30 results about "Load redistribution" patented technology

The invention provides an intelligent fault diagnosis and safety protection method for a combine harvester, and the method comprises the steps: carrying out the quantitative analysis of the influence degree of a fault on the system safety and operation continuity through a risk assessment model, calculating a fault danger grade score through combining historical fault data and equipment aging state information, and carrying out the calculation of the fault danger grade score. A five-level danger classification result from slight danger to serious danger is obtained; acquiring system response data after isolation control is executed, monitoring running state changes and load redistribution conditions of residual normal parts, adjusting control parameters through a real-time feedback mechanism, and judging the effectiveness of isolation measures and the maintenance degree of system stability; a closed-loop feedback mechanism is adopted to continuously monitor the fault isolation effect and the system operation performance, key data in the processing process are collected to update fault diagnosis model parameters, the risk level evaluation accuracy and the protection range division rationality are optimized, and a continuously improved intelligent fault diagnosis and safety protection system is obtained.

The invention provides a method for controlling the precision of an inner molded surface of a cast-in-place slab hung below a net rack, and belongs to the technical field of wind tunnel construction.The method comprises the steps that a C-shaped clamping piece and a sensor system are installed on a net rack lower chord, a monitoring network is established, fatigue characteristic data are analyzed through a minimum cut algorithm, and a load imbalance coefficient is calculated; a high-precision adjustable suspender and a prestress adjusting device are installed to construct a dynamic supporting system, a three-level precision compensation mechanism is established to achieve preventive compensation before construction, real-time monitoring compensation in the construction process and corrective compensation after pouring is completed, and multi-target collaborative optimization is achieved through a dynamic adjusting algorithm and a game optimization model. A complete technical system including fatigue point prediction, load redistribution and precision real-time control is formed, and the technical problem that in the construction process of hanging the cast-in-place slab under the net rack, the plane precision control lacks a systematic dynamic adjustment mechanism is solved.

The invention provides a scientific and technological intelligence resource integration method based on a real-time data stream, which comprises the following steps: monitoring the task queue depth of each node through a load migration trigger condition, and if the queue depth of a certain node exceeds a preset upper limit value, starting a task migration process, selecting a target node with the shortest response time as a migration object in combination with the node response time statistical data, and obtaining a load redistribution instruction; and according to the load redistribution instruction, a processing result synchronization mechanism is adopted to coordinate the data processing progress among the nodes, if a source node completes task migration, a global task state table is updated, if a target node successfully receives a task, a confirmation signal is sent, and a closed-loop control mechanism for cooperative processing among the nodes is formed.

The invention discloses a cascade fault analysis method based on digital twinning and physical power and related components. The method comprises the following steps: establishing a double-layer coupling network model through a digital twinning virtual network and a physical power network; setting a cascade fault propagation process through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy so as to construct a multi-layer network cascade fault model; updating the multi-layer network cascade fault model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascade fault model; and performing cascade fault analysis on a specified power system by using the target cascade fault model. According to the method, the target cascade fault model is constructed based on the coupling symbiosis between the digital twin virtual network and the physical power network in combination with the communication delay, and the analysis accuracy and reliability of the cascade fault of the power system can be effectively improved through the target cascade fault model.

The invention discloses a reinforcement learning-based virtual power plant enhanced recovery strategy method and system. According to the method, information-physical fusion modeling is adopted, and a power layer, an information layer and a coupling layer of a virtual power plant are constructed, so that the influence of key nodes and key lines is accurately evaluated, and a fault recovery sequence is optimized. Firstly, node efficiency, load importance and information-power coupling strength are calculated through a complex network theory, key nodes and lines are identified, and the prevention capability of a system is improved. And secondly, after a fault occurs, an intelligent load redistribution strategy is adopted, power flow adjustment of an overload line is optimized, and cascade failure diffusion is prevented. And finally, optimizing and recovering scheduling by utilizing a reinforcement learning method, and realizing dynamic scheduling optimization. According to the invention, the recovery capability of the virtual power plant under the cascade fault condition is improved, and the toughness and stability of the power grid are improved.

