58 results about "Loop scheduling" patented technology

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of resource scheduling, and particularly discloses a government affair collaborative emergency resource dynamic scheduling method and system.The method comprises the steps that event spatio-temporal information is obtained through a government affair data sharing platform, multi-source data are fused to construct a three-dimensional situation map, and a dynamic demand list is generated; positioning distributed resource nodes and establishing supply capability portraits; constructing an optimization model with response time efficiency, resource utilization efficiency and demand satisfaction balance degree as targets, and generating a multi-target scheduling scheme set by adopting a particle swarm algorithm; generating a visual decision billboard based on the scheme set, determining a final scheme through multi-party collaborative consultation, and analyzing the final scheme into an operable instruction; monitoring an instruction execution state and a resource track in real time, and automatically triggering scheme re-optimization when deviation or situation change occurs to form a closed-loop scheduling system; according to the invention, the method achieves the precise, intelligent and adaptive dynamic adjustment of resource scheduling, and improves the efficiency and robustness of emergency response.

The invention relates to the technical field of resource scheduling, in particular to a digital-intelligent non-accompanying service management scheduling system, which comprises a service baseline construction module for instantiating a service item into a standard plan event flow; the state portrait generation module is used for collecting service object data flow, establishing a dynamic time sequence signal by applying a recurrent neural network model, and quantifying the occurrence probability of a future abnormal event through multi-step prediction; when the triggering is abnormal, executing causal two-way collision positioning reasons by applying the knowledge graph; the task load calculation module is used for integrating the plan event flow into a plan task component, and generating a worker resource list in combination with the predictive risk score and the worker real-time load and space reachability analysis containing the fatigue index; the system further comprises a task distribution module, the optimal staff is determined and a closed-loop scheduling instruction is generated by minimizing a composite cost function containing predictive load cost, and digital intelligent unattended service management from prediction to scheduling is realized.

The invention discloses an intelligent production scheduling management system for a hardware mold, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of slow scheduling reconstruction under disturbance. According to the method, rapid response and local adjustment of resource disturbance events such as equipment faults and personnel absence are realized by constructing a disturbance identification and scheduling reconstruction mechanism, the system acquires equipment utilization and scheduling information, generates a joint time sequence, calculates a disturbance sensitivity index and constructs a task resource influence map, and the task resource influence map is subjected to resource scheduling. Positioning a disturbed task and a critical path; a scheduling adjustment strategy in a minimum disturbance range is generated in combination with task priority factors, equipment load balance coefficients and scheduling cost, scheduling confusion caused by global rearrangement is avoided, a system is linked with mold allocation and task queue control, a closed-loop scheduling execution process is formed, and the scheduling efficiency is improved. And the production scheduling stability, the self-adaptive capability and the task completion efficiency can be improved in a complex manufacturing scene.

The invention relates to the technical field of mine disaster monitoring and prevention and control, in particular to a deep mine disaster big data analysis and prevention and control decision-making system. Comprising a data pollution quantification unit used for collecting a multi-source heterogeneous data stream and reference prediction data and carrying out noise pollution quantitative analysis to obtain a data pollution index; the toughness entropy evaluation unit is used for collecting physical risk factors, resolving the basic safety entropy, correcting the basic safety entropy according to the data pollution index to obtain the toughness entropy of the mine system, and judging the toughness entropy of the mine system to obtain a risk level; the anti-fragility decision-making unit is used for dynamically adjusting a risk aversion coefficient according to the risk level, and performing utility function solution on a preset decision to be selected to obtain an optimal decision; and the closed-loop scheduling control unit is used for matching a preset scheduling instruction set according to the optimal decision and the risk level, and generating and executing a hierarchical control instruction. The problem that a traditional risk assessment model fails due to serious pollution of deep mine monitoring data is solved.

