275 results about "Response control" patented technology

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 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 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 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 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 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.

A response system for user voice interruption in a voice interaction process comprises an information importance mapping (ICM) generation module, an interruption response configuration file (BRP) generation module, an acoustic feature extraction module, a response control module, a semantic understanding module, a rhythm generation module, a transient response trigger and a response manager, according to the method, interruption intentions can be distinguished, the context importance can be evaluated, and differentiated responses with low delay, certainty and controllable sound characteristics can be realized.

A power transformation equipment oil temperature prediction and abnormity early warning method based on a fuzzy algorithm comprises the following steps that real-time operation parameters and oil temperature historical data of power transformation equipment are collected, the data are preprocessed, and a high-quality input data set is constructed; constructing a fuzzy inference system considering multiple input factors, and setting a fuzzy membership function and an inference rule base; according to the fuzzy prediction result and the real-time oil temperature change, a self-adaptive dynamic safety threshold model is compared, a potential abnormal trend is identified, and automatic grading early warning is carried out according to the overtemperature grade; and corresponding exception type identifiers and disposal suggestions are generated and are pushed to the operation and maintenance platform through the communication module, so that remote operation and maintenance scheduling and response control are supported, and intelligent cooperative processing is realized. According to the invention, intelligent prediction of the oil temperature change trend under the influence of multiple factors is realized, the capability of sensing temperature rise abnormity in advance is improved, the accuracy of sensing the operation state of the equipment and the timeliness of early warning response are also remarkably improved, and the risk of equipment failure caused by the abnormal oil temperature is reduced.

The invention relates to the technical field of equipment operation and maintenance management, in particular to a medical equipment operation and maintenance method and system.The method comprises the following steps that fault units are screened based on multi-source heterogeneous data, analysis factors and fault association identification abnormal intervals, distribution state identification key nodes are extracted, offset is identified, and a propagation track is extracted; and extracting a trend superposition efficiency layer to identify synchronous change, and outputting a fault behavior linkage response control instruction set. According to the method, through structured integration and labeling of multi-source heterogeneous data, a reference state model can be formed in the early stage of operation, abnormal fluctuation recognition and threshold screening are combined, potential risks are rapidly positioned, manual troubleshooting delay is avoided, key node recognition and influence area visualization are achieved, accurate processing is facilitated, and the method is suitable for large-scale popularization and application. Through time continuous offset analysis and track identification, the fault evolution trend can be captured, a quick response instruction is generated in combination with overlay analysis of operation factors and recovery efficiency, and the reliability and safety of equipment are improved.

The invention provides an e-commerce customer service response control method and system, and relates to the field of driver tests.The method comprises the steps that an oscillogram of a driver serves as a use case to be analyzed, and independent analysis is conducted on different corresponding stages in the use case, so that automatic analysis of waveform data is achieved; the analysis capability and the identification precision of the driver data are improved; in addition, the method does not need a large amount of marked data in the analysis process, and the problem that in the prior art, when AI means such as deep learning are adopted for data analysis, resource consumption is large is solved.

A control system for human-robot collaboration, including: a passive reactive control path implemented through a virtual damping system and configured to generate reactive control signals in response to human-applied interaction inputs; a predictive control path configured to generate predictive control signals based on predicted human performance objective inferred online from measured contact wrenches; and a signal blending component configured to combine the reactive control signals and the predictive control signals, wherein the predictive control signals are bounded in magnitude such that passivity of the reactive control path is preserved and stability of closed-loop human-robot interaction is maintained during collaboration.

The invention relates to the technical field of electric power, and discloses a rapid load response control method for deep peak regulation of a thermal power generating unit, which comprises the steps of collecting multi-dimensional parameters for load index prediction, predicting a climbing rate based on historical operation data of the unit, comparing the deviation between an actual load and a predicted load, and correcting a load index prediction result. Fusing the corrected load index with a climbing rate prediction result, establishing a key operation parameter change model, carrying out parameter adjustment in advance, combining the corrected load index and the climbing rate, verifying the safety domain boundary of the unit, and guaranteeing the operation safety of the unit. According to the method, the problem of adjustment lag or excessive adjustment caused by inaccurate load pre-judgment in traditional response is solved, panoramic perception of load fluctuation is achieved, pre-judgment adjustment is achieved at the same time, the unit parameter change rhythm and the load demand are synchronized, the time difference between load change and unit adjustment is remarkably shortened, and the load response speed is improved. And the response capability to the rapid fluctuation load is improved.

