662 results about "Simulated annealing" patented technology

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention provides an urban functional area gridding identification and classification method based on multi-source spatio-temporal data, and relates to the technical field of area identification, and the method comprises the steps: carrying out the dynamic grid division of an urban area through employing a self-adaptive grid division algorithm, obtaining the multi-source spatio-temporal data of a gridding area, carrying out the space-time alignment, and generating a multi-dimensional time sequence feature set; extracting multi-dimensional feature vectors of the grid units through a multi-modal feature fusion algorithm, acquiring long-range time sequence features by using a bidirectional gating recursion unit, calculating spatial correlation features based on a multi-head attention mechanism, and inputting the two types of features into a feature fusion network with jump connection to generate enhanced spatial-temporal features; and an optimization strategy is adopted, a functional area clustering center point is determined through density peak value clustering in sequence to obtain an initial division result, the initial result is optimized by combining spectral clustering of spatial adjacency constraints, and the boundary of the functional area is optimized by using a simulated annealing algorithm, so that accurate identification and classification of the urban functional area are realized.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention discloses an intelligent construction method based on data driving, and relates to the field of intelligent construction, and the method comprises the steps: collecting multi-source heterogeneous data of an intelligent construction project; performing feature selection on the acquired multi-source heterogeneous data by adopting a recursive feature elimination (RFE) algorithm to obtain a feature subset reflecting project progress, quality and safety; according to the feature subset, a multi-target model used for predicting project progress, quality and safety is constructed through a support vector machine (SVM) algorithm; a non-dominated sorting genetic algorithm NSGA-II is adopted to solve the multi-target model, a simulated annealing strategy is introduced in the iterative search process of the NSGA-II to jump out of a local optimal solution, and an optimized Pareto optimal solution set is output; and selecting an optimal parameter combination according to the Pareto optimal solution set, and adjusting the BIM model of the current intelligent construction project by using the optimal parameter combination. In view of low multi-objective optimization efficiency in intelligent construction project management in the prior art, the optimal model parameters considering different management objectives are obtained, and the efficiency is improved.

The invention relates to the technical field of intelligent laser processing path planning, and discloses a laser flight processing path planning method and device for a complex curved surface, and the method comprises the steps: obtaining the geometric continuity features of the curved surface, carrying out the region division and boundary feature extraction, obtaining the geometric features of the boundary points of each region, and obtaining the geometric features of the boundary points of each region; performing track generation based on boundary geometric features to obtain an initial processing track path, then performing heat accumulation analysis and track screening, identifying a track section needing to be adjusted, executing multi-target path scheduling optimization, and obtaining adjusted track execution feature data; processing sequence optimization is carried out based on the feature data, and an optimized processing sequence is obtained; performing thermal input analysis and parameter correction according to the processing sequence to obtain an updated processing parameter set; and finally, performing global coordination based on a simulated annealing algorithm, generating a final processing track set, completing track optimization and instruction set generation, and outputting a complete processing scheme. According to the method, the problem of insufficient track fitting degree can be solved.

The invention provides an optical imaging satellite task scheduling method based on a hybrid heuristic memetic evolution algorithm, solves the problem of how to accurately and efficiently obtain satellite images, belongs to the engineering field, and is based on a constructed satellite data set, a task data set, a satellite dynamics model, a task decision model and constraint conditions, takes maximizing the satellite task completion benefit value as an objective function, and constructs a satellite scheduling decision model; constructs a hybrid heuristic memetic evolution algorithm: obtains an initial observation path solution set through clustering processing and a constraint check model; inputs the initial solution into the satellite scheduling decision model to obtain a total benefit value of the initial solution; adopts a neighborhood search algorithm to obtain a feasible solution and a total benefit value of the initial solution, and obtains the quality of the feasible solution from the total benefit value difference; combines a simulated annealing algorithm and an ant colony evolution algorithm to search, and obtains an optimal solution for optical imaging satellite task scheduling after a preset stop condition is met; the invention realizes accurate and efficient acquisition of satellite images.

