543 results about "Simulating annealing" patented technology

The invention discloses a multi-target task and resource intelligent modeling method, particularly relates to the field of complex adversarial simulation, is used for solving the problems of dynamic constraint optimization and robustness improvement in multi-dimensional task planning, and aims at realizing multi-dimensional coupling of space-time resource parameters by constructing a three-dimensional hypergraph model, mining a parameter association rule by means of tensor decomposition, and realizing multi-dimensional optimization of the space-time resource parameters. Dynamic constraint quantization is supported, multi-dimensional index priority evaluation is fused in a constraint layered injection stage, hard constraints are recognized, a solution domain is compressed, a hybrid optimization strategy regulates and controls balance between global exploration and local optimization, and after annealing is simulated to jump out of a local extreme value, a multi-target particle swarm algorithm is used for screening a space-time resource equilibrium solution in a trimming solution domain. Digital twinborn verification promotes physical and virtual space interaction data closed loop, a parameter correlation degree matrix is corrected, scheme robustness is enhanced, efficient generation and adaptive optimization of a task planning scheme under complex constraints are realized, and system stability and multi-target cooperation capability under sudden disturbance are improved.

The present invention relates to the technical field of coal mine water inrush disaster prevention and control, and relates to an artificial intelligence-based method for identifying a water inrush point in a mine and parameters of a simulation model, comprising the following steps: step 1: establishing a numerical model on the basis of observation data, and determining prior information of parameters to be identified including position coordinates of a water inrush point; step 2: on the basis of the numerical model and the prior information of the parameters, generating a training sample dataset and a test sample dataset for an alternative model; step 3: constructing and training an alternative model neural network; step 4: testing the accuracy of the alternative model; and step 5: executing a simulated annealing algorithm to identify the position of the water inrush point and parameters of a simulation model. By using the method provided by the present invention, the problem in accurate positioning of water inrush points in underground coal mines can be solved, which is of great significance in carrying out scientific disaster management and rescue efforts in a timely manner.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

According to the tray cycle scheduling system and the application method, historical data and a production plan are fused, an improved algorithm is adopted to predict tray requirements, and an attention mechanism is introduced to improve precision; the method comprises the following steps: acquiring multi-dimensional state information of a tray through a multi-modal sensor, eliminating noise by using a data fusion algorithm, and constructing a digital twin model to realize state synchronization; a double-layer optimization architecture is constructed, an upper layer solves a global scheme by combining an improved particle swarm and a simulated annealing algorithm, and a lower layer dynamically adjusts a path through reinforcement learning; an instruction is generated based on a digital twin model, an event triggering mechanism is adopted to reduce communication load, and virtual-real interaction closed-loop control is realized; a real-time evaluation index system is established, a meta-learning algorithm is utilized to quickly adapt to a new environment, and system parameters are continuously optimized. Multi-module collaborative innovation is achieved, the tray scheduling efficiency and the intelligent level are remarkably improved, production logistics whole-process collaborative optimization is achieved, and core support is provided for cost reduction and efficiency improvement of enterprises.

The present disclosure provides an artificial intelligence-based method for enhancing mine safety by identifying and predicting locations of water inrush points in a mine, including the following steps: S1: constructing a numerical model to determine priori information of parameters to be recognized based on observation data, including coordinates of locations of water inrush points; S2: generating a training sample dataset and a test sample dataset of an alternative model based on the numerical model and the priori information of the parameters; S3: constructing and training a neural network of the alternative model; S4: testing an accuracy of the alternative model; and S5: performing a simulated annealing algorithm to identify the locations of water inrush points and simulation model parameters.

The invention discloses a task scheduling method and system for a dense warehousing system, and the method comprises the steps: building an elevator task distribution model, a shuttle vehicle selection model and a warehouse-in and warehouse-out task time mathematical model according to the characteristics of warehousing operation, and preliminarily determining a task distribution scheme of an elevator through employing a heuristic rule on this basis, an improved A * algorithm is used for carrying out path planning on the shuttle vehicle, the time needed by the shuttle vehicle for completing a task is calculated, it is ensured that the elevator and the shuttle vehicle start to run, the scheme is further optimized through an improved PSO-SA algorithm, and the algorithm combines particle swarm optimization and a simulated annealing algorithm, so that the algorithm is optimized. A specific scheduling result of cooperative task execution of the elevator and the shuttle vehicle can be obtained, a scheduling scheme which enables the cooperative work effect of the whole system to be optimal is finally obtained, and maximization of the work efficiency is ensured.