The invention relates to the technical field of home safety, and discloses an intelligent door lock identification and hierarchical response control method, which comprises the following steps: acquiring multi-modal data in a learning mode to train and construct an authorized behavior baseline model; in the working mode, the current feature vector is extracted in real time, the abnormal score of the current feature vector relative to the baseline model is calculated, when the abnormal score is larger than a threshold value, potential threats are judged, the potential threats are responded, the micro actuator is driven to apply physical disturbance signals, and feedback signals are synchronously collected; analyzing the time sequence characteristics of the feedback signal to discriminate whether the event category of the potential threat is benign or malicious, and executing a hierarchical response strategy; and dynamically correcting the model based on the event category, if the model is benign, updating the authorized behavior baseline model, and if the model is malicious, extracting malicious feature signatures and storing the malicious feature signatures in a high-priority threat signature library. Through active discrimination and closed-loop adaptive learning, the recognition accuracy is significantly improved, and the false alarm rate is reduced.

The invention relates to a dynamic defense system and method for durability degradation of a reinforced concrete structure. The system comprises a composite fabric network pre-buried in the reinforced concrete structure, a sensing unit for monitoring the health state of the structure and a response control unit, the composite fabric network comprises conductive row fiber bundles and conductive column fiber bundles which are intercrossed and woven into a two-dimensional matrix skeleton and can be addressed independently; arranging a plurality of repairing microcapsules which adopt a specific sensitive polymer wall material to package a chemical repairing agent at cross nodes, and additionally arranging function execution assemblies at corresponding nodes according to requirements; and the response control unit applies an excitation signal exceeding a preset threshold value to the target node, and generates or triggers the function execution component to form a corresponding external excitation energy field to act on the sensitive polymer wall material of the repairing microcapsule, so that the sensitive polymer wall material is broken or subjected to phase change to release the chemical repairing agent. According to the method, the threat position, the threat type and the threat level can be accurately positioned, and the repair range and strength are dynamically adjusted.

The invention relates to the field of data processing, and discloses an off-line target detection and tracking method based on a localization platform, which comprises the following steps: establishing a full-localization hardware platform without a wireless communication and positioning module; closed off-line data acquisition, labeling and training are carried out based on the platform; a YOLOv13 model improved by enhancing feature channel interaction and multi-scale detail feature fusion is adopted to carry out high-precision personnel target detection; a Deepsort algorithm improved through optimization state prediction and a matching strategy is adopted to carry out collaborative continuous tracking; and the countering device is linked to perform guidance and response control on the tracking target. By constructing a full-link autonomous and controllable software and hardware system and an optimized detection tracking algorithm, the comprehensive technical problems that an existing system is poor in autonomous controllability, high in data security risk, insufficient in specific target detection precision and lagged in response in high-security scenes such as confidential scenes are solved.

The invention provides an intelligent driving safety control method based on multi-source data fusion, and the method comprises the steps: collecting multi-source data such as high-precision map data, vehicle-mounted sensor data and Internet of Vehicles data, carrying out the fusion analysis of the multi-source data to generate fusion environment information, calculating a dynamic risk level based on the information, and carrying out the calculation of the dynamic risk level. And executing a grading response control strategy according to the risk grade. Through multi-source data fusion, the sensing limitation of a single sensor under severe weather or complex road conditions can be overcome, and the accuracy and robustness of environment sensing are improved; the dynamic risk level calculation integrates the lane occupancy rate, visibility and other multi-dimensional factors, and realizes the accurate assessment of real-time risks; the grading response control strategy is gradually upgraded from early warning to active intervention according to the risk level, the problem of response lag or overreaction of a traditional scheme is avoided, and therefore the driving safety and the system adaptability are remarkably improved. The method is suitable for intelligent driving vehicles with different configurations, and the accident risk can be effectively reduced.

