108results about How to "Improve search ability" patented technology

The invention discloses a fishery culture pond temperature control optimization method, and belongs to the technical field of PID control optimization, and the method comprises the specific steps: S1, constructing a fishery culture pond temperature control system; s2, improving a dandelion optimization algorithm; s21, using a multi-elite opposite learning strategy to improve a mathematical model of a decline stage of the dandelion optimization algorithm; s22, improving a mathematical model of a landing stage of the dandelion optimization algorithm by using a self-adaptive segmented search strategy; s3, optimizing PID controller parameters in the temperature control system of the fishery culture pond by using an improved dandelion optimization algorithm; s4, applying the optimal three parameters Kp, Ki and Kd obtained in the S3 to a PID controller of a temperature control system of the fishery culture pond, and optimizing the temperature control of the culture pond; the fishery culture pond temperature control system is optimized through an improved dandelion optimization algorithm, and the temperature control performance and the control precision of the system are integrally improved.

The invention provides a distributed energy storage output scheduling optimization method, medium and system, and belongs to the technical field of distributed energy storage output scheduling, and the method comprises the steps: firstly, collecting the electrical parameters, load parameters, renewable energy power generation parameters and energy storage equipment parameters of a transformer area, and building an energy storage grid-connected equation set which comprehensively considers the factors; and predicting a load, renewable energy output and energy storage demand curve in a period of time in the future according to historical data. Then, a multi-time-period energy storage scheduling optimization model is constructed, and objectives comprise operation cost minimization, renewable energy consumption maximization and power grid fluctuation minimization; and solving the multi-objective optimization problem by adopting an improved particle swarm optimization algorithm to obtain a plurality of candidate schemes. And finally, comprehensively evaluating the candidate schemes by using a multi-objective decision-making method, and selecting an optimal energy storage scheduling scheme. The technical problem that in the prior art, only parameters of an energy storage system are considered, and influences of power grid input electrical parameters and load parameters are ignored is solved.

The invention discloses a dynamic optimization algorithm-based citrus red wine brewing formula design method. The method comprises the following steps of S1, constructing an initial multi-dimensional data set; s2, generating an initial formula setting through a fuzzy adaptive coevolution algorithm; s3, dividing the brewing process into a primary fermentation stage, a middle-stage fermentation stage and a later-stage curing stage; s4, controlling exploration and development balance of each particle by using an improved particle swarm optimization algorithm, and performing real-time optimization by using a multi-agent coordination mechanism; s5, dynamically adjusting the variation amplitude in each stage by applying an adaptive variation distribution mechanism; s6, optimizing a specific brewing target in different brewing stages through a staged multi-target optimization system; s7, analyzing brewing data of each batch by applying a traceability analysis system; and S8, adjusting the initial formula setting of the next batch based on the traceability analysis result. According to the method, the fuzzy adaptive coevolution algorithm, the improved particle swarm optimization algorithm and the like are utilized, and multi-stage intelligent control over the brewing process of the citrus red wine is achieved.

The invention discloses an unmanned aerial vehicle cluster ground attack task allocation method and system based on an improved particle swarm algorithm, belongs to the technical field of intelligent optimization algorithms, and aims at solving the problem of unmanned aerial vehicle cluster ground attack task allocation. And analyzing two types of constraints including the target type constraint and the unmanned aerial vehicle type constraint, and establishing a mathematical model for maximizing the attack cost-effectiveness ratio. The method comprises the following specific steps: step 1, establishing a target function for distributing and establishing a target attack revenue and a target attack cost to the ground attack task, wherein the target function is used for maximizing the target attack revenue and minimizing the target attack cost; 2, analyzing constraint conditions of ground attack task allocation; 3, integrating the target function and the constraint condition of the ground attack task allocation, and establishing a mathematical model of the ground attack task allocation; and 4, solving the mathematical model established in the step 3 by using an improved particle swarm algorithm to obtain a final distribution result.

