1783 results about "Data dimension" patented technology

The invention relates to the technical field of data processing, and discloses a multi-dimensional financial pressure testing and financial early warning method and system. The method comprises the following steps: forming a financial situation awareness data set through multi-dimensional data acquisition integration and standardization processing; adaptive weight distribution and data dimension reduction are carried out on the five dimension indexes, and a risk measurement standard spectrum is output; a third-level pressure scene is established according to the standard spectrum, a risk conduction path is analyzed, and a financial pressure fluctuation spectrum is generated; a deep learning early warning model is constructed based on the fluctuation pedigree, an early warning threshold value is determined, and a risk pulsation graph is formed; constructing a multi-level early warning system based on the pulsating diagram, and establishing a financial early warning intelligent chain; and matching historical cases based on the early warning intelligent chain, designing a prevention and control strategy, and constructing a risk solution scheme library. According to the invention, by constructing a multi-level risk assessment system, accurate identification, dynamic monitoring and intelligent disposal of financial risks are realized, and the accuracy and timeliness of risk early warning are improved.

The invention relates to the technical field of medicine quality management, in particular to a medicine quality management method and system based on big data and artificial intelligence, and the method comprises the steps: obtaining the quality influence parameters of a medicine product in the production, transportation and storage processes through a multi-modal data collection device; noise is removed through data cleaning, data dimension reduction is carried out through principal component analysis and an auto-encoder, the analysis efficiency is improved, and the efficiency problem of mass data processing of a traditional method is solved. Time sequence analysis is carried out through the long-short-term memory network, abnormal nodes are recognized in time, manual sampling inspection is replaced, and accurate early warning is achieved. The graph neural network constructs a path tracking model, analyzes the relevance between nodes, traces the quality problem, and reckons the batch of affected drugs. Based on a traceability result, an XGBoost model generates quality risk scores of all links by analyzing historical and real-time data and sets a threshold value, and quality risks are early warned in real time.

The invention provides a privacy protection method and system based on big data security and privacy calculation, and relates to the technical field of data processing, and the method comprises the steps: obtaining original data feature extraction, obtaining data features, selecting a data desensitization algorithm to carry out data desensitization, carrying out encryption processing through a hybrid fully homomorphic encryption algorithm, and carrying out encryption processing through a hybrid fully homomorphic encryption algorithm. Homomorphic encrypted ciphertext data is generated and sent to the privacy protection computing platform; a data preprocessing protocol for privacy protection is constructed, data filtering and data dimension compression are carried out, multi-party security calculation is executed, a ciphertext calculation result is generated and returned to a data provider, verification is carried out, and if verification passes, decryption and filtering are carried out through a decryption algorithm to obtain a pure plaintext calculation analysis result; and constructing a reverse data desensitization model for reduction to obtain an analysis calculation result, constructing a dual-target optimization model for solving, obtaining an optimal balance point and generating an optimal decision result, desensitizing, uploading to a privacy protection block chain network, and performing distributed credible storage and sharing.

The invention provides a traffic safety detection method and system based on big data analysis, and the method comprises the steps: obtaining a to-be-detected traffic internet-of-things data set, carrying out the fusion representation information mining of the to-be-detected traffic internet-of-things data set, and obtaining first fusion representation information; the first fusion representation information comprises multiple pieces of data dimension representation information corresponding to multiple data dimensions; indexing a plurality of historical traffic internet-of-things data sets matched with the traffic internet-of-things data set to be detected according to the first fusion representation information, and obtaining a traffic congestion level mark corresponding to each historical traffic internet-of-things data set according to second fusion representation information corresponding to each historical traffic internet-of-things data set and the traffic congestion level mark corresponding to each historical traffic internet-of-things data set; generating a traffic situation fusion template according to the first fusion representation information of the traffic internet-of-things data set to be detected; and according to the traffic situation fusion template, detecting a traffic congestion level mark of a traffic internet-of-things data set to be detected. According to the invention, the traffic jam grade marks can be efficiently and accurately obtained.

