623 results about "Hybrid algorithm" patented technology

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The invention discloses a fault detection and root cause analysis system for a multi-source energy system, and relates to the technical field of energy management and fault diagnosis. The system obtains various operation data and image data of the multi-source energy system in real time; preprocessing the operation data and the image data; inputting the preprocessed data into a hybrid algorithm combining wavelet transform, CNN and LSTM, and detecting an abnormal mode and a potential fault in the multi-source energy system; the digital twinborn model is dynamically updated according to data acquired in real time; and in combination with the feature extraction capability of a digital twin model and a hybrid algorithm, positioning key causes causing the fault, simulating a system operation state, analyzing potential causes of the fault, and outputting an analysis result. According to the invention, the comprehensive analysis capability of the system on various data sources is effectively improved, and a comprehensive and accurate solution is provided for intelligent operation and maintenance of the energy system.

The invention discloses a rockburst risk early warning method based on TBM multi-source data fusion and a hybrid algorithm, and the method comprises the steps: collecting TBM tunneling parameters, geological exploration data and micro-seismic monitoring data of different rockburst levels, achieving feature complementation through multi-source data fusion, improving the completeness and reliability of early warning, constructing a hybrid neural network model based on CNN, LSTM and an attention mechanism, and carrying out the early warning of the rockburst risk. Feature weight distribution is optimized by introducing an attention mechanism, so that the model can adaptively focus key risk signals, and the applicability of model early warning is improved; and in combination with a fuzzy comprehensive evaluation algorithm and a Bayesian probability model, outputting four rockburst grades of no rockburst, slight rockburst, medium rockburst and strong rockburst and occurrence probabilities thereof, and realizing real-time dynamic early warning and probabilistic early warning of the rockburst grades. Compared with an existing method, the method has the advantages that the accuracy, timeliness and engineering applicability of rockburst early warning are remarkably improved, and real-time and accurate early warning of potential rockburst and the grade of the potential rockburst can be achieved.

The invention discloses a method for multi-AGV and multi-task allocation, and the method comprises the steps: obtaining real-time tasks, and sorting the tasks according to dynamic priorities; according to AGV states and task information, a charging decision is considered, an upper layer model of multi-AGV and multi-task allocation is established, scheduling optimization is carried out by using an APSO-NSGA-III hybrid algorithm, and a task queue to be executed is allocated for each AGV; according to task queue information generated by the upper-layer model, conflict resolution is considered, a lower-layer model with the lowest total path cost as a target is established, and an improved A * algorithm is used for performing optimal path planning of multiple AGVs; the optimal path planning result generated by the lower-layer model is fed back to the upper-layer model, the upper-layer model forms a new task queue scheme according to the feedback and continues to influence the lower-layer model, the upper-layer model and the lower-layer model mutually feed back, and the optimal scheme of multi-AGV multi-task allocation is generated.

The invention relates to a deep integrated electric drive assembly design method and system, and the method comprises the steps: obtaining the parametric modeling data of an electric drive assembly, and carrying out the digital twin synchronous motor modeling, and obtaining a drive three-dimensional model; topological structure data of the electric drive assembly are obtained, and mixed algorithm optimization calculation is carried out on the topological structure data and the drive three-dimensional model to obtain a system optimization solution set; performing model prediction control construction based on the system optimization solution set, and performing gear contour line modification processing on the electric drive assembly to obtain a speed reduction transmission strategy; performing heat dissipation management extraction optimization on the system optimization solution set according to a preset variable cross-section micro-channel heat dissipation algorithm to obtain a dynamic heat dissipation strategy; preliminarily integrating the speed reduction transmission strategy and the dynamic heat dissipation strategy to obtain an initial design scheme; eMI filtering optimization is carried out on the initial design scheme, multi-stage electromagnetic shielding construction is carried out, and a system performance optimization scheme is obtained. The comprehensive performance of the system can be improved through integral design, regulation and control.

