458 results about "Adaptive decision making" patented technology

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

The invention discloses an intelligent regulation and control method and system for sewage treatment and a program product, and relates to the technical field of sewage treatment.The method comprises the steps that a multi-modal water quality sensing network is constructed, and sewage quality parameter information and overall treatment target numerical value information are obtained; according to the equipment operation state and the overall treatment target numerical value information, sewage treatment process parameters are generated, and treatment units in the sewage treatment system are distributed in a plurality of areas; triggering a process regulation and control instruction according to the sewage treatment process parameters and the overall treatment target numerical value information; obtaining partition processing effect detection information, and comparing the partition processing effect detection information with the corresponding index value to obtain a partition difference value; if the partition difference value exceeds a preset change threshold range, triggering a partition self-adaptive regulation and control updating instruction; obtaining partition adjustment parameter information according to the partition self-adaptive regulation and control updating instruction; and triggering an adjustment instruction. The invention provides an intelligent regulation and control method based on multi-dimensional perception and adaptive decision.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, which can realize deep association and complementarity mining of multi-modal information and improve the accuracy and robustness of multi-modal understanding. The method comprises the following steps: an environment sensing module adjusts an environment sensing strategy according to feedback information transmitted by a self-adaptive decision module, and acquires multi-modal data according to the environment sensing strategy; the multi-modal encoding module encodes the multi-modal data into multi-modal feature vectors of the same dimension; a cross-modal fusion module fuses the multi-modal feature vectors to obtain fusion features; the self-adaptive decision-making module selects a decision-making network matched with the task type from a predefined network library according to the task type of the current decision-making task, inputs the fusion features into the decision-making network, and generates feedback information according to the decision-making process of the decision-making network; and the meta-learning controller evaluates the system performance of the current multi-modal data processing system and adjusts system parameters according to an evaluation result.

The invention discloses a territorial space planning dynamic monitoring method based on multi-source data fusion, and belongs to the technical field of territorial space planning intelligent monitoring, and the method comprises the steps: obtaining data, and generating a multi-source heterogeneous data set; performing space-time alignment processing by using a preset regional association rule of a planning knowledge base to generate a space-time unified data set; constructing a dynamic knowledge graph taking planning elements as a core based on the data set, and updating node relation weights in real time; guiding a multi-source data fusion direction through the map relation weight to generate a fusion feature vector; incremental learning monitoring processing is carried out on the feature vectors, parameters are optimized, and a planning implementation state monitoring result is output; and updating the knowledge graph node relation weight in a closed loop manner according to a monitoring result, and synchronously optimizing incremental learning monitoring processing. According to the method, a dynamic knowledge graph is adopted to guide data fusion and an incremental learning closed-loop optimization mechanism in real time, and accurate perception and adaptive decision support of a planning implementation state can be realized.

The invention relates to the technical field of intelligent gardens and precise irrigation, and particularly discloses an intelligent irrigation monitoring method and system. According to the method, a multi-source sensor array is deployed, Kalman filtering is adopted to fuse environmental data, and a three-dimensional state vector input reinforcement learning model is constructed to generate an irrigation decision; predicting a vegetation water demand by combining a gradient lifting decision tree, forming a graded irrigation strategy and converting the graded irrigation strategy into a water pump control instruction; soil humidity feedback data are collected in real time, decision model parameters and filtering rules are dynamically adjusted, and closed-loop optimization is achieved. Through multi-source data fusion and a self-adaptive decision-making mechanism, the irrigation precision and the water resource utilization rate are remarkably improved, meanwhile, the response capacity of the system to the vegetation growth dynamic state and the environment change is enhanced, and the beneficial effects of optimization process closed loop, decision dynamic adaptation and controllable resource consumption are achieved.

The invention relates to the technical field of integrated circuits and computer networks, and particularly discloses a transmission control and intelligent scheduling system for an integrated chip, and the system sets a dual-mode decision and dynamic switching mechanism for each routing node. Calculating dynamic characteristic parameters including an instantaneous value, a first-order trend and a second-order acceleration; when the parameter is matched with a pre-stored abnormal feature set and the load exceeds a threshold value, the node is immediately atomized and switched to a predefined security scheduling strategy loaded from a shared storage area, and otherwise, the node generates a scheduling decision according to self-adaptive decision logic continuously optimized based on historical performance feedback; all the nodes carry out data packet forwarding control according to the current execution strategy; and the system also periodically realizes federated global knowledge evolution according to the quality evaluation result of the self-adaptive decision of each node.

