482 results about "Response control" patented technology

The invention, which relates to the technical field of network awareness anomaly detection, discloses a network awareness anomaly detection system based on big data, comprising a data acquisition module, a feature fusion module, a map construction module, a model calculation module, a root cause reasoning module, a threshold decision module and a response control module. The data acquisition module receives network flow data, equipment state data and system log data and outputs a standardized feature set; the feature fusion module is connected with the data acquisition module, dynamically calculates a weight coefficient of each data source based on information entropy, performs feature aggregation of privacy protection through a federated learning framework, and outputs a fusion feature vector; the atlas construction module is connected with the feature fusion module, maintains a network equipment node set and a communication edge set in real time, and updates a space-time association atlas according to a topology change event; and the model calculation module is connected with the atlas construction module, extracts topological features through a space-time diagram convolutional network, and updates a detection model based on an incremental learning mechanism.

The invention relates to the technical field of power system automation, in particular to a substation management system and method based on the Internet of Things technology, and the system comprises a ubiquitous Internet of Things sensing matrix, an electric power intelligence brain evolution center, a space-time fusion twin module, a nonlinear optimization decision module, and a multi-level vibration risk management and control model. Wherein the ubiquitous internet-of-things sensing matrix acquires substation equipment, environment and power grid data in real time; the electric power intelligence brain evolution center carries out health assessment, life prediction and abnormity positioning on the equipment; the space-time fusion twinborn module constructs a digital twinborn body, fuses equipment space-time data and historical data, and performs operation and maintenance simulation and fault reproduction; the nonlinear optimization decision-making module is used for generating a response regulation and control strategy aiming at the nonlinear and uncertain problems in the operation of the power grid; and the multi-level vibration risk management and control model carries out dynamic assessment and strategy optimization on equipment faults, power grid safety and environmental risks. Therefore, the problems of low inspection efficiency, high false detection risk, poor transmission stability and the like in the prior art are solved.

The invention provides an intelligent monitoring and early warning method and device for photovoltaic energy storage equipment, and the method comprises the steps: carrying out the multi-time scale sliding window feature extraction of the operation data of a photovoltaic energy storage system, calculating a power coupling degree quantitative index and a system response feature index, and generating a real-time feature data set; based on the real-time feature data set, performing deviation propagation path identification under thermal dynamic constraint to obtain a real-time deviation propagation path diagram marking deviation sensitive nodes; performing multi-level dynamic deduction in combination with the real-time feature data set and the deviation propagation path map to obtain a dynamic deviation accumulation situation map displaying a deviation accumulation risk level and a development trend; and performing adaptive threshold early warning judgment according to the dynamic deviation accumulation situation map, and executing response control according to a hierarchical early warning mechanism to obtain a hierarchical early warning response control instruction. According to the method, the cumulative effect of the power prediction deviation can be effectively identified, and early warning of coordination imbalance of the photovoltaic energy storage system is realized.

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.

The invention discloses a flight control method and system for an electric power inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection flight control. According to the flight control method of the electric power inspection unmanned aerial vehicle, the current flight position of the unmanned aerial vehicle is obtained in real time, deviation analysis is performed on the current flight position and a target inspection position, and when the deviation exceeds a preset threshold value, disturbance sensing data in a set area is further obtained and a disturbance sensing intensity index is calculated; according to a comparison result of the index and a disturbance attribution judgment threshold value, an offset cause is judged, and a corresponding disturbance response control strategy or a flight error correction strategy is executed respectively, so that the flight stability and the control precision of the electric power inspection unmanned aerial vehicle are dynamically guaranteed; according to the invention, through dual discrimination of the disturbance perception intensity index and the flight error adjustment index, external interference and flight control error causes are accurately distinguished, path offset cause identification and strategy matching are realized, and the flight stability and control precision of the electric power inspection unmanned aerial vehicle in a complex environment are improved.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

The invention discloses an intelligent water supply management system and method based on wind-light-water multi-source data fusion, and relates to the technical field of electric digital data processing. The system comprises a time alignment deviation quantification module, a time alignment regulation and control module, a data fusion timeliness response quantification module and a timeliness response regulation and control module. According to the method, the time alignment deviation quantization result is obtained by quantizing the multi-source data time parameters, whether time alignment regulation is carried out or not is judged, if yes, the timeliness response parameters are obtained after regulation, and if not, the timeliness response parameters are directly obtained and quantized to obtain the data fusion timeliness response quantization result; and whether PLC scanning interval regulation and monitoring verification are carried out or not is judged based on the data fusion timeliness response quantification result, so that the timeliness of water supply management demand response is improved, and the problem that in the prior art, the water supply management execution response flexibility is poor due to the fact that the time difference of multi-source data processing is not fully considered is solved.

