62results about How to "Improve scheduling flexibility" patented technology

The invention discloses a hydrogen-containing energy storage micro-grid dispatching optimization method in combination with ARIMA-LSTM and DDPG. Comprising the following steps: firstly, acquiring historical renewable energy productivity data, historical power load data, historical electricity price and historical energy storage equipment state of the hydrogen-containing energy storage microgrid; secondly, historical renewable energy productivity and power load data are predicted through an ARIMA-LSTM model, a complete state space is constructed in combination with the renewable energy productivity, the electricity price, the energy storage state and the like of the current time slot and input into an Actor and Critic network of the DDPG, a DDPG network model is trained, and a reward function model in the DDPG network model aims at optimizing the annual operation cost; the constraints comprise equipment operation constraints, renewable energy source output constraints, power imbalance and the like; and finally, a micro-grid scheduling strategy responding to external environment change is obtained, dynamic scheduling optimization of each device is realized, and adaptability and robustness of the system are improved.

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.

The invention discloses a configurable flexible job shop scheduling method based on a multi-agent game model, and the method comprises the steps: mapping a scheduling problem into a dynamic disjunction graph model to describe the relation between a process and equipment, representing process nodes, equipment nodes and the mutual relation through a node set and an edge set, and combining the flexibility of a dynamic disjunction graph to obtain a flexible job shop scheduling model. And matching of equipment and processes is dynamically adjusted according to changes of production requirements, so that the equipment utilization rate is optimized. And a clustering method based on structure entropy is adopted, and process nodes are clustered into sub-graphs, so that the search space of a scheduling problem is reduced, and the calculation complexity is reduced. Construction of an order agent and an equipment agent and a game and cooperation process between the order agent and the equipment agent are covered. And each agent optimizes a decision through a deep Q network, and cooperates or competes according to a respective target. An automatic optimization configurator is used for optimizing hyper-parameters and a network structure of a deep Q network in a multi-agent system, and scheduling performance and adaptability are further improved.

The invention discloses a virtual power plant multi-objective optimization scheduling method, system and device, and a storage medium. The method comprises the following steps: constructing a virtual power plant architecture; the virtual power plant comprises internal resources and distributed power generation equipment; the internal resources comprise a base station standby energy storage system, an internet data center, an interruptible load and a gas turbine; the distributed power generation equipment comprises wind power generation and photovoltaic power generation; internal resources of the virtual power plant are modeled, specifically, base station standby energy storage system modeling, internet data center modeling, interruptible load modeling and gas turbine modeling are included; establishing an objective function of multi-objective optimization of the virtual power plant; establishing constraint conditions of multi-objective optimization of the virtual power plant; solving based on the objective function and the constraint condition, obtaining a scheduling instruction, and executing scheduling; according to the invention, the operation economy and scheduling flexibility of the virtual power plant system are improved.

The invention provides a layered optimization scheduling method and system for a wind power cluster and a medium, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: determining evaluation indexes, obtaining the variable weight of each evaluation index, employing a membership function and a fuzzy comprehensive evaluation method, and obtaining the variable weight of each evaluation index; obtaining a membership score of each wind power plant of the wind power cluster under each evaluation index, and obtaining a comprehensive degradation score of each wind power plant; taking the ultra-short-term power predicted value of the wind power plant as the basis for judging the power change trend of the wind power plant, determining the climbing classification of the wind power plant, and determining the scheduling sequence of the wind power plant; on the premise that the power balance constraint between the wind power plants is met, the total output power of all the wind power plants is adjusted and controlled in real time according to the dispatching requirement for power increasing or power reducing of the wind power cluster and based on the dispatching sequence of the wind power plants of all the wind power plant groups. According to the method, the time-varying property and uncertainty of the wind power plant are fully considered, and the fatigue and fluctuation conditions of the whole wind power cluster are reduced.

The embodiment of the invention discloses a job task scheduling method and system based on a computing power resource pool. The method comprises the steps of obtaining a target user identifier and a target scheduling mode through a computing power application portal subsystem, and determining a candidate computing power resource pool partition and a target computing power quota based on the target user identifier; if the target scheduling mode is detected to be a query scheduling mode through the computing power application portal subsystem, obtaining a target job task and a target job demand; performing partition screening on the candidate computing power resource pool partitions based on a target job demand through a computing power scheduling subsystem to determine a target computing power resource pool partition; and controlling the target computing power resource pool partition to execute the target job task based on the target computing power quota through the computing power base platform. Through the technical scheme of the embodiment of the invention, the job task can be quickly and accurately scheduled to the effective computing power resource pool, and the scheduling flexibility and the user experience are improved.