The invention discloses a ship shore-end intelligent management method, system and device and a storage medium, and the method comprises the steps: carrying out the ship position prediction based on an electronic chart, the current navigation state data of a ship, and the current environment data, and obtaining the information of a to-be-docked ship according to the future position information of the ship, constructing a port operation knowledge graph based on the information of the ships to arrive at the port, the equipment data of the ships approaching and leaving the port, the historical tug operation data and the port data, and predicting the tug workload demand based on the port operation knowledge graph; taking tug operation data as a state space, taking a tug scheduling strategy and a cargo stacking layout adjustment strategy as an action space, taking maximization of wharf operation efficiency as a reward target, and adopting a reinforcement learning method and a simulated annealing method to carry out wharf operation scheduling optimization to obtain an optimal wharf operation scheduling strategy. According to the method, the port working efficiency is improved, manual interference is not needed when the wharf operation scheduling strategy is obtained, and autonomous decision-making can be completely achieved.

The invention discloses a filling body multi-age strength prediction and material inversion model construction method, and belongs to the technical field of metal mine paste filling, and the method comprises the steps: carrying out the preprocessing operation according to the characteristic variables of a material ratio parameter and a tailing characteristic parameter, and obtaining a characteristic variable data set; performing data division on the characteristic variable data set according to a preset proportion in a preset multi-age period; constructing a multi-age strength prediction model, and training the model by using the training set to obtain an unconfined compressive strength prediction result in a preset multi-age; verifying the constructed multi-age strength prediction model by using a test set by adopting a Shapley addition explanation method; and adding a simulated annealing algorithm to the verified multi-age strength prediction model to obtain a reverse optimization model. The method has higher generalization ability and universality, and the filling design period is greatly shortened by combining model prediction of the multi-age strength prediction model with the simulated annealing optimization algorithm.

The invention provides a charging pile energy consumption scheduling method based on a load prediction model, and the method comprises the steps: carrying out the data collection and preprocessing, obtaining multi-source data, such as charging, equipment, environment and electricity price, from a charging station management system, and cleaning abnormal values; and then using grey correlation analysis to determine weights of factors influencing energy consumption, constructing a grey prediction model, and combining preprocessing and weight distribution data to predict future charging pile load demands. And the control center receives a load prediction result and vehicle reservation information, preliminarily plans a charging distribution scheme according to the real-time state of the charging pile, and optimizes scheduling by taking the minimization of the total charging cost and consideration of the user satisfaction and the stability of the power system as targets through a simulated annealing algorithm. During operation, an actual state and an external environment are continuously monitored, a charging plan is rapidly adjusted when deviation occurs, finally, vehicle charging bills of all departments are generated according to an execution scheme, and system performance indexes are regularly evaluated to realize continuous optimization. The method can effectively reduce energy consumption cost and guarantee efficient and stable operation of the charging station.

The invention discloses a dynamic equipment management service system and method for deep learning. The method comprises the following steps: S1, collecting real-time operation data of equipment and preprocessing the real-time operation data; s2, constructing a long-short-term memory network model, and outputting comprehensive early warning indexes of the equipment; s3, constructing an optimization problem based on the comprehensive early warning index of the equipment, and setting an objective function and constraint conditions; s4, performing global search on the optimization problem by adopting an improved dragon fly algorithm, and generating a plurality of resource scheduling and maintenance candidate schemes; s5, performing local optimization on the candidate scheme by using a simulated annealing algorithm, and determining an optimal resource scheduling and maintenance decision scheme; and S6, feeding back the real-time prediction result and the optimal decision result to form closed-loop control. Through fusion of deep learning prediction, the improved dragon fly algorithm and the simulated annealing algorithm, real-time closed-loop control of accurate early warning of the equipment state and dynamic resource scheduling decision is realized, so that the failure rate is effectively reduced, and the equipment management efficiency is improved.

The invention discloses a power system unit commitment model solving method based on an improved simulated annealing algorithm, and the method comprises the steps: carrying out the optimization solving of a model objective function through employing the improved simulated annealing algorithm on the basis of a standard simulated annealing algorithm, and carrying out the optimization solving of a model objective function through adaptive temperature control and a multi-starting point strategy. And the global search capability and the convergence speed are improved when the unit commitment optimization problem of the power system is solved. Through the improvement of the invention, the algorithm can more effectively avoid falling into local optimum during solving, and the solving process is accelerated.