The invention relates to a visual transformer substation intelligent inspection system and method based on a multi-mode large language model, and belongs to the technical field of power system intelligence. The system obtains image, temperature, vibration and noise data of substation equipment in real time through a multi-modal data acquisition module, and performs preprocessing and fusion. A high-precision three-dimensional semantic model is constructed by using a three-dimensional dynamic modeling module, and the device attributes are automatically labeled by fusing LLM semantic understanding capability. A multi-modal large language model (LLM) engine is combined with cross-modal feature extraction, a dynamic knowledge base and a self-adaptive reasoning unit to realize accurate diagnosis of equipment faults. An augmented reality (AR) interaction module displays the real-time state of equipment through AR glasses and supports natural language interaction. The self-interpretation decision support module generates interpretable fault reports and maintenance suggestions, and the communication and feedback module is responsible for data uploading and remote alarm. According to the method, multi-dimensional perception, dynamic knowledge reasoning and self-adaptive decision support of the equipment state are realized, the intelligent level of substation inspection is remarkably improved, the inspection efficiency is improved by more than 40%, the omission ratio is reduced to less than 1%, and rapid diagnosis of more than 95% of novel faults is supported.

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual processing edge adaptive decision-making method for resource and task perception, and aims to solve the problem of scheduling mismatch caused by resource dynamic change and task demand diversity in visual task processing in an edge computing environment. The method comprises the following steps: collecting multi-dimensional resource state data of edge nodes in real time to form a resource state vector with high time resolution; analyzing the visual task request, and constructing a quantifiable task feature vector; and establishing a resource-task association mapping model based on a dynamic weight distribution mechanism. The method also supports cross-edge domain collaborative decision, and processes a pipeline dynamic reconstruction and security isolation mechanism. According to the technical scheme, the fluctuation of the resource utilization rate is reduced to 15% or below, the average task processing delay is reduced to 60%, the scheduling satisfaction degree is improved by 40% or above, and the self-adaptability and the service quality guarantee capability of the edge vision system are remarkably enhanced.

The invention relates to the technical field of logistics park management, and particularly discloses a logistics park intelligent early warning and dynamic evaluation method, and the method comprises the steps: carrying out the time-space alignment of vehicle trajectory data, cargo pressure distribution data and environment data collected by a heterogeneous sensor network, and inputting the data into a dynamic threshold adjustment module to generate a self-adaptive warning threshold; the dynamic threshold adjustment module establishes a dynamic calculation model containing the equipment utilization rate and the vehicle density based on the matching relationship between the historical operation mode and the real-time operation state; based on a multi-dimensional evaluation system constructed based on a self-adaptive warning threshold, generating a comprehensive evaluation index through collaborative analysis of three groups of indexes including transportation efficiency, safety risk and resource utilization; and when the comprehensive evaluation index deviates from the preset range, triggering a grading early warning mechanism associated with the deviation degree. According to the invention, dynamic evaluation and intelligent early warning are realized, and real-time monitoring and intelligent evaluation regulation and control of the operation state of the logistics park are realized through space-time correlation analysis and an adaptive decision-making mechanism of multi-source heterogeneous data.

The invention discloses a customized production-oriented edge node lightweight AI model adaptive compression method, which belongs to the technical field of intelligent manufacturing and edge computing, and comprises the following steps of: dynamically integrating compression strategies such as pruning, quantification and knowledge distillation by analyzing demand constraints and edge node hardware resources of customized production tasks; constructing an adaptive decision engine by utilizing reinforcement learning and Bayesian optimization, and generating an optimal compression scheme; in the deployment stage, compression parameters are dynamically adjusted through real-time monitoring and a closed-loop feedback mechanism, and the balance of model precision, reasoning efficiency and resource occupation is achieved. According to the method, the adaptability of the model in a heterogeneous edge environment can be remarkably improved, the deployment cost is reduced, and the small-batch and multi-task quick response requirement in a customized production scene is met.

The invention discloses a text and image fused online comment toxicity detection and filtering method and system, and relates to the technical field of natural language processing, and the method comprises the steps: extracting a semantic vector of a comment text through a RoBERTa-Marge model; the visual features of the image are extracted through an OfficientNet-V2 model; aligning heterogeneous modal features by adopting a double-flow contrast loss function; self-adaptive decision making of culture sensitivity: loading a regional sensitive rule table according to a user IP address, and dynamically adjusting symbolic semantics; calculating an intimacy correction factor based on the social relationship between the publisher and the receiver; and performing context weighted toxicity scoring, calculating a user historical behavior weight, and outputting a final toxicity probability. According to the method, a deep dynamic mapping mechanism of text and visual features is constructed, heterogeneous features are extracted through RoBERTa-Large and OfficientNet-V2 double-flow architectures, semantic space alignment is forced by utilizing comparative learning, and the problem of image-text splitting detection in the traditional technology is solved.