The invention discloses an agricultural machinery automatic driving operation method applied to an automatic driving harvester. The method belongs to the technical field of automatic driving of agricultural machinery, and comprises the following specific operation steps: firstly, acquiring internal environment information of a greenhouse by using a millimeter wave radar, and constructing a two-dimensional grid map by using a Cartograph algorithm; secondly, according to the drawn grid map, an improved PSA algorithm is adopted to complete global path planning of the automatic driving harvester; and finally, the automatic driving harvester carries a ZED camera to collect image information within 5-6 m around the vehicle, an xLSTM model is used to identify the collected image information, whether there is an obstacle around the vehicle is determined, and if there is an obstacle, a DCS algorithm is used to complete local path planning of the operation vehicle. According to the invention, farmland crops can be harvested more quickly and accurately, the labor cost is reduced, and the harvesting efficiency is improved.

The invention relates to the technical field of data analysis, and discloses a laboratory data analysis method and system based on a machine learning technology. The method comprises the following steps: firstly, introducing an adaptive threshold, and identifying and deleting abnormal data in original laboratory data by using an improved isolated forest algorithm to obtain a processed laboratory data set; secondly, improving the generative adversarial network to obtain an improved generative adversarial network filling model, and performing data filling on the processed laboratory data set for multiple times to obtain a processed laboratory data set; according to errors of experimental result data, an improved black-wing optimization algorithm is used for searching for the minimum error, and optimization of laboratory data is achieved; and finally, establishing a laboratory database, performing data storage on the processed laboratory data set, and completing laboratory data analysis. By analyzing and processing the laboratory data, the purpose of analyzing the laboratory data is achieved, and the method is accurate and objective.

The invention discloses an effluent total phosphorus prediction model construction method based on a pigeon flock optimization echo state network, and the method comprises the steps: obtaining sewage treatment data, and carrying out the data preprocessing; determining an auxiliary variable set and a prediction variable according to the preprocessed sewage treatment data; wherein the predictive variable is effluent total phosphorus; determining the number of input nodes and the number of output nodes of the echo state network according to the auxiliary variable set and the prediction variable; and determining optimal parameters of an echo state network reserve pool layer based on a self-adaptive improved pigeon inspired optimization algorithm in combination with a tabu search algorithm. According to the method, the global search capability of the pigeon inspired optimization algorithm and the local search capability of the tabu search algorithm are utilized, the optimization capability of the algorithm is further improved by improving an operator updating formula in the pigeon inspired optimization algorithm, and the parameters and the structure of the echo state network are optimized by applying a self-adaptive adjustment mechanism. The original algorithm gets rid of a local optimal solution, and the model prediction precision can be improved.

The invention discloses a trusted task unloading method based on autonomous collaboration of vehicle side cloud. The method can be applied to the dynamic heterogeneous Internet of Vehicles to solve the low-efficiency problems of task accumulation, resource preemption, poor cooperation and the like caused by false tasks and malicious nodes. Firstly, an unloading task and cooperative node trust evaluation model is provided, and false tasks and malicious nodes are accurately identified and filtered by analyzing data sensitivity, resource preemption, historical unloading success rate and cooperative feedback evaluation; then, a discrete particle swarm algorithm is utilized to iteratively obtain a vehicle edge cloud collaborative unloading decision, particle codes, a fitness function and a correction mechanism are redesigned, and random quantities, greedy and mapping functions are introduced to enhance the diversity of unloading decisions. Compared with a traditional task unloading method, the method shows better energy consumption and delay performance in a scene with compact and uniformly distributed tasks, and the unloading efficiency and safety are effectively improved.

