82 results about "Dynamic priority scheduling" patented technology

The invention relates to the technical field of intelligent navigation and control of ships. The invention provides a complex channel ship intelligent navigation and emergency decision-making method and system. The method comprises the following steps: acquiring environment data through a multi-source heterogeneous sensor array, and establishing a channel three-dimensional dynamic environment model; establishing a multi-objective optimization function, and performing dynamic path planning by adopting an improved model prediction control algorithm; synchronizing motion state parameters of an actual ship and a virtual ship model in real time, constructing an emergency decision tree in combination with an expert knowledge base, and verifying the feasibility of an emergency decision through Monte Carlo simulation; carrying out local route optimization by adopting edge computing nodes, carrying out multi-ship trajectory prediction through a federated learning mechanism, and generating a corresponding collaborative collision avoidance strategy; and establishing a dynamic priority scheduling mechanism, implementing hierarchical response, and confirming a global avoidance scheme through a distributed consensus algorithm. The problems that an existing inland ship intelligent system is limited in perception, rigid in decision and weak in collaboration in a complex scene are solved.

The invention relates to the technical field of new energy vehicle-mounted data processing, and discloses a new energy vehicle vehicle-mounted data processing method and system based on intelligent networking, and the method comprises the steps: collecting and preprocessing data through a multi-source sensor, carrying out the slicing fusion of the data based on time-space correlation, and extracting features through a lightweight convolutional neural network. Constructing an inter-vehicle communication topology model to realize collaborative feature learning, adopting a federal learning framework to train the model and protect privacy, applying a reinforcement learning algorithm to make a decision, establishing a multi-objective optimization model, deploying a dynamic priority scheduling module to manage resources, and optimizing the model based on an incremental learning algorithm. The system covers a plurality of modules such as data acquisition and preprocessing, data dynamic slicing and fusion and the like. According to the method, the data processing efficiency and accuracy are improved, intelligent decision and multi-objective optimization are realized, data privacy is guaranteed, computing resources are reasonably distributed, and the overall performance of the new energy automobile in an intelligent network connection environment is improved.

The invention relates to the technical field of electric data processing, and discloses a system and a method for hybrid processing of mass offline data and mass real-time data, and the system comprises a queue management module which receives task requests, identifies and marks task types, distributes tasks to a stream processing queue or a batch processing queue, and sorts the tasks according to priorities; the task types comprise flow tasks and batch tasks; the thread pool management module establishes a thread pool, sequentially processes tasks according to priorities and collects real-time parameters; the calculation module is responsible for calculating task priorities and calculating task jitter factors based on real-time parameters; the scheduling decision module compares the task jitter factor with a reference value, determines a weight adjustment coefficient, corrects the priority and formulates a scheduling strategy; the alarm module judges whether to trigger an alarm. According to the invention, by introducing the dynamic priority scheduling and preemptive processing mechanism driven by the jitter factor, the real-time response capability and the resource utilization efficiency of the system are improved.

The invention provides a multi-modal feature perception and meta reinforcement learning adaptive algorithm scheduling system and method, and the system comprises a multi-modal feature coding module which is used for carrying out the cross-modal feature alignment and fusion of input image data, physical signals and structured parameters; the deep reinforcement learning decision module is used for constructing a multi-objective optimization strategy by adopting a TD3 algorithm framework; the self-adaptive scheduling execution module is used for realizing strategy distillation and dynamic priority scheduling; and the multi-modal feature coding module comprises a dynamic graph convolutional network and a hierarchical cross attention mechanism. Through technical innovation, comprehensive breakthrough is achieved in the aspects of multi-modal data processing precision and efficiency, reinforcement learning scheduling stability and adaptability, engineering deployment lightweight and robustness and the like, the defects in a dynamic scene in the prior art are effectively overcome, and the intelligent level and the practical application value of an algorithm scheduling system are remarkably improved.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