The invention relates to the technical field of service scheduling, and discloses a dynamic rule engine and state tracking service request automatic scheduling processing system. According to the system, scheduling is realized through a five-stage processing chain of request intake, state acquisition, rule fusion, decision generation and execution monitoring which are connected in sequence. The request intake stage captures a service request stream and generates a request feature topology; in the state acquisition stage, resource operation data is acquired in real time, and a state multi-dimensional map is constructed; in the rule fusion stage, time-space alignment is carried out on the two, and scheduling fitness is output through rule reasoning; in the decision generation stage, based on the scheduling fitness, a search algorithm is adopted to generate a scheduling command sequence; and tracking an execution result in the execution monitoring stage, calculating a scheduling deviation value and feeding back the scheduling deviation value to the request intake stage, and adjusting a feature topology generation parameter. According to the system, a closed-loop scheduling mechanism is formed, the dynamic adaptation of the service request and the resource state is realized, and the flexibility and pertinence of scheduling processing are improved.

The invention relates to the technical field of logistics automation and information processing, and discloses a sorting center intelligent management method and system based on a cloud platform. The method comprises the following steps: acquiring an image and input information of an abnormal package, and extracting a preliminary attribute; if the preliminary attribute is incomplete, analyzing the high-definition image, calculating a damage index and generating a repair queue; according to the repair queue, matching from a historical feature library to obtain inferred physical attributes; the input information is analyzed, all known attributes are synthesized, and a final destination is determined; generating an initial path according to the final destination and the real-time working condition; the preliminary path is optimized by fusing historical congestion records; and carrying out load balancing based on the optimized path, generating a final allocation scheme, and issuing and executing. According to the method, a complete process from multi-dimensional information intelligent completion to dynamic closed-loop scheduling is constructed, so that the technical problem of low abnormal package processing efficiency in the prior art is solved.

The invention provides a computing power resource scheduling method and device for a robot cluster and computer equipment, and relates to the field of computers and robots, in particular to the fields of intelligent robots, robot systems, edge computing, computing power distribution and scheduling and the like. Providing the state awareness information to a state and computing power mapper to determine a computing power resource scheduling request; based on the computing power resource scheduling request, determining a target computing power unit which can meet the computing power resource scheduling request and a target constraint condition from a global resource pool; based on a preset path decision-making mechanism, determining a target path for applying the target computing power unit to the robot; and the target computing power unit is applied to the robot based on the determined target path, so that end-side computing power real-time sensing and closed-loop scheduling facing the dynamic physical task can be realized, and the flexibility and efficiency of computing power resource scheduling in a robot cluster scene are greatly improved.

The invention discloses an urban road intelligent drainage scheduling system and method based on the Internet of Things. The method comprises the following steps that an urban road drainage Internet of Things sensing network is constructed, and a multi-source heterogeneous data set corresponding to an urban road target area is collected; performing space-time alignment and noise filtration processing on the multi-source heterogeneous data set, and meanwhile, performing ponding increase rate and pipe network pressure bearing evaluation to obtain urban ponding increase rates and drainage pipe network pressure bearing degrees corresponding to different urban road subnetworks; calculating drainage pipe network negative pressure indexes corresponding to the different urban road section sub-networks according to the urban water accumulation increasing rates and the drainage pipe network pressure bearing degrees corresponding to the different urban road section sub-networks; and acquiring an urban road network topological structure, performing drainage hierarchical scheduling processing, issuing the urban road network topological structure to drainage facilities in corresponding areas to execute drainage scheduling and feed back scheduled drainage effect data, and re-executing closed-loop scheduling if the drainage effect data is not accordant with the drainage effect data. According to the invention, accurate drainage scheduling of the urban road area can be realized.