The invention discloses a dynamic short-term and temporary intelligent forecasting system for mine time-delay rockburst, and belongs to the field of short-term and temporary forecasting of rockburst disasters. The system comprises a deep well three-dimensional micro-seismic field global sensing module, a micro-seismic characteristic parameter extraction module, a rockburst catastrophe evolution data set construction module, a data preprocessing module, a deep learning prediction module, an interpretability analysis module and a dynamic short-term and temporary intelligent forecasting module. A dynamic tracking type micro-seismic sensing network is constructed through a three-level annular nested layout strategy, micro-seismic event data are collected in real time, spatial-temporal characteristic parameters are extracted, a rockburst time sequence data set is constructed in combination with a sliding time window method, and the occurrence probability and intensity level of rockburst are predicted. The system adopts a grey wolf-simulated annealing hybrid algorithm to optimize model parameters, and improves the credibility of a prediction result through SHAP value analysis. The system can realize dynamic short-term and temporary early warning of rockburst disasters, and provides scientific decision support for mine safety management.

The invention discloses a multi-satellite task scheduling method based on a genetic algorithm, and belongs to the technical field of satellite task scheduling. Establishing a scheduling model based on satellite and ground station visible window data, and integrating a scheduling period constraint, a task uniqueness constraint, an equipment protection time constraint and a frequency band and orbit type matching constraint by taking task income maximization as a target; a simulated annealing local search mechanism is introduced into the genetic algorithm, local optimum is jumped out through a probability acceptance inferior solution, and a self-adaptive strategy of mutation probability is dynamically adjusted according to population fitness change; setting an early stop mechanism to terminate iteration in advance when the fitness is continuously not improved; and finally generating a scheduling scheme. According to the method, the problems that a traditional genetic algorithm is prone to falling into local optimum and constraint processing is rigid are solved.

The invention discloses a production scheduling method, a production scheduling system and electronic equipment, and belongs to the technical field of production management and scheduling. The production scheduling method comprises the steps that according to production requirements and a preset production correlation model, a fitness function and an initial production scheduling scheme set are determined, and the fitness function comprises at least two of the following production scheduling strategies: total delay time minimization, resource utilization rate maximization, production line load balancing and intermediate product minimization; at least two preset algorithms are fused, the initial production scheduling scheme set is optimized based on the fitness function, the optimal production scheduling scheme is obtained, and the preset algorithms comprise any one of a genetic algorithm, a simulated annealing algorithm and a tabu search algorithm. The method can solve the problems of insufficient processing of resource constraints, low production scheduling efficiency and incapability of effectively finding the optimal production scheduling scheme in related technologies.

The invention relates to the technical field of aircraft optimization, and discloses an expressway low-altitude aircraft optimization arrangement method, which comprises the following steps: constructing an evaluation index system, determining the comprehensive weight of each evaluation index by using an analytic hierarchy process and an entropy weight method, and identifying the importance of the evaluation indexes; constructing a low-altitude aircraft optimization arrangement model, and solving by utilizing a simulated annealing algorithm to obtain static optimal quantity configuration of the low-altitude aircrafts of the service site; dynamically adjusting the static optimal number configuration of the low-altitude aircrafts of the service site by using a social emotion resonance avoidance algorithm; and according to the final number configuration of the low-altitude aircrafts of the service site, combining the social emotion data to optimize the flight path of the low-altitude aircrafts. According to the method, various complex factors are comprehensively considered, efficient arrangement and path optimization of the low-altitude aircraft are achieved, investment of the low-altitude aircraft is more global and economical, and important technical support is provided for application of the low-altitude aircraft industry.

