504 results about "Influence analysis" patented technology

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data consanguinity map processing method, device, equipment and medium, and the method comprises the steps: collecting metadata change information and data operation event information, and building a mixed consanguinity map model; monitoring a metadata change event and a data operation event, and triggering incremental updating based on an event type; and performing data change influence analysis based on the updated blood relationship map model, generating an analysis result, and outputting the mixed blood relationship map model and the data change influence analysis result. According to the invention, by collecting metadata, incrementally updating the blood relationship map and carrying out data change influence analysis, full-link data tracing and influence analysis are realized, the problems of blood relationship breakage and insufficient real-time data tracing in traditional data blood relationship analysis are solved, and the accuracy of data tracing and the timeliness of business decision are improved.

The invention provides an artificial influence weather-based image recognition method and system, and the method comprises the steps: firstly obtaining a meteorological image data set of a target region containing a plurality of original meteorological images in sequential arrangement, carrying out the feature extraction of the original meteorological images, and obtaining a meteorological element distribution feature and a dynamic evolution feature; and then, based on a preset weather influence analysis model, carrying out weather influence analysis on the meteorological element distribution characteristics and the dynamic evolution characteristics, generating weather intervention decision characteristics, finally, according to the weather intervention decision characteristics, generating an operation strategy of artificial influence weather, and sending the operation strategy to operation execution equipment to trigger weather intervention operation. Therefore, meteorological image features can be comprehensively and deeply analyzed, the operation strategy is scientifically and accurately generated, and the effect and efficiency of artificial influence weather operation are improved.

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 discloses a thermal power generating unit load distribution optimization method and system, and relates to the technical field of load optimization distribution, and the method comprises the steps: quantifying the contribution degree of the position of each operation state monitoring device to a load prediction error based on operation influence data, and screening out a representative monitoring point set covering a unit core energy consumption node; constructing a multi-scale influence analysis model, identifying the change influence degree of the operation state data of each representative monitoring point on the load data of the thermal power generating unit, and generating a load distribution initial scheme; running state data of representative monitoring points are monitored in real time, a multi-target optimization mechanism is introduced, the weight value of each target is dynamically adjusted, and the load distribution of the thermal power generating unit is optimized in real time in combination with a load distribution initial scheme. Accurate dynamic load distribution based on multi-scale analysis and multi-objective optimization is realized, and the comprehensive benefit of a unit is improved.

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 discloses a temperature and pressure integrated transmitting measurement method and system based on nonlinear compensation, and relates to the technical field of measurement. The method comprises the following steps: synchronously acquiring a temperature original signal and a pressure original signal of a target environment to carry out cross influence analysis, and constructing a temperature-pressure cross influence matrix; performing nonlinear segmented compensation on the temperature original signal and the pressure original signal according to the temperature-pressure cross influence matrix to generate a temperature compensation signal and a pressure compensation signal; and fusing the temperature compensation signal and the pressure compensation signal, generating an initial fusion measurement value, carrying out data change diagnosis, carrying out multi-stage verification according to a diagnosis result, and determining a temperature and pressure integrated transmission measurement value. The technical problem that in the prior art, temperature and pressure signals interfere with each other in the temperature and pressure measurement process, effective compensation and correction cannot be achieved, and consequently measurement is inaccurate is solved, and the technical effect of improving temperature and pressure integrated transmitting measurement precision is achieved.

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 relates to the technical field of generative AI watermarks, and discloses a domestic credible watermark embedding and tracing method and system based on generative AI output. An acquisition module of the system acquires original content data output by a generative AI, and establishes a trusted watermark configuration database mapped with the original content data; the watermark target setting module configures a watermark embedding target based on the database and covers watermark robustness optimization, watermark invisibility optimization and watermark traceability association optimization; the watermark classification identification module determines target classification identifications, wherein the target classification identifications comprise a main watermark target, an auxiliary watermark target and a reference holding target; after the key watermark parameters are selected by the watermark influence analysis module, the influence analysis of the key parameters on the watermark embedding target is executed; the watermark traceability module establishes a watermark optimization constraint according to the influence analysis result, executes watermark parameter directional embedding, and completes localized credible watermark embedding; and the traceability path construction module generates a traceability path based on the embedding result to obtain a traceability verification result.