The invention relates to the technical field of gas leakage detection, and discloses a gas sensor-based gas leakage detection system and method. The system comprises a dynamic gas concentration acquisition module, a leakage feature extraction module, a risk area modeling module, a multi-mode verification module and a grading response control module. The acquisition module acquires multi-dimensional concentration data through a distributed sensor array and a self-adaptive sampling mechanism; the feature extraction module separates steady-state background and transient leakage features through spatial-temporal feature decomposition and generates feature vectors; the risk modeling module constructs a three-dimensional diffusion model, and outputs potential leakage point coordinates with confidence in combination with a hydrodynamic equation; the multi-modal verification module generates a fusion result through infrared spectrum and sound wave detection cross validation; and the grading response module is matched with the strategy library to dynamically generate a gradient control instruction. According to the system, accurate detection, positioning and graded response of gas leakage are realized, and the comprehensiveness of leakage monitoring and the pertinence of emergency treatment are improved.

The invention discloses a power utilization potential prediction and response regulation and control system, and the system comprises a panoramic sensing module which is used for the synchronous collection of data; the dynamic modeling module is used for fusing multi-source data according to space, time and semantic association rules to generate a standardized electric power characteristic spectrum; the situation deduction module is used for identifying industrial adjustable capacity, commercial peak shifting potential and resident elastic interval and constructing a three-dimensional potential matrix; the collaborative optimization module is used for establishing an economy-environmental protection-user satisfaction multi-objective function by taking power grid security as a hard constraint, and driving a basic strategy library, a real-time optimization engine and emergency mode hierarchical regulation and control; and the efficiency verification module is used for tracking closed-loop optimization of indexes such as load peak-valley difference reduction rate and the like. According to the method, adjustment potential space-time quantification, multi-target collaborative optimization and strategy dynamic evolution are realized, and the adjustment potential evaluation time of the actual measurement region is shortened.

The invention discloses a battery thermal runaway multi-feature fusion early warning method, device and equipment and a storage medium, and relates to the technical field of battery safety, and the method comprises the steps: obtaining thermal runaway features based on temperature channel data and environment temperature; adjusting a dynamic threshold and a weight corresponding to the thermal runaway feature based on the thermal runaway feature to obtain an adjusted dynamic threshold and an adjusted weight; based on the thermal runaway characteristics, adjusting a dynamic threshold value and adjusting a weight, and determining a thermal runaway risk level; and executing a corresponding grading response control strategy according to the thermal runaway risk grade. The technical means of dynamically updating the characteristic threshold value and fusing the weight based on the real-time operation data is adopted, so that the technical problems of misalignment of early warning sensitivity and high false alarm rate under battery aging, environment change and dynamic working conditions of the existing fixed threshold value method are solved, and earlier and more accurate identification of the thermal runaway risk is realized.

The invention relates to the technical field of intelligent health monitoring, and discloses a multi-dimensional data fusion interaction processing system of a multi-modal intelligent health monitoring terminal. The system comprises a multi-dimensional data acquisition module, an intelligent fusion processing module and an interactive response control module. The multi-dimensional data acquisition module forms multi-modal original data through the data fusion interface unit; after receiving the data, the intelligent fusion processing module processes and outputs a feature fusion signal and an abnormal risk early warning signal through a data fusion engine, a health state evaluation unit obtains an evaluation result by combining historical health data of a user, and a parameter optimization unit outputs a personalized health guidance threshold according to the evaluation result; in the interactive response control module, an adaptive feedback unit adjusts intervention parameters, a terminal cooperative control unit adjusts working parameters of a monitoring terminal and collects feedback signals, and a human-computer interaction interface module outputs a comprehensive health risk level. According to the system, effective fusion and personalized interaction response of multi-dimensional data are realized, and the comprehensiveness and applicability of health monitoring are improved.

