580 results about "Probability model" patented technology

The invention discloses a satellite internet-based model-data combined drive power transmission line icing state sensing and operation risk assessment method and device, and the method comprises the steps: obtaining line icing monitoring data and meteorological monitoring data, collected by a data collection system, of an icing affected region, the method comprises the following steps: inputting meteorological monitoring data into an icing prediction model for icing prediction to obtain an initial prediction result, constructing a prediction correction model according to line icing monitoring data, inputting the initial prediction result into the prediction correction model for correction, and obtaining a target icing prediction result. A line fault probability model of an icing affected area is constructed based on a target icing prediction result, and power transmission line icing risk assessment is performed on the icing affected area according to the line fault probability model, so that the monitoring range is greatly expanded, the timeliness and accuracy of monitoring data are ensured, the line risk assessment efficiency and accuracy are effectively improved, and the power transmission line icing risk assessment method is suitable for popularization and application. And the problem of prediction result deviation caused by single data prediction is avoided.

Even when there is little measurement data of abnormal signals, it is an object to manage the abnormality of the signals. 【Means for solving the problem】 The abnormality management device 1 uses, as teacher data, normal data indicating a normal frequency spectrum among the frequency spectra of a plurality of signals, for each frequency component intensity, and the parameter of a probability model that outputs the posterior probability that each frequency component intensity is normal is learned by maximum likelihood estimation. And a derivation unit 12 configured to derive a probability distribution of abnormal data indicating a frequency spectrum including a frequency component with an abnormal intensity, based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of being normal.

The invention belongs to the technical field of 4D millimeter-wave radar perception, and particularly relates to a 4D millimeter-wave radar space occupation probability estimation method based on laser radar supervision, which comprises the following steps: constructing an environment occupation probability graph as a supervision signal of a laser radar; feature extraction is carried out on the sparse point cloud data of the 4D millimeter wave radar; performing cross-modal feature alignment on the supervision signal of the laser radar and the radar point cloud feature, and constructing a first-stage occupancy probability model; and obtaining a reference occupancy probability model through laser radar point cloud self-supervision by adopting the same network and supervision mode, performing supervision and fine tuning on the first-stage occupancy probability model by using the reference occupancy probability model, and deploying the first-stage occupancy probability model to a downstream task. According to the invention, the geometric priori knowledge of the laser radar is utilized to constrain 4D millimeter wave radar data learning, the problem of insufficient modeling precision caused by point cloud sparsity, noise and penetrability of the 4D millimeter wave radar is solved, and low-cost and high-robustness environmental geometric perception is realized.

The invention discloses an available energy estimation method and system for a prefabricated cabin type energy storage system, and belongs to the field of energy storage systems.The method comprises the steps that probability distribution of environmental parameters is constructed; historical operation data of equipment is collected, a probability relation between the environment temperature and equipment energy consumption is established, and an equipment energy consumption confidence interval is generated according to the probability relation; determining a confidence interval of the SOH by combining the battery aging experiment data and the real-time monitoring error; inputting the probability distribution of the environmental parameters, the equipment energy consumption confidence interval and the battery health state confidence interval into a probability model to generate a probability interval of available energy; and the optimal confidence level is selected in combination with the power grid dispatching requirement and the risk tolerance, and the available energy of the prefabricated cabin type energy storage system is obtained on the basis of the optimal confidence level according to the probability interval of the available energy. According to the method, confidence analysis is introduced, environmental parameters, equipment energy consumption and uncertainty of battery attenuation are quantified through a probability model, a probability interval of available energy is generated, and a basis is provided for scheduling decision making.

The invention discloses a distributed photovoltaic power prediction method, system and device based on a Gaussian mixture model and a medium, and belongs to the technical field of photovoltaic power prediction.The distributed photovoltaic power prediction method comprises the steps that a time sequence vector is collected, principal component analysis is carried out on the time sequence vector, low-dimensional feature representation is obtained, and a power feature vector of each photovoltaic power station is formed; performing clustering analysis based on a probability model on the power feature vector to generate a plurality of photovoltaic power station clusters; for each cluster, acquiring meteorological input data through a set data source priority rule and a completion mechanism; and constructing a neural network power prediction model based on the accumulated power data in the cluster and the corresponding meteorological features, and outputting a future power generation power prediction value of the photovoltaic power station in the corresponding cluster. According to the invention, N photovoltaic power stations in a region are divided into M clusters through a GMM clustering method, so that the design is simplified; and the power prediction of the whole area is realized.

