298 results about "Global optimization algorithm" patented technology

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The invention provides an underground water dynamic evolution prediction method, and belongs to the technical field of underground water prediction based on deep learning. Hydrological parameters and geographic position data of monitoring points are collected, and a regional hydrogeological input tensor is constructed; then establishing an initial finite element model to carry out space extrapolation and flow field completion on underground water level distribution; based on an initial finite element model structure, designing a global optimization algorithm based on target decomposition, carrying out real-time structure optimization on the finite element model, then simulating underground water evolution of a non-monitoring area through a finite element numerical solution, and generating multi-node physical consistency hydrological time series data after resolution is improved; and inputting the generated hydrological time series data into a designed perceptual water level prediction model, and outputting underground water level prediction results at multiple moments and multiple positions in the future and corresponding underground water level thermodynamic diagrams. According to the method, more accurate, continuous and interpretable high-precision prediction of the underground water system is realized, and reliable support is provided for scientific management of underground water resources.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention relates to the technical field of image processing, and discloses a steel bar positioning control method, system and device based on intelligent scanning and a medium. The method comprises the following steps: establishing a global reference coordinate system through target ball space measurement; scanning the mold table according to the reference coordinate system and extracting boundary features; dividing an operation area and planning a path based on mold table positioning data; performing hand-eye calibration to obtain a pose matrix; filtering the point cloud data and extracting steel bar features; and analyzing the steel bar intersection points and generating an optimized binding instruction. According to the method, the reinforcing steel bar intersection points with different intervals are recognized in a self-adaptive mode, the optimal binding path is generated through the global optimization algorithm, and meanwhile the smoothness and continuity of movement of the mechanical arm are guaranteed.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention relates to a quantum gate simulation and control parameter optimization method based on numerical calculation, and the method sequentially comprises the following three modules: a model Hamiltonian matrix representation module can calculate the matrix representation of a model Hamiltonian according to a specific problem; a time evolution and fidelity calculation module of the system discretely solves a Schrodinger equation by using a Hamiltonian matrix, gives a corresponding time evolution matrix, and calculates gate operation fidelity by using the obtained time evolution matrix; and the control parameter optimization module calls the first two modules, optimizes control parameters and quantum gate operation time by adopting a local optimization algorithm or a global optimization algorithm, and gives optimal control parameters and gate operation time. The problem that the gate fidelity of a current superconducting quantum computing system cannot meet the requirement for reliable operation of a practical quantum algorithm is solved, the detailed process of quantum gate operation can be simulated through a numerical calculation method, and the quantum gate operation fidelity is improved by optimizing control parameters.

The invention relates to the technical field of Internet of Things data processing, and discloses an efficient data processing system for Internet of Things equipment. According to the method, multi-node cooperative training and reasoning are supported through the federated learning driven edge computing cluster, and localized efficient processing is realized while data privacy is protected; the cloud edge collaborative intelligent center solves the compatibility problem of heterogeneous equipment and various scenes through a lightweight model dynamic adaptation technology, the communication overhead is reduced, and the model generalization ability is improved; the cross-layer dynamic scheduling subsystem is combined with space-time flow prediction and a global optimization algorithm to realize elastic allocation of computing resources, so that burst loads are effectively dealt with and node overload is avoided; the full-link trusted execution system integrates data desensitization, security aggregation and block chain evidence storage, constructs hierarchical protection from equipment to the cloud, resists hostile attacks and ensures operation traceability; the AI-driven data processing assembly line greatly reduces the redundant data transmission and storage cost through adaptive compression and exception filtering.

The present invention provides a ray tracing-based method for indoor multi-base station location optimization in a millimeter wave frequency band, comprising: constructing an indoor millimeter wave network model; determining an indoor millimeter wave network model optimization constraint condition; constructing a cost function of multi-base station location deployment on the basis of the constraint condition; determining the initial location of each base station in the indoor millimeter wave network model; and by taking the initial location of each base station as a starting point, determining an optimal location of each base station by means of axial search together with pattern search. According to the present invention, while maintaining low complexity and the advantages of a global optimization algorithm, an indoor multi-base station optimization problem can be solved under the constraint condition that base station locations are necessarily within a feasible interval, and by taking the path loss and signal-to-noise ratio as optimization parameters, a good balance can be achieved between the accuracy of optimization results and the complexity of optimization algorithms for the multi-base station deployment optimization problem, so that the optimized base station locations can provide high-quality signal coverage for an indoor millimeter wave network.