The invention discloses a monitoring method and device for an intelligent digital air conditioner system, and belongs to the technical field of intelligent air conditioner control and environment monitoring. Triggering the environment data collaborative awareness protocol layer to perform composite sampling on the target area, writing sampling data into the first cache area, and analyzing disturbance factor weights corresponding to the monitoring indexes; when the weight accumulated value reaches a dynamic threshold value, a response event identification signal is generated, and a response model corresponding to the control level is selected; loading the model to a decision scheduling layer, and generating a fine tuning instruction; after the instruction is issued, feedback information is collected to a second cache region; when the feedback deviation exceeds a correction threshold value, the sampling process is restarted, and the disturbance weight distribution matrix is updated; quick recognition and response control of multi-parameter coupling disturbance can be achieved, and the environmental adaptability and control precision of the air conditioning system are improved.

The invention discloses a high fundamental frequency permanent magnet motor low carrier wave ratio control method and device based on an optimal switching vector mode. The method comprises the steps of sampling a motor current and a rotor position, and obtaining a dq-axis reference current; calculating a reference stator flux linkage, a reference modulation ratio and a reference voltage phase angle; looking up the table according to the carrier wave ratio and the reference modulation ratio to obtain an optimal switching vector mode and an optimal flux linkage apocentric distance of offline optimization; determining an optimal switching vector mode of the current control time domain; calculating an optimal reference stator flux linkage; calculating a reference voltage vector according to sampling and calculation delay; calculating a switching vector sequence and action time; a switching signal is generated. Through off-line optimization, on-line table look-up and real-time synthesis calculation of the optimal switching vector mode, low-current harmonic and high-dynamic response control of the permanent magnet motor under a low carrier wave ratio is realized, the complexity of a control algorithm is simplified, and the efficiency and reliability of the system are improved.

The invention provides an access control terminal response method based on data line signal reading, which realizes a closed loop process of temporary charging, identity signal reading and system verification through a data line of an access control, and is in communication connection with an access control management system through a data line with data communication and charging functions configured by an access control terminal. Output management, signal reading and response control are realized; and when the user fails to try to scan the access control terminal or the electric quantity of the user terminal is lower than a preset threshold value, the access control terminal prompts the user to connect the user terminal to the access control terminal through the data line, reads data, verifies the data and performs access control. Through the processes of data line connection, temporary charging, signal reading and system verification, the problem that a user cannot open a door due to terminal power failure / code scanning failure is solved, and meanwhile, the safety of identity verification is ensured through multiple safety mechanisms.

The invention relates to the technical field of automatic control, and particularly discloses an automatic response control device and system based on artificial intelligence, and the system comprises a data collection module which is used for collecting the operation state data of a mechanical arm of an automatic robot in the part grabbing process, the data calculation module is combined with the operation state data of the mechanical arm after cleaning in the part grabbing process of the automatic robot, and the data can reflect the load condition of the mechanical arm in each grabbing operation process; due to the fact that the load condition of the mechanical arm can reflect the adaptation condition of the mechanical arm to inertia force generated during grabbing, artificial intelligence can make a decision on whether the grabbing speed of the mechanical arm needs to be adjusted or not on the basis of the data, and therefore the grabbing speed of the mechanical arm is automatically adjusted. And the situation that the service life of the mechanical arm is shortened due to the fact that the mechanical arm generates microcracks under the high-load condition is avoided.

The invention relates to the technical field of driving motor control, in particular to an intelligent hierarchical control method for driving motor shell collision and related equipment. The intelligent hierarchical control method for the collision of the driving motor shell comprises the following steps: collecting a multi-source sensing signal in real time; wherein the multi-source sensing signal at least comprises a pressure signal from the interior of the driving motor, a vibration signal from a shell of the driving motor and a rotating speed signal of the driving motor; filtering the collected multi-source sensing signal, and comparing and analyzing the processed multi-source sensing signal with a preset dynamic threshold value and a time window; performing information fusion on the pressure signal, the vibration signal and the rotating speed signal based on a comparison analysis result so as to diagnose whether a collision event occurs or not; according to a diagnosis result of information fusion, determining a severity level of collision; and executing a grading response control action corresponding to the severity level.

