428 results about "Hybrid algorithm" patented technology

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 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 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 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 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 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 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.

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 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.

The invention relates to the technical field of artificial intelligence, in particular to an injection molding process parameter optimization method and system based on hybrid algorithm and model fusion, and the method comprises the steps: optimizing a parameter combination of a support vector regression model through a simulated annealing algorithm, building a weighted fusion model based on the optimized support vector regression model and a random forest, and optimizing the model; constructing a hybrid model of an adaptive selection weighted fusion model and an optimized support vector regression model; constructing a three-objective optimization model including buckling deformation, volume shrinkage and production energy consumption, and searching a Pareto optimal solution set in a process parameter space by adopting a multi-objective genetic algorithm by taking the hybrid model as a target value evaluation tool; carrying out local correction on the key process parameters by adopting a gradient descent method until the deviation falls back to be within a preset threshold value, and obtaining optimized process parameters; the defect rate of products can be reduced, and meanwhile production energy consumption is reduced.

The invention discloses an on-orbit spacecraft attitude estimation method and system based on ISAR image feature selection, and belongs to the field of aerospace control systems. The method comprises the following steps: acquiring an ISAR image of a spacecraft, and acquiring a complex linear structure set and three-dimensional feature points of an on-orbit spacecraft; then, according to the obtained three-dimensional-two-dimensional projection model, the CRLB of each reference structure in the complex linear structure set is deduced to carry out attitude estimation error analysis; calculating the trace of the CRLB covariance matrix of each reference structure to select an optimal feature structure, correcting the scattering point trace of the optimal feature structure by using polynomial fitting, and switching the reference structures as a new optimal feature structure according to the scattering point loss rate and a preset sequence; and optimizing the spacecraft attitude angle solving function by using a particle swarm and LM hybrid algorithm to obtain attitude angle parameters. The target with high-precision target attitude real-time estimation can be completed aiming at the problems that high-order frequency change in a dynamic environment is difficult to capture and resolution and noise suppression are contradictory due to a fixed window time-frequency analysis method.

The invention discloses an underground carry-scraper path planning method and system, and the method comprises the steps: obtaining environment data information of a target underground mine roadway, constructing an environment map, and carrying out the preprocessing of the environment map, and obtaining a binary grid map; constructing a generalized Voronoi diagram, extracting a roadway center line and carrying out region division; performing path guidance and segmentation by adopting an algorithm to obtain a reference state set of the target underground mine roadway; and based on a hybrid algorithm, a regional dynamic node expansion strategy and a multi-target heuristic cost function, realizing path planning of the underground carry-scraper, and realizing path planning of the underground carry-scraper. According to the invention, through data acquisition and data processing of a target underground mine roadway, a roadway center line and divided areas are extracted, then path guidance is carried out through an algorithm, and based on a divided area dynamic node expansion strategy and a multi-target heuristic cost function optimization hybrid algorithm, path guidance is carried out. By combining a hybrid algorithm, a Dubins curve, a collision detection scheme, path smoothness punishment and path distance punishment, the path planning of the underground carry-scraper is realized, the reliability is higher, the accuracy is better, and the effect is better.

An improved BA-P&O hybrid algorithm based MPPT power supply control method for a satellite, comprising: a solar cell array regulates bus voltage by means of a DC-DC unit to supply power to a load; a storage battery is connected to a bus by means of a diode to supply power to the load; a bat algorithm iteration stage and a perturbation observation stage are used for tracking the maximum power of the solar cell array when the solar cell array and the storage battery supply power together; a bus voltage regulation stage is used for maintaining the bus voltage stable when the solar cell array supplies power independently, and determining the update direction of the current duty ratio on the basis of the current bus voltage sampling value; and the three stages jump to each other on the basis of set conditions. The present invention is used for MPPT power supply control.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention discloses a soil water content prediction method, a soil water content prediction device, soil water content prediction equipment and a computer readable storage medium based on a spectral analysis technology. The method comprises the following steps: acquiring target spectral data of a target soil area and standard spectral data of the target soil area after drying; performing optimal coupling spectrum calculation based on the target spectrum data and the standard spectrum data to obtain optimal coupling spectrum data; carrying out wavelength screening processing on the optimal coupling spectrum data by adopting a wavelength optimization algorithm to obtain model input data; wherein the wavelength optimization algorithm is a hybrid algorithm of a competitive adaptive reweighted sampling algorithm and a continuous projection algorithm; reasoning model input data based on the fusion prediction model to obtain a soil water content estimation value; wherein the fusion prediction model is a prediction model obtained by training and fusing an initial partial least square regression model and an initial convolutional neural network model based on spectrum training data.

