14 results about "Effective capacity" patented technology

The invention discloses a capacity market clearing and pricing method and device and a medium, which are applied to an electric power system, and the method comprises the steps: obtaining the new energy real-time data of a new energy station in the electric power system, inputting the new energy real-time data into a capacity contribution evaluation network model, and obtaining an equivalent reliable capacity; calculating a traditional unit capacity, an energy storage capacity and a demand side response capacity respectively; and respectively inputting the equivalent reliable capacity, the traditional unit capacity, the energy storage capacity and the demand side response capacity into a clearing model to obtain a bid-winning subject and a clearing price of the corresponding capacity. According to the method, the effective capacity of various resources can be accurately quantified, and a price signal capable of truly reflecting the capacity scarcity degree of the system is formed on the basis, so that the optimal power supply structure investment is effectively guided, and long-term reliable operation of the high-proportion new energy power system is guaranteed.

The present application relates to power system power source planning method technical field, especially a kind of multi-region power system power source planning method considering capacity sharing, including the construction load growth driven time sequence uncertainty probability model;Establish the effective capacity refined evaluation model under the random failure of multi-source unit;System capacity distribution analytical transformation based on semi-invariant and series expansion.The present application breaks the limitation of traditional single region independent planning by establishing the capacity sharing mechanism between receiving end and sending end regional power grid.Can dynamically adjust inter-regional power resource allocation according to the degree of capacity scarcity, significantly reduce cost under the premise of guaranteeing reliability.

The application discloses a large language model key value management system and method based on a memory pool, and belongs to the technical field of computers.The system comprises: a first layer storage module, which is GPU memory and stores key value cache data of an inference request being processed; a second layer storage module, which comprises local main memories of various computing nodes and stores key value cache data generated after inference of each computing node is completed in the form of prefix data blocks; and a third layer storage module, which is a memory pool and is used for storing prefix data blocks repeatedly accessed by a large number of different requests; and a three-layer storage scheduling module, which is used for transmitting the prefix data blocks of the second layer storage module to the third layer storage module for exclusive storage according to an access frequency, and bidirectionally expelling and writing back when the capacity of the local main memories of the computing nodes and the third layer storage module reaches an upper limit.The application can eliminate a network bottleneck, reduce inference delay, improve effective capacity of storage space, and improve a cache hit rate.

This application provides a capacity testing system, method, integrated charge / discharge machine, dielectric, program product, and terminal. The capacity testing in this application is performed synchronously with the charge / discharge process, eliminating the need for separate testing. Furthermore, it employs an embedded modular design, enabling online capacity testing without external equipment. The capacity testing method uses the traditional two-point method capacity calculation result as the initial capacity observation value, and then dynamically corrects the initial capacity observation value through aging state transition and stress mapping, resulting in significantly higher capacity testing accuracy than the traditional two-point method. The capacity testing system of this application is applicable to different cell types, overall pack designs, and thermal management strategies. This application obtains highly accurate battery effective capacity and estimated state of equilibrium (SOH), which can be used for subsequent battery operating status assessment, life analysis, and user display. It can also provide a basis for equipment operation strategy optimization or maintenance decisions, and is continuously iterated and updated during subsequent operation.

The application discloses a kind of power supply full life cycle flat equalization capacity cost calculation method, system, equipment and medium, it is related to power system analysis and planning technical field, including obtaining the multiple cost parameters of each year of target power supply in entire life cycle, and according to the operation state data of target power supply in life cycle, calculate capacity reliability representation parameter, each year multiple cost parameters are uniformly converted to reference time point using the preset time value discount factor, and summation is carried out, obtain the present value of total cost in full life cycle, based on the present value of total cost in full life cycle and cumulative effective capacity, the amortization cost of unit effective capacity in life cycle is calculated, i.e.flat equalization capacity cost.The application is quantified to power supply full life cycle flat equalization capacity cost by combining capital recovery, dynamic environmental cost, capacity reliability reduction and comprehensive operating cost, provides fair evaluation basis for the capacity value of power supply of different technical route.

The application relates to the technical field of wireless communication, and particularly discloses a GFDM effective capacity optimization method and system for a jungle channel, the method comprising the following steps: obtaining a raw binary data stream at a transmitting end, pre-processing and demodulating a signal received at a receiving end to obtain a demodulated binary data stream, and calculating a bit error rate based on the raw binary data stream and the demodulated binary data stream; calculating effective capacity based on the bit error rate, taking the effective capacity as a utility function, and step-by-step optimizing GFDM waveform design parameters by applying a particle swarm algorithm, wherein the GFDM waveform design parameters comprise a roll-off coefficient and a subcarrier number; and the method can optimize the GFDM effective capacity under the jungle channel, and obtain the best effective capacity and corresponding subcarrier number and pulse shaping filter coefficient.

The application discloses a storage pool creation method and device, equipment, medium and product, applied to the storage technical field, including: obtaining the number of fault domain units and the number of copies; when the number of fault domain units is greater than the number of copies, whether each fault domain unit meets the first preset capacity balance condition is judged based on the minimum effective capacity corresponding to each fault domain unit and the expected effective capacity; if the first preset capacity balance condition is not met, the target effective capacity is obtained by grouping and recursively calculating each fault domain unit, and whether the second preset capacity balance condition is met is judged based on the target effective capacity and the expected effective capacity; in the case of not meeting the second preset capacity balance condition, the main pool is created based on the target effective capacity, and the auxiliary pool is created based on the remaining capacity. In this way, the utilization rate of storage resources can be improved.