The invention provides a remote intelligent operation monitoring method and system for an intelligent substation, relates to the technical field of intelligent operation and maintenance of substations, and relates to multi-source heterogeneous sensing, depth feature modeling, fault prediction evaluation and model self-optimization. According to the method, electrical, environmental and meteorological data are collected through heterogeneous sensors, a structured original data set is constructed, time sequence prediction is carried out in combination with a convolution-LSTM model, a Transform fusion network is utilized to output a fault probability and a confidence interval, online early warning and response control are realized, and the method has a federated learning driven adaptive updating capability.

The invention relates to the field of laboratory environment control, and discloses a laboratory environment abnormity self-adaptive linkage control system based on multi-source heterogeneous perception, which comprises the following steps: when any environment parameter exceeds a set safety threshold value, judging whether comprehensive environment abnormity exists in combination with the numerical value change trend of other types of sensors; based on historical monitoring data and real-time sensing data, constructing a multi-dimensional environment characteristic spectrum, and identifying internal association and change paths between abnormal signals; whether linkage control measures are needed or not is evaluated by analyzing the current anomaly type and the influence area; dynamically generating a linkage control path and a priority sequence according to a system topological structure and an equipment operation state, and determining equipment objects needing to be linked; and according to the generated control path and parameters, the operation state of related equipment is automatically adjusted, active response control for abnormity is completed, and an environment feedback result after control is monitored. The method has the advantage of improving the intelligent control level.

The invention relates to a low-delay edge reasoning deployment method for power terminal equipment. The method comprises the following steps: acquiring data under a unified time reference to form an operation data sequence; inputting the operation data sequence into a pre-trained reasoning model to generate a reasoning parameter set adaptive to the local operation capability; performing reasoning calculation based on the reasoning parameter set to obtain a load identification result and an electric quantity prediction result; determining a communication scheduling strategy according to the reasoning result, wherein the communication scheduling strategy comprises communication priority information, sending time sequence information and target receiving node information; a communication instruction is generated according to the communication scheduling strategy, and data content is sent to a target receiving node according to the communication instruction within a limited time, so that rapid data synchronization and dynamic response control based on load state change are realized; according to the invention, intelligent data processing and rapid communication scheduling can be locally realized at the power terminal equipment, the system response time delay is reduced, and the processing efficiency of a load change event is improved.

The invention relates to an intelligent inspection control system and method. The system comprises an environment sensing module, a situation recognition module, a task management and scheduling module, a path planning module, an execution control module and a communication linkage module. The system can collect various environmental parameters of an inspection area in real time, dynamically adjust the priority of an inspection task after identifying an abnormal state, and generate an optimal path to drive a robot to execute response operation; meanwhile, abnormal information, images, videos, sensor snapshots and other contents are uploaded to a background management platform through a communication linkage module, and sound-light alarm equipment, broadcast equipment and other equipment are linked to carry out field response. The control method has task preemption and path reconstruction capabilities, and supports distributed sensing deployment and whole-process execution state monitoring. Compared with the prior art, integrated fusion of environment situation awareness, task dynamic scheduling and response control is achieved, and the adaptive capacity, the response speed and the safety guarantee level of the system to complex scenes are improved.

