3884 results about "Control data" patented technology

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

Job clusters take time to instantiate. A computing system is provided comprising a plurality of non-interactive job clusters, a control database storing a task queue, and a controller. The controller instantiates one or more clusters of the plurality of non-interactive job clusters based on a size of the task queue and monitoring if the one or more clusters are successfully instantiated. Each of the one or more clusters, after successfully being instantiated by the controller, executes a dispatcher process that includes: querying the control database to identify an available task from the task queue; obtaining and processing the available task; and, after completion of the available task, further querying the control database prior to terminating.

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

Provided are a device and a method for controlling data traffic by a core network control plane node. The method may include receiving, from an application function control entity, one or more pieces of information among help information on application traffic characteristics, a PDU-Set QoS parameter, an identifier for grouping flows belonging to a multi-modal application, and a synchronization threshold; transmitting, to a base station, one or more pieces of information among help information on application traffic characteristics, a PDU Set QoS parameter, an identifier for grouping flows belonging to a multi-modal application, and a synchronization threshold during a PDU session establishment or modification procedure; and instructing transmission of a user packet including PDU-Set information to a core network user plane node.

The invention discloses a digital financial data sharing system and method, and belongs to the technical field of digital financial data analysis, and the system comprises a data standardization management module, a data access conversion interface module, a cross-mechanism authentication authority management module, a safety audit risk control module and an intelligent analysis cooperation platform module. The data standardization management module is used for unifying a data format to be XBRL, metadata definition and cleaning rules and eliminating data semantic differences between mechanisms, and the data access conversion interface module is used for supporting multi-source heterogeneous data access and providing a standardized output interface. The cross-institution authentication authority management module is used for realizing decentralized identity authentication based on a block chain technology, zero-knowledge proof and a federated learning technology, and controlling a data access range in a hierarchical manner in combination with role authority, and the security audit risk control module is used for monitoring data flow in real time. According to the invention, on the basis of realizing digital financial data sharing, the problem of data islands can be solved, and cross-mechanism data authentication can be facilitated.

The invention provides an equipment health examination method and system based on multi-agent collaboration. The method comprises the following steps: a data acquisition step: acquiring target data to generate a multi-dimensional feature vector; the target data comprises frequency data, process data and control data; an equipment monitoring step: monitoring the equipment based on the generated multi-dimensional feature vector, and outputting a monitoring result; and a deployment processing step: deploying and implementing a corresponding processing scheme according to a monitoring result. The invention provides an equipment early warning and diagnosis method and system based on multi-agent cooperation, and the method comprises the steps: combining the advantages of a professional mechanism model, an AI model and a large language model through the multi-agent cooperation of feature extraction, threshold adjustment, fault diagnosis, decision generation, summary review and closed-loop feedback; the problems that a traditional method is single in model, high in false alarm rate, lagged in decision and the like are solved.

The invention discloses a multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method, and relates to the technical field of liquid cooling energy efficiency optimization. The multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method comprises the following steps: S1, collecting and preprocessing two-phase liquid cooling control data; s2, the thermal load level of each area is evaluated periodically, a discrete time linear state space model is constructed, and the thermal load change rate is extracted; s3, the cold distribution amount of each area is quantified periodically, the pump speed and the valve opening target value are calculated, and an adjustment control instruction is generated; and S4, high heat risk and low heat stability areas are recognized, the cooling capacity compensation requirement is evaluated, a cooling capacity enhancement and recovery distribution value is output, and a corresponding control instruction is generated. The problems that an existing two-phase liquid cooling system generally only depends on temperature feedback for adjustment, so that cooling capacity distribution response lags behind, a short-time overheating phenomenon is prone to occurring in a high-heat area, and cooling capacity waste exists in a low-heat area at the same time are solved.

The invention discloses an adaptive modulation anti-multipath unmanned aerial vehicle swarm communication method and system based on MIMO-OFDM, and relates to the technical field of wireless communication, and the method comprises the steps: receiving a down-conversion baseband; synchronization and compensation are carried out; removing a cyclic prefix and carrying out fast Fourier transform; estimating a channel and noise on the training and pilot frequency, and correcting a common phase; performing frequency domain equalization and soft decision, forward error correction and cyclic redundancy check according to the estimation to obtain a control parameter, decoding a payload according to the control parameter, and performing statistics to form a link quality indication; modulating coding, bandwidth and spatial mode, configuring mapping, power and weight, and packaging physical layer protocol data; carrier sensing conflict avoiding sending, confirmation and retry are adopted to generate media access statistics; and the routing selects the next-hop multi-hop forwarding according to the transmission quality, and performs end-to-end statistics and recharge. Through cooperation of hierarchical synchronization and phase correction with cyclic prefix and FFT, crosstalk is suppressed, channel noise scale optimization soft decision is robust, data decoupling is controlled, and end-to-end availability is improved through multi-hop self-healing recharge.

