900 results about "Time distribution" patented technology

The invention provides an intelligent factory production scheduling method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time monitoring data set, including equipment operation states, material flow tracks and environment sensing data, of a physical workshop; generating a three-dimensional digital twinborn model containing an equipment dynamic operation topology, a material real-time distribution thermodynamic diagram and an environment state simulation layer, then performing operation state correlation analysis on the three-dimensional digital twinborn model, and generating a feature set of equipment operation efficiency, material flow bottleneck, environment interference and the like; generating a dynamic scheduling parameter set containing equipment load balancing, material priority distribution and environment adaptation compensation parameters, and finally calling a preset scheduling strategy optimization algorithm to perform multi-objective optimization processing on the dynamic scheduling parameter set to generate an equipment scheduling, material distribution and environment regulation and control instruction set. And synchronizing to a physical workshop to execute real-time scheduling operation, thereby realizing intelligent and efficient production scheduling.

The invention relates to the technical field of computer hardware monitoring, in particular to a case internal temperature intelligent monitoring system which comprises a temperature acquisition synchronization module, an environment difference analysis module, a cold control response judgment module, a heat source positioning recognition module and an energy consumption load evaluation module. According to the invention, by performing uniform timestamp sorting and collection on the temperature data at different positions, the time consistency of multi-node temperature information is improved, high-precision dynamic tracking of temperature change is realized, the timeliness and integrity of a time sequence are ensured, and the accuracy of the temperature change is improved. The temperature difference between the nodes and the environment and the time distribution characteristics of the nodes support accurate identification and positioning of thermal anomalies, fan response opportunities are judged according to the peak value and fluctuation rate of the temperature difference, fan numbers and control paths are calibrated, the pertinence of response is enhanced, and the core position of a heat source is positioned through temperature difference space comparison of adjacent nodes. The fan rotating speed and current load change analysis establishes dynamic association between a heat source and energy consumption, and thermal anomaly and energy efficiency trend evaluation is realized.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

The invention provides a cold and hot data object life cycle feature extraction method based on deep learning, and the method comprises the steps: carrying out the clustering of feature vectors through employing a clustering algorithm, dividing the data into different viscosity level data clusters according to a clustering result, and enabling the data in each viscosity level data cluster to have similar access modes and life cycle features; for each viscosity level data cluster, analyzing the access time distribution and life cycle stage of the data cluster, and judging the heat type to which the data cluster belongs, including continuous heat, periodic heat, temporary heat and cold; according to the current popularity type and the historical access mode of the viscosity level data cluster, predicting the popularity change trend of the viscosity level data cluster in a future period of time, and obtaining a popularity change curve of each cluster; by monitoring the access condition and the heat change of the data in real time, the migration and replication modes of the data between different storage layers are dynamically adjusted, and when the actual access mode of the data is deviated from the predicted mode, data re-clustering and storage optimization are triggered.

The invention relates to the technical field of distributed file processing, in particular to a distributed file processing method and system, and the method comprises the following steps: based on a task distribution node and a data exchange interface, extracting path characteristics of a task input / output file, analyzing a file exchange sequence of a task path, and calculating a topological structure and length of the path. According to the method, the flexibility and the adaptability of task allocation are realized by combining task state monitoring and real-time adjustment of dependent path weights and task priority marks, resource allocation and a short-time task sequence are optimized by utilizing request frequency and time period records, high-frequency request resource conflicts are effectively relieved, the resource allocation efficiency is improved, and the resource allocation efficiency is improved. Through node historical resource distribution data and time distribution trend analysis, task distribution is adjusted and resource loads are balanced in combination with differences, the stability and balance of the system are enhanced, a file transmission path is optimized based on a load state and a task execution path, and a concurrent task distribution strategy is improved.

The invention relates to the technical field of industrial control, in particular to a robot scheduling method and system based on cooperative control, and the method comprises the steps: obtaining a task proportion and target node information in a task adjustment scheme, judging an optimal task distribution path and an optimal execution sequence according to a task priority order and a node adaptation rule, and carrying out the task scheduling according to the optimal task distribution path and the optimal execution sequence; determining an inspection task distribution blueprint; according to the routing inspection task distribution blueprint, the tasks are distributed in real time by adopting a low-delay scheduling framework, and a dynamic distribution map and real-time distribution update data of the tasks among the nodes are obtained according to node response delay and communication overhead among the nodes; the latest load state of each node is obtained through a dynamic distribution map and real-time distribution update data, and whether a potential calculation pressure overload risk exists or not is judged according to a distribution uniformity index and distribution anomaly detection. The invention aims to solve the problems of large performance difference, unbalanced load and low task scheduling efficiency in a multi-robot collaborative inspection process in a narrow space.

