473 results about "Feature combination" patented technology

The invention discloses a multi-field part size and appearance defect intelligent detection system, and relates to the field of image analysis. The system comprises an image acquisition module, an image preprocessing module, a feature extraction module, a defect identification and size measurement module, a data processing and analysis module, an automatic control module and a man-machine interaction module. According to the method, CNN and LBP, Hough transform and SIFT algorithms are fused, high-level semantics and bottom-level texture / geometric features are considered, 2304-dimensional fusion feature vectors are formed through feature splicing, the feature extraction integrity of parts in multiple fields is improved, texture detail features can be accurately extracted, geometric shape features can be accurately obtained, and the method is suitable for large-scale popularization and application. Meanwhile, the adaptive feature selection mechanism dynamically optimizes the feature combination according to the detection result, the problem of calculation redundancy is reduced, and the detection precision is ensured while the detection efficiency is improved.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention belongs to the technical field of landslide susceptibility analysis, and relates to a landslide susceptibility ensemble learning evaluation method considering spatial heterogeneity partitioning and factor feature screening, which comprises the following steps: generating a landslide sample based on historical landslide catalog data, and selecting a non-landslide sample through environmental factor frequency ratio analysis; the method comprises the following steps of: extracting static and dynamic environment factor data sets, realizing factor space interpretation force transformation by utilizing a t-SNE-ISO clustering algorithm and a feature screening strategy, eliminating high-correlation factors through a Pearson correlation coefficient method, quantifying interpretation force of each factor on landslide space differentiation by combining a geographic detector, screening optimal feature combinations under global and partition frameworks respectively, and performing landslide space differentiation on the landslide space. According to the method, a Stacking integrated learning framework is combined with CNN, DNN, MLP-based learners and LR element learners, a landslide susceptibility probability prediction model is formed, the generalization ability and prediction accuracy of the model are improved, and the method is especially suitable for landslide high-incidence areas with severe topographic relief and complex geological conditions.

The invention provides a display panel defect detection method and system based on multi-modal data fusion, and the method comprises the steps: obtaining multi-type representation data generated in a detection process of a display panel, and carrying out the feature mapping of the multi-type representation data; respectively mapping the panel appearance characterization data, the panel signal characterization data and the panel interlayer characterization data to a preset feature space to generate a cross-type mapping feature set, and performing feature association enhancement on the cross-type mapping feature set to obtain a cross-type mapping feature set; establishing an association relationship among the appearance mapping features, the signal mapping features and the interlayer mapping features, generating an association enhanced feature set, performing defect feature reasoning on the association enhanced feature set, extracting a feature combination conforming to a defect feature mode, and generating a defect feature reasoning result; and outputting a defect detection result of the display panel. According to the invention, the accuracy and reliability of the defect detection result of the display panel can be improved.

The invention provides a coral reef remote sensing image multi-modal feature generation and restoration method and system, and relates to the technical field of remote sensing image restoration, and the method comprises the steps: obtaining coral reef image data based on a multi-source sensor, and carrying out the preprocessing; performing image morphological feature and spectral feature extraction on the preprocessed coral reef image data by using a convolutional neural network, and establishing a feature database; carrying out weighted fusion on the multi-source features based on a feature combination network, generating comprehensive feature expressions, and storing the comprehensive feature expressions into a feature database; the coral reef image to be restored is matched with the feature database, and guidance parameters are generated; and inputting the guidance parameters into the generative adversarial network, and performing pixel-level reconstruction on the missing region of the coral reef image to be restored. According to the method, a complete closed-loop system is constructed, multi-source information scheduling, pixel-level guide reconstruction and semantic feedback verification are covered, image information can be supplemented, ecological information can be reasonably reconstructed, and a high-quality data basis is provided for subsequent classification, monitoring and protection work.

The invention provides an artificial intelligence advertisement pushing method based on multi-dimensional historical data analysis, and the method comprises the steps: constructing second-level, minute-level, hour-level and other behavior feature views of different time granularities for extracted original features, and forming multi-time scale feature representation; analyzing feature views under different time scales through a graph mining algorithm, mining an association rule between feature nodes, and constructing a cross-scale feature association graph; in the feature correlation map, according to the spectral clustering result of the feature nodes and the gravitation direction of the feature edges, the temporal correlation degree of different feature combinations is judged; if the time correlation degree of the feature combination exceeds an empirical threshold value, endowing the time weight of the feature combination with a high weight coefficient of power law distribution; and dynamically adjusting the network depth and the connection density of the association atlas according to the vertical dimension and the semantic hierarchy of the features, and optimizing the feature weight distribution in the atlas.

