1285 results about "Search algorithm" patented technology

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses a distributed training scheduling and communication optimization method and system of a multi-modal large model on a domestic computing power platform. The method comprises the following steps: virtualizing a heterogeneous computing unit of a preset platform into a virtual device pool, and fusing first-order gradient of a multi-modal sample and Hessian matrix information based on quantitative perception training to generate a sample sensitivity grading atlas; virtual device pool attributes and the sensitivity grading atlas are used as input, an optimal hybrid parallel configuration scheme is automatically generated through a configuration search algorithm, and a parallel combination mode, resource mapping and a high-sensitivity sample scheduling strategy are defined; a distributed training code of an integrated communication optimization strategy is automatically generated according to a configuration scheme, pipeline parallel communication and data parallel gradient synchronization constraint are executed in a topology adjacent equipment subset, and a hierarchical aggregation mechanism is adopted; and dynamically screening a core training set and scheduling a calculation task to complete distributed training. According to the method, efficient cooperative training of the multi-modal large model on the domestic computing power platform is realized.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention provides a track board drilling parameter intelligent optimization method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: recognizing a feature region in three-dimensional scanning data through a deep learning semantic segmentation network, building a local coordinate system, calculating hole site deviation, constructing a pose correction field, and achieving the intelligent correction of drilling process parameters. And an optimal drilling track is generated by using a state space search algorithm. The method can adapt to actual geometric errors of the track plate, the drilling precision and efficiency are improved, the drilling failure rate is reduced, and the manufacturing quality of the track plate is improved.

The invention discloses a method and a system for determining optimal parameters of an X-ray excited extra-high voltage composite apparatus, and relates to the technical field of extra-high voltage electrical equipment detection. The method comprises the following steps: remotely operating the X-ray machine and calculating partial discharge intensity; for the current irradiation position, the tube voltage and the tube current are gradually adjusted, parameters are dynamically optimized based on the quantitative relation between the partial discharge intensity and the radiation dose rate change rate, and the optimal parameters are recorded; and changing position repetition parameter optimization, and constructing a BP neural network model after data set training optimization so as to predict an optimal irradiation position. The optimized BP neural network adopts an improved sparrow search algorithm to optimize an initial weight and a threshold value, and combines Bayesian to optimize a learning rate and a hidden node number. Accurate optimization of excitation parameters and irradiation positions is achieved, detection accuracy and safety are both considered, the optimal irradiation position positioning efficiency is improved, and the method is suitable for ultra-high voltage GIS detection of different voltage grades.

The invention belongs to the technical field of seismic exploration, and discloses a robust full-waveform inversion method, system and device based on generative modeling, and a medium, and the method comprises the steps: obtaining seismic observation data; the method comprises the following steps: starting from random Gaussian noise, establishing a model space comprising a plurality of initial velocity models through an unconditional score model, and determining a global optimal initial velocity model of the model space by adopting a strategy search algorithm; de-noising is carried out on the global optimal initial velocity model based on back diffusion, seismological observation data are introduced as observation constraints, the velocity model is updated by calculating the mismatch gradient of forward modeling data of the current velocity model and the seismological observation data, and an implicit condition sample is obtained; performing forward diffusion processing on the implicit condition sample to obtain a speed model for a subsequent annealing time step; and when the annealing process reaches a preset condition, outputting a final speed model. According to the method, the robustness and accuracy of full-waveform inversion are improved, and the bottleneck problem of traditional full-waveform inversion is solved.

The invention relates to the technical field of laser spectrum gas detection, and particularly discloses a TDLAS (tunable diode laser absorption spectroscopy) double-gas concentration synchronous calibration and decoupling method in the presence of spectrum aliasing interference, and the method comprises the following steps: acquiring a multi-band harmonic signal subjected to adaptive optimization while acquiring environmental parameters; an optimal frequency band signal is determined based on the signal quality index; and calculating a spectral aliasing coefficient according to the characteristics of the optimal frequency band signal, dynamically adjusting operation parameters of a search algorithm based on a numerical value interval in which the spectral aliasing coefficient is located, and determining an initial range of dual-gas concentration in a search space. According to the method, the improved genetic algorithm based on the spectral aliasing coefficient dynamic adjustment search strategy is combined with the incremental support vector regression model, stage processing from coarse calibration to fine decoupling is achieved, and the calculation efficiency and the search precision can be automatically balanced when the spectral aliasing degree changes; and online correction is carried out on the model in combination with aging characteristics.

