1083 results about "Evolutionary algorithm" patented technology

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention relates to a coal mine safety production intelligent decision-making system based on digital twinning, and the system comprises a physical sensing layer which collects coal mine environment parameters, equipment states and personnel positioning data through the deployment of a multi-mode sensor network, and generates a structured data flow; the edge calculation layer is used for operating an incremental multi-objective evolutionary algorithm, quickly generating a cache strategy in combination with a strategy cache pool preloading mechanism, uploading the processed data to the digital twinborn layer, receiving a global instruction of the intelligent decision-making layer and decomposing the global instruction into a device-level control signal; the digital twinborn layer is used for receiving the real-time data uploaded by the edge calculation layer, updating the state of a digital twinborn body and feeding back an optimization demand to the intelligent decision-making layer; and the intelligent decision-making layer is used for generating a global strategy by means of digital twin-guided hybrid optimization and a special FPGA acceleration card for a coal mine, and fusing the cache strategy of the edge calculation layer and the global strategy of the intelligent decision-making layer to generate a global instruction.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention relates to the technical field of wetland carbon evaluation, and discloses a wetland carbon flux evaluation and management system based on three-dimensional digital twinning. The system comprises a three-dimensional modeling module, a flux monitoring module, a model optimization module and a twin updating module. The three-dimensional modeling module integrates wetland geographic space coordinates, an ecological parameter set and elevation, vegetation coverage density and soil type layering information, constructs a three-dimensional terrain grid model, and generates a three-dimensional digital twinborn body with a geographic mark; the flux monitoring module is used for collecting data such as atmospheric temperature gradient based on real-time environmental parameters, extracting carbon flux change characteristics through time sequence analysis and outputting a dynamic distribution state value; the model optimization module uses an adaptive evolutionary algorithm to adjust the parameter matching degree, iteratively updates constraint conditions, and generates an optimization parameter set; and the twin updating module recognizes abnormal grid units according to the abnormal grid units, corrects attributes by combining measured data, calibrates a topological relation, and outputs an updated evaluation model.

The invention discloses a BIM (Building Information Modeling)-based hoisting construction safety monitoring and early warning system. The system comprises a terminal sensing layer which is used for collecting environmental parameters and personnel behavior data in a closed space in real time; the edge computing layer is used for carrying out cleaning, compression and encrypted transmission on original data by utilizing an explosion-proof edge computing gateway; the cloud collaboration layer is used for storing full data based on a BIM digital twinborn platform, constructing a'danger mode-construction feature-disposal measure 'three-dimensional meta-knowledge graph by adopting an MAML + + algorithm, meanwhile, coupling a physical mechanism data enhancement engine with a multi-physics field coupling model and a physical constraint generative adversarial network, generating virtual data conforming to mass conservation and energy conservation, and sending the virtual data to the cloud collaboration layer; performing mixed training with real data; according to the intelligent decision-making layer, a space-time adaptive threshold evolutionary algorithm encodes a space-time context through a graph attention network and Transform, an alarm threshold is dynamically optimized through deep reinforcement learning, meanwhile, a digital twin deduction engine calculates a shortest safety path in real time, and rescue resource allocation is optimized.

The invention relates to the technical field of post-disaster path planning, in particular to a post-disaster unmanned aerial vehicle path planning method and system based on double-population constraint multi-objective optimization. The method comprises the following steps: based on a post-disaster task scene model, establishing an unmanned aerial vehicle voyage multi-objective collaborative optimization objective function and constraint conditions, including constructing a multi-objective function system, setting system constraint conditions and establishing a constraint violation degree evaluation mechanism; performing path optimization by using a double-population constraint multi-objective evolutionary algorithm, including establishing a multi-unmanned aerial vehicle path coding mechanism and initializing a double-population architecture, implementing a double-population collaborative genetic reproduction operation, and determining a double-stage constraint processing strategy; environment selection based on elite perception sorting is implemented; the multi-target collaborative optimization model and the accurate risk quantification mechanism constructed by the invention effectively solve the key problems of single target and rough risk processing of the existing method.