The invention discloses an effective energy efficiency optimization method for an MU-MIMO-NOMA system, and the method comprises the steps: dividing users into a plurality of clusters according to the difference of the service time delay service quality requirements of all users of a multi-user MIMO system; performing block diagonalization precoding on different clustered transmission signals to eliminate inter-cluster interference; carrying out power domain power distribution in each cluster by adopting a non-orthogonal multiple access technology to obtain distribution power; calculating the user transmission rate based on the distribution power, and obtaining the effective capacity and the effective energy efficiency of the system; constructing an optimization problem with effective energy efficiency maximization as a target, wherein the optimization problem takes system transmitting power and NOMA power distribution factors as optimization variables; and solving the optimization problem by using an improved EAO algorithm to obtain optimal system transmitting power and NOMA power distribution factors, thereby realizing effective energy efficiency optimization.

The invention relates to the field of AP management, in particular to a self-adaptive AP capacity planning management method and device, equipment and a medium. Respectively calculating an aggregation bandwidth demand of each terminal type according to the number and the concurrency rate of each terminal type in the target wireless network scene and the bandwidth demand of a single terminal; calculating the effective capacity of the single AP for each terminal type according to the theoretical support capacity of the single AP for different terminals and the physical layer transmission efficiency; obtaining the total number of first APs needing to be deployed in the daytime according to the aggregation bandwidth requirement of each terminal type and the effective capacity of the single AP for each terminal type; obtaining the total number of second APs needing to be deployed at night according to the online rate of each terminal type at night and the number of each terminal type; and according to the total number of the first APs, the total number of the second APs and the plane position coordinates of all the APs in the target wireless network scene, determining the APs needing to be closed at night. And the overall utilization rate of network resources is improved.

The embodiment of the present application relates to the application field of storage devices, and discloses a data write protection method and a storage device, the method comprising the following steps: when the storage device is in a write state, obtaining the compression ratio and the residual effective capacity of each valid super block of the storage device; calculating a first approximate compression ratio according to the compression ratio of each valid super block; and determining the write protection strategy of the storage device according to the comparison result of the first approximate compression ratio and a preset compression ratio, and the comparison result of the residual effective capacity and a capacity threshold. The write protection strategy of the storage device is determined according to the comparison result of the first approximate compression ratio and the preset compression ratio, and the comparison result of the residual effective capacity and the capacity threshold, so that the problem that the storage device is down due to the continuous data writing of the host can be solved, and the stability of the storage device is improved.

The invention provides an end-to-end service level adaptive cloud data center resource collaborative optimization method. The method is characterized by comprising the following steps of: predicting a workload based on historical task data; under the constraint of global SLA violation budget, dynamic violation budget allocation and resource quota calculation are carried out on each application type based on dynamic planning; calculating the effective capacity of the virtual machine based on a historical utilization rate and a security margin factor, and supplying a security excess allocation virtual machine; a virtual machine-to-physical machine placement step based on an anti-affinity principle; and a double-layer load balancing task scheduling step based on deep reinforcement learning. According to the method, workload prediction, SLA violation budget allocation, resource supply and placement and online task scheduling are unified in one framework, and the problem of global sub-optimization caused by isolated optimization of a resource configuration layer and a scheduling layer is avoided.

The invention provides an instruction compression method and device, a processor core and computer equipment, and relates to the technical field of data compression. The instruction compression method comprises the steps of obtaining a plurality of micro-operations corresponding to at least one instruction in a current period; according to the execution unit type corresponding to each of the plurality of microoperations and a plurality of preset compression types, determining a plurality of compression modes for the plurality of microoperations; according to the compression lengths of the various compression modes, selecting a target compression mode from the various compression modes; and compressing the plurality of microoperations into the reordering buffer area by adopting a target compression mode. According to the method, multiple compression modes with the high coverage rate are determined based on execution unit types corresponding to the microoperations and multiple preset compression types, then an optimal target compression mode is determined from the multiple compression modes based on the compression length, and the multiple microoperations are compressed into a reordering buffer area in the target compression mode. In this way, more microoperations can be accommodated for the same reordering buffer area resources, the effective capacity of the reordering buffer area is improved, and then the renaming speed is increased.

This application provides a packet discarding method and a communication device relating to the field of communications. The method includes the steps of: a second communication device transmitting first instruction information to a first communication device, wherein the first instruction information indicates adjusting the duration of a discard timer corresponding to first data at the PDCP layer to a second duration, and the first data being a first data packet or a first data packet set of target importance; and the first communication device adjusting the duration of the discard timer from a first duration to a second duration based on the first instruction information. According to the method, the first communication device can quickly and flexibly adjust the duration of the discard timer for first data to adapt to different communications environments, increase the effective capacity of the system, and improve the user experience. For example, in scenarios where network congestion occurs, the duration of the discard timer for less important data can be shortened. In this way, less important data can be preferentially discarded, and it can be ensured that more important data can be transmitted normally.

The application discloses a data transmission optimization method and device of a communication system, a terminal and a medium, and comprises the following steps: modeling an IRS-aided channel according to a cascade channel model; obtaining the AoI violation probability of each device according to the definition of AoI; constructing an effective capacity maximization problem of information timeliness guarantee; converting a long-term random optimization problem into a per-slot deterministic optimization problem according to a Lyapunov optimization algorithm; converting the original effective capacity optimization problem into a Markov decision process; and jointly optimizing the phase shift of the IRS and the transmission power of the device according to an SAC algorithm to obtain an optimized data transmission strategy. The application solves the effective capacity maximization problem of information timeliness guarantee in an IRS-aided NOMA communication system with imperfect CSI.