823 results about "Linear transform" patented technology

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The embodiment of the invention provides a multi-modal knowledge graph construction method in cross-media retrieval. The method comprises the steps that extracted multi-modal features are mapped to a multi-modal feature space through a linear transformation layer; in the multi-modal feature space, the intra-modal attention weight of each modal feature is calculated according to a self-attention mechanism, the cross-modal attention weight of different modal features is calculated according to a cross attention mechanism, the two weights are fused to obtain a final fusion weight, each modal feature is weighted and input into a graph attention network, and the multi-modal feature is obtained. Obtaining a multi-modal fusion graph structure; performing semantic analysis on each modal feature, matching with a preset multi-modal semantic knowledge base, determining potential semantic association, performing semantic alignment on the multi-modal fusion graph structure according to a preset graph matching algorithm and the potential semantic association to obtain a multi-modal knowledge graph, performing cross-modal data retrieval according to the multi-modal knowledge graph, and performing cross-modal data retrieval according to the multi-modal knowledge graph. The multi-modal data cross-media retrieval method and device can improve the efficiency and accuracy of multi-modal data cross-media retrieval.

The invention provides a cellular network flow space-time prediction method and system based on a hybrid neural network, and belongs to the technical field of intelligent communication. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a feature fusion layer and an output layer. The data embedding layer maps a historical traffic sequence, cross-domain external data and metadata into high-dimensional features; the space-time coding layer is used for respectively fusing one-dimensional causal convolution and a Mama neural network to extract multi-scale time features and densely connecting convolution and a multi-head attention mechanism to capture multi-scale space features through time and space modeling branches; the feature fusion layer realizes adaptive weighted fusion of spatial-temporal features, cross-domain features and metadata features by using a gating fusion mechanism; and the output layer performs linear transformation on the fusion features to generate a final prediction result. According to the method, the spatial-temporal dynamic capture of the service traffic is accurate, the prediction curve is highly fit with the true value, and the accurate prediction of the multi-service traffic of the cellular network is realized.

A method and system for optimizing compute runtime and memory footprint of a linear transformation process are provided. The method includes determining a set of optimal rotation parameters, wherein the optimal rotation parameters provide an optimal tradeoff between runtime compute resources and a memory footprint for a runtime execution of the linear transformation process; initializing the linear transformation process to run a boosting technique with the determined set of optimal rotation parameters, wherein the boosting technique, when executed at runtime as part of the linear transformation process, performs at least one iteration that yields rotated ciphertexts, and wherein the at least one iteration is based on the determined set optimal rotation parameters and at least one key switching key (KSK); and loading the initialized linear transformation process to an internal memory of a hardware accelerator.

The invention discloses a dynamic feature retrieval generation and management method based on a cross attention mechanism, which belongs to the technical field of natural language processing, and comprises the following steps: step 1, converting knowledge base document fragments into atomic knowledge units, each atomic knowledge unit comprising question and answer pairs, codes as key value pairs, and adding dynamic priority weights; self-attention query is replaced with a double-channel query structure, one channel is used for cross attention, a cross attention query vector is generated through linear transformation, and key value pairs of atomic knowledge units are used; and step 3, training a cross attention adapter, freezing the weight of the language model, optimizing parameters of the adapter, and dynamically adjusting a loss function by using pre-judgment parameters based on input sequence context complexity. By means of the method, deep correlation description of user input and knowledge fragments can be achieved, semantic ambiguity is eliminated, knowledge injection and context information are balanced, distortion is avoided, and the strict requirement for semantic precision in the professional field is met.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.

Disclosed in the present invention is a deep learning-based power distribution network load prediction method, comprising: acquiring regional load data and renewable energy power generation data to form a data set, and preprocessing the data set to obtain a first data set; using a convolutional neural network to extract a time series feature in the first data set, and converting the time series feature into a data form of a deep learning model by means of an embedding layer to obtain one-dimensional time series data; performing fast Fourier transform on the one-dimensional time series data to obtain a frequency curve, extracting amplitude values on the frequency curve to calculate corresponding periods, and selecting the corresponding periods to slice the one-dimensional time series data and form same into two-dimensional matrixes; using a two-dimensional convolutional network to perform feature extraction, reshaping the two-dimensional matrixes that have undergone feature extraction into one-dimensional arrays, and performing adaptive fusion on the one-dimensional arrays to obtain a first time series feature; and inputting the first time series feature into a fully connected layer for weight calculation and linear transformation to obtain a power distribution network load prediction result, thereby improving the stability and reliability of electric power supply.