The invention provides an electrocardiosignal reconstruction method based on hybrid optimization and multi-modal feature fusion, and the method comprises the steps: obtaining a 12-lead electrocardiosignal, processing the 12-lead electrocardiosignal through a linear regression model, a genetic algorithm and a simulated annealing algorithm in sequence to obtain an optimal three-lead electrocardiosignal, and reconstructing the optimal three-lead electrocardiosignal according to the optimal three-lead electrocardiosignal. The optimal three-lead electrocardiosignal is subjected to one-dimensional convolution layer and maximum pooling processing in sequence to obtain time domain features, the time domain features, the frequency domain features and expert features are subjected to multi-modal feature fusion to obtain fusion features, and the fusion features are input into a Transform mechanism to be processed to obtain a reconstructed electrocardiosignal. The method has remarkable effects in long-range time sequence modeling and local waveform detail optimization. The traditional CNN / RNN is limited by the problem of local receptive field or gradient disappearance, the global rhythm and the local form are difficult to balance, the QRS is adopted to perceive a Transform architecture, and a multi-head self-attention and nonlinear feed-forward network is combined, so that the width error of a QRS wave group is smaller.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention relates to an ecological system carbon sink evaluation method, which comprises the steps of generating a continuous climate distribution grid based on a global ecological system type and climate element data, extracting multi-dimensional characteristic parameters of temperature, rainfall and a carbon sink value through an improved weighted K-means clustering algorithm, quantifying climate transition zone boundary fuzziness in combination with a covariance matrix, and evaluating the ecological system carbon sink evaluation result. Dynamically adjusting the weight of the spatial variation coefficient; a simulated annealing algorithm is utilized to optimize correlation between the division scale and the carbon sink response, and a multi-scale regression model is constructed to generate a high-precision carbon sink distribution diagram; further fusing the remote sensing vegetation index and the ground actual measurement data, and eliminating the evaluation error caused by the conflict between the boundary fuzziness and heterogeneity of the transition zone; according to the method, the problems of insufficient climate-ecology interaction dynamic response modeling and low boundary division precision in the prior art can be solved, the reliability of carbon sink evaluation in complex areas such as forest-grassland interlaced areas and coastal wetlands is remarkably improved, and scientific data support is provided for ecological restoration project site selection and carbon trading markets.

The invention discloses a system and a method for multi-parameter cooperative control in PVC steel wire hose production, and relates to the technical field of plastic hose production, the system comprises a parameter monitoring module, a central processing module, a parameter adjusting module and a quality detection module, the parameter monitoring module is a high-precision intelligent sensor and has a self-adaptive filtering function, and key parameters are measured and transmitted to the central processing module; the central processing module uses an improved multi-objective optimization algorithm to calculate an optimal cooperation value, a simulated annealing-genetic hybrid algorithm is used for iteration, the parameter adjustment module uses intelligent PID, adaptive variable structure control and model prediction control adjustment equipment, and the quality detection module uses multi-modal fusion deep learning and high-precision physical performance detection equipment. And if not, recalculating the adjustment parameters. The production quality and efficiency of the PVC steel wire hose are improved, the cost is reduced, parameters are precisely and cooperatively controlled, the hose is stable in physical performance and few in surface defect, the raw material and equipment maintenance and energy consumption cost is reduced, and the enterprise revenue and market competitiveness are improved.

The invention relates to the technical field of artificial intelligence, in particular to an injection molding process parameter optimization method and system based on hybrid algorithm and model fusion, and the method comprises the steps: optimizing a parameter combination of a support vector regression model through a simulated annealing algorithm, building a weighted fusion model based on the optimized support vector regression model and a random forest, and optimizing the model; constructing a hybrid model of an adaptive selection weighted fusion model and an optimized support vector regression model; constructing a three-objective optimization model including buckling deformation, volume shrinkage and production energy consumption, and searching a Pareto optimal solution set in a process parameter space by adopting a multi-objective genetic algorithm by taking the hybrid model as a target value evaluation tool; carrying out local correction on the key process parameters by adopting a gradient descent method until the deviation falls back to be within a preset threshold value, and obtaining optimized process parameters; the defect rate of products can be reduced, and meanwhile production energy consumption is reduced.

The invention discloses a national secret algorithm data encryption transmission method and system. The method comprises the steps of obtaining to-be-encrypted data and network parameters, establishing a Bayesian network probability ablation model, performing Monte Carlo sampling ablation national secret encryption and evaluating attack risks, and generating a probability security encryption strategy; and extracting a Brinell feature set, constructing a long and short-term memory network time prediction model, and optimizing by using a simulated annealing algorithm to obtain an optimal encryption parameter configuration sequence. Generating a key pair according to the sequence and SM2, establishing a shared key by means of an elliptic curve Diffie-Hellman protocol, deriving an SM4 session key through SM3, and establishing a hybrid encryption key system; constructing a teacher and student network model, optimizing multi-thread scheduling through adversarial distillation training and a retrieval enhancement technology, and generating a multi-thread parallel encryption architecture; and network parameters are monitored in real time, a reinforcement learning adaptive decision engine is constructed, a strategy is dynamically adjusted, and adaptive encryption transmission is completed. According to the invention, the optimal balance between the security and the efficiency in the data encryption transmission process is realized.

