7 results about "Allocation Code" patented technology

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.

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.