The invention discloses a distributed electrochemical energy storage fire alarm system based on an intelligent algorithm, and relates to the technical field of energy storage battery safety monitoring. Technical processes of multi-source data acquisition and preprocessing, key health factor extraction, multi-modal feature fusion, depth model detection, cross-system intervention and adaptive strategy optimization are provided; a high-quality data stream is generated through real-time synchronous sampling and noise filtering, weak fault symptoms such as internal short circuit or material deterioration are highlighted by applying a multi-dimensional feature structure, a deep learning model is utilized to accurately recognize and output early warning, and then load reduction, isolation and temperature control measures are carried out in cooperation with a BMS, an EMS and a fire fighting system. An evolutionary algorithm or a reinforcement learning continuous iteration intervention strategy is introduced in a simulation environment, high-sensitivity capture and rapid suppression of latent risks under multi-scene complex working conditions are achieved, the rate of missing report and false report can be remarkably reduced, and the overall safety of the system is remarkably enhanced.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention discloses an automatic scheduling method and system for ship unloading equipment, and relates to the technical field of port automation. According to the method, a high-precision digital twinborn model for ship unloading operation is constructed, physical equipment is abstracted into a digital intelligent agent with an autonomous decision-making capability, real-time multi-dimensional data and historical data are utilized to perform deep fusion to drive system synchronization, and a future multi-step scheduling strategy is deduced in a parallel simulation manner in a virtual space based on rolling time domain control, so that the real-time multi-dimensional data and historical data are subjected to real-time multi-dimensional data synchronization driving system synchronization is realized. Dynamic evaluation and optimization are carried out by adopting a multi-objective evolutionary algorithm combined with a cooperative game mechanism, and conflicts and cooperation among equipment are effectively coordinated by defining an individual utility function and introducing cooperative game negotiation and a meta-controller to dynamically adjust target weights, so that system-level global optimal scheduling is realized under multiple objectives of efficiency, energy consumption, safety and the like, and the scheduling efficiency is improved. The intellectualization, the self-adaptability and the comprehensive operation benefit of port ship unloading operation are comprehensively improved.

The present invention discloses an online evolutionary algorithm and training platform for intelligent agents based on self-game, including: a task and environment module, an intelligent agent training module, a strategy evaluation module, a data management module and a user interaction module. The task and environment module dynamically defines training tasks and environmental parameters through a task generation unit and an environment simulation unit. The intelligent agent training module includes an initial strategy generation unit, a course learning unit and a self-game optimization unit to work together to strengthen the learning framework. The strategy evaluation module tests the advantages and disadvantages of intelligent agent strategies through performance evaluation, diversity analysis and comparative analysis. The data management module records and analyzes training data to manage the historical strategy library. The user interaction module provides task configuration, training monitoring and result visualization functions. The present invention improves the adaptability and robustness of intelligent agents by introducing a self-game mechanism and dynamic opponent generation, and is suitable for strategy optimization and training in complex collaboration and confrontation scenarios.

The invention discloses an acquisition and processing system and method for dry data of a transformer, and relates to the technical field of data processing. Comprising the following steps: step 1, multi-source data real-time acquisition and edge preprocessing; 2, constructing a drying end point prediction model; 3, constructing a composite objective function, and solving the composite objective function by adopting a multi-objective evolutionary algorithm; 4, performing real-time correction and feedback; according to the method, key parameters in the drying treatment process are monitored in real time, an accurate drying end point prediction model is automatically constructed, heating and air exhaust curves are dynamically optimized according to the real-time working condition and the production strategy, and traditional manual blind judgment and static presetting are replaced; and meanwhile, the operation energy consumption is remarkably reduced, the optimal balance of the energy consumption and the production efficiency is realized through a multi-objective evolutionary algorithm and an online Kalman filtering correction closed loop mechanism, the drying quality and the equipment safety are improved, the energy cost is greatly saved, and the dual requirements of modern intelligent manufacturing for flexibility, energy conservation and consumption reduction are met.

The invention relates to the technical field of control systems, in particular to a melt mixing parameter real-time feedback control system for color master batch production. The method comprises the following steps: acquiring a corresponding process parameter reference through a parameter reference setting module; carrying out risk assessment through a grading module to obtain risk grades and carrying out grading processing; constructing a global optimization model through a control sequence module; a target evolutionary algorithm is started in combination with the technological parameter benchmark, and the technological parameter adjustment amount is calculated and a regulation and control sequence is generated by analyzing the dynamic coupling correlation among the technological parameters and the comprehensive influence on the whole; and finally, the process parameters are adjusted through a closed-loop control module, and real-time closed-loop feedback control is formed. The defects exist in real-time feedback and accurate control links of parameters in a color master batch melting and mixing process. Therefore, the invention provides a melt mixing parameter real-time feedback control system for color master batch production.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

