12 results about "Allocation Code" patented technology

A system including: a memory, an encoder, a decoder, and a processor, the processor being connected to the memory, the encoder, and the decoder. The system is configured to: receive, at the encoder, an input image, divide, by the encoder, the input image into a plurality of image patches, select, by the encoder, codes corresponding to the plurality of image patches of the input image, from a codebook including the codes. The system is further configured to determine, by the encoder, an assigned code matrix including the codes corresponding to the plurality of image patches of the input image, receive, by the decoder, the assigned code matrix from the encoder. The system is further configured to generate, by the decoder, a reconstructed image based on the assigned code matrix.

The invention relates to the technical field of underwater energy storage system optimization, and discloses a cluster distributed underwater flexible compressed air system topology optimization method comprising the following steps: establishing a cluster distributed gas storage system mathematical model; coding the individuals into a capacity allocation coding part, a spatial layout coding part and a topological connection coding part by adopting a hierarchical coding strategy; optimizing the air bag capacity distribution vector by adopting a capacity distribution method based on pressure loss weighting; executing an improved multi-objective evolutionary algorithm, and performing synchronous optimization on the capacity allocation coding part, the spatial layout coding part and the topological connection coding part through a co-evolutionary strategy; and outputting a Pareto optimal solution set. According to the method, an air bag capacity distribution vector, a space coordinate matrix and a connection topology matrix are used as joint design variables, so that three highly-coupled sub-problems of capacity distribution, space layout and pipeline topology connection are synchronously optimized.

Methods, systems, and devices for transmitting metadata using unassigned codes from an encoder are described. The method may include inputting data and metadata associated with the data into an encoder. The data may include a first set of codewords modulated using a first modulation scheme, including symbols, each representing one bit of digital information. Furthermore, the method may include generating, using the encoder, a first subset of a second set of codewords representative of the data, and a second subset of the second set of codewords representative of the metadata. The second set of codewords may be modulated using a second modulation scheme, including symbols, each representing more than one bit of digital information.Furthermore, the procedure can include sending the data and metadata using the second set of codewords.

SYSTEM AND METHOD FOR POLICY DETERMINATION The present disclosure relates to a system (108) and a method (400) for determining a plurality of policies for a plurality of user equipments (UEs) (104). The method (400) includes a policy control function (PCF) (110). The PCF (110) includes 5 receiving a policy creation request from at least one UE (104) of the plurality of UEs (104) and extracting information from the received policy creation request, where the information includes international mobile equipment identity (IMEI) corresponding to the at least one UE (104). The PCF (110) includes detecting whether the extracted IMEI is present in a list stored in a database, where the list includes a plurality of IMEI-Type 10 Allocation Codes (TACs) and installing at least one of a plurality of policies for the at least one UE (104) based on detecting that the extracted IMEI is present in the list stored in the database.

Embodiments of the present disclosure relate to methods, apparatuses, devices, and products for allocating code tasks. The method includes determining a data basis for the submission history information based on the submission history information of the code tasks, where the submission history information includes task information, personnel information, and personnel skill information. The method includes determining, by a target model, a knowledge graph associated with personnel, skills, and code tasks based on the determined data basis, where the target model is configured with a time decay model and an attention mechanism. The method includes determining, by the target model, a set of personnel matching each code task based on each code task and the knowledge graph. The method further includes, in response to a code task going wrong, allocating the code task to a person matching the code task by the target model based on the comprehensive score of each person in the set of personnel, where the comprehensive score is determined based on the knowledge graph and the time decay coefficient of each person.

Disclosed is an NFT object management system including an NFT object management server that issues an NFT for an NFT object and assigns a code to the NFT, and a first user terminal that generates the NFT object and transmits the NFT object to the NFT object management server. The NFT object management server includes an NFT issuance module and a code management module. The NFT issuance module receives basic information about the NFT object from the first user terminal and issues the NFT for the NFT object. The code management module receives the basic information about the NFT object and device information of the first user terminal and generates the code for the NFT object based on the basic information and the device information.