The invention provides a new energy transport capacity task scheduling method and system based on an energy complementation scene, and relates to the technical field of transport capacity task scheduling, and the scheduling method comprises the steps: collecting and preprocessing the real-time data of the energy, position, energy consumption and the like of a vehicle, carrying out the vehicle endurance evaluation and energy complementation judgment according to the real-time data, according to the endurance evaluation and energy complementation judgment result, screening energy complementation sites meeting preset conditions, carrying out scheme calculation and cost calculation on the screened energy complementation sites, comparing calculation results through an intelligent scheduling decision algorithm to obtain a final scheduling scheme, sending a scheduling instruction, carrying out cycle scheduling on newly added waybills or transport capacity tasks, and carrying out scheduling on the newly added waybills or transport capacity tasks. The scheduling scheme is dynamically adjusted and updated in real time according to actual conditions so as to adapt to efficient operation of a complex scene; the scheduling flexibility is enhanced, the scheduling efficiency and economic benefits of the new energy transport capacity in the energy complementing scene can be remarkably improved, and powerful support is provided for wide application of new energy automobiles in the logistics transportation field.

The invention relates to the technical field of automatic control systems, in particular to an intelligent substation sequence control method and system, and the method comprises the following steps: based on an equipment operation instruction of an intelligent substation, analyzing the logic sequence of equipment operation, extracting the state change information of equipment, and carrying out the operation analysis of equipment state and time correlation; and judging the dependency relationship through the time difference of the equipment state, and generating an equipment operation dependency model. According to the method, the resource occupation condition of the equipment is monitored through a data stream compression algorithm, load balancing is carried out, the load level of the equipment is evaluated in real time, dynamic distribution and adjustment of loads are achieved, the pressure of high-load equipment is effectively relieved, potential conflicts of the equipment during parallel execution are recognized and solved in real time by introducing an operation conflict judgment mechanism, and the safety of the equipment is improved. Smooth execution of parallel tasks is guaranteed, delay and conflict possibly occurring in the equipment execution process are effectively reduced, and continuity and reliability of a power system are guaranteed.

The invention discloses a cross-provincial flexible scheduling method and system for a pumped storage power station, and the method comprises the steps: monitoring the operation state of each unit of the pumped storage power station in a region, and displaying the basic data and operation conditions of each unit on a front-end web page, and formulating a pumped storage calling method according to the capacity demand of each small region in the whole large region for the pumped storage power station and the available capacity of the inter-provincial tie line, and finally realizing rear-end data transmission and development and display of a front-end data simulation platform by using a Django framework. The method has the advantages that the current situation that pumped storage is ignored under the background of vigorous development of new energy is solved, meanwhile, the blank that pumped storage participates in trans-provincial calling is filled up, the energy calling process is visually displayed through the computer technology, and the pumped storage calling frequency and efficiency are improved.

The invention belongs to the technical field of new energy power generation system control, and discloses a photovoltaic power error-considered power distribution network intra-day scheduling correction method, system and device and a medium. The method comprises the following steps: acquiring a preset intra-day initial scheduling instruction of the power distribution network, and controlling the power distribution network to start intra-day initial scheduling based on the instruction; and obtaining a preset correction period parameter, and after the intra-day initial scheduling of the power distribution network is started, executing a period rolling optimization step on the intra-day initial scheduling instruction of the power distribution network based on the correction period parameter. According to the method, the flexibility, the accuracy, the economic benefit, the safety and the stability of intra-day dispatching of the power distribution network are remarkably improved by introducing the periodic rolling optimization step, comprehensively considering the economic index, enhancing the adaptability of photovoltaic power prediction, presetting the correction constraint condition and the like.

The invention discloses an operation control method and device applied to a cargo access system, a computer readable medium, electronic equipment and a computer program product. According to one specific embodiment, the method comprises the steps that the operation end point of a cargo carrying table is responded to be a target position, the cargo carrying table is controlled to operate to an adjacent position above the target position, and the cargo carrying table in the target position has the possibility of colliding with a transfer trolley in at least part of the operation state; the cargo carrying platform at the adjacent position has no collision possibility with all the transfer trolleys in the running state; in response to the situation that the operation authority of the unit area below the target position is not obtained by a first transfer trolley in the first operation state in the transfer trolleys, it is determined that no collision possibility exists between the transfer trolley and the cargo carrying table in the target position; and controlling the cargo carrying table to run from the adjacent position to the target position. On the basis of ensuring the operation safety of the cargo access system, the scheduling flexibility and the access efficiency can be improved.

