427results about How to "Strong explainability" patented technology

The invention discloses a roadbed settlement prediction method based on IGWO-BP and ARIMA. The roadbed settlement prediction method comprises the following steps: firstly, obtaining a seasonal permafrost region settlement data composition, wherein the seasonal permafrost region settlement data composition comprises short-term linear characteristics and long-term nonlinear change characteristics; preprocessing the original monitoring data, dividing the data into a training set and a test set, and constructing an IGWO-BP model based on the training set; then, an IGWO-BP model is trained on the training set, a training residual sequence is further obtained, and meanwhile, the trained IGWO-BP model is adopted to predict the test set; constructing a model ARIMA, and predicting a network residual error based on the model; finally, the prediction result of the IGWO-BP model and the residual error sequence prediction result of the ARIMA model are connected in series, error correction of the prediction result is completed, and a final combined prediction result is obtained.By means of the method, the freezing injury occurrence characteristics of the seasonal frozen soil region can be well captured, and the defect of prediction of a single model is overcome.

The invention relates to the technical field of nursing scheme intelligent recommendation systems, in particular to a cardiology patient personalized nursing scheme intelligent recommendation system optimization method, which comprises a dynamic rare case recognition engine for accurately capturing and recognizing cardiology rare cases by using medical data and text data, and comprises a data preparation module, a data retrieval module, a data processing module and a data processing module, the candidate sample classification and recognition module is used for recognizing whether rare cases exist or not, and the problems existing in the prior art are effectively solved by introducing innovation modules such as a dynamic rare case recognition engine, a knowledge graph builder and a rare case simulation generator. According to the invention, continuous learning and updating can be realized, rare cases can be accurately identified, and customized nursing suggestions can be provided based on individual features of patients. Meanwhile, the decision-making process of the system has good interpretability, decision-making basis can be provided for doctors, and usability and credibility of the system in clinical practice are greatly improved.

The invention discloses an in-structure sound field bounded uncertainty analysis method based on a physical information neural network. The method comprises the following steps: establishing a physical information loss function and establishing a physical information driven neural network; obtaining an input uncertain parameter value range of the internal sound field, obtaining a plurality of parameter levels, and training a sound pressure prediction model under the parameter levels; acquiring sound pressure of a sample point and establishing a data set; establishing a data driving residual error based on the data set, establishing an overall loss function by using the data driving residual error and a physical information loss function, and training a parameter-containing physical information driving neural network to obtain a parameter-containing internal sound field sound pressure prediction model; and establishing an interval model, and obtaining an internal sound field sound pressure uncertainty distribution range by using a global search algorithm and the parameter-containing internal sound field sound pressure prediction model. The method can solve the problem of prediction of the sound field in the structure containing uncertain parameters, and can effectively improve the calculation efficiency on the premise of ensuring that the calculation precision meets the engineering requirements.

The invention discloses an energy carbon data analysis method and system, electronic equipment and a computer storage medium, and belongs to the technical field of large models. The method comprises the following steps: receiving an energy carbon data analysis request of a user side; analyzing the energy-carbon data analysis request through a pre-constructed large language model to obtain an overall task corresponding to the energy-carbon data analysis request; decomposing the overall task into a plurality of sub-tasks, wherein each sub-task processes data analysis in a specific field; calling a corresponding sub-model for each sub-task, and obtaining an intermediate result output by the sub-model; and fusing the intermediate results to obtain an energy-carbon data analysis result corresponding to the energy-carbon data analysis request. Through the method, the technical problems that a traditional decision analysis method is single in data source and limited in dimension are solved.

The invention relates to the technical field of video source switching, and discloses an intelligent video source switching system and method for a director switching station. The method comprises the following steps: acquiring an input video stream of a current video source through a director switching station, and performing N-gram feature vector calculation and inter-frame difference analysis to obtain video frame segmentation data; performing scene analysis on the video frame segmentation data to obtain a scene feature signature; performing spatial-temporal feature extraction and fusion on each video frame in the input video stream according to the scene feature signature to obtain a scene spatial-temporal fusion feature set; inputting the scene space-time fusion feature set into an LSTM-based lightweight time sequence convolutional network model to carry out video switching behavior prediction so as to obtain video switching behavior prediction data; and inputting the video switching behavior prediction data into a double-depth Q learning model based on a Markov decision process to carry out video source switching decision to obtain a video source switching decision result, thereby improving the intelligent video source switching accuracy of the director switching station.