The invention discloses an attention mechanism optimization method and device, a storage medium and a processor. In the scheme, rotation position coding is carried out on a Query vector and a Key vector which are obtained through linear transformation of an input sequence, and an original attention value is obtained; based on the relative distance of each position in the input sequence, constructing a bias item; the bias item comprises a sine bias item; and carrying out dynamic fusion on the original attention value and the offset item, and outputting an optimized attention value. Compared with the position coding technology in the prior art, the method has the obvious advantages that the performance of the model is obviously reduced when the model is used for processing the super-long sequence, and the extrapolation is insufficient.

The invention discloses a lithium ion battery capacity inflection point prediction method and system based on a multi-parameter data fusion decision. The method comprises the following steps: collecting multi-parameter data of a lithium battery and preprocessing the multi-parameter data; constructing an inflection point prediction model based on deep learning, and extracting time sequence data characteristics of current, voltage and temperature; based on an attention-enhanced graph convolutional neural network AGCN, an attention mechanism is introduced into a graph convolutional neural network GCN to dynamically learn the association weight of a multi-parameter feature matrix, and multi-parameter data fusion features are obtained; dynamic decision making is carried out on the battery multi-parameter data fusion features, linear transformation is carried out on a dynamic decision making result to obtain a predicted value of an inflection point, and construction of an inflection point prediction model is completed; carrying out training optimization on the whole model, and predicting the residual cycle period of the battery to the inflection point; according to the method, inflection point high-precision prediction of any stage of the battery can be realized by depending on relatively short cycle period data.

The invention discloses a model reasoning method and device. The method comprises the following steps: preprocessing an input text to convert the input text into a semantic vector sequence; calculating a compression key and a compression value matrix corresponding to the semantic vector sequence through dimension reduction mapping and key-value mapping, and storing the compression key and the compression value matrix into a preset cache space; in response to the fact that the loop end condition is not met, the following steps are executed in a loop mode: based on the target semantic vector and first linear transformation of a compression key and a compression value matrix stored in a preset cache space, current attention representation is calculated; generating a new semantic vector based on the current attention representation and updating a semantic vector sequence; and through dimension reduction mapping and key-value mapping, calculating a compression key vector and a compression value vector corresponding to the new semantic vector, and updating a compression key matrix and a compression value matrix. According to the model reasoning method, the reasoning efficiency can be remarkably improved while the model reasoning effect and performance are ensured.

The invention provides a building identification method and system based on three-dimensional point cloud, and particularly relates to the technical field of computer vision and three-dimensional target identification, and the method comprises the steps: carrying out the undersampling, oversampling and data enhancement preprocessing of inputted building roof point cloud data, and carrying out the T-net network space alignment to output the aligned data. Then, an NEWPCT model is obtained by improving a PCT model, aligned data are input into the NEWPCT model, a point cloud feature matrix is generated through a linear coding layer, query, key and value matrixes are obtained through linear transformation, multi-head attention output is calculated by introducing an offset matrix, intermediate correlation features are obtained after splicing and fusion, and a global feature vector is generated through global maximum pooling; and finally, inputting the vector into a classifier, and outputting a building roof type, and the method solves the technical problems of geometric transformation sensitivity and local feature loss while improving the precision and robustness of three-dimensional point cloud building identification, thereby effectively improving the identification precision and robustness.