The invention discloses a multi-stage coordinated regulation and control method and system for a virtual power plant, and the method comprises the steps: carrying out the quantitative description of an adjustable space of various distributed devices in the virtual power plant through a unified modeling method, and building an evaluation model of real-time output and response capabilities; aiming at the problem of online coordinated scheduling between the virtual power plant and the power distribution network, a centralized-distributed hierarchical scheduling structure is adopted, and the scheduling efficiency of the system and the response capability of equipment are effectively improved through unified modeling and hierarchical optimization. According to the method, the total operation cost of the virtual power plant can be effectively reduced, the problem of comprehensive information sharing needed by traditional centralized scheduling is avoided through privacy protection design, efficient interaction and information safety between the virtual power plant and the power distribution network are achieved, the requirement for real-time economic scheduling in a complex scene can be met, and high expandability and adaptability are achieved. And meanwhile, the method accords with the development direction of optimal dispatching of the current power distribution network, and plays a crucial role in promoting energy structure transformation and accelerating renewable energy consumption.

The invention discloses a warehousing system scheduling control method, device and equipment and a storage medium, and the method comprises the steps: receiving a task scheduling request which is used for requesting to schedule target equipment in a warehousing system to execute a corresponding equipment instruction; in response to the task scheduling request, searching whether a matched task template matched with the template identifier exists in a task template database or not; if not, acquiring a substitute task template; on the basis of the equipment instruction, a task execution step in the substitute task template and a task instruction corresponding to the task execution step, updating the substitute task template to obtain a target task template; and sending a task scheduling instruction to the warehousing system to schedule the target device to execute a corresponding device instruction based on the target execution information in the target task template. According to the method and the device, when the task execution logic is changed, only the corresponding template needs to be called, or the substitute task template is updated based on the instruction, and program codes do not need to be rewritten, so that human resources are saved, and the calling efficiency is improved.

The invention discloses an SMT production line dynamic scheduling optimization method and system based on big data analysis, and belongs to the technical field of dynamic scheduling optimization. Collecting equipment load data and qualified rate data of equipment in the SMT production line; constructing a qualified rate-load scatter coordinate system; in the qualification rate-load scatter coordinate system, respectively constructing a qualification rate line segment and a load mean value line segment; constructing an optimal performance point, a load mean value range and a qualified rate data range; constructing an optimal range circle; marking equipment outside the optimal range circle as priority scheduling equipment, and carrying out load adjustment; according to the method, the performance data of the equipment is visualized by constructing the scatter coordinate system of the qualified rate and the load, and it is ensured that the product quality is not affected in the load adjusting process; the optimal range circle is constructed, priority scheduling is performed, and the equipment load is dynamically adjusted, so that the scheduling flexibility and response speed of the production line are effectively improved, and the equipment can be quickly adapted when the production demand changes.

The invention provides a TSN-5G high-speed rail vehicle-mounted network traffic scheduling method based on an enhanced immune algorithm. The method comprises the following steps: acquiring all possible routes of a traffic set to be scheduled in a TSN-5G high-speed rail vehicle-mounted network and the time delay of each route; various operators of the enhanced immune algorithm are set, a DQN network is used as a clone mutation operator of the enhanced immune algorithm, and time delay and load are comprehensively considered to serve as an optimization target of the enhanced immune algorithm; the enhanced immune algorithm utilizes various operators to iteratively obtain the sending sequence and routing of each flow and the sending time of each flow in a TSN switch; and obtaining a gating scheduling list of the TSN switch according to the sending time of each flow, and performing end-to-end deterministic transmission on each flow by the TSN switch according to the gating scheduling list. According to the method, the immune algorithm and reinforcement learning are combined, the defects of an existing scheduling method are overcome, the scheduling efficiency and flexibility are improved, and deterministic scheduling of the key traffic in the TSN-5G train on-board network is achieved.

