447 results about "Data fragment" patented technology

The invention relates to the technical field of underground coal mine safety, and discloses an underground coal mine early warning system and method. The method comprises the following steps: collecting multidimensional monitoring data of an underground coal mine environment; performing dynamic window division on the multi-dimensional monitoring data to generate a data fragment set; performing abnormal mode recognition based on the data fragment set, and extracting abnormal feature points exceeding a dynamic threshold in each data fragment; inputting the abnormal feature points into a pre-constructed space-time correlation model, and analyzing diffusion paths of the abnormal feature points in a time dimension and a space dimension; according to the diffusion path, generating an abnormal propagation map containing the propagation direction and the intensity attenuation gradient of the abnormal feature points; hierarchical clustering is carried out on the abnormal propagation atlas, and abnormal clusters with similar propagation characteristics are divided; generating an early warning signal containing a priority label by using the spatial and temporal distribution characteristics of the abnormal cluster; performing synchronous verification on the early warning signal and underground equipment state data, and screening out a target early warning signal needing to be responded; and triggering a corresponding emergency control instruction according to the target early warning signal.

The invention relates to the technical field of data processing, in particular to an intelligent supervision method and system based on a data visualization platform, and the method comprises the steps: collecting multi-source water conservancy data through dual-channel redundancy check, converting the multi-source water conservancy data into a standardized space-time matrix, carrying out the data cleaning through a containerization adaptation and variational auto-encoder, and marking the confidence; hierarchically storing the data based on a confidence threshold, and generating a routing strategy through reinforcement learning to distribute data fragments; calculating a flood peak evolution prediction result by using space-time diagram convolution, calculating an output risk assessment value by combining a Bayesian network and long and short-term memory, and activating federal learning parameter updating when the confidence coefficient is insufficient; the digital twin model loads physical constraint parameters to deduce a flood control scene, and a scheduling instruction set and a rehearsal animation are generated through multi-objective optimization; and dynamically rendering to generate an interactive visual interface, associating a causal deduction path, and triggering federal model parameter updating by user feedback, thereby realizing dynamic collaboration of the forecasting and early warning rehearsal plan function chain.

The invention discloses an archive data security backup method and system based on cloud computing, and the method comprises the steps: obtaining archive data and a cloud backup pool, obtaining an edge verification package based on to-be-backed-up data, carrying out the grouping encryption of the edge verification package through a national password, and enabling the cloud backup pool to carry out the splitting and storage of the backup data according to a regeneration code strategy, the method comprises the following steps: acquiring data fragments and fragment roots, writing Hash of the fragment roots into an alliance chain channel, hosting a decryption key of a confidential envelope according to a storage period, storing fragments of the decryption key into a cloud HSM, and if a cloud backup pool monitors a disaster trigger event, pushing decryption key sub-fragments to a disaster recovery undertaking pool according to a disaster level, the subset of the readable file data is recovered to the edge node, and a recovery log is written back to the alliance chain. According to the method, data backup, safety and reliability are improved through desensitization grading, differential safety protection, regeneration code strategy splitting and multi-node disaster recovery.

The present application provides a video segmentation method, a server, a storage medium, and a program product. In the method of the present application, video data to be segmented is segmented into multiple data segments, unimodal features of the data segments, including text features of a text modality and visual features of a visual modality, are respectively extracted by means of a video topics segmentation model, and then the text features and visual features of the data segments are fused, so that the fusion of multimodal information can be performed at the intermediate representation level, the relationship and interaction between different modalities can be better captured, and higher-quality multimodal fusion features of the data segments are obtained. Furthermore, on the basis of the multimodal fusion features of the data segments, whether the data segments are topic boundaries is predicted, so that the topic boundaries of the video data can be accurately predicted, improving the accuracy of topic boundary recognition, thereby improving the accuracy and quality of video topics segmentation results.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The invention relates to a fingerprint lock dynamic encryption authentication method and system and a medium. The method comprises a registration stage and an authentication stage. Wherein in the registration stage, lock body environment real-time physical parameters are collected, and a dynamic environment secret key is generated through a chaotic mapping function; acquiring a user fingerprint, identifying core and non-core feature regions, and encrypting the two types of regions by combining a dynamic environment key and a user key to generate registration encrypted fingerprint data; and dividing the data into two data fragments by using a quantum noise source true random sequence, storing the first data fragment in a local security chip, and encrypting the second data fragment and storing the encrypted second data fragment in a cloud server. In the authentication stage, environment parameters are collected in real time to generate a verification key, and the verification key is compared with a registration key; and when the two fragments are consistent, recombining the two fragments to restore the encrypted fingerprint data, authenticating the fingerprint input by the user, unlocking if the authentication succeeds, and triggering risk grading processing if the continuous failure reaches a threshold value. The overall safety and reliability of the fingerprint lock system are improved.