The invention discloses a WMS warehouse task dynamic scheduling method and system based on multi-objective optimization, and the method comprises the steps: collecting scheduling parameters, such as task priority, equipment load and goods location distance, through a RidgeOS intelligent warehouse scheduling platform, and inputting a dynamic scheduling task uncertainty perception model to generate an uncertainty feature vector; constructing a scheduling decision space by adopting an elastic time slice-reinforcement learning hybrid algorithm, dividing an elastic time slice interval, and iteratively updating a scheduling strategy in the interval; sorting the to-be-executed tasks of the warehouse according to the updated strategy, wherein the uncertainty feature vectors are used as constraints during sorting; a scheduling instruction is sent to the equipment through the platform, and meanwhile parameter change data in the equipment execution process is collected and fed back to the sensing model. The system comprises six units which are connected. According to the method, the task execution uncertainty can be accurately perceived, the scheduling strategy flexibility and iteration efficiency are improved, and the intelligent storage multi-target optimization scheduling requirement is met.

The invention discloses a charging pile intelligent heat dissipation method and device based on heat conduction line optimization and a medium, belongs to the technical field of charging pile heat dissipation, and aims to solve the technical problem of how to avoid the defects of high thermal resistance, response lag and large energy consumption in a traditional charging pile heat dissipation scheme and improve the heat dissipation efficiency and operation reliability of a charging pile. According to the technical scheme, a heat conduction structure is optimized; deploying a thin film thermocouple and constructing a three-dimensional temperature field model through infrared thermal imaging; establishing a thermal resistance-flow-power transfer function model by adopting a fuzzy PID (Proportion Integration Differentiation) and model predictive control hybrid algorithm; historical charging data are analyzed based on an LSTM neural network, a power peak value is predicted 300 ms ahead of time, and heat dissipation equipment is pre-started.

The invention discloses a charging pile dynamic load balancing scheduling system based on reinforcement learning, and the system comprises a data collection module which comprises 5G communication units installed at the charging piles, and is used for obtaining the power of the charging piles, vehicle battery parameters and power grid load data in real time; the reinforcement learning decision module maps the data in the data acquisition module into a state vector through a feature extraction network, and generates a power distribution strategy by adopting a PPO + DQN hybrid algorithm; the communication protocol module realizes state synchronization and control instruction transmission between the charging piles based on an OCPP2.0 standard; the execution control module converts the control instruction into a PWM control signal, and adjusts the output power of each charging pile in real time; through the reinforcement learning decision module, the system can analyze the charging pile power, the vehicle battery parameters and the power grid load data in real time, generate an accurate power distribution strategy, ensure accurate matching of the charging pile power and the electric vehicle demand power, and thus significantly improve the charging efficiency.

The invention relates to the technical field of cables, and particularly discloses a cable terminal simulation and state evaluation method and system in a humid environment, multiple types of sensor arrays are deployed, distributed temperature and humidity sensors are installed at key parts of a cable terminal, and aiming at the technical problems of one-sided single-dimensional monitoring, simulation model solidification and insufficient static weight adaptation, the cable terminal simulation and state evaluation method and system are provided. Cooperative acquisition of multiple parameters such as temperature and humidity, partial discharge and corrosion current is achieved through deployment of multiple types of sensor arrays, the nonlinear interaction relation between humidity permeation and electric field distortion is dynamically reflected in combination with a multi-physics field coupling simulation model, the one-sidedness of single parameter monitoring is solved, and the risk misjudgment rate is reduced by 40% or above; a dynamic water film thickness model is adopted to update interface parameters of the sealing ring in real time, a simulation result is updated per hour in combination with a finite element-boundary element hybrid algorithm, an electric field distortion simulation error is controlled within 12%, and the simulation precision is improved by more than one time compared with a fixed boundary condition.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

The invention discloses an SSL VPN security gateway communication method fused with a post quantum cryptography technology. Comprising the following steps: S1, a client and a server respectively configure a serial hybrid signature digital certificate containing an SM2 algorithm and a serial hybrid encryption digital certificate containing a PQC algorithm, and a corresponding PQC encryption key pair private key, a PQC signature key pair private key, an SM2 encryption key pair private key and an SM2 signature key pair private key; s2, newly adding a hybrid algorithm password suite using an SM2 algorithm and a PQC algorithm in a password suite list of the client, and forcibly jacking the priority of the hybrid algorithm password suite; and S3, a message structure in a handshake protocol is transformed to use a series hybrid encrypted digital certificate to realize that PQC algorithm processing is added on the basis of an original national cryptographic algorithm to realize quantum key negotiation resistance. According to the method, the PQC algorithm is added on the basis of the original national secret algorithm, and the handshake protocol is transformed, so that anti-quantum-attack key agreement and identity authentication can be realized, the communication security is remarkably improved, and meanwhile, the interoperability with the standard SSL VPN is kept.

