50 results about "Consistency model" patented technology

The invention provides a real-time face blind restoration method and device based on identity constraint and frequency domain enhancement, and relates to the technical field of image processing, and the method comprises the steps: S1, generating an identity code through the fusion of CLIP and ArcFace, extracting spatial structure features in combination with ControlNet, and obtaining a high-frequency component through the decomposition of learnable wavelets; s2, an input image is mapped to a potential space, and identity, space and high-frequency features are fused to construct a condition vector; s3, performing iterative denoising based on the consistency model, and superposing lightweight wavelet residual correction to enhance high-frequency details; s4, according to the high-frequency energy dynamic distribution weight, reconstructing a multi-scale fusion image through inverse wavelet transform; according to the method, the problems of identity distortion, detail loss and low calculation efficiency of a low-quality face image in restoration are solved, and semantic consistency, detail fidelity and real-time performance of a restoration result are improved through identity-space-frequency domain ternary constraint, lightweight residual correction and degradation perception enhancement.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention discloses an engineering quality safety real-time supervision method and system based on multi-source data fusion, and belongs to the technical field of engineering data processing, and the method comprises the steps: collecting original data, and carrying out the preprocessing of the original data, and obtaining a first data set; establishing a trend model, inputting the first data set into the trend model, and outputting a structure health state prediction sequence; combining the structure health state prediction sequence with the multi-dimensional information of the engineering structure to construct a knowledge graph; reasoning by using a knowledge graph, calculating a risk level of each node, and generating a safety early warning according to the risk level; and inputting a preset intervention strategy set and a risk event set, sorting the intervention strategies according to the risk levels and the construction priorities, allocating and executing the intervention strategies, and finally outputting an intervention execution record. By constructing a unified multi-source sensing and fusion framework, standardized processing and consistent modeling can be carried out on multi-class heterogeneous data, and the dynamic sensing capability on the overall state of an engineering site is remarkably improved.

A system may access content comprising text content, visual content, and / or audio content. The system may perform, based on a harmonization model and / or consistency model, a harmonization check and / or a consistency check on the content. The system may recognize, based on the harmonization check and / or the consistency check, a conflict to be corrected. The system may identify a property of the content that should be changed based on the recognized conflict. The system may generate a corrective action based on the property of the content that should be changed.

The invention discloses a progressive fine tuning method and system for a multi-modal pre-training model, and belongs to the field of deep learning, and the method comprises the steps: obtaining a high-dimensional visual feature vector from a visual encoder of a pre-trained multi-modal large model, obtaining a text feature vector from a text encoder, and carrying out the construction to obtain a heterogeneous modal feature; the contribution degrees of different modes are analyzed through a resource allocation strategy, the whole fine adjustment process is dynamically guided, and limited computing resources are allocated to a multi-mode large model component which contributes to the current task most; and processing the heterogeneous modal features through a cross-modal comparison consistency model to obtain a final optimization target. According to the invention, a larger batch can be trained or a larger batch size can be used for fine tuning training under a limited hardware condition.

The invention discloses a heterogeneous computing system, a cache consistency maintenance method and device, equipment and a medium, and relates to the technical field of heterogeneous computing. The method comprises the steps that when a data operation request is detected, if a receiving party and a transmitting party are a cluster cache layer and a global cache layer, the protocol format of the data operation request is converted according to the mapping relation between a group consistency protocol and a global consistency protocol. And when the data is forwarded to the local cache layer, executing corresponding operation based on the memory consistency model of the calculation unit to which the data belongs, and maintaining the consistency of the local cache. And when the data is forwarded to a cluster cache layer, corresponding operation is executed based on the cluster consistency protocol, and the consistency of the cluster shared cache is maintained. And when the data is forwarded to a global cache layer, maintaining the consistency of the shared caches of the clusters based on a global consistency protocol. The method and the device can solve the problem of poor compatibility and expansibility of related technologies, can be compatible with various computing units and different memory consistency models thereof, and are easy to expand.