The invention discloses an industrial control data processing system and method based on an embedded real-time operating system, and relates to the technical field of industrial automation control, and the industrial control data processing system comprises a dynamic weight evaluation module, a resource elastic distribution module and a dual self-healing verification module; and the dynamic weight evaluation module is used for generating a dynamic weight value according to the task type, the data security level, the deadline margin and the historical execution abnormal rate. According to the industrial control data processing system and method based on the embedded real-time operating system, the task criticality is quantified in real time through the dynamic weight evaluation module, and the real-time response capability and the resource utilization efficiency of the industrial control system are improved in combination with elastic resource allocation driven by load prediction. A dynamic weight mechanism ensures that the emergency task can instantly preempt resources, and the problem of low-priority task blocking caused by traditional static priority scheduling is avoided.

The invention belongs to the field of pollution prevention and control data processing, and particularly relates to a laboratory instrument data automatic acquisition and quality tracing system and a laboratory instrument data automatic acquisition and quality tracing method. A distributed search traceability mapping space is constructed through the storage mapping module to realize space-time-semantic composite indexing and classification detection of data, and a causal reasoning model of the quality judgment module is used for performing anomaly judgment; a reverse Hash traceability mechanism is innovatively designed, an index-traceability node graph and a segmented Hash heterogeneous node anchoring strategy are combined, Hash head, body and tail fragments are stored in three types of nodes of acquisition, storage and detection, and tamper-proof traceability of abnormal data and dynamic optimization of acquisition parameters are realized through cross-node aggregation verification; according to the invention, the problems of low data credibility and poor abnormal traceability efficiency in ecological environment monitoring are solved, and full-chain closed-loop management of data acquisition-analysis-traceability-acquisition configuration adjustment is supported.

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

A data management system establishes connections with access control systems which are delegated by a domain to control data access and maintain data access history associated with the domain. The system receives a group access permission of a set of data resources to a group of named entities and heterogeneous sets of metadata related to the data access history and generates graph objects. The graph objects include named entity nodes and resource nodes. The named entity node represents a named entity associated with an organization, and the resource node represents a data resource. The system traverses access paths that connect the named entity node and the resource node determines a utilization level of a set of access paths. Based on the utilization level, the system revokes the set of access paths, thereby revoking an access permission of the set of named entity in the group to the specific data resource.

The invention provides a construction method for a robot teleoperation body-sized model and a robot teleoperation system, and the method comprises the steps: carrying out the training and fine adjustment of a pre-trained body-sized model through master-slave robot control data, and obtaining a fine-adjusted body-sized model; deploying the fine-tuning body large model to provide robot control service, and collecting real machine execution data; obtaining scores of the real machine execution data, forming reinforcement learning training data pairs by the real machine execution data and the corresponding scores, and adding the reinforcement learning training data pairs into a reinforcement training data set; and performing reinforcement learning training on the fine-tuning body size model by using the reinforcement training data set. According to the method, the pre-training model is finely adjusted, the cost of manual intervention is effectively reduced, the reinforcement learning training data is collected by deploying the fine adjustment body model, the reinforcement learning method and the pre-training body model are organically combined, the advantages of the reinforcement learning method and the pre-training body model are fully played, and efficient learning and performance improvement of the robot in a complex environment are achieved.

The invention relates to the technical field of hydropower engineering, and discloses a single-core self-healing type looped network networking method and system, and the method comprises the steps: dividing a whole network into a plurality of sub looped networks; each sub-ring network operates independently; the light switch in each sub-ring network adopts an A / B end connection mode; reversely connecting the B end of the last measuring point to the A end of the initial measuring station by using an optical fiber to form a closed physical structure; configuring a protocol on the switch, and controlling a data flow path through the protocol; different sub-ring networks are connected through RJ45 ports, and a CAT5E shielded twisted pair or an Ethernet interface is used; when a certain node or link fails, the protocol automatically triggers a switching mechanism; the master station optical transceiver monitors the states of all nodes in real time and displays fault points through a network management interface. The system comprises a sub-ring network module, a control data flow module and a fault point display module. The problems that a traditional networking method of hydropower engineering is poor in reliability, and self-healing cannot be achieved after an intermediate node is disconnected are successfully solved.