The invention relates to the technical field of die parameter optimization, and discloses a process optimization method and system for a multi-part adaptive self-adjusting stamping die, and the method comprises the following steps: generating a structured process input parameter set through input parameters, and storing the structured process input parameter set in a central database; searching related cases and rules from the knowledge graph according to the input parameters; and calculating the similarity between the current parameter and a historical case, using a hybrid algorithm, carrying out weighted averaging, explaining the weight of each parameter, processing special constraints, and finally carrying out normalization to obtain a comprehensive similarity index. Real-time monitoring and dynamic adjustment are achieved through an intelligent control system, self-adaptive mold design optimization and centralized material characteristic database management are combined, an integrated quality detection system automatically recognizes and classifies defects through image recognition and machine learning technologies, and the problems that key parameters cannot be adjusted in time according to actual working conditions, and the working efficiency is high are effectively solved. The production efficiency is low; and the product quality is unstable.

The invention discloses a ship block construction process-level production scheduling method and system based on a particle swarm-genetic hybrid algorithm. The method comprises the following steps: constructing a standardized process-level process design data model; based on an operation plan decomposition rule, decomposing the segmented production task into a part-level operation production task matched with the process; establishing an optimization target model with the shortest delivery time, the least overdue tasks, the most balanced load and the like, and constraint rules such as lane and line separation and customization station rules, process route and process constraint rules, material and capacity constraint, priority and the like; optimizing the segmented production plan through a particle swarm-genetic hybrid algorithm until an optimization result meeting the requirement is obtained; and according to the feedback of the actual execution condition of the plan, the orders are classified based on a clustering algorithm, and the operation cycle parameters of different processes are dynamically compensated. The method has the advantages of being high in plan fineness, small in operation progress estimation deviation and the like, is high in calculation speed and low in algorithm complexity, fully considers support of process design data of a design end on algorithm implementation, and is very suitable for engineering application.

The invention relates to the technical field of intelligent warehousing monitoring, and discloses an intelligent warehousing monitoring system based on digital twinning, which comprises a data acquisition layer, a digital twinning modeling layer, an intelligent analysis layer and an execution control layer, and is characterized in that three-dimensional coordinates and temperature distribution data of goods are acquired through a laser radar array and an infrared thermal imager; constructing a dynamic point cloud data set in combination with RFID positioning; an improved YOLOv7 model is adopted to be embedded into a CBAM attention mechanism to recognize the cargo form risk, and an LSTM time sequence model is fused to predict a temperature anomaly trajectory; an AGV obstacle avoidance path is optimized based on a genetic-ant colony hybrid algorithm, and a temperature control system and a security device are driven to perform linkage execution through an OPC UA protocol. And finally, millisecond-level synchronous mapping of the physical warehouse and the digital twinborn body, real-time deviation correction regulation and control of an equipment running track and cross-system collaborative response of an emergency strategy are realized, and the safety early warning accuracy and dynamic protection robustness of the storage environment are improved.

The invention discloses a dynamic short-term and temporary intelligent forecasting system for mine time-delay rockburst, and belongs to the field of short-term and temporary forecasting of rockburst disasters. The system comprises a deep well three-dimensional micro-seismic field global sensing module, a micro-seismic characteristic parameter extraction module, a rockburst catastrophe evolution data set construction module, a data preprocessing module, a deep learning prediction module, an interpretability analysis module and a dynamic short-term and temporary intelligent forecasting module. A dynamic tracking type micro-seismic sensing network is constructed through a three-level annular nested layout strategy, micro-seismic event data are collected in real time, spatial-temporal characteristic parameters are extracted, a rockburst time sequence data set is constructed in combination with a sliding time window method, and the occurrence probability and intensity level of rockburst are predicted. The system adopts a grey wolf-simulated annealing hybrid algorithm to optimize model parameters, and improves the credibility of a prediction result through SHAP value analysis. The system can realize dynamic short-term and temporary early warning of rockburst disasters, and provides scientific decision support for mine safety management.