The invention discloses a medicine bottle defect detection method and system based on machine vision and deep learning, belongs to the technical field of defect detection, and solves the problems that a twin defect classification network is sensitive to a highlight area caused by light reflection in appearance image information and the defect detection accuracy of the twin defect classification network is interfered in the existing method. The method comprises the following steps: constructing a defect detection model based on deep learning in combination with a YOLOV5 model, carrying out conjoint analysis detection on a time sequence image set by the defect detection model in combination with a medicine bottle simulation model, carrying out defect identification on the time sequence image set by the defect detection model, positioning a medicine bottle defect area, and carrying out defect detection on the medicine bottle. Classifying and marking defect types of the defect areas of the medicine bottles in a short circuit and random vector machine combined mode; according to the invention, the mean filter is introduced into the defect detection model, so that background filtering can be carried out on the image acquired under machine vision under the condition of light reflection of the medicine bottle, effective information of a small-size target image is effectively reserved, and the defect classification and recognition accuracy is ensured.

The invention discloses a production plan and workshop scheduling integrated optimization method of a hydraulic cylinder manufacturing enterprise. The method comprises the following steps of performing a series of assumptions on a production plan and workshop scheduling integrated model of the hydraulic cylinder manufacturing enterprise; establishing a multi-objective optimization function taking minimization of machining time as a main objective and minimization of machining cost as a secondary objective, and setting constraint conditions of the multi-objective optimization function; an improved particle swarm algorithm is adopted to solve the multi-objective optimization function, the improved particle swarm algorithm adopts double-layer coding to represent the positions of particles, the first layer of the double-layer coding is the machining sequence of each workpiece, and the second layer of the double-layer coding is machining equipment of each machining sequence; and the improved particle swarm optimization algorithm performs iterative optimization on the positions of the particles, and outputs a scheme which meets the completion time and has the minimum processing cost as an optimal production plan and workshop scheduling scheme.

The invention discloses a transformer fault diagnosis research method and system, and relates to the technical field of transformer fault diagnosis, and the method comprises the steps: introducing an SHAP value to carry out the feature optimization of transformer fault data based on a decision tree and an integration method thereof; dividing the optimized data into a training set and a test set, and performing data cleaning and oversampling processing on the training set; and optimizing the deep mixed kernel extreme learning machine based on the improved dung beetle algorithm, constructing a transformer fault model, and performing fault diagnosis by using the transformer fault model. According to the feature system provided by the invention, the input features of the transformer fault diagnosis model can be effectively optimized, transformer fault data can be effectively utilized, and the influence of redundant features on the diagnosis model is reduced. Compared with other heuristic artificial intelligence algorithms, the improved dung beetle algorithm provided by the invention has more excellent optimization ability, and can find more excellent parameters of the deep mixed kernel extreme learning machine in the same number of iterations and range.

The invention discloses an automatic wiring and optimizing method for a superconducting quantum chip, and belongs to the field of automatic design of superconducting quantum chips. According to the method, in a modeling stage, a'combined wiring template 'model under a fine-grained grid is adopted, a problem is discretized through a combined wiring template, complex geometric operation of the arc wiring problem in the solving process is avoided, and the search space of an algorithm is greatly reduced; in the legal solution search stage, a single-side wiring algorithm is adopted, a line sequence search problem is extended according to wiring result characteristics of the algorithm, a line sequence tree search framework is designed, and on the basis, the blocking relation between lines is explored, and a series of search optimization strategies are designed; an exchange winding action is adopted to meet the special wire sequence requirement of a read wire; in the wiring optimization stage, an iterative optimization wiring scheme based on space limitation is adopted, and multi-stage target optimization is performed on the overall wiring result on the basis of a legal solution, so that the wiring result meets design requirements.

The present invention discloses a LSTM sewage water quality prediction method based on improved DE and attention mechanism, comprising the following steps: 1) pre-processing important feature data sets of sewage treatment plants; 2) constructing and initializing an ATT-LSTM water quality prediction model; 3) optimizing the hyperparameters of the ATT-LSTM water quality prediction model using ADE to obtain an optimal hyperparameter ADE-ATT-LSTM water quality prediction model; 4) constructing a training set and a test set based on the pre-processed data to train the optimal hyperparameter ADE-ATT-LSTM water quality prediction model; 5) inputting the collected data into the trained ADE-ATT-LSTM water quality prediction model to obtain a DO prediction result. The water quality prediction model of the present invention can ensure that the prediction model parameters are optimal, thereby effectively improving the water quality prediction accuracy; and the present invention greatly improves the computational efficiency of the water quality prediction model by combining ADE with ATT and LSTM models.

