95 results about "Independent data" patented technology

The invention provides a Hadoop cluster incremental backup recovery method based on data block optimization, and relates to the technical field of data management, and the method comprises the following steps: obtaining distribution state information and historical change records of data blocks in a distributed file system, extracting space-time correlation characteristics, and constructing a data block dependency graph containing dependency direction and dependency intensity; identifying the changed data blocks, performing topological sorting, and identifying key path data blocks and independent data block clusters; calculating a distribution entropy value according to a copy distribution position, and determining a backup granularity type; a backup execution sequence is generated according to a topological sorting result, key path data blocks are backed up preferentially, independent data block clusters are backed up in parallel, and backup metadata containing dependency relationship identifiers and distribution entropy values are generated; and performing dependency consistency verification after recovery based on the dependency relationship identifier. According to the method, the backup recovery efficiency is improved, the data consistency is guaranteed, and the storage overhead is reduced.

The embodiment of the invention provides a data collection method and system, electronic equipment and a storage medium, and is applied to the technical field of industrial communication. According to the data acquisition method and system, the electronic equipment and the storage medium provided by the embodiment of the invention, the register objects with the same acquisition period, the same slave station ID, the same function code and continuous register addresses are aggregated in the same second register aggregation group, and the data request frame is generated for the same second register aggregation group; the register objects in the second register aggregation group can share one data request frame. Compared with the prior art in which each register object generates an independent data request frame, the data acquisition method and device have the advantages that the number of the data request frames can be obviously reduced, so that excessive system resources are prevented from being occupied, and the data acquisition efficiency is further improved.

The invention discloses an industrial chain logic deduction method, computer equipment and a program product, and the method comprises the steps: constructing a data asset library which comprises charts, data indexes and research reports as independent data assets; the method comprises the following steps: constructing a visual editing framework based on a Web technology, wherein the framework provides a predefined graphic element library and canvas; in the canvas, dragging elements in the graphic element library and establishing connection to construct an industrial chain logic deduction graph; carrying out association binding on nodes in the logic deduction graph and charts, data indexes or research reports in a data asset library; and performing logic deduction and demonstration in response to user operation based on the bound logic deduction graph. By supporting interactive demonstration and path multiplexing based on the logic deduction graph, effective transmission of analysis thoughts and continuous precipitation of knowledge are realized, and the depth and efficiency of industrial chain research are comprehensively improved.

The application discloses an antiviral peptide prediction method and system based on federated learning and a neural network, utilizes multiple independent data sets to respectively perform training on a prediction model, constructs a double-channel deep neural network through the model, extracts features of different dimensions from original variable-length sequence data, analyzes peptide segment sequences from two aspects of sequence data and evolution levels, and finally transmits model gradients and parameters obtained to an intermediate server through encryption technology; the server aggregates the transmitted model parameters to form an intermediate model without contacting the data itself; the server transmits parameters of the intermediate model to participants, completes updating of participant models, and the above steps are circularly performed until model training is completed. The combination of federated learning and the neural network can accurately and quickly predict the antiviral activity of peptide segments under the premise of guaranteeing data security.

Systems, methods, and other embodiments associated with a centralized product catalog with standardization from independent data sources are described. In one embodiment, a computing system is configured to access and download product information for a plurality of products that is maintained in a non-standard format. Metadata is identified and extracted. Feature vectors may be generated from the metadata and used to predict and assign class codes to products. The product attributes are extracted from each product, which include a non-standard product description. The non-standard product description is converted into a standardized text description. Standardized records are generated for each product record and a centralized database is generated with the standardized records.

The invention discloses a dangerous goods vehicle complex environment monitoring method and system based on satellite positioning, and relates to the technical field of vehicle monitoring, and the method comprises the steps: obtaining the historical monitoring data of a dangerous goods vehicle, and carrying out the information extraction to obtain historical satellite positioning data, historical dangerous goods vehicle motion data and historical geographical environment data; constructing a combined positioning module according to the historical satellite positioning data and the historical dangerous goods vehicle motion data; according to the combined positioning module and the historical geographical environment data, constructing a risk assessment early warning model; and obtaining current satellite positioning data, current dangerous goods vehicle motion data and current geographical environment data, and inputting the data into the risk assessment early warning model to obtain a risk assessment early warning result to perform complex environment monitoring on the dangerous goods vehicle. The problems that a traditional monitoring method is insufficient in positioning continuity, single in risk assessment and mutually independent in data information are solved, and multi-dimensional and intelligent risk assessment early warning of the dangerous goods vehicle in a complex environment is achieved.

