35 results about "Bidirectional map" patented technology

The application discloses a prefix caching method and device suitable for a mixed attention model architecture. In view of the heterogeneous management problem of key-value caching and state snapshot, a unified prefix index mechanism based on chain hashing is proposed, so that the two types of caches share a hash space. The method determines the boundary of the two types of caches consistent and effective through the longest prefix matching and forward traversal verification, combines local snapshot query and remote confirmation, realizes the collaborative recovery and reuse of key-value caching and linear attention snapshot, and reduces the heterogeneous state step-out risk. In the reasoning process, the two types of states are captured synchronously and unified hash index is adopted, and a bidirectional mapping between cache block identification and snapshot hash is established through a double cache coordination manager; when the expulsion condition is met, the linkage cleaning of the two types of caches is realized based on the mapping. The application realizes the unified storage and life cycle collaborative management of heterogeneous caches under the mixed attention architecture, guarantees the whole process consistency, and improves the reasoning performance and system robustness.

This application provides a method and related equipment for constructing a skill tree profile of maintenance personnel based on work order task topology mapping, which can improve overall work efficiency. The method includes: acquiring historical work order data from the maintenance work order system, which includes equipment information, task information, and personnel information; performing structured processing on the historical work order data to construct a full lifecycle map of equipment and a personnel skill tree, establishing a bidirectional mapping relationship between task nodes in the equipment lifecycle map and skill nodes in the personnel skill tree through personnel and equipment information; when receiving new work order data, extracting feature information from the new work order data to generate a composite identifier, and verifying the composite identifier against historical composite identifiers in a pre-stored topology cache library, where the composite identifier includes equipment type and task level; if the verification result is invalid, calculating the mapping relationship in real time based on the equipment lifecycle map and the new work order data to obtain the target mapping relationship.

The application relates to the technical field of single sign-on, in particular to a plug-and-play OAuth2 single sign-on authentication service system and method, which comprises the following steps: redirecting to a client according to a login address carrying an authorization code preset by the client; after receiving the authorization code, automatically calling a token interface of an authentication center system to obtain an OAuth2 token, calling a token generation interface provided by an original system by a plug-in client jar package, and obtaining an original system token based on user information in the OAuth2 token; the plug-in client jar package establishes a bidirectional mapping relationship between an OAuth2 access token and an original system access token and between an OAuth2 refresh token and an original system refresh token, and stores the tokens in Redis; and when the OAuth2 token state changes, synchronously updating the original system token state based on the mapping relationship in Redis. The method does not need to make large-scale modification on an existing business system, and reduces integration cost and development difficulty.

The application provides a bridge structure parameterized modeling and drawing method, constructs a bridge general element library with unique topological coding, takes the unique topological coding in the bridge general element library as an associated index, establishes a bidirectional mapping rule of design parameters, element models and drawing elements, generates a bridge three-dimensional model and a corresponding initial drawing framework after inputting design parameters based on the bidirectional mapping rule through parameterized modeling and engineering drawing technology, identifies component priority based on the unique topological coding, constructs a multi-constraint global optimization target, obtains a globally optimal labeling layout scheme through improved genetic algorithm iteration, completes intelligent labeling of the initial drawing, and generates a construction drawing. The application realizes intelligent labeling and synchronous compliance verification of the bridge construction drawing through the unique topological coding of the bridge element and the multi-constraint global optimization solution of the improved genetic algorithm.

This invention provides an isolation architecture, a method and system for managing waveform display script objects. The isolation architecture includes: an internal object layer, comprising an internal object factory for creating internal objects corresponding to script commands; a handle mapping layer, comprising a handle allocation module and a bidirectional mapping module, wherein the handle allocation module allocates integer handles to internal objects, and the bidirectional mapping module maintains the bidirectional mapping relationship between integer handles and internal object pointers; and a script interface layer, comprising a custom command set adapted to script syntax specifications, wherein the parameters and return values ​​of each command in the custom command set conform to the basic script data types and do not contain internal object pointer information, and the script interface layer returns integer handles as return values ​​to the script. This solution, by designing a handle mapping mechanism across interpreter boundaries, achieves a clear separation between the script environment and internal object management, improving system security, performance, and maintainability.