The invention discloses a multi-modal open intention recognition method and system based on pellet characterization, and belongs to the technical field of artificial intelligence and multi-modal intention understanding, and the method comprises the steps: carrying out the feature extraction and modal fusion of multi-modal input data; carrying out structural modeling on the feature representation of each mode and the fusion mode through an adaptive particle and ball clustering method, and generating a multi-granularity particle and ball set; the mass centers of the pellets serve as multi-granularity anchor points, and the pellets with the same labels in different modalities are aligned; introducing a weighting mechanism based on purity and sample scale into the fusion mode; generating a boundary-constrained pseudo-distribution outer sample in the fusion modal space; and constructing a self-adaptive decision boundary based on the fusion modal particle ball obtained by training, and carrying out known class classification and unknown class detection. According to the invention, by introducing the multi-granularity anchor point and the structure perception particle-ball representation mode, the joint recognition of the known category and the unknown category in the multi-modal scene is realized, and the accuracy and robustness of intention recognition are remarkably improved.

The invention belongs to the field of intelligent vehicle-mounted technology, and particularly relates to a multifunctional vehicle-mounted sensing detection system based on multi-source information fusion, which comprises multi-source information acquisition, high-precision space-time synchronization and heterogeneous data preprocessing. A heterogeneous sensing system with feature level deep fusion, vehicle state dynamic performance evaluation and adaptive decision and risk evaluation capabilities is constructed, and a driving decision is dynamically adjusted through fusion perception and vehicle performance. The vehicle state and dynamic performance evaluation module is constructed by deeply integrating vehicle internal state data acquired by a vehicle-mounted diagnosis system into a perception fusion and decision planning process, and the core contradiction of disjunction of a perception result and a vehicle dynamic performance strategy is effectively solved. The system can evaluate key performance parameters such as power, braking, steering and the like of the vehicle and potential faults of the key performance parameters in real time, and the driving strategy and the safety margin are dynamically adjusted in the self-adaptive decision and risk evaluation module according to the actual physical limitation of the vehicle.

The invention discloses a large model calculation network scheduling method based on task combination automation, and relates to the technical field of data processing, and the method comprises the steps: constructing a task factor flow graph; simulating resource linkage between nodes by using a cascade pulse propagation mechanism, generating a pulse propagation topological graph and a pulse intensity matrix, and constructing a computing force field situation awareness network in combination with a pre-trained pulse graph neural network; predicting the resource matching degree of the task factor and the computing power node by using a preset space-time convolution predictor, and generating an affinity tensor; and constructing a joint strategy space, searching and generating a fusion strategy in the joint strategy space by using a multi-target equalization algorithm, and embedding the fusion strategy into the deep reinforcement learning framework to generate an optimal scheduling strategy. By constructing the computing power field situation awareness network, continuous tracking and awareness of the load evolution process, the resource coupling relation and the performance bottleneck dynamic migration of each computing power node are realized, and the self-adaptive decision-making capability and the resource matching efficiency of a scheduling system in a multi-source heterogeneous environment are improved.

The invention discloses an elderly cognitive function adaptive training method and system based on multi-modal interaction feedback, and relates to the technical field of smart medical treatment. The method comprises the steps that basic information of a user is collected for initial cognitive ability evaluation, a user cognitive portrait is constructed according to an evaluation result, and an initial training task with the corresponding difficulty is allocated; collecting multi-modal interaction data in real time according to the initial training task; carrying out fusion analysis on the multi-modal interaction data by utilizing a machine learning model to obtain a quantized real-time state index; based on the real-time state index and the performance data of the current task, dynamically adjusting a subsequent training task through an adaptive decision rule engine; all-dimensional data of each training task is recorded, a visual cognitive competence development trend report is generated through longitudinal comparative analysis, and a machine learning model and a self-adaptive decision rule engine are continuously optimized and trained by utilizing accumulated user data to form an optimized training closed loop. The cognitive function training effect of the old people can be improved.

