95 results about "Algorithmic skeleton" patented technology

The invention provides a multi-modal feature perception and meta reinforcement learning adaptive algorithm scheduling system and method, and the system comprises a multi-modal feature coding module which is used for carrying out the cross-modal feature alignment and fusion of input image data, physical signals and structured parameters; the deep reinforcement learning decision module is used for constructing a multi-objective optimization strategy by adopting a TD3 algorithm framework; the self-adaptive scheduling execution module is used for realizing strategy distillation and dynamic priority scheduling; and the multi-modal feature coding module comprises a dynamic graph convolutional network and a hierarchical cross attention mechanism. Through technical innovation, comprehensive breakthrough is achieved in the aspects of multi-modal data processing precision and efficiency, reinforcement learning scheduling stability and adaptability, engineering deployment lightweight and robustness and the like, the defects in a dynamic scene in the prior art are effectively overcome, and the intelligent level and the practical application value of an algorithm scheduling system are remarkably improved.

The invention discloses a T-S fuzzy and TD3-based slow dynamic unknown coal-fired power generation system coordination control method, which comprises the following steps: firstly, decomposing a coal-fired power generation system into a fast subsystem and a slow subsystem by using a singular perturbation theory, and decomposing an original control task into a stabilization task of the fast subsystem and a tracking task of the slow subsystem; for the fast subsystem, a fuzzy set is selected on a definition domain of the fast subsystem by using the slow variation characteristic of steam pressure, and a T-S fuzzy model is constructed; a fast subsystem controller is obtained by solving algebraic Riccati equations corresponding to a plurality of linear systems. For a slow subsystem, a double-Q network is adopted to reduce overestimation of a Q value, a dynamic learning rate and a batch size adjustment mechanism are introduced to accelerate training convergence, and learning is carried out under a TD3 algorithm framework to obtain control input of the slow subsystem. The fast subsystem and the slow subsystem control inputs to be combined and then act on the original system to obtain state information at the next moment, and the intelligent agent interacts with the original system to complete collaborative optimization.

The invention relates to a dependent task-oriented resource allocation method, and belongs to the technical field of wireless communication. The method comprises the following steps: S1, in an industrial internet scene, constructing an industrial internet system model comprising a cloud server, an edge server and industrial internet equipment, and modeling a dependency relationship among tasks into a directed acyclic graph; s2, constructing a system total time delay energy consumption weighted sum mathematical model of the industrial internet network; and S3, modeling the problem model as a Markov decision process, and using a deep reinforcement learning (DRL)-based SAC algorithm framework to obtain a task-dependent approximate optimal unloading strategy in an industrial internet IIOT equipment scene with a plurality of edge servers.

The invention discloses a body intelligent controller architecture based on an end side intelligent model and an operation method, and belongs to the technical field of industrial body intelligence. According to the architecture, a CPU, a GPU and an FPGA are integrated by adopting a heterogeneous multi-core processor, and high-speed interconnection is realized through a star-type + NoC hybrid topology; a multi-level task scheduling mechanism is constructed based on a real-time operating system, and it is ensured that control tasks are executed preferentially. End-side visual model lightweight (YOLOv5s model parameters are reduced to 3.5 M) and FPGA hardware acceleration (control instruction time delay is 10ns) are innovatively realized on an algorithm framework layer, and a transmission bottleneck is eliminated through a zero-copy data path. The system supports model hot switching less than or equal to 10ms and FPGA bit stream online updating less than or equal to 50ms, and has MD5 check and an automatic rollback mechanism. Compared with a traditional separation type scheme, the processing time delay is reduced by 25 times, the hardware cost is reduced, the size is reduced, and the real-time performance, reliability and deployment flexibility of the industrial robot and other intelligent agents with the body are remarkably improved.

The invention discloses an unmanned ship control method based on deep reinforcement learning in a multi-task scene. The method comprises the following steps: constructing dynamics and kinematics models of an unmanned ship; the method comprises the following steps: constructing a high-fidelity simulation environment based on Isaac Sim and a parallel training framework thereof, and respectively designing a state space and an action space for various unmanned ship tasks; reward functions are respectively designed; constructing an unmanned ship control strategy, and designing an algorithm framework of a centralized importance sampling and shearing strategy optimization mechanism based on an end-to-end deep reinforcement learning algorithm; and for different task scenes, multiple times of training-verification are performed on the strategy network through a PPO algorithm, and the trained strategy network is used for realizing multiple control tasks of the unmanned ship. According to the method, a high-fidelity simulation environment is constructed through Isaac Sim and a parallel training framework thereof, parallelization support which is crucial to deep reinforcement learning training is achieved, and various actual control tasks of the unmanned ship can be achieved.