This invention provides an intelligent design method for building walls based on a semantic-physical dual closed loop, comprising the following steps: acquiring project data and environmental element data of the target project, generating semantic tags and project context, retrieving standard provisions based on semantic tags, and compiling the standard provisions into constraint expressions; constructing an executable design grammar for wall design, establishing a bidirectional mapping relationship between the executable design grammar and the building information model, and generating multiple candidate wall schemes conforming to the executable design grammar under the constraints of the constraint expressions; using a surrogate model to perform multiphysics performance evaluation on the candidate wall schemes, triggering a high-precision solver to verify based on the uncertainty evaluation results, and selecting a set of compliant schemes; iteratively optimizing the set of compliant schemes under the penalty mechanisms of hard and soft constraints, outputting a Pareto optimal solution set, parsing the Pareto optimal solution set based on the executable design grammar, and generating design documents and a compliance audit report.

This application discloses a method, apparatus, and device for auditing vulnerability mechanisms, relating to the field of software security technology. The method includes: extracting vulnerability causal elements and sorting out causal relationships from multi-source evidence associated with vulnerabilities, which is linked to corresponding nodes in a causal graph through globally unique evidence citation identifiers; obtaining a causal graph with vulnerability causal elements as nodes and causal dependencies as edges; extracting observed variables from the multi-source evidence based on the causal graph and constructing a structural equation model based on causal dependencies; solving the structural equation model to obtain the quantitative contribution of each vulnerability causal element to the occurrence of the vulnerability; performing a bidirectional mapping match between the vulnerability causal elements and a pre-set security compliance clause library based on the quantitative contribution to obtain a bidirectional mapping relationship; and integrating multi-source evidence, quantitative contribution, and bidirectional mapping relationship to obtain a structured audit report. This enables complete tracing and accountability for the explanation of vulnerability mechanisms.

This invention proposes a single-view X-ray 3D CT reconstruction method based on a self-optimizing dual-domain cyclic diffusion probability model, aiming to solve the problem of degraded reconstruction quality caused by severe information loss in single-view CT in traditional CT. This method constructs diffusion denoising processes in both the projection domain and the CT volume domain, and establishes a bidirectional mapping relationship between forward and back projection through a perceptual transformation module. Cyclic consistency constraints are formed during the back diffusion process, achieving cross-domain structural collaborative correction. Simultaneously, a condition generation module based on structural entropy is designed to quantify and dynamically filter uncertainties in multi-view features to enhance model stability. Furthermore, a self-optimizing mechanism is introduced, performing multiple rounds of denoising and error correction within each back diffusion time step to gradually correct prediction bias and suppress error accumulation. By collaboratively optimizing the projection domain and the CT volume domain through a dual-domain joint loss function, a unity of physical consistency and structural realism is achieved, thereby realizing high-precision 3D CT reconstruction under single-view X-ray conditions.

The application relates to the field of computers and digital twinning technology, and discloses an industrial asset visualization method and system based on a knowledge graph and digital twinning. The method comprises the following steps: fusing multi-source heterogeneous industrial asset data, constructing a knowledge graph based on an ontology model; establishing a high-fidelity digital twinning model, and realizing bidirectional mapping with the knowledge graph through a unique device identifier; synchronously rendering a three-dimensional geometric model and a semantic relationship network in a visualization interface, and supporting interactive linkage query and dynamic state display. The system comprises the following modules: multi-source data access, data standardization, knowledge graph construction, digital twinning modeling, bidirectional synchronization, visualization rendering, and interactive response. Through semantic and geometric deep fusion, the application realizes intelligent visualization of industrial assets in the whole-element and whole-life-cycle mode, and improves operation and maintenance decision efficiency and system scalability.

The application discloses a power system station automation protocol conversion method and device based on docker virtualization, belongs to the technical field of power automation, adopts an isolated deployment mode of one device one container, creates an independent running container for each physical 103 protocol device, internally embeds a 103 protocol analysis, data mapping engine and a virtual 61850 IED Server, and builds a high-fidelity virtual 61850 IED Server digital twin body consistent with the real device; the data semantic recovery and intelligent bidirectional mapping are realized through the mapping engine, the whole life cycle management and standardized configuration of the virtual 61850 IED Server cluster are completed in combination with a centralized configuration and management platform and a container arrangement tool, the device-level and semantic protocol conversion is realized, the information islands of new and old devices are broken, the system reliability and operation and maintenance efficiency are improved, the technical life cycle of the stock device is prolonged, a smooth transition path is provided for the digitalization and twinning upgrade of the power system, and the full-scene construction demand of the intelligent substation is adapted.