The invention discloses a self-adaptive sound effect management method and system with a scene analysis module, and relates to the field of self-adaptive sound effect management, and the method comprises the following steps: S1, collecting environmental acoustic characteristics and user behavior data in real time through the scene analysis module; s2, dynamically extracting user hearing preference parameters and scene classification labels; s3, constructing a virtual audition sweet zone matched with the physical space; and S4, dynamically adjusting the phase delay and the energy distribution proportion of each frequency band, and generating an optimal loudness equalization curve meeting the multi-scene constraint. According to the method, adaptive matching of sound field characteristics is realized in a complex dynamic scene through a multi-level perception-optimization closed-loop architecture, acoustic feature extraction and physical space modeling are deeply fused, the technical boundary of traditional pure signal processing is broken through, and the problem of multi-target conflict is solved based on cross-modal collaborative learning and a global optimization algorithm.

The invention discloses a parameter optimization method based on an evaporation waveguide and troposphere scattering mixed channel, and the method comprises the steps: calculating the loss of a microwave propagation path through simulating a near sea surface evaporation waveguide and troposphere scattering environment in combination with an evaporation waveguide model, a troposphere scattering model, an atmospheric absorption attenuation model and a parabolic equation model; and obtaining the optimal transmitting antenna height, the optimal receiving antenna height and the optimal electromagnetic wave frequency under the evaporation waveguide and troposphere scattering mixed channel by using the target function and a global optimization algorithm. The method disclosed by the invention can adapt to application scenes such as complex offshore beyond visual range communication, can provide a flexible optimization scheme for different offshore propagation conditions and distances, and realizes all-weather beyond visual range communication by combining the advantages of an evaporation waveguide channel and a troposphere scattering channel; the influence of communication system parameters on electromagnetic wave propagation under an evaporation waveguide and troposphere scattering mixed channel can be effectively reflected, the loss of an electromagnetic wave propagation path is remarkably reduced, and the reliability of signal transmission is improved.

Disclosed in the present application are a method and apparatus for acquiring the positions of measurement points of a combustion chamber of a turbine engine. By minimizing a condition number of a design matrix A, the minimum number of measurement points and an optimal measurement position are determined by using a global optimization algorithm. In the embodiments of the present application, on the basis of data of the minimum number of measurement points and an optimal measurement position that are determined, a temperature field of a turbine engine can be reconstructed, thereby solving the problems with respect to the low efficiency and high costs of conventional rotary-rake-based methods during combustion chamber measurement, and reducing the test cycle and cost during turbine engine development.

The invention discloses a photovoltaic support structural member strength analysis method and system based on a wind load, and relates to the technical field of photovoltaic equipment detection, the method improves the accuracy and economy of photovoltaic support wind resistance design through multi-modal data fusion and intelligent algorithm iteration, and improves the photovoltaic support structural member strength analysis efficiency. Firstly, wind speed / wind direction separation modeling based on machine learning breaks through the limitation of traditional static wind pressure calculation, and the extreme wind condition prediction accuracy is effectively improved; secondly, non-dominant dangerous working conditions such as a 52-degree inclination angle can be automatically positioned through linkage of a parameterized model and a global optimization algorithm, and the efficiency is obviously improved compared with manual trial calculation; and finally, a stress threshold dynamic early warning mechanism realizes conversion from passive checking calculation to active defense.

