56 results about "Dependent model" patented technology

The invention discloses a field specialized large model fine tuning and deployment method and system based on adaptive optimization, belongs to the technical field of artificial intelligence and machine learning, and aims to solve the technical problem of how to improve the rapid adaptation capability of a large model on small-scale field data. In order to meet the requirements of special fields of law, medical treatment, finance and the like on professional terms and complex contexts, the technical scheme adopted by the invention comprises the following steps of: data processing and sample generation: performing cleaning, feature extraction and small sample expansion on field data, and generating a high-quality training sample through a field feature guide mechanism; small-sample fine tuning and migration: realizing rapid field adaptation of a large model through a small-sample learning technology and dynamic Prompt optimization, and reducing dependence on large-scale annotation data in combination with cross-field migration learning; compressing and optimizing the model; performing automatic deployment and collaborative reasoning; and monitoring and adaptive optimization are carried out.

The invention discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention discloses a reinforcement learning training method and system for relieving illusion of a multi-modal large model, and belongs to the field of reinforcement learning training of a multi-modal large language model. Firstly, a planning and visual description generation step is introduced in an early stage to guide a model to perform structured reasoning, then a grouping relative strategy optimization algorithm is used, reward values are calculated for multiple candidate responses generated by the model after cold start, and particularly, a visual perception reward mechanism is set. The reward mechanism evaluates the consistency of the generated text description and the visual information by using an external large language model. Then, based on a vision description attention score advantage distribution method, learning of the model on key vision signals is dynamically enhanced, and the perception ability of the model on the vision signals is improved; and finally, the perception and reasoning performance of the model is further improved by adopting multiple rounds of rejection sampling and supervised fine tuning. The scheme does not depend on a model architecture, the extra overhead is small, the illusion problem caused by early image-text inconsistency is effectively solved, and the accuracy and the reliability are improved.

A method can include receiving seismic data of a seismic survey of a subsurface region; performing a full waveform inversion using the seismic data and a model of the subsurface region to determine one or more characteristics of the subsurface region, where the model of the subsurface region includes one or more angle-dependent model parameters; and outputting the one or more characteristics of the subsurface region.

The invention discloses an electromechanical industry digital process evaluation method based on data analysis, and particularly relates to the technical field of digital evaluation, a behavior path map, a time sequence causal dependence model and an operation frequency density model are constructed based on information provided by the electromechanical industry, whether structural deviation or behavior abnormity exists is analyzed, and the evaluation result is obtained. If the initial deviation label is generated, calculating a behavior consistency score and a process integrity score, inputting the scores into a fuzzy logic device for risk reasoning, and outputting a credible level; according to the credibility level, judging whether the data is suitable for digital process evaluation, and if the data does not meet the preset requirement, feeding back a corresponding optimization strategy; according to the method, digital behavior structured analysis is realized by constructing a behavior path map, a time sequence causal dependence model and an operation frequency density model; a deviation triggering mechanism is set, and abnormal operation behaviors are accurately recognized; the credibility level is output based on the scoring result, the information applicability is judged according to the credibility level, an optimization strategy is fed back, and the evaluation intelligence level is improved.

The invention discloses a multi-granularity data cascade updating method and device based on a dynamic topological graph and a medium, and relates to the technical field of data processing. The method maintains a multi-level data dependency model (DAG) in memory. The system recursively updates a target node by monitoring the change of a source node and utilizing an incremental propagation algorithm. For a high-concurrency scene needing isolation verification, the method introduces an overlay shadow topology mechanism: on the premise of not destroying a main graph structure and not copying a total graph, only instantiating a shadow copy of an affected node based on a Context ID, and constructing an overlay Mapping pointing to a father node; when the propagation path is calculated, the numerical value of the shadow node is preferentially read based on the context, and the propagation path of the original node is logically blocked in the current context. The invention further provides a path convergence mechanism and rendering frame synchronization technology based on topology in-degree, and the problems of resource competition, data consistency and front-end rendering flicker under the complex dependence network are effectively solved.