It aims to appropriately manage the bias of the signal processing amount. 【Solution means】 The abnormality management device 1 uses, as teacher data, normal data indicating the number of normal packets distributed to each device for each time zone, and learns, by maximum likelihood estimation, the parameters of a probability model that outputs the posterior probability that the number of packets distributed to each device for each time zone is normal. A learning unit 11, the posterior probability for each device for each time zone estimated by the learned probability model, the probability distribution of the normal data for each device estimated based on the normal data, and the prior probability of being normal. A derivation unit 12 that derives the probability distribution of abnormal data indicating the number of abnormal packets distributed to each device in the time zone, and the probability distribution of the normal data and the probability distribution of the abnormal data for each device for each time zone are equal. And a setting unit 13 that sets, as a threshold value for abnormality determination of the number of packets distributed to each device, the number of packets corresponding to the value of the probability distribution that becomes the value.

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention discloses a tunnel rockburst type prediction method and system based on multi-dimensional mechanism fusion and a medium. Relates to the technical field of tunnel rockburst. Dynamically acquiring multi-dimensional basic information in a tunnel construction process; inverting first proportions of different fracture modes of the micro-seismic event according to the micro-seismic information fusion moment tensor, and performing energy evolution on the acoustic emission information to obtain second proportions of different fracture modes of each acoustic emission event; the dynamic failure weights of different fracture modes are comprehensively obtained; spatial clustering analysis is carried out based on the dynamic damage weight and the multi-dimensional basic information, rockburst type probability models of different rockburst types are constructed, and the probabilities of different rockburst types are predicted; according to the scheme, the rockburst type probability model is constructed in combination with real-time multi-dimensional basic information, dynamic judgment and prediction of rockburst types are achieved, and technical guarantee is provided for deep tunnel construction safety.

The invention belongs to the technical field of automatic driving, and discloses an autonomous driving decision-making method based on safety enhancement multi-agent reinforcement learning. The safety consciousness module and the multi-agent decision module are designed, the Bayesian reasoning and action shielding technology is introduced, the safety of the vehicles in the high-speed afflux area is improved, meanwhile, multi-vehicle cooperation is achieved through deep reinforcement learning, and the afflux efficiency is improved. The safety awareness module performs modeling on collision time and blind area threats by constructing a probability model, and flexibly captures environmental uncertainty by adopting a piecewise likelihood function in combination with Gaussian and uniform distribution. The multi-agent decision-making module defines a state space, an action space and a reward function, and dynamically adjusts a reward value to solve the dynamic interaction problem of the highway merging area. The invention also constructs a shared network structure which comprises an actor network and a commentator network. According to the invention, the safety, efficiency and adaptability of the automatic driving vehicle in the highway confluence area are improved.

It aims to more reliably manage abnormal communication. 【Solution means】 The abnormality management device 1 is configured such that a first learning unit 12 treats each observed value of a normal presence time series as discrete values independent of each other, and estimates probability parameters of a probability model representing the normal presence time after quantization based on the appearance frequency of each observed value; and a second learning unit 13 configured to fix the generator parameters of a generator 131 that generates pseudo-normal data sufficiently deviated from the distribution of true normal data, with each observed value of the normal presence time series being true normal data, and update only the discriminator parameters of a discriminator 132 that discriminates between true normal data and pseudo-normal data in a direction to maximize the discrimination accuracy, based on the probability parameters of the normal presence time estimated by the first learning unit 12.