The invention discloses an electric energy meter full-performance test task allocation management method and system. The method provided by the invention comprises the following steps: constructing a resource state matrix and a resource dependency matrix by monitoring and acquiring the state characteristics of test resources and the dependency relationship among the test resources in real time, and calculating the initial utilization rate of the test resources by using a resource utilization rate calculation algorithm; calculating the priority of the task by using a priority evaluation algorithm; calculating a matching priority between the task and the test resource by using a bidirectional matching priority calculation algorithm, performing a preliminary decision of task allocation, and constructing a task allocation matrix; and calculating by using a task allocation balance optimization algorithm to obtain a target function, further optimizing task allocation by using a gradient descent method, and calculating to obtain updated task allocation matrix elements. According to the method, the current state and the mutual dependency relationship of each test resource can be accurately mastered, and resource waste and test task failure caused by incomplete or wrong information during task allocation are avoided.

The invention relates to a gearbox fault diagnosis method and system. The method comprises the following steps: acquiring fault data of a gearbox; performing feature extraction by using the convolutional neural network fault data to obtain fault features; inputting the fault features into an extreme gradient lifting classifier to obtain a fault diagnosis result of the gearbox; wherein the extreme gradient lifting classifier adopts an improved big sugarcane-mouse algorithm to carry out parameter optimization, and the improved big sugarcane-mouse algorithm is combined with a nonlinear weight, an adaptive factor, sine and cosine and a sparrow warning mechanism. According to the invention, the features of the gearbox fault data are mined by using the convolutional neural network, accurate fault classification and identification are carried out on the basis of the features by using the extreme gradient lifting classifier, and the hyper-parameters of the extreme gradient lifting classifier are optimized by introducing the improved big sugarcane-mouse algorithm. And the optimization capacity, the stability and the convergence speed are improved, so that the overall performance of the model is effectively improved.

The invention provides a passenger roller fire personnel evacuation simulation method and system, and the method comprises the steps: building a passenger cabin scene model, carrying out the numerical simulation of a passenger cabin fire, and obtaining the data of a fire product; analyzing a change rule of the fire product, and calculating a fire evacuation speed influence coefficient; improving a particle swarm algorithm, and fusing the fire product data into the improved particle swarm algorithm to obtain a personnel evacuation path optimization model under the fire condition; and simulating a personnel evacuation process in the passenger cabin scene model based on the personnel evacuation path optimization model to obtain an optimal personnel evacuation path under the fire condition. According to the method, the optimization space can be effectively expanded, the capacity of the algorithm for obtaining the globally optimal solution is improved, the optimization capacity of the algorithm in the initial stage is enhanced, and the high-level development capacity is kept in the final stage of search, so that the convergence speed and effect of the algorithm are remarkably improved, and the path optimization result is improved; and the optimal path is ensured to be the shortest path.

The invention provides a path planning method based on an improved ant colony algorithm. The method is mainly used for optimizing and improving the efficiency and accuracy of the ant colony algorithm in path planning. A traditional ant colony algorithm has the problems of low convergence speed, easy falling into local optimum and the like. In order to solve the problems, the invention provides a series of improvement measures. First, the present invention initializes pheromone concentrations using non-uniform initialization. Secondly, an enhanced direction guiding mechanism is used, and the convergence speed of the algorithm is increased; in addition, the distance between the to-be-selected node and the target point is added into the heuristic function, so that the purposiveness and efficiency of path searching are improved. Finally, according to the invention, a strategy of combining a plurality of transfer strategies is adopted for transfer, and finally, an optimal path with a shorter distance and a relatively smooth path can be obtained more quickly.