The invention relates to a quality optimization method for offshore multi-source space-time monitoring data. The method comprises the following steps: acquiring an offshore multi-source space-time monitoring data set; constructing a multi-level evaluation index system; determining the weight of each index in the evaluation index system; obtaining a quality score of each independent data sample; multi-source data collaborative quality evaluation is carried out through a two-stage fusion strategy; and screening the original monitoring data according to the comprehensive quality evaluation, constructing an optimal data set oriented to a specific application scene, inputting the optimal data set into the application model, and outputting an environmental element inversion result. The method has the advantages that progressive quality optimization from samples to platforms and then to application results is achieved in a unified framework, low-quality samples can be effectively removed, and the utilization efficiency of multi-source data and the accuracy of inversion results in offshore ecological environment monitoring can be improved.

The invention discloses a distributed file system oriented to consumer-level equipment and a data security management method of the distributed file system, and belongs to the technical field of computer systems. The system comprises a virtual namespace management module used for maintaining a virtual namespace tree formed by combining a local main namespace tree and a remote snapshot sub-tree; the access control and encryption module is used for executing access control based on the attribute set of the virtual namespace tree nodes; and the data processing pipeline module is used for partitioning the file and realizing pipeline operation of transmission and decryption through independent data flow and control flow channels. According to the invention, the access control and the encryption strategy are tightly coupled based on the unified attribute set, so that file re-encryption is not needed when the authority is changed, and the system flexibility is improved; and the security operation delay is hidden through data partitioning and pipeline processing, so that the file access efficiency is improved, and the problem of balance among security, flexibility and high efficiency of the distributed file system of the consumption equipment is effectively solved.

The invention discloses a data customization production delivery method and system based on a block chain and cloud, and relates to the technical field of data processing and information security, and the method comprises the steps that a data customization party and a production party register and bind a public chain address in the system; deploying an intelligent contract with an automatic fund transfer function on a public chain based on the transaction demand; the customization party prepares a production environment, including creating a production cloud container and a cloud database, and generating an asymmetric key pair; the producer completes data production in the isolated cloud container, encrypts the whole-process data by using the public key of the customizing party and stores the encrypted data into the private block chain; and the system then starts an independent data verification container, decrypts the data by using a built-in private key, carries out integrity comparison and quality verification, and automatically triggers an intelligent contract to carry out settlement according to a verification result. The problems that the data is easy to reuse, the ownership is unclear and the settlement efficiency is low in data customization production are effectively solved, and safe, credible and efficient data production and delivery are realized.

The invention discloses a unified label index library construction method based on a multi-dimensional application field, and relates to the technical field of financial data management. The method comprises the following steps: extracting a general rule, establishing a reusable label index template, and intensively constructing three types of basic libraries; integrating real-time data of the message center of the head office and local data sources of the branches, and synchronizing customer transaction data by everyday increment ETL; a unified real-time query interface is provided, the real-time query interface is packaged through an Flink + ES engine, and all target data are returned through a single request; setting an independent data item space, a control data authority, sensitive information desensitization processing and a parameterized flexible configuration interface for each branch; and establishing a causal chain between metadata and a future trend thermodynamic diagram based on the business application timeliness iteration library content. According to the unified label index library construction method based on the multi-dimensional application field, the problems of fragmentation, low timeliness and high security risk of branch data assets are solved, and'sharing reuse and business technology fusion 'of data are realized.

The invention discloses an unauthorized business asset discovery method and system based on a multi-source log. The method comprises the following steps: determining a first network entity based on heterogeneous security logs of different data sources; when it is determined that the first network entity does not exist in the configuration management database CMDB, determining that the first network entity is a suspicious network entity; and when it is determined that the suspicious network entity appears in at least two independent data sources, the activity in a preset time window meets a preset persistence condition, and a service port exists, determining that the suspicious network entity is a persistent unauthorized service asset. Therefore, through multiple independent data source conditions, activity persistence conditions and service port conditions, suspicious target entities related to transient temporary connection are excluded, and persistent unauthorized business assets are accurately identified.