The invention discloses a distribution network dispatching method considering multi-time scale air conditioning load demand response, which belongs to the technical field of power system optimization operation, and solves the problem of how to optimize the demand response of air conditioning load through multi-time scale dispatching, so as to improve the operation efficiency and economy of the power grid. The invention obtains the line information of the distribution network and the air conditioning load data of the users at the distribution network nodes; constructs the constraint conditions of the distribution network operation; according to the line information of the distribution network, considers the operation characteristics of the air conditioning load under multi-time scales, and constructs the differentiated constraint conditions of the air conditioning load demand response under different time scales; constructs a multi-level objective function considering the multi-time scale air conditioning load demand response; solves the dispatching method model considering the multi-time scale air conditioning load demand response, and obtains the generalized controllable resource dispatching operation scheme of multi-stage and different time scales. The invention can effectively reduce the operation cost of the power grid, improve the reliability of the power grid, and improve the dispatching flexibility and response speed of the power grid.

The invention discloses a virtual power plant optimization scheduling method based on an improved dung beetle optimization algorithm, and the method comprises the steps: taking the operation economic cost and environmental protection cost of a virtual power plant as targets, and constructing a virtual power plant mixed environmental economy multi-target optimization scheduling model; setting a multi-dimensional dynamic constraint condition according to the power generation power of the power generation main body and the charge state of the energy storage system; performing optimization solution on the virtual power plant mixed environment economy multi-target optimization scheduling model through an improved dung beetle optimization algorithm, performing non-dominated sorting on dung beetle individuals, then calculating crowding distances of the dung beetle individuals, and adjusting dung beetle population distribution according to the crowding distances; adjusting the search radius and disturbance intensity of dung beetle individuals according to the number of iterations; utilizing a boundary control mechanism to update dung beetle individuals; iteratively screening out an optimal power dispatching scheme through an improved dung beetle optimization algorithm; the scheduling efficiency is improved by using the improved dung beetle optimization algorithm, the operation economic cost is reduced, and the support for environmental protection is enhanced.

The present invention is applicable to the technical field of new energy, and provides a method and device for load regulation, wherein the method for load regulation includes: collecting real-time microgrid load, electric energy storage capacity of energy storage module and real-time power generation; calculating the electric energy consumption within a preset time period according to the real-time microgrid load; regulating the power generation module, energy storage module and / or main grid to supply power to the microgrid load according to the electric energy consumption, electric energy storage capacity and real-time power generation. This technical solution achieves a significant improvement in the microgrid in terms of supply and demand balance, resource optimization configuration, power supply reliability and safety, and better balances the power supply and demand between the microgrid load and multi-source power supply (power generation module, energy storage module and main grid, etc.).

The invention discloses a new energy photovoltaic intelligent management method and management system. The method comprises the following steps: collecting photovoltaic, inverter, energy storage, weather and power grid data in real time through a multi-source sensor network; time alignment and feature extraction are carried out, and abnormities such as hot spots, insulation degradation and battery attenuation are identified; building a day-ahead, intra-day and second-level three-layer collaborative optimization model, and dynamically generating a power scheduling instruction; and intelligent fault diagnosis and self-healing control are realized based on the knowledge graph. The system comprises a multi-source data acquisition module, a data fusion module, a multi-time scale optimization module, a fault diagnosis module and a man-machine interaction module. According to the invention, through full-dimension perception and multi-level cooperation, the energy utilization efficiency, the scheduling flexibility and the fault recovery speed are significantly improved, and high-permeability photovoltaic safety grid connection is supported.

The invention provides a control method and device for a gas-steam combined cycle unit, and relates to the technical field of power generation. The method is applied to a gas-steam combined cycle unit, the gas-steam combined cycle unit comprises a gas turbine, a waste heat boiler and a steam turbine, a first flue branch between the gas turbine and the waste heat boiler is provided with a first baffle, and a second flue branch between the gas turbine and the waste heat boiler is sequentially provided with a second baffle, an energy storage device and a third baffle. The energy storage device is made of a heat conduction material. The method comprises the steps that real-time operation data of the gas-steam combined cycle unit are obtained to determine the real-time operation state of the gas-steam combined cycle unit; in response to switching of the real-time operation state of the gas-steam combined cycle unit, a switching control strategy corresponding to state switching is determined, and the switching control strategy comprises opening and closing control over at least one of a first baffle, a second baffle and a third baffle; and executing the switching control strategy on the gas-steam combined cycle unit.