The invention relates to the technical field of power distribution networks, in particular to an environment-related intelligent regulation and control method and system for a power distribution network, and the method comprises the steps: obtaining the operation parameters of each monitoring node of the power distribution network, and enabling the operation parameters to comprise node state parameters and external environment parameters; based on the operation parameters, calculating time correlation between the node change characteristics and the environment data, and generating a node operation state report; identifying an abnormal event according to the node operation state report, and determining an abnormal influence range based on time distribution, space distribution and fluctuation characteristics of the abnormal event; according to the abnormal influence range, the load state of the node is evaluated, the emergency response priority is determined based on the influence weight of the node in the network, and a load regulation and control strategy is generated; and executing a load regulation and control strategy, dynamically adjusting node power supply capability, distributing standby power source resources, monitoring load change in real time, completing intelligent regulation and control of the power distribution network, and realizing accurate monitoring and intelligent regulation of the operation state of the power distribution network.

The invention relates to the technical field of optical machining surface shape simulation, in particular to a surface shape simulation calculation optimization method for a multi-mechanical-arm cooperative machining scene, and the method comprises the steps: planning a polishing region and a polishing track belonging to each mechanical arm according to the surface shape sampling size of a workpiece to be machined and the number and position of the mechanical arms for machining; and a residence point in the polishing track of each mechanical arm; the residence point of each mechanical arm is matched with the surface shape data of the workpiece to be machined, and the surface shape data corresponding to the residence point of each mechanical arm is determined; and finally, according to the surface shape data corresponding to the residence point of each mechanical arm and the removal function of the workpiece to be machined, final residence time distribution of each mechanical arm during machining is obtained through calculation. According to the method, a surface shape matching simulation calculation method is utilized, the secondary frequency superposition problem occurring during multi-mechanical-arm machining is effectively eliminated, and meanwhile residence time distribution with good smoothness can be obtained.

The invention discloses a cooling cooperative control method, and the method comprises the steps: obtaining a temperature parameter, a load state parameter and a cooling system parameter of a transformer, and forming a parameter set; constructing a gating circulation unit model, and predicting thermal load distribution of each region of the transformer by adopting the parameter set through the gating circulation unit model; and performing real-time distribution on cooling resources by adopting a dynamic region distribution algorithm. According to the cooling cooperative control method, temperature and load changes of all areas of the transformer are predicted in real time through a gating circulation unit model; intelligent distribution of cooling resources is achieved through a dynamic area distribution algorithm, the cooling cooperative control method can dynamically adjust the flow speed of a cooling medium and the rotating speed of a fan according to real-time temperature and load changes, it is ensured that all areas of a transformer obtain appropriate cooling resources, and extra cooling is preferentially provided for areas with the temperature abnormally increased; and a cooling strategy is optimized through multiple iterations in combination with historical operation data, so that the response speed and accuracy of the cooling system are remarkably improved.

The invention provides an information resource demand prediction method and system based on a user behavior pattern, and the method comprises the steps: firstly obtaining a user behavior data set of a target user in a preset time period, and enabling the user behavior data set to comprise a plurality of behavior event units composed of user operation types, operation timestamps and associated resource identifiers; performing behavior feature extraction on the user behavior data set to obtain an operation intensity feature, a time distribution feature and a context association feature, and inputting the operation intensity feature, the time distribution feature and the context association feature into a pre-trained demand prediction model to generate a resource demand prediction set; according to the method, priority ranking is carried out according to the feature weights of the resource demand items in the resource demand prediction set, a resource allocation optimization strategy is generated, and finally, the resource allocation optimization strategy is sent to a resource scheduling system to trigger a resource preloading operation, so that the accuracy and efficiency of information resource allocation can be improved.

The invention discloses a laser cutting parameter adjusting system based on machine learning, and relates to the technical field of laser cutting. According to the method, multiple laser cutting parameters in the laser cutting process are collected, a short-term state index and a long-term state index are calculated based on the cutting parameters, and a laser cutting instant index and a laser cutting long-term index are generated respectively; based on the laser cutting instant index and the laser cutting long-term index, optimized laser cutting parameters are generated, and working parameters of the laser cutting machine are adjusted in real time; the adjustment result and the related data are sent back to the data storage module for updating the historical database; a real-time distribution curve is generated and compared with a target distribution curve, deviation is calculated, and a parameter optimization target curve is generated; and according to the optimized sub-targets, laser cutting parameters are adjusted in real time, and the stability and high efficiency of the laser cutting process are ensured. According to the system, accurate control over the laser cutting path is achieved through multi-layer and non-uniform control unit division and time-space correlation model construction.