The invention relates to the field of geological disaster recognition, in particular to a road slope landslide debris flow disaster recognition method, device and equipment and a medium. According to the invention, through the high-spatial-resolution optical remote sensing image of the highway slope in the alpine and valley region and DEM data in the same region, deep fusion of spectral features of the optical image and DEM elevation features is realized through the cross-modal cycle fusion module, and texture damage and earth surface deformation information of landslide debris flow can be captured at the same time. Meanwhile, when the optical image is seriously covered by cloud snow, the DEM data branch can be used as an independent data source for identification, and continuity and stability of identification work are guaranteed. According to the method, two kinds of remote sensing data with different modes and information complementation are organically combined, the accuracy of landslide debris flow geological disaster recognition is improved, the requirement for rapid general survey of disaster points along highways and major engineering areas in alpine and valley areas can be met, the manual workload can be reduced, and the working efficiency is improved.

The application discloses a kind of neural patient rehabilitation training methods based on task training and brain-computer interaction, belong to rehabilitation training technical field, specifically include: based on the motion decoding model corresponding to the training task machine;Real-time acquisition data signal in current rehabilitation training of patient and carry out segmented processing, obtain several independent data blocks, carry out frequency domain conversion to independent data block, extract time-frequency feature and calculate the KL divergence between adjacent data blocks;If the KL divergence of any adjacent independent data block exists is greater than or equal to preset threshold, then obtain historical data signal and modify the motion decoding model, according to the motion decoding model after modification to the patient motion decoding, according to this to patient carries out rehabilitation training, the present application is selected corresponding model according to current rehabilitation training task, dynamic adjustment is combined with real-time signal characteristics, ensure that decoding result and training target are highly matched, improve the rehabilitation training effect of patient.

The invention provides a medical multi-center data-oriented federated learning and differential privacy training method and system and a storage medium, which are suitable for carrying out joint modeling on highly sensitive medical data, and can be used for carrying out joint modeling on the highly sensitive medical data on the premise of ensuring that the original data are not out of the local. Tasks of disease prediction, typing, risk assessment and the like of NMR (nuclear magnetic resonance) spectrum data and multi-omics-clinical data fusion are realized, and the comprehensive requirements of medical data privacy protection, model auditing and cross-mechanism generalization ability are met. The model discrimination performance close to centralized training can be realized on the premise of not converging original data; differential privacy protection with strict mathematical guarantee is provided, and compliance requirements that epsilon is smaller than or equal to 2.0, delta is equal to 1 * 10 <-5 > and the like are met; the calibration and generalization ability of the model on external independent data are remarkably improved; the communication burden is reduced, and the fault-tolerant capability for the offline of the client is enhanced; a whole-process traceable and auditing training system is constructed, and third-party supervision verification is supported.

A methods, systems, and devices for secure linking and matching of data elements across independent data systems of a first and a second enterprise are disclosed. The system includes a first private data system having a first private database that may include all attributes known to the first enterprise A reference data system having a reference database and is also part of the first enterprise. The reference database may contain a plurality of identity data records that only contain identity attributes that are used for resolving and verifying identities of unknown data records that are external to the reference data system. A first set of query attributes may be used determine whether a subject represented by the first set of query attributes associates uniquely with a subject of any private data record stored in the first private database. A similar comparison may be made in the reference database.

The invention discloses a construction method and application of a safety evaluation risk prediction model after child medication marketing, and the method comprises the steps: constructing a three-level index system, and determining the weight of each level of index through an analytic hierarchy process; based on the three-level index system, determining a risk feature set, constructing a three-level model architecture, and performing model training by adopting a machine learning algorithm; collecting multi-source real world data, and establishing a standardized data pool; the performance of the model is improved through hyper-parameter optimization and integrated learning, the performance of the model is verified and evaluated by adopting internal cross validation and an external independent data set, and the transparency of the model is enhanced through an interpretability technology; and establishing a dynamic updating mechanism, deploying the model as a cloud API service, and realizing risk prediction and early warning functions. According to the invention, multi-source real world data can be effectively integrated, the children medication safety risk can be accurately identified, the method is used for safety evaluation after children medication is listed, and the method is of great significance to supplement clinical test insufficiency before children medication is listed, reduce medication risk and guarantee children medication safety.