The invention relates to the field of internet attack detection, in particular to a data security event real-time monitoring method and system, and the method comprises data collection, multi-modal fusion, threat detection, causal reasoning, dynamic response and feedback optimization. Compared with the traditional security analysis which depends on isolated data dimension or simple rule association, is difficult to capture a cross-data-source complex attack mode, and is faced with the limitations of low calculation efficiency, slow link traceability, storage access bottleneck and the like in mass data association analysis, the scheme integrates multi-source heterogeneous data into a dynamic association network through graph structure modeling, so that the security analysis efficiency is improved. Hidden association and behavior patterns among users, equipment and IPs are deeply mined by utilizing a GNN framework, the suspicious degree among entities can be accurately quantified, and hidden attack chains can be identified; and meanwhile, a hybrid storage architecture and a query optimization technology are adopted, so that a security analyst can backtrack a complex attack path in a second level while breaking through the bottleneck of large-scale graph data access performance, and the threat hunting efficiency and the high-level attack traceability are remarkably improved.

The invention discloses a power distribution area anomaly positioning method and system based on multi-modal dynamic feature analysis, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining area multi-source fusion data according to a sliding time window, and determining a hidden danger path corresponding to an instantaneous drop event in combination with a line topology; s2, performing state fluctuation analysis according to the real-time state data and the historical state data of the monitoring point to determine a state fluctuation factor, and constructing an impedance prediction model according to the path disturbance data to determine an environment disturbance factor; s3, performing activation processing on the hidden danger path according to the state fluctuation factor and the environment disturbance factor to obtain a transformer area degradation graph; and S4, dynamically cutting the transformer area degradation graph through the hidden danger risk values corresponding to the monitoring points to determine degradation blocks, and determining corresponding early warning measures according to the degradation degrees corresponding to the degradation blocks. According to the scheme, the limitation of single data dimension analysis and static evaluation is overcome, and the monitoring point hidden danger recognition accuracy and intelligent processing efficiency in a complex scene are remarkably improved.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention discloses a network traceability data processing method, system and device and a medium, and the method comprises the steps: collecting network data, carrying out the preprocessing of the network data, and generating the preprocessed network data; extracting behavior characteristics from the preprocessed network data to obtain network behavior characteristics; constructing an attack graph according to the obtained network behavior characteristics, and forming a dynamic graph representing an attack path; and carrying out traceability decision on the dynamic atlas, and positioning an attack source and an attack path through the traceability decision. Through the technical means of six-dimensional feature vector construction, quantum derivation genetic algorithm optimization detection and attack atlas construction through the tense graph convolutional network, the problems of data dimension missing, correlation analysis lagging and insufficient traceability precision are effectively solved.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The invention relates to the technical field of textile machinery fault detection, and discloses a spinning machine fault detection method and system based on deep learning, and the method comprises the steps: collecting vibration, temperature and current signals through a plurality of sensors, constructing a data stream after principal component analysis and dimension reduction, employing a variational auto-encoder to reconstruct an error positioning abnormal data segment, and obtaining a spinning machine fault detection result. The method comprises the following steps: extracting local features of a multi-source signal in combination with a convolutional neural network, dynamically distributing feature weights by using an attention mechanism, screening time sequence key features through a forgetting gate, introducing an LSTM network to model a feature sequence dynamic relationship, completing fault category mapping based on a support vector machine, and triggering LSTM secondary classification calibration for samples with insufficient confidence. A closed-loop technical system of data dimension reduction, anomaly detection, feature optimization, time sequence modeling and classification verification is formed, and efficient analysis and accurate recognition of multi-modal fault features under complex working conditions are achieved.

