693 results about "Priority scheduling" patented technology

The dynamic bandwidth allocation and resource scheduling method based on the 5G network comprises the steps that S1, a base station collects service requests, network loads and channel information of user equipment of the 5G network, and the services are divided into real-time services, big data services, key services and conventional services; s2, the real-time service calculates the bandwidth by using deep learning; the big data service is distributed according to the idle bandwidth and the data volume proportion; key service main and standby links distribute bandwidths; conventional businesses come first and then are subjected to service distribution; s3, the base station maps bandwidth resources by using a multi-carrier and dynamic subcarrier algorithm, and optimizes signal transmission in combination with a beam forming technology; s4, processing service data according to a priority scheduling queue, and verifying signal transmission according to a channel quality adjustment coding mode; and S5, updating the data and evaluating the effect according to the user feedback, and re-optimizing the scheduling strategy as required. Through intelligent dynamic bandwidth allocation and resource scheduling, efficient, flexible and reliable transmission of different types of services is realized, and the network resource utilization rate and the service quality are improved.

The invention provides a logistics support dynamic scheduling and emergency response system based on intelligent data fusion, and relates to the technical field of logistics support. Comprising a data acquisition module, a data fusion and processing module, a scene prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, a self-adaptive network optimization module and a moral priority scheduling module, according to the logistics resource dynamic scheduling system, global efficient optimization of logistics resource dynamic scheduling and intelligent analysis and strategy optimization of real-time data are realized through advanced technologies such as a variable component optimization algorithm, fusion of quantum annealing and a genetic algorithm, deep reinforcement learning and combination of Transform and a genetic algorithm; and the rationality of resource allocation and the scheduling efficiency are greatly improved.

The invention discloses a heterogeneous AI computing power resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous AI computing power resource pool, wherein the heterogeneous AI computing power resource pool integrates the computing resources of a plurality of heterogeneous AI acceleration chips; obtaining a scheduling demand of the AI task, wherein the scheduling demand comprises a task type, a resource request quantity, a priority identifier and a task group association relationship; generating a multi-dimensional scheduling strategy according to task requirements, wherein the scheduling strategy comprises a priority scheduling rule, an affinity scheduling rule and a resource preemption rule; based on a multi-dimensional scheduling strategy, the AI tasks are dynamically allocated to target computing power nodes of the heterogeneous AI computing power resource pool, and the task execution state and the resource utilization rate are monitored in real time; and dynamically adjusting computing resource allocation according to the resource utilization rate. Through the heterogeneous AI computing power resource pool, the resource utilization rate is remarkably improved, dynamic resource allocation is realized through a multi-dimensional scheduling strategy, and meanwhile, a communication path is optimized through an affinity scheduling strategy, so that the problem of task starvation caused by resource fragmentation is avoided.

The invention relates to a robot distribution task priority scheduling method, which comprises the steps of inputting priority scoring parameters to a preset dynamic weight scoring model to obtain priority scores of distribution tasks, the priority scoring parameters comprising task emergency degree, distribution distance, goods weight and time efficiency requirements, the dynamic weight scoring model represents the influence degree of each priority scoring parameter on priority scoring; acquiring conveying capacity parameters of the plurality of robots, and allocating the robots to the delivery tasks according to the conveying capacity parameters and the priority scores of the delivery tasks; determining a planning path required by the robot to complete the corresponding distribution task; in the moving process of the multiple robots, the moving information of the multiple robots is obtained in real time, and whether path conflicts exist in the multiple robots or not is detected; and under the condition that the existence of the path conflict is detected, the planned path of the robot is optimized based on the conflict solving strategy so as to avoid collision of a plurality of robots. And efficient cooperative operation of a plurality of conveying robot systems is realized.

The invention relates to a multi-type industrial equipment PLC control method and device based on a hardware abstraction layer, and the method comprises the steps: constructing the hardware abstraction layer comprising a core layer and an adaptation layer; the core layer is provided with a standardized interface set, the adaptation layer is provided with a special adaptation module, and a control protocol is converted through a protocol analysis engine. And generating a unified instruction set based on the standardized interface set, and establishing a data interaction channel supporting priority scheduling. And identifying the newly-added equipment, matching the equipment configuration library, and generating a newly-added adapter drive if the equipment configuration library fails. And acquiring a device state by using a data interaction channel, and constructing a cooperative control logic to realize linkage. Equipment load and task queues are monitored in real time, and dynamic task scheduling and grading fault processing are carried out. And when an adapter configuration updating instruction is detected, the adaptive layer thermally loads the updated drive, and unified, intelligent, efficient and stable PLC control of multiple types of industrial equipment is realized through cooperation of the core layer and the adaptive layer.

