic source network load storage. The method comprises the following steps: acquiring a power distribution network topological structure, photovoltaic output data and a feeder section switch state in each photovoltaic grid-connected scene; calculating the equivalent impedance of a photovoltaic grid-connected point according to the topological structure of the power distribution network and the section switch state of a feeder line to obtain impedance change characteristics; extracting output fluctuation characteristics according to the photovoltaic output data; calculating a correlation coefficient between the impedance change characteristic and the output fluctuation characteristic in each photovoltaic grid-connected scene so as to adjust a feeder section switch state and obtain an initial topology adjustment scheme; calculating transient characteristics of power grid nodes under the initial topology adjustment scheme, and performing iterative simulation on the initial topology adjustment scheme to obtain an optimized topology adjustment scheme; and carrying out optimal scheduling of photovoltaic source network load storage by adopting an optimal topology adjustment scheme. By adopting the embodiment of the invention, the consumption capability of the power distribution network on photovoltaic power generation can be effectively improved, and meanwhile, safe and stable operation of the power grid is ensured.

The invention relates to the technical field of power distribution network operation management, and discloses a power distribution network capacity-increase-free access system based on dynamic coordination. The system comprises a multi-source data acquisition and fusion unit which acquires multi-source data and fuses the multi-source data to generate dynamic power grid state characteristics; the multi-time-scale collaborative optimization architecture unit is used for constructing a layered architecture to realize optimization of different time scales; the space-time load prediction unit is used for predicting load distribution based on the space-time diagram model; and the multi-target dynamic optimization solving unit is used for constructing a model to solve an optimal scheduling instruction. In addition, a layered communication protocol unit is arranged to guarantee communication, and a voltage stability cooperative control unit and a distributed energy storage cooperative control unit are arranged to improve the stability of the power grid. Through cooperation of multiple technologies, capacity-increase-free access of the power distribution network is realized, the operation efficiency is improved, the loss is reduced, the voltage stability is ensured, and challenges brought by distributed energy and electric vehicle charging are effectively dealt with.

The invention, which relates to the warehouse management field, discloses an intelligent WMS system with an intelligent scheduling function, comprising a cargo sensing module, a data processing and analysis module, a scheduling decision module, a decision optimization module, and an execution control module. And the multi-agent scheduling module is used for defining each independent operation task in the warehouse as an agent, modeling the warehouse environment, and setting a reward function for each agent for guiding the agent to make an optimal decision. According to the intelligent WMS system with the intelligent scheduling function, the task completion time, the resource utilization rate, the cost saving and the service quality can be comprehensively considered through the set reward function, an intelligent agent can be guided to make an optimal decision, the accuracy and efficiency of future decision making can be improved through learning, and the intelligent WMS system has good application prospects. Therefore, the intelligent agent can gradually master the optimal scheduling strategy in different environments, and maximization of the overall warehouse operation efficiency is realized.

The invention discloses a multi-source data fusion-based optical storage load collaborative optimization scheduling system and method, and relates to the technical field of photovoltaic energy storage optimization, and the method comprises the steps: building a photovoltaic power generation scene tree model and a load prediction model through obtaining historical photovoltaic power generation data, load power consumption data and meteorological data; constructing a first optimization target and a second optimization target by considering the operation income and the energy storage aging cost, considering the photovoltaic uncertainty based on the photovoltaic power generation scene tree model, and establishing an adaptive optimization scheduling model in combination with the load demand predicted by the load prediction model; the photovoltaic power generation scene tree model and the load prediction model are embedded into the adaptive optimization scheduling model, and the optimal energy storage charging and discharging strategy of the current time period is solved, so that the robustness and reliability of energy storage scheduling are improved while the economical efficiency of photovoltaic power generation is ensured.

The invention relates to the technical field of civil construction scheduling, and discloses a BIM-based civil construction intelligent scheduling method and system. The method comprises various resource parameters, construction procedures and site arrangement information. Performing construction progress dynamic simulation on the BIM model, generating a multi-dimensional progress plan in combination with a resource database, identifying a process logic relationship and potential resource conflict points, and forming a conflict early warning list with priorities; establishing an intelligent scheduling model according to the progress plan and the conflict early warning list, optimizing a scheduling algorithm by using a conflict early warning result, and dynamically adjusting resource allocation parameters; comparing the real-time construction data with the intelligent scheduling model, identifying resource supply and demand deviations and process delay risks, marking key conflict events and feeding the key conflict events back to the BIM model for visual analysis, and obtaining a conflict influence range; and performing construction process deduction on the BIM model based on a conflict identification result, identifying a potential optimization path and a deployment scheme, and determining an optimal scheduling strategy and implementation steps through feasibility verification.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