Embodiments of the present application provide a granular material non-recurrent history coding elastic-plastic constitutive method and system, which obtains the complete strain history sequence of the granular material from the initial to the current, and inputs the resampled sequence into the history encoder to generate a low-dimensional history state vector in a non-recurrent one-time coding manner. Secondly, a constitutive decoder, a state evolutioner and a physical state encoder are constructed: the constitutive decoder maps the low-dimensional history state vector into current mechanical state variables satisfying the elastic-plastic mechanics criterion; the state evolutioner predicts the updated history state based on the current history state and strain increment; and the physical state encoder reversely maps the mechanical state back to the latent space, forming a bidirectional mapping closed loop between the physical state and the latent state. Finally, a fusion training loss function is established, and the encoder, the decoder, the evolutioner and the physical encoder are jointly trained by using the loss function, and finally the elastic-plastic constitutive model of the granular material is obtained.

This invention provides a multimodal data annotation method and apparatus that combines frame-overlapping and single-frame annotation, applicable to the field of data processing technology. This application first acquires core source data such as 2D images, 3D / 4D point clouds, target object motion states, and dual coordinate systems. Relying on a real-time dual coordinate system transformation algorithm, combined with displacement thresholds, it identifies the dynamic and static attributes of the target object, generating a classification-adapted annotation base dataset. Then, it constructs multi-dimensional annotation association nodes according to dynamic and static classifications, generates a bidirectional mapping association graph through 2D-3D / 4D bidirectional projection, and obtains annotation space verification feature vectors by mining spatial consistency through a multi-coordinate system verification model. These feature vectors are then imported into an optimization engine, combining static cross-frame propagation, dynamic trajectory calibration, and manual correction mechanisms to construct a multimodal annotation base model. Finally, it integrates real-time motion data and deviation correction signals for dynamic annotation, balancing accuracy and efficiency, to generate multimodal annotation information combining frame-overlapping and single-frame annotations adapted to complex scenes.

This invention provides a predictive testing system for electric struts based on digital twins, overcoming the limitations of traditional testing which focuses on cycle count. Through quantitative analysis of performance degradation, it visualizes and quantifies the entire lifecycle degradation process of the electric strut, accurately capturing early failure signs and addressing the core pain point of traditional testing, which can only determine failure after the fact and cannot provide insight into the degradation process. Using a high-fidelity sensor array, it simultaneously collects multi-dimensional data on current, displacement, vibration, acoustics, and the environment, capturing early failure signs such as gear wear and increased lead screw backlash that traditional testing cannot identify. This allows for early prediction of failure risks throughout the lifecycle, significantly improving the test's early warning capabilities. By constructing a multi-physics coupled digital twin of the electric strut, it achieves real-time bidirectional mapping between the physical entity and the virtual model. Test data can be used to optimize product structure, and virtual simulation can guide adjustments to test conditions, breaking down the barriers between testing and R&D and shortening product development iteration cycles.

The application discloses a layered decoupling deterministic idempotent identification generation method and system. The method physically separates the session access stage and the service identification generation stage: the session access stage generates a session access certificate based on non-service attributes to prevent replay attacks; the service identification generation stage only constructs a service unique key based on service parameters and generates an idempotent handle through a deterministic algorithm; a minute boundary double window atomic query and TTL guarantee threshold checking mechanism are adopted to provide a clock drift tolerance of ±30 seconds; and offline verification capability is realized through reverse verification of the server. The application eliminates the long-term bidirectional mapping index of the service key to the identification, and the storage overhead can be reduced by more than 70%, the generation delay can be optimized to less than 1ms, and 100,000 + high-concurrency scenarios per second are supported; 100% cross-node consistency, zero-downtime configuration hot update, financial-grade security salt value rotation and cross-system offline verification capability are provided, and the application can be widely applied to distributed payment, e-commerce transaction and other scenarios.

