32 results about "Throughput (business)" patented technology

The invention belongs to the technical field of block chains, and particularly relates to a financial and tax data fusion and audit traceability management system based on a block chain, which comprises an under-chain multi-source data acquisition layer, an on-chain dynamic fusion verification layer and a cross-domain collaborative feedback control layer, and is pre-bound to a data source through a business rule engine. Through combination of a lightweight fusion unit of space-time alignment and an incremental consensus mechanism based on a state channel, semantic consistency, fine-grained tracing and real-time compliance intervention of finance and tax data in a full life cycle are realized. Through a business rule pre-binding and source event capturing mechanism, finance and taxation data is prevented from breaking away from compliance context in a generation stage; a space-time alignment fusion unit is utilized to construct a composite transaction object with causal logic, and semantic segmentation among multi-source data is eliminated; the incremental consensus architecture based on the state channel is realized, and the final consistency of key transactions is ensured while the high throughput performance is maintained.

The invention relates to the technical field of e-commerce, in particular to an e-commerce intelligent prediction management method and system based on big data, and the method comprises the following steps: obtaining a real-time message in a buffer area, generating a throughput rate sequence based on a sliding window, carrying out the difference and mapping of throughput rates of adjacent periods, and constructing a gradient slope sequence; and extracting a transient abrupt change ratio by means of a moving average model to generate a pre-trigger level identifier, extracting a set boundary extreme value, carrying out quantitative evaluation to generate discrete parameters, correcting a transfer matrix, and executing steady-state iterative solution to obtain prediction confidence to construct an elastic control instruction. According to the method, the sudden change trend of the business time domain dimension is accurately captured and static reference deviation interference is eliminated, so that deep analysis and real-time correction of the dynamic transfer characteristics of the core transaction link are realized, and the dilemma of research and judgment distortion and scheduling lag existing in a traditional rule when facing burst traffic is thoroughly avoided; and the full-link prediction precision and the elastic resource management and control efficiency in a complex transaction scene are remarkably improved.

The invention discloses a dynamic self-verification method based on service flow-test flow homologous heterogeneous execution. The method comprises the following steps: uniformly describing service logic and a corresponding interface test contract through a strong type of domain specific language (DSL); the method comprises the following steps: simultaneously compiling homologous DSL descriptions into stateful business workflows executed by a process engine and stateless and combinable atomic test contract sets called by a test executor by utilizing a double-path compiling engine; during operation, loose coupling synchronous triggering and execution of a service flow and a test flow are realized based on a high-throughput event bus, and real-time and high-fidelity contract verification, performance monitoring and chaos probe testing are performed on cross-system interface calling in a production environment. According to the invention, the traditional post-event and off-line integration test is converted into the in-event, on-line and continuous system endogenous capability, and the industrial problems that the complex enterprise system group integration test is difficult and the operation and maintenance monitoring is passive are effectively solved.

The embodiment of the invention provides a multi-account concurrent processing management system and a multi-account concurrent processing management method, which are applied to the technical field of multi-account high-concurrency business processing, and are used for analyzing a plurality of received account operation requests into a micro-operation set, packaging micro-operations into an operation object identifier, an operation type and a dependency relationship, and storing the operation object identifier, the operation type and the dependency relationship. Comprising resource dependence, granularity dependence and business logic dependence; determining a lock execution path based on the operation object identifier, the operation type and the dependency relationship; constructing a dependency tensor model, performing conflict density mapping to form conflict density distribution, and performing tensor conflict operation on the microoperation in combination with a lock execution path to obtain a conflict intensity value; a high-conflict area is identified based on conflict density distribution, a potential deadlock graph is constructed, a micro-operation directed acyclic graph is generated, nodes with zero in-degree and out-degree are identified to form a first execution sequence, a second execution sequence is constructed according to residual micro-operations, a micro-operation execution sequence is formed through splicing, and efficient parallel scheduling and throughput capacity improvement are achieved.