The invention provides a method for carrying out fault judgment based on a computer hard disk state index, and relates to the technical field of computers.The method comprises the steps that a periodic data collection mechanism is set, and multi-dimensional feature data including disk rotation instability, the data transmission rate, the read-write error rate and the like are obtained; carrying out normalization and de-noising processing on the data; then, data dimension reduction is achieved through an auto-encoder, key feature vectors are extracted, and then fault probability calculation is conducted through a support vector machine; on the basis, a threshold fine tuning model is constructed, and the fault probability threshold can be dynamically adjusted according to the relevance between the physical state and the operation state collected in real time and the change trend of the physical state and the operation state; the accuracy and timeliness of fault judgment are improved, the probability of false alarm is reduced through a dynamic adjustment mechanism, and the safety of data storage and the normal operation of the system are ensured.

The invention relates to the technical field of power equipment state monitoring, in particular to a power equipment operation state evaluation and prediction method based on big data, which comprises a multi-source data acquisition module, a feature engineering processing module, an intelligent evaluation and prediction module and a decision support output module. By setting a multi-source sensing end, when the operation state of the power equipment is evaluated, the definiteness of state evaluation of different types of equipment is ensured by formulating multi-modal data fusion standard parameters and setting different state sensing weights for different types of equipment; and meanwhile, real-time state sensing is performed by fusing electrical parameters, mechanical vibration, thermal distribution and environmental stress data, so that whether a sensing blind area problem caused by a single data dimension occurs in an equipment evaluation process or not can be detected in real time, the comprehensiveness and accuracy of equipment operation state evaluation are ensured, and state sensing fragmentation errors are further reduced.

The invention relates to the technical field of flow anomaly identification, and discloses a flow anomaly identification method for a numerical control machine tool protocol, which comprises the following steps of: 1, multi-source data acquisition: acquiring various types of data from a numerical control machine tool and a control system thereof by arranging a data acquisition module, the data sources include network protocol flow data, machine tool sensor data, operation log data and equipment environment data, and the data formats of the data sources are kept uniform; the collected original data is subjected to standardization processing, the problems of data dimension inconsistency, noise interference and missing values are solved, and alignment of the data on a time axis is guaranteed through a synchronization algorithm; through a multi-source data acquisition and high-precision sensor system, the real-time monitoring of the flow of each key fluid system of the numerical control machine tool is realized. And by combining a plurality of feature extraction methods, the traffic anomaly can be effectively identified.

The invention relates to the technical field of industrial equipment state monitoring, and provides a servo cable bending stress prediction method based on multi-sensor fusion. Aiming at the problems of single data dimension, poor generalization ability of a machine learning model and insufficient real-time performance in the prior art, the method comprises the following steps of: firstly, acquiring and preprocessing a multi-source historical data set by deploying a multi-modal sensor; and then, constructing a neural network fusion layer to fuse the preprocessed data set, and generating comprehensive time-frequency domain fusion data. Thirdly, constructing a trainable deep neural network hybrid architecture, performing deep feature extraction on the fused data, and executing pattern correlation analysis to obtain a comprehensive correlation matrix; and finally, directly inputting new data acquired in real time into the prediction neural network, and performing time sequence prediction reasoning in combination with the comprehensive incidence matrix to obtain final stress prediction data. According to the method, the accuracy of predicting the bending stress of the servo cable is remarkably improved, and the adaptability and robustness of the system are enhanced.