This application provides a heterogeneous computing resource scheduling method and system for communication information processing, relating to the field of communication computing resource scheduling technology. First, it constructs a dynamic coupling relationship between communication information processing tasks, heterogeneous computing resources, and available communication links, generating dynamic coupling features. Next, it establishes a bidirectional mapping logic between task requirements and resource states to obtain the bidirectional mapping results. Then, it hierarchically deconstructs the collaborative relationships and extracts collaborative elements, generating scheduling execution logic to form a resource link scheduling scheme. Finally, it collects full-process data to extract coupling optimization factors and iteratively updates the dynamic coupling features and bidirectional mapping logic to complete dynamic adaptation scheduling. This invention improves the efficiency and quality of communication information processing and achieves efficient utilization of heterogeneous computing resources.

The application discloses a 3D profile measurement method and system based on a semi-supervised reversible phase unwrapping network, relates to the technical field of screen profile image processing, and utilizes a teacher branch to directly reconstruct a deformation fringe image without a label into a predicted 3D profile, and selects high-quality samples as pseudo labels through a pseudo label threshold screening mechanism. Then, a small amount of labeled data and a large amount of unlabeled data are jointly input into a student branch: the student branch directly reconstructs a fringe into a 3D profile in a first reconstructor, and then sends the reconstructed profile into a renderer to reversely render the 3D profile into a fringe. Thus, with the aid of the bidirectional mapping characteristics of the reversible network, a fringe consistency cyclic constraint is formed. The method realizes supervision of the student branch through the pseudo labels, balances the supervised and unsupervised items in combination with a bidirectional mixed loss function, effectively alleviates the problem of insufficient labeled data, and realizes high-precision screen 3D profile reconstruction driven by a small amount of labeled data.

This invention discloses a digital twin modeling method for a reservoir intelligent scheduling decision support system, belonging to the field of decision modeling technology. A multi-source data fusion module integrates data from multiple sources such as satellites and UAVs, improving data quality through cleaning and spatiotemporal alignment. A physical mechanism modeling module constructs hydrodynamic and joint scheduling models to ensure simulation accuracy. A data-driven modeling module uses LSTM, graph convolutional networks, and other techniques to predict hydrological and equipment status. A twin mapping and interaction module establishes a bidirectional mapping to achieve virtual-real interaction. A decision support module provides scientific decision-making solutions based on multi-objective optimization algorithms. All modules work together to improve the intelligence level of reservoir scheduling. This invention extends the flood forecast lead time and reduces errors; optimizes multi-objective scheduling to improve the comprehensive benefits of water resources; achieves precise synchronization between the digital twin model and the physical entity, enhancing dam safety monitoring capabilities; provides intelligent decision support, shortens response time, and promotes the transformation of reservoir management towards intelligence and efficiency.

The application provides a building wall intelligent design method based on a semantic-physical double closed loop, comprising the following steps: obtaining project data and environmental element data of a target project, generating semantic labels and project context, retrieving specification articles based on the semantic labels, and compiling the specification articles into constraint expressions; constructing an executable design grammar for wall design, establishing a bidirectional mapping relationship between the executable design grammar and a building information model, generating multiple wall candidate schemes conforming to the executable design grammar under the limitation of the constraint expressions; performing multi-physical field performance evaluation on the wall candidate schemes by using a proxy model, triggering a high-precision solver review based on the uncertainty evaluation results, and screening out a compliance scheme set; iteratively optimizing the compliance scheme set under a penalty mechanism of hard constraints and soft constraints, outputting a Pareto optimal solution set, analyzing the Pareto optimal solution set based on the executable design grammar, and generating a design document and a compliance audit report.

The invention discloses an intelligent fault diagnosis method of a fault tree based on dual drive of a genetic algorithm and a gain factor, and the method comprises the following steps: 1, building a bidirectional mapping fault tree model according to the phenomenon and reason of a fault event; 2, in the fault tree model, a dynamic sub-tree lightening and extinguishing mechanism is introduced, and the reason probability is recalculated in real time according to a fault field observation result; 3, constructing a shared bottom event gain factor model, optimizing adjustable parameters, and adjusting the cause probability calculated in the step 2 by using the optimized model; and step 4, taking the adjusted cause probability as an evaluation index of fault diagnosis, and outputting a maintenance priority list. According to the method, the interpretability of the fault tree is maintained, meanwhile, the diagnosis precision and efficiency under the multi-concurrent fault scene are remarkably improved, and technical support can be provided for equipment maintenance, fault prediction and task decision making.

