1759 results about "Device status" patented technology

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention belongs to the technical field of multi-target global dynamic production scheduling, and discloses a manufacturing resource dynamic production scheduling data comprehensive processing method, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a field mapping table, constructing an equipment fingerprint database in combination with historical equipment performance data, dynamically adjusting confidence coefficient weight, and further judging an equipment state. Based on an equipment state judgment result, integrating operator report data and multi-source heterogeneous data through a preset rule base trigger event, and generating an abnormal event flow with a context tag; based on the abnormal event type and the equipment state judgment result, a recommendation scheme is constructed through a production scheduling knowledge graph, and a production scheduling instruction set is generated; according to the production scheduling instruction set, generating a local optimization scheme and a global optimization strategy, and forming a final execution scheme; calculating a resource utilization rate and a bottleneck process index through the 3D virtual workshop model, and generating a difference analysis report; according to the method, global resource scheduling and closed-loop iteration are realized, and the production efficiency and the system robustness are improved.

The invention discloses a digital twin enabling intelligent pump station preventive operation and maintenance system. Comprising a dynamic twin construction module, a multi-source heterogeneous multi-modal data acquisition module, an edge computing and cloud collaboration module, an equipment health degree evaluation module, a predictive maintenance decision module, a cross-system data fusion module, a self-evolution knowledge graph module, an intelligent diagnosis and early warning module, a self-adaptive maintenance decision module and a man-machine collaboration interaction module. And the dynamic twin construction module comprises a physical-virtual synchronous calibration mechanism and an equipment degradation parameter dynamic updating mechanism. According to the method, the limitation problem of a traditional static model is solved, the method can adapt to nonlinear changes under complex working conditions, the comprehensive judgment and prediction capability of the system on the equipment state can be enhanced, the energy utilization efficiency is improved, the energy consumption is reduced, the decision and verification mechanism is perfected, and the data acquisition and processing problem is improved; the problems of timeliness and flexibility of the model are solved, and the computing architecture and the response capability are optimized.

The invention relates to the technical field of project management, in particular to an intelligent management method and system for a civil engineering project, and the method comprises the following steps: obtaining personnel distribution, equipment state and material inventory data, extracting a key task node and an execution state, recognizing a progress lagging task, screening a key path node, analyzing a time difference, and marking a progress trend. And identifying an equipment influence section, judging resource allocation imbalance, and generating a progress monitoring index. According to the invention, through multi-source data acquisition of personnel distribution, equipment state and material inventory, difference identification between a task state and a plan node is enhanced, progress lag early warning efficiency is improved, progress fluctuation dynamic classification and trend identification are realized, construction rhythm optimization is assisted, and through coincidence analysis of equipment operation and a task fluctuation area, the progress lag early warning efficiency is improved. The construction efficiency bottleneck is identified, the scheduling accuracy is improved, fine progress monitoring is realized through investment and period difference comparison, and the progress control capability and the collaboration of on-site resource allocation are comprehensively enhanced.

The invention relates to the technical field of data processing, and discloses a PTP network performance monitoring method and system based on big data analysis, and a medium. The method comprises the following steps: acquiring PTP message data, equipment state data and RTP stream data; preprocessing the data and calculating a clock error; constructing a time sequence database and an analysis model; real-time monitoring and anomaly detection are executed; performing performance scoring and fault positioning by applying machine learning; and carrying out network optimization and predictive maintenance. According to the PTP network performance monitoring method based on big data analysis, global state monitoring, intelligent anomaly detection, accurate fault positioning and predictive maintenance of the PTP network are achieved, and therefore the high-precision time synchronization requirement of a broadcast and television IP making and broadcasting system is guaranteed.

