61 results about "Cuckoo search" patented technology

The invention provides a highway tunnel decision-making maintenance system and a maintenance method thereof. The method comprises the steps of obtaining tunnel static and dynamic data and tunnel surrounding geological environment data; according to the geological environment data, carrying out geological risk analysis by adopting a geological statistical simulation algorithm and a time sequence decomposition algorithm to obtain a safety risk assessment result of the tunnel structure influenced by geological factors and an underground water dynamic change trend prediction result; according to the geological risk analysis result, fusing the tunnel technical condition data by adopting a heterogeneous integration model of a dynamic Bayesian network and an extreme gradient boosting tree, predicting the tunnel technical condition, and generating maintenance decision parameters; optimizing the maintenance decision parameters by adopting a cuckoo search algorithm so as to minimize the maintenance cost under the influence of the geological risk; and outputting decision results including the total maintenance cost distribution based on the geological risk, the technical condition improvement expectation, the rock stratum stability safety factor prediction value and the underground water seepage risk early warning level.

The invention discloses a data governance method and system based on artificial intelligence, and relates to the technical field of data governance, and the method comprises the steps: effectively improving the recognition capability of structural abnormal behaviors in a device file through introducing an abnormal track extraction mechanism combining image enhancement and cuckoo search, and improving the recognition efficiency. The method combines an isolated forest model optimized by a genetic algorithm, achieves the high-confidence screening of candidate abnormal data, remarkably improves the accuracy and robustness of anomaly detection, and achieves the recognition of a redundant entity through the construction of a semantic conflict graph and the introduction of a graph matching network. The problem that in a traditional method, the field similarity is insufficient to support complex conflict judgment is effectively solved, finally, a standard equipment file is generated through a field-level fusion strategy, a governance evolution graph is synchronously updated, a complete closed loop from discovery, evaluation and fusion to optimization is formed, automation, intelligentization and self-adaptive evolution of equipment master data governance are achieved, and the efficiency is improved. And the consistency, availability and governance efficiency of the equipment data are remarkably improved.

The invention discloses a water purifier water quality anomaly detection method based on deep learning. The method comprises the following steps: S1, collecting multi-dimensional index data of water quality sensors inside and outside a water purifier; s2, constructing a spatial adjacency matrix according to the physical distribution of the water quality sensors; s3, a time-space diagram neural network model is adopted to generate water quality time-space feature representation; s4, calculating the abnormal score of the water quality state based on the reconstruction error and the prediction error; s5, optimizing hyper-parameters of the water quality anomaly detection model by adopting an improved cuckoo search algorithm; s6, updating the positions of cuckoo individuals; and S7, applying the optimized water quality anomaly detection model to anomaly detection of the real-time water quality data flow. According to the method, the time-space diagram neural network model is combined, the cuckoo search algorithm and the anomaly detection and evaluation technology are improved, and deep learning-based water quality anomaly detection of the water purifier is realized.

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.

The invention discloses a tourist route planning method and system based on artificial intelligence. The method comprises the following steps: step 1, collecting multi-modal input data and constructing a multi-modal data set; step 2, inputting the multi-modal data set into a Perciver IO model for encoding; 3, constructing an original urban environment map; 4, generating a personalized urban environment map based on the intention-environment bidirectional remapping network; 5, constructing a tourism path set; step 6, optimizing the travel path set based on an improved Rhododendron search algorithm, and introducing a heterogeneous reconstruction egg exchange mechanism to realize jumping type evolution updating; and step 7, ending evolution updating when a preset evolution termination condition is met, generating an optimized travel path set, and selecting the travel path with the highest fitness as a current recommended path. According to the method, multi-modal modeling and the improved Rhododendron search algorithm are fused, and efficient planning of the personalized travel route is realized.

The invention discloses a Cuckoo search algorithm-based grid-connected inverter multi-objective optimization control method, which comprises the following steps of: constructing a three-level grid-connected inverter mathematical model, and calculating to obtain an inverter side current reference value; constructing a current prediction model of the inverter side; constructing a value function control item of the inverter side current by adopting a value function in a square error form; constructing a cost function by taking the neutral-point potential balance and the switching frequency as control items; and iteratively calculating a weight factor by adopting an improved self-adaptive cuckoo retrieval algorithm to realize multi-objective optimization control of the grid-connected inverter. According to the multi-objective optimization control method for the grid-connected inverter based on the cuckoo search algorithm, the problem that the control performance is limited due to the fact that weight factors are difficult to set in multi-objective optimization of the grid-connected inverter in the prior art is solved.