The invention discloses an idle computing power resource dynamic scheduling method based on node allocation, and relates to the technical field of node allocation. Sequentially executing to-be-scheduled calculation tasks to perform lightweight simulation execution to generate task feature vectors, collecting idle node hardware architecture feature data to generate node hardware feature tags, calculating a matching degree based on a bidirectional adaptability evaluation matrix, allocating main execution nodes, and performing task scheduling; the redundancy is determined by combining the task key level and the overall reputation score of the node pool, the standby redundant nodes are allocated for parallel execution, and the closed-loop scheduling process of the consensus verification window period and the arbitration mechanism is synchronously set, so that compared with the prior art, the accuracy of idle-time computing power scheduling and the credibility of an execution result can be improved, and the scheduling efficiency is improved. Therefore, the problems of low resource utilization rate and high error rate of calculation results caused by disjunction of task and node adaptation and lack of effective verification in the execution process in the existing idle computing power scheduling can be solved.

The invention is suitable for the technical field of communication, and provides a 5G lightweight core network-based computing network integration intelligent scheduling method, which comprises the following steps of: analyzing a computing task and a resource demand vector thereof by acquiring a task demand, computing power and a network resource state, further matching the demand with the resource state, and applying an improved particle swarm optimization or convex optimization algorithm and the like to obtain a computing network integration intelligent scheduling result. Generating an optimal task scheduling strategy, determining a mapping relation between the task and a computing power node and a network path, distributing the task to an edge node for execution according to the strategy, guaranteeing the data transmission efficiency through a UPF local shunting technology, and finally updating a global scheduling model through federal learning iteration based on a task execution result. And a closed-loop scheduling system with continuous optimization capability is formed. Unified management and integrated operation of computing power and network resources are realized, and through a lightweight core network and an intelligent scheduling algorithm, the task time delay is remarkably reduced, and the resource utilization rate is improved.

The invention provides a terahertz communication sensing and computing integrated system based on hierarchical resource scheduling and mixed waveforms. The system comprises a unified radio frequency front end which adopts a high-integration-level terahertz phased array chip and supports ultra-large bandwidth transmission and flexible multi-beam forming; the baseband processing unit (BBU) is used for realizing real-time processing and large-scale parallel computing of signals; the OCSE waveform generation module is used for generating an ''orthogonal traffic signal-perception embedded (OCSE)'' waveform; the parallel receiver processing module comprises a communication demodulation path and a sensing parameter extraction path which are arranged in parallel; and the three-layer intelligent resource scheduler constructs an application-function-physics three-layer closed-loop scheduling model based on a deep reinforcement learning (DRL) algorithm, and is used for dynamically adjusting the distribution proportion of physical resources between communication and sensing functions according to an upper-layer application real-time task demand and a bottom-layer physical environment sensing result so as to maximize the comprehensive efficiency of the system.

This disclosure provides a method, apparatus, and computer device for scheduling computing resources in robot swarms, relating to the fields of computers and robots, particularly intelligent robots, robot systems, edge computing, and computing power allocation and scheduling. The implementation scheme includes: acquiring state perception information to indicate the physical state of the robot; providing the state perception information to a state-to-computing power mapper to determine a computing resource scheduling request; based on the computing resource scheduling request, determining a target computing power unit from the global resource pool that can satisfy the request and meet the target constraints; determining a target path for applying the target computing power unit to the robot based on a preset path decision mechanism; and applying the target computing power unit to the robot based on the determined target path. This enables real-time perception and closed-loop scheduling of edge computing power for dynamic physical tasks, significantly improving the flexibility and efficiency of computing resource scheduling in robot swarm scenarios.

The invention provides an AIoT device data priority scheduling method based on an SD-WAN, and relates to the technical field of data processing, and the method comprises the steps: analyzing device data at an AIoT gateway or an SD-WAN edge device into data atoms containing a security domain identifier, a service type identifier, a data size, a deadline, a basic value, an attenuation coefficient and a reliability requirement; an instant value function is generated; the edge device collects network measurement data for the superposition tunnel path according to a preset measurement period, calculates a path time delay quantile and a path packet loss rate quantile, obtains a deliverable coefficient and an expected delivery value of a data atom on each path, and further calculates a unit byte scheduling index; selecting the maximum scheduling index to send under the constraint of credit balance; when the deliverable coefficient is lower than the reliability requirement, double-path copying or system code action is executed, and a receiving end carries out duplicate removal and expired discarding; and the edge device reports statistical information, and the controller updates the next window allocation to realize closed-loop scheduling and resource isolation.

