30 results about "Kriging algorithm" patented technology

The invention discloses a wireless sensor network temperature monitoring method based on an improved Kriging algorithm. The wireless sensor network temperature monitoring method comprises the steps of: firstly, establishing a layer cluster type wireless sensor network topological structure, analyzing historical temperature data of a target region, setting a threshold value rho and a fusion cycle T, performing periodic fusion on in-cluster node data at a cluster head node based on the threshold value rho, acquiring initial temperature data, transmitting the initial temperature data to a monitoring host by means of a wireless link and saving the initial temperature data in a local database; importing the initial temperature data, and performing variable structure analysis on the initial temperature data to generate a variation function expression; and optimizing a variation function model by utilizing an improved Nelder-Mead simplex method, designing a regular grid terrain, performing temperature interpolation by adopting an ordinary Kriging algorithm, carrying out error correction, and outputting a regular grid temperature field pattern. The wireless sensor network temperature monitoring method optimizes the variation function model by utilizing the improved Nelder-Mead simplex method, corrects weights of the ordinary Kriging algorithm, performs error compensation on the interpolation result, and makes the interpolation result more scientific and closer to the actual situation.

The invention discloses an increment ionosphere refraction error correction method which is applied to a GNSS single-frequency user. The method comprises a monitoring station utilizes ionosphere model parameters broadcasted by a GNSS system itself to calculate ionosphere vertical TEC1, and meanwhile, utilizes double-frequency observation data to calculate ionosphere vertical TEC2, and difference value [delta] TEC between the TEC1 and the TEC2 is obtained; a plurality of monitoring stations transmit the [delta] TECs of themselves to a master control station through a communication link; the master control station receives each [delta] TEC, establishes an ionosphere increment variation model, obtains ionosphere increment variation model broadcast parameters a, b and c in a region, and transmits and broadcasts the a, b and c to the user through a broadcast link; a user receiver receives a navigation signal and the a, b and c, utilizes the ionosphere model parameters broadcasted by the GNSS system itself to calculate TEC <GNSS>, utilizes the a, b and c to estimate optimum estimation value (shown in the specification ) of the ionosphere increment through a common Kriging algorithm, and adds the TEC <GNSS> with the optimum estimation value (shown in the specification ) to obtain TEC ; and the TEC is utilized to calculate ionosphere addition time delay, so that high-precision ionosphere refraction error correction for the GNSS single-frequency user is realized.

The invention discloses a ground settlement space monitoring method through Kriging interpolation based on a genetic algorithm. The method comprises the following steps of: acquiring coordinates and ground settlement of monitoring points; calculating distance among the monitoring points; dividing the distance into distance groups; calculating variation function values corresponding to the distance groups; determining a variation function model and fitting a variation curve; solving a Kriging weight coefficient; calculating settlement estimated values and conducting iterative computation; and encrypting settlement monitoring results through a grid method. On the basis of the traditional Kriging algorithm, the genetic algorithm is introduced, the variation function model is fit and natural logarithm is taken for regionalized variables, so as to reduce influences caused by distribution problems and abnormal values. Compared with the traditional settlement monitoring model, the ground settlement space monitoring method through Kriging interpolation based on the genetic algorithm has the characteristics of high estimation accuracy, high reliability and the like.

An electric power system measurement and load parameter analysis and identification method relates to an electric power system parameter identification method, and comprises the following steps: firstly, clustering and analyzing loads according to load measurement information, and determining a load recording device installation place; secondly, constructing a comprehensive load model of the parallel static characteristic load of the motor, and establishing an identification target function; performing parameter identification by adopting a hybrid optimization algorithm combining an ant colonyalgorithm and a gradient algorithm through measured data of a load recording device; optimizing parameters on multiple identification results by utilizing unbiased and optimal estimation characteristics of a Kriging algorithm; and finally, counting load models of different stations to form a load model parameter library. The speed of parameter identification is increased, parameter optimization is realized for multiple identification results by adopting a Kriging algorithm, and the precision of parameter identification is improved. The method can provide a model basis for research in the fields of electric power special planning, electric power system stability analysis, electric power system energy efficiency evaluation and the like.

