50 results about "Metric tensor" patented technology

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

The invention relates to the field of highway engineering safety monitoring, in particular to a highway slope stability image monitoring and risk early warning system which comprises an image acquisition module, a slope parameterization representation module, a multi-scale curvature flow analysis module, a Riemannian manifold learning module and a risk early warning module. Differential features are extracted by calculating a measurement tensor and a curvature tensor of the curved surface; constructing multi-scale representation of the slope curved surface by applying a curvature flow theory, and identifying deformation characteristics under different scales; according to the method, riemannian manifold learning and a geodesic convolutional network are utilized to extract advanced features, geological disaster precursors such as landslide, collapse and debris flow are accurately identified, the system has a self-adaptive monitoring frequency adjustment function, monitoring modes are automatically switched according to risk levels, the slope disaster risk is greatly reduced, and a powerful guarantee is provided for safe operation of roads.

The invention relates to the technical field of multi-modal sensing, and discloses a multi-modal large model detection and recognition robot recognition system for a complex scene, the system constructs a dynamic manifold modeling module, realizes cross-modal joint denoising through a stochastic differential equation and depth score matching, constructs a drift term and an anisotropic diffusion term by using an optical flow field, and realizes multi-modal detection and recognition of a multi-modal large model. Dynamic noise interference such as rain fog and motion blur is eliminated; on the basis, designing an information geometric alignment module, and based on Riemannian manifold optimization and orthogonal projection matrix calculation, realizing geometric equidistant mapping of vision-Li DAR features through multi-scale measurement tensor fusion; a dynamic external parameter calibration module is further provided, SE (3) manifold Kalman filtering is combined with a noise self-adaptive scaling technology, and external parameter offset is tracked and compensated in real time. Compared with a traditional method, the method has the advantages that the core problems of cross-modal data geometric mismatch, external parameter drift accumulation, low semantic fusion efficiency and the like are solved, and the sensing precision and robustness of the automatic driving system in a complex dynamic scene are remarkably improved.

The invention relates to the field of transaction anti-fraud, and discloses a real-time transaction anti-fraud system based on a multi-modal behavior atlas, and the system comprises an acquisition processing module which is used for obtaining time sequence data of equipment, transaction and geographic modals and carrying out the preprocessing of the time sequence data, and obtaining the processed data; the multi-mode construction module is used for calculating the time-varying fluctuation rate of a transaction mode, the local dispersion degree of an equipment mode and the trajectory bending degree of a geographic mode, and constructing the processing data into random manifold features containing a Riemannian metric tensor; and the cross-modal interaction module is used for modeling a time-varying driving relation between random manifold characteristics based on a stochastic differential equation and a Poisson process. The method comprises the following steps: converting equipment, transaction and geographic data into random manifold features containing Riemannian metric tensor, reserving nonlinear time sequence association of multi-modal data, and describing a random drive of'equipment motion-geographic trajectory 'and a time-varying trigger relationship of'transaction-equipment activity' through a stochastic differential equation and a Poisson process.

The invention relates to the technical field of intelligent construction scheduling and path planning, and discloses a cold regeneration rolling path intelligent planning system for multi-machine collaborative operation, and the system comprises an operation area modeling module which is used for obtaining the boundary and terrain elevation data of a construction area and the compaction task density information of each position in the area; the measurement tensor construction module is used for generating a measurement tensor of a construction area based on the terrain elevation data and the compaction task density information, and constructing and forming a two-dimensional Riemannian manifold model with weighted measurement; and the path cost construction module is used for constructing a path cost functional based on the two-dimensional Riemannian manifold model and the compaction task density information. According to the method, on the basis of geodesic path optimization and multi-machine dynamic allocation and scheduling, the path is reconstructed in combination with equipment kinematics, and the construction effects that the operation path is reasonable, equipment scheduling is efficient, the path is smooth and executable, and real-time adaptability is achieved are achieved.