The invention provides a steam extraction energy storage and heat energy storage integrated regulation control method and system, and relates to the technical field, and the method comprises the steps: building a digital twinborn model of a steam extraction energy storage and heat energy storage integrated system, and respectively obtaining corresponding data; establishing a multi-objective optimization model based on the collected data; solving the multi-objective optimization model by using an improved multi-objective differential evolution algorithm in combination with an adaptive variation strategy and a non-dominated sorting mechanism to obtain an optimal adjustment strategy; on the basis of the optimal adjusting strategy, the opening degree of a steam extraction valve of each stage of steam extraction point is adjusted in real time through a distributed control system, and the steam extraction flow is controlled; the inflation power and the deflation power of the compressed air energy storage unit are dynamically adjusted, and meanwhile the charging and discharging power of the flywheel energy storage unit is controlled; the heat charging and discharging rate of the phase change material heat storage unit is adjusted in a self-adaptive mode, and meanwhile the heat charging and discharging flow of the fused salt heat storage unit is controlled.

The invention discloses a task allocation method and system based on a hybrid game genetic algorithm, and belongs to the field of intelligent manufacturing, and the method comprises the following steps: obtaining task allocation data based on an industrial Internet of Things and an edge computing technology; constructing a task allocation-path planning-energy consumption control collaborative optimization model based on the task allocation data; the collaborative optimization model comprises a three-dimensional optimization objective function and a constraint system; the constraint system comprises an equipment health state constraint, an AGV dynamic motion constraint, a process connection space-time constraint and a multi-agent game equilibrium constraint; and solving the collaborative optimization model based on a hybrid game genetic algorithm to obtain a task allocation optimal solution. Through deep coupling of a multi-agent game and a dynamic evolutionary algorithm, the problem of decision dimension splitting of traditional single-target static scheduling is solved.

The invention discloses a common concrete and ultra-high performance concrete performance prediction and mix proportion multi-objective optimization design method and system, and the method comprises the steps: obtaining a comprehensive data set which covers the mix proportion, performance data and cost data of common and ultra-high performance concrete; correlation analysis is carried out, and influence factors strongly correlated with the target variable are determined; constructing a concrete performance prediction model by using a machine learning algorithm, training and testing, then carrying out interpretability analysis, and selecting an optimal concrete performance prediction model; constructing a mix proportion optimization design objective function and an ultra-high performance concrete cost function; establishing a multi-objective optimization design mathematical model in combination with constraint conditions, a cost function and an optimization objective function; and solving by adopting a multi-objective evolutionary algorithm, generating an approximate Pareto frontier solution set, and determining an optimal mix proportion optimization design scheme. According to the method, the concrete performance can be predicted, the mix proportion with the optimal comprehensive performance and cost effectiveness is obtained, and multi-objective optimization design of the concrete mix proportion is achieved.

The invention discloses a robot control method based on a multi-modal large model, and relates to the technical field of robot control, and the method comprises the following steps: 1, collecting the sensing data of a robot in real time through installing a multi-modal sensor group, and constructing a posture three-dimensional model of the robot to obtain the posture change data of the multi-modal sensor group in real time; a multi-mode sensor group is installed to collect sensing data of a robot in real time, a posture three-dimensional model is constructed, environment videos, audios, a contact state and posture change data are processed, a hidden state and a memory state are dynamically updated through a long-short-term memory network, and a predictable robot behavior strategy is generated. A dynamic evolutionary algorithm is adopted to optimize the adjustment amount of the behavior strategy, evolution of the optimal behavior strategy is achieved, the Q value of the robot behavior strategy is updated according to instant rewards, feedback circulation is formed, and the robot is helped to adjust and optimize the behavior strategy in real time.

The invention discloses a sewage treatment plant greenhouse gas optimization control method and device and a storage medium, and relates to the technical field of environmental protection, and the method comprises the steps: discretizing a sewage treatment process of an aeration tank into a plurality of complete mixing reactors connected in series, and constructing a sewage treatment process mechanism model based on an activated sludge model; optimizing parameters of the sewage treatment process mechanism model according to the target greenhouse gas concentration spatial distribution data, the target greenhouse gas emission flux and the water quality spatial distribution data to obtain a digital twinborn model; based on a digital twinborn model, a dissolved oxygen set value of each complete mixing reactor partition in an aeration tank is used as a decision variable, a multi-objective evolutionary algorithm is adopted for solving, a Pareto optimal control strategy is generated, and the technical problem that in the prior art, an optimal control strategy of greenhouse gas is not accurate is solved. And precise quantification and collaborative optimization control of greenhouse gas emission are realized.