The invention discloses a multipath relay transmission method and system for underground confined space construction data, and belongs to the technical field of wireless communication network routing, and the method comprises the steps: obtaining a signal quality parameter of a communication link and a distribution density parameter of an ad hoc network node, and combining the load state of the node and the information of a neighbor node, decision calculation is executed on a plurality of relay nodes in parallel, relay decision vectors corresponding to the relay decision vectors are generated, a plurality of candidate transmission paths are formed through evaluation, construction data blocks to be transmitted are fragmented and distributed to the corresponding optimal candidate transmission paths to be sent in parallel, all received data fragmented sub-blocks are verified and recombined, and the construction data blocks to be transmitted are obtained. And recovering a complete construction data block. A distributed relay decision based on network state feature fusion, data adaptive fragmentation based on path stability and a multipath parallel transmission and feedback optimization mechanism are adopted, so that the data transmission reliability in an underground complex environment can be improved, the transmission delay is reduced, and the dynamic optimization of network performance is realized.

The invention discloses a target equipment upgrading method and device based on an equipment grading strategy, equipment, a medium and a product, and relates to the field of mobile terminal software upgrading, and the method comprises the steps that a server collects hardware features of target equipment and automatically grades the equipment by using a K-means clustering algorithm; generating a differential OTA upgrade package based on the grading result; segmenting the differential packet into data fragments with fixed sizes, and generating a hash value and a digital signature for each fragment; the equipment end downloads one by one, verifies integrity and signatures in real time, and supports breakpoint resume; the server side predicts the OTA upgrade failure risk through the trained random forest model, and dynamically adjusts a push strategy; the equipment adopts an A / B partition mechanism to realize safe rollback; in a non-secure network environment, the server and the device cooperatively generate a temporary key based on a device ID and a timestamp to encrypt and transmit fragments. The volume of the upgrade package is effectively reduced, the transmission efficiency is improved, the security is enhanced, and the upgrade failure rate is remarkably reduced.

The invention relates to an emotion regression recognition method based on multi-modal signals. The method comprises the steps that original electroencephalogram signals and data fragments of all modals of electrocardiosignals are obtained; inputting the modal data fragments into corresponding shallow layer encoders and fusing the modal data fragments to obtain shallow layer fusion features; inputting the shallow spatio-temporal features of each mode into a corresponding Transform deep encoder to obtain deep features, and performing deep fusion on the deep features and the shallow fusion features to obtain deep fusion features; splicing the deep fusion features and the feature-activated manual features to obtain final features; and outputting an emotion continuous value regression result through the multi-layer perceptron network. According to electroencephalogram and electrocardiosignals, original signals and manual features are combined, step-by-step extraction is carried out in the feature learning process, a multi-modal fusion mechanism of the network is utilized, the performance and generalization ability of the model are improved, meanwhile, continuous prediction of the emotional state is achieved, fine fluctuation of the emotion is more accurately captured, and the application prospect is wide.

