458 results about "Response delay" patented technology

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to the technical field of database operation and maintenance, in particular to a database operation and maintenance decision-making method and device based on a large language model.The method comprises the steps that real-time operation indexes and historical log data of a database are collected, and multi-dimensional time sequence features are extracted; and analyzing the characteristics by using a large language model, judging whether a performance bottleneck or an abnormal risk exists or not, and determining the relevance between the performance bottleneck or the abnormal risk and a database performance problem. If the risk exists, acquiring a system load sudden change trend and a resource request rate, quantifying a system pressure degree, and inputting a performance optimization model to generate an adaptive tuning strategy; calculating statement complexity through an SQL audit log, and selecting an index adjustment strategy in combination with an index optimization model; evaluating the influence of the tuning strategy and the index strategy on the throughput, the response delay and the resource utilization rate, and determining a target high-performance strategy; according to the scheme, accurate optimization and efficient operation and maintenance of database performance are realized through intelligent analysis and dynamic adjustment and optimization.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention relates to the technical field of safety monitoring, in particular to a safety monitoring management method and system based on the Internet of Things, and the method comprises edge intelligent perception, multi-mode cognitive fusion, danger reasoning and root cause positioning, digital twinborn decision deduction and alarm intelligent merging and response. Compared with the technical defects that in the prior art, response delay is high and key alarms are prone to being missed due to the fact that cloud centralized processing is relied on, a special AI reasoning chip is deployed on the edge side to execute lightweight model real-time preliminary screening, and high-value feature data are uploaded only after abnormity is confirmed; meanwhile, constructing a space-time sensing network deep fusion multi-modal evidence chain at the cloud; according to the architecture, a decision chain of industrial dangerous events from perception to cognition is shortened to be within a second level, collaborative optimization of millisecond-level local blocking of major risks and cloud deep analysis is realized, and the security defense timeliness and reliability of high-risk scenes are greatly improved.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention discloses a server resource dynamic scheduling method for dealing with video stream high concurrent access, and particularly relates to the technical field of computer network and intelligent scheduling. A user access behavior data set is constructed; a deep learning model is adopted to train an access hot spot prediction model based on the time sequence features to predict a future access hot spot area and a peak trend; acquiring response delay, CPU / GPU occupancy rate and bandwidth load information of the heterogeneous server group, and generating a resource state multi-dimensional parameter set; carrying out joint modeling on the model and the parameter set, constructing a resource scheduling priority model by adopting a graph neural network, and generating an optimal scheduling path graph based on an A * improved algorithm; task transfer, instance elastic expansion, cache preheating and other scheduling operations are executed according to the model; according to the method, the resource utilization efficiency and the service quality of the video system in a high-concurrency scene can be improved, and the method has the advantages of being high in real-time performance, intelligent in scheduling and high in self-learning capability.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention relates to the field of automatic driving vehicle control, in particular to a sightseeing vehicle anti-collision control method and system based on a multi-mode radar. Comprising the following steps: acquiring a multi-modal radar original signal, and performing timestamp alignment and noise reduction processing to generate standardized fusion data; performing multi-target classification based on a convolutional neural network, and outputting a target recognition result in combination with Doppler frequency shift compensation and distance calculation; generating a hierarchical response strategy through dual-threshold comparison and environment interference dynamic weight adjustment; the strategy is converted into a PWM control signal, and a control instruction is generated through relay driving adaptation and hardware compatibility detection; a relay is driven in real time, motor response is collected, and execution effect evaluation and time sequence marking are completed; and brake release and system parameter synchronization are realized through PID closed-loop control. The problems of insufficient multi-target identification precision, poor environmental interference adaptability and response delay are solved, and the active safety performance of the sightseeing vehicle is remarkably improved.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant optimal scheduling system and method, and the system comprises a data obtaining module, an edge calculation module, a prediction module, a scheduling controller, a topology reconstruction module, and an intelligent terminal device cluster. According to the invention, the edge computing module carries out localization processing and prediction on the sensing data, so that rapid generation and issuing of a scheduling scheme are realized, and the problem of response delay caused by network transmission and centralized computing of a traditional centralized architecture is avoided, thereby supporting millisecond scheduling feedback and improving the scheduling efficiency. The real-time response capability under the sudden load fluctuation or fault condition is remarkably improved, a multi-dimensional perception and prediction mechanism is constructed based on an LSTM neural network prediction model, the recognition and trend prediction capability of the system on meteorological disturbance, equipment aging and operation abnormity is enhanced, the intelligent level of the virtual power plant system is improved, and the real-time performance of the virtual power plant system is improved. The system can dynamically generate an optimal scheduling strategy to ensure stable operation of the virtual power plant under various working conditions.