The invention provides a target tracking method and system based on global and local two-way extraction and asynchronous enhancement, and the method comprises the steps: carrying out the initialization of a template image and a search image, and carrying out the sequence division through block embedding; extracting global features and local features of the template image and the search image at the same time, and performing feature fusion on the global features and the local features of the template image and the search image; the output of the global and local joint module is input into an asynchronous enhancement module for asynchronous interaction and feature enhancement; and inputting the finally enhanced output feature to a prediction head to obtain a tracking result. According to the method, global and local features are extracted at the same time through the feature combination module, and the features after interaction are enhanced by using asynchronous enhancement operation in the feature fusion stage, so that the robustness and the feature expression ability of the model are improved.

The invention provides an intelligent electric energy meter service life prediction method and device and a readable storage medium, and belongs to the technical field of device service life prediction.The method comprises the steps that multi-dimensional input is constructed by fusing key environment data and an operation time sequence, and the microcosmic, periodic and long-term characteristics of the input are extracted through multi-scale convolution support; the feature combination is dynamically optimized through an adaptive fusion module; constructing a time sequence prediction model, embedding a convolution attention mechanism in an encoder, and compressing redundant information of key features by combining sparse self-attention distillation through key features of feature combination of channel-space double attention dynamic enhancement optimization; the decoder adopts a mask probability sparse attention and iterative prediction strategy to generate an error prediction sequence based on the global features of the encoder to avoid future information interference; and calculating the residual life according to the intersection point of the error prediction sequence and the failure threshold. According to the method, the long sequence prediction precision, the key feature utilization rate and the calculation efficiency are improved, and reliable support is provided for preventive maintenance of the intelligent electric meter.

The invention belongs to the technical field of medical clinic and health monitoring, and discloses an operation hypotension early warning system and prediction method based on multi-modal physiological parameters, and the system comprises a data acquisition module, a feature engineering module, a multi-modal integration and prediction module, a decision support module, a result display module and the like. The feature engineering module constructs a multi-dimensional feature space including key indexes, composite indexes and dynamic indexes based on various real-time physiological parameter data of a patient, the multi-modal integration and prediction module carries out feature screening, constructs optimal feature combinations corresponding to different prediction time windows, and carries out prediction on the optimal feature combinations. And designing two-stage model processing to obtain a hypotension risk prediction result of a plurality of prediction time windows. According to the method, the possibility of hypotension events in multiple time periods in the future in the operation process can be predicted, and clinical intervention suggestions are provided, so that medical personnel can intervene and prevent the hypotension events in advance, and postoperative complications related to hypotension are reduced.

The invention relates to the field of nondestructive testing and automatic sorting of agricultural products, and discloses an X-ray-based walnut internal defect feature optimization detection method and device, and the method comprises the steps: collecting an X-ray image of a moving walnut, extracting a region of interest (ROI) through preprocessing, extracting and fusing spatial domain and frequency domain texture features to form a multi-dimensional feature set, and obtaining a feature set; an optimal feature combination is screened out through dimensionality reduction; and finally, the types of the internal defects of the walnuts are identified by a pre-trained classifier, and a result is output. The device comprises a feeding mechanism, a conveying mechanism, an X-ray detection mechanism, a sorting execution mechanism and a processing and control system electrically connected with all the mechanisms. The problems of single identification category and insufficient model stability and precision are solved, and high-throughput and automatic lossless sorting of walnuts is realized.

The invention discloses a terrain classification method based on a random forest algorithm, a server and a storage medium, and belongs to the technical field of terrain classification, and the method comprises the steps: obtaining DEM data and remote sensing image data of a research area, and extracting terrain features, spectral features, index features and texture features; designing a feature combination scheme, and generating an optimal feature combination data set as input data of terrain classification; calculating an optimal parameter combination of a random forest algorithm, and generating an optimal random forest classifier; establishing a terrain classification system suitable for the research area, and constructing a terrain classification training sample set and a verification sample set; performing terrain classification by using a random forest classifier and the optimal feature combination data set to obtain a terrain classification result of the research area; and calculating and evaluating the precision of a terrain classification result by utilizing a verification sample set and a Kappa coefficient evaluation method. By adopting the method, the terrain classification refinement degree and the terrain classification calculation efficiency and classification efficiency can be improved.