The invention relates to the technical field of computer authentication, and discloses a cross-system non-inductive switching authentication method and device for e-commerce, electronic equipment and a storage medium. The method comprises the following steps: acquiring user behavior data, and generating a user behavior data set; extracting user behavior features, and constructing a feature vector group; matching a user behavior mode in combination with a historical abnormal file, and giving an initial score to an abnormal risk; if the score exceeds a threshold value, performing cross validation on the login time distribution and the operation instruction sequence, and determining an abnormal risk level; according to the risk level, matching an authentication strategy, and generating a targeted authentication process scheme; and issuing the scheme to the front end of the equipment for execution, and determining an authentication result. According to the method, through dynamic and accurate risk assessment and adaptive adjustment of the authentication strength, the problem of low user experience and safety caused by a fixed authentication strategy in the prior art is solved.

The invention provides a safety early warning method and device for abnormal behavior trajectory analysis and a medium, and relates to the technical field of safety early warning, and the method comprises the steps: collecting behavior trajectory data of a target region through a multi-source sensing device, and carrying out the multi-dimensional feature extraction of a behavior trajectory data set; according to the stay frequency characteristics, carrying out abnormity determination on the track space-time distribution characteristics and the speed change characteristics, and constructing an abnormal behavior determination vector; performing wandering mode recognition based on the abnormal behavior judgment vector, determining an abnormal behavior level, and triggering a graded early warning mechanism according to the abnormal behavior level; and generating an abnormal behavior alarm signal according to the grading early warning mechanism, combining the abnormal behavior alarm signal with the electronic fence information of the target area to generate a safety early warning report, and pushing the report to a monitoring terminal. According to the invention, the technical problem of abnormal behavior recognition accuracy in the prior art can be solved, and the technical effect of improving the behavior judgment accuracy is achieved.

The invention discloses a rock burst advanced early warning method based on cumulative damage spatial-temporal distribution characteristics, and the method comprises the steps: obtaining all micro-seismic data of a to-be-predicted region, the micro-seismic data being composed of the three-dimensional coordinates and corresponding energy of each point in the predicted region; dividing the to-be-predicted area into a plurality of grids, and obtaining all microseismic data in each grid; based on all the micro-seismic data in each grid, calculating the fractal dimension of the roadway surrounding rock damage of each grid; based on all microseismic data in each grid, the number of crack planes, the crack area and the crack shape in each grid are obtained; and on the basis of the parting dimension, the number of the crack planes, the crack area and the crack shape in each grid, the rock burst danger degree of each grid is obtained, and early warning is performed according to the danger degree. According to the method, the parting dimension is obtained based on the microseismic data, meanwhile, various indexes of cracks are introduced, the danger degree of rock burst is obtained, early warning is conducted, and the timeliness and accuracy of early warning are improved.

The invention relates to the technical field of data processing, in particular to a power distribution terminal with a power supply fault abnormity monitoring and early warning function, and the power distribution terminal comprises a data collection module which is used for obtaining a K value range according to the difference between a real-time data sequence of the power distribution terminal in a current period and a historical data sequence in a historical period; the data analysis module is used for acquiring mutation data in the real-time data sequence; the data processing module is used for obtaining the mutation distribution density according to the distribution condition of the difference between the mutation data and the data in the local range, and obtaining the time distribution density according to the data distribution in the local range of the mutation data; and the abnormity early warning module is used for obtaining a self-adaptive K value according to the abrupt change distribution density, the time distribution density and the K value range of the abrupt change data, and obtaining an LOF value of the abrupt change data by using an LOF algorithm, so that power supply fault abnormity monitoring and early warning are carried out on the power distribution terminal, and the accuracy of fault abnormity early warning is improved.

The invention relates to the technical field of power distribution network fault research and judgment, discloses a power distribution network fault real-time research and judgment and accurate positioning processing method and system, and aims to improve the operation and maintenance efficiency and the power supply reliability of a power grid. According to the method, historical fault records and environment monitoring data of a power distribution network system are collected and preprocessed to form a basic data set; then performing data analysis, including fault type and environmental factor correlation analysis and fault area clustering analysis, to construct a training data set; the core is to construct a power distribution network fault research and judgment model adopting a dual machine learning model, and the accurate research and judgment of fault types are realized by fitting fault features and fault influences and utilizing a residual error to construct the model to estimate a causal effect. And after training and optimizing the model, studying and judging real-time power distribution network monitoring data, identifying a fault risk, and formulating a targeted disposal strategy. According to the method, various data are comprehensively considered, and the accuracy and the handling efficiency of fault research and judgment are improved.