The invention provides an enterprise production safety monitoring and checking system based on multi-source data fusion, and the system is characterized in that the system comprises a data collection module which collects production environment data, processes the data, and generates a time-space alignment data set; the feature screening module is used for screening features highly related to security from the space-time alignment data set to generate a feature matrix; the model construction module is used for constructing a risk assessment model, calculating a dynamic risk value according to the multi-dimensional features, and performing risk grading on the dynamic risk value; the response control module is used for starting a corresponding response strategy according to the risk classification and outputting an early warning instruction set and an equipment control signal; the monitoring calculation module is used for monitoring control, comparing risk value changes before and after treatment and calculating response efficiency; and the adjusting and optimizing module is used for carrying out dynamic adjustment according to the response efficiency and optimizing the response strategy. And the limitation of traditional single-dimensional monitoring is broken through, accurate correlation analysis of equipment, environment and personnel risks is realized, and the composite hidden danger recognition capability is remarkably improved.

The invention relates to the technical field of man-machine interaction, in particular to a dialogue Agent interaction method based on multi-modal intention understanding, which comprises the following steps: S1, collecting multi-modal data in a user interaction process in real time, and calculating a time synchronization deviation value of each modal data source; s2, constructing a space-time fusion feature vector; s3, analyzing a dominant action instruction and a recessive behavior clue in the space-time fusion feature vector; s4, generating a multi-level intention analysis tree; s5, when the corrected confidence coefficient of any node in the intention analysis tree is lower than a set threshold value, activating a targeted sensor to complementarily collect data; and S6, analyzing a tree drive response decision according to the finally confirmed intention. According to the method, high-precision identification and response control of the dialogue Agent on the user intention in a complex scene are realized by constructing a multi-modal interaction method with space-time consistency fusion capability, an explicit and implicit intention analysis mechanism and an adaptive modal clarification strategy.

The invention relates to the technical field of game development and artificial intelligence, and discloses a Qt interactive game role action response control method fused with AI emotion recognition, which comprises the following steps: S1, acquiring multi-modal data such as facial expression, voice intonation and limb action of a player by using sensors such as a camera and a microphone on game equipment; s2, the collected multi-modal data are transmitted to an AI emotion recognition module, a deep learning algorithm is adopted to analyze and process the data, and the current emotion state of the player is recognized; and S3, obtaining current game scene information and other operation instructions input by the player through the Qt framework. According to the Qt interactive game role action response control method and system fused with AI emotion recognition, through the AI emotion recognition technology, a game role can sense the emotion state of a player and make the emotion interaction action matched with the game role, the emotion resonance between the game role and the player is enhanced, and the interaction experience and immersion of a game are greatly improved.

The invention discloses a response control method for a charging station participating in a low-voltage treatment scene, and the method comprises the following steps: S1, collecting the voltage data of a charging pile, calculating a deviation, and judging a low-voltage grade; s2, when the deviation is in a light interval, outputting reactive power support voltage in proportion; s3, when the deviation exceeds a heavy threshold value, reducing active power and starting V2G feedback; s4, constructing a graph structure model, and extracting inter-node power coupling and user behavior factors; s5, inputting the model into an improved prediction controller, predicting a future voltage state, and optimizing and outputting a current period power control value; s6, power adjustment is executed, and feedback power is slowly output in the V2G stage; and S7, comparing the control deviation with the behavior factor change, updating the node control priority, and feeding back for rolling optimization of the next period. According to the invention, while the charging regulation and control precision is improved, the user experience and the voltage stability are effectively considered, and the method is suitable for various low-voltage treatment scenes.

A system and method for detecting anomalies in encrypted network traffic without decrypting encrypted payloads is disclosed. The invention comprises a hardware-accelerated pipeline including a packet acquisition unit configured to extract observable header attributes and timestamp values, a signal analysis unit implemented using digital signal processing circuitry to generate temporal and spectral characteristics of encrypted packet flows, and a morphometric extraction unit that produces encrypted-flow descriptors based on statistical dispersion, burst patterns, and ciphertext variability. A behavioral graph construction unit generates graph-structured representations of encrypted flows using incremental state-transition encoding. An artificial-intelligence inference unit employing a tensor-processing processor performs multi-stage anomaly analysis through temporal reconstruction, attention-based weighting of descriptors, and graph-structural deviation assessment to compute anomaly scores without decrypting any payload. A secure response control unit evaluates the scores against policy thresholds and performs mitigation actions while preserving confidentiality.