The invention relates to the technical field of micro-grids, in particular to an alternating current and direct current hybrid micro-grid cooperative control method. According to the technical scheme, the alternating current and direct current hybrid micro-grid cooperative control method comprises the following steps that S1, a multi-stage micro-grid framework is constructed and comprises a main micro-grid, a direct current sub-micro-grid and an alternating current sub-micro-grid, and the main micro-grid and the direct current sub-micro-grid are connected through a bidirectional interconnection converter; s2, deploying a hierarchical Multi-Agent control architecture, wherein the hierarchical Multi-Agent control architecture comprises a main micro-grid agent, a direct current sub-micro-grid agent and an alternating current sub-micro-grid agent; and S3, executing three-level cooperative control, receiving a main network scheduling instruction by the main micro-grid agent, decomposing the main network scheduling instruction into sub-micro-grid control targets, coordinating power distribution among sub-micro-grids based on a global optimization algorithm, and maintaining voltage and frequency stability by the DC sub-micro-grid agent and the AC sub-micro-grid agent through a local control algorithm. According to the method, the power grid consumption efficiency and the operation stability in a high-proportion new energy access scene are remarkably improved.

The invention provides a distributed optical fiber-based vibration source transverse distance determination method and system, and relates to the technical field of optical fiber sensing, and the method comprises the steps: collecting distributed optical fiber vibration signals, extracting features, constructing a multi-resolution scanning region, carrying out the traversal through employing a global optimization algorithm, and calculating the propagation speed of the vibration signals. According to the method, velocity field distribution characteristics based on Gaussian process regression are constructed, velocity field parameters are optimized in combination with a probability sampling optimization method, the transverse distance of a vibration source is accurately determined through discretization space traversal and anisotropy correction, and the precision and reliability of vibration source positioning are improved.

The invention relates to the technical field of electric energy metering monitoring, in particular to an electric energy metering online monitoring method based on adaptive filtering and a deep convolutional neural network. According to the technical scheme, the method comprises the following steps: based on monitoring of correlation between electrical parameters and equipment characteristics, carrying out preliminary correlation analysis by using an intelligent algorithm according to electrical parameters, mutual inductor characteristics and electrical topological structure information collected by an electric energy metering device, and then carrying out global optimization algorithm processing. Noise interference in data is effectively removed through the adaptive filtering technology, the data quality is improved, a reliable data basis is provided for subsequent analysis and model training, the accuracy of a monitoring result is improved, high-precision description of errors of a measuring instrument is achieved, the precision of online monitoring of electric energy measurement is remarkably improved, and the method is suitable for popularization and application. Potential problems of the measuring instrument can be found in time, electric power transaction disputes and power grid operation risks caused by measuring errors are reduced, and stable operation and economic operation of an electric power system are guaranteed.

The invention discloses an inducer cavitation erosion resistance optimization design method based on an optimal meta model, which is applied to the technical field of pump inducer optimization design. Determining an inducer hydraulic performance simulation model by adjusting the coefficient of the cavitation model, and determining a cavitation calculation model by comparing the cavitation prediction error of the existing cavitation calculation model based on the unsteady numerical simulation result of the hydrofoil; on the basis of a small sample test design method, geometric parameters with the influence weight on the cavitation erosion area and the pump performance larger than a preset influence weight threshold value are determined, a hydraulic performance simulation model and a cavitation erosion calculation model are used for prediction, and a sample database is established; and training different meta-models based on the sample database, determining the meta-model with the best prediction precision, and solving the meta-model by using a global optimization algorithm. According to the induction wheel, the cavitation erosion phenomenon of the induction wheel can be effectively avoided, and the cavitation erosion resistance of the induction wheel is improved.

The invention discloses a culture medium optimization method and system based on metabolic stimulation. The method comprises the following steps: (1) constructing a relation model of component concentration difference and cell response amount at each culture moment relative to an initial culture moment; (2) taking a preset cell response amount or a weighted value of a plurality of preset cell response amounts as an optimization target, and searching an optimal component concentration difference by adopting a global optimization algorithm; and (3) determining a control quantity according to a preset culture scheme by taking the optimal component concentration difference as a control target, obtaining a culture solution component addition concentration of the culture medium or the culture medium series under the culture scheme by adopting an optimal control algorithm, and developing a culture medium formula of the preset culture scheme. According to the method, synergistic adjustment among components of the culture medium at different culture moments is considered; modeling analysis is carried out on data by adopting machine learning, so that the research and development period of culture medium optimization can be shortened, and repeated tests are reduced. Meanwhile, nonlinear correlation among different components in the culture medium can be considered.