A user management method for a multi-terminal cloud mobile phone system comprises the following steps: verifying the identity of a user when the user requests to log in a cloud mobile phone through a terminal device, and establishing a connection between the cloud mobile phone and the terminal device after the verification is passed; when a plurality of terminal devices perform same-screen interaction through the cloud mobile phone, the cloud mobile phone allocates data authority to the terminal devices based on user identities; and when the terminal device is disconnected from the cloud mobile phone, the cloud mobile phone stores the real-time data of the terminal device, and calls the stored real-time data after the terminal device is connected with the cloud mobile phone again. The invention provides a user management method and system for a multi-terminal cloud mobile phone system, computer equipment and a storage medium, which can allocate an independent data authority to each terminal equipment when a plurality of terminal equipment interacts on the same screen, can improve the overall data security, and avoids the situation of privacy data leakage.

The invention relates to a driver test vehicle AEB + ACE dual-system fusion docking test evaluation system and method, and the system comprises a main controller, a millimeter wave radar, a vehicle-mounted automatic diagnosis system, an automatic emergency braking execution unit, an accelerator mistaken stepping prevention execution unit, and a driving test evaluation terminal. The main controller comprises a microprocessor module, a CAN bus module and a gigabit network card module. The CAN bus module builds three independent data transmission channels by adopting a design of combining physical partition with vehicle-mounted equipment communication protocol isolation, and is bidirectionally connected with the microprocessor module, the main controller receives millimeter wave radar detection data and vehicle running state data and then carries out fusion analysis, the risk level of a front obstacle is accurately judged, and the safety of the vehicle is ensured. When the driver exam car runs, the ACPE pedal mistaken stepping suppression or AEB automatic emergency braking action is triggered, safety action information is synchronized to the exam equipment through the gigabit network card module, the exam judgment result is not interfered, accurate output of a safety defense instruction is achieved, and the running safety and stability of the driver exam car are effectively improved.

The invention discloses an urban traffic intelligent cooperative monitoring method and system based on mechatronics, and belongs to the technical field of urban traffic. The invention aims to solve the problems of poor multi-source data collaboration, low prediction precision and insufficient real-time performance in the existing urban traffic congestion monitoring. According to the method, a traffic jam flow threshold value is set, historical data is collected to construct a basic data set, a traffic characteristic prediction model is established based on linear fitting, an independent data cluster is established by using a multi-source collection terminal arranged in a mechanical-electrical integration mode, and the time length difference degree is calculated. And finally, screening an optimal prediction sequence through an effective feedback rate and outputting a mean value result. The matching system comprises a data acquisition module, a prediction model construction module, a data cluster processing module and an effective feedback and result output module, and the modules cooperatively work according to data circulation. According to the invention, accurate and real-time prediction of urban key road section congestion can be realized, congestion early warning is provided for traffic management departments, traffic dispersion in morning and evening peak hours is assisted, and road section congestion duration is reduced.

The invention discloses an insurance information automatic filling and verification method and system, a product and a medium, and relates to the field of artificial intelligence. The method comprises the following steps: responding to a batch insurance instruction, receiving and managing identity information of an insured person, and constructing an independent data object to form an insured person information set; traversing the set, analyzing the birth date and calculating the age; taking the age as a matching factor, matching the age with an insurance product rule base, and determining an applicable product plan range and an insurance premium calculation factor; generating and binding a differentiation rule set for each data object; generating an aggregation type interactive interface based on the context and the differentiation rule set; receiving the input information of the exclusive problem area, and verifying according to the differentiation rule set; and summarizing all the input information passing the verification, and generating an aggregation type insurance order. By implementing the technical scheme provided by the invention, the automatic processing efficiency in a batch insurance buying scene is improved.

