78 results about "Load time" patented technology

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.

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 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.

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.

The present application relates to a kind of optimal energy storage capacity determination method of industrial park based on load timing characteristics, comprising the following steps:1), obtain industrial park basic data, electrical equipment parameters etc.;2), based on Monte Carlo scheduling sampling, construct the random sampling model of industrial park production operation schedule;3), industrial park electricity load curve generation;4), industrial park photovoltaic power generation curve generation;5), calculate the load timing characteristics of park in k schedule, repeat calculation until the feature of all n times of load curve is completed;6), each group photovoltaic power generation curve and industrial park electricity load curve are independently optimized and solved, and the optimal energy storage capacity corresponding to each group of scene is sequentially solved;7), based on load timing characteristics and optimal energy storage capacity, regression fitting model is constructed.The present application fully considers the timing randomness and volatility of industrial park load, and considers the calculation accuracy and efficiency, can quickly and accurately determine the optimal energy storage capacity of industrial park.

Embodiments of the present application provide a kind of application running method, device, equipment and storage medium, relate to computer technical field.The method is executed by terminal device.The method includes: when application starts running, download assembly function package from server, at least one function subpackage of application is included in assembly function package;During the running of application, if it is detected that the first function is missing in running memory, the first function subpackage is obtained from assembly function package;Application is run based on the first function.When application starts, assembly function package is downloaded to terminal device, and if it is detected that the first function is missing in running memory during the running of application, first function subpackage does not need to be requested to server again, avoid the time required for data transmission between terminal device and server, reduce the loading time of calling first function subpackage, weaken the sense of application's lag.

The application discloses a power load scheduling method and device based on a large language model, relates to the technical field of power scheduling, and mainly aims to solve the problem of poor accuracy of existing power load scheduling. The method comprises the following steps: acquiring scheduling text data of power load, wherein the scheduling text data comprises load time sequence data, meteorological text data, equipment log data and power grid index data; performing feature extraction on the scheduling text data to obtain multi-modal features, and encoding the multi-modal features into a semantic space to obtain multi-modal feature encoding; and performing scheduling prediction processing on the multi-modal feature encoding after consistency processing based on a large language model after model training to obtain a power load scheduling result.

The invention provides a power grid power supply scheduling method and system based on load prediction, and the method comprises the steps: carrying out the preprocessing of original time series data of the operation of a power grid, and generating net load time series data; predicting a net load mean value and a load fluctuation entropy spectrum in a future scheduling period in parallel to obtain a net load mean value prediction result and a load fluctuation entropy spectrum prediction result; analyzing a fluctuation risk type of a future scheduling period, and obtaining performance parameters of available adjustment resources from the heterogeneous resource feature library; adjusting resources are matched based on the obtained performance parameters, and a prepared adjusting resource pool is determined; and generating a comprehensive scheduling instruction including a basic output plan and an auxiliary service plan in combination with the net load mean value prediction result and the preparatory adjustment resource pool, and issuing the comprehensive scheduling instruction to a target control unit in the power grid. According to the method, the problems that the utilization of heterogeneous adjustment resources is not fine enough and does not have perspectiveness due to the fact that a scheduling strategy mainly pays attention to load mean value prediction but neglects fluctuation characteristic quantization in the prior art are solved.

The invention provides a computing coupling system capacity configuration method considering load time shifting characteristics, and belongs to the technical field of integrated energy system planning and operation optimization. By constructing a wind and light output typical scene clustering and double-layer optimization configuration model, equipment locating and sizing and load time shifting scheduling are collaboratively optimized; the new energy utilization rate is effectively improved, the comprehensive cost and network loss of the system are reduced, the node voltage level is improved, and a systematic solution is provided for high-quality power supply of the data center under high-proportion new energy access.

The invention provides a power load prediction method and device based on order driving, a medium and a product, and the method comprises the following steps: obtaining historical power load data and production business data corresponding to a target time period, the production business data comprising an order material list and a workshop production scheduling plan of a manufacturing execution system; according to the process operation time window and the equipment energy consumption reference, constructing a pre-scheduling production feature sequence aligned with the historical load time; generating a time sequence input tensor through multi-dimensional splicing, and inputting the time sequence input tensor into the recurrent neural network model to obtain an initial predicted value; and monitoring the time deviation between the actual production progress and the production scheduling plan in real time, correcting the feature sequence based on the actual progress and inputting the model again when the deviation exceeds a threshold value, and outputting a corrected predicted value. By implementing the technical scheme provided by the invention, dynamic coupling of load prediction and production rhythm is realized, and the prediction precision is remarkably improved.