The invention relates to the technical field of defect detection, in particular to an inner package defect detection system which comprises an illumination control module, a boundary recognition module, a defect gathering module, a grade mapping module and a response linkage module. According to the method, through the convergence degree and Euclidean vector distribution of the boundary contour form, boundary offset distribution information is established, tiny offset or abnormal positions are accurately captured, aggregation extraction and spatial clustering of defect boundaries are achieved, abnormal clusters are screened through distance and number distribution parameters, and the defect detection accuracy is improved. And a defect level is mapped by combining a structure level and a functional risk factor, and differential station response operation is triggered according to level evaluation, so that on the basis of improving image quality, the component identification precision is guaranteed, the space representation capability of defect clustering is enhanced, and the grading judgment granularity of defect detection in multi-component assembly is enhanced. Dynamic linkage response control in the inner packaging process is achieved, and the accuracy of overall defect recognition and the timeliness of control decision making are improved.

The invention discloses a new energy automobile charging pile multi-dimensional data fusion monitoring and early warning method and system, and relates to the technical field of new energy automobile charging safety monitoring and control. The new energy vehicle charging pile multi-dimensional data fusion monitoring and early warning method comprises the following steps: S1, constructing a charging state original data set, and preprocessing the charging state original data set; s2, performing early warning grade division in combination with a disturbance trend analysis result and discriminator output; s3, a response control condition is matched according to the early warning level state; and S4, recording the operation structured data of the charging pile, constructing an event chain, and comparing the current and historical features to identify the coincidence degree. The problem that due to the complex operation working conditions of high-frequency plugging, large day and night temperature difference and heavy environmental dust of a new energy automobile charging pile in an urban fast charging station, an existing monitoring method cannot achieve multi-dimensional sensing and dynamic adjustment, and then abnormal early warning lags is caused is solved.

The invention provides a lane changing intention recognition method based on a driving style. The method comprises the following steps: firstly, acquiring driving behavior data in a car-following scene through a driving simulation experiment, and constructing a multi-dimensional data set comprising a self-car state, a front-car state and time-space car-following characteristics; then, on the basis of an expected safety margin model, identifying reaction characteristics, steady-state risk characteristics and operation characteristics of a driver, and extracting behavior parameters forming a driving style, including indexes such as reaction time, an expected safety margin interval and operation sensitivity; and further dividing the drivers into different style types by using a clustering algorithm, and endowing style labels with the drivers. On the basis, lane changing intention time information subjectively marked by a driver is collected through a non-control observation experiment and is aligned with the trajectory data, and a trajectory data set with a real intention label is constructed. And finally, constructing a dynamic Bayesian network model based on the data set, taking a historical driving state sequence in a fixed time window as input, fusing a driving style, interaction characteristics of a vehicle and surrounding vehicles and traffic flow characteristics, and realizing real-time identification of whether a lane changing intention exists at the current moment. The method has good interpretability, individual difference adaptability and engineering deployment, and is suitable for dynamic identification and response control of driver intention in an intelligent driving assistance system.

The invention discloses a driver abnormity monitoring system based on large model collaborative decision-making. The system comprises a multi-path camera, a voice interaction module, a DTOPA visual pre-alignment module, a Qwen-Apan lightweight language reasoning module, a collaborative decision-making module and a safety response control module. Wherein the multi-path camera is used for extracting an environment video stream in a vehicle in real time; the voice interaction module is used for reminding a driver and interacting with the driver; the TOPA visual pre-alignment module is used for converting a video stream into structured text description; the Qwen-Apan lightweight language reasoning module is used for performing semantic analysis and abnormal risk assessment on the text description; the collaborative decision module is used for triggering a voice interaction and vehicle control instruction based on the risk assessment result; the safety response control module is used for executing side parking, emergency contact person notification or multi-level early warning operation; according to the invention, the safety of aided driving is obviously improved.

The invention provides a meter management method fusing remote parameter configuration and local response control, and the method comprises the steps: constructing a self-adaptive configuration parameter set comprising basic parameters, dynamic regulation factors and environment adaptation coefficients through a remote configuration center according to a diversified operation environment and equipment difference, and transmitting the self-adaptive configuration parameter set to a target meter; the meter completes parameter analysis and rule mapping fusion locally, a control instruction sequence containing control logic and threshold expression is generated, then real-time monitoring and control operation is executed, and a comprehensive operation data set is formed through collection; by analyzing operation data and an instruction response result, a control adaptation adjustment factor is calculated to reflect control deviation, local rule mapping is updated, a self-adaptation feedback data set is generated and transmitted back to a remote configuration center, and parameter configuration and closed-loop optimization of a control strategy are completed; the method improves configuration flexibility and local response precision, and is suitable for intelligent management of large-scale heterogeneous meters.