The invention provides a multi-scene vehicle scheduling optimization algorithm based on a parallel evolution framework, and provides the multi-scene vehicle scheduling optimization algorithm based on the parallel evolution framework through interactive population dynamic evolution and a Knee point evaluation strategy on the basis of a non-dominated sorting genetic algorithm (NSGAII) in combination with a KnEA algorithm. The established multi-scene multi-target vehicle path model with capacity constraint is optimized, and a compromise delivery route set (PCOS) which achieves a good effect in multiple scenes is found out. In order to solve the limitation of single crossover operator optimization, a self-adaptive crossover mechanism for a migration solution and a Knee point is designed on a multi-scene vehicle scheduling optimization algorithm framework of a parallel evolution framework. A three-population crossover operator is designed, and the updating of an optimized population quantity proportion matrix is realized by calculating the distance between a Knee point generated after the population is optimized by each operator and a migration solution to an extreme value line, so that the quantity of the population optimized by each operator is dynamically adjusted, and the quality of the PCOS can be remarkably improved.

The invention provides an automatic learning engine device based on a packaging large model training platform, and the device comprises a unified access specification module which carries out the standardized training and reasoning parameter configuration through a YAML configuration template file; the multi-device support and parallel computing framework module is compatible with three hardware devices including a CPU (Central Processing Unit), a GPU (Graphic Processing Unit) and an NPU (Network Processing Unit), and supports two parallel computing frameworks including Accelerate and DeepSpeed; the training engine module adopts a three-layer architecture to realize task allocation, state monitoring and exception handling; and the training algorithm framework module is used for analyzing the running configuration file, carrying out data set splitting and data format conversion, supporting various fine tuning training methods and evaluating the model. According to the overall scheme, the technical threshold of large model fine adjustment is lowered, non-professional personnel can complete complex model training tasks through simple configuration, and popularization and application of the large model technology are promoted.

The invention discloses a reinforcement learning-based global structure perception R-Tree construction method, which comprises the following steps: designing a Markov decision process constructed by an R-Tree, the Markov decision process constructed by the R-Tree comprising state design, action design and reward design; a reinforcement learning agent algorithm framework is applied, and an Actor-Critic framework and a near-end strategy optimization algorithm are selected to construct a reinforcement learning agent; training a reinforcement learning agent by adopting a self-chess-playing mechanism to carry out R-Tree incremental construction; a trained reinforcement learning agent replaces a traditional variant local optimal child selection algorithm to automatically construct an R-Tree in a construction process of inserting data one by one. According to the method, the construction performance and the query efficiency of the R-Tree are remarkably improved, meanwhile, good flexibility and expansibility are achieved, and the real-time updating requirement in a dynamic data scene can be met.

The invention discloses a quay crane operation queue intelligent scheduling data processing method, and belongs to the technical field of intelligent scheduling, and the method comprises the steps: 1, preparing data, processing the data including ship information, equipment information, queue information and rule configuration, and then setting constraint conditions including a preset limiting condition and a target constraint condition; 2, building a CWP algorithm framework; 3, initializing parameters, carrying out data preprocessing, initializing parameters and variables required by an algorithm, and executing scheduling; step 4, constructing a neural network, establishing a neural network model of reinforcement learning, and realizing data progressive extraction from an input layer, a processing layer, a feature fusion layer, a decision layer to an output layer; and 5, training the network and updating parameters. The defects of a traditional scheduling algorithm are overcome, scheduling mode diversification is achieved, global optimization of intelligent scheduling is achieved, and a more flexible, efficient and intelligent job scheduling solution is provided.

The invention discloses a smart power grid false data injection identification method based on deep reinforcement learning. The intelligent power grid false data injection identification method based on deep reinforcement learning comprises the following steps that a preset node in a power grid is selected as a reference node, a system node state set is obtained, and the system state set comprises a phase angle of a system at a detection moment and a measurement value of a detector at the detection moment; constructing a direct-current power grid system model based on the system node state set, and defining an attack strategy in the direct-current power grid system model; randomly selecting the system node state set as initial sample data, inputting the initial sample data into the direct-current power grid system model, and obtaining a countermeasure sample in combination with a deep Q learning algorithm; and building a multi-layer attack detection algorithm framework to train the adversarial sample to obtain a power grid hostile attack recognition model. According to the method, the problem of low false data attack detection precision is solved through adversarial sample generation and a multi-level attack detection algorithm framework.

