54 results about "Load modeling" patented technology

The invention discloses a method for rapidly evaluating the fatigue life of a steel trestle for extreme heavy load impact, and relates to the technical field of bridge fatigue life evaluation. Establishing an extreme impact load probability model; multi-source bridge monitoring; establishing and correcting a finite element model; efficiently simulating a fatigue damage field; carrying out multi-stage modeling and prediction on crack propagation; performing multi-scale damage accumulation fusion analysis; and carrying out residual life evaluation and multi-dimensional risk grading. Through combination of extreme impact load modeling, multi-source data fusion and a physical constraint neural network technology, the impact load can be accurately identified and the stress response of the structure can be calculated under an extreme heavy load working condition, and meanwhile, a three-stage crack propagation model is introduced to comprehensively describe the whole process from microscopic damage to structure fracture. And the efficiency and reliability of fatigue life evaluation are obviously improved.

The invention relates to the technical field of power system modeling and simulation, in particular to a load model identification error analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining the prediction output and the measurement output of a preset load model, and building a target function based on the error between the prediction output and the measurement output; decomposing the measurement output to obtain an actual output and a noise component of a preset load model; and based on the objective function and the actual output, establishing a linear model between the identification error of the preset load model and the noise component through a first-order approximation method, and performing linear regression according to the linear model to obtain an analysis result of the identification error. Therefore, by constructing the theoretical model of the load model identification error, the problems that the identification error is difficult to predict, the data processing strategy selection lacks theoretical guidance and the like in related technologies are solved, and a theoretical basis is provided for selecting an optimal load modeling data processing strategy.

The invention relates to the technical field of electric pile energy switching, and discloses a bidirectional charging pile energy switching control method and system supporting vehicle-network interaction, and the method comprises the steps: collecting the load state of a power grid, and constructing a load evaluation model in which a historical load is combined with real-time data; selecting a common charging mode or a V2G mode according to an evaluation result; switching the charging module according to the charging mode requirement; constructing a load attention model according to power grid dispatching and load distribution; optimal configuration is applied to charging control, and peak clipping and valley filling are carried out in cooperation with a power system; the system comprises a load acquisition module, a state judgment and mode driving module, a module interface switching control module, a local load modeling module, a multi-channel power optimization module, a strategy execution control module and a storage module. According to the method, a power grid load identification mechanism constructed by historical load statistics and a time period standard deviation model is introduced, and a power boundary threshold value is dynamically set through formula modeling, so that whether a load is in a tension state or not can be judged.

The application discloses a kind of based on working condition division concentrator electric load modeling and production plan optimization method.The method first defines normal production, low production operation, three production states and corresponding load coefficients of shutdown maintenance, in combination with typical daily load curve, daily random fluctuation factor and seasonal variation factor, construct multi-layer composite annual load refinement model;Again, initial production plan is generated by state coding, and the scattered maintenance day and low production operation day are integrated into continuous period by using centralized scheduling optimization algorithm, to reduce the frequency of production state switching;Finally, the optimized production plan and load model are integrated to generate a 8760-hour refined load sequence throughout the year.The application realizes the deep coupling of production plan and electric load, significantly improves the stability and accuracy of load prediction, provides reliable data support and decision basis for power grid planning, demand side response and enterprise energy efficiency optimization, and is highly adaptable and easy to implement.

The invention discloses a load modeling method and system for large power grid electromagnetic transient simulation, and belongs to the technical field of large power grid electromagnetic transient simulation. The method comprises the following steps: for a target load power grid, determining the types of basic units of an electromagnetic transient model of the target load power grid, and determining a connection relationship between different basic units; according to different basic units, different modeling modes are used for modeling, unit models corresponding to the different basic units are generated, and the unit models are connected through the connection relation; and obtaining model parameters of the electromagnetic transient model of the target load power grid, and performing parameter setting on the connected unit models according to the model parameters to generate an equivalent electromagnetic transient model of the target load power grid. According to the method, the external characteristics of the load power grid containing high-proportion power electronic equipment are accurately reflected, and compared with an electromagnetic transient simulation model of a detailed load power grid, hardware simulation resources can be effectively reduced, and the simulation efficiency is improved.