The invention provides a vehicle fault root cause diagnosis method, equipment, equipment and a medium, and the method comprises the steps: obtaining a vehicle target fault phenomenon, determining a to-be-detected fault event set based on a preset fault logic relation, constructing an initial scoring matrix, enabling a row vector to correspond to a fault event, enabling a column vector to correspond to the state feature parameters of a plurality of evaluation dimensions, and carrying out the detection of the fault event set; executing the detection task of the highest comprehensive score fault event, obtaining feedback data, updating the initial score matrix parameters based on the feedback data to obtain an updated score matrix, and calculating the comprehensive score of each event based on the updated score matrix again. Iteratively executing the detection task corresponding to the updated highest score event to identify whether the detection event is a fault root cause or not until the fault root cause of the target fault phenomenon is determined; according to the method, through a dynamic priority scheduling mechanism, real-time feedback data is fused in multi-dimensional evaluation, a high-value diagnosis task is executed preferentially, the resource consumption of traditional traversal diagnosis is remarkably reduced, and the fault positioning efficiency is effectively improved.

The invention provides a path planning method and system based on four-AGV cooperative carrying, and relates to the technical field of automatic logistics transportation. The method comprises the steps that a high-precision grid map is constructed, a global path is generated in combination with a simulation improved A * algorithm, dynamic weight factors are adaptively adjusted according to the obstacle density, and the path search efficiency is optimized; a time window constraint method is adopted to divide path segments and dynamically allocate time windows; real-time sensing of dynamic obstacles is fused, and a local path is adjusted based on Kalman filtering and a dynamic priority scheduling algorithm; a one-master and three-slave cooperative control architecture is adopted, a master AGV tracks a simulation path, and slave AGVs dynamically adjust control parameters through a fuzzy adaptive PID algorithm; the synchronous jacking operation of the four AGVs feeds back the state to the central server in real time through the Ethernet, so that the synchronism and safety of collaborative carrying are ensured. According to the invention, the technical problems of high path conflict rate, low trajectory tracking precision and slow obstacle avoidance response of a multi-AGV system in a complex environment are solved.

The invention discloses a distributed FPGA (Field Programmable Gate Array) reconstruction management method and system, and relates to the technical field of computer embedding. And establishing connection with an external microsystem through the proxy FPGA, remotely reading and encrypting configuration data in the memory, and performing efficient data transmission by using a RapidIO interface. The data security and transmission efficiency are ensured, and the reconstruction delay is reduced. According to configuration data and external task requirements, the dynamic performance and resource allocation characteristics of each FPGA are analyzed through a dynamic priority scheduling algorithm, a priority reconstruction index is evaluated, a reconstruction sequence is determined, the resource utilization rate is optimized, in the FPGA reconstruction process, operation environment and configuration state data are obtained, comprehensive analysis is conducted through a data fusion algorithm, and the optimal configuration state is obtained. The operation health indexes of the FPGAs are obtained, the health states of the FPGAs are monitored in real time, and the reliability of the system is improved. The abnormal state in the reconstruction process is analyzed according to the operation health index, operation and maintenance personnel are helped to find potential problems in the FPGA reconstruction process in time, and the overall operation efficiency and the management level of the FPGA are improved.

The invention discloses a multi-dimensional demand-driven large-model multi-task dynamic priority scheduling and multi-model collaboration method. The method comprises the steps of multi-task input and environment state perception: receiving an input concurrent task set and real-time environment parameters; multi-dimensional demand feature extraction: semantically analyzing the task set, and extracting a security demand level, a timeliness threshold, a social association degree and a personal comfort influence value of each task; dynamic weight generation based on a style model: calling a pre-training style decision model to generate a dynamic weight distribution rule; task priority quantitative calculation: carrying out tensor product calculation on the demand features and the weight vectors to obtain a comprehensive priority score; and multi-model collaborative scheduling execution: according to priority ranking, distributing high-priority tasks to a real-time response type model, distributing low-priority tasks to a resource optimization type model, and monitoring in real time and dynamically adjusting a queue. The invention also discloses a scheduling cooperation system for realizing the method, and the system has a wide application scene.

