17results about How to "Improve scheduling flexibility" patented technology

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 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.

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.

The invention relates to the technical field of cloud computing and data center operation and maintenance, and provides a data center resource scheduling optimization method based on artificial intelligence, and the method comprises the following steps: collecting multi-dimensional data; preprocessing the data; constructing and training a hybrid intelligent scheduling model; and real-time scheduling decision generation, scheduling execution and closed-loop optimization are carried out. According to the artificial intelligence-based hybrid intelligent model-driven data center resource scheduling optimization method provided by the invention, through multi-dimensional data acquisition, feature engineering optimization, hybrid intelligent model construction and closed-loop iterative optimization, multi-target collaborative optimization of a resource utilization rate, an SLA standard-reaching rate and energy consumption is realized; and the intelligent level and the dynamic adaptability of resource scheduling of the data center are improved.

The application discloses a dynamic robust feasible region solving method for integrated energy system scheduling robust optimization, comprising the following steps: S1, constructing an integrated energy system model according to the integrated energy system; S2, converting the integrated energy system model into a compact form to obtain a compact model, and constructing an uncertainty set according to the compact model; S3, defining the correlation and continuity of decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set comprises robust feasible regions of the preset number of time periods; S4, rolling updating the robust feasible regions of the preset number of time periods in the first set; optimizing first constraints according to the first set to obtain second constraints; S5, solving the dynamic robust feasible region according to the second constraints and the first set to obtain a second set; the second set comprises robust feasible regions of the preset number of time periods. The application further discloses a dynamic robust feasible region solving system for integrated energy system scheduling robust optimization.

The application provides a method and system for collaborative optimization of a commercial model of an energy storage power station based on multi-objective programming, relates to the technical field of energy storage power station operation, and comprises the following steps: obtaining a commercial model feature set by performing space-time dimension decoupling on operation data of the energy storage power station; establishing a multi-objective programming constraint system; obtaining a commercial model combination scheme based on a Pareto front search strategy; generating a dispatch execution instruction according to the commercial model combination scheme; and identifying and adjusting the capacity allocation of the commercial model in conflict. The application realizes dynamic collaborative optimization of multiple commercial models of the energy storage power station, and improves economic benefits and operation safety.

This invention discloses an API-driven intelligent scheduling and dynamic adaptation method for service containers, applicable to Kubernetes clusters and / or Docker Swarm clusters. The method receives service container operation requests through an API gateway, authenticates and authorizes them via a security control module, and generates target instructions after a parameter validator verifies image information, resource quotas, and runtime parameters. The container management module then creates, starts, stops, deletes, and monitors the status of containers. The resource scheduling module selects target nodes and controls scaling based on business priorities and node resource usage. The log management module centrally collects, stores, and analyzes requests, runtime status, and scheduling results, providing a basis for blue-green deployment, rolling upgrades, and anomaly rollback of service versions. This method achieves full lifecycle management and cross-environment dynamic adaptation of containers based on a unified API, reducing the operational threshold and improving resource utilization and service stability.

This invention relates to the field of combined heat and power (CHP) energy system technology, and discloses a high-efficiency CHP system based on hydrogen-oxygen synergistic conversion. The system includes a housing with a water supply module inside, and further comprises: an electrolyzer with its inlet connected to the water supply module; a hydrogen production cabinet electrically connected to the electrolyzer; a solid hydrogen storage tank with its inlet connected to the hydrogen output of the electrolyzer; an oxygen cylinder with its inlet connected to the oxygen output of the electrolyzer; a chiller connected to the solid hydrogen storage tank; a fuel cell with its hydrogen input connected to the solid hydrogen storage tank; and a thermal management module composed of water pipes passing through the electrolyzer and the fuel cell. The housing also includes a control panel and an electrical cabinet for electrical control and connection between the various modules, thereby achieving a four-way energy closed loop of electrolytic hydrogen and oxygen production, hydrogen storage, fuel cell power generation, synergistic electrothermal output, and waste heat recovery.

This invention discloses a method and system for bidirectional energy interaction and multi-period regulation of electric vehicle aggregators (EVs) for ancillary services, relating to the fields of smart grid and electric vehicle aggregator operation optimization technology. The method includes: calculating the energy and power feasible domain boundaries of the EV cluster within the scheduling cycle based on the framework of EV aggregator (EVA) participation in ancillary services; establishing a user willingness discrimination model to determine the probability of EV users' willingness to participate in regulation; combining charging demand response strategy (CBDR) and discharging demand response strategy (DBDR) to determine the user participation ratio; constructing an optimization model with maximizing the operating revenue of EV aggregators as the objective function; and obtaining the optimal charging and discharging scheduling scheme by solving this optimization model. This method employs a composite strategy when the EV access scale expands, helping to improve regulation capacity and user acceptance, and enhancing the stability of revenue.