The invention discloses an optimal scheduling method and platform for distributed collaborative mutual aid of multiple virtual power plants, relates to the technical field of intelligent scheduling of power systems, and is used for solving the problems of limited data privacy, slow optimization convergence and opaque transaction execution among the existing multiple virtual power plants. The method comprises the following steps: firstly, calculating power generation cost increments under different output levels according to wind power and photovoltaic output prediction and load demand prediction data of each virtual power plant, and generating a quotation curve reflecting transaction willingness; then, each virtual power plant quotation curve is used as an input, a distribution network topology parameter and a line capacity constraint are combined, a distributed collaborative optimization model based on Lagrangian relaxation is constructed, and parallel solving of local power optimization and coordination layer dual updating is realized; in the iteration process, a penalty factor is adaptively adjusted through the ratio of an original residual error to a dual residual error, and the stability and convergence efficiency of the algorithm are improved; after optimization convergence, the mutual aid power and the transaction price are written into a block chain intelligent contract, settlement and deviation punishment are automatically executed based on metering data on the chain, and a closed-loop scheduling system from distributed optimization to trusted execution is constructed.

The invention discloses a highway engineering construction resource dynamic optimization scheduling system based on the Internet of Things and an implementation method, and the system comprises a multi-dimensional perception layer, an edge calculation transmission layer, an intelligent decision scheduling layer and an execution feedback layer, and each layer forms a perception-calculation-decision-execution-feedback closed-loop scheduling system. The multi-dimensional sensing layer is deployed on a highway construction area and various construction resources; the edge calculation transmission layer performs real-time preprocessing and local calculation on the multi-source data acquired by the sensing layer; a dynamic optimization scheduling model based on reinforcement learning is built in the intelligent decision scheduling layer, and a resource scheduling optimization scheme is output; the execution feedback layer is used for driving the construction resources to execute the scheduling instruction. According to the method, the core problems of one-sided perception, calculation lag, decision-making rigidity, feedback deficiency and the like in a traditional mode are solved, road construction resource scheduling is promoted to be transformed from experience-driven to data-driven and intelligent decision-making, and finally optimal configuration of construction resources and efficient management and control of the construction process are achieved.

The present application relates to the technical field of resource scheduling, in particular to a digital intelligent unaccompanied service management scheduling system, comprising: a service baseline construction module, which instantiates service items into standard plan event streams; a state portrait generation module, which collects service object data streams, applies a recurrent neural network model to establish dynamic time series signals, and quantifies the occurrence probability of future abnormal events through multi-step prediction; when an abnormality is triggered, a knowledge graph is applied to perform bidirectional collision positioning of causes; a task load calculation module integrates the plan event streams into plan task components, combines the predictive risk score, and further combines the real-time load of staff containing a fatigue index and spatial accessibility analysis to generate a staff resource list; the system further comprises a task allocation module, which determines the optimal staff and generates closed-loop scheduling instructions by minimizing a composite cost function containing a predictive load cost, thereby realizing digital intelligent unaccompanied service management from prediction to scheduling.

The invention discloses a cedarwood forest-oriented periodic tending scheduling method, device and equipment and a storage medium, and relates to the technical field of intelligent forestry driven by artificial intelligence, and the method comprises the steps: optimizing a preset initial tending period through a tending period adjustment model based on LSTM training, and obtaining a target tending period; when the period is started, collecting multi-mode cedarwood forest data, and preprocessing the multi-mode cedarwood forest data to form target data; inputting the target data into a cunninghamia lanceolata forest growth prediction model constructed by CNN and PointNet, and outputting a breast diameter growth prediction value, a species richness prediction value and a tending standard reaching probability; and an equipment control strategy is generated, and intelligent felling equipment, an automatic replanting device or mechanical brush cutting equipment is scheduled to execute tending operation. According to the method, closed-loop scheduling from period setting, state sensing and intelligent decision making to automatic execution is realized, and periodic intelligent tending scheduling can be realized on the premise of avoiding excessive interference on the Chinese fir forest.