The invention discloses a method for supporting fast switching of multiple models by NPU (Network Processing Unit) firmware. According to the invention, the system real-time performance and the resource utilization rate in a multi-task scene are obviously improved. Through a dynamic hierarchical caching strategy and a priority scheduling mechanism, the system can intelligently allocate cache resources and preferentially guarantee rapid loading of a high-frequency and high-urgency model, for example, in an automatic driving scene, switching delay of a path planning model can be reduced to a millisecond level, and non-perceptual switching of key tasks is guaranteed. The incremental parameter loading technology and firmware-level context management are deeply fused, only model difference data are transmitted, and hardware is utilized to accelerate and recover a calculation state, so that bandwidth waste caused by traditional full-amount loading is greatly reduced, the model switching efficiency of edge computing equipment is improved by more than 10 times when multiple tasks such as voice recognition and image processing are carried out in parallel, and the efficiency of the edge computing equipment is improved. And meanwhile, the calculation precision is kept lossless.

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

The invention relates to an implementation method and device for an EtherCAT master station system based on a non-operating system, and the method comprises the steps: achieving the adaptation of an SOEM protocol stack to a hardware abstraction layer of an EtherCAT master station through the byte order processing based on a bit mask and a staged shift strategy and the EtherCAT frame receiving and transmitting control based on annular buffer area index management; on the basis of non-real-time task priority scheduling processing of a hardware timer, main circulation driven by a single-thread event and overtime monitoring of a blocking communication synchronization mechanism in combination with the timer, adaptation of an SOEM protocol stack to an EtherCAT master station operating system abstraction layer is achieved; through network scanning and communication parameter configuration, a memory management mechanism based on CoE standard mapping and a periodic position mode of a slave station, adaptation of an EtherCAT master station application layer is realized. According to the invention, through optimization and adaptation of the SOEM protocol stack, operation-system-free and microsecond-level communication jitter of the EtherCAT master station system is realized.

The invention provides a bus network data transmission control method based on priority scheduling, which relates to the technical field of network data transmission, and comprises the following steps: dividing a bus network into an edge layer, a core layer and a terminal layer; the terminal layer comprises a plurality of actuator nodes; performing priority classification on the terminal data set in the edge layer; distributing a transmission time slot of priority rating data on the core layer based on a preemptive scheduling algorithm; a priority data transmission strategy is dynamically adjusted in combination with the dynamic time slot allocation table and the local load state; and when a plurality of actuator nodes initiate transmission requests at the same time, outputting a final transmission sequence according to a priority data transmission strategy. Through a static rule base and a dynamic correction mechanism, key data features are accurately identified, the real-time performance and reliability of high-priority data are ensured, and the key business delay risk is reduced. Intelligent allocation of bandwidth resources is achieved through a preemptive scheduling algorithm, real-time service requirements are guaranteed to the maximum extent, and the network throughput efficiency is remarkably improved.

The invention specifically relates to the technical field of edge computing, and discloses a multi-platform concurrent transmission edge computing data acquisition method, which comprises the following steps: S1, concurrent access of multi-platform equipment: establishing a mapping relationship between equipment identity and a communication link; s2, data concurrent collection and preprocessing: obtaining a preprocessed data stream; s3, concurrent transmission priority scheduling: allocating a transmission bandwidth for each data stream; s4, edge node parallel computing: distributing the subtasks to a plurality of computing nodes; s5, cloud collaboration and global optimization: generating a global optimization strategy; s6, carrying out full-link monitoring and dynamic regulation and control; concurrent access of heterogeneous equipment is realized by generating data streams in a uniform format, multi-platform compatibility is effectively enhanced, bandwidth is dynamically allocated to the data streams according to priority weight indexes by calculating the priority weight indexes of the data streams, data transmission efficiency is improved, and the data transmission efficiency is improved by allocating sub-tasks to a plurality of computing nodes. And the resource utilization rate of the edge computing gateway is improved.