The invention provides a photovoltaic prediction method, which comprises the following steps: data preprocessing: aligning time sequences of historical power generation data and meteorological data through a time sequence warping strategy, constructing an equipment health index, and inputting the equipment health index into an input layer; designing a space-time embedding layer, and performing space-time coding on data transmitted by the input layer and all meteorological data branches; defining a neural network module, designing a local-global attention layer, designing a network layer, and injecting meteorological condition constraints through a physical regularization item; a joint attention fusion layer is designed, and key features are effectively integrated; an output layer is designed, and feature integration, linear transformation, activation function setting, physical regularization and final photovoltaic power prediction generation and output are completed; and offline prediction is realized through edge optimization. The invention provides a photovoltaic intelligent prediction method fusing time-space sparse attention and a time sequence neural network, and solves the problems of insufficient data isomerism, calculation efficiency, physical interpretability, dynamic environment adaptability and the like in the prior art.

The invention discloses a cable fault detection method and system, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: obtaining cable signal data, carrying out the standardization processing, converting the cable signal data into a matrix, carrying out the parallel calculation of attention heads, carrying out the feature extraction of the cable signal data, and carrying out the splicing and linear transformation of the output of the attention heads, the method comprises the following steps of: obtaining fused feature representation, introducing position codes into a self-attention mechanism, processing the structure and dynamic change of a sequence by utilizing the position codes, detecting and analyzing cable fault signals by utilizing extracted feature information, optimizing model parameters through detection and training results, and distinguishing the fault signals from noise. According to the method, position coding and a multi-head self-attention mechanism are combined, so that the model not only can capture the long-range dependency relationship, but also can accurately locate the time sequence characteristics of fault occurrence, and the accuracy of fault detection is improved.

The invention provides a multi-modal data classification method and system, computer equipment and a storage medium, and the method comprises the steps: receiving multi-modal data through edge equipment, and carrying out the preprocessing of the multi-modal data; aligning to the same latitude through linear transformation or a projection layer; a comprehensive information representation vector is generated through fusion of an attention mechanism or a Transform architecture; quantifying the multi-modal large model, and deploying the multi-modal large model in edge equipment; sending the comprehensive information representation vector to a multi-modal large model for reasoning, and processing the comprehensive information representation vector through a multi-layer Transform architecture; and generating a classification result according to task requirements. According to the method, the real-time performance, the accuracy and the resource utilization efficiency of the system are improved through localized deployment on the edge equipment, sensitive data can be processed locally, data privacy and safety are guaranteed, cross-modal feature extraction and alignment are achieved, and the recognition capability in a complex scene is improved. Dependence on large-scale annotation data is reduced, and adaptability is improved.

The invention discloses a ship route planning method and system in a polar region sea ice environment and a program product. The ship route planning method comprises the steps of preprocessing satellite remote sensing radiation intensity data, performing image reconstruction of inter-ice water channel detection through an improved Vision Transform model, planning and visualizing a ship route and the like. The deep features of the image blocks are extracted through a Transformer encoder which comprises a multi-head self-attention mechanism and is processed by a multi-layer perceptron; and the Transform encoder firstly performs layer normalization on the input, and then performs multi-head attention mechanism and multiple linear transformation and regularization processing. And realizing path planning in an inter-ice water channel environment by fusing a dynamic planning cost function through a fast marching method. Compared with a traditional algorithm, the method has the advantages that the calculation precision is higher in the same scene, the calculation efficiency is greatly improved, and the planning result obtained after multiple costs are comprehensively considered is given instead of pursuing the shortest distance.

The invention discloses a large model dynamic batch processing method based on sequence splicing. The method comprises the following steps: receiving input sequences of a plurality of users in a batch; converting the input sequence into a corresponding token sequence; carrying out heterogeneous splicing on all token sequences along a sequence length dimension to form a unified joint token; the spliced joint tokens pass through a normalization layer, standardization operation is executed on the joint tokens, and data distribution is unified; carrying out linear projection on the joint token through a shared linear transformation layer to generate a joint query vector, a joint key vector and a joint value vector, and splitting the joint query vector, the joint key vector and the joint value vector into sub-vector groups corresponding to each user; executing multi-head attention calculation to obtain attention output of the user; and carrying out linear transformation on the attention output, and inputting a transformed result into a shared MLP to carry out nonlinear feature extraction and enhancement so as to obtain a final output corresponding to each user. According to the method, the problems of efficiency bottleneck and resource consumption when a large model processes mass data are effectively solved.