The invention relates to the technical field of data distributed storage, in particular to a data distributed secure storage system and method based on computer science and technology. The system comprises a data fragmentation and strategy binding module, a strategy declaration evidence storage and state tracking module, a strategy execution voucher generation module and a voucher verification and data assembly module. The system carries out fragmentation processing on original service data through a dynamic fragmentation algorithm to generate encrypted data fragments and a strategy binding statement; storing the strategy binding declaration to a distributed account book and maintaining a strategy state table; generating a policy execution voucher including an access qualification proof, a digital signature response, a policy hash value and a timestamp based on the system real-time state and the access request; and the distributed storage node implements decentralized data access control by verifying the policy execution credential. According to the method, the single-point fault and strategy tampering risk of centralized authority management are effectively solved, and a reliable data security sharing scheme is provided for a supply chain financial service scene.

The invention relates to the technical field of document management, and relates to a legal document key point automatic extraction and association method and system.The new legal document data of a target case is obtained, a preset document fingerprint algorithm is used for automatically comparing the new legal document data with a historical reference document, differential text data fragments are extracted, and the text data fragments are extracted; the method comprises the following steps of: performing deep semantic analysis on different text data fragments to generate candidate triple data containing entities and relationships, intelligently mapping the candidate triple data into a pre-constructed case knowledge graph, quickly positioning graph anchor nodes with data change, and then, performing data change on the basis of change types of the different text data fragments. And executing atomic updating operation on the map anchor node, including updating the dynamic mark of the node state attribute, and finally generating the change source node data with the latest state identifier. The problems that the current legal case data updating depends on manpower, is low in efficiency and is easy to make mistakes are effectively solved.

The invention provides a concurrent scheduling and fault-tolerant optimization method and a concurrent scheduling and fault-tolerant optimization system for distributed jobs of a Spark application layer. The method comprises the following steps: acquiring a scheduling parameter, and analyzing the parameter to generate a configuration object; dividing data in a starting and ending date range according to business logic based on the configuration object, and generating a discrete data fragment list; based on the discrete data fragment list and the concurrent thread count, creating a thread pool, packaging each discrete data fragment into an independent task, and storing the independent task into a to-be-processed fragment queue; obtaining discrete data fragments through a thread pool working thread, calling SparkSession to execute service processing logic, and submitting a job to a cluster to obtain an execution result; performing judgment processing on the execution result, and recording a task processing state; based on all processing states, an execution report is generated. According to the method, the execution efficiency and the resource utilization rate of the Spark application are remarkably improved.

The invention relates to the technical field of industrial process control and data analysis, in particular to a polyacrylamide production process monitoring data analysis system based on reinforcement learning, which comprises a data acquisition step: acquiring process data at a first frequency, and receiving quality data at a second frequency; a strategy generation step: outputting an action by using a neural network, and calculating an instant agency reward by using an agency model; an asynchronous experience playback step: constructing a nonlinear storage structure, and mapping lagging quality data back to historical process data fragments to complete a causal closed loop; a model updating step: calculating a delay correction reward, and updating network parameters based on the mixed reward; according to the invention, decoupling of real-time decision and delay calibration is realized through a dual-time scale architecture, and the response capability and high-precision control of a test vacuum period are ensured.

The invention discloses an intelligent computing center data security protection method and system based on a computer network, and relates to the technical field of network data security protection. Comprising a data classification identification module, a data encryption and fragmentation module, a distributed storage management module, a network attack detection and defense module, a data integrity verification and recovery module, a security policy optimization module, a security situation awareness module and a central control module. The distributed storage management module records storage positions, hash values and access permissions of data fragments based on the block chain technology, the distributed storage management module dynamically adjusts the access permissions of storage nodes, node access is only allowed to be achieved through multi-factor authentication of fingerprints and digital certificates, the data streams are fragmented and then encrypted, and therefore the data streams are encrypted, the data streams are encrypted, and the access permission of the storage nodes is dynamically adjusted. And the data is obtained by reading the storage position, the hash value and the access permission, and the access permission of the storage node is dynamically adjusted, so that the data security can be ensured while the data can be conveniently accessed.