The invention discloses a pollutant spatio-temporal distribution uncertainty characteristic analysis method and system and a storage medium. The method comprises the steps that time attributes are added to a space coordinate system to form a space-time coordinate system according to existing pollutant monitoring data; according to a preset threshold value, concentration values of pollutants at each coordinate point in the space-time coordinate system are indicated, and indicator data is processed by means of a spatio-temporal indicator Kriging algorithm to obtain spatio-temporal distribution of the pollutant monitoring data to further obtain space-time prediction cube data; by conducting data mining on the space-time prediction cube data, analysis data of pollutant spatio-temporal distribution uncertainty characteristics is obtained. According to the method, probabilistic analysis is conducted on pollutants reaching a certain mass level to improve credibility of analysis results.

The invention relates to the technical field of universal machinery hydraulic performance optimization, in particular to an automatic adjustment method for a centrifugal pump front guide vane and a device for the automatic adjustment method. According to the automatic adjustment method, provided by the invention, for the centrifugal pump front guide vane, firstly, overall performance data samples of a coded centrifugal pump and the coded front guide vane are written in a computer and led in a single chip microcomputer control unit; then current centrifugal pump flow and the guide vane opening degree are obtained through a flow detection unit and an angle sensor correspondingly and are transmitted to the single chip microcomputer control unit; moreover, a Kriging interpolating calculation module is adopted for predicting hydraulic performance under the current centrifugal pump flow, and the optimal guide vane opening degree is determined through an optimization algorithm module; finally, based on the difference value of the current guide vane opening degree and the optimal guide vane opening degree, a PID control module is adopted for controlling a stepping motor to reach the optimal guide vane opening degree; and a guide vane opening degree control mechanism with automatic feedback adjustment is established based on a Kriging algorithm, so that the centrifugal pump is controlled to be in the optimum operating condition, and thus operation energy saving is achieved.

The invention discloses a deterministic method for qualitatively identifying the top and bottom interfaces of hydrate in the sea, which includes the following steps: A, getting multiple sensitive attributes for identifying hydrate through seismic reflection characteristic research, AVO, elastic wave impedance inversion and other research methods; B, calculating the weight coefficients of the attributes by analyzing the relationship between the sensitive attributes and hydrate characteristics, and getting attributes used to identify the hydrate characteristics through information fusion analysis; C, studying the spatial correlation between the weighted attributes and sonic skew log variables, and building a model capable of reflecting the hydrate characteristics through a collaborative Kriging algorithm based on the information of the weighted attributes; and D, on the basis of collaborative Kriging modeling, carrying out global optimization broadband constrained inversion to qualitatively identify the top and bottom interfaces of hydrate. The limitation brought by a single technology and the multi-solution interference are reduced. A favorable target of hydrate can be found more accurately, and the top and bottom interfaces of hydrate can be identified.

The invention provides a vehicle instability pre-warning system and method. The vehicle instability pre-warning system comprises a detecting module, a data collecting module and a pre-warning module, wherein the detecting module is used for detecting a state parameter of a vehicle and providing a detection signal that expresses the state parameter; the data collecting module is used for filtering the detection signal from the detecting module so as to obtain detection data; the pre-warning module is used for judging whether vehicle turnover danger exists or not according to a surrogate function on the basis of the detection data and giving pre-warning if the vehicle turnover danger exists, wherein the surrogate function is fitted by using Kriging algorithm in advance on the basis of the detection data. As the surrogate function is built by using the Kriging algorithm, the function relation between the state parameter of the vehicle and the vehicle body stability can be established to improve the pre-warning accuracy.