The invention provides a passive perception radar target classification method and system based on electromagnetic detection feature extraction, and the method comprises the steps: carrying out the multi-dimensional signal decomposition of an original scattered signal sequence through obtaining an electromagnetic radiation signal set of a target region; obtaining a time-frequency distribution feature set and a polarization response feature set corresponding to each signal acquisition period, extracting a target electromagnetic feature combination from the time-frequency distribution feature set and the polarization response feature set according to a preset electromagnetic feature association rule, generating a weight distribution parameter, and performing weighted fusion processing on the target electromagnetic feature combination to obtain a target electromagnetic feature combination; and the fusion feature vector set is input into an adaptive classification network, hierarchical classification operation is performed on the fusion feature vector set, the category identifier of the target scatterer and the corresponding classification confidence are output, and a radar detection optimization instruction is generated based on the category identifier and the corresponding classification confidence. According to the method, high-precision distinguishing of multiple types of scatterers is achieved under the passive sensing condition, and meanwhile the environmental adaptability and the calculation efficiency of the classification process are guaranteed.

The invention relates to the technical field of low-altitude aircraft chip safety monitoring, and discloses a low-altitude aircraft chip safety monitoring system which comprises a state acquisition module, a risk assessment module, an anomaly detection module, a strategy generation module, an instruction execution module, an effect verification module, a threshold setting module, a data output module, a feedback optimization module and a system regulation module. The state acquisition module analyzes the operation parameters and the environment interference to obtain a state value; the risk assessment module extracts and screens abnormal protection feature combinations; the anomaly detection module combines a sample variation matching feature with an evaluation combination; the strategy generation module maps a feature strategy based on real-time data and sets a rule; the instruction execution module captures feature data and analyzes a trend; the effect verification module adjusts rules according to errors; the threshold setting module determines a monitoring threshold interval; the data output module generates standardized data; the system realizes comprehensive monitoring of the chip state through cooperation of multiple modules, and the operation safety and stability are improved.

The invention discloses an intelligent medical big data information management system, and the system comprises a data collection layer which serves as a sensing neural network, employs an Internet of Things gateway to be compatible with a medical equipment protocol, and deploys an edge calculation node for physiological signal preprocessing; the intelligent preprocessing layer is used for cleaning and standardizing the original data transmitted by the data acquisition layer; the informatization service layer is used for realizing fine-grained authority control based on an RBAC (Role Based Access Control) model, realizing inspection process automation by integrating an LIS (Line Information System) interface through a Hyperdger Fabric block chain evidence storage key operation hash, and constructing a zero-footprint DICOM (Digital Imaging and Communications in Medicine) viewer; the intelligent analysis layer is used for carrying out data life cycle management by adopting a mixed storage architecture, carrying out feature engineering on data, carrying out label matching and classified storage by using efficient neighbor search and similarity / distance measurement, quantifying contribution of each feature or feature combination to a single prediction result through an SHAP value, generating an interpretation result, and outputting the interpretation result; and presenting an interpretation result to a clinician or related personnel through a query interface.

The invention discloses a mineral spectral feature unmixing device using a generative adversarial network, which comprises a multi-modal data preprocessing module used for correcting and enhancing original mineral spectral data, solving the problem of small sample training and providing high-quality input for subsequent unmixing, and a generative network module used for fusing noise and geological text description, and providing high-quality input for subsequent unmixing. The dynamic adversarial training module is used for initializing end member features based on comparative learning of a mineral symbiosis sequence and improving priori cognition of the model on a mineral combination rule, and the end member reconstruction verification module is used for cyclically reconstructing a single albedo matrix by using a generator network unit and a discriminator network unit; nonlinear scattering and atmospheric noise interference are effectively eliminated through the model conversion unit and the 3D convolution kernel, the data synthesis capability of the conditional generative adversarial network is combined, the small sample training bottleneck is relieved, and the prior constraint of an end member feature combination rule is enhanced based on mineral symbiosis sequence comparative learning through a dynamic adversarial training mechanism.

