105results about How to "Relieve computing pressure" patented technology

The invention relates to a method and system for screening face images. The method comprises the following steps: according to preset face integrity standards, face size threshold and face position threshold, obtaining a first screening result; according to a preset definition threshold, obtaining a second screening result; according to a preset best angle threshold, obtaining a third screening result; and according to the sum of similarities between each face image and other face images, the definition of each face image and the face deflection angle, obtaining the weighting sum of each face image in the third screening result, and acquiring a face image with the maximum weighting sum for face identification. The method can be used for solving the problem that the face is mistakenly identified due to the low quality of an input image, the data volume of the face images is effectively reduced, and the operation load during face identification is relieved; and moreover, the data volume of the face images is reduced, front face images with high quality are effectively screened to be identified, and the accuracy and real-time performance of face identification are improved.

The invention relates to a text to speech (TTS) method and system. The method comprises: performing text analysis and linguistic analysis on an inputted text so as to generate front-end scripts comprising corresponding speech units; obtaining, verifying and correcting the front-end scripts; and obtaining the corrected front-end scripts for synthesizing correction speech. By adopting the provided TTS method and system, the errors of the front-end scripts such as word-segmentation errors and polyphone phonetic notation errors can be corrected so that the synthesized speech is more understandable and more user friendly for users, a conventional TTS's shortcomings of insufficient prediction accuracy of rhythm can be overcome, and the synthesized speech is more natural and more expressive.

The invention provides an expressway vehicle detection and multi-attribute feature extraction method based on a local image, and relates to the technical field of intelligent transportation. A video acquisition terminal reads expressway monitoring video in real time and transmits the expressway monitoring video to an edge end, and the edge end analyzes the real-time video data by adopting a background difference method to select a key frame; a cloud end uses a VOC2007 data set and vehicle pictures collected by an expressway to train a YOLO-v3-tiny detection model, the edge end loads the trained YOLO-v3-tiny detection model to predict the position of a vehicle bounding box in the selected key frame, and then a local image of a vehicle is obtained and transmitted to the cloud end; a ResNet-50 residual neural network model is trained by the cloud end by utilizing the training set data with the multi-label type, the edge end loads the trained ResNet-50 residual neural network model, and the acquired local image of the vehicle is input into the neural network model to realize the extraction of multi-attribute features of the vehicle; and the extracted multi-attribute features of the vehicle are made into a label, and the label is uploaded to the cloud end.

The invention discloses an active content caching method based on federated learning, and belongs to the technical field of wireless communication. The method comprises steps that firstly, in each round of communication, a user downloads a global model, training is conducted locally through a stacked automatic encoder, and a local model and implicit features of the user and a file are obtained; secondly, in each round of communication, the user updates the model and sends the model to a server, and all the local models are aggregated to generate a global model; thirdly, after training is finished, the user sends the implicit features of the user and the file to the server, the server firstly calculates the user similarity and the file similarity, then a certain user is randomly selected, and a decoder of the stacked automatic encoder is used for recovering pseudo score matrixes of the user and the file; and finally, scores of the group of users on all the files are calculated by usingcollaborative filtering, and the file with the highest average score is selected for caching. On the premise of ensuring the cache hit rate, a problem of data sharing between neighbor users is effectively avoided, so private data of the user is safer.

The invention discloses a wireless body area network resource allocation and task unloading algorithm with maximum system revenue. The algorithm is based on a classic bargaining game model in the gametheory. An edge server for providing computing resources is used as a resource seller in the model; and the wireless body area network distributed in the service range of the server is used as a resource buyer, the two parties perform bargaining by taking maximization of own benefits as a target according to a selfish behavior mode in allusion to CPU resources and wireless channel resources, andfinally, an optimal resource allocation scheme and an optimal task unloading scheme of the whole system are obtained. The timeliness and reliability of wireless body area network data processing can be improved.

The invention relates to a mobile unloading migration algorithm based on an improved HRRN (High Resolution Response Next) algorithm and a multi-attribute decision. The method comprises the following steps: step 1, a single wireless body area network generates a plurality of tasks with different priorities at a certain moment, if the tasks can be locally processed, the tasks are directly locally processed, otherwise, the tasks are uploaded to an edge server; 2, establishing a benefit function of an edge server, and taking the edge server with a high benefit value as an initial edge server of the current task; 3, judging whether the current edge server is suitable or not; 4, judging whether the edge server meets two criteria or not, wherein one of the criteria can be used as a candidate edgeserver; and step 5, according to the benefit function of the edge server established in the step 2, calculating the benefit values of all candidate edge servers, and selecting the edge server with the highest benefit value to migrate.