The invention provides a damage assessment method based on heterogeneous double-flow network and multi-strategy adaptive decision fusion. The method aims at solving the technical defects of an existing method in the aspects of dual-time-phase feature alignment, multi-scale perception and fusion strategy self-adaption. The method comprises the following steps: registering and enhancing unmanned aerial vehicle images before and after damage; afterwards, feature extraction is carried out through a heterogeneous double-current feature extraction module, a cross-temporal window alignment mechanism is introduced into a global context awareness stream, strict spatial alignment of double-temporal features is ensured, and capture of multi-scale damage details is enhanced through multi-receptive-field optimization of a local detail enhancement stream; finally, through a multi-strategy self-adaptive decision fusion module, a strategy selector is used for dynamically calculating the weight, weighted decision is carried out on output of the association perception fusion strategy, the explicit change detection strategy and the robust weighted fusion strategy, and finally the damage level is output through a classifier. According to the invention, the accuracy, robustness and adaptive ability of damage assessment in a complex battlefield environment are effectively improved.

The invention belongs to the technical field of efficient fine tuning and continuous learning of visual language models, and discloses a visual language model continuous learning method based on a dynamic hybrid expert adapter. The method comprises the following steps: constructing a dynamic hybrid expert adapter on a part of layers of a pre-trained visual language model, wherein the expert adapter and a routing network dynamically expand along with an incremental task; whether a new expert adapter is added or not is adaptively decided through the dynamic expert extension controller, and parameter redundancy of static hybrid experts is avoided; a potential embedded self-selector is integrated in a model, potential features output by a freezing layer are utilized to automatically judge data distribution and select corresponding routes, an independent external distribution discriminator is replaced, and a unified framework is formed. According to the method, the problem of disastrous forgetting in continuous learning is effectively relieved, parameter redundancy and calculation burden are remarkably reduced, and meanwhile, the zero sample generalization ability of the model for unseen data is kept.

The invention discloses a mine water storage layer leakage risk early warning and emergency decision intelligent system, which is characterized in that the system acquires osmotic pressure gradient, microseismic events, tracer migration rate and rock stratum displacement data in real time through distributed sensors, and generates a standardized multi-parameter data set through processing such as wavelet threshold denoising and variation mode decomposition; outputting a leakage probability value P and a potential fracture azimuth angle theta by using a fuzzy neural network model; early warning in three levels according to the P value, wherein Plt is greater than or equal to 0.3; when 0.6, regulating and controlling pore pressure, wherein 0.6 < = Plt; when P is larger than or equal to 0.85, sampling is encrypted, a grouting path is generated, and when P is larger than or equal to 0.85, an optimal evacuation path is calculated; constructing a grouting pressure gradient field according to the theta and the early warning grade, and dynamically matching the ratio of the leaking stoppage material; and online updating of model parameters is realized through closed-loop control. The system realizes multi-physics field coupling monitoring and dynamic adaptive decision making, and improves leakage risk assessment accuracy and emergency response efficiency.

The invention relates to the technical field of industrial vehicle intelligence, in particular to an intelligent flexible forklift system based on multi-mode sensing and self-adaptive path planning. The intelligent forklift comprises an intelligent forklift body, a vehicle-mounted integrated control system, a multi-mode sensing and navigation module, a self-adaptive decision-making and path planning module, a cooperative communication and task management module and a cloud scheduling and management platform, and the intelligent forklift body is provided with a pallet fork mechanism with a 3D camera and a force sensor; the vehicle-mounted integrated control system serves as a core to achieve multi-module data interaction and instruction output, the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion, the self-adaptive decision-making and path planning module completes path planning, obstacle avoidance and pre-verification, the cooperative communication and task management module achieves multi-vehicle cooperation and task disassembly, and the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion. And the cloud scheduling and management platform realizes global monitoring and management. According to the method, the adaptability to complex scenes and the operation safety are improved, and accurate forking and flexible path planning are realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of mechanical arm grabbing, financial science and technology, medical treatment and health and the like, and discloses an action sequence generation method and device, equipment and a medium. Respectively generating a visual feature vector and a language feature vector by using a visual encoder and a language encoder; fusing the visual feature vector and the language feature vector to obtain a multi-modal fusion feature, and inputting the feature into a language model for processing to generate an initial action strategy; an action sequence is generated according to an initial action policy by an action decoder integrated with a language model. According to the method, by fusing vision and language information, self-adaptive decision making in a complex task environment is realized, and the adaptive capacity of the system in a dynamic change environment is enhanced; and through multi-modal feature fusion, the operation precision and the generalization ability of the system are effectively improved.