The invention discloses an intelligent battery low-temperature top-speed heating method, system and device and a storage medium, and the method comprises the steps: obtaining the battery temperature and battery state information of a target battery, and the battery state information comprises an SOC state and a battery open-circuit voltage; when the battery temperature is smaller than a preset target temperature, determining an EIS impedance spectrum according to the battery temperature and the SOC state, and determining a target equivalent circuit model according to the EIS impedance spectrum; performing parameter updating on the target equivalent circuit model through a global optimization algorithm to obtain equivalent circuit parameters; and updating pulse parameters according to the equivalent circuit parameters, the battery open-circuit voltage and a preset boundary condition, and performing variable bidirectional pulse cyclic heating on the target battery according to the pulse parameters. The low-temperature heating efficiency of the lithium ion battery is improved, the energy consumption and the cost are reduced, and the method can be widely applied to the technical field of battery management.

The invention relates to the technical field of weighing measurement and metering, and discloses an intelligent dynamic weighing system and a weighing method thereof, and the system comprises a first-level hierarchical module which is used for achieving local real-time processing through a weighing sensor and a local data collection module; the second-level hierarchical module is used for carrying out fusion and cross validation on multi-sensor data through edge computing nodes; and the three-level hierarchical module is used for carrying out global optimization and monitoring analysis through the cloud computing node. Through hierarchical distribution design, the real-time performance and fault-tolerant capability of the system are remarkably improved, and the problems of precision deviation, overhigh energy consumption and response delay of a traditional system in a high-load and high-frequency dynamic weighing scene are solved. The local adaptive algorithm and the global optimization algorithm cooperate with each other, so that the system can dynamically adjust the weighing parameters in a complex environment, and the weighing precision is ensured not to be affected by the change of the external environment. And a multi-sensor data fusion algorithm is combined with edge calculation, so that the data reliability and the system stability are further improved.

The invention discloses a distributed cooperative control method and system for silicone rubber glass fiber sleeve production equipment, and relates to the technical field of industrial automation control. Through the block chain technology, the non-tampering and credibility of the data are ensured, the problems that in a traditional method, the data are prone to being tampered, and a trust mechanism is missing are solved, efficient cooperation and task distribution between devices are achieved in combination with a multi-agent system, device operation parameters are optimized, the production efficiency and product quality are improved, and the method is suitable for large-scale popularization and application. A virtual model is constructed by using a digital twin technology, simulation verification is performed on optimized parameters, the achievement of a production target is further ensured, and the system dynamically adjusts equipment operation parameters and enhances the flexibility and dynamic response capability of the system through a global optimization algorithm and a feedback control mechanism.

The invention belongs to the technical field of geophysical exploration, and particularly relates to a method and a system for quantitatively measuring intrinsic parameters of bedrock fractures based on all-angle domain amplitude and phase joint inversion. The method comprises the following steps: acquiring geological radar reflection data of a target fracture at a plurality of incident angles; synchronously extracting data sequences of amplitude change along with angles and phase change along with angles; establishing a fracture reflection theoretical model; constructing a target function jointly constrained by the amplitude residual and the phase residual; and adopting a global optimization algorithm to minimize the target function, and synchronously inverting to obtain the fracture opening, the filler dielectric constant and the occurrence parameter. According to the invention, through the combined constraint of the amplitude and the phase, the inversion multiplicity is effectively inhibited; accurate system effect correction is adopted, so that the reliability of large-angle data is ensured; according to the method, multi-parameter synchronous inversion without prior hypothesis is realized, the inversion precision is small in relative error, the method is obviously superior to a traditional method, and the method has important engineering application value.