The invention provides a narrow-gauge 50kg / m steel rail 6 single turnout, which comprises a concrete switch tie, and a switch group, a frog, a rail support sliding bed backing plate assembly and a III-type switch tie bolt assembly which are arranged on the concrete switch tie, the rail support sliding bed backing plate assembly is arranged between the concrete switch tie and the switch group, and the III-type switch tie bolt assembly is fixedly connected with the concrete switch tie; the rail support sliding bed backing plate assembly comprises a rail support sliding bed backing plate used for installing a straight stock rail and a curved stock rail and a sliding bed table rail bearing surface used for installing a straight switch rail and a curved switch rail, and rail gauge adjusting pieces are arranged between the straight stock rail and the rail support sliding bed backing plate and between the curved stock rail and the rail support sliding bed backing plate. The radius of a guide curve can be increased, and the overall length of the turnout is shortened; the point end of the switch rail is of a hidden point type structure, the point end of the switch rail can be protected from being punctured by wheels, and the switch rail can keep good vertical stability under the action of dynamic loads. The adjustable rail brace is arranged on the outer side of the stock rail, stability of the stock rail is enhanced, and camber is reduced. Railway operation safety is enhanced, and service life of steel rails and wheels is prolonged.

The invention provides an automatic guided vehicle scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of production scheduling, and the method comprises the steps: obtaining the number of workpieces transported by an automatic guided vehicle, the number of procedures and the number of parallel machines for machining the workpieces; according to the number of the workpieces, the number of the working procedures and the number of the parallel machines, the priority of workpiece transportation and the parallel machine selection of each workpiece in each working procedure are coded, and an initial scheduling scheme is obtained; for any workpiece, determining a transportation plan of the workpiece in the current working procedure, corresponding to the state of the parallel machine and the position of the automatic guided vehicle in the idle state, and determining a transportation plan of the workpiece in the next working procedure; constructing a parallel multi-population hybrid genetic algorithm, and carrying out iterative optimization on the initial scheduling scheme according to a transportation plan of any workpiece transportation in the next process to obtain an optimal scheduling scheme; and scheduling the automated guided vehicle according to the optimal scheduling scheme. Therefore, the utilization efficiency of AGV scheduling can be effectively improved.

The application provides a mobile terminal real scene performance real-time on-demand and synchronous control method based on a 4G / 5G network, and belongs to the technical field of Internet of Things remote control and low-latency communication. The method comprises the following steps: a mobile terminal accesses a cloud platform through a 4G / 5G network, and a user selects a program to initiate an on-demand instruction; the cloud splits a program time sequence script into multiple device control frames, and after compression, encryption and time stamp marking, the device control frames are sent to an on-site edge control host; the host performs packet loss detection, out-of-order rearrangement and time delay compensation, and based on a unified clock reference, the host links and synchronously operates devices such as lights, fountains, lasers and sound systems; the system supports real-time pausing, program switching and emergency shutdown, and the end-to-end time delay is less than or equal to 100 ms; when the signal fluctuates, a buffer queue and an active retransmission mechanism are automatically enabled to ensure the continuity of performance. The application realizes wide-area and long-distance real-time on-demand, millisecond-level multi-device synchronization and weak network adaptive transmission, and is suitable for outdoor real scene performance scenes such as a light show, a music fountain and a laser show in a cultural and tourist scenic area.

This invention relates to an intelligent scheduling method and system for new energy logistics fleets that coordinates dynamic task allocation and energy replenishment. The invention constructs a multi-dimensional disturbance vector by collecting parameters such as the time window offset, spatial dispersion, load mutation rate, and emergency level transition of new tasks. Within a sliding time window, Shannon entropy is used to quantitatively characterize the uncertainty of the task flow, distinguishing between high-disturbance and steady-state regions. The invention adaptively adjusts the energy constraint set based on the task uncertainty state, dynamically relaxing the lower energy limit in high-entropy regions and compensating for the impact of battery aging and degradation in low-entropy regions. It achieves globally optimal vehicle-task-energy ternary matching under the constraints of path continuity and task uniqueness using mixed-integer linear programming, and realizes online adaptive correction of the entropy judgment threshold based on actual execution feedback. This invention improves the responsiveness and energy adaptability to complex dynamic scenarios, effectively reduces the impact of uncertainty, and optimizes operational efficiency.

The present invention proposes a method, system, medium and device for robust optimization scheduling of a building microgrid, which belongs to the technical field of building microgrid optimization scheduling, including: generating a number of source load samples based on the day-ahead source load prediction value, representing the possible realization value of the source load on the next day, thereby constructing a source load uncertainty set; establishing a day-ahead scheduling layer, including: dividing the source load uncertainty set into a larger part of the source load uncertainty set, a smaller part of the source load uncertainty set and establishing a three-stage day-ahead robust optimization scheduling model; on the basis of the day-ahead scheduling layer, establishing an intraday real-time optimization scheduling layer based on model reinforcement learning, and outputting an intraday real-time optimal scheduling plan.