The application discloses an AI acceleration card reasoning computing power benchmark test method and device, obtains reasoning load data and hardware configuration parameters, generates a load anchor point sequence through business scene feature joint labeling, constructs an initial load test set accordingly, quantifies concurrent throughput backoff features to generate a computing power benchmark atlas, directionally decomposes cumulative accuracy deviation in the test set, identifies two components of computing power sensitivity and bandwidth sensitivity, respectively obtains computing power correction and bandwidth bottleneck anchor points through utilization rate false reporting traceability and memory conflict frequency domain association, generates accuracy correction instructions and implements accuracy re-correction on the initial test set, performs cold start path loss detection and accuracy gradient monotonicity failure identification based on the corrected test set, locates false reporting consistency blind area anchor points, executes global optimization on the computing power benchmark atlas, and finally outputs benchmark test results, thereby improving the accuracy and business adaptability of AI acceleration card reasoning computing power benchmark test results.

The invention relates to the technical field of manufacturing data processing, and discloses a production business data processing system for a discrete manufacturing workshop, which comprises an asynchronous data acquisition module for acquiring an asynchronous event pulse sequence of a discrete execution node; the mapping transformation module establishes an addressing mapping relation between an event source and a memory logic bit plane and generates an original differential bitmap; the bitmap filtering module is used for hedging paired logic flipping signals by utilizing an exclusive-OR logic self-reversal principle and filtering jitter interference at the edges of the signals so as to generate a net value difference bit plane; the state synchronization processing module performs in-situ upset updating on the state global bitmap through address offset addressing according to the bit identifier, and through a bitmap differential synchronization mechanism, asynchronous state updating without transaction lock constraint is achieved, calculation blocking generated by high-frequency pulse impact is relieved, and the real-time performance and stability of system data throughput are enhanced.

The invention relates to the field of data processing, in particular to a service credential migration-oriented full-process monitoring method and system, and the method comprises the steps: obtaining a resource consumption estimation coefficient of each service module according to the change relation of each throughput curve along with an available computing resource curve; screening out a plurality of migration tasks from each service module for pausing, wherein the size relationship of pause indexes of the migration tasks paused in different service modules and the size relationship of resource consumption estimation coefficients between different service modules have the maximum consistency; recording the difference between the pause index of the unpaused migration task in each business module and the pause index of the paused migration task as a first difference; and after all the migration tasks are paused, the paused migration tasks are obtained again by using the variation of the available computing resource curve along with the throughput curve of each service module and the first difference. According to the method, the risk existing in the credential migration process when computing resources are insufficient and resource allocation is improper is reduced.

The present application relates to big data platform security operation and maintenance and task scheduling technical field, specifically for the security operation and maintenance task scheduling method for big data intelligent platform, including: collecting business task dependency data, service level agreement data, historical business throughput time sequence data, real-time business load state data and the occupation consumption characteristic data of the to-be-scheduled security operation and maintenance task;Build the ideal business processing benchmark under the state of no operation and maintenance interference;Characteristic injection generates the theoretically interfered business state;Align to generate real deviation data and theoretical deviation data, evaluate the goodness of fit and generate the predicted business load state;According to the business capacity boundary, output the execution permission, suspended execution or forced termination instruction, and update the task disposal priority in the subsequent management period according to the actual execution business feedback data;The present application further improves the stability of long-term scheduling.

The application provides a micro-service architecture-oriented intelligent chaos test method and device, relates to the technical field of software testing, and solves the problems of one-sided fault coverage of traditional tests, rigid strategies, easy triggering of system risks and disconnection with business scenarios. The method comprises the following steps: collecting a service call chain through a distributed tracking technology, and constructing a dynamic dependency graph comprising node importance and link throughput; adopting an LSTM neural network to analyze a business log sequence to identify a business process stage, and establishing a "stage-link-fault" triplet rule; integrating topology features, business stages and real-time operation indexes to construct a state space, and optimizing a fault injection strategy based on a reinforcement learning algorithm and a multi-objective reward function; and limiting the injection intensity and range through a conflict detection and pre-check mechanism. The application is used for reliability verification of a micro-service system, and improves test efficiency and safety.

The application relates to a kind of enterprise multi-source data real-time synchronization systems based on data platform, the system adopts containerization deployment mode and layered architecture, including access layer, processing layer, distribution layer and control layer;Access layer communicates with data platform, obtains original data from data platform;Access layer is provided with data access adapter module, processing layer is provided with data format processing module, distribution layer is provided with data routing distribution module, control layer is provided with consistency guarantee module;Data access adapter module is used for the unified access of multi-source heterogeneous data;Data format processing module is used for data standardization processing to the multi-source heterogeneous data accessed;Data routing distribution module is used for intelligent data routing and distribution;Consistency guarantee module is used to ensure the consistency of data synchronization.The application reduces the delay of data synchronization, improves the system throughput, effectively guarantees the consistency of data, and is especially suitable for data synchronization and cross-system data integration scenarios of enterprise core business systems.