The invention discloses an online car-hailing order allocation method and system based on an artificial intelligence algorithm, and relates to the technical field of intelligent scheduling, and the method comprises the steps: obtaining to-be-allocated order data and driver data; determining a candidate driver set by adopting a dynamic search radius and a multi-state driver screening strategy, and calculating an estimated arrival time in combination with a real-time traffic condition; establishing a multi-factor order receiving willingness evaluation model, and calculating the order receiving probability of each order-driver combination; constructing a comprehensive matching degree evaluation model, calculating the matching degree of all order-driver combinations, and generating a matching degree matrix; processing the matching degree matrix by adopting a global optimization algorithm, and outputting an order-driver allocation result; and pushing an order to a driver in the order-driver allocation result, monitoring a response state, and performing reallocation processing on a non-response condition. Through the multi-factor order receiving willingness evaluation and the global optimization algorithm, the success rate and matching precision of order allocation are improved, the order waiting time is shortened, and the overall allocation efficiency is improved.

The invention relates to the technical field of flue gas treatment, and particularly provides a coal-fired power plant intelligent environmental protection island platform management system, which comprises a sensing layer, an application layer and a decision-making layer, and is characterized in that the sensing layer comprises a plurality of data acquisition devices and is used for collecting data of all working devices in an environmental protection island; the application layer comprises a flue gas management and control module, and the flue gas management and control module establishes a flue gas management and control prediction model based on the data collected by the sensing layer and historical data records; and the decision-making layer determines an optimal collaboration strategy of the environmental protection island based on the model and data established by the application layer. According to the environment-friendly island platform management system provided by the invention, parameter information is obtained through the sensing layer, modeling is performed through the application layer, emission prediction is realized, the application layer uploads a prediction model and sensing layer parameters to the decision-making layer, the decision-making layer performs particle swarm global optimization algorithm on output parameters and each model, and the minimum cost is taken as a target. And the range of the parameters and the pollutant emission limit value are adjusted as constraint conditions, and a control strategy of the optimal system is obtained.

The invention provides a wind generating set tower tube weld joint optimization method based on hydrodynamics, and relates to the technical field of hydrodynamics, the wind generating set tower tube weld joint optimization method comprises the following steps: obtaining an image of a junction of to-be-welded positions of a wind generating set tower tube, and extracting a junction center line in the image of the junction; according to the extension direction of the boundary center line, a constraint condition for keeping energy continuity of the molten metal at the inflection point of the extension direction is constructed, and the heat flux density formed by the molten metal at the inflection point of the center line is calculated; according to the heat flow density needing to be met in the step S2, the welding heat needing to be absorbed by the to-be-welded material when the welding seam is formed at each inflection point of the tower barrel welding seam is calculated; and a welding parameter-inflection point heat mapping model is constructed, optimal welding parameters are output through a global optimization algorithm, the fatigue strength of a welding seam under the wind load effect is improved, and the service life of a tower drum of the wind generating set is prolonged.

The invention discloses a wind power plant network following / constructing type fan proportion optimization configuration method and device, a medium and a product, and belongs to the technical field of new energy power system stability control, and the method comprises the steps: collecting the operation data of a wind power plant and a power grid, and carrying out the standardization preprocessing; constructing a typical operation scene set based on the preprocessed data; establishing an equivalent model of a following network type fan and a construction network type fan and a coupling model with a power grid; constructing a frequency, voltage and transient stability index system based on the coupling model; designing constraint conditions containing stability indexes; constructing a multi-objective optimization function with stability priority; solving by adopting a global optimization algorithm to obtain an optimal fan proportion solution set; and performing simulation verification and scheme screening on the optimal solution set, and determining a final configuration proportion. On the premise of ensuring the stability of the power grid, the proportion of different types of fans in the wind power plant is scientifically configured, and the safe and stable operation capability of a high-proportion new energy power system is effectively improved.

