32 results about "Mean vector" patented technology

The invention relates to the technical field of environmental parameter measurement, in particular to an indoor environment quality intelligent evaluation method based on multi-dimensional data fusion, which comprises the following steps: step 1, performing time-space synchronous acquisition on multi-source data; 2, a dynamic correlation model is constructed, a parameter coupling matrix is generated based on inter-parameter time-delay cross-correlation analysis, the time-delay cross-correlation analysis is achieved through sequence translation in a sliding time window and correlation coefficient calculation, an environment state class is constructed according to a parameter mean vector, fluctuation intensity and abnormal event marks, and a dynamic correlation model is constructed; each environment state class is bound with an independent parameter contribution degree weight set; step 3, performing double-stage environment quality evaluation; and step 4, adaptive increment optimization: when the deviation between the comprehensive evaluation index and the subjective evaluation exceeds a threshold value, adjusting a parameter contribution degree weight set of an environment state class, and periodically reconstructing a parameter coupling matrix. The prediction capability is improved by considering the time-delay coupling between the parameters; the system has self-learning and optimization capabilities, and the stability of long-term operation of the system is improved.

The invention discloses an equipment operation safety detection method and system of an energy storage system, and relates to the technical field of energy storage system safety detection.The method comprises the steps that multichannel original data are collected and preprocessed, and multichannel signals are obtained; extracting an instantaneous frequency through adaptive decomposition and Hilbert transformation based on the multi-channel signal, performing modal reconstruction based on the instantaneous frequency, extracting a switch frequency band energy feature, an EMI feature, a DC ripple and a coherent feature, and constructing a fingerprint vector; collecting health state historical data, calculating a mean vector and a covariance matrix, and constructing a health template; based on the health template and the real-time fingerprint vector, analyzing a safety state through dual statistical judgment, and outputting a safety statistical magnitude; and judging abnormity based on the current safety statistics, performing LASSO sparse reconstruction by using a predefined typical fault template, and outputting a fault type. Therefore, efficient and accurate fault diagnosis and automatic alarm are realized.

The invention provides a quality control system based on a multivariate exponential weighted moving average control chart, and belongs to the technical field of quality management and statistical process control. The system comprises a quality data acquisition module, a data preprocessing module, a statistical process control module, an anomaly judgment module and a quality evaluation and improvement module, and is used for monitoring and controlling the production process of a product with a plurality of quality characteristics in real time. The method comprises the following steps of: acquiring quality data of a plurality of quality characteristics in a production process by a system, preprocessing the quality data, and constructing a multivariate quality characteristic data set; estimating a mean vector and a covariance matrix in a controlled state based on the historical quality data, performing weighted updating on the multivariate quality data by adopting a multivariate exponential weighted moving average method, calculating a corresponding MEWMA statistic and generating an MEWMA control chart; and comparing the MEWMA statistical magnitude with a preset control limit to judge whether the production process is in an out-of-control state or not, and outputting early warning information and a corresponding quality evaluation result and improvement suggestion when abnormality is detected. The method can comprehensively analyze related information among multivariate quality characteristics, improves the sensitivity and accuracy of anomaly detection in the production process, and effectively improves the quality control level of the production process in the manufacturing industry.

The invention provides an ISAC system signal detection method based on block diagonal expected propagation, and the method comprises the steps: obtaining a communication signal and a target echo signal transmitted by multi-antenna user equipment through a receiving end, carrying out the whitening of the received signal and noise, and minimizing the interference of a radar to communication; converting the optimized complex signal into a real value system model; initializing a mean vector and a covariance matrix parameter of the expectation propagation algorithm, and setting the number of iterations; calculating diagonal elements of the covariance matrix by adopting a block diagonal Neumann series approximation method; updating a mean vector and adjusting an iteration step length through a dynamic residual factor; and after a preset number of iterations, outputting a final detection signal. According to the method, the complexity is reduced by calculating the diagonal elements of the matrix inversion instead of directly solving the matrix inversion, and a special averaging method is designed for the diagonalization characteristic of the matrix with the similar channel hardening characteristic to optimize the precision.