The invention discloses an industrial control data processing system and method based on an embedded real-time operating system, and relates to the technical field of industrial automation control, and the industrial control data processing system comprises a dynamic weight evaluation module, a resource elastic distribution module and a dual self-healing verification module; and the dynamic weight evaluation module is used for generating a dynamic weight value according to the task type, the data security level, the deadline margin and the historical execution abnormal rate. According to the industrial control data processing system and method based on the embedded real-time operating system, the task criticality is quantified in real time through the dynamic weight evaluation module, and the real-time response capability and the resource utilization efficiency of the industrial control system are improved in combination with elastic resource allocation driven by load prediction. A dynamic weight mechanism ensures that the emergency task can instantly preempt resources, and the problem of low-priority task blocking caused by traditional static priority scheduling is avoided.

The invention provides an interaction method based on artificial intelligence equipment, belongs to the technical field of artificial intelligence, and remarkably optimizes the efficiency of an AI equipment interaction system through distributed resource dynamic scheduling, multi-framework heterogeneous collaboration and full-link intelligent monitoring. Based on an elastic capacity expansion and contraction and priority scheduling strategy of Kubernetes, GPU resource islands are eliminated, and the computing power distribution efficiency is improved; a Flink checkpoint mechanism and Spark RDD blood relationship tracking are integrated, training task breakpoint continuous calculation and millisecond-level fault recovery are achieved, and repeated data processing is avoided; gPU node performance indexes are collected in real time through Prometheus Exporter, and in combination with Grafana visual early warning, the anomaly detection response speed is shortened to the second level; seamless access of frames such as TensorFlow / PyTorch is supported, heterogeneous device protocol conversion is achieved through a unified API gateway, and the cross-platform migration cost is reduced.

The invention discloses a semiconductor device communication system and method based on an SECS / GEM protocol. The system comprises an SECS / GEM protocol processing module, a PLC communication module and a service logic processing module. The protocol processing module realizes message coding and decoding and state management through an HSMS / GEM layered architecture, and supports equipment capability dynamic negotiation; the PLC communication module adopts a factory mode to adapt to a multi-manufacturer controller to realize online mapping of a physical register and a logic address; and the business logic module dynamically loads the command processor based on a reflection mechanism, and realizes multi-condition event triggering through a rule engine. The method comprises the processes of system initialization, message priority scheduling, composite alarm processing and exception recovery, and the equipment integration efficiency and the communication reliability are remarkably improved. The method solves the problems of rigid parameter mapping, single event processing and poor equipment compatibility of a traditional scheme, and can be widely applied to equipment automation control of scenes such as semiconductor wafer manufacturing and packaging testing.

The invention relates to the technical field of construction management, in particular to a BIM-based building construction management system, which comprises a path compression module, a priority scheduling module, an equipment allocation module, a material arrangement module and a progress coordination module. According to the method, the BIM model construction path node set is obtained, the node compression condition is judged based on the labor resource availability index of the construction scheduling schedule, high coordination of the construction path and resource configuration is achieved, the problems of resource mismatching and construction efficiency reduction are avoided, and the construction efficiency is improved based on the density comparison between the space area to which the component belongs and the construction equipment in the area. Reasonable allocation of construction equipment is achieved, full utilization of mechanical equipment is guaranteed, structural hierarchical indexes, space coordinates and construction logic dependency relationships are associated through component identification numbers, a construction feasible dependency graph and material entering batch sorting are formed, and a material entering time window is accurately determined; coordination conflicts between material approach and storage area arrangement are mitigated.

The invention discloses a Token-based visual task generation method, and belongs to the technical field of intelligent task automation, and the method comprises the steps: S1, cross-modal alignment; s2, performing visual Token processing; s3, constructing a task description Token sequence: constructing the task description Token sequence based on a predefined visual task template library according to requirements of a user or a specific application scene; s4, checking task feasibility; s5, task priority scheduling; s6, training a task generation model; s7, dynamic task allocation; and S8, optimizing the model. According to the method, the long sequence processing capability is optimized through a hierarchical merging strategy, the compactness of feature expression is realized while space position information is reserved, linear projection and enhanced position coding are combined to form a visual Token sequence with strong representation capability, local detail features are contained, a global context relationship is kept, and the method is suitable for the visual Token sequence with high representation capability. High-information-density feature input is provided for subsequent task processing, and the processing precision of various visual algorithms is effectively improved.