The invention discloses a vehicle-mounted controller network communication control system, relates to the technical field of network communication control, and effectively relieves systematic defects of a traditional vehicle-mounted network under a congestion working condition through deep coupling of a dynamic start-stop prediction model and a self-adaptive communication strategy. A probability model is constructed in real time based on brake operation characteristics, a start-stop period is accurately pre-judged, a driving instruction recombination module generates a composite control frame, and the number of redundant messages is remarkably reduced; by introducing a cross splicing technology and a verification flag bit, efficient fusion and fault-tolerant analysis of multi-source data are realized on the premise that the bandwidth is not increased; the adaptive arbitration unit intelligently optimizes bus resource allocation by dynamically closing an ACK confirmation mechanism and priority adjustment, and instruction stacking and conflicts in a high-frequency start-stop scene are avoided.

The invention relates to the technical field of network security, in particular to a network security situation awareness prediction method and device based on a knowledge graph, and the method comprises the steps: integrating a network topological structure, asset importance, security defects and traffic mode information, and generating a network basic state graph containing initial nodes and edges; according to the application, a knowledge graph structure is constructed on the basis of a network topology structure, and then multi-modal information such as network asset importance, security defects and traffic modes is incorporated to form a network basic state graph model; further, the basic state map model is mapped into a network attack path map structure based on a network attack inherent mode, a probability model is introduced, the security situation of nodes in the network is quantitatively calculated, and a network security situation static map is formed; and finally, carrying out quantitative prediction on high-risk nodes, high-risk attack paths and the overall situation of network security in the network.

The purpose is to easily manage abnormal program operation. [Solution] The abnormality management device 1 includes a first learning unit 11 that uses normal data indicating processing resource usage data in which the processing resource usage corresponding to each process ID is normal as training data from among a plurality of processing resource usage data each including the usage of processing resources used in executing a process corresponding to each process ID, and learns parameters of a probability model that outputs a posterior probability that the processing resource usage corresponding to each process ID is normal by maximum likelihood estimation, and a derivation unit 12 that derives a probability distribution of abnormal data indicating processing resource usage data including abnormal processing resource usage based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of normality.

The invention discloses a self-adaptive time sequence prediction method based on risk perception, which dynamically adjusts a prediction step length by perceiving the change of a prediction error, avoids the problem of high error caused by multi-step prediction, and ensures the accuracy of a prediction result. The method comprises three key modules: a feature extraction and fusion module based on multi-scale fragments, used for capturing periodic, seasonal and trending features with intricate service demands, and extracting long-term dependency and local semantic information therein; the time sequence probability prediction module based on the diffusion model is used for carrying out probability modeling on the prediction result and describing a credible value of the prediction result by probability distribution of a prediction moment; and the prediction optimization module for adaptive risk management is used for evaluating and predicting the risk and carrying out adaptive truncation. According to the invention, a new solution is provided in the aspects of accuracy and reliability of time sequence prediction.

The invention relates to the technical field of hydrological data processing, in particular to a water conservancy intelligent internet-of-things sensing method and system, and the method comprises the following steps: combining statistical data into a feature set based on hydrological monitoring data including flow, water level and rainfall information, judging whether the data accords with normal distribution, and generating a data distribution feature vector set. According to the method, the change trend of the data is captured through multi-level data distribution characteristic analysis, and the monitoring precision of the data exception is enhanced. In the prediction process, new data and weight adjustment are gradually fused, so that the model can adapt to environmental changes in real time, and the hydrological state in a future short period is accurately predicted. Besides, abnormal data can be identified in real time based on analysis of quantiles, potential problems can be quickly found, and state division of different levels can be carried out according to abnormal degrees. In sampling scheduling, the priority of data acquisition is adjusted according to a probability model of state transition, so that monitoring resources are efficiently configured.

The invention relates to the technical field of data processing, in particular to a cloud database data compression method based on an adaptive quantization algorithm. The method comprises the following steps of: 1, regarding to-be-compressed original data in a cloud database as a matrix, dividing the data into a plurality of non-overlapped data blocks according to predefined statistical characteristics, and calculating a mean value and a variance of each data block; 2, defining the complexity of each data block, and calculating the self-adaptive quantization step length of each data block according to the mean value and variance of each data block; step 3, performing non-uniform adaptive quantization on each data item in each data block to obtain a quantized value; and 4, establishing a probability model for a quantization result of each data block, calculating a coding length, obtaining a compression ratio, and carrying out entropy coding. The quantization step size of the data block is dynamically adjusted to be matched with the local characteristic of the data, so that the quantization error is reduced, and the data recovery precision is improved.