The invention provides a multi-channel national secret task scheduling method and system based on dynamic priority, and belongs to the technical field of information security. The method comprises the following steps: determining an initial urgency factor according to remaining time from a national secret task to deadline; determining a dynamic enhancement factor by adopting an adaptive immune optimization enhancement algorithm to correct the initial emergency factor to obtain a final emergency factor; a Bayesian linear regression model corresponding to the Bayesian updating channel is used to generate a new value of a posterior mean vector and a new value of a posterior covariance matrix, a basic suitability factor is determined according to the new value of the posterior mean vector, and the basic suitability factor is corrected according to the new value of the posterior covariance matrix to obtain a final suitability factor; multiplying the dynamic enhancement factor, the final urgency factor and the final suitability factor to obtain the priority of the national secret task; and constructing a global optimal allocation model according to the priorities of the national secret tasks, and solving to obtain a national secret task scheduling strategy. According to the invention, the system resource use efficiency is improved.

The invention discloses a distributed particle filtering algorithm based on Gaussian mixture and Gossip fusion, which relates to the technical field of distributed state estimation and comprises the following steps: acquiring local posteriori distribution by a sensor node through local particle filtering, exchanging sufficient statistics of GMM between the node and a randomly selected neighbor node based on a Gossip protocol, and obtaining a distributed state estimation result. Iterative fusion of the GMM is realized through weighted mixed importance sampling; and finally, optimizing a global state estimation result through a posterior correction step. According to the method, only full statistics such as a mixed coefficient, a mean vector and a covariance matrix of a Gaussian mixture model (GMM) are exchanged among nodes, original particle transmission is replaced, the communication overhead is remarkably reduced, and the communication complexity is linearly increased along with the network scale; meanwhile, an adaptive GMM component adjustment, a regularization weighting EM algorithm and an iteration updating strategy of random weight matrix control are adopted, the calculation process is simplified on the premise that the estimation precision is guaranteed, the single-node calculation burden is reduced, and large-scale sensor network deployment with limited adaptive resources is achieved.

The invention provides a batching method for a flash furnace. The method comprises the following steps: generating a plurality of batching schemes; each batching scheme is input into the flash furnace digital model for analog calculation, and a heat balance parameter corresponding to each batching scheme is obtained; calculating a comprehensive loss value corresponding to each batching scheme so as to update a mean vector and a covariance matrix in a covariance matrix adaptive evolutionary algorithm; judging whether the difference value between the predicted value and the target value of the heat balance parameter of each batching scheme exceeds an allowable error or not; if the batching scheme which does not exceed the allowable error exists, outputting the batching scheme and ending; and if all the batching schemes exceed the allowable error, repeating the above steps according to the updated mean vector and covariance matrix. The batching schemes are generated through an algorithm, simulation calculation is carried out, the heat balance parameter of each batching scheme is predicted, and the batching schemes are evaluated by judging the heat balance parameters, so that the optimal batching scheme is obtained.

The invention relates to the technical field of machine learning data preprocessing, in particular to a machine learning sampling method and system capable of keeping a feature proportional relation, and aims to ensure that the proportional relation between features and target variables is not distorted by dividing category subsets and constraining that sampled feature mean vectors are consistent with original data. And misjudgment of feature importance by the model is avoided. And by verifying the consistency of the covariance matrix, the correlation structure between the features is maintained, and the generalization ability of the model depending on the statistical characteristics is improved. While the problem of sample imbalance is solved, the proportional relation between the features in the original data set and the target variables and the proportional relation between the features are strictly maintained, so that the accuracy and reliability of machine learning model training are improved.

