796 results about "Fuzzy logic" patented technology

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

A system is provided for managing Internet of Things (IoT) networks. The system includes a learning module configured to employ machine learning models with hyperparameters optimized through a hyperparameter optimization process; wherein the process includes evaluating a set of hyperparameters against a performance metric to select optimal hyperparameters that enhance the adaptability and efficiency of dynamic membership functions within an adaptive fuzzy logic engine (AFLE).

According to the optical storage direct flexible system scheduling method based on multi-objective optimization and an adaptive scheduling strategy, monitoring devices are installed on a photovoltaic array, an energy storage unit and a load side, a data sensing network covering the whole link of'source-storage-load-network 'is constructed, and key data are collected in real time. A multi-objective optimization model with maximization of economical efficiency, reliability and clean energy consumption rate as objectives is established, and a hybrid optimization mechanism of a genetic algorithm and particle swarm optimization is adopted to generate a day-ahead scheduling reference scheme. And designing an adaptive scheduling algorithm and a dynamic parameter adjustment mechanism based on fuzzy logic, and combining a rolling optimization window to realize real-time optical storage coordination control and power real-time balance. According to the invention, the operation efficiency, stability and flexibility of the optical storage direct-flexible system are effectively improved, the capability of coping with emergencies is enhanced, and the optimization of the overall performance of the system is realized.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention relates to the technical field of forklift risk management, in particular to a multi-scene-oriented forklift self-adaptive safety early warning decision-making method, which comprises the following steps of: through multi-scene dynamic modeling, constructing multi-dimensional scene feature vectors according to forklift operation environment spatial features, cargo physical attributes and driver behavior parameters; establishing a scene classification model by means of transfer learning; a multi-modal sensor data fusion technology is utilized, a heterogeneous sensor network is deployed to collect data, and data reliability is optimized through space-time alignment and confidence evaluation; a dynamic safety threshold value is generated through fuzzy logic and reinforcement learning, and a driver behavior correction factor is introduced for real-time adjustment; calculating a comprehensive risk index by using a dynamic weight distribution algorithm, and triggering graded early warning and response; a federated learning closed-loop optimization risk assessment model is adopted, robustness is verified in combination with digital twinning, and cooperative obstacle avoidance of multiple forklifts is achieved through inter-vehicle communication. According to the method, the operation safety and decision-making accuracy of the forklift in a complex and changeable scene are remarkably improved.

The invention relates to the technical field of communication engineering, and discloses a power line carrier and wireless dual-mode fusion optimized communication method, which comprises the following steps of: 1, acquiring signal quality, noise and delay data of a power line and a wireless link in real time, and constructing a prediction model by utilizing historical data; step 2, dynamically adjusting a judgment threshold value of link switching in real time based on the prediction model, and ensuring that switching is triggered only when the signal change exceeds a preset range; step 3, based on the dynamic threshold, combining multiple indexes of signal-to-noise ratio, delay, packet loss rate and network load; according to the method, the necessity of link switching can be more intelligently judged by dynamically adjusting the judgment threshold of link switching and adopting a fuzzy logic decision mechanism, frequent switching during tiny fluctuation of signals is avoided by dynamic threshold adjustment, and a fuzzy logic system integrates multiple factors, so that the decision is finer, and the reliability of link switching is improved. And unnecessary delay and resource waste are reduced, so that the stability of a communication system is improved.

The invention discloses an airport bird condition automatic detection and intelligent bird repelling device and method, and the method comprises the steps: collecting airport airspace environment data in real time through a multi-source sensor array, carrying out the preprocessing of the environment data, and generating a fusion data set; a target detection model is constructed based on a deep learning algorithm to perform bird target recognition on the fusion data set, and target feature information is extracted and comprises bird positions, movement tracks, the number and types; calculating a threat level of bird activities to flights through a risk assessment model established based on fuzzy logic and a Bayesian network in combination with airport flight take-off and landing time sequence data and target feature information; triggering a grading early warning signal according to the threat grade, and linking the bird repelling equipment to execute directional bird repelling operation. According to the invention, an airport bird situation automatic detection and emergency prevention and control system is formed, the bird strike risk detection precision, the early warning accuracy and the bird repelling efficiency are improved, and the aviation operation safety is guaranteed.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