The invention discloses a plateau multi-energy complementary agricultural energy management system and a plateau multi-energy complementary agricultural energy management method, and belongs to the technical field of new energy. Wind, light and terrestrial heat multi-energy complementation is cooperated with a battery energy storage system and a phase change heat storage system, and a central intelligent management system is used for carrying out priority scheduling and real-time monitoring and prediction on a load; efficient energy supply of irrigation, processing and greenhouse heating is achieved, the renewable energy utilization rate and the load satisfaction degree are remarkably improved, the operation cost and environmental pollution are reduced, safe and efficient operation of the system is ensured, and good economic and environment-friendly effects are achieved.

The invention provides a timed task execution optimization method and system, and aims to solve the problems of inflexible static priority scheduling, unintelligent resource allocation and the like in the prior art. According to the method, a distributed timed task intelligent scheduling framework is constructed and comprises five core components including a task management console, a scheduling decision engine, a resource monitoring agent, a task execution cluster and metadata storage. Task basic information is obtained through task feature extraction, a dynamic priority score is calculated based on decision factors such as SLA urgency, resource matching degree and service weight, and task priority ranking is achieved. And performing intelligent task allocation by adopting a BestFit algorithm, and allocating the task to the optimal execution node. The system monitors the CPU utilization rate, the memory occupation and the IO waiting time of the nodes in real time, and when the CPU utilization rate exceeds 85%, an elastic resource allocation strategy is triggered. And when the task execution time exceeds the pre-estimated duration, performing task splitting and rescheduling based on a dynamic fragmentation algorithm.

The invention discloses a computing power distribution method based on a dynamic window and multi-version correction, and relates to the technical field of intelligent resource scheduling, the method comprises the following steps: constructing a scheduling state set by monitoring a to-be-scheduled task set and a platform resource state of a cloud platform resource scheduling system in real time; based on time window dynamic judgment, extracting a stable and effective task set; calculating a scheduling priority coefficient in combination with task resource intensity, urgency and historical completion performance, determining a priority scheduling set and executing a first version scheduling scheme; the difference between the current scheduling version and the historical optimal scheme is evaluated through the residual vector and the offset coefficient, and whether version correction needs to be carried out or not is judged; and based on resource use feedback in the scheduling execution process, calculating a feedback change rate and judging whether a scheduling execution state is stable or not, and if the scheduling execution state is unstable, dynamically adjusting scheduling parameters. According to the method, accurate allocation of computing power resources, adaptive scheduling optimization and abnormal risk control are realized.

The invention relates to a data access prediction scheduling method and system based on a dynamic threshold value, and belongs to the technical field of data processing. The method comprises the following steps of: dynamically adjusting an abnormal detection threshold in an access behavior by utilizing reinforcement learning and an error feedback mechanism, realizing preposition identification of potential high-frequency access data by combining abnormal point classification processing and access trend prediction, and generating a scheduling request to drive cache preheating or interface priority scheduling of the data. According to the method, access behavior modeling, anomaly recognition, trend prediction and strategy feedback mechanisms are combined, interface access optimization requirements in a mass data storage system are met, and the key problems that access behaviors are unpredictable, scheduling response lags and the like are solved.

The invention provides a distributed event processing method and electronic equipment, which can be applied to the field of distributed technologies. The distributed event processing method comprises the following steps: receiving an event processing request at a request receiving entrance of a distributed system, and searching a priority and a mutual exclusion execution type corresponding to an event type from a preset event registration library according to an identifier representing the event type in the event processing request; based on the priority and the timestamp of the event processing request, inserting an event object obtained after packaging the event processing request into a priority scheduling queue; after an event object used for execution is extracted from the priority scheduling queue, conflict detection is carried out on a mutual exclusion execution type corresponding to the event type based on a global mapping table maintained in the distributed system; and under the condition that the conflict is not detected, calling a service processor corresponding to the event type to execute the event object, and updating the current execution count of the event type in the global mapping table based on the execution state.