The invention discloses an unmanned aerial vehicle dynamic game model based on PER-MATD3 deep reinforcement learning and a working method thereof, the unmanned aerial vehicle dynamic game model comprises an MATD3 algorithm module, a PER sorting module and a sample importance evaluation module, an algorithm framework of MATD3 is constructed, a Priority Expersion Replay (PER) method is introduced to improve the algorithm framework, and the algorithm framework of the MATD3 is optimized. According to the PER method, priority ranking is carried out on empirical data according to the importance of the empirical data, more important experiences can be selected for multiple times during sampling, and therefore the learning efficiency is improved, however, due to the fact that samples with high TD errors are frequently used in the PER method, even though the learning efficiency can be improved, deviation can be inevitably brought, and the error can be inevitably caused. In order to avoid oscillation and even discretization caused by deviation during training, an importance sampling method is introduced, and the sample weight with high TD error is properly reduced, so that the influence of each sample during gradient descent during training is the same, and the convergence of a training result is ensured.

The invention relates to a hybrid flow shop scheduling robust optimization method and device based on a hybrid genetic algorithm, which can realize the improvement of the robustness of a hybrid flow shop scheduling scheme, emphasizes the mean value performance while paying attention to the worst situation, and has relatively strong capabilities of global search and jumping out of a locally optimal solution. The method comprises the following steps: determining a hybrid flow shop scheduling problem mathematical model based on robust optimization; constructing a first optimization algorithm based on a genetic algorithm according to the constraint of the hybrid flow shop scheduling problem; constructing a second optimization algorithm based on a simulated annealing algorithm framework; constructing a scheduling scheme evaluation method based on the stochastic simulation algorithm and considering the worst situation and the average performance; and fusing the first optimization algorithm and the second optimization algorithm to obtain a target optimization algorithm, and performing scheduling scheme optimization based on the target optimization algorithm and the scheduling scheme evaluation method to obtain an optimal scheduling scheme.

The invention relates to a geometric calculation teaching method for driving training, which comprises an abstract algorithm framework. According to the framework, teaching points in a driving training project are abstracted into an object algorithm framework with unified characteristics, all point positions in current teaching are calculated in real time according to the current state of a vehicle according to calculation methods of different subjects, a first uncompleted point position is found through traversal, and whether the vehicle reaches the point position or not is judged; the same project has a corresponding method for calculating the position of the point location, the method has a unified data format and calling mode, and a configuration file for configuring the feature of the point location exists for loading of an algorithm framework. According to the method, the teaching point is calculated in real time, corresponding guidance can be provided according to the actual running state of the vehicle, the method is suitable for changes of various vehicle initial postures, effective correction suggestions can be provided when trainees deviate from a standard path, and the effectiveness and flexibility of driving training are greatly improved.

The invention discloses a scheduling decision obtaining method and device for a renewable energy power system, and belongs to the technical field of power system control, and the method comprises the steps: enabling a constraint Markov process to be embedded into a Lagrange-depth deterministic strategy gradient algorithm framework; further, reinforcement learning training is carried out on the initial decision network under the Lagrange-depth deterministic strategy gradient algorithm framework; the implicit relation between the system state containing the uncertainty information and the actual decision is learned through the powerful fitting ability of the neural network, and finally the current system state in the constrained Markov process corresponding to the renewable energy power system is input into the target decision network to obtain an actual scheduling decision. According to the method, the constraint in the dispatching state transition process of the power system is considered, and the purposes of ensuring the economy and safety of the action and efficiently solving the dispatching plan during decision making of the intelligent agent are achieved.

The invention discloses a task allocation method and system based on an improved whale optimization algorithm framework, relates to the technical field, and is used for optimizing order type adaptive cross-domain traffic control network task allocation and improving the key task response capability of a time-sensitive traffic system. ICWOA initializes a population through Chebyshev mapping, and introduces Levy flight disturbance to enhance the optimization ability; dynamically balancing local and global search by means of adaptive parameters; relieving population diversity attenuation through randomness retention, diversity maintenance and boundary constraint; dimension type pinhole imaging reverse learning is fused to reduce high-dimensional optimization dimension interference. According to the algorithm, the convergence speed and the solving precision are better, sub-second calculation time is kept under different task scales, the task distribution efficiency is improved, and the time-sensitive scene task success rate is remarkably improved. The method solves the problems that an existing algorithm is insufficient in adaptive order type'order application-order sending 'structure and prone to falling into local optimum, population diversity attenuation and calculation speed.

