543 results about "Time distribution" patented technology

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 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 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 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 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 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 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.

The invention relates to a system and method for low-voltage judgment of a power failure area based on mobile optical modem equipment, and the method comprises the steps: collecting the static data and offline event data of each mobile optical modem in real time, and storing the data after the data cleaning operation; based on historical offline event data of each mobile optical modem, an initial weight is distributed for each optical modem offline event, and then the offline weight of each mobile optical modem is dynamically adjusted according to the spatial and temporal distribution characteristics of the real-time offline event data of each mobile optical modem; in combination with the offline weight of each mobile optical modem, determining the power failure judgment weight of the corresponding area through space-time scanning statistics; and comparing the power failure judgment weight of each area with a corresponding power failure threshold value to judge whether a power failure fault occurs in the area, and if the power failure fault occurs, outputting position information and power failure warning information corresponding to the area. Compared with the prior art, the method can timely and accurately identify the regional power failure event and determine the fault region.

The invention discloses a cyberspace surveying and mapping threat detection method and a cyberspace surveying and mapping threat detection system, and belongs to the technical field of network security. Performing multi-dimensional feature extraction including spatial distribution feature extraction, time distribution feature extraction, protocol interaction feature extraction and load response feature extraction to construct feature vectors, and outputting a prediction feature vector of a next time step according to a plurality of feature vector pairs in a latest time window through a learning model; performing error comparison with the actual feature vector to screen out abnormal features, performing attack intention assessment according to the abnormal features to assess an attack intention comprehensive threat coefficient, and finally assessing a threat risk coefficient in combination with the abnormal features and the attack intention comprehensive threat coefficient. The accuracy of attack intention assessment is improved through the multi-dimensional features, and the assessment accuracy of the threat risk coefficient is improved by combining the abnormal features and the attack intention comprehensive threat coefficient.

The invention provides a self-adaptive cache evaluation method and device based on multi-dimensional evaluation parameters and a medium, and relates to the technical field of data storage. The method comprises the following steps: acquiring an evaluation access frequency f of to-be-cached data, evaluation semantic popularity h and evaluation space-time demand density d of the to-be-cached data by a to-be-cached terminal, and evaluation terminal performance e of the to-be-cached terminal; obtaining an access frequency evaluation weight alpha, a semantic popularity evaluation weight beta, a space-time demand density evaluation weight gamma and a terminal performance evaluation weight delta; and obtaining the cache value of the to-be-cached data in the to-be-cached terminal, wherein the cache value is equal to alpha * f + beta * h + gamma * d + delta * e. According to the method, the cache value of the to-be-cached data in the to-be-cached terminal is evaluated from the four dimensions of the access frequency, the semantic popularity, the space-time demand density and the terminal performance, deep demands such as semantic association, space-time distribution and terminal suitability are considered while high-frequency access is captured, resource mismatching is effectively avoided, and the cache content value can be evaluated more accurately.

The invention discloses a power distribution network source load space-time distribution collaborative prediction system based on a deep learning model, and particularly relates to the technical field of power system operation and power distribution automation. The system accesses and aligns multi-source data under a unified time base, constructs graph priori carrying version weight in combination with operation topology, and outputs multi-step prediction of each node. Dynamic updating is introduced, wherein switching, reconstruction and power flow information is obtained through SCADA / power distribution automation, and graph priori is updated by event driving according to planned / instantaneous dual modes; performing time matching of the operation and the work order / instruction and historical plan probability weighting to obtain a planning coefficient, and setting a hysteresis threshold for discrimination; events such as tripping and protection action are directly judged to be instantaneous; and the undefined but continuously existing persons are conservatively classified according to the threshold duration. And the state duration threshold is self-adaptive along with the planning coefficient and the topological disturbance intensity. The system is matched with consistency auditing and gray release, and stable operation is supported.