The invention belongs to the technical field of fan operation control, and particularly relates to a multi-manufacturer fan unified control system based on Web forwarding and protocol adaptation. Accessing a unified fan control instruction sent by a dispatching center or an external system; the accessed unified fan control instruction automatically selects a corresponding protocol adaptation module based on a manufacturer protocol interface model to obtain a manufacturer fan control instruction; original data is converted into a unified data structure through field mapping; constructing a wind power mapping function according to the digital relationship between the fan output power and the wind speed change; performing health state quantitative judgment on the controllability of the fan based on the alarm quantity of the unified data structure; and based on the unified data structure, the wind power mapping function and the health state quantitative judgment calculation, generating a closed-loop scheduling instruction of the fan. The control system can realize unification of fan control interfaces, standardization of fan data and coordinated control of fan clusters under the conditions of mixed use of multiple protocols, complex fan scenes and diverse fan manufacturers.

The application discloses a park comprehensive energy system optimization scheduling method based on carbon emission constraint and efficiency orientation; the method comprises the following steps: constructing an electricity-heat-cold-gas multi-energy flow topology and an energy conversion model set of coupling equipment; obtaining electricity / heat / cold load, wind and light output prediction and carbon factor; establishing a comprehensive optimization model containing economic cost, wind and light abandonment penalty, carbon cost and cascade utilization efficiency item, and setting multi-energy flow balance, energy storage SOC, equipment output / climbing, power grid interaction and carbon emission total amount / intensity constraint; solving to obtain equipment output and energy storage charging and discharging sequence generation scheduling instruction in each period, triggering rolling re-optimization according to the measured carbon emission overrun in operation, enhancing the adaptability to disturbance, forming a low-carbon and high-efficiency closed-loop scheduling which is monitorable, constraintable and deviation-correctable, and realizing safe, economic and low-carbon collaborative operation of the park comprehensive energy system.

The invention discloses an AGV cluster distribution scheduling method based on multi-agent cooperative control, and the method comprises the following steps: constructing a self-organizing cognitive map control network based on virtual potential flow, and building a mapping relation between a physical layer and a cognitive layer; establishing a virtual potential flow model in the cognitive layer, and generating an initial virtual potential flow field; potential flow distribution is monitored based on information entropy, a balance area and a disorder area are divided, and a virtual potential flow state diagram is generated; potential flow adjusting nodes are generated in the disorder area, and an adjusted virtual potential flow field and a coordination instruction set are formed; issuing the adjusted potential flow field to a physical layer, and generating a path occupation set; and the cognitive layer performs conflict detection and time sequence coordination to form a closed-loop scheduling control system driven by the virtual potential flow. Based on the virtual potential flow self-organizing cognitive map, autonomous collaborative scheduling of the AGV cluster is achieved, and the method has the advantages of being high in real-time performance, high in coordination and capable of achieving self-adaptive optimization.

The application provides a kind of multi-robot scheduling method based on context adaptive decision and congestion perception planning. Online tasks, robot states, road bottlenecks, commitment paths and historical feedback are obtained according to a predetermined period to construct context features. A context confidence upper bound model is used to select among multiple task allocation strategies online, and the model is updated based on task completion, waiting time, task backlog and planning time. The selected strategy is compared with the congestion perception anchor strategy through the service protection mechanism, and the system will safely fallback when the constraints are not met. A commitment path is generated through rolling horizon path planning, and bounded local repair is performed on conflict clusters to form a closed-loop scheduling. Compared with the prior art, the application has the following advantages: 1) it can adaptively switch strategies according to dynamic load and congestion state; 2) it makes the upper allocation aware of the lower congestion through shadow prices and execution feedback; 3) it does not require black box models and high-overhead global re-planning, improving scheduling efficiency, stability and safety.