The invention discloses a mail system based on a time sequence database and object storage and a data processing method.The system is constructed based on a three-layer network architecture including a metadata storage layer, a content storage layer and a service interaction layer, and correspondingly comprises a metadata storage module which is used for creating an independent data table for each user, storing associated fields in combination with a hot partition and a cold partition, and storing the associated fields in the data table; meanwhile, a secondary index is established for index optimization, and multi-condition combined query is supported; the content storage module is used for creating a text storage sub-module and an attachment storage sub-module in the object storage, respectively storing a mail text and an attachment, and configuring an attachment hash duplicate removal mechanism and a redundancy storage strategy; and the service interaction module is configured with a plurality of functional modules to guarantee data transmission security and stable operation of the system. According to the scheme, real linear expansion, cost reduction and efficiency improvement are realized in a high-concurrency and high-load mailbox scene, and the reliability and the user experience are remarkably improved.

The application provides an independent data isolation system based on hardware-level physical isolation, comprising: a special security processing unit which is a multi-core heterogeneous system-level chip, and integrated with the following through a hardware security bus: a national encryption algorithm acceleration core for realizing encryption and decryption and operation processing of national encryption algorithms; a general control core for executing task scheduling, inter-core communication and external interface protocol processing; a security storage core for generating a unique physical key and supporting quantum key injection; an isolation control core for controlling physical isolation of data flow through a hardware mutex and a bus tag controller; and a fuse self-destruction module connected with the special security processing unit and controlled by the isolation control core, for quickly destroying key data and paths. The application has the beneficial effects of optimizing data security isolation, key protection and emergency response, effectively improving the overall security of the system and resisting more complex attacks.

The invention discloses a multi-department data co-processing system suitable for building a company, relates to the technical field of data co-processing, and solves the problems of serious data island phenomenon, difficulty in realizing real-time information synchronization, high data transmission delay and poor consistency caused by independent deployment of a data management system by each department. In order to solve the technical problems that data sharing is limited, decision optimization and resource allocation cannot be effectively supported and the overall efficiency is low due to lack of unified data standards and access control mechanisms in the prior art, centralized management and real-time synchronization of data of various departments are realized through a distributed database architecture of a central data storage unit, data splitting is eliminated, and the data sharing efficiency is improved. Therefore, the problem of data islands can be solved, high consistency of cross-department data is ensured by utilizing low-delay communication and high-concurrency processing mechanisms of the real-time data transmission unit and combining data compression, encryption and integrity verification, and the defects of data transmission delay and inconsistency are overcome.

The application discloses a kind of automated filling and checking method, system, product and medium of insurance information, and it is related to artificial intelligence field.The method comprises the following steps: in response to batch insurance instruction, the insured identity information is received and managed, and the independent data object is constructed to form the insured information set;Age is calculated by traversing the set and analyzing birth date;Age is used as a matching factor, matched with an insurance product rule base, to determine the applicable product plan range and premium calculation factor;Generate and bind a differentiated rule set for each data object;Based on context and differentiated rule set, generate an aggregated interactive interface;Receive input information in the exclusive problem area, and check according to the differentiated rule set;All input information that passes the check is summarized to generate an aggregated insurance order.Implementation of the technical solution provided in the application improves the efficiency of automated processing in the batch insurance scenario.

The invention discloses an industrial park power utilization monitoring system and method, and relates to the technical field of power utilization monitoring. According to the invention, the power utilization monitoring data of the enterprises are independently collected through the deployed monitoring equipment group, and reliable data support is provided for accurately mastering the power utilization characteristics of the enterprises; during peak clipping processing, adjustable enterprises are screened based on plan adjustability and power utilization adjustment cost, power utilization loads of the enterprises are transferred to idle periods, and power utilization loads of non-production areas of the enterprises and power utilization loads of public areas of the park are sequentially interrupted for still existing adjustment areas; the pertinence of a peak clipping scheme is guaranteed through independent data acquisition, interference on core production activities of enterprises is avoided to the greatest extent through gradient adjustment logic of load transfer-non-production area power failure-public area power failure, power supply overload of the park is avoided, the safety of power supply equipment is protected, and the power supply efficiency is improved. The production continuity and the operation benefit of an enterprise are considered, and the dual goals of park power utilization energy-saving optimization and enterprise production guarantee are achieved.

The invention discloses a method for screening disease diagnosis markers based on multi-omics data analysis, and particularly relates to the technical field of graph neural network and network disturbance stability evaluation. Firstly, a co-expression network is constructed, a disturbance influence index PII and a module stability score MSS are introduced, an improved graph neural network model fusing expression features and disturbance information is constructed on this basis, and node embedding expression learning is achieved. The method comprises the following steps of: identifying a functional module through clustering or graph pooling operation, determining a candidate diagnostic marker based on a centrality score and a combined screening mechanism of PII and MSS, and finally verifying the diagnostic performance in an independent data set, so that the stability and clinical applicability of a screening result are improved.

The application relates to a device point position interaction method and system based on a plan view, wherein the device point position interaction method based on the plan view comprises the following steps: acquiring an initial image with indoor device point position information, converting the initial image into a target plan view with initial pixel coordinates and displaying the target plan view in a display interface of a terminal device; monitoring a target operation on the target plan view, converting the initial pixel coordinates into current pixel coordinates according to a current zooming multiple; performing aggregated analysis on indoor devices according to the current pixel coordinates and a dynamically adjusted overlap threshold, generating device aggregated point data and independent data; and rendering the target plan view based on the aggregated point data and the independent data. Through the application, technical problems such as inaccurate device positioning, poor visual effect and weak interaction in an indoor scene are solved, and high-precision, low-cost and strong-interactive visual monitoring and management of devices in a complex indoor environment are realized.

The invention discloses a monitoring method and device for quality data of a self-correlation mixing process, and relates to the technical field of disease monitoring. The method comprises the following steps: obtaining a group of multivariate self-correlation data in a historical controlled state and a group of multivariate self-correlation data in a historical out-of-control state; the method comprises the following steps: respectively calculating a sample mean value and a covariance matrix, carrying out decorrelation on samples through a Cholesky decomposition method, constructing an MEWMA sequence by utilizing standard independent data after decorrelation, training a classifier model of a random forest, designing a control chart method based on the random forest, and monitoring a linear autocorrelation process; on the basis of data decorrelation, according to the random forest theory, a novel control chart is generated, the modeling and monitoring problems of quality data in the self-correlation mixing process are effectively solved, the trained random forest model automatically learns and captures complex and nonlinear modes of data in controlled and out-of-control states, and the quality of the self-correlation mixing process is improved. And effective modeling and monitoring can be carried out no matter the data is in skewed, multi-peak or completely unknown distribution.

The application provides a multi-dimensional health data processing system and method for internal medicine patients, which performs dimensional filtering on multi-dimensional health data flow through priority scores of each independent data dimension to obtain first intermediate data; performs correlation degree learning on orthogonal coupling relationships between each independent data dimension based on the first intermediate data to obtain a time-varying nonlinear strength coupling matrix, and then performs sparse reconstruction on the correlation topological structure between each independent data dimension according to the strength coupling matrix to obtain second intermediate data; and finally, based on the second intermediate data, a derived data view of the current application scenario is obtained in response to an external query condition of the current application scenario, and the common feature mode of the internal medicine patient group is visually presented using the derived data view. Based on the above scheme, dimensional correlation reconstruction of multi-dimensional health data can be realized, thereby improving the scene adaptability of internal medicine health data processing.

The present application belongs to the technical field of privacy calculation, cryptography and data security, and particularly relates to a privacy protection aggregation query method for multi-party data cooperation, comprising the following steps: electing two collaborative role servers in multiple participating servers; a client respectively performs an oblivious pseudo-random function with each server in a first round to obtain a pseudo-random representation of a query identifier; each server performs secret sharing splitting on an associated value corresponding to a local record, and sends the value to the two collaborative role servers respectively; the client further performs an interaction of subsequent oblivious pseudo-random functions with the two collaborative role servers respectively, and cooperates to shuffle a share set; the two collaborative role servers respectively complete matching and share aggregation, and return aggregated shares to the client; and the client reconstructs to obtain a final aggregation query result. The present application can adapt to the data cooperation demand between multiple independent data sources, and overcome the dependence of the existing scheme on full staff hits.