The invention discloses a road three-dimensional intelligent inspection system and method based on multi-source perception, particularly relates to the technical field of traffic intelligent inspection, and is used for solving the problem of low reliability of road multi-source data fusion. According to the invention, camera shooting, thermal imaging and a vehicle or airborne platform are deployed in a road scene to collect multi-source data; boolean consistency gating and mutual exclusion override are executed in a unified time window, high-credibility candidates are generated through cross validation, cross-modal external parameters are solved by means of road geometric anchor points and a random consistency model, and long-term stable alignment is ensured in cooperation with external parameter change rate threshold rollback; then, necessary and sufficient condition confirmation is carried out on foreign matters, accumulated water, smog and marking defect according to an inspection anomaly truth table, and a finite-state machine is used for tracking evolution and confirming upgrading nodes to schedule vehicle and machine platforms based on the shortest path of a topological graph and link field facilities to form a closed loop of discovery and disposal, so that the efficiency and reliability are remarkably improved, the cost is reduced, and misjudgment is reduced. The method has the advantages of intelligence, globalization and self-adaption.

The invention provides a probabilistic multi-scale-based missile loading vehicle tail end relative attitude estimation method and system, and belongs to the technical field of aviation equipment automation. Comprising the following steps: firstly, performing coordinate system conversion and noise suppression processing on acquired point cloud data, and constructing a probability multi-scale consistency model; then, on the basis of weighted features obtained by the probability multi-scale consistency model, density self-adaptive multi-scale feature fusion is executed; and finally, according to the multi-scale features after feature fusion, performing dual-stage pose estimation. Through dynamic noise suppression, component-level multi-scale feature modeling and a real-time optimization strategy, millimeter-level and high-reliability alignment between the end effector of the missile loading vehicle and the missile loading frame is achieved.

The application discloses a kind of few sample X-ray defect intelligent detection methods based on diffusion generative model, belong to industrial nondestructive testing and artificial intelligence technical field;The method first acquires preprocessed X-ray image dataset, constructs defect condition embedding that retains physical characteristics;Build the condition diffusion generative model that introduces rectified flow reparameterization and consistency model distillation mechanism optimization, trains high-fidelity defect sample in stages generation;Build multiscale Transform detection network, through mixed sample training, dynamic loss optimization, course learning and feature consistency constraint, by the intermediate feature of detection network is fed back to generative model to adjust defect condition weight, realize the joint training of generative model and detection network;Finally input image to be detected completes defect positioning classification.The application can generate multiple types of real defect samples, eliminate sample imbalance and domain bias, with high detection accuracy and real-time performance under few sample conditions.

This application provides an image generation method, apparatus, device, medium, and program product. The method includes: acquiring text data; determining an initial latent code; iteratively reasoning on the text data and the initial latent code using a target model to obtain a target latent code; wherein the target model includes a latent consistency model and a text-to-image model; the text-to-image model includes a parameter-adjusted Unet module; the parameter-adjusted Unet module is trained using target sample data; the target sample data includes a set of images carrying text labels; the image set includes at least a first set; the first set includes a set of scene images corresponding to a target scene; the target scene includes the application scene of the target model; and generating a target image based on the target latent code. This application improves the efficiency of target image generation.

The invention relates to a verification method and device for each material code in a mold production process, and relates to the technical field of material code verification, the verification method comprises the following steps: S1, coding rule initialization: constructing coding rules of various materials through a coding rule data model for initialization; s2, inputting material codes; s3, the codes are analyzed in a segmented mode; s4, rule matching verification: calculating a regular matching degree for each candidate segment, and judging whether a coding format is legal or not according to the regular matching degree; s5, performing cross-system consistency verification: performing calculation and verification based on the consistency model; s6, verification result processing: according to the legality of the coding format and the cross-system consistency result, executing release, abnormity prompt or blocking operation; according to the invention, automatic verification can be realized, accurate matching of coding rules is realized based on a mathematical model and a formula, and the human error rate is reduced; dynamic rule configuration is supported, and coding requirements of different product structures are met through a parameterized rule model R.

The application provides a surface feature-based anti-counterfeiting information generation method, device, equipment and medium, which comprises the following steps: firstly, a microstructure intensity map is obtained by extracting surface micro-relief features through a phase consistency model; a first height map is obtained by reconstructing an initial surface height field by using a MiDaS depth estimation network; a local displacement field caused by embedding a preset mark is quantified by using a RAFT optical flow model; an initial stress disturbance distribution is constructed by fusing the height field and the displacement field as physical input of a coupling prediction model, and then the surface morphology of a concrete sample after solidification is predicted; finally, the microstructure intensity map, the local displacement field and the first height map are taken as initial surface features at the sampling time, and the initial surface features and target surface features are encoded as anti-counterfeiting information, so that the reliability of sample identification is effectively improved.