The invention relates to the technical field of communication, in particular to a micro-segmentation-based multi-tenant zero-trust security system, which comprises a tenant network topology sensing module for acquiring network structures and service dependencies of tenants and generating a tenant-level network topology graph and a communication graph; a strategy rule automatic generation module generates a micro-segment access control strategy; the context-aware risk assessment module collects a user identity, an equipment state and a behavior track, and generates an access risk score; the strategy dynamic adjustment and application module updates the access control strategy; and the tenant isolation and zero-trust interaction module deploys a security sandbox to complete trust verification and isolation protection of cross-tenant requests. According to the method, the micro-segmentation strategy is generated by automatically identifying the service dependency boundary, the access control rule is dynamically adjusted based on the context, and refined isolation and risk-driven defense in a multi-tenant environment are realized in combination with the security sandbox and the zero-trust mechanism, so that the security and controllability of the system are remarkably improved.

The invention relates to the technical field of audio and video analysis and data processing, in particular to an AI-based multimedia equipment operation and maintenance management system, which comprises a data acquisition module, an analysis module, an equipment state modeling module, a fault prediction module, a maintenance strategy optimization module, a resource allocation module and a visual platform. The data acquisition module acquires temperature, vibration, audio and video feature data of the equipment through the sensor and the video equipment, and the data are preprocessed and then transmitted to the cloud; the equipment state modeling module constructs a dynamic baseline model based on historical data, abnormal signals are generated through real-time comparison, and the visual platform realizes multi-dimensional state monitoring through 3D topology rendering, AR auxiliary diagnosis and early warning linkage. The system solves the problems of response lag and low resource utilization rate caused by dependence on manual operation and maintenance in the prior art through a closed-loop management mechanism driven by a data stream, and improves the fault diagnosis precision and the operation and maintenance efficiency.

The invention discloses an intelligent campus operation and maintenance management system based on digital twinning, which relates to the technical field of intelligent campus operation and maintenance management and comprises a digital twinning monitoring module, an intelligent task scheduling and resource optimization module and a space optimization and path planning module. According to the system, the visual and intelligent level of equipment management is improved by monitoring the equipment state in real time and constructing an accurate equipment virtual model, the task execution efficiency is improved by optimizing task scheduling and resource allocation, meanwhile, the energy consumption of the equipment is reduced, the service life of the equipment is prolonged, the space conflict between the equipment is solved, and the equipment management efficiency is improved. The cooperative work capability of the equipment is improved, the path planning and task execution sequence is optimized, and the safety and stability of the intelligent campus operation and maintenance system are improved.

The invention relates to the technical field of smart energy management, in particular to a public energy consumption equipment operation and maintenance management system suitable for a smart park. The system comprises an equipment operation state sensing module, an energy consumption characteristic dynamic analysis module, an operation parameter optimization module, an equipment state balance regulation and control module, an equipment operation safety constraint verification module and an equipment cooperative operation execution module. According to the invention, through combination of time sequence analysis and load fluctuation trend extraction, dynamic monitoring and trend arrangement are realized, the accuracy and comprehensiveness of operation data are improved, the operation diagnosis capability is improved, the parameter adjustment flexibility is enhanced, multi-device cooperative regulation and control and load balancing are realized, energy consumption fluctuation is reduced, and the system efficiency is improved. And in combination with safety verification and offset risk analysis, potential risk areas are adjusted in real time, the operation reliability is enhanced, a global regulation and control scheme is dynamically generated, energy consumption waste is reduced, the management efficiency of public energy consumption equipment is comprehensively improved, and the efficient energy-saving target of the smart park is achieved.

