160results about How to "Flexible allocation" patented technology

The invention relates to the technical field of supply chain risk analysis, in particular to a supply chain risk management and decision-making system based on a large model, and the system comprises a multi-layer supply chain data collection module which collects supply chain operation data from a multi-layer supply chain node; the multi-layer supply chain structure model construction module is used for constructing a multi-layer supply chain structure model among the supply chain nodes through the collected operation data of each supply chain node, and automatically identifying hidden risks in each supply chain node; the supply chain risk propagation path analysis module is used for analyzing a hidden risk propagation path of the hidden risk in each supply chain node after identifying the hidden risk, and analyzing a transmission mechanism of the risk between different nodes of the supply chain; and the decision support module supports the enterprise to adjust the supply chain strategy to reduce the risk. According to the method, an enterprise can grasp a conduction rule of risks in a supply chain from a global perspective, hidden risks of key paths and nodes are processed preferentially, and the early warning level and the anti-risk capability of the supply chain are remarkably improved.

The invention discloses a power efficiency optimization method based on intelligent load management. The method comprises the following steps: S1, generating an initial load characteristic matrix; s2, decomposing the initial load characteristic matrix into a short-term behavior characteristic matrix and a long-term trend characteristic matrix through pseudo-symmetry decoupling; s3, multi-level load demand prediction is carried out through a fusion structure of the multi-scale convolution and the recurrent neural network; s4, coding a cognitive gene sequence, triggering gene mutation and recombination operation when significant change occurs, and generating a load distribution strategy; s5, a load distribution strategy is transmitted into a causal reasoning and self-supervision feedback control module, and if the deviation between feedback data and a prediction result exceeds a threshold value, a self-learning mechanism is triggered; s6, dynamically distributing the load priority, and generating a decision path of load management; and S7, performing adaptive optimization through a multi-layer feature feedback closed-loop optimization system. The intelligent load management method is adopted, and the power efficiency is optimized through multi-layer feature decoupling and closed loop.

The invention relates to the technical field of communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

The invention provides a multi-agent cooperation system, which comprises a multi-agent cooperation engine module, a task decomposition and distribution module and a tool calling normal form module, and is characterized in that the task decomposition and distribution module is used for decomposing tasks and distributing decomposed sub-tasks to agents; the multi-agent cooperation engine module is used for managing cooperation and communication between agents; and the tool calling module enables the intelligent agent to dynamically call different tools. According to the multi-agent collaboration system, the collaboration efficiency among the agents, the flexibility of task decomposition, the adaptivity of tool calling and the intelligent level of user interaction are remarkably improved, and remarkable technical advantages and effects are shown in complex task processing and dynamic environment processing.

The invention provides a resource adjustment method and device for edge computing nodes, equipment and a medium, and belongs to the technical field of computers.The method comprises the steps that the use condition and service scene data of target resources associated with all the edge computing nodes are monitored, the target resource comprises at least one of a central processing unit (CPU), a memory and a network bandwidth; according to the monitored data, a target scheduling strategy is selected from a pre-established scheduling strategy library, and the scheduling strategy library comprises resource scheduling strategies for different service scenes and resource use conditions; and according to the target scheduling strategy and the current use condition of the target resource by each edge computing node, adjusting the resource currently allocated to each edge computing node.

The invention discloses an adjustment method and system for 5G network slices, and relates to the field of 5G networks, and the method comprises the steps: obtaining historical demand information of a target service; determining an initial network slicing strategy based on the historical demand information; determining an initial network slice based on the initial network slice strategy; monitoring performance data of each initial network slice; obtaining priority information of a target service corresponding to each network slice; the initial network slice is adjusted based on the performance data and the priority information to determine an optimized network slice.

The invention relates to a time slot allocation method and device of a dynamic TDMA protocol and a storage medium. The method comprises the following steps: carrying out time slot allocation on a network allocation frame in a broadcast time slot according to the priority size of node packet sending, resending a time slot request frame in an arbitration time slot when a certain node applies for sending high-priority data, and after a plurality of nodes send the time slot request frame in the arbitration time slot, sending the time slot request frame in the arbitration time slot. And the main node calculates to obtain an arbitration value corresponding to each node according to the weight corresponding to the priority category of each data to be sent and the weight of the number of frames to be sent, and inserts the new time slot allocation table into the next stage according to the arbitration values from large to small to continue to send the data. By adopting the method, when temporary high-priority data is inserted, high-priority emergencies can be responded in time.