The invention discloses a sea surface wind speed inversion method and device based on marine environment noise, and a storage medium. The method comprises the following steps: S1, obtaining marine noise data; s2, acquiring wind speed data according to time and latitude and longitude information in the ocean noise data; s3, audio data in the ocean noise data are processed, and center frequency point noise spectrum level data are obtained; s4, summarizing the noise spectrum level data of the center frequency point and the corresponding wind speed data into a neural network data set through time and latitude and longitude information; step S5, constructing an initial Elman network; s6, performing iterative optimization on the network weight and bias of the Elman network by adopting a cuckoo search algorithm to generate optimized network parameters; step S7, initializing an Elman network by using the optimized network parameters, and generating a CS-Elman model; training a CS-Elman model by using the neural network data set; and step S8, carrying out quantitative evaluation on the CS-Elman model. The method can achieve the high-precision prediction of the sea surface wind speed.

The invention relates to a time-frequency difference passive positioning method based on an adaptive step cuckoo search algorithm, and belongs to the technical field of passive positioning, and the method comprises the following steps: S1, constructing a time-frequency difference positioning mathematical model; s2, proposing a time-frequency difference positioning algorithm based on an adaptive step cuckoo search algorithm ASCS, and solving the time-frequency difference positioning mathematical model; according to the time-frequency difference positioning algorithm based on the adaptive step cuckoo search algorithm ASCS, an adaptive step strategy and the discovery probability of nonlinear logarithmic decline are introduced on the basis of the cuckoo search algorithm; according to the adaptive step length strategy, the position is adaptively updated by introducing a global historical optimal solution in the optimization process.

The invention relates to a method for determining parameters of a dynamic compensation filter based on an improved cuckoo search algorithm, belongs to the field of intelligent sensors, and solves the problems that a compensation filter of an existing sensor is low in precision and difficult in parameter design. Comprising establishing a dynamic compensation filter model; acquiring original input data and output data of the sensor as a sample data set; based on the sample data set, using an improved cuckoo search algorithm to optimize parameters of the dynamic filtering compensator, and using a dynamic compensation filter model to calculate a fitness value of each nest in the optimization process; obtaining a global optimal solution, transmitting the global optimal solution to a dynamic compensation filter model, performing parameter optimization on the dynamic compensation filter model, and obtaining the optimized dynamic compensation filter model as a sensor dynamic compensation model; and inputting the input data into a sensing system consisting of the sensor and the sensor dynamic compensation model in real time to obtain a compensated sensor output value. And real-time dynamic compensation of the sensor is realized.

The invention discloses a power grid intelligent power flow control method based on an improved cuckoo search algorithm. The method comprises the following steps: firstly, constructing a mathematical model of an AC-DC hybrid system, analyzing the characteristics and interaction of each device in the system, and determining a composite function taking the minimization of power generation cost, active loss and generator voltage deviation as targets according to an operation target; and then, decomposing the complex objective function into a plurality of sub-problems through hierarchical processing, and realizing respective solving. In hierarchical calculation, an improved cuckoo algorithm is adopted for optimization in a solution space, and an optimal power flow distribution and control strategy is obtained. And finally, intelligent layered power flow control of the alternating-current and direct-current hybrid system is realized according to an optimization result. According to the invention, the system response speed and the regulation efficiency are obviously improved, and the effect in the aspect of optimized operation is outstanding; compared with the traditional Cuckoo search CS algorithm and NSGA algorithm, the cost consumption is respectively saved by 2.21% and 4.49%; the experimental model is low in weekly average cost and high in power transmission efficiency, the power transmission efficiency is effectively improved, and the loss cost is reduced.