The invention discloses an irregular ship scheduling method and system considering uncertain harbor time, and a medium, and belongs to the technical field of shipping management. The method comprises the following steps: collecting ship, cargo, port and interference event data; generating an initial scheduling scheme by adopting a greedy strategy; generating multiple groups of random interference scenes based on Monte Carlo simulation; a two-stage stochastic programming model is constructed, in the first stage, income maximization serves as a target, and an initial plan is generated through damage and repair operator iterative optimization by means of a self-adaptive large neighborhood search algorithm; in the second stage, aiming at each interference scene, a recovery network is constructed for the influenced goods, and an optimal recovery strategy and adjustment cost are solved by utilizing a shortest path algorithm; and finally, outputting a scheduling scheme including a ship path, a navigational speed and a multi-scene recovery strategy through iterative optimization. According to the method, the earnings and the operation efficiency of the shipping companies are improved, the adjustment cost caused by uncertainty is reduced to the greatest extent, the benefits of the shipping companies and cargo owners can be balanced in a complex scheduling problem, and the service quality is improved.

The invention is suitable for the technical field of road construction, and provides a wind power plant road intelligent line selection method based on multi-dimensional constraint dynamic modeling, and the method comprises the steps: obtaining a multi-dimensional constraint condition of wind power plant road line selection, and constructing a constraint model; generating a slope grid map based on a digital elevation model, and fusing the slope grid map with a constraint map layer reflecting geological stability and a land cover type to generate a dynamic cost surface; adopting an improved heuristic search algorithm to perform parallel path search from the road starting point to each target fan position, generating a plurality of initial feasible paths, and calculating a comprehensive cost value; screening a candidate line set according to a comparison result of the comprehensive cost values; receiving real-time updating data in a construction process and environment monitoring, inputting the real-time updating data into the constraint model to trigger model updating, performing iterative optimization on the candidate line set based on the updated constraint model, and outputting a final line selection scheme; and the line selection efficiency, economy and reliability are effectively improved.

The invention relates to the technical field of power system operation and control, and discloses an improved model-free adaptive flexible low-frequency power transmission oscillation suppression method, which performs zero-phase lag filtering on noisy signals through a nonlinear tracking differentiator, effectively solves the problem of control divergence caused by signal noise, and improves the suppression efficiency. A tight-format dynamic linearization and linear expansion state observer technology is adopted, accurate capture of dynamic characteristics of a strong nonlinear system and real-time observation and compensation of internal and external full-dimensional disturbance are achieved without dependence of a physical model, and particularly, an adaptive estimation algorithm of an output variable quantity penalty factor is introduced. The problem that model parameter identification jumps severely under the broadband oscillation working condition is solved, the robustness of a control system is enhanced, and the self-adaptive optimality of a control strategy is guaranteed and interference of long-term online operation on steady-state performance is avoided by combining parameter optimization of a cuckoo search algorithm and an event triggering mechanism of time-frequency double criteria.

Disclosed in the present invention are an automatic delay calibration method for a smart speaker system, a device, and a storage medium. The method comprises: initializing a smart speaker system and performing environmental testing to preliminarily configure system parameters; exciting a target sound channel and playing a target test signal; locating a first test signal on the basis of a voice activity detection (VAD) algorithm, determining the start and end of the first test signal, and identifying a time difference of the first test signal to obtain a delay of main channels; locating a time of arrival of a second test signal on the basis of a fast peak search algorithm, and identifying a delay of a subwoofer; and performing delay calibration on the subwoofer and the main channels, and dynamically adjusting delay calibration parameters of the channels on the basis of a delay calibration amount, thereby achieving accurate time alignment between the subwoofer and the main channels. The present invention solves the problem of low-frequency signal delays that are difficult to process in conventional technologies, and particularly makes a breakthrough progress in the coordination of a subwoofer and other sound channels.