The invention provides a three-dimensional scene rendering method and device, equipment and a medium, relates to the field of computers, in particular to the technical field of page development, and can be used for application scenes such as generative search, intelligent document editing, intelligent assistants, virtual assistants and intelligent e-commerce. The method comprises the following steps: determining a plurality of elements in a to-be-rendered three-dimensional scene; based on a preset rule, putting the plurality of elements into a plurality of independent rendering layers; determining respective rendering priorities of the plurality of rendering layers; and based on the corresponding rendering priorities, sequentially rendering the plurality of rendering layers.

The invention relates to a low-power-consumption control method, device and equipment for a sports bracelet and a storage medium. The method comprises the following steps: constructing an initial mapping model of a motion state and chip power consumption according to first user motion data and chip power consumption data collected by a motion bracelet; inputting the second user motion data into the initial mapping model for processing to obtain a predicted power consumption value and calculating a power consumption residual error; performing priority ranking on the function tasks of the sports bracelet to obtain a task priority queue; performing frequency adjustment on the plurality of cores of the processor to obtain the working frequency of each core; performing chip voltage regulation and power consumption distribution to obtain an initial power consumption control strategy; and inputting the data of the plurality of sensors into the adaptive state observer for fusion processing, and updating to obtain a target mapping model and a target power consumption control strategy. According to the intelligent low-power-consumption control method capable of achieving self-adaptive learning and dynamic adjustment, the increasing power consumption management requirement of the sports bracelet is met.

The invention relates to a task processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring data processing tasks from an edge processing node, and acquiring task demand information corresponding to each data processing task; acquiring node state information of each processing node in the computing power network; according to the node state information and task demand information carried by the data processing task, determining a target processing node by taking resource utilization rate maximization of each processing node in the computing power network as a target; determining a target transmission path and a target transmission protocol by taking maximization of transmission bandwidth and minimization of transmission delay as targets according to data size information and time delay requirements carried by the data processing task, and a network state and a transmission protocol of a target processing node; and transmitting the data processing task to the target processing node through the target transmission path and the target transmission protocol. By adopting the method, the task processing efficiency can be improved.

The invention discloses a task scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring rescue request information of all accident points; determining all schedulable rescue teams, and obtaining position information of each schedulable rescue team; calculating a rescue distance between each accident point and each dispatchable rescue team based on the rescue position information of each accident point and the position information of each dispatchable rescue team; constructing a rescue distance array based on the rescue distance between each accident point and each dispatchable rescue team; constructing an emergency level array and a resource demand array; and according to the rescue distance array, the emergency level array and the resource demand array, performing task scheduling on each schedulable rescue team by using a pre-configured greedy scheduling strategy. The invention further relates to the technical field of block chains, and the rescue request information is stored on a block chain network. The rescue efficiency and coverage can be comprehensively improved.

The invention discloses an industrial internet trusted data communication method and system, and relates to the technical field of data communication, and the method comprises the steps: taking an equipment end and an employee end as data collection points, building an initial trust value for each data collection node, and obtaining a dynamically adjusted trust value for each data collection node according to the historical performance and real-time state of the data collection node; classifying and aggregating the collected data according to the data type and the trust value; dynamically allocating bandwidth resources according to the trust value and the data type; after data transmission is completed, a bidirectional confirmation mechanism is started, a sender and a receiver confirm whether the data is successfully sent and received through a feedback mechanism, and if the sender does not receive a confirmation message within a specified time, a timeout mechanism is triggered, and a data packet is sent again until the data packet is confirmed to be received; and calculating an average transmission success rate, and dynamically adjusting strategies of data acquisition, compression and bandwidth allocation.

The invention discloses a task unloading method and computer equipment, and is applied to an edge computing system, and the method comprises the steps: obtaining a directed acyclic graph task of each terminal equipment, and determining a current subtask based on the directed acyclic graph task; for the current sub-task of each terminal device, obtaining state information of the edge computing system at the current moment, inputting the state information at the current moment into the multi-target reinforcement learning model, generating a task unloading strategy corresponding to the current sub-task, and based on the task unloading rate in the task unloading strategy corresponding to the current sub-task of each terminal device, the terminal device and / or the server execute a corresponding task unloading operation on the current sub-task. According to the method, the task unloading strategy of the directed acyclic graph task of the terminal equipment can be provided, so that the optimal task unloading decision and the optimal computing resource allocation decision can be obtained.