Systems provide load redistribution and reduced stress at otherwise highly loaded threads. A system for a threaded connection includes a shaft that rotates about an axis. The shaft is a hollow cylinder with an inner surface and a wall defined around the inner surface. A fastener is in threaded engagement with the shaft. The fastener applies a compressive load holding the shaft under a tensile load. The threaded connection extends from a loaded end adjacent a place of application of the compressive load, to an unloaded end opposite the loaded end. The inner surface has a contour, and the wall has one thickness at the loaded end and another greater thickness at the unloaded end, so the wall has more compliance at the loaded end as compared to at the unloaded end to reduce a stress resulting from the load and redistributing stress throughout the threaded connection.

The present application belongs to the technical field of high formwork monitoring, and relates to a real-time monitoring and early warning system for construction risks of high formwork of house building. First, a frame structure topology model containing pole number, spatial coordinates and connection relationship is established, and the poles with defects such as base void, base half contact, eccentric force of support or support point contact are marked in the initial stage of concrete pouring, the poles carrying initial bearing state labels are constructed, and the load sensitivity and stiffness degradation of each pole are continuously monitored. After being bound with the initial bearing state label, the bearing state evolution track of the pole is generated. Then, the load redistribution path caused by the bearing capacity failure of the initial defect pole is identified, the instantaneous overload degree of the healthy pole on the path is analyzed to evaluate whether there is a group pole imbalance risk, and finally, the preset multi-level early warning rule is matched to output a risk analysis report. The present application effectively overcomes the monitoring blind area of only focusing on the response after force in the prior art, and improves the accuracy of the early warning of the construction risks of high formwork.

The invention discloses a cascading fault defense and self-healing optimization method for an air-space-ground integrated network. According to the method, aiming at the problem that cascading faults are easily caused by node heterogeneous coupling and topology dynamic change of the space-air-ground integrated network, a multi-factor coupled cascading fault propagation dynamic model is firstly established, and the synergistic effect of node heterogeneity, link dynamics and load redistribution is quantified; then, a closed-loop cooperative defense system integrating early warning, isolation and self-healing is designed, intelligent decision making is conducted through machine learning, reinforcement learning and the game theory, and the real-time performance and the energy efficiency performance on resource-limited edge nodes are ensured through a lightweight algorithm; and finally, an NS-3 and MATLAB joint simulation platform is constructed, and the model and the strategy are verified in various application scenes of the Internet of Things. Experimental results show that the prediction error of the propagation model provided by the invention is controlled within 10%, the failure recovery rate is improved to 85-90% through a defense strategy, the communication interruption duration is reduced by more than 60%, the resource overhead is reduced by 20-25%, and theoretical support and technical guarantee are provided for stable operation of the space-air-ground integrated network in a safe and tough key environment.

This invention designs an IES attack detection method based on non-invasive heat load detection modeling, belonging to the field of integrated energy technology. Addressing the thermal inertia of the heating network in an integrated electric-thermal energy system, a load redistribution attack model is constructed. Based on this, a non-invasive detection model is used to establish dual models on the load side: a radiator model and a building storage heat dissipation model, to detect amplified attack deviations in the room temperature state matrix. State prediction is performed using a sliding window method for state matrix similarity matching, replacing the attacked and contaminated state matrix with the predicted state matrix. The MILP method is used to enable rapid load recovery after an attack on the integrated electric-thermal energy system. This invention's detection method is fast and effective, enabling online detection, and has a certain theoretical basis and practical engineering significance.

The application discloses a load balancing distribution control method and system in parallel operation of multiple energy main machines, relates to the technical field of load distribution, and comprises the following steps: collecting real-time operation parameter sets of each energy main machine in the system; calculating real-time health degree coefficients of each energy main machine; solving energy efficiency deviation degrees of each energy main machine under current load demand, and constructing a load redistribution optimization function; solving the load redistribution optimization function, dynamically generating and issuing individualized load setting instructions of each energy main machine; monitoring overall energy efficiency improvement rates of the system and operation stability indexes of each energy main machine, and dynamically backtracking and adjusting parameter weights of the main machine health degree attenuation model according to the overall energy efficiency improvement rates and the operation stability indexes. The technical problem that the existing load distribution method ignores the service life and maintenance state of the main machine, resulting in overall performance attenuation, is solved.