The invention discloses a marine water environment abnormal state secondary and derivation probability modeling and estimating method, which belongs to the field of state modeling estimation, and comprises the following steps: acquiring real-time data flow of marine environment parameters, performing segmentation processing by adopting a sliding window mechanism, and reducing data dimension by utilizing principal component analysis. Then, constructing a graph structure model based on a time sequence, and capturing correlation and a space-time dependency relationship among parameters; and performing nonlinear mapping on the graph structure model by adopting a radial basis function core, and capturing complex nonlinear characteristics among the marine environment parameters. And finally, classifying the abnormal states by using a multi-class support vector machine algorithm, and outputting secondary and derivative probability estimation results of the abnormal states in real time. According to the method, abnormal events in the marine environment can be found in time, dynamic modeling support can be provided for marine environment monitoring, and the accuracy and real-time performance of marine environment monitoring are remarkably improved.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention discloses a rotor surface magnetic field characteristic real-time monitoring and damage assessment system and method, and relates to the technical field of damage assessment. According to the method, a rotor surface magnetic field is monitored in real time under different rotating speed conditions, detailed magnetic field change data information is collected, the monitoring means can dynamically capture the magnetic field fluctuation condition, and the damage assessment accuracy is improved. According to the method, the damage prediction model is constructed by using the deep learning technology, the relevance between the damage prediction model and the rotating speed of the rotor is analyzed, early detection and classified evaluation of the unbalance condition are ensured, accurate data support is provided for further damage analysis, and accurate evaluation of the surface damage of the rotor is realized by using the deep learning technology to construct the damage prediction model and combining the unbalance type and the magnetic field change data. Meanwhile, the damage evaluation index is subjected to linear normalization processing, the influence between different data dimensions can be eliminated, and the evaluation result is more stable and reliable. And whether the damage state of the rotor surface is abnormal or not is judged in time by comparing and analyzing the damage evaluation index and a threshold value.

The invention provides a Gaussian modeling method combined with laser scanning point cloud data processing, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: carrying out the registration of a plurality of point cloud data sets through employing a point cloud fusion algorithm, and obtaining a target point cloud data set; performing data precision, position deviation and noise influence analysis on the plurality of point cloud data sets, determining point cloud data precision, a position deviation coefficient and a noise influence coefficient, and comprehensively evaluating and determining point cloud acquisition precision; setting a data dimension reduction proportion according to the point cloud acquisition precision, and performing covariance matrix dimension reduction on the target point cloud data set; and selecting a predetermined kernel function, performing data fitting and Gaussian process regression modeling on the dimension reduction point cloud data set, and outputting a surface fitting model. According to the method, the technical problems that the requirements for storage and computing resources are too high and efficient three-dimensional modeling cannot be realized due to the fact that a large number of covariance matrixes need to be calculated in a traditional Gaussian modeling method can be solved; the dimensionality of data can be effectively reduced, and the efficiency of three-dimensional modeling is remarkably improved.

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention discloses a circuit breaker fault detection optimization method and system based on SVD-XGBoost. The method comprises the steps of obtaining an original vibration signal generated when a circuit breaker is in an operation state; performing data enhancement on the original vibration signal based on an NLMS algorithm so as to adaptively filter noise, and obtaining a time domain signal after data enhancement; constructing an SVD-XGBoost model based on combination of SVD (singular value decomposition) and an XGBoost algorithm, converting an enhanced time domain signal into a frequency domain signal through fast Fourier transform, performing data dimension reduction on the frequency domain signal by adopting singular value decomposition, and extracting target features; according to the method, the extracted target features are input to the trained SVD-XGBoost model, the optimized network is adopted to carry out fault classification on the circuit breaker vibration signals, a circuit breaker fault identification result is obtained, SVD dimension reduction is introduced to convert high-dimensional features into low-dimensional features, the number of the features is reduced, the calculation efficiency is improved, and the fault classification precision and effect are improved.

The invention discloses a listed company operation risk early warning method based on multi-source auditing and text semantic fusion, and relates to the technical field of auditing, and the method comprises the steps: S1, crawling and converging multi-source heterogeneous data of listed company financial newspapers, auditing suggestions, supervision announcements, inquiry letters, news public opinions and market transactions; according to the method, unstructured texts are subjected to cleaning, blocking and semantic vectorization processing, each text segment is embedded into a high-dimensional semantic space, a vector index is established, a bottom-layer knowledge base of an RAG framework is formed, in the stage, it is ensured that the data structure is uniform, the source is traceable, standardized input is provided for subsequent semantic retrieval and modeling, and the reliability of the system is improved. S2, a query expression is constructed based on a target company, a time window and a risk topic, dense semantic retrieval and sparse BM25 retrieval methods are comprehensively used, a time decay and source credibility weighting mechanism is introduced, and the problems that a traditional method is single in data dimension and information is split are solved.