The invention discloses a Tibetan teaching evaluation method and system based on artificial intelligence. The Tibetan teaching evaluation method comprises the following steps: S1, collecting multi-modal original data formed in a Tibetan teaching process; s2, preprocessing is carried out, a corresponding Tibetan dialect classification result is obtained, and parameter configuration required by the semantic coding model is loaded; s3, generating a student semantic embedding representation and a standard answer semantic embedding representation based on the semantic coding model; s4, constructing a semantic consistency scoring model and a training sample set; s5, optimizing parameters of the semantic consistency scoring model by adopting a fractional order cuckoo search algorithm; and S6, executing an automatic scoring task of the answer text of the student. According to the method, semantic coding and the fractional order cuckoo algorithm are fused, automatic scoring of Tibetan answering is achieved, and the method has the advantages of being high in adaptability, accurate in scoring and high in fairness.

The invention discloses an active power distribution network flexible resource collaborative planning method based on an improved CS algorithm, and the method comprises the steps: constructing a planning and operation integrated active power distribution network double-layer optimization model, enabling an upper planning layer to take the minimum annual comprehensive economic cost as a target, optimizing the locating and sizing of a distributed power supply and energy storage, and covering the investment cost, the operation and maintenance cost and the power purchase cost; the lower operation layer generates an energy storage charging and discharging strategy and a power flow scheduling scheme based on a typical day scene by taking minimum network loss and maximization of new energy consumption as targets, guarantees node voltage and line current safety margin through probability constraint, and constructs a multi-resource coordinated planning-operation closed-loop model; and adopting an improved cuckoo search algorithm to carry out iterative solution on the bilayer programming model. And improving a cuckoo search algorithm, combining a particle swarm optimization algorithm with the improved cuckoo search algorithm, converting double-layer multiple targets into a fitness function by adopting a linear weighting method, and solving an optimal planning scheme.

The invention discloses a one-key intelligent filling control system and method based on a digital twin model, and the method comprises the following steps: S1, collecting filling data in real time, and constructing a standardized input data set; s2, constructing a multi-scale digital twinborn model, and outputting simulation state data; s3, acquiring a response sensitivity matrix and a risk factor matrix; s4, introducing a cuckoo search algorithm, calculating a disturbance weight according to the response sensitivity matrix, and initializing a control strategy population; s5, adjusting the jump direction and step length of the Levy flight by using the risk factor matrix; s6, calculating a fitness value; s7, deleting the control strategy which is marked to be failed according to a historical strategy, and performing disturbance adjustment on a scheme which is close to an efficient strategy; and S8, if the convergence condition is not met, repeatedly executing the steps S4 to S7, otherwise, outputting the control strategy with the maximum fitness value, and generating a one-key filling control instruction. According to the method, digital twinning and cuckoo search algorithms are fused, and intelligent control over the whole medicament filling process is achieved.

The invention discloses a gravity value extraction method based on orthogonal distance minimization, relates to the field of gravity meter interference fringe fitting gravity value extraction method design, and solves the problems that in the prior art, when interference fringe fitting is carried out, phase extraction of interference fringes faces many problems, and then the measurement efficiency and precision of an atomic gravity meter are limited. According to the method, related parameters of interference fringes are initialized; setting a minimum sum of orthogonal distances from the data points to the curve as an optimized objective function; fitting the interference fringes by adopting a self-adaptive cuckoo search algorithm; according to the method, when the target function is optimized, multiple optimization strategies are introduced into the self-adaptive cuckoo search algorithm, the global search capability of the algorithm is improved, and the method can be better applied to a scene of gravity value extraction. According to the method, the extraction precision of the gravity value can be improved to a certain extent.

The invention discloses a farmland drainage channel optimization layout method based on machine learning, which comprises the following steps: S1, processing farmland microtopography original data by using an improved ConvNeXt network multi-modal feature extraction model to generate a microtopography feature result; s2, constructing a structured evaluation index data set based on a microtopographic feature result; s3, establishing a farmland drainage channel multi-objective optimization model according to the structured evaluation index data set; s4, performing global search on the channel network layout parameters under the farmland drainage channel multi-objective optimization model by adopting an improved Cuckoo search algorithm, and outputting optimal channel network layout parameters; and S5, according to the optimal channel network layout parameters, a channel line vector data set is generated, the channel line vector data set comprises channel line vectors, channel section node catchment partitions and channel section parameters, the channel line vector data set is imported into a farmland geographic information management platform, and a drainage channel construction drawing and an earthwork volume list are generated. According to the method, the key attention on the terrain slope change sensitive area is realized, so that the potential trend area of the channel line trend can be positioned more accurately.

