718 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 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 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 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 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 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 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 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 mangrove forest space expansion-atrophy monitoring method based on a continuous time sequence spectral index. The mangrove forest space expansion-atrophy monitoring method comprises the following steps: step (1), preprocessing a remote sensing image; (2) generating an original time sequence; (3) removing abnormal observation data in the time sequence; step (4), constructing a vegetation index time sequence sensitive to the mangrove forest within the year; step (5), extracting a mangrove forest potential area every year based on a threshold method; step (6), identifying and correcting potential areas of the mangrove forest in data abnormal years; (7) capturing the years of space expansion and atrophy of the mangrove forest; (8) drawing a space-time distribution diagram of expansion and atrophy of the mangrove forest; and (9) quantitatively evaluating the annual mangrove forest area increase and loss. The method draws the spatial distribution diagram of mangrove forest expansion and atrophy based on long-time series multispectral remote sensing data, can accurately monitor the time point when the mangrove forest is subjected to atrophy and expansion events, and ascertains the spatial and temporal characteristics of the mangrove forest distribution range.

The invention relates to the technical field of building intelligent control and energy saving, and discloses a building equipment intelligent control optimization management system based on a green low-carbon building. The system comprises five subsystems, wherein an environmental parameter sensing subsystem is used for acquiring temperature and humidity gradients, illumination intensity distribution and personnel activity thermodynamic diagram data inside and outside a building through a distributed sensing network to form a multi-dimensional environmental state vector; the equipment operation state acquisition subsystem acquires an operation power curve, an energy efficiency conversion rate curve and an equipment health degree index of heating ventilation air conditioning, illumination and renewable energy equipment in real time; the dynamic load prediction subsystem establishes an energy consumption load space-time distribution prediction model according to historical data time sequence relevance; the control strategy generation subsystem is used for generating an optimized instruction set containing an equipment start-stop time sequence, a power regulation gradient and an energy distribution weight in combination with the current environment vector and a predicted load; the execution terminal adapts the subsystem to convert the instruction into a device compatible signal and calibrate a response delay.

The invention provides a control method and system of double-station transceiving integrated coupling equipment, and the method comprises the steps: obtaining defect coordinates and pinhole characteristic data of a target region, carrying out the analysis to obtain a transmittance abnormal region, generating an initial path of a spiral search algorithm according to the transmittance abnormal region, adjusting the search step size and angle, and obtaining a preliminary scanning boundary of a to-be-scanned region; analyzing the closeness degree of the scanning path and the defect, if the closeness degree is within a preset range, determining a power reduction condition according to the defect coordinate, adjusting the power, processing the light spot to obtain the size, and calculating a broadening coefficient; and extracting dynamic change data of the light spot size from the distributed power distribution, scanning the defect area to obtain a heat accumulation risk real-time distribution diagram, and dynamically adjusting the light spot size and power until the peak coupling power stability recovers to a preset range.

The invention provides an edge calculation optimization method for a seawater parameter monitoring Internet of Things system, and belongs to the technical field of edge calculation, and the method comprises the steps: constructing a seawater parameter monitoring sensor network, and calculating an energy consumption evaluation reference value based on a data change rate and a disorder degree; performing feature extraction on the seawater parameter data; deploying a double-model collaborative architecture comprising a terminal lightweight model and a center complete model; setting a dynamic energy consumption threshold judgment mechanism to allocate calculation tasks; executing lightweight edge calculation at the sensing nodes; transmitting the processed data through a low-power wide area network; the calculation center verifies and fuses the data; constructing a seawater parameter space-time distribution model; dynamically adjusting an energy consumption evaluation reference value by using a Pareto optimal solution set method; and periodically evaluating system energy consumption efficiency and updating model parameters. According to the method, dynamic balance of energy consumption and calculation efficiency of the seawater parameter monitoring system is realized, and the continuous operation capability and the monitoring reliability of the system are remarkably improved.

The invention relates to an industrial robot health state evaluation method based on multi-modal servo information mapping. The method comprises the following steps: obtaining the influence of a complex service condition on the performance of an industrial robot and a degradation mechanism; health perception features are extracted from multi-mode servo information collected in the operation process of the industrial robot; according to the collected health perception characteristics, a health perception model of the industrial robot under the complex working condition is established; an incremental learning model is established, a degradation model is obtained along with continuous addition, continuous learning and updating of new data, failure time distribution of the degradation model is predicted, decision support is provided for maintenance of industrial robot equipment, and a multi-modal servo information mapping model is constructed by deeply analyzing a performance degradation mechanism of an industrial robot under complex working conditions. And accurate sensing of the health state of the robot is realized. And a self-supervised contrast representation learning theory is utilized to solve the problem of fault sample deficiency, and the accuracy of health perception is improved. Based on an incremental learning theory of multi-modal servo information, an autonomous diagnosis model is established, and rapid diagnosis and performance degradation prediction of a new fault are realized.

