242 results about "Data contention" patented technology

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention relates to the technical field of accounting informatization, and discloses an accounting document cross-platform synchronization system based on a block chain and a conflict processing method, and the system automatically judges a multi-mechanism concurrent operation conflict based on a preset medical authority level and a millisecond timestamp. And when a plurality of branch hospitals modify the same patient voucher at the same time, preferentially adopting a high-authority mechanism to operate, if the authority is at the same level, selecting the operation with the latest timestamp, and if logic conflicts exist, pushing to a general hospital for manual auditing. And an operation log is recorded in real time by adopting Apache Kafka, and a non-tampering historical version is generated through a block chain technology. All modification records are stored in a Merkle tree structure, a complete operation track at any time point can be inquired through a patient ID, the complete operation track comprises modification content, operator identity and block chain transaction hash, a national medical insurance platform is connected in real time, and cost compliance is automatically verified; the system can solve the problems of data conflicts, version chaos and auditing tracing among heterogeneous ERP (Enterprise Resource Planning) systems.

The invention provides a distributed multi-mode data cross-trust-domain data sharing method and system, and relates to the technical field of data sharing, and the method comprises the steps: carrying out the type recognition and format standardization processing of heterogeneous data provided by a plurality of data source nodes; dividing the data source node into different trust domains based on the security level, the authorization range and the protection strategy; constructing a cross-trust-domain data mapping model to establish a data synchronization channel; verifying the access authority through the smart contract and recording an access log; a distributed consensus mechanism based on a Byzantine fault-tolerant algorithm is adopted to synchronize data states, and data conflicts are solved through version control. According to the invention, secure sharing and state consistency maintenance of heterogeneous data are realized, and the reliability and fault tolerance of cross-trust-domain data sharing are improved.

The invention relates to the technical field of information auditing management, in particular to a personnel archive information extraction intelligent auditing method and system based on a large language model, and the method comprises the following steps: generating a timeline sequence and an attribute feature set which are separated through spatial-temporal feature decoupling; performing cross-modal alignment verification on the timeline sequence and the attribute feature set, and outputting a time-space consistency map with conflict marks; generating a risk conduction probability matrix by analyzing abnormal nodes; calculating timeline credibility, attribute credibility and association credibility to form a three-dimensional credibility vector; and automatically executing logic self-consistent repair or generating an audit report comprising a contradictory traceability path. According to the method, the traceability path and the influence weight of the data conflict are provided, a visual basis is provided for manual auditing, and the conflict judgment efficiency in a complex data environment is improved.

The invention belongs to the technical field of distributed databases, and particularly relates to a decentralized copy control method of a distributed database, which comprises the following steps: step 1, copy management: determining the number and storage positions of copies according to data volume and system load requirements, and reasonably distributing data copies to different nodes; 2, replica consistency: selecting a consistency model to balance system performance and data consistency requirements; 3, synchronizing the copies: determining a data synchronization strategy between the copies, and adjusting according to the real-time requirement of the system; 4, conflict detection and resolution: detecting data conflict by using a timestamp, a version number or a logic clock mechanism, and designing a conflict resolution strategy; and 5, replica recovery: monitoring the state of each node, quickly detecting replica faults, and recovering data from other replicas. The stability, reliability and performance of the distributed database system can be effectively improved, and the distributed database system adapts to constantly changing service requirements.

The invention relates to the field of group quality management, in particular to a group quality management system and method based on a dynamic data source. Comprising a data source management module, a quality data acquisition module, a quality data storage and management module, a quality data analysis and statistics module, a quality control and decision support module, a user management and authority control module and a system setting and management module which are connected through data interfaces, the data flow, the use frequency and the updating condition are monitored, and an early warning mechanism for data source connection abnormity, data access timeout and data conflict is arranged. According to the method, data integration and sharing can be improved, the accuracy of data quality is improved, user experience is improved, and safety is enhanced. According to the method, quality abnormity and fluctuation can be quickly and accurately found, targeted control measures are taken in time, quality problems are restrained in the bud state, and defective products and waste products are effectively reduced.