The invention provides a power system resonance suppression method and device, equipment and a storage medium, and relates to the technical field of power quality analysis and control. The method comprises the following steps: acquiring electrical signals of a plurality of nodes in a power system, and identifying a source node which generates harmonic resonance currently and a corresponding resonant frequency based on the electrical signals; a load redistribution scheme is generated with the aim of destroying the resonance condition of the source node, the scheme is used for transferring at least one load under the source node to other nodes so as to change the system impedance of the source node, and the system impedance is equal to the parallel equivalent value of the impedance of all the loads under the source node; a load redistribution scheme is performed to suppress harmonic resonance. Resonance suppression can be realized only by optimizing the existing network load distribution, the system transformation cost is remarkably reduced, the additional operation loss is reduced, the instability risk is controlled, meanwhile, the advantages of rapid response, high adaptability and the like are achieved, and effective technical support is provided for constructing a safe and flexible novel power system.

The application discloses a kind of based on multilayer network cascading failure multimodal transport network resilience evaluation method, belong to multimodal transport network operation and resilience evaluation technical field.First, according to multimodal transport network multilayer structure and freight flow transfer characteristics, ML-space method is used to construct highway and water weighted undirected multilayer network model;On this basis, fusion network topology and freight attribute double perspective, calculate node multilayer centrality and freight level to determine node importance, combined with the heterogeneity of subnetwork to build load-capacity cascading failure model containing initial load, node capacity and differential load redistribution strategy;Then based on the resilience triangle theory, build the whole stage resilience evaluation system covering resistance, absorption capacity and recovery capacity, through MATLAB software numerical simulation, compare the network resilience under different cascading failure and recovery strategy, reveal the influence law of node capacity coefficient on resilience.The application starts from the actual operation scene of multimodal transport network, considers multilayer characteristics and cascading failure propagation law, proposes the precise evaluation method of multimodal transport network resilience under cascading failure scenario, provides scientific reference for the planning construction, risk prevention and control and resilience improvement of multimodal transport network.

The invention relates to a two-stage sea-railway combined transportation network toughness optimization method under uncertain cascade failure, which comprises the following steps of: S1, acquiring a failure hub node, and generating a container operation load redistribution scheme based on a sea-railway combined transportation network dynamic load redistribution stochastic programming model under the uncertain cascade failure, the sea-railway combined transportation network dynamic load redistribution stochastic programming model under the uncertainty cascade failure aims at minimizing the total loss expectation of all alternative nodes and the container freight volume which cannot be redistributed, and the original container operation demand of the original failure hub node is distributed to the surrounding uninterrupted alternative nodes; s2, taking the generated container operation load redistribution scheme as the input of the sea-railway combined transportation network transportation organization optimization model under the interruption of the key interval, generating a new train operation demand, reconstructing a path through the sea-railway combined transportation network transportation organization optimization model when the new train operation demand is subjected to the interruption of the key interval, and performing the operation of the sea-railway combined transportation network transportation organization optimization model. And a new feasible restorative transportation scheme is generated. Compared with the prior art, the method has the advantages.

The invention discloses an elastic load balancing and tuning method and system, and belongs to the technical field of communication, and the method comprises the steps: determining the load difference of an edge server and the total time delay of a terminal task through constructing a terminal equipment-edge server two-layer system model; constructing an optimization function and setting a constraint condition by taking minimization of the weighted sum of the two as a target; and finally, solving through a two-stage bionic algorithm, training a plurality of initial deep neural networks by means of a multi-group intelligent algorithm, optimizing through a genetic algorithm to obtain an optimal model, and inputting current scene parameters to obtain a final equalization strategy. According to the method, the problem of rapid load redistribution in an abnormal scene is solved, the search efficiency and the generalization ability are considered, the system efficiency and the load balance degree are improved, the energy consumption is reduced, overload or idling of the server is avoided, and the performance is superior to that of a traditional algorithm.

This invention relates to the field of new energy vehicle technology and proposes a remote control method for intelligent charging piles. The method includes: deploying an edge intelligent acquisition terminal between the charging pile and the charging gun port to synchronously acquire high-frequency electrical timing signals in the charging circuit and environmental physical quantity signals corresponding to the charging pile; using the high-frequency electrical timing signals and environmental physical quantity signals, generating an edge event trigger vector for the charging pile to set the adaptive control action corresponding to the charging pile at the current moment; when the adaptive control action is a preset high-priority control action, the cloud control platform determines the load redistribution coefficient of each charging pile within the charging group, and based on the load redistribution coefficient, generates differentiated power adjustment commands for each charging pile within the charging group to execute remote control of the charging pile. This invention enables real-time matching between remote control commands for charging piles and dynamic operating conditions at the charging site.