This invention provides a method and apparatus for predicting the risk of credit bond investment transactions, relating to the field of artificial intelligence technology. The method includes: collecting transaction data during the credit bond investment transaction process, the transaction data including training set data and prediction set data; performing supervised training based on the training set data and a support vector machine algorithm to obtain a credit bond risk prediction model; performing risk prediction on the prediction set data based on the credit bond risk prediction model and determining the prediction accuracy; using the prediction accuracy as the fitness value of a cuckoo search algorithm, iteratively optimizing the penalty factor and kernel parameters in the credit bond risk prediction model using the cuckoo search algorithm to obtain the risk prediction model; and performing risk prediction on target transaction data during the target credit bond investment transaction process based on the risk prediction model. This invention improves the prediction accuracy of the risk prediction model and reduces learning costs, thereby enhancing the accuracy of credit bond investment transaction risk prediction.

The application provides a census clustering method for fairness and balance constraint optimization, comprising the following steps: step 1, obtaining a census sampling data set X; step 2, selecting an initial population clustering center by using a cuckoo search algorithm for optimization; step 3, selecting an optimal K value of population survey clustering by using a contour coefficient comparison method in a K value selection interval; step 4, performing a weighted Euclidean distance optimization iteration; step 5, introducing fairness and balance constraints; and step 6, iterating until a convergence condition is reached. The method eliminates the influence of data bias by optimizing the number of samples in the clustering cluster, and guarantees the fairness and representativeness of each group in the clustering result. The optimization method of the application can significantly improve the accuracy, fairness and stability of the clustering result when dealing with complex census data, especially in scenes involving sensitive attributes and sample imbalance, and effectively improve the reliability and fairness of census data analysis.

The invention relates to the technical field of high-speed rail virtual tests, in particular to a novel high-speed rail infrastructure virtual test system. The method comprises the following steps: screening out speed reduction demand track sections; for any speed reduction demand track segment, randomly generating a position point as a bird nest position, and executing a cuckoo search algorithm; the fitness of each nest position is obtained according to the position feature of each nest position on the track and the maximum operation adjusting speed of the track segment where the nest position is located and the next track segment; judging whether the bird nest position is abandoned or not by combining tunnel parameters and position features of different position points in a neighborhood range; if it is judged that the bird nest position is abandoned, iteratively updating the bird nest position to obtain a new bird nest position until the maximum number of iterations is reached, and taking the bird nest position corresponding to the maximum fitness value as an optimal coasting position point; and the high-speed rail is operated. According to the method, by obtaining the accurate fitness and the abandoning mode when the algorithm is carried out, the effectiveness of the operation decision is improved, and the effect of the virtual test is optimized.

The application discloses a full-slope erosion and sediment transport simulation method combining soil properties and land use, collects corresponding erosion and sediment transport basic data of full slopes of beam and valley slopes in a basin, including soil data, terrain data, land use data, vegetation data and water and sediment data; based on the erosion and sediment transport basic data, terrain conditions and land use type boundary conditions, the erosion environment type of the full slope is automatically distinguished, and the initial to-be-optimized parameters affecting the erosion are obtained based on the erosion environment type; the initial to-be-optimized parameters are updated based on the cuckoo search algorithm until the optimal parameters are obtained; the slope combination and slope length in the terrain conditions and the land use type and vegetation coverage combination in the land use type boundary conditions are changed, a plurality of different scenes are obtained, the full-slope sediment production and transport evolution process is simulated based on the different scenes and the optimal parameters, and the slope sediment transport rate under different conditions is obtained.

The invention discloses a Cuckoo search algorithm-based network node interaction method and system, and the method comprises the steps: setting wireless sensor network parameters, constructing a node energy consumption model containing a free space and a multipath attenuation mode, and adaptively selecting an energy consumption mode according to the relation between a transmission distance and a threshold value; selecting a single-hop or multi-hop transmission mode according to the distance between the cluster head nodes and the base station, determining the optimal number of cluster heads, and optimally selecting the cluster head nodes by using a cuckoo search algorithm; and optimizing a data transmission path, and selecting a relay node through a fitness function to realize interaction with a core network control function node. The system comprises a network initialization and parameter configuration module, an energy consumption modeling module, a cluster head selection and optimization module and a path optimization and core network interaction module. According to the invention, network energy consumption can be effectively reduced, network life is prolonged, data transmission efficiency and network adaptability are improved, and intelligent network management is realized.