The invention relates to the technical field of data processing, and discloses a college budget management system fused with a new quality productivity theory, comprising: a state characterization interface collects process characterization data and maps the process characterization data into a discrete innovation state bit vector; the differential efficiency mapping unit responds to the state bit jump to calculate an efficiency marginal increment and generate an injection instruction; the physical bypass monitoring channel collects physical consumption time sequence data, and the entropy analysis closed-loop unit calculates a metabolic entropy representing the resource consumption order degree; the budget dynamic response control unit generates a state locking instruction when the state bit is constant and the metabolic entropy meets the low-entropy convergence condition, the physical working condition entropy is used for correcting the logic state, and the project resource state is maintained by recognizing the low-entropy resource consumption characteristics when no dominant output exists; the problem that discrete evaluation indexes cannot represent blind areas of continuous scientific research activities is solved.

The invention relates to the technical field of intelligent disaster assessment, in particular to a multi-source heterogeneous disaster investigation and assessment model elastic integration method and system, and the method comprises the following steps: generating an assessment reference layer based on multi-source heterogeneous data, recognizing abnormal fluctuation and influence factor deviation, screening key nodes and combinations, and extracting a propagation trajectory and a dynamic path. And analyzing trend and efficiency superposition characteristics, and outputting a disaster behavior linkage response control instruction set. According to the method, a space-time identification mechanism is introduced through structured recombination of multi-source heterogeneous data, so that multi-dimensional unified expression and time sequence continuity of disaster information are realized, abnormal intervals and factor deviations are identified, interference data are effectively eliminated, disaster identification precision and evaluation consistency are improved, and positioning capability is enhanced; the disaster diffusion direction is determined through area offset and trajectory recognition, recovery efficiency information is superposed to analyze response association, a control instruction set with spatial-temporal characteristics is output, and closed-loop regulation and control of disaster assessment, prediction and response are achieved.

The invention discloses a seat control method and a vehicle, and belongs to the technical field of vehicle control. Through the technical scheme provided by the embodiment of the invention, the problem of unstable comfort experience caused by lack of comprehensive dynamic adjustment of the real-time physiological state, personalized preference and environment information of the user in related technologies is solved. By synchronously acquiring and fusing the three types of key information, user demand changes can be identified, and the defect that environmental response control neglects individual differences and the influence of manual operation on driving concentration are avoided. In other words, the matching relation between the current state of the user and the environmental condition is considered in the determination process of the target seat control parameters, and user preference information is introduced for adaptive adjustment, so that it is ensured that the synergistic effect of the ventilation and heating functions is always in the optimal state, and the stability and individuation level of riding comfort are improved.

The invention relates to the technical field of supervision and control, in particular to a polyethylene chlorination reaction control system which comprises a hot-pressing time difference extraction module, a viscosity variable-speed recognition module, a chlorine pressure collaborative trend division module, a response centralized recombination module and a stirring amplitude management and control module. According to the method, a multi-source variable data structure is unified through period numbers, cooperative sections with consistent trend directions are constructed, response feature fragments in a continuous period are recognized by combining fluctuation frequency and adjustment amplitude feature extraction, the behavior boundary of each variable in the time dimension is unified, and the recognition precision of multi-variable abnormal evolution is improved; a regulation and control reference is established through trend classification and strength grading, structured expression and controllability analysis of periodic behaviors are realized, the matching between the regulation rhythm and the variable trend is enhanced, and a regulation and control strategy is enabled to have clear boundary recognition, period tracking and behavior intervention capabilities.