The invention belongs to the technical field of computer information processing, and relates to a scientific research performance information analysis and evaluation system and a processing method thereof.The scientific research performance information analysis and evaluation system comprises a data collection module, a weight distribution module, a real-time feedback module and an execution engine function module; weight distribution receives the first signal and analyzes a current strategic target priority identifier and a historical performance evaluation record contained in the first signal, a weight parameter space dynamically matched with a target priority is constructed through an online learning algorithm, and a second signal containing a real-time weight mapping relation is generated; real-time feedback is carried out, a multi-dimensional performance evaluation matrix is constructed based on the weight mapping relation in the second signal, and a third signal is generated; the execution engine receives the third signal, analyzes an abnormal positioning code of the third signal, and aggregates intervention records in a period to form a fourth signal; according to the method, the defects that traditional performance information analysis depends on static weight, the data dimension is single, and real-time dynamic adjustment is lacked can be overcome, and the analysis efficiency is improved.

A measurement anomaly analysis method and apparatus, a storage medium, and a computer device. The measurement anomaly analysis method comprises: acquiring first-order analysis data analyzed by a source-end system and, on the basis of the first-order analysis data, determining at least one data filtering rule (101); on the basis of the data filtering rule, performing a filtering operation on monitoring data of the source-end system to obtain target measurement anomaly data (102); performing contrastive analysis on the target measurement anomaly data and pre-configured anomaly case data to determine at least one piece of target case data from among the anomaly case data (103); and performing multi-dimensional clustering analysis on the target measurement anomaly data so as to obtain the degrees of aggregation of the target measurement anomaly data in a plurality of data dimensions and, on the basis of the target case data and the degrees of aggregation, analyzing a measurement anomaly cause (104).

The invention relates to the technical field of fruit detection, in particular to a blueberry acidity detection method based on hyperspectral imaging, which comprises the following steps: randomly selecting blueberry samples to be detected, and constructing a reference sample database; acquiring full-band hyperspectral image data of the to-be-detected blueberry sample by using hyperspectral imaging equipment; performing spectrum preprocessing on the hyperspectral image data; eliminating redundant information of redundant wave bands by combining a continuous projection algorithm to obtain a final characteristic wave band set, and extracting a spectral intensity matrix corresponding to the final characteristic wave band set from the hyperspectral image data based on the final characteristic wave band set; constructing a blueberry acidity detection model; and outputting the acidity predicted value of the blueberries. According to the method, the data dimension is greatly reduced, the sensitivity and modeling efficiency of the characteristic wave band are improved, and compared with a traditional destructive detection method, the method has the advantages of being high in speed, free of damage and suitable for large-scale sample detection.

The invention belongs to the technical field of machine room safety monitoring, and discloses an image review method fusing semantic understanding and visual recognition, which comprises the following steps: calculating visual / semantic feature dynamic credibility in real time through an exponential weighted moving average algorithm in combination with environment interference and equipment state parameters; resNet50 is adopted to extract visual features in global and local branches, and a BERT model is adopted to encode semantic features; correcting the feature correlation degree according to the scene, calculating a dynamic weight, carrying out heterogeneous calibration through an attention mechanism, and calling priority rules such as'physical security features are higher than behavior features' for arbitration during conflicts; the method is advantaged in that low-credibility feature interference fusion is avoided, abnormity identification accuracy in a complex scene of a machine room is improved, multi-modal data cooperation demands are adapted, scene labels are marked based on time, work orders and historical data dimensions, an exclusive sub-model is constructed for a high-density scene through DBSCAN density clustering, and low-frequency scene parameters are migrated to a similar model.