The invention relates to the technical field of software engineering automation of digital data processing, and discloses a game development automation method based on a multi-agent system. The method is used for solving the problem that a machine-readable cross-product dependence model and consistency verification are lacked in a traditional method. According to the method, based on a task list freezing project context, a constraint packet and a threshold aperture, and based on a demand account book and registration demands, constraints, codes, configuration, resources and other entry abstracts, version information and strong and weak reference relationships; the multiple agents only submit change packets in a temporary storage area by writing tokens and occupying fragmentation write-in gating, and an influence range report binding verification list is generated for each change packet; boundary compliance, reference integrity, compilation verification, determinacy operation verification and acceptance assertion verification are sequentially carried out on the temporary storage area snapshots, and when conflicts occur, rollback or confinement repair is triggered to output traceable and acceptance deliverables based on reproducible evidence judgment.

The invention discloses a GPU resource aware matrix multiplication parallel performance analysis model construction method, which relates to the technical field of high-performance calculation, depends on the Roofline principle, combines a bandwidth model, a resource load model and an instruction dependence model to construct a performance analysis model, and is successfully applied to a matrix multiplication scene. And the parallel performance quantification under the matrix multiplication application of resource awareness is realized. The method is not limited by a platform, resource awareness can be achieved, maximized resource use and parameter setting of optimized performance can be distinguished through a performance analysis model in a matrix multiplication scene, and a foundation is laid for follow-up parallel program optimization work; the hardware parameters used in the method are completely based on the GPU hardware parameters which can be obtained publicly, so that non-GPU professionals can also use the model to evaluate the performance of the program.

The invention discloses a learner cognitive diagnosis method oriented to an intelligent education scene, and belongs to the technical field of cognitive diagnosis and education data analysis. According to the method, concept-level multi-dimensional modeling is carried out on the ability and exercise difficulty of a learner by constructing multi-dimensional representation of concept perception so as to describe mastering characteristics of the learner on different cognitive levels; and meanwhile, distinguishing a pre-correction dependency relationship and a semantic approximation relationship, establishing a relationship-perceived concept dependency model, and deducing a potential exercise-concept association structure according to the relationship-perceived concept dependency model. Further, the annotated exercise-concept incidence matrix and the inference incidence matrix are fused in a unified diagnosis layer, and comprehensive evaluation of the knowledge mastering state of the learner is achieved. According to the method, an end-to-end mode is adopted for optimization, the fineness, knowledge coverage and stability of cognitive diagnosis can be effectively improved, better prediction performance and generalization ability are shown on multiple real education data sets, and the method is suitable for intelligent education application scenes such as learning analysis and personalized teaching.

The invention discloses a method and device for determining an operator shape of an AI model and related equipment, and relates to the technical field of artificial intelligence. Determining the degree of influence of each parallel strategy on the shape of a first operator included in the AI model in a plurality of parallel strategies provided by a computing system running the AI model; obtaining a target parallel configuration of the AI model, wherein the target parallel configuration comprises a configuration value of each parallel strategy in a plurality of parallel strategies; and determining the shape of the first operator under the target parallel configuration according to the configuration value of each parallel strategy in the plurality of parallel strategies, the basic shape of the first operator and the influence degree of each parallel strategy on the shape of the first operator. Therefore, according to the influence degree of each parallel strategy on the shape of the operator in the AI model, the shape of the operator under the target parallel configuration can be determined without depending on the specific structure of the AI model, so that the generalization and universality of determining the shape of the operator can be effectively improved, and meanwhile, the efficiency of determining the shape of the operator can also be improved.