The invention belongs to the technical field of smart home control, and particularly relates to a smart home generation strategy optimization system based on adaptive learning, and the system comprises the steps: collecting the operation history, sensor data and equipment use modes of a user in a smart home system, and determining an evaluation standard and an optimization direction; extracting features according to user behaviors, environment changes and equipment states, and constructing a feature matrix; selecting a reinforcement learning method for modeling according to an optimization problem, designing an adaptive learning mechanism, and enabling the model to be continuously adjusted according to real-time data in an operation process; according to the method, historical data is used for training the model, a reward mechanism in reinforcement learning is used for guiding the implementation of an optimization target, a control strategy adapting to the current environment and demand is generated based on model prediction, and the method has the effects of better meeting the use demand of a user for the smart home and improving the intelligent level of the system and the user experience.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention discloses a remote printer monitoring and fault prediction system based on the Internet of Things, and relates to the technical field of the Internet of Things and artificial intelligence, and the system comprises a data collection module which is used for collecting equipment state data of a remote printer in real time and carrying out the preprocessing of the equipment state data; the data processing module is used for integrating the preprocessed equipment state data into a data matrix and a label vector; the model construction module is used for outputting a final feature set by using a random forest model and combining a Gumbel-Sigmoid activation function; and the fault prediction module is used for constructing a fault prediction model by using the bidirectional LSTM to output the predicted fault probability of the current time point and dividing a fault interval data pool. By introducing the combination of a random forest algorithm and a Gumbel-Sigmoid activation function, a feature selection process can be automatically optimized, a feature set which is crucial to fault prediction can be accurately screened out, and a bidirectional LSTM model is adopted to capture time sequence information, so that the fault prediction precision and the real-time response capability are remarkably improved.

The invention discloses an intelligent house household electric appliance remote control system, which relates to the technical field of intelligent homes, and comprises an instruction receiving module, an instruction analysis module, a priority judgment module, a decision processing module and a state verification and feedback module: the instruction receiving module is used for receiving control instructions from a plurality of users and an automatic program; by introducing authority priority management, multi-dimensional judgment and intelligent scheduling, the system effectively solves instruction conflicts, and stability and reliability are improved. In family and security scenes, high-priority instructions respond in time, low-priority instructions are delayed reasonably, and the correct equipment state is ensured. The transparent feedback mechanism improves the trust of the user, and optimizes the operation efficiency and experience at the same time.

The invention relates to the technical field of operation and maintenance control, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent equipment configuration changing method and device, equipment and a medium. Generating a preliminary equipment configuration instruction sequence based on the change intention information; rechecking the configuration instruction through the intelligent checking component, and generating a rechecked configuration instruction sequence; sending the rechecked configuration instruction to the target equipment and executing the rechecked configuration instruction; and verifying whether the equipment state meets the expectation, and if not, executing a rollback operation. Configuration accuracy is ensured through knowledge representation, configuration errors are automatically detected and corrected through intelligent verification, configuration efficiency and safety are improved through automatic issuing and state verification, abnormity is quickly recovered through automatic rollback, and reliability and stability of configuration change are improved.

The invention discloses a communication infrastructure intelligent operation and maintenance management system based on cloud computing. The system collects multi-source operation and maintenance data through the data acquisition module, and the multi-source operation and maintenance data is transmitted to the cloud computing center module after being processed by the data transmission and preprocessing module. The cloud computing center module performs deep analysis on data by using a plurality of intelligent algorithm-based sub-modules such as equipment state evaluation, fault prediction, resource optimization and allocation, operation and maintenance decision suggestion and the like to generate an operation and maintenance strategy, the operation and maintenance strategy is implemented by the operation and maintenance operation execution module, and the operation and maintenance condition is visually presented by means of the visual display and monitoring module. According to the method, the powerful computing power and storage capacity of cloud computing are utilized, and multiple algorithm advantages such as machine learning and time sequence analysis are combined, so that intelligence and automation of operation and maintenance management of the communication infrastructure are realized, the operation and maintenance efficiency and quality are effectively improved, and stable and reliable operation of communication service is guaranteed.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The present invention provides a multi-project scheduling optimization algorithm and system based on resource constraints. By loading test tasks, personnel information and equipment status, a task dependency graph is constructed to determine the execution order. Through the priority calculation module, each task is assigned a priority according to the deadline, resource requirements and task urgency, ensuring that high-priority tasks are executed in a timely manner. The scheduling execution module uses the shortest job first (SJF) or backtracking algorithm to optimize task scheduling. In addition, the real-time monitoring module monitors the task progress and resource status, and the conflict detection and processing module can adjust the personnel and task allocation in real time to cope with resource contention. Finally, the evaluation and optimization module generates an evaluation report after the project is completed, records the resource utilization efficiency, and provides improvement suggestions for subsequent projects. The present invention effectively improves the efficiency of multi-project management in a resource-constrained environment, and is suitable for scientific research, engineering and other multi-task collaborative fields.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention relates to the technical field of artificial intelligence, in particular to a family service method and system based on artificial intelligence, and the method comprises the following steps: obtaining a statement analysis action sequence and a task direction, collecting the feedback of a device to generate a state structure, judging an instruction trend, recombining a statement to generate a behavior chain, and disassembling an action recognition conflict to extract a main control path. Analyzing a behavior time sequence generation prominent trend, and adjusting a display sequence to generate a function scheme. According to the method, through semantic component extraction and equipment state mapping, the accuracy of instruction target recognition is improved, word order rearrangement and logic connection enhance the continuity of a behavior chain, a control path is optimized and sorted according to verb density and object association, the task scheduling precision is improved, and behavior time sequence analysis recognizes a high-frequency operation forward trend; according to the method, priority dynamic adjustment and rearrangement of recommended content in combination with instruction frequency and time sequence characteristics are realized, the response initiative and the service matching degree are enhanced, and collaborative optimization of semantic understanding, behavior prediction and content recommendation is integrally realized.