An intelligent regulation and control method for a DevOps platform monitoring period comprises the following steps: firstly, acquiring a multi-dimensional index data stream of a system, and forming an instant multi-dimensional vector through stream-oriented calculation, window processing, feature combination and the like so as to judge the state of the system; and quantifying event information by using an event level model to determine an emergency degree. A confidence vector is obtained through operation of a preset rule base, a corresponding scheduling strategy is triggered, and resources are allocated according to the fuzzy logic and the event emergency degree. Meanwhile, the event priority is determined in combination with expert experience and fuzzy logic, and a flexible management plan is executed and a result is evaluated accordingly. And finally, according to the rule base evaluation, dynamically adjusting the rule weight ratio, and optimizing the judgment on the multi-dimensional indexes of the system. According to the invention, dynamic regulation and control of a monitoring period, accurate evaluation of systems and events, intelligent scheduling and handling are realized, and the stability, high efficiency and self-adaptability of a DevOps platform are improved.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention discloses an unmanned aerial vehicle cargo distribution management method and system based on strategy optimization, and particularly relates to the technical field of cargo distribution management. Through an improved incremental K-means clustering algorithm, a KD-Tree index is combined to optimize nearest neighbor search, order area grouping efficiency is improved, based on an order cluster set O, mixed integer programming and a Hungary algorithm are adopted to carry out unmanned aerial vehicle task matching, a task distribution moment M is optimized, a distribution path is dynamically adjusted to deal with real-time weather and airspace limitations, and in the distribution process, the distribution efficiency is improved. Data such as order liquidity index, battery consumption rate and task balance degree are acquired through an unmanned aerial vehicle sensor, and the scheduling delay condition is judged by using fuzzy logic reasoning, so that the system can still realize efficient, balanced and intelligent unmanned aerial vehicle scheduling in a high-load scene, the distribution delay is greatly reduced, the no-load rate is reduced, and the overall logistics efficiency is improved.

The invention relates to the field of power system monitoring and fault prediction, in particular to a lightning arrester fault type prediction method and system based on an extremely cold environment working condition. Probabilistic prediction of future key indexes of the lightning arrester is realized, and physical state data is generated through real-time simulation of a digital twin platform; through weighted least square method fusion correction and fuzzy logic, an integrated decision model constructed by a dynamic Bayesian network, system output fault mode classification and early warning decision, the effects of early warning of fault risks in advance in an extremely cold environment and improvement of the safety of a power system are achieved.

The invention discloses an expressway tunnel monitoring method, system and device and a medium, and relates to the technical field of tunnel monitoring. Tunnel environment data are collected in real time through a multi-source heterogeneous sensor array, and the data at least comprise video images, millimeter wave radar point cloud, laser radar three-dimensional coordinates, temperature and humidity and CO concentration parameters. According to the system, an improved YOLOv7 algorithm is used for carrying out target real-time detection, an optical flow method is combined to predict the motion trail of a target object, a tunnel dynamic characteristic spectrum is constructed, real-time monitoring of the tunnel environment is achieved, by deploying a risk level assessment engine, the system can extract space-time correlation characteristics, and the risk level assessment efficiency is improved. A comprehensive risk index is generated in combination with a fuzzy logic decision tree, and when the risk index exceeds a dynamic threshold value, the system triggers a grading early warning mechanism, links tunnel emergency equipment, synchronously generates an emergency plan and pushes the emergency plan to an operation and maintenance terminal to guarantee tunnel operation safety.

The invention belongs to the technical field of power systems, and particularly relates to an audit data early warning method and system, and the method comprises the steps: collecting the node voltage and current of a power supply network and the charging and discharging efficiency data of an energy storage system in real time through a distributed sensor network, and generating an original data flow; carrying out localized cleaning and standardized format conversion through an edge computing node; generating a dynamic risk assessment result and a scheduling scheme based on fuzzy logic reasoning and digital twinborn simulation; and analyzing and predicting deviation through meta-learning, and realizing closed-loop optimization by adopting a model distillation technology. The system comprises a distributed sensor network, an edge computing node, a multi-source data fusion module and the like. According to the method, the anomaly detection real-time performance, the risk assessment accuracy and the energy storage scheduling economy of the power system are improved.

The invention relates to the technical field of electric vehicle control, and provides an intelligent new energy locomotive power distribution and energy recovery control system and method. The system comprises a multi-source information sensing layer, an intelligent decision control layer and an execution feedback layer. The multi-source information sensing layer collects the state of a vehicle and external environment data. The intelligent decision control layer generates a power distribution and energy recovery instruction through data fusion and preprocessing, fuzzy logic assistance, reinforcement learning decision and cooperative work of a multi-objective optimization module; and the execution feedback layer executes the instruction, monitors feedback in real time, and optimizes related actions. According to the method, advanced technologies such as reinforcement learning and fuzzy logic are fused, complex and changeable driving scenes are accurately dealt with, self-adaptive optimization of power distribution and energy recovery strategies is achieved, the energy recovery efficiency is improved, power distribution is accurate and efficient, dynamic adjustment can be achieved according to driving intentions and working conditions, the driving safety and comfort are guaranteed, and meanwhile the driving efficiency is improved. And efficient utilization of energy is realized.