The invention discloses an intelligent networking distributed storage interaction system based on multi-cabin cooperation and a method thereof, and belongs to the technical field of camping intelligent cabin interaction, and the method comprises the following steps: taking each intelligent cabin as an independent node, initializing and accessing an intelligent networking distributed system, and enabling the nodes of the intelligent cabins to establish initial connection with other nodes in the system; each intelligent cabin collects user data, environment data and equipment state data through a built-in sensor and stores the collected data in a local storage unit in a classified mode. According to the method, the data synchronization among the nodes of the intelligent cabin is realized through a distributed database consistency algorithm, the data consistency under the condition of network delay or interruption is ensured, the data conflict and damage are prevented, and the system reliability is improved. Meanwhile, resource allocation is dynamically optimized based on an intelligent scheduling algorithm of machine learning, different load requirements are met, resources are saved, and the system efficiency and the intelligent level are improved.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention belongs to the technical field of airport operation data processing, and discloses an airport flight data fusion processing method and system based on timestamps. According to the method, data streams of different data sources are captured in real time through a message queue, and a database is constructed; the captured database is analyzed, key data fields are extracted, and preprocessing operation is carried out; synchronizing and sorting the analyzed data by using a timestamp; performing data processing in real time according to a preset fusion rule; and storing the fused real-time data into a database, and carrying out data analysis and monitoring on a fusion result to find potential problems. According to the method, the consistency and accuracy of the data in the fusion process are ensured, and data conflicts and errors are avoided. According to the method, configuration of various data sources and fusion rules is supported, and a user can flexibly select the data sources and set the fusion rules according to actual requirements so as to meet data processing requirements in different scenes.

The invention discloses a PLC scheduling algorithm based on a multi-core soft real-time operating system, and relates to the technical field of voltage control, and the algorithm comprises the following four steps: 1, starting a target industrial control system, completing hardware self-inspection, memory allocation and communication module starting, and sending an initialization ready signal after confirming that an assembly is normal; 2, according to PLC task functions and real-time performance, four types of standardized tasks are divided, priority levels are defined, sub-priorities are configured, a dependency relationship is identified, and a task chain is established; 3, counting the number of tasks and task chains, distributing resources by using multiple binding cores, and constructing a global ready queue to adapt to dynamic scheduling; and 4, monitoring an interrupt event through an interrupt manager, realizing high-priority task preemption by semaphores, and allocating tasks to corresponding CPU cores, so that the problems of soft real-time multi-core task preemption and data conflict are solved, the scheduling real-time performance and the data security are improved, the system failure rate is reduced, and the industrial control efficiency and stability are improved.

An example operation may include one or more of receiving, by a resolver node, asset data to be recorded on a blockchain, executing, by the resolver node, a smart contract to apply an asset data validation format to the asset data, rejecting, by the resolver node, non-conforming asset data from being stored on the blockchain based on the execution of the smart contract, and accepting the asset data validated by the smart contract.

The invention relates to the technical field of medical information, and particularly discloses an ERP (Enterprise Resource Planning) integration-based medicine transaction data synchronization system, which comprises a data acquisition module, a mapping analysis module, a data fusion module, a trend analysis module and a center console module. According to the scheme, the multi-dimensional feature space is constructed, the field structures in different service systems are identified and mapped to the uniform target field set to construct the dynamic mapping table, and the adaptability to different service systems is enhanced; a dynamic updating mechanism based on event driving is designed, complex business logic is flexibly handled, the system load is reduced, the system operation efficiency is improved, the compliance of a business process is ensured through cooperation of a rule engine, and the problem of data conflict possibly occurring in the data synchronization process of multiple business departments is solved; and a center console is arranged to monitor efficient circulation of medicine transaction data, so that the stability and safety of data exchange are ensured, a reliable tracing basis is provided for medicine supervision, and subsequent analysis and maintenance are facilitated.

The invention provides a data synchronization method and device based on Spring Batch and a large model. Synchronously initializing a large model micro service and a Spring Batch framework, and establishing a model service hot standby mechanism; obtaining data source features through a metadata scanning engine driven by a large model, and generating an adaptive synchronization strategy; optimizing a query plan based on the synchronization strategy, dynamically controlling data fragmentation and executing streaming preprocessing and semantic mapping conversion; a target table structure is adapted, a three-level conflict detection mechanism is adopted to eliminate data conflicts, and meanwhile transaction processing and batch writing logic are optimized; the data quality is verified through three-level verification, performance parameters are dynamically adjusted and optimized, and fault-tolerant recovery and resource elastic expansion and contraction are achieved; metadata are synchronized, an execution report is generated, knowledge base self-learning is completed based on task full-link indexes, and a data consanguinity map and a reusable processing case library are constructed. According to the scheme, diversified and dynamic enterprise data synchronization requirements can be met.