The invention discloses a greenhouse intelligent response control method based on environmental parameters, and relates to the related technical field of agricultural intelligent control, and the method comprises the steps: obtaining a target crop, and collecting the infrared temperature of a canopy in real time; the distributed environment parameters of the greenhouse are dynamically collected through a distributed multi-source sensor array; traversing predetermined related indexes in the distributed environment parameters to obtain a first crop heat demand index; performing normalization weighting calculation on the factor environment data to obtain an environment influence coefficient; calibrating the first crop heat demand index to obtain a second crop heat demand index; and calling the response control plan to perform intelligent control on the greenhouse. The technical problems that in the prior art, environment regulation neglects crop physiological feedback, environment parameters are distributed unevenly, multi-factor coupling effect analysis is insufficient, and consequently greenhouse control precision and adaptability are poor are solved, and the technical effects of dynamically and accurately responding to crop requirements, optimizing an environment regulation strategy and improving greenhouse control precision and adaptability are achieved.

The invention discloses an intelligent sensing control system for riding protection equipment, relates to the technical field of riding safety protection, and is used for solving the problem that inertial sideslip is inaccurately intervened in advance. The method is composed of riding data collection, behavior event recognition, behavior matching, risk evolution analysis and pre-response control, the system constructs a behavior map through a multi-source sensor, the behavior map and an inertia sideslip template are subjected to feature difference value and structure consistency verification, and the behavior map is obtained by combining a trend change mode and a minimum continuous threshold value. Pre-response (pre-inflation, prompt rhythm and optical signal adjustment) is triggered at the early risk stage, closed-loop comparison is performed on the execution state and the risk state, and compared with a single-threshold triggering scheme, false alarm and missing alarm can be reduced, the response lead and intervention accuracy can be improved, unnecessary triggering can be reduced, and the self-adaptive reliability under a complex road can be enhanced.

The invention discloses a lithium battery thermal runaway prediction and multistage response system based on AI intelligent evaluation, and the system comprises a multi-sensing monitoring module, an AI prediction evaluation module, a thermal runaway risk evaluation module, a safety response control module, and a safety execution module. Through fusion of multi-dimensional sensing data and an artificial intelligence algorithm, real-time prediction and evaluation of the thermal runaway risk of the battery are realized, and graded safety response measures are actively triggered at the early stage of thermal runaway. And aiming at different risk levels, the system automatically executes corresponding multi-level protection actions. The system also has online learning and model adaptive optimization capabilities, and can continuously improve the prediction accuracy and response efficiency along with the change of the state of the battery. And the thermal runaway early warning sensitivity and the response timeliness are remarkably improved, and the method has an intelligent level and engineering practicability and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to the technical field of robot flexible operation and intelligent control, and discloses a modeling uncertainty-oriented dexterous hand collaborative grabbing control method and system, and the method comprises the following steps: S1, building a dexterous hand system dynamics model; s2, virtual coupling is introduced among multiple fingers of the dexterous hand system to construct a communication topological structure; a collaborative grabbing error is defined, and a corresponding potential energy function is constructed; s3, designing a gradient descent distributed control law based on time-varying gain; s4, motion gravity change is processed through a gravity item integral compensator, and kinematics parameter uncertainty is compensated based on a time-varying gain Jacobian matrix adaptive law; and S5, integrating the distributed control law in the step S3 and the adaptive processing mechanism in the step S4, so that the dexterous hand system realizes collaborative grabbing control within the preset time of the user. The method is accurate in control and flexible in response, and can realize efficient, robust and quick response control of the grabbing task in collaborative operation of small-batch customized products.

The invention discloses a storage battery real-time monitoring method based on edge calculation, and the method comprises the following steps: S1, collecting multi-source operation parameters of a storage battery, and constructing an operation parameter set; s2, processing the operation parameter set to generate a standardized state vector; s3, on the basis of a micro neural network model, generating graph structure reasoning characteristics are fused; s4, performing global search on a connection structure between the sub-network clusters by adopting a spider-now-monkey optimization algorithm, and updating the structure configuration of the micro neural network model; s5, on the basis of the graph structure reasoning features and the optimized sparse connection structure, forming a monitoring judgment result; s6, performing edge response control according to a monitoring judgment result, and generating a response record; and S7, based on the response record and the monitoring determination result, completing edge side dynamic optimization of the miniature neural network model. Sparse neural clusters and edge reasoning are adopted, and real-time monitoring and dynamic optimization control of the state of the storage battery are achieved.