124 results about "Load time" patented technology

The invention discloses a multi-target AGV task allocation optimization method, and the method comprises the steps: obtaining the position information, load information, task information and related constraint conditions of an AGV according to a manufacturing workshop environment and order production demands; performing initialization coding, and generating a task relation matrix, a task data matrix and an AGV state matrix by adopting integer coding for representing a task allocation scheme; constructing a multi-target AGV task allocation optimization model by taking the total transportation distance, the total energy consumption and the load time deviation as optimization targets; solving the multi-objective optimization model by adopting an improved hunting optimization algorithm to obtain an optimal task allocation optimization scheme; according to the method provided by the invention, the problems of load unbalance and the like in AGV task allocation can be effectively solved, the balance and efficiency of task allocation are improved, the task completion time is shortened, the energy consumption of the system is reduced, and the overall operation efficiency of the multi-AGV system is improved.

The invention discloses a resource allocation method of a vehicle-mounted information system and a program product. The method comprises the following steps: acquiring historical load time sequence data of a vehicle-mounted information system in a historical time period, and determining load prediction data of the vehicle-mounted information system in a prediction time period according to the historical load time sequence data and a load prediction model; under the condition that the load prediction information meets a preset load condition, determining user operation prediction data of the vehicle-mounted information system in the prediction time period, and determining a user interaction task according to the user operation prediction data; the method comprises the steps of obtaining a system task needing to be executed by the vehicle-mounted information system within a prediction time period, determining the system task and a user interaction task as a to-be-executed task, determining a priority score of the to-be-executed task, and allocating resources to the to-be-executed task according to the priority score so as to realize dynamic allocation of task resources.

The embodiment of the invention discloses a power grid load prediction method and device based on fractal analysis, and the method comprises the steps: obtaining historical load time series data of a power grid, and carrying out the preprocessing, and generating a standardized load data set; fractal features of the load data set are extracted by using a fractal analysis algorithm; judging dynamic behavior characteristics of the load time sequence based on fractal characteristics, adaptively selecting short-term prediction or long-term prediction model parameters, and dynamically adjusting the time scale of a prediction model to adapt to load fluctuation characteristics; analyzing an abnormal mode in the historical load time sequence based on fractal features, and generating an abnormal detection result; constructing a load prediction model based on the fractal features, the anomaly detection result, the model parameters and the external features; and predicting the power grid load in the future time period by using the load prediction model to obtain a prediction result. According to the power grid load prediction method provided by the invention, the prediction accuracy and stability are improved.

The invention discloses a load management system based on a load model in the plastic product industry. The load management system comprises a load time sequence model building module which classifies and marks equipment according to operation behaviors of the equipment to obtain a load time sequence model; the data acquisition and fusion module acquires power, current, voltage and start-stop state data of equipment in real time through a monitoring point position, and generates a real-time load data set in combination with real-time production state data; the load prediction and adjustability evaluation module generates a load prediction curve by using a long short-term memory network, generates an adjustability score by adopting a support vector machine model, and determines an adjustable process list in combination with real-time production state data; and the scheduling execution module determines candidate adjustment equipment according to the load prediction curve, the adjustability score and the adjustable process list, constructs a multi-objective optimization function through a multi-objective optimization algorithm, and generates a load adjustment strategy by using a particle swarm optimization algorithm to execute scheduling on the equipment. According to the invention, the refinement level of load management is improved.

The invention discloses a bus peak load prediction method fusing transfer learning and peak load adaptive identification, and the method comprises the steps: constructing a dynamic weight-driven transfer learning framework, and achieving the precise migration of multi-source bus knowledge to a target bus; introducing a self-adaptive threshold mechanism based on load volatility and a peak value proportion, and dynamically identifying peak values and conventional load time periods; and designing a customized loss function for strengthening the peak error weight and using the customized loss function for fine adjustment of the model, thereby remarkably improving the load prediction precision of the bus in a key period. The target bus prediction precision is obviously improved through multi-source bus transfer learning; a self-adaptive threshold identification mechanism realizes dynamic judgment of a peak time period sensitive to load fluctuation, and the perception capability of the model to a key load time period is enhanced; through the synergistic effect of the technical means, the overall prediction accuracy is finally improved, meanwhile, the prediction precision and stability of the peak load are remarkably improved, and the actual requirements of a power grid for high precision and high robustness of load prediction are met.

