356 results about "Influence analysis" patented technology

The invention relates to the field of power systems and intelligent power grids, in particular to an intelligent analysis system and method for the influence of multi-scene new energy power generation on a distribution network, and the system comprises a data collection module, a multi-scene modeling module, an influence analysis module, an intelligent evaluation module and an optimization suggestion generation module. And the data acquisition module, the multi-scene modeling module, the influence analysis module, the intelligent evaluation module and the optimization suggestion generation module are in communication connection with an external data bus. The hybrid digital twinborn model is constructed through the multi-scene modeling module, four typical scenes of new energy output fluctuation, extreme weather, AC / DC hybrid access and power grid topology change are dynamically simulated, and dynamic configuration of scene parameters (such as new energy permeability and load growth rate) is supported. According to the model, through fusion modeling (such as a power flow equation, an LSTM time sequence and a graph neural network GNN) driven by a physical mechanism and data, the running state of the distribution network under the multi-scene coupling effect can be reflected more truly.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention discloses a civil aviation airport ground guarantee resource scheduling method and device and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a real-time flight plan, and constructing a guarantee resource coupling network based on the real-time flight plan and a predefined guarantee flow rule base; performing conflict prediction and conflict influence analysis on the guarantee work of the future flight by traversing the guarantee resource coupling network, and generating a structured conflict early warning report; triggering an on-demand scheduling mechanism of a virtual resource pool for querying and allocating available resources on demand according to the structured conflict early warning report, querying available shared guarantee resources in the virtual resource pool, and generating a conflict resolution scheme which can be directly executed; and executing the conflict resolution scheme, monitoring an execution result through the Internet of Things data, feeding back the execution result to the guarantee resource coupling network, and calibrating a duration parameter in the guarantee resource coupling network. Through the constructed support resource coupling network, the prospective performance and the cooperative robustness of ground support scheduling are improved.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention relates to the technical field of silver paste preparation, in particular to a silver paste conductivity data modeling and formula optimization system based on machine learning, which comprises a data acquisition and storage module, a preprocessing module, a characteristic influence analysis module, a formula optimization module, a simulation verification module and the like. The method comprises the following steps: acquiring original data of a silver paste formula, preprocessing, calculating influence coefficients of all components on target performance by utilizing a machine learning model, and identifying high and low influence components; the formula optimization module is combined with component content constraints and adopts a multi-objective optimization algorithm to generate candidate formulas; and the simulation verification module verifies the performance of the formula through sintering simulation and process adaptation, and feeds back optimization. According to the invention, the full-process intelligentization of the silver paste formula from data processing to optimization verification is realized, the conductivity and other performances of the silver paste are accurately improved, the research and development cost is reduced, the period is shortened, the suitability of the formula process is enhanced, and the research and development and industrial upgrading of the silver paste are promoted.

The invention relates to an engineering construction stage user multi-dimensional demand analysis system and method, and the system comprises a multi-modal semantic model construction module which integrates a multi-source knowledge base, and extracts triple description statements from the provisions of the multi-source knowledge base to construct an engineering object ontology knowledge graph; the demand structured guidance and intelligent analysis engine module dynamically identifies an engineering stage and a user role, guides a user to perform text demand input, and performs semantic completion and standardization processing; the demand conflict detection and coordination correction module is used for detecting conflicts in user demands and generating a conflict influence analysis report and a coordination processing scheme; the intelligent drawing and dynamic optimization module is used for automatically drawing according to the triple obtained through demand analysis; and the demand analysis visualization and flow closed-loop module is used for generating an executable parameterized demand document and an association chart, and completing demand-design-verification engineering closed-loop management, so that intelligent analysis and standardization of user multi-dimensional demands are realized, and the system has the advantages of high efficiency, accuracy and intelligence.

The invention discloses an OLED (Organic Light Emitting Diode) production equipment fault diagnosis terminal capable of being remotely monitored, which relates to the technical field of fault diagnosis, and comprises the following steps: acquiring key process parameters of equipment to obtain equipment operation data; performing operation state analysis according to the equipment operation data to obtain an abnormal index; performing fault mode identification according to the abnormal index to obtain an equipment fault type; performing abnormal influence analysis according to the equipment fault type to obtain key influence parameters; performing remote abnormality diagnosis according to the key influence parameters to obtain a diagnosis result; and performing abnormal trend analysis according to the diagnosis result to obtain the equipment health state. Through parameter deviation degree calculation and multi-parameter correlation analysis, the collected key process parameters are subjected to real-time comprehensive analysis, the parameter deviation degree is quantified, coupling abnormity among the parameters is identified, the accuracy and comprehensiveness of fault diagnosis are improved, and the false alarm rate and the missing report rate are reduced.