The invention relates to a fine-grained dynamic unit task scheduling strategy, aims to solve the problem of real-time scheduling of DAG (directed acyclic graph) tasks on a multi-core platform, and belongs to the field of real-time embedded system task scheduling. Static priority distribution is adopted in an existing priority preemptive scheduling method, the parallel execution degree of tasks is limited, and response time is possibly increased. According to the method, the DAG vertex is divided into a plurality of unit nodes, the independent priority is allocated to each unit node, execution is performed with the shorter worst case execution time (WCET), the priority is dynamically adjusted, the limitation of the static priority is broken through, the task parallelism degree is improved, and the calculation limit of the worst response time (WCRT) is optimized. Meanwhile, a unit node priority distribution algorithm is designed, and the task scheduling efficiency is further improved.

The embodiment of the invention provides a distributed function scheduling optimization method and device under a server-free architecture, and the method employs a dynamic priority scheduling strategy, dynamically adjusts the priority of a function task according to a predefined priority rule and real-time monitoring data, guarantees the priority processing of a key task, and improves the scheduling efficiency. And the system response speed and the resource utilization efficiency are improved. A distributed cache is adopted to store function tasks, execute environment snapshots and a dependency library, a preheating mechanism is supported, cold start delay is reduced, and function task execution efficiency is improved. Through a monitoring feedback mechanism, index data related to system operation are collected in real time, and according to the index data, a priority and a caching strategy are dynamically adjusted, so that adaptive optimization is realized, and the stability and robustness of the system are improved. And through dynamic priority scheduling and cache optimization, the system throughput and the resource utilization rate are improved.

The invention discloses a batch task optimization method based on dynamic priority scheduling. The method comprises the steps of creating tasks, setting priority information of the tasks, adding the tasks into a scheduling queue of a task execution system, sorting the tasks according to an initial priority, scanning all the tasks in the scheduling queue at a set first time interval so as to obtain the priority of each task, performing resource data acquisition on the task execution system at a set second time interval to obtain a resource utilization rate of the task execution system, judging whether the resource utilization rate is lower than a corresponding safety threshold or not, if the resource utilization rate is lower than the corresponding safety threshold, controlling tasks with high initial priorities to be executed preferentially, and otherwise, controlling tasks with high initial priorities to be executed preferentially. And adjusting the initial priority of the task. The task sequence is dynamically adjusted according to the priorities and resource conditions of the tasks, so that the tasks with high priorities are processed preferentially, and the overall task processing efficiency and response speed are improved.

The invention relates to the technical field of building energy saving, and discloses a building energy-saving control system and method.The method comprises the steps that firstly, multi-source environment data inside and outside a building is collected, and a time-space fusion data set is generated through timestamp synchronization and data alignment; dynamic energy consumption characteristics are extracted, and the future energy consumption state of each region is predicted by using a hidden Markov model; decomposing a historical energy consumption mode according to a prediction result and a non-negative matrix factorization algorithm to generate a multi-objective optimization control strategy; and finally, adjusting an equipment operation sequence through a dynamic priority scheduling algorithm and outputting an instruction. The system comprises a multi-source data acquisition module, a feature extraction module, a state prediction module, a strategy generation module, an equipment scheduling module and a feedback correction module. According to the method, energy consumption can be accurately predicted, multi-target optimization control, dynamic equipment scheduling and online strategy correction are realized, the building energy utilization efficiency is effectively improved, energy consumption is reduced, the comfort level is guaranteed, and equipment loss is reduced.