The embodiment of the invention provides a data conflict automatic solution method and device, electronic equipment and a medium, and the method comprises the steps: automatically recognizing a data conflict through text comparison and semantic analysis; based on a rule base and a reinforcement learning algorithm, dynamically selecting an optimal conflict resolution rule based on the identified data conflict, and executing the conflict resolution rule; selecting an optimal version to be executed according to the conflict resolution rule, carrying out data merging, and storing key information of conflict data; under the condition that the integrity of AI language large model training data is guaranteed, redundant data is removed from the merged conflict data, and incremental updating is carried out.

The invention discloses an informatization asset identification method based on machine learning. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, cross-platform data integration; 3, designing and training a context awareness model; and 4, dynamic asset identification and decision making. By designing the cross-platform data integration module and combining the machine learning model to perform data deduplication and consistency check, standardization and efficient integration of multi-source data are realized. Compared with a traditional manual data arrangement mode, the method has the advantages that structured and unstructured data can be automatically processed, and the accuracy and integrity of the integrated data are ensured, so that the condition of information redundancy or data conflict is avoided.

The invention discloses a service entity intelligent prediction system and method oriented to multi-dimensional decision support, and relates to the technical field of service prediction. The system comprises a service multi-source data screening module, a service multi-source data correction module and a service entity dimension index output module. According to the method, data fusion is carried out on the multi-source data of each dimension index, an adaptive data screening strategy is selected according to the data conflict detection condition in the process, and then data range verification is carried out on each to-be-input data determined by the data screening strategy; and determining whether to perform data deviation determination based on the verification result so as to determine whether to execute data correction measures, finally, after data fusion, inputting the determined to-be-input data into the prediction model, and outputting each dimension index after index data range determination and a determination result prompt. The intelligent prediction precision of the service entity is improved, and the problem that the intelligent prediction precision of the service entity is reduced in the prior art is solved.

The invention provides a trusted data exchange and shared space management system supporting multi-party cooperation. The trusted data exchange and shared space management system supporting multi-party cooperation comprises a data exchange and sharing module used for uploading, exchanging and sharing data by a plurality of users in real time and ensuring data consistency, the data exchange and sharing module realizes data consistency through a conflict detection and solution mechanism under the condition of data conflict, and the data exchange and sharing module is used for realizing data exchange and sharing; and intelligently sorting the updated contents through a priority algorithm. According to the trusted data exchange and sharing space management system supporting multi-party collaboration, through modular design, the efficiency, safety and transparency of multi-party collaboration data exchange and sharing are remarkably improved. The data exchange and sharing module solves the data conflict problem when a plurality of users operate data at the same time through a timestamp mechanism, a priority ranking mechanism and a rollback mechanism, and data consistency and accuracy are effectively guaranteed.

The invention discloses a foreground and background state information intelligent synchronization method in project management. The method comprises the following steps: S1, constructing a three-layer architecture of a foreground system, a state storage center and a background system; s2, initializing a state; s3, realizing state synchronization of the foreground system and the background system through at least one synchronization mode; s4, constructing a version control and conflict resolution mechanism, when the state of each project is updated, the foreground system and the background system carry version numbers, checking the consistency between the client version number of the foreground system and the server version number of the background system when the update is submitted, if the client version number of the foreground system and the server version number of the background system are consistent, executing state updating and progressively increasing the version number, and if the version number of each project is not consistent, executing state updating and progressively increasing the version number; if yes, judging that the data conflict exists, automatically executing a preset conflict solving strategy, and broadcasting a resolution result to a foreground system and a background system. The method has the advantages that the item switching state is not lost, background state change and foreground real-time response are achieved, and conflicts are automatically eliminated without manual intervention.

The invention provides a data acquisition component optimization method based on a distributed storage all-in-one machine, a medium and equipment. A distributed lock and version control combined method is adopted. Specifically, the distributed lock mechanism can ensure that only one node performs write operation on certain data at the same moment, so that data conflict and write competition are avoided. In the version control, a version number is added to each piece of data, so that the system can detect and solve conflicts occurring in the data synchronization process. When one node prepares to write data, whether the version number of the data is consistent with the latest version or not is checked firstly, if not, the data of the latest version needs to be acquired again, and the data is written after being merged and changed. Therefore, the data consistency is ensured, and the stability and reliability of the system are improved. Through the method, the problem of data consistency in the optimization process of the data acquisition component can be effectively solved, so that the overall performance of the distributed storage all-in-one machine is improved.