The DoS attack strategy making method based on the multilayer cut points is provided for consistency control of the multi-agent system, and the state consistency of the multi-agent system is effectively influenced under the condition that it is guaranteed that the attacker consumes relatively little energy. The method comprises the steps that a multi-agent system consistency model and a DoS attack model are established, an objective function is obtained by taking energy consumption of an attacker and a system consistency error as two indexes, multi-layer cut points are used for replacing an attack action space to improve the optimization efficiency of the objective function, and an attack strategy is obtained by optimizing the objective function. The attack research essence is to guarantee the improvement of the system security performance, and the method provided by the invention can be used as a link for testing the system security to help discover the defects in the aspect of system defense.

The invention is suitable for the technical field of deep learning, and provides a data set generation method, a model training method and a computer, and the method comprises the steps: obtaining a video stream containing a target object, and enabling a camera to move around the target object, and carrying out the shooting of the video stream; receiving interactive annotation input of a user on the target object in a starting frame image of the video stream; calling an image segmentation model to generate an initial mask based on the interactive annotation input; based on the initial mask, automatically tracking and generating an object mask sequence of subsequent multiple frames of the video stream through a time sequence consistency model; and according to an object model of the target object, the initial mask and the object mask sequence, performing pose labeling on the multiple frames of images in the video stream to obtain real pose data. Through the method, the training data set can be automatically generated without manual annotation under the condition of not depending on equipment such as a mechanical arm and a rotary table.

The invention provides an anti-fake information generation method and device based on surface features, equipment and a medium. The method comprises the steps that firstly, surface micro-fluctuation features are extracted through a phase consistency model to obtain a microstructure strength diagram; reconstructing the initial surface height field by using a MiDaS depth estimation network to obtain a first height map, and quantifying a local displacement field caused by embedding of a preset identifier through an RAFT optical flow model; the height field and the displacement field are fused to construct initial stress disturbance distribution to serve as physical input of a coupling prediction model, and then the surface appearance of the concrete sample after solidification is predicted; and finally, the microstructure strength diagram, the local displacement field and the first height diagram are used as initial surface features during sampling, and the initial surface features and the target surface features are coded into anti-counterfeiting information, so that the reliability of sample identification is effectively improved.

The application provides a data center liquid cooling square tank cold source multi-parameter fusion monitoring and early warning method, relates to intelligent operation and maintenance, and comprises the following steps: collecting multi-source time sequence data representing fluid dynamics state and environment heat and humidity state through a multi-modal perception unit arranged in a liquid cooling square tank cooling loop and a cabin environment, and constructing a state observation sequence; based on the state observation sequence, using a physical consistency model embedded with fluid dynamics constraints, heat exchange constraints and loop response consistency constraints, the unobservable state in the cooling loop is inverted to obtain a hidden state vector; according to the hidden state vector and the state observation sequence, a time-varying coupling relationship reflecting parameter change amplitude and response time difference is established to generate a multi-dimensional correlation feature representation; taking the multi-dimensional correlation feature representation as a driving condition, a heat and humidity coupling digital twin model is constructed with the time-varying coupling relationship as a boundary driving condition, and a spatial temperature field, a humidity field and a condensation risk distribution are obtained, which can accurately locate the condensation risk area before the fault occurs and actively intervene, thereby improving the safety and reliability of the liquid cooling square tank operation.

The present invention discloses a heterogeneous computing system and a cache consistency maintenance method, device, equipment, and medium, which relate to the field of heterogeneous computing technology. The method includes: when a data operation request is detected, if the sending and receiving parties are a cluster cache layer and a global cache layer, the protocol format of the data operation request is converted according to the mapping relationship between the group consistency protocol and the global consistency protocol. When forwarded to the local cache layer, the corresponding operation is performed based on the memory consistency model of the computing unit to which it belongs, and the consistency of its local cache is maintained. When forwarded to the cluster cache layer, the corresponding operation is performed based on the group consistency protocol, and the consistency of its cluster shared cache is maintained. When forwarded to the global cache layer, the consistency of each cluster shared cache is maintained based on the global consistency protocol. The present invention can solve the problems of poor compatibility and scalability of related technologies, can be compatible with multiple computing units and their different memory consistency models, and is easy to expand.