The invention provides a coupling space-spectrum conversion operator transformer multi-physics field distribution prediction method and system, and the method comprises the steps: mapping the initial condition distribution of multi-physics field distribution to a potential space of a unified dimension through independent linear transformation, generating an initial potential feature vector, and carrying out the prediction of the initial potential feature vector; performing spatial expansion on the cross-field coupling state of the multi-physical field distribution in combination with the GRU, splicing the obtained current global cross-field coupling state and the current potential feature input, and obtaining the potential feature input of the next layer of the GRU through a space-spectrum conversion operator layer; and inputting the potential features of all the current physical field distribution and the global cross-field coupling state into a GRU unit, updating the cross-field coupling state including cross-physical field interaction, minimizing weighted error optimization parameters of a physical field distribution prediction result, inversely mapping the reconstructed final potential features to a target space, and generating the prediction result of the physical field distribution. The method has higher generalization ability, the modeling precision is improved, and the calculation complexity is reduced.

The invention provides an improved YOLOv8 underwater image target detection method, device, medium and program, a fine-grained feature extraction module is arranged to replace a C2f module on the basis of YOLOv8, the fine-grained feature extraction module comprises a visual state space model and a local feature extraction module which are arranged in parallel, feature distribution is adjusted through linear transformation in the visual state space model, and the local feature extraction module is used for extracting local features in the visual state space model. A multi-scale space context is extracted in combination with depth separable convolution and a SiLU activation function; in the local feature extraction module, detail information of a target is focused through a convolution kernel channel attention mechanism, and noise interference is suppressed; the two outputs are subjected to element-by-element addition fusion and then enter a multi-scale cross attention fusion module, local and global features of different scales are fused in the multi-scale cross attention fusion module through dynamic weighting, and fine-grained features are output. The improved YOLOv8 is used for underwater target detection, and the identification capability of a fuzzy target and a complex background in underwater image target detection can be remarkably improved.

The invention discloses a route planning method and system, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: calculating a cross-modal attention weight based on a feature vector, combining the attention weight with the feature vector to obtain a weighted feature vector, calculating an attention score based on the weighted feature vector, generating an integrated feature based on the attention score through linear transformation, and carrying out the calculation of the integrated feature. And integrating the features and combining the communication topology, and inputting the features into a Transform architecture to generate 3D environment representation. A cross-modal attention mechanism is utilized, attention weight dynamic modulation feature vector contribution of each modal is calculated, the environmental perception accuracy in a complex environment is improved, feature vectors of neighbor unmanned aerial vehicles are aggregated through a distributed communication topology construction method in combination with a multi-head attention mechanism, and the multi-modal attention mechanism is established. The group stability is still kept when communication is interrupted or obstacles change, and the environmental adaptability, the cooperation efficiency and the navigation precision of the unmanned aerial vehicle group in a complex environment are improved.

The invention provides an urban traffic flow prediction method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: receiving traffic data, and carrying out the linear transformation processing through a full connection layer; the processed traffic data are input into multiple layers of time-space modules of the same structure for feature extraction, each layer of time-space module is composed of a time convolution module and a mixed time-varying graph module, and time features output by the time convolution module and spatial features output by the mixed time-varying graph module are spliced to form output of the time-space modules; parameterized learning is carried out on the mixed time-varying graph module; performing jump connection on the outputs of all the space-time modules to generate fused space-time features; and processing the fused spatial-temporal characteristics through a ReLU activation function, and transmitting a processing result to a full connection layer to obtain a final prediction result. According to the method, the urban traffic condition and the change trend can be accurately judged, and effective decision support is provided for urban planning and traffic management.

