40results about How to "Reduce training" patented technology

The invention discloses an image generation method and system based on diffusion model adaptive reasoning. The method comprises the following steps: constructing a next noise intensity prediction module TPM; improving an original diffusion model, and adding a TPM; the improved diffusion model is trained by using a reinforcement learning method, the whole denoising process is expanded for multiple times, the next time step is predicted in each iteration, an award is calculated according to the quality of a finally generated image and the number of steps, and TPM parameters are updated by using a PPO loss function; in the actual reasoning process, noise is randomly sampled from Gaussian noise and input into the trained diffusion model, the diffusion model is used for predicting a denoising result, meanwhile, the TPM is used for outputting distribution of time steps based on next denoising, and a distribution mode is used as the next time step used in actual reasoning; and when the predicted time step is smaller than a set threshold value, a final image generation result is obtained based on model output. According to the method, the image generation process can be dynamically adjusted, and a reasoning result with both efficiency and quality is realized.

The invention relates to the technical field of marine ecology, in particular to a marine ecology multi-agent construction method and an interaction system thereof. The construction method comprises the following steps: a marine ecology large model construction step: constructing a fine adjustment data set, and training based on the fine adjustment data set to obtain a marine ecology large model; a parallel application module construction step: constructing a domain knowledge base and a marine ecological multi-intelligent body, constructing a multi-modal large model, and deploying the domain knowledge base, the marine ecological multi-intelligent body, the multi-modal large model and the marine ecological large model of the parallel application module in a GPU server, and the marine ecology large model is used for analyzing the user request and distributing the user request to the parallel application module, triggering the parallel application module to generate a response result, feeding the response result back to the marine ecology large model for semantic integration, and outputting the result. According to the invention, the intelligent level in the field of marine ecology is improved, the prediction capability of the model is enhanced, and the prediction result is more accurate and reliable.

The invention discloses a real-time three-dimensional multi-machine conflict resolution method based on graph reinforcement learning, and the method comprises the steps: constructing a three-dimensional air traffic interaction scene, carrying out the modeling of the scene into a graph, and obtaining the convolution features of the graph as model input; and establishing a conflict buffer model, and designing a reward function to guide the aircraft to minimize an additional flight distance while ensuring a safety interval. A D3QN reinforcement learning algorithm is adopted for training, the optimal avoidance action of the aircraft is obtained, and real-time multi-aircraft conflict resolution in the high-density airspace is achieved.

The invention provides a data set keyword generation and screening method based on a large language model, and belongs to the field of natural language processing and artificial intelligence. The method comprises the following steps: collecting comment text data and known keywords thereof; dividing the text data into blocks; generating preliminary keywords by the large language model; performing preliminary screening on the preliminary keywords in the preliminary keyword list in combination with a TF-IDF algorithm and a TextRank algorithm; and performing semantic and emotion correlation screening on the preliminarily screened keywords through a large language model to obtain final keywords. According to the method, the keyword generation and screening technology is remarkably improved, and the method is particularly suitable for large-scale text analysis tasks needing high precision and high efficiency.

The invention belongs to the technical field of Internet engineering services, and discloses an engineering management promotion service system based on the Internet. The data acquisition module is responsible for capturing and integrating data required by engineering management from various sources; the data processing module is used for receiving the original data from the data acquisition layer and carrying out cleaning, integration, conversion and storage processing; the business logic module is responsible for realizing specific business processes and rules of project management; and the application service module meets diversified requirements of project management personnel. Through the well-designed interface layout, the operation process conforming to the habits of the user and rich interaction elements, it is ensured that the user can easily and happily interact with the system, the operation efficiency of the user is improved, the satisfaction degree and loyalty of the user are enhanced, and the user experience is improved through continuous optimization and iteration. The system can continuously adapt to the requirements and changes of the user, and provides more personalized experience.