The invention relates to the technical field of intelligent ore processing, and discloses a multi-element grade prediction method based on multi-task collaboration and covariance modeling. The method comprises the steps that an ore grade prediction model is constructed, the ore grade prediction model comprises a feature coding layer, a fusion processing layer and an output layer, the feature coding layer is provided with a plurality of encoders for multi-modal data of ore, the fusion processing layer is provided with fusion blocks based on an attention mechanism, and the output layer is constructed based on a multi-layer perception mechanism; the method comprises the following steps: acquiring multi-modal data of to-be-detected ore, inputting the multi-modal data into an ore grade prediction model, respectively acquiring corresponding single-modal features through a plurality of encoders, acquiring fusion features by a fusion processing layer based on the single-modal features, acquiring a mean vector and a covariance matrix by an output layer based on the fusion features, and performing prediction distribution. And analyzing the ore grade data according to the prediction distribution result and regulating and controlling the process flow. The problem that the ore grade cannot be effectively and accurately detected by the existing method for predicting the ore grade by adopting machine learning is solved.

The invention discloses an ion implantation uniformity adjusting method, which comprises the following steps: step 1, establishing a wafer ion implantation amount model, dividing a wafer into n wafer areas and dividing a scanning range of an ion beam into m implantation positions in the model, the model comprising an n * m first matrix, an m * 1 second matrix and an n * 1 third matrix; the first to third matrixes are respectively an injection rate matrix, an injection time matrix and an injection amount matrix, and the third matrix is obtained by multiplying the first matrix by the second matrix. Step 2, performing iterative optimization, including giving values of a second matrix and a third matrix, and calculating a mean vector matrix and a covariance matrix required for enabling the first matrix to conform to normal distribution; and calculating the value of the first matrix through the mean vector matrix and the covariance matrix. And predicting and adjusting the value of the second matrix according to the value of the first matrix, and obtaining the value of the adjusted third matrix according to the value of the first matrix and the adjusted value of the second matrix. The method can reduce the number of iterations.

The embodiment of the invention discloses a fire point detection method based on satellite data and related equipment, and the method comprises the steps: obtaining a plurality of pieces of satellite time series data when there is no fire point, obtaining a potential space vector through a preset encoder, calculating a mean vector and a covariance matrix, and enabling the covariance matrix to describe the discrete degree of data in different directions, the shape of the fire-point-free sample cluster can be described more accurately, and the method is not limited to hypersphere hypothesis any more. For a to-be-detected target point, after target satellite time sequence data and a target potential space vector of the to-be-detected target point are obtained, the submerged space anomaly is obtained in combination with a mean vector and the covariance matrix, and the method for obtaining the submerged space anomaly in combination with the covariance matrix fully considers the actual distribution form of a fire-point-free sample in the submerged space. Therefore, the accurate potential space anomaly degree is obtained, the potential space anomaly degree is input into the preset classifier, the accuracy of fire point detection can be effectively improved, and abnormal point misjudgment caused by false hypothesis is avoided.

The invention relates to the field of computers and communication, and provides a passive Internet of Things positioning method and device, electronic equipment, a medium and a program product, the method comprises the following steps: preprocessing label data collected from passive Internet of Things equipment, the label data comprising data of at least two dimensions; based on the preprocessed label data, respectively estimating a mean vector and a covariance matrix of the label data of the target label; constructing a multivariate Gaussian distribution model corresponding to each target label based on the mean vector and the covariance matrix; performing data enhancement by using a multivariate Gaussian distribution model to generate an enhanced data sample of the target tag; merging the original data sample and the enhanced data sample of the label data to generate an enhanced data sample; constructing a sample set according to the data sample of each label in the passive Internet of Things equipment; and inputting the real-time label data into a positioning model obtained based on sample set training, and outputting a label positioning result. According to the method, effective data enhancement is realized, the sample balance is improved, the accuracy and robustness of a label positioning result are improved, and the positioning precision is improved.