The invention discloses a method and an apparatus in a node used for wireless communication. A first node receives first DCI (Downlink Control Information), and the first DCI schedules K1 cells; the subcarrier intervals corresponding to at least two cells in the K1 cells are not equal, and K1 is a positive integer greater than 1; the first minimum applicable scheduling offset is indicated by physical layer dynamic signaling in the first time slot; the first cell is one of the K1 cells, the first cell is different from a cell to which the physical layer dynamic signaling belongs, and the first minimum applicable scheduling offset is applicable to the first cell starting from a second time slot of the first cell; the second time slot depends on the first time slot and a first offset value, and the first offset value is related to the subcarrier interval corresponding to the first cell and the subcarrier interval corresponding to the cell to which the physical layer dynamic signaling belongs. According to the invention, the problem of the effective time of the minimum applicable scheduling offset when one PDCCH schedules a plurality of carriers at the same time is solved.

This invention discloses a collaborative batch scheduling method and system for dual production lines in tire production, specifically for pre-fabricated rubber packs. Relating to the fields of intelligent manufacturing and production scheduling, the technical solution involves: acquiring basic tire production data; converting tire demand into rubber pack demand based on the mapping relationship between tire type and rubber pack type; generating predicted demand for each rubber pack type within a scheduling time window; establishing a collaborative batch scheduling model for both the rubber pack and tire production lines based on the predicted demand and the basic data; constructing a set of candidate scheduling schemes; and performing joint decoding, evaluation, and feedback correction on a unified time axis to output the optimal scheduling scheme. The beneficial effects of this invention are: achieving collaborative optimization between pre-fabricated rubber packs and tire production; reducing inventory and material shortages; improving equipment utilization and on-time order delivery capabilities; and enhancing the system's adaptability to demand fluctuations and resource changes.

A sidelink communication method, a communication apparatus, and a communication system are provided, to perform sidelink communication configuration in a relay scenario. The method includes: A remote device sends first information to a relay device; the relay device receives the first information from the remote device, where the first information includes a first paging parameter of the remote device; the relay device sends second information to a first network device, where the second information includes the first paging parameter; the first network device receives the second information from the relay device; and the first network device performs sidelink configuration based on the second information. In this way, the first network device can obtain the first paging parameter of the remote device, and perform sidelink communication configuration based on the first paging parameter. The first network device performs sidelink communication configuration based on the first paging parameter, so that quality of sidelink communication between the relay device and the remote device is improved, and scheduling flexibility of the first network device is improved.

The application discloses a plan-driven radio block center virtual marshalling control method and system, and is applied to the technical field of data processing.The radio block center is taken as a core, a marshalling plan of a centralized dispatching system, train data, trackside and interlocking information are acquired first, cross-source mapping is established through associated line and station basic data, and initial legality checking is completed, and target associated plan data is generated.Then, structured control data is obtained through splitting processing, topological relationship generation and a priority caching mechanism, and targeted control commands are generated in combination with real-time states of trains and state machine logic.Through optimization of state machine migration conditions and weights, a dynamic optimization model is constructed, and finally, based on the model and train-ground and train-train communication states, virtual marshalling dynamic control results and abnormal protection instructions are output, and efficient and safe train virtual marshalling control is realized.

The application belongs to the technical field of new energy power generation control, and discloses a kind of multi-path direct current convergence photovoltaic inverter network construction control method and device, method includes: selecting at least one photovoltaic branch as leading branch, the rest photovoltaic branch as subordinate branch;Leading branch operates in maximum power point tracking mode, subordinate branch operates in fast power control mode;Coordination controller generates global power reference instruction based on direct current bus voltage deviation, and combines the output capacity reference provided by leading branch and the actual output power of inverter, generates the active power instruction of each subordinate branch and issues to each subordinate branch;Coordination controller adaptively adjusts inverter active instruction based on the relationship between actual output power of inverter and photovoltaic branch estimated output limit;Direct current / alternating current controller obtains inverter active instruction, generates driving signal of inverter power switch based on network construction type control strategy through current-voltage double closed loop regulation, to realize the dynamic coordination of multi-path photovoltaic power.