The invention relates to the technical field of coal quality identification and monitoring, and discloses a coal quality dynamic detection method and system based on an image identification mining face, and the method comprises the steps: obtaining images of a first region, a second region and a third region of the mining face from top to bottom in the mining operation process of a coal mining machine; extracting stratification features from the first area image and the second area image to obtain a stratification line distribution diagram; based on the stratification line distribution diagram, calculating the maximum value of the interlayer spacing and the vertical coordinate variance; determining the uniformity of the coal seam based on the maximum value of the interlayer spacing; determining the flatness of the coal seam based on the vertical coordinate variance; based on the coal seam uniformity and the coal seam flatness, the coal seam quality is obtained; extracting geometric features of the third area image, and generating a bright spot distribution map; calculating the average area of the hot spots based on the hot spot distribution map; according to the average area of the bright spots, the crushing degree of the coal rock is determined, and then the quality of the coal rock is determined. According to the invention, the coal seam quality and the coal rock quality can be analyzed.

The invention relates to a low-light image enhancement method suitable for haze climate, and belongs to the field of digital image processing. According to the method, firstly, denoising information in an image pair can be effectively learned in the learning process through a GCA (Group Channel Attention) group channel attention structure, and interference caused by external noise can be solved; secondly, the transformer block of the method enables the network to have a better window adaptive capability through a self-attention mechanism, and has a better effect in solving a local overexposure phenomenon; moreover, according to the method, fine adjustment can be carried out through a small number of field samples, the adaptive capacity to the required environment is greatly improved, a new task can be learned on a low-light enhancement task, and the influence of non-illumination factors on the image quality is solved.

The invention provides a key information extraction and mass attitude evaluation method based on a large language model. The method comprises the following steps: collecting comment texts of the masses on a target decision, and extracting known keywords; screening a comment text related to the known keyword by utilizing a search model; further screening a comment text related to the known keyword by utilizing a semantic understanding model; rechecking the correlation between the comment texts and the known keywords by using a large language model, and filtering out the comment texts which are misjudged to be related to the known keywords to obtain a key comment set; and a multi-agent system is constructed, each agent independently evaluates the attitude of each comment text in the key comment set to the target decision, and the user agent integrates the evaluation results of all agents and outputs the evaluation results. According to the method, a multi-level screening mechanism and an attitude evaluation mechanism based on multi-agent consensus are adopted, the calculation amount in the similarity judgment process is reduced, and the attitude evaluation accuracy and the system processing efficiency are improved.

The invention relates to a method for generating a complex-shape three-dimensional building model based on frame field deep learning. The problems that in the prior art, when a three-dimensional building model is generated, training difficulty is large, the three-dimensional building model is easily affected by smooth corners, and artifacts are generated are solved. The method comprises the following steps: S1, acquiring remote sensing data and point cloud data; s2, preprocessing the acquired image data; s3, carrying out data normalization processing; s4, designing a frame field and optimizing details; s5, optimizing the activity skeleton model; and S6, generating a multi-shape contour and a three-dimensional model. The method has the advantages that the generation precision and the generation efficiency of the three-dimensional building model are improved, buildings with complex shapes can be processed, and the method has a wide application prospect.

The invention relates to an image generation method and device based on a Brown bridge diffusion model, equipment and a medium, and the method comprises the steps: receiving a group of image combinations which comprise a satellite image and a ground panoramic image corresponding to the satellite image; extracting sharing features of the satellite image and the ground panoramic image through a cross-view-angle image joint encoder; performing polar coordinate transformation on the satellite image, and encoding the satellite image and the ground panoramic image after polar coordinate transformation into a potential space to obtain an initial satellite image potential vector and a ground panoramic image potential vector; in the potential space, gradually adding noise to the potential vector of the ground panoramic image to obtain a potential vector of a satellite image; noise in the satellite image potential vector is gradually removed based on the satellite image potential vector and the shared features, and a target potential vector is generated; and decoding the target potential vector to generate a target ground panoramic image. According to the invention, the efficiency and quality of converting the satellite image into the ground panoramic image are improved.