The invention relates to the technical field of remote water meter abnormity early warning, and discloses a remote water meter abnormity early warning method and system based on historical water volume, and the method comprises the steps: dividing a user water meter into three classes A, B and C according to the installation time of the user water meter, and employing a differentiated abnormity detection strategy. For the A-type users, establishing a water consumption prediction model based on historical consumption data of the A-type users; for the B-type users, finding similar reference water meters from the A-type users by using a K-means clustering algorithm; and for the C-type users, monitoring whether the accumulated water consumption for continuous N days is completely equal or not after the C-type users are installed. After real-time usage data is obtained, usage deviations of various users are calculated respectively, if the deviations exceed a set threshold value, abnormal early warning is triggered, and the early warning level is dynamically adjusted according to the number of abnormal duration days. According to the method, technologies such as machine learning and clustering analysis are comprehensively utilized, a set of comprehensive, flexible and efficient water consumption abnormity early warning system is constructed, and the refinement and intelligence level of urban water supply management can be remarkably improved.

The invention relates to a full-route bump feature mining and EDR estimation method based on airborne QAR data. The method comprises the following steps: forming an EDR basic data set; mining and extraction of advanced features are realized; forming an optimized feature set; obtaining a trained neural network model; and calculating the full-route EDR value of the real route. Through combination of discrete wavelet transform and a neural network model, the invention provides an accurate and reliable EDR estimation method, and compared with a traditional single-parameter method, detail description is more obvious and is completely and automatically calculated by a computer without manual reasoning. According to the method, after model training and optimization, the EDR value on a single course can be estimated in real time, and the accuracy and practicability of analyzing an airplane bumping event through flight data in the aviation field are improved.

The invention discloses a transformer fault voiceprint diagnosis method based on a Transform algorithm, and the method comprises the steps: installation of collection equipment: installing a sound collection device at the periphery of a transformer, and collection of sound signals: collecting the sound signals in the operation process of the transformer through the sound collection device, carrying out normalization, pre-emphasis, MRSVD noise reduction processing, filtering processing and signal feature extraction on the acquired sound signals; extracting a dominant frequency, a voiceprint component variation, a peak factor and a signal sound pressure level of the sound signals; according to the invention, the method can realize early warning and accurate diagnosis of faults of the transformer, and improves stable operation and reliability of the transformer. The method comprises the following steps of: acquiring a sound signal characteristic vector, caching the vectors of the four characteristics into a system, comparing the sound signal characteristic vector which is being diagnosed with the characteristic vector cached in the last time period, and calculating the variable quantity of each characteristic, and the like. The method can realize early warning and accurate diagnosis of the faults of the transformer, and improves stable operation and reliability of the transformer.

The invention relates to the technical field of computer vision and deep learning, and relates to a deep forgery detection method based on mask supervision. According to the technical scheme, an image is input to a preprocessing module for preprocessing; the processed image is respectively input into a mask supervision module for extracting spatial domain features and a frequency domain feature extraction module for extracting frequency domain features; and the spatial domain features and the frequency domain features are connected and input to a classification module to obtain a final prediction result. The method has the beneficial effects that the attention of the model to the irrelevant region is supervised and corrected through the mask, and a pixel-level label is used for guiding the model to face the synthesis region of the face, so that more accurate extraction of spatial features is ensured; the depth forgery detection method based on mask supervision has remarkable advantages in the aspects of detection accuracy, robustness, generalization ability, calculation efficiency, interpretability, overall performance optimization and the like, and a new solution is provided for the field of depth forgery detection.

The invention provides an electric vehicle charging load space-time prediction method based on hybrid model driving, and relates to the field of electric vehicle charging, and the method comprises the steps: building a user travel model based on a Markov chain; constructing a multi-angle traffic road network model and a load space-time prediction model based on multi-graph feature fusion; acquiring user historical travel data, electric vehicle charging load historical data, traffic network historical information and external historical data of the to-be-predicted area; generating a user MDP trip chain according to the user historical trip data of the to-be-predicted area through a user trip model; extracting a multi-angle spatial relation graph from the traffic road network historical information of the to-be-predicted area through the multi-angle traffic road network model; the electric vehicle charging load of the to-be-predicted area is predicted through the load space-time prediction model based on multi-graph feature fusion according to the user MDP trip chain, the multi-angle spatial relation graph and the external historical data, and the method has the advantage of improving the accuracy of electric vehicle charging load prediction.