Systems and methods, implemented as computer programs on one or more computers for training a consistency model for use in generating a frame of data, such as a frame of image data, and methods of using a trained consistency model to generate a frame of data. A consistency model is used to generate a frame of data by predicting a succession of de-noised frames starting with an initial, noisy frame at an initial time and ending with a final frame, without noise, at a final time. The consistency model is trained to generate self-consistent predictions over a trajectory of predicted frames corresponding to these times. Implementations of the described techniques divide the trajectory into segments and only require the model to generate self-consistent predictions over each segment. This can facilitate the rapid generation of high quality frames of data.

The invention belongs to the field of image consistency processing, and discloses an image consistency model adaptive discretization method for optimizing visual angle inspiration, which comprises the steps of diffusion model setting, adaptive discretization and the like. From the perspective of optimization, a joint target of local consistency and global consistency is constructed, a time step updating strategy in an analysis form is exported by using a Gaussian-Newton method and is seamlessly integrated into a training process, and self-adaptive adjustment and efficient convergence of time steps are realized. According to the method, various existing discrete strategies are unified, the training efficiency and final performance of image generation are remarkably improved, and a new theoretical tool and key technical support are provided for design and scheduling of a high-performance image generation model.

A consistency model is trained to mimic the output of a diffusion model at various points along the denoising trajectory. A trajectory of the diffusion model is determined by generating the data point with the diffusion model by sampling a noised data point and applying denoising steps of the diffusion model to obtain the denoised output. At each of the noise levels, the consistency model is applied to the corresponding data point to remove the remaining noise. The resulting data point from the consistency model is compared with the denoised output of the diffusion model. An error for the consistency model may then be determined based on the comparisons at the various points in the trajectory.

The application discloses a representation enhancement aspect sentiment triple extraction method based on contrast learning, and belongs to the technical field of artificial intelligence. In view of the problems of the existing method, such as too fine contrast granularity of word units, inaccurate boundaries, incomplete aspect word or sentiment word extraction, and lack of cross-sample semantic unified modeling, the method proposes a cross-sample segment-level contrast learning mechanism: obtaining word unit representation through a pre-training model, constructing a grid annotation matrix and a structure contrast loss, simultaneously extracting segment representation from multiple samples and calculating a segment-level contrast loss, and loss weighting back propagation optimization model. The method can enhance the consistency modeling capability of the model for similar semantic segments, and improve the extraction accuracy of semantic segments composed of multiple words and the generalization and accuracy of triple extraction.

This paper discloses a semi-supervised segmentation method based on annotation and prediction consistency. First, a dataset of image samples with fuzzy segmentation targets is obtained and preprocessed. Then, an annotation and prediction consistency model is constructed and trained. Finally, the trained annotation and prediction consistency model is input into the test set image information, and a segmentation mask corresponding to the input image information is output. This method introduces multiple annotations into semi-supervised segmentation, leveraging the diversity and complementarity of multiple annotations to extract key information about object boundaries for accurate segmentation. Compared to other semi-supervised methods that rely solely on a single annotation, the annotation and prediction consistency network performs well in segmenting medical images with fuzzy boundaries.

This invention relates to the field of speech synthesis technology, specifically to a speech synthesis method based on an implicit continuous consistency model. The method involves constructing a dataset containing audio and its text annotations; building a residual vector quantization variational autoencoder, training it using a joint loss until convergence, and extracting the mean and variance of all audio data mapped to a latent vector distribution; sampling Gaussian noise latent variables based on the mean and variance, sampling Gaussian noise and time steps, and adding noise to obtain speaker features; calculating the continuous consistency loss using the time steps, speaker features, text, audio data, and the consistency model; and optimizing the continuous consistency loss until the consistency model converges. This method utilizes residual vector quantization technology to achieve high-rate audio feature compression and decoupling, and combines the single-step sampling characteristics of the consistency model in the latent space to improve the inference efficiency and training stability of speech synthesis, enabling the model to generate high-fidelity speech in a very small number of iterations.

The invention discloses a multi-agent system dynamic event triggering Nash equilibrium search method and device based on an unreliable network, and a storage medium, and the method comprises the steps: defining a revenue function of each user, and obtaining a revenue function of each user; based on a gradient descent model, a leader follower consistency model and a dynamic event triggering mechanism, a user can update own actions according to own revenue functions and estimated values of neighbors, and a Nash equilibrium solution is searched accurately; introducing a DoS attack model, a gradient descent model and a leader-follower consistency model into the multi-agent system model; the invention discloses a multi-agent system dynamic event triggered Nash equilibrium search method and device based on an unreliable network, and a storage medium. According to the invention, users can be ensured to find the Nash equilibrium point under DoS attack, communication resource consumption is effectively reduced, and system stability is maintained.

