Construction of Enterprise Management Business Model Based on Internet of Things RFID Technology

This paper proposes a new tag anticollision algorithm. On the basis of the common query tree algorithm, this algorithm increases the number of query bits, so that the algorithm has a faster tag identification amount in the state of full time slots. Collision bit and total response bit are used to estimate the number of tags, and combined with the highest collision bit to determine whether it is continuous, the number of query crosses is adaptively adjusted, and a variable bit dynamic query tree tag anticollision algorithm is designed. In conjunction with the settlement environment, improvements have also been made in the status of the identified tags. The new algorithm has improved progress in reducing idle time slots and reducing search depth. Under the premise of ensuring stability, the recognition efficiency of the algorithm is improved. From the perspective of different dimensions of corporate network and marketing dynamic capabilities, based on the strength of network relationships and product development processes, it can promote value creation model innovation in business model innovation. That is, when the network partners communicate very frequently, they can provide new ideas and resources for enterprise product development and creation and then can better integrate innovation through product innovation, quality improvement, product development cycle shortening, and development advance management. Typed products and resources are put into the operation process of the industrial chain of product output to better create new products for customers and lay the foundation for realizing the innovation of corporate value creation models. Based on the network density and customer relationship process, it can promote the value proposition model innovation in the business model innovation. Enterprise business model innovation should not only pay attention to the collaborative innovation between different elements within the enterprise but also pay attention to the overall coordination mechanism of the company’s external interest network, pay attention to the different value propositions of stakeholders through the enterprise network, and cultivate the enterprise’s rapid response to market changes. Network centrality has the greatest positive impact on the supply chain process. The company develops new products, manages customer relationships across departments, and implements supply chain management across departments.

Handling Complex Queries Using Query Trees

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

Handling Complex Queries Using Query Trees

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

AUTOMATICAL PRETRANSLATION OF SQL-QUERIES TO A REGULAR PLAN IN CLUSTERIX-LIKE DBMS

Previously completed designs for pretranslating SQL queries did not bring significant success. Nevertheless, such a pretranslator is necessary for the possibility of using Clusterix-like database management systems (DBMSs) by a wide circle of specialists. The article proposes an original approach to pretranslating SQL queries without writing operations to the regular plan for Clusterix-like DBMSs. The possibility of implementing such a pretranslator is discussed. The concepts of a hard and a simple SQL query are given. The basis of the pretranslator is a library for parsing arbitrary grammars antlr4, which is used to build a SQL query tree. A software-implemented pretranslator uses it to build queries that meet the regular plan.

Method for Retrieving Digital Agricultural Text Information Based on Local Matching

In order to improve the retrieval results of digital agricultural text information and improve the efficiency of retrieval, the method for searching digital agricultural text information based on local matching is proposed. The agricultural text tree and the query tree are constructed to generate the relationship of ancestor–descendant in the query and map it to the agricultural text. According to the retrieval method of the local matching, the vector retrieval method is used to calculate the digital agricultural text and submit the similarity between the queries. The similarity is sorted from large to small so that the agricultural text tree can output digital agricultural text information in turn. In the case of adding interference information, the recall rate and precision rate of the proposed method are above 99.5%; the average retrieval time is between 4s and 6s, and the average retrieval efficiency is above 99%. The proposed method is more efficient in information retrieval and can obtain comprehensive and accurate search results, which can be used for the rapid retrieval of digital agricultural text information.