The invention discloses a meta-cognition level prediction method based on voiced thinking driven retrieval enhancement, which is characterized in that an information extraction method in a deep learning technology is adopted to extract answer features, a voiced thinking response analysis library is constructed, a correlation mode between various answer contents and corresponding meta-cognition scores is established, and a prediction result is obtained. The method comprises the following steps: establishing a mapping model, bridging the internal relation between the answer content and the meta-cognitive level by using the established mapping model, matching the sound thinking answer with the existing answer sample in a sound thinking response analysis library to realize the prediction of the meta-cognitive level, and specifically comprising the steps of data collection, data enhancement, big five personality correction, mapping model establishment, matching, retrieval and the like. Compared with the prior art, the method has the advantages that a more scientific and targeted problem design scheme can be provided on the basis of real-time evaluation of the meta-cognition level of the student when the type of problems proposed to the student is determined, so that the method has remarkable advantages in the aspects of education evaluation and personalized learning support.

The invention belongs to the field of geological engineering construction, discloses an automatic analysis method and system for tunnel advanced geological forecast by an elastic wave method, and solves the problems of few training samples, poor universality of intelligent model engineering and low automation level in the research of applying artificial intelligence to tunnel advanced geological forecast analysis in the prior art. The method comprises the following steps: identifying an elastic wave method parameter image to obtain elastic wave method parameter data, and establishing an elastic wave method parameter database; a mapping relation of elastic wave method key geophysical prospecting parameters-geological indexes-geological quality is established by combining a rock mass mechanics theory, a geological disaster formation mechanism and the like so as to establish a classifier classification principle; and finally, adopting a machine learning method to construct a classifier model according to a classifier classification principle, and obtaining training and testing samples based on an elastic wave method parameter database to train and verify the model. By utilizing the model, real-time whole-process automatic analysis can be realized, so that the analysis accuracy and efficiency are improved.

The invention relates to a transformer comprehensive safety assessment method and system based on symbol regression. The method comprises the following steps: acquiring monitoring variables of an electrical signal dimension, an equipment state dimension and an operation environment dimension of a transformer; based on the maximum mutual information coefficient, screening the dimension monitoring variables to obtain a plurality of monitoring variable sets with closely associated variables; performing sign regression on the plurality of monitoring variable sets to obtain a nonlinear function expression between any reference monitoring variable and other monitoring variables in the monitoring variable sets, namely a mutual check function, and realizing sign regression by using a genetic algorithm; establishing a single-variable fault probability function according to the influence of the multi-dimensional monitoring variable on the transformer equipment; and building a system dynamics model based on the mutual verification function and the fault probability function to realize comprehensive safety evaluation of the transformer. Compared with the prior art, the method has the advantages of improving the safety assessment level of the transformer, being high in interpretability and the like.

The invention discloses an interpretable deep learning-based innate immune gene expression prediction method, which comprises the following steps of: 1, collecting and preprocessing a DNA (Deoxyribose Nucleic Acid) promoter sequence and epigenetic modification data, and dividing a preprocessed data set into a training set and an independent test set so as to verify the prediction performance of a model; 2, constructing a human innate immune gene expression prediction network based on residual connection, a convolutional neural network and an attention mechanism; 3, performing model training on a plurality of cell lines to obtain an optimal human innate immune gene expression prediction model, and evaluating the prediction effect of the model on the independent test set; and 4, carrying out post interpretability analysis on the model through an integral gradient method, and identifying a key motif which is most likely to influence gene expression. According to the method, DNA sequence information and epigenetic modification information are extracted more effectively, and a new tool is provided for deeply understanding a regulation mechanism of an innate immune gene and developing individualized medical treatment.

The invention provides an unsupervised dimensionality-reduction online monitoring method for the abnormity of a secondary circuit of a voltage transformer, and the method comprises the steps: collecting the time sequence voltage data of the voltage transformer in real time, segmenting the data according to a time window, obtaining a time-domain and frequency-domain high-dimensional data sample set in each time window, giving consideration to the time-domain characteristics of the data and the frequency-domain characteristics under an abnormal condition, and achieving the online monitoring of the abnormity of the secondary circuit of the voltage transformer. According to the method, a time domain similarity matrix and a frequency domain similarity matrix are constructed, feature vectors of the similarity matrixes are extracted and dimensionality reduction is carried out, finally, normal and abnormal moment data are distinguished by using an unsupervised Gaussian mixture model method, normal and abnormal transformer secondary circuits can be accurately distinguished by using KL divergence, and more accurate abnormal secondary circuit positioning is realized. And the normal operation of the power system is further ensured.