The invention belongs to the technical field of unmanned system reliability, and provides an unmanned cluster fault mode analysis and transverse and longitudinal propagation analysis method and system based on a task chain, electronic equipment and a storage medium. The method comprises the steps of model construction, fault mode recognition, severity setting, fault transmission recognition, adjacency matrix construction, network model construction and fault transmission influence analysis. According to the method, the unmanned cluster is subjected to hierarchical division, so that the problem of complex calculation process of a physical model is avoided, and the real-time performance of fault mode analysis is improved; through fault mode identification, severity setting and fault transmission identification, analysis of complex cooperation relation and interaction among individuals is realized, and adaptability of a dynamic change environment is improved. Through adjacency matrix construction, network model construction and fault transmission influence analysis, the analysis of the mutual dependency relationship between different fault modes is improved, and the accuracy of the propagation path is improved.

The invention relates to an automatic magnesium oxide coating analysis system and method, and belongs to the technical field of coating quality analysis. The method comprises the following steps: acquiring attributes of a magnesium oxide coating, an infrared spectrum and environment temperature and humidity data, and constructing a multi-source feature set; analyzing the distribution dispersion and local enrichment risk of free water based on a dynamic migration model, and analyzing the bonding weakening degree and dissociation risk of bound water based on a chemical stable state model; respectively generating free water treatment process parameters for inhibiting structural damage and combined water treatment process parameters for preventing performance degradation according to the abnormal analysis result and the coating attribute; by performing coupling influence analysis on the two types of process parameters, the balance requirements of the structural integrity and chemical stability of the coating are coordinated, an optimized process scheme is generated, and cooperative regulation is realized. According to the method, accurate identification, independent analysis and cooperative control of the free water and the bound water are realized, and the accuracy, reliability and quality consistency of coating water treatment are improved.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention discloses an intelligent media content association propagation and influence analysis method based on a knowledge graph, and the method comprises the following steps: collecting multi-source media data from social media, a news website, a video platform and a forum, and constructing and forming a heterogeneous knowledge graph; performing feature initialization and embedding on nodes in the heterogeneous knowledge graph to generate initial node embedding representation; constructing a dynamic heterogeneous graph attention network based on the node initial embedding representation to obtain a node dynamic propagation state representation; calculating a propagation influence score based on the node dynamic propagation state representation to obtain a node influence sorting result and a core propagation path; and introducing a causal consistency training mechanism based on the core propagation path, optimizing parameters of the dynamic heterogeneous graph attention network, and outputting a propagation influence evaluation result. According to the method, the dynamic heterogeneous graph attention network is adopted, and intelligent analysis of the media content propagation influence is realized.

The invention relates to an AI production decision-making method and system based on industrial big data, and the method comprises the following steps: collecting real-time sensor data of an industrial production field, and obtaining industrial production data; detecting whether abnormal parameters exist in the industrial production data or not by setting a parameter threshold range, and if yes, performing production link positioning on the industrial production field based on the abnormal parameters to obtain abnormal production links; performing influence analysis on the industrial production field based on the abnormal production link to obtain an influence analysis result; and carrying out production decision analysis on the industrial production field based on an AI model and the influence analysis result, and generating a production decision suggestion. The root cause of the abnormal generation and the propagation path and the influence range of the abnormal generation in the whole production process are difficult to explain.

Systems, computer program products, and methods are provided for code revision impact analysis. The method includes generating a system map based on data received from a plurality of network devices; receiving a data transmission including a text file; processing the text file via a natural language processing engine, where an output of the natural language processing engine comprises a plurality of expected updates; determining, based on the system map, at least one downstream effect of the plurality of expected updates; and performing a remedial action.

An impact analysis device acquires a list of method information related to a difference between a new version of a library and an old version of the library, based on a source code of the new version of the library and a source code of the old version of the library; extracts, from the list, a first difference related to a method existing in a first call graph of software that uses the old version of the library; extracts, from the list, a second difference related to a method added to a second call graph of the software that uses the new version of the library; extracts a set of method information of a caller of a transition added to the second call graph in comparison with the first call graph; and outputs a union of the first difference, the second difference, and the set, thereby enhancing support for determining a likelihood that a library update could affect operations of software.

