52 results about "Data evolution" patented technology

The invention provides a humanoid robot inertial navigation and vision fusion positioning method, device and equipment. The method comprises the following steps: firstly, pre-evaluating the quality of IMU inertial data and visual SLAM data to generate a data credibility identifier; establishing a causal relationship chain between the data sources through time sequence correlation analysis; constructing a multi-strategy superposition state based on data credibility, and determining an adaptive fusion strategy through collapse judgment; constructing a data evolution path by using a causal relationship, and generating an expected behavior feature and a causal deviation feature; the fusion parameters are optimized according to the deviation characteristics, and a plurality of fusion positioning estimations with different weights are generated; and through space aggregation analysis and confidence weight calculation, selecting optimal positioning estimation and outputting a high-precision fusion positioning result. According to the method, deep understanding of sensor data, intelligent selection of fusion strategies and robust output of positioning results are realized, and the positioning performance of the humanoid robot in a complex environment is improved.

The invention relates to the technical field of data processing, in particular to a computer feature extraction processing system and method fusing multi-source heterogeneous data, and the system comprises a structure correlation mapping module, a density mutation recognition module, a disturbance path analysis module, a path logic matching module and a risk logic output module. According to the method, by optimizing a data processing mode, important features in data can be accurately recognized, manual intervention and experience dependence are reduced, large-scale data analysis and processing efficiency is improved, the accuracy of a data model is enhanced, and by deeply analyzing the fluctuation amplitude and the change trend of the data, potential associated areas and abrupt change grids can be accurately positioned; key disturbance information is effectively identified, path logic matching can reveal a risk behavior mode in data evolution, potential early warning events are marked in time, the efficient processing capacity of the model for a complex data mode is ensured, and particularly, the problems of low efficiency and insufficient precision in a traditional scheme are solved in the face of large-scale data.

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

The invention provides an equipment hidden fault reasoning method and system in combination with multi-source Internet of Things data. The method comprises the following steps: firstly, acquiring a multi-source Internet of Things data set comprising core component operation parameters, surrounding environment perception and inter-component interaction behavior data of smart home equipment; performing time sequence evolution mark extraction on the data set to generate a multi-dimensional data evolution mark set; then constructing an initial structure of a fault traceability deduction tree based on equipment physical operation logic; dynamically matching the multi-dimensional data evolution mark set with initial structure nodes of the deduction tree, adjusting branch growth directions and node weights, and generating a self-adaptive fault traceability deduction tree; and finally, mining implicit association logic of the imprint and the node based on the deduction tree, and generating an equipment implicit fault tracing conclusion. The hidden fault of the smart home equipment can be comprehensively and accurately inferred, and the operation stability of the system is improved.

The invention discloses a rural energy system multi-energy load scene construction method, and relates to the field of comprehensive energy, and the method comprises the steps: obtaining a historical multi-energy load time sequence of a rural energy system; according to the historical multi-energy load time sequence, a scene generation model is adopted to generate a multi-energy load scene set in a future time period; the scene generation model is constructed based on a Transform model and a regularization relative loss generative adversarial network, and a cosine annealing algorithm and a hot restart mechanism are adopted to dynamically adjust the learning rate when the scene generation model is trained; and reducing scenes in the multi-energy load scene set by adopting a dynamic time warping distance method and a K-Medoids clustering method to obtain a typical multi-energy load representative scene set. According to the method, on the basis that the generated scene keeps a certain diversity, the historical data evolution law and the coupling characteristic between the source and the load are restored to the maximum extent, and extraction and reduction of the representative scene are achieved.

The invention discloses a student archive data management method and system, and relates to the technical field of education informatization management, and the method comprises the specific steps: S100, collecting structured and unstructured data and generating a data chain, S200, splitting the data chain, S300, carrying out the simulation verification of the data chain, S400, changing the data chain, and S500, cloning the data chain. According to the student archive data management method, potential development paths of students can be accurately simulated according to development parameters input by authorized users, various possible development paths of the students can be simulated by adjusting probability distribution of related nodes in a prediction network, the deviation degree of each path and current dominant chain data is calculated, and the student archive data management efficiency is improved. Therefore, teaching contents and methods are adjusted in a targeted manner according to potential development requirements of students, data evolution paths of the students are completely recorded based on a chain structure of the timestamps, and data clone backtracking of any node is supported.