The invention particularly relates to a pose calculation method and device based on point cloud and an article grabbing system. The method comprises the steps of collecting a current scene image to be processed and a corresponding scene depth image, and performing image recognition on the scene image to determine position information of a target to be captured; wherein the scene image comprises at least one target to be captured; acquiring corresponding scene point cloud data based on the scene depth image, and determining at least one target point cloud data corresponding to the to-be-captured target in combination with the position information of the to-be-captured target; performing plane fitting processing on the target point cloud data to obtain a corresponding target fitting plane; projecting the target point cloud data to the corresponding target fitting plane to obtain a projection point cloud, and calculating a convex polygon corresponding to the projection point cloud; calculating a target quadrangle corresponding to the convex polygon with dense point clouds; and determining the pose information of the target to be grabbed according to the target quadrangle.

The invention relates to the field of automatic driving, and provides an out-of-distribution point cloud object detection method based on fuzzy membership supervision and evidence deep learning. Comprising the following steps: obtaining a candidate foreground object by using a 3D target detection model; foreground object point cloud feature extraction; calculating a fuzzy membership degree based on fuzzy C-mean clustering; the fuzzy membership degree value is used as mark information, and evidence deep learning model training is carried out; and inferring an uncertain value of a foreground object by using the trained evidence deep learning model, and judging whether the object belongs to an out-of-distribution sample or not. According to the method, the fuzzy membership degree obtained by fuzzy clustering is used as supervision information, evidence deep learning is used for training on the point cloud target detection data set, and the uncertain value predicted by the evidence deep learning is used for point cloud open set target detection, so that robustness and interpretability are high; and the open set detection effect can be ensured in a large-scale sparse point cloud scene.

The invention relates to an electric power quantity accumulated temperature effect duration prediction method and device and a medium, and the method comprises the following steps: obtaining original data, dividing the original data into a plurality of groups of data, and carrying out the wavelet transformation of temperature-sensitive load data and temperature data in a high-temperature time period in each group, so as to obtain time-frequency distribution data; performing causal inference by using a frequency domain causal analysis method and integrating the time-frequency distribution data to obtain a causal effect of the temperature on the temperature-sensitive power and electric quantity data, and determining occurrence time of an explicit causal relationship; daily load data, daily electric quantity data and daily temperature data of multiple consecutive days are collected as to-be-analyzed data; and predicting the duration of the accumulated temperature effect according to the data to be analyzed by using a high-order Granger causality method. Compared with the prior art, the method has the advantages of high accuracy, high interpretability and the like.

The invention discloses a formation resistivity determination method and device for a post-casing transient electromagnetic logging curve, equipment and a medium, and relates to the technical field of oil exploration and development, and the formation resistivity determination method comprises the following steps: obtaining a post-casing electromagnetic logging curve, and carrying out time sequence conversion and data preprocessing to obtain time sequence data; converting the data into frequency domain data by adopting wavelet transform, and performing multi-scale refinement splitting to obtain low-frequency features and high-frequency features; fitting the low-frequency features and the high-frequency features by using a symbol regression method to obtain a nonlinear high-dimensional symbol regression formula for the low-frequency data and a nonlinear high-dimensional symbol regression formula for the high-frequency data, and generating a post-casing electromagnetic logging curve nonlinear fitting formula; the mapping relation between the post-casing transient electromagnetic curve and the formation resistivity is determined through the post-casing electromagnetic logging curve nonlinear fitting formula, the formation resistivity is obtained, the uncertainty of manual selection of model parameters is avoided, the capacity of processing nonlinear problems is improved, and the accuracy and reliability of high-dimensional relation mapping and nonlinear fitting are improved.

The invention belongs to the field of vulnerability detection, and discloses a graph matching-based CVE vulnerability detection method, which comprises the following steps of: extracting a plurality of signature graphs according to PoC codes for representing CVE vulnerabilities; extracting system call event data, and constructing an origin graph based on the system call event data; calculating a final score of the matching confidence of the signature graph and the origin graph; if the final score of at least one matching confidence exceeds a threshold value, the CVE vulnerability corresponding to the signature graph exists in the origin graph; otherwise, the CVE vulnerability corresponding to the signature graph does not exist. According to the invention, efficient and accurate vulnerability detection is realized.

