17 results about "Initial load" patented technology

The invention relates to a self-adaptive wind load passive slowing-down design method based on threshold triggering. The method comprises the steps that aerodynamic parameters during non-load-shedding are calculated according to a nominal program angle; calculating an initial load shedding program angle ideal value; when the flight Mach number is within the allowable load shedding range and the qalpha value exceeds the current amplitude limiting value, load shedding is carried out; only the part exceeding the qalpha amplitude limiting value is subjected to load shedding; and carrying out amplitude limiting processing on the angle correction of the load shedding program in combination with the dynamic characteristics and maneuverability of the rocket body. According to the method, the load shedding opportunity is triggered through the qalpha amplitude limiting value, amplitude limiting processing is carried out on the load shedding program angle and the angular velocity correction amount, the structural strength of a rocket body can be fully utilized, the dynamic lag characteristic and the actual control capacity of the rocket are considered, the rocket body can track the corrected program angle with high precision, and the method has high application value in engineering.

The invention relates to a power equipment load scheduling optimization method and device. The method comprises the following steps: carrying out load operation data acquisition through monitoring units deployed in power equipment to obtain a distributed load operation data set, and obtaining strategy game relationship network data between the equipment based on the distributed load operation data set; obtaining initial load adjustment intention intensity data of each power device according to the policy game relationship network data between the devices; sending the initial load adjustment intention intensity data and the global collaborative scheduling optimization target to a distributed negotiation chain to obtain final load adjustment strategy data after negotiation consensus; and obtaining an optimized distributed load scheduling model according to the final load adjustment strategy data after negotiation of the consensus, distributing the optimized distributed load scheduling model to each power device, and executing decentralized power device load scheduling optimization. By adopting the method, the load scheduling of the power equipment can be optimized.

The invention discloses a load prediction method and system based on user order demands, and relates to the technical field of power load prediction. Calculating a standard production load of a single product based on the historical production log and the historical power load data, and determining a characteristic operation power value corresponding to each product type according to the standard production load; generating a baseline load prediction curve based on planned starting and ending time and a characteristic operation power value in the production plan data; combining the baseline load prediction curve and the historical power load data to obtain an input feature, and inputting the input feature into the time sequence prediction model to obtain an initial load prediction curve; determining an efficiency correction factor and an environment correction factor based on the historical production log and the historical power load data; and correcting the initial load prediction curve according to the efficiency correction factor and the environment correction factor to obtain a final load prediction curve. By implementing the technical scheme provided by the invention, the accuracy of power load prediction of the industrial enterprise is improved.

The invention discloses a flexible load regulation and control method and system based on global optimization and edge collaboration, and belongs to the field of power systems, and the method comprises the steps: predicting a power grid flexible load prediction value corresponding to a target regulation and control day according to the flexible load data of a current power grid; iteratively optimizing the initial load regulation and control solution according to the power grid flexible load predicted value and a preset random swarm intelligent optimization algorithm to obtain an optimal load regulation and control solution; according to the adjustable power range corresponding to the flexible load of each edge device, decomposing the optimal load regulation and control solution to obtain a first load regulation and control solution corresponding to each edge device; according to the difference value between the actual value and the predicted value of the flexible load of each edge device in the target regulation and control day and a preset distribution rule, adjusting each first load regulation and control solution to obtain the compensation load capacity of each edge device; and according to each compensation load capacity, each edge device is controlled to execute flexible load regulation and control. According to the invention, the problem of poor flexible load regulation and control effect in the prior art can be solved.

The invention relates to an operation control method and device of a fused salt heat storage system, computer equipment and a readable storage medium. The method comprises the following steps: acquiring historical operation basic data of the fused salt heat storage system; iteratively training an initial load prediction model of the fused salt heat storage system according to the historical operation basic training data to obtain a target load prediction model; after the target load prediction model is tested to reach the standard based on the historical operation basic test data, inputting the real-time operation basic data into the target load prediction model, and performing prediction through the target load prediction model to obtain an operation load prediction value of each discrete prediction time interval within the load prediction time domain length; generating a corresponding operation control rule for each discrete prediction time interval according to all the operation load prediction values; and performing operation control on the fused salt heat storage system within the load prediction time domain length according to all the operation control rules. By means of the method, the control effect of operation control over the fused salt heat storage system is improved.