The invention provides a method and a model for predicting the temperature continuity characteristic of a low-temperature MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor), and the method comprises the following steps: designing and preparing a verification structure which covers a full box of the model based on the process size range of the MOSFET; performing direct current characteristic test on the verification structure at a plurality of test temperatures, acquiring actual measurement data, and completing low-temperature characteristic characterization of the verification structure; the method comprises the following steps: defining a sub-circuit based on a cryo-BSIM model architecture, and establishing a sub-circuit temperature dependent model driven by sub-box parameters; according to the low-temperature electrical characteristic parameters, through parameter fitting and discrete model integration, establishing a sub-circuit temperature continuous intensive model for low-temperature electrical characteristic simulation; when the consistency verification of the fitting result and the measured data fails, backtracking and adjusting the sub-box parameters, and completing the model iteration of the sub-circuit temperature dependent model; and when the consistency verification of the fitting result and the measured data is successful, obtaining the temperature continuous model of the low-temperature MOSFET. According to the invention, the electrical characteristics of the transistor in a full-temperature zone can be reflected more accurately.

Disclosed herein is an apparatus associated with a network controller in an Open-Radio Access Network (O-RAN). The apparatus is configured to receive a request from a RAN function application from among a plurality of RAN function applications associated with the network controller. The request is indicative of a change in one or more parameters associated with the RAN function applications. The apparatus is configured to determine, based on a parameter dependency model, whether a conflict will occur as a result of the change in the one or more parameters. The parameter dependency model defines dependencies among a plurality of parameters associated with the plurality of RAN function applications. The apparatus is further configured to trigger, based on a set of mitigation rules, one or more reconciliation actions upon a determination that the conflict will occur as a result of the change in the one or more parameters.

The application discloses a cross-media model error attribution and auxiliary correction method based on causal decoupling, which comprises the following steps: image segmentation is performed on an input image, and the input image is divided into a plurality of sub-regions; a target function is solved according to the sub-regions, the minimum key region which supports the generation of a target word element to the greatest extent and removes the generated ability is sorted according to the contribution to the generation decision, and an attribution saliency map is generated; an influence score is calculated according to the change of the generation probability of the target word element in the process of inserting the sub-regions in the ordered subset; the ordered subset, the attribution saliency map and the influence score are evaluated from the dimensions of fidelity, positioning ability and error correction guiding ability, and an evaluation result is obtained; the evaluation result is obtained without relying on the internal gradient, attention weight or activation map of the model, the dependence on the internal structure of the model is reduced, the regional attribution of an arbitrary word element set is realized, and the relative dependence of the generation process on visual evidence and language prior is quantified.

A computer-implemented method for ascertaining an item of environment information is provided. The item of environment information relates to material in surroundings of a third article and / or an interaction of the third article with the material. The method includes receiving an X-ray image and an item of sensor information, and determining model parameters or limiting possible parameter values of the model parameters of a three-dimensional model of the third article as a function of the X-ray image in order to specify an X-ray-dependent model. The three-dimensional model describes a three-dimensional shape and / or pose of the third article as a function of the model parameters. The method includes ascertaining the item of environment information as a function of the X-ray-dependent model, where the item of environment information and / or the X-ray-dependent model additionally depends on the item of sensor information. The item of environment information is provided.

The application relates to a periodic operation component multivariate perception abnormal mode time sequence dependence mining method, which comprises the following steps: S1, a component multivariate perception variable abnormal event capture platform is constructed; S2, abnormal mode time sequence data of the perception variable is captured, and an abnormal time sequence database Event_seqDB is constructed; S3, a multivariate perception abnormal mode time sequence database KDD_SeqDB is constructed; and S4, multivariate perception abnormal mode time sequence dependence mining is carried out based on an improved PrefixSpan. Through the construction of a key component multitransmission variable perception mode time sequence dependence model and algorithm, the time sequence cause-effect relationship formed in the component operation and maintenance process can be effectively mined, thereby providing support for preventive maintenance and design iteration optimization.