The invention discloses a virtual power plant optimization scheduling method and device for power system regulation capability improvement, and relates to the technical field of virtual power plants. The method comprises the following steps: acquiring basic data of virtual power plant aggregation resources; constructing a resource operation constraint model under multiple scenes based on the basic data; embedding a conditional value-at-risk (CVaR) risk control mechanism in the operation constraint model, and establishing a multi-scene random optimization scheduling model; and solving the model by adopting a CVX-SeDuMi algorithm and a particle swarm optimization (PSO) hybrid algorithm to obtain an optimal scheduling scheme considering adjustment capacity maximization and risk management and control. On the premise of ensuring safe and stable operation of the power system, the regulation potential of the virtual power plant can be fully mined, meanwhile, the scheduling risk in an extreme scene is effectively avoided, and a scientific optimization scheduling method is provided for the virtual power plant to participate in power system regulation.

The invention discloses a laser wireless energy transfer dynamic MPPT control method based on an RL-PSO hybrid algorithm and a reconfigurable array system, and aims to solve the problems of power oscillation and mismatch loss caused by light spot offset of a photovoltaic array under laser dynamic irradiation. According to the method, reinforcement learning (RL) and particle swarm optimization (PSO) are fused, dynamic parameters such as the light spot moving speed and irradiance are sensed in real time through the RL, the inertia weight and learning factors of the PSO are adjusted in a self-adaptive mode, and collaborative optimization of global search and local tracking is achieved. According to the system level, a reconfigurable photovoltaic array is designed, and according to data of a light spot position sensor, battery series-parallel topology is dynamically adjusted through a switch matrix, and current matching is optimized. According to the method, double-loop control is adopted, wherein an outer-layer RL decision module dynamically switches PSO working modes according to the environment, and an inner-layer PSO module optimizes the working voltage. The system efficiency in a dynamic scene can be improved by 15%-30%, mismatch loss is reduced by 40% or above, and the method is suitable for unsteady-state energy supply scenes such as unmanned aerial vehicles and mobile robots.

The invention relates to the technical field of coal washing equipment, in particular to a lifting type peripheral transmission rake thickener operation system for coal washing, which comprises a working condition sensing module used for collecting slime water concentration, rake frame torque, slag layer thickness and equipment operation temperature data; the intelligent decision-making module is used for constructing a slime water sedimentation prediction model through an LSTM-GRU hybrid algorithm according to the data, and generating an equipment operation regulation and control instruction; the lifting transmission regulation and control module is used for adjusting the vertical height of a rake frame according to a regulation and control instruction to adapt to different slag layer thickness treatment requirements, and meanwhile, combining real-time feedback of the torque of the rake frame, dynamically adjusting the rotating speed of a peripheral transmission motor and optimizing coal slime and water separation conditions; and the safety early warning module is used for monitoring parameter mutation through a sliding window algorithm, and triggering corresponding strategies of different levels when the harrow torque and the slime water concentration exceed multiple groups of adaptability thresholds. Therefore, the problems that in the prior art, the height of the rake frame is fixed, regulation and control depend on manpower, and an early warning mechanism is extensive are solved.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention discloses an unmanned aerial vehicle flight path planning method based on a hybrid WOA-CEM-DRL algorithm, and the method is based on a hybrid WOA (whale optimization algorithm), a cross entropy method (CEM) and deep reinforcement learning (DRL), and aims at solving the problems that an existing algorithm is slow in convergence speed and is liable to fall into a local optimal solution in a complex three-dimensional environment. The invention provides a three-stage dynamic coevolution framework. In the early stage of evolution, population diversity is enhanced through global disturbance of CEM; in the middle stage of evolution, exploration and development are balanced; and in the later stage of evolution, performing fine tuning on the elite solution by using gradient information of the DRL. According to the method, global search of WOA, efficient sampling of CEM and rapid convergence capability of DRL are fused, and the method is mainly used for accurately generating a safe and smooth optimal track for an unmanned aerial vehicle in a complex three-dimensional environment.