The invention discloses a high-performance CPU-GPU (Central Processing Unit-Graphics Processing Unit) cooperative processing architecture of an audio frequency integrated signal processor, belonging to the technical field of audio frequency integrated signal processing, the architecture is based on a dynamic priority scheduling model and realizes efficient collaboration of a CPU and a GPU through hierarchical resource management and protocol level optimization, a hardware layer adopts a multi-GPU cluster and a distributed storage node, and the CPU-GPU cooperative processing architecture has the advantages that the multi-GPU cluster and the distributed storage node are integrated; high-concurrency task processing is supported; the transmission layer fuses RapidIO and an Ethernet protocol, and adapts to a short frame control signal and a long packet data stream respectively; and the application layer calculates task resource demands through dynamic priority weights, and ensures low time delay of key tasks in combination with a normalized allocation algorithm. According to the architecture, in an audio signal processing scene, the task scheduling efficiency is improved by 40%, the short frame transmission delay is as low as 0.5, the throughput of a long data stream reaches 100 Gbps, and the requirements for real-time performance and calculation precision in a complex acoustic environment can be met.

The invention relates to the technical field of photovoltaic power generation cloud platforms, and discloses a multi-site energy storage cluster cloud scheduling platform based on photovoltaic power generation prediction, and the platform employs a real-time data collection and space-time synchronization module to carry out the timestamp alignment and space grid processing of multi-source heterogeneous data. In combination with a dynamic priority scheduling mechanism of a prediction task distribution module based on volatility and a power grid security situation, the effects of cooperative execution of prediction tasks and priority guarantee of key tasks are achieved; through containerized parallel reasoning and consistency Hash load balancing technology of a distributed prediction calculation module, time consistency of a multi-node prediction result is ensured, and a rolling optimization algorithm and a time sequence constraint embedding strategy of a unified scheduling decision module are adopted. Accurate calculation of power generation unbalanced power and time sequence consistency generation of an energy storage strategy are achieved, and through instruction verification and conflict detection and filtering frequency reduction processing of a smoothing module, the effects of prolonging the service life of equipment and guaranteeing stable operation of a power grid are achieved.

The invention relates to the technical field of artificial intelligence, provides an edge computing task unloading method based on DRL and dynamic scheduling collaboration, and solves the problem that a task unloading decision is not matched with execution efficiency in an MEC environment. Task unloading is modeled as a Markov decision process, a state space containing task attributes, a transmission rate and computing resources is defined, a discrete action space covers local or edge server execution, and a reward function is designed based on time delay and overtime penalty. And carrying out offline training by adopting an LSTM-D3QN algorithm integrated with a long short-term memory network, extracting time sequence features and optimizing a state-action value function. A time-aware dynamic priority scheduling mechanism is introduced, an emergency area, a balance area and a waiting area are divided according to task remaining deadline, and remaining time sorting, dynamic weight priority and FIFO variant strategies are adopted respectively. In the online decision-making stage, the real-time environment state is input into the model to generate an unloading action, and task execution is scheduled according to the dynamic priority.

The invention relates to a priority dynamic scheduling and load self-adaption method for an unmanned aerial vehicle in a complex task scene based on an AUTOSAR real-time operating system. According to the method, the task priority is dynamically adjusted through a multi-dimensional dynamic priority scheduling algorithm, multi-core load balancing is achieved in combination with a prediction-based task migration strategy and a hybrid load balancing algorithm, resource allocation is optimized by adopting resource pooling dynamic allocation and PID feedback control, and the problem that when the AUTOSAR is applied to flight control of the unmanned aerial vehicle, the load balancing efficiency is high is solved. The problems that a static priority mechanism cannot respond to an emergency scene, the task load balancing control capability is lacked, task execution is easy to interfere and resource allocation is low in efficiency are solved, the real-time responsiveness, the task completion rate and the resource utilization rate of an unmanned aerial vehicle flight control system are effectively improved, and the flight safety and the operation reliability of the unmanned aerial vehicle in a complex scene are guaranteed.