The invention discloses an indoor layout automatic generation method based on a deep neural network and simulated annealing optimization, and the method comprises the three main steps: firstly, generating an indoor scene initial layout through employing a variational auto-encoder VAE; secondly, performing multi-constraint optimization on the initial layout by adopting a deep learning enhanced simulated annealing algorithm (DL-SA); and finally, converting the optimized layout into a three-dimensional indoor scene. According to the method, the deep neural network model and the simulated annealing optimization method are combined, efficient and attractive indoor layout generation is achieved, and the method is widely applied to the fields of smart home, indoor design and the like.

The invention provides a non-equidistant lattice distribution optimization method based on local dose constraint. The method comprises the following steps: generating an initial lattice based on a patient image and a delineated target region; calculating and storing a dose kernel in advance based on a Monte Carlo algorithm; calling a dose core to carry out rapid dose optimization and calculation; establishing an independent peripheral subspace dose evaluation region and a local dose constraint condition for each lattice target region; establishing a multi-objective function for lattice target region position optimization; dynamically adjusting lattice positions and intervals by using a rapid simulated annealing method or a gradient descent method until target region position distribution and dose distribution meeting a dose multi-objective function and constraint conditions are generated; according to the invention, through dynamic adjustment of the lattice spacing and position and combination of the patient specific anatomical structure, accurate control of the valley dose around each lattice is realized, so that the problems of non-uniform target dose distribution and insufficient valley dose control in traditional equal spacing lattice radiotherapy are solved.

The invention relates to a path planning method fusing D * Lite, simulated annealing and a genetic algorithm. The path planning method comprises the following steps: S1, acquiring map environment information by using an RGB-D camera; s2, modeling is carried out on map environment information; s3, initializing parameters of the D * Lite and a genetic algorithm; s4, performing genetic algorithm population initialization fused with a D * Lite algorithm; s5, the fitness value of the initialized population is calculated, and path redundant point optimization is carried out; s6, applying the generated population to a selection operator of a fusion simulated annealing algorithm, and introducing an elitism strategy; s7, applying the population subjected to operator selection to adaptive crossover and mutation operators; s8, the fitness values of all individuals of the population are calculated again, and path redundant points are optimized again; and S9, judging whether the path meets an optimal condition or not, and outputting an optimal solution after iteration. The method not only has strong global search capability in a complex environment, but also improves local search capability, has good universality and robustness, and meets actual requirements.

The invention discloses an autonomous inspection system of an offshore wind plant operation and maintenance unmanned aerial vehicle, and relates to the technical field of inspection operation and maintenance. An autonomous inspection system of an offshore wind plant operation and maintenance unmanned aerial vehicle comprises an autonomous inspection preparation module and an autonomous inspection adjustment module. According to the method, historical operation and maintenance data and meteorological risk factors are fused, the routing inspection priority of each wind turbine generator set is scientifically evaluated, optimal path planning is realized through a digital twinning and simulated annealing algorithm, and the path rationality and routing inspection efficiency are improved; by means of a knowledge graph and a hypergraph neural network, a path risk avoiding strategy can be optimized in real time, and safe execution of an inspection task under severe sea conditions is guaranteed; task allocation is dynamically adjusted according to the unmanned aerial vehicle cluster state, task merging and path sharing are achieved, loads are effectively balanced, energy consumption is reduced, repeated flight is avoided, and the overall cooperation efficiency is improved.

The invention discloses an unmanned aerial vehicle cluster path planning and communication resource allocation joint optimization method for logistics distribution. The method comprises the following steps: constructing scene models of unmanned aerial vehicle cluster logistics distribution, wherein the scene models comprise an unmanned aerial vehicle cluster distribution model and an unmanned aerial vehicle cluster communication model; ground customers, namely target points, in the unmanned aerial vehicle cluster distribution model are subjected to clustering analysis, the target points are divided into Kclus clusters, so that the target points in the same cluster are relatively concentrated in the geographic position, meanwhile, it is ensured that the number distribution of the target points contained in each cluster is balanced through density constraint, and optimization of an initial logistics distribution task is achieved; performing unmanned aerial vehicle cluster path planning by using a simulated annealing algorithm to obtain an optimal flight path of the unmanned aerial vehicle cluster; and based on the position of the unmanned aerial vehicle in each time slot t in the future determined by the optimal flight path of the unmanned aerial vehicle cluster, performing distributed communication resource allocation on the unmanned aerial vehicle cluster in the dynamic flight process by using a competitive architecture depth Q network.

