Patent ID: 11861552
Assignee: CHENGDU PUHUIDAO SMART ENERGY TECHNOLOGY CO., LTD.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. The Internet of Things system according to claim 6, wherein the LNG tanker initiates a request for matching location after arriving at a location of the LNG distributed energy intelligent terminal, completes a two-way location matching authentication with the LNG distributed energy intelligent terminal through the LNG distributed energy management platform, and performs tanking for the LNG distributed energy intelligent terminal; wherein the two-way location matching authentication includes:
for performing the tanking, sending, by the LNG tanker, request information for tanking and vehicle location information of the LNG tanker corresponding to the LNG distributed energy intelligent terminal to the LNG distributed energy management platform;
sending, by the LNG distributed energy intelligent terminal, request information for tanking corresponding to the LNG tanker number and the location information of the LNG distributed energy intelligent terminal to the LNG distributed energy management platform;
performing, by the LNG distributed energy management platform, information matching according to the task plan and issuing an instruction for tanking to the LNG tanker and the LNG distributed energy intelligent terminal respectively if the matching succeeds; if the matching fails, sending, by the LNG distributed energy management platform, an alarm to a platform manager;
after receiving the instruction, opening, by the LNG tanker and the LNG distributed energy intelligent terminal, valves for the tanking respectively;
wherein the information matching performed by the LNG distributed energy management platform according to the task plan specifically includes:
constructing, by the LNG distributed energy management platform, an address matching model and extracting a semantic feature from the location information of the LNG tanker and the location information of the LNG distributed energy intelligent terminal respectively; querying, by the LNG distributed energy management platform, the task plan for tanking stored in a database according to the request information for tanking, and obtaining the location information of the LNG distributed energy intelligent terminal and vehicle location information of the LNG tanker in the task plan for tanking; and matching the extracted semantic feature with an address of the task plan for tanking in the database for semantic matching of address text, and outputting a matching result;

a process of constructing an address matching model by the LNG distributed energy management platform specifically includes:
address dataset division: obtaining address data of historical tanking task plans of the LNG distributed energy management platform to form an address database, constructing a set of address data with a manual marking, and dividing the set of address data into a training set, a verification set, and a testing set;
a word vector training for an address element: using a Word2vec model in a gensim natural language processing library to perform a word vector training for the training set in the address database to obtain a word vector of a glossary in a current application context;
semantic matching of address text: using an ESIM mode as a basic model of the semantic matching of address text, and performing local modeling according to a contextual dependency relation of the address element to obtain the address matching model; and
verification and testing of address matching: performing a verification of address matching on the address matching model according to the verification set, adjusting a count of hidden layer nodes, a learning rate, and a mini-batch size of the model according to a verification result, and finally performing testing of address matching with the testing set;

the word vector training for an address element specifically includes:
the Word2vec model belonging to an unsupervised neural network language model, adopting the Word2vec model in the natural language processing library to perform the word vector training for the training set in the address database; first performing modeling according to a distribution of a current word and a local context of the word to obtain a word vector of the current word; a model used in a training process being a CBOW model, and a training manner being Negative Sampling; and then generating a glossary of address elements of a corpus and a 256-dimensional word vector corresponding to the corpus.