The present application relates to the technical field of parallel processing, and provides a parallel processing method and system for batch write-off tasks. The method comprises: extracting a plurality of pieces of query data of a preset write-off window, comprising external write-off codes and write-off services; performing file import on the basis of the data, and generating transaction files with unique identifiers; when file name identifiers are verified, parsing the external write-off codes, and generating fund files; generating pending write-off files by means of procurement-settlement mapping authentication; identifying request time identifiers to identify write-off conflicts, and generating conflicting file clusters; updating the configuration of a parallel processing module on the basis of the conflict condition; and utilizing the updated module to process the conflicting file clusters in parallel. The present application solves the technical problems of data conflict, irrational resource allocation and low processing efficiency caused by improper parallel processing in batch write-off tasks, and realizes real-time extraction, verification, parsing and authentication of write-off data, thereby ensuring the data accuracy and consistency.

The invention discloses a data conflict processing method and device, equipment and a medium, and relates to the technical field of object storage. According to the scheme, a conflict detection model used for conflict detection and a conflict processing model used for conflict processing are generated in advance by using historical object storage service operation records; when an object storage service operation record of the data center is collected, conflict detection can be realized through the conflict detection model; when it is confirmed that conflicts exist, the optimal conflict processing strategy can be automatically recommended through the conflict processing model, and therefore the requirement for manual intervention is reduced while the detection accuracy is improved; compared with a traditional rule, the automatic conflict detection and recommendation conflict processing scheme is especially suitable for a complex conflict situation, and the data conflict processing efficiency is improved.

The invention discloses a data access method, device and equipment. According to the method, high-frequency concurrently read / written data is stored in a DPU (Data Processing Unit) memory of a memory node, and other data is stored in a node memory; and if the computing node determines that the first target data to be read / written is stored in the DPU memory, sending a first request to the memory node to request a DPU processor of the memory node to read / write the first target data in the DPU memory, otherwise, sending a second request to request an RDMA network card of the memory node to read / write the first target data in the node memory. The DPU processor reads and writes the data in the DPU memory, so that the reading and writing efficiency can be guaranteed when data conflicts exist; according to the method, the RDMA network card reads and writes the data in the node memory, the read-write efficiency can be guaranteed when no data conflict exists, and therefore the read-write efficiency of the data can be guaranteed no matter whether the data conflict exists or no data conflict exists, the system efficiency is improved, and the timeliness of service processing is guaranteed.

The invention relates to an inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, and discloses an inventory data synchronization and consistency verification system which realizes continuous recording of inventory operation during a network disconnection period through network state detection and local cache, performs batch transmission after network recovery through a synchronous uploading and verification mechanism, and ensures data integrity. According to the inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, data conflicts are automatically solved through a conflict detection and strategy processing module, a difference report is generated through consistency verification, and secondary synchronization is executed, so that safe synchronization and final consistency guarantee of inventory data are achieved, and it is ensured that the service process is continuous and stable. The system is characterized by comprising a network state detection module, a local cache module, a transaction log management module, a synchronous uploading module, a conflict detection module, a conflict resolution strategy module, a consistency verification module and an abnormity alarm and manual intervention interface module.

The invention relates to the technical field of distributed data synchronization, in particular to a three-terminal message synchronization method, which comprises the following steps of: generating FALCON public and private keys for three terminals respectively, negotiating a two-threshold threshold public key, writing a message key hash with a vector clock into a multi-branch Verkle tree, and updating a root abstract; a sending end carries out integrity proof broadcasting through a Bloom bitmap, a compression model, a root abstract and zero knowledge; a receiving end compares the bitmaps to obtain a difference set, and a coding window, a redundancy proportion and power distribution are obtained through reinforcement learning; a sending end uses sparse Fountain coding to send difference set packets in wireless and optical links in a shunting manner; a receiving end carries out linear solution decoding, verification and certification, batch box opening, supplementary signing aggregation and root abstract refreshing; and the completion rate and the link quality are continuously written, and self-adaptive operation is carried out in offline, alternating or low-power scenes. The method has the advantages of quantum security, low bandwidth, low time delay and energy conservation, and transaction omission and data conflict can be avoided.