The application provides a vulnerability repair method and system, and relates to the technical field of network security vulnerability verification and enterprise vulnerability management. The application obtains a task containing a vulnerability identifier, asset information and to-be-verified repair content, processes the task in parallel according to a pipeline split according to business execution steps, and configures a resource pool matching external dependencies for each stage; a stage fingerprint is generated when the task is transferred, a result is reused when a subtask is executed, otherwise the execution is scheduled according to vulnerability harm, asset importance and threat intelligence, and the vulnerability repair is completed according to the verification result. The application solves the problems of low throughput of the existing general pipeline, slow response of high-priority tasks, and easy starvation of low-priority tasks, and achieves the effect of collaborative optimization of throughput improvement, emergency task response acceleration and low-priority task starvation prevention without changing the original business and hardware.

The invention relates to the technical field of data processing, and discloses an enterprise data real-time analysis and synchronization system based on intelligent governance, and the method comprises the steps: obtaining real-time state data reflecting the current operation condition of the system; based on the real-time state data, through a pre-configured time sequence prediction model, generating a prediction result of a system load in a future preset time period; maintaining a strategy library comprising at least two data synchronization strategies; dynamically selecting a data synchronization strategy adaptive to the prediction result of the system load from a strategy library as a target synchronization strategy according to the prediction result of the system load and a preset business service level protocol; and executing the data synchronization task according to the target synchronization strategy. According to the method and the device, a load prediction mechanism is introduced, so that a high-throughput strategy can be switched in advance or resource capacity expansion can be completed before a load peak arrives, and sudden system performance drop or service interruption caused by untimely strategy switching is effectively avoided.

The application relates to the technical field of manufacturing data processing, and discloses a production business data processing system for a discrete manufacturing workshop, which comprises the following modules: an asynchronous data acquisition module which collects asynchronous event pulse sequences of discrete execution nodes; a mapping transformation module which establishes an addressing mapping relationship between an event source and a memory logic bit plane and generates an original differential bit map; a bit map filtering module which uses the self-reflection principle of exclusive or logic to pair up logical flip signals, filters out the jitter interference of signal edges to generate a net value differential bit plane; and a state synchronization processing module which implements in-situ flip update of a state global bit map through address offset addressing according to a bit identifier. Through the bit map differential synchronization mechanism, the application realizes asynchronous state update without transaction lock constraint, alleviates the calculation blockage caused by high-frequency pulse impact, and enhances the real-time performance and stability of system data throughput.

This invention belongs to the field of integrated stream and batch computing technology, specifically relating to a design method for a unified stream and batch integrated data engine based on Flink. This invention achieves unified management and scheduling of stream processing, batch processing, and mixed scenarios through business tag-driven access and computing task identification. It eliminates the scenario adaptation problems caused by the generalization of traditional Flink stream and batch processing strategies. Data fingerprints are generated at the parsing, execution, and target layers, and a data fingerprint chain is constructed to achieve full-link tracking of data field structure, parsing rules, and processing procedures, improving data integrity and traceability. Real-time monitoring of the running status is provided, and dynamic parameter adjustment strategies are generated in conjunction with business tags, enabling the system to optimize resource allocation, processing latency, and throughput according to different business processing needs, thereby improving the execution efficiency and stability of stream and batch tasks.

The invention discloses a capacity calculation method and device based on a full-link pressure test. The method comprises the following steps: analyzing according to a transaction service scene to obtain a transaction link; the transaction business scene is used for describing a specific business process and a system interaction process experienced from initiation, processing to completion of the transaction; building a test environment according to the transaction link, and deploying a test transaction model; based on the test environment, the transaction link and the test transaction model, initiating an end-to-end transaction test, monitoring a test execution process, and obtaining monitoring data; determining a mapping relationship between the resource utilization rate and throughput capacity of each subsystem according to the monitoring data, and calculating the throughput capacity of each subsystem according to the mapping relationship and the available resource utilization rate of each subsystem in the production environment; the available amount of the resource utilization rate represents the amount of resources meeting the safe operation of the subsystem. According to the invention, the capacity of each physical subsystem on the transaction link can be intelligently estimated, and the capacity estimation accuracy is improved.