The invention discloses a multi-scale fusion fast network traffic anomaly detection method based on comparative learning, and the method comprises the steps: carrying out the preprocessing of traffic data, extracting features, converting the traffic data into a traffic image, carrying out the normalization processing of the traffic image, and dividing the traffic image into small blocks; constructing a multi-granularity multi-layer perceptron model, inputting traffic images after normal traffic data and abnormal traffic data division into the multi-granularity multi-layer perceptron model, and extracting feature information; defining a comparative learning loss function, training the multi-granularity multi-layer perceptron model by comparing the characteristics of the normal traffic and the abnormal traffic, and iteratively adjusting the model parameters of the multi-granularity multi-layer perceptron model to reduce the comparative learning loss function value until convergence; and inputting the preprocessed traffic image into the trained multi-granularity multi-layer perceptron model, wherein the multi-granularity multi-layer perceptron model can distinguish normal traffic from abnormal traffic.

The invention belongs to the field of social risk prevention and control, and discloses a credit card risk prevention and control method and system based on Stacking ensemble learning, and the method comprises the steps: preprocessing a data set, extracting key features, and employing a resampling technology to balance positive and negative examples; constructing a shallow feature rule by means of a diversified machine learning method, and establishing a deep neural rule by using deep learning technologies such as a multi-layer perceptron; based on a Stacking ensemble learning method, a feature rule and a neural rule are fused. According to the invention, the risk can be identified and predicted more accurately, so that the accuracy and timeliness of early warning are improved; through fusion optimization of multiple rules, the problems of ambiguity, conflict, non-uniformity and the like in current social governance and risk prevention and control rules are solved.

The invention discloses a gene amplification fluorescence in situ hybridization image intelligent identification system based on deep learning. According to the technical scheme provided by the invention, in the system, an image acquisition module is used for performing image acquisition on a sample marked by a FISH technology by using a microscope to obtain an initial image; the SAM segmentation module is used for carrying out image segmentation on the initial image based on an SAM model to determine a segmented image; the ResNet comparison module is used for comparing each quasi-cell image by using the standard cell image, and topping a real cell image; the fluorescent dot counting module is used for carrying out fluorescent dot identification and counting based on a color filtering technology, and carrying out gene amplification detection based on a target gene signal and a chromosome centromere signal. According to the method, more accurate intelligent segmentation of the FISH image is realized based on the SAM model, the accuracy of cell judgment is further improved by using a deep learning technology, the identification and counting of target gene signals are quickly realized, and the gene amplification level is better judged.

The application provides a location-based search processing method, device and equipment and a storage medium. The method comprises the following steps: in response to a location search request of a user, inputting search information into a preset multi-task search model; simultaneously processing the search information based on a city jump decision task and a search suggestion task in the multi-task search model to obtain target city location features corresponding to the city jump decision task and search suggestion features corresponding to the search suggestion task; obtaining a search processing result based on the target city location features and the search suggestion features; obtaining a search suggestion list with the highest matching degree with the search processing result in a preset search suggestion library, and displaying the search suggestion list on a search interface. Through the above method, the training and development and maintenance costs of multiple models can be effectively reduced.

The application discloses a Transformer oil well production prediction method and device based on gating constraints and Kalman filtering, and a medium, which comprises the following steps: first, obtaining oil well historical production data and preprocessing; then, using a mutual information method to screen input features related to target production, and constructing conditional gating constraint features representing the opening and closing states of the oil well; then, inputting the processed time sequence samples into a Transformer prediction model to obtain initial prediction results of the oil well production; then, performing state constraint on the initial prediction results according to the conditional gating constraint features, and performing causal recursive residual correction on the prediction results of subsequent time based on the current time prediction residual by using Kalman filtering to obtain final prediction results; further, performing grid search on Kalman filtering parameters Q and R for optimization, and determining the optimal parameter range by combining a heat map. The application can effectively reduce the prediction error, and improve the accuracy, stability and robustness of the oil well production prediction.