This invention relates to a method for optimizing and evaluating the reliability of a two-unit co-load system. Based on the Weibull distribution of lifetime, the failure distribution function is obtained for any load-bearing unit. The average lifetime function of the load-bearing unit is obtained based on the description of the scale parameters of the load-bearing unit and the load size in the failure distribution function using an inverse power-law model. Based on the average lifetime function, the allocation weight is calculated with the goal of making the average lifetimes of the first and second load-bearing units equal. Based on the allocation weight, the load is redistributed between the first and second load-bearing units, thereby improving the lifetime and reliability of the co-load system after load redistribution. The entire co-load system is optimized, and the method is simple to calculate, easy to implement, and convenient for engineers to master and use.

The application relates to the technical field of cloud computing resources, and discloses a resource management method and system for green cloud computing and a storage medium. The method comprises the following steps: performing weight optimization on CPU and memory data through a coyote optimization algorithm to obtain a server energy consumption coefficient matrix; using a DDQN decision to allocate the CPU of a virtual machine based on the matrix to obtain a resource allocation parameter table; adjusting the storage location of a data backup copy to obtain a backup node configuration table; monitoring the PUE values of servers to obtain a list of servers with excessive power consumption; and using a particle swarm algorithm to calculate a virtual machine migration path to obtain a load redistribution table. The application solves the problems of inaccurate energy consumption prediction, lack of intelligent scheduling strategy, separation of backup storage and main task scheduling, and low efficiency of abnormal power consumption processing in the existing cloud computing resource management, and improves the energy utilization efficiency of the cloud computing environment and the intelligent level of resource scheduling.

This invention discloses a load balancing scheduling optimization method for multi-motor cooperative drive, relating to the field of multi-motor cooperative control technology. The invention includes the following steps: Step S1, initialization of multi-motor drive system parameters and dynamic load perception; Step S2, forward static load balancing allocation based on an improved equal-increase rate method; Step S3, verification of multi-motor dynamic response constraints and identification of infeasible periods; Step S4, dynamic margin release and load redistribution through finite-depth backward backtracking; Step S5, multi-motor cooperative execution and real-time data feedback; Step S6, iterative optimization and adaptive parameter update. This invention constructs a scheduling framework of "forward calculation - dynamic verification - backward correction - closed-loop iteration," balancing load balancing, dynamic feasibility, and operational economy, significantly reducing the number of dynamic response failures and improving the stability of multi-motor cooperative drive systems. It is applicable to multi-motor cooperative scenarios such as industrial robots and new energy vehicle drives.

The application provides a control method for improving wedge precision by rolling load redistribution in a short process hot rolling, comprising: adopting a constant pressure reduction mode to perform rolling mill load distribution; limiting F1 and F2 rack pressure reduction rates to fixed values, and calculating F3-F7 rack pressure reduction rates according to the fixed pressure reduction rates of F1 and F2 racks in a secondary control system; performing rolling load redistribution; after plate passing, the difference between actual rolling load of the last rack and set load is compared, when the deviation between actual rolling force and set rolling force reaches the minimum force difference effective proportion, part of the load is distributed to the front stage rack; and performing short stroke control of rolling mill guide plate opening; for F2-F7 racks, the head short stroke opening, the middle short stroke opening and the tail short stroke opening are set. The application solves the technical problem of low wedge precision in two-phase zone rolling in a short process hot rolling, improves the stability and continuity of two-phase zone rolling in the short process hot rolling, ensures the smooth production, and realizes the improvement of wedge precision in two-phase zone rolling in the short process.