The invention provides a weapon station muzzle disturbance optimization method based on the Kriging algorithm. The method comprises the steps of: firstly, setting the ranges of the buffer damping, the barrel elasticity modulus and the barrel Poisson ratio of an overhead weapon station; secondly, selecting N sample points by using the Latin Hypercube experiment design; thirdly, calculating a muzzle disturbance comprehensive function; fourthly, combining the N groups of sample points and N muzzle vibration comprehensive parameters to form an initial training sample point set; fifthly, performing optimizing on a Kriging surrogate model by using the genetic algorithm to find out the optimal point and the expectation maximum raising point and obtain optimized structure parameter values. The weapon station muzzle disturbance optimization method based on the Kriging algorithm can minimize the muzzle disturbance quantity through the optimization design of the elasticity modulus of a support frame, the quality of a cartridge box and the rigidity of a buffer.

The invention provides a multi-working-condition structure optimization method of a weapon station on the basis of a Kriging algorithm. The method comprises the following steps: 1: giving the range of a support frame elasticity modulus, the range of cartridge box quality and the range of buffer rigidity; 2: adopting Latin hypercube experiment design to select and obtain N groups of sample points; 3: calculating gun muzzle vibration comprehensive parameters; 4: combining the N groups of sample points with N gun muzzle vibration comprehensive parameters to form an initial training sample point set; 5: using a genetic algorithm to carry out optimization on a Kriging surrogate model to find an optimal point and a maximum expectation improvement point; 6: on the basis of a dual Kriging model sequence iterative optimization algorithm, carrying out optimization on a structure parameter optimization problem until convergence is realized; and 7: obtaining the structure optimization parameters of different angles of fire, and substituting the optimal structure parameter of each angle of fire into weapon station dynamics models under other working conditions, and looking for the structure parameter with a maximum average optimization range as a final structure optimization parameter.

The invention provides an overhead weapon station structure optimizing method based on an improved Kriging algorithm. The method includes the following steps of firstly, presetting the ranges of the elasticity modulus of a supporting frame, the mass of an ammunition can and the rigidity of a buffer; secondly, designing, selecting and obtaining N sets of sample points through a Latin hypercube test; thirdly, calculating vibration comprehensive parameters of gun muzzles; fourthly, establishing an initial training sample point set by combining the N sets of sample points with the vibration comprehensive parameters of the N gun muzzles; fifthly, optimizing a Kriging agent model through a genetic algorithm, and finding the optimal point and the maximum expectation improvement point; sixthly, optimizing the structural parameter optimizing problem on the basis of a duplex Kriging model sequence iterative optimizing algorithm till the algorithmconverges. By means of the method, through the optimization design of the elasticity modulus of the supporting frame, the mass of the ammunition can and the rigidity of the buffer, the disturbance quantity of the gun muzzles is made the minimum.

The invention discloses an RSM-Kriging-GA algorithm based energy consumption optimizing method and system for an air conditioning system in a wide region. The method comprises the following steps: reading current course arrangement, and setting start and stop of an air conditioner according to the course arrangement; automatically selecting a corresponding season mode according to the date on thatday, and obtaining historical data under the corresponding season mode; adopting an RSM cross model to fit to a relationship among number of people attending the source, an environment temperature, aclassroom size and a real-time refrigeration equivalent in the historical data; obtaining current environment parameters in real time, and calculating needed refrigeration equivalent according to theRSM cross model in real time; constructing a constraint relationship, and adopting a Kriging algorithm to establish a mathematic model between the environment parameters and energy consumption E; solving the optimal parameter solution based on a GA algorithm, and performing energy consumption optimizing automatic control on a set temperature, air speed and refrigeration time length according to the optimal parameters. The RSM model is based to fit to the historical big data, so that a constraint model is constructed, and a wide region air conditioning system energy consumption optimizing model based on the RSM-Kriging-GA algorithm is constructed, and therefore, air conditioner energy consumption is optimized, and resources are saved.