The invention provides an industrial glass defect detection method based on Visionmaster. The industrial glass defect detection method is provided by aiming at the pain point of current glass defect detection and combining the actual demand of an enterprise. A traditional industrial glass defect detection method has many problems and deficiencies, such as poor environmental adaptability, lack of robustness, low precision, dependence on manual adjustment and the like. In order to solve the problems, the innovation comprises the following steps: preprocessing an image, detecting whether the glass is transversely broken or longitudinally divided, detecting glass edge defects, detecting glass surface defects, and summarizing results to judge the qualification of the glass. Firstly, noise interference is removed through an image processing technology, the position and the edge of a glass area are obtained, and an image enhancement means is used for improving the contrast ratio, so that an image is clearer; based on Visionmaster, whether glass is transversely cut or longitudinally divided, whether edge defects exist and whether surface defects exist are judged by utilizing a traditional visual technology and setting a proper threshold range. And finally, determining whether the industrial glass is qualified or not by summarizing detection results of all links. And if not, determining the defects of the glass. The industrial glass defect detection method has the advantages that the coding difficulty is reduced, the parameter adjustment process in the detection process is simplified, and the accuracy, universality and anti-interference capability of industrial glass defect detection are improved; and meanwhile, the labor cost is reduced, the product quality is better guaranteed, and the detection speed and efficiency are improved. In addition, the method has high interpretability and adaptability, and is suitable for complex and changeable industrial field environments. According to the method, while the production efficiency is improved, challenges existing in a traditional method are effectively handled, and a more effective and reliable detection method is provided for the industrial glass production process.

The invention discloses a tracked vehicle driving state prediction model training method, a tracked vehicle driving state prediction method and a tracked vehicle driving state prediction product, and relates to the technical field of tracked vehicle intelligent driving. Inputting the measurable control vector data into the trained transmission-track model to obtain actual control vector data; acquiring state vector data of the tracked vehicle at the previous moment, and sequentially calculating prior estimations of the state vector data and the measurement vector data to obtain prior state estimation and prior measurement estimation; converting the priori state estimation, the priori measurement estimation and the actual control vector data into a differential sequence; and training the recurrent neural network by using the differential sequence to obtain a tracked vehicle driving state prediction model. According to the method, the neural network with a good nonlinear fitting characteristic is combined into the Kalman filtering algorithm, effective state estimation of a strong nonlinear system is realized, a Kalman filtering state estimation principle is integrated, and to-be-learned parameters of the neural network are reduced.

The invention discloses an electrical coupling system operation reliability evaluation method, which comprises the steps of analyzing a network topology, an energy flow direction and an element logic relationship of an electrical coupling system, and constructing a Bayesian network model of the system from a power supply source to a load point; establishing an element reliability model, and obtaining different failure rate information of the non-key element by changing different age limits and load rates; taking different failure rate information of the non-key elements as input data of nodes of the Bayesian network model, outputting an analytical model coefficient, and obtaining a reliability index in combination with a key element combination state probability; training an LLSVM model by taking different failure rate information of the non-critical elements as input and taking an analytical model coefficient as output; and during online application prediction, obtaining different failure rates of corresponding non-key elements by combining element long-term and short-term prediction results with the element reliability model, inputting the different failure rates into the trained LLSVM model, and outputting a reliability index. According to the method, effective, rapid and efficient reliability evaluation of the electrical coupling system is realized.

The invention discloses an automatic labeling method combined with multi-source log data. The method comprises the following steps: 1) preprocessing an application and a tool: generating a corresponding application program log by using related information in a target application program; meanwhile, modifying an attack plan and an attack tool of an attacker; 2) attack execution: executing the attack by using the preprocessed attack tool and the designed attacked application program, and generating an attack log at the same time; 3) data marking: constructing a provenance graph by using an audit log; positioning attack injection points of different types of logs; all system calls in the thread are divided into different processing programs, processing program IDs are recorded, and a tree structure of a processing program derivation relation is constructed; and 4) marking malicious sub-graphs. According to the method, the auditing log, the traffic log and the application program log are integrated, so that the full view of the network attack can be captured from different dimensions, and the accuracy of attack behavior recognition is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a power abnormal data risk grading and handling method and system, and the method comprises the following specific steps: collecting data from a multi-source channel, and carrying out the preprocessing and feature engineering of the collected data; building a classifier based on a plurality of decision tree models, and selecting information gains as evaluation indexes to construct decision trees; finely tuning the locally deployed GLM open source large model; disposing suggested retrieval enhanced questions and answers; according to the method, various information sources and the processing knowledge base are combined in real time, more comprehensive and accurate processing suggestions are provided, and real-time updating of the processing knowledge base and dynamic generation of the processing suggestions are achieved through a retrieval enhancement scheme.