The invention discloses a civil aviation element configuration optimization method based on mixed integer dynamic programming. The method comprises the following steps: S1, constructing a Chinese civil aviation core element and carbon emission spatial-temporal characteristic database; s2, establishing a mixed integer dynamic programming model with the purpose of minimizing the total cost of the system in the programming period; and S3, establishing decision variables, constraint conditions and a fusion solution algorithm framework required by the mixed integer dynamic programming model. And S4, based on the fusion solution algorithm framework, solving the mixed integer dynamic programming model by using a mathematical programming solver integrated with a robust optimization module so as to obtain a civil aviation core element dynamic configuration scheme which is optimal in total system cost in multiple periods in the future and meets the uncertainty scene. According to the method provided by the invention, a multi-source data fusion algorithm is optimized, a carbon emission measuring and calculating method is deepened, and solver parameters are adjusted, so that the collaborative requirements of improving the operation efficiency and reducing the carbon emission from the perspective of carbon cost are met.

The invention relates to a multi-target logistics distribution path optimization method based on AI, and the method comprises the following steps: obtaining order data, road network data and vehicle data of logistics distribution of a customer point, extracting basic information from the three types of data, and constructing the multi-dimensional features of the customer point; inputting the multi-dimensional features of the customer points into a pre-trained graph attention network for feature embedding, generating a distribution network feature graph fusing customer demands and a road network relationship, inputting a pre-trained reinforcement learning agent, and generating an initial path population with quality and diversity balance; performing iterative optimization on the initial path population based on a Memetic algorithm framework; and calculating the fitness of each individual on a plurality of preset optimization objectives, maintaining a Pareto solution set by adopting a multi-objective evolutionary algorithm with reference points, and recommending a final path scheme from the Pareto solution set based on user preferences. The method has the effect of remarkably improving the quality, efficiency and dynamic adaptability of path optimization.

The invention discloses an AUV trajectory tracking deep reinforcement learning method based on an improved curiosity mechanism. On the basis of an internal curiosity mechanism, a self-adaptive internal reward coefficient mechanism is provided, and an improved internal curiosity module IICM is constructed, so that the AUV can dynamically adjust the exploration capability of the AUV according to the actual tracking effect. Meanwhile, the IICM is combined on the SAC algorithm framework, an SAC + IICM algorithm is provided, the exploration behavior of the AUV is stimulated through a self-adaptive internal reward mechanism, and the understanding depth of the AUV on the environment is improved. Besides, in order to improve the tracking effect and the training efficiency, a composite reward function fusing factors such as path errors, speed changes and yaw angle errors is designed, and a state and action space highly matched with a tracking task is constructed. The method has the advantages of being high in autonomy, good in adaptability, high in convergence speed, high in control precision, high in robustness and the like, and is suitable for an AUV autonomous operation scene in a complex marine environment.

Aiming at the construction of an automatic warehouse logistics system of a digital workshop production line, the invention provides a single-load automatic guided vehicle workshop path planning method based on directed acyclic graph modeling, and the method comprises the functions of task selection, task execution sequence planning and the like. The method comprises a fusion algorithm framework based on a binary discrete particle swarm optimization algorithm and a genetic algorithm component, and can solve and fully consider the diversity of task selection strategies in a short time according to information such as known task arrival time and transportation starting point, so that normal production is ensured; and the task execution sequence with the shortest total driving distance is the optimal (or close to the optimal) task execution sequence.

The invention provides an interpretable safe longitudinal federal representation learning method and device for multi-modal big data. According to the method, for the problem of target domain modal data scarcity, in the target domain modal representation learning process, an attention mechanism is used for supplementing information of a source domain modal into a target domain modal, and an algorithm framework is established under a longitudinal federated learning framework, so that on one hand, it is guaranteed that data of a source domain and data of a target domain are not locally output, and the algorithm framework is established under a longitudinal federated learning framework; therefore, the data security is improved; on the other hand, the problem of insufficient modal data of the target domain is solved, and the performance of downstream tasks is improved; besides, in the process of constructing the loss function, an information bottleneck theory is introduced, redundant information between input and intermediate representation is minimized, and related information between representation and a target task is maximized, so that efficient information compression and feature extraction are realized, and the interpretability of extracted representation for downstream tasks is improved.