The invention discloses a wind and light energy storage power generation distribution control method and system. The method comprises the following steps: acquiring related parameters of a wind and light energy storage system; a multi-target control function is constructed based on a multi-target Harris eagle optimization algorithm, and system constraint conditions are combined in the directions of wind and light output fluctuation suppression, energy storage charge and discharge loss reduction and power grid supply and demand matching degree improvement; monitoring and adjusting the state of charge of the multi-mode hybrid energy storage unit through a wind storage combined wide-area SOC virtual synchronous control strategy, establishing association mapping and determining an initial threshold value; preliminarily distributing a power difference value between wind and light output and a power grid load by using the platform; iteratively optimizing the preliminary distribution power and then outputting target power; and sending a control instruction to realize real-time distribution. The number of the system is six, and all the units work cooperatively. According to the invention, the energy storage resource utilization efficiency and the system operation stability are improved, and the dynamic adaptation capability of the wind-solar energy storage system to the power grid load is enhanced.

The invention discloses a battery remaining service life prediction method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining an impedance data set through the electrochemical impedance spectroscopy test of a battery in multiple aging states, synchronously constructing an equivalent circuit model of the battery, and carrying out the modeling in combination with relaxation time distribution; extracting mechanism features from the electrochemical impedance spectroscopy to form a mechanism feature data set; and after fusing the impedance data set and the mechanism feature data set, screening features strongly related to battery performance through correlation analysis to obtain a target feature data set, and inputting the target feature data set into a preset prediction model to output a prediction result of the remaining service life of the battery. By means of combination of electrochemical impedance spectroscopy testing and relaxation time distribution modeling, conversion from original impedance data to mechanism characteristics is achieved, the characteristics are directly associated with battery aging essence, dependence on pre-calibration is eliminated, and the problem that an empirical model is insufficient in adaptability under complex working conditions is solved; and the model input quality is optimized through feature fusion and correlation screening.

The invention relates to the technical field of circuit breaker control, in particular to a control system and method for an intelligent circuit breaker controller, and the system comprises a data collection module, an abnormality judgment module, an abnormality determination module, a type determination module, an adjustment module and an execution module. According to the invention, the current, the contact displacement and the contact temperature are collected in real time, so that the operation state of the circuit breaker can be sensed in a multi-dimensional manner; in the hidden anomaly judgment, anomaly candidate generation and type identification process, the synchronous change trend of displacement and temperature is compared with historical anomaly characteristics, and a trigger amplitude threshold value and a delay tripping window are adjusted based on the anomaly type and the confidence coefficient; and furthermore, the peak amplitude threshold value is adjusted or the tripping action is executed according to the time distribution of the implicit anomaly judgment result in the preset correction duration, so that the problem of insufficient early warning capability caused by lack of a refined state recognition and prediction mechanism is effectively solved.

The invention discloses a production line unit task allocation method based on reinforcement learning, which comprises the following steps: extracting a specific task instruction of each production line unit from a final task allocation matrix, verifying the feasibility of the instruction under the constraint of completion time through a simulation execution module, determining an instruction set passing verification, and performing task allocation on the instruction set; wherein the instruction set is obtained by fusing the solution of the multi-target conflict; according to the verified instruction set, production line feedback data such as actual execution time and energy consumption records are obtained, deviation is analyzed from the feedback data, if it is judged that the deviation is larger than a preset threshold value, a self-adaptive adjustment mechanism is triggered, and a corrected distribution strategy is obtained; and the optimized resource allocation indexes are extracted from the corrected allocation strategy, the indexes are pushed to the production line equipment through the real-time distribution system, the execution monitoring cycle after pushing is determined, and the monitoring cycle is obtained by continuously tracking the low-efficiency risk.

The invention provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, and the method comprises the steps: carrying out the multi-period sampling and time window division of an original signal inputted by an industrial field sensor or a switching value, and extracting the time distribution characteristics of the signal; performing signal stability judgment and pseudo edge elimination based on the signal time distribution characteristics, and outputting a stable edge event set; performing event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary time sequence trigger signal; environment characteristic data are collected, real-time feedback of an actuator is obtained, and system judgment parameters are dynamically corrected; and generating a control command and verifying an execution result based on the preliminary time sequence trigger signal and actuator feedback, the method can dynamically distinguish real signals and noise under vibration interference, simultaneously ensures the real-time performance and trigger reliability of the system, and solves the problems of insufficient edge detection stability and false trigger phenomenon caused by mechanical vibration.