The invention relates to a transformer fault positioning method and device based on multi-point oil chromatographic data. The method specifically comprises the following steps: setting more than or equal to 3 sampling points on a transformer to obtain concentration sequence data of dissolved gas in transformer oil; establishing a relation function based on the distance from each sampling point to the fault point, the peak concentration and the peak concentration time; and based on the acquired concentration sequence data, solving the relation function through a global optimization algorithm to obtain the coordinates of the fault point of the transformer. According to the method, the coordinate of the fault point with the maximum correlation coefficient can be quickly and effectively searched in the range of the transformer oil tank, and compared with a traditional fault positioning method, the method is free of electromagnetic interference, wider in coverage range, higher in safety and capable of reducing the possibility of misjudgment.

The present invention provides a method, device, and apparatus for determining the instantaneous angular velocity of a rotating machine, wherein the method includes: obtaining a time-frequency diagram corresponding to an acceleration signal of the rotating machine within a preset time period; determining multiple first continuous ridge structures within the first sub-time period based on the local peak value of the spectrum corresponding to any moment in the time-frequency diagram; updating the multiple first continuous ridge structures based on a preset global optimization algorithm and the second ridge point of the time-frequency diagram in the second sub-time period to determine multiple second continuous ridge structures of the time-frequency diagram within the preset time period; determining a target instantaneous frequency within the preset time period based on a preset frequency domain sparsity evaluation index and the multiple second continuous ridge structures, and determining a target instantaneous angular velocity based on the target instantaneous frequency. The solution of the present invention can improve the accuracy of extracting the instantaneous angular velocity.

The invention relates to the technical field of oil and gas field surface engineering, and discloses a compressor distribution and working condition parameter planning optimization technology in a shale gas pressurization process, and an optimization method comprises the following steps: establishing a mathematical model for accurately describing pipe network pressure loss and compressor operation power; the outlet pressure of each intermediate booster stage is defined as a decision variable, and a nonlinear programming model with the lowest system total power or the lowest full life cycle cost as an objective function is constructed; solving the model by adopting a global optimization algorithm to obtain a group of optimal intermediate interstage pressure values; and by analyzing the optimal solution, determining an optimal compressor physical distribution scheme and accurate working condition parameters of each stage of compressor under the scheme. According to the method, a complex engineering planning problem is converted into a mathematical problem with a clear solving path, a global optimal planning scheme considering technology and economy can be provided for the shale gas pressurization system, and the energy consumption and total cost of the system are remarkably reduced.

The invention provides a karst underground water level prediction method based on a PSA-CNN-LSTM-Attention deep learning algorithm. The karst underground water level prediction method based on the PSA-CNN-LSTM-Attention deep learning algorithm comprises the steps that 1, data and input indexes are obtained; 2, data processing; 3, model selection and construction; and 4, verifying the model. According to the method, a large amount of underground water level data is processed and analyzed by comprehensively applying a convolutional neural network (CNN), a long short-term memory (LSTM) network, an attention mechanism and a PID search optimization algorithm (PSA). A high-precision underground water level prediction model is constructed by coupling a convolutional neural network (CNN) and a long short-term memory (LSTM) network technology and integrating a global average pooling layer and an SE attention mechanism, and the method is particularly suitable for dynamic change prediction of the underground water level in a karst area. Besides, model parameters are adjusted by adopting a PSA global optimization algorithm to ensure that the model can exert optimal performance under different environments and data conditions, so that not only is the adaptability of the model enhanced, but also the prediction accuracy of the model under a complex geological background is improved.

The invention discloses a parameter identification test system and method for a tuned mass damping inerter, and the method comprises the steps: generating a signal through a signal generator in a TMDI parameter identification process, converting the signal into mechanical vibration through a vibration exciter after amplification, and enabling the mechanical vibration to act on a main structure equipped with TMDI; a force sensor and a laser displacement sensor are used for obtaining the exciting force borne by the main structure provided with the TMDI and the displacement time sequence under the action of the exciting load; on the basis, based on a mathematical model of the TMDI, seven key parameters including the mass, the rigidity and the damping coefficient of the main structure with the TMDI and the mass, the rigidity, the damping coefficient and the system inertia coefficient of the TMDI are effectively recognized through a global optimization algorithm; the method is accurate in parameter identification, high in identification efficiency and suitable for performance analysis and optimization of various TMDIs.