16 results about "Loop analysis" patented technology

The invention relates to the technical field of fund information management, in particular to a fund exchange information management system based on big data analysis. The system comprises a multi-source heterogeneous data acquisition and fusion module, an enterprise dynamic behavior portrait construction module, a hidden association network mining module and a trade background authenticity dynamic evaluation module. The method comprises the following steps: firstly, collecting and processing data from a plurality of data sources to generate a fused fund exchange record; constructing an enterprise dynamic behavior portrait based on the record; furthermore, a hidden association network graph is constructed based on the data, the data are received at the same time, the transaction behavior deviation degree is identified through behavior consistency check, fund closed-loop analysis is carried out on abnormal transactions, and finally a trade background authenticity index and a risk decision signal are generated. According to the invention, dynamic evaluation and risk early warning of supply chain financing trade background authenticity are realized, and the risk control capability of a financial institution is effectively improved.

The invention discloses a URL (Uniform Resource Locator) security analysis method and device for an end-side large language model and a medium, and belongs to the field of network and information security. The method comprises the steps of collecting multi-modal data such as a URL analysis structure, DNS / certificate metadata, front-end code sampling, an OCR text and a dynamic behavior event sequence; performing exponential decay weighted aggregation on the dynamic events in the sliding time window; generating a static structure feature, a text semantic feature and a threat intelligence similarity based on the multi-modal data; fusing the multi-modal features by adopting a gating attention mechanism; inputting the fusion features and the readable context into an end-side large language model, and calling a security analysis tool set under limited decoding; a risk score and a minimum sufficient evidence are obtained through two-stage scoring; and executing an end-side security policy according to the scoring threshold value and leaving a trace by using a JSONL structure. According to the method, in-end closed-loop analysis is realized, the detection accuracy is remarkably improved, and the method is suitable for various deployment scenes such as mobile terminals and edge gateways.

The invention discloses a memory alloy part durability experiment data analysis method and system, relates to the technical field of data processing, and aims to solve the problem of poor alloy performance detection effect caused by environmental noise interference and signal scattering of existing memory alloy durability experiment data. A differential signal processing strategy is adopted to carry out noise reduction optimization on multi-dimensional performance response data, characteristic factors representing deformation recovery, residual deformation and energy dissipation are extracted and corrected, and combined logic of the characteristic factors is dynamically judged by combining a recognition rule set based on an application scene so as to determine a specific performance degradation mode. Corresponding residual use cycle prediction models are constructed according to different degradation modes, the credibility range is calculated, finally, a performance optimization strategy is generated through matching, and full-process closed-loop analysis from data preprocessing and feature extraction to degradation mode recognition, service life prediction and strategy generation is achieved. And the performance prediction precision and the reliability evaluation efficiency of the memory alloy part under the complex working condition are remarkably improved.

The embodiment of the invention provides a visual analysis method for diagnosis and optimization of an urban drainage system and related equipment, and belongs to the technical field of smart city and data visualization. According to the method, a closed-loop analysis framework formed by collaboration of a map view, an analysis view and a comparison view is constructed. The map view integrates a hydrological and hydraulic model simulation result, visualizes a drainage system state and supports interactive modification; the analysis view is used for extracting a key drainage subsystem based on rules, and performing causal visualization by adopting an optimized mulberry-based graph integrated with terrain elevation information, so that overflow causes are accurately positioned; a comparison view is visualized through a parallel histogram and version inheritance relation, and multi-scheme comparison and iterative optimization management are supported. According to the method, the problems of difficulty in causal analysis, poor interpretability and lack of iterative optimization support in the prior art are solved, the whole-process integrated support from problem identification and diagnosis to optimization verification is realized, and the analysis efficiency and decision scientificity of the urban drainage system are remarkably improved.

The application discloses a deep evaluation model and analysis method based on a computing power application scene, relates to the field of information technology and digital infrastructure planning, and specifically relates to a deep evaluation model and analysis method based on a computing power application scene. The model is constructed from ten dimensions of policy adaptability, regional industry integration, infrastructure support, construction operation mode, implementation strategy landing, operation and maintenance service, energy consumption control, cost return calculation, market supply and demand matching, and risk prevention and control system, taking actual landing requirements as the core. The importance of each item is graded and quantitatively scored, a feasibility determination standard is set, a full-closed-loop analysis system is formed, full-dimension and hierarchical deep analysis of the feasibility of computing power project construction and operation is realized, and support is provided for project decision-making.