The application provides a PC power supply load adjustment method and device based on AI prediction and a medium. By continuously collecting power supply signals at a PC power supply output end, processor running state signals and graphic component working state signals, load time series data is generated. After pattern recognition, power consumption signatures corresponding to different computing tasks are separated to obtain a power consumption signature set. The load prediction model is input into a pre-trained load prediction model. Through time series analysis and processing, the load intensity change profile in the future power supply cycle is obtained. The load intensity change profile is fed back and calibrated with the current double-path power supply state information of the PC power supply to generate a power supply power distribution strategy. The working points of the primary conversion path and the secondary conversion path in the PC power supply are adjusted according to the power supply power distribution strategy, so that the power supply output power and the load change are dynamically matched. The application reduces unnecessary power loss, thereby ensuring stable operation of the PC system while realizing fine and dynamic adjustment of the power supply load.

The present application relates to the technical field of ocean engineering, in particular to a load loading method and device for ocean structure based on numerical simulation. The load loading method for ocean structure based on numerical simulation provided by the present application calculates the ocean environmental load of the ocean structure through the numerical simulation of the numerical model, thereby realizing the fluid simulation calculation of the ocean structure in the real ocean environment, obtaining the load time history suffered by the ocean structure, and considering the grid elements of the numerical model when establishing the finite element model, thereby facilitating the loading of the pressure data corresponding to as many grid elements as possible according to the grid parameters of the two models, so that the change of the load of the model at different nodes in the real ocean environment can be more accurately fitted, and thus the safety performance of the ocean structure can be more accurately evaluated, which is helpful to improve the design safety and reliability of the ocean structure.

The application provides a user-side micro-grid load regulation method, system and equipment based on artificial intelligence, and relates to the technical field of micro-grid management.The method comprises the following steps: analyzing multi-dimensional heterogeneous data to obtain load time series data, and determining the demand weight of each user according to power demand data through an analytic hierarchy process; processing the load time series data through empirical mode decomposition to obtain intrinsic mode function components; dynamically predicting the intrinsic mode function components, and obtaining a multi-modal load prediction result by weighting and fusing the prediction results through an attention mechanism; optimizing the multi-modal load prediction result in combination with the operation target of a micro-grid cluster to generate a basic load regulation strategy of the micro-grid cluster; and generating a final regulation strategy corresponding to each user in combination with the demand weight corresponding to the user according to the basic load regulation strategy, and regulating the final regulation strategy. The application significantly improves the regulation accuracy of the user-side micro-grid load.

The application provides a short-term charge load time sequence prediction method, device and equipment and readable storage medium. After obtaining a power load historical data set, a preset load sequence prediction model can be further used to process each load sequence of the obtained power load historical data set, so that a short-term power load time sequence can be obtained. The preset load sequence prediction model can be trained by taking the training power load historical data set as a sample and taking the short-term power load time sequence included in the training power load historical data set as a sample label. Through the preset load sequence prediction model, the power load historical data set can be processed, the load prediction randomness can be reasonably decomposed, the influence of systematic errors and noise interference can be removed, the convergence speed of the load prediction model can be improved, the local optimal solution can be avoided, the global search capability can be enhanced, and the prediction accuracy can be improved to ensure the load prediction accuracy.

The invention discloses a hydraulic support load prediction method, device and system and a storage medium, and the method comprises the steps: collecting the load time sequence data of a coal face hydraulic support, and carrying out the normalization processing of the load time sequence data; decomposing the preprocessed load time sequence data to obtain a plurality of intrinsic mode components; meanwhile, redundant components are recognized and eliminated, and an effective feature input sequence is constructed; extracting local detail features, short-term dependency features and long-term trend features of the load data in parallel through convolution kernels of different scales; focusing long-term stable key features through a static attention mechanism, and adaptively adjusting weight distribution according to a feature input sequence through a dynamic attention mechanism to obtain output of residual connection; and according to the output of the residual connection, establishing a deep dependency relationship between the feature input sequence and the load time sequence data by using a long-short-term memory neural network, and realizing accurate prediction of the support load in the future time period.