The invention discloses a short time sequence Alzheimer disease progress prediction method based on multi-relation mixed perception, and belongs to the field of progress prediction, and the method comprises the steps: providing a time sequence evolution generative adversarial network, enhancing the dynamic fitting capability of data evolution, and improving the generalization of a long time sequence learning model to a short time sequence. Secondly, a multi-relation mixed sensing module embedded with global noise filtering is designed, complex relations among subjects can be captured, and pairwise and high-order interaction relations can be efficiently learned. Specifically, the module realizes joint modeling of a pairwise relationship and a high-order relationship by constructing a noise filtering population graph and a population hypergraph. Then, the mixed representation of the multiple relationships is fed to an extended long-short term memory module for time series learning.

The invention provides a multi-source sensor monitoring method and device, electronic equipment and a storage medium, and the method comprises the steps: configuring a heterogeneous sensor network according to a monitoring target and environment; a sensor node topological relation graph is established, a unique space-time identifier is distributed, and timestamp synchronous correction is carried out; processing data of different sampling frequencies and then extracting core feature vectors to form a multi-dimensional feature matrix; constructing a dynamic graph structure, learning a space-time dependency relationship between sensors, and generating a node embedding vector; establishing a space-time propagation model to predict a data evolution trend, constructing a normal working model, and calculating a reconstruction error threshold value; when abnormality is detected, triggering a sensor network adaptive reconfiguration mechanism, and adjusting the sampling frequency and sensitivity parameters of the key sensor; and constructing a hierarchical decision tree, fusing the monitoring result, generating confidence evaluation, outputting a differential early warning signal according to the risk level, and generating a monitoring report. According to the invention, the monitoring precision and the abnormal detection and fault diagnosis capability are improved.

A computer-implemented system for performing updates to a multi-client application without downtime, wherein the system includes: a client policy store configured to manage client-specific rollout and compliance policies; an update orchestrator configured to generate a client-specific rollout plan from a versioned dependency graph of application components; a version compatibility layer configured to enforce dual-mode compatibility between simultaneously active service and data versions; a data evolution engine configured to perform database schema changes using an expand-migrate contract procedure with versioned read views and a transactional outbox for dual-write with checkpoint-based backfill; a traffic shaper configured to perform programmable L4 / L7 routing, including session pinning, shadow mirroring, stepwise canary operation, and atomic client-specific cutover; and a telemetry and health manager configured to calculate client-specific service level indicators and to control phase progress and rollback decisions; where the coordinated cooperation of the update orchestrator, the version compatibility layer, the data evolution engine, the traffic shaper, and the telemetry and health manager updates one or more clients without interrupting ongoing sessions or violating data consistency requirements.

Techniques for extending blockchain to support controlled branching and consistent data reconciliation for collaborative data evolution include: storing multiple data assets on a global ledger used by multiple nodes in a network; generating a first local cache for a first subset of the multiple nodes; storing, by a node in the first subset, a write transaction to the first local cache; and merging the write transaction from the first local cache onto the global ledger.

The invention discloses a multi-source data fusion and analysis method and system for a complex scene, belongs to the technical field of data processing, and aims to solve the contradiction between a static semantic model and dynamic data evolution. The method comprises the following steps: firstly, configuring a data pipeline, fully extracting source data from an original database to a target database to obtain a source data set, and respectively obtaining a first data set and a second data set through cleaning, dump and link validity judgment; analyzing the semantic constraint relationship of the entity attributes of the ontology model to obtain a rule set, and mapping the second data set field to the target entity attributes of the ontology model to obtain a preliminary alignment data set; generating temporary attributes for unmatched fields through context association analysis, and expanding the temporary attributes to the ontology model to obtain a semantic alignment data set; standardizing the numerical value to obtain a third data set; and finally, carrying out multi-modal feature conjoint analysis on the third data set to generate a dynamic decision index.