The invention relates to the technical field of state monitoring, in particular to an electric drive system overheating protection monitoring method and system based on big data, and the method comprises the following steps: obtaining a power rising section and extracting a temperature curve, judging whether the temperature rise lags behind or not, marking response delay, recognizing thermal fluctuation lag through an expansion window, and extracting peak valley to judge trend deviation. And the positioning node tracks the heat path, identifies the interrupt structure and generates an early warning identifier. In the electric drive system operation process, the coupling relation between the power change trend and the temperature rise time sequence is combined, accurate recognition of temperature rise response delay is achieved, the position distribution of a heat path interruption structure is defined through continuous tracing of the heat node temperature rise sequence relation, and linkage performance between heat interruption structures is combined, so that the reliability of the electric drive system is improved, and the reliability of the electric drive system is improved. Concurrent characteristics of overheating abnormity are identified, the response capability and early warning accuracy of an electric drive system in the early stage of overheating risks are improved, and rapid positioning of potential thermal fault risks and intelligent division of risk levels are achieved.

The invention relates to the technical field of man-machine cooperation, and aims to solve the problems that the traditional man-machine cooperation process mainly depends on a preset flow and timing rhythm control, and is difficult to dynamically capture personnel misoperation, machine response delay or cooperation rhythm imbalance, so that response strategy lagging and low regulation and control efficiency are caused in the face of complex and variable man-machine interaction situations. According to the enterprise intelligent man-machine cooperation method and system, through comprehensive analysis with behavior response offset, cooperation rhythm consistency and a personnel fatigue state as core indexes, a set of intelligent man-machine cooperation process which is distinct in hierarchy, logic closed-loop and sustainable iterative optimization is constructed; the real-time response capability of man-machine cooperation and the production takt time stability are improved, the cooperation efficiency fluctuation can be dynamically monitored, the precise regulation and control strategy is output in time, quantitative evaluation and actual improvement of man-machine cooperation efficiency are achieved, and efficient production of a production line and the quality level of products are guaranteed.

The invention relates to the technical field of the Internet of Things, and discloses an animal physiological feature data acquisition system and analysis method based on the Internet of Things, and the method comprises the following steps: cleaning and standardizing original sensor data, including filling missing data, eliminating noise interference through a dynamic filtering technology, unifying the dimension of multi-source data, and analyzing the data; the method comprises the following steps of: acquiring a Beidou time service time reference, eliminating redundant features, interpolating and aligning asynchronous sampling data to the Beidou time service time reference, correcting a time sequence phase difference of physiological and behavior data through a dynamic path matching technology, and correcting GPS / Beidou positioning drift in combination with Kalman filtering. Through multi-source sensing fusion and an edge-fog-cloud cooperative computing architecture, the effective collection rate of animal physiological data is achieved, abnormal response is delayed and compressed, the flexible energy supply technology and dynamic communication optimization are combined, the endurance and communication success rate is maintained in a pasture, and the false alarm rate is reduced compared with a traditional method; common diseases such as abnormal body temperature, digestive system diseases and the like are accurately warned and covered.