The invention discloses a Bates model parameter estimation method and device and a related product. The method comprises the following steps: determining observation parameters, to-be-estimated parameters and scalar features; dividing intervals for the scalar features, calculating to obtain a cumulative probability corresponding to each interval, and forming an observation statistic vector; wherein the scalar features are extracted from the observation parameters; for the candidate value of any to-be-estimated parameter, simulating n trajectories through a numerical method, and extracting target features of each trajectory; dividing intervals for the target features, calculating to obtain a cumulative probability corresponding to each interval, and forming a first simulation statistic vector; according to each first simulation statistic vector, obtaining a mean vector of the simulation statistic and a covariance matrix of the simulation statistic; and under the condition that the observation statistic vector obeys Gaussian distribution, synthesizing an approximate likelihood function according to the mean vector of the observation statistic vector simulation statistic and the covariance matrix of the simulation statistic, and estimating the model parameters according to the approximate likelihood function.

The invention relates to the field of laser radars, and provides a laser radar point cloud modeling method, system, equipment and medium under a dynamic sea condition, and the method comprises the steps: obtaining unmanned vehicle radar data, unmanned vehicle motion data and water surface environment data of an unmanned vehicle; performing point cloud inter-frame registration and dynamic point elimination to obtain static point cloud data; the method comprises the following steps: performing motion compensation through motion data of an unmanned vehicle in a wake flow area to obtain compensation radar data, performing kernel density estimation to obtain a Poisson wake flow parameter, calculating a Poisson wake flow model parameter through the Poisson wake flow parameter, and obtaining a Poisson term coefficient based on the kernel density estimation; calculating the total number of Gaussian components, calculating the mean vector and Gaussian component weight of the static point cloud data, obtaining a covariance matrix, and constructing a dynamic surface model; and constructing a parallel maximization likelihood function, and performing model parameter estimation through the parallel maximization likelihood function to obtain updated parameters, thereby obtaining target point cloud data and completing modeling of the point cloud data.

The invention discloses a power distribution system collaborative optimization operation method and device, and the method comprises the steps: constructing a generalized energy storage collaborative optimization model of a power distribution system according to the charging and discharging characteristics of an electric vehicle, the charging and discharging characteristics of a fixed energy storage device, and the load characteristics; constructing a context vector learning model containing a mapping relation between context vectors and optimization parameters by using historical operation data of the power distribution system and a generalized energy storage collaborative optimization model of the power distribution system and adopting a Gaussian process regression method; according to the mapping relation, a context vector of the current scene is extracted, initial distribution of parameters is predicted and optimized, and an initial mean vector and a covariance matrix of a CMA-ES algorithm are obtained; and executing a CMA-ES iterative optimization process by adopting the initial mean vector and the covariance matrix, and outputting a scheduling scheme of the electric vehicle and the fixed energy storage device containing the charging and discharging power of each time period. According to the scheme, hot start of the CMA-ES algorithm is realized through context vector learning, the optimization efficiency is improved, and falling into a local optimal solution is avoided.

The application provides a polysilicon production equipment fault diagnosis method and device, a server and a storage medium, and relates to the field of equipment fault diagnosis. The method comprises the following steps: acquiring target equipment collected sample data and first historical sample data within a preset time window; determining a first set composed of equipment parameters and a second set composed of equipment parameters and corresponding upstream and downstream process parameters; further determining the first robust distance of the equipment parameters according to the equipment mean vector and the equipment covariance matrix; determining the second robust distance of the equipment parameters and the corresponding upstream and downstream process parameters according to the overall mean vector and the overall covariance matrix; if the first robust distance is greater than the first robust distance threshold and / or the second robust distance is greater than the second robust distance threshold, the running state is determined to be an abnormal state. The comprehensiveness of the diagnosis and the accuracy of the diagnosis result are ensured, the stability of the benchmark during diagnosis is ensured, and the diagnosis efficiency is improved.