The invention belongs to the technical field of wind and light hydrogen storage resource allocation, and discloses a wind and light hydrogen storage scheduling method based on a sparrow search algorithm and related equipment. The wind-solar hydrogen storage scheduling method based on the sparrow search algorithm comprises the steps of constructing a wind-solar hydrogen storage operation day-ahead profit model according to a scheduling target of a wind-solar hydrogen storage system, obtaining a profit maximization objective function through the wind-solar hydrogen storage operation day-ahead profit model, and solving the profit maximization objective function by adopting the sparrow search algorithm. Obtaining a globally optimal solution, and scheduling the wind and light hydrogen storage system according to the globally optimal solution; the method can overcome the defects that in the prior art, the optimization speed is reduced during optimization scheduling, the randomness of an optimization scheduling result is high, and local optimum is likely to happen.

The invention relates to a sweeper path planning method, system and device suitable for a complex environment and a medium. The method comprises the steps that a global map of a sweeping area is acquired, feature vertexes are determined to decompose the global map, and a structural sub-area and a special sub-area are generated; determining a starting point of the structure sub-region according to the feature vertex, and generating a first path covering the structure sub-region by adopting an internal spiral method based on the starting point; determining a non-linear constraint region of the special sub-region, converting the non-linear constraint region based on coordinate system transformation, and generating a second path of the special sub-region; and determining a starting point and a target point of any path, connecting the starting point and the target point based on an improved path search algorithm, and generating a target path of the cleaning area. Therefore, according to the invention, through accurate path planning and dynamic adaptability optimization, the path planning level and the completion quality of the sweeping operation are improved, so that the sweeping vehicle can adapt to the actual scene of a complex environment, and the application range is greatly expanded.

The invention relates to the technical field of low-altitude safety, and provides an aircraft optimal trajectory screening method and system based on adjustable phase quantum search. Candidate trajectories are generated according to flight mission constraints, cost values are calculated, and Boolean markers are distributed based on cost threshold values to identify high-quality solutions; performing iterative search and measurement on the balanced quantum superposition state of the coding track index by utilizing a Grover quantum search algorithm based on the adjusted search phase to obtain an output index; and mapping the index into a track, obtaining an optimized track through classic local optimization, adaptively adjusting the phase parameter and the cost threshold value based on performance evaluation, and repeatedly executing until an end condition is met, thereby determining an optimal track. On-line reconfiguration of a strategy is realized by introducing an adjustable phase mechanism, a feedback closed loop is established to realize adaptive optimization of parameters, and the real-time requirements of scenes such as urban air traffic control and unmanned aerial vehicle clusters are met.

The invention provides an efficiency optimization control method of a permanent magnet synchronous motor and related equipment. The method comprises the following steps: acquiring an output current of an inverter; constructing an inverter loss model based on the output current of the inverter and the inverter loss coefficient; constructing a total loss model based on the electromagnetic loss model, the inverter loss model and an equivalent conversion method; constructing a current population by taking the plurality of d-axis current values as individuals; and performing optimization solution based on the total loss model, the current population, a PID search algorithm and an artemisinin optimization algorithm to obtain a global optimal solution and control the permanent magnet synchronous motor. According to the scheme, the driver loss is added into the total loss model, and efficiency optimization can be carried out on the whole motor driving system; in order to solve the problem that a traditional method is not suitable for an IPMSM, a PID search algorithm and an artemisinin optimization algorithm are combined, parameter search is carried out on a motor efficiency optimal point by simulating an incremental PID controller adjusting mechanism, a global optimal solution is obtained, and therefore optimal control over the click efficiency is achieved.

The invention discloses a multi-constraint path planning method and device based on intelligent heuristic search, and is suitable for a mobile robot in a complex and narrow environment. According to the method, a multi-source sensor is used for environment perception and fusion mapping, and a grid map is generated; a neural heuristic search algorithm based on a Mama structure and a selective state space model (Selective SSM) is adopted on the cost map, and a guide graph is generated; a continuous and collision-free initial path is generated through self-adaptive step size control in combination with robot kinematics constraints and channel characteristics; further establishing a multi-target constraint model based on a robot center Euclidean symbol distance field (RC-ESDF), and executing path smoothing and safety optimization by using a priori-guided particle swarm optimization algorithm (Prior-guided PSO) to obtain an optimal path meeting dynamic consistency; the system corrects the path in real time through a closed-loop feedback mechanism and triggers dynamic re-planning, so that the path feasibility, safety and real-time adaptive capacity of the robot in a limited environment are remarkably improved.