The application discloses a mixed flow shop multi-objective scheduling method based on an improved genetic algorithm and application. The mixed flow shop multi-objective scheduling method comprises the following steps: a multi-objective optimization model with multiple optimization objectives is established; an initial population is generated by combining random initialization and greedy initialization based on double-layer encoding / decoding rules; a selection operation is performed to generate a next generation population; a crossover and mutation operation is performed; and iteration updating is repeatedly performed. The multi-objective scheduling method based on the improved genetic algorithm can realize multi-objective optimization scheduling by adopting a double-layer encoding rule combining a first process processing sequence code and a device allocation code. The improved genetic algorithm adopts the combination of random initialization and greedy initialization in initialization, and an elite reservation strategy is introduced in the selection operation, so that the solution quality is improved, and the convergence speed of the algorithm is also accelerated.

This invention discloses a work order requirement identification method and system integrating big data analysis, relating to the field of work order data processing technology. It achieves accurate work order requirement identification through multi-module collaboration and multi-source data fusion. First, it obtains the original strings of multi-source candidate identifiers and converts them into half-byte sequences. The half-byte length is calculated, and the tail padding type label is determined based on the half-byte length, while filtering out non-protocol pseudo-strings. The half-byte sequence is processed according to the tail padding type label to obtain a pure numeric half-byte sequence. A 14-bit numerical sequence is extracted, and a 15-bit standard IMEI is calculated. Then, a half-byte verification sequence is generated in reverse, and a reversibility verification flag is generated after comparison. Based on the reversibility verification flag, the device allocation code (TAC) is extracted, and the device information is obtained by querying the device knowledge base in conjunction with the tail padding type label. The 15-bit standard IMEI, TAC, and device information are combined into a work order requirement identification triplet and written into the work order structured field, completing the work order requirement identification data encapsulation.

Method for using volatile and non-volatile memory, the method comprising: Storing one or more static code segments of an executable application in a non-volatile storage device; Storing one or more static data segments of the application in the non-volatile storage device; Storing one or more volatile data segments of the application in a volatile storage device; Assigning an address mapping table having at least a first address pointer for pointing to each of the one or more static code segments, at least a second address pointer for pointing to each of the one or more static data segments, and at least a third address pointer for pointing to each of the one or more volatile data segments; initially storing all data segments of the software application in the non-volatile storage device, for a given data segment within all of the data segments; Transferring the given data segment to the volatile storage device when a rewrite request for the given data segment is received during execution of the application; and Updating the address mapping table using the respective address pointer to a memory section allocated in the volatile memory device.

The invention discloses an artificial intelligence-based edge computing load balancing method, which belongs to the technical field of edge computing, and comprises the following steps of: obtaining an edge computing task queue, sequentially taking out a plurality of target edge computing tasks from the edge computing task queue according to a fixed number, and then aiming at the plurality of target edge computing tasks, carrying out load balancing on the target edge computing tasks; carrying out allocation relation coding on the edge computing server by adopting an intelligent coding method to obtain a plurality of task allocation codes, and carrying out load balance evaluation by adopting an artificial intelligence algorithm to determine the load balance corresponding to the task allocation codes; and finally, according to the load balance corresponding to the task allocation codes, determining a current optimal code, optimizing other task allocation codes according to the current optimal code, obtaining a final task allocation code, and on the basis of the final task allocation code, performing task allocation. And the target edge computing task is allocated to the edge computing server for processing, so that load balancing of edge computing is realized, overload of the edge computing server can be effectively avoided, and the overall data processing efficiency is improved.

This invention provides a flexible production workshop batch flow scheduling method, electronic device, and storage medium. The method comprises the following steps: constructing process sequence codes, machine selection codes, and worker allocation codes based on the production batch size of each order product and the process route of each workpiece, and calibrating them using a pointer calibration method to construct a sub-batch strategy code; establishing a scheduling model that targets maximum completion time and total production energy consumption; optimizing the hunting exploration phase and escape development phase of the Secretary Bird algorithm through discretization operations, and dynamically adjusting the neighborhood search strategy using reinforcement learning to obtain an improved discrete Secretary Bird algorithm; and solving the scheduling model using the improved discrete Secretary Bird algorithm based on a four-layer coding strategy of sub-batch strategy codes, process sequence codes, machine selection codes, and worker allocation codes to generate a scheduling solution. This method achieves synergistic and efficient results in shortening production cycles, reducing energy consumption, and improving scheduling flexibility.