The invention provides a multiphase collaborative purification intelligent treatment system for heavy metal wastewater, which relates to the technical field of wastewater treatment and comprises a pretreatment module, a main treatment module, an advanced treatment module, a post-treatment and recycling module, a sludge treatment module and an intelligent control module. The intelligent control module comprises a data acquisition layer, a control layer and a management layer, and intelligent control on each treatment module is realized by monitoring parameters such as pH value, ORP (oxidation-reduction potential), conductivity and heavy metal ion concentration of the wastewater on line. The data acquisition layer acquires processing parameters in real time through various sensors; the control layer executes control logic based on the PLC / DCS and monitors the operation state through SCADA (Supervisory Control And Data Acquisition); the management layer adopts AI algorithms such as a water quality-energy consumption correlation regression model and a time sequence water quality fluctuation learning model to optimize processing parameters. According to the system, the treatment process can be intelligently adjusted according to the characteristics of the wastewater, the maximization of the heavy metal wastewater treatment efficiency, the minimization of chemical consumption and the minimization of energy consumption are realized, and the treatment effect and the system stability are improved.

The invention discloses a logging-while-drilling reservoir parameter dynamic prediction method based on double attention convolution LSTM, and the method comprises the steps: obtaining logging-while-drilling data and drilling data in real time, and carrying out the preprocessing of the data; establishing a DTW dynamic time window model based on a gradual transition strategy, and adaptively adjusting the length of an input sequence according to the stratum change speed; a double attention convolution LSTM model is combined with the DTW dynamic time window model to establish a feature-time double attention mechanism, and two dimensions of features and time are adaptively fused based on the convolution LSTM; setting a buffer area, storing data and controlling data quality; setting an online increment fine tuning strategy; and performing instance training, and performing multi-angle evaluation on the performance of the double attention convolution LSTM model by adopting multiple indexes. According to the scheme, the influence of features and time on the prediction accuracy is fully considered, and the prediction accuracy is improved.

The invention relates to the technical field of temperature control, in particular to a temperature controller nonlinear compensation control method and system based on fuzzy logic, and the method comprises the steps: constructing a double-closed-loop control architecture of a temperature control system; main control data is generated through the outer loop fuzzy decision layer, and feedforward compensation and lag compensation processing is carried out on the main control data through the inner loop compensation execution layer to obtain a final control quantity; and adjusting operation parameters of the temperature control system according to the final control quantity. Through a double compensation mechanism of feed-forward compensation and lag compensation, accurate pre-judgment and self-adaptive adjustment of control data are realized, and the problems of control lag and nonlinear instability caused by sensor delay, inertia of an execution mechanism and environmental interference in a temperature control system are systematically solved.

The invention relates to the technical field of robot joint control, and discloses a robot dog mechanical joint control method. The method comprises the following steps: acquiring a whole-body joint real-time sensing data set which covers the torque reading of a multi-axial force sensor, the angle data of a joint encoder and the body attitude data of an inertial measurement unit; motion state compensation is carried out on the sensing data based on a dynamic window mechanism, and a dynamic compensation matrix containing kinematics compensation parameters is generated; and inputting the multi-modal motion control data set into a gradient descent model under space-time constraint to complete multi-modal motion feature fusion so as to obtain a fusion motion control data set. Joint behavior abnormity is detected through a Lyapunov stability analysis algorithm, and abnormal nodes are identified; and iteratively optimizing control parameters through a fuzzy PID optimization algorithm to generate an optimized control parameter set, finally constructing a three-dimensional virtual motion space model, and establishing a dynamic mapping relationship between a virtual track and an actual mechanism.

The invention discloses a two-phase immersed liquid cooling liquid level intelligent dynamic regulation and control system and method, and relates to the technical field of liquid cooling intelligent processing. The two-phase immersion type liquid cooling liquid level intelligent dynamic regulation and control method comprises the steps that S1, state observation data, execution control data and log data in the liquid cooling liquid level regulation and control process are collected and preprocessed, and a standardized liquid cooling state data set is constructed; s2, the synchronism of the evaporation strengthening trend is evaluated, and the opening degree of an electronic control valve is adjusted; s3, analyzing a control feasible region under gas-liquid balance constraint, and optimizing the working mode of the two-way pump; and S4, the consistency of the feedback trend and the rolling decision is analyzed, and the circulating flow of the cooling coil and the heat exchange intensity of the condenser are adjusted. The problems that when power consumption of an existing two-phase immersion liquid cooling system changes dramatically, due to lag control of a single sensor and lack of multi-dimensional data prediction, evaporation and liquid supplementing actions are prone to being mismatched, and liquid level decline, pressure abnormity and chip dry burning are caused are solved.