The invention relates to the technical field of tunnel construction geological disaster early warning, and discloses a tunnel construction geological disaster early warning method and system. According to the method, multi-source geological monitoring data and environment dynamic parameters of a tunnel construction area are collected; and then through data fusion and feature engineering processing, data contradiction and redundancy are eliminated, and a standardized training data set is formed. And then training and verifying a disaster risk prediction model by using the data set and adopting a machine learning algorithm so as to obtain an optimized model to accurately predict the disaster risk. And performing risk value prediction on different geological structure units by applying the optimization model, generating a risk distribution map, and determining an optimal geological structure unit through a search algorithm. Based on this, numerical simulation of geological disaster triggering and propagation is executed, a disaster evolution rule is analyzed, and a path simulation data set is output. And finally, performing multi-objective optimization on the data set according to a safety early warning objective, generating a geological disaster early warning scheme, and performing iterative calibration.

The invention provides a cable path intelligent optimization method fusing a cellular automaton and an ant colony algorithm, and the method comprises the steps: obtaining a three-dimensional boundary range of a target region, constructing a three-dimensional underground space voxel model, and dividing the target region into voxel units containing five-dimensional attribute tensors; constructing and inputting the five-dimensional attribute tensor into a physical constructable potential field model, and generating a corresponding three-dimensional physical constructable potential field; performing construction scoring according to the three-dimensional physical constructable potential field and the five-dimensional attribute tensor; constructing a composite cost function in combination with the three-dimensional physical constructable potential field and the construction score, generating a composite passing cost of each voxel, and generating an optimal cable path in combination with a path search algorithm which takes an improved ant colony algorithm as a control mechanism and introduces a rule of a cellular automaton on a path state propagation structure; and performing three-dimensional geographic mapping, structure reconstruction and geometric fairing on the optimal cable path, and outputting a standardized path result.

The invention relates to a thermodynamic diagram guiding type multi-agent path planning method for a high-density storage environment, and the method comprises the steps: employing a thermodynamic diagram guiding-based explicit estimation conflict search algorithm HEECBS, constructing a thermodynamic diagram to record the aggregation degree of a robot at different positions and time, and carrying out the searching of a path at a bottom layer, generating a robot initial path based on thermodynamic diagram guidance and updating the thermodynamic diagram; in the high-level search, on the basis of the initial path of the robot and the updated thermodynamic diagram, path set nodes with the highest priority are selected for extension, conflicts in the path set nodes are detected, if conflicts exist, splitting is carried out according to the heat priority, new constraint nodes are generated, and then bottom-level path search is carried out again; and continuously repeating the above steps until all the robots obtain a conflict-free path or reach a preset termination condition. Compared with the prior art, the method has the advantages that conflicts can be accurately predicted, path planning time is shortened, and synchronism and stability in the task execution process are improved.

The invention discloses a natural language structured query generation method and system based on pattern reasoning, and relates to the technical field of natural language processing and database query. The method comprises the steps of mode knowledge graph construction in an offline stage and three-step reasoning generation in an online stage. In the offline stage, through structure analysis, semantic enrichment and data portrait processing, a database mode is converted into a knowledge graph Schema-KG rich in structure and semantic information. The online stage adopts three-step reasoning: firstly, decomposing a natural language of a user into an intention component through LLMParser, and linking the intention component to a graph entity; secondly, reasoning an optimal JOIN path on the Schema-KG based on a graph search algorithm, and generating a mode perception intermediate representation SA-IR; finally, the SA-IR is compiled into an SQL, and pre-verification is executed. According to the method, the accuracy of complex query is remarkably improved through a structured reasoning mechanism, errors during operation are effectively avoided through pre-execution verification, a clear reasoning process is provided through SA-IR intermediate representation, and the interpretability and debugging efficiency of the system are greatly improved.