The invention discloses a dangerous goods transportation charging method based on risk assessment, and relates to the technical field of dangerous goods transportation charging, and the method comprises the following steps: collecting the data of traffic flow, accident occurrence frequency, environmental factors and the like of each road segment in a dangerous goods transportation network, and calculating a dangerous goods transportation charging result based on a population coverage risk model and an accident expected value calculation method; and evaluating the potential risk level of each road section. Through multi-dimensional data fusion and an advanced algorithm, real-time assessment and path optimization of transportation network risks are realized. A population coverage risk model and an accident expected value calculation method are adopted, high-risk road sections are accurately recognized, transportation tasks are reasonably distributed, and safety is improved. Meanwhile, the genetic algorithm is utilized to optimize the transportation path, balance the cost and risk, reduce the transportation cost and guarantee the safety. Global path distribution is optimized through a simulated annealing algorithm, excessive load of high-risk road sections is avoided, task distribution is dynamically adjusted according to risk prediction, reasonable resource allocation is ensured, and transportation efficiency and safety are improved.

The invention discloses a traffic shaping method and system based on deep learning and an antagonism strategy. The shaping method comprises the following steps: monitoring network traffic in real time through a traffic monitor, capturing information of traffic data, carrying out data preprocessing, carrying out traffic feature extraction through the preprocessed traffic data, and training a DNN classifier; jointly inputting the trained DNN classifier and traffic data into a patch generation module, preliminarily generating an antagonistic patch according to a dual simulated annealing algorithm added with a multi-objective optimization algorithm, and optimizing the antagonistic patch through a self-adaptive adjustment strategy and a bandwidth overhead adjustment strategy; and patch injection: injecting the optimized antagonistic patch into the original flow data through an injection controller, and shaping the original flow data. According to the invention, the problem that the confrontation capability and the privacy security protection capability of the Internet of Things equipment when facing equipment identification and traffic analysis attacks need to be enhanced is solved.

The invention discloses a positioning method and system based on an RSSI fingerprint database and a generalized regression neural network, and relates to the technical field of indoor positioning. The method comprises the following steps: acquiring RSSI values and corresponding position information of a plurality of sampling points in a target space, and constructing a fingerprint database based on the RSSI values and the position information; establishing a generalized regression neural network prediction model based on the fingerprint database, and globally optimizing model parameters of the generalized regression neural network prediction model by adopting a quantum particle swarm algorithm to obtain preliminary model parameters; performing local optimization on the preliminary model parameters by adopting a simulated annealing algorithm to obtain target model parameters, and training based on the target model parameters to obtain a target generalized regression neural network prediction model; and collecting a real-time RSSI value of a to-be-positioned target, inputting the real-time RSSI value into the target generalized regression neural network prediction model, and outputting a target position coordinate of the to-be-positioned target. By implementing the technical scheme provided by the invention, parameter optimization can be prevented from falling into a locally optimal solution, and the positioning precision is improved.

The invention provides a highway bridge intelligent maintenance decision-making system and decision-making method. The system comprises a data acquisition module, a processing and storage module, a bridge condition evaluation module, a maintenance decision making module, an implementation and management module, and a monitoring and feedback module. The data acquisition module collects various types of data of bridges, environments, traffic and the like through multiple means such as fiber bragg gratings and vibration sensors. And the processing and storage module cleans the data by using a DBSCAN algorithm, processes the classified data by one-hot coding, and constructs distributed database storage based on a block chain. And the evaluation module adopts an Isomap algorithm to evaluate technical conditions and predict diseases. And the decision module generates a scheme by using a particle swarm optimization-simulated annealing algorithm, and analyzes and optimizes by using an analytic hierarchy process-grey clustering. And maintenance implementation is managed by means of a BIM model. The monitoring module collects data in real time and feeds back an adjustment strategy. The system can acquire data in real time through multiple sensors, accurately process data, accurately evaluate diseases, optimize maintenance decisions, dynamically adjust strategies, guarantee bridge safety and prolong the service life.

The invention provides a reinforcement corrosion electrochemical parameter inversion method based on LSTM (Long Short Term Memory) time sequence prediction, which comprises the following steps: S1, acquiring electrochemical time sequence data in a reinforcement corrosion process through an electrochemical workstation to form an original reinforcement corrosion electrochemical time sequence data set; s2, preprocessing is carried out to obtain a training set, a verification set, a test set and normalization coefficients of all parameters; s3, constructing and training an LSTM time sequence prediction model; s4, constructing and calibrating a steel bar corrosion electrochemical parameter forward modeling model; and S5, constructing an inversion framework fusing a particle swarm optimization algorithm, a simulated annealing algorithm and an Adam optimization algorithm, forming closed-loop cooperation by the particle swarm optimization algorithm, the simulated annealing algorithm and the Adam optimization algorithm so as to minimize an error between a target electrochemical response parameter and a theoretical electrochemical response parameter, and outputting an inversion result. According to the method, through organic combination of time sequence prediction and multi-algorithm cooperation, the problems that a traditional inversion method is low in precision and poor in stability are solved, and a reliable technical means is provided for reinforced concrete structure health monitoring.