The application relates to the technical field of big data intelligent outpatient management, and discloses an outpatient information query reservation management method based on big data. The method comprises the following steps: collecting original outpatient records to establish an environment model; according to the environment model, time stamp alignment and semantic association are performed on real-time registration, diagnosis and treatment state and resource consumption data to form an outpatient associated data set; mode fragments are extracted and projected into a dynamic characteristic space composed of service efficiency and demand urgency to generate an outpatient dynamic characteristic cloud; in the space, a core service path is aggregated according to the density and evolution direction of characteristic points; finally, real-time requests are matched with the core path to generate a reservation guide strategy. The method realizes dynamic and quantitative representation of the outpatient operation state, can automatically mine efficient service paths based on real data evolution, and thus improves the accuracy and adaptability of outpatient scheduling and guidance.

The invention discloses a nursing data intelligent analysis method and system, and relates to the technical field of medical data intelligent analysis, and the method comprises the steps: employing a semantic reasoning mechanism of a semantic mapping system, and carrying out the recognition of classified nursing data; constructing a nursing data evolution path with context sensing ability by identifying a relationship between the classified nursing data and combining time sequence information of state change in a nursing process; the nodes in the evolution path are matched, the matched priority is dynamically presented through a visual interaction interface, nursing suggestions are put forward, and intelligent analysis of nursing data is completed. According to the method, the semantic understanding and intelligent analysis capability of the nursing data is improved, the scientificity, the real-time performance and the individuation level of nursing decision making are remarkably improved, and the method has a good popularization prospect.

The invention discloses a wireless networking fire-fighting early warning method and system based on multi-source data, and relates to the technical field of wireless networking. The method comprises the following steps: deploying an array integrated with multiple types of sensors in a target space in a wireless networking mode, and collecting and uploading electrical, gas and temperature multi-source parameters to a data medium station in real time; when monitoring parameter triggering is abnormal, obtaining a time verification credibility array based on multi-source data evolution time verification, and performing spatial evolution verification on gas parameters based on an air flow field to obtain spatial verification credibility; basic early warning parameters are calculated according to the multi-source parameters, correction is carried out on the basic early warning parameters by fusing the credibility of space-time dual verification, final early warning parameters are generated, and fire-fighting early warning judgment is executed. According to the invention, through a time sequence and space dual verification mechanism, false alarms caused by sensor faults or environmental interference are effectively discriminated, the early warning accuracy is improved, and early and reliable fire risk identification and hierarchical linkage response are realized.

This application discloses a data grading method for the evolution of data space information, including: in response to the process of generating second data based on a data operation on first data, obtaining the first data level of the first data and the operation type of the data operation; determining the correlation strength evaluation value between the first data and the second data based on the first data and the second data, as well as the operation type of the data operation; and determining the second data level of the generated second data based on the first data level, the correlation strength evaluation value, and the operation type of the data operation. This method solves the problem of repeated grading overhead in the dynamic evolution of data and successfully builds a traceability system for data evolution levels, providing technical support for the efficient management and intelligentization of data space.

This invention relates to the field of medical data technology and discloses an AI-based method for managing cardiovascular chronic disease data. The method constructs a cardiovascular data pool containing patients' historical medical records and real-time physiological signals; establishes a dynamic ontology in the cardiovascular data pool based on a clinical knowledge graph, which defines the semantic relationships between different data entities; maps and generates data evolution trajectories on the dynamic ontology according to a preset treatment target path; activates an intelligent agent configured to traverse the dynamic ontology along the data evolution trajectory, resolving causal constraints between data entities based on the semantic relationships encountered during the traversal; the intelligent agent adaptively reorganizes new data flowing into the cardiovascular data pool in real time according to the resolved causal constraints, generating data slices with temporal correlation; inputs the data slices into a pre-trained deep learning network for feature fusion, outputting a dynamic profile of the patient's cardiovascular health status.

Techniques for extending blockchain to support controlled branching and consistent data reconciliation for collaborative data evolution include: storing multiple data assets on a global ledger used by multiple nodes in a network; generating a first local cache for a first subset of the multiple nodes; storing, by a node in the first subset, a write transaction to the first local cache; and merging the write transaction from the first local cache onto the global ledger.