The invention discloses an intelligent adaptive personalized learning method and system based on AIGC and a knowledge graph. The technical problems that an existing online learning system is low in individuation degree, a knowledge system is fragmented, the evaluation dimension is single, the adaptive capacity is weak, and different requirements of teachers and students are not distinguished are solved. The method comprises the following steps: constructing and updating a domain knowledge graph library with weights, establishing a self-adaptive course resource library associated with knowledge entities, respectively generating multi-dimensional portraits of teachers and students, generating differentiated personalized learning / teaching paths based on an A * search algorithm, and realizing self-adaptive content recommendation, continuous self-adaptive evaluation and dynamic adjustment through a mixed recommendation algorithm. A closed-loop intelligent ecology is formed; the corresponding system comprises core modules such as a knowledge graph library, a self-adaptive course resource library and a teacher and student portrait module. According to the method, all-dimensional personalized support of teachers and students, dynamic self-adaptive adjustment and resource generation according to needs are realized, the teaching and learning efficiency is remarkably improved, and the method is suitable for practical subjects and has system self-evolution ability.

The invention provides a boiler heating surface pipe wall leakage detection method, and belongs to the technical field of industrial intelligent detection based on machine learning. According to the method, a heterogeneous sensing architecture combining adaptive signal decomposition optimized by an improved sparrow search algorithm and acoustic array positioning corrected by a physical field is adopted, and complementary features are extracted from time domain statistical features and a frequency domain energy spectrum through a specially designed double-flow space-time attention fusion neural network; therefore, the weak leakage signals in the strong noise environment can be captured and identified. Based on an identification result, a hearth three-dimensional temperature field distribution model is used for carrying out gridding correction on a sound wave propagation path, a nonlinear positioning equation set is constructed, and three-dimensional space sub-m-level accurate positioning of a leakage sound source is realized without manual intervention; the problems of false alarm and missing alarm caused by background noise interference and positioning deviation caused by temperature gradient in traditional acoustic monitoring are thoroughly solved.

The invention provides a logistics resource sharing and dynamic optimization configuration method based on the industrial internet in order to solve the technical problems that an existing distribution task distribution mode is mostly based on static data and is not flexible and efficient enough, and most distribution systems do not fully utilize real-time state information of vehicles, so that dynamic adjustment and optimization cannot be achieved in the distribution process. On the basis of perception of logistics resource real-time state information and customer demand dynamic information, a vehicle path model suitable for mixed taking and delivering is constructed, and the model is a multi-target model considering vehicle cost, time window deviation punishment cost and the like. In combination with the state information of the current logistics resources and the dynamic change of the customer demand, the hybrid delivery vehicle path model is solved through an improved adaptive large-scale neighborhood search algorithm to obtain an optimized delivery path scheme, so that the logistics vehicles can dynamically respond to the customer demand in the delivery process, and the delivery efficiency is improved. The method is more flexibly suitable for dynamically changing distribution requirements and complex urban traffic environments, and efficient logistics services are realized.

The invention discloses a load power balance regulation and control method and device based on knowledge graph and random sequence optimization, a medium and a product, and relates to the field of power system dispatching, and the method comprises the steps: constructing a power grid topology knowledge graph according to power grid physical equipment and corresponding topology connection and operation constraints; performing probability distribution fitting on the power output of the renewable energy according to the output data to obtain the output of the renewable energy; obtaining a plurality of typical output scenes; taking the state of the power grid physical equipment after fault isolation as an initial state, and constructing a random Markov decision process; searching in a state space by using an improved heuristic search algorithm by taking an initial state as a starting point, minimizing an accumulated cost function as a target, taking a hard constraint as a constraint condition and taking a topological distance as a heuristic function to obtain an optimal operation sequence; verifying the optimal operation sequence to obtain a final operation sequence; the power of the power grid is regulated and controlled; according to the invention, the fault response speed can be improved, and rapid adaptive decision in a complex scene can be realized.

The invention discloses a dynamic knowledge graph construction and application method, system and device for energy policy influence conduction analysis and a medium, and the method comprises the steps: carrying out the multi-dimensional knowledge extraction of an energy policy text through employing a first adaptive extraction model, and outputting a structured knowledge triple; constructing a dynamic multi-subject knowledge graph; performing deduction search on the potential association path through a first influence search algorithm, and identifying a key policy influence conduction chain; an event-driven knowledge graph closed-loop self-evolution mechanism is established, policy change events are captured in real time through automatic monitoring, and incremental updating and structure correction of the dynamic multi-subject knowledge graph are triggered; and constructing a scene-oriented personalized application service interface according to the dynamic multi-subject knowledge graph, converting a user natural language query into a graph query instruction, and generating a scene response in combination with a retrieval result. According to the invention, the cost of manual arrangement and maintenance is obviously reduced, and the working efficiency and expandability of the system are improved.