The invention provides a capacitor voltage transformer error online detection method and system, and the method comprises the steps: collecting a secondary output signal of a three-phase capacitor voltage transformer, and carrying out the data preprocessing; based on the collected data, constructing a deep neural network learning model based on a stacked auto-encoder, and extracting deep features of the data through multilayer nonlinear transformation to obtain reconstructed data; taking the original data and the reconstructed data as input data, respectively carrying out co-integration analysis, and calculating a co-integration matrix and an equalization error; combining the reconstruction data with the equilibrium error, constructing a composite statistical magnitude and calculating a statistical magnitude threshold value; and collecting online measurement data of the capacitor voltage transformer, calculating monitoring statistics and carrying out abnormity diagnosis. The technical problems that disturbance components and error information in measurement data are difficult to distinguish, non-linear and non-stationary components in measurement errors of the capacitor voltage transformer are difficult to analyze, and the online detection accuracy and real-time performance of the measurement errors are low are solved.

The invention discloses a CT (Computed Tomography) image pulmonary embolism segmentation and classification method combined with quality evaluation, which relates to the technical field of image processing, and comprises the following steps: inputting a 256 * 256 pulmonary embolism CT image and a quality score thereof into a quality score guide encoder, expanding a quality score dimension through linear transformation, and carrying out point product fusion with a feature map extracted by ResNet34 layer by layer to obtain a final product; generating multi-scale coding features; performing wavelet domain decomposition and reconstruction on the coding features through a wavelet transform fusion jump link module, and optimizing feature transmission; a multi-scale cross enhanced decoder is adopted to carry out multi-scale deconvolution fusion on the features, a segmentation result is output in combination with an efficient channel attention mechanism, meanwhile, pulmonary embolism existence judgment is output through a classification head, and the method provides powerful support for early diagnosis of pulmonary embolism, development of an image auxiliary diagnosis system and clinical application, and has good application prospects. Wide application prospects and profound social significance are realized.

The invention relates to the technical field of sentiment analysis, in particular to a sentiment analysis method and system. The sentiment analysis method comprises the steps of collecting a dialogue voice signal, extracting an acoustic feature and a semantic feature from the dialogue voice signal, performing double-flow extraction of the acoustic feature and the semantic feature on the collected dialogue voice signal, performing normalization on the acoustic feature and the semantic feature respectively, and performing sentiment analysis on the acquired dialogue voice signal. The normalized acoustic features and semantic features are spliced to form a joint feature vector, the joint feature vector is mapped into a high-dimensional intermediate emotion vector through linear transformation, controllable Laplace noise is added to the intermediate emotion vector, and the intermediate emotion vector is mapped into a high-dimensional intermediate emotion vector; and outputting an encrypted intermediate emotion vector, inputting the encrypted intermediate emotion vector into a pre-trained emotion analysis neural network, outputting a nine-dimensional emotion vector spectrum, and generating visual feedback on a local device by using the nine-dimensional emotion vector spectrum. According to the invention, emotion understanding and communication optimization in a dialogue scene are greatly improved.

The invention discloses a non-parametric probabilistic load flow calculation method considering complex uncertainty for a power system. The method comprises the following steps: firstly, constructing source-load nonparametric probability distribution based on a multivariate Gaussian mixture model; then, constructing an alternating current power flow model under a polar coordinate system in the power system, and performing linear expansion on the alternating current power flow model under the polar coordinate system in the power system at a mean point of source-load non-parametric probability distribution by adopting a first-order Taylor series approximation method to obtain a system operation simplified model based on alternating current power flow linearization of the power system; and finally, according to the linear transformation property of the Gaussian distribution, obtaining the probability distribution of the power flow distribution of the power system under each Gaussian component, and in combination with the total probability law, carrying out weighted summation on the probability distribution under each Gaussian component according to the weight to obtain the non-parametric probabilistic power flow probability distribution of the power system. The method provided by the invention has stronger generalization ability and accuracy, and has important reference significance for operation uncertainty quantification of the power system under complex uncertainty.

The invention discloses a cloud mobile phone equipment fingerprint disguising method and related equipment, and relates to the technical field of cloud mobile phone security, and the method comprises the steps: obtaining hardware entropy source data which comprises current timestamp data, a hardware unique identifier hash value and an environment noise sampling value; non-linear transformation is carried out on the hardware entropy source data based on a preset chaotic mapping algorithm, a virtual parameter set is generated, and the virtual parameter set meets a preset equipment parameter rule; covering the virtual parameter set to a multi-level system interface of the real equipment fingerprint through kernel-level injection operation; monitoring the use state of the virtual parameter set based on a preset life cycle model, and determining a parameter updating trigger condition; and when a parameter updating triggering condition is met, the nonlinear transformation is executed again to generate an updated virtual parameter set, and the updated virtual parameter set covers the multi-level system interface.