The invention relates to a hydropower station safety comprehensive management system based on a digital twinborn scene, which designs a hydropower station data resource coding system, establishes a hydropower engineering digital twinborn base, integrates all related geospatial data, basic data, monitoring data and business management data of a hydropower station, and constructs the hydropower station digital twinborn scene. Predicting displacement and deformation conditions of a building structure of the hydropower station and a change trend of important facility and equipment state indexes of the hydropower station, and predicting and pre-judging safety risks therein; the reservoir flow of the hydropower station, the upstream and downstream river section water level, the flow change and the power generation output and the power generation capacity of the hydropower station are forecasted or predicted, corresponding plans are provided for flood early warning or meteorological disaster early warning or water level over-limit early warning, and key links of the plans are simulated in combination with a simulation engine; and performing simulation calculation on different power generation and reservoir scheduling strategies to obtain an optimal scheduling scheme. According to the invention, the intelligent management of hydropower station engineering safety, scheduling safety and power generation safety is realized.

The invention provides a multi-level geothermal well collaborative scheduling method and system based on multi-time scale prediction, and relates to the technical field of data processing. The method comprises the following steps: constructing a well group output model based on operation parameters of a shallow geothermal well group and a deep geothermal well group; constructing a multi-scale load prediction model based on the historical load data and the historical meteorological data; determining a load prediction result of the target time period by using the multi-scale load prediction model, wherein the load prediction result comprises a day-ahead load, a day-mid load and a real-time load; and performing joint calculation by using the well group output model, the load prediction result and the multi-objective optimization function to obtain optimal scheduling parameters of the shallow geothermal well group and the deep geothermal well group in the current scheduling period. According to the technical scheme, the output ratio of the shallow geothermal well and the deep geothermal well can be dynamically coordinated according to the predicted load requirements of different time scales, and efficient utilization of multi-level geothermal resources is achieved.

The invention relates to the technical field of power systems, in particular to a power distribution network multi-scale optimization scheduling method and system based on deep reinforcement learning, and the method comprises the steps: carrying out the modeling of the source load prediction error probability distribution of a scheduling day through employing a probability box theory, and obtaining a net load uncertainty interval; based on the multi-time scale scheduling hierarchy, a multi-agent deep reinforcement learning algorithm is adopted to construct a power grid optimization scheduling model based on a centralized training and decentralized execution architecture; taking the minimum day-ahead total operation cost as an optimization target, and according to the net load uncertainty interval, solving through the power grid optimization scheduling model to obtain a day-ahead scheduling strategy; and performing multi-scale rolling optimization based on the day-ahead scheduling strategy to obtain a multi-scale full-period collaborative scheduling strategy. According to the method, through comprehensive utilization of probability box theory modeling, multi-time scale hierarchical division and a multi-agent deep reinforcement learning algorithm, flexible resource optimization configuration of the power distribution network in day-ahead, intra-day and real-time scheduling is realized, and the adaptability to source load uncertainty is enhanced.

The invention provides a task allocation and optimal scheduling method and system for a multi-robot collaborative industrial assembly line, and relates to the technical field of robots, comprising the steps of constructing a task allocation strategy network through deep reinforcement learning, encoding workpiece types and attribute information into a state vector input network, a multi-objective optimization function including total assembly time minimization, load balance degree maximization and energy consumption minimization is designed, a near-end strategy optimization algorithm is adopted to train network parameters, a robot task mapping matrix is output as a distribution scheme, and the network parameters are dynamically optimized and updated in an online learning mode. The task allocation efficiency of the multi-robot collaborative assembly production line can be remarkably improved, the dynamic optimization scheduling of the production line is realized, the system energy consumption is reduced, and the assembly efficiency is improved.

The invention discloses a green data center scheduling optimization method under computing power and electric power coupling, data load types of a data center comprise a transferable load, a reducible load and a rigid load, and the method comprises the following steps: constructing a double-target scheduling optimization model by taking the minimum power consumption cost of the data center and the maximum green power fitting degree as targets; the power consumption cost of the data center is used for representing the sum of expenses generated by the data center for purchasing power from a power grid in order to meet the power required for processing the data load in each time period, and the green power fitting degree is used for evaluating the matching degree between the power consumption demand of the data center and the renewable energy source supply; solving the dual-target scheduling optimization model by using a multi-target dogvessel swarm algorithm to obtain a Pareto frontier; and determining an optimal scheduling scheme based on the Pareto frontier, wherein the optimal scheduling scheme comprises a data center power consumption curve, an energy storage power station charging and discharging curve and a transferable load scheduling strategy. According to the invention, the power consumption cost of the data center can be reduced, and the renewable energy consumption capability can be improved.