The invention relates to a self-adaptive evaluation and precise learning path generation system based on a knowledge graph. The method comprises the following steps: constructing a standard knowledge graph and an error region knowledge graph; performing misunderstanding mode matching based on the learning interaction data of the students, updating the misunderstanding knowledge graph of the students, and generating a personal misunderstanding knowledge graph of the students; setting a graph conversion operation from the student personal misunderstanding knowledge graph to a standard knowledge graph, and determining a target graph conversion operation sequence through a search algorithm by taking the student personal misunderstanding knowledge graph as an initial state and taking a sub-graph corresponding to a learning target in the standard knowledge graph as a target state; the target image conversion operation sequence is a personalized learning path. The problems that cognitive errors are difficult to eradicate and learning paths are poor in adaptability can be effectively solved, and meanwhile by means of scientific cost evaluation and an intelligent search algorithm, it is ensured that the generated learning paths conform to student individual cognitive characteristics and have high efficiency and feasibility.

The invention provides a wind and light storage micro-grid optimization scheduling method and system containing small hydropower storage, and the method comprises the steps: obtaining historical operation data, and carrying out the prediction according to the historical operation data, and obtaining a full-dimension prediction data set; according to the full-dimension prediction data set, constructing a multi-objective optimization model with the goal of minimizing residual load, minimizing power abandoning cost, minimizing cascade water abandoning amount, maximizing green energy collaborative utilization efficiency and minimizing time weighted voltage-power comprehensive error; and solving the multi-objective optimization model by adopting a hybrid optimization algorithm fused by a Hanniac optimization algorithm and an improved sparrow search algorithm to obtain a final output. According to the invention, efficient consumption of green energy can be realized, the electricity abandoning phenomenon is reduced, carbon emission is reduced, and the stability, economical efficiency and environmental protection property of a power system are improved.

The invention discloses an intelligent outpatient service guidance and triage management method and system based on artificial intelligence. The method comprises the following steps that patient information, symptoms and outpatient service data are collected to generate triage input; constructing a triage optimization model based on the input, setting waiting time, path cost, load rate and priority satisfaction degree as targets, and setting a time window, resources and dependency constraints; an improved squirrel search algorithm is initialized, a population code is adopted to represent a distribution scheme, and a fitness function is established; updating the candidate scheme according to a fitness function in iteration; repairing and locally optimizing the updating scheme; when the operation data fluctuates, self-adaptive adjustment is triggered, and updating is continued; and when the fitness function converges or the iteration upper limit is reached, outputting an optimal triage scheme, including departments, time windows and path allocation, and executing incremental optimization when the patient arrives or the shift arrangement is changed. The triage efficiency is improved, the load is balanced, and the requirements of priority patients are met.

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

The invention discloses an urban and rural sewer intelligent planning method and system based on artificial intelligence, and relates to the technical field of urban and rural sewer intelligent planning, and the method comprises the following steps: obtaining topological structure data and historical rainwater flow data of an urban and rural sewer system, constructing a first pipe network feature set, and carrying out the pipe diameter change analysis; calculating a flow velocity difference at a pipe diameter sudden change position so as to identify an air retention area; constructing a sewer graph model, and traversing the sewer graph model by adopting a graph search algorithm to obtain an air retention subnet; carrying out feature constraint on the air retention subnet to obtain a diversion judgment node set and identifying a diversion pipe network path set; according to the method, flow evaluation is carried out on the diversion pipe network path set, and the diversion pipe network path set is screened according to an evaluation result to obtain an optimal diversion pipe network path which is used for dynamic diversion of the sewer, so that the problem that in urban and rural sewers, the flow velocity difference of rainwater peak flow is caused by sudden change of the pipe diameter is solved; and air retention is formed, so that normal flowing of fluid is hindered.