The embodiment of the invention discloses an intelligent terminal control method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining and storing a to-be-executed linkage rule; generating a control instruction for controlling the intelligent terminal according to the linkage rule; and sending the control instruction to the intelligent terminal to enable the intelligent terminal to execute an instruction task represented by the control instruction. According to the method, the intelligent gateway is constructed locally, the local area network between the intelligent gateway and the intelligent terminal is constructed, the intelligent gateway can download the linkage strategy used for controlling the intelligent terminal through the server side,and a control instruction is issued in the constructed local area network through the linkage rule to control the intelligent terminal. The linkage rule is placed in the local intelligent gateway, sothat the current situation that the intelligent terminal needs to depend on an external network to be unblocked under control is avoided.

The invention relates to a distributed uniting coordination control method of a large-scale electric automobile charging load, and belongs to the technical field of energy management. Client terminals determine and send the initial charging power to a coordination center according to basic information of an automobile. The coordination center calculates an optimized load curve and an optimized judging threshold value. A current load curve and a representation value of the current load curve are calculated according to the charging power of the client terminals. If the representation value is smaller than the judging threshold value, a stopping mutual order is sent, and the client terminals charge the electric automobile according to the charging power, and if not, a probability transfer matrix is calculated and is sent to the client terminals. The client terminals calculate a transfer matrix according to the probability transfer matrix, and the charging power is updated and sent to the coordination center. Interaction is executed repeatedly until the representation value is smaller than the judging threshold value. Staggering-peak charging can be effectively achieved, the calculating pressure of the coordination center is relieved, the charging demand privacy of a user is protected, the data communication resources between an upper layer and a lower layer are saved, and the distributed uniting coordination control method is suitable for orderly controlling large-scale electric automobiles.

The invention provides an edge cloud resource scheduling method managed and controlled by an edge autonomous center. The method comprises the following steps: receiving a service request from a terminal; obtaining a state space of the edge cluster, the state space being used for representing a resource state of the edge cluster; inputting the state space of the edge cluster into a service request assignment model to obtain a state transition probability for assigning a service request action, wherein the service request assignment model comprises a deep reinforcement learning network; and determining a target cluster used for responding to the service request according to the state transition probability, the target cluster comprising the edge cluster or the cloud cluster, and the edge cluster comprising edge nodes. The invention further provides an edge cloud resource scheduling device controlled by the edge autonomous center and an edge cloud resource scheduling system controlled by the edge autonomous center.

The invention discloses a transformer substation monitoring system based on edge computing. The system comprises an external monitoring device, an internal monitoring device, an acquisition device, aprocessing device, a judgment device, a storage device, a first alarm device and a supervision platform, and is characterized in that the external monitoring device is arranged outside a transformer substation monitoring point; the internal monitoring device is arranged in a transformer substation monitoring point; the acquisition device, the processing device, the judgment device, the storage device and the first alarm device are connected with one another and correspondingly arranged near a substation monitoring point; the processing device is used for preprocessing, matching and identifyingthe image data acquired by the external monitoring device; and the storage device is in wireless connection with the supervision platform, and the supervision platform comprises a database, a secondalarm device and a display terminal. High-quality image resources are obtained, detection and recognition of various different parts and defects are achieved, various factors of the transformer substation are comprehensively monitored, an alarm is given in time and backed up to a supervision platform, and the computing pressure of a cloud processing center is relieved.

The invention provides an edge-cloud collaborative micro-service scheduling method and device and equipment, and aims at optimizing the service completion time and the resource utilization rate of an edge server by decoupling an application service program into a plurality of micro-services based on an improved particle swarm optimization algorithm. A micro-service scheduling strategy for realizing multi-objective optimization is finally obtained through continuous iteration. On one hand, service execution is close to a data source as far as possible through service scheduling, and the low-delay requirement of a user is met; and on the other hand, micro-services of part of non-delay sensitive applications are scheduled to a cloud server, so that the calculation pressure of an edge server is well relieved, and the problem that the edge server is overloaded and the service quality is influenced is avoided.