The invention provides a method and a system for diagnosing and estimating the state of health of a fuel cell based on polarization characteristics, and relates to the technical field of fuel cells, and the method comprises the following steps: constructing a proton exchange membrane fuel cell model based on a multi-condition multi-physical field, extracting health state characteristics of the to-be-diagnosed battery from the proton exchange membrane fuel cell model under a preset typical working condition; and inputting the health state characteristics into a trained fuel cell health state diagnosis model to obtain the health state of the to-be-diagnosed battery evaluated by the fuel cell health state diagnosis model. The method not only can accurately diagnose the health state of the fuel cell, but also is low in implementation difficulty and cost and relatively high in interpretability, so that the dependence of high-precision diagnosis of the health state of the fuel cell on expert experience is reduced.

The invention discloses a big language model learning method for collaborative reasoning and action based on agency, which comprises the following steps: defining a task demand, and initializing a universal prompt vector; designing a support data set of small sample scene learning, so as to enable the large language model agent learning to cooperate with the logic of reasoning and action; designing an interface for calling an external tool and acquiring external information by the model agent; selecting a proper tool combination from a preset tool library; through an agent packaging prompt, an external tool and a large language model, the designed support data set serves as a prompt word and is submitted to the large language model for agent so as to carry out small sample learning, and a'reasoning-action 'cooperative normal form is mastered; and applying the trained model agent to a new task instance, evaluating an output effect, correcting a logic error or a fact error through human intervention, and performing adjustment and optimization. The method is visual, easy to design, universal, flexible, efficient, stable, aligned with human thinking logic and highly controllable.

The invention discloses a data-driven calculation method, device and equipment for hydrogen storage capacity of a solid-state hydrogen storage device, and belongs to the technical field of solid-state hydrogen storage. The method comprises the steps that distributed parameters of the solid-state hydrogen storage device are obtained through a multi-physical-field full-coupling model of the solid-state hydrogen storage device, and the multi-physical-field full-coupling model is constructed based on structural characteristics of the solid-state hydrogen storage device; obtaining a dynamic response database of the multi-physics field full-coupling model, wherein the dynamic response database is used for representing a corresponding relation between distributed parameters of the multi-physics field full-coupling model and the hydrogen storage capacity of the solid hydrogen storage device; performing dynamic identification on the multi-physical field full-coupling model through a dynamic response database to obtain an identification model of the solid hydrogen storage device; and inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data driving calculation on the target distributed parameters through the identification model so as to output the predicted hydrogen storage amount of the solid-state hydrogen storage device. According to the method, the calculation cost and the solving time can be reduced.

The invention provides a virtual server load response model establishment method, a medium and a system, belongs to the technical field of virtual server load response, and is characterized in that multiple groups of load tests are carried out on a virtual server by setting an initial test condition and changing the initial test condition for multiple times, test data under different conditions are acquired, and a load data set is formed. And establishing a load response characteristic equation set containing all parameters, and performing fitting by using a data set. Then, establishing a resource conversion model and a resource competition model, and reflecting a resource mapping relationship between the physical server and the virtual server and resource competition between the virtual servers; integrating the two models, and solving the fitted equation set to obtain a numerical solution matrix; finally, a virtual server load response model is constructed based on the matrix and used for describing resource use and performance of the virtual server under different load conditions, and the technical problem that a server load response model established in the prior art is not high enough in accuracy is solved.

The invention discloses a shale lithology identification method based on a prior Bayesian cascade forest, and relates to the field of rocks, the shale lithology identification method based on the prior Bayesian cascade forest comprises the following steps: 1, firstly establishing a mineral pre-training model data set of continental facies shale, then taking logging data in the data set as features, and taking the logging data as the features; taking the mineral data as a label, using a Bayesian extreme gradient lifting regression device for training, and storing a mineral pre-training model; compared with the prior art, the method has the advantages that the class imbalance problem of logging data is considered, self-adaptive multi-target group fusion is used for resampling a data set, X-ray diffraction total rock mineral analysis data is used, feature generation is carried out by means of a Bayesian gradient lifting regression device, and the method is high in accuracy and high in accuracy. And then the Bayesian gradient lifting classifier is used for identifying the shale lithology, so that the cost is low under the condition of high accuracy, and a reliable scheme is provided for identifying the shale lithology.