The invention discloses a bypass operation drainage wire rapid lap joint tool structure force field analysis method, system and device and a medium, and belongs to the technical field of electrical tool design, and the method comprises the steps: automatically generating a tool geometric model through adaptive multi-resolution three-dimensional modeling, and carrying out local encryption of a grid for a mechanical key area; then integrating a multi-target material performance evaluation algorithm to realize material optimization, and applying a multi-physics coupling load based on a high-fidelity load modeling method; a high-risk area is dynamically corrected through a structure-interface self-correction stress simulation model, and a simulation drive design mechanism is introduced for iterative optimization until the overall performance is optimal; and verifying and solidifying the structure scheme through an extreme working condition inversion test. According to the method, the analysis and prediction precision of the tool under complex working conditions is improved through adaptive multi-resolution modeling and multi-physics field coupling simulation; and in combination with an evolutionary iterative optimization mechanism, collaborative spanning of mechanical, electrical and corrosion-resistant properties is realized, and the structural reliability is enhanced.

The invention discloses a BIM wide area network collaborative deepened design modeling method and system, and relates to the technical field of intelligent building construction design, and the method comprises the steps: collaborative preparation: selecting and defining a collaborative center, and loading a program at least capable of modeling in the collaborative center; determining a network environment to establish a central intranet, performing intranet penetration, and opening the permission of an extranet to access a specified central file; obtaining a central file based on the user behavior, and uploading the central file to a collaboration center; performing working set division on the central file through the loaded modeling program to generate working set data; s2, collaboration: identity information of a worker is acquired, and a matched work task is identified; allocating a work set according to the work task of each worker; and obtaining data updating of any worker on the working set, storing and synchronizing to the collaboration center. The method has the effect of improving convenience and efficiency during cooperative work of building design.

The invention relates to a method for realizing a resource adaptive pressure test based on load scaling. The method comprises the following steps: load modeling; constructing a distributed test cluster; service simulation and Mock construction are carried out; load prediction; self-adaptive pressurization is carried out; and evaluating a result. The invention also relates to a system and a device for realizing the resource adaptive pressure test based on load scaling, a processor and a computer readable storage medium thereof. By adopting the method, the system and the device for realizing the resource adaptive pressure test based on the load scaling, the processor and the computer readable storage medium thereof, the resource utilization rate is greatly improved, the test efficiency is greatly improved, manual intervention is reduced through an automatic test process, and the test period is shortened. The environment adaptability is enhanced, and the method has good universality for different types of service systems. The cost effectiveness is outstanding, the dependence on a special high-performance test environment is greatly reduced, the existing low-configuration equipment is fully utilized, and the hardware investment and the maintenance cost are saved.

The invention discloses a main and distribution network fusion method and system based on active power distribution network modular modeling, and the system comprises an active power distribution network modular modeling module which is used for carrying out the topology analysis of a power supply network of an active power distribution network, and automatically generating a detailed power supply network small system simulation model and an equivalent load simulation model of a 220kV transformer substation below a 110kV bus side; the main and distribution network model mapping relation dictionary management module is used for establishing a mapping relation dictionary among a transformer substation name in an offline simulation calculation model adopted by main network simulation, a transformer substation name in an online simulation calculation model adopted by load modeling, a 110 kV / 35 kV transformer substation, a distribution line and a load node name; the main distribution network model fusion module is used for automatically splicing the detailed power supply network small system simulation model or the equivalent load simulation model of the active power distribution network with the main network simulation calculation model; the main and distribution network joint simulation solving module is used for performing main and distribution network integrated joint simulation analysis; and the precision, efficiency and flexibility of joint simulation of the main network and the distribution network are improved.

The application relates to the field of power distribution system simulation, in particular to a distributed power distribution system unified scale load modeling simulation method and system, which comprises the following steps: denoising and fitting a wideband signal to obtain a multiscale signal; obtaining a Hankel matrix time-varying extended model based on the multiscale signal and historical modal parameters and solving to obtain real-time modal parameters; constructing a coupling matrix based on the real-time modal parameters, fitting a fast action layer state equation and a slow action layer state equation by using the coupling matrix to obtain a unified coupling equation; transforming and decoupling the unified coupling equation to obtain a mixed differential-algebraic equation group; and solving the mixed differential-algebraic equation group by using a preset step length switching rule based on a dominant dynamic type to obtain full-time domain simulation results of the distributed power distribution system. The application can effectively solve the problems of time scale fragmentation and insufficient parameter identification accuracy in traditional load modeling.