The present invention relates to the field of computer application technology, and discloses a software product component version control method, system, storage medium and device, including: responding to a project code compilation instruction, processing the code modified by annotations, obtaining a dependency tree of the project code, building a dependency model according to the dependency tree, assembling several components and their version numbers in the dependency model into version record information; obtaining software and hardware information under the compilation environment, obtaining compilation environment information; assembling the version record information and the compilation environment information into a reporting object, and requesting an encrypted public key, using the encrypted public key to encrypt the reporting object, obtaining a ciphertext, reporting the ciphertext and receiving the returned component information. The mental burden of developers is reduced, and it is convenient for developers to timely understand component updates, perform updates and vulnerability repairs, and improve the overall stability and security of the project.

The invention relates to the technical field of reliability management of electromechanical equipment, in particular to an electromechanical equipment reliability evaluation method based on a switching Markov process and fractional Brownian motion, which comprises the following steps: constructing a switching Markov process model, the description module is used for describing a multi-stage degradation process experienced by equipment in a life cycle and state transition thereof; based on the external working condition parameters and the switching Markov process, constructing a stage dependency model; establishing a nonlinear degradation model of the electromechanical equipment based on the fractional Brownian motion process, and introducing a random effect into the model to represent individual differences; and constructing a two-stage parameter estimation method, and sequentially estimating unknown parameters in the switching Markov process and the degradation model. The performance degradation of the electromechanical equipment is accurately modeled, and the nonlinearity, randomness, individual difference, long-term memorability and stage dependence of the multi-stage degradation of the electromechanical equipment are effectively captured, so that the reliability of the electromechanical equipment is accurately evaluated.

This invention relates to the field of data processing technology for predictive purposes, and discloses a method for adjusting the power generation performance of an energy system throughout its entire life cycle. The method determines the parameter boundaries of a wind-solar-storage power supply system, divides the entire life cycle planning period into multiple consecutive life stages, and determines the load demand input for the entire life cycle. It sets a baseline power generation performance at the initial commissioning stage, calculates the degradation maintenance level for each stage, establishes a monotonic mapping relationship between the degradation maintenance level and maintenance intensity, constructs a stage-dependent model for calculating available power generation capacity coupled with degradation and maintenance, and obtains available power supply capacity parameters for each stage of the entire life cycle. It generates a maintenance time series distribution through Monte Carlo simulation and constructs a set of life cycle power generation performance evolution scenarios. With the goal of minimizing the total cost throughout the entire life cycle, a mixed-integer linear programming model is constructed to obtain the optimal capacity configuration and adjust the power generation performance throughout the entire life cycle. This method solves the problems of ignoring degradation maintenance and inaccurate planning, achieving the goal of accurate and reliable life cycle data at a low cost.

A method can include receiving seismic data of a seismic survey of a subsurface region; performing a full waveform inversion using the seismic data and a model of the subsurface region to determine one or more characteristics of the subsurface region, where the model of the subsurface region includes one or more angle-dependent model parameters; and outputting the one or more characteristics of the subsurface region.

The invention provides a decoration project intelligent construction planning system based on artificial intelligence, and relates to the technical field of construction planning, and the system comprises the steps: obtaining a resource library and a construction task sequence in a decoration project; constructing a resource task constraint model by using the resource library and the construction task sequence, determining a regulation and control optimization feature matrix, and performing constraint optimization on the resource task constraint model according to the regulation and control optimization feature matrix to obtain a resource task optimization model; constructing a construction process dependence model in the decoration engineering process, and performing decoration core measurement on each process dependence node in the construction process dependence model to obtain a decoration core coefficient of each process dependence node; and generating a resource scheduling planning sequence in the decoration project on the basis of the decoration core coefficients of all the process dependent nodes and the resource task optimization model, and planning and scheduling resources in the decoration project according to the resource scheduling planning sequence. Therefore, resource collaborative scheduling in the decoration project is realized.