The invention discloses an intelligent regulation and control method and system for temperature and humidity data of a logistics warehouse, and relates to the technical field of data identification, and the method comprises the steps: collecting the spatial and temporal distribution data of a temperature and humidity field in the warehouse through a multi-source sensor network; processing the data by adopting a multi-source time sequence data alignment algorithm and a feature extraction method; constructing and updating a spatio-temporal evolution prediction model; generating a coordinated regulation and control instruction sequence; a regulation and control instruction is issued, and closed-loop feedback is formed. The system comprises an environment sensing module, a data fusion and feature extraction module, a dynamic prediction modeling module, a multi-target optimization decision module and an instruction execution and feedback control module. According to the method, the spatial-temporal heterogeneity of the temperature and humidity field can be captured at high resolution, the temperature and humidity evolution trend in a future time window can be accurately predicted, the environmental safety and the energy consumption efficiency are comprehensively considered, the stability of the storage environment is remarkably improved, and energy consumption optimization is realized on the premise that the cargo storage safety is guaranteed.

The invention discloses a BIM (Building Information Modeling) and real-time monitoring-based cylindrical pier cast-in-place cantilever variable cross-section continuous beam bridge construction early warning system and method, and relates to the technical field of construction early warning. According to the system, alignment and noise reduction are carried out on monitoring data through BIM constrained drift correction, a curvature sequence and an acceleration sequence are obtained, an energy response index and an energy characteristic index are extracted, and a space-time distribution matrix is formed. And identifying a curvature deviation development trend by using a time sequence prediction model, and dynamically evaluating structural stress distribution and a deformation state in combination with finite element analysis. When the deviation value or the stress result exceeds a multi-level threshold value, finite element reanalysis is triggered for correction. And the decision execution module judges the real-time safety level and generates an early warning signal by integrating the monitoring characteristics and the simulation result based on a robust intelligent identification model subjected to ring loss regularization training. The method can improve the monitoring precision and robustness of the construction stage, and achieves the real-time, foresight and closed-loop management and control of the structure safety.

The invention relates to a tunnel partition illumination dynamic optimal control method and system based on traffic flow space-time distribution prediction, and belongs to the technical field of tunnel illumination energy-saving operation. The method aims to solve the problems of black hole effect caused by response lag, trailing illumination caused by rough partition and lack of pre-judgment ability in the existing control technology. According to the technical scheme, data are collected through a multi-source traffic perception layer, and advanced prediction is carried out by fusing a macroscopic and microscopic model and deep learning through a traffic flow space-time trajectory prediction layer; the dynamic partition optimization decision-making layer maps a prediction result into a virtual light packet and solves an optimal dimming instruction meeting illuminance, uniformity and vehicle speed correlation type brightness change rate constraints through model prediction control, and finally the optimal dimming instruction is output through the smooth execution and closed-loop correction layer. According to the system, the black hole effect can be fundamentally eliminated, the safety is improved, fine energy-saving control over the lamp moving along with a vehicle is achieved, the visual comfort is guaranteed, and the system has high adaptability and robustness for multiple traffic scenes.

The invention discloses a space-time collaborative optimization-based virtual power plant adjustable capability evaluation method and system. The method comprises the following steps of S10, obtaining operation parameters of distributed energy resources in a virtual power plant; s20, performing space-time analysis on the operation parameters, extracting space distribution characteristics and time change characteristics of the distributed energy resources, and generating a space-time distribution matrix; s30, constructing a joint scheduling model of the virtual power plant based on the spatial-temporal distribution matrix, and obtaining an adjustable capability evaluation result of the virtual power plant by solving the joint scheduling model; and S40, generating a regulation and control instruction according to the adjustable capability evaluation result, and sending the regulation and control instruction to the distributed energy resources to realize collaborative optimization scheduling. According to the method, the operation parameters of the time dimension and the space dimension of the distributed energy resources are jointly scheduled, and the dynamic data of the virtual power plant are acquired in real time for analysis and evaluation, so that the regulation and control process is more comprehensive, and the evaluation result of the adjustable capability of the virtual power plant is optimized.