The invention relates to the technical field of data acquisition, in particular to a bus real-time data acquisition method and system, and the method comprises the following steps: collecting data from a plurality of sensors of a bus in real time; dividing the data into a plurality of categories according to the timeliness and importance of the data; multi-level dynamic compression algorithm selection based on data categories: adopting multi-level dynamic compression algorithms for different categories of data; during data transmission, a self-adaptive priority scheduling mechanism is adopted, and the uploading priority of different types of data is dynamically adjusted according to the current network bandwidth and the transmission delay; in a bus network, short-distance wireless communication is established among a plurality of buses, and data cached in one bus is shared to other surrounding vehicles through a bus-bus linkage mechanism; according to the invention, real-time sharing and temporary supplement of data are realized through short-distance wireless communication, the problem of data missing caused by network disconnection of a single vehicle in a traditional data acquisition system is solved, and the reliability of overall data is greatly enhanced.

The invention relates to a priority scheduling generation method and system for Beidou satellite short message communication, and the method comprises the steps: collecting multi-dimensional features, such as message service types, user identities, spatio-temporal information, channel states and the like, dynamically calculating the priority score of each message through a configurable weighting model, and then, carrying out the dynamic calculation of the priority score of each message; the method comprises the following steps of: abstracting a message into a conflict graph vertex, establishing an edge according to a receiving conflict relationship, distributing candidate message sets which are not conflicted with each other for each scheduling time slot by adopting an independent set selection algorithm, and performing joint optimization on a message sending sequence and power distribution by using a genetic algorithm in each time slot by taking a maximized comprehensive utility value as a target, and generating a final scheduling instruction. Through dynamic evaluation, conflict avoidance and resource collaboration, intelligence and self-adaption of a scheduling strategy are realized, and the guarantee capability of a system for key messages and the overall resource utilization efficiency are remarkably improved.

The invention relates to the technical field of smelter equipment control, in particular to a smelter feeding multi-equipment cooperative control method, system and equipment, and the method comprises the steps: constructing a real-time communication network between smelter feeding equipment, synchronizing the clock signals of the smelter feeding equipment, and scheduling the transmission time sequence of control instructions according to the priority; continuously collecting operation state data of each smelting furnace feeding device according to a real-time communication network, and generating a device operation index according to a preset abnormality judgment rule; when the device operation index indicates that the first device is in an abnormal state, dynamically allocating a control task of the first device to at least one second device based on a current topological structure of the real-time communication network, and adjusting a link connection relationship of the real-time communication network; and according to the instruction interface type of the target equipment, converting the cooperative control instruction into an instruction format adaptive to the target equipment. According to the invention, the problems of multi-device communication delay, high abnormal detection misjudgment rate and low protocol conversion efficiency in a complex environment are effectively solved.

The invention discloses a dynamic concurrency control and resource optimization method of a smart phone outbound system. According to the method, the calling resources are dynamically allocated through the multi-dimensional weight model, and line loads and risk management and control are effectively balanced on the premise of ensuring compliance requirements of operators. Real-time state monitoring of the calling number is combined with an intelligent cooling strategy, so that a risk control mechanism is prevented from being triggered by overuse of a single line, and high-efficiency access of high-value customers is ensured through priority scheduling. The concurrency scale and the calling rhythm are accurately controlled through double constraints of the double-token mechanism, the instant flow is prevented from impacting the bearing limit of the system, it is ensured that the outbound call rate is always within the safety threshold value of an operator, and therefore the call failure rate and resource waste are greatly reduced. A parameter optimization mechanism based on dynamic feedback can automatically adjust a resource allocation strategy according to historical data and operation environment changes, for example, rate limitation is temporarily relaxed and remainder smooth scheduling is started in a burst traffic scene, so that smooth transition of a service peak value is realized.