The invention discloses a self-adaptive optimization control system and method for the combustion process of a biomass generator set. The self-adaptive optimization control system comprises a multi-dimensional sensing array module, an edge calculation module, a self-adaptive decision server, an execution mechanism cluster and a closed-loop verification terminal. The multi-dimensional sensing array module comprises a temperature field monitoring unit, a flue gas component analysis unit and a fuel characteristic detection unit; a data fusion processor is arranged in the edge calculation module; the self-adaptive decision-making server carries a dual-module decision-making engine; the executing mechanism cluster comprises a frequency conversion fan, a precise feeding machine and an adjustable fire grate. Through multi-source sensing data fusion and a self-adaptive decision-making mechanism, real-time response to biomass component fluctuation and load change is achieved, and the combustion efficiency is improved; a deep learning prediction model is combined, the air-coal ratio and the hearth temperature set value are adjusted in advance, and CO emission is reduced; the manual intervention frequency is reduced through an optimization mechanism, and the equipment maintenance period is prolonged.

The invention relates to the technical field of robot and artificial intelligence crossing, in particular to a robot cooperative autonomous decision-making energy management method and system based on an AI agent, and the system comprises an edge sensing module which is used for collecting original data including energy data, environment parameters and equipment working conditions in real time and carrying out the preprocessing, obtaining state data; the communication module is used for establishing operational coupling among the modules so as to realize data and strategy transmission; the AI agent module takes a deep reinforcement learning engine as a core, performs joint modeling on the state data, and outputs an energy decision strategy; and the robot collaboration module is used for receiving the energy decision strategy, converting the strategy into a task which can be executed by at least one robot, and scheduling the at least one robot to execute the task. By adopting the method, the problem that the prior art lacks a novel energy management scheme capable of breaking through the limitation of the traditional EMS can be solved, and the method has the characteristics of high adaptive decision-making capability, low time delay and high reliability.

The invention relates to the technical field of image analysis, in particular to a metal composite material surface defect intelligent detection method based on machine vision, which comprises the following steps: acquiring an original image and converting the original image into an observation matrix; decomposing the matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; utilizing a nuclear norm constraint low-rank matrix to establish a background statistical model, and utilizing a norm constraint sparse matrix; positioning a defect area in the sparse matrix and extracting a gray level co-occurrence matrix feature vector; performing connected domain analysis on the sparse matrix, calculating geometrical morphology parameters and evaluating a stress concentration coefficient; and establishing a self-adaptive decision model to carry out fusion judgment so as to judge the physical damage. According to the method, through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background are realized.

A mine earthquake adaptive positioning method and system based on multi-index fusion discrimination belong to the field of mine earthquake positioning, and the system comprises a data acquisition module, a feature extraction and quality evaluation module, a comprehensive index calculation module and an adaptive decision module. The method comprises the following steps: firstly, carrying out multi-dimensional physical examination on input waveform data, station layout and a speed model by the system, and generating a quantized physical examination report comprising a signal-to-noise ratio, a pickup error, station geometric coverage, model credibility and the like, namely a multi-dimensional feature vector; and then, a built-in rule engine automatically screens out an optimal single method or combined strategy under the current condition from multiple positioning methods according to the report and a demand target set by a user, and outputs a decision result with a clear basis. Through the system and the method, full-process automation from data input to strategy output is realized, and efficient and reliable technical support is provided for mine safety monitoring and disaster early warning.

The invention relates to the technical field of vehicle safety, and provides a remote lane departure early warning method and system based on remote driving, and the method comprises the steps: guiding a fusion network to extract a lane line feature vector through employing a two-way decoder, and generating a vehicle state parameter set through combining with a dynamic modulation Kalman filter; a dual-mode risk quantification framework is applied, the early warning level is judged through a situation self-adaption decision, and an early warning information message is reported; performing time sequence alignment on the comprehensive control instruction set, the driver state data and the environment context data, and generating a control instruction message through optimization of an environment adaptive instruction intention filter; and the vehicle-mounted terminal calculates a man-machine control fusion weight based on the comprehensive risk and the driver state, a second comprehensive control instruction set and a safety deviation correction instruction are fused to generate a bottom layer control signal, and the remote cockpit executes graded early warning and generates a multi-mode feedback signal. According to the invention, prospective risk research and judgment and adaptive control optimization are cooperated, and a man-machine cooperative early warning and closed-loop control mechanism for remote driving is constructed.