The invention relates to the technical field of enterprise motorcade management, in particular to a motorcade dynamic scheduling and energy consumption collaborative optimization method and system based on digital twinning, and the method comprises the steps: building a motorcade digital twinning scheduling cloud platform, and accessing a plurality of motorcade participants cooperatively executing transportation tasks; the method comprises the following steps: carrying out space-time division on a transportation task according to a collaborative scheduling authority, generating and issuing motorcade scheduling blocks, carrying out initial parameter definition on key factors such as vehicle distribution, path planning, load optimization and dynamic road condition response by each participant based on block information, and constructing a plurality of motorcade dynamic scheduling modeling spaces by combining with traversal search of a scheduling parameter library; an improved teaching and learning optimization algorithm module is introduced, a dynamic competition optimization algorithm is embedded to enhance the adaptability, and a motorcade scheduling collaborative optimization algorithm module is formed. Global optimization is carried out in multiple modeling spaces based on the algorithm, an optimal scheduling parameter set is output, and dynamic scheduling and energy consumption collaborative optimization of transportation tasks are achieved.

The invention relates to the technical field of laboratories, and discloses an ice period flow velocity prediction method based on a WSO-ELM network model, and the method is characterized in that the method comprises the following steps: building a dynamic ELM network model based on the WSO-ELM network model through employing a WSO optimization algorithm, employing the corresponding characteristics based on limited historical observation data and side sweeping Reida observation signals, and carrying out the prediction of the ice period flow velocity through employing a WSO algorithm; and carrying out optimization solution on model parameters by adopting a white shark optimization algorithm WSO, constructing a WSO-ELM network optimization model meeting the requirement of observing the ice-period river by the side-sweeping rada, and measuring the characteristics of the ice-period river by combining the real-time observation signal characteristics of the side-sweeping rada. Aiming at the difficulty of traditional ice period river measurement, in combination with a real-time observation result of a side sweeping rada system on an ice period river, an ELM network model based on a WSO optimization network coefficient is adopted, historical observation data is utilized, the network model is trained, and an optimal network structure meeting a training sample is obtained.

The invention provides a permanent magnet synchronous motor fault prediction method and early warning system, and the method comprises the steps: collecting motor operation parameters through a sensor combination, and the motor operation parameters comprise an electric control bus voltage, an electric control bus current, a cell temperature, a motor rotation speed, and a motor torque; preprocessing motor operation parameters based on an MIV algorithm to screen key characteristic variables and construct a fault data set; constructing a BP neural network; optimizing a weight and a threshold value of the BP neural network based on an IPSO algorithm; performing data training on the BP neural network by using the optimized weight and threshold as initial values of the BP neural network to obtain a corresponding MIV-IPSO-BP neural network model; and inputting the real-time operation parameters to the MIV-IPSO-BP neural network model, and obtaining fault prediction data, so as to solve the technical problems that an existing method is very easy to cause misdiagnosis, and the signal acquisition process is very easy to be interfered by the external environment, and the fault prediction result is influenced.

The invention discloses a rolling bearing weak fault diagnosis method based on an improved sparrow algorithm. The method comprises the following steps: firstly, optimizing parameters alpha and k in the VMD by using a sparrow optimization algorithm (OCSSA) fusing eagle and Cauchy variation; decomposing the signal by using the optimized VMD, and screening components meeting conditions as optimal components by taking EWK as an evaluation index; secondly, multi-scale morphological energy entropy (WMMEE) based on wavelet decomposition serves as a fitness function to optimize MCKD so as to enhance fault features, and envelope spectrum analysis is adopted to extract actual fault feature frequency to achieve fault diagnosis; under the action of the two fitness functions, the OCSSA optimization algorithm is used for optimizing hyper-parameters in VMD and MCKD, the influence of the parameters on feature extraction is reduced, and meanwhile the problem that the parameters in the VMD and MCKD algorithms are difficult to determine is solved to a certain extent.