The invention discloses an electronic machine account intelligent early warning system based on big data analysis. A distributed data medium table (based on a Hive number bin of a Hadoop 3. X framework and a Spark SQL component, adopting an ORC column type storage format, having daily processing capacity greater than or equal to 5 million pieces, realizing dynamic capacity expansion through YARN resource scheduling, supporting lateral expansion to ten-million-level throughput, and adopting date and business dimension dual hash for a data partitioning strategy); the Flink streaming computation engine is used for ensuring data consistency by adopting Exactly-Once semantics, realizing Checkpoint barrier synchronization based on a Kafka message queue, controlling delay within 200ms, and supporting dynamic back pressure regulation, and the peak value processing capability reaches 100,000 pieces per second; provided is a visual early warning billboard. The system response delay is controlled within 500ms (the actually measured industrial scene average delay is 378ms) through the multi-source data parallel access and stream processing channel, the efficiency is improved by 6 times compared with the traditional scheme, and the real-time performance is improved.

The invention discloses a transaction process-oriented risk sample adaptive selection and labeling method, particularly relates to the technical field of industrial risk control data processing, and is used for solving the problems of business exception redundancy pushing and checking overload. The method comprises the steps that firstly, a transaction flow and a physical flow log are fused to construct a multi-modal space-time transaction feature matrix, a high-frequency deviation sample is extracted in combination with a graph structure relative entropy and a hysteresis damping coefficient, and false positive interference is eliminated; then, analyzing an isomorphic risk cluster based on density clustering, stripping homogenized redundancy by using information entropy and centrality in the cluster, extracting representative samples to generate a high-entropy refined subset, and reducing auditing redundancy; and finally, the concurrent throughput and the cognitive load threshold are monitored to establish a dynamic quota game mechanism, the optimal sample slices are intercepted to be subjected to terminal identification labeling, a closed-loop system from feature fusion to adaptive pushing is constructed, and scientific support is provided for industrial transaction risk control.

According to the batch database operation execution sequence optimization method based on time delay detection, random I / O is converted into quasi-sequence I / O, and the execution efficiency of operations such as large-scale deletion and updating is improved. GMM statistical modeling and LLM cognitive analysis are combined to form a dual verification framework, the storage performance level can be adaptively recognized, an abstract statistical peak value can be converted into the insight with clear business semantics, and the analysis depth and accuracy of the method are far better than those of a traditional method. The physical aggregation relationship of the data to be operated can be deduced only by detecting the data with a minimum proportion by adopting a strategy of'intelligent sampling + sequence correlation full-quantity mapping '. The mapping process can be efficiently completed in the database, and minimum disturbance and global optimization coverage of the production system are achieved. And in combination with a cognitive throttling mechanism based on a real-time load, high throughput, low delay and predictability of an optimization process are ensured, and the stability of a core service is ensured.

This invention discloses a dynamic self-verification method based on the homogeneous and heterogeneous execution of business flow and test flow. The method includes: uniformly describing business logic and corresponding interface test contracts using a strongly typed Domain-Specific Language (DSL); utilizing a dual-path compilation engine to simultaneously compile the homogeneous DSL description into a stateful business workflow for execution by the process engine and a stateless, composable set of atomic test contracts for invocation by the test executor; and at runtime, achieving loosely coupled synchronous triggering and execution of business flow and test flow based on a high-throughput event bus, performing real-time, high-fidelity contract verification, performance monitoring, and chaos probe testing on cross-system interface calls in a production environment. This invention transforms traditional post-event, offline integration testing into in-process, online, and continuous system-native capabilities, effectively solving the industry problems of difficult integration testing of complex enterprise system groups and passive operation and maintenance monitoring.

The invention discloses a risk prediction method and system for operation and maintenance of a large-scale server cluster, and the method comprises the steps: collecting multi-dimensional data of the server cluster before the current moment, carrying out the preprocessing of the multi-dimensional data, and obtaining a standardized data set, the multi-dimensional data comprises operation monitoring index data, service operation characteristic data and real environment interference factor data; and inputting the preprocessed data into a pre-trained risk prediction model to output future prediction values of a CPU usage rate index, a memory usage index, a disk IO index and a network throughput index, the risk prediction model being an LSTM model. The four core indexes including the CPU utilization rate, the memory occupancy, the disk IO and the network throughput are subjected to collaborative prediction at the same time.