The invention relates to the technical field of data processing, and discloses a large steel structure hoisting precision real-time monitoring method and system. The method comprises the following steps: scanning and collecting the edge of a steel structure through a distributed sensor network, and obtaining structure data to obtain an original monitoring data set; extracting and analyzing structure transient response features based on the original monitoring data set, and generating a state feature matrix; analyzing a coupling relationship between local deformation and overall torsion to obtain a dynamic response prediction matrix; performing space-time registration and data fusion on the monitoring data, and constructing a state evaluation data set; carrying out optimization calculation on the hoisting parameters to generate a hoisting control data packet; and performing intelligent reasoning analysis in combination with historical case data, and outputting a control instruction sequence. Real-time monitoring of the coupling relation between local deformation and overall torsional deformation in the hoisting process of the large steel structure is achieved, and the technical defect that in the prior art, the structural deformation development trend cannot be accurately recognized and predicted is overcome.

The invention provides a situation awareness traffic control method and system based on a time sequence large model, and a medium, and belongs to the technical field of traffic control. The method comprises the following steps: processing a road monitoring video stream through a time sequence large model to determine a situation awareness data set corresponding to a first preset time period; and analyzing a sensing data change result of the situation awareness data set based on the historical situation awareness time sequence data, and matching each associated road section corresponding to the monitored road section of the first monitoring equipment so as to construct a road network space of a corresponding associated type. And according to the similar road correlation characteristics corresponding to the road network space, matching a correlation weight set corresponding to the multi-source data type so as to perform weighted fusion on the multi-source data of the monitoring equipment group in the road network space, and obtaining a road control grade judgment index corresponding to the road network space. And determining a corresponding traffic control strategy according to each road control grade judgment index and a preset situation awareness control data set, and sending each traffic control strategy to a traffic control management terminal.

The invention discloses an intelligent energy consumption distribution method for a multi-source energy system. The method comprises the steps of energy supply data fusion, dual-channel joint prediction, energy response lag correction, multi-objective optimization and intelligent energy consumption distribution. The invention relates to the technical field of data processing of power management and resource scheduling, and the method comprises the steps: carrying out the unified collection and archiving of operation control data and environment information of power grid energy, distributed photovoltaic energy and an energy storage system, and constructing an energy supply fusion data set; respectively extracting energy availability and user load characteristics by adopting a double-domain attention decoupling fusion joint supply and demand prediction method, and realizing double-channel joint modeling; modeling, estimating and correcting response lags of different energy sources in combination with a dynamic response curve reconstruction method; and further constructing a layered target penalty function containing real-time response error, economic cost, carbon emission and response penalty terms, and embedding the layered target penalty function into a particle swarm optimization algorithm to obtain an energy distribution strategy under multi-target optimization.

The invention discloses a landscape design system based on artificial intelligence man-machine interaction, and the system comprises a multi-mode analysis module which is used for extracting keywords and semantic description, and generating user intention data; the structure segmentation module is used for processing the site image, generating a semantic segmentation mask graph and constructing structure control data in combination with the user intention data; the text prompt module is used for converting the user intention data into text generation prompt information; the diffusion generation module is used for receiving the text generation prompt information and the structure control data and generating a landscape design drawing; the interactive feedback module is used for displaying the landscape design drawing, updating the semantic segmentation mask graph and text generation prompt information and generating control condition data; the three-dimensional modeling module is used for performing three-dimensional reconstruction and generating three-dimensional landscape representation; and the space feedback module is used for extracting space parameters, outputting an ecological analysis result and feeding back the result to the interactive interface. According to the invention, intelligent optimization of multi-modal intention-driven landscape map generation and three-dimensional structure modeling is realized.