The present invention provides a pedestrian trajectory prediction method based on a sparse spatiotemporal graph Transformer network, which belongs to the field of computer vision technology. The technical problem of low efficiency in predicting pedestrian trajectories in crowded environments and over long distances is solved. The technical solution is as follows: it includes the following steps: S1: obtaining the position information of pedestrians from image frames; S2: using a dynamic spatial Transformer, and using a multi-head attention mechanism to jointly model multiple spatially dependent models; S3: using a self-attention mechanism to achieve bidirectional temporal dependency modeling across multiple time steps; S4: obtaining a spatiotemporal Transformer network with sparse transformations; S5: designing a model framework of a spatiotemporal block chain based on a spatiotemporal Transformer. The beneficial effects of the present invention are: the present invention improves the accuracy of pedestrian trajectory prediction in crowded environments and over long distances.

This document can use a "Perception Statistical Performance Model" (PSPM) that models perception slices of the runtime stack of an autonomous vehicle or other robotic system, e.g., for safety / performance testing. The PSPM is configured to: receive computed perception ground truth; determine a probabilistic perception uncertainty distribution from the perception ground truth based on a set of learned parameters, the parameters being learned from a set of actual perception outputs generated using the perception slice to be modeled. The PSPM includes a time-dependent model such that the perception output sampled at the current moment depends on at least one of: an earlier one of the perception outputs sampled at the previous moment, and an earlier one of the perception ground truth computed for the previous moment.

The invention discloses a crane track seam real-time detection method and system, and belongs to the technical field of cranes, and the method comprises the steps: obtaining seam images under various different track surface conditions, extracting a region of interest, combining a model activation consistency index and background disturbance sensitivity, calculating a background dependency index, and carrying out the dependency classification of a model; according to the method, the recognition accuracy of a high-dependency model in a new environment is predicted based on track background features, a prediction result and dependency are subjected to joint analysis, and directional optimization is performed on the model by adopting an attention-guided confrontation decorrelation training mechanism and the like, so that the problem that the recognition capability of a traditional CNN model is reduced in a background change environment is effectively solved, and the recognition accuracy of the model is improved. The generalization ability and deployment robustness of the model are significantly improved, and the environment migration cost and the misjudgment risk are reduced.

The invention provides a super junction structure parameter optimization method and apparatus, a terminal device and a storage medium. The method comprises the steps of establishing a temperature dependence model of each physical parameter in a super junction structure; wherein the temperature dependence model is used for describing the change rule of the physical parameters along with the temperature; establishing constraint conditions according to a planar junction theory and an avalanche breakdown principle; wherein the constraint conditions comprise a collision ionization integral breakdown condition and a buffer layer boundary condition; obtaining target information based on the design target of the super junction structure; wherein the target information comprises breakdown voltage, depth-to-width ratio and temperature; solving a performance parameter optimization value of the super junction structure under the target information according to the temperature dependence model and the constraint condition; wherein the performance parameters comprise depletion layer thickness, doping concentration and specific on-resistance. According to the method, the optimization calculation of the performance parameters of the super junction structure can be realized in a larger temperature range.

The application discloses a kind of based on double-layer dependent perception Web3D application mobile edge cache method, first according to the data of content object in Web3D application mobile network, double-layer dependent relationship modeling is carried out, the logical dependence model and space dependence model between Web3D application content object are obtained, then based on double-layer dependent model, mobile edge cache problem with the minimum system expected loading total delay as target is constructed, and edge server carries out caching and replacement to the resource module in each content object according to the cache decision variable matrix obtained by solving.The logical dependence model and space dependence model between Web3D application content object are obtained by double-layer dependent relationship modeling in the application, and caching decision is carried out based on the above two models, and the hit rate and resource loading efficiency of Web3D application mobile edge cache are improved.