The invention relates to the field of distribution box operation state monitoring, and discloses a distribution box operation state monitoring system and method, and the method comprises the steps: collecting temperature, current and environment humidity signals, and constructing a dynamic operation feature matrix; forming an exclusive safety threshold library according to the dynamic operation characteristic matrix of each node in combination with line attenuation characteristics and switch contact aging states; the safety threshold value of each node is used as a constraint state parameter to be introduced into a loop analysis framework, and a multi-layer causal association structure is constructed; based on the multi-layer causal association structure and the historical operation features, constructing an extreme operation condition structure, executing set statistics and entropy feature evolution analysis on corresponding operation states, and generating a risk candidate list; according to the conduction result of the safety threshold constraint in the multi-layer causal association structure and the potential risk candidate list, the nodes on the key causal path are subjected to prospective adjustment of the output power, an operation regulation and control mechanism is formed, and the method has the advantage of improving the early risk identification capability.

The application discloses a kind of remote sensing time series data analysis method and system considering the spatio-temporal correlation of geographical object, belong to data analysis technical field.The method includes: obtaining multi-source multi-temporal remote sensing image and auxiliary data and pre-processing, generate consistent time series data stack;In three-dimensional space-time domain, image is divided into voxel and is adjacent tracking, identify geographical process object with evolution behavior and record its attribute;Determine the spatio-temporal topological relationship between geographical process object, generate unified spatio-temporal relationship table by composite reasoning;With geographical process object as node, spatio-temporal relationship as edge constructs geographical process object spatio-temporal graph model, extracts and standardizes the attribute characteristics of node and edge;Importance index of each node is calculated by using graph convolution network and topological dynamic mechanism joint modeling.The application realizes closed-loop analysis from data processing, relationship modeling, importance evaluation to decision support, improves the interpretability and decision effectiveness of remote sensing time series change detection.

The invention relates to the technical field of deep groove bearing detection, in particular to a deep groove bearing rolling body surface defect detection method and system. Through layered vibration signal acquisition, low-frequency reference noise self-adaptive elimination and rolling body motion trail modeling, accurate detection of micron-sized defects on the surface of a deep groove bearing rolling body is realized, different signal channels of the rolling body and a bearing seat are effectively covered by constructing a multi-source vibration sensing layered acquisition system, and the detection precision of the deep groove bearing rolling body is improved. By synchronously acquiring low-frequency reference noise and high-frequency micro-impact signals and performing self-adaptive time domain noise cancellation, interference such as equipment background noise and gear meshing is remarkably suppressed, a rolling body motion and impact pulse correlation model is established by using structural parameters and real-time rotation parameters, and pure micro-impact signals are directionally enhanced, so that the precision of the rolling body motion and impact pulse correlation model is improved. Through multi-dimensional feature extraction and three-dimensional closed-loop analysis, accurate positioning and authenticity verification of defect features are realized, and graded early warning of rolling body surface defects is completed in combination with a defect grade threshold and an early warning rule.

The application provides a large-span steel structure closed loop analysis method and system, the analysis method comprises the following steps: constructing a finite element model and a design parameter space, identifying key design parameters and key regions affecting the first N modal strain energy of the structure through initial sensitivity and modal analysis, and establishing a mapping relationship between the two; according to the parameter sensitivity contribution, a non-uniform sampling strategy is formulated, the sampling is encrypted in the high sensitivity parameter subspace, an initial sample set is generated, based on the sample and the finite element response, a hierarchical mixed kernel Kriging surrogate model containing a global long-range kernel and a multi-region local short-range kernel is constructed, the local kernel weight increases as the input point approaches the high sensitivity region, the iteration termination and the performance limit state are set, the composite point adding criterion integrating the prediction variance, the limit state distance and the cross-validation error is used to iteratively update the sample and the model, and after the condition is met, the structure performance analysis is completed by using the surrogate model.

The invention provides a collaborative innovation platform based on a DIKWP model and a TRIZ theory, and belongs to the technical field of artificial intelligence and innovative design. The platform takes an artificial consciousness operating system (AI-OS) as a core architecture, and deeply fuses data-information-knowledge-intelligence-intention (DIKWP) five-layer semantic storage and a TRIZ algorithm library reasoning solving function: when a user submits an innovation demand or a technical problem, the platform firstly uses a DIKWP model to perform hierarchical analysis on problem semantics, and identifies potential contradictions and key targets; and then a built-in TRIZ contradiction solving engine and a causal chain analysis engine are matched with corresponding conflict models and inventive principles, and an innovative solution is automatically generated. When the scheme is generated, a general problem solving thought is converted into an executable scheme in combination with domain knowledge, and an inference chain of semantic conversion, a TRIZ principle and a result is output for explanation and verification. The platform also supports plug-in extension, and can be in butt joint with an industry-specific knowledge graph, simulation software and external AI service to enhance the solving capability of problems in specific fields. Compared with the prior art, the method has the advantages that the target-driven semantic closed-loop analysis and the TRIZ algorithm are combined, the value alignment of the solution and the user intention is realized, the process can be explained, the robust iterative optimization capability is realized, the innovation threshold is remarkably reduced, and the problem solving efficiency is improved.