The invention provides a data transmission method and device, and the method and device are applied to a cloud channel controller of a cloud end, the cloud channel controller is connected with a first message queue and a side channel controller of an edge end, and the side channel controller is connected with a second message queue of the edge end. The method comprises the following steps: receiving edge application data acquired and sent by an edge channel controller from a target theme in a second message queue; and storing the edge application data to the target theme in the first message queue, so that the cloud application with the read-write permission of the target theme in the first message queue obtains the edge application data from the target theme in the first message queue. According to the scheme, the distributed message queues are deployed at the cloud end and the edge end respectively, the network limitation problem in cloud edge data interaction is solved by combining the cloud edge data channel, in addition, the cloud application and the edge application publish and subscribe data in the subscribed data theme, and the data conflict problem is effectively solved.

The invention discloses an OpenCL parallel defect detection method and device based on static analysis and a storage medium, relates to the field of computer compilation, and aims to discover parallel defects of OpenCL, improve code quality and automatically detect potential data conflicts and deadlock defects by processing specific keywords of OpenCL and analyzing on the basis of parallel grammar features of OpenCL. According to the method, problems can be found in the early stage of code development, compared with a dynamic detection method, the problems can be found before executable codes are generated by analyzing code files of OpenCL, the executable codes do not need to be modified and run, and the production environment and data cannot be affected. Compared with an analysis method based on a control flow diagram, the method has the advantages that the mode based on symbolic execution is adopted, the expansion diagram with the fence nodes is constructed, and branch information loss at branch instructions in a program is avoided.

The invention relates to the technical field of data management, in particular to a cross-platform advertisement putting and SEO collaborative optimization system which comprises a resource time sequence scheduling module, a keyword screening module, a putting path construction module, a data conflict comparison module and a content archiving and auditing module. According to the invention, through association mining of multi-platform advertisement data and user behaviors, real-time mapping of time sequence distribution of put resources and user group states, and dynamic integration of budget weights and audience portraits in a keyword selection process, a multi-parameter interactive screening mechanism is formed for advertisement paths; text similarity judgment and conflict source tracing are synchronously introduced into channel content, an archiving standard is jointly defined by content change frequency and source credibility, data in each link automatically adjusts an attribution boundary according to a change trend, multi-source data validity is accurately distinguished in an archiving process, and structure-level linkage is realized by advertisement circulation, content integration, path adaptation and conflict detection. And the consistency and uniqueness of data updating and archiving are guaranteed.

The invention discloses a third-party cloud service data synchronization processing method and system based on a timed task, and the method carries out the attribute configuration of the timed task through a management interface, and the objects of the attribute configuration comprise execution time, frequency, a third-party cloud service data source and target system information. Storing the object of the attribute configuration in an attribute database; when the timed task is triggered, acquiring data from a third-party cloud service in an asynchronous mode, storing the data to a local cache, and recording a data identifier; synchronizing the processed data to a target system by adopting an incremental synchronization strategy according to the data identifier, and performing conflict detection and conflict resolution in the synchronization process; the execution state of the timed task is monitored in real time, and if the execution state of the timed task is abnormal, a report is generated and automatic retry is tried. According to the method, the problems of resource waste, data delay, system performance reduction, data conflict loss and the like confronted by third-party cloud service data synchronous processing in the prior art are solved.

The invention relates to a data batch synchronization method and device and computer equipment. The method comprises the following steps: in a source database, acquiring to-be-synchronized data needing to be synchronized in a timestamp query mode, and performing conflict detection processing on each piece of to-be-synchronized data based on timestamp information corresponding to each piece of to-be-synchronized data to obtain data conflict information among the to-be-synchronized data; based on the data conflict information between the to-be-synchronized data, conflict adjustment processing is carried out on the to-be-synchronized data through a conflict resolution strategy to obtain target to-be-synchronized data, and data synchronization processing is carried out on the target to-be-synchronized data through a data synchronization program to obtain synchronized data; and storing the synchronized data in the source database through an encryption transmission strategy, and updating the timestamps of the synchronized data in the source database. By adopting the method, the multi-data synchronization efficiency and accuracy can be improved.

According to the heterogeneous memory management system and method oriented to the sparse hybrid expert model, the medium, the program product and the terminal provided by the invention, the interference of cold data on hot data access delay is eliminated by constructing a double-Bank physical isolation architecture in the physical heterogeneous storage module, so that the bandwidth contention of the cold and hot data is avoided; the cold storage unit adopts a low-power-consumption memory and supports a sleep mode, so that near-zero-power-consumption storage of cold data is realized; in the semantic prefetching unit, the prefetching hit rate is increased to 89% by adopting a cosine similarity matching method, so that a dynamic data migration mechanism with a high hit rate is constructed; and the gating collaborative migration engine triggers data migration, and the calculation continuity of the calculation module is guaranteed through DMA control.