299 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 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 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 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 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 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 heating furnace group control system and method based on the Internet of Things, and relates to the technical field of industrial Internet of Things heating furnace control. The adaptive edge calculation module fuses algorithm processing data and reduces response delay; the dynamic thermal efficiency optimization module predicts and improves thermal efficiency; the digital twinborn decision support module realizes fault prediction; the collaborative energy scheduling module optimizes multi-furnace operation; the block chain security management module guarantees data security; and the cognitive interaction interface module realizes multi-modal interaction, and the system also comprises extension modules such as a multi-scale combustion optimization module and the like, so that accurate control and intelligent management are realized. According to the invention, accurate acquisition and real-time processing of multi-dimensional data of the heating furnace group are realized, the heat efficiency is improved, and energy consumption and emission are reduced; the system has the capabilities of fault prediction, cooperative scheduling and data security guarantee; and the man-machine interaction experience is improved, the production efficiency is improved, and intelligent upgrading of industrial heating is promoted.

The invention relates to the technical field of linkage control, in particular to a real-time industrial state prediction and linkage control method, which comprises the following steps of: acquiring data of surface sensors of a main pump, a cooler and a steam generator, eliminating an abnormal sampling group, generating a configuration state sequence, extracting a maximum amplification point index, constructing a linkage track set, extracting cross-equipment characteristics and judging sudden change intensity. And generating a pre-triggering section, analyzing trend consistency and response delay, generating a control driving sequence, and outputting a linkage regulation and control instruction set. According to the invention, the method can accurately lock a state change section and recognize a trend pre-triggering segment through sliding a construction window and fusing the sudden change intensity of the features such as current and temperature, and effectively improves the accuracy of the control cooperation judgment through combining the dual constraints of trend consistency and delay time limit. And after the consistency of the control action is judged by combining the change direction and amplitude stability, an instruction is issued, so that the real-time prediction capability of the equipment operation state under the complex working condition can be enhanced, and the response time is shortened.

The invention relates to the technical field of voice interaction, in particular to a dialogue type AI low-delay response control method and system based on semantic analysis. The method comprises the following steps: firstly, generating a driving intensity correlation factor of a current time window according to the change of CAN bus data; further monitoring the output of the NLU, analyzing the acoustic intonation raising characteristics, and obtaining an interaction suppression coefficient in combination with the driving intensity correlation factor; further acquiring a time pressure coefficient based on the mute duration after the user stops sounding; further comparing the interaction suppression coefficient and the time pressure coefficient of the current time window to obtain a response judgment value; further analyzing the change trend of the interaction inhibition coefficient, and updating an environment improvement flag bit; and finally, according to the driving intensity correlation factor, the environment improvement flag bit and the response decision value of the current time window, carrying out multi-level response decision control, and carrying out selective reset of an interaction state, thereby solving the problems of wrong truncation and response delay of voice interaction under dynamic driving.

According to the large language model reasoning optimization method and device, the storage medium and the computer equipment provided by the invention, the multi-dimensional monitoring data is acquired, and then the multi-dimensional monitoring data is input into the pre-trained environment decision network to obtain the optimal model configuration. The current resource state is evaluated according to the multi-dimensional monitoring data, and the optimal execution path is determined according to the resource state and the optimal model configuration. And finally, adjusting the target model based on the optimal model configuration, and performing model reasoning according to the optimal execution path in the reasoning process of the target model. In the process, the resource state is evaluated and the optimal model configuration is determined by collecting the multi-dimensional detection data, and then the optimal execution path is dynamically determined, so that the model structure and the calculation strategy can be dynamically adjusted according to the state during actual operation, and the response delay is effectively reduced and the resource utilization efficiency is improved while the reasoning precision is guaranteed.

The invention discloses an AGC information physical system reliability verification method and system, and relates to the technical field of power dispatching automation. The problems that an existing AGC system only adopts a static operation mode and an ideal communication condition in a simulation test, and coupling response of continuous operation and sudden disturbance of a power grid under a long time scale is difficult to reproduce truly are solved. The state evolution under event driving is realized by constructing three types of simulation event sources of a time sequence operation sample, a master station control instruction and external disturbance and setting a priority mechanism of external disturbance gt, master station instruction gt and time sequence propulsion; an uplink channel fault model is introduced to simulate on-off, delay and errors of measurement data, and a downlink channel fault model is introduced to simulate transmission abnormity of a control instruction; closed-loop simulation of frequency response and electrical quantity updating is realized by combining promotion of a rotor motion equation and dynamic load flow calculation; and finally, by analyzing indexes such as frequency deviation, tie line deviation and control response delay, comprehensive reliability evaluation of the AGC system in a complex information physical environment is completed.