The invention provides a mass spectrometer operation state monitoring method, system and device and a medium, and relates to the technical field of fault monitoring, and the method comprises the steps: collecting environment parameters in a mass spectrometer operation environment, carrying out the vacuum degree influence analysis of a mass spectrometer according to the environment parameters, and obtaining vacuum degree influence parameters; analyzing the mass axis offset influence of the mass spectrometer according to the environmental parameters to obtain mass axis offset parameters; testing to obtain an actual vacuum degree influence parameter and an actual mass error parameter of the mass spectrometer, performing state influence verification by combining the vacuum degree influence parameter and the mass axis offset parameter, and processing to obtain a vacuum verification coefficient and a mass verification coefficient; and according to the vacuum verification coefficient and the quality verification coefficient, performing fusion state anomaly analysis on the vacuum degree influence parameter and the quality axis offset parameter to obtain a state anomaly coefficient, and performing fault judgment and early warning. The technical problem that fault early warning is not timely in the monitoring process of the operation state of the mass spectrometer in the prior art is solved.

The invention relates to the technical field of intelligent monitoring management, particularly discloses an intelligent assembly component whole-process management system, and aims to optimize production efficiency and product quality through real-time data acquisition and analysis. The system comprises a data acquisition module which monitors and records the switching time of each process and the quality inspection qualified rate of each component in real time through a sensor and automation equipment on an integrated production line, a production plan evaluation module which calculates a switching time abnormal coefficient, identifies a bottleneck or low-efficiency operation in the process switching time, and evaluates the quality inspection qualified rate of each component according to the bottleneck or low-efficiency operation. And the process switching time is dynamically adjusted based on the specific numerical values of the influence interference factors and the positive and negative characteristics of the influence interference factors, the production quality evaluation module calculates a quality abnormal coefficient according to the quality inspection qualified rate and judges the quality fluctuation condition of each component, and the influence analysis and adjustment module predicts the quality qualified rate by using a gradient enhanced regression model. The process switching time is dynamically adjusted to eliminate potential quality problems.

The invention belongs to the technical field of semiconductor manufacturing management, and particularly relates to a semiconductor manufacturing-oriented gaseous molecular pollutant online monitoring system, which comprises a small-size traceability dynamic historical influence analysis module, a multi-source real-time monitoring and early warning module, a prospective early warning analysis module, an active control detection module and an intelligent feedback interface, the small-size traceability dynamic historical influence analysis module is based on a hybrid model, historical data and real-time data are combined, the future influence trend of pollutants can be predicted and graded, and therefore enterprises can make coping strategies in advance, reasonably arrange production plans and reduce the influence of the pollutants on the product yield. According to the method, pollutant diffusion paths can be simulated, high-risk stations can be positioned, pollutant sources and propagation paths are analyzed through the Bayesian network, and a root cause probability sorting list is output, so that a basis is conveniently provided for prevention, control and treatment of pollutants, and targeted measures can be quickly and accurately taken.

The invention relates to the technical field of risk assessment based on big data, and provides a post-disaster regional operation risk assessment method based on a Beidou technology, and the method comprises the steps: building an impact analysis model based on disaster types through multi-dimensional information, and combining with the condition of a target work site, so as to assess the risk of post-disaster field operation. In order to achieve the purpose, the post-disaster regional operation risk assessment method based on the Beidou technology is provided and comprises the steps that daily multi-source data collection and standardization processing are carried out on a target region, and indoor and outdoor scene dynamic judgment models are established respectively for risk index calculation; and after the disaster occurs, detecting the target area again, comparing the detection result with the comprehensive risk index, and accurately evaluating the risk again. According to the method, after-calamity risk assessment is converted from fuzzy judgment to quantitative accuracy, operation preparation is converted from experience driving to data driving, and direction guidance is provided for after-calamity safe and efficient operation in the power industry through risk assessment.