The invention discloses a data processing method, device and equipment and a readable storage medium, and the method comprises the steps: carrying out the feature extraction of sample image data through a visual coding model, and obtaining a visual coding feature vector; performing linear transformation on the visual coding feature vector through an initial projection layer to obtain an initial projection feature vector, and generating first image data through an initial generation model and the initial projection feature vector; adjusting the initial projection layer and the initial generation model to obtain a target projection layer and a target generation model; performing linear transformation on the visual coding feature vector through a target projection layer to obtain a target projection feature vector, and generating second image data through a target generation model and the target projection feature vector; and adjusting the visual coding model to obtain a visual enhancement coding model. According to the invention, the accuracy of the visual features extracted by the visual coding model can be improved.

The invention discloses a PLC anomaly detection method and system based on a self-attention mechanism and an OCNN, and relates to the technical field of industrial automation control, and the method comprises the steps: collecting PLC operation data through a Modbus communication protocol, carrying out the standardization processing, constructing a time sequence sample, constructing a feature extraction network based on the self-attention mechanism, and carrying out the detection of the PLC anomaly. Generating a query matrix, a key matrix and a value matrix through three linear transformation layers to perform global feature extraction, further performing local feature extraction, designing an improved OCNN detector, splicing global and local features through a special fusion layer, introducing residual connection, and calculating an abnormal score through an abnormal boundary learning layer; joint optimization of a feature extraction network and a detector is realized, a multi-task loss function is designed, and a two-stage training strategy is adopted for optimization; and finally, online anomaly detection is executed, real-time data preprocessing and feature extraction are carried out, multi-level early warning judgment is carried out, and an early warning information report containing processing suggestions is generated in combination with space-time correlation analysis.

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.

The embodiment of the invention provides a full-automatic mouth following meal assisting method and system based on machine vision, and belongs to the technical field of man-machine interaction of feeding assisting mechanical arms. The meal assisting method comprises the steps that a mechanical arm base coordinate system is established according to the position relation between a mechanical arm and a depth camera; obtaining a face two-dimensional image; obtaining mouth postures of key points of the face two-dimensional image based on an improved direct linear transformation algorithm; judging whether a feeding condition is met or not according to the mouth posture; when it is judged that the feeding condition is met, the mechanical arm obtains a feeding track according to teaching learning so as to carry out feeding operation; whether the mechanical arm completes the feeding operation or not is judged; under the condition that it is judged that the mechanical arm completes the food taking operation, the mouth posture is updated, and a food delivery track is obtained according to the mouth posture; and the mechanical arm executes meal delivery operation according to the meal delivery track. According to the invention, the direct linear transformation algorithm is improved to obtain the mouth posture, so that the detection precision and robustness are improved, and the feeding track is generated according to the real-time mouth posture, so that the feeding precision is improved.

The invention relates to a virtual power plant resource flexible aggregation method and device based on linear programming projection, and the method comprises the steps: constructing a linear state equation corresponding to each to-be-aggregated resource according to the resource characteristic data of each to-be-aggregated resource, and carrying out the linear transformation; aggregating the energy use flexible feasible region of each to-be-aggregated resource, and solving a coordinate projection problem to obtain a preliminary aggregated energy use feasible region; constructing a network security constraint and embedding a solution process of the preliminary aggregation energy use feasible region to obtain an aggregation energy use feasible region considering the network security constraint; and constructing a multi-parameter planning model, and fusing an aggregation cost function into a solving process of an aggregation energy use feasible region considering network security constraints to obtain a virtual power plant resource flexibility aggregation result. Therefore, the problems that in the prior art, energy use feasible regions and adjustment cost of massive heterogeneous resources cannot be aggregated at the same time, network security constraints cannot be embedded, and high-dimensional aggregation errors are large are solved, and higher calculation efficiency and compatibility are achieved.