The invention discloses a federated learning method based on network unloading, which is used for improving the performance of the federated learning method while ensuring (improving) the privacy of terminal data. The method comprises the following steps: in a wireless communication system with a plurality of terminals and an edge server, jointly training an artificial intelligence network by using the terminals and the edge server; compared with a traditional federal learning working mode, carrying out the training of partial networks by unloading a terminal to an edge server; in the training process, selecting a terminal which is good in wireless communication network environment and small in calculation and transmission load to participate in training of the network model. According to the invention, the calculation load of the communication terminal is effectively reduced, and the communication load caused by a federated learning method is reduced.

The invention relates to the technical field of artificial intelligence, in particular to a model compression method and device, equipment and a storage medium. The model compression method comprises the following steps: acquiring a test image and a to-be-compressed model, wherein the to-be-compressed model comprises a plurality of cascaded feature extraction layers; inputting a test image into each feature extraction layer, and performing feature extraction on the test image through a filter to obtain a multi-channel feature map, wherein each feature channel in the multi-channel feature map corresponds to one sub-feature map; converting the sub-feature map into a visual feature map, and determining the importance of a feature channel corresponding to the sub-feature map based on a plurality of feature values in the visual feature map; determining a target pruning channel according to the importance degree of the feature channel; and performing pruning processing on the target pruning channel to obtain a compressed target model. According to the method, the model pruning accuracy can be effectively improved, accurate compression of the model is realized, the model calculation amount can be effectively reduced, and deployment is convenient.

The invention discloses an electric power video image analysis system and method based on cloud edge collaboration, and the system comprises an electric power video image platform, a business processing system, an artificial intelligence cloud platform, and a plurality of edge analysis devices, and the edge analysis devices are used for collecting electric power video image data in an electric power production environment, and performing first-layer analysis processing on the electric power video image data, and the electric power video image platform is used for uploading the acquired electric power video image data to the cloud platform and reporting an analysis result to the service processing system; and the artificial intelligence cloud platform is used for carrying out second-layer analysis processing on the video image data. According to an existing cloud end centralized processing mode, computing power distribution is carried out on a cloud end and an edge end by calculating the cloud end analysis capability, the edge end analysis capability, the algorithm complexity and the algorithm calculation amount, and the problems of real-time performance of data processing, unreasonable occupation of analysis resources and the like are effectively solved.

The present disclosure relates to a method, an apparatus and a computer readable storage medium for processing an order request, and relates to the technical field of data processing. The method comprises the following steps of: obtaining an intelligent contract corresponding to a target item stored in a block chain; Obtaining a corresponding number of tokens according to the smart contract in response to the user booking the target item; In response to the user initiating an order request for the target item, determining whether the user has a sufficient number of tokens to place an order according to the smart contract; Validation of the user's order request, provided that the user has a sufficient number of tokens; All validated order requests are sent to the nodes in the block chain for validation to determine if the order was placed successfully. The technical scheme disclosed by the invention can avoid the waste of resources and improve the credibility of order processing.

The invention relates to the technical field of virtual power plant energy management and control optimization, in particular to a virtual power plant energy management and control optimization method and system based on end-side cloud architecture. Based on an operation mechanism of the end-side cloud architecture, distributed control architecture is adopted, the computing pressure of a cloud center is greatly relieved, the operation efficiency of the system is improved, meanwhile, the system further considers the regulation potential of a user-side flexible load, an energy optimization scheduling model of the system is further improved, and the utilization efficiency of energy is improved. According to the method and the system, a plurality of flexible resource clusters are constructed in different areas for different flexible resources, so that the scheduling control of a virtual power plant on each flexible resource can be enhanced.

The invention discloses a sensor node system with a multiple logical sensor function to meet diverse needs of state data sampling of a sensor node in actual application. The method for a physical sensor to realize a multiple logical sensor provided by the invention is to employa software technology to further diversifiedly calculate the data after physical sensor sampling on the hardware basis of the physical sensor to realize the packaging of a plurality of specific perception functions of the physical sensor. The realization of the method comprises the storage organization mode of physical sensor data, the mapping mode of the physical sensor tothe multiple logical sensor, the configuration ofthe logic sensor and the operation control ofthe logic sensor. The method for realizing multiple logical sensorfunctions provided by the invention can be absolutely applied to the sensor node of wireless sensor network.