The invention relates to the technical field of new energy power generation and control, and discloses a new energy station operation decision support system based on multi-objective optimization, which comprises a multi-source state space reconstruction module, a Riemannian manifold geometry engine module, a self-adaptive inertia Hamiltonian evolution module and a symplectic geometric integral and instruction mapping module. The system collects station data to construct a dimensionless state space, constructs a Riemannian metric tensor according to physical constraints to reconstruct a Riemannian manifold space, and calculates a geometric connection strength factor; the factor is used to adaptively modulate a virtual inertia matrix, and a dissipative Hamiltonian kinetic model is constructed; and finally, solving the steady-state generalized coordinates through a pungent-preserving numerical integration algorithm, and decoding the steady-state generalized coordinates into an equipment control instruction. According to the method, physical constraints are converted into geometric measurements, and a self-adaptive inertia mechanism is introduced, so that the problem of optimization convergence under multivariable strong constraints is solved, and the safety and accuracy of a control instruction are ensured.

The invention relates to the technical field of artificial intelligence and traditional Chinese medicine diagnosis, in particular to a traditional Chinese medicine tongue diagnosis image intelligent analysis system based on deep learning, which comprises a tongue picture acquisition module, a tongue picture measurement module, a tongue picture comparison module, a tongue picture comprehensive analysis module, a health consultation module and a data management module. Accurate extraction and quantitative analysis of tongue picture color features are realized by constructing a color manifold space and introducing Riemannian metric tensor and geodesic distance calculation; performing dynamic evolution tracking on tongue image change by adopting a thermal equation and a curvature flow model on a manifold; based on the geodesic distance, constructing a mapping relation between color features and syndromes, and generating syndrome probability distribution; meanwhile, tongue images collected at different times are compared and analyzed, the treatment effect is evaluated, the system is further combined with a traditional Chinese medicine knowledge graph, syndrome diagnosis, disease interpretation and dialectical treatment suggestions are provided, and the limitation that traditional tongue diagnosis is high in subjectivity and lacks quantitative standards is broken through.

The invention relates to the technical field of oil and gas reservoir development and geological modeling, and discloses a multi-scale factor constraint-based tectonic network fracture modeling method, which comprises the following steps of: synthesizing a Riemannian metric tensor field on the basis of earthquake, logging and geomechanics data, and defining non-Euclidean distance cost of a fracture expanded in an anisotropic medium; initial seed points are screened according to the elastic strain energy density, and initial growth potential energy in a limited range is distributed; solving the eikonal equation by using an anisotropic fast marching algorithm to carry out wavefront competitive growth, and dividing a grid region into generalized Voronoi units; identifying a wavefront contact interface, and extracting gradient features to judge a fusion or truncation type so as to establish fracture topological connection; and finally, tracking a geodesic line path along an anti-gradient direction to generate a three-dimensional discrete fracture network. According to the method, macro and micro constraints are unified through Riemannian geometry, clear physical significance is given to the fracture by utilizing an energy mechanism, and automatic and accurate construction of the complex fracture network topology structure is realized.

A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.

A system and method for implementing persistent cognitive computation through geometric representation of thought in a dynamic latent manifold. The system encodes inputs into a curved space characterized by time-evolving metric tensors, compression pressure fields derived from Ricci curvature, and goal potential fields that shape attention flow. Cognition occurs through geodesic traversal of this manifold, with attention following paths that minimize cognitive action while balancing semantic density and goal relevance. A Cognitive Dynamics Engine maintains manifold geometry, computing optimal trajectories and managing thought bundle operations including consolidation, expansion, and higher-order abstraction. During idle periods, autonomous dreaming processes reorganize the manifold through perturbation, recombination, and topological surgery. This architecture enables persistent memory through geometric encoding, where frequently accessed concepts develop high-curvature regions and cognitive shortcuts emerge from usage patterns, transforming artificial intelligence from stateless computation to structured motion through shaped memory space.

The invention discloses a machine room resource capacity intelligent planning system and method based on multi-source data fusion, and belongs to the technical field of data center management, and the system comprises a multi-source data collection module, a resource manifold modeling module, a geodesic optimization module, a self-adaptive planning module and a verification and inspection module. The method comprises the following steps: mapping multi-dimensional resource parameters such as space, power, heat dissipation and load bearing of a machine room into a Riemannian manifold mathematical model, and constructing a resource measurement tensor representing a resource distribution density and a constraint relationship; calculating a resource allocation optimal path on the resource manifold, constructing a multi-objective function including space utilization rate, energy efficiency, heat dissipation efficiency and cost effectiveness, and generating an optimal deployment scheme of the machine room equipment; in combination with topological characteristic analysis of resource manifolds, potential resource bottlenecks are predicted, and a resource allocation scheme is dynamically adjusted; and the security and compliance of the evaluation scheme are verified through digital twinborn simulation, so that the problem of unbalanced resource allocation caused by independent planning of each system in traditional machine room planning is solved.