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 the technical field of construction management, and discloses a construction progress recognition method and system based on image recognition. The method comprises the steps that real-time image data of a construction scene are obtained, a construction progress feature monitoring database mapped with the real-time image data is established, and database monitoring indexes comprise image definition, feature matching degree and the like; configuring a directional recognition target, including key node recognition, construction stage division and the like, and then determining a target classification identifier; key identification parameters are selected through the main identification target and the auxiliary identification target, directional identification target influence analysis is carried out, parameter directional identification is executed after parameter identification constraints are established, and construction progress identification is completed. When parameter directional recognition is executed, a time efficiency and accuracy evaluation function is also established, and a balance adaptation function is constructed to screen recognition schemes. The system comprises a data acquisition module, a target configuration module and the like and can implement the method. Through multi-dimensional analysis and optimization, efficient and accurate recognition of the construction progress is achieved, and the construction progress management requirement is met.

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.

The invention provides a professional influence evaluation and dynamic deduction method and system based on a knowledge graph and a storage medium. The method comprises the steps of 1, constructing a structured knowledge set representing entities and relationships in a professional field; 2, calculating a global influence absolute score of a target entity based on the structured knowledge set in the step 1; step 3, based on the global influence absolute score in the step 2, performing context-aware context influence analysis; and 4, performing prospective influence deduction based on disturbance simulation of the structured knowledge set in the step 1. The method has the beneficial effects that 1, the holography and objectivity of an evaluation basis are realized, and the credibility and fairness of an evaluation result are fundamentally improved; 2, a uniform and data-driven evaluation scale is established, and cross-organization and cross-region fair benchmarking is realized for the first time; and 3, a context-aware multi-level analysis capability is provided, so that the evaluation has unprecedented depth and fineness.

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 garden environment informatization management system based on diversified analysis, which belongs to the technical field of garden environment management and comprises a management platform, a parameter monitoring module, a vegetation growth evaluation module, an environment influence analysis module, an early warning unit and a decision execution module. According to the method, a partition management mode is adopted, the vegetation growth state of a local area is comprehensively analyzed based on soil data and vegetation apparent images of a single area, the vegetation abnormal growth type of an abnormal management area is obtained through abnormal recognition, and historical data tracing analysis is combined, so that the vegetation abnormal growth state of the abnormal management area is obtained. The method specifically comprises the steps of performing root cause judgment based on historical meteorological data and facility data so as to generate different strategy information according to a root cause judgment result, constructing a closed-loop management system from anomaly recognition to data attribution to decision execution, and performing decision execution by using diversified data and an intelligent analysis technology and taking data as driving. And comprehensive monitoring, multi-dimensional analysis and accurate decision making of environmental elements are realized.

The invention relates to the technical field of water source heat pumps, particularly discloses a recharge pressure influence analysis method based on recharge test data of a water source heat pump, and is used for solving the problems that in the prior art, recharge capacity evaluation is single, parameter identification precision is low, and multi-working-condition changes cannot be dynamically responded. Comprising the following steps: collecting experimental data, identifying a stable section, inverting permeability parameters, analyzing unit parameters, evaluating recharge performance, constructing a dynamic prediction model, dynamically calculating a permeability coefficient and an influence radius by constructing a stable section identification mechanism, combining high-frequency automatic monitoring data and utilizing a segmented inversion method, and introducing a well structure correction factor to improve the calculation precision; through the recharge pressure influence analysis method integrating high-frequency monitoring, stable section recognition, dynamic parameter inversion and multi-factor prediction, the limitation of traditional single-variable static evaluation is broken through, and fine modeling and dynamic response analysis of the recharge capacity of the water source heat pump system under multiple working conditions are achieved.