The invention discloses an EHA drive control system three-closed-loop control system and a dynamic hysteresis compensation method. The technical problems that an existing electro-hydrostatic actuator is large in hydraulic hysteresis error, poor in dynamic response and insufficient in environmental adaptability are solved. The method comprises a three-stage control structure of a position control loop, a speed control loop and a current control loop, the position control loop introduces load disturbance feed-forward compensation based on a state observer, the speed control loop adopts an adaptive sliding mode control strategy, the current control loop executes a decoupling vector control algorithm, and the load disturbance feed-forward compensation is realized. And a dynamic hysteresis compensation model based on historical input and disturbance estimation is established in the controller. According to the system, the servo motor, the gear pump, the hydraulic cylinder and the sensor module are integrated into an integrated drive control structure, and the effects of improving the control precision, reducing the response time delay and correcting the hydraulic hysteresis error are achieved.

The invention provides a storage resource dynamic allocation method and system based on artificial intelligence, and relates to the technical field of storage resource dynamic allocation, and the method comprises the steps: obtaining the real-time response delay, data transmission bandwidth and node computing power data of each storage node in the current stage of a target task, and carrying out the cooperative processing of the data, thereby achieving the dynamic allocation of storage resources. Obtaining a storage resource real-time performance data packet containing the real-time load state of each storage node; analyzing the data packet based on an artificial intelligence model corresponding to the task stage to obtain a storage resource allocation target value; through a dynamic allocation optimization algorithm, a resource scheduling strategy is formulated according to a target value, a real-time control signal is generated, and dynamic allocation and scheduling of storage resources among storage nodes are executed accordingly, so that the dynamic allocation and scheduling of the storage resources among the storage nodes can be realized based on an artificial intelligence-based storage resource dynamic allocation method, and the dynamic allocation and scheduling of the storage resources can be realized through data co-processing, AI analysis adaptive to a task stage and the dynamic optimization algorithm. Accurate and dynamic allocation and scheduling of storage resources meeting target task requirements are realized.

The invention relates to a distributed data unified management and intelligent scheduling method based on data braiding, and the method comprises the steps: collecting the real-time state data of a node, and generating a node capability portrait; constructing a dynamic incidence matrix of the metadata and the node capability portraits; establishing a multi-dimensional scheduling evaluation model, inputting a business data dependency relationship, performance data in the node capability portrait and a node future load predicted by the LSTM model, and outputting an initial scheduling scheme; a static threshold value and a dynamic prediction threshold value are preset to serve as scheduling optimization triggering conditions, the initial scheme is optimized through a reinforcement learning algorithm, and an optimized scheduling decision is obtained; and sending a scheduling instruction containing a priority identifier to a corresponding node, collecting feedback data such as response delay and an error rate in real time, updating the association strength of the dynamic association matrix according to the feedback data, and optimizing the parameters of the multi-dimensional scheduling evaluation model. Unified management of distributed data is achieved, scheduling intelligence and accuracy are improved, node state changes can be dynamically adapted, and data processing efficiency and reliability are effectively guaranteed.

The invention relates to the technical field of on-line monitoring, in particular to a flue gas cooler leakage monitoring system and method.The method comprises the steps that a multi-source real-time sensing layer module continuously collects tube plate weld joint area temperature data, cooling water flow characteristics and flue gas sulfur-containing gas concentration, and the sampling period is 250 milliseconds; the intelligent feature fusion module performs noise reduction processing and dynamic weight fusion on the multi-source data and outputs a leakage probability vector; the dual-system collaborative early warning module maps the probability vectors into confidence levels, a 65% confidence threshold triggers an acoustic verification system to start, an acoustic emission sensor array captures fractured sound wave features and then compares the fractured sound wave features with a voiceprint library through a convolutional neural network, and an early warning signal is output when the fractured sound wave features exceed a 0.85 similarity threshold; and the dynamic knowledge base updates the case characteristics online according to the early warning result. According to the system, second-level identification and continuously optimized closed-loop monitoring at the initial stage of leakage germination are realized, and the problem of response delay caused by intermittent detection in the prior art is solved.

The invention discloses a joint motor control method and system based on an angle sensor, a medium and a product, and relates to the field of general control or adjustment systems.The method comprises the steps that real-time angle data and temperature data of the angle sensor are collected, and a real-time data packet is generated; determining a rapid compensation parameter corresponding to the temperature value, performing preliminary compensation on the angle value to obtain a preliminary compensation angle value, and generating a motor control parameter to control a joint motor to operate; storing a plurality of continuous real-time data packets into a compensation cache region; when the number of the data packets in the compensation cache region reaches a preset number threshold value, temperature change features and angle drift features are extracted, and accurate compensation parameters are calculated; and performing secondary compensation on the preliminary compensation angle value based on the accurate compensation parameter to obtain an accurate compensation angle value, and updating the motor control parameter based on the accurate compensation angle value. According to the invention, response delay caused by a compensation algorithm can be reduced.