The invention discloses a photoelectric red envelope acousto-optic cooperative trigger response control method, and belongs to the technical field of photoelectric control, and the method comprises the steps: extracting the ripple content from a filtered signal, comparing the ripple content with a reference value, and obtaining the quantitative representation of the ripple increase amplitude; calculating an excess between the ripple increase amplitude and a preset ripple threshold; and inputting the excess into a dynamic adjustment model to obtain a current-limiting resistance value, a filter capacitor capacity and a shaping circuit time constant of the waveform shaping circuit, and obtaining the stability of the driving current waveform and the repeatability of acousto-optic cooperative starting by iteratively adjusting the current-limiting resistance value, the filter capacitor capacity and the shaping circuit time constant. The core contradiction that the triggering sensitivity and the system stability are difficult to consider in a complex working environment in the existing light source driving technology is solved, and the technical effects of improving the photoelectric triggering accuracy, enhancing the anti-interference capability of the system and ensuring the high repeatability of acousto-optic cooperative starting are achieved.

The application provides a remote intelligent operation monitoring method and system of a smart substation, relates to the technical field of intelligent operation and maintenance of a substation, and relates to multi-source heterogeneous sensing, deep feature modeling, fault prediction evaluation and model self-optimization.The application collects electrical, environmental and meteorological data through heterogeneous sensors, constructs a structured original data set, carries out time series prediction in combination with a convolution-LSTM model, and outputs a fault probability and a confidence interval by using a Transformer fusion network, so that online early warning and response control are realized, and the application has the self-adaptive updating capacity driven by federated learning.

The invention relates to the technical field of after-sales process scheduling, in particular to a clock after-sales task assignment method and system based on a process engine, and the method comprises the following steps: obtaining a request extraction time identifier state, replacing a path to adjust a node update sequence after a transfer instruction is generated, completing task assignment, and generating a record. According to the method, the response time node is generated by extracting the task request time to realize accurate definition of the task response period, whether the task is overtime or not is dynamically judged to construct a delay identification mechanism, and path reconstruction is carried out in combination with a delay identifier to realize self-adaptive replacement and priority redefinition of a task flow path. The method comprises the following steps: establishing a task, identifying and executing redundant nodes, dynamically applying an acceleration node template to optimize an execution link, constructing automatic mapping between the task and a terminal to complete final task assignment, realizing unification of task response control, exception handling, adaptive path optimization and efficient task delivery, and improving clock after-sales task processing efficiency and service response quality.

The application is suitable for the technical field of vehicle acceleration control, and provides a commercial vehicle acceleration closed-loop control method considering load and running resistance, which comprises the following steps: step 1, driving system dynamics modeling; step 2, driving process dynamic response transfer function modeling; step 3, acceleration dynamic response control parameter determination; and step 4, acceleration adaptive closed-loop control. The method derives the dynamic response transfer function of the driving process by constructing the driving system dynamics model; after the key parameters most sensitive to the acceleration dynamic response are determined through analysis, the method of combining feedforward control and closed-loop feedback adaptive control is adopted to realize the closed-loop adaptive control of the acceleration dynamic response. When the vehicle mass and the running resistance change, the method can effectively guarantee the consistency of the acceleration dynamic response, and further improve the driving quality.

The embodiment discloses a response control method and device, electronic equipment, medium and computer program product, which are applied to a request end device, the request end device is used for network communication with a response end device by using a remote direct memory access (RDMA) method, the response control method comprises the following steps: acquiring a current network state of a network between the request end device and the response end device; determining a congestion control stage currently entered by the request end device; determining a response acquisition frequency based on the current network state and the congestion control stage currently entered; wherein the response acquisition frequency represents a frequency of acquiring response information, and the response information represents a response of the response end device to a data packet sent by the request end device.