The invention discloses a load modeling and regulating method and device participating in demand response, equipment and a medium. The method comprises the steps that firstly, operation parameters of a partial monitoring air conditioner are collected, and the single adjusting capacity is calculated based on a second-order equivalent thermal parameter model; quantifying cluster parameter heterogeneity by using a Gaussian mixture model, optimizing parameters through K-means and expectation maximization algorithms, and extrapolating the capacity to obtain a total adjustable capacity; then constructing a Markov chain state space containing a temperature interval, an action and a counter and a transfer matrix; establishing a function taking a power grid demand tracking error and economical efficiency as targets, and solving by adopting an alternating direction multiplier method to obtain the optimal temperature rise probability of each temperature interval; and finally generating a low-dimensional broadcast signal to guide the local autonomous decision of the air conditioner cluster. According to the method, the problems of accurate modeling and cooperative control of large-scale heterogeneous air conditioner loads are solved, accurate evaluation of the adjusting capacity and accurate guidance of group response are achieved, and communication and calculation expenses are remarkably reduced.

The invention discloses a load model selection method and system based on time-space characteristic evolution rule quantitative evaluation, a storage medium and electronic equipment, and the method comprises the steps: obtaining multi-business-state time sequence data for load modeling, and constructing the time sequence characteristics of the multi-business-state time sequence data; performing quantitative preliminary screening on the multi-business-state time sequence data through a constructed dynamic envelope channel, and identifying a parameter drift event; based on the multi-business-state time sequence data, the time sequence features and the parameter drift event, constructing a comprehensive feature vector; obtaining a prediction error of the comprehensive feature vector through a trained LightGBM regression model, and judging a parameter drift event through the prediction error; and when the parameter drift event is judged to be valid, updating a load model in a preset scenarized candidate load model library based on a preset rule, and generating an optimal load model decision sequence.

The invention belongs to the field of human factor engineering and human-computer interaction, and provides a pilot load modeling method, system and device based on a cognitive network and a medium, and the method comprises the steps: constructing a human-computer interaction sequential network model; calculating and correcting the cognitive demand of each task according to the time overlapping condition; aggregating cognitive demands of tasks with the same operator node, the same system resource node and the same interaction direction in the sequential network model, constructing an incidence matrix, and generating a cognitive social network graph for visualizing operator cognitive workload distribution based on the incidence matrix; the change of the distributed cognitive workload is identified by comparing cognitive social network graphs, and optimization suggestions are proposed according to the result. According to the method, the problem that the distributed cognitive workload cannot be quantized, visualized and compared and analyzed in the prior art can be solved, and scientific and intuitive decision support is provided for optimization design of man-machine function distribution.

The invention provides an air conditioner load-temperature relation robust regression clustering method and system, and the method comprises the steps: obtaining and processing the historical data of air conditioner operation power and meteorological temperature, and generating an air conditioner load-temperature sample set; the number of regression relations is determined according to the classification of the air conditioner operation state division in the user-defined time period; constructing a fuzzy regression model fused with a maximum entropy principle by introducing an entropy penalty term and an L1 norm; initializing the fuzzy regression model according to the number of regression relationships and self-defined parameter values; and taking the air conditioner load-temperature sample set as the input of the initialized fuzzy regression model, carrying out iterative optimization, and determining the regression relationship between the air conditioner and the air temperature of the building to be evaluated according to the output result. Compared with a traditional method, the method and the system have higher robustness and generalization ability, and can be suitable for air conditioner load modeling requirements in different areas and under different climate conditions.

