41results about How to "Realize Segmentation" patented technology

The high precision vision detection system includes the digital staff guage system, the illuminating system with adaptive adjusting optical intensity, the auto focus system, the optical image system, the vision detection software, the foundation, the computer or microprocessor system. The digital staff guage system is used to provide the high precision length detection base in large scale; the illuminating system is used to adjust the optical intensity according to the different of the detection distance, detection angle and the surface reflectivity of detected workpiece to provide the uniform and stable illuminating intensity to pledge the image quality and the detection precision; the auto focus system is used to adjust the working distance of the detection system to pledge the image has the good quality and constant amplification ratio and pledge the repeatability and detection precision of the detection system; the optical image system is the main part of the detection system, which is used to transform the geometry outlook of detected workpiece to the computer or processor through the optical lens image, the vidicon photoelectricity switch and collecting control circuit; the vision detection software is used to process the image and get the detection result; the foundation is the supporting frame of vision detection system, which is used to fix, support and move every part of system; the computer or microprocessor system is the core part of the whole vision detection system, which is used to coordinate and control every system.

The embodiment of the invention provides a case grouping method and device, electronic equipment and a storage medium. The method comprises the steps of determining a disease treatment category of a to-be-grouped case, and obtaining data of a first disease treatment category, wherein the first disease treatment category is the disease treatment category where the to-be-grouped case is located, andis obtained based on the main diagnosis type and the main operation type of the to-be-grouped case; calculating a CCI index of the to-be-grouped case according to the data of the first disease treatment category; determining expense influence characteristic of the to-be-grouped case, wherein the expense influence characteristic is a characteristic that the influence of the medical expense of theto-be-grouped case is higher than a preset threshold value; and inputting the CCI index and the expense influence characteristic of the to-be-grouped case into a case grouping model, and determining aDIP disease severity degree group where the to-be-grouped case is located, wherein the case grouping model is obtained by training based on the CCI index of the sample case and the expense influencecharacteristic.

The present invention discloses a method and apparatus for mining massive intelligent power consumption data based on cloud computing. The method comprises the following steps of: storing massive power consumption data generated by a peripheral system in a distributed file system; a user actively initiating a service request, and a master node receiving the request and analyzing the service request, selecting slave nodes required to participate in mining and a mining algorithm according to an actual situation, and assigning tasks to the slave nodes after decomposition of dimension; and each slave node according to the assigned task, performing data storage and task execution, using the data mining algorithm selected by master node to perform a power consumption data mining task independently, and interacting with task management. The apparatus comprises a data management module, a task management module, a task execution module, a data storage module, a mining model library module and a data dimension model module. According to the method and apparatus for mining massive intelligent power consumption data based on cloud computing, power consumption information of massive users is efficiently mined, and forecast of power consumption of domestic consumers is achieved, so as to develop an optimal power consumption strategy.

The invention discloses an annular combustion furnace with a temperature subdividing function and belongs to the field of equipment for analyzing the sulfur content of combustible substances in bituminous coal. The annular combustion furnace comprises a lower combustion furnace cover, a combustion furnace body, an upper combustion furnace cover, a rotary table, a stepping motor and a rotary shaft, wherein the lower combustion furnace cover is arranged on a combustion furnace frame; the stepping motor is arranged on a motor frame; one end of the rotary shaft is connected with the rotary table, and the other end of the rotary shaft is connected with an output shaft of the stepping motor. A flange hole is formed in the center of the top of the lower combustion furnace cover, a rotary shaft hole is formed in the center of the lower combustion furnace cover in the axis direction, and second heat insulation plates are evenly arranged in the circumferential direction of the lower combustion furnace cover. Twenty crucible holes are evenly distributed in the rotary table in the circumferential direction. A third heat insulation plate and a fourth heat insulation plate are arranged between every two adjacent crucible holes. Five to ten heating resistance wires are arranged on the inner wall of the combustion furnace body. According to the annular combustion furnace, the structure is simple, the service life of the resistance wires is longer, multiple temperature zones are achieved, and the temperature subdividing function is achieved.

The invention discloses a package optimizing system and method based on a GPU and neighboring mass data rapid analysis. The package optimizing system based on the GPU and neighboring mass data rapid analysis comprises a parameter input module, wherein the parameter input module is used for transmitting parameter information to a data generation module and a package optimization module, the data generation module transmits data to the package optimization module, the parameter input module and the data generation module extract information from a client information database, the package optimization module comprises an evolutionary algorithm module and a GPU acceleration module, communication is carried out between the evolutionary algorithm module and the GPU acceleration module, the GPU acceleration module comprises a plurality of package adaptive value evaluation modules, and each package adaptive value evaluation module comprises a similarity calculation module. According to the package optimizing system and method based on the GPU and neighboring mass data rapid analysis, automatic high-speed processing of mass teledata can be achieved, a package scheme which highly meets the demand of a target user group is obtained, the problem that high time complexity is caused due to modeling by using a complex model is solved, and reliability of optimization results is improved.

The invention discloses a user grouping method, which includes: determining one or more exclusive attributes of the category according to the browsing behavior of users who have purchased commodities under the category within the first predetermined browsing time range; Traversing and combining each product attribute value with each product attribute value with other exclusive attributes to obtain the user group corresponding to each combination; the users who have the willingness to purchase products under this category within the second predetermined browsing time range Users are assigned to corresponding user groups. The exclusive attribute is a commodity attribute with multiple commodity attribute values, and the multiple commodity attribute values ​​completely cover all commodities under the category; the same commodity attribute selected by the same user group in the process of browsing commodities under the category The product attribute value of is the most concentrated, and has a low degree of overlap with other product attribute values ​​of this product attribute. By adopting the present invention, user groups can be distinguished according to the real purchase needs of users.

An absolute coding raster phase subdivision method based on a charge coupling device (CCD for short) is disclosed, comprising the following sequential steps of: (1) reading code channel graphics of the absolute coding raster with a CCD reading head; (2) subjecting a periodical discrete space function sequence formed by a row of the lowest code channel graphics in the obtained code channel graphicsof the absolute coding raster to Flourier conversion so as to result in spectral distribution thereof; (3) taking out a fundamental component of the spectral distribution obtained in the step (2); (4) subjecting the obtained fundamental component to inverse Flourier conversion so as to result in a fundamental wave signal and a phase thereof; (5) generating the obtained fundamental wave signal instep (4) into a continuous phase by using N periodical phases; (6) expressing measurement resolution of the lowest code channel graphics in the absolute coding raster by using the average of the continuous phase. Such a method can realize the subdivision of pixels and remarkably enhance resolution and measurement accuracy of the absolute coding raster.

The invention discloses a character separation method for consecutive character strings in a complex environment, and belongs to the technical field of image processing. The invention uses the envelope information of the character string to initially locate the primary division point, and then generates the secondary division point according to the filtering process of the primary division point, and then obtains the primary division point, and finally calibrates the primary division point to accurately locate Segmentation position, so as to realize the cutting processing of the collected word string image, and achieve the purpose of separating tightly connected word strings in complex environments. The application of the present invention realizes the segmentation of tightly connected character strings in a complex environment, and is applicable to different fonts, with low computational complexity and high segmentation accuracy.