The invention discloses a highway toll station flow prediction method and system, and relates to the field of intelligent traffic management and big data analysis, and the method comprises the steps: collecting multi-source heterogeneous traffic data, carrying out the preprocessing, carrying out the feature extraction of the preprocessed multi-source heterogeneous traffic data, mapping the extracted features into a common feature space, and carrying out the prediction of the flow of a highway toll station. The method comprises the steps of performing feature fusion, generating a comprehensive feature vector, obtaining a traffic network diagram based on the comprehensive feature vector, constructing an STGATwE model according to the traffic network diagram, training the STGATwE model by using the comprehensive feature vector, adjusting parameters of the STGATwE model, generating a trained STGATwE model, inputting multi-source heterogeneous traffic data collected in real time into the trained STGATwE model, and obtaining a flow prediction result. By collecting multi-source heterogeneous traffic data and introducing an external influence factor mapping function, the data dimension is enriched, and the sensitivity to external factors is enhanced.

The invention discloses a tumor chemoradiotherapy reaction automatic prediction system, and particularly relates to the technical field of medical automation, the tumor chemoradiotherapy reaction automatic prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction and selection module, a model training module, a prediction analysis module, an early warning module and a feedback module; the multi-source data acquisition module comprehensively gathers patient information, the quality is improved through the preprocessing module, a foundation is built for the feature extraction and selection module to accurately screen key features, the data dimension is greatly reduced, and the model training efficiency is remarkably improved; the model training module applies a machine learning algorithm to deeply mine a data complex relationship, so that the prediction is more accurate and reliable; on the basis, the prediction analysis module outputs detailed chemoradiotherapy reaction prediction and provides confidence evaluation to help doctors to make decisions and select; and the early warning module is responsible for predicting, warning abnormity in time and helping doctors to plan intervention in advance.

The present application provides an image processing method and apparatus, a computer device, a storage medium and a product. The method comprises: obtaining an input image containing a preset object, and obtaining text data; performing encoding processing on the text data to obtain text embedding features; performing image feature extraction processing on the input image according to a plurality of predefined data dimensions, so as to obtain identity embedding features of the preset object under each data dimension; and, by means of using an interlaced condition mechanism, performing fusion and recognition processing on the identity embedding features of the preset object under each data dimension and the text embedding features to generate an output image comprising the preset object and comprising the features described by the text data. The present application can extract multi-dimensional identity features and, according to the interlaced condition mechanism, fuse the extracted multi-dimensional identity features and text features to generate personalized images, thus improving the accuracy of the personalized images.

The invention discloses a forest ecological product value accounting method, and relates to the technical field of ecological value accounting, and the method comprises the specific steps: S100, data collection: employing a novel sensor technology and an unmanned aerial vehicle remote sensing technology, carrying out the data collection of a forest ecological system, and combining the data of a conventional fixed monitoring point, and obtaining the value of a forest ecological product; forming a data set of multi-source data fusion; by utilizing a novel sensor technology and an unmanned aerial vehicle remote sensing technology, the method realizes high-frequency and high-precision data acquisition of a forest ecological system, and the technologies not only cover key information such as vegetation growth conditions, soil humidity and animal activities of the forest, but also are combined with traditional fixed monitoring point data, so that the forest ecological system is more accurate. A multi-source data fusion monitoring system is formed, the innovative data acquisition mode greatly enriches data dimensions, the comprehensiveness and accuracy of data are improved, and a solid data basis is provided for subsequent value accounting of forest ecological products.

The invention provides a charging pile ordered charging load prediction method and system, electronic equipment and a storage medium, and belongs to the technical field of electric traffic and power load prediction.The method comprises the steps that electric vehicle operation data at different time points in a to-be-predicted area are obtained, and an electric vehicle operation database is constructed; constructing an electric vehicle user portrait classification model according to the electric vehicle operation database to form a user portrait set; according to the user portrait set in combination with the positions of the charging piles, predicting the charging behavior possibility and the charging amount of each charging pile at different time points; selecting a prediction date, judging the type of the prediction date, and performing classified fitting on a charging pile load curve; and matching the charging pile load curve with the possibility of the charging behavior of each charging pile at different time points and the charging amount, fitting the charging pile load curve into a charging pile load prediction curve of each charging pile, and performing prediction. According to the method, data dimension expansion is realized, prediction deviation is avoided, the accuracy is high, and the application range is wide.