The present invention discloses a real-time data warehouse ETL method for parallel parsing of business library logs. The real-time data warehouse ETL method for parallel parsing of business library logs includes the following steps: the business library generates a change log, a log parsing service parses and replays the change log, the log parsing service sends the parsed structure to a message queue, and a real-time synchronization program consumes the data in the message queue to achieve data synchronization. In the industry, in the log parsing process, in order to achieve accurate once, the serial parsing method is basically maintained. This method will cause serious delays under high load. Using parallel instead of serial has higher throughput. Parsing logs in a serial manner also relies on the performance of a single-core CPU. Even if the server is multi-core, it cannot fully utilize the resources. Parallel parsing can use cheaper CPUs to reduce usage costs. It can be deployed across servers and use cheaper servers for parallel parsing to achieve horizontal expansion.

The application discloses a risk sample adaptive selection and labeling method for transaction process, and particularly relates to the technical field of industrial risk control data processing, and is used for solving the problems of business exception redundancy push and audit overload. First, a multi-modal space-time transaction feature matrix is constructed by fusing transaction flow and physical flow log, high-frequency deviation samples are extracted by combining graph structure relative entropy and lag damping coefficient, and false positive interference is excluded; then, isomorphic risk clusters are analyzed based on density clustering, homogenization redundancy is stripped by using cluster information entropy and centrality, representative samples are extracted to generate a high-entropy refined subset, and audit redundancy is reduced; finally, a dynamic quota game mechanism is established by monitoring the concurrent throughput and cognitive load threshold, the optimal sample slice is intercepted to the terminal discrimination labeling, and a closed-loop system from feature fusion to adaptive push is constructed, which provides scientific support for industrial transaction risk control.

The embodiment of the present application provides a kind of flattening speed control method and device, can be obtained by each storage node executing flattening task and the business throughput generated by service throughput, when the flattening throughput and service throughput satisfy preset speed reduction condition, control storage node reduces the rate of execution flattening task.In the embodiment of the present application, the flattening throughput and service throughput on the storage node are obtained by management node, determine the processing service resource condition on each node, and then the rate of execution flattening task of storage node can be controlled according to service resource condition.That is, when there are more background flattening tasks, the flattening task speed can be dynamically adjusted, and the influence on normal service performance can be avoided.

The present application belongs to the technical field of supply chain management, and particularly relates to a multi-tenant cost accounting method and system, a storage medium and equipment, wherein a cost calculation task is asynchronously generated in response to a core business event and stored in a task queue, thereby decoupling business operation and cost calculation; on this basis, preset cost accounting dimension data is used as a dimension key to group and scan the to-be-processed tasks in the task queue, and a repeated task group with the same dimension key is identified; then, a preset deduplication strategy is applied to each repeated task group to merge and clean up, and only the tasks that need to be executed are retained. Since multiple repeated calculation tasks for the same accounting object are combined into one execution from the source, a large amount of redundant calculation is avoided, so that the consumption of computing resources can be significantly reduced, and the processing efficiency and throughput of the system can be greatly improved.

The application belongs to the field of natural language processing, and discloses an LLM business text analysis and classification method based on structured prompt words, which comprises the following steps: constructing a multi-level business label system and a structured prompt word template library with a full-process control module; preprocessing business text; combining Prompt to call a large language model, extracting core elements and repairing JSON under the constraint of original text fidelity; hierarchical matching label and recording basis; calculating confidence score through three-dimensional index weighting; if the constraint verification is unqualified, it is returned to re-extraction; assembling structured data output according to the fixed protocol. The system cooperates with the fixed core layer, the dynamic configuration layer and the processing engine module, realizes the decoupling of business rules and core logic, greatly reduces the expansion cost of new business, guarantees the data fidelity and matching traceability, quantifies the analysis quality, avoids the format exception, reduces the downstream artificial cost, improves the batch throughput and stability, and has good practical value and application prospect.