The invention provides a holographic photoetching mask design method based on an alternating projection algorithm, and the holographic photoetching resolution is improved. According to the method, a pixel representation mask is adopted, and finite series discretization is carried out on an amplitude matrix and a phase matrix corresponding to the mask in combination with mask manufacturing constraint conditions. In order to avoid stagnation in the optimization process, a step-by-step discretization strategy is adopted. And evaluating the imaging quality of the designed mask through a relative pattern error between the photoresist contour and the target pattern. According to the method, the alternative projection iterative algorithm and the advanced mask alignment photoetching technology are combined, different initial conditions are adopted according to mask types, and holographic photoetching mask design of any mask pattern can be effectively achieved.

The invention discloses a method for controlling the spraying speed of a greening wet spraying machine, which belongs to the technical field of PID control optimization. The specific steps are as follows: step one, constructing a control system model of the greening wet spraying machine; step two, improving the mathematical model of the aurora optimization algorithm, and the specific improvement strategies are as follows: D1, using an adaptive scaling Levy flight strategy to improve the mathematical model of the aurora elliptical complex variant Ao in the aurora optimization algorithm; D2, using a transition perturbation strategy to improve the mathematical model of the particle collision stage of the aurora optimization algorithm; step three, using the improved aurora optimization algorithm to optimize the PID controller parameters of the control system model of the greening wet spraying machine, and obtaining the optimal three parameters of Kp, Ki, and Kd; step four, using the optimal three parameters of Kp, Ki, and Kd obtained in step three for the spraying speed control of the greening wet spraying machine, and optimizing the control effect of the spraying speed of the greening wet spraying machine.

The invention discloses a turbonator rotor winding turn-to-turn short circuit fault early warning method and device, and relates to the field of data processing, and the method comprises the steps: obtaining historical monitoring data and an actual value of excitation current, carrying out the preprocessing of the historical monitoring data, obtaining the preprocessed historical monitoring data, and forming a monitoring sequence; constructing an excitation current prediction model based on Bi-LSTM, and optimizing the training parameters to be optimized by adopting an improved pelican optimization algorithm to obtain optimized training parameters; training an exciting current prediction model based on the optimized training parameters to obtain a trained exciting current prediction model; and inputting the monitoring sequence into a trained exciting current prediction model to obtain a predicted value of the exciting current, and judging whether the turbonator has a rotor winding turn-to-turn short circuit fault or not according to the predicted value of the exciting current and the actual value of the exciting current. The problems that an existing model is poor in optimization capacity, low in convergence speed and low in prediction accuracy are solved.

The present invention discloses a dual-variable power source hydraulic system and control method for an electric loader boom, belonging to the field of energy-saving technology for electric engineering machinery. The hydraulic system configuration is composed of a permanent magnet synchronous servo motor and an electro-hydraulic proportional variable device to form a dual-variable power source direct-drive arm hydraulic cylinder. The one-way valve realizes the oil cylinder, motor oil replenishment and flow reproduction functions according to the hydraulic cylinder working condition. The direct-acting overflow valve realizes the overflow protection of the hydraulic system. The two-position two-way electromagnetic reversing valve realizes the hydraulic cylinder pressure-maintaining function. The control method of the hydraulic system configuration is that the host computer outputs the target instruction to the dual-variable parameter optimization controller, and then the dual-variable parameter optimization controller outputs the optimal speed or displacement signal to the motor pump group, and the pressure closed loop is realized by feedback of the pressure signal by the pump outlet pressure sensor. The present invention can effectively realize the energy recovery function in configuration, and can greatly improve the energy efficiency characteristics of the power source in control method, and effectively improve the endurance time of the electric loader.