The invention provides an electronic book borrowing management system based on OPAC bidirectional hitching, and relates to the technical field of library informatization. Comprising the steps that a hardware acceleration layer constructs a dynamic resource distribution matrix through an FPGA coprocessor and calculates a matrix norm in real time, and request queue scanning and a redundant copy generation algorithm per 100 ms are achieved in combination with an ASIC anti-conflict controller; a system service layer deploys a dynamic priority scheduling engine, realizes intelligent resource distribution based on a cross-campus coordination coefficient and an edge cloud coordination strategy, records borrowing operation through a block chain evidence storage service and executes automatic copyright division auditing; the user interaction layer provides a bidirectional association retrieval interface and a priority borrowing channel based on credit scores. According to the system, the resource scheduling efficiency in a high-concurrency scene is improved through matrix norm calculation accelerated by hardware, an elastic copy allocation mechanism and an anti-conflict algorithm solidified in ASIC, and cross-campus resource collaboration and accurate copyright revenue allocation are realized in combination with geographic position scoring and block chain technologies.

The invention discloses a wireless body area network transmission rate optimization method based on a cooperative game. The method comprises the following steps: S1, constructing a basic model; s2, performing parameter definition on a Nash bargaining model based on a cooperative game; s3, constructing a utility function based on a Nash bargaining model and parameters; s4, solving a Nash bargaining solution based on the utility function; s5, designing an MAC protocol based on the general solution of the transmission rate; and after receiving the beacon frame, the sensor node adopts a corresponding strategy to access a channel according to the priority. In the GTS stage, the sensor nodes with the priorities of P1 and P2 only transmit data in the distributed time slots, so that high reliability of data transmission is ensured. In the competition access stage CAP, all the sensor nodes perform channel competition by adopting a CSMA / CA strategy based on the priorities of the sensor nodes, so that network resources are effectively utilized. Through a dynamic priority scheduling and resource allocation strategy, the coordinator can flexibly cope with the change of the network state, and the high efficiency and reliability of the wireless body area network in various application scenarios are ensured.

The invention discloses a value maximization calculation unloading method and system based on dynamic priority scheduling and reinforcement learning. According to the method, an optimization framework with maximization of the'long-term average task value 'of the system as a core target is constructed, and the task value is defined by introducing an exponential value function, so that the problem of'value neglect' existing in a traditional'average delay or energy consumption optimization 'strategy is solved. According to the value-oriented optimization mechanism, the multi-agent system can autonomously learn a strategy which preferentially ensures high-value task execution when resources are limited in the training process. In addition, the invention further provides a dynamic priority function which can accurately measure the actual emergency level of the task at different moments, so that the defect that a static priority model is difficult to deal with the real-time change of the emergency state is overcome. Particularly, the dynamic priority method is not only used for task selection in a waiting queue, but also innovatively introduced into a resource dynamic allocation mechanism of a running queue.

The invention relates to the technical field of software engineering and automatic operation and maintenance, and particularly provides a micro-service automatic deployment and intelligent upgrading method and device, which aim at solving an off-line environment in the industry, and comprises the following steps: S1, initializing the environment; s2, task arrangement and scheduling; s3, performing dependency perception deployment; and S4, performing intelligent upgrading and rollback. Compared with the prior art, the method has the advantages that through dynamic priority scheduling and dependency topology sorting, the time consumed for starting the core service is reduced by 50%, and the overall deployment efficiency is improved by 40%.

The invention belongs to the technical field of electrochemical water treatment, and provides an electrochemical water treatment equipment cluster monitoring method and system based on cloud collaboration, and the method comprises the steps of data collection and preprocessing, low-delay high-credibility data transmission, intelligent fault diagnosis, dynamic energy efficiency optimization and collaborative scheduling, and whole-process monitoring visualization. According to the method, through dynamic priority scheduling, multi-link backup and full-link encryption transmission mechanisms, low-delay and high-reliability transmission of key data is realized on the premise of ensuring data security. Especially in emergency scenes such as sudden water quality change, through 5G slicing and differential compression technologies, real-time performance and bandwidth cost are effectively balanced, millisecond response of a control instruction is ensured, through federal transfer learning and digital twinning scene adaptation, on the premise that privacy data of each water plant is not shared, diagnosis capability of small sample faults is improved, and fault diagnosis efficiency is improved. And rapid parameter optimization in an extreme water quality scene is realized.