The invention relates to the technical field of emergency rescue resource scheduling, in particular to an annealing algorithm improved genetic algorithm-based resource scheduling scoring method, which comprises the following steps of: S1, pre-constructing a resource information database; s2, obtaining a final resource set meeting the emergency demand of the current user; s3, constructing a target function, generating a plurality of initial populations of the resource scheduling scheme, and performing global optimization calculation on the initial populations in combination with a genetic algorithm and a simulated annealing algorithm; and S4, obtaining an optimal resource scheduling scheme based on a global optimization calculation result. Through modular design and genetic algorithm optimization, high efficiency and applicability of resource scheduling are guaranteed, and the emergency rescue response speed and the resource utilization efficiency are remarkably improved. The genetic algorithm is combined with the simulated annealing algorithm, the resource scheduling scheme is quickly generated, the emergency response time is shortened, and the scheduling efficiency is improved; according to the genetic algorithm, advantages and disadvantages of a scheduling scheme are clearly measured through a fitness function, and quantitative indexes are provided to guide scheduling decisions.

The invention provides a demand response bus intelligent scheduling method and system driven by real-time data. A demand response bus intelligent scheduling method driven by real-time data comprises the following steps: S1, collecting and fusing multi-source data in real time, and constructing a dynamic data set; s2, carrying out short-time demand prediction and clustering analysis based on LSTM and DBSCAN; s3, dynamic service area division and greedy algorithm line generation; s4, elastic vehicle scheduling based on combination of mixed integer programming and a simulated annealing algorithm; s5, dynamically adjusting the real-time ticket price driven by the supply and demand game model; s6, passenger personalized service matching is achieved through a collaborative filtering algorithm; s7, performing multi-target simulation verification on the digital twin platform; and S8, reinforcing a learning-driven dynamic feedback optimization mechanism. According to the real-time data-driven demand response bus intelligent scheduling method and system provided by the invention, the operation efficiency, the passenger satisfaction degree and the enterprise benefit of the demand response type customized bus can be remarkably improved, and the method and the system are suitable for construction of an urban intelligent bus system and have remarkable advantages.

The invention relates to the field of thermal fault early warning of machine room inspection robots, in particular to a multi-modal sensing thermal fault early warning method of a machine room inspection robot, which comprises the following steps: acquiring infrared thermal image, acoustics, vibration and gas concentration data through a multi-modal sensor array, and generating voxelization temperature distribution; according to the method, temperature features are extracted through wavelet transform and a fractional Brownian motion algorithm and are fused into an equipment global temperature feature set; constructing noise, vibration and gas concentration tower extraction auxiliary features, and combining non-extensive entropy and a graph attention model to be fused into a high-dimensional fault feature pool; building a fiber bundle model by taking the machine room topology as a base space, and locating a thermal fault after optimizing parameters by a simulated annealing algorithm; and finally, a dynamic thermodynamic diagram and the like are generated through digital twinning, so that accurate early warning is realized.

The invention discloses a parameter optimization method for a rotary inertia double-tuned mass damper and a vibration reduction system. The parameter optimization method comprises the steps that S1, a main structure motion equation comprising the rotary inertia double-tuned mass damper is constructed; s2, selecting combined variables from a design variable space of the rotary inertia double-tuned mass damper, adopting a genetic simulated annealing algorithm for each combined variable, and optimizing parameters by taking minimization of the maximum acceleration frequency response of a main structure as an objective to obtain an optimal parameter data set; s3, cleaning an optimal parameter data set based on an IQR criterion; s4, constructing a correction factor, and establishing a parameter correction factor nonlinear mapping model through tensor basis function expansion; and S5, multiplying the theoretical closed solution by the parameter correction factor to obtain design parameters of the rotary inertia double-tuned mass damper. Compared with an analytical formula design, the RIDTMD designed by using the corrected formula has the advantages that the vibration reduction efficiency is improved by about 10% at most, and the asymmetric three-peak phenomenon of a core frequency band can be effectively eliminated.