The application discloses a high-efficiency beam training method, device, medium and product under near field communication, relates to the technical field of communication, and comprises the following steps: selecting code words from the first layer of a stage one codebook according to the index of optimal code words; finding out the optimal code words from the selected code words and updating the index; feeding back the updated index to a base station; determining whether the first cycle parameter is greater than the first maximum cycle parameter, executing the above steps when the first cycle parameter is greater than the first maximum cycle parameter, and setting the second cycle parameter otherwise; selecting code words from the first layer of a stage two codebook according to the index of the current optimal code words; finding out the optimal code words from the selected code words and updating the index; feeding back the updated index to the base station; determining whether the second cycle parameter is greater than the second maximum cycle parameter, outputting the code word at the optimal code word index after updating when the second cycle parameter is greater than the second maximum cycle parameter, and returning to execute the above steps otherwise. The application can effectively improve the reachable rate performance in near field communication and significantly reduce the training and feedback overhead.

The invention provides a welding seam segmentation method based on stripe feature enhanced extraction. The welding seam segmentation method comprises the following steps: carrying out efficient encoding and decoding learning on welding seam stripe information in an encoder and a decoder by using transfer learning and cascade information learning thoughts respectively; through the two structures, richer low-layer and high-layer semantic information can be extracted, and the extraction capability of welding seam stripes of the segmentation model is ensured; besides, the weld stripe image contains complex noise such as arc light, smoke dust and molten drop splashing, the defects of an existing jump connection mode are analyzed, a multi-layer feature fusion mechanism fusing a global attention mechanism is designed, information association in different coding layers can be learned through the model, and the fusion precision is improved. And the influence of noise can be greatly weakened, so that effective fusion of shallow-layer semantic information and high-layer semantic information is realized, and the segmentation capability of the model to weld seam stripe features is enhanced.

The invention belongs to the technical field of garbage cleaning, and particularly relates to a sweeper garbage collecting and leading-in mechanism which comprises a mounting frame, a sweeping cavity, a garbage collecting and leading-in device and a garbage collecting and leading-in device, the mounting frame is internally provided with a conveying cavity, the right side wall of the conveying cavity is provided with a discharging port, and the conveying cavity is internally provided with a conveying belt; a collecting and guiding-in assembly is arranged in the sweeping cavity and used for collecting and guiding in garbage. Garbage of different sizes can be guided into the sweeping mechanism without difference, the size and the type of the garbage do not need to be perceived and recognized through an AI technology, and efficient sweeping is achieved; in addition, the research, development and manufacturing cost of equipment is saved, and the cost of large model training, vehicle-mounted computing power and sensing parts is saved.

This application discloses a cross-domain multimodal recommendation method and system based on multi-agent attribute-driven architecture. Embodiments of this application can be applied to product recommendation scenarios in an e-commerce platform. The method includes: receiving multimodal features; processing the multimodal features using a first operation to generate reconstructed original multimodal features; the reconstructed original multimodal features include attribute-related representations and attribute-independent representations; then, processing the reconstructed original multimodal features using a second operation to generate a modal cue vector; then, processing the modal cue vector and the attribute-related representation to generate a new feature vector; and finally, processing the new feature vector using a sequence encoder to generate attribute-level multimodal sequence states. By sharing basic embeddings, the parameter overhead is significantly reduced, solving the problem that the parameter size of traditional cross-domain multimodal encoders increases linearly with the number of domains.