The invention discloses a multi-feature emotional electroencephalogram recognition method and device based on density map convolution and a medium. The method comprises the following steps of collecting original electroencephalogram signals and performing frequency band filtering division; extracting the frequency characteristics of each frequency band, calculating the electrode spatial correlation related to the emotional state, and constructing an adjacent matrix; extracting multi-dimensional spatial feature combinations in different emotional states through a density map convolutional network; integrating the spatial features by adopting a dynamic weighted fusion and channel splicing technology; enhancing the key features by using a channel attention mechanism; and outputting a recognition result through feature classification. According to the method, time domain, frequency domain and space domain features are fused, emotion related space features are fused through dynamic weighting, and the weight is adaptively optimized; constructing a multi-space adjacency matrix in combination with prior space distribution, and extracting topological association by using graph convolution; introducing a channel attention mechanism to screen key features, and collaboratively optimizing data driving and priori knowledge; and through multi-space feature fusion and dynamic modeling, the recognition precision is improved.

The invention provides a circuit breaker mechanical wear prediction system based on vibration characteristics, and compared with the prior art, the circuit breaker mechanical wear prediction system further comprises a vibration signal acquisition module, a signal processing module, a characteristic extraction module, a characteristic screening and fusion module, a control and evaluation module, a correction module and a communication module. According to the system, original vibration signals of a circuit breaker in the opening and closing process are collected, multiple vibration features are extracted after filtering, denoising and time alignment processing, then an optimal feature combination is obtained through a feature screening and fusion algorithm, and a mechanical wear prediction model is constructed by adopting a random forest. And introducing an environmental health factor to correct a prediction result, and finally outputting a corrected wear degree prediction value RHI. The system has the advantages of high prediction precision, strong environmental adaptability, flexible deployment and the like, solves the problems of unstable performance and large prediction error of a traditional method under complex working conditions, and is suitable for online monitoring and predictive maintenance of circuit breaker equipment.

The invention discloses a geological multi-scale data hierarchical retrieval method and electronic equipment, and belongs to the technical field of geological data management and retrieval, and the geological multi-scale data hierarchical retrieval method comprises the following steps: S1, constructing a multi-scale data system, namely a three-level data structure system of a digital outcrop model, a rock specimen and a rock sample; s2, establishing an association mechanism among the three-level data structure systems, digital outcrop model-rock specimen association and rock specimen-rock sample association are carried out; and S3, performing multi-scale data hierarchy retrieval by adopting bidirectional association retrieval and feature retrieval. The digital outcrop-rock sample-rock sample association retrieval method has the advantages that the digital outcrop-rock sample-rock sample association retrieval efficiency is improved, the multi-dimensional retrieval capability is achieved, multi-class feature combination query, lithology + era + components and the like are supported, and seamless connection of macroscopic-mesoscopic-microscopic data can be realized; a bidirectional traceable chain is established, and data traceability is complete.

The invention discloses a transformer area topology identification method and device based on multi-feature fusion, and the method comprises the steps: S1, distributing an initial branch node for each user, constructing an initial topological structure identification model, and obtaining a preliminary branch node-user association relation adjacency matrix; s2, constructing a topological optimization model based on difference characteristics, and enabling the error between branch node power difference and user power difference combinations to be minimum; s3, constructing a topological optimization model based on peak-valley feature matching, and enabling the matching degree of the peak-valley feature combination of the branch node and the peak-valley feature combination of the user to be maximum; s4, constructing an optimization model based on total fitting error correction, and enabling the total error between the actual power and the fitting power of the branch node to be minimum; s5, judging whether undistributed users exist or not, if not, obtaining a final topology identification result, and otherwise, performing S1 to S4 again; according to the method, the area topology connection relation is effectively identified.

The invention relates to the field of traditional Chinese medicine pulse condition collection, and provides a traditional Chinese medicine six-channel identification cognition method and system based on heart rate variability, and the method comprises the steps: obtaining heart rate variability data of a detected object, and extracting parameters such as time frequency from the heart rate variability data; constructing a hierarchical feature extraction network, inputting parameters such as time frequency into the hierarchical feature extraction network, and distributing weights for the parameters such as time frequency based on a qi-blood-body fluid theory to obtain a fusion feature vector; inputting the fused feature vector into a particle swarm optimization algorithm, and performing feature selection by adopting a six-channel transmission constraint function and a syndrome affinity particle update strategy to obtain an optimized feature combination; the optimized feature combination is converted through the semantic mapping relation between the heart rate variability parameters and the pulse condition descriptors, and pulse condition feature parameters are obtained; and performing syndrome classification calculation based on the pulse condition characteristic parameters, and outputting six-channel syndrome types. According to the invention, automatic identification conversion from physiological signals to traditional Chinese medicine syndromes is realized, and the precision and reliability of six-channel syndrome identification are improved.