The invention discloses a rope net ladder data production scheduling and traceability system based on big data storage, and particularly relates to the field of data scheduling and traceability, comprising the following steps: the system obtains total factor data of equipment, process, quality and the like through a data perception and digital acquisition module and standardizes the total factor data; the integrated storage and quantitative analysis module constructs functions such as efficiency evaluation and quality risk to quantify production indexes; the adaptive decision optimization and scheduling module generates an optimal scheduling scheme and a dynamic rescheduling instruction based on a multi-target comprehensive decision function; the full-link tracing and self-feedback optimization module realizes full-link bidirectional tracing and drives process parameter and model self-feedback optimization; the system realizes the intelligent control of the whole production process, balances the efficiency, quality, cost and delivery target, improves the resource utilization rate and production stability, and is suitable for the precise production scene of structural members such as rope net ladders.

The invention relates to the technical field of intelligent switching, in particular to a multi-mode dynamic coupling intelligent switching method and system based on road condition recognition and a vehicle. The method comprises the steps that gradient, vehicle speed, accelerator and brake signals are collected in real time, and a multi-dimensional feature vector fusing driving habits is constructed after preprocessing; a real-time road condition label is automatically generated through unsupervised clustering, and the clustering effectiveness is verified online by using a contour coefficient. Enabling the road condition label and the current power mode to form a system state, and inputting the system state into a self-learning decision network based on reinforcement learning; and the network updates the state-action value matrix through a reward function in a time sequence difference mode, and outputs a target power mode with the highest accumulated reward. And calculating the motion confidence coefficient of the target mode based on the value matrix, if the motion confidence coefficient exceeds a threshold value, generating a switching instruction, and smoothly adjusting the torque by an execution mechanism through a torque transition algorithm to realize impact-free switching. Automatic road condition recognition, self-adaptive decision making and non-inductive execution are achieved, and smoothness and adaptability are improved.

The invention discloses a network system security monitoring device, which comprises an intelligent sensing layer for scanning network traffic, logs and user behaviors by deploying AI-driven detection nodes; when a potential threat is detected, a structured alarm is generated immediately and pushed to a trusted transmission layer; the trusted transmission layer is used for generating a unique block identifier by adopting a Hash algorithm after the data integrity is verified through a consensus node; automatically triggering a repair process by using an intelligent contract; the self-adaptive decision-making layer is used for synchronizing configuration, flow and vulnerability states of physical equipment; after the vulnerability information is received, simulating various repair schemes in a virtual environment, evaluating the influence of the schemes on services, dynamically adjusting the priority, generating an optimal strategy, and pushing the optimal strategy to the automatic execution layer; the automatic execution layer is used for automatically executing and monitoring the repair progress through an agency-free technology after receiving the repair instruction of the self-adaptive decision-making layer, and triggering a rollback mechanism and generating a fault report if abnormity is detected; and after repairing is completed, a verification script is automatically generated.

The invention discloses a power conversion control method of a household energy storage system and a magnetic component, and the method comprises the steps: collecting an input voltage range, an output power demand and a frequency range according to the working characteristics of the system, selecting a nanocrystalline magnetic core material, and designing a multi-layer distributed winding structure to construct the magnetic component; acquiring operation monitoring data through a multi-dimensional data acquisition network, and generating a working condition description set through feature extraction and classification identification; a multi-target optimization model is constructed through bottleneck point identification and capacity estimation, and a power optimization distribution scheme is generated; then a control strategy selection space is constructed, an adaptive decision engine is operated, a PWM control strategy is selected, multiple PWM generation algorithms are realized, parameters are adjusted in real time, and a control signal is output; and finally, realizing system security monitoring and self-adaptive protection response through differential privacy protection processing and an anomaly detection model. According to the invention, the adaptability of the system in a wide voltage input range can be improved, and the loss of magnetic components under different load conditions is reduced.

The invention relates to the technical field of information, and discloses a comprehensive operation supervision system based on an intelligent supply chain. The system comprises a real-time data acquisition module, a multi-modal feature extraction module, a dynamic risk assessment module, a self-adaptive decision generation module and an execution feedback regulation and control module. By constructing a real-time data acquisition and multi-modal feature extraction module, efficient fusion and deep feature extraction of internal and external multi-source heterogeneous data of the supply chain are realized, the sensing sensitivity and accuracy of the system to market fluctuation and emergencies are remarkably improved, and the prediction deviation is reduced from the data source. The dynamic risk assessment module adopts a dual anomaly detection mechanism, various potential risks can be quickly and comprehensively identified, risk level quantification is carried out in combination with a time sequence mode, and accurate and timely risk situation assessment is provided for subsequent decision making.