The invention relates to the field of temperature regulation and control, in particular to an intelligent regulation and control method and system for the temperature of a test box, and the method comprises the steps: calculating an optimal disturbance range in a simulated annealing algorithm, and obtaining the temperature in the test box in a next time period through the simulated annealing algorithm; the calculation method of the optimal disturbance range comprises the steps that a data set is obtained, the data set comprises multiple pieces of temperature data in the current time period, the dispersion degree of the data in the data set is calculated, the disturbance range optimization factor in the current time period is calculated, and the disturbance range optimization factor is in positive correlation with the dispersion degree. And taking a product of the disturbance range optimization factor and a preset disturbance range as an optimal disturbance range. The method has the effect of improving the calculation efficiency and the control precision of the simulated annealing algorithm.

The invention discloses an integrated energy system planning optimization method and computing equipment, and the method comprises the steps: obtaining various equipment parameters, load demand data and energy prices in an integrated energy system, taking the lowest annual total cost of the integrated energy system as a first target, and taking the highest integrated energy efficiency as a second target; the total annual cost comprises the annual investment cost, the energy purchase cost, the annual operation and maintenance cost, the stepped carbon transaction cost and the wind curtailment and light curtailment penalty cost, and the comprehensive energy efficiency is used for representing the ratio of the total energy supply amount to the total energy consumption amount of each type of equipment in the comprehensive energy system; and solving the planning optimization model by using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution, and determining the optimal capacity configuration of each type of equipment in the integrated energy system based on the optimal solution. According to the method, the optimal capacity configuration of each type of equipment in the integrated energy system can be reasonably planned on the premise of meeting the load requirement of the system.

The invention relates to the technical field of machine learning optimization, in particular to a machine learning optimization method based on a QUBO model and quantum annealing, and the method comprises the following steps: firstly, discretizing continuous parameters of the machine learning model into binary variables, and constructing a QUBO objective function; then optimizing the QUBO model by using a quantum annealing algorithm; and finally, solving a globally optimal solution by adjusting annealing parameters, wherein the annealing parameters comprise the initial temperature, the cooling coefficient and the number of iterations. The QUBO model can discretize continuous variables (including parameters such as weights and offsets) in models such as AR, SVM and CNN in machine learning into binary variables, so that a nonlinear relation is better processed, and the calculation complexity in the training process is reduced. Through the QUBO model, the regularization item can be better controlled, and the problem of overfitting is avoided. Meanwhile, the QUBO model can be solved in parallel through quantum calculation or a simulated annealing algorithm, and the calculation efficiency under large-scale data is remarkably improved.

The invention discloses a wing design optimization method based on agent-assisted multi-initial-point simulated annealing, and belongs to the technical field of optimization design. Comprising the steps of 1, initializing algorithm parameters and a training data set; 2, constructing an agent model; 3, executing multi-initial-point parallel simulated annealing, and generating a batch of candidate new solution sets; 4, executing a double-elite active learning strategy based on the new solution set, screening the most potential sample to carry out real evaluation, and updating the agent model; 5, cooling the temperature and reducing the step length; 6, judging whether the cumulative evaluation times of the expensive objective function reach the set maximum evaluation times or not; if not, returning to the step 2; if yes, optimization is stopped; and finally, traversing the training data set, and selecting a sample point with the minimum real objective function value as a global optimal solution. According to the method, a multi-initial-point parallel simulated annealing search mechanism and a double-elite active learning strategy are combined, and the global optimal solution is quickly approached under the limited simulation times.