The invention discloses a high-dimensional heterogeneous traffic dynamic feature optimization method based on double-track coevolution, and aims to solve the problems of slow convergence and poor local optimization in high-dimensional data of a traditional algorithm. According to the method, through cooperative work of a global exploration orbit and a local enhancement orbit, a dynamic parameter adjustment and information interaction mechanism is combined, and efficient search and fine adjustment of a feature space are achieved. Performing wide feature combination search on the global orbit by adopting an adaptive crossover rate, a disturbance rate and a structured recombination strategy; the local rail dynamically adjusts the feature weight through gradient driving optimization and time sequence sliding window constraint. Coevolution between double tracks is realized through feature vector mapping and gradient feedback, and a nonlinear balance factor is introduced to dynamically fuse global and local optimization results. Finally, an integrated detection model is constructed in combination with DNN, LightGBM and SVM, and the accuracy and robustness of malicious traffic detection in a complex network environment are improved. The global exploration and local optimization capabilities are effectively balanced, and the feature selection precision and the convergence efficiency are remarkably improved.

The invention discloses a large language model robustness visual diagnosis method based on multi-dimensional features and adversarial attacks. The method aims to break through a mode that traditional evaluation only depends on a single aggregation index, and a multi-dimensional text feature exploration system covering vocabularies, syntax, semantics and a structural layer is constructed, and a large-scale antagonism disturbance mechanism and a task self-adaptive quantification strategy are combined. And generating structured feature-adversarial instruction-robustness diagnosis data comprising the cue word to be evaluated and the corpus. On the basis, an interactive visual analysis system is constructed, and through bidirectional linkage of a feature statistical view and a semantic projection view, a user is supported to realize progressive exploration from macroscopic feature screening to microscopic semantic attribution under the double view angles of cue words and corpora, so that a root cause causing the fragility of the model is deeply diagnosed. According to the method, the key feature combination influencing the stability of the model can be identified, so that a basis is provided for directional optimization of the model, and the diagnosis depth of robustness evaluation is improved.

The invention provides a soft clustering-based positive sample expansion graph comparative learning method, which comprises the following steps of: data enhancement: performing structure enhancement and feature enhancement on an original graph to generate an enhanced view; according to the structural enhancement, edge disturbance is guided through structural similarity between nodes, key edges are reserved, and potential similar edges are supplemented; according to feature enhancement, diversified feature combinations are generated through fine-grained masks; positive sample dynamic expansion: calculating the membership degree of nodes to each category based on a fuzzy clustering algorithm, and screening a high-confidence node set; expanding a positive sample for each target node in combination with the graph structure constraint and the first-order neighborhood; and multi-task joint training: performing joint optimization on the comparison loss, the clustering uncertainty loss and the edge prediction loss, and training a graph neural network model.

The embodiment of the invention provides an intelligent monitoring method and system based on multi-source heterogeneous data fusion. The intelligent monitoring method based on multi-source heterogeneous data fusion comprises the steps that production data sets in different production scenes are collected and stored in a classified mode; mining association features of equipment operation behavior records and material circulation track information through semantic association analysis, and extracting influence features of production environment perception data in combination with environmental factor mapping to obtain multi-source qualitative features; generating a node relation chain according to the business process association relation, and configuring a matching algorithm of qualitative scene features; screening feature subsets conforming to association rules, and combining and associating the feature subsets to obtain a scene feature combination; creating a scene exception reasoning model by using the scene feature combination and performing exception feature evolution analysis to obtain an exception reasoning result; and the abnormal propagation path is traced, the root cause node is positioned, and the abnormal monitoring label is generated, so that the accuracy of abnormal root cause positioning and the scene adaptability are improved.

The invention relates to a multi-modal data fusion-based infection dynamic visualization evaluation method, which comprises the following steps of: acquiring multi-modal data which comprises a medical image modal, a microbiological modal, a spatio-temporal behavior modal and a physiological and biochemical modal; the multi-modal data is quickly searched through quantum parallel computing, and an optimal feature combination is obtained; carrying out fusion association on the optimal feature combination to obtain fusion features, and constructing a space-time coupling infection risk model at the same time; the space-time coupling infection risk model performs risk field construction based on the fusion features, and generates infection dynamic visualization content; compared with traditional single-mode data, the infection direction can be evaluated more accurately, after quantum calculation is introduced, the operand can be reduced, the calculation efficiency and the accuracy of optimal feature combination obtaining can be improved at the same time, the pathogen propagation path can be visually checked through the infection dynamic visualization content generated through the space-time coupling infection risk model, and the infection risk can be effectively improved. Therefore, blocking can be timely cut off, and disease prevention and control are achieved.

