1607 results about "Analysis data" patented technology

The present disclosure is directed to enabling asynchronous analytics via an intercept device. The intercept device may intercept a request transmitted by a user device to a host server, the request indicating data of interest to be provided by the host server in a response; transmit the request to the host server; asynchronously extract analytic data from the request; transmit the analytic data to an analytics server for performance of analytics on the analytic data; receive, from the analytics server, a result of performance of the analytics on the analytic data; determine, based at least in part on analyzing the result, whether the response from the host server is to be modified; selectively effect modification of the response based at least in part on determining whether the response from the host server is to be modified; and transmit the response to the user device. Various other aspects are contemplated.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention relates to the technical field of data management and information, and discloses a data asset identification system and method based on multi-dimensional rule engine blood relationship analysis, and the method comprises the steps: obtaining static code information and dynamic operation tracking information of to-be-analyzed data; constructing a theoretical data blood relationship map based on the static code information; constructing a real-time data blood relationship map based on the dynamic operation tracking information; generating a fusion data consanguinity map by adopting a time window alignment strategy; constructing a five-dimensional data asset identification rule model; starting a blood relationship driven multi-dimensional rule engine; executing the multi-dimensional rule engine, and evaluating and determining the nature of the to-be-analyzed data; in the evaluation process, a conflict resolution mechanism is started. According to the method, the multi-dimensional rule engine is driven through dynamic and static combined blood relationship analysis, and conflict judgment is performed by using blood relationship information.

The invention discloses a method for identifying an engineering drawing detail table and generating a BOM table, and belongs to the crossing field of automation technology and image processing, and the method comprises the steps: obtaining a scanning or electronic image of an engineering drawing; through preset datum line positioning, recursively detecting a nested rectangular region conforming to an area difference threshold value, and determining a title bar, a detail list region coordinate and a table image; identifying lines and cross points, analyzing the line and column boundaries of the table, and constructing a topological structure; performing character recognition by adopting multi-engine OCR integration; the characters and the cells are associated, a header is recognized through semantics, and analysis data subjected to integrity verification are generated; and automatically generating a structured BOM table in a preset standard format based on the data, and outputting an editable file. According to the method, full-process automation is achieved, manual intervention is not needed, and manual input cost and personal errors are greatly reduced.

The invention discloses a fire fighting system intelligent management method and device based on intelligent property management, and relates to the technical field of data management. The intelligent management method and device for the fire fighting system based on the intelligent property management comprises the following steps: S1, dynamically triggering and collecting a multi-source fire fighting data set through multiple protocols, and temporarily storing the multi-source fire fighting data set at the edge in a normalized manner; s2, constructing an event full-period window and a ternary association index of the event, the time sequence data and the original message; s3, performing double-track storage of the semantic analysis data and the original message, and constructing multi-dimensional index association; s4, reconstructing a visual playback alarm process through edge cloud increment synchronization and a multi-dimensional time sequence chain; and S5, performing transient analysis, protocol upgrading and responsibility evidence obtaining based on the cloud data collaborative link and the original message data set. According to the method, the complete acquisition of the fire-fighting data and the reproducibility of the event process are effectively improved, and the problem that in the prior art, only polling recording of single-point alarm is carried out, and an original message is discarded, so that the transient process and message-level evidence is lost is solved.

The invention discloses a supervision method and device based on construction project quality detection and a medium, and relates to the technical field of project quality supervision, and the method comprises the steps: collecting construction site data, carrying out the preprocessing, and generating a site data set; performing real-time comparison and verification on the building information model and the field data set to obtain verification data, inputting the verification data into the building information model, performing construction consistency analysis and deviation identification, and generating a quality evaluation report; performing risk assessment and priority classification on the quality analysis data to obtain risk assessment data, and performing problem priority analysis and rectification scheme formulation on the risk assessment data to generate rectification execution data; and performing rectification effect analysis and overall quality evaluation according to the rectification execution data, and generating a project quality summary report. The real-time performance and the accuracy of construction quality monitoring are improved, the construction deviation is reduced to the maximum extent, the quality management is optimized, and the smooth proceeding of a project is guaranteed.