The invention discloses a data-driven production line layout intelligent optimization and reconstruction method, and relates to the field of intelligent manufacturing engineering. According to the method, the cuckoo search algorithm, the group collaborative search mechanism and the firefly algorithm are fused, and intelligent optimization and dynamic reconstruction of the production line layout are realized. According to the invention, the stability of layout optimization and the performability of the scheme can be improved.

The present application relates to a kind of based on multi-index fusion evaluation power quality disturbance feature screening method, comprising: step 1: input power quality disturbance signal, extract multiple power quality disturbance features, construct power quality disturbance feature universe;Step 2: based on the fusion evaluation index of intersection degree index, redundancy index, separation index, using Cuckoo search method is searched and each dimension power quality disturbance feature subset selected is obtained;Step 3: based on cost factor, the selected power quality disturbance feature subset is evaluated, and the optimal power quality disturbance feature subset is obtained, and based on the optimal power quality disturbance feature subset, power quality disturbance signal is classified.The method is conducive to improving power quality disturbance classification efficiency and accuracy.

The application provides a kind of random scheduling method for ship maintenance task under milestone constraint, and relates to task scheduling technical field.The application obtains the data of the task to be scheduled in project, encodes the task to be scheduled, and generates a task list;process the task list based on heuristic rules and probability selection method, and generate an initial population;use the initial population as an initial bird nest, minimize the maximum completion time and minimize the delay cost as the goal, optimize the initial bird nest by improved discrete valley bird search algorithm, and obtain the optimal solution;the application takes minimizing the maximum completion time and minimizing the delay cost of task as the goal, studies the task scheduling method of time-varying task duration and resource demand under milestone task, and proposes several heuristic rules based on the characteristics of milestone task and random scheduling.It fills the gap in current research, and obtains a task scheduling scheme of high-end equipment manufacturing that is more in line with the actual production situation.

The present invention belongs to the field of electronic countermeasure technology, and particularly relates to a method for coordinated deployment of a mobile chaff screen. While maintaining the advantages of traditional chaff screen jamming itself, a chaff screen jamming is configured by coordinating the deployment of chaff projectiles through unmanned aerial vehicles (UAVs) and marine platforms. Based on an improved cuckoo search algorithm, the deployment rate and direction of chaff projectiles are controlled to make the energy centroid of the chaff screen move continuously over time, effectively dealing with the anti-jamming technology of radar seekers and ensuring the successful escape of marine platforms. The advantages of the present invention are as follows: facing the characteristics of airborne threat radar seekers and actual confrontation scenarios, the deployment parameters of chaff projectiles for UAVs and ships are obtained by means of the optimization mechanism of the cuckoo search algorithm. Through the coordination of UAVs and ships, the controllable sequential deployment of multiple chaff projectiles is completed, enabling the centroid of the chaff screen to move continuously over time according to a certain law, accelerating the escape speed of marine platforms, and effectively dealing with anti-jamming technologies such as moving target indication and track-while-scan of radar seekers.

The invention relates to an operation control method of a dust removal fan, and belongs to the technical field of automatic control. Comprising the following steps of firstly establishing and forming a historical operation data set, then establishing a prediction model according to an FEDform time sequence prediction algorithm, inputting data in the historical operation data set into the dust removal fan operation parameter prediction model for model training, and finally establishing an objective function of dust removal fan operation control. And performing optimization calculation on the target function through a cuckoo search optimization algorithm to obtain an optimal negative pressure set value in the operation control time period. According to the method, a time sequence prediction algorithm in an FEDform mode is improved, a structure capable of extracting time domain information of fan operation data is added to an encoder part of an existing time sequence prediction algorithm, and meanwhile, a multi-query attention mechanism structure is adopted to replace a multi-head self-attention structure in the existing algorithm; therefore, the prediction result of the improved FEDform-form time series prediction algorithm is more accurate, and the optimization accuracy is improved.

