12results about How to "Solve the generalization problem" patented technology

The present application relates to the technical field of image sensor calibration, and particularly relates to a processing method and system of a hybrid event and frame sensor, computer equipment and a medium; the method comprises the following steps: acquiring original data of the hybrid event and frame sensor; performing a noise calibration operation on an intensity frame to generate an active pixel sensor noise model and a net intensity signal; performing an event probability coupling operation on an event stream to generate an event visual sensor event probability model; and performing unified collaborative processing on the active pixel sensor noise model and the event visual sensor event probability model to output uniformly calibrated sensor data. In this way, a unified and reproducible cross-modal calibration framework can be provided to solve the technical problems of existing calibration technologies, such as weak physical coupling, insufficient calibration granularity and black box implementation, and to provide a unified, reproducible and high-precision sensor data calibration output for high-speed and high-dynamic-range mobile perception applications.

The application discloses a reservoir intelligent inversion method and related device, relates to the technical field of reservoir intelligent inversion, and comprises the following steps: obtaining multi-source heterogeneous data of a target area and preprocessing; constructing an initial geological model generator based on a conditional diffusion model, and optimizing hidden space parameters in a manner of seismic wave forward simulation; constructing a reservoir knowledge graph and adaptively pretraining and migrating the model online; performing task demand analysis, task decomposition and dynamic collaborative execution on a reservoir modeling task based on a thinking chain technology and a language large model technology, and obtaining iteratively optimized hidden space parameters; inputting the iteratively optimized hidden space parameters into the optimized geological model generator, outputting a three-dimensional distribution model, and extracting a confidence interval feature; incrementally updating the reservoir knowledge graph, determining a reservoir model conforming to a geological mode feature and matching a seismic waveform, and realizing reservoir intelligent inversion. The application can improve the accuracy and efficiency of reservoir inversion.

The application provides a multi-dimensional perception-oriented super-resolution imaging method and system. The method comprises the following steps: acquiring an original low-resolution polarization mosaic image collected by a defocus plane polarization camera, and calculating a first Stokes parameter and a second Stokes parameter according to light intensity components of multiple polarization directions contained in the original low-resolution polarization mosaic image; inputting the original low-resolution polarization mosaic image, the first Stokes parameter and the second Stokes parameter into a pre-trained polarization reconstruction model to obtain a high-resolution polarization image subjected to super-resolution reconstruction; wherein the pre-trained polarization reconstruction model is constructed through a multi-scale large kernel attention mechanism; the multi-scale large kernel attention mechanism is arranged at an encoder layer of the pre-trained polarization reconstruction model and is used for global feature modeling. Based on this, the super-resolution reconstruction of the polarization image is realized, which is physically faithful, efficient in feature extraction and strong in robustness.

The application provides an incremental learning method and device based on a cloud-edge collaborative architecture, equipment and a medium. The method comprises the following steps: training a cloud-end basic model based on original data collected from each edge node; training an edge model corresponding to each edge node based on self-owned data of each edge node and the cloud-end basic model; screening incremental data generated by each edge node to obtain an incremental data set, wherein the incremental data is generated by the edge node based on the edge model; and performing incremental learning training on the cloud-end basic model through the incremental data set to obtain an updated cloud-end basic model. Through the cloud-edge collaborative architecture, the application solves the problem that three important indicators, i.e., model generalization ability, model accuracy and reasoning time delay, are difficult to meet business requirements at the same time, and ensures that the cloud-end basic model can maintain good generalization ability in multiple scenarios through a cloud-end training strategy.

The application discloses a kind of high-voltage motor fault prediction method and system, to solve the problem that prior art cannot carry out high sensitivity early warning to weak, progressive early fault under the premise of not relying on fault sample.The method comprises: synchronously collecting three-phase current, three-axis vibration and stator temperature signal;Based on historical normal data, train multivariate LSTM-VAE prediction model;Real-time calculation multidimensional residual vector;Residual covariance matrix is calculated in sliding window;Generate anomaly score by structural similarity with reference covariance matrix;When anomaly score is continuously over threshold, trigger early warning.The system comprises multi-source sensor array, synchronous acquisition module, prediction model, residual calculation module, covariance analysis module and early warning decision module.The application realizes early fault high robustness early warning when signal amplitude is not over limit by monitoring multivariate residual covariance structure evolution, significantly improves predictive maintenance efficiency.

The invention discloses a short-term photovoltaic power prediction method and system based on data mechanism combined driving. The method comprises the following steps: acquiring the geographic latitude, the solar declination angle and the solar hour angle of a photovoltaic power station to be predicted; calculating a solar elevation angle according to the geographic latitude, the solar declination angle and the solar hour angle, and determining the projection direction of the sun in the horizontal plane to obtain a solar azimuth angle; calculating the ideal irradiance of a photovoltaic panel plane based on the solar elevation angle and the solar azimuth angle in combination with the photovoltaic panel arrangement parameters of the photovoltaic power station to be predicted; introducing ideal irradiance of a photovoltaic panel plane to establish a photovoltaic power prediction probability neural network PNN model; and constructing a photovoltaic power prediction neural network PVFNN model by combining a long short-term memory network LSTM on the PNN model, and realizing short-term photovoltaic power prediction by using the PVFNN model. According to the method, the irradiance model in ideal weather is organically embedded into the neural network, learning in a data driving process is constrained, and a predication result with interpretability can be obtained.