The invention relates to a reinforcement learning-based text-to-image consistency acceleration method and system, which can model the reasoning process of a consistency model into a Markov decision process (MDP), and directly optimize a task-specific reward function by using reinforcement learning, thereby realizing rapid training and efficient reasoning text-to-image generation.

The invention discloses an end-to-end reservoir parameter inversion method based on a consistency model and phase control constraint, and relates to the technical field of reservoir engineering. The method comprises the following steps: firstly, acquiring geological parameters and production dynamic data of a target area, constructing a sample database and carrying out data preprocessing, then constructing a consistency model and an improved U-shaped network, and integrating the consistency model and the improved U-shaped network to construct an end-to-end oil reservoir parameter inversion model considering phase control constraint; and training and verifying the end-to-end oil reservoir parameter inversion model by using the database, and after the end-to-end oil reservoir parameter inversion model is optimized, performing oil reservoir end-to-end history fitting on a specified oil reservoir by using the end-to-end oil reservoir parameter inversion model, and inverting an oil reservoir geological parameter field. According to the method, the physical mechanism constraint is combined with a model efficient generation method, the consistency model and the sedimentary facies distribution constraint are introduced into the end-to-end oil reservoir parameter inversion model, the inversion robustness of the end-to-end oil reservoir parameter inversion model is improved, and technical support is provided for accurate inversion of an oil reservoir geological parameter field.

The application discloses a kind of under DoS attack based on dynamic event triggering's spacecraft formation system's self-adapting consistency control method.The method is first based on multi-agent consistency model theory, establishes the multi-agent system model of multi-agent spacecraft formation system, then consider based on time series DoS attack, by the frequency and duration of DoS attack are analyzed and researched, to solve the consistency problem of multi-agent spacecraft formation under DoS attack, then design a dynamic event triggering mechanism to reduce communication transmission frequency, and it is proved that there is no Zeno behavior existence, finally based on Lyapunov stability theory analysis, design a kind of event triggered distributed adaptive controller, solve the consistency problem of multi-spacecraft formation.The method is applied to multi-spacecraft formation system, guarantee the normal operation of system under DoS attack.

Systems and methods are disclosed that perform a truncated consistency model training framework that includes two stages. For example, in the first stage, embodiments of the present disclosure may train a consistency model using first and second time step samples. The first time step samples may be obtained based on sampling from a plurality of time steps. Following, a truncated time range that does not include all of the time steps from the plurality of time steps is obtained. Then, third time step samples are obtained based on sampling from the truncated time range and fourth time step samples are determined based on the third time step samples and a time step difference. Afterwards, in a second stage, the consistency model is further trained using the third time step samples and the fourth time step samples.

The application relates to the technical field of data collection and discloses a multi-protocol extensible data collection system architecture and method of a unified interface standard, which comprises a data source management module, a protocol adaptation module, a data conversion module, a data verification module and an exception detection module, a data quality monitoring module and a warning module and a result output module. The data source management module is used for storing data source configuration information and distributing protocol identifiers. The protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification. The data conversion module is used for generating target data through format conversion and semantic mapping. The data verification module and the exception detection module are used for monitoring data integrity based on an integrity algorithm and threshold comparison. The data quality monitoring module and the warning module are used for evaluating data quality through a consistency model and threshold comparison. The result output module is used for transmitting warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring and warning and the like. The application improves system expansibility and data reliability and is suitable for multi-scene data collection.

The application discloses a big data-based information technology detection system with security protection, comprising the following modules: a log access and preprocessing module for collecting multi-source heterogeneous log big data and generating standardized feature data stream; a label alignment and embedding module for performing local label standardization and embedding mapping on the standardized feature data stream at each participating node and generating a fusion feature vector; a model training and updating module for generating an encrypted gradient; a federal coordination module for obtaining a global model; a distillation guidance module for generating a distillation consistency model; a blockchain storage module for writing parameters in the global model of the current round into a side chain storage; and an inference deployment module for deploying the fused global model and the distillation consistency model to each node. The application provides basic support for security and governance in a multi-agency collaborative environment.