The application relates to the technical field of intelligent warehouse scheduling, in particular to a cross-aisle cooperative scheduling and congestion control method of a CTU cluster, which comprises the following steps: in a scheduling stage, performing congestion influence evaluation on a generated candidate cross-aisle scheduling scheme, wherein the congestion influence evaluation represents the expected influence degree of the candidate cross-aisle scheduling scheme on the overall operation state of shared passing resources when the candidate cross-aisle scheduling scheme is executed; according to the congestion influence evaluation result, performing cross-aisle access control at the scheduling level on the candidate cross-aisle scheduling scheme to obtain a corresponding cross-aisle access control result, and according to the cross-aisle access control result, limiting or allowing a target CTU to execute the corresponding cross-aisle scheduling scheme. Through the evaluation, access and cooperative control of the candidate cross-aisle scheduling scheme, executable cross-aisle scheduling schemes are retained as much as possible, the scheduling flexibility is improved, and the system-level efficiency decline caused by excessive concentration of cross-aisle scheduling is avoided.

The utility model relates to the technical field of ferrous metallurgy RH vacuum refining, particularly discloses a silicon steel production oriented RH refining device based on double vacuum pump cooperation and a direct connection feeding system, and mainly solves the problems that the RH treatment period of silicon steel production is mismatched with the continuous casting production rhythm and the alloy feeding efficiency is bottleneck. According to the scheme, a second set of vacuum pump system is additionally arranged on the basis of traditional three-vehicle five-position arrangement, two sets of independent vacuum pumping systems are formed, vacuum treatment can be conducted on two treatment stations at the same time, and the comprehensive treatment period of the RH link is shortened. Meanwhile, each station is provided with an independent ferrosilicon feeding system which comprises a high-capacity overhead bunker, a weighing hopper and a vacuum charging bucket directly connected with an articulated chute, so that the ferrosilicon feeding time is shortened. According to the three-vehicle five-position RH vacuum refining device, the vacuum treatment efficiency and the feeding process of the three-vehicle five-position RH vacuum refining device are synergistically optimized, and efficient matching of the RH refining period and the continuous casting rhythm during silicon steel production is achieved.

The utility model discloses a double-bus large-current switch gas box capable of stably supplying power, which belongs to the technical field of electrical equipment, and comprises a box body, a mounting rack is fixedly mounted in the box body, three wall bushings are arranged on the mounting rack, and the wall bushings are arranged on the mounting rack. A first three-position disconnecting switch and a second three-position disconnecting switch are installed on the inner walls of the two ends of the box body respectively, a circuit breaker body is installed on the inner wall of one end of the box body, and the first three-position disconnecting switch and the second three-position disconnecting switch can be arranged in the box body and correspondingly connected with a first ejection sleeve set and a second ejection sleeve set. When one group of buses breaks down or needs to be overhauled, each power supply and each loop load can be distributed to other groups of buses, power supply can be quickly recovered, and in-turn overhauling is realized without power failure, so that stable power supply to equipment is ensured.

The application relates to an LNG cold energy multi-user cascade utilization system based on a temperature level matching matrix and a control method thereof, which comprises an LNG multi-stage gasification shunt unit, a multi-user cold quantity distribution bus, a user cold end heat exchanger group, a sensing monitoring network and a temperature level matching matrix controller. Each temperature level section outputs a cold quantity mother pipe through a segmented heat exchanger, and an independently controllable valve serving as a matrix switching valve island is arranged at the intersection of the mother pipe and a user branch pipe. The controller takes the minimum square weighted sum of the deviation between the temperature level section center temperature and the user target temperature as the target, solves the temperature level matching matrix under the constraints of complete distribution in the same temperature level section and meeting the cold quantity demand of each user, and issues the opening degree of the valve according to the matrix elements. The control method comprises offline modeling, initial value solving, online collection, instruction issuing, deviation determination, matrix recalculation, buffer absorption, fault tolerance reconstruction and iterative optimization feedback. The temperature level-user distribution is sublimated into matrix optimization, and dynamic adaptability and fault tolerance are combined.

The present application relates to the technical field of power system dispatching operation, and particularly relates to a power grid adaptive dispatching optimization method for new energy access, comprising: extracting probability distribution characteristic parameters of new energy and load prediction error; generating a power interval prediction sequence for defining a source and load uncertainty set; constructing a double-layer optimization dispatching model; solving the double-layer optimization dispatching model to output unit start-stop plan, base point output plan, spinning reserve capacity requirement and real-time adjustment instruction; calculating real-time power deviation, feeding back the real-time power deviation to update the probability distribution characteristic parameters, and adaptively adjusting the weight coefficient of the robustness penalty term based on the comparison result of the deviation and the current robustness constraint boundary to form a closed-loop feedback. Through deep fusion of prediction information and strengthening of online adaptive adjustment capability, not only the economy and safety of new energy power grid dispatching are improved, but also solid technical support is provided for the intelligentization of the power system.

The invention discloses a multi-scene power resource aggregation scheduling integrated system based on a regional range, and relates to the technical field of power scheduling. The data uploading module is used for acquiring equipment data in the target area, acquiring target operation data of the target equipment and uploading the target operation data to the cloud; the scheme optimization module is used for enabling the cloud to receive the operation data, and performing power resource scheduling optimization according to the operation data to obtain a target scheduling scheme; and the scheme transmission module is used for encrypting the target scheduling scheme to obtain a final optimization scheme, and sending the final optimization scheme to the user side, so that the user side executes the final optimization scheme. Through a regional adaptation algorithm, a standardized protocol and full-process security protection, the problems of scheduling adaptability, resource collaboration, security access and the like are solved, the resource utilization efficiency, the system stability and the scheduling flexibility are improved, and the regionalization requirements of a novel power system are met.