The invention discloses a double-stage pre-training system for reading of an industrial inspection instrument, and belongs to the technical field of industrial visual inspection and intelligent inspection. The system comprises a structure interpretable parameterized instrument data synthesis module, a mask auto-encoder pre-training module, a multi-task joint pre-training module and an industrial deployment fine tuning module. Constructing a plurality of pointer type instrument structure templates based on a parametric modeling mode, randomizing parameters to generate high-diversity synthetic instrument data, and automatically generating corresponding structure and semantic annotations; performing unsupervised pre-training by using a mask auto-encoder to learn the relationship between the underlying structure features of the instrument image and the global space; then, multi-task joint pre-training is introduced into the shared backbone network, and multi-task collaborative optimization is carried out; in the industrial deployment stage, through multiple task heads and newly added target detection task heads, small sample fine adjustment is carried out in combination with a small amount of field data, and rapid adaptation to a specific industrial environment is realized. The method is suitable for an automatic instrument reading scene in a complex industrial environment.

The invention provides a document analysis method for qualification review and similarity comparison, and relates to the field of document intelligent analysis, the method adopts an OCR, CV and NLP multi-modal fusion technology to realize bidding document deep analysis, fuses text, layout and image features through an attention mechanism, and converts unstructured data into a structured semantic map. And fusing text, image and quotation multi-dimensional similarities, and dynamically generating a comprehensive risk score through a learnable function. And qualification review adopts a rule engine and NLP double-engine cooperation, a rigid rule guarantees a review base line, flexible semantic reasoning processes fuzzy conditions, and NLP weights are dynamically adjusted according to historical behaviors of an enterprise. Manual auditing feedback is used as continuous learning data, and the model and the rule base are optimized. According to the method, full-process automation of bidding clearing is realized, the review efficiency and accuracy are improved, dynamic configuration of rules is supported, the risk of range bidding is effectively prevented, the bidding clearing cost is reduced, and continuous evolution of the system is realized.

The application belongs to the field of robot positioning, and relates to a self-adaptive layered robot positioning method and device for degenerative scenes, which comprises the following steps: monitoring the data quality obtained by a heterogeneous sensor in real time, mapping the state of the heterogeneous sensor to a unified quantization space, and outputting a unique degenerative level identifier D1-D4; for a D1 environment, the uncertainty of feature matching is quantified, and the most contributive feature subset is selected; for a D2 environment, a singular value decomposition is used to locate a degenerative subspace; for a D3 environment, an expected error of the heterogeneous sensor engine is estimated through a performance prediction network, and a continuous confidence weighting is used instead of hard switching; for a D4 environment, a pre-training-fine-tuning-online adaptation three-level architecture is used to provide a cross-platform generalized pure inertial odometer; and historical events are stored as experience data. The application realizes fine differentiation and directional processing of degenerative scenes, and builds an experience-driven closed-loop optimization mechanism.

The invention discloses a bearing residual life prediction method based on a multiple working condition information fusion large model, and the method comprises the following steps: constructing a cross-modal data set, extracting time sequence features through a signal encoder, mapping the time sequence features to a large model semantic space through a projection layer, achieving the alignment with a working condition text, carrying out the fine adjustment of the large model based on an LoRA technology, and carrying out the prediction of the residual life of a bearing. Receiving a sequence fusing the instruction, the working condition and the signal characteristics for joint reasoning; in training, an asymmetric dynamic penalty mean square error loss function is adopted, and the penalty weight of optimistic prediction errors is dynamically increased. According to the method, physical signals and semantic information are effectively fused, and generalization and industrial safety of prediction results are improved.

The invention relates to the technical field of distribution network operation and maintenance management, and discloses a distribution network operation and maintenance management method and system based on multi-dimensional intelligent perception, and the method comprises the steps: constructing a distributed cooperative sampling perception architecture, collecting distribution network operation and maintenance data through multiple types of sensors, and carrying out the space-time alignment compensation of the distribution network operation and maintenance data through combining with a digital twin space-time coordinate system; establishing an electromagnetic interference self-adaptive filtering architecture to filter interference data, performing equipment characteristic decoupling of equipment type adaptation, performing decoding at a cloud side after encoding, and reconstructing an equipment core operation state; constructing a three-dimensional topological twin body of the distribution network, establishing a health assessment model to predict a degradation track, calculating a fault risk entropy, and substituting the fault risk entropy into a corrected life prediction result; constructing an operation and maintenance decision space in combination with the fault risk entropy and the distribution network three-dimensional topological twin state, and solving an optimal decision; and injecting the decision into the distribution network three-dimensional topology twinborn simulation verification security constraint, evaluating the fault risk entropy change, and feeding back and updating the health evaluation model and the decision engine.

The present application belongs to the technical field of radar signal processing, and provides a 94Ghz radar baseband signal solving method and system, which comprises clock signal synchronization, signal receiving and preprocessing, analog-to-digital conversion, multi-stage parallel signal processing, parameter dynamic adjustment, multi-target solving and resource dynamic allocation. The present application introduces a FPGA multi-stage parallel processing architecture, improves the multi-target detection and tracking capability of the system in complex scenes, realizes dynamic optimization of signal processing parameters according to real-time environment through a deep reinforcement learning algorithm, ensures the accurate detection capability of the system in different scenes, enhances the real-time tracking and solving capability of the system for multi-target through a graph neural network multi-target cooperative processing algorithm, and realizes dynamic resource allocation through an energy perception algorithm, thereby greatly reducing the power consumption of the system while ensuring high-speed processing.