39results about How to "Fix slow issues" patented technology

The invention discloses a character string matching method and device. The character string matching method comprises the following steps of: getting a character mapping table, wherein all characters which are different from the characters in a preset character string in the character mapping table correspond to a first mapping character, and the first mapping character is different from the mapping characters in the mapping table, which correspond to the characters in the preset character string; receiving input data; getting the mapping characters in the character mapping table relative to the characters in the input data; and matching the character string constituted by the obtained mapping characters with the mapped character string in the character mapping table, which correspond to the characters in the preset character string. According to the character string matching method disclosed by the invention, all the non-appeared characters in the preset character string are mapped to the same character, so that the occupancy of a memory during matching is reduced and matching speed and matching efficiency are improved by only identifying whether the certain character is matched with the characters in the preset character string or not without respectively identifying all the non-matched characters during the matching process.

The invention discloses an image encryption method based on a Lorenz system optimal sequence and K-L transformation, and a Lorenz chaotic system is selected as a key generator. At the same time, the K-L transformation method is introduced, which effectively improves the performance of the encryption result in the adjacent pixel correlation test. At the same time, this method is relatively easy to implement in subsequent hardware implementations, and finally the Lorenz-based system proposed in this paper is obtained. The image encryption algorithm of optimal sequence and K-L transformation has achieved a good balance between algorithm security and algorithm speed. The encryption algorithm of the present invention is simple and easy to implement, solves the problem of slow image replacement algorithm speed, and at the same time avoids the problem of insufficient security of the image scrambling algorithm, and achieves a good balance between algorithm security and algorithm speed The balance of the image; the index of image pixel correlation is further improved, and the security of the finally obtained image encryption algorithm is further improved.

The invention belongs to the technical fields of robot active visual perception, language interaction, radar obstacle avoidance and deep learning, and in particular relates to a robot human-computer interaction method. This method captures the RGB image and depth map of the environment, detects obstacle information to obtain a laser radar array, normalizes the acquired data, constructs a problem encoding network in human-computer interaction to encode the problem; constructs an image feature extraction network, Extract RGB image and depth image information into a feature matrix, splice lidar data, question code and feature matrix to get feature fusion matrix; use convolutional network to obtain data fusion matrix as the data fusion matrix of surrounding environment; train a cyclic neural network As a navigator, the network takes the data fusion matrix as input, outputs the navigation result, and controls the robot's movement direction. The method realizes the robot's self-navigation, self-exploration, human-computer interaction and other functions, and improves the intelligence of the robot.

The present invention provides a target detection method and system based on depth-separable convolution, the method comprising: acquiring image data; the image data includes a target object; inputting the image data into a depth-separable neural network, extracting Image features in the image data; fusion detection is performed according to image features of different levels, and a prediction result of the target object is output. The present invention solves the problem of slow speed when using standard convolutional neural network for target object recognition. After using different levels of image features for detection, feature fusion is performed, which ensures the high efficiency and accuracy of target object detection and realizes low calculation cost. , Effectively and accurately detect the purpose of the target object.

The invention discloses a control method and device of an electronic expansion valve and an air conditioner. The method includes: starting the compressor of the air conditioner; controlling the opening of the electronic expansion valve of the air conditioner to a first opening value, and starting timing; detecting the first operating parameter and the first operating condition of the compressor, wherein the first The operating parameter is the operating parameter of the compressor when the timing time is between the first preset time and the second preset time, and the first operating condition is when the timing time is between the first preset time and the second preset time. the operating condition; calculate the second opening value according to the first operating parameter and the first operating condition; and control the opening of the electronic expansion valve to the second opening value. The present invention solves the problem in the related art that the compressor in the air-conditioning system reaches a stable running state after startup due to low control precision of the electronic expansion valve.

Embodiments of the present invention provide a data request processing method and a communication network system, which relate to the technical field of communication. The communication network system includes: a base station and an edge computing device, and the method includes: the edge computing device determines whether the received data request is a target request; if so, the edge computing device determines whether there is pre-saved response data for the data request, and The saved response data is obtained by saving the response data corresponding to the target request to the edge computing device under preset conditions. The preset condition is that the access frequency of the response data corresponding to the target request is greater than or equal to the preset frequency threshold; if it exists, Then the edge computing device returns the pre-saved response data to the base station. In this way, the problem of slow network speed in densely populated areas can be solved, and frequently accessed data can be saved in advance, and directly returned to subsequent users, achieving the beneficial effect of improving network speed.