The invention relates to the technical field of urban climatic science, in particular to an urban climatic environment rapid evaluation system and method. According to the technical scheme, the system comprises a data acquisition module, an index standardization and feature extraction module, an index weighting and dynamic parameter adjustment module and a climatic environment comprehensive index generation module. According to the method, based on meteorological and environmental multi-source data of an urban area, a multi-dimensional evaluation index set is firstly constructed through standardization and normalization processing, and then a fuzzy logic harmonic function and a non-linear harmonic function are utilized to identify the coupling relation and the cooperation strength among the indexes; and meanwhile, a dynamic optimization mechanism is introduced to adaptively adjust each weight coefficient according to the real-time index sensitivity, an urban climate environment index is finally generated, and spatial visual expression and grade early warning are realized. According to the method, rapid evaluation and trend judgment of the urban climate system state can be realized, and the method is suitable for urban planning, climate risk monitoring and toughness analysis.

The invention provides a centralized control automation strategy optimization method and system based on fuzzy logic, and the method comprises the steps: collecting the real-time operation state data of power grid operation equipment in a target centralized control region, covering the operation parameters of a power grid and a parameter fluctuation range, and carrying out the fuzzy membership processing of the data according to the parameter fluctuation range, and generating a fuzzy rule set containing a plurality of fuzzy rules and parameter dynamic adjustment logic corresponding to each fuzzy rule, optimizing a current control strategy by applying fuzzy logic based on the fuzzy rule set, and generating an optimized control strategy containing a control instruction and a trigger condition thereof, and then the strategy is sent to execution equipment according to a trigger condition, execution feedback data is monitored, the validity of the strategy is verified according to a matching result of the execution feedback data and a preset threshold value, the control strategy is dynamically adjusted and optimized, a centralized control automation strategy can be effectively optimized, and the power grid operation control effect is improved.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention discloses a cloud mobile phone distributed task scheduling method and related equipment, and relates to the technical field of cloud mobile phones, and the method comprises the steps: obtaining metadata of a cloud mobile phone task request; performing priority discrimination on the metadata based on a fuzzy logic classifier to obtain a priority score; acquiring a multi-dimensional resource index of each cloud mobile phone node in the cloud mobile phone cluster in real time; determining a task allocation strategy through a multi-objective optimization algorithm based on the dynamic priority score and the multi-dimensional resource index; and in response to the task allocation strategy, performing dynamic migration and resource allocation operation on the cloud mobile phone task request through the containerization execution layer. According to the invention, through combination of fuzzy logic priority evaluation, multi-dimensional resource perception and a multi-target optimization scheduling algorithm, intelligent, refined and global optimal distribution of cloud mobile phone tasks is realized, and the response speed of high-priority tasks, the resource utilization efficiency and the system load balancing capability are effectively improved.

The invention discloses a data management method of a cross-domain data automatic clustering algorithm, which relates to the technical field of data processing, fully considers the characteristics of cross-domain data through distributed collaborative clustering, and adopts a clustering algorithm based on multi-domain kernel density estimation and adaptive neighbor search. According to the method, the bandwidth and the neighbor weight of kernel density estimation can be dynamically adjusted according to the distribution characteristics of data in different fields, the clustering structure in the data can be identified more accurately, and meanwhile, in the global clustering optimization process, a classification method based on fuzzy logic and evidence theory fusion is applied, so that the clustering optimization efficiency is improved. The feature weight of the field where the data points are located, the distance from the data points to the clustering center and global density information of clustering are comprehensively considered, it is ensured that clustering attribution of the data points is more reasonable and accurate, the problems of clustering errors and instability caused by the fact that a traditional clustering method cannot adapt to cross-field data complexity are effectively solved, and the clustering efficiency is improved. Therefore, a more reliable basis is provided for subsequent data analysis.

The invention discloses an intelligent driving multi-target path planning method based on fuzzy game optimization, and the method comprises the following steps: S1, collecting the real-time environment data of a vehicle, and carrying out the preprocessing; s2, constructing a current road environment model, obtaining candidate paths, and quantifying an optimization target of each candidate path; s3, performing fuzzification processing on the optimization target through fuzzy logic to obtain an optimization target weight of each candidate path; s4, constructing a game model, and calculating a game optimization value for each candidate path; s5, performing multi-objective optimization by adopting an improved particle swarm optimization algorithm to generate an optimal path; and S6, executing the optimal path through a vehicle control system, and performing navigation guidance. According to the method, the fuzzy logic, game optimization and the improved particle swarm optimization algorithm are combined, multi-target path planning is achieved, and the method has the advantages of being efficient, safe and intelligent.