The invention discloses a software development cloud computing system with a big data processing module, which relates to the technical field of computer systems and data processing, and comprises a development input acquisition module used for receiving source code data, construction script data, dependency declaration data, configuration file data and development environment description data, and respectively executing integrity verification, structure analysis, dependency analysis, parameter extraction and environmental element analysis processing to form a standardized development input data set. According to the method, unified feature abstracting and aggregation are carried out on source codes, construction scripts, dependency declarations, configuration parameters and development environment elements, version fingerprint data capable of stably representing the software version state is formed, the strict degree of analysis, verification and influence analysis is remarkably enhanced in a high-difference version scene, and the software version state can be stably represented. Therefore, the problems of misjudgment, missed judgment or resource waste caused by adoption of a fixed threshold value or manual parameter configuration in the prior art are avoided.

The invention discloses a river flood control influence analysis method based on a graph neural network, and the method comprises the following steps: S1, collecting terrain, hydrology, weather and water flow data, and constructing a graph structure model; s2, generating node and edge features, and forming a graph model; s3, inputting the graph model to a Laplacian network, and generating graph feature representation; s4, combining real-time data updating features, and constructing a dynamic graph model; s5, executing neighborhood sampling, and constructing a local subgraph; s6, inputting an attention mechanism, and generating an attention graph representation; s7, inputting a graph generation module, simulating a flood situation, and generating an expansion graph structure; and S8, training the dynamic graph model, and outputting a flood range, a risk level and flood control measures. According to the invention, graph modeling and reasoning integration of flood control analysis is realized.

The invention discloses a water environment pollution monitoring method and system based on data analysis, and belongs to the technical field of pollution monitoring. The system comprises an environment monitoring module, an influence analysis module, an abnormity identification module and a visualization and storage module. The environment monitoring module collects a GIS map and historical records and collects monitoring indexes and hydrological indexes of a river water body through monitoring equipment. The influence analysis module marks the position of each monitoring device on the GIS map, analyzes the upstream and downstream relationship and sets a relative time difference; establishing an influence relation chain for each monitoring index, and calculating an influence coefficient between the monitoring devices; the anomaly recognition module takes the monitoring indexes currently collected by the monitoring devices as measured values, calculates predicted values according to the influence relation chain, calculates anomaly indexes of the monitoring devices in combination with influence coefficients and divides anomaly regions; and the visualization and storage module displays the position of each abnormal area, prompts a worker to check each abnormal area one by one according to an abnormal index in an inverted order, and stores the collected index data into a historical record.

The invention relates to the technical field of ecological environment monitoring, in particular to an environment assessment method and device based on carbon footprint and regional ecological data. The method comprises the following steps: acquiring carbon footprint data and regional ecological data of a target region; performing dimension alignment on the carbon footprint data and the regional ecological data to obtain an associated data set associated with a preset grid of the target region; and based on the associated data set, through at least one of a time sequence similarity analysis strategy, a spatial distribution overlapping degree analysis strategy and a causal relationship influence analysis strategy, determining an environment assessment result of the target area. Compared with the prior art, a manual evaluation mode is replaced, and the analysis efficiency is improved. And on the other hand, the carbon footprint data and the regional ecological data are aligned according to the preset grids of the region, so that the problem of non-uniform comparison dimensions and indexes is solved. And meanwhile, three analysis strategies are provided for reflecting the internal relation of the two types of data, so that the data analysis precision is improved.

The invention relates to the technical field of supplier full life cycle technologies, and provides a supplier full life cycle management method based on multi-dimensional evaluation, which comprises the steps of demand source searching, supplier classification and selection, supplier relationship management, contract management, purchase management, order execution, invoice reconciliation and expenditure analysis. According to the method, a whole-process closed-loop management system from demand source searching to expenditure analysis is established, so that information barriers among links are broken, and seamless circulation and sharing of data are realized; a standardized evaluation model and a classification system are adopted, traditional supplier decision-making depending on subjective experience is converted into scientific decision-making based on quantitative data, and the management efficiency and decision-making accuracy are remarkably improved. By establishing a contract performance early warning mechanism and an abnormal order grading processing flow, full-flow dynamic monitoring of contract risks and execution abnormities is realized; based on the risk assessment matrix and the change influence analysis model, various potential risks can be identified and responded in time.