The invention provides an aviation composite material hot-press forming workpiece loading method based on a hybrid optimization algorithm. The method comprises the steps that workpiece demand information and autoclave platform information in composite material hot-press forming manufacturing are obtained; minimizing the opening times of the autoclave as a target function; establishing a two-dimensional multi-layer platform hot press molding workpiece loading model, defining space constraints, distribution constraints, resource constraints and type constraints among workpieces, and incorporating the constraints into model constraint conditions; a GA-SP is adopted to optimize a workpiece loading sequence and a rotation state, an optimal layout is generated through chromosome coding, decoding, selection, intersection and mutation operations, a hybrid optimization algorithm is constructed in combination with a neighborhood search strategy, efficient arrangement of workpieces on different platforms is realized in combination with a simulated annealing criterion, and an optimized loading scheme is output. The production efficiency of the hot press molding procedure can be improved, the manufacturing cost is reduced, and efficient technical support is provided for intelligent manufacturing of aviation composite materials.

The invention provides an LLM reasoning-oriented heterogeneous core particle architecture simulation and search method and system, and the method comprises the steps: constructing a heterogeneous core particle joint simulation platform which integrates behavior-level modeling aiming at various core particle types, simulates the calculation and data transmission behaviors of a heterogeneous core particle architecture and power consumption area characteristics, forms a simulation architecture, and carries out the simulation of the heterogeneous core particle architecture; carrying out performance evaluation on the simulation architecture; an improved simulated annealing search strategy is adopted to explore the design space of the simulation architecture, and the strategy comprises the following steps: configuring core particles of different types or scales for core particle groups executing tasks of different layers based on calculation and memory access characteristics of different network layers of LLM by adopting packet heterogeneous search, optimizing the search process by adopting a simulated annealing algorithm integrated with Pareto frontier optimization; a hybrid parallel strategy of TP, PP, DP and EP and grouping heterogeneous search are subjected to collaborative optimization, and optimal parallelism combination and task mapping based on core particle grouping are automatically explored.

The invention provides a steel cutting and production scheduling method and device based on dynamic risk assessment, and relates to the field of steel processing pre-production management. Order, equipment and inventory data are collected in real time, and a multi-dimensional numerical matrix is constructed to achieve data structured expression; adopting a Monte Carlo method to generate a candidate production scheduling sequence, combining leftover material loss and delay risk dynamic evaluation, and fusing simulated annealing and an adaptive threshold mechanism to carry out iterative optimization; and through probability similarity screening and regression fitting, an optimal production scheduling scheme is determined in an error allowable range. The method effectively balances the production efficiency and the resource utilization rate, reduces the cutting loss and the delivery risk, and is suitable for the intelligent production scheduling decision of a complex manufacturing scene.

The invention discloses a multi-station robot photographing task allocation method for hub surface defect detection, and the method comprises the steps: forming a hub triangular patch model, and carrying out the Gaussian mapping of the hub triangular patch model; finding out the task area boundary of each station robot based on a simulated annealing algorithm, wherein the included angle between the normal vector of the triangular patch in the same task area and the Z axis is in a set range; generating a camera viewpoint of each station robot for completing task area shooting, and adjusting the viewpoint with collision into a collision-free viewpoint; planning a local optimal path passing through all collision-free camera viewpoints based on a greedy algorithm; and carrying out collision detection on the local optimal path, and re-planning the road section with collision. According to the multi-station wheel hub defect detection method, the task area corresponding to each station is determined according to the wheel hub curved surface features by adopting the simulated annealing algorithm, finally, automatic balanced distribution of the multi-station robot operation tasks is achieved, collision detection is conducted on the generated shooting path, and the reliability of multi-station wheel hub defect detection is improved.

The invention discloses a multi-objective optimized intelligent job scheduling system and a scheduling method thereof, and belongs to the technical field of supercomputers. According to the scheduling method, a Pareto solution set in multiple targets is searched through a simulated annealing algorithm, simulated annealing algorithm search and job scheduling are combined, and an optimal job execution sequence is sought. According to the method, the simulated annealing algorithm and the job scheduling scene demand are combined, and the optimal scheduling mode is searched, so that the job scheduling is more intelligent, and the scheduling efficiency is improved to a certain extent. In the super computer sharing the burst buffer architecture, the burst buffer requirement of the job can be effectively utilized, the backfilling limitation existing in traditional scheduling can be broken through, and the job scheduling efficiency and the system resource utilization rate are improved.