The invention discloses an optimization system and method for participation of an electric vehicle cluster in an electric power standby market, and is applied to the field of an electric power auxiliary service market, and the system comprises a market uncertainty modeling module which is used for constructing a joint uncertainty model of market price and standby calling probability, wherein the model comprises a probability distribution model and a conditional probability model; the risk quantitative evaluation module is used for evaluating risk exposure degrees of different decision-making schemes, including establishing a multi-period risk accumulation model, and comprehensively evaluating balance indexes of expected income, risk openness and opportunity cost; the robust optimization decision-making module constructs a decision-making model according to the market uncertainty modeling module and the risk quantitative evaluation module, and generates an optimal strategy that the electric vehicle cluster participates in the standby market; the adaptive learning optimization module is used for optimizing the decision model through reinforcement learning; according to the invention, risk-controllable revenue maximization can be realized, so that the electric vehicle cluster can efficiently participate in the electric power standby market.

The invention discloses an optical storage micro-grid economical efficiency optimization scheduling method considering demand response and source load uncertainty, and the method comprises the steps: firstly generating a plurality of probability models, namely scenes, of photovoltaic power generation power and user load power based on a Monte Carlo method, and then carrying out the k-means clustering, scene reduction and expectation solving, so as to deal with the source load uncertainty; and obtaining the photovoltaic power generation power and the user load power in each time period after considering the uncertainty of the source load. On this basis, the constraint condition and the objective function of the energy scheduling model of the energy storage system of the optical storage micro-grid are determined, and a flexible load and demand response mechanism is introduced, so that the optical storage micro-grid has the capability of adjusting demand fluctuation, and the economic optimization scheduling of the micro-grid is further realized. The high-proportion access of the optical storage micro-grid to the main power grid and the efficient utilization of the optical storage micro-grid are realized.

The invention discloses an automatic control gradient LED lamp energy-saving illumination method, which comprises the following steps: S1, asynchronous sampling is carried out through an illumination sensor A and an illumination sensor B, ambient light change data is collected, a sampling timestamp is recorded, a time difference weight is obtained based on the timestamp, data fusion is carried out through the time difference weight, and ambient light intensity data is obtained; s2, performing data calibration on the ambient light intensity data through a state space model, and performing optimization through a weighted moving average method to obtain an ambient light intensity change rate; and S3, acquiring a light change condition based on an environment illumination intensity change rate, adjusting a brightness duty ratio through a probability model based on Bayesian reasoning to output an initial control variable, performing optimization based on the initial control variable in combination with a particle filtering algorithm to obtain a final control scheme, and performing optimization through the probability model and the algorithm to obtain a final control scheme. A relatively accurate brightness adjustment scheme can still be given, and the intelligent level of lamp control is improved.

The invention discloses an accident risk assessment method considering risk avoiding behavior heterogeneity. The method comprises the following steps: acquiring trajectory data of a vehicle and preprocessing the trajectory data to enhance trajectory quality; driver risk avoiding behavior characteristics are extracted; the method comprises the following steps: carrying out probability modeling on response time, maximum deceleration and maximum jerk of a driver by utilizing relative motion state and risk avoiding behavior information among vehicles and adopting a Monte Carlo simulation method, and calculating probability collision time PCT; and comparing the calculated PCT value with a preset risk threshold value, and performing accident risk assessment in real time. According to the method, dynamic risk avoiding behaviors of a front vehicle and a rear vehicle in a real collision scene are brought into a risk assessment model, and a collision risk curve with a confidence interval is generated through parameterized modeling of heterogeneity distribution of response time, maximum braking deceleration and jerk. Compared with a traditional time collision index, the method breaks through a homogeneous driving behavior hypothesis, and can remarkably improve the accuracy and timeliness of risk perception in a complex traffic scene.