The embodiment of the invention discloses a flexible job shop scheduling method based on preference-driven graph reinforcement learning, and relates to the field of shop dynamic scheduling in an intelligent manufacturing technology. According to the method, by constructing a multi-objective optimization model, multiple objectives of the job shop can be optimized at the same time, including the minimum completion time, the total delay and the total cost. Wherein an imperfect maintenance model is constructed, and the maintenance demand and the maintenance opportunity of each machine are dynamically determined. And capturing a complex relationship between the operation and the machine by using an improved graph neural network. In combination with a preference-driven mechanism, a maintenance plan and workshop scheduling are adjusted in real time through a graph reinforcement learning method, and efficient priority scheduling rules under different preferences are learned, so that an integrated decision of machine allocation, an operation sequence and maintenance arrangement in dynamic scheduling is realized.

The invention discloses a dual-stage resource reservation and scheduling system for vehicle networking sudden accident emergency calculation, and relates to the technical field of edge calculation and intelligent traffic. The system comprises a traffic situation sensing module, a risk prediction decision module, a resource pre-freezing execution module, a task grading analysis module and a hybrid scheduling optimization module. The system innovatively divides a traffic accident handling process into an early warning stage and a response stage: in the early warning stage, the system adjusts a resource reservation ratio of edge nodes based on traffic flow density and historical task load conditions; in a response stage, the system performs hierarchical scheduling according to task types and dependency relationships by using a mixed integer programming model and a priority perception strategy. Through an end-side cloud collaborative architecture and a resource reservation mechanism, the system can significantly improve the task processing efficiency under emergencies and reduce the response time delay of high-priority tasks, has good expansibility and practicability, and is suitable for various vehicle networking application scenes such as intelligent expressways and urban traffic.

The invention belongs to the technical field of vehicle-mounted edge computing (VEC), and particularly relates to an unloading and resource allocation method for a DAG task in a vehicle-mounted edge computing environment. The method comprises the following steps: firstly, constructing a VEC unloading system in a two-way lane scene, and collecting task and equipment information and establishing an optimization model in combination with a vehicle moving model, a communication model and a calculation model; and secondly, aiming at a task in a directed acyclic graph (DAG), a task priority scheduling method based on a DAG topological structure is designed, so that a task scheduling strategy is optimized. On the basis, the problem is modeled as a Markov decision process by taking minimization of task completion time delay and system energy consumption as optimization objectives. The invention relates to the field of resource allocation, in particular to a task-dependent computing unloading and resource allocation algorithm based on a depth deterministic policy gradient (DDPG), and further provides a task-dependent computing unloading and resource allocation algorithm based on the depth deterministic policy gradient (DDPG) so as to solve the optimization problem.

The invention discloses a vehicle-mounted electrical control unit (ECU) debugging simulation system and method based on digital twinning. The system is composed of a data aggregation and time reference module, a consistency evaluation module, a synchronization and correction module, a semantic communication and scheduling module, an arbitration and security debugging module, and a cross-layer adaptive degradation and execution and iteration module. Channel-level time delay is estimated through a unified time reference, a consistency difference spectrum containing amplitude, time sequence, event and derivative components is constructed, confidence is given, time delay compensation and event-level alignment are calculated, and deterministic and priority scheduling is implemented according to semantic benefits; actions such as parameter recharge, message injection, task rhythm perturbation and environmental stimulation are generated and limited under the constraint of a security invariant set, control right soft handover and gating are realized based on confidence, and grading and degrading are performed when the confidence is low or the constraint is triggered; millisecond-level bidirectional collaborative debugging of twin and physical ECUs is realized, and consistency convergence, safety and bandwidth time arrival are improved.

The invention discloses a downlink resource scheduling method and device for a 5G differentiated scene and a storage medium. The method comprises the following steps: acquiring information, wherein the information comprises time slot network state information of each time slot; converting the information into historical state track information; the historical state track information serves as input vectors to be input into a DQN neural network and a TD3 neural network respectively, and core parameters are output; respectively training a DQN neural network and a TD3 neural network according to the historical state trajectory information and the core parameters to obtain a trained DQN neural network and a trained TD3 neural network; the time slot network state information of the current time slot serves as a current input vector to be input into the trained DQN neural network and the trained TD3 neural network, and a current core parameter is output; calculating the priority of each user for each resource block based on a priority formula of a heuristic algorithm according to the current core parameter; and performing resource scheduling on the resource blocks based on a greedy strategy according to the priority. According to the method, the defects of a traditional priority-based scheduling algorithm in a complex network environment are overcome.

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.