The invention relates to an electric vehicle logistics distribution path planning method based on an improved genetic algorithm, and the method comprises the steps: obtaining an initialized population through employing a natural number coding mode; an improved genetic algorithm is constructed on the basis of the genetic algorithm to serve as an improved genetic algorithm for electric vehicle logistics distribution path planning; judging the genetic algorithm by using the maximum number of iterations to obtain an optimal solution of the genetic algorithm; and sending the target data set into the constructed genetic algorithm to complete identification of the logistics distribution path of the electric vehicle. A genetic algorithm is improved, an initialized population with better quality is obtained through a greedy strategy, meanwhile, a large-scale neighborhood search algorithm fused with simulated annealing is introduced to improve the search precision of the algorithm, and an electric vehicle path planning model is solved by using the improved genetic algorithm, so that the total distribution cost is minimum; and the effect of better processing the logistics distribution path planning of the electric vehicle under the multi-target and multi-constraint conditions is achieved.

The invention provides a rotor-bearing system fault diagnosis method and system based on virtual-real fusion, and relates to the technical field of signal detection, and the method comprises the steps: deducing a mixed eccentric unbalanced magnetic pull expression, and building a rotor-bearing system twinborn model in combination with a Hertz contact theory; introducing rotor eccentricity and bearing inner / outer ring faults, and constructing a multi-fault working condition twinborn model; performing multi-parameter identification on the model through an improved genetic algorithm (introducing a feature sensitivity evaluation factor, a self-adaptive crossover mutation probability and simulated annealing) to obtain a corrected twin model; generating a twin fault sample based on the correction model, and training by using a one-dimensional cyclic generative adversarial network (designing a comprehensive loss function) with a classifier to generate a sample close to real distribution; and virtual and real fusion samples form a balanced data set, and fault classification is realized through one-dimensional convolutional neural network training. According to the method, the problems of fault data shortage, large difference between a twin model and real data and the like are solved, and the fault diagnosis precision and reliability are improved.

The invention discloses a distribution network engineering mechanization construction quota optimization method and system based on artificial intelligence, and relates to the technical field of quota optimization, and the method comprises the following steps: constructing a multi-target construction collaborative optimization model, solving and generating a quota detection optimization scheme, the solution is to obtain an initial solution of a simulated annealing algorithm based on a genetic algorithm and optimize and screen; obtaining quota detection data based on a quota detection optimization scheme to construct a quota prediction model, and performing quota anomaly judgment based on a dynamic threshold value which is obtained based on a variational auto-encoder; the root cause features of the abnormal result are recognized, the contribution degree of the root cause features is analyzed in combination with an SHAP method, and the root cause features are recognized through a causal reasoning model; according to the invention, through dynamic detection and intelligent optimization of the mechanical construction quota, the problems of low fixed threshold determination sensitivity and incapability of adapting to construction dynamic change in quota detection, and weak abnormal traceability and lack of interpretable support in the quota optimization process in the prior art are solved.

The invention discloses a hardware-in-the-loop test method and device and electronic equipment, relates to the technical field of automotive electronics, and adopts a simulated annealing algorithm in an optimization stage, adjusts parameters according to characteristics of an electronic stability control system, optimizes data distribution, and improves test data quality and fault detection capability. In the verification stage, the generated test data is subjected to actual operation test, the data problem is fed back by comparing actual output with an expected result, and the optimized test data which does not pass verification is sent to the target hardware-in-loop test model for optimization adjustment, so that a closed-loop optimization verification process is formed. The iterative closed-loop process comprehensively covers all possible working conditions and fault modes, key test points are prevented from being omitted, the reliability of the electronic stability control system is enhanced, the accuracy, effectiveness, comprehensiveness and sufficiency of hardware-in-the-loop test of the electronic stability control system are ensured, and the reliability of the electronic stability control system is improved. And the limitation of hardware-in-the-loop test of the electronic stability control system is reduced.

The invention discloses a workshop dynamic scheduling method considering equipment occupation under emergency order insertion, which comprises the following steps: receiving a trigger event in a production system, and obtaining workshop production real-time state data according to the trigger event; according to the real-time state data, a dynamic scheduling decision engine is activated, and a global scheduling problem is decomposed into deterministic sub-problems at the current decision moment; according to the deterministic sub-problem, identifying the state of a work-in-process and generating an unfinished process set, and re-bringing the unfinished process set into a rearrangement resource pool; selecting a rearrangement strategy based on a preset influence threshold according to the unfinished process set; according to the rearrangement strategy, a planning model is constructed, and the planning model aims at minimizing the maximum completion time and minimizing the order insertion change degree; according to the planning model, an improved genetic simulated annealing algorithm is adopted for solving, and a dynamic scheduling scheme is output and issued to a production execution layer. According to the invention, the disturbance of order insertion on the original production system can be minimized.