The invention discloses a three-dimensional Kriging algorithm collaborative acceleration technology based on a CUDA (Compute Unified Device Architecture) and an OpenMP (Open Messaging Protocol). The method comprises the following basic steps: constructing a KD tree search structure, calculating coordinates with interpolation points, carrying out OpenMP parallel search, asynchronously constructing a Kriging equation by a CUDA (Compute Unified Device Architecture) concurrent stream, and solving a final result. The invention has the beneficial effects that the computing resources of the CPU and the GPU can be fully utilized, so that the computing parts of the GPU and the CPU are executed asynchronously in the interpolation process, and the invention has the advantages of rapidness and high efficiency; meanwhile, when more variation models participate in calculation, the invention has a better acceleration effect.

The invention relates to a pervasive regional environment information mobile sensing and prediction method, which comprises the following steps: acquiring data by using a mobile sensing node and transmitting the data to a cloud server; performing space and structure analysis on the data by adopting a regionalized random variable analysis method at a cloud end, and obtaining expressions of a covariance and a variation function; using an improved random L-BFGS algorithm to improve a Kriging algorithm, and using the algorithm to carry out pervasive regional environment information space prediction to obtain a space prediction result of the environment information; carrying out space-time prediction analysis by utilizing the relationship between the time and the space of collecting the environment data by the shared bicycle to obtain a space-time prediction result of the target area; and finally, giving a final regional global environment prediction result graph according to different prediction results of time and space.

The invention relates to a shale reconstruction method combining a convolutional neural network and shale soft data, and the method comprises the following steps: 1), obtaining a three-dimensional image of a shale sample as a target image, and randomly obtaining the hard data of a part of shale images from the target image; 2) performing interpolation processing on the hard data image to obtain anestimated value of the shale data distribution condition, and taking the estimated value as input soft data; and 3) inputting the soft data image into a convolutional neural network, and outputting areconstructed image. Compared with the prior art, the method has the advantages that the shale hard data is interpolated by using the simple Kriging algorithm to obtain the estimated value of the shale data distribution condition, the estimated value is used as the soft data input by the convolutional neural network, the soft data image is input into the convolutional neural network, the reconstruction result with high accuracy can be obtained, the consumed time is short, and the method is convenient for large-scale application.

The invention discloses a deterministic method for qualitatively identifying the top and bottom interfaces of hydrate in the sea, which includes the following steps: A, getting multiple sensitive attributes for identifying hydrate through seismic reflection characteristic research, AVO, elastic wave impedance inversion and other research methods; B, calculating the weight coefficients of the attributes by analyzing the relationship between the sensitive attributes and hydrate characteristics, and getting attributes used to identify the hydrate characteristics through information fusion analysis; C, studying the spatial correlation between the weighted attributes and sonic skew log variables, and building a model capable of reflecting the hydrate characteristics through a collaborative Kriging algorithm based on the information of the weighted attributes; and D, on the basis of collaborative Kriging modeling, carrying out global optimization broadband constrained inversion to qualitatively identify the top and bottom interfaces of hydrate. The limitation brought by a single technology and the multi-solution interference are reduced. A favorable target of hydrate can be found more accurately, and the top and bottom interfaces of hydrate can be identified.

The invention discloses a method and system for optimizing energy consumption of a pan-park air-conditioning system based on the RSM-Kriging-GA algorithm. The method includes: reading the current course arrangement, setting the start and stop of the air conditioner according to the course arrangement; automatically selecting according to the date of the day According to the corresponding seasonal mode, obtain the historical data under the corresponding seasonal mode; use the RSM cross model to fit the relationship between the number of courses, ambient temperature, classroom volume and real-time cooling equivalent in the historical data; obtain the current environmental parameters in real time, according to the RSM Calculate the real-time cooling equivalent required by the cross model; construct the constraint relationship, and use the kriging algorithm to establish a mathematical model between environmental parameters and energy consumption E; solve the optimal parameter solution based on the GA algorithm, and set the temperature and wind speed according to the optimal parameters , Refrigeration time to optimize energy consumption and automatic regulation. The present invention fits historical big data based on the RSM model, constructs a constraint model, and establishes an energy consumption optimization model of a pan-park air-conditioning system based on the RSM-Kriging-GA algorithm, thereby optimizing energy consumption of the air-conditioning system and saving resources.