The invention discloses a DCQCN parameter adaptive tuning method and system based on a power business scene, a medium and a processor, relates to the technical field of congestion control, and solves the problems of rate fluctuation, poor structural data adaptation and difficulty in considering hybrid business QoS (Quality of Service) caused by existing DCQCN fixed parameters. The method comprises the following steps: collecting power service network flow characteristic data, and quantifying service priorities based on an analytic hierarchy process; reinforcement learning core elements are determined according to the traffic characteristics, and a DQN reinforcement learning model is constructed and trained; and constructing a heuristic search algorithm fusing the service priority and the reinforcement learning model, and screening an optimal DCQCN parameter. The system comprises a preprocessing module, a first construction module, a second construction module and an adjusting and optimizing module. The method can dynamically adapt to mixed service requirements of real-time power monitoring, video streaming, structured data and the like, time delay optimization and throughput improvement are realized, and stable and efficient operation of a power network is guaranteed.

This invention discloses a computing resource planning method and system based on a large model. The computing resource planning method includes: constructing a structured performance database based on benchmark test documents; generating a set of configuration data points corresponding to each configuration node based on the performance database; generating optimal data points based on the configuration data point sets; obtaining the business target concurrency; traversing the optimal data points corresponding to the configuration nodes; generating at least one candidate deployment scheme that meets the business target concurrency; calculating the cost information of the candidate deployment schemes; and generating the target deployment scheme based on the cost information. Specifically, the optimal data point has a first token latency less than a preset latency time threshold, a single-user throughput greater than or equal to a preset throughput threshold, and the concurrency number is the maximum value in the configuration data point set. This invention achieves accurate prediction of concurrency carrying capacity under strict latency constraints through structured modeling and interpolation calculation, exhibits strong cross-scenario generalization ability, and reduces computing resource planning errors.

The invention discloses a dynamic task scheduling method, system and device and a medium, and belongs to the technical field of data processing.The method comprises the steps that online and offline events of service nodes are registered, and a storage cluster hash table is updated; carrying out competitive selection on the plurality of service nodes through the atomization script to obtain a first service node; traversing the storage cluster hash table according to a second interval preset time through the first service node, and detecting the latest state of each service node according to a heartbeat maintenance strategy; in response to a change event of any service node in the service cluster, updating service node fragmentation parameters through a cluster task rebalance process and a modulo fragmentation strategy; and performing dynamic task scheduling on the affected service nodes according to the updated service node fragment parameters. According to the method, the data is filtered by directly using the modulo fragmentation strategy in the SQL query of the database, and the divisor is automatically adjusted when the number of the nodes changes, so that the allocation throughput is improved by 5-10 times, and the memory calculation overhead is eliminated.

The invention belongs to the technical field of supply chain management, and particularly relates to a multi-tenant cost accounting method and system, a storage medium and equipment, and the method comprises the steps: asynchronously generating a cost calculation task in response to a core business event, and storing the cost calculation task in a task queue, thereby achieving the decoupling of business operation and cost calculation; on this basis, taking preset cost accounting dimension data as a dimension key, performing grouping scanning on the to-be-processed tasks in the task queue, and identifying a repeated task group with the same dimension key; and combining and cleaning each identified repeated task group by applying a preset duplicate removal strategy, and only reserving tasks which need to be executed. The multiple repeated calculation tasks aiming at the same accounting object are merged into one-time execution from the source, and a large amount of redundant calculation is avoided, so that the consumption of calculation resources can be remarkably reduced, and the processing efficiency and throughput of the system are greatly improved.

The invention discloses a dynamic multitask-based network slice access control method and system, and relates to the technical field of communication network management and control, and the method comprises the steps: obtaining network related parameters, carrying out the business throughput capacity calculation and load evaluation based on the network related parameters, and obtaining the throughput capacity of a business in a slice and the load ratio of the slice; based on a preset multiplication coefficient, calculating a time delay coefficient and a throughput coefficient respectively based on the network related parameters and the throughput of the service in the slice; calculating an access criterion value of the slice and a control priority of a new arrival service based on the load ratio, the time delay coefficient and the throughput coefficient of the slice; and based on the service type label of the newly arriving service with the highest control priority of the newly arriving service, selecting the slice with the maximum access criterion value of the slice from the slices of the same type for access, determining the waiting times of the newly arriving service which is not accessed, and if the waiting times of the newly arriving service which is not accessed exceed a preset threshold value, accessing the newly arriving service which is not accessed. And dynamically adjusting the control priority of the corresponding new arrival service, so that the corresponding new arrival service is preferentially in the subsequent access slice.