The present application relates to the technical field of smart water affairs and urban public safety, and particularly relates to a kind of artificial intelligence driven urban pipe network flood resilience evaluation method and system, comprising: constructing Riemann metric tensor by using metric matrix containing potential barrier function, and establishing the Riemann manifold embedding space of pipe network physical state;Then, based on fluid energy, a Hamilton function is constructed, and a dynamics prediction model with physical conservation is obtained by using symplectic neural network and symplectic discrete integral format training;Subsequently, a second-order Hamilton-Jacobi-Aleksandrov partial differential equation describing stochastic differential game is constructed, and a physical perception neural network is used to solve and extract the resilience safety boundary;Finally, real-time data is mapped to the manifold space, the Riemann gradient is calculated, and a quadratic programming problem is solved to generate control instructions. The problem of lack of physical constraints and safety bottom line in pipe network control under extreme random working conditions is solved, and real-time closed-loop control with physical consistency is realized.

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

The present application relates to the field of highway engineering safety monitoring, in particular to a highway slope stability image monitoring and risk early warning system, the system comprises an image acquisition module, a slope parameterization representation module, a multi-scale curvature flow analysis module, a Riemann manifold learning module and a risk early warning module, the system regards the slope as a Riemann manifold, extracts differential features by calculating the metric tensor and the curvature tensor of the surface; the multi-scale representation of the slope surface is constructed by applying the curvature flow theory, and the deformation characteristics under different scales are identified; senior features are extracted by using Riemann manifold learning and geodesic convolution network, and precursors of geological disasters such as landslides, collapses and debris flows are accurately identified, the system has an adaptive monitoring frequency adjustment function, automatically switches the monitoring mode according to the risk level, greatly reduces the slope disaster risk, and provides a strong guarantee for highway safety operation.

This invention relates to the field of robot motion control technology, and more particularly to an obstacle avoidance adaptive dynamic control system, comprising a multimodal environment and physiological temporal perception module with communication connections, a photodynamic Riemannian metric tensor mapping module, a neural constant differential cognitive evolution prediction module, a robust forward invariant tube synthesis module, a Riemannian space barrier constraint solving module, and a differential manifold impedance execution module. The perception module extracts circadian rhythm representation data; the mapping module reconstructs Euclidean space into a Riemannian manifold space based on the photodynamic cost scalar field; the neural constant differential module calculates the cognitive delay evolution trajectory by combining multi-source features; the synthesis module synthesizes a robust forward invariant tube for envelope prediction error; a Riemannian control barrier function constraint proposition is constructed by combining geodesic distance; and the execution module solves for the optimal safe control vector and inversely maps it to impedance torque distribution. This invention achieves deep coupling between physiological rhythms and non-Euclidean geometric control, constructing a physically compliant obstacle avoidance safety defense line.

The invention relates to the technical field of image detection, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a feature representation analysis method, device and equipment of a self-supervised image and a medium, and the method comprises the steps: extracting basic features of a target image, and recognizing a spatial position corresponding to the basic features; generating a measurement tensor corresponding to each spatial position, and adjusting a local geometric perception range of the convolution kernel through the measurement tensor; determining a geometric propagation distance threshold value of the target image according to the local geometric perception range, and determining geometric consistent features of feature vectors in the basic features through the geometric propagation distance threshold value; performing geometric regularization on the metric tensor to obtain geometric regularization loss, inputting geometric consistent features into a self-supervision task head, and outputting target feature representation; and optimizing the target feature representation according to the task loss and the geometric regularization loss to obtain the feature representation of the self-supervised image. And the accuracy of image feature representation is improved.