The invention provides a non-zero mean clutter neutron space signal adaptive detection method and system. The method comprises the following steps: firstly, jointly estimating a mean vector and a covariance matrix of clutters by using to-be-detected data and auxiliary data; then, on the basis of the subspace signal model, deriving a detection statistic by utilizing a generalized likelihood ratio criterion; and finally, comparing the detection statistics with a preset threshold to complete target detection. According to the method, the non-zero mean value of the clutter is explicitly estimated and compensated, so that the adaptive detector keeps a false alarm rate characteristic for a clutter covariance matrix and also has a false alarm characteristic for the non-zero mean value of the clutter in a non-zero mean value clutter environment, and the detection probability is remarkably improved; the technical problem that a traditional subspace detector fails in the environment is solved.

This invention belongs to the field of communications and provides a method and apparatus for diagnosing communication faults, comprising: acquiring a backscattered time-domain signal from a communication optical cable, obtaining multiple intrinsic mode functions through variational mode decomposition, selecting the signal with the largest kurtosis as the analysis signal, reconstructing the phase space of the analysis signal, and determining a set of relevant integral radii; calculating the relevant integral for a preset embedding dimension and candidate delay time values ​​to obtain difference statistics and correlation statistics, constructing a global index function and determining the optimal delay time, extracting the optimal delay time, corresponding curvature, and standard deviation of the difference statistics to form a real-time state feature vector, calculating the Mahalanobis distance based on the normal state benchmark mean vector and covariance matrix, comparing the distance with a preset threshold, and completing the diagnosis of communication optical cable faults.

The invention discloses a prediction method, system and equipment named as a power load prediction method based on a time sequence, relates to the technical field of power prediction, and aims to solve the problems of insufficient date feature expression, non-uniform sample distribution and insufficient description of a model on date sequential logic and semantic association in an existing method. The method comprises the steps of obtaining date type labels of multi-level date types according to historical power loads of dates; obtaining a mean vector and a covariance matrix of each date type as load mode characteristics according to historical loads and date type labels; according to the load mode characteristics, a time sequence prediction model is obtained based on a gating circulation unit, so that rich multi-level date type characteristics and load mode characteristics can be combined; and finally, updating the time sequence prediction model in a rolling manner after the actual power load of one day is newly added every time. Through the scheme, the adaptability and accuracy of power load prediction in a complex and variable load scene are further enhanced.

This invention provides a method and apparatus for blast furnace process fault diagnosis based on dynamic canonical correlation analysis, belonging to the field of blast furnace process fault diagnosis technology. The method includes: acquiring offline and online blast furnace data; performing data identification processing based on the offline and online blast furnace data using a data-driven method to obtain offline zero-mean vectors and online zero-mean vectors; performing fault verification using the SD-CCA method based on a preset fault threshold and the offline and online zero-mean vectors to obtain SD-CCA-based residual vectors and fault detection results; when the fault detection result indicates a fault, performing fault retrieval based on the SD-CCA-based residual vectors and obtaining the fault type based on a fault database. This invention is an accurate and efficient method for blast furnace process fault diagnosis based on dynamic canonical correlation analysis.

The invention provides an anomaly detection method and device, electronic equipment and a storage medium. The method comprises the steps of collecting a data packet obtained by performing database query based on a service code; processing the data packet to obtain index data; determining a corresponding multi-dimensional mean vector and a covariance matrix from a corresponding preset baseline model according to the query statement ID; calculating a comprehensive abnormal score based on the multi-dimensional mean vector, the covariance matrix and the index data; calculating an independent abnormal score based on the index data of each dimension and the historical index data; and if the comprehensive exception score is greater than a preset threshold, generating alarm data based on the comprehensive exception score and the independent exception score of the performance index of each dimension. The method comprises the following steps: establishing index data of a multi-dimensional performance index for each SQL query statement ID; and overall anomaly detection is carried out based on the comprehensive anomaly score determined by the performance index data, so that potential memory overflow and system avalanche risks can be avoided.