The invention discloses a power proxy service system and method based on artificial intelligence, and the method comprises the steps: collecting power data in real time through an intelligent electric meter and a sensor, and uploading the power data to a cloud platform through the Internet of Things for preprocessing and marking; predicting the power load by using time sequence analysis, regression, SVM and LSTM methods in combination with weather, season and holiday factors; power grid load dispatching is optimized through reinforcement learning or a genetic algorithm, and automatic allocation is achieved; the sensor monitors the state of the power equipment in real time, predicts the fault risk and gives an alarm in advance; the system provides energy-saving suggestions according to historical power consumption data of a user, and adjusts an equipment operation mode in real time; the intelligent customer service server pushes personalized electricity consumption suggestions based on natural language processing; an encryption technology and multiple identity authentication are adopted to guarantee data security, regular security audit and vulnerability repair; according to the electric power proxy service system and method based on artificial intelligence, integration with an AI system and construction and optimization of an intelligent power grid and renewable energy technology are promoted.

The invention discloses a computer network equipment fault positioning method and system and a storage medium, and relates to the technical field of fault positioning. The method comprises the following steps: performing multi-source operation acquisition on computer network equipment to generate an equipment state data set; according to the equipment state data set, abnormity judgment is carried out according to the dynamic baseline, monitoring extraction is carried out according to a judgment result, and an abnormal event sequence is constructed; performing association mapping based on a computer network equipment topological graph, constructing a network equipment dependency graph, traversing the network equipment dependency graph according to an abnormal event sequence to perform traceability positioning, and determining a fault source equipment identifier set; and performing fault root cause analysis according to the fault source equipment identification set, determining fault generation root cause data, and feeding back the fault generation root cause data to the network operation and maintenance unit to execute automatic fault handling. The technical problems of low network equipment fault positioning efficiency and poor positioning accuracy in the prior art are solved, and the technical effect of accurately and efficiently positioning the fault source is achieved.

The invention discloses a smart home control system and method based on a graph attention network, and belongs to the technical field of smart home. The method comprises the steps that S10, multi-source data from a smart home system are collected, and real-time data are collected through a plurality of sensors and a user interaction interface; s20, constructing a graph structure model by using the multi-source data; s30, calculating a correlation weight between the nodes by using an attention mechanism, and generating an optimized equipment control strategy; s40, generating an equipment control instruction according to an output result of the graph attention network; s50, based on user operation feedback, continuously adjusting and optimizing the graph attention network model through a self-supervised learning mechanism; and S60, continuously monitoring the environment parameters and the equipment state, and updating the equipment control strategy in real time. The method has the beneficial effects that efficient fusion of multi-source data, intelligent coordination of user preferences, function coordination of equipment and self-adaptive adjustment of users and environments can be realized.