The application relates to the technical field of visualization processing, in particular to a data asset visualization method and system based on business requirements, which comprises the following steps: acquiring multi-source heterogeneous data, and preliminarily dividing the data into multiple homogenous data categories based on data sources; for a single homogenous data category, acquiring stable difference values of a single data in each collection cycle; acquiring a stability analysis confidence of the single data; grouping stable difference values of all kinds of data in each collection cycle into a to-be-divided sample, and acquiring an effective difference distance between any two to-be-divided samples; clustering and dividing all to-be-divided samples, extracting effective collection cycles, and establishing a bidirectional mapping association relationship between data in the effective collection cycles and enterprise management indexes, so as to realize the accurate visualization processing of data assets.

The application relates to a text relation triple extraction method based on multimodal association representation and global semantic consistency of entity pairs, which comprises a lightweight entity pair label mechanism, span representation based on global semantics and local adhesion, multimodal token pair association representation, global semantic consistency constraint for entity pairs, and triple decoding with bidirectional mapping of boundaries. A general representation of a span is formed based on global semantics and local adhesion, and multimodal token pair association representation and relation token pair global semantic consistency constraint are modeled in two stages before and after embedding the relation. In addition, the relation representation embedded with the text semantics is combined with a lightweight bidirectional mapping decoding mechanism to complete one-step extraction of the relation triple. Compared with the prior art, the adhesion characteristics between span tokens are considered, the generation probability of irrelevant noise entity pairs is reduced, the resources occupied by the model during training are less, and the performance is better.

This invention provides a method and system for autonomous iteration and real-time control of forging process based on machine vision for extracting time-frequency map features. The method transforms the thermal and deformation history data during forging into a time-frequency map through time-frequency transformation, and establishes a mapping relationship between the time-frequency map and the microstructure using a convolutional neural network (CNN). Then, a generative model is used to construct a bidirectional mapping model between process parameters and the time-frequency map. Based on this, combined with a model predictive control (MPC) framework, the process parameters are optimized in real time with microstructure targets as constraints. After the process is completed, the model parameters are iteratively updated autonomously, thereby achieving iterative optimization of the microstructure quality of the forging. This invention can effectively extract deep features of the thermal deformation history, establish a reliable mapping relationship between "process parameters - thermal deformation history - microstructure," and achieve online optimization of process parameters during forging, providing an intelligent production process optimization solution for the high-end equipment manufacturing field.

This application relates to the field of simulation integration technology, specifically to a data processing system for co-simulation. The system includes an interaction module, a script parsing module, and a function adaptation module. The interaction module receives function instructions and source scripts. The script parsing module converts the source script into a lexical sequence; determines the type information corresponding to dynamic type variables in variable nodes; determines the parameter type characteristics of function call nodes based on a bidirectional mapping table; determines the intermediate code corresponding to the functional modules and sends the intermediate code to the algorithm platform. The function adaptation module generates adaptation rules for function instructions; adapts and transforms the input parameters to obtain adaptation parameters; constructs a scheduling strategy; and sends the function instructions, adaptation parameters, and scheduling strategy to the algorithm platform. This application solves the technical problem of low data interaction efficiency between algorithm platforms and general simulation platforms in related technologies.

This invention discloses a prefix caching method and apparatus suitable for hybrid attention model architectures. Addressing the heterogeneous management problem of key-value caches and state snapshots, a unified prefix indexing mechanism based on chained hashing is proposed, enabling both types of caches to share a hash space. The method determines the consistent and valid boundary of both types of caches through longest prefix matching and forward traversal verification, combined with local snapshot queries and remote confirmation, achieving collaborative recovery and reuse of key-value caches and linear attention snapshots, reducing the risk of heterogeneous state synchronization failures. During inference, both types of states are captured synchronously and a unified hash index is used. A bidirectional mapping between cache block identifiers and snapshot hashes is established through a dual-cache coordinator; when eviction conditions are met, the two types of caches are cleaned up in conjunction based on the mapping. This invention achieves unified storage and collaborative lifecycle management of heterogeneous caches under a hybrid attention architecture, ensuring consistency throughout the process and improving inference performance and system robustness.