The present invention relates to the field of load monitoring technology, specifically to an integrated deployment method of a non-invasive load monitoring system based on edge computing technology, comprising the following steps: based on the load parameters of the edge device, obtaining current, voltage and power information, extracting load characteristic values, detecting the power change amplitude by matching the current, voltage and time points, judging whether it exceeds the threshold, marking the part exceeding the threshold as abnormal and generating load abnormality marking data. In the present invention, by dynamically evaluating the load status of adjacent devices, intelligent transfer of high-frequency fluctuating loads is achieved, which can effectively reduce the risk of equipment overload and avoid the occurrence of local overload of the system. Based on the load transfer list and real-time load status, low-load devices are dynamically selected for the transfer of high-frequency fluctuating loads. Through global load redistribution, the load status between devices is balanced, the pressure on high-load devices is reduced, and the operating efficiency and safety are improved.

The application provides a power system load redistribution attack defense method and system, and the method comprises the following steps: a load redistribution attack defense framework based on a phase-shifting transformer is designed, a three-layer game model containing a defender-attacker-system is constructed, and the three-layer game model is used for decision-making of key positions and quantities of phase-shifting transformer deployment; the three-layer game model of attack influence elimination based on the phase-shifting transformer is decomposed into a solvable optimization problem, and an iterative algorithm capable of obtaining a global optimal solution is designed; the solution of the attack influence elimination optimization problem is simplified, and a high-efficiency simplified algorithm is designed to realize fast solution. The application solves the technical problems of poor attack influence elimination effect, high economic loss caused by load redistribution attack and poor load redistribution attack influence elimination effect.

A method of operating a washing machine appliance may include directing a spin cycle of the washing machine appliance. The method may include identifying an out-of-balance condition during the spin cycle. The method may include suspending the spin cycle in response to identifying the out-of-balance condition during the spin cycle. The method may include providing a redistribution notification at a user interface following suspending the spin cycle. The redistribution notification may indicate a potential load redistribution procedure.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a load change and load redistribution cooperative attack method oriented to a power distribution network and a defense method thereof. According to the attack method, a double-layer Stackelberg game model between an attacker and a scheduler is constructed, the model is converted into a single-layer mixed integer linear programming problem by utilizing a KKT condition, efficient solving of an optimal cooperative attack strategy is achieved, a mathematical model of the attacker fuses double means of load change attack and load redistribution attack, and the optimal cooperative attack strategy is effectively solved. Physical layer disturbance is realized by directly tampering the real load of the node, and a scheduler is misguided to perceive by forging measurement data, so that collaborative attack of physical and information layers is realized under the same game framework, and the attack is more hidden and destructive; in order to cope with the attack method, a three-layer attack and defense model of soft open point defense is also introduced, the solving capability is stronger, the application range is wider, and the attack risk of the power distribution network can be more truly evaluated and effectively defended.

The application discloses a kind of composite material mixed connection performance modularization quick prediction method, comprising the following steps: S1. the circuit model of composite material connecting structure is constructed, and the circuit model is established based on the mechanical equivalent principle of spring stiffness method;S2. the displacement response signal and load information of connecting structure under the action of load are acquired by simulating composite material connecting structure using the circuit model;S3. based on the displacement response signal, whether the structure is damaged and damage type is judged in combination with corresponding damage criterion;S4. the circuit model is adjusted according to the judgment result;S5. based on the circuit model after adjustment, the complete load-displacement curve of the composite material connecting structure is output in combination with the load information predicted.The application can efficiently predict the nonlinear mechanical response of bolt connection, glue joint connection and glue screw mixed connection structure before damage occurs, and accurately simulate the whole process behavior of stiffness degradation and load redistribution after damage occurs.

The invention relates to a loading device and method for simulating the moving load of a real train, the device comprises a loading assembly, a load moving circulating assembly and a track model, a walking part is arranged above a loading rod, a bogie is arranged below the loading rod, the walking part is connected with a forward crawler belt and a counter-force cross beam, wheels are arranged below the bogie, and the loading rod is connected with the bogie. The counter-force cross beam applies a train load to the bogie through the walking part and the loading rod, the counter-force cross beam has the functions of providing counter force and being matched with the walking part for limiting and guiding, and the advancing track drives the walking part and the loading rod to achieve circulating movement of the bogie on the track model; and a nonlinear elastic element is arranged in the loading rod to realize the expansion and contraction of the loading rod. Compared with the prior art, the method has the advantages that the redistribution of the load of the wheel rail near the poor bearing sleeper and the sub-rail foundation can be considered, the test result is closer to the real wheel rail stress condition, and the working condition of normal service of the sub-rail infrastructure and the poor bearing damage condition caused by deterioration of the sub-rail foundation can be considered at the same time.