This invention belongs to the field of intelligent edge network technology and discloses an industrial sensing, communication, computing, and closed-loop scheduling method and system based on digital twins. Within the current closed-loop scheduling time slot of multiple industrial AGVs, it acquires environmental perception data, the state information of each AGV and the edge gateway, constructs a state representation model in a digital mapping space, establishes a collaborative benefit evaluation mechanism, obtains the collaborative benefit of a single time slot, and derives a comprehensive benefit function. It then employs a closed-loop scheduling decision algorithm to optimize the strategy, outputs the sensing frequency strategy and task allocation strategy for each AGV in the next time slot, and distributes them to the corresponding AGVs, updating the state representation model and realizing the collaborative scheduling of the industrial multi-AGV system. This invention can uniformly optimize multiple aspects such as perception, communication, computing, and scheduling feedback, and uses the updated state representation model in the digital mapping space to support the generation of scheduling decisions for the next time slot, thereby improving the closed-loop scheduling capability and overall operating efficiency of the industrial multi-AGV system.

The invention relates to the technical field of semiconductor and electronic testing, and discloses a power consumption management device for multi-site parallel testing, which comprises a vector power consumption feature library for storing expected power consumption; a plurality of site-level energy buffer units for responding to the instantaneous power consumption demand; the local state monitors are used for monitoring the real-time energy state of the buffer unit; the central power supply scheduler firstly generates a preliminary phase shift scheduling plan for staggering the preliminary execution starting time of each station on the basis of the expected power consumption; then in the test execution process, the real-time energy state fed back by the local state monitor is received in real time; and based on the real-time energy state, adaptively correcting the initial execution starting time to obtain final execution starting time, and issuing an instruction. Through combination of predictive open-loop scheduling and real-time closed-loop correction and cooperation with local energy buffering, smoothing of total power consumption and adaptive management of instantaneous fluctuation are realized.

The invention discloses an intervention task closed-loop scheduling method and system based on a learning condition risk radar, and belongs to the technical field of online learning education, and the method comprises the steps: collecting original learning condition log data, carrying out the preprocessing, and obtaining a smooth feature vector; carrying out trend risk estimation and mutation risk modeling on the smooth feature vector to obtain a fusion risk score; based on the questions, the knowledge points and the error cause labels, constructing an error cause link diagram, and obtaining an error cause link priority; constructing a candidate intervention task set; performing utility calculation on the candidate intervention task set to generate a constraint matrix; performing feasibility solution on the candidate intervention task set by adopting a multi-strategy king butterfly optimization algorithm, and searching an optimal task combination of a current period; performing result distribution on the optimal task combination of the current period to obtain a receipt set; and carrying out weight adaptive adjustment according to the receipt set, carrying out iterative loop until a preset convergence condition or a maximum number of iterations is reached, and outputting the intervened task combination.

The invention discloses a production line scheduling optimization method based on real-time data feedback, and relates to the technical field of production scheduling, and the method comprises the steps: collecting multi-source heterogeneous data of a production line in real time, carrying out the time sequence alignment and difference normalization, and generating a deviation state vector; based on the scheduling feedback intensity index, constructing a lightweight deep reasoning model, performing dynamic mapping, generating a corresponding scheduling intervention level, performing multi-dimensional disturbance influence modeling on the deviation state vector through a graph neural network, and constructing a disturbance causal map; on the basis of the scheduling intervention level and the disturbance causal atlas, key nodes and propagation paths are identified, rule reasoning is carried out in combination with a scheduling rule, and multiple scheduling adjustment actions are generated; and performing multi-stream coupling disturbance cost prediction on the plurality of scheduling adjustment actions through discrete event simulation, and selecting a candidate scheduling adjustment action with the minimum disturbance cost and the optimal comprehensive benefit as a production line scheduling optimization scheme. According to the method, high-robustness and self-adaptive closed-loop scheduling optimization is achieved.