The invention discloses a submarine pipeline scour-vibration response analysis method based on a cross-scale coupling framework. The method comprises the following steps: establishing a multi-physics field coupling model according to geographical and structural information of a pipeline layout area and initializing the multi-physics field coupling model; marine environment data are obtained through real-time monitoring, the model is imported for scouring dynamic deduction, and a scouring boundary data set is generated. And reconstructing a multi-physics field solving environment, and forming a real-time physics solving environment package. After the model is updated, pipeline vibration response is analyzed, and a vibration data set is obtained. And performing fatigue damage analysis, deducing a flow guide field disturbance coefficient, updating the model again, performing next time step coupling calculation, and finally obtaining a full-period coupling analysis data set. And evaluating the pipeline operation safety situation based on the data set, and generating and pushing a safety report. Various physical field effects on the submarine pipeline in the marine environment are comprehensively considered, the safety of the submarine pipeline is evaluated more comprehensively, and cross-scale coupling analysis between the scouring effect and the vibration effect is achieved.

The invention belongs to the technical field of wind power weather forecasting, and provides an all-region three-dimensional wind speed correction method and system, and the method comprises the steps: constructing a weather numerical forecasting model, and obtaining wind field forecasting data; fusing the preprocessed multi-source data by adopting an optimal interpolation method to obtain three-dimensional space-time continuous wind field analysis data; based on the wind field forecast data and the wind field analysis data, features are extracted and fused, then a historical forecast error sample set is constructed, a wind speed correction model is constructed, and the historical forecast error sample set is utilized to train the wind speed correction model; introducing an initial value, a physical parameter and boundary condition disturbance, calculating a mean value and a standard deviation of each set result, extracting a probability distribution feature of a wind speed, constructing a confidence interval, estimating a probability density function, and quantifying an occurrence probability of an extreme wind speed event; and the prediction result of the wind speed correction model and the multi-source wind field observation data are fused to generate final three-dimensional wind field data, so that the actual requirements of wind power prediction and power grid dispatching can be met.

The invention provides an ocean live big data fusion method based on deep learning, and relates to the technical field of ocean big data fusion, and the method comprises the steps: obtaining meteorological driving variables, ocean historical reanalysis data, satellite observation data and ocean field observation data, and performing unified format conversion processing on the basis of the acquired meteorological driving variables, the ocean historical reanalysis data, the satellite observation data and the ocean field observation data, and configuring a unified ocean environment feature tensor set. A unified marine environment feature tensor structure is configured, meteorological driving variables, marine historical reanalysis data, satellite remote sensing data and on-site buoy observation data are subjected to space resampling, time interpolation and missing complementation and are unified into a space-time grid under a geographic reference coordinate system, a four-dimensional tensor set is configured, and the four-dimensional tensor set is obtained. The problem of fusion obstacles caused by non-uniform data formats and inconsistent temporal-spatial resolutions in the prior art is effectively solved.

The invention provides a user attribution method and system capable of meeting various configurations and conversions, and belongs to the technical field of computer networks, and the method comprises the following steps: according to a unified identity mapping table, ordering behavior records on each device according to a time sequence, integrating the behavior records into a continuous behavior path data set, and determining a full-link view of a user conversion path; carrying out fragmentation storage and parallel analysis by adopting a distributed computing framework through the behavior path data set, and optimizing resource allocation to obtain a real-time processed user conversion path result set; according to a user conversion path result set processed in real time, a cache mechanism is constructed to store high-frequency access path data, and if a new query request is received, data is extracted from a cache preferentially, and a quick-response path analysis result is determined; through a path analysis result, an encryption technology is applied to protect a user identity identifier and behavior data, and in combination with an access control strategy, a safe and compliant attribution analysis data set is obtained. According to the method, the problem of cross-device user behavior tracking is solved, a full-link user conversion path view is provided, the path analysis requirement can be quickly responded, and data support is provided for precise marketing decision making.

The invention discloses an automobile part assembly precision intelligent compensation method and a self-adaptive regulation and control system, and relates to the field of intelligent compensation, and the method comprises the steps: synchronously collecting part geometric parameters, tool poses and environment data, and constructing an associated data matrix; analyzing data by using an improved random forest-attention model, and outputting a deviation factor contribution degree sequence; a compensation calculation model is designed accordingly, initial compensation amounts are generated in a segmented mode in combination with a precision margin threshold value, and correction is conducted through historical data similarity matching; selecting an execution path according to the compensation amount and the core deviation type; an actual precision value is obtained through laser detection after assembly, and a compensation error is calculated; based on an error triggering model optimization mechanism, model parameters are iteratively updated by using a gradient descent algorithm, and the deviation identification and compensation precision is improved. The method has the advantages that the model attribution deviation is improved, the precise compensation amount is calculated in combination with historical data, flexible execution, real-time monitoring and model self-optimization are matched, and the assembly precision and the production efficiency are efficiently improved.