Provided are a compiling device, a compiling method, and a compiling program with which the speed of a program can be increased when a loop in a program includes an access to adjacent data. The compiling device includes: a loop analysis unit for determining whether vectorization of a loop in a source program is possible when an access to adjacent data is included in the loop; and a vectorization execution unit for generating an instruction for reading elements required for computation from a memory to a vector register when access to the adjacent data is included and the vectorization of the loop is possible.

A password function identification method based on cycle analysis and binary similar code analysis, simulates execution of binary function code to be analyzed, extracts dynamic execution characteristic sequence in the simulation execution process, calculates the Jaccard coefficient of the characteristic sequence and the characteristic of the password function in the characteristic library as a similarity score, takes the password function in the characteristic library with the highest similarity score to the function to be analyzed as the identification result, and realizes automatic detection of the password algorithm. The application guarantees the accuracy of password algorithm identification under different compiler versions and optimization options, considers the efficiency and scalability of analysis, improves the accuracy of standard password algorithm identification in such scenes, reduces the time overhead of the analysis process, and has the advantages of cross-compiler configuration and cross-architecture.

The invention belongs to the technical field of information technology processing, and particularly relates to an analysis method and system for mobile terminal AI application software, and the method comprises the following steps: employing a dynamic symbolic execution algorithm DSE + + to analyze an encryption model structure, employing a heterogeneous perception federal average algorithm FedAvg + + to carry out cross-device collaborative analysis, and carrying out cross-device collaborative analysis; a deep reinforcement learning DRL algorithm is adopted to perform adaptive resource scheduling, and an analysis system for mobile terminal AI application software comprises an end side analysis module, an edge aggregation module, a cloud decision module and a security detection module. Through combination of dynamic symbolic execution and an attention mechanism, an encryption model analysis problem is solved, a FedAvg + + algorithm is proposed to break through a federated learning isomerism bottleneck, resource adaptive scheduling is realized through DRL, and the three parts cooperate to form a closed-loop analysis system.

This invention provides a method and apparatus for measuring latency in audio and video playback environments. The method includes: connecting test audio directly to an audio input port via an audio output port to obtain a recording of the test audio; performing loop analysis on the recording to calculate the sum of the audio output latency and the audio input latency; playing a test video and calculating the time difference between the played sound and the video to obtain the audio output latency; subtracting the audio output latency from the sum of the audio output latency and the audio input latency to obtain the audio input latency; directly measuring various latency parameters that can correct audio and video playback results, ultimately improving the user experience.

The invention relates to the technical field of intelligent analysis of a power distribution network, and discloses a regional current situation power distribution network development diagnosis and weak index analysis method. The method comprises the following steps: constructing a power distribution network basic cognitive model based on a general large model, and processing original operation data to generate a power distribution network knowledge graph containing an equipment state and a network topology; the knowledge graph is finely adjusted through industry documents and standards, industry rules are injected into the model, and an industry knowledge enhancement model with professional cognition is formed; performing cooperative processing on the structured knowledge by using a multi-agent cooperative system to generate a multi-dimensional diagnosis report; and collecting evaluation deviations through a scene feedback mechanism, driving the model and the system to carry out reinforcement learning training, and forming a closed-loop analysis process of iterative optimization. According to the method, deep combination of professional knowledge and artificial intelligence is realized, the problems of insufficient professional adaptability and stiff static analysis of a traditional method are solved, and the diagnosis accuracy and the self-adaptive capability of the system are improved.

The application discloses an underground pipeline intelligent leakage detection method and device, relates to the field of intelligent leakage detection, collects a high-frequency pressure sequence, establishes a time-temperature equivalent equation to calculate a moving factor, maps the high-frequency pressure sequence to a reduced time axis by using the moving factor, obtains a temperature equivalent pressure sequence, and is converted into a full history stress-strain memory field tensor, constructs a neural lag operator network, takes the full history stress-strain memory field tensor as input, carries out non-local memory characteristic and hysteresis loop analysis, carries out point-by-point difference processing on the high-frequency pressure sequence and the pipe material intrinsic nonlinear hysteresis response signal, extracts a fidelity fluid dynamics abnormal residual error, constructs a spatiotemporal evolution Poincare section, inputs into a convolutional neural network, and determines real leakage negative pressure waves and sensor random drift through attractor shape difference on the spatiotemporal evolution Poincare section, so that periodic false alarms caused by material rheological characteristics are eliminated.