The invention provides a robust subspace signal correction Wald detection method under strong clutters, which comprises the following steps of: constructing a complex domain binary hypothesis detection model, acquiring estimated values of a clutter mean vector, a covariance matrix and a signal coordinate vector through maximum likelihood estimation, deducing based on a correction Wald detection criterion to obtain an effective detector Wald-NMC, and detecting the robust subspace signal under the strong clutters through the effective detector Wald-NMC. And determining a detection threshold in combination with a preset false alarm probability to realize robust detection of the target signal. According to the method, the detector design is completed in the complex domain, the complete information of the signal is reserved, the method has a relatively good inhibition capability on non-zero mean strong clutter, shows excellent robustness in a signal mismatch scene, and can be widely applied to the engineering fields of radar target detection, sonar underwater target identification and the like.

The invention discloses a near infrared spectrum data enhancement method and device based on Gaussian joint distribution sampling, and relates to the field of data enhancement, and the method comprises the following steps: obtaining near infrared spectrum data and chemical values of a sample; the method comprises the following steps: preprocessing a near infrared spectrum, performing standardization and zero-mean centralization processing on a preprocessed spectrum matrix, performing synchronous centralization on chemical values, performing dimension reduction processing on the standardized and centralized spectrum matrix, and splicing the dimension-reduced spectrum matrix and the centralized chemical values in sequence to construct a joint matrix; then, a sample covariance matrix is calculated, regularization processing is carried out, sampling is carried out from multivariate Gaussian distribution based on the zero-mean vector and the regularization covariance matrix, and centralized virtual samples with the required number are obtained; and recovering the centralized virtual sample to an original scale, and separating virtual dimension reduction data and virtual chemical values. According to the method, the problems of model overfitting, low prediction precision and the like caused by insufficient samples during modeling in a small sample scene can be solved.

The application provides an ISAC system signal detection method based on block diagonal expectation propagation, wherein a communication signal and a target echo signal sent by a multi-antenna user equipment are acquired by a receiving end, the received signals are subjected to noise whitening processing, and interference of radar on communication is minimized; the optimized complex signals are converted into real value system models; mean vector and covariance matrix parameters of an expectation propagation algorithm are initialized, and an iteration number is set; diagonal line elements of the covariance matrix are calculated by using a block diagonal Neumann series approximation method; the mean vector is updated, and an iteration step is adjusted by using a dynamic residual factor; and finally, a final detection signal is output after a preset number of iterations. The diagonal line elements of the matrix inverse are calculated to replace direct solution of the matrix inverse, so that the complexity is reduced, and a special mean calculation method is designed for the matrix diagonalization characteristics with similar channel hardening characteristics, so that the precision is optimized.

This invention discloses a method for identifying and locating abnormal bearing temperatures in hydro-generator units. The method involves acquiring bearing temperature data and unit operating parameters from a database; identifying the current operating condition based on unit speed and active power; using a multi-criteria fusion approach to identify abnormal bearing temperatures; extracting preset interval statistical features from three dimensions—cooling system, lubrication system, and shaft vibration—to form a multi-dimensional feature vector; calculating the mean vector and covariance matrix corresponding to each predefined typical bearing system fault to form a fault standard library; and using the mean vector of each fault type as a reference center and the covariance matrix as a distribution scale, calculating the statistical distance from the multi-dimensional feature vector of the current abnormal window to each fault category using the low-rank Mahalanobis distance method, and determining the category with the smallest distance as the most likely fault type, outputting the fault location result.