The invention discloses a virtual power plant scheduling method based on a large language model and deep reinforcement learning, and belongs to the technical field of virtual power plant scheduling. Comprising the following steps: constructing a virtual power plant multi-agent cloud edge collaborative scheduling framework based on large language model driving; predicting wind power, photovoltaic power and load power based on a large language model; constructing a mathematical model of virtual power plant optimization scheduling; converting the virtual power plant optimization scheduling model into a Markov game process in combination with a large language model; performing initialization training on the strategy network of the edge layer intelligent agent by adopting imitation learning to obtain a pre-trained edge layer intelligent agent strategy network; and based on the pre-training strategy network of the boundary layer intelligent agent, combining with a large language model and adopting an improved multi-agent near-end strategy optimization algorithm to solve a scheduling strategy.

The invention relates to the technical field of intelligent logistics distribution, in particular to an automobile market logistics distribution scheduling system based on AI optimization, which comprises a data acquisition module, an order management module, a traffic flow analysis module and a scheduling decision module. Wherein the data acquisition module is used for acquiring current position data and traffic flow data of a delivery vehicle and order information of a customer in real time; the order management module is used for carrying out priority ranking on the orders; the traffic flow analysis module is used for predicting traffic conditions in a period of time in the future and identifying a road section and a time period which are about to be congested; and the scheduling decision module is used for optimizing the scheduling and distribution paths of the vehicles to generate an optimal scheduling scheme. According to the invention, by combining real-time traffic prediction, order priority ranking and a path optimization algorithm, the logistics distribution efficiency is effectively improved, distribution delay and resource waste are reduced, and timely distribution of high-priority orders is ensured.

The invention provides a calculation task scheduling method and system applied to a numerical control machining system, and the method comprises the steps: obtaining a task request set containing a machining process type and a real-time constraint identifier, extracting a processing complexity feature and a resource demand feature, calling a task priority evaluation model to generate a priority sequence, and carrying out the calculation of a task. Generating an initial scheduling scheme according to the sequence and current available resource information of the system, adaptively adjusting the initial scheduling scheme based on task execution state information fed back in real time, and generating a final scheduling scheme including a task execution sequence and resource allocation parameters, thereby comprehensively considering task complexity, resource requirements and real-time constraints, and improving the scheduling efficiency. Dynamic matching and optimal scheduling of tasks and resources are achieved, and the machining efficiency and the resource utilization rate of the numerical control machining system are effectively improved.

The invention provides a task scheduling method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of task scheduling. The method comprises the following steps: acquiring tasks, resources and user behavior data from a user side and a cloud server through an information collection module, and storing the tasks, the resources and the user behavior data into an information base; the task evaluation module analyzes the data and generates a task value score; the scheduling strategy generation module formulates a preliminary execution scheme in combination with a preset rule, and optimizes through a reinforcement learning algorithm to obtain a dynamic optimal scheduling decision; the task redundancy analysis module is used for identifying mergeable redundant tasks and generating a report for further optimizing a scheduling decision; the dynamic priority adjustment module adjusts task priorities and resource allocation according to the real-time monitoring data; and the scheduling execution module is responsible for performing specific execution and generating a state report. The multi-module collaborative design realizes the intellectualization and adaptivity of task scheduling, and improves the utilization rate of system resources.

The invention discloses a real-time temperature measurement and optimal scheduling method for a rail-mounted intelligent inspection robot, and the method comprises the steps: obtaining the temperature of equipment along a rail in real time through a temperature sensor on the inspection robot, analyzing the working state of the equipment along the rail, and recognizing a potential overheating risk region; establishing a multi-robot collaborative inspection task scheduling model according to the position information and the emergency degree of the potential overheating risk area; planning an inspection path for the multi-robot collaborative task scheduling data by taking the shortest time as an index, optimizing the inspection path by adopting a fusion improved ant colony algorithm and combining a TEB strategy, and obtaining a global planning path; a trajectory optimization strategy based on Minimum Snap is adopted for the global planning path, a Bezier curve is utilized to optimize the path trajectory, a multi-robot collaborative task scheduling model is combined with an auction algorithm to dynamically allocate tasks, efficient utilization of resources and priority processing of emergency tasks are ensured, the inspection efficiency and safety are improved, and the operation cost is reduced.

The invention relates to the technical field of network management, in particular to an Internet of Things gateway control method and system based on AI computing power, and the method comprises the following steps: obtaining channel real-time signal intensity, noise level and occupancy proportion, monitoring protocol type and transmission condition, analyzing the relationship between retransmission times and interference, deducing protocol combination interference and throughput, and obtaining a real-time channel signal intensity, noise level and occupancy proportion. And configuring an optimal scheduling scheme, evaluating resource consumption and load states, packaging and requesting permission entrustment, and judging permission information in combination with an interaction record and an access control list. According to the method, different protocol distribution combinations are simulated and deduced, the data throughput of the whole network is further estimated and optimized, potential protocol conflicts are reduced, the use efficiency and the overall performance of network resources are effectively improved, consumption of a local computing unit and network bandwidth is comprehensively considered, packaging processing of request information is optimized, and the network performance is improved. And the real-time response capability and manageability of the network state are obviously improved.