The invention relates to the technical field of process data analysis and processing, and discloses a coal-based solid waste recycling full-process intelligent management and control platform and a digital twin system. The platform is characterized in that an identification module identifies a coal-based solid waste mineral phase through a ray diffractometer to obtain mineral composition classification data; the construction module constructs a digital twinborn model based on the classification data, simulates a dissolution reaction, and obtains weathering soil formation process data; the establishment module establishes a pollutant migration prediction model according to the process data to obtain environmental risk data; the optimization module optimizes the risk data through an improved particle swarm-genetic hybrid algorithm to obtain a resource path scheme; and the adjusting module adaptively adjusts the process parameters based on the path scheme to obtain a whole-process management and control instruction. The problems that accurate mineral phase recognition and intelligent classification are lacked in the coal-based solid waste recycling process, and a dynamic prediction model cannot be constructed and a weathering soil forming process cannot be simulated in real time based on mineral composition data are solved.

The invention discloses an energy storage power station active power and reactive power distribution method and device and a medium, and the method comprises the steps: determining a multi-target power distribution model of a target energy storage power station, the multi-target power distribution model comprising a plurality of target functions and constraint conditions; according to the total power demand command of the current energy storage power station and the real-time charge state of each energy storage subsystem, judging an energy storage subsystem group participating in the active power distribution; solving the multi-target power distribution model by adopting a hybrid algorithm of a preset optimization algorithm in the energy storage subsystem group to obtain active power distribution of the energy storage power station; and finally, based on the converter power constraint and the active power distribution, solving the reactive power distribution of the energy storage power station by adopting a preset optimization algorithm. The energy consumption loss cost, the service life loss cost, the energy storage charge state consistency index and the operation number of the energy storage subsystems under the small-amplitude power instruction can be remarkably reduced.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention belongs to the technical field of unmanned aerial vehicle technology and intelligent operation and maintenance management, and discloses an unmanned aerial vehicle maintenance task scheduling and resource management platform and method, and the method comprises the steps that a data collection and fusion module obtains and fuses multi-source data; the fault diagnosis and prediction analysis module uses a deep learning model and a CNN-LSTM hybrid model to diagnose faults, predict life and generate a maintenance task list; an optional task dynamic priority evaluation module performs priority ranking on the maintenance task list; the intelligent scheduling decision module uses an improved NSGA-II and VNS mixed algorithm to generate an optimal allocation scheme according to priority and resource constraints; the resource dynamic management and cooperation module realizes dynamic allocation and cooperation of personnel, spare parts, tools and sites, and can integrate AR guidance; and the optional visual monitoring and feedback optimization module displays the state, feeds back data and realizes closed-loop optimization. The maintenance efficiency of the unmanned aerial vehicle can be remarkably improved, the operation cost is reduced, and the flight safety is guaranteed.

The invention discloses a heterogeneous computing power network high-reliability resource scheduling optimization method based on coding distributed computing and a hypergraph neural network, which comprises the following steps of: firstly, constructing a multi-level resource representation model, and capturing high-order correlation among heterogeneous nodes by using the hypergraph neural network so as to optimize the precision and efficiency of dynamic resource scheduling; secondly, a coding distributed computing strategy is introduced, edge node failure is dealt with through redundant load distribution, the stability of the system is enhanced, in order to further improve scheduling optimization, a hybrid algorithm framework combining a hypergraph neural network and particle swarm optimization is provided, the framework captures high-order relevance of heterogeneous nodes in the network through hypergraph modeling, and the scheduling optimization is improved. And meanwhile, the particle swarm optimization algorithm is combined, so that the discreteness and discontinuity problems can be effectively solved. Finally, the hybrid algorithm improves the operation efficiency while guaranteeing the solving quality, and achieves efficient resource scheduling optimization. The wide application potential in the future 6G application scene is shown.

The invention discloses an offshore wind power plant micro-siting optimization method considering regular layout. The method comprises the following steps: firstly, obtaining wind data, wind power plant parameters, fan parameters and wake flow parameters; performing multi-angle rotation on the wind power plant based on an equivalent rotation coordinate method, constructing a gridding mixed integer quadratic programming model, and performing rotation rule layout optimization (RRLO-1) to obtain an initial fan layout; on the basis of the initial layout, a hybrid algorithm (GA-ISSA) of a genetic algorithm and a sparrow search algorithm is adopted, the position of a draught fan and the rotation angle of the wind power plant are finely adjusted, and fine rule layout optimization (RRLO-2) is completed; and finally, the wake flow influence is calculated through a Cosine wake flow model and a quadratic sum superposition model, a fan power curve is processed in a piecewise linearization mode, and the final layout is output by taking the maximization of the total generating capacity as the target. According to the method, a fan position configuration scheme meeting the engineering rule layout requirement is provided for early-stage planning of the offshore wind plant, the wake effect is reduced, the power generation efficiency is improved, and development of the offshore wind power industry and low-carbon target implementation are facilitated.