The invention relates to the technical field of electric power, and provides a frequency modulation method and related equipment for a hybrid wind power plant, and the method comprises the steps: constructing a frequency response model of a network-forming type fan, a frequency response model of a network-following type fan, and a frequency response model of a synchronous unit; establishing a system frequency response model of the hybrid wind power plant according to the frequency response models of the net-forming type fan, the net-following type fan and the synchronous unit; constructing a multi-objective function containing a frequency change rate maximum value, frequency response steady-state deviation and a frequency deviation maximum value based on a system frequency response model; an improved particle swarm genetic hybrid algorithm is adopted to carry out minimization solution on the multi-objective function, and the optimal primary frequency modulation coefficient of the net-building type fan and the optimal droop coefficient and virtual inertia coefficient of the net-following type fan are obtained; and performing frequency modulation on the hybrid wind power plant according to the primary frequency modulation coefficient, the droop coefficient and the virtual inertia coefficient. The result verifies that the method can effectively improve the frequency response characteristic of the system containing the hybrid wind power plant.

According to the method for designing the reasonable arch axis of the large-span arch bridge based on the hybrid calculation algorithm and the self-adaptive load distribution, an analytic equation method, a curve fitting method and an intelligent optimization algorithm are combined, and by selecting the initial arch axis, introducing a dynamic load distribution model and applying a genetic algorithm and a particle swarm optimization algorithm, the reasonable arch axis of the large-span arch bridge is designed. Global and local optimization is carried out, and the optimal shape and load distribution of the arch axis are ensured. And finally outputting design parameters, such as a control point, a bending moment, thrust and the like. According to the method, the design precision, flexibility and safety can be effectively improved, the material utilization rate is optimized, the cost is reduced, and the method is particularly suitable for design of large-span bridges and has high practical value.

The invention provides a load prediction and intelligent real-time control system based on hybrid algorithm fusion. Relates to the field of energy storage systems and energy scheduling, and comprises a data acquisition and processing module used for acquiring and preprocessing multi-source heterogeneous data of the energy storage system in multiple scenes to generate a structured time sequence feature set; the multi-source heterogeneous data comprises historical load data, time variables, environmental data, economic data and scene exclusive data; the power load prediction module outputs a load prediction result by integrating two different types of prediction models, and the two prediction models comprise a first prediction model based on historical sequence similarity and a second prediction model based on time decomposition; and the intelligent real-time control module is used for dynamically generating an energy storage system charging and discharging instruction and an energy scheduling strategy through a rolling optimization strategy based on a load prediction result and a preset target function, so that multi-target energy management is realized. According to the invention, the load response capability and prediction control precision of the energy storage system in multiple scenes are improved.

The invention discloses a thermal power generating unit transition peak regulation intelligent cruise control method based on an intelligent control algorithm, and relates to the technical field of intelligent control algorithms, and the method comprises the steps: constructing a distributed sensor network through deploying distributed sensors at all key positions of a thermal power generating unit, and obtaining the operation parameters of the thermal power generating unit in real time; external data are introduced and integrated, and short-term load demand prediction is generated according to an intelligent control algorithm; constructing a dynamic optimization function by combining a power grid dispatching instruction and a load prediction result and by taking the minimum coal consumption, the shortest peak regulation response time and the minimum equipment life loss as targets; adopting a hybrid algorithm of model predictive control and reinforcement learning to obtain an optimal control instruction; the control instruction is transmitted to a boiler combustion system and a steam turbine speed regulating system, and the fuel amount, the water supply amount and the valve opening degree are regulated; and dynamically adjusting model parameters and fuzzy rule weights of model prediction control through reinforcement learning to realize adaptive control of the thermal power generating unit.

The invention discloses a virtual power plant source load interaction optimization scheduling model based on low-carbon response and a solving algorithm, and belongs to the technical field of power system optimization scheduling. A low-carbon scheduling framework containing a distributed power supply, energy storage, a flexible load and a carbon transaction mechanism is constructed, the carbon emission intensity of each link is quantified to form a carbon flow scheduling signal, and a dynamic carbon emission factor and energy cost are coupled. A multi-objective optimization model is established, a complex function is processed by piecewise linearization, and a hybrid algorithm of an improved genetic algorithm and a commercial solver is designed to improve the solving efficiency. The prediction error is dynamically corrected through a'prediction-optimization-feedback 'closed loop, and the strategy is adjusted. According to the scheme, low-carbon and economic collaborative optimization is realized, renewable energy consumption and system stability are enhanced, user satisfaction and real-time scheduling are considered, and a solution is provided for low-carbon intelligent operation of the power distribution network.