The invention relates to the technical field of machine learning, in particular to a cooling pump optimization algorithm of a central air-conditioning water chilling unit based on a knowledge graph, which comprises the following steps of: acquiring an alignment relationship between a pump start-stop signal and current response time, extracting an action section and a path node sequence to construct a logic sequence, and identifying control signal alternation and direction deviation to extract a path limit number, and screening response delay and a load fluctuation section to correspondingly adjust an identification area. According to the method, behavior mapping between the pump action and the signal time sequence is constructed, alternating signals and direction deviation fragments in a control chain are extracted, a path limiting identifier is generated to be used for restraining control interference, a trigger area is regulated and controlled in combination with matching of load response and an equipment action section, and an accessible path is screened to replace a limited section; the path structure is recombined, instruction chains are sorted, the matching capability of path scheduling and the response collaboration of linkage nodes are enhanced, and the control process is promoted to be kept continuous and stable in a differentiated state.

The invention relates to the technical field of pressure control, in particular to a butterfly valve pressure self-adaptive adjusting method, device and equipment and a storage medium. According to the method, a pre-constructed neural network model is introduced, and user set pressure, actual measurement pressure, current control signals and error parameters serve as input variables; dynamic generation and real-time self-adaptive adjustment of PID control parameters are realized, the technical bottlenecks that parameter setting of a traditional PID controller is complex and adaptability to non-linear and time-varying systems is insufficient are effectively solved, and the parameter adjustment period is shortened; the weight parameter of the neural network is continuously optimized through a back propagation algorithm, so that the inhibition capability of the system on external disturbance is remarkably enhanced, the overshoot is reduced, and the long-term stability and control precision of pressure control are improved; the target step number is calculated in combination with butterfly valve hardware parameters, the accuracy and rapidity of stepping motor driving are ensured, and the problem of response delay of traditional PID control is effectively solved.

The invention discloses a distribution box room environment parameter integrated measurement method, and particularly relates to the field of multi-parameter measurement, and the method comprises the steps: firstly constructing a machine room three-dimensional model and a sensor correlation degree matrix, then building a heat balance, humidity diffusion and airflow motion model, and calibrating parameters; deploying a measurement system and calibrating through dual synchronization and cross check; establishing a sensor confidence evaluation system and an adaptive threshold based on 72-hour reference data; fusing calibration data by adopting a three-level correlation calibration mechanism; four types of measurement modes and conversion logics are designed, and measurement resources are dynamically allocated through environmental risk assessment; multi-dimensional state parameters are extracted, a comprehensive evaluation value is calculated through normalization, dynamic weighting and combinatorial algorithms, and five-level early warning response is achieved; according to the method, multiple models and an intelligent algorithm are integrated, the parameter measurement precision and the environment risk identification efficiency are improved, the response delay is reduced, the fault diagnosis accuracy is improved, and reliable technical support is provided for safe operation and maintenance of the distribution box room.

The invention relates to the technical field of control and adjustment, in particular to an intelligent control system and method for an induction cooker, and the method comprises the steps: collecting the material and temperature data of cookware through a multi-source sensing module; the collaborative prediction module dynamically adjusts the window size of the time convolution network and generates a power compensation parameter and a frequency tuning parameter; the simulation execution module executes triple calculation of particle swarm optimization, thermal inertia calibration and nonlinear constraint verification, and outputs optimization control parameters; and the optimization feedback module updates the weight of the thermal inertia model in real time through a double-loop feedback mechanism and calibrates prediction parameters. Perceptual data synchronous processing is achieved through edge calculation, knowledge graph driving load sudden change rapid decision is achieved, the simulation efficiency is improved through dynamic step length control, and material parameter mapping is optimized through historical deviation statistics. The problem of multi-link cooperative response mismatch under the load sudden change working condition is solved, the power regulation response time delay is shortened, and the heating efficiency stability and the safety protection precision are remarkably improved.