This invention relates to the field of construction monitoring technology, specifically to an intelligent monitoring method for construction progress based on BIM and digital twins, comprising the following steps: acquiring the BIM model and construction plan data of the project; performing lightweight processing on the BIM model to establish a digital twin; and bidirectionally mapping the physical construction site and the virtual model; collecting image and video data, environmental and equipment status data, and construction management data from the construction site, and outputting multi-source data after preprocessing by edge computing nodes; mapping the multi-source data into the digital twin in real time to generate actual progress data points; comparing the actual progress data points with the construction plan progress curve to calculate the progress deviation value, and determining the progress status according to a preset threshold; generating a graded response strategy based on the progress status and pushing it out; through the above method, accurate and real-time matching of virtual and physical progress is achieved, improving the level of intelligence in construction progress management.

This disclosure relates to a content recommendation method, device, and medium, applicable to the field of computer application technology. It includes: training a semantic alignment model by constructing an aligned sample set consisting of initial semantic identifiers and corresponding target object text descriptions; establishing a bidirectional mapping relationship between the two; aligning the semantic identifiers, originally limited to business contexts, with the natural language semantic space; and obtaining enhanced semantic identifiers that retain the feature representations of business data while deeply integrating the pre-trained knowledge of a general-purpose model, thereby significantly improving the performance of the generative recommendation model in terms of semantic understanding depth. This semantic alignment mechanism effectively solves the semantic gap problem in existing technologies, enabling the generative recommendation model trained using enhanced semantic identifiers and corresponding target object text descriptions to fully utilize the knowledge transfer capabilities of the general-purpose model, significantly enhancing accuracy in handling cold-start scenarios and target object recommendations.

The application relates to the technical field of computers and discloses a semantic scheduling query system and method based on a weak feedback neural algorithm, wherein the semantic scheduling query method based on the weak feedback neural algorithm comprises the following steps: constructing a modal relation graph network, mapping different modal data to a unified semantic space; constructing a condition generation model; and performing weighted integration on a prediction result based on a modal importance evaluation network; constructing a bidirectional mapping self-encoder, realizing knowledge transfer between an existing modal and a newly added modal, and introducing an adversarial verifier to judge whether the converted representation maintains the original semantics; constructing a hierarchical scheduling protocol, performing resource allocation based on query importance and modal resource demand; and dynamically adjusting system parameters by analyzing user implicit feedback signals; the application can provide reliable query services under the condition of modal loss, reduce the adaptation threshold of a new modal, and continuously optimize system performance based on limited implicit feedback signals.

The present application belongs to the field of traffic infrastructure carbon emission accounting, and discloses a highway carbon quota calculation method based on machine learning and knowledge graph. In order to solve the problems of non-uniform accounting boundary in the design stage, dispersed and inconsistent emission factors, and lack of corresponding model for budget quota subentry, the present application constructs a carbon emission knowledge graph containing activity template and emission factor constraint, encodes subentry semantics and bidirectionally maps template, fills slots to generate activity instances, performs neural symbol reasoning under the constraint of the enabling stage, and matches emission factors with consistent caliber hard pruning, calculates carbon emissions and unit engineering carbon quota, and realizes the technical effects of scientific quantification and scheme comparison and optimization.

This invention discloses a cross-domain routing method for UAV swarms that coordinates node identifiers and IP addresses, belonging to the field of UAV ad hoc network communication technology. The method includes: constructing a hierarchical node identifier (NID); establishing a forward lookup table from NID to IP and a reverse recalculation bidirectional mapping from IP to NID, dynamically maintained based on timestamps, leases, and version control; the source node initiates cross-domain requests as needed based on the DID comparison result, the active gateway responds and establishes a temporary guidance state; cross-domain data packets carry a fixed DstNID and dynamic CurDstAddr and CurDID, the boundary nodes perform encapsulation forwarding or address rewriting, advancing domain by domain to the target domain; and a three-level progressive anomaly recovery is performed for guidance address failure, mapping failure, and local repair failure. This invention can eliminate the impact of address conflicts, reduce control overhead, and improve the robustness and maintainability of cross-domain communication under dynamic topology.