This invention proposes a robotic arm obstacle avoidance path planning method based on an improved ant colony algorithm, relating to the fields of robot path planning and intelligent optimization algorithms. This method endows the joint configuration space with a non-uniform metric structure induced by a metric tensor G(q), where G(q) is derived from the normalized joint inertia matrix W. I The algorithm consists of three parts: the singularity gradient outer product term and the obstacle spacing gradient outer product term. Local geodesic distances are approximated using the mean of the metric tensors at both ends of the node. All edge weights are pre-calculated and cached during the PRM graph construction phase. The ant colony uses the reciprocal of the geodesic distance as a heuristic function, drives non-uniform pheromone evaporation using the normalized value of the metric tensor trace increment, and uses the weighted sum of geodesic length cost and inertia-weighted velocity mutation penalty as the comprehensive path cost. The beetle whisker algorithm performs pre-search and completes non-uniform pheromone initialization under geodesic metrics. This method effectively improves path safety, continuity, and dynamic adaptability.

The invention discloses a novel power grid adjustable resource demand response method based on wide-area measurement data, and relates to the technical field of power regulation and control, and the method comprises the steps: building a gauge tensor through metric mapping based on a slice data set, and generating cluster topological data through a Riemann clustering algorithm according to a geodesic distance; constructing Hamiltonian by adopting quantum mapping based on cluster topological data, and generating a resource scheduling strategy through a quantum annealing algorithm; based on a resource scheduling strategy, multi-modal response verification is carried out on the multi-source measurement data through topology dynamic verification, and an instruction response verification report is generated; and according to the instruction response verification report, counting the proportion of the number of abnormal nodes in the cluster to generate a topology recombination decision, and optimizing a resource scheduling strategy through consistency verification. The intelligent degree gauge tensor is constructed by fusing the physical characteristics of the power grid and the communication topology, deep coupling cooperation of response clusters is achieved, and the bottleneck of large-scale real-time decision making is broken through.

The present application relates to motor drive control and hardware ring simulation test technical field, specifically to a kind of motor operating state simulation test system.It includes: signal acquisition module, for collecting the control instruction issued by measured controller, and obtains the environmental parameter and load parameter of test setting;Manifold space construction module, for reflecting the high-dimensional riemann manifold space of motor energy state, and change into the metric tensor of manifold space;Dynamic curvature evolution unit, for mapping external load disturbance and multi-physical field coupling effect into the dynamic change of metric tensor, geodesic trajectory represents the evolution path of motor operating state, to solve the operating state of motor next time;Physical quantity reconstruction module, for projecting the geodesic trajectory coordinates on manifold space back to Euclidean space, restore as electric signal physical quantity, and feedback to measured controller, form closed loop test.The present application significantly improves the physical authenticity and confidence of motor running simulation under high temperature or magnetic saturation state.

The invention relates to the technical field of motor driving control and hardware loop simulation test, in particular to a motor running state simulation test system. Comprising a signal acquisition module used for acquiring a control instruction sent by a tested controller and obtaining environment parameters and load parameters set by testing; the manifold space construction module is used for reflecting a high-dimensional Riemannian manifold space of a motor energy state and converting the high-dimensional Riemannian manifold space into a measurement tensor of the manifold space; the dynamic curvature evolution unit is used for mapping external load disturbance and a multi-physical field coupling effect into dynamic change of a measurement tensor, and a geodesic track represents an evolution path of a motor running state, so that the running state of the motor at the next moment is solved; and the physical quantity reconstruction module is used for projecting the geodesic track coordinates on the manifold space back to the Euclidean space, restoring the geodesic track coordinates into electric signal physical quantities, and feeding the electric signal physical quantities back to the tested controller to form a closed-loop test. According to the invention, the physical trueness and confidence of operation simulation of the motor in a high-temperature or magnetic saturation state are remarkably improved.

The invention relates to the technical field of coal mining mechanical equipment state monitoring, in particular to a coal mining equipment state monitoring and diagnosing system based on digital twinning. The system comprises an equipment state monitoring center, a manifold space mapping unit, a state drift monitoring unit, a curvature fault positioning unit and a closed-loop control decision unit. The system analyzes sensor operation data by constructing a manifold topology and a Riemannian metric tensor field; the core of the method is to carry out parallel movement prediction based on a metric tensor, determine a state drift amount, carry out Riemannian curvature singularity analysis on abnormal drift, and decouple to obtain a fault contribution index; generating a regulation and control instruction according to the index; according to the invention, the problem of high-dimensional nonlinear coupling fault monitoring is solved, the transformation from traditional Euclidean space analysis to manifold geometric depth perception is realized, and the monitoring sensitivity is remarkably improved.