The invention relates to a scraper chain tension dynamic optimization method based on real-time load feedback, and belongs to the technical field of scraper chain control. The method comprises the following steps: acquiring load data of a scraper chain in various operation states; carrying out standardization processing on the load data of various operation states and then extracting features to obtain load time sequence features; carrying out time attention mechanism processing on the load time sequence characteristics and the load data of the multiple operation states to obtain importance weights; a tension value is calculated according to the load data of each operation state and the importance weight of the load data; calculating a real-time working condition coefficient of the current time node, and dynamically adjusting a preset safety tension range to obtain a real-time safety tension range of the current time node; comparing the tension value with a real-time safe tension range, and generating a control instruction according to a comparison result; and executing the control instruction. The tension control method of the scraper chain is optimized, and the control precision of the tension of the scraper chain is improved.

Techniques for incorporating machine learning recommendations into presentation of network information within predefined load times are described herein. For example, a computer system can determine, based on a selection via a user interface of a client device presenting first network information, a request for a second network information. The computer system can cause a processor to use a machine learning model to generate a first recommendation based on contextual data and indicating a first parameter to present the second network information. The computer system may access a pre-computed second recommendation based on the contextual data. The second recommendation indicates a second parameter. The computer system can cause of the client device to present the second network information such that the second network information is presented by at least using the first parameter or the second parameter based on a response time for the machine learning model generating the first parameter.

The invention belongs to the technical field of power load prediction, and relates to a power load prediction method based on interpretable multi-mode enhancement, which comprises the following steps: 1, constructing a text representation mode of an original load time sequence through a multi-mode enhancement module; 2, time sequence modal information and text representation information of the load are embedded into a high-dimensional vector space through a two-channel coding module; 3, receiving an embedded vector of a time sequence mode through a multi-mode prediction module, and taking a representation vector of a multi-mode text as input; 4, realizing an interpretable multi-mode alignment module; according to the method, the effects among the multi-modal information are divided into uniqueness, redundancy and collaboration; by constructing a negative sample pair for training, alignment of multi-modal information representation is enhanced, and then the prediction performance of the model is improved.

The invention discloses a network scanning task intelligent segmentation and load balancing method and system. The method comprises the following steps: identifying abnormal fragments by executing integrity verification on a scanning task, analyzing a task dependency relationship to determine a divisible position, and generating a segmentation scheme in combination with granularity constraint; node resource states are collected to calculate load indexes, and scheduling weights are hierarchically configured according to load levels to establish parallel scheduling channels; constructing a node cooperation group based on load difference, formulating an inter-group circulation rule to form a cooperation scheduling space, fragmenting tasks according to node capacity, and generating a parallel execution plan; monitoring the execution progress to identify overstocked nodes, diagnosing bottleneck types and planning a dredging path to execute task migration; the resource time sequence data is collected to identify the low-load time period, and the task is distributed to the optimal time slot to be executed, so that the task segmentation reasonability and the node load balancing degree are improved, and the distributed scanning execution efficiency is improved.

The invention discloses a short-term power load prediction method and device, terminal equipment and a storage medium, and belongs to the field of power grid load prediction.The method comprises the steps that a power load time sequence in a preset interval is obtained, the power load time sequence is processed through metamorphosis mode decomposition, a plurality of mode components are obtained, and the center frequency of each mode component is calculated; inputting the modal component of which the center frequency is smaller than or equal to a preset threshold value into a bidirectional gate control unit to obtain a first prediction result; inputting the modal component of which the center frequency is greater than a preset threshold value into a preset long-short-term memory network to obtain a second prediction result; a memory enhancement unit is introduced into the preset long-short-term memory network; the memory enhancement unit is used for updating and storing historical information; and fusing the first prediction result and the second prediction result to obtain a load prediction value. Therefore, the technical problem that in the prior art, an effective load time sequence data processing mechanism cannot be constructed, and the long-term dependency feature of the sequence cannot be fully captured can be solved.