The invention provides a cloud configuration item intelligent optimization system and method, and relates to the technical field of cloud configuration data optimization. Comprising the following steps: constructing a dynamic configuration relation graph by mapping configuration item data to different hierarchies of a rule dependence model, generating a multi-modal evidence body and a dynamic trust chain rule based on the dynamic configuration relation graph and key attributes of configuration items, constructing a causal dependence graph through instantiation, calculating a robustness index for the causal dependence graph, and establishing a dynamic trust chain rule. Performing conflict resolution arbitration according to the state of the arbitrated configuration item; the data accuracy and the self-healing capability are improved through automatic conflict resolution, the real-time performance and the consistency of configuration optimization are enhanced through causal dependence analysis, and the optimization efficiency is improved while the reliability is ensured.

The invention relates to the technical field of combustion safety management and control, and discloses a combustion safety management and control system and method, and the system comprises an acquisition module which is used for collecting spectral data in real time, processing and analyzing the spectral data, obtaining real-time key parameters, and calculating a real-time combustion evaluation value according to the real-time key parameters; the judgment module is used for judging whether management and control are needed or not according to the real-time combustion evaluation value, and if yes, a plurality of real-time management and control characteristics and corresponding parameters needing to be adjusted are determined; the generation module is used for generating a to-be-controlled parameter of each parameter needing to be adjusted based on the control parameter-key parameter dependency model and obtaining a plurality of first management and control strategies; and the management and control module is used for performing management and control simulation on the plurality of first management and control strategies, determining a second management and control strategy according to a simulation result, issuing a management and control instruction, accurately evaluating a parameter state, formulating a reasonable management and control strategy and improving combustion safety management and control efficiency.

The application relates to the technical field of computer vision and deep learning, and particularly discloses a fabric defect detection method based on a visual saliency model, which utilizes a convolutional neural network to construct a spatial detail extraction branch, accurately captures the edge and texture details of local tiny defects through a self-adaptive weighted context coordination mechanism, simultaneously introduces a visual state space model to construct a global information extraction branch, and efficiently establishes a long-distance dependence model of a global background with the aid of a two-dimensional space selective scanning technology. On this basis, a bidirectional feature fusion decoder is used to perform deep cascading and dynamic integration on local details and global contexts, thereby effectively suppressing the interference of regular texture backgrounds, enhancing the feature saliency of irregular defects, and realizing high-precision and high-efficiency automatic detection of fabric defects in a low-contrast scene.

The application discloses a Go language library migration method based on semantic dependency graph and complexity scheduling, and comprises the following steps: S1, source code is acquired, and a unified semantic dependency graph is generated; S2, a minimum migration unit is generated; S3, multi-dimensional complexity quantification and risk modeling calculation are carried out on each minimum migration unit, a risk score is generated, all minimum migration units are sorted according to the risk score from low to high, and a migration priority queue is constructed; S4, according to the migration priority queue, the migration unit with lower risk is preferentially executed, and a migration state is recorded; S5, a test verification process is carried out; and S6, migration scheduling is carried out. The application realizes automatic migration planning based on program analysis, graph theory structure and complexity scheduling algorithm, does not depend on model training or external knowledge enhancement mechanism, and has high engineering applicability.

A computer-implemented method for determining environmental information (22) based on at least one two-dimensional X-ray image (23) depicting at least a section of a third object (24-26) located within a patient (42), and at least one sensor information (27) based on at least one measurement value from at least one sensor (28-31) of the third object (24-26), wherein the environmental information (22) relates to material (32-34) in the vicinity of the third object (24-26) and / or an interaction of the third object (24-26) with this material (32-34), comprising the steps: - Receiving the X-ray image (23) and the sensor information (27), - Determining model parameters (35) or restricting the possible parameter values ​​of the model parameters (35) of a three-dimensional model (36) of the third object (24-26) depending on the X-ray image (23) to specify an X-ray-dependent model (37), wherein the three-dimensional model (36) describes a three-dimensional shape and / or pose of the third object (24-26) depending on the model parameters (35), - Determining the environmental information (22) as a function of the X-ray image-dependent model (37), wherein on the one hand the environmental information (22) and / or on the other hand the X-ray image-dependent model (37) additionally depend on the sensor information (27), and - Providing the environment information (22).