The invention discloses an urban bridge and tunnel group vehicle load modeling method and system based on a depth probability graph model, and belongs to the technical field of urban bridge and tunnel group health monitoring. The method comprises the following steps of: 1, preprocessing acquired historical data of a dynamic weighing system and acquired data of each passing vehicle; 2, time-space factors are collected; step 3, establishing a bridge-tunnel network based on the preprocessed data in the step 1 and the space-time factors in the step 2; 4, constructing a vehicle load depth probability model based on the bridge-tunnel network established in the step 3; 5, training based on the vehicle load depth probability model in the step 4; step 6, generating a load of a random traffic flow vehicle based on the model trained in the step 5; and 7, carrying out random traffic flow vehicle load deduction based on the model trained in the step 5. The method is used for solving the problems of bridge and tunnel operation and maintenance and bridge and tunnel reliability analysis, the influence of time difference on the model, and unclear correlation among nodes in the urban bridge and tunnel network.

The invention provides a layered space-time clustering method of a time-varying load model, and relates to the technical field of power system load modeling. The method comprises the steps of obtaining a load model data set of load nodes through periodic identification, wherein the load model data set comprises a dynamic load proportion, an active static load parameter, a reactive static load parameter and a dynamic load model parameter; clustering the identification result of each load node by using a time clustering algorithm, and extracting a node typical load model set; and uploading the typical load model of each load node to a control center for spatial clustering, and further extracting a typical load model set of the system, including a typical active static load model set, a reactive static load model set and a dynamic load model set of the system. By introducing a clustering algorithm based on local density and distance deviator, the load modeling precision and efficiency can be improved, the problem caused by response lag of a traditional method to load change is avoided, and the adaptability and reliability of a load model are improved.

The invention discloses a data-driven load model key parameter analysis method supporting trusted simulation analysis. On the basis of extracting multi-type load time-frequency domain features, a mechanism type dynamic feature knowledge base is constructed, a data driving model is converted into an interpretable equivalent load model through approximate Bayesian estimation and an alternating direction multiplier method, and the precision loss of dynamic feature response curves of the two models is compared in the same simulation scene, so that the accuracy of the equivalent load model is improved. Therefore, the simulation credibility of the data-driven load model is improved. According to the invention, the mechanism reconstruction of the data-driven load model is realized. According to the equivalent load model, a load physical mechanism structure is explicitly introduced while the data fitting precision is kept, so that a model result has clear physical meaning and engineering interpretability, and the model is more reasonable in load modeling expression.

The application discloses a kind of building adjustable potential evaluation methods based on hierarchical progressive evaluation.The application comprises extracting multiple building features to obtain a comprehensive feature set, identifying key influencing factors through the comprehensive feature set, and finally screening high adjustment potential buildings through the key influencing factors;Collecting high adjustment potential building data for establishing building load baseline model, then obtaining the load curve of high adjustment potential building from the building load baseline model and extracting baseline load value to obtain building load baseline;Collect information on high adjustment potential buildings and construct a building thermodynamic model, use the building thermodynamic model to extract the adjustable capacity of each high adjustment potential building, and generate an adjustable potential feature curve.The application realizes the accurate identification and evaluation of building adjustable potential by combining energy consumption data screening, sub-item load modeling and thermodynamic simulation, providing reliable technical support and decision basis for power grid load dispatching and demand response.

The invention discloses a multi-scale Transform-based computing network resource load feature representation method and device and a storage medium, and the method comprises the steps: obtaining multi-source computing network resource load related historical time sequence data, and dividing the data according to different time scales of hours, days and weeks to form a multi-scale data sub-sequence set; for the data sub-sequence of each time scale, respectively carrying out feature embedding and position coding processing to obtain a feature vector sequence reflecting the load change under the scale; a plurality of groups of parallel Transform encoders are designed, and depth feature extraction is carried out on the feature vector sequence of each scale; a scale attention module is introduced and is used for establishing a relation among representations of different scales and fusing global dependency information under each scale; the output features of the Transformer encoders of all scales are combined into a uniform load feature representation vector according to a certain mode, and the uniform load feature representation vector serves as comprehensive representation of the current load state of calculation network resources; and the load characteristic representation is input into the prediction module for realizing prediction of the load at the future moment. According to the method, the characteristics of the computing network resource load data under different time scales can be effectively fused, and the accuracy, the stability and the generalization capability of load modeling are improved.