The invention discloses a self-adaptive apple maturity lossless picking flexible claw and a picking method. The device comprises a base assembly, and a visual recognition module, a flexible clamping jaw assembly, a distributed pressure sensing module, a transmission execution module and a control unit are integrated on the base assembly. And the visual module adopts a depth camera and a YOLO11n model, judges the maturity grade in real time based on the apple red area proportion and is matched with the corresponding grabbing threshold force. The pressure sensing module is a thin film pressure sensor, and multi-point pressure feedback is achieved through a plurality of pressure detection points. The transmission module adopts a single motor to drive a lead screw nut lifting table, opening and closing of clamping jaws are synchronously controlled, the structure is light and simple, and cost is low. The picking method follows the process of recognition, matching, force control and picking, a cutter is not needed, lossless and high-success-rate self-adaptive picking is achieved, the problem of damage or failure caused by traditional fixed force control is effectively solved, the method is suitable for dense orchards, and the intelligent and lossless levels of apple picking are improved. The control unit is low in response delay and small in overshoot, and it is ensured that the grabbing force is accurately matched with the fruit state.

The embodiment of the invention provides a multi-level cache access method, system and device and a storage medium, and relates to the technical field of data cache access, and the method comprises the following steps: obtaining a key field in a user auditing request, and converting the key field into a feature vector; searching whether a reasoning result corresponding to the feature vector exists in a first preset cache or not; if yes, a reasoning result is returned to the user; if not, a corresponding semantic partition is found in a second preset cache, and a historical cache vector with the highest similarity and a corresponding reasoning result are determined from the semantic partition; and determining a reasoning result corresponding to the feature vector according to the feature vector, a historical cache vector and a reasoning result corresponding to the historical cache vector. In this way, a two-stage cache mechanism based on the feature vector is constructed, a corresponding reasoning result is found in combination with semantic partition, the cache hit rate is increased, repeated reasoning of a large model is reduced, response delay is reduced, and therefore the requirements for high redundancy and high real-time performance of cache data reasoning are met.

The invention relates to the technical field of precision shell contour metering, and discloses an online metering method for a precision shell contour, and the method comprises the steps: obtaining an original sampling point cloud and a feature point group of a to-be-measured workpiece in a conveying state; resolving a real-time pose matrix of the to-be-measured workpiece relative to a preset measurement reference by using a spatial topology constraint relationship of the feature point groups; carrying out differential processing on a real-time pose matrix in the sampling sequence, and synthesizing an instantaneous motion vector corresponding to a sampling moment; in combination with a signal response time delay constant of the sampling system, phase compensation is carried out on a motion displacement deviator induced by signal conversion delay, and a corrected sampling point cloud is generated; according to the method, the geometric distortion of the point cloud generated by response delay of the sensor is solved, the problem of instantaneous drift of the measurement reference in an unsteady state conveying state is solved, and the contour reduction precision in a complex working condition is improved.

The invention relates to the technical field of mechanical state monitoring, and discloses a guide rail curvature online detection method and system, and the method comprises the steps: obtaining the multi-mode synchronous measurement data of a guide rail; carrying out preprocessing and space-time registration on the multi-modal synchronous measurement data; performing temperature compensation on the high-precision registration data; the curvature distribution of the guide rail fused with the displacement data is calculated, and mutation points are recognized through a curvature change rate threshold method for segmented modeling; calculating a multi-dimensional curvature parameter, and carrying out abnormal mode recognition by adopting a convolutional neural network; short-term early warning is carried out at the edge end, long-term evolution prediction is carried out at the cloud end, and predictive maintenance suggestions are obtained; according to the invention, transient bending deformation in a high-speed operation state can be captured, edge end data processing and early warning response delay are shortened, and real-time monitoring is truly realized.

The invention relates to the technical field of software testing, and particularly provides an automatic operation method for instrument testing software, which comprises the following steps: if a determined state parameter exceeds a preset threshold range, triggering an abnormal alarm signal to obtain an abnormal state classification result; for the abnormal state classification result, obtaining a pre-established instruction mapping table, and matching a corresponding automatic operation sequence from the instruction mapping table to obtain a preliminary operation instruction set; sending the final synchronization instruction sequence to the remote instrument equipment through a network transmission channel, obtaining equipment response feedback data, and obtaining a response timestamp record; comparing the response timestamp record with the initial state parameter, judging whether the response delay meets a preset standard or not, and obtaining a delay evaluation index; if the delay evaluation index is displayed abnormally, adjusting the matching logic of the instruction mapping table, and determining an optimized mapping table version; and regenerating an operation instruction set by adopting the updated mapping table version to obtain an optimized instruction sequence for a subsequent test cycle.