The invention discloses a load collaborative management method, system and device based on multi-dimensional perception, and the method comprises the steps: obtaining multi-dimensional data which can affect a load through a deployed edge node; extracting features of the multi-dimensional data in different dimensions to form an initial load feature vector; inputting the initial load feature vector into a pre-trained model to obtain a load feature vector output by the model; clustering the load nodes based on the load feature vectors, and correspondingly forming group-level units by clustering groups obtained by clustering; and selecting a representative node of each group-level unit, receiving a scheduling instruction through the representative nodes, and coordinating the allocation of resources in the group-level units. Due to the fact that the multi-dimensional data including the environment, the user behavior, the equipment state and the like influencing the load are collected, the high-quality data are extracted from the multi-dimensional data, the load feature vector is formed and used for determining the load management decision, and the accuracy of the load management decision is improved.

The invention relates to the technical field of distributed control, in particular to a multi-target balanced load coordination control method for a distributed virtual power plant, and the method comprises the steps: analyzing the similarity between historical daily load data, introducing an LOF algorithm to evaluate the abnormal degree of the similarity of historical curves, and determining the abnormal degree of the similarity of the historical curves; determining the reference of a predicted value obtained through historical data at the same time in previous days; determining an initial load prediction value at the next moment according to the data change trend before each moment, predicting a first prediction value at each moment by adopting a prediction algorithm according to the load data at the same moment in the previous day, and evaluating the predictability of the data at each moment by analyzing the regularity of the data at each time point in the previous day; through multi-source information complementation, a final load demand is determined, and then load value regulation and control of each power supply load of the virtual power plant are performed, so that the accuracy of virtual power plant load prediction is significantly improved, the precision and robustness of load prediction are improved, and subsequent precise regulation and control are supported.

The application discloses a variable cross-section group pile optimization design method and system, which is applied to the technical field of variable cross-section group piles and comprises the following steps: obtaining load data of each distribution position of a sample variable cross-section group pile; determining each parameter load data section; obtaining a load influence parameter set; obtaining a load mapping relationship based on the load influence parameter set and the load data; obtaining initial load prediction data based on the load mapping relationship and a to-be-optimized parameter data set of a target variable cross-section group pile; analyzing the to-be-optimized parameter data set and the load data, processing the initial load prediction data according to an analysis result, and obtaining a load prediction condition; and optimizing the to-be-optimized parameter data set based on the load prediction condition, and obtaining a target design parameter set of the target variable cross-section group pile. The variable cross-section group pile optimization design method and system can accurately optimize design parameters and improve the reliability of the design parameters.

A method, apparatus, and computer program are provided for load monitoring for use in an industrial facility comprising a plurality of devices. The method comprises : obtaining energy data associated with the industrial facility, wherein the energy data relates to an aggregate energy consumption of the plurality of devices within the industrial facility over a time period; determining load identification data and initial load disaggregation data for each of the plurality of devices using a respective joint machine learning model; and providing an output comprising a final load disaggregation data for the plurality of devices based, at least in part, on the load identification data and the initial load disaggregation data.

The invention provides a load current waveform extraction method, electronic equipment and a storage medium, which are applied to the technical field of computers, and the method comprises the steps: carrying out the operation test of a simulation circuit model of a microprocessor architecture through a preset test set, and obtaining an initial load current waveform of the microprocessor architecture; waveform fragment extraction is carried out on the initial load current waveform by using a plurality of different time windows to obtain target current waveform fragments corresponding to the time windows, and finally each target current waveform fragment is determined as a target load current waveform of the microprocessor architecture. Because the duration of the time window is related to load current components of the initial load current waveform at different frequencies, the method utilizes a plurality of different time windows to carry out waveform fragment extraction on the initial load current waveform, can obtain load current waveforms corresponding to different frequency bands, and further comprehensively measures the load characteristics of the microprocessor architecture. The accuracy and reliability of PI simulation are improved, and the simulation test requirement is met.

The invention discloses a power system load data generation method, system and device and a medium, and belongs to the field of load data generation, and the method comprises the steps: taking a load sequence model as a current load sequence model, and selecting a sample load sequence from a sample load sequence set as a current load sequence, repeatedly executing a model updating action based on the current load sequence until all sample load sequences are polled to obtain a target load sequence model; inputting the initial load sequence into a target load sequence model to obtain a target load sequence set of the power system of the to-be-detected region; the model updating action is that first and second load MMD losses are obtained based on the load sequence data volume, the first and second load potential vectors and a load MMD loss algorithm; and obtaining a load sequence total loss based on the first and second load MMD losses and a load sequence total loss algorithm, and updating the current load sequence model. Therefore, the operation condition of the power system can be reflected more accurately through the power load sequence.