The invention relates to the technical field of artificial intelligence and information propagation analysis, and discloses a deep learning-based network information trend prediction method and system.The deep learning-based network information trend prediction method comprises the steps of performing neural architecture search through combination of an evolutionary algorithm and reinforcement learning; an optimal neural network structure suitable for different types of network information is automatically found; dynamic reconstruction of a model structure is realized through an environment perception and event triggering mechanism; according to a deployment environment resource constraint, adopting an importance-perceived neuron self-adaptive pruning technology; turning point features in network information propagation are specially extracted and enhanced; knowledge migration from a large-scale high-precision model to a lightweight model is realized; an online learning and continuous optimization mechanism is adopted to prevent disastrous forgetting; according to the method, the key turning point of network information propagation can be accurately predicted, the prediction accuracy is improved, the early warning time is shortened, and the computing resource consumption is reduced.

The invention discloses a multi-agent evolution reinforcement learning method based on multi-strategy optimization and scene memory, and the method comprises the following steps: initializing a multi-agent reinforcement learning team and an evolution learning team, including observation characterization and strategy characterization of agents; updating a reinforcement learning team strategy according to the interaction experience and the scene memory; updating an evolutionary learning team strategy according to the population fitness by using an evolutionary algorithm; performing feature extraction and dimension reduction on the global state through a state encoder; updating the memory in the buffer pool based on a scene memory mechanism; and carrying out strategy interaction on the reinforcement learning team and the evolutionary learning team to obtain a current optimal strategy. Through a multi-strategy optimization mechanism, the problem of limited strategy interaction between a reinforcement learning team and an evolutionary learning team in a traditional evolutionary reinforcement learning method is solved, and meanwhile, the decision-making quality and learning efficiency of the intelligent agent are optimized by utilizing a scene memory mechanism. The method not only can improve the convergence speed of the algorithm, but also can enhance the adaptability of multiple agents to a dynamically changing non-stationary environment, thereby making a decision favorable for a team.

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

The invention discloses a multi-mode short-term photovoltaic power prediction method and device based on a satellite cloud atlas, and the method comprises the steps: obtaining real-time meteorological data, historical photovoltaic power data and cloud cluster image data, and carrying out the preprocessing of the data; a GPAformer model is established, and the meteorological data after noise reduction are predicted; the method comprises the following steps: establishing an SA-Convlstm model, capturing a cloud cluster movement track, extracting time change characteristics, predicting cloud cluster image data, introducing a cloud shielding model, and correcting the prediction deviation of SA-ConvLSTM under the condition that a cloud layer is dense or changes rapidly; establishing a photovoltaic power KAN-COGCN prediction model, and performing photovoltaic power prediction by taking the meteorological factors, the cloud cluster motion time sequence characteristics and historical photovoltaic power data obtained by prediction of the GPAformer and the SA-ConvLSTM model as input; using an improved alpha evolutionary algorithm IAE to optimize hyper-parameters of the three models; and performing error correction on a prediction result by establishing an adaptive wavelet RBF neural network to obtain a final prediction result. According to the invention, the precision and effectiveness of photovoltaic power prediction can be improved.

The invention discloses an intelligent optimization method and system for territorial space planning based on a multi-objective evolutionary algorithm, and relates to the technical field of territorial space planning, and the method comprises the steps: collecting multi-source basic data needed by territorial space planning, and carrying out the optimization of the territorial space planning based on ecological environment bearing capacity and construction land development suitability indexes; constructing a nonlinear mapping model to quantitatively evaluate the development risk of each grid unit, constructing a multi-objective optimization model covering economic, ecological and social benefits by taking the area or quantity of each land type as a decision variable, and calculating the development risk of each grid unit in combination with the initial land utilization state and developable spatial data. The FLUS model is adopted to carry out simulation expansion on the urban development boundary, and the planning scale and the land use growth trend are responded in real time by dynamically adjusting the conversion rule in the simulation process. According to the method, the data integration accuracy and the model response flexibility are remarkably improved, the scientificity and the intelligent level of planning decision making are enhanced, and powerful technical support is provided for regional sustainable development.

The invention discloses an offshore pile foundation evaluation system construction method based on evolutionary algorithm optimization and PIML linkage. A standardized engineering data set is obtained; parameterizing the pile-soil coupling dynamics boundary condition, the pile-soil interaction specification criterion and the limit state and use state criterion, and storing the parameters as a physical constraint set; obtaining an initial performance evaluation model; generating an alpha evolutionary optimization performance evaluation model; outputting a bearing capacity-settlement relation index, a dynamic and static stiffness degradation curve index and a residual bearing capacity probability distribution index to form a structural performance index set; generating a credible structure performance index set containing confidence boundaries and extracting an influence degree sorting result; and automatically generating a structured evaluation report according to a verification result. The model output has physical consistency and engineering interpretation all the time, and the problem that a traditional pure data driving model is unreliable in result under data sparsity and environment sudden change is remarkably solved.

