50results about How to "Realize resource allocation" patented technology

The present invention provides a method and device for resource allocation in a V2V scenario. The method includes: after determining that the user equipment UE supports V2V services, allocate at least one time-domain resource of a semi-static resource to the UE through a first RRC signaling message; After receiving the resource allocation request sent by the UE, determine whether the UE has allocated corresponding time domain resources; if so, according to the size of the data packet and the transmission period of the service to be transmitted by the UE, allocate it to the UE through the medium access control unit MAC CE The frequency domain resources of the corresponding semi-static resources, if not, allocate at least one time domain resource of semi-static resources to the UE through the second RRC signaling message, and at the same time, according to the packet size and transmission period of the service to be transmitted by the UE, through the media interface The access control unit MAC CE allocates frequency domain resources corresponding to the semi-static resources for the UE, and activates the semi-static resources through the MAC CE. The present invention effectively realizes the semi-static resource allocation in the V2V scene.

The invention discloses an image processing system with a distributed architecture. The image processing system comprises a core controller and a plurality of embedded GPUs, wherein the core controller comprises an address channel, an image channel and a result channel which are parallel; wherein the address channel is used for receiving configuration parameters sent by remote control equipment; wherein the image channel is used for receiving image data to be processed and decoding the image data, distributing the image data to the corresponding embedded GPUs according to a target equipment mask obtained by decoding, and executing parallel data processing by the plurality of embedded GPUs; the result channel is used for receiving and outputting a processing result of the embedded GPU on the image data; based on the architecture of the FPGA core control panel and the plurality of embedded GPUs, the image processing speed is greatly improved, the structure simplification, high integration and system miniaturization of the system are realized, the use flexibility of the system is enhanced, and the test requirements of a large-size and high-resolution display panel are met.

The invention provides a new boundary calculation method for a visible light communication multiple access channel capacity region, and provides a new inner boundary and a new outer boundary of the multiple access channel capacity region in a visible light communication network under the constraint of a peak value and average optical power. In particular, the proposed inner bound is established byimplementing an input profile for each user using a single user capacity. The proposed external boundaries are derived by determining a single user capacity for each user and relaxing the input constraints to calculate a sum capacity upper limit. Numerical results show that the proposed new boundary is very tight, and is superior to existing boundaries over the wide area of SNR.

The present invention relates to industrial wireless network technology, specifically, an industrial wireless network resource allocation method based on multi-agent deep reinforcement learning, including the following steps: establishing an industrial wireless network with terminal-edge coordination; establishing industrial wireless network terminal-edge resources Allocation optimization problem; establish Markov decision model; use multi-agent deep reinforcement learning method to construct resource allocation neural network model; offline training neural network model until the reward converges to a stable value; based on the offline training results, industrial wireless network online Perform resource allocation, process industrial tasks. The present invention can implement end-edge collaborative resource allocation for industrial wireless networks in real time and with high energy efficiency, and minimize system overhead under the constraints of limited energy and computing resources.

The invention provides a terminal safety control method and a system using the terminal safety control method. The method includes the following steps: a controller receives detection data information reported by each terminal. Build a safety problem statistic table according to the detection data information; according to the safety problem statistical table, a first current time slice sum and a first strategy, the controller decides a first distribution time slice of each terminal and system left time slices, and judges whether the amount of the system left time slices is smaller than or equal to the preset amount; if the amount of the system left time slices is larger than the preset amount, the controller uses the system left time slices as a second current system time slice sum, and recalculates the second distribution time slice of each terminal according to the first strategy, if the amount of the system left time slices is smaller than or equal to the preset amount, the controller obtains target distribution time slice of each terminal according to the first distribution time slice and the second distribution time slice; a management central server treats safety problems of terminals according to the target distribution time slice of each terminal.

The invention discloses a resource allocation method for unmanned aerial vehicles based on the quantum bird swarm evolution mechanism, which belongs to the field of autonomous control of unmanned aerial vehicles. The steps of the method of the present invention are as follows: establish a UAV resource allocation model; determine the type of mission performed by the UAV, and initialize the quantum bird group; perform fitness calculation according to the fitness function, and determine the global best position of the group; through quantum rotation The gate and the quantum NOT gate update and measure the quantum position; calculate the fitness according to the fitness function; update the local best position of each quantum bird and the global best position of the whole group; judge whether the maximum number of iterations is reached, and output if reached The global optimal position of the swarm is mapped to a task resource matrix. The present invention fully takes into account that UAVs have different needs for resources when they perform different tasks, obtains the UAV resource allocation scheme with the optimal resource configuration ratio at less time cost, and at the same time satisfies the performance requirements of UAVs, and obtains a more reasonable solution. UAV resource allocation scheme.