The invention relates to a multi-dimensional data intelligent analysis method and device for sewage treatment, equipment and a medium. The method comprises the steps of obtaining multi-dimensional data, and performing standardization processing on the multi-dimensional data to obtain standardized data; based on the standardized data, performing feature analysis on the standardized data through a preset feature extraction model to obtain feature data; according to the feature data, performing correlation analysis on the feature data through a preset correlation analysis model to obtain correlation analysis data; performing deviation degree analysis on the correlation analysis data through a preset anomaly detection model to obtain abnormal data; and according to the abnormal data, performing comprehensive evaluation analysis on the abnormal data through a preset decision analysis model to obtain a sewage treatment analysis result. By adopting the method, the abnormal root can be accurately identified from the complex association, and the intelligent monitoring level, the risk early warning capability and the operation optimization effect of the processing process are improved.

The invention discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.

The invention provides a curved surface segmented welding deformation prediction method and system, and relates to the field of data processing. According to the method, the welding heat input quantity is generated by obtaining the assembly error of a rib plate and a curved surface outer plate in a curved surface section, establishing the relation between the welding bead deposition area and the assembly error, constructing a welding heat input evaluation model and introducing a welding bead remelting coefficient. And determining a welding equivalent mechanical load based on the material thermodynamic parameters, inputting the welding equivalent mechanical load into the finite element model, and generating heated welding deformation prediction data. The gravity of the curved surface segments and the restraining force of the pressing block are converted into uniform volume force, and deformation precision analysis data are obtained; and constructing a contact unit model to analyze a contact state and a springback effect, and generating stress welding deformation prediction data. And finally, generating a welding deformation prediction result of the curved surface segment based on the data. By implementing the technical scheme provided by the invention, the prediction accuracy of the curved surface segmented welding deformation can be improved.

The invention relates to the field of intelligent report generation, in particular to an intelligent insight report generation method and system based on an AI large model, and the method comprises the steps: inputting an insight demand, and generating an insight data package comprising insight contents, associated data and industry labels; extracting a basic keyword set of the insight data packet to form a mixed feature code; after mixed feature coding preprocessing, weight distribution is carried out; and after weight distribution of the AI large model, injecting a high-weight feature vector into a semantic understanding core layer, injecting a low-weight feature vector into a logical reasoning layer, and outputting analysis data to form an intelligent insight report. According to the method, word embedding parameters are optimized according to field semantic characteristics, a parameter verification mechanism is introduced, field text characteristics are adapted by adjusting vector dimensions, context windows and low-frequency vocabulary filtering threshold values, window parameter validity is verified through cosine similarity, word frequency threshold value reasonability is verified through standard deviation, and field text characteristic matching is achieved. And it is ensured that the feature vectors can accurately capture domain-specific semantics.

The invention discloses an agent training data set construction method and system combined with cross-modal learning, and relates to the technical field of agent training, and the method comprises the steps: obtaining multi-modal intelligence data from a multi-source database, extracting feature information, and projecting the feature information to a preset semantic space to obtain cross-modal alignment features; constructing an agent function call syntax tree based on cross-modal alignment features, analyzing the syntax tree into an instruction sequence, constructing an analysis reasoning chain, and generating a sample label and an interaction track; constructing a task dependency graph by using the sample labels and the interaction tracks, decomposing the task dependency graph into a plurality of parallel decision branches, and dynamically adjusting a processing strategy to form a training data set; mapping the training data set to an agent target space, optimizing and analyzing an inference chain through a training feedback channel, and outputting a standardized sample library; and finally inputting to-be-analyzed data into the trained agent, and generating an intelligence analysis report according to the analysis reasoning chain.