The invention discloses a digital archive online management system based on big data. The digital archive online management system comprises a digital acquisition layer which is used for carrying out semantic perception and structured extraction on heterogeneous archives; and the intelligent classification layer is used for mapping the collected archive entities, attributes and relationships to a dynamically expanded domain knowledge graph based on a knowledge graph construction technology and a graph neural network, realizing association mining and dynamic classification of cross-modal archives through node embedding and link prediction, adapting to evolution requirements of archive themes in combination with a weak supervised learning mechanism, and realizing dynamic classification of the cross-modal archives. A classification system with causal interpretation is formed; the storage retrieval layer is used for encoding the generation time, the space trajectory and the event causal chain of the archive into space-time causal metadata; the archive utilization layer is used for actively pushing associated archives and generating a personalized analysis report by analyzing user behavior preferences and scene requirements; and the backup layer is used for dynamically sensing the threat type and calling an evolutionary algorithm to adjust the backup frequency, the storage position and the recovery path.

The invention discloses a hydrogen energy system safety assessment method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing the dynamic operation parameters of a hydrogen energy system, and generating an input feature matrix; s2, inputting the feature matrix into a multi-resolution feature sensing module to generate a high-dimensional feature map; s3, inputting the high-dimensional feature map into a dynamic time bending network to generate a parameter incidence matrix; s4, optimizing feature weight distribution based on an adaptive differential evolution algorithm; s5, abnormal distribution is captured through the dynamic graph convolutional network, and a dynamic abnormal graph is generated; s6, simulating an anomaly propagation path, and generating a cascade anomaly analysis result in combination with a dynamic traceability mechanism; s7, a cascade anomaly analysis result is optimized through a grey wolf-firefly hybrid optimization algorithm; and S8, dynamic operation parameters are adjusted based on a final cascade risk prediction result, and hydrogen energy system safety assessment is completed. According to the method, deep learning, a hybrid optimization algorithm and the like are combined, and safety evaluation of abnormal detection and risk prediction of the hydrogen energy system is realized.

A system and method for field service assistance for telecommunications operations are described, which utilize a data acquisition module configured to receive multimodal data inputs including structured and unstructured data from field operations. A preprocessing module normalizes the multimodal data inputs to generate pre-processed data. A vectorization module transforms the pre-processed data into numerical vector representations using domain-specific embedding models trained on telecom equipment data, implementing convolutional neural networks for image feature extraction and transformer-based encoders for text vectorization. A contextual retrieval module retrieves contextually relevant historical data from a vector database by computing similarity metrics between current job vectors and stored job completion vectors. A response generation module processes the numerical vector representations and retrieved contextual data using an evolutionary algorithm engine to generate structured job summaries and real-time field recommendations.

The invention discloses a digital modeling steel structure multi-dimensional collaborative optimization design method and system, belongs to the technical field of steel structure design, and aims to solve the problems that multi-dimensional comprehensive consideration and automatic optimization tools are lacked, and complex multi-objective optimization requirements are difficult to quickly respond. Comprising the steps of establishing a full-parameterized digital model containing construction process parameters and environmental protection indexes, constructing a multi-objective evaluation system of structural safety, economical efficiency, construction feasibility and environmental influence, performing automatic optimization by adopting an improved multi-objective evolutionary algorithm, selecting 10 groups of candidate schemes, generating a visual interface, supporting human-computer interaction decision, and generating an optimization report. According to the method, multi-dimensional collaborative optimization is realized by constructing a multi-target evaluation system of structural safety, economy, construction feasibility and environmental influence, construction process parameters and environmental protection indexes are integrated through the digital modeling unit, it is ensured that factors of all parties are comprehensively considered in design decision, and the design efficiency is remarkably improved.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention provides a rescue scheduling method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: building a multi-target optimization model based on a target function for minimizing travel time and resource cost, and constraint conditions of the target function; solving the multi-objective optimization model by adopting an evolutionary algorithm, determining an optimal rescue point corresponding to each accident point, and determining an optimal planning path with the shortest travel time from the optimal rescue point to the accident point; and based on the optimal rescue point and the optimal planning path corresponding to each accident point, determining a rescue scheduling strategy and carrying out rescue. According to the method, the optimal rescue point can be quickly and scientifically selected under the conditions of limited rescue resources and simultaneous occurrence of a plurality of accidents, and the optimal path can be planned while the optimal rescue point is selected, so that the overall rescue efficiency and the rescue effect are improved, and efficient and scientific rescue scheduling is realized.