The embodiment of the invention discloses a safety calculation method and device for robot cluster collaboration and a medium, and relates to the technical field of safety calculation, and the method comprises the steps: carrying out the participant authentication of a robot cluster and a production enterprise node in advance, and carrying out the dynamic identity verification of each robot after the participant authentication is passed, the operation feature data of each target robot passing verification are extracted and encrypted, and encrypted feature vectors are determined; splitting the encrypted feature vector into a plurality of data fragments, storing the data fragments to a distributed node, obtaining a predetermined task cooperation strategy, and distributing the task cooperation strategy to an edge computing node in a secret sharing manner; executing safety calculation in a ciphertext state through a task cooperation strategy and the plurality of data fragments, determining a joint motion control instruction of each target robot, and generating corresponding zero-knowledge proof data; and after the verification is passed, the encrypted control instruction is distributed through the block chain smart contract, so that safety calculation of robot cluster collaboration is realized.

The invention provides a dynamic self-adaptive data stream accelerated transmission method and device oriented to a credential environment. The method comprises the following steps: carrying out capability detection on a central processing unit, an operating system, a network interface and a password acceleration unit in a credential computing node to obtain environment capability information, and organizing the environment capability information into a credential environment capability portrait; analyzing the service type, the data scale, the timeliness level and the security level of the data stream, and generating a data stream portrait corresponding to the data stream in combination with a preset classification rule; acquiring running state information in a cross-layer manner and organizing the running state information into running state vectors; based on the credential environment capability portrait, the data stream portrait and the operation state vector, executing a preset strategy decision process, and determining a transmission strategy parameter set for the data stream; and sending the data fragments according to the transmission strategy parameter set. The resource utilization rate of the creative environment can be improved, the multi-type data stream transmission efficiency is improved, and the manual tuning overhead of transmission parameters is reduced.

The invention discloses a high-performance reliable transmission system implementation method, and relates to the related field of data transmission control, and the method comprises the following steps: a sending end fragments to-be-sent data according to a preset loading size, outputs a plurality of fragments of the to-be-sent data, and extracts a plurality of protocol heads; the sending end sends a plurality of to-be-sent data fragments to the receiving end in sequence, and the sent data fragments are stored in a sent queue of the sending end; the receiving end performs validity check on the received at least one data fragment, and replies a confirmation message to the sending end according to a check result; and configuring a transmission coordination mechanism, monitoring the transmission process of the plurality of to-be-sent data fragments by using the transmission coordination mechanism, and carrying out coordination construction on the transmission coordination mechanism through the sent queue of the sending end, the received queue of the receiving end and the to-be-sorted queue. The technical problems that an existing data transmission system is low in efficiency and insufficient in reliability are solved, and the technical effect of improving data transmission performance and reliability is achieved.

The invention provides a method and system for realizing progressive user interface rendering through real-time completion, and the method comprises the steps: S1, receiving data fragments provided by a large language model and forwarded by a server based on a streaming protocol in real time, adding the data fragments to the tail of a buffer region according to a time sequence, and updating the buffer region; s2, extracting all JSON character strings from the updated buffer area, and complementing the JSON character strings on the basis of complementing logic of a heuristic algorithm to generate new JSON character strings; s3, analyzing the new JSON character string to obtain a current object; s4, obtaining an object used in the last successful rendering in the list container from the UI framework, comparing the current object with the object used in the last successful rendering, and updating the list container through difference comparison; and S5, rendering of the UI framework is carried out on the updated list container.

The invention discloses an automobile production line parameter monitoring and early warning method and system based on dynamic threshold and multi-dimensional comparison. The method comprises the following steps: completing deployment of a sensor device for data acquisition and construction of a data set; constructing a model infrastructure, wherein an input layer synchronously receives a process card parameter vector, an equipment state vector and a process-equipment association feature vector; a time sequence segmentation learning mechanism is introduced into the LSTM layer, and data fragments are divided according to the equipment operation cycle; the model processing layer adopts a dynamic weight adjustment mechanism, and the Attention layer firstly endows features at different moments and in different equipment states with basic weights, and then carries out real-time calibration according to the abnormal contribution degree of each feature in historical early warning data; the output layer calculates and outputs the probability value of the actual parameter meeting the process requirement through a Softmax function; and taking a probability value output by the model as a core, establishing a hierarchical early warning response mechanism and matching a standardized process. And long-term accurate monitoring and efficient early warning of the parameters of the automobile production line are realized.