The present invention belongs to the field of video analysis technology, and discloses a method for software-hardware collaborative optimization of a video structured analysis system based on parameter value selection, including step 1: construction of multi-module algorithm control parameters and implementation parameter sets; based on the industry's mainstream YOLO+byteTrack+ResNet+SIFT CDVS feature video structured analysis system framework, combined with the encoding algorithm characteristics and pipeline constraints of heterogeneous multi-computing platforms, a hardware-friendly algorithm framework is selected for each module; step 2: multi-module coupling parameter optimization value selection; according to the pipeline throughput constraint, the control parameters of a certain concurrency granularity and concurrency intensity are configured to achieve a balance between system throughput and hardware consumption. The present invention supports the joint optimization of six algorithm modules, supports the joint optimization of target performance such as bit rate, computational complexity, algorithm accuracy, and system processing retrieval delay; and realizes the joint optimization of multi-target performance of multiple algorithm modules.

Traditional algorithms for solving constrained optimization problems are complicated to implement, difficult to interpret, and require significant computational resources. Disclosed embodiments convert constrained optimization problems into parametric optimization problems, in which at least a subset of the constraints are converted into parametric quadratic penalty (PQP) terms that each depends on a translational parameter. The parametric optimization problem may be used for optimization in a power system (e.g., for optimal power flow, economic dispatch, etc.). When solving the parametric optimization problem, the translational parameters are updated to ensure convergence. The parametric optimization problem can be solved with reduced computational expense, using only a linear equation solver to solve a sequence of primal variables only, thereby reducing computational complexity and expense. In addition, the disclosed embodiments provide a means to incorporate constraints into machine-learning algorithms. The disclosed algorithmic framework also provides interpretability and insights for analysis.

The invention discloses a multi-agent-based micro-grid energy management method and device containing flexible resources, and a storage medium. The method comprises the following steps: step 1, determining each main body of a micro-grid; 2, constructing a state space, an action space and a reward function of each main body in the three main bodies, thereby constructing a sequential decision model of the micro-grid intelligent body under a GPRO algorithm framework; 3, determining an objective function and constraint conditions; step 4, generating a training sample, and performing multiple rounds of training on the sequential decision model of the micro-grid intelligent agent under a GPRO algorithm framework through the training sample to obtain a micro-grid energy management model; and 5, inputting the current state of each main body in the micro-grid to be managed into the micro-grid energy management model to realize micro-grid energy management. The device and the storage medium are used for implementing the method. According to the method, flexible resources on the load side are fully considered, memory occupation is reduced, and training resources are remarkably reduced.

The application provides a mechanical state monitoring method based on an interpretable sparse optimization unfolding network, comprising the following steps: S100, performing mechanical state data monitoring test to obtain mechanical state signals; S200, constructing a sparse optimization model, and using sparse feature representation for the extracted mechanical state features; S300, using an alternating multiplier method to derive an iterative optimization solving algorithm of the sparse optimization model; S400, introducing learnable parameters to replace the parameters in the iterative optimization solving algorithm; S500, using an unfolding algorithm framework to construct an interpretable sparse optimization unfolding network; S600, using the interpretable sparse optimization unfolding network to identify the health state of a test machine; the application embeds a Q-adjusted wavelet transform into a sparse optimization model, and uses the multi-scale characteristics and wavelet dictionary structure for sparse feature representation, so that accurate capture of key state information can be realized in the feature extraction stage.

The invention discloses a robot path planning method based on an adaptive fuzzy and hybrid strategy, and belongs to the technical field of robot motion planning, and the method comprises the steps: carrying out environment perception, and obtaining environment information; the method comprises the following steps: on the basis of an RRT-Connect algorithm framework, fusing a fuzzy control theory, a hierarchical hybrid expansion mechanism and a dynamic path optimization thought to obtain an improved RRT-Connect algorithm; and an improved RRT-Connect algorithm is utilized, and robot path planning is realized based on environment information. According to the method, the fuzzy control theory is utilized to endow the path planning process with a macroscopic intelligent decision-making capability, and a microscopic accurate execution and fault-tolerant capability is provided through a hierarchical mixed strategy, so that the method is an efficient and robust universal solution; the success rate, efficiency and path quality of path planning of the robot in a complex high-dimensional space are remarkably improved, and more efficient and better collision-free path generation can be achieved.