The invention relates to the technical field of agricultural planting, and discloses a crop planting suggestion generation method adapting to different climate conditions. Real-time climate data are collected through a multi-source climate sensor, climate dynamic feature data are generated through a spatial-temporal feature fusion algorithm, and planting strategy parameters are obtained by inputting the climate dynamic feature data into a pre-trained mixed decision model. And constructing a multi-objective optimization model taking the crop adaptability matching degree and the resource utilization efficiency as objectives, and globally optimizing a planting scheme by adopting a hierarchical dynamic programming algorithm integrating fuzzy logic constraint and an adaptive decomposition strategy. A multi-level regulation and control framework comprising a decision-making layer, an adaptation layer and an execution layer is constructed, the decision-making layer generates a global planting sequence, the adaptation layer uses a sliding window optimization algorithm to adjust local parameters, the execution layer regulates and controls soil humidity and illumination intensity based on a robust feedback control algorithm, and finally a planting control instruction is output. And accurate and efficient climate adaptability planting decision is realized.

The embodiment of the invention provides an X-band weather radar non-rainfall data quality control method and system. The method is applied to the technical field of weather radars and comprises the following steps: acquiring X-band weather radar data; identifying and eliminating the electromagnetic interference echo according to the continuity characteristic of the electromagnetic interference echo and the distribution condition of the correlation coefficient CC and the differential reflectivity factor ZDR; judging whether the difference value of the reflectivity factors of each echo point before and after hardware filtering is greater than a preset threshold value or not, and if so, marking the echo point as a rainfall echo; if not, fuzzy logic recognition elimination based on characteristic parameters is carried out; wherein the non-precipitation echo probability PNPR and the precipitation echo probability PPR of each echo point are calculated according to the characteristic parameters, whether the PNPR is larger than the PPR or not is judged, and if yes, the echo point is marked as a non-precipitation echo; if not, non-rainfall weak echo discrimination is carried out; whether the echo is a non-rainfall echo is determined by comparing the reflectivity factor mean value # imgabs0 #, the correlation coefficient mean value # imgabs1 # of the elevation angle of the whole layer of the echo and the ratio ValCC of the effective distance library number of the correlation coefficients with a judgment threshold value, and if the echo is the non-rainfall echo, the echo point is marked as the non-rainfall echo; and if not, continuously filtering scatter and plaque noise around the echo point by using the reflectivity factors in the radial direction and the azimuth to obtain the rainfall echo. In this way, the quality of weather radar data can be improved.

The invention discloses an efficient and energy-saving type intelligent management and control system and method for an integrated cold station, and particularly relates to the technical field of intelligent management and control of cold stations. A plurality of sensors are arranged in a cold station system, the operation state of each component and the external environment change are monitored in real time, historical data and external environment information are combined, and a load fluctuation prediction model is established by adopting a long-short-term memory network, so that the load demand is accurately predicted, and the operation parameters of each component of the system are adjusted in advance; meanwhile, on the basis of real-time monitoring data and load prediction deviation, dynamic optimization is conducted on the cold station system through fuzzy logic, and therefore the equipment response speed and the energy-saving efficiency are balanced; abnormal detection is carried out on the system through a machine learning model, potential fault risks are found in time, and early warning is given out; the system can quickly and accurately adjust the operation state when the load fluctuates, the stability, the energy efficiency and the fault prevention capability of the cold station system are improved, and the overall operation efficiency and the energy-saving effect are remarkably improved.

The invention discloses a terminal equipment interface display method and system based on user habits, and belongs to the technical field of digital display, key features such as user intention deviation degree and interface attention stability index are extracted by collecting behavior data of a user for interaction objects and function modules, and a user preference weight table is calculated and dynamically updated; interface module sorting is further carried out in combination with a deep learning model, a fuzzy logic evaluation mechanism is introduced to carry out interpretable scoring on a sorting result, when a scoring result is lower than a threshold value, the system automatically corrects a sorting strategy based on user feedback, and finally optimized interface display content is output. Personalization, dynamics and controllability of interface display are achieved, and the man-machine interaction experience and the user satisfaction degree of the terminal equipment are effectively improved.

The invention discloses a satellite flash connection intelligent switching decision-making system and method based on equipment state prediction, and particularly relates to the technical field of satellite flash connection. In order to solve the problem of star-flash network linkage failure caused by multi-mechanical-arm cooperative movement and electromagnetic disturbance coupling in an industrial cooperative robot system, a prediction model fusing space attitude cooperative change characteristics and transient electromagnetic disturbance amplitude gradient characteristics is constructed, and high-sensitivity recognition and risk early warning of the coupling state of a star-flash connection network are achieved. A communication channel and communication parameters are dynamically adjusted through a fuzzy logic decision mechanism, and link stability and interference robustness are effectively improved; and meanwhile, a model adaptive optimization mechanism driven by switching effect feedback is introduced, so that the prediction accuracy and the system self-evolution capability are remarkably improved, and the continuity, the safety and the high reliability of multi-node cooperation in an industrial communication scene are guaranteed.