The invention provides an SAR image vegetation coverage inversion method and system based on multi-dimensional feature optimization and model optimization, a storage medium and electronic equipment, and the method comprises the following steps: carrying out the preprocessing of a Sentinel-2 image, calculating a normalized difference vegetation index (NDVI) through a wave band calculation formula, and carrying out the calculation of the NDVI; a reference vegetation coverage (FVC) of a research area is obtained by combining a pixel bipartite model (DPM), and then an optimal SAR feature combination is selected by applying an improved genetic algorithm ET-GA, using XGBoost as an adaptability evaluation function and using a root-mean-square error (RMSE) as an evaluation index. According to the method, SAR features are extracted through multiple feature decomposition methods, multi-dimensional feature optimization, intelligent algorithm optimization and a deep learning model are combined, the core problems of feature redundancy, insufficient precision, low efficiency and the like of SAR data in vegetation parameter inversion are solved, and an innovative technical scheme is provided for the field of remote sensing quantitative inversion.

The invention discloses a hydrological forecasting method based on multi-feature combination and a Transform model. The method comprises the following steps: acquiring measured data of a target watershed hydrological station, building a physical hydrological model, acquiring output data and derivative feature data of the physical hydrological model, and integrating to obtain basic hydrological data; creating enhanced hydrological physical features, and forming a multi-dimensional original feature pool; constructing a plurality of combination strategies based on the basic hydrological data and the multi-dimensional original feature pool; capturing a long-term dependency relationship of the hydrological time sequence by using an improved Transform model; and the model performance is improved through automatic hyper-parameter optimization. According to the method, the influence of different input feature combinations on the flood forecasting precision is highlighted, and effective technical support is provided for water resource management and flood control and disaster reduction.

The invention provides a converter valve key component burning defect identification method based on a neural network, and belongs to the technical field of power electronic equipment fault diagnosis, and the method comprises the steps: collecting multi-modal data through an infrared thermal imager and a plurality of sensors, and inputting the pre-processed multi-modal data into a specially designed neural network architecture; the framework comprises an image feature extraction module, a time sequence feature extraction module, a feature fusion module and a classification positioning module. According to image processing, improved ResNet50 is combined with an attention mechanism, time sequence features are extracted through a bidirectional long-short-term memory network and a time convolution network, and effective feature combination is achieved through a dynamic weight fusion mechanism. Meanwhile, a double-phase heat conduction model is introduced to analyze temperature distribution, accurate identification of burning defects of key components of the converter valve in a complex environment is realized through large-scale data set training and a two-stage optimization strategy, and key technical support is provided for safe operation of a power system.

The invention relates to the technical field of image processing, in particular to a damper valve system adjustment method based on an artificial intelligence technology, a product, equipment and a storage medium. The method comprises the steps that parameter combinations of all valve systems of the shock absorber are collected; performing data quantization on the parameter combination to obtain a feature combination; and inputting the feature combination into a neural network model to obtain a change curve of the damping force along with the piston speed predicted by the neural network. According to the method, the external characteristic functions of the passive hydraulic shock absorber under different valve system structures are predicted through the artificial intelligence algorithm, and the number of tests in the chassis adjustment process is reduced.

The invention discloses a conveying system and method based on AI commodity recognition, and relates to the technical field of conveying recognition, and the system comprises a collection module which is used for collecting image data and sensor data of a to-be-conveyed commodity in conveying equipment in real time; the AI feature combination module is used for carrying out feature extraction on the image data of the to-be-transmitted commodity and the sensor data based on an AI model, and combining the extracted features to obtain a first feature; the commodity recognition module is used for inputting the first feature into a pre-trained commodity recognition model for recognition and determining a recognition result; the adjusting module is used for adjusting the transmission information of the to-be-transmitted commodity based on the identification result; the cooperation mechanism of the conveying mechanism equipment and the recognition system is optimized, and the conveying efficiency of the conveying mechanism equipment is improved.