36results about How to "Simple steps to follow" patented technology

The invention relates to a semi-automatic antarctic subglacial lake recognition method based on an ice radar technology. The semi-automatic antarctic subglacial lake recognition method based on the ice radar technology has the advantages that in consideration of the characteristics that the interface between a subglacial lake and ice on an ice radar fracture surface image has high echo signal intensity and is flat, the initial computer recognition of a subglacial lake surface is realized by using an automatic extraction method in which the surface roughness and the radar reflection power are combined, so that the recognition efficiency and the recognition accuracy are greatly improved; the location of a subglacial lake is corrected through manual modification, no excessive workload is increased, errors caused by automatic extraction can also be made up, so that the final recognition result is relatively accurate, and the speed and the reliability are relatively high.

The invention relates to a remote sensing estimation method for the black carbon concentration in accumulated snow. The method performs regional scale black carbon concentration estimation based on the combination of MODIS daily accumulated snow synthetic products (MOD10A1 and MYD10A1) with a 500 meter spatial resolution and actually-measured black carbon concentration data of ground stations. The method comprises the steps of performing image fusion, map projection conversion and data format conversion on the MODIS daily accumulated snow products to acquire research area accumulated snow albedo data; performing cloud-removing synthesizing processing on images, extracting the accumulated snow albedos of ground station positions, referring to corresponding documents, establishing a relation model of snow albedo and black carbon concentration, and performing inspection and repeated optimization on the relation model; processing the accumulated snow albedo images by using the relation model and acquiring the spatial and temporal distribution result of the black carbon concentration in accumulated snow by using research area vector boundary data interception. Compared with the conventional methods, the method employs the remote sensing technology and thus can obtain the spatial and temporal feature data of the black carbon concentration distribution in large-scale accumulated snow effectively.

The invention relates to a sea ice thickness estimation method based on experimental data of a multi-beam altimeter (MABEL). The method comprises the following steps: preprocessing MABEL data, extracting altitude and coordinate information for matching, and removing an atmospheric effect; then performing time shaft equal division and altitude division, constructing a two-dimensional grid, and extracting an interval with the largest number of photons; performing treatment of calculating mean variance and the like to further eliminate invalid photons to acquire a sea surface contour line; and finally, estimating the sea ice thickness by combining a hydrostatics equilibrium formula according to the sea ice height out of water. Compared with the traditional method, the MABEL data is introduced into the method provided by the invention to estimate the sea ice thickness, so that the precision is higher.

The present invention relates to a glacier identification method based on coherence coefficient threshold. Firstly, the SAR image pair is processed by raw data reading, image registration, interferogram calculation, coherence estimation, removal of flat phase, phase filtering and geocoding to obtain coherence. Coefficient map, the glacier area presents low coherence characteristics on the coherence coefficient map. Then fit the curve relationship between the coherence coefficient value and the corresponding number of pixels in the coherence coefficient map of the study area, and calculate the first derivative of the curve relationship. The transition point to the non-glacial area is the selected threshold b. Then classify the coherence coefficient map through the rule-based object-oriented classification method, and set up the classification rules as: γ

The invention provides a cup dropping device and a lid dropping device for an automatic fruit juice vending machine, aiming to solve the problem that the structures of the cup dropping device and the lid dropping device are relatively complicated in the prior art. The cup dropping device includes a cup barrel, a drive assembly and two dials; the cup barrel is provided with two oppositely set aside grooves; the dial is composed of a roller and picks evenly distributed on the outer wall of the roller, and the roller and the drive assembly Connected; the two dials correspond to the dialing slots one by one. The cover drop device includes a cup cover tube, a drive assembly and two dials; two oppositely arranged pull-off grooves are arranged on the side wall of the lower end of the cup cover tube; It consists of a plurality of paddles, and the rollers are connected with the drive assembly; the two dials are respectively arranged on both sides of the cup lid cylinder and correspond to the pull-out slots one by one. In the present invention, the juice cup or the cup cover can be smoothly removed by two dial wheels, and the overall structure is simplified.

The invention relates to a planeness error evaluation method and device. The method includes initiating a rotary angle and a current optimal planeness error; projecting a to-be-tested dot set of a component plane to a projection face under the rotary angle; determining a two-dimensional boss of a projection point in the projection face; determining the minimum dot line distance in the two-dimensional boss; judging whether a minimum area condition is met or not; updating the current optimal planeness error; judging whether a convergence condition is met or not for determining a final planeness error evaluation result. The method provided by the invention is small in calculation amount. The two-dimensional boss of the projection dot set on the projection face is generated by adopting a boss quick generation method and easy implementation is achieved. At the same time, by changing the rotary angle of the projection face, different two-dimensional bosses can be obtained and a solution meeting the minimum area condition can be found. The execution steps are simple and are easy to realize through programming. Therefore, the method and device provided by the invention is easy to apply in practical engineering.

A method and device for generating an index for a table in a database are provided. Provided is a method for generating an index for a table in a database, including: in response to receiving a query statement for querying the table, searching the table for one or more rows satisfying the query condition in the query statement; based on a or a plurality of row identifiers to build a result list; and generate an index for the query statement, the index includes information associated with the query condition and the result list. There is also provided a method for operating on a table in a database based on an index, comprising: parsing a query condition and a result list from the index; in response to receiving a second statement operating on the table, determining that the query condition matches at least a portion of the second condition in the second statement; and executing the second statement based on one or more rows in the table indicated by the identifier in the result list.

The invention relates to a remote sensing classification method for sea ice types, which uses satellite radar altimeter Cryosat-2 data to classify polar sea ice, and realizes sea ice classification based on a new method and on a large scale. The steps include obtaining the latitude and longitude coordinates of the measurement point and the corresponding radar echo waveform from the CryoSat-2 altimeter data, extracting the relevant characteristics of the echo waveform; downloading the AARI sea ice type data, and extracting the sea ice type information at the corresponding measurement point ;Perform geographic matching on the above-mentioned extracted data information, and convert it into point data with latitude and longitude coordinates through MATLAB programming; the waveform features at the obtained points and the corresponding sea ice types are used as training data, and the random forest classifier is used for For training, the sea ice classification is carried out on the data to be classified; the classified data are processed in ArcGIS through projection transformation, gridding, etc., and a sea ice type data set with a spatial resolution of 25km is obtained.