The invention discloses a high-dimensional data evolution feature selection method based on dynamic clustering and mixed agent combined assistance, and belongs to the field of high-dimensional data feature screening, and the method comprises the following steps: S1, constructing an initial population; s2, on the basis of the initial population, combining a paired comparison agent model and a sample agent model to form a mixed agent model; s3, approximately evaluating the individual fitness of the initial population by using the sample agent model, initializing an individual optimal solution and a global optimal solution, entering an iterative optimization process, and outputting an optimal feature subset; and S4, based on the classification performance of the output optimal feature subset, the feature scale and the operation time verification result, feeding back the update frequency of the optimization hybrid agent model and the increment adjustment parameter of the feature clustering. The high-dimensional data evolution feature selection method based on dynamic clustering and mixed agent combined assistance has high robustness and engineering adaptability, and realizes optimal balance of classification precision, feature simplification and calculation efficiency in a high-dimensional data scene.

This application proposes a data calculation method for a city information model based on CIM, belonging to the field of data processing. The method includes: semantic parsing of the target test scenario description information to construct a test scenario feature vector; constructing an evolutionary parameter space for at least one evolutionary prediction model based on the CIM city information model; training the probability function mapping relationship between the test scenario feature vector and the evolutionary parameter space, and outputting an adaptive evolutionary parameter combination based on the probability function mapping relationship; importing the data into the evolutionary prediction model to perform data evolution calculation, obtaining a dynamic evolutionary time-series dataset, which is then transmitted as test cases to the test system terminal containing the target test scenario description information to obtain the test performance results of the tested target. By adaptively adjusting the evolutionary parameter combination according to the test scenario characteristics, the dynamic time-series data output by the evolutionary prediction model better matches the needs of various test scenarios, thereby improving the accuracy and computational efficiency of test cases.

This invention relates to the field of carbon emission forecasting technology, providing a carbon emission forecasting method, system, computer equipment, and storage medium. The method includes: decomposing the industrial-specific final energy consumption data from the overall industry-specific final energy consumption data based on historical annual industrial end-product energy consumption data by category and type; predicting and analyzing this data to obtain the target year's overall industry-specific final energy consumption forecast data; and calculating the primary energy consumption forecast data based on the target year's overall industry-specific final energy consumption forecast data using power generation ratio prediction data obtained from historical annual electricity data evolution analysis, thereby obtaining the predicted value of regional energy consumption carbon emissions. This invention can capture the natural laws governing the impact of energy structure adjustment, technology diffusion, and resource constraints on energy demand and carbon emissions, improving the reliability and accuracy of regional energy carbon emission forecasting.

The application provides a kind of data security system and method based on multi-party cooperation, belong to information security technical field, the data security system based on multi-party cooperation of the application, including data slicing device, encrypted storage, node manager, key coordinator and instantaneous view generation device, the method is through data slicing and isolated storage, heterogeneous authentication key mechanism separates the readability of data and use mode in technology and is in the control of different participants, so that the disclosure of any single component or repository does not expose meaningful complete information, node manager as process arbitration core, the accessibility of data and the promotion state of business process, data evolution state are strongly bound, fundamentally eliminate the data misuse risk caused by process disorder or illegal operation, without setting complex encryption algorithm, the security processing of data can be realized from system level, improve the security and efficiency of data management.

The application provides a kind of old patient sign monitoring method and system based on multi-modal perception, it is related to intelligent health monitoring technical field, including: acquisition and fusion physiological and behavioral data, construct unified representation and modal correlation matrix.Baseline features are calculated and tensor fusion is carried out, abnormal fragments are identified and dominant influence factors are traced back by encoding reconstruction.Risk level is mapped by deducing data evolution trajectory and calculating deviation from health benchmark.Simulate the timeliness of intervention scheme, determine the target scheme and generate monitoring report.The application realizes the deep correlation analysis and early warning of abnormality of multi-modal data, can automatically determine the risk dominant factor and generate individualized intervention scheme, improves the accuracy and initiative of old health monitoring.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a health data evolution analysis method, device and equipment and a medium, and the method comprises the steps: obtaining user historical data of a user, and constructing an individual health map of the user according to the user historical data; generating health analysis suggestion data of the user according to the individual health map, and collecting multi-modal health data according to the health analysis suggestion data; extracting multi-modal features in the multi-modal health data, and constructing a health evolution graph according to the multi-modal features; calculating an analysis candidate set of the user according to the health evolution graph; and generating a health data evolution visualization report of the user according to the analysis candidate set. The accuracy of health data evolution analysis can be improved.