The invention provides a distributed power supply group control scheduling method and system based on source-load cooperation, and relates to the technical field of distributed power supplies. Decomposing the flexible load into a multi-dimensional characteristic parameter set with response delay, maximum adjustable power, sustainable response time and a user satisfaction function; and according to the current supply and demand target, based on the multi-dimensional characteristic parameter set, setting a target function with the minimum source-load matching error and the optimal scheduling cost, performing group control rolling optimization, generating a combined control strategy, performing target equipment execution instruction sequence decomposition, and sending to the distributed power supply and the energy storage equipment for execution control. According to the method, the technical problems of low utilization efficiency of the flexible load and difficulty in accurately adapting to the power supply demand due to lack of a multi-dimensional characteristic modeling and dynamic sorting mechanism for the flexible load group in the prior art are solved, and the technical effect of improving the dispatching accuracy and flexibility of the distributed power supply is achieved.

The invention relates to a surgical robot motion control method applied to a neuroendoscope, and belongs to the technical field of surgical robos.The surgical robot motion control method comprises the steps that an entry point of a target area is determined based on the long axis of the target area, kinematics parameters are established according to the motion mode of the endoscope, and a motion area in the target area is constructed; flexible operation of an end effector of the mechanical arm in a motion area is achieved through an RCM constraint algorithm of the mechanical arm; according to the method, the motion control precision is improved, the response delay is reduced, the adaptability to personalized operation requirements is enhanced, and the safety and effectiveness of the neuroendoscopic operation are guaranteed. When the mechanical arm end executor is controlled to move, the defects that in the prior art, a surgical robot motion control method applied to a neuroendoscope is low in precision, delayed in response and poor in adaptability are overcome.

The invention discloses a voice equipment response method and device and electronic equipment. The method comprises the following steps: acquiring a user instruction received by intelligent voice equipment; performing intention recognition on the user instruction by adopting a terminal intention recognition model to obtain a first intention recognition result; when the similarity between the first intention recognition result and a target intention in a local intention storage library is greater than or equal to a preset threshold value, generating a first response instruction according to the target intention; under the condition that the similarity between the first intention recognition result and all intentions in the local intention storage library is smaller than a preset threshold value, a cloud intention recognition model is adopted to conduct intention recognition on the user instruction, a second intention recognition result is obtained, and a second response instruction is generated according to the second intention recognition result. The technical problems of too high response delay and insufficient intention recognition precision in real-time voice instruction control caused by computing power limitation and complex environmental noise interference of wearable equipment in related technologies are solved.

The invention relates to the technical field of data processing and distribution, and discloses an internet of things data distribution engine algorithm. The method comprises the following steps: firstly, cleaning and standardizing collected original power data; performing feature extraction and semantic analysis, and generating a dynamic priority score representing the real-time emergency degree of the data and a static importance factor representing the inherent importance of the equipment by calculating a plurality of dynamic feature indexes of the equipment and combining the business metadata of the equipment; a hierarchical scheduling strategy is adopted, secondary judgment based on the change trend is started for uncertain data, and accurate routing of the data to channels with different priorities is achieved; finally, the system dynamically adjusts a distribution threshold value according to actual service utility feedback of data distribution, and closed-loop optimization is formed; according to the method, the problems of key information submerging, response delay and extensive resource allocation in massive Internet of Things data are effectively solved, and low-delay transmission of high-value data and self-adaptive optimization of system resources are realized.

The invention relates to the technical field of virtualization orchestration, in particular to an industrial control virtualization orchestration system and method, and the system comprises a task extraction module, a node behavior monitoring module, a stability evaluation module, an orchestration load adjustment module and a control task migration module. According to the method, the scheduling cycle closed-loop feature recognition is performed by collecting the control task scheduling mapping table and the execution instruction queue and combining the cycle control mark and the interrupt mark, so that an orchestratable task set can be effectively recognized, the behavior success rate is calculated according to parameters such as node response delay and task completion proportion, and node stability screening is performed; stability grade division of control nodes is achieved, a scheduling priority list is formed by setting a release threshold value, then the task migration opportunity is judged based on task residence time and IO completion conditions, migration targets are accurately selected in a behavior pattern matching mode, and the stability of task scheduling, the precision of resource allocation and the adaptability of control logic are remarkably improved.