The invention discloses a thermal power plant equipment control method and device combined with load optimization configuration and a medium, and relates to the technical field of thermal power plant equipment control, and the method comprises the steps: inputting an operation state parameter set into a load optimization calculation model, and dynamically generating an initial load configuration scheme; performing multi-constraint correction on the initial load configuration scheme to obtain a multi-target correction result, and performing weighted priority adjustment to generate a constraint correction load configuration scheme; performing real-time scheduling decision on the load prediction result to generate a scheduling execution instruction set; and issuing the scheduling execution instruction set to thermal power plant equipment for load adjustment, collecting equipment response data in real time, and generating a load adjustment result through data fusion and state evaluation operation. And through high-precision real-time data preprocessing and intelligent anomaly filtering, the accuracy and stability of operation state parameters are guaranteed, and a reliable basis is provided for load optimization calculation.

The disclosed techniques relate to the reuse of speculative results from an incorrect execution path. In some embodiments, if a control transfer instruction is mispredicted, a load instruction may have been executed on the wrong path. In the disclosed embodiments, the result storage logic records information indicating the target registers of speculatively executed load instructions, including an initial load instruction. The control flow tracing logic can store information indicating a reconvergence point for the control transfer instruction.The reuse control switching logic can track registers written by instructions prior to the reconvergence point, determine that the first load instruction does not depend on data from any instruction between the control transfer instruction and the reconvergence point, and as a consequence of the first load instruction, use a value from a recorded destination register written based on the speculative execution of the first load operation, regardless of the control transfer instruction's misprediction.

The application discloses a kind of path planning methods, systems, equipment and medium for distribution network backup resource planning, comprising the following steps, in response to the acquisition of the position information of distribution network failure element, distribution network is divided into regions, obtain multiple failure influence areas;The load node of each failure influence area is prioritized, and the available backup resource is determined by the priority sorting result;By the position of available backup resource, obtain the initial load recovery path of available backup resource to each load node;The initial load recovery path is screened in a first level, and the target load recovery path of each load node is obtained;The target load recovery path in target area is optimized across regions, and the optimized load recovery path is obtained.The application further improves the rationality of the optimized load recovery path of low-priority load node under the premise of guaranteeing the power supply recovery of high-priority load node.

The invention discloses a computing power resource adjustment method and apparatus, a device, a storage medium and a program product. The method comprises the steps of obtaining a tenant identifier and computing power resource information of any tenant of a cloud platform; wherein the computing power resource information comprises an initial load utilization rate and an initial computing power node number; determining an estimated computing power node number of the tenant according to the tenant identifier and the computing power resource information; sending the pre-estimated computing power node number to the cloud platform, and obtaining a reference load utilization rate after the cloud platform adjusts the computing power resource of the tenant based on the pre-estimated computing power node number; evaluating the reference load utilization rate to obtain an evaluation result; and when the evaluation result is that the number of the pre-estimated computing power nodes is not accurate, adjusting the number of the pre-estimated computing power nodes according to the evaluation result to obtain a target computing power node number, and sending the target computing power node number to the cloud platform. By adopting the embodiment of the invention, the regular or irregular fluctuation of the service can be accurately adapted, the service congestion is avoided, and meanwhile, the deployment and operation and maintenance cost of the cloud platform is reduced.

The invention discloses an SPI (Serial Peripheral Interface) dynamic scheduling method, device and equipment based on scene awareness and a storage medium, which are used for solving the technical problems of CPU (Central Processing Unit) redundancy consumption and incapability of dynamic adaptation in the SPI scheduling method. Comprising the steps of obtaining rule data of a preset SPI interface when the preset SPI interface is in an initial starting stage; collecting operation parameters of multiple dimensions to generate an initial scene label; matching the initial scene label with the rule data, determining an implementation ID of the SPI interface, determining an initial loading strategy according to the implementation ID, and loading an implementation class corresponding to the implementation ID; when new operation parameters of the preset SPI interface are collected in the dynamic stage during operation, a new label is generated; judging whether the new label is the same as the initial scene label; if not, obtaining a new implementation ID according to the new label; querying the loading state of the corresponding implementation class according to the new implementation ID; and determining a new loading strategy according to the loading state, and loading a new implementation class corresponding to the new implementation ID.

The invention provides a data acceleration processing method and system based on a low-code platform, and the method comprises the steps: S2, decomposing a trend component and a periodic component according to a hardware utilization rate sequence in an initial load feature set, and determining a hardware performance attenuation rate as a quantitative index; s4, fitting the resource demand increment estimation and the hardware performance attenuation rate, training a load change rule model, and obtaining a load prediction curve in a future time window; s5, comparing the load prediction curve with a current system stability index to obtain a deviation value sequence, and determining the position and severity of a potential resource bottleneck point; and S7, extracting a key threshold from the optimized planning parameter set, and returning the key threshold to the updating process of the initial load feature set by adopting a feedback cycle mechanism to obtain a refined load change rule model so as to realize continuous matching. The resource utilization efficiency and the system stability of a low-code platform in a complex service scene are remarkably improved.