The present application relates to the field of laser radar, and provides a laser radar point cloud modeling method, system, device and medium under dynamic sea conditions, comprising obtaining unmanned vehicle radar data, unmanned vehicle motion data and water surface environment data of a measured unmanned vehicle; performing point cloud inter-frame registration and dynamic point elimination to obtain static point cloud data; performing movement compensation in the wake area through the unmanned vehicle motion data to obtain compensated radar data, performing kernel density estimation to obtain Poisson wake parameters, calculating Poisson wake model parameters through the Poisson wake parameters, obtaining Poisson term coefficients based on the kernel density estimation; calculating the total number of Gaussian components, calculating the mean vector and Gaussian component weight of the static point cloud data, obtaining a covariance matrix, and constructing a dynamic surface model; constructing a parallel maximum likelihood function, performing model parameter estimation through the parallel maximum likelihood function to obtain updated parameters, thereby obtaining target point cloud data and completing the modeling of the point cloud data.

This invention relates to the field of intelligent ore processing technology and discloses a multi-element grade prediction method based on multi-task collaboration and covariance modeling. The method includes: constructing an ore grade prediction model, which comprises a feature encoding layer, a fusion processing layer, and an output layer. The feature encoding layer uses multiple encoders for multimodal ore data; the fusion processing layer uses fusion blocks based on an attention mechanism; and the output layer is constructed based on a multilayer perceptron. Multimodal data of the ore to be detected is acquired and input into the ore grade prediction model. Corresponding single-modal features are obtained through multiple encoders. The fusion processing layer obtains fused features based on the single-modal features. The output layer obtains the mean vector and covariance matrix based on the fused features and performs a predicted distribution. The ore grade data is analyzed based on the predicted distribution results, and the process flow is adjusted accordingly. This method solves the problem that existing methods using machine learning for ore grade prediction cannot effectively and accurately detect ore grade.

The invention relates to the technical field of molecular identification of pig varieties, and discloses an identification method of southern Anhui black pigs, which comprises the following steps: step 1, extracting genome DNA of a sample to be detected; step 2, carrying out genetic typing on the 16 SNP sites according to the claim 1 to obtain genotype information of each SNP site; step 3, carrying out numerical coding on the genotype information obtained in the step 2 to obtain a 16-dimensional feature vector X of the sample to be detected; the rule of numerical coding is as follows: the homozygous coding of the reference allele is 0, the heterozygous coding is 1, and the homozygous coding of the substitution allele is 2; and step 4, performing standardization processing on the feature vector X to obtain a standardized feature vector X '. According to the method, 16 specific SNPs are used as a core marker set, unified 0, 1 and 2 codes, standardized parameters (including a mean vector mu and a standard deviation vector sigma) and a threshold judgment rule are matched, a detection end process is clear, repeatability is high, and consistent implementation among different laboratories is facilitated.

The application provides a method for batching a flash furnace, which comprises: generating a plurality of batching schemes; inputting each batching scheme into a digital model of the flash furnace for simulation calculation to obtain a corresponding heat balance parameter of each batching scheme; calculating a corresponding comprehensive loss value of each batching scheme to update a mean vector and a covariance matrix in a covariance matrix self-adaptive evolution algorithm; judging whether a difference between a predicted value and a target value of the heat balance parameter of each batching scheme exceeds an allowable error; if there is a batching scheme that does not exceed the allowable error, outputting the batching scheme and ending; and if all the batching schemes exceed the allowable error, repeatedly executing the above steps according to the updated mean vector and covariance matrix. The batching scheme is generated by an algorithm, and simulation calculation is performed to predict the heat balance parameter of each batching scheme, and the batching scheme is evaluated by judging the heat balance parameter, so that the optimal batching scheme is obtained.

Provided are a method, apparatus, and a computer program for compressing NDT map data for an autonomous driving system. Various embodiments of the present invention provide a method of compressing normal distribution transform (NDT) map data generated by modeling a three-dimensional (3D) point cloud for a predetermined area as a normal distribution set for an autonomous driving system, which is performed by a computing device, the method including: processing mean vector data included in the NDT map data; processing covariance matrix data included in the NDT map data; and generating compressed NDT map data using the processed mean vector data and the processed covariance matrix data.