The invention discloses a power distribution network dynamic multi-target regulation and control method and system based on graph-model driving, and belongs to the technical field of power system automation. The method comprises the following steps: generating a dynamic topological graph model with a time mark correction label based on an acquired power distribution network topological state and preset power grid graph model verification; in combination with the dynamic topological graph model and the electrical parameters, executing state estimation and security risk analysis, and generating a security risk space-time distribution matrix; based on the security risk space-time distribution matrix, generating a multi-target strategy set for simultaneously optimizing a network loss index, a voltage fluctuation index and a load balancing index; and executing the multi-target strategy set, collecting power grid state response data after execution, calculating a control effect index, and judging whether to iteratively update the dynamic topological graph model or not. The method is used for solving the problems that topology modeling is static, state estimation granularity is coarse, risk assessment lacks a spatio-temporal evolution mechanism and strategy closed-loop capacity is weak in a traditional method.

The invention relates to the technical field of semiconductor manufacturing, in particular to an accurate detection method and system for a flat-edge sheet wafer placement rotary chuck, and the method comprises the steps: when a wafer is placed on the rotary chuck, scanning the lap joint condition of a wafer edge and a chuck contact area through a laser sensor, and obtaining the real-time distribution data of an edge lap joint sheet; flat edge position information is extracted from the edge lap joint distribution data, an image matching algorithm is adopted to compare a preset orientation reference, and a specific angle value of orientation deviation is determined; according to the orientation deviation angle value and the edge lapping piece distribution data, the rotating speed and the angle of the rotating chuck are adjusted through a cooperative control algorithm, and a final positioning result of the wafer on the chuck is obtained; after a final positioning result is obtained, the in-place state of the wafer is classified and judged through a deep learning model, and whether the residual deviation or wafer lapping phenomenon exists or not is determined; and extracting abnormal data from a classification judgment result, and adjusting collaborative parameters of carrying and placing according to the abnormal data to obtain an optimized process control scheme.

The invention relates to the technical field of enterprise office collaboration, and discloses an enterprise series intelligent office collaboration data processing system based on edge computing. The system comprises a collaborative data acquisition module, a collaborative feature analysis module, an edge task scheduling module and a dynamic storage optimization module. The collaborative data acquisition module is deployed at a local edge node of an enterprise, captures a collaborative operation behavior flow of the multi-source office terminal in real time, and extracts space-time distribution features to generate an original collaborative feature vector; the collaborative feature analysis module analyzes the coupling strength of related data through a multi-dimensional correlation analysis engine, quantifies the frequency fluctuation amplitude of a communication session, and fuses to generate a collaborative efficiency quantized value; the edge task scheduling module dynamically divides task load levels based on the value, and matches execution priorities to generate a distributed calculation task allocation strategy; and the dynamic storage optimization module responds to the strategy, monitors the throughput delay of the storage nodes, reconstructs a high-frequency access data storage index, and generates a cross-node storage topology configuration.

The invention relates to the technical field of machine learning, and provides a building equipment abnormity identification method and system based on machine learning, which are used for realizing accurate detection and accurate early warning of building equipment abnormity. The method comprises the following steps: acquiring a continuous operation data set of target building equipment, wherein the continuous operation data set comprises multiple segments of equipment state recording units with timestamp marks; performing time-frequency domain feature extraction processing on the continuous operation data set to obtain a time-frequency domain feature set of the equipment state recording unit; calling a pre-constructed hybrid machine learning model to perform anomaly detection processing on the time-frequency domain feature set, and generating an anomaly recognition result of the equipment state recording unit; determining the anomaly type of the target building equipment and the distribution feature information of the anomaly type in the time dimension according to the anomaly recognition result; and generating a target early warning instruction containing a time positioning identifier based on the exception type and the time distribution feature information, and sending the equipment early warning instruction to a target equipment management terminal.