The invention relates to the technical field of computer storage systems, and provides a cache optimization method and device for a power data system, computer equipment, a readable storage medium and a program product. The method comprises the following steps: determining target cache data in a fixed period access scene according to a historical access rule of a power data system; according to a historical access rule of the power data system, predicting the current cache time of the target cache data; according to the current cache time of the target cache data, prolonging the retention period of the target cache data in the cache region; and when a data generation request related to target cache data is received, reading the target cache data from the cache region, and generating data according to the target cache data. By adopting the method, the loading time of the target cache data can be shortened, and the data processing efficiency of the power data system can be remarkably improved.

The invention belongs to the technical field of power load monitoring and artificial intelligence crossing, and particularly relates to a user side load non-intrusive identification method and system, and the method comprises the steps: constructing a load feature library of typical electric equipment, and storing a steady-state feature set and a transient feature set of each piece of equipment; extracting user power consumption behavior characteristics, and combining the load characteristic library to synthesize a user power consumption gateway load time sequence data sample through a data generation model; extracting a multi-scale steady state characteristic quantity and a transient state characteristic quantity from the sample, and performing characteristic fusion to form a fusion characteristic vector; inputting the fusion feature vector into a trained load identification model, and outputting the operation state and type of each electric device at the user side; the four core obstacle problems of feature confusion, data scarcity, difficulty in concurrent identification and model stiffness are solved, and the accuracy, robustness and practicability of load identification are remarkably improved.

The invention provides a Web page first screen loading acceleration method and system, a medium and equipment, and relates to the technical field of Web front-end performance optimization and network transmission, the method comprises the following steps: after obtaining a request of a user for accessing a URL (Uniform Resource Locator), analyzing an HTML (Hypertext Markup Language) and constructing a DOM (Document Object Model) tree; marking first screen resources to obtain a first screen resource list; key resources are inserted into the head of the loading queue, and non-key resources are marked as defer; if the weak network is detected, degrading the picture quality; after the CSSOM is ready, text and layout are rendered immediately, and placeholders are replaced after pictures are asynchronously loaded; when the delay is high, switching to an edge CDN node or starting a QUIC protocol; and pre-fetching resources are predicted based on user behaviors, and the pre-fetching resources are stored in a Service Worker cache. According to the method, the first screen loading time can be shortened, the maximum content drawing time is shortened, the network transmission cost is saved, and the cache utilization efficiency is improved.

The invention provides a PC power supply load adjusting method and device based on AI prediction and a medium, and the method comprises the steps: continuously collecting a power supply signal of a PC power supply output end, a processor operation state signal and a graphic part working state signal, generating load time sequence data, separating power consumption signatures corresponding to different calculation tasks after mode recognition, obtaining a power consumption signature set, and carrying out the calculation of the power consumption signature set. Inputting a pre-trained load prediction model, obtaining a load intensity change profile in a future power supply period through time sequence analysis processing, carrying out feedback calibration on the load intensity change profile and the current double-circuit power supply state information of the PC power supply, generating a power supply power distribution strategy, and outputting the power supply power distribution strategy. And adjusting working points of a primary conversion path and a secondary conversion path in the PC power supply according to the power distribution strategy of the power supply, so that the output power of the power supply is dynamically matched with the load change. According to the invention, unnecessary power loss is reduced, so that stable operation of a PC system is guaranteed, and refined and dynamic adjustment of the power supply load is realized.