The invention relates to the technical field of big data processing, in particular to a hydrological information forecast data collection automatic processing method, which comprises the steps of multi-source data automatic acquisition and access, data quality intelligent diagnosis processing and abnormity early warning, multi-index comprehensive drought evaluation model construction and drought trend intelligent prediction. GIS spatial analysis and visual display are carried out, and drought evolution is dynamically reflected; and user-defined configuration and adaptive learning are carried out. The equipment state can be displayed, and related personnel can be prompted to maintain the observation station in time; existing hydrological data of the hydrological bureau are fully applied, rainfall, river flow, soil moisture content and other single items are normatively determined to determine the drought level, and meanwhile, according to multiple indexes, comprehensive drought judgment is performed according to the proportion, and based on the GIS technology, an interpolation method and a proximity association method are utilized to determine a Xinjiang whole administrative division plane drought map; and according to the analysis data, automatically sorting out soil moisture content brief report materials.

The invention relates to the technical field of business data, and discloses a process parameter iterative optimization method and system based on multi-source data, and the method comprises the steps: collecting multi-source business data, a resource association table and a task time sequence set of a target business process, analyzing the steady-state and abnormal characteristics in the data, and obtaining a target business flow; therefore, a reasonable adjustment interval of resource configuration and a to-be-improved task are determined, and a preliminary optimization scheme is formed. And constructing a resource collaboration graph by taking resources as a main body and fusing a task time sequence and a dependency relationship. After the preliminary scheme is mapped to the map, the full-node load change can be simulated and deduced. And comparing the load added value of the node in the full-node load change set with the available resource capacity of the node, and identifying a resource overload risk set of the business process. Adjusting the preliminary optimization scheme set according to the resource overload risk set to obtain a collaborative optimization execution sequence of the target business process; according to the invention, the optimization accuracy of the technological parameters of the target business process can be improved.

The invention provides a large geothermal heat exchange station efficient construction system and method based on a BIM technology. The scanning device is used for constructing a three-dimensional model of the geothermal heat exchange station through space scanning; the pre-assembly module is used for generating a BIM model with a precision control rule based on a drawing and a professional model, supporting standard value / standard update value double-precision grades, and automatically triggering precision switching by a user instruction or a conflict detection result; the conflict detection module is used for identifying building, structure, equipment and pipeline collision problems; the performance analysis module is used for generating analysis data such as fluid mechanics and thermal performance; the fusion module is used for aligning the conflict information and the performance data by using the fusion matrix to generate a high-precision analysis result; and the machine learning model is used for generating a performance evaluation result and a design optimization instruction based on historical data, and dynamically adjusting the model precision. Through dynamic precision control, multi-source data fusion and machine learning closed-loop optimization, the problems of low cooperation efficiency, frequent design change and operation and maintenance disjunction in traditional construction are solved.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention relates to the technical field of water supply network operation and maintenance detection, in particular to a water supply network multi-pipeline leakage detection and positioning method, which comprises the following steps of: acquiring pressure time sequence data of a monitoring point of a water supply network, processing to generate a standardized pressure fluctuation sequence, and analyzing the data by utilizing a pre-trained leakage reasoning calculation model; leakage suspected degree scores and leakage type classifications are obtained, high-suspected-degree leakage events are screened, and corresponding positions and timestamps are obtained. A transient flow state inversion equation set is built in combination with a pipe network topological structure and a hydraulic model, a standardized pressure sequence is used as a boundary condition, pipe section hydraulic state changes are reversely deduced through a state inversion algorithm, pressure gradient abrupt change points are recognized, corresponding pipe sections are positioned, and leakage types and positioning information are fused to generate a complete detection positioning report. According to the method, leakage types can be accurately distinguished, multi-pipeline concurrent leakage pipe sections can be accurately positioned, non-leakage signal interference is reduced, and the method is adaptive to a complex pipe network leakage detection scene.

The invention relates to a digital quality evaluation method, device and equipment for cooperative learning of higher vocational schools and foreign vocational schools, and a medium. The method comprises the following steps: firstly, acquiring multi-source original data of Chinese and foreign education systems, determining a core term definition through a term anchoring technology, and synchronously adopting a noise cleaning algorithm to remove invalid data to form a basic data set; performing format standardization conversion on the basic data set, quantifying a Chinese and foreign education standard deviation value based on an algorithm, establishing a data stream mapping relation, and generating an analysis data set; then, on the basis of the analysis data set, professional core features are screened through a feature vector extraction technology, grouping parameters are dominated by using a clustering algorithm in combination with a clustering boundary right confirmation rule, and a classification data set is obtained; and finally, constructing a dynamic evaluation index system according to the classification data set, obtaining an evaluation result, and outputting an evaluation report. According to the method, standardized and traceable digital technical support is provided for dynamic monitoring, difference analysis and optimization improvement of cooperative learning quality of higher vocational schools and foreign vocational schools.