The invention discloses a dynamic scheduling optimization method for a low-altitude logistics distribution network, and the method comprises the steps: a server side builds a multi-source sensing network through satellite remote sensing, an unmanned plane airborne sensor and ground traffic monitoring, fuses meteorological data, airspace control data and order distribution data which are collected in real time, and generates a four-dimensional space-time grid map; based on the four-dimensional space-time grid map, the server side adopts a TD3-GA hybrid intelligent algorithm to carry out path planning of the logistics distribution network; the edge calculation end generates an optimal scheduling scheme of the unmanned aerial vehicle group through a multi-objective optimization function based on the global path; and the server side performs security risk assessment on the optimal scheduling scheme by using a Bayesian network model, and dynamically adjusts a space-time routing strategy of the unmanned aerial vehicle cluster according to an assessment result. According to the method, in a large-scale unmanned aerial vehicle concurrent scheduling scene, the scheduling efficiency can be effectively improved, the response time delay is reduced, the risk prediction accuracy is improved, and the timeliness and safety of a low-altitude distribution network are remarkably improved.

The invention discloses a visual setting calculation system and method, and relates to the technical field of power grid setting calculation, and the method comprises the following steps: obtaining operation data of a target system, extracting feature data, and constructing a knowledge graph of entities and relationships, the feature data including control data; constructing a system structure diagram, and generating a semantic enhancement diagram in combination with semantic information in the knowledge graph; inputting the semantic enhancement graph into a pre-trained graph neural network model, performing node feature aggregation and edge weight learning in combination with a prior rule of a knowledge graph, and outputting sensitivity scores of all control data nodes on performance indexes; according to the sensitivity score, constructing a boundary constraint condition, and generating a control data setting candidate solution based on a CSP solver; performing parameter response simulation verification on the control data setting candidate solution, and screening out an optimal control data setting candidate solution; according to the application, accurate sensitivity scoring can be realized by fusing the knowledge graph, the semantic enhancement graph and the graph neural network.

The invention discloses a pilot behavior evaluation method and a pilot behavior evaluation system. The pilot behavior evaluation method comprises the steps that multi-modal data are acquired, and the multi-modal data at least comprise flight control data, airframe state data and airframe environment data; carrying out preprocessing on the multi-modal data; evaluation features are extracted from the preprocessed multi-modal data; and loading the evaluation characteristics to an evaluation model, and outputting an evaluation result of the pilot in real time according to the evaluation model. According to the technical scheme, the accuracy of pilot evaluation can be effectively improved, and the occurrence of flight accidents is reduced.

The invention relates to the technical field of industrial operation, and discloses a multi-interface industrial control device based on an OpenHarmony industrial operating system and a control method thereof, and the method comprises the steps: scanning an interface resource list of an industrial control computer after the OpenHarmony industrial operating system is started; industrial equipment connected to each interface is automatically found according to the interface resource list, and an equipment connection table is obtained; performing protocol conversion based on the device connection table to obtain a device communication data stream, and extracting a feature data set from the device communication data stream; and calculating an interface coordination scheme based on the feature data set, issuing a control instruction to each industrial device, and collecting execution feedback to obtain unified management and control data. Unified control and remote cooperation of the industrial control equipment are realized through the OpenHarmony distributed soft bus, the conflict problem during multi-protocol concurrent access is effectively solved, and local intelligent decision and cloud resource scheduling of the industrial operation system are realized.

An apparatus is provided having cache circuitry providing a plurality of cache lines to store data for access by a processing element, and cache control circuitry to control storage of data in the cache circuitry. The cache control circuitry is arranged to manage storage of data within the cache circuitry for data blocks of size X exceeding a size of data that is storeable within a single cache line of the cache circuitry. The cache control circuitry is arranged, for each data block to be stored in the cache circuitry, to determine in which state of a plurality of states to store that data block in the cache circuitry, the plurality of states comprising an uncompressed state where the data block is stored within a plurality Y of cache lines and at least one compressed state where the data block is represented in a form occupying less than Y cache lines within the cache circuitry. The cache control circuitry is further arranged to maintain a plurality of metadata entries in memory, where each metadata entry is associated with a data block of size X and is arranged to identify in which state that data block is represented when stored in the cache circuitry.

The invention provides a universal measurement and control system data flow control bus architecture method and system, and relates to the technical field of measurement and control, and the method comprises the steps: distributing independent address spaces for a plurality of driving modules through a main control module, building a parameter mapping table and a buffer region, and enabling a plurality of test channels to share an interrupt signal and store the interrupt signal in an interrupt vector register; generating an interrupt signal based on a relationship between the buffer data volume and a threshold; sequencing according to the channel priority reference value, and dynamically adjusting the priority based on the load rate; and the main control module determines an interrupt source according to the interrupt vector and dynamically adjusts a prefetching strategy according to the measurement and control data flow. The system data processing efficiency and the resource utilization rate are improved.