This invention discloses a load modeling system and method based on a multimodal model that fuses geological parameters and water pressure time-series data, belonging to the field of computer technology. The method includes: standardizing and spatiotemporally aligning geological parameters and water pressure time-series data to generate multimodal feature data; extracting and encoding prior knowledge of geological structures using a graph neural network; dynamically modeling the modulation effect of geological conditions on water pressure response using a cross-modal attention and gating fusion mechanism to generate a unified feature representation; reconstructing the current load field and predicting its future evolution based on an encoder-decoder architecture; simultaneously employing multiple random forward propagations to quantify uncertainty and generate confidence intervals for predicted values; and dynamically optimizing and updating the model using a sliding window online learning strategy, combined with online monitoring feedback. This invention effectively solves the problem of spatiotemporal semantic mismatch in multi-source data, improving the accuracy, physical consistency, generalization ability, and engineering practical value of load inversion.

The invention relates to the technical field of power systems, and discloses a power electronization load modeling method, device and equipment considering a converter and a storage medium, and the method comprises the steps: obtaining a double-loop control strategy of a plurality of power electronization loads to be modeled; for each double-loop control strategy, determining a double-loop control structure of the converter based on the double-loop control strategy, and determining a critical voltage value based on the double-loop control structure; for each power electronic load, obtaining a preset protection delay and a preset protection voltage threshold value of a built-in converter; dividing load operation conditions of the power electronic load based on the preset protection delay, the preset protection voltage threshold and the critical voltage value, wherein the load operation conditions comprise a stable operation condition, a transient regulation condition and a protection condition; and constructing a total load model of the power electronic load based on the load operation condition and the common physical structure. A load model capable of accurately describing external performance of a load is established based on a load operation condition division mechanism in combination with a common physical structure.

This application discloses a precise correction method for the GNSS vertical velocity field based on multi-source load modeling, relating to the fields of geodesy and geophysics. The method includes: acquiring multi-source surface mass load data; constructing a multi-source load dataset; calculating the vertical displacement response of the Earth's surface under point loads based on an elastic Earth model and using Farrell's load Green's function theory, obtaining the load Green's function by solving for the load Love number and summing Legendre series; using the spherical Green's function integration method to calculate the vertical displacement of each grid point on the global grid caused by all mass loads, constructing a global load deformation field, performing time series analysis, and extracting the load correction at the locations of each GNSS station globally; subtracting the load correction from the global GNSS vertical velocity field, while simultaneously subtracting the glacial isostatic adjustment effect, to obtain the corrected GNSS vertical velocity field. This application can obtain accurate and reliable GNSS vertical velocity information.

The application discloses an online service system abnormality detection and root cause analysis method and system based on causal inference and business load, belongs to the technical field of micro-service fault detection and root cause positioning, and has the technical scheme that the method collects distributed tracking data under normal flow of the online service system, constructs a causal graph facing the business load by using service call topology prior constraints, regresses the structural equation between each load modeling index based on the causal graph and estimates the residual error distribution benchmark, then receives the distributed tracking data in real time, calculates the structural residual of each load modeling index through the structural equation, further standardizes the residual error, aggregates the standardized residual error, judges the abnormality based on the residual error distribution benchmark, and positions the abnormal root cause node from the causal graph by using the root cause preference score after the abnormality occurs; the system is used for realizing the method; the structural equation between each load modeling index based on the causal graph is regressed, the residual error distribution benchmark is estimated, the precise detection and root cause positioning of the silent business fault are realized.

The invention discloses a new energy fluctuation stabilizing method and device based on data center space-time load adjustment. The method comprises the following steps: carrying out data center load modeling and quantitative analysis to establish a quantitative model of data center power consumption and space-time scheduling capability; establishing equivalent output of a new energy fluctuation stabilizing target according to the set constraint meeting the power fluctuation at the continuous time; constructing a space-time scheduling optimization model of the data center based on the quantitative model and the equivalent output; and solving the space-time scheduling optimization model to obtain a space-time scheduling strategy that the data center satisfies the minimization of the new energy fluctuation stabilization cost.