The invention relates to the technical field of network security, in particular to a network intrusion detection method and system based on behavior analysis, and the method comprises the steps: collecting real-time data of a target network, the real-time data comprising network flow data, user access logs, equipment state parameters, inter-user interaction logs and trust relationship data; performing space-time correlation analysis on the real-time data to generate a dynamic behavior graph; performing anomaly detection on the dynamic behavior map to generate an abnormal node list; and executing attack blocking based on the abnormal node list. According to the invention, the recognition and defense capabilities of abnormal behaviors are effectively improved.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention discloses a service-based calculation task dynamic abstraction method and system, and relates to the technical field of calculation task scheduling. The service-based calculation task dynamic abstraction method comprises the following steps: receiving and analyzing a to-be-executed task, and dividing the to-be-executed task into a plurality of independent service units; constructing a directed acyclic graph representing the dependency relationship between the service units, and executing topological sorting based on the directed acyclic graph to determine an execution sequence; and according to the resource demand of each service unit and the current system equipment state. According to the method, the technical problems that a traditional task scheduling method comprises but is not limited to the following technical problems that resource allocation is rigid, a heterogeneous computing environment cannot be dynamically adapted, and the hardware utilization rate is low; parallel arrangement is low in efficiency, depends on manual definition of an execution sequence, lacks an automatic arrangement capability and is difficult to deal with a complex task process; the real-time performance is insufficient, the task execution process is solidified, and the resource allocation and execution path cannot be dynamically adjusted according to the runtime state.

The invention provides an intelligent factory decision control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining equipment operation state data, customer cooperation data, customer order data and material feature data, determining a customer value level according to the customer cooperation data, and carrying out the health assessment based on the equipment operation state data, thereby achieving the intelligent factory decision control. Equipment state evaluation parameters are obtained, an initial scheduling plan is generated according to the customer order data, the customer value level and the equipment state evaluation parameters, a preset production line digital twin model is called in combination with the material feature data to perform scheduling verification optimization, scheduling optimization compensation parameters are obtained, and an optimized scheduling plan is obtained after optimization adjustment; according to the method, multi-dimensional association is constructed through clients, equipment and materials, global resource optimal allocation of scheduling is achieved, delay risks are found in advance through a twin model, adjustment and optimization are conducted through dynamic feedback, active optimization is achieved based on pre-verification and real-time compensation, decision global optimization coverage is reserved, and dynamic adaptability is improved.

The invention relates to the technical field of operation ticket processing, and discloses an operation ticket generation method and device, equipment and a storage medium, which are used for improving the adaptability of the operation ticket generation method to different substation operation scenes and improving the accuracy, safety and efficiency of the generated operation ticket. The operation order generation method comprises the steps of performing semantic analysis on a to-be-processed scheduling order to obtain structured scheduling order data; performing dynamic topology analysis on the structured scheduling order data according to the real-time equipment state data to obtain a real-time topology analysis result; matching in a preset operation ticket database according to the structured scheduling order data, and inputting the processed historical topology analysis result, the ticket forming template, the structured scheduling order data, the real-time topology analysis result, the self-defined ticket forming rule and the cue word template into a large language model for ticket forming to obtain an initial operation ticket; performing safety verification on the initial operation ticket to obtain a safety verification result; and generating a target operation ticket based on the interaction information of the security verification result.

The invention relates to an efficient test optimization method, system and device for chip test equipment, and the method comprises the steps: obtaining the equipment state data and test management information of the chip test equipment, carrying out the test unit division of the equipment state data based on the test management information, and obtaining the regional test information; acquiring real-time operation data of the chip test equipment, and performing matching analysis on the real-time operation data and the regional test information to obtain a real-time test task group; acquiring chip performance data acquired by the chip test equipment, performing load prediction on the regional test information to obtain a test load trend, and performing resource planning analysis with a real-time test task group to obtain an initial test scheme; performing exception identification on the chip performance data to obtain chip exception information; and performing global optimization on the initial test scheme according to the priority test sequence to obtain a global test strategy. According to the invention, fine management of different test tasks can be realized.

The invention discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.