The invention relates to the technical field of data processing, and provides a missing data interpolation method and system based on a generative adversarial network. The method comprises the following steps: clustering a missing data matrix to obtain a clustering cluster containing a cluster label; based on the clustering cluster, performing classification prediction on a data feature vector corresponding to the missing data matrix through a logistic regression algorithm to obtain a cluster label prediction model; performing probability distribution modeling on the data feature vector through a Gaussian mixture model to obtain a probability model; training a generative adversarial network framework based on the cluster label prediction model and the probability model to obtain an interpolation model; and interpolating data to be interpolated through the interpolation model to obtain an interpolation data matrix. According to the invention, the interpolation precision and stability of nonlinear data are improved.

The invention provides a quantum error mitigation method and device, and the method comprises the steps: obtaining a sample quantum state through linear superposition of the results of a plurality of first unitary transformations which have a first probability and act on an accurate quantum state, constructing a quantum channel model, carrying out the inverse solution of the quantum channel model, obtaining a quasi-probability model, and carrying out the calculation of the quasi-probability model. The second initial state passes through a quantum circuit with noise to obtain an output quantum state, error correction is performed on the output quantum state by using a quantum error correction code method to obtain a quantum state to be subjected to error mitigation, and error mitigation is performed on the quantum state to be subjected to error mitigation by executing an operation process in a quasi-probability model on the quantum state to be subjected to error mitigation. According to the quantum error correction method, the number of physical bits required for realizing quantum error correction is reduced, the requirement on physical resources can be remarkably reduced on the premise of not sacrificing performance, so that the construction and maintenance of a quantum computing system are more economical and efficient, and higher computing accuracy can be kept under the conditions of higher noise and limited operation precision.

The invention discloses a statistical eye pattern generation method and device and a storage medium. The method comprises the following steps: inputting a step signal at a transmitting end of a channel, and determining a step response function based on the step signal; introducing a dithering signal at the transmitting end, and determining a dithering probability model based on the dithering signal; based on a jitter probability model, determining a jitter value set and a jitter probability set; determining a state transition probability matrix between the first moment and the second moment in the input step signal; based on the state transition probability matrix and the step response function, establishing an equivalent response distribution kernel for the target sampling moment; performing convolution operation on the voltage probability distribution at the previous sampling moment of the target sampling moment and the equivalent response distribution kernel, determining the voltage probability distribution at the target sampling moment, and determining the voltage probability distribution at multiple sampling moments; and generating a statistical eye pattern based on the voltage probability distribution at the plurality of sampling moments. The method has the technical effect of assisting in improving the credibility of simulation evaluation.

The invention discloses a nuclear magnetic resonance free induction decay signal reconstruction method based on a diffusion probability model, and relates to the field of magnetic resonance spectrums.The method comprises the steps that a simulation data set is constructed based on a mathematical model of free induction decay signals, and the simulation data set is divided into a training set, a verification set and a test set; sampling each data set by adopting a Poisson gap non-uniform sampling strategy, and simulating an actual under-sampling condition; constructing a diffusion probability model, gradually adding Gaussian noise through a forward diffusion module to generate a noise-containing sample, and recovering an original signal from a noise signal through a reverse denoising module; and inputting test data into the optimal model for multiple times to obtain a plurality of reconstruction results. According to the method, the reconstruction method based on the diffusion probability model is constructed, training optimization is carried out through Poisson gap non-uniform sampling and simulation signal data sets, and high-precision reconstruction of the under-sampling nuclear magnetic resonance free induction decay signals is achieved.

The invention relates to the technical field of potential safety hazard identification, in particular to an AI-based method and system for identifying potential safety hazards of hoisting machinery, which comprises the following steps of: collecting operation data of the hoisting machinery, including position, speed and load capacity, integrating meteorological conditions and information of near-field obstacles, and combining real-time transmission data to obtain a real-time transmission result of the hoisting machinery; and generating comprehensive real-time data. According to the method, comprehensive assessment and early warning of potential risks are achieved by integrating the operation data and the environment variables of the hoisting machinery, the operation environment can be simulated more accurately, occurrence of risk conditions can be predicted more accurately, the operation strategy is adjusted in time through dynamic risk scoring, the early warning accuracy and the operation safety are effectively improved, and the safety of the hoisting machinery is improved. The state definition and the probability model which are updated in real time can continuously adapt to environment changes, it is ensured that the operation strategy of the hoisting machinery is always kept in the optimal state, the accident occurrence probability is reduced, and meanwhile economic losses caused by equipment faults or misoperation are reduced.