A gravity value extraction method based on orthogonal distance minimization relates to the field of design of gravity value extraction methods using gravimeter interference fringe fitting. It addresses the numerous difficulties faced in extracting the phase of interference fringes during interference fringe fitting in the prior art, which in turn limits the measurement efficiency and accuracy of atomic gravimeters. The method of the present invention is implemented by initializing interference fringe-related parameters; setting the minimum sum of the orthogonal distances from data points to the curve as the optimization objective function; fitting the interference fringes using an adaptive cuckoo search algorithm; and calculating the gravity value based on the phase obtained after fitting. When optimizing the objective function, the method uses an adaptive cuckoo search algorithm that introduces multiple optimization strategies, improving the algorithm's global search capability and enabling better application in gravity value extraction scenarios. The method of the present invention can improve the accuracy of gravity value extraction to a certain extent.

The present application discloses a task scheduling method based on an improved particle swarm optimization algorithm, which includes: obtaining task data to be scheduled, encoding particles according to the task data; iterating the particles by a particle swarm optimization algorithm; in response to that the particle swarm optimization algorithm does not fall into a local optimal solution, outputting a scheduling scheme; and in response to that the particle swarm optimization algorithm falls into the local optimal solution, outputting the scheduling scheme by fusing the particle swarm optimization algorithm with a cuckoo search algorithm. The present application introduces a cuckoo search algorithm when the particle swarm optimization algorithm falls into a local optimal solution, solving the dilemma of the particle swarm optimization algorithm falling into a local optimal solution, while improving the global search capability of the algorithm.

The application discloses a target attribute recognition method and system based on three-dimensional force information of a mechanical finger tip, and five kinds of silica gel models with different hardness and three kinds of shapes are prepared; a mechanical hand with an optical three-dimensional force sensor is used to collect force data of the gripping experiment of the models, original data are preprocessed by feature dimension reduction, and the data set is divided into a training set and a test set; the data set is classified based on a support vector machine algorithm optimized by an improved cuckoo search; the training set is used to train the support vector machine classifier model, the cuckoo search method is used to optimize the support vector machine model hyperparameters, the test set is input into the classifier model to realize attribute classification, and the accuracy of the real class and the predicted class and the confusion matrix during attribute classification are obtained; and the newly collected data is classified and a three-dimensional force time sequence diagram is drawn, so that the hardness and shape attributes of the target gripped by the mechanical hand can be effectively classified, and the application has higher accuracy compared with other algorithms.

The invention provides a nickel-based alloy strip stamping springback prediction method, and relates to the technical field of nickel-based alloy stamping manufacturing, and the method comprises the steps: obtaining initial springback values corresponding to a plurality of groups of stamping process parameter combinations in the stamping process of a nickel-based alloy strip, and calculating the stamping springback value of the nickel-based alloy strip based on all the stamping process parameter combinations and the corresponding initial springback values; constructing a training data set and a test data set; the training data set serves as input data, the initial springback value corresponding to the training data set serves as output data, and a springback prediction model driven by physical information is constructed; using an adaptive cuckoo search algorithm to search a predicted springback value as an optimal springback value, wherein the difference value between the predicted springback value and the expected springback value is minimum in the springback prediction model driven by physical information, and using a stamping process parameter combination corresponding to the optimal springback value as an optimal process parameter combination; optimization can be carried out in a complex parameter space, the optimization efficiency and stability are effectively improved, and local optimization is avoided.

The application discloses a slope auxiliary driving control method, device and equipment of a heavy haul train and a storage medium, relates to the technical field of intelligent transportation and vehicle engineering, and comprises the following steps: analyzing train attributes, a running line and running environment information of the heavy haul train to obtain line slope, slope length information, speed limit information and slope distribution position data; according to the data, a multi-objective optimization model containing a fail-safe function, a smoothness function and an energy consumption function is constructed; according to the line slope, the multi-objective optimization model is solved by using a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm, a multi-objective cuckoo search mechanism and a Gaussian disturbance strategy to obtain train control parameters; and the heavy haul train is subjected to auxiliary driving control according to the train control parameters and the speed limit information. The application realizes safe, smooth and energy-saving multi-objective optimization intelligent auxiliary driving control for the long and large slope running scene of the heavy haul train.