The invention provides an emergency call method, system and device based on the VONR technology, and relates to the technical field of emergency calls, and the method comprises the steps: determining an initial deployment scheme; quickly deploying the unmanned aerial vehicle base station in the air; n unmanned aerial vehicle base stations are started, and a VONR technology is started; n unmanned aerial vehicle base stations receive and analyze the emergency call data in real time, and output N dynamic resource priority schedules; the 5G core network allocates communication resources; dynamic flow sharing and rescue dynamic task allocation of the unmanned aerial vehicle base station are synchronously executed by adopting N dynamic resource priority scheduling and dynamic resource allocation scheduling, and emergency call guarantee is carried out. According to the invention, the technical problem that the rescue efficiency is affected due to poor emergency communication quality caused by inflexible communication resource allocation in an emergency in the prior art can be solved, and high quality and low delay of emergency communication are ensured through a VONR technology and intelligent resource scheduling, so that the rescue efficiency is improved.

The invention relates to the technical field of embedded systems, in particular to a data backup and recovery method applied to an embedded device, an electronic device and a storage medium, comprising: in response to creation of a user partition and a backup partition, storing data in the user partition, and backing up the data stored in the user partition in the backup partition; wherein the user partition comprises a plurality of user sub-partitions, the backup partition comprises a plurality of backup sub-partitions, and each user sub-partition and each backup sub-partition store verification files; when it is confirmed that data consistency verification is started, whether data stored in the user sub-partition is consistent with data stored in the corresponding backup sub-partition or not is verified through the verification file; and if the user sub-partitions and the backup sub-partitions are not consistent, data recovery operation is performed, so that the data stored in the corresponding user sub-partitions and the backup sub-partitions are kept consistent, and the defects of an existing data backup and recovery mechanism of the embedded system are effectively overcome.

The invention relates to the technical field of electric power communication, in particular to an electric power communication planning method, system, equipment and medium based on cross-layer slicing, and the method comprises the steps: classifying all services in an electric power communication network, and obtaining the service type of the electric power communication network; dividing the power communication network resource into a plurality of slices among a plurality of network layers according to the service communication key index of each service type and the available state of the current network resource, and forming a cross-layer slice model comprising the plurality of network layers; determining cross-layer slicing resources of the cross-layer slicing model by using a reinforcement learning algorithm according to the service communication key indexes of the service types; and searching an optimal power communication path by using a path optimization algorithm according to the current power communication network state and the cross-layer slice resources. Through the cross-layer slicing model and the reinforcement learning algorithm, dynamic scheduling and efficient management of power communication network resources are realized, and the utilization rate of the network resources is improved.

The invention belongs to the technical field of medical detection, and provides an automatic specific protein analyzer for comprehensive multi-item detection, which mainly comprises an online conveying track, an urgent treatment assembly, a double-sample-arm sampling mechanism, a sample disc, a reagent disc, a reaction disc, an ISE electrolyte module and a double-light-path detection module. The online conveying track is used for automatic detection and transmission of samples and online connection of adjacent detection instruments, the urgent treatment assembly is used for urgent detection of the samples, the double-sample-arm sampling mechanism is used for selective sampling of different samples, the ISE electrolyte module is used for electrolyte detection, and the double-sample-arm sampling mechanism is matched to achieve electrolyte module detection. The detection sequence and speed of other samples are not delayed; the double-light-path detection module is used for detecting different samples, the photosensitive sensitivity is improved, and the detection efficiency is improved. By adopting the structure, the detection requirements and sampling conditions of a current detection instrument on different samples are met, and the compatibility and detection efficiency of the detection instrument are improved.

The invention discloses a hybrid energy storage power distribution method based on a power grid frequency modulation demand, and relates to the field of hybrid energy storage frequency modulation. The method comprises the steps that when a primary frequency modulation signal instruction and a secondary frequency modulation signal instruction are consistent in direction, a first total response instruction of the hybrid energy storage system and the working condition of the hybrid energy storage system are determined; based on the working condition of the hybrid energy storage system, aiming at prolonging the continuous regulation capability of the hybrid energy storage system, distributing a power instruction for each energy storage unit according to a first total response instruction of the hybrid energy storage system; when the directions of the primary frequency modulation signal instruction and the secondary frequency modulation signal instruction are opposite, taking the primary frequency modulation signal instruction as a second total response instruction, and distributing an initial power instruction for each energy storage unit; dynamically adjusting an initial power instruction of each energy storage unit by using a fuzzy controller; and determining a final power instruction of each energy storage unit according to the adjusted power instruction and rated power of each energy storage unit. The frequency modulation accuracy and stability can be improved, and the energy loss can be effectively reduced.