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 provides a greenhouse intelligent control system based on machine learning. The greenhouse intelligent control system comprises the steps of collecting real-time monitoring data of a multi-dimensional sensor in a greenhouse, splicing continuous sampling data according to a time step, constructing a time sequence state feature chain representing an environment factor time-varying rule, and inputting a deep neural network for resolving to obtain a first group of equipment control parameters; deducing a future environment state change rate according to the parameters based on an environment dynamic model, calculating an environment factor change vector included angle cosine value to represent a mutual exclusion influence value, converting the value into an attention mask, and attenuating a historical data fragment weight causing a mutual exclusion trend in a time sequence state feature chain; and inputting the feature chain after weight attenuation into the model, generating a second group of control parameters, extracting a high-energy-consumption execution mechanism action instruction in the parameters, predicting energy consumption and environment steady state disturbance gain, constructing a balance coefficient to correct the action duration of other execution mechanisms, and outputting a control instruction sequence.

The invention provides a distributed database data migration method which can be applied to the technical field of big data and the field of distributed technologies. The distributed database data migration method comprises the steps that a data migration plan is obtained, wherein the data migration plan comprises distribution information that each source data fragment in a source distributed database cluster is distributed to a target node of a target distributed database cluster and parallel migration degree representing the number of source data fragments of concurrent migration; executing the data migration plan, including: controlling m source data fragments to migrate in parallel, m being a positive integer and m not greater than the parallel migration degree; and migrating each source data fragment to the allocated target node based on the allocation information, and obtaining a target data fragment corresponding to the source data fragment at the target node after migration is completed. The invention further provides a distributed database data migration device and equipment, a storage medium and a program product.

The invention discloses a distributed data storage and access control method based on a block chain, and relates to the technical field of data security. The method comprises the following steps: fragmenting user data, encrypting the fragmented user data by using an encryption algorithm, and storing the encrypted user data in a plurality of distributed nodes; taking the hash value and the storage position of the data fragment as metadata to be recorded in a block chain, and performing integrity verification through a smart contract; when a user accesses, user attributes are verified based on an attribute-based encryption technology, and an access control strategy is dynamically executed through an intelligent contract; and an erasure code technology is adopted to carry out redundancy storage so as to realize data recovery and fault tolerance. The method effectively solves the problems of single-point failure, privacy disclosure and inflexible access control in traditional centralized storage, has the beneficial effects of improving data security, guaranteeing integrity, realizing fine-grained dynamic access control and enhancing system reliability, and is suitable for data security management in cloud computing, Internet of Things and big data environments.

The invention relates to the technical field of space-time big data processing, and discloses a Web-end-oriented space-time big data efficient rendering optimization method and system, a storage medium and electronic equipment, and the method carries out unified and intelligent management on the whole rendering process by introducing a central rendering task scheduler. Space-time big data fragments are split into fine-grained vector tile rendering tasks, a dynamic rendering priority score is calculated for each task, and the vector tile rendering tasks with high priorities are sequentially taken out from the head of a dynamic priority queue according to a current available rendering resource threshold value of a Web end. The method comprises the following steps: acquiring a plurality of rendering tasks, distributing the rendering tasks to a plurality of parallel execution rendering task execution units for real-time rendering processing, outputting a final spatio-temporal data visual image, and dynamically adjusting a task execution sequence and resource allocation in combination with real-time system monitoring, thereby preferentially ensuring that contents most concerned by a user are quickly rendered under limited Web end resources, and improving the user experience. And an efficient and smooth visual effect is realized.