A vehicle infrared radiation simulation method based on temperature field mapping comprises the following steps: S1, performing thermal influence analysis on an original vehicle model to generate a simplified vehicle model; s2, grid division is carried out on the simplified vehicle model, and a temperature field of the simplified vehicle model is solved by adopting a numerical analysis method; s3, respectively extracting node coordinates of the original vehicle model and the simplified vehicle model, respectively calculating geometric centers of the original vehicle model and the simplified vehicle model, and realizing spatial registration of the original vehicle model and the simplified vehicle model through translation transformation; s4, mapping the temperature value of the simplified vehicle model to a corresponding grid node of the original vehicle model by adopting a KNN algorithm based on a KD tree and combining an inverse distance weighted interpolation algorithm; and S5, on the basis of the mapped original vehicle model temperature field data, in combination with the material emissivity and the environmental radiation parameters, calculating the infrared radiation characteristics through a ray tracing method. According to the method, the calculation efficiency can be remarkably improved while the simulation precision is ensured, and the advantages are obvious.

The invention discloses a method and a device for analyzing and correcting influence of phase errors between sub-bands of step-frequency ultra-wideband SAR (Synthetic Aperture Radar) data, and belongs to the technical field of synthetic aperture radar signal processing. The method comprises the following steps: carrying out amplitude error estimation and compensation on each sub-band echo signal; preliminarily estimating a phase error by using the partial overlapping characteristic of the sub-band spectrum; performing distance compression on each sub-band signal; establishing an optimization model based on a maximum norm, and solving an accurate phase error by taking the preliminarily estimated phase error as an iteration initial value; carrying out sub-band splicing and distance resolution compression on the sub-band signals after phase error compensation; and finally, a corrected SAR image is obtained through a CS imaging algorithm. According to the method, extra calibration signals are not needed, high-precision phase error correction can be achieved only based on echo data, the calculation efficiency is high, and the method is suitable for a high-resolution SF-SAR system.

The invention provides a banking business data change influence analysis method and related equipment. The method comprises the following steps: acquiring a business data blood relationship map constructed for a banking business system; each node in the business data blood relationship map corresponds to each bank data entity, and each edge in the business data blood relationship map corresponds to a data flow relationship among the bank data entities; based on the change type, determining a target sub-graph corresponding to the change type from preset sub-graphs; taking the change node as a starting point, and screening out candidate nodes matched with the root node attribute of the target sub-graph from the business data blood relationship graph; based on the target sub-graph and each candidate node, performing sub-graph matching in the business data blood relationship graph to obtain a matching result isomorphic with the target sub-graph; based on a preset influence scoring rule, scoring each matching path in the matching result; and on the basis of each matching path and the score corresponding to the matching path, a change influence analysis result is generated, so that the change influence analysis efficiency and accuracy are improved.

The invention provides a vulnerability early warning method and system based on asset fingerprint driving. The method comprises the following steps: acquiring vulnerability information data from a plurality of vulnerability information sources; asset fingerprint information of assets in the target system is collected; based on semantic analysis, version comparison and a fingerprint matching algorithm, performing fine-grained association matching on the vulnerability information data and asset fingerprint information, and identifying target assets affected by vulnerabilities; performing influence range analysis, risk level evaluation and repair priority ranking on the target assets to generate a vulnerability influence analysis report; based on the vulnerability impact analysis report, sending an early warning notification to a related person in charge, and automatically generating an electronic work order; the processing state of the electronic work order is tracked, the repair progress is recorded, and full-life-cycle closed-loop management of vulnerabilities from recognition to repair is achieved. The missing report rate and the false report rate are effectively reduced, and compared with manual matching, the full-automatic mode can form a complete closed loop, and the early warning efficiency is improved.

The invention relates to the technical field of wind turbine generator management, in particular to a wind turbine generator control method and system based on component interaction influence analysis, and the method comprises the steps: collecting the multi-source operation data of a wind turbine generator impeller system and a gear box system and the turbulence intensity of a wind field in real time; based on the multi-source operation data, calculating a dynamic interaction influence index between the impeller and the gearbox; according to the dynamic interaction influence index and the turbulence intensity of the wind field, judging whether the wind turbine is in a low-interaction influence working condition or a high-interaction influence working condition currently; when it is judged that the working condition is the low-interaction-influence working condition, a first control strategy based on historical optimal parameters is adopted to control the wind turbine; when it is judged that the working condition is the high-interaction-influence working condition, a second control strategy based on the prediction model is adopted to control the wind turbine; the dynamic interaction influence of the impeller and the gearbox can be evaluated in real time, and grading self-adaptive control is achieved accordingly.

