35results about How to "Realize intelligent optimization" patented technology

The invention discloses an internet-of-things and cloud computing based intelligent monitoring and fault diagnosis system for mine ventilators. An operating process of the system includes the steps of 1, establishing a ventilator monitoring and fault diagnosis system for each ventilator; 2, integrating all ventilator monitoring and fault diagnosis systems in a mine and a ventilation safety monitoring system to establish mine ventilator air network monitoring systems, through an internet-of-things technology; 3, integrating the mine ventilator air network monitoring systems to establish a group-level ventilator air network monitoring system, through the internet-of-things technology; 4, integrating ventilator users, ventilator manufacturers and ventilator air network fault diagnosis experts in a work environment through a cloud computing platform, improving functions of the ventilator monitoring and fault diagnosis systems to multi-ventilator intelligent control so as to achieve air network intelligent optimization and ventilator fault auxiliary diagnosis. The system has the advantages that a ventilation system is more efficient and reliable, safety production of the mine is guaranteed, an air network is efficient and stable, energy is saved, and consumption is reduced.

The invention relates to a parameter identification method of an asynchronous motor based on an improved particle swarm optimization algorithm. Based on a standard particle swarm optimization algorithm, the maximum weighting coefficient (as the following formula) and the minimum weighting coefficient (as the following formula) are set in batches respectively; a random mutation operator is added, and the strategy of adding the mutation operator to perform random mutation on gbest improves the ability of the algorithm to jump out of local convergence, improves the problem of premature falling into local optimum of the particle swarm, expands the search scope of particles, improves the global searching ability and convergence speed of the particle swarm optimization algorithm, reduces the risk of falling into the local optimum, and takes both precision and efficiency of an optimization process into account. According to the parameter identification method of the asynchronous motor based on the improved particle swarm optimization algorithm provided by the invention, the measuring value of each working characteristic of the asynchronous motor is obtained by measuring, the improved particle swarm optimization algorithm is used to realize the static parameter identification of the asynchronous motor, and the method still has higher identification accuracy in the presence of noise.

The invention provides an intelligent method for optimizing working procedure of a semiconductor packaging production line, applying particle swarm optimization (PSO) to optimize the working procedure in a semiconductor packaging process, modeling and finding the solution, taking processing center as a unit, building a mapping relation between problem to be solved and particle swarm, using particles to express optional solutions to the problem, and finding the optimized solution under the guidance of corresponding target; each particle swarm optimization subproblem is using processing time of the machine on the production line for different products to encode the particles to determine particle swarm optimization space and making time sequence decoupling on the optimization problem to be solved, obtaining the corresponding subproblem and then dividing the problem space and determining a mapping relation between the divided space and each subproblem to find particle swarm optimization solution of each subproblem, thus obtaining an optimized machine processing time allocating solution and machine occupation ratio. The practice indicates that PSO has the advantages simple concept, convenient implementation, rapid convergence, etc.

The invention provides an omnibearing electric power economic business index analysis system and method, and relates to the research field of the relationship between the electric power economic business index and economic growth. The system comprises a first input module, a second input module, an index prediction module and an output module. A cross-discipline method is applied, on the basis of cross-correlation analysis in statistics, mature methods in the fields of economy, finance, industry, foreign trade and the like are combined, prosperity analysis is completely introduced into the power industry, and a novel statistical power economy prosperity index is extracted through multi-dimensional power utilization data mining. The operation condition and development situation of provincial macroeconomy are objectively reflected, the important function of watching economy through electric power is achieved, and a quantitative index is provided for the economic operation trend.

Embodiments of the present invention provide a resource configuration method, device, and electronic equipment, wherein the method includes: acquiring the operating parameters of each of the multiple tasks, and calculating the objective function of the task according to the operating parameters of each task, Obtain the total amount of preset resources of the server cluster, and calculate the optimal resource usage of each task according to the preset total amount of resources and the objective function of each task, and send the optimal resource usage of each task to the server Cluster, so that the server cluster can run corresponding tasks according to the optimal resource usage of each task. The embodiment of the present invention realizes intelligent optimization of resources manually configured by business personnel, effectively coordinates resource usage of different tasks, achieves global optimal effect, effectively improves server cluster resource utilization, and reduces resource waste.

The embodiment of the invention provides an attendance checking method and device and equipment and a storage medium, and the method comprises the following steps: notifying a plurality of employees of a first attendance checking rule according to employee setting information of the first attendance checking rule; obtaining the fitness between the plurality of employees and the first attendance checking rule; determining a second attendance checking rule corresponding to each of the plurality of employees according to the fitness; and notifying the plurality of employees of the corresponding second attendance rules. Through the attendance checking scheme, the attendance checking rules more suitable for the employees can be provided for the employees, so that the work enthusiasm of the employees is stimulated, and the improvement of the overall performance of an enterprise is promoted.

The invention discloses an overpass layout method, system, terminal and readable storage medium for automatically changing and moving existing roads along a railway line. The method includes: step S1: constructing an optimization model of road and railway overpass lines; step S2: Carry out cell grid division in the research area, and decompose the path search into two non-intersecting segment searches and intersecting segment searches; Step S3: perform path search on two non-intersecting segments and intersecting segments based on the optimization model; Step S4: use step The N paths, M paths and P intersecting partial lines in S3 are spliced ​​and integrated to form several complete lines, and the optimal line is determined with the optimization function as the objective function, and the curves of the plane and longitudinal sections are carried out based on the optimal line Fitting to get the final route scheme. The invention decomposes into path search of non-intersection section and intersection section, weakens the coupling interaction between connection point and intersection point, and realizes the optimization of highway and railway overpass.

The invention discloses an aircraft assembly measurement field layout method based on a genetic algorithm, comprising the following steps: establishing an assembly measurement field layout model based on 0-1 planning; solving the assembly measurement field layout model through a genetic algorithm to obtain an optimized layout of the measurement field. The present invention realizes the intelligent optimization of the measurement field layout by establishing a measurement field layout optimization model based on 0-1 planning, and then using a genetic algorithm to solve it, and lays a foundation for high-precision measurement orientation points. The layout optimization model of the measurement field is established by using 0-1 planning, so that the layout problem of the complex measurement field becomes clear, concise and easy to solve; and then the genetic algorithm is used to solve the model, which is efficient and convenient.

The invention relates to a structure optimization method for a low frequency electromagnetic sensor based on a genetic algorithm. The method is suitable for the structure design of a COMSOL analog low frequency electromagnetic sensor model and the shape reconstruction for defect detecting and belongs to the field of nondestructive detection. The method comprises the following steps: on the basis of simplifying the COMSOL analog low frequency electromagnetic sensor model, parameterizing an optimization model of the COMSOL analog low frequency electromagnetic sensor and defining the reading paths for different variables; setting a genetic optimized fitness function and main parameters including an algorithm running parameter and a control parameter; compiling a dynamic link function, calling for the COMSOL model by MATLAB and constructing a channel for mutually transferring data; calling a COMSOL genetic algorithm for optimizing the parameters of the low frequency electromagnetic sensor model, and while acquiring the fitness function meeting the convergence criterion, acquiring an optimized result, namely, the optimized structure parameter of the low frequency electromagnetic sensor. According to the method provided by the invention, the limitation of the COMSOL single assignment operation is broken through and the intelligent optimization of the COMSOL model parameters is realized.