The described technology is generally directed towards automated and dynamic creation of a recommended treatment for a patient's medical condition. A potential treatment can be generated by application of patient data to a multimodal model representing radiomic data and pathomic data integrated with transcriptomic data. The potential treatment can be applied to a probability model to determine a risk score for the potential treatment. The probability model comprises a collection of treatments and medical conditions, in conjunction with respective measures of success of application of a respective treatment to a respective condition. The multimodal model can comprise a network of nodes and edges, wherein the nodes represent knowledge of a patient condition, medical knowledge / medical research regarding the respective patient conditions, and the like. An output of the multimodal model is one or more recommended treatments for the medical condition.

The invention provides a sensor network target distribution method and device, and the method comprises the steps: obtaining the threat weight of a monitoring target according to the initial weight of the monitoring target in a sensor network; obtaining a sensor target monitoring efficiency function based on a sensor target sensing probability model and the threat weight of the monitoring target; and based on the sensor target monitoring efficiency function and the sensor utilization rate function, taking a randomly generated target allocation scheme as a giant panda initial population, obtaining a sensor network target allocation optimization result through a giant panda optimization algorithm, and performing target allocation on the sensor network based on the sensor network target allocation optimization result. Therefore, the multi-target distribution requirement of an automatic control device for the sensor network is effectively met, the monitoring precision can be improved, the sensor utilization efficiency is improved, and the energy consumption of the sensor network can be reduced.

The invention discloses a human body behavior prediction method and system, and the method comprises the steps: carrying out the skeleton point sequence extraction of a real-time behavior video image of a target person, and obtaining a joint point coordinate set and a skeleton motion sequence; determining a spatio-temporal feature sequence of the joint point coordinate set, and determining a skeleton motion sequence to perform spatio-temporal attention coding to obtain global spatio-temporal dynamic features; fusing the action probability distributions corresponding to the spatial-temporal feature sequence and the global spatial-temporal dynamic features to obtain short-time action probability distribution data; candidate action screening is carried out on the short-time action probability distribution data, and candidate action comprehensive features are obtained; performing feature coding on the historical action sequence of the target person to obtain a historical context vector, and splicing the historical context vector with the candidate action comprehensive features to obtain a fusion feature; and inputting the fusion features into a probability model for intention probability evaluation to obtain a behavior prediction result of the target person. According to the method, the accuracy of behavior prediction is improved.

The invention discloses a unified representation and dynamic knowledge reasoning method and device for an intelligent cluster system, and the method comprises the steps: determining a decyclization task sub-graph based on a composite task state space model of the intelligent cluster system through the priori information; constructing a real-time data sub-graph by using a real-time data feedback mechanism, and constructing a traceability mode sub-graph based on the decyclization task sub-graph; candidate traceability sub-graphs with the highest matching similarity with the traceability mode sub-graphs are determined from the real-time data sub-graphs to serve as traceability sub-graphs; constructing a reasoning path set based on the decyclization task subgraph, and calculating a probability value of each reasoning path in the reasoning path set by using a path probability model constructed based on a Bayesian conditional probability chain rule; and screening out the reasoning paths of which the probability values are smaller than a preset probability value in the reasoning path set by utilizing a dual path pruning strategy, and adding the reasoning paths containing abnormal nodes in the reasoning path set into an alarm path set so as to provide support for state prediction and risk management of the intelligent cluster system.

Methods and systems use an additional determination to generate predictions of future data processing load predictions. Specifically, the methods and systems generate additional predictions based on other data sets and data calculation methodologies (e.g., data with non-homogeneous time dependencies). The methods and systems then use these additional predictions to determine whether or not the prediction of the real-time average of data processing loads is an outlier. Thus, the methods and systems generate a time-specific data processing prediction.