The embodiments provide a system and method for interpreting predictions from machine learning systems using natural language. The system includes a prediction module and an explanation module. The prediction module includes a machine learning model to make predictions for quantities such as recommended coverage amounts for insurance policies. The explanation module includes an impact analyzer that calculates impact values, which represent the degree of influence that each attribute has on predicted values. The explanation module also includes a natural language processing system for transforming generating natural language explanations indicating how the user attributes have influenced the predicted value.

The invention relates to the technical field of building carbon emission monitoring, and discloses a building construction carbon emission dynamic monitoring system and method. The method comprises the following steps: acquiring carbon emission activity data in a construction process, and constructing a carbon emission dynamic monitoring database corresponding to a current construction stage; configuring a dynamic optimization target based on the database, wherein the dynamic optimization target comprises optimization of energy consumption, a material turnover rate, equipment operation efficiency and a transportation path; target classification identifiers are determined and divided into a main optimization target, an auxiliary optimization target and a benchmark maintaining target; key monitoring parameters are selected according to the main optimization target and the auxiliary optimization target, and dynamic optimization target influence analysis is carried out; and establishing a parameter optimization constraint according to an analysis result, carrying out parameter dynamic optimization, and updating a carbon emission dynamic monitoring strategy according to an optimization result. According to the method, construction carbon emission data can be dynamically captured, and the accuracy and management efficiency of carbon emission monitoring are improved through multi-dimensional target collaborative optimization.

The invention discloses a large-model-driven data change influence analysis method and device, equipment, a storage medium and a product, and relates to the technical field of financial science and technology and data engineering, and the method comprises the steps: receiving a data change notification sent by an upstream business system; based on a semantic analysis and standardization mechanism of a large language model, performing semantic analysis and standardization processing on original change content in the data change notification, and outputting standardized change description with clear structure and semantic consistency; based on a pre-constructed data change knowledge base, matching and retrieving the standardized change description; obtaining model prediction context information according to the standardized change description; and according to the model prediction context information and the retrieval result, performing influence analysis and reasoning on the data change influence by using a large language model to obtain a data change influence analysis result. According to the technical scheme, the efficiency, accuracy and result reliability of data analysis are improved, and the data consumption risk is reduced.

A plurality of logs for a cloud security service for a plurality of tenants are captured. A network traffic pattern associated with the plurality of logs is analyzed and a security related impact analysis is determined. An action is performed based on the security related impact analysis.

The application relates to an automatic magnesium oxide coating analysis system and method, and belongs to the technical field of coating quality analysis. The method comprises the following steps: acquiring the properties, infrared spectrum and environmental temperature and humidity data of the magnesium oxide coating, and constructing a multi-source feature set; analyzing the distribution dispersion and local enrichment risk of free water based on a dynamic migration model, and analyzing the bond weakening degree and dissociation risk of combined water based on a chemical stability model; generating free water treatment process parameters for inhibiting structural damage and combined water treatment process parameters for preventing performance attenuation according to abnormal analysis results and coating properties; and through coupling influence analysis on the two types of process parameters, the balance requirements of coating structural integrity and chemical stability are coordinated, an optimized process scheme is generated, and synergistic regulation and control are realized. The application realizes accurate identification, independent analysis and synergistic control of free water and combined water, and improves the accuracy, reliability and quality consistency of coating moisture treatment.

The invention discloses a harmonic suppression method and system of a rail transit electric traction system. The system comprises a traction unit harmonic response acquisition module, a traction group construction and intra-group influence analysis module, a traction group collaborative behavior analysis module, a collaborative suppression strategy generation module and a suppression resource regulation and control module. The method comprises the following steps: dividing a plurality of traction units on the same train into traction groups, analyzing harmonic response information of the traction units on a traction group level to generate comprehensive harmonic influence characteristics, analyzing action results of the plurality of traction groups on a traction power supply section level, and judging whether a harmonic collaborative amplification or collaborative instability trend exists or not; and thus, a collaborative harmonic suppression strategy for the traction group or the traction unit is generated. Through differential regulation and control on harmonic suppression resources and in combination with feedback correction, dynamic adjustment of a harmonic suppression process is realized, and pertinence of harmonic suppression under a complex operation condition and operation stability of a traction power supply system are improved.