The present application relates to a kind of triangular mesh autoregressive generation method based on geometric curvature perception state space modeling, belong to artificial intelligence, computer vision and computer graphics field.First, the input triangular mesh is serially represented, obtains the grid mark sequence and feature for state space modeling;Second, by establishing geometric curvature perception module, the curvature information and multi-scale geometric statistical information of input triangular mesh are extracted, obtain geometric condition vector;Then, grid mark sequence and feature, geometric condition vector are modeled in state space, obtain the output feature for predicting the conditional probability distribution of next grid mark;Finally, by constructing autoregressive generation model based on geometric quality constraint and adaptive decoding, combined with conditional probability distribution generates target grid mark sequence, finally generates target triangular mesh.The present application not only can realize adaptive generation of triangular mesh, but also can consider the geometric rationality of sequence modeling efficiency and generation result.

The application discloses a kind of based on knowledge graph's cancer cell line drug response prediction large language model construction method.The method fuses the structured information of biological knowledge graph and the semantic reasoning ability of large language model, realizes the multi-level modeling and explainable prediction of drug-cell reaction mechanism.The method comprises the following steps: first, integrate multi-source data to construct multi-modal dataset;Second, establish the drug response knowledge graph containing drug, gene and other entities;Then, fine-tune the model using LoRA technology and inject graph embedding, realize cross-modal alignment;Further, dynamic knowledge retrieval and enhancement are carried out using RAG technology;Finally, output drug sensitivity prediction and natural language explanation.The application significantly improves the prediction accuracy and explainability, and can reveal the key gene pathway, providing an efficient intelligent tool for precision cancer treatment.

A target detection method and apparatus based on a monocular image. The method comprises: obtaining the monocular image, and determining a two-dimensional frame of a target in the monocular image and a priori size of the target, the priori size being a preset size determined according to the target (S101); calculating space coordinates of two bottom corner points of the two-dimensional frame in a three-dimensional coordinate system (S102); and calculating three-dimensional information of the target according to the priori size of the target and the space coordinates of the two bottom corner points (S103).

The present invention provides an indoor heading estimation method, device and medium, wherein the method includes grouping IMU data to obtain local features, integrating them with preset additional features, inputting the local features into a deep learning model to obtain correlation to update the additional features, and finally reducing the dimension of the integrated features to obtain the heading estimation result. The method can carefully mine the local features of the data and capture specific information in different regions; update the additional features through correlation to achieve global feature extraction and enrich feature representation; the dimensionality reduction process can reduce the computational complexity, reduce the training and inference time, remove redundant information and noise, and improve the generalization and robustness of the model. Compared with the existing technology, it can better adapt to the complexity of the indoor environment and improve the accuracy and reliability of heading estimation.

According to a first aspect, a network system for generating output data values from input data values according to one or more learned data distributions includes an input receiving a set of observations, each observation including a respective first data value of a first variable and a respective second data value of a second variable dependent on the first variable. The system may include an encoder neural network system configured to encode each observation in the set of observations to provide an encoded output for each observation. The system may further include an aggregator configured to aggregate the encoded output for the set of observations and provide an aggregated output. The system may also include a decoder neural network system configured to receive a combination of the aggregated output and the target input value, and provide a decoder output. The target input value may include a value of the first variable, and the decoder output may predict a corresponding value of the second variable.

The invention discloses a lightweight method for defect detection of solar cell sheets based on data enhancement, comprising the following steps: S1: screening the acquired fault pictures of the solar cell sheets to obtain true fault samples; and manually marking the types of the pictures; S2: building an improved ACGAN network, using a generator to generate false fault samples and corresponding categories according to randomly generated noise data and type information; then inputting the false fault samples and the real pictures into a discriminant network together, and the discriminant network determines the true and false, and the following actions are repeated in a cycle, and the generating network and the discriminant network are trained in turn; the invention expands the data set based on the data enhancement algorithm of the ACGAN network, alleviates the overfitting problem caused by insufficient training data, and proposes optimizing the convolutional neural network structure, performing lightweight processing, reducing network training parameters, compressing the model, shortening the model training and defect detection time, and improving the defect detection efficiency.