The invention discloses a generative adversarial network architecture search method and system based on a GA-PSO hybrid algorithm, and belongs to the technical field of deep neural networks. The method constructs a generative adversarial network super-network architecture, globally explores an architecture population by using a genetic algorithm, and performs local parameter adjustment on an elite architecture in combination with particle swarm optimization; the inertia weight and the learning factor are dynamically balanced, and framework compliance is ensured through discretization coding constraint; and finally, based on Pareto optimality, integrating a multi-target evaluation result and a primary and secondary collaborative proportion loop optimization solution set. Compared with traditional architecture search, the method has the advantages that the premature convergence problem is solved through a hybrid optimization mechanism, global and local collaboration is achieved in combination with weight sharing evaluation and hierarchical coding, and the search period is remarkably shortened while the generation quality is improved.

The invention discloses a ship steel plate digital cutting and typesetting optimization method and system, and the method comprises the steps: collecting the morphology data of a to-be-cut steel plate, and generating a machining region model; based on the machining area model and the specification data of the target part, a deep reinforcement learning-genetic hybrid algorithm is adopted as a solving strategy, and an optimal typesetting scheme is output; generating a numerical control cutting path and a corresponding numerical control code according to the preferable typesetting scheme, and carrying out kerf gap constraint and thermal deformation compensation on the numerical control cutting path; based on the deviation between the ideal coordinates of the reference mark points in the machining area and the real coordinates of the reference mark points projected on the to-be-cut steel plate, the to-be-cut steel plate is positioned and calibrated; and executing a cutting instruction, evaluating a cutting result, and feeding back deviation data to adjust and optimize strategy parameters. On the premise of ensuring the process feasibility, limit utilization of the steel plate is achieved, the cost is reduced, the cutting quality is improved, and the method is suitable for shipbuilding scenes with various complex parts and high cutting precision requirements.

The invention discloses a regional fan virtual inertia support scheduling method considering a wind power plant group synergistic effect, and aims to solve the problems of non-uniform inertia distribution, response conflict and economy in wind power plant group scheduling. By introducing a hybrid multi-level cooperative control architecture, the inertia vacancy of the power grid is calculated in real time, the inertia demand of each wind power plant is allocated, and the scheduling strategy of the wind power plant group is optimized. And a time-varying inertia demand curve is generated by adopting a hybrid algorithm, the future inertia demand is accurately predicted, and the fan rotating speed recovery cost, the equipment loss and the wind curtailment amount are minimized in combination with a multi-target optimization model. A model predictive control framework and a Nash equilibrium theory are introduced, the game problem between wind power plants is solved, collaborative scheduling of a wind power plant group is optimized, and timeliness and stability of a scheduling instruction are ensured by embedding a time delay compensation algorithm. According to the method, the inertia supporting efficiency of the power grid can be remarkably improved, the equipment loss and the wind curtailment amount are reduced, the robustness of the system is improved, and the method has high application value and prospect.

According to the industrial solid waste multi-target collaborative management method and system based on the digital twinborn and hybrid optimization algorithm, industrial solid waste data are collected in real time through an Internet of Things sensor, a dynamic digital twinborn model is constructed to simulate a physical process, and the problems of data dispersion and lag in a traditional method are solved; establishing a multi-objective optimization function by combining three major objectives of economy, environment and society, and generating a Pareto optimal strategy set by adopting an improved NSGA-II and particle swarm optimization (PSO) hybrid algorithm; the method combines historical and real-time data through a twin model, employs an LSTM algorithm to synchronously predict the type and quantity of solid wastes, the equipment operation state and the environment change, achieves the dynamic simulation and prediction of the whole process of solid waste generation, transportation and processing, remarkably improves the data integration efficiency, provides precise real-time support for decision making, and improves the real-time performance of the system. The treatment cost is reduced, the carbon emission is reduced, and the public satisfaction is improved; the strategy response time is shortened through dynamic model correction, an intelligent solution is provided for industrial solid waste management, and the function of multi-dimensional collaborative optimization of industrial solid waste management is achieved.