The invention discloses a dynamic noise analysis method and device for an urban elevated road and a medium, and belongs to the technical field of noise monitoring and analysis. The method comprises the following steps: acquiring a three-dimensional geographic information model and real-time traffic flow data of a target elevated road; processing the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; fusing the real-time traffic flow data with a noise source intensity calculation model to generate a dynamic noise source intensity sequence; calculating a real-time equivalent sound pressure level of each grid unit through a noise superposition algorithm in combination with the dynamic noise source intensity sequence and the noise propagation path grid; performing rolling prediction on the real-time equivalent sound pressure level based on a time window sliding mechanism to generate a noise space-time distribution thermodynamic diagram; and according to the noise space-time distribution thermodynamic diagram, identifying a noise exceeding area, and outputting a dynamic noise evaluation report. According to the method, the technical effect of dynamically predicting and analyzing the noise of the elevated road is achieved.

The invention discloses an earth and rockfill dam material seepage deformation inspection system and method, and relates to the technical field of earth and rockfill dam engineering.The method comprises the steps that a sample of an earth and rockfill dam material is prepared and arranged in a test cavity of a permeameter; maintaining a steady-state seepage field in the sample; in the maintaining process of the steady-state seepage field, acoustic response signals and thermal response signals excited in the sample due to the seepage effect are synchronously collected through a distributed sensing system arranged around the sample; analyzing the acquired acoustic response signal and thermal response signal, and identifying and positioning an acoustic abnormal area with abnormal sound energy release and a thermal abnormal area with abnormal thermal change; and after the early warning signal is generated, the spatial and temporal distribution characteristics of the acoustic abnormal region and the thermal abnormal region are used as input, a parameter distribution field representing the damage evolution of the microstructure in the sample is calculated through a coupled physical inversion algorithm, and the permeability stability of the sample is evaluated based on the parameter distribution field.

The invention discloses a hydraulic rammer tamping construction parameter adjusting method based on sensing parameter real-time feedback, and relates to the technical field of hydraulic rammer tamping construction.The hydraulic rammer tamping construction parameter adjusting method comprises the steps that a multi-mode sensing monitoring network is constructed to collect full-amount construction data, a wavelet packet decomposition algorithm is adopted for noise layered suppression, and a multi-mode sensing monitoring network is established; constructing a working condition associated data set in combination with the construction stage labels; based on the working condition associated data set, establishing a dynamic tamping effect evaluation model, and outputting a deviation index moment of time-space distribution; training a parameter adjustment intelligent model based on the deviation index matrix and a transfer learning mechanism, generating a multi-parameter collaborative adjustment strategy, and carrying out working condition adaptation degree scoring and adjustment risk early warning on strategy output; and adjusting the intelligent model based on incremental learning and model distillation technology optimization parameters. According to the method, the multi-modal sensing network, the geological dynamic quantitative model, the improved entropy weight method, the migration and reinforcement learning and the lightweight deployment technology are fused, so that full-chain intelligent dynamic optimization and safe controllable execution of hydraulic rammer construction parameters are realized.

The invention provides a method for predicting the residual life of a fuel cell, which belongs to the technical field of fuel cells, and comprises the following steps: carrying out data preprocessing on collected fuel cell operation time-voltage data to obtain output voltage data of a time sequence; introducing the verified EIS data, and further analyzing and extracting attenuation characteristic parameters of the fuel cell through relaxation time distribution; analyzing a peak value and a frequency of relaxation time distribution analysis and a Pearson's correlation coefficient of output voltage data, distributing a weight, performing nonlinear fitting on a recession characteristic parameter, and establishing a time-frequency health index; constructing a fuel cell attenuation model, and performing optimization training; the attenuation characteristic parameters and the output voltage data are used as input and are sent into the fuel cell attenuation model after optimization training, and the residual life of the fuel cell is predicted and output. The defect that a conventional prediction method based on data driving cannot be suitable for fuel cell attenuation prediction is overcome, and the attenuation process can be quantitatively analyzed.

The invention discloses a distributed photovoltaic meteorological resource real-time monitoring station group site selection method and system, and the method comprises the steps: 1, obtaining photovoltaic historical resource data of a target region, and constructing a historical data set; 2, based on the historical data set, analyzing space-time distribution characteristics of distributed photovoltaic radiation resources in a target area, and dividing clusters with consistent radiation resource changes; 3, respectively selecting an optimal center point of each cluster; and step 4, performing micro-region site selection based on each cluster center point to determine an optimal site establishment position. According to the method, the existing distributed layout condition is considered, the future distributed development planning is also considered, and the method has the characteristics of full coverage of regional monitoring information, high representativeness of monitoring stations, scientific and reasonable site selection and excellent economical efficiency of a monitoring layout scheme.