The invention belongs to the field of artificial intelligence, and particularly relates to a security defense method, device and equipment of a large language model for malicious query. The method comprises the following steps: obtaining a query question; obtaining a candidate lexical element set of a current word responding to the query problem by the large language model, and adjusting probability distribution of each lexical element in a safer direction in the candidate lexical element set; and selecting the lexical element with the highest probability from the candidate lexical element set as the current word of the response. According to the invention, the security defense method for the malicious query is efficient and does not obviously influence the performance.

The invention discloses a dynamic routing method and device based on a self-purification expert pool, and the method comprises the steps: firstly, constructing an expert feedback pool supporting dynamic extension, driving a group of heterogeneous domain expert models to carry out the reasoning of a cross-domain data set problem, and storing a result; secondly, implementing a double-layer self-purification mechanism, performing data cleaning and expert model cleaning on a feedback pool based on real answers, and automatically eliminating low-quality data and low-performance experts; further, on the basis of the purified high-quality data, a lightweight BERT model is trained to serve as a routing learning device, so that the routing learning device learns an expert which predicts the most adaptive expert according to the semantic features of the problem; and finally, in practical application, the route learner is used for performing rapid reasoning and routing on a new input question, and the new input question is distributed to the predicted optimal expert model to obtain an answer. Through dynamic self-purification of data and resources and data-driven routing decision, intelligent management and efficient utilization of heterogeneous expert model resources are realized, routing accuracy and interpretability are ensured, and meanwhile, the capability of a system for processing complex cross-domain problems in a scene with limited computing power is remarkably improved.

The invention relates to the technical field of abrasive disc processing equipment, in particular to a method for intelligently manufacturing a metal mineral polished section, which comprises the following steps: S1, roughly grinding a sample through water and sand adding, surface grinding and plane treatment operations; s2, the sample is subjected to advanced fine grinding treatment through a grinding material; s3, performing high-precision wafer grinding treatment on the sample through timed grinding with grinding powder and polished wafer thickness fine detection operation, and using a thickness sensor and a roughness sensor in combination to ensure that the surface quality of the sample meets a high standard; and S4, under the condition that the thickness reaches the standard, the sample is polished, a roughness sensor is used for detecting the surface smoothness of the sample, the polishing machine stops working after the specified smoothness is achieved, and the metal mineral polished section meeting the requirement is obtained. Detail parameters in the grinding and polishing processes are monitored and adjusted in real time through a sensor system, and high consistency and high standard quality of final products are ensured.

The invention discloses an intelligent network attack detection method based on traffic analysis and electronic equipment. The method comprises the following steps: acquiring traffic data in a network; a pre-trained automatic encoder AE is constructed, the automatic encoder AE comprises an encoder module, a potential space and a decoder module, the encoder module adopts two layers of long short-term memory networks, and a Dropout layer is inserted between the two layers of long short-term memory networks and is used for receiving input traffic data and performing data compression and feature extraction to obtain key traffic features; and the potential space adopts a bidirectional gating circulation unit to carry out key traffic feature classification, and outputs a detection result indicating whether the traffic data has an abnormal traffic type or not through a decoder module. According to the method, the important traffic characteristics in the traffic can be fully extracted, and the problems of low detection rate and high false alarm rate caused by solving the problem of data imbalance by using a sampling technology are avoided while data dimension reduction is completed.

The invention provides a data processing method, device and system, a storage medium and a program product, and relates to the field of data processing. The method comprises the following steps: selecting a first sample set from a target domain sample set; the second sample set is removed from the source domain sample set to obtain a third sample set, and a first area range corresponding to the first sample set is the same as a second area range corresponding to the second sample set; and fusing the first sample set and the third sample set to obtain a fused sample set for training a target detection model. The problem that the source domain sample set dominates network training can be reduced, in addition, due to the fact that fusion between the source domain sample set and the target domain sample set is achieved, the target detection model is made to learn domain-invariant universal features independent of specific configuration or environment, and therefore the adaptability of the model in the target domain is improved.