35 results about "Composite index" patented technology

The invention provides a method and device for constructing a knