399 results about "Full data" patented technology

The invention discloses a BIM (Building Information Modeling)-based hoisting construction safety monitoring and early warning system. The system comprises a terminal sensing layer which is used for collecting environmental parameters and personnel behavior data in a closed space in real time; the edge computing layer is used for carrying out cleaning, compression and encrypted transmission on original data by utilizing an explosion-proof edge computing gateway; the cloud collaboration layer is used for storing full data based on a BIM digital twinborn platform, constructing a'danger mode-construction feature-disposal measure 'three-dimensional meta-knowledge graph by adopting an MAML + + algorithm, meanwhile, coupling a physical mechanism data enhancement engine with a multi-physics field coupling model and a physical constraint generative adversarial network, generating virtual data conforming to mass conservation and energy conservation, and sending the virtual data to the cloud collaboration layer; performing mixed training with real data; according to the intelligent decision-making layer, a space-time adaptive threshold evolutionary algorithm encodes a space-time context through a graph attention network and Transform, an alarm threshold is dynamically optimized through deep reinforcement learning, meanwhile, a digital twin deduction engine calculates a shortest safety path in real time, and rescue resource allocation is optimized.

The invention relates to the technical field of electrical equipment analysis, and particularly discloses an electrical equipment operation state monitoring system and method, and the method comprises the steps: the system is deployed at an electrical equipment site, integrates a low-power-consumption processor and an FPGA acceleration module, and is used for collecting multi-source data in real time, and executing lightweight model calculation and local early warning; and storing full data and operating a global analysis model, and communicating with the edge computing node to realize data collaborative analysis. According to the electrical equipment operation state monitoring system and method provided by the embodiment of the invention, through edge computing lightweight deployment, data preprocessing and local early warning are completed on the equipment site, and high efficiency of monitoring response and low-power-consumption hardware adaptation are realized; by means of an electromagnetic interference dynamic evaluation module, an acquisition strategy is intelligently adjusted, noise is suppressed, the reliability of multi-source data is guaranteed, the efficiency, precision and intelligent capability of electrical equipment state monitoring are remarkably improved, and technical support is provided for reliable operation of power system equipment.

The invention discloses a GPU resource scheduling method and system based on dynamic weight calculation, and relates to the technical field of algorithm services, and the method comprises the steps: carrying out the pre-operation of an algorithm according to the initial weight of the algorithm and the algorithm flow of analysis task design; obtaining algorithm computing power resource data, and pre-evaluating algorithm computing power resources; according to the algorithm computing power resource data, the pre-evaluated algorithm computing power resource and algorithm task analysis, setting a priority sequence and a dynamic weight for executing an algorithm process, computing the algorithm computing power resource and generating a dynamic scheduling scheme; verifying and adjusting the computing power resource of the algorithm; and loading a full data operation algorithm, executing an algorithm process, and monitoring an execution process of the algorithm process. According to the method, under the condition that servers and GPUs are limited, more users can execute analysis tasks as much as possible, the situation of task failure caused by queuing waiting or resource competition does not occur, and efficient and stable operation of an algorithm platform is ensured.

The invention discloses a reinforcement learning model training method and device based on dynamic data selection and electronic equipment, and the method comprises the steps: carrying out the screening from an original mathematical problem data pool according to preset data quality, diversity and difficulty evaluation indexes, and obtaining a candidate training set; calculating a data validity score of each training sample in the candidate training set; difficulty filtering is carried out on the candidate training set, and an adaptive training course is constructed; further filtering the candidate training set according to the solution reasoning process and the reward score of the candidate training set; dynamically selecting a data subset for the current training period by setting a temperature parameter attenuated along with the training process and a probability threshold based on the further filtered candidate training set and the data validity score; the selected data subsets are put into a reinforcement learning model for training, and the reinforcement learning model is trained in an optimized mode according to the correctness reward and the format reward. The model training period can be remarkably shortened, and the performance equivalent to or even better than that of full data training is achieved.

The invention relates to the technical field of chip design, and provides a critical path screening method based on a chip time sequence report, which comprises the following steps: collecting chip time sequence report data, screening out first N critical paths from the data based on time sequence index data, and establishing a path ID-delay type-scene three-dimensional data model; and calculating the delay ratio of the unit delay to the interconnection line delay in each critical path. Performing single-type delay, type relevance and RC condition influence comparison on different scenes to obtain delay characteristic data. The invention discloses a system for executing the method, which can avoid full data redundancy and improve the analysis efficiency by screening the first N critical paths. And the delay change rule in different scenes is quantified. The delay characteristic data reflects the actual influence of the key path delay on the time sequence, so that a designer can quickly position the delay bottleneck of the key path, and a basis and a reference are provided for the targeted optimization of the total delay of the chip in the later period.

The present disclosure relates to the field of data processing, and in particular to a question-and-answer (QA) data generation method and apparatus, and a computer device and a storage medium. The method comprises: acquiring a plurality of segmented document blocks, wherein each segmented document block includes a first preset number of pieces of text data; on the basis of the text data in the segmented document blocks and a target questioning model, obtaining target question data corresponding to the segmented document blocks; acquiring full data, wherein the full data is content information which is associated with the target question data and is included in a complete document composed of the plurality of segmented document blocks; on the basis of the segmented document blocks, the target question data, the full data and a target answering model, obtaining target answer data corresponding to the target question data; and generating QA data on the basis of the target question data and the target answer data. The present disclosure solves the problems in the related art of low efficiency and poor data quality in QA data mining for a document.

The invention discloses an ETL full-increment dynamic mapping data integration system and method, and the system comprises a data source configuration module which is used for configuring data source connection information and verifying the connectivity of a source database and a target database; the log collection module is used for deploying a log collection tool to monitor file change of the source database and capture a data change event of the source database; the full data updating module is used for triggering full data updating operation, acquiring all logs stored by the log collection tool from the message queue middleware, and updating existing data in the source database to the target database; and the incremental data updating module is used for receiving a new data event from the message queue middleware after the existing data in the source database is updated, comparing change log position information recorded locally, obtaining updated operation information from the new data, obtaining an updating statement by using a dynamic mapping updating engine and executing the updating statement. According to the invention, the real-time performance and the accuracy during data updating are guaranteed.

The invention discloses a multi-dimensional credit asset traceability system and method. The system comprises a data acquisition and encryption module (10) which is used for performing encryption and digital signature on credit asset data based on a trusted execution environment (TEE) and a hardware security module (HSM) at a data generation end; the parallel Hash processing module (20) is used for performing parallel Hash operation on the encrypted data and generating leaf node Hash values based on GPU acceleration and a multi-thread technology; the nested hash and zero-knowledge proof module (30) constructs leaf node hash values into a compression type Merkle tree and generates a zero-knowledge proof (ZKP), so that a third party can verify the authenticity and consistency of data without accessing full data; the on-chain evidence storage module (40) is used for recording root Hash (RootHash) and zero knowledge proof of the compression type Merkle tree to the block chain; the dynamic authorization and secure multi-party computing module (50) performs access clipping on the credit asset data based on the access intention and realizes verification of the minimum data dimension through secure multi-party computing (SMPC). Through combination of technical means such as trusted hardware acquisition, parallel hash calculation and privacy protection verification, rapid verification is realized on the premise of not exposing original data, and verification performance, data security and privacy protection capability are remarkably improved.

The present application provides a graphical monitoring dynamic refresh method and device, which are applied to the field of information technology. By setting an interceptor in the persistence layer service, the change event of the database can be captured in real time. The change event is then pushed to the global Actor using the transaction message middleware. The global Actor is responsible for maintaining the full data view, and the local Actors filter out customized data on demand to meet the personalized monitoring needs of different users. Finally, the socket service is used to accurately push incremental data and only transmit the data that the user is concerned about, thereby significantly reducing unnecessary data transmission, greatly reducing the pressure on the database and network, and realizing millisecond-level customized dynamic refresh of monitoring data, which significantly improves the real-time performance and accuracy of the monitoring system and meets the strict requirements of terminal production operations for real-time monitoring.

The embodiment of the invention provides a model training method and a text processing method, and the model training method comprises the steps: obtaining at least two reference models, and obtaining at least two target data processing networks based on each reference model, the target data processing network comprises at least one full data processing network and at least one compressed data processing network; a data processing model is generated according to the target data processing networks, the data processing model comprises at least one data coding layer, and the data coding layer comprises at least one full data processing network and at least one compressed data processing network; and obtaining target sample data, and training the data processing model based on the target sample data until the data processing model reaches a model training stop condition. The target data processing network is constructed through the total data processing network and the compressed data processing network, so that the resource consumption of large model training can be effectively reduced, and the data processing efficiency is improved on the premise of ensuring the data processing accuracy.

The invention discloses an Internet of Things full data persistent storage system and method based on multi-stage time granularity. The system comprises a data acquisition module, a multi-stage storage management module, a dynamic index module, a compression engine module and a query engine module. The data acquisition module is used for receiving equipment data in real time, the multi-level storage management module is used for dynamically dividing time windows and executing a cold and hot data migration strategy, the dynamic index module is used for constructing a joint index of time dimensions and equipment IDs, and the compression engine module is used for selecting an optimal compression algorithm according to data features and transmitting the optimal compression algorithm to the storage management module. And the query engine module is used for analyzing a query condition, positioning a data block through an index and optimizing an IO reading sequence. Compared with a traditional time sequence database, the storage cost is reduced through a dynamic time granularity division and hierarchical compression algorithm; and on the basis of query optimization of multi-level indexes, the query efficiency in the complex time range is improved by several times, and the query response time in the complex time range is reduced from the minute level to the sub-second level.

The invention relates to the technical field of databases, in particular to an incremental data writing method and system based on incremental erasure codes. The method comprises the following steps: acquiring a write-in position (offset value) and a data length of incremental data, calculating an incremental erasure code according to incremental change data of each data block starting from the write-in position and having the length of the data length, and updating verification data at a corresponding position of a verification block; according to the method, calculation and updating of the incremental erasure codes in the incremental data writing process are achieved, and therefore in a distributed storage scene, it is not needed to obtain full data of data blocks for erasure code calculation, and occupation of data transmission and calculation resources is reduced.

The invention provides a real-time data updating method and device based on AI prediction and computer equipment. The method comprises the steps that a server side collects a historical behavior data set of a user to train an LSTM time sequence model; the client establishes a long connection with the server, and reports the current operation behavior of the user to the server in real time; the server predicts the next operation behavior of the user based on the LSTM time sequence model; the server side compares a local version number carried by the client side with a server version number by maintaining the version number of the data item, adopts a Diff algorithm to calculate incremental data, and issues full data if the incremental data volume exceeds a full data threshold value; compressing the incremental data by using a compression algorithm, and actively issuing the incremental data to a client through long connection; and the client side merges the incremental data and then executes MD5 verification, and if the verification is inconsistent, the client side requests to update the total data. The method has the beneficial effect that the data updating efficiency and reliability in the long connection environment are improved.

The invention provides a prepaid early warning method for intelligent electric meters, which comprises the following steps of: acquiring data of each intelligent electric meter in real time and transmitting the data to a database, and transmitting the data of the database to a constructed TensorFlow at regular time; if data missing exists, automatically supplementing the historical data and the recent data through TensorFlow to form complete data, and returning the complete data to the database; the total data in the database is subjected to electricity consumption prediction after n days through TensorFlow according to historical data and recent data, and energy consumption early warning and notice are sent to a user after energy consumption balance calculation is combined. According to the technical scheme, faults in short communication time are made up by combining the data complementation and prediction capability of the AI large model, and personalized energy consumption reminding services can be carried out for different user groups.

Disclosed in the present invention is a defect detection and avoidance method based on gypsum board production full data collection, comprising the following steps: performing timing analysis on adjustment amounts of gypsum board production device adjustment items to extract companion relationships between the gypsum board production device adjustment items, and abstracting the companion relationships into a companion network among the gypsum board production device adjustment items; and on the basis of the number of real-time gypsum board production device adjustment items, determining real-time settings of production conditions of the gypsum board production device. According to the present invention, learning and training is performed on characterization data of gypsum board defect items and adjustment amounts of gypsum board production device adjustment items to obtain a defect adjustment calculation model, timing analysis is performed on the adjustment amounts of the gypsum board production device adjustment items to extract companion relationships between the gypsum board production device adjustment items, and the companion relationships are abstracted into a companion network among the gypsum board production device adjustment items, thereby achieving full detection and full avoidance of potential defects of gypsum boards, and improving the proactiveness of defect detection and avoidance.

The invention provides a memory management method and a storage device. The method comprises the following steps: before executing a complete data reading operation in response to a reading instruction of a target page, acquiring slowly-changed metadata and quickly-changed metadata of the target page; obtaining a first voltage adjustment parameter according to the slow change metadata; acquiring a second voltage adjustment parameter according to the quick change metadata; according to the first voltage adjustment parameter and the second voltage adjustment parameter, adjusting the reading voltage of the target page from a preset reading voltage to a target reading voltage; according to the target reading voltage, the complete data reading operation is executed on the target page, and the complete data reading operation is executed under the condition that historical reading failure does not occur for the target page. Therefore, the data reading efficiency of the storage device can be improved.

The invention discloses a rail vehicle intelligent welding all-data collection and analysis system based on structural parameters and a use method, and relates to the technical field of rail transit welding digital production. The system comprises a welding file compiling unit, a welding monitoring execution unit, a welding data processing and analyzing unit, a welding data collecting unit, a welding parameter switching unit and a welding control unit. According to the method, the digital welding process file and the welding parameter package are compiled and issued to the welding machine, the welding monitoring activity is carried out in the mode of binding persons, machines, materials and methods, the welding parameters of the welded welding seam are strictly controlled, the one-time qualification rate of the welding seam is increased to 90%, and the welding quality is improved; welding parameters are monitored in real time, so that a welder carries out welding strictly according to a welding process procedure, and the welding quality problem caused by human factors is avoided.

The invention provides a self-adaptive data consistency maintenance method for an electric power information system based on incremental updating, and relates to the technical field of big data processing, and the method comprises an initialization step, a detection step and a feedback step. In the initialization step, a network including regional power data nodes and a distributed account book is constructed, and full power data is stored; in the detection step, power data changes are confirmed, key attributes are used for marking data, automatic conflict detection is carried out through a distributed consensus algorithm, solutions for coping with conflicts are dynamically selected, and then an incremental updating mechanism is adopted for synchronously changing the data; in the feedback step, a monitoring system with a closed-loop feedback mechanism is established, the data consistency state is automatically tracked when the electric power data is changed, and abnormity is recognized. Compared with a traditional data synchronization method which needs to synchronize full data, the method provided by the invention only synchronizes the changed data fragments through an incremental updating mechanism, thereby ensuring the consistency of the data, remarkably improving the data processing efficiency and saving the network bandwidth.

The invention discloses a full-data backup method for a fast and slow operation period of an industrial controller. The full-data backup method comprises the following steps: detecting whether a main power supply is powered down or a backup command is received by an FPGA (Field Programmable Gate Array); if yes, the FPGA writes the latest fast-cycle integrity data into the power-down nonvolatile storage chip by comparing the integrity marks and the timestamps of the fast-cycle data of the data A area and the data B area of the memory chip; then, the FPGA writes the completeness data of the latest slow period into the power-down nonvolatile storage chip by comparing the completeness marks and the timestamps of the data of the slow period of the data A area and the data B area of the memory chip; according to the data backup method, data does not need to be selected, one cycle of complete data can be recovered under the condition of power failure or no power failure, common memory chips and power failure non-volatile storage chips are used, and cost and implementation complexity are effectively reduced.

The invention relates to a medical image retrieval method and system based on a DICOM protocol and a storage medium, by setting a unified proxy forwarding service and combining with an image prefetching and caching mechanism linked with a clinical business event, the configuration difference of a back-end heterogeneous PACS system is shielded, single access point connection of a client is realized, and the user experience is improved. The complexity of system deployment and maintenance is reduced; meanwhile, the image data are pre-stored in the cache, so that the retrieval waiting time of the remote image is shortened, and the data access performance and the user experience are improved; and centralized authority authentication and log auditing are realized through the agent layer, so that the controllability and the security of a cross-mechanism data sharing process are guaranteed under the condition of avoiding high cost of centralized storage of full data.

The invention provides an auxiliary switching method and device for a new system and an old system, electronic equipment and a storage medium, and is applied to the field of finance or medical treatment. The method provided by the invention comprises the following steps: after a new system and an old system are switched, respectively and correspondingly comparing total data of a new database and an old database, output parameters of interfaces of the new system and the old system and configurations of the new system and the old system, and displaying corresponding difference data; the method comprises the following steps: when a new system is subjected to pilot run, injecting a preset proportion of flow into the new system, monitoring the new system, and if the new system is monitored to be abnormal, switching the flow back to an old system; and obtaining the interface calling time of the new system during pilot run to obtain a difference value of the corresponding interface calling time of the new and old systems, and if the difference value exceeds a preset threshold value, performing early warning. According to the auxiliary switching method for the new and old systems, switching of the new and old systems can be assisted, the system switching time cost is reduced, and the accuracy and integrity of system switching can be guaranteed.

The present disclosure provides a method, device, equipment, medium and program product for processing diversified data. The processing method is applied to the B-side system, and the processing method includes: obtaining diversified data and a data template corresponding to enterprise needs; obtaining fields in the diversified data and fields in the data template; in response to obtaining a mapping relationship between the B-side system fields and the enterprise's full data fields, matching the fields in the diversified data with the fields in the data template based on the mapping relationship. The present disclosure automatically matches the fields in the diversified data with the fields in the data template through the mapping relationship between the B-side system fields and the enterprise's full data fields in combination with the data template corresponding to the enterprise's needs, which facilitates and simplifies user operations while fully meeting the differentiated, customized and personalized needs of different scenarios, reducing the workload of manual pre-processing of data and improving data processing efficiency.

The invention provides a method, a device and equipment for processing maritime target playback data, which are applied to a client, and the method comprises the following steps: sending a playback data push request to a server, the push request comprising a sea chart geographical range of the playback data, a query duration density and a request cache duration of the playback data; receiving a plurality of target playback data segments pushed by the server according to the push request, the plurality of target playback data segments being a plurality of target playback data segments queried by the server querying playback data in a database according to the query duration density; the sea target position of the target playback data segment is within the geographical range of the sea chart, and the query duration density is smaller than the cache request duration of the playback data; and caching the data of the plurality of target playback data segments into a full data cache according to a time sequence according to the identifier of the marine target. According to the scheme of the invention, the chart ship trajectory can be played back more quickly, and the continuity of target movement is ensured.

The invention relates to a network rule confirmation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring full data from a Neutron database; extracting a service table and a data table from the total data; determining a target node from the service table, and establishing a mapping relationship based on the target node and the configuration information in the data table; extracting a configuration information set corresponding to a target node from the mapping relation, and sending the configuration information set to the target node to indicate the target node to collect an actual rule set on the target node; generating an expected rule set based on the configuration information set; and comparing the actual rule set with the expected rule set to obtain at least one of a hit rule, a residual rule or a missing rule. By adopting the method, the network rules can be acquired regularly and compared, so that problems existing in the network can be found efficiently and timely and processed.

The present application provides a CEPH metadata migration and upgrade method and device based on a distributed database, the method comprising: modifying the write mark of the migration bucket to double write, and updating the latest uploaded / deleted / modified metadata to SHRAD and the distributed database at the same time; migrating the existing metadata of SHRAD to the distributed database; performing a full comparison between SHRAD and the distributed database, and then performing two incremental comparisons, migrating the HEADER data of the migration bucket on SHARD to the distributed database, switching the read and write state of the migration bucket to a single-read single-write database, and turning on the mark-delete mark of the migration bucket; performing a full data comparison between SHRAD and the distributed database again, and deleting the data marked as mark-delete in the migration bucket from the distributed database; cleaning up the metadata of SHARD; the method migrates the metadata stored on SHARD in the original bucket to the distributed database without interrupting the user's business, realizing the function of unlimited number of object storage in a single bucket of the user, and improving the quality of user use.

The invention provides an automatic process management method and system applied to foreign trade business intellectualization, and relates to the technical field of foreign trade business management.The method comprises the steps that firstly, initial order data, preset document template data, logistics resource configuration data and order change data of foreign trade business are obtained and integrated to form a foreign trade business total data set; then, carrying out change influence definition processing on the order change data, and determining influenced foreign trade business links and associated data; performing linkage adaptation processing on the associated data based on a defining result, and adjusting preset document template data and logistics resource configuration data; according to a linkage adaptation processing result and a foreign trade business process knowledge base, generating operation execution instructions of all links; finally, the instructions are distributed to the corresponding execution main bodies according to the execution sequence, operation execution results are received and integrated to form feedback information, automatic and intelligent management of the foreign trade business process is achieved, and the management efficiency and accuracy are improved.

The invention provides a block chain-based data security processing platform, which relates to the technical field of computer software and information, and comprises a block chain architecture system, a data security processing system and a data sharing system, the block chain architecture system comprises a data provider node, a data user node, a supervision node and a consensus node; according to the invention, multiple system modules are covered, a data security processing system combines AES and RSA encryption, integrity is verified by means of a Hash algorithm, access control is set by means of a smart contract, a data sharing system sets a sharing rule according to the smart contract, data is traced by means of a block chain, and sharing is promoted by means of an incentive mechanism; various block chain types and adaptive consensus mechanisms are provided, a system management and maintenance module checks nodes every day, full data backup is performed to three places every week, a system is audited every month, node faults are processed within 24 hours, safety problems are repaired in seven workdays, and stable, safe and compliant operation of a platform is guaranteed.

The invention provides a garbage collection method and device, a storage control chip and a storage medium. The method comprises the steps that the Flash starts garbage collection, the Flash is configured with an initial target block and an initial free block, the target block is used for collecting data, and the free block is used for serving as a next target block when the target block collects full data; a to-be-recycled block queue is confirmed, the to-be-recycled block queue comprises a plurality of source blocks, and the to-be-recycled block queue meets a preset recycling condition; the target blocks and the to-be-recycled block sequence are processed according to a preset recycling mechanism, and the preset recycling mechanism comprises the steps that on the basis of the to-be-recycled block sequence, the effective data page of the current source block is transferred into the current target block so that the recycled source blocks can be converted into free blocks, and all the source blocks are sequentially processed till garbage recycling is completed. By means of the technical scheme, it is ensured that the number of the free blocks is at least two after garbage collection is executed every time, the continuity of a write-in data address is ensured, and the Flash read-write speed is increased.

The invention provides a data processing method and device for a database, a computer program product, equipment and a storage medium, related embodiments can be applied to various scenes such as database migration and full data backup, and the method comprises the following steps: analyzing binary log information to obtain a to-be-processed transaction; detecting an event distribution state of the to-be-processed transaction to obtain a distribution state detection result; determining a thread matched with the to-be-processed transaction according to the distribution state detection result; executing the to-be-processed transaction through the thread to obtain an execution result of the to-be-processed transaction; and sending the execution result of the to-be-processed transaction to the target database, thereby not only controlling the transaction execution process according to the relevance between the transactions and ensuring the ordered execution between the associated transactions, but also controlling a plurality of threads to perform parallel execution on the transactions and improving the transaction execution efficiency. And the time for waiting for transaction execution is shortened.

The invention provides a real-time data synchronization method and device for a property management system, equipment and a storage medium, and the method comprises the steps: configuring a preset Flink CDC task for each business database, starting a target Flink CDC task corresponding to a source database, obtaining a plurality of source table names from a mapping table, and determining a source data table in the corresponding source database; the corresponding source data table is synchronized to a first ODS layer of the Dores database from the source database in a full-amount mode; and after all the target Flink CDC tasks are polled, monitoring a binlog of the source database in real time based on the name of the source table, and incrementally synchronizing the updated source data table in the source database to the first ODS layer from the source database. Source table names of source data tables needing data synchronization can be recorded in an editable mapping table, and flexible adjustment of synchronous data is achieved; through one Flink CDC task, full data synchronization and incremental updating of the whole business database can be realized, the data synchronization efficiency is improved, and the data management efficiency is improved.