The invention discloses an equipment fault identification method and system and a storage medium, and the method comprises the steps: carrying out the dimension reduction processing of an original fault data set obtained for equipment operation data through a principal component analysis method, extracting key features through a feature weight initialization mode and a regularization term coefficient, obtaining a fault feature set after the dimension reduction, and carrying out the recognition of an equipment fault. The feature redundancy is reduced; for the determined rare fault mode, generating a synthetic sample by adopting an SMOTE oversampling technology, and obtaining a balanced fault data set through feature correlation analysis and data set distribution balancing, so as to improve feature sparsity; and a fault judgment result is obtained from the optimized simulated annealing algorithm, through characteristic interpretability analysis, if a rare fault mode exists in the judgment result, an early warning mechanism is triggered, and a targeted maintenance suggestion is generated according to a node fault recovery strategy. According to the method, rare faults can be effectively identified and processed, and the accuracy and efficiency of fault diagnosis are improved.

The invention discloses an intelligent cloud computing resource scheduling method and system based on a digital technology, and relates to the technical field of cloud computing, and the method comprises the steps: collecting and preprocessing multi-dimensional resource use data, constructing a resource demand prediction model to carry out resource scheduling prediction, extracting feature factors based on predicted resource demands, and carrying out resource scheduling prediction. And calculating a comprehensive priority score, designing a scheduling strategy, optimizing a resource scheduling strategy by adopting an adaptive strategy combining cat group optimization and ant colony algorithm and simulated annealing, and implementing resource scheduling. Through combination of cat group optimization, ant colony optimization and simulated annealing algorithms, efficient combination of global search capability and local optimization capability is realized in a scheduling process, an efficient adaptive task scheduling mechanism is formed, the problems that priority division is unreasonable, a scheduling path is easy to fall into local optimum and the like in a traditional method are avoided, and the scheduling efficiency is improved. Therefore, the scheduling efficiency and the global resource utilization are optimized.

The invention discloses a data security situation intelligent monitoring method and system based on a power service chain. The method comprises the following steps: S1, forming a power service chain data set; s2, forming a preprocessed power business chain data set; s3, extracting a key feature vector reflecting the running state and the abnormal feature of each link; s4, identifying and marking a power business chain data sample with potential abnormal behaviors; s5, using a power business chain data sample with potential abnormal behaviors as verification data, constructing an anomaly detection performance evaluation index, and performing global optimization on key hyper-parameters in the improved isolated forest model by using a simulated annealing algorithm; and S6, applying the optimized improved isolated forest model to the power service chain data flow acquired in real time, and performing anomaly detection on the newly acquired power service chain data. According to the method, the recognition sensitivity of the model to the key power abnormal signal can be remarkably improved, so that the path division of the abnormal behavior better fits the business risk reality.

The invention discloses a carbon neutralization target setting and tracking system based on big data and an artificial intelligence technology, and relates to the technical field of carbon neutralization. The system comprises a plurality of key modules, a data acquisition module widely collects multi-source data of energy, industry, traffic, building, weather and the like, a satellite remote sensing data acquisition technology is introduced, and the carbon emission condition is comprehensively reflected; the data preprocessing module adopts a transfer learning technology to improve the processing efficiency; the big data storage module is combined with a block chain technology to guarantee data credibility; a reinforcement learning mechanism is integrated into a target setting sub-module of the artificial intelligence analysis module, a knowledge graph technology is introduced into a tracking analysis sub-module, and a carbon neutralization target is accurately set and tracked; the visual display module provides immersive experience by applying VR and A technologies; and the decision support module gives comprehensive decision suggestions in combination with a simulated annealing algorithm and a multi-objective decision theory. The system can scientifically set targets, accurately track the progress and intelligently assist in decision making, and provides powerful technical support for carbon neutralization.

The invention provides an FPGA parallel Isin model optimization method and system based on a GHMC simulated annealing algorithm, relates to the field of power system combination optimization, and solves the technical problem that an existing FPGA-based Isin model solver adopts a traditional Monte Carlo method and is low in convergence precision and solution quality. The method comprises the following steps: receiving a question input by a user through an upper computer, and mapping the received question into a Hamiltonian parameter of an Isin model; the Hamiltonian parameter is transmitted to an FPGA through a communication interface, and a mixed GHMC sampler and a temperature scheduling module are arranged in the FPGA; the hybrid GHMC sampler carries out parallel solution on Hamiltonian parameters; and the temperature scheduling module gradually reduces the system temperature according to a preset annealing curve, and drives the Isin model to converge to a global optimal solution. The method is used in the power system combination optimization process.