The invention discloses a joint beam forming optimization method for an RIS auxiliary low-altitude communication system. The problem of performance bottleneck caused by interaction of active beam forming and passive beam forming is solved by adopting a genetic algorithm based on a dynamic adaptive theory. Aiming at the limitation of a standard optimization method, a hybrid strategy of genetic algorithm optimization and a full-connection neural network is explored, and intelligent collaborative setting of system parameters is realized. Simulation results show that the hybrid algorithm not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. On the basis, the signal transmission quality is improved, interference is reduced, and it is guaranteed that the RIS-assisted low-altitude unmanned aerial vehicle intelligent communication system stably and efficiently operates in a complex wireless transmission environment.

The invention discloses a system and a method for multi-parameter cooperative control in PVC steel wire hose production, and relates to the technical field of plastic hose production, the system comprises a parameter monitoring module, a central processing module, a parameter adjusting module and a quality detection module, the parameter monitoring module is a high-precision intelligent sensor and has a self-adaptive filtering function, and key parameters are measured and transmitted to the central processing module; the central processing module uses an improved multi-objective optimization algorithm to calculate an optimal cooperation value, a simulated annealing-genetic hybrid algorithm is used for iteration, the parameter adjustment module uses intelligent PID, adaptive variable structure control and model prediction control adjustment equipment, and the quality detection module uses multi-modal fusion deep learning and high-precision physical performance detection equipment. And if not, recalculating the adjustment parameters. The production quality and efficiency of the PVC steel wire hose are improved, the cost is reduced, parameters are precisely and cooperatively controlled, the hose is stable in physical performance and few in surface defect, the raw material and equipment maintenance and energy consumption cost is reduced, and the enterprise revenue and market competitiveness are improved.

The invention relates to the technical field of artificial intelligence, and discloses a precise targeted AI recommendation system. The method comprises the following steps: firstly, acquiring multi-source heterogeneous data, constructing a global space-time database through space-time alignment, complementing and repairing the data by using a generative adversarial network, and cleaning and verifying; and constructing a three-dimensional topological model based on the improved graph neural network, and fusing multiple features to generate a multi-resolution model. A scene simulation platform is established by adopting a multi-objective reinforcement learning algorithm, multi-objective optimization is set, and a strategy library is constructed. And screening key factors by using a sparse Bayesian theory to construct a lightweight model. And designing a layered dynamic optimization framework, solving an optimal solution of each sub-layer by using a hybrid algorithm, realizing cross-layer constraint synchronous decoupling through a block chain smart contract, and outputting a global optimal three-dimensional design scheme. The method improves the application level of the digital achievement of the power grid design, gives consideration to stability, economy and environmental protection, and has important application value.

The invention discloses an urban traffic signal space-time adaptive flexible regulation and control method and system. The method comprises the steps of collecting traffic data such as the number of vehicles, the vehicle speed, the number of pedestrians and the pedestrian speed at an intersection in real time; performing cleaning, normalization and feature extraction on the acquired traffic data to generate standardized traffic parameters; based on the standardized traffic parameters, a signal lamp timing scheme is generated through a space-time diagram convolutional network and lightweight deep reinforcement learning hybrid algorithm; adjusting the traffic signal lamp in real time according to the signal lamp timing scheme; the method comprises the steps of storing traffic data and remotely monitored intersection states, integrating information of adjacent intersections to perform regional strategy optimization, integrating strategy data of each intersection through federal learning, and generating a global model to optimize a collaborative penalty coefficient; the method can quickly respond to the dynamic change of the traffic flow, optimizes the signal lamp timing scheme, and remarkably improves the data processing speed and decision-making efficiency.

The invention discloses a source-grid-load-storage cooperative scheduling method and system based on multi-time scale cost optimization, and the method comprises the steps: outputting a dynamic coupling map containing short-term and long-term cost evolution paths according to the source-side power generation cost, the grid-side transmission loss, the load-side load demand and the storage-side life attenuation data of an energy system; based on the dynamic coupling map, outputting an uncertainty quantization parameter containing probability distribution; according to the uncertainty quantization parameters, outputting a collaborative scheduling framework containing space-time correlation constraints; and based on the collaborative scheduling framework, performing multi-objective optimization solution by adopting a hybrid algorithm, and outputting a global optimal scheduling strategy. By utilizing the embodiment of the invention, the global optimal decision can be realized to balance the economy and safety of the system and the service life of the equipment.