The invention provides an updating method and system for aviation ice accretion identification based on aircraft real-time detection. The method comprises the following steps: acquiring real-time data of an aircraft to judge icing generation information; constructing an ice accumulation report membership function according to the ice accumulation generation information, wherein the function is used for representing the space and time distribution of the ice accumulation generation probability; and through combination of the icing report membership function and a basic icing index, an icing potential index is obtained through calculation and is used for identifying three-dimensional distribution of an icing area. According to the method, accurate positioning, dynamic prediction and real-time early warning of the accumulated ice are realized, and the aviation safety and the flight efficiency are improved.

The invention belongs to the technical field of geothermal energy development and geological engineering, and discloses a geothermal reservoir fracture network dynamic modeling method based on geomechanical constraints. The method comprises the following steps: firstly, acquiring multi-source monitoring data of the geothermal reservoir, wherein the multi-source monitoring data comprises rock mass surface deformation data, microscopic damage space-time distribution data and acoustic emission fracture signal data; constructing a microscopic-macroscopic correlation model based on a bonded particle model and a moment tensor theory, and obtaining a characteristic value of rock mass damage evolution; performing three-dimensional fracture source positioning on the acoustic emission fracture signal; constructing a cubic cluster model based on a positioning result, and generating an initial topological structure of the fracture network; dynamically adjusting the geometrical morphology and connectivity of the fracture network through a random walk algorithm and a fracture intersection correction mechanism according to the evolution characteristic value in combination with a geomechanical constraint condition; and finally predicting fracture expansion trends and damage evolution laws under different geomechanical conditions. According to the method, the accuracy and reliability of fracture network evolution prediction are remarkably improved.

The invention discloses a full-life-cycle monitoring and risk early warning system for equipment leasing, particularly relates to the field of equipment leasing supervision, and is used for solving the problem of an illegal subleasing recognition blind area in the existing equipment leasing, and the system comprises the steps: extracting an operation period log corresponding to a bound account, and generating an operation behavior record; constructing an operation atlas according to the account control instruction sequence, and identifying behavior fragments with rhythm change and functional path mutation in the atlas as control mutation candidate sections; through structured regression comparison with a historical behavior template, screening out a non-regressive control path offset section which cannot be matched with a historical control mode; and in a plurality of continuous monitoring periods, counting time distribution of non-regressive behaviors, constructing a control jump distribution sequence, mapping a high-density section to a corresponding lease period, and completing illegal sublease risk early warning labeling in a contract database to realize whole-process risk monitoring of lease behaviors.

The invention relates to the technical field of intelligent control, and discloses a water conservancy water resource dynamic scheduling method and system based on big data, and the method comprises the steps: collecting the collected real-time hydrological data, meteorological data and water demand data into multi-source real-time water conservancy data of a water conservancy region; based on a long-term dependency relationship in historical water resource scheduling data, determining a water consumption characteristic and a water resource space-time distribution rule; adaptively distributing water utilization priority weights of water resources to obtain water resource supply and demand conditions; mapping the water resource supply and demand condition to a scheduling rule base of historical water resource scheduling data to obtain a preliminary water resource scheduling scheme; the preliminary water resource scheduling scheme is corrected in real time in combination with the multi-source real-time water conservancy data, and a target water resource scheduling scheme is obtained; responding to the target water resource scheduling scheme, obtaining difference information between an actual response effect and an expected effect, and dynamically adjusting facility operation parameters according to the difference information; according to the invention, the efficiency of dynamic scheduling of water resources can be improved.

The invention relates to the technical field of power system planning, and discloses a new energy grid-connectable capacity margin evaluation method based on spatio-temporal distribution characteristics, which comprises the following steps: constructing a joint probability distribution model based on nonparametric kernel density estimation and a Copula function by using historical and meteorological data of a wind and light station, and generating a random output scene reflecting the spatio-temporal complementarity of wind and light resources; constructing a multi-dimensional stability constraint set containing a generalized short circuit ratio and broadband oscillation as a safety boundary of a random output scene; establishing a grid source interaction control strategy considering energy storage adjustment and direct current power modulation by taking grid-connected capacity maximization as a target, solving an optimal installed capacity scheme, and activating the grid source interaction control strategy to perform dynamic correction when a scene is out of limit; the maximum grid-connected capacity distribution meeting the constraint is counted, and the shadow price of the constraint is calculated to identify the bottleneck. According to the method, the wind and light resource complementary benefit and the system regulation potential can be quantified, and the new energy grid-connected capacity evaluation margin is improved on the premise of guaranteeing the safety.