This invention provides an e-book lending management system based on OPAC bidirectional linkage, belonging to the field of library information technology. It includes: a hardware acceleration layer that constructs a dynamic resource distribution matrix and calculates the matrix norm in real time using an FPGA coprocessor, combined with an ASIC anti-collision controller to implement request queue scanning every 100ms and a redundant copy generation algorithm; a system service layer that deploys a dynamic priority scheduling engine, achieving intelligent resource allocation based on cross-campus collaboration coefficients and edge-cloud collaboration strategies, while recording borrowing operations and performing automated copyright revenue sharing audits through blockchain notarization services; and a user interaction layer that provides a bidirectional association search interface and a priority borrowing channel based on credit scoring. The system improves resource scheduling efficiency in high-concurrency scenarios through hardware-accelerated matrix norm calculation, an elastic copy allocation mechanism, and an anti-collision algorithm embedded in ASICs. Combined with geolocation scoring and blockchain technology, it achieves cross-campus resource collaboration and accurate allocation of copyright revenue.

The invention provides an Internet of Vehicles redundant data supplementary transmission method and system based on dynamic priority scheduling, and relates to the technical field of new energy vehicle Internet of Vehicles. The method comprises the following steps: acquiring multi-mode vehicle state time sequence data; analyzing the multi-modal vehicle state time sequence data through an early warning prediction model, and predicting the early warning probability of the data; determining a multi-modal data supplementary transmission priority based on the timeliness, the data volume and the early warning probability of the multi-modal data; performing hierarchical compression processing on different types of data based on the multi-modal data supplementary transmission priority to obtain compressed data; and storing the compressed data into the emergency cache region and / or the main storage region according to a preset rule, and carrying out supplementary transmission according to the supplementary transmission priority after the network is recovered. The method and the device are used in the process of complementary transmission of redundant data of the Internet of Vehicles based on dynamic priority scheduling, and solve the technical problems of high redundancy overhead and low complementary transmission efficiency of vehicle-mounted complementary transmission data in the prior art.

The invention discloses a data acquisition and processing method of a multi-core single-chip power distribution intelligent terminal, which relates to the technical field of data acquisition and processing, and obviously optimizes the real-time response and data processing capability of the multi-core power distribution terminal through dynamic priority scheduling and intelligent memory management. The dynamic priority model fuses task types, nuclear loads and aging attenuation factors, intelligent queue jumping processing of emergency interruption is achieved, and time sequence conflicts between periodic sampling and sudden interruption are effectively relieved; the shared memory fragmentation design isolates real-time data streams from historical caches, and an independent access channel and a conflict arbitration mechanism are combined, so that the inter-core communication blocking risk is reduced; the self-adaptive sampling frequency tracks the fluctuation of a power grid, combines with an improved compression algorithm, and considers the high-frequency signal acquisition efficiency and storage resource optimization.

The invention provides an instruction set online circulation method. By acquiring the real-time disaster data and generating the standardized instruction set through the preset instruction process, the core defect that a traditional system is poor in adaptability is overcome. Furthermore, based on a hierarchical communication protocol and a dynamic priority scheduling algorithm, accurate transmission of the instruction in a multi-level node architecture is realized, and particularly, continuity is maintained through a node ad hoc network when communication is interrupted. In the execution link, double verification of identity authentication and role matching is carried out through an authority white list mechanism, and running state data is collected in real time in combination with a structured identifier to form an execution effect evaluation closed loop. According to the method, the whole-process optimization of the emergency instruction from intelligent generation and robust transmission to safe execution and dynamic tracking is realized, information islands and response delay are eliminated from the source, and the flexibility and reliability of disaster response are remarkably improved.