The invention relates to the technical field of universal machinery hydraulic performance optimization, in particular to an automatic adjustment method for a centrifugal pump front guide vane and a device for the automatic adjustment method. According to the automatic adjustment method, provided by the invention, for the centrifugal pump front guide vane, firstly, overall performance data samples of a coded centrifugal pump and the coded front guide vane are written in a computer and led in a single chip microcomputer control unit; then current centrifugal pump flow and the guide vane opening degree are obtained through a flow detection unit and an angle sensor correspondingly and are transmitted to the single chip microcomputer control unit; moreover, a Kriging interpolating calculation module is adopted for predicting hydraulic performance under the current centrifugal pump flow, and the optimal guide vane opening degree is determined through an optimization algorithm module; finally, based on the difference value of the current guide vane opening degree and the optimal guide vane opening degree, a PID control module is adopted for controlling a stepping motor to reach the optimal guide vane opening degree; and a guide vane opening degree control mechanism with automatic feedback adjustment is established based on a Kriging algorithm, so that the centrifugal pump is controlled to be in the optimum operating condition, and thus operation energy saving is achieved.

A universal mobile sensing and forecasting method for regional environmental information, which uses mobile sensing nodes to collect data and transmits it to the cloud server; Then use the improved stochastic L-BFGS algorithm to improve the Kriging algorithm, and use this algorithm to carry out universal regional environmental information spatial prediction, and obtain the spatial prediction results of environmental information; use the shared The relationship between the time and space of the environmental data collected by the bicycle is used for spatio-temporal prediction and analysis, and the spatio-temporal prediction result of the target area is obtained. Finally, the final regional global environment prediction result map is synthesized based on the different prediction results of time and space.

The invention discloses an oil reservoir modeling method based on geological time domain coordinates. The method comprises the steps that S1, a regional structure oil reservoir attribute model is calculated; s2, determining a constraint method of the oil reservoir attribute model in the S1, and modeling by adopting the determined constraint method; s3, establishing a three-dimensional velocity field according to a DS I discrete smoothing algorithm, a Kriging algorithm and the constraint method determined in the S2, and performing time-depth and depth-time conversion to obtain an underground structure condition of the regional structure; s4, simulating by utilizing a geological time domain coordinate system and combining the underground structure condition obtained in the S3; and S5, constructing a three-dimensional geologic model for quantitatively representing the oil reservoir. By applying the method, the limitation of fault constraint on a common geological grid is overcome. An oil reservoir modeling geological grid based on the UVT technology can be randomly cut and displaced by a fault; the geologic model does not need to give any prerequisite assumption condition, completely follows the geological conditions of the real structure and the underground complex sedimentary stratum, and establishes a geologic model which is closer to the objective reality.

The invention discloses a city field intensity reconstruction method based on adaptive hybrid interpolation, and relates to the field of city radio wave propagation. Due to the fact that an existing city field intensity reconstruction technology is difficult to meet local and wide-area situations of city field intensity at the same time, the reconstruction effect is poor. According to the technical scheme adopted by the invention, the method comprises the following steps: actually measuring and collecting original data composed of longitude and latitude and field intensity, and gridding a to-be-reconstructed region; calculating the density of sampling points around the check point, and respectively calculating the field intensity of the check point by using an inverse distance weighted interpolation algorithm and a Kriging interpolation algorithm; introducing a density threshold to carry out density diversity on grid points, using a particle swarm algorithm to optimize the density threshold, classifying the grid points greater than the threshold to be dense, using an inverse distance weighting algorithm to calculate the field intensity of the grid points, and classifying the grid points less than the threshold to be sparse, and using a Kriging algorithm to calculate the corresponding field intensity. According to the method, the interpolation method is automatically selected according to the density of the city field intensity measured data, the interpolation reconstruction result effectively shows the local and wide-area situation of the city field intensity, and the interpolation precision is improved.