The invention discloses an exoskeleton attitude prediction system for building construction, particularly relates to the field of attitude prediction, and comprises a multi-dimensional sensing module, a data preprocessing module, an attitude prediction model module and a control decision module. A multi-dimensional sensing module collects joint movement, muscle movement and human-computer interaction force data through a nine-axis IMU, a surface myoelectricity sensor and a miniature strain type force sensor; the preprocessing module obtains standardized feature data through filtering, standardization and exception elimination; the attitude prediction model uses a CNN-LSTM hybrid architecture, and outputs a target attitude of 0.5-2 seconds; and the control decision module compares a prediction result with a safety threshold value, generates control instructions in three levels to drive exoskeleton correction, synchronous feedback and recording, solves the problem of response delay of a traditional exoskeleton, and improves the construction safety and efficiency.

The invention discloses an OpenHarmony multi-queue scheduler intelligent distribution method based on multi-dimensional load feature perception, and relates to an OpenHarmony multi-queue scheduler intelligent distribution method. The problems that the CPU utilization rate is low, the average response delay is high, and resource scheduling self-adaptive adjustment and optimization cannot be achieved are solved. The method comprises the following steps: step 1, deploying a data acquisition module; step 2, feature preprocessing and coding; step 3, task load classification; step 4, executing dynamic optimization of scheduler parameters according to a prediction result; and step 5, performing performance feedback and online model updating. The invention belongs to the technical field of operating system resource management and artificial intelligence.

The invention relates to the technical field of information security management, in particular to a multi-modal data dynamic desensitization method and system based on machine learning, multi-modal features such as texts, images and time sequences are jointly extracted through CNN-Transform-LSTM, 1-5 sensitive levels are evaluated by adopting a cross-modal attention model, a self-adaptive desensitization strategy is generated through an improved MOEA / D-DE algorithm, and the dynamic desensitization of the multi-modal data is realized. And realizing cross-modal collaborative desensitization by combining GNN. The system dynamically adjusts parameters through TD3 reinforcement learning, the response delay is less than or equal to 100ms, the Bi-LSTM-AE model evaluates privacy and utility in real time, and the block chain evidence storage whole process is realized. The method solves the problems of precision and utility imbalance, poor dynamic adaptability and insufficient multi-mode collaboration in the traditional technology, and is suitable for the fields of finance, medical treatment and the like.

The invention provides a virtual power plant load prediction and demand response optimization method and system, and relates to the technical field of power systems, and the method comprises the steps: carrying out the multi-scale time sequence decomposition of historical data, obtaining a hierarchical feature set, recognizing a load fluctuation transmission link through cross-equipment correlation analysis, calibrating a load prediction time reference based on response delay time, and carrying out the optimization of demand response. The adjustable capacity and response time delay of energy equipment are calculated, the equipment is divided into a plurality of virtual aggregation units, a collaborative scheduling rule is established, and a distributed demand response instruction is generated through Nash equilibrium optimization. According to the invention, the load prediction precision and demand response capability of the virtual power plant are improved.

The invention discloses a computer system based on a cross-model Internet of Things equipment access efficiency calculation model, which integrates an equipment performance sub-model, a network state sub-model and a response prediction sub-model. The identification module is used for identifying a device inherent identifier of the Internet of Things device to be accessed so as to obtain a response time statistical characteristic value and a historical communication parameter set; the prediction module is used for acquiring network state parameters in real time, calling the response prediction sub-model to calculate expected response time, and optimizing the historical communication parameter set to generate an initial communication parameter set; the comparison module is used for monitoring the actual response delay and the data packet loss rate of the current access equipment and obtaining a delay deviation value; and the adjusting module is used for dynamically adjusting the bandwidth allocation strategy in the initial communication parameter set when a preset condition is met, and synchronously updating the data analysis priority to generate a target communication parameter set. According to the invention, differentiated access control of different types of Internet of Things equipment can be realized.