The invention provides an unmanned aerial vehicle path planning method and system in an obstacle avoidance process, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining an obstacle region on a navigation path of an unmanned aerial vehicle; representing the obstacle region as a closed differential form for describing a three-dimensional structure of the obstacle region by combining an algebraic topology algorithm; establishing an unmanned aerial vehicle safety potential field under the constraint of the minimum turning radius of the unmanned aerial vehicle in combination with a closed differential form and an unmanned aerial vehicle navigation path; defining a Riemannian metric tensor for describing a flight path of the unmanned aerial vehicle in a three-dimensional space by combining a safety potential field of the unmanned aerial vehicle; determining the flight path constraint of the unmanned aerial vehicle by combining the closed differential form and the unmanned aerial vehicle navigation path; and under the constraint of the flight path constraint of the unmanned aerial vehicle, carrying out shortest path optimization by combining a Riemannian metric tensor, and outputting an optimal flight path of the unmanned aerial vehicle. Through the shortest path optimization, the method can adapt to a complex flight environment in real time, effectively avoids the problem of circular obstacle avoidance, reduces the energy consumption, and improves the flight efficiency.

The invention relates to the technical field of mobile robot autonomous navigation, and discloses a mobile robot path anti-interference method based on three-dimensional curved surface equivalent expansion, and the method comprises the steps: obtaining multi-source heterogeneous sensing data of a mobile robot to construct an initial Riemannian manifold, and extracting features based on a transmittable confidence model; and calculating specific attribute confidence and a global conflict coefficient of the grid patches, performing anisotropic transformation on the initial measurement tensor by using the attribute confidence, and performing topological tearing on a high-conflict region according to the global conflict coefficient to generate an anti-disturbance manifold. The method comprises the following steps: solving an anisotropic Laplacian-Beltrami equation on an anti-disturbance manifold to establish mapping to a two-dimensional parameter plane, planning a path on the two-dimensional plane, generating a three-dimensional reference trajectory through inverse mapping, and adjusting process noise parameters of an integrated navigation filter by using Jacobian field deviation feedback. According to the method, robust path planning under multi-source interference is realized through a processing mechanism of physical risk geometries and information conflict topologies.

This invention provides an autonomous obstacle avoidance method and system for industrial robots, relating to the field of industrial control technology. The method includes: acquiring multiple frames of environmental images of the industrial robot along a preset travel path; determining whether obstacles exist in each frame of environmental images; if so, retaining multiple consecutive environmental images containing obstacles; based on the retained environmental images, constructing a dynamic risk density function that fuses the current position and predicted displacement of the obstacle; mapping the dynamic risk density function to the configuration space of the industrial robot; establishing a Riemannian metric tensor that dynamically evolves over time; establishing a geodesic equation to transform the path planning problem in the autonomous obstacle avoidance process of the industrial robot into a geodesic solution problem; acquiring the current configuration and target configuration of the industrial robot; using the current configuration and target configuration as boundary conditions, solving the geodesic equation and outputting the optimal obstacle avoidance path; guiding the industrial robot to avoid obstacles according to the optimal obstacle avoidance path; otherwise, re-acquiring images.

The invention discloses a CAE simulation model grid division method based on AI. The method comprises the following steps that CAD, loads and boundaries are obtained, an initial grid is generated, the AI gives a density field, a measurement tensor and uncertainty, and a newly-added upper limit is set; scheduling and spectrum cutting are combined according to uncertainty, and an interface and a protection band are calibrated; establishing an anisotropic Monge-Amprore two-side value problem according to the interface fragments, and applying boundary and interface constraints; solving by using monotonous discretization and filtering formats in cooperation with convexity projection, and scoring sheet convex potential and displacement candidates; performing iteration by using an error and a quality fixed point, performing equal distribution and fairing, and projecting to a feasible region to obtain a target displacement; within the upper limit, selecting a refining region and refining the size, increasing the order and repositioning according to the TV with consistent sub-module effectiveness and measurement; and performing topology updating according to the target displacement and the refined region, and outputting a target grid. According to the invention, anisotropy control, interface continuity and efficiency are improved.