The invention relates to a continuous intelligent refueling control method for a fracturing unit, and the method achieves the integration of the monitoring of the fuel oil liquid level of a diesel-driven fracturing unit and the control of a continuous refueling device through the construction of a data center, and achieves the prediction of the oil consumption of each device of the fracturing unit through the introduction of an LSTM oil consumption prediction model and a dynamic priority scheduling algorithm. Dynamic priority refueling scheduling ensures timely fuel oil supply of the whole unit, and fully ensures continuity in the construction process of the fracturing unit. The system effectively solves the problems that a current continuous refueling automatic control system only supplies fuel oil simply according to high and low liquid level domain values, cannot cope with the working condition that multiple large-scale fracturing devices need to be refueled at the same time, and needs manual control; according to the invention, the labor intensity of operators can be greatly reduced, and the continuity of the construction operation of the diesel-driven fracturing unit is improved.

The invention relates to a multi-agent cooperative communication method based on dynamic priority scheduling, and the method comprises the steps: determining an initial priority through priority prediction and distributed auction, carrying out the local optimization based on dependence conflicts, carrying out the real-time adjustment for tiny dependence changes, and achieving the dynamic priority distribution; then, the intelligent agents make a sequence decision according to a priority sequence, an autoregressive action decoder is used for carrying out action inference, a message generation network and a message reconstruction network are combined, information transmission between the intelligent agents is optimized, and the overall performance is improved in a mode that all modules are trained in a centralized mode; through the design, the problems of limitation of a fixed priority sequence, lack of dynamic adjustment and difficulty in coordination in multi-agent communication are solved, the adaptability and flexibility of a multi-agent system in a dynamic environment are improved, the efficiency of information transmission and decision-making processes among the agents is improved, and the communication efficiency is improved. Therefore, the cooperation efficiency and decision-making ability in the multi-agent environment are finally improved, and efficient cooperation of the multi-agents is realized.

The invention provides an intelligent door lock cleaning service system and method based on multi-mode perception and dynamic scheduling, and belongs to the technical field of intelligent identity recognition. According to the system, a multi-mode interaction and infrared sensing unit is integrated through an intelligent door lock, and a cleaning instruction of a user is received and verified. And the housekeeper service system generates cleaning tasks by adopting a dynamic priority scheduling algorithm fused with user behavior prediction, and performs optimal allocation in combination with the real-time state of the intelligent door lock. A temporary authorization mechanism based on a one-time dynamic password or a two-dimensional code is provided for cleaning personnel, the mechanism is bound with the door lock state, and the safety is high. And the system monitors the task execution track and state in real time, and feeds back the progress to the user side through the encryption link. According to the invention, the problems that the traditional cleaning service is low in efficiency, opaque and disjointed with an access control system are solved, the intelligent and credible closed-loop management of the whole process from demand triggering to completion is realized, and the user experience and the service management efficiency are improved.

The invention provides an automatic processing method for experimental process state data based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the feature extraction and fusion of multi-source heterogeneous data obtained in an experimental process, so as to output a real-time state data set reflecting an experimental process; according to the real-time state data set and the experimental process characteristics, performing dynamic priority scheduling and resource allocation on a plurality of concurrent decision calculation tasks to obtain an allocation result; and according to the distribution result, executing the scheduled task so as to output a series of decision-making instructions adaptive to the current experiment process. According to the invention, experimental stage identification and decision support based on an artificial intelligence rich knowledge base and interdisciplinary knowledge can be provided, the automation level of experimental intervention and the experimental safety are improved, the personnel participation risk is reduced, and the experimental record precision and the experimental result analysis depth are improved; and the experiment efficiency, the experiment safety and the discovery probability of a new experiment path and a new experiment result are remarkably improved.