The invention provides a photovoltaic power generation hydrogen production control method and system, a program product and a storage medium, and relates to the technical field of photovoltaic power generation. The system determines whether adjustment is needed or not by judging the relation between the current illumination parameter and the preset threshold value. In the adjusting state, the system calculates the total energy receiving power, and dynamic adjustment of the target power point is achieved. When the target power point is larger than the total energy receiving power, the system analyzes according to the temperature of the electrolytic cell and calculates needed energy and excess energy, so that whether the temperature of the electrolytic cell is increased or the working voltage point is adjusted is determined. According to the dynamic adjustment mechanism based on multiple parameters, the system can flexibly distribute energy according to real-time working conditions, the situation that electric energy is forced to be connected to a grid is avoided, meanwhile, the performance of the system is improved through temperature adjustment when the hydrogen production power is not high, and finally the energy utilization efficiency is improved.

The invention discloses a virtuality and reality combined method and system for dynamically reconstructing diversified network topology, and belongs to the technical field of novel power systems, and the method comprises the following steps: virtualizing physical equipment in a power system business scene network by using NFV to obtain virtual equipment; constructing a network architecture for realizing a service scene network of the power system by adopting a virtualization mode; physical equipment and virtual equipment in the service scene network of the power system are controlled in a centralized manner based on the SDN; and the SDN controller dynamically reconstructs the service scene network of the power system according to a set strategy. According to the invention, dynamic allocation of network resources and real-time reconstruction of topology are realized through a virtuality and reality combination technology, a software defined network technology and a network function virtualization technology, and virtualized resources and resources of physical equipment can be effectively integrated so as to quickly respond to changes of application requirements.

The invention provides a video data transmission system based on a PCIE interface, the system comprises a PCIE RC terminal system and a PCIE EP terminal system, each of the PCIE RC terminal system and the PCIE EP terminal system comprises a PCIE interface module and a PCIE virtualization module, the PCIE RC terminal system and the PCIE EP terminal system are connected through the PCIE interface module, and the PCIE interface module is connected with the PCIE EP terminal system through the PCIE virtualization module. The PCIE virtualization module is connected with the PCIE interface module; wherein the PCIE interface module is used for carrying out bidirectional video data transmission between the PCIE RC terminal subsystem and the PCIE EP terminal subsystem, and the PCIE virtualization module is used for controlling the PCIE interface module to execute corresponding operation according to the transmission requirement of video data and respectively creating a corresponding virtual device node for each path of video data.

The invention discloses a multi-node intelligent scheduling service segmentation cooperative control method and system, and relates to the technical field of the Internet. By deploying a heartbeat detection mechanism, heartbeat signals are sent and monitored regularly, and abnormal signals are identified; marking the node agent of the abnormal signal as a fault node, obtaining a resource index of the fault node, and identifying a fault type; sending a fault notification to other nodes according to the fault type, and judging whether to elect a new master control node agent or not according to the fault notification; the master control node reads the service execution information of the fault node, splits the uncompleted service into sub-services, obtains the remaining available resources of the available nodes and the resource requirements of the sub-services, and allocates the remaining available resources to the available nodes according to the resource requirements of the sub-services; the fault node is repaired, and if the repair is successful, the master control node returns the service; if the fault node cannot be successfully repaired, performing service redistribution and replacement on the fault node; and processing and load balancing of the fault node are realized.

The invention relates to a single-root virtualization hardware implementation method for an RDMA (Remote Direct Memory Access) network card supporting Ethernet. The method comprises the following steps of: offsetting an input read-write address through offset information; tlp header framing is carried out on the request information through the VF configuration information; the framing of tlp header and the distribution of an interrupt number are carried out on the request information; and maintaining a reqquery register of the rcq descriptor through a qpnum register, maintaining a target MAC register through a destmac register, carrying out message forwarding on the obtained target MAC address and the queue number information through the target MAC address, and reporting the corresponding queue number information. According to the method, the change of data and control paths is small, the resource consumption is low, the resource allocation is flexible, and the flexibility and compatibility are extremely high.

The invention provides a power management system and a control method, which are applied to the technical field of power electronics. The power management system comprises a power switching module, a control module and a driving module, the power supply switching module comprises at least two input paths, and each input path is used for receiving different input voltages; wherein the at least two input channels comprise a main input channel. The control module is used for acquiring input parameters of the main input path; and the control module is used for receiving the input parameters of the main input channel, judging whether the current input state of the power management system is abnormal according to the input parameters of the main input channel, and if so, sending a control signal to the power switching module to cut off the main input channel and conduct any other channel except the main input channel. On the basis, the condition of multi-power-supply input can be maintained, the electrical system can more flexibly distribute electric power resources, meanwhile, after one power supply breaks down, input adjustment of the power supplies is achieved, normal operation of the electrical system is guaranteed, and the overall efficiency of the system is improved.