The invention relates to the technical field of medical image processing, in particular to an ultrasonic image processing system based on image enhancement, which comprises a field construction step: constructing a pixel-level structure tensor matrix, and mapping original ultrasonic image data into tensor field data; a flow direction locking step: performing feature space decomposition on the tensor field data to generate a diffusion guide graph containing a main feature vector; a nonlinear evolution step: constructing an anisotropic diffusion tensor based on characteristic value information, and executing time step iterative solution based on a partial differential equation; a reunion output step: performing boundary continuity reunion on the enhanced image data; according to the method, the direction of the tissue is accurately locked through the Riemannian metric tensor field, smooth texture smoothing and inverse texture sharpening are achieved, speckle noise is greatly restrained, and meanwhile the microstructure features are reserved.

This invention provides a semiconductor wafer stress distribution measurement system based on polarization imaging, applicable to chip design and process optimization. The system includes a polarization light source module, a wafer processing module, a multi-channel optical system, a polarization phase processing module, a data acquisition and processing module, and a result visualization module. Its core innovation lies in applying differential geometry theory to polarization phase information processing. A polarization state manifold construction unit maps polarization state parameters onto a Poincaré sphere and calculates the local metric tensor on the manifold. A stress-phase mapping unit calculates the connection coefficient and curvature tensor to determine the stress distribution. A multi-scale analysis unit constructs the scale space of the polarization state manifold, enabling seamless analysis of stress distribution from micro to macro scales. This significantly improves stress measurement accuracy and spatial resolution, allowing simultaneous analysis of stress distribution at the micrometer and millimeter scales.

The application discloses a standard meter performance degradation online detection method, which comprises the following steps: constructing a six-dimensional state vector, lifting the standard meter error data to a high-dimensional phase space, and accurately describing the dynamic characteristics of the equipment. The probability density distribution and Jacobian matrix of the state vector are calculated by adaptive kernel density estimation and weighted least squares method, and the local entropy density, geometric weight and topological analysis are further fused to form a continuous and differentiable entropy field structure. The entropy field gradient is used to derive the metric tensor, affine connection coefficient and curvature tensor to complete the construction of the Riemannian geometry structure. Based on the curvature scalar, topological invariant and entropy density criterion, the performance degradation is accurately identified, and early warning is provided. The application can deeply mine the dynamic changes and system structure evolution in the equipment error data, significantly improve the monitoring accuracy and early warning ability of the performance degradation, has strong innovation and practicality, and is suitable for long-term monitoring and fault warning of power metering equipment.

The application relates to the technical field of financial customer credit rating, and discloses a method for realizing rapid credit rating of financial customers by means of AI, which comprises the following steps: first, obtaining multi-modal credit data of the financial customers, and mapping the multi-modal credit data to an initialized credit feature manifold space by using an embedding operator to obtain manifold representation of the credit features of the customers; calculating curvature features of the credit feature manifold space, combining a counterfactual weight feedback item, and dynamically adjusting a metric tensor of the credit feature manifold space through an evolution equation to realize adaptive evolution of the credit feature space. By introducing a metric evolution equation based on Ricci flow to dynamically adjust the geometric structure of the credit feature manifold, high-fidelity deep fusion of structured financial features and unstructured text semantic features is realized, the problem of fuzzy determination boundary caused by insufficient sample quantity of long-tail customers is solved, and the recognition accuracy and spatial separability of the model for the credit state of new financial customers are significantly enhanced.

The invention relates to the technical field of industrial intelligent manufacturing and equipment health management, in particular to an industrial intelligent agent equipment analysis system and method based on reinforcement learning, and the method comprises the steps: collecting multi-dimensional real-time sensor data of industrial equipment; mapping multi-dimensional real-time sensor data to a continuous potential Riemannian manifold space by using a configured variational auto-encoder embedding module; manifold state coordinates and measurement tensors are output; constructing a directional degradation gradient vector field by using a configured virtual aging operator; generating an anti-fact fault sample; calculating a geodesic distance and a curvature gradient; generating a geometric curvature risk field signal representing the deformation degree; generating an equipment control instruction for guiding the equipment to avoid the high-curvature area; executing the equipment control instruction, and collecting actual moving track data generated by the equipment; calibrating calculation parameters of the measurement tensor; according to the method, the fault prediction problem under the zero sample condition is effectively solved, and advanced deduction of the potential evolution trend of the equipment is realized.