The invention belongs to the technical field of data processing, and particularly relates to a real-time control simulation system for air cooling island freezing prevention, and the system obtains a temperature field, a back pressure field and a fan operation state in a target area in real time through a monitoring module; the analysis module constructs a space-time distribution field of the temperature and the back pressure according to the temperature and the back pressure, and further fuses the space-time distribution field into a temperature-back pressure causal distribution mapping field; the inversion module carries out fault type discrimination based on the mapping field and a back pressure abnormity identification model, and generates an inversion mapping space containing an early warning level, starting time, air volume, air speed and air direction in combination with a fan operation inversion function; the response simulation module performs real-time anti-freezing control by applying a strengthening adjustment strategy and a simulation algorithm, so that the temperature and the back pressure are maintained in a standard interval; according to the invention, intelligent identification, accurate prediction and self-adaptive regulation and control of the air cooling island freezing injury risk are realized, and the accuracy of anti-freezing control and the reliability of system operation are effectively improved.

The invention relates to the technical field of operation and maintenance management, and discloses a historical building operation and maintenance management method and system based on digital twinning, and the method comprises the steps: building a digital substrate based on historical data and point cloud, dividing a monitoring region according to a bearing structure, value elements and a function space, and associating an identifier. And mapping the operation data to a corresponding component storage space according to the area to which the operation data belongs, and forming a dynamic operation and maintenance file and a monitoring data stream. And performing visual coding on the data stream by taking the acquisition time as a horizontal axis and the spatial topology as a longitudinal axis to generate a spatial-temporal distribution map. And determining an abnormal area according to fluctuation form difference and trend deviation degree of real-time and historical data in the map, and binding identifiers and time periods to generate an abnormal list. Updating the file by taking the list as an index, and matching the disease record with a historical maintenance record to generate an operation and maintenance management scheme; the efficiency of historical building operation and maintenance management based on digital twinning can be improved.

The invention provides an irrigation area sediment deposition prediction method and system, computer equipment and a storage medium, and belongs to the field of water conservancy project intellectualization, and the method comprises the steps: collecting historical hydrological data, real-time monitoring data and remote sensing observation data of a to-be-predicted irrigation area; the method comprises the following steps: resampling historical hydrological data and real-time monitoring data, carrying out forward filling completion on missing data, filling remote sensing observation data into a unified time axis, and filling missing spatial features based on spatial feature values of an adjacent time reference to obtain multi-source data with time-space alignment; extracting features of the multi-source data to obtain four features; and combining the four features and calculating correlation weights among the features so as to determine the sediment flow, the sediment concentration and the sediment flow velocity of the to-be-predicted irrigation area. According to the method, aiming at the defect of non-uniform spatial and temporal distribution among multi-source data sources, spatial and temporal alignment is realized through time re-adoption and spatial feature completion, the input information can accurately reflect the sediment transportation process of an irrigation area, and the prediction accuracy is improved.

The invention relates to the technical field of electric vehicles and charging scheduling, and discloses an electric vehicle ordered charging guiding optimization scheduling method and system based on big data, and the method comprises the steps: collecting and preprocessing electric vehicle data, traffic data, electric power data and charging facility data; based on the preprocessed user historical data, dividing the electric vehicle users into different user clusters by adopting a K-Means clustering algorithm, and predicting the time-space distribution characteristics of the charging load by using a time sequence prior fitting network TimePFN model; and constructing a charging guide model under the power-traffic constraint, taking the predicted space-time distribution characteristics of the charging load as input, taking the user comprehensive overhead minimization as a target, considering the power constraint, the traffic constraint and the charging facility constraint, and adopting a point-by-point transfer learning Gaussian Bayesian mixture optimization PPTL-MGTO algorithm to solve an optimal charging scheduling scheme. According to the invention, accurate prediction of the charging load can be realized under the condition of scarcity of multi-source heterogeneous data.