The invention relates to a cloud computing resource load prediction method based on TCN-PatchTST (Trusted Cryptographic Network-PatchTST). Comprising the following steps: collecting CPU load time sequence data to form a training sample set; carrying out preprocessing and feature construction on the training sample, and selecting a memory utilization rate load value which has obvious influence on a CPU load value as an input feature to predict the CPU load value; according to the method, a TCN-PatchTST neural network is constructed, a two-channel feature extraction architecture is designed, local time sequence features are extracted through causal convolution and expansion convolution of TCN, a long-term dependency relationship is captured by using partitioning processing and a channel independence mechanism of PatchTST, and a feature fusion layer is designed to integrate multi-scale information; the TCN-PatchTST neural network is trained; and inputting a test sample into the trained TCN-PatchTST neural network to obtain a CPU load value prediction result of the test sample. According to the method, load prediction is carried out through the historical load time sequence data, the prediction precision of the CPU load value of the cloud computing resources is effectively improved, and technical support is provided for intelligent scheduling of the cloud resources.

Provided in the present invention are a task scheduling method and apparatus based on cloud-edge collaboration. The method comprises: acquiring, from an edge end, a task to be allocated; acquiring time queues of resource load values of the edge end and a cloud end; extracting resource load time-series features of the edge end and the cloud end; on the basis of the resource load time-series features, performing refined feature interaction based on a spatial distance measurement, so as to obtain a resource load time-series collaborative feature; and on the basis of the resource load time-series collaborative feature, splitting said task, and respectively scheduling same to the edge end and the cloud end, such that the edge end and the cloud end respectively execute said task. In the present invention, a resource load time-series feature of an edge end and a resource load time-series feature of a cloud end are extracted, resource load conditions of the cloud end and the edge end are taken into consideration, and refined feature interaction based on a spatial distance measurement is then performed to obtain a resource load time-series collaborative feature, so as to realize the flexible splitting and scheduling of a task, such that the overall operation stability of a system can be improved, thereby improving the task processing efficiency.

The invention discloses a load prediction method, system and device based on EMD multi-scale feature fusion enhancement and a storage medium, and belongs to the technical field of intelligent power distribution networks, and the method comprises the steps: carrying out the normalization processing of historical power load data, obtaining a standardized load time sequence, carrying out the multi-scale feature modeling, and obtaining a load time sequence; obtaining a plurality of subsequences reflecting different time scale characteristics; inputting each sub-sequence into a feature extraction structure, and extracting time sequence features of each sub-sequence to obtain feature representations of a plurality of sub-sequences; acquiring external factor data corresponding to the historical electrical load data, and performing format processing on the external factor data; fusing the feature representations of the plurality of subsequences with the external factor data after format processing to generate a fused feature vector; performing multi-factor correlation modeling on the fused feature vector to obtain an enhanced feature representation vector; and inputting the enhanced feature representation vector into the mapping structure, and outputting a predicted electrical load result.

Aspects presented herein relate to methods and devices for display processing including an apparatus, e.g., a CPU or a DPU. The apparatus may monitor at least one display task associated with a set of display software threads. The apparatus may also detect that a loading time of the at least one display task is less than a loading time threshold or that a panel frame rate is less than a frame rate threshold. Further, the apparatus may obtain a first configuration for at least one thread of the set of display software threads and a second configuration for the at least one thread. The apparatus may also adjust an execution of the at least one display task from being associated with the first configuration for the at least one thread to being associated with the second configuration for the at least one thread.

The invention discloses a load prediction method and device, a storage medium and computer equipment. According to the load prediction method, the scale of a sliding window is dynamically adjusted based on fluctuation characteristics of historical load time series data. And according to the window data, generating a global feature vector comprehensively representing a historical load mode through a time sequence model. And based on the feature intensity of the global feature vector, adaptively selecting a target probability model in a preset probability model set so as to predict the load of the verification time according to the global feature vector by using the target probability model. And comparing a prediction result of the verification time with an actual observation result, and quantifying a prediction error. And if the offset does not accord with the preset threshold range, optimizing parameters of the time sequence model to update and select the target probability model. Therefore, the load prediction model is constructed according to the time sequence model with the minimum prediction error and the target probability model, the generalization and stability of the load prediction model in a data sparse or severe fluctuation scene are improved, and the redundancy calculation overhead is reduced.