The invention discloses a forest fire vehicle scheduling method for an original forest region of northern great khingan. The method comprises the following steps: step 1, collecting forest fire data; 2, determining constraint conditions of the model; 3, establishing a mixed integer linear programming model according to the constraint conditions determined in the step 2 by taking the aim of achieving the shortest rescue time of the fire fighting truck as a target; and 4, designing a genetic algorithm to solve the obtained mixed integer linear programming model to obtain an optimal path of the forest fire rescue vehicle. According to the improved genetic algorithm, a high-quality solution can be quickly obtained for the forest fire emergency rescue vehicle scheduling problem, rescue workers can be helped to make a quick response under the forest fire emergency situation, and the rescue vehicles can be scheduled for the forest fire at the first time according to a feasible scheme obtained through solving; the loss caused by the forest fire is reduced, and the method has important significance for improving the emergency rescue capability under the sudden forest fire condition.

The present invention relates to a multi-sensor error compensation method based on ISSA-BP neural network, belonging to the field of sensor compensation. The method uses a trained ISSA-BP neural network to perform environmental error compensation on three sensors of temperature, humidity and pressure carried by the system respectively, thereby eliminating the detection errors caused by environmental temperature, humidity and air pressure. The method solves the problem that the prior art does not fully consider the influence of multiple environmental factors such as temperature, humidity and air pressure in the detection environment of the multi-sensor system on the detection data, resulting in poor robustness of the compensation method, and the problem that the traditional sensor error compensation algorithm has low accuracy and slow time.

The application discloses a kind of based on the improved NSGA-II algorithm abrasive belt grinding multi-objective process parameter optimization method, first establish the mathematical model of carbon emission, surface roughness and material removal rate of abrasive belt grinding process, then according to the improved NSGA-II algorithm multi-objective optimization model is established, and using the improved NSGA-II algorithm multi-objective optimization is realized the intelligent optimization of process parameter of grinding process.The method of the application fully considers the mutual influence between each process parameter in the process of abrasive belt grinding difficult-to-machine material, the improved NSGA-II algorithm proposed uses new sequencing method, improves the population diversity of algorithm, so that the algorithm has better solution set uniformity, and the searching ability of improved NSGA-II to global optimal solution is stronger, under the same condition, the number of Pareto optimal solution of improved NSGA-II algorithm is more than NSGA-II and MOPSO algorithm, which means that improved NSGA-II algorithm can find out better global optimal solution.

The invention discloses a multi-energy load virtual energy storage intra-day optimization scheduling method and system, and the method comprises the steps: building a target function for a multi-energy load virtual energy storage intra-day optimization scheduling target with the minimum deviation between the actual output of equipment of a multi-energy load virtual energy storage system and a day-ahead scheduling plan, and building an operation constraint condition; and under the operation constraint condition, solving the objective function to obtain a multi-energy load virtual energy storage intra-day optimization scheduling strategy and executing the multi-energy load virtual energy storage intra-day optimization scheduling strategy. According to the method, the deviation between the multi-energy load virtual energy storage system and a day-ahead scheduling plan due to the uncertainty of data prediction can be reduced, so that the operation cost is reduced, meanwhile, by improving a population initialization mode and a random number generation method of a traditional algorithm, optimization model solving is carried out, and the optimization efficiency of the multi-energy load virtual energy storage system is improved. According to the method, local optimization can be avoided, the obtained optimization result is high in quality, the convergence speed is high, and the problem of multi-energy load virtual energy storage intra-day optimization scheduling is well solved.

The present invention relates to the technical field of Beidou positioning, and discloses a Beidou positioning system, method, and application in vehicles and marine equipment. The present invention first obtains the initial vehicle and marine equipment positioning data, performs extreme data clearing and repairing, completes data preprocessing, and then calculates the Beidou satellite position coordinates; secondly, calculates the Beidou satellite pseudo-range single difference measurement value, establishes the processed pseudo-range measurement single difference equation, and performs error elimination to obtain the pseudo-range measurement single difference equation after error elimination; finally, constructs a weight matrix, performs linear processing and coordinate correction on the pseudo-range measurement single difference equation after error elimination, and outputs the final coordinates of the vehicle and marine equipment. The present invention achieves the Beidou positioning purpose of the vehicle and marine equipment by analyzing and processing the positioning data of the vehicle and marine equipment, and the method is accurate and objective.