The invention relates to a driver fatigue state data determination method and device and computer equipment. The method comprises the following steps: acquiring environment state data corresponding to a running automobile and driver action data corresponding to a driver; according to the environment state data, risk events of the running automobile in a future preset time domain are predicted and analyzed, and task complexity analysis data are obtained; performing mutual information analysis on a corresponding relation between the environment state data and the driver action data to obtain driver-man mutual information analysis data; according to the task complexity analysis data and the driver mutual information analysis data, calculating a fatigue index of the driver at the current moment to obtain an information efficiency attenuation fatigue index; and performing sequential statistical test analysis on the information efficiency attenuation fatigue index to obtain fatigue state data corresponding to the driver. By adopting the method, the generalization ability and the stability of determining the fatigue state of the driver can be effectively improved.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance, and discloses a credit evaluation method and device for multi-source data co-processing and related equipment. The method comprises the steps of receiving credit evaluation data of at least two types of preset data sources, and performing cleaning, time scale unification and numerical standardization processing on the credit evaluation data to form standardized multi-source feature data; constructing credit portrait data based on the standardized multi-source feature data; performing scoring reasoning on the credit portrait data through a pre-trained credit extension evaluation model, and generating credit extension evaluation result data through a limit mapping rule; receiving the use behavior data and the performance behavior data, performing time sequence analysis and deviation identification, and generating credit use behavior analysis data; and updating credit extension evaluation model parameters based on the credit use behavior analysis data, loading the credit extension evaluation model parameters to a model operation state, outputting credit extension evaluation result data, and recording model parameter updating information. According to the method, the dynamic adaptive capacity of multi-source credit assessment is improved.

The invention provides a meteorological element field microstructure intelligent generation and reconstruction method and device, and relates to the field of model training, the method can obtain reanalysis data and historical forecast data of global meteorological elements, and construct a training sample set based on the reanalysis data and the historical forecast data; taking sparse meteorological element fields of a plurality of different height layers in a continuous time period as model input, combining ground station observation data and topographic data related to meteorological evolution as constraint conditions, and training the initial diffusion generation model by using the training sample set to obtain a final diffusion generation model after training is completed; the constraint condition is used for guiding the final diffusion generation model to generate a three-dimensional meteorological element field conforming to observation consistency and terrain adaptability; modeling the three-dimensional high-resolution meteorological element field through the final diffusion generation model to obtain a reconstructed three-dimensional meteorological element field microstructure; the high resolution is higher than the specified resolution.

Methods, systems, and computer storage media for providing iterative data processing optimization using an iterative data processing optimization engine in a data intelligence system are described. Iterative data processing refers to handling data where the processing steps are repeated multiple times, across multiple views or modalities, to train machine learning models, filter and score data or generate output. The iterative data processing optimization engine employs expectation step machine learning models that are simple but with fast language models to efficiently and effectively probe and analyze data, while iteratively refining maximization step machine learning models that are optimized and fast to approximate the probing mechanism of the expectation step machine learning models more efficiently, for example, using metadata, external information, and compressed representation. The iterative data processing optimization engine can operate based on an agentic framework using lightweight artificial intelligence (AI) agents to perform model fitting, featurization, and report generation autonomously.

The invention discloses an intelligent analysis method for major project site selection and generation, and the method comprises the steps: 1), building an analysis database based on layer data; 2) establishing an operator library, a model library and an index library; 3) constructing a site selection intelligent analysis model based on the operator library, the model library and the index library; 4) obtaining one or more project intentional land use ranges uploaded by a user, inputting the ranges into the intelligent site selection analysis model, determining evaluation indexes of the project intentional land use ranges, and binding the index values to the corresponding layer data items; 5) establishing a rule engine based on a decision tree; and 6) performing intelligent judgment on a calculation result by utilizing a rule engine in combination with a service rule, optimizing a calculation process, and generating an intelligent analysis report. According to the method, through a spatial parallel computing technology, a mode of overlay analysis of global mass basic data is adopted, and help is provided for proprietors to expose and solve major contradictions in the early stage of a project in advance.