The invention discloses a load balanced distribution control method and system in parallel operation of multiple energy hosts, and relates to the technical field of load distribution, and the method comprises the steps: collecting a real-time operation parameter set of each energy host in a parallel system; calculating a real-time health degree coefficient of each energy host; solving the energy efficiency deviation degree of each energy host under the current load demand, and constructing a load redistribution optimization function; solving the load redistribution optimization function, and dynamically generating and issuing a personalized load setting instruction to each energy host; and monitoring the overall energy efficiency improvement rate of the parallel system and the operation stability index of each energy host, and dynamically backtracking and adjusting the parameter weight of the host health degree attenuation model according to the overall energy efficiency improvement rate and the operation stability index. The technical problem that an existing load distribution method ignores the age limit and the maintenance state of a host, and consequently the overall performance is degraded is solved.

The invention discloses a permanent magnet motor cluster management method and device, and the method comprises the steps: predicting a future thermodynamic diagram based on a historical cluster state frame sequence, and precisely capturing the temperature evolution trend of each motor; then combining a potential overheating area disclosed by the prediction thermodynamic diagram with a real-time state, and intelligently triggering an optimization mechanism; and finally, in a digital twin environment, by taking a current state as a starting point and a future heat source load as a constraint, cooperatively simulating a ventilation wall heat dissipation effect and a load redistribution strategy, generating a motor operation plan considering overall performance and local heat safety, and issuing a control instruction in real time. In this way, the limitation of traditional single-dimension optimization is broken through, and continuous, efficient, stable and long-life operation of the cluster under the complex dynamic working condition is ensured through fusion of data-driven prediction and physical model simulation.

The invention relates to the technical field of cloud computing resources, and discloses a green cloud computing-oriented resource management method and system and a storage medium. The method comprises the following steps: performing weight optimization on CPU and memory data through a subwolf optimization algorithm to obtain a server energy consumption coefficient matrix; based on the matrix, using DDQN to decide virtual machine CPU allocation, and obtaining a resource allocation parameter table; adjusting the storage position of the data backup copy to obtain a backup node configuration table; monitoring the PUE value of each server to obtain a list of servers with power consumption exceeding the standard; and calculating a migration path of the virtual machine by using a particle swarm algorithm to obtain a load redistribution table. The problems of inaccurate energy consumption prediction, lack of intelligence of scheduling strategies, separation of backup storage and main task scheduling and low power consumption exception processing efficiency in existing cloud computing resource management are solved, and the energy utilization efficiency of a cloud computing environment and the intelligent level of resource scheduling are improved.

The invention discloses a fault machine tool power management method based on energy optimization, and relates to the technical field of machine tool power management, and the method comprises the steps: installing a three-axis vibration sensor and a current and voltage sensor at a main shaft and a motor driving side of each machine tool at the same time, so as to collect a vibration signal and an electrical signal of the machine tool in real time; a fault alarm strategy is set, and after a fault is triggered, a load redistribution process is started; establishing a multi-criterion decision-making model, dynamically distributing priority weight for each machine tool, establishing a load distribution algorithm, and distributing power for each machine tool; and dynamically adjusting the motor driving voltage and frequency according to the actual load distribution. According to the method, the problems that the voltage and the frequency are adjusted only according to the single machine load in a constant voltage-frequency ratio method, the total power supply output cannot be automatically reduced when one machine tool is shut down, and energy is wasted in no-load operation and machine tool level energy consumption prediction is caused are solved.

A hardware right to repair prioritization system comprising a graphical user interface (GUI) to receive partial rankings of requirements for prioritizing an failing component replacement, where each requirement describes a characteristic having varied values across the failing components, a processor to generate a priority requirement ranking for all of the requirements, based on the partial rankings, determine a replacement priority rank for each failing component, based on characteristic values for each failing component and priority requirement ranking; determine a change in carbon dioxide (CO2) footprint resulting from replacement of each of the failing components, generate a CO2 optimized replacement priority rank by weighting the replacement priority rank by the change in CO2 footprint for each of the failing components; identify a failing component associated with a highest CO2 optimized replacement priority rank as a top replacement candidate, and the GUI to recommend immediate replacement of the top replacement candidate.