The invention provides a method for determining a well location in a high water-cut oil field based on an evolutionary algorithm, comprising the steps of: establishing a reservoir model of a predetermined oil area, dividing the reservoir model into grids; Combined to obtain the remaining oil potential enrichment area; calculate the remaining oil potential enrichment area in the remaining oil potential enrichment area, which represents the distribution of remaining oil and the well location constraint value of the injected water sweep volume, and obtain the well location advantage area; use the evolutionary algorithm to iterate the oil wells in the advantage area Calculate the fitness function, use the kriging algorithm to evaluate the fitness function, the evolutionary algorithm is adjusted according to the fitness function, the kriging algorithm considers multi-dimensional space, nugget effect and local weight; iteratively calculates according to the evolutionary algorithm, and obtains the maximum fitness degree function value to determine the well location. The invention uses the improved evolutionary algorithm and Kriging algorithm as optimization tools, constructs a dynamic potential index to narrow the search space of the evolutionary algorithm, and greatly increases the convergence accuracy and speed of optimization calculation.

The invention discloses a temperature monitoring method for a wireless sensor network based on an improved Kriging algorithm. Firstly, a layer-cluster wireless sensor network topology structure is established, and historical temperature data of a target area are analyzed, and a threshold ρ and a fusion period T are set. Then at the cluster head node, based on the threshold value ρ, the data of the nodes in the cluster are periodically fused to obtain the initial temperature data, which is transmitted to the local database stored in the monitoring host through the wireless link; the initial data is exported, and the variation structure analysis is performed on it. Generate the variation function expression; use the improved Nelder-Mead simplex method to optimize the variation function model, design the regular grid terrain, use the ordinary Kriging algorithm for temperature interpolation, perform error correction, and output the regular grid temperature field map. The invention optimizes the variation function model by using the improved Nelder-Mead simplex optimization method, corrects the weight of the ordinary kriging algorithm, and compensates the error of the interpolation result, so that the interpolation result is more scientific and closer to the actual situation.

The invention provides a vehicle instability pre-warning system and method. The vehicle instability pre-warning system comprises a detecting module, a data collecting module and a pre-warning module, wherein the detecting module is used for detecting a state parameter of a vehicle and providing a detection signal that expresses the state parameter; the data collecting module is used for filtering the detection signal from the detecting module so as to obtain detection data; the pre-warning module is used for judging whether vehicle turnover danger exists or not according to a surrogate function on the basis of the detection data and giving pre-warning if the vehicle turnover danger exists, wherein the surrogate function is fitted by using Kriging algorithm in advance on the basis of the detection data. As the surrogate function is built by using the Kriging algorithm, the function relation between the state parameter of the vehicle and the vehicle body stability can be established to improve the pre-warning accuracy.

The invention provides a multi-working-condition structure optimization method of a weapon station on the basis of a Kriging algorithm. The method comprises the following steps: 1: giving the range of a support frame elasticity modulus, the range of cartridge box quality and the range of buffer rigidity; 2: adopting Latin hypercube experiment design to select and obtain N groups of sample points; 3: calculating gun muzzle vibration comprehensive parameters; 4: combining the N groups of sample points with N gun muzzle vibration comprehensive parameters to form an initial training sample point set; 5: using a genetic algorithm to carry out optimization on a Kriging surrogate model to find an optimal point and a maximum expectation improvement point; 6: on the basis of a dual Kriging model sequence iterative optimization algorithm, carrying out optimization on a structure parameter optimization problem until convergence is realized; and 7: obtaining the structure optimization parameters of different angles of fire, and substituting the optimal structure parameter of each angle of fire into weapon station dynamics models under other working conditions, and looking for the structure parameter with a maximum average optimization range as a final structure optimization parameter.