The invention relates to a power grid load model parameter online updating method, and belongs to the technical field of power system load modeling, and the method comprises the steps: building a reference load model of a 10kV transformer area based on a typical transformer area load model parameter library and power grid related data; monitoring voltage data of the 10kV transformer area in real time, and obtaining actually measured load model parameters of the 10kV transformer area influenced by voltage disturbance; fusing the actually measured load model parameters with the reference load model parameters to obtain corrected load model parameters; based on the corrected load model parameters, correcting a typical transformer area load model parameter library, and synchronously updating the load model parameters of each 10kV transformer area; and combining relevant parameters of the power grid, aggregating load model parameters of each 10kV transformer area, and generating a comprehensive load model of the 220kV transformer substation. According to the method, respective advantages of a statistical integration load modeling method and an overall measurement and discrimination load modeling method are brought into full play, and an effective path is provided for realizing online updating of large power grid load model parameters.

The application relates to the technical field of mobile robot posture control, and provides a mobile robot posture control method, device, equipment and medium, which can detect whether a load change of a mobile robot occurs in real time, and reconfirms based on a robot dynamics equation, real-time motion and posture data, so that the detection accuracy is improved; an extended Kalman filtering algorithm and real-time motion and posture data are used for new load modeling, a center of mass is reconstructed according to a new load mass estimation value and a new load position estimation value, the whole processing process does not need to stop and does not need to rearrange sensors, and the compatibility is improved; a model predictive control model is called, the mobile robot is controlled to adjust the posture according to the reconstruction result and the real-time motion and posture data, dynamic compensation for the center of gravity change can be realized, instability, skidding or falling caused by the center of mass drift can be effectively prevented, and the task execution stability of the mobile robot in a changeable and unpredictable environment is improved.

The invention discloses a load modeling and system control method considering an annular plasma source under multi-physics field coupling in the technical field of plasma sources. According to the method, plasma impedance Zp is equivalent to a series circuit model of nonlinear impedance Rp and inductive reactance Lp, a polynomial fitting relational expression of impedance and inductive reactance is established by constructing a multi-physical field coupling mathematical model, then a simulation model of the impedance Zp is constructed, and the model can predict Zp values under different physical parameters. The predicted value participates in control of a driving power supply, and a basis is provided for constant current / constant power mode switching of the system. The load modeling method is non-invasive high-precision measurement, invasive limitation of a Langmuir probe is avoided, the error of the predicted impedance parameter is small, and the plasma source is accurately and stably controlled in a complex environment. Meanwhile, according to the system control method, full-load-range ZVS conduction is achieved through dynamic intercept prediction, reactive power is restrained, and system efficiency and stability are improved.

The invention discloses a low-power-factor power system load modeling method based on a time-varying power function, and belongs to the technical field of power system load analysis, and the method comprises the steps: collecting power sample data of a power system; preprocessing the power sample data to obtain a sample data set; performing clustering analysis on the sample data set through a clustering algorithm, and dividing the clustered data into dynamic load categories; constructing a time-varying power function load model; establishing a PSO-FFRLS hybrid optimization algorithm, and performing identification optimization on parameters of the time-varying power function load model under each dynamic load category through the PSO-FFRLS hybrid optimization algorithm to obtain a model parameter set of each dynamic load category; and collecting to-be-tested data of the power system, and analyzing dynamic load parameters of the to-be-tested data of different dynamic load categories according to the model parameter set and the time-varying power function load model. According to the method, the technical problem that modern load modeling with low power factor and strong nonlinearity is difficult is solved.

The application discloses a kind of active power distribution network setting calculation modeling method, device, equipment and medium.Therein, the method is by obtaining the distributed photovoltaic parameter management data of target power distribution network, distributed photovoltaic access quantity and equipment parameter;According to the comparison result of distributed photovoltaic access quantity and preset threshold value, and distributed photovoltaic parameter management data, determine first modeling mode, wherein first modeling mode includes conventional load modeling mode and current source modeling mode, current source modeling mode includes controlled current source fine modeling mode, constant current source fine modeling mode and constant current source aggregation modeling mode;If determine that first modeling mode is current source modeling mode, then according to preset priority, determine second modeling mode, based on second modeling mode and equipment parameter, target power distribution network is modeled and setting calculation is carried out, and the setting value interval is determined.This technical solution, by analyzing the distributed photovoltaic parameter management data of active power distribution network and distributed photovoltaic access quantity, to realize the differential modeling of active power distribution network, improve the adaptability of setting value.