The application discloses a rural energy system multi-energy load scene construction method, relates to the field of comprehensive energy, and comprises the following steps: acquiring a historical multi-energy load time sequence of a rural energy system; generating a multi-energy load scene set of a future time period by using a scene generation model according to the historical multi-energy load time sequence; the scene generation model is constructed based on a Transformer model and a regularized relative loss generation adversarial network, and the scene generation model dynamically adjusts a learning rate by using a cosine annealing algorithm and a hot restart mechanism during training; and the scenes in the multi-energy load scene set are reduced by using a dynamic time warping distance method and a K-Medoids clustering method, so that a typical multi-energy load representative scene set is obtained. According to the application, the generated scenes can restore the coupling characteristics between the historical data evolution law and the source load to the maximum extent on the basis of maintaining a certain diversity, and the representative scenes can be extracted and reduced.

This disclosure presents a method and system for automatically discovering sensitive data, comprising: monitoring a task queue to obtain sensitive data discovery tasks to be processed; parsing the sensitive data discovery tasks, dynamically adapting connection strategies using a database fingerprinting mechanism, and connecting to a target database; extracting configuration information, determining the target database table name and target field name, generating and executing an intelligent sampling strategy and data query SQL statement, scanning and obtaining data samples, performing sensitive information pattern matching and identification, and generating discovery results and a data evolution tracking system; writing the discovery results into a result table and generating a data lineage graph; updating the execution progress of the sensitive data discovery tasks, closing the database connection after execution, and updating the status of the sensitive data discovery tasks to complete. This method can improve the efficiency, accuracy, and traceability of sensitive data discovery.

The invention provides a data classification method and device, equipment, a medium and a product, and relates to the field of data classification management. Obtaining current database metadata; preprocessing the current database metadata to obtain first data; carrying out vector fusion processing on the first data according to a multi-modal fusion algorithm to obtain a fusion vector; performing similarity screening processing according to the template vector set and the fusion vector to obtain a classification result matrix; judging whether the classification result matrix contains a newly added category or not through a category identification strategy; and if the classification result matrix contains a new category, updating the template tree, obtaining the metadata of the current database again, and performing data classification. According to the method and the device, the technical problems that the prior art mostly depends on a static template, cannot adapt to data evolution and lacks application of a field example data statistical rule, so that the data classification efficiency is relatively low are solved.

The invention provides a credit risk prediction method and device, a storage medium and electronic equipment, and is applied to the technical field of financial risk prediction. According to the method, for a credit application object, structured time series data, unstructured texts, images and other multi-modal original data are collected, standardization processing is carried out, and multi-modal feature vectors are extracted. Inputting the feature vectors into a state transfer function of a preset world model to obtain a current state vector; and inputting the state vector into a pre-trained credit risk prediction model, outputting a risk score, and generating a credit evaluation result according to the risk score, thereby effectively capturing a data evolution rule through multi-modal data fusion, standardization and dynamic state modeling, and remarkably improving the accuracy and reliability of credit risk prediction.

The present application relates to a kind of data traceability system and method based on DDS, the system includes two layers of monitoring layer and terminal layer, terminal layer includes subsystem data collection module and subsystem data stream upload module, subsystem data stream upload module uploads data to monitoring layer by DDS;Monitoring layer includes interactive data acquisition module based on non-intrusive, inter-subject data item correlation analysis module, subject-based data evolution sequence generation module and visual display module;The technical scheme supports real-time data traceability in the process of system operation, not post-analysis, not dependent on system operation log;For the dynamic addition of subject in running state, the subject publishing and subscribing relationship and data flow path can be updated in real time;Using publish / subscribe mechanism, it brings non-intrusive data acquisition mode, so it is not necessary to modify the code of running system, and it is not necessary to set probe, so that the data traceability method has good flexibility and scalability.