The invention relates to an energy storage data transmission method and system and a storage medium. The energy storage data transmission stability and the energy storage data assembly overhead can be balanced. The method comprises the steps that a data sending end obtains first network state information, dynamic fragmentation processing is carried out on energy storage data to be transmitted according to the first network state information, data fragments are obtained, and each data fragment carries a corresponding fragment identifier and a fragment sequence number; the data sending end encapsulates the data fragments to obtain corresponding messages to be transmitted; the data sending end sends the to-be-transmitted message to a message queue in a message queue server for storage according to the first network state information; and the data receiving end obtains the to-be-transmitted message from the message queue of the message queue server, and recombines the data fragments in the to-be-transmitted message according to the fragment identifier and the fragment sequence number carried by the data fragments in the to-be-transmitted message to obtain the to-be-transmitted energy storage data.

A process of collecting inference results that are obtained by inputting operation data to a trained machine learning model that has been trained based on training data, a process of generating clusters by performing density-based clustering on the collected inference results, a process of estimating, for each of the clusters, estimation labels associated with the corresponding clusters from among correct answer labels that correspond to all correct answers that can potentially be the inference results, and a process of performing fine-tuning on the trained machine learning model based on the pieces of operation data that belong to the corresponding clusters and based on the estimation labels associated with the corresponding clusters are performed.

A computer is operable to store a set of encoded data slices of a data segment, where each encoded data slice includes a corresponding checksum of a plurality of checksums. Based on ones of the plurality of checksums corresponding to the set of encoded data slices, it is determined when the set of encoded data slices includes at least one corrupted encoded data slice. At least a threshold number of encoded data slices of the set of encoded data slices are retrieved, and a reconstructed data segment is generated using the at least the number of encoded data slices of the set of encoded data slices. At least one at least one reconstructed encoded data slice is formed utilizing the error correcting information dispersal algorithm on the reconstructed data segment, and the at least one corrupted encoded data slice is replaced with the at least one reconstructed encoded data slice.

The present application relates to the field of streaming audio and video technology, and provides a multi-source audio and video file cross-time axis synchronous retrieval and playback method and device. Each terminal of the present application respectively collects and encodes multiple audio and video sources into independent storage files, and each frame of audio and video data of each storage file carries corresponding absolute time information; each terminal retrieves a data segment corresponding to a playback time specified by a user from each storage file according to the playback time; and a client corresponding to each terminal aligns the retrieved multiple data segments according to the absolute time information and synchronously decodes and plays the data segments. The present application solves the problem that the prior art has different delays in the transmission and playback process of multiple video sources, resulting in that the playback of each audio and video file can only be asynchronous, and the demand for synchronous playback of multiple video sources at a time point cannot be met.

The invention discloses a data migration method and device, a storage medium, electronic equipment and a program product, and relates to the field of distribution.The method comprises the steps that original transaction data of a host side is split into N data fragments according to service types and time windows; encrypting each data fragment into a target data fragment; leader nodes corresponding to the target data fragments are selected from the L cluster nodes, and each target data fragment and the corresponding data version vector are sent to the corresponding leader node; performing consensus confirmation on the target data fragment received by the leader node through the leader node and the M replica nodes to obtain a consensus result of the target data fragment; and migrating the target data fragment from the host end to a storage node in a cluster. The technical problem of low consistency judgment efficiency before and after data migration caused by lack of a data version control mechanism during data migration of a distributed storage system in the prior art is solved.

The application provides a kind of mass network data distributed parallel processing method and system, it is related to data processing field, solve the problem of low efficiency of distributed parallel processing caused by the mismatch of data characteristics and computing power characteristics and the large overhead of global rearrangement in prior art.The method comprises: obtaining network data to be processed;Based on the first feature information of network data, the time consumption of prediction calculation is determined based on the time consumption of calculation, and a plurality of data fragments are obtained based on the slice boundary;Based on the mapping relationship between the first feature information of data fragment and the second feature information of computing node, the data fragment is distributed to the corresponding computing node;In the parallel processing process of data fragment, in response to the deviation between the actual progress and the estimated progress of computing node exceeds the preset threshold, the task of computing node that has not been completed is divided into subtask slices, and is migrated to other computing nodes for local redistribution.The application is used for mass network data processing.