The invention relates to the technical field of power supply, in particular to an emerging load power supply potential improvement method and system, and the method comprises the following steps: obtaining power demand data of a user in a plurality of time periods, calculating a load change rate in each time period, constructing a load behavior mode feature set, and obtaining load behavior mode feature data. According to the invention, by analyzing the user power demand data and calculating the real-time power demand comparison mapping information, the power demand can be effectively identified and adjusted, the coping capability and accuracy of power supply are improved, and by dynamically adjusting the discharge priority and time distribution of the energy storage unit, the power supply efficiency is improved. The use efficiency and response speed of energy storage equipment are improved, peak load shifting is facilitated, high efficiency and safety of power grid operation are kept, the overall performance and reliability of a power system are improved, the emergency response capacity of the power system to emergencies is enhanced, and the supply potential of power is optimized.

The invention discloses an electric connector fault prediction method, and the method comprises the steps: synchronously obtaining multiple types of time series data of a contact area of a target electric connector in a preset time window based on a preset data collection device, and the time series data comprise temperature spatial and temporal distribution time series data, contact resistance transient time series data and a surface image sequence; performing feature analysis on the multi-source time sequence data by using a preset time sequence coupling sign extraction mechanism to obtain a multi-source phase change feature vector of the target electric connector in the current time window; inputting the multi-source phase change feature vector into a pre-trained electric connector health state prediction model, and outputting a prediction result of the health state of the target connector at the current moment, including a health state index; and performing differential early warning based on the predicted health state index of the target connector. Therefore, early and quantitative accurate prediction of the state of the electric connector is realized.

The invention discloses a distributed resource control system and method based on cross-domain dynamic coupling, and the system comprises a space-time dynamic coupling analysis module which is used for analyzing collected distributed resource operation data to obtain a distributed resource space-time distribution condition, constructing an adjustable resource scheduling optimization model, and carrying out the optimization of the adjustable resource scheduling optimization model; inputting actual to-be-analyzed resource data into the adjustable resource scheduling optimization model to generate a resource optimization scheduling scheme; the multi-level differential regulation and control module is used for generating a multi-level differential regulation and control model according to the resource optimization scheduling scheme and the layered and clustered architecture so as to adopt different regulation and control strategies for a regional power grid, a power distribution network and a micro-grid in the layered and clustered architecture; and the cloud edge collaborative synchronization module is used for issuing regulation and control indexes generated according to the regulation and control strategy to edge side equipment, and adjusting the multi-level differential regulation and control model according to feedback data collected by the edge side equipment. By implementing the method and the system, effective regulation and control of massive distributed resources are realized, and the regulation and control efficiency is improved.

The invention discloses a surface wear volume time-space distribution prediction method combining abrasive particle and vibration monitoring, which comprises the following steps: constructing an abrasive particle size distribution driven Markov process, representing a random wear process, and realizing dynamic and probability representation of real-time wear volume time evolution; a physically constrained double-branch mapping model is constructed, fuzzy mapping of the surface wear volume and the vibration time-frequency characteristics is established, and precise observation of wear volume space distribution is achieved; the two physical mechanisms are fused on the basis of a state space modeling framework, a digital twinborn model combining abrasive particle monitoring and vibration monitoring is constructed, and accurate prediction of the abrasion volume of the friction pair part in the time dimension and the space dimension is achieved. According to the method, the wear volume space-time distribution is predicted by fusing the wear particle information and the physical correlation between the vibration characteristics and the wear process, and the precise monitoring of the wear process is realized.

The invention relates to a conflict-free AGV scheduling method for punctualized material distribution in a workshop, and the method comprises the following steps: obtaining a material distribution task, and dividing a decision period into a plurality of stages; an independent scheduling algorithm is used in each stage for AGV scheduling, tasks and departure time distributed by each AGV in each trip are optimized, a conflict-free optimal path is planned, an AGV scheduling scheme for minimizing time window penalty and distribution cost is realized, the scheduling algorithm comprises task allocation and path planning, in the task allocation, the path planning is performed, and in the path planning, the time window penalty and the distribution cost are reduced. According to the method, travel division is carried out based on departure time estimation, AGV assignment of each travel is carried out based on a heuristic rule, initial path planning is carried out by adopting an A * algorithm according to a task allocation result in path planning, conflict detection is carried out based on a path time window, and conflict resolution is carried out in combination with waiting and local re-planning. Compared with the prior art, cooperation of task allocation and path planning under the punctualization distribution requirement is achieved, and the calculation efficiency and the resource utilization rate can be improved.