This patent discloses a scheduling control method and system for assembly lines based on continuous state evolution, applicable to complex assembly lines under multi-process and multi-resource constraints. The method first continuously models the execution conditions of assembly processes, forming a state expression that reflects the executability of the processes. Based on this, a scheduling focus is constructed according to the current main constraints of the production line, guiding the overall direction of scheduling decisions. Simultaneously, the assembly quality state is introduced into the scheduling state system in the form of reliability decay, allowing quality factors to participate in state evolution during the scheduling process. During scheduling operation, the system forms an ordered execution sequence and generates scheduling instructions. After the scheduling instructions are issued to the execution layer, their execution feedback drives the continuous updating of relevant states, thus forming a closed-loop scheduling control process. Through the above methods, adaptive adjustment of scheduling decisions is achieved, improving the operational stability and scheduling efficiency of the assembly line under complex constraints.

The present application relates to the field of film and television production and virtual shooting technology, and discloses an artificial intelligence driven virtual scene intelligent scheduling method, comprising the following steps: S1, shooting task initialization and AIGC pre-scheduling stage; S2, multi-system linkage calibration and benchmark parameter synchronous scheduling stage; S3, real-time shooting closed-loop dynamic scheduling stage; S4, intelligent emergency scheduling stage of shooting exception; S5, shooting end and resource archiving scheduling stage; Through the closed-loop scheduling architecture driven by artificial intelligence, the present application realizes intelligent unified control of the whole process of virtual shooting, greatly reduces the dependence on manual operation in the shooting process, simplifies the operation process of virtual scene resource management and multi-system cooperation, effectively shortens the debugging period before shooting and the adjustment time during shooting, optimizes the configuration mode of virtual scene resources and rendering computing resources, and improves the utilization efficiency of various system resources.

The invention discloses a logistics supply chain scheduling system based on data analysis, and relates to the technical field of industrial data mining. According to the system, traffic states, order attributes and dynamic constraint information are collected and fused in real time, and structured dynamic data vectors are formed after processing; a road condition congestion index and an order priority coefficient are extracted by using a dynamic feature analysis model, and order adaptive clustering is realized based on a watershed algorithm; an order delivery cluster is used as a scheduling unit, a multi-target dynamic path planning model is constructed, and the time window fulfillment rate, the driving path and the transport capacity cost are synchronously optimized; and a real-time path scheduling instruction is generated and issued, and changes are dynamically responded through a monitoring and re-planning mechanism. According to the invention, full-link closed-loop scheduling from data perception, intelligent clustering to dynamic optimization is realized, and adaptability, efficiency and performance quality of logistics distribution are improved.

The embodiment of the invention provides a robot task closed-loop scheduling method and device based on atomic skills, and the method comprises the steps: carrying out the task planning and disassembly according to the current environment information and task information through a task decision model, generating the current atomic skills, and carrying out the task planning and disassembly according to the current atomic skills; the task scheduling method comprises the steps of acquiring environment information and task information, generating a corresponding atomic instruction through a preset skill instruction mapping model, driving a robot to execute the atomic instruction to obtain an execution feedback result, and if task scheduling is not completed, repeatedly executing the step of acquiring the current environment information and task information until task scheduling is completed, so that complete decoupling of task decision and bottom layer execution is realized, and the task scheduling efficiency is improved. The universality and the mobility of the system on different robot platforms are obviously improved; a closed-loop scheduling mechanism based on environment feedback and atomic skill execution results is introduced, and the method can sense the task execution state in real time and dynamically adjust the subsequent task sequence, so that the robustness and the completion success rate of the long-time task in the dynamic environment are remarkably enhanced.