The application provides an IRS transmission power optimization method based on a quasi-affine transformation evolution algorithm, and comprises the following steps: step 1, system model construction and parameter initialization; step 2, channel modeling; step 3, constructing a target for minimizing transmission power while meeting the constraint condition of the signal-to-noise ratio of all users; step 4, searching by using a quasi-affine evolution-based meta-heuristic algorithm; and step 5, outputting an IRS phase shift vector and an AP beamforming vector matrix. The application has the beneficial effect of improving the quasi-affine transformation evolution algorithm framework, aiming to overcome the problems of the traditional optimization method, such as the dramatic increase in the calculation complexity under the condition of a large number of IRS unit numbers N and user numbers K, and the defects of the existing heuristic algorithm (such as the particle swarm optimization) such as being prone to local optimization and low search efficiency.

The invention relates to an urban rail transit network train application and crew plan collaborative optimization method. On the basis of a space-time network model, a train application and crew planning space-time network is constructed, and a branch-pricing-cutting algorithm framework is adopted to form the collaborative optimization method. According to the invention, the problems of too small crew plan adjustment space and multi-time feedback adjustment of the train application plan caused by the existing staged programming of the train application and crew plan are solved. Besides, according to the branch-pricing-cutting algorithm method based on the space-time network model provided by the invention, an accurate optimal solution can be solved and a convergence curve of upper and lower boundaries can be given within the calculation time shorter than that of integer programming solution software. The method provided by the invention can be popularized to more traffic space-time path coupling optimization problems, and redundant transportation capacity generated by respective compilation of different plans is fully excavated. The method is of great significance for improving the current rail transit network operation efficiency, the passenger service level, cost reduction and efficiency improvement of operation enterprises and the like.

The invention belongs to the field of remote sensing small target detection algorithms in a complex environment, and particularly relates to a small target detection method for a complex background remote sensing image, which comprises the following steps of: constructing a basic YOLO-V5 algorithm network structure; a Res2Unit network based on a residual network idea is constructed; establishing a dense connection network and a fourth scale, and performing effective and accurate detection on a remote sensing small target under a complex background; and the improved YOLO-V5 target detection algorithm is trained and tested. Based on a YOLO-V5 algorithm network structure, the feature extraction performance of an algorithm network is improved by constructing a Res2Unit network based on a residual network thought, gradient attenuation is avoided to the greatest extent during algorithm network training by adopting a dense connection network, and meanwhile, a fourth scale is introduced into the algorithm structure, so that the feature extraction performance of the algorithm network is improved. Therefore, the method adapts to effective detection of small target objects in a complex background. According to the method, by means of a YOLO-V5 algorithm framework, the omission ratio and the accuracy of the algorithm are improved to the maximum extent on the basis of ensuring rapid detection of a remote sensing small target in a complex environment.

The invention relates to the field of human resource management, in particular to a human resource management method based on multi-modal data fusion and adaptive learning, and adopts the technical scheme that the method comprises the following steps: constructing a time sequence data portrait of a target object through static attribute data, dynamic behavior data and external environment data; and inputting the multi-modal data into a preset feature extraction model for feature extraction to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a pre-trained human resource analysis model to obtain an analysis result, and finally executing a corresponding human resource management operation according to the analysis result. The personnel and post matching degree score, the demission risk probability and the recommended development path are analyzed, intelligent resume screening and personnel and post matching optimization can be carried out, employee demission risk prediction and intervention suggestion can be carried out, personalized employee development path planning can be carried out, and the employee development path planning efficiency is improved. And deep learning and dynamic optimization capabilities are improved through an algorithm framework of multi-modal data fusion and dynamic adaptive learning.

The invention provides a mechanical state monitoring method based on an interpretable sparse optimization expansion network, and the method comprises the following steps: S100, carrying out the monitoring and testing of mechanical state data, so as to obtain a mechanical state signal; s200, constructing a sparse optimization model, and representing the extracted mechanical state features by adopting sparse features; s300, deducing an iterative optimization solution algorithm of the sparse optimization model by using an alternating multiplier method; s400, introducing learnable parameters to replace parameters in an iterative optimization solution algorithm; s500, constructing an interpretable sparse optimization expansion network by using an expansion algorithm framework; s600, using the interpretable sparse optimization expansion network to identify the health state of the test machine; according to the method, the Q-switched wavelet transform is embedded into the sparse optimization model, and sparse feature representation is carried out by using the multi-scale characteristic and the wavelet dictionary structure, so that the key state information can be accurately captured in the feature extraction stage.