The invention provides a wireless audio data transmission method and related equipment, and relates to the technical field of wireless audio, a first device communicates with N1 second devices in continuous equal time intervals based on N1 communication links, one equal time interval comprises a plurality of sub-events, and the first device communicates with N1 second devices based on N1 communication links. One sub-event comprises a plurality of audio transmission time slots used for carrying out Bluetooth low-power audio data transmission, and the method comprises the following steps: configuring a time slot resource allocation instruction, the time slot resource allocation instruction is used for allocating a corresponding audio transmission time slot in a first sub-event to target equipment which needs to perform audio data transmission in the first sub-event; sending the time slot resource allocation instruction and performing audio data transmission in the first sub-event; the first sub-event is any sub-event in the current equal time interval; n1 and M1 are natural numbers, and M1 is smaller than or equal to N1. The method provided by the invention can improve the link efficiency when the audio data stream is transmitted.

The embodiment of the invention relates to the technical field of computers, and discloses a data sharing information system and method based on mass data. The data sharing information system based on mass data introduces innovative technologies such as multi-source sharing, intelligent scheduling and repairing mechanisms and dynamic performance evaluation indexes; the system aims to solve many challenges faced by an existing data sharing system when facing mass data, and provides powerful support for efficient and reliable data sharing in a mass data environment by optimizing a data synchronization strategy, improving data consistency and reliability and realizing flexible allocation of resources.

The present application discloses a detachable mobile energy storage device management system, method, equipment and medium, which mainly relates to the field of energy storage device application technology, and is used to solve the existing mobile energy storage system that contains only one energy storage module, which affects efficiency, disconnects the node at the fault, has poor power supply continuity and low distributed power supply utilization efficiency, and the mobile route of the existing mobile energy storage system has an unbalanced power supply problem. It includes: an island division component to obtain a number of power grid islands; a route division component to obtain the calculated difference of distributed power sources; determine the priority of distributed power sources according to weights and calculated differences; complete the connection between the detachable energy storage component and the distributed power source; obtain the calculated difference corresponding to the distributed power source in real time, and when it is less than the preset difference threshold, the distributed power source is disconnected from the detachable energy storage component until an extreme fault elimination signal is received, or the calculated differences of the distributed power sources are all less than the preset difference threshold.

The invention discloses a large model reasoning method and device based on cross-domain edge device collaboration, and the method comprises the steps: dynamically sensing the available resource condition of a graphics processor in a plurality of working nodes based on a distributed hash table, and distributing a large model block to the working nodes according to the available resource condition; the available resource condition represents an unallocated working node set; receiving a user reasoning request, wherein the reasoning request at least comprises one or more reasoning tasks; based on the large model blocks allocated by the working nodes, allocating the reasoning tasks to one or more management nodes; an area where each management node is located comprises a working node set; the management node distributes the reasoning task to the working node set of the region for reasoning; the working node set comprises a plurality of working nodes. By means of the method and device, the efficiency of the distributed reasoning task can be improved, and flexible allocation of resources and high reliability of the system can be achieved.

The invention provides a product recommendation method and device based on a virtual scene, electronic equipment and a medium, and can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the steps that a pre-configured virtual business hall is acquired, the virtual business hall is simulated through the augmented reality technology, and the virtual business hall comprises a product display area; responding to login of a target user to the virtual business hall, and obtaining user static information of the target user; based on the static information of the user, identifying a product interest point and a product demand of the user by using a machine learning model, and outputting a demand identification result; selecting a target recommended product based on the demand identification result, and displaying the target recommended product to a target user through the product display area; and collecting user dynamic information generated when a target user interacts with the product display area, and updating the target recommended product in real time based on the user dynamic information.

The invention provides a parallel power control method for a bidirectional interface converter of an AC / DC hybrid micro-grid, and the method comprises the steps: adding an inertia control link and a capacity distribution link into an active control loop of the parallel bidirectional interface converter; according to the alternating current side frequency deviation and the direct current side voltage deviation of the parallel bidirectional interface converter, an active power transmission reference value of each bidirectional interface converter is calculated through inertia control and capacity allocation links; and the active power transmission reference value of each bidirectional interface converter is sent to a power current control ring to obtain a signal for driving a switch in the three-phase bridge type conversion circuit. Power distribution is realized through inertia control and capacity distribution links, the control convenience and reliability of the parallel bidirectional interface converter are improved, the circulating power of the parallel bidirectional interface converter is solved, the complex calculation of control parameters of the parallel bidirectional interface converter is reduced, the power balance state of the parallel bidirectional interface converter is formed, and the reliability of the parallel bidirectional interface converter is improved. And meanwhile, the dynamic response of alternating-current frequency and direct-current voltage and current is improved.