The invention discloses a large model cluster shunting system and method based on performance perception. The system comprises a routing decision module, a resource pooling module, a KV cache perception module and a performance perception module. Through dynamic resource pool division, KV cache index matching, instance health state monitoring and load evaluation, and in combination with a hierarchical scheduling strategy and a hit rate load scoring mechanism, intelligent routing of requests and efficient allocation of resources are realized. The method effectively solves the problems of non-uniform resource allocation, KV cache reuse failure, single-point bottleneck, resource waste and the like in a large model reasoning task, remarkably improves the system throughput, reduces the response delay, and has good fault-tolerant and self-recovery capabilities.

The invention relates to the technical field of database optimization, in particular to a database preloading method and device based on a hybrid architecture and hotspot prediction.The method comprises the steps that on the basis of a preset sliding window mechanism, time sequence characteristics of historical access logs are extracted to recognize high-frequency hotspot data; based on a preset cache preheating mechanism, dynamically generating a loading priority queue according to the data access frequency of the high-frequency hotspot data so as to preferentially load the key data to a memory hotspot; and based on the monitoring data of the LRU list, the flush list and the check point state, a preset self-adaptive mechanism is linked to dynamically adjust a preloading strategy, and the preloading strategy is deeply integrated with a database check point mechanism to form closed-loop control, so that the preloading of the database is realized. Therefore, the problems that frequent changes of an access mode are difficult to cope with due to the fact that a database preloading method mostly depends on a static strategy or simple statistics, and particularly when the concurrency is high and the load fluctuation is large, hot area identification lagging, resource waste, cache hit rate reduction, response delay increase and the like are easily caused are solved.

The invention provides a multi-park-oriented equipment operation and maintenance work order assignment method and a path planning system, and belongs to the technical field of multi-park management. The method comprises the following steps: constructing a digital twinborn model of a'park-building-space-equipment-accessory-operation and maintenance personnel 'six-level topology, splitting an operation and maintenance work order into feature vectors of skill requirements, accessory requirements and energy consumption sensitivity, and generating an initial order dispatching scheme and path planning based on a topological relation and the feature vectors; and the order dispatching scheme and the path planning are dynamically adjusted according to the real-time feedback of the equipment monitoring system in the execution process. According to the method, integrated intelligent decision and dynamic optimization of accessories, personnel, paths and energy consumption are realized, the problems of difficult resource coordination and response delay in multi-park operation and maintenance are effectively solved, and the operation and maintenance efficiency and the resource utilization rate are remarkably improved.

The invention belongs to the technical field of power distribution network fault removal, and discloses a power distribution system fault rapid removal method, which comprises the following steps: in the operation process of a power distribution system, collecting multi-dimensional operation data and a corresponding control log in real time, and applying a timestamp mark to the multi-dimensional operation data based on a synchronous triggering index mechanism; mapping the multidimensional operation data with the timestamp mark into a compressed state expression, constructing a topological electrical parameter constraint graph, and generating a topological perception state matrix; in combination with instruction response delay in a control log, action lag factors are extracted, state representation is embedded, and a composite state vector sequence is formed; on the basis of the composite state vector sequence, behavior events with path influence in the power distribution system are extracted, behavior influence accumulation is calculated by adopting an index time decay mechanism in combination with occurrence time and action intensity, and a dynamic convolution behavior index is generated; the timeliness and reliability of fault response of the power distribution system are improved, and safe and stable operation of the power system is guaranteed.

The invention belongs to the field of load response and regulation, and particularly relates to an adjustable load aggregation and demand response control system of a virtual power plant, which is characterized in that dispersed adjustable load resources are aggregated into a load aggregation cluster containing shared resources through a clustering module; utilizing a screening module to construct an overlapping cluster hypergraph taking the shared resources as hyperedges, and screening a high-value candidate set; an iteration module is adopted to calculate a Shapley value revenue share of each load aggregation cluster through a cooperative game, a joint demand response strategy is generated by taking alliance total revenue maximization and Gini coefficient minimization as targets, and a system state is dynamically updated; and finally, integrating all strategies through a strategy generation module to generate an executable control instruction. According to the method, the problems of low resource integration efficiency, response delay and large execution deviation in the prior art are solved, optimal scheduling and cooperative control of load resources are realized, and the economical efficiency and the stability of the virtual power plant participating in the power grid demand response are remarkably improved.