The invention discloses an increment ionosphere refraction error correction method which is applied to a GNSS single-frequency user. The method comprises a monitoring station utilizes ionosphere model parameters broadcasted by a GNSS system itself to calculate ionosphere vertical TEC1, and meanwhile, utilizes double-frequency observation data to calculate ionosphere vertical TEC2, and difference value [delta] TEC between the TEC1 and the TEC2 is obtained; a plurality of monitoring stations transmit the [delta] TECs of themselves to a master control station through a communication link; the master control station receives each [delta] TEC, establishes an ionosphere increment variation model, obtains ionosphere increment variation model broadcast parameters a, b and c in a region, and transmits and broadcasts the a, b and c to the user through a broadcast link; a user receiver receives a navigation signal and the a, b and c, utilizes the ionosphere model parameters broadcasted by the GNSS system itself to calculate TEC <GNSS>, utilizes the a, b and c to estimate optimum estimation value (shown in the specification ) of the ionosphere increment through a common Kriging algorithm, and adds the TEC <GNSS> with the optimum estimation value (shown in the specification ) to obtain TEC ; and the TEC is utilized to calculate ionosphere addition time delay, so that high-precision ionosphere refraction error correction for the GNSS single-frequency user is realized.

The invention provides a Kriging interpolation-based rapid division method for an outburst danger area of a stope face, which comprises the following steps of: monitoring and preprocessing micro-seismic data, quantifying the micro-seismic monitoring data, performing Kriging interpolation on the quantified micro-seismic monitoring data, and dividing the outburst danger area. According to the method, a micro-seismic monitoring technology is applied to a coal and gas outburst mine; dynamic evaluation based on micro-seismic monitoring data is established by taking a coal and gas outburst mechanism and geostatistics as theoretical basis and taking a GIS technology and a Kriging algorithm as research tools; the following problems are effectively solved: an existing coal and gas outburst prediction method is large in drilling work amount; the interference of human factors is large; furthermore, the production is generally influenced to a certain extent; continuous monitoring cannot be realized in time domain; a point evaluation form is embodied in space domain; and the regional coal rock stress environment and the coal and gas outburst danger dynamic evolution process in the mining disturbance process are difficult to reflect. The danger level visualization of the outburst danger area of the stope face is realized.

The invention provides a weapon station muzzle disturbance optimization method based on the Kriging algorithm. The method comprises the steps of: firstly, setting the ranges of the buffer damping, the barrel elasticity modulus and the barrel Poisson ratio of an overhead weapon station; secondly, selecting N sample points by using the Latin Hypercube experiment design; thirdly, calculating a muzzle disturbance comprehensive function; fourthly, combining the N groups of sample points and N muzzle vibration comprehensive parameters to form an initial training sample point set; fifthly, performing optimizing on a Kriging surrogate model by using the genetic algorithm to find out the optimal point and the expectation maximum raising point and obtain optimized structure parameter values. The weapon station muzzle disturbance optimization method based on the Kriging algorithm can minimize the muzzle disturbance quantity through the optimization design of the elasticity modulus of a support frame, the quality of a cartridge box and the rigidity of a buffer.

The invention provides a multi-granularity ore body three-dimensional modeling uncertainty expression and transmission method. Based on an uncertainty reasoning network, a geostatistics Kriging algorithm and a sequential inertial Gaussian simulation algorithm, the multi-granularity ore body three-dimensional modeling uncertainty expression and transmission method concludes and summarizes reasoningtype uncertainty and calculation type uncertainty generated by errors in a modeling process, analyzes the algorithms, establishes an uncertainty expression and transfer model framework of a multi-granularity ore body three-dimensional attribute model in combination with error distribution and transfer conditions in the ore body three-dimensional model establishment process, and realizes quantitative analysis of the uncertainty of the ore body three-dimensional model. The beneficial effects of the multi-granularity ore body three-dimensional modeling uncertainty expression and transmission method are that the multi-granularity ore body three-dimensional modeling uncertainty expression and transmission method achieves the description of the uncertainty expression and transmission process ofthe three-dimensional model of the ore body, and provides a theoretical basis for the design, planning and production of a mine enterprise through employing the three-dimensional model.