A comprehensive short-term power load prediction method based on an LSTM neural network and a CGAN network belongs to the short-term power load prediction technology, and comprises the following steps: collecting historical load data of a province and NWP data corresponding to the province, arranging original data, and obtaining historical power consumption data of each city of the province in previous years; calculating weather-load time sequence coupling similarity, sorting the weather-load time sequence coupling similarity according to the similarity, selecting the first n days as a similar day set, and extracting and sorting load data of the first n similar days to form a training set; an LSTM neural network is constructed and trained, a historical 96-point load sequence is used as input, rolling prediction is carried out, and future 96-point loads are predicted; if the prediction day is a non-holiday, the prediction result is a final prediction result; and if the to-be-predicted day is a holiday, performing load correction by using the CGAN network, and adding the correction amount and the initial prediction result to obtain a final prediction result value. By adopting the method, the short-term load prediction precision can be greatly improved.

The invention discloses a multi-dimensional index collaborative Kubernetes container intelligent telescoping system, and belongs to the technical field of cloud native. Comprising a load prediction module, a monitoring module, a multi-dimensional cooperative telescoping module and an execution module. The load prediction module is used for predicting a future container load demand based on the historical load data and outputting a load prediction value; the load prediction module adopts an SLMD-LightGBM model, performs multi-scale feature extraction on a load time sequence through local mean decomposition to obtain a plurality of product functions and residual components, then performs modeling prediction on each component through a LightGBM framework, and finally performs aggregation to obtain a load prediction result, and the model comprises a self-updating mechanism. The training set is updated by regularly adding new data to adapt to load feature evolution. The load prediction module predicts the load change in advance through an SLMD-LightGBM model, so that the execution module triggers the capacity expansion and contraction operation in advance, the resource mismatching caused by the fact that a traditional HPA (High Power Amplifier) detects first and then responds is avoided, and the risk of service fluctuation is remarkably reduced.

The application relates to the technical field of intelligent power system optimization scheduling, and discloses a power load optimization distribution method and system based on an intelligent algorithm, which comprises the following steps: collecting historical load data of a power system and preprocessing; calculating the fractal dimension of a load time sequence, identifying a load change critical point, and dynamically adjusting the size of a load prediction time window; inputting the dynamically adjusted time window data into an LSTM model for load prediction; and according to the load prediction result, intelligently optimizing and distributing the power load by using an optimization algorithm. The application makes the optimization distribution of the power load not only have high precision, but also be able to flexibly cope with various sudden fluctuations and complex constraint conditions in the power system. Through the steps, the application improves the operation efficiency, stability and economy of the power system, reduces the uncertainty and risk in the power system load distribution, and provides a new idea and technical support for future smart power grids and power dispatching systems.

The invention discloses a load ultra-short-term prediction method based on multi-feature fusion and deep learning, and the method comprises the steps: 1, collecting historical load data, removing noise and abnormal values in the load data through wavelet transform, and completing the preprocessing of the load data; 2, extracting weather, time correlation, electricity price, load statistical characteristics and historical load characteristics according to the load data and the corresponding timestamps, constructing a characteristic set, and normalizing the characteristic set; step 3, using a TCN neural network to extract global and local features on the load data set to complete feature fusion; and 4, performing load time sequence prediction by using a BiGRU (Bidirectional Gated Recirculation Unit) neural network and an attention mechanism.

This invention discloses a method for predicting load time-series adjustment potential based on error correction, comprising the following steps: S10, acquiring raw load response data; S20, performing RF processing on the raw load response data to obtain complete time-series data; S30, using SSA decomposition to extract time-series sub-modes and performing potential prediction, then summing the predictions of each sub-mode to obtain a preliminary time-series adjustment potential prediction result; S40, subtracting the preliminary time-series adjustment potential from the raw time-series adjustment potential to obtain the time-series adjustment potential error, and using a dynamic mode decomposition algorithm to correct the time-series adjustment potential error in the preliminary time-series adjustment potential prediction result to obtain the final time-series adjustment potential prediction result. This invention solves the problems of excessive complexity and limited generalization ability in traditional time-series potential prediction, effectively handles the influence of data disturbances, and improves the accuracy of potential analysis.