The invention discloses a simulation plant intelligent light control method and device and electronic equipment, and relates to the technical field of intelligent control / Internet of Things. The method comprises the following steps: acquiring environment illumination intensity, sound intensity, human body existence state and activity state information, acquiring time information, generating a time period identifier, and receiving a user instruction and preference setting information of a mobile terminal; performing fusion processing on the data to generate a standardized environment perception data set; executing a scene feature extraction algorithm based on the data set to generate a scene feature vector, and mapping the feature vector into a scene type identifier by adopting a predefined scene recognition rule and a K-nearest neighbor hybrid algorithm; according to the scene type identifier, generating a light control strategy by adopting a corresponding strategy decision mode; and executing a strategy to drive a light-emitting unit in the simulation plant, and monitoring an actual lighting effect parameter in real time for self-adaptive correction. According to the invention, the self-adaptive adjustment of the lighting effect is realized, and the user experience is remarkably improved.

The invention relates to the technical field of traffic engineering management, and particularly discloses a traffic engineering quality safety dynamic supervision and management system and method, and the method comprises the steps: constructing a multi-dimensional sensing network which comprises an entity, an environment and a behavior sensing layer, and collecting data; a three-dimensional model is built based on the BIM technology, and standardized data and model millisecond mapping are fused and visualized; an LSTM and random forest hybrid algorithm is adopted to establish a three-level early warning model, and risks are identified and pushed in a graded manner; matching the disposal flow according to the early warning level, tracking the progress by the mobile terminal, and checking the closed loop in a grading manner; and key node data are stored through a multi-level block structure, an alliance chain permission, a consensus mechanism and an intelligent contract, so that reverse tracing is realized. According to the invention, real-time acquisition, dynamic modeling, intelligent early warning, co-processing and credible tracing of quality safety data in the traffic engineering construction process are realized, the quality safety risk is reduced, and the management efficiency and supervision transparency are improved.

The invention discloses a charging pile fault prediction remote maintenance system based on an edge calculation and AR technology, and relates to the technical field of charging pile maintenance, and the system comprises a data collection and transmission module which monitors the data of a charging pile in real time, and carries out the edge calculation of the data; the data processing and fault prediction module is used for performing fault prediction by combining a mixed algorithm of an LSTM (Long Short Term Memory) network and an FFT (Fast Fourier Transform) module based on the data after the edge calculation; the fault diagnosis and grading module is used for establishing a fault prediction data analysis model based on the fault prediction data and generating a fault grade and an early warning signal; the AR auxiliary operation and maintenance module is used for scanning the charging pile by using AR equipment and displaying fault information, maintenance steps and remote expert marks; and the remote support and monitoring module provides remote support and continuously monitors the state of the charging pile. According to the method, the LSTM network and the FFT technology are combined, fault prediction is carried out on the equipment, the system can predict potential faults in advance, and slow response and increase of maintenance cost of a traditional maintenance mode are avoided.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel lining long-term safety assessment method based on a PSO-SA hybrid algorithm, and the method comprises the steps: determining a surrounding rock creep constitutive model, and obtaining initial surrounding rock parameters through an inversion algorithm; the influence of a supporting structure on surrounding rock deformation is introduced, and corrected surrounding rock parameters are obtained to improve authenticity; based on the time-varying factors, the surrounding rock parameters and the lining parameters are iteratively updated, and long-term safety dynamic evaluation is achieved; inputting the iteratively updated surrounding rock parameters and lining parameters into numerical analysis software, and obtaining stress deformation characteristics of the lining structure through the numerical analysis software; according to the method, the long-term safety of the tunnel lining structure is evaluated according to the stress deformation characteristics and related specifications, a dynamic evaluation model which truly reflects the time-varying characteristics of the tunnel structure is constructed, qualitative leap in evaluation precision, reliability and foresight is brought, and a powerful technical guarantee is provided for long-term safety of an operation tunnel.