The disclosure provides a load prediction method, a load prediction model training method and related equipment, which relate to the field of artificial intelligence such as deep learning, and the method comprises the following steps: obtaining historical load time series data, historical exogenous time series data and predictable exogenous time series data in a prediction time range; obtaining first load change information of the historical load time series data under the influence of the historical exogenous time series data, and extracting first features corresponding to the first load change information; obtaining first predicted load time series data in the prediction time range under the influence of the predictable exogenous time series data according to the first features; and obtaining target predicted load time series data in the prediction time range according to the first predicted load time series data. In the disclosure, the load prediction based on the exogenous time series data is realized, the accuracy of the load prediction is improved, the load prediction method has better applicability and practicability, and the load prediction method is optimized.

This invention discloses a method for constructing a multi-state model of hydropower units that takes into account load temporal fluctuations. It focuses on the impact of regional time-period water inflow restrictions and system safety constraints, corrects the outage characteristics of the hydropower unit model within a fixed period, correlates the outage characteristics of the units with load locations, and converts the unit failure rate into a dynamic calculation process. Simultaneously, considering the impact of system safety constraints, it optimizes the unit commissioning sequence under the constraints of system ramp-up capacity reserve and spinning reserve, obtaining richer system operation information, including system reliability indicators, safety margin indicators, and unit start-up and shutdown frequency, providing a reference for power system safety assessment.

The application discloses a loading method and device of a server hardware device, equipment and a storage medium, and relates to the technical field of servers. The method comprises the following steps: in response to a starting instruction of a server, a first loading time configuration list is acquired; a query command of loading time information is sent to a baseboard management controller, and a second loading time configuration list returned by the baseboard management controller based on the query command is received; for each hardware device, the loading time threshold of a loading timer corresponding to the hardware device is configured based on the device identifier information corresponding to the hardware device, the first loading time configuration list and the second loading time configuration list; the loading timer corresponding to the hardware device is started, and the hardware device is loaded based on the loading timer until the loading of at least one hardware device is completed. The method can improve the starting stability of the server.

Devices, systems, and methods for determining a number of shot characteristics associated with shooting a hockey puck are described herein. In some examples, one or more embodiments include a memory and a processor to execute instructions stored in the memory to determine shot characteristics including a shot time, a load time, a release time, and a shot score associated with shooting a hockey puck based upon data from a sensor, such as a pressure sensor and / or a force sensor, and an accelerometer on a bracket attached to a blade of a hockey stick.

The invention provides an automatic operation and maintenance scheduling system and method in a cloud computing environment. The method comprises the following steps: predicting a load demand curve through historical load time sequence data and real-time load indexes of an online service cluster; generating an arbitration decision of resource operation and maintenance scheduling according to the simulated resource occupation contour of the online service cluster constructed by the load demand curve and the resource request queue corresponding to the offline task cluster; issuing a reserved resource permission to the second control ring by the arbitration decision, and injecting a virtual node resource view complementary with the reserved resource permission in time and space into the first control ring; and when the real-time load index triggers the first control ring to perform resource scaling operation, the first control ring executes instance scaling in a resource boundary limited by the virtual node resource view, and the second control ring starts and stops the offline task instance in the corresponding resource block according to the current reserved resource permission. According to the technical scheme provided by the invention, collaborative scheduling of operation and maintenance of the online service cluster and the offline task cluster can be realized under load fluctuation.

This invention relates to a method for cross-version binary compatibility of Linux kernel modules, belonging to the field of computer operating system kernels. It includes the following steps: During the compilation phase, the compiler identifies specific keywords (__kabi_reloc_member, __kabi_check_member, __kabi_sizeof) and records instruction offsets, type identifiers, member variable name offsets, and relocation types, generating a .kabi_relocs segment. During the loading phase, the loader parses the .kabi_relocs segment, combines it with module BTF and kernel BTF information, and dynamically repairs instructions based on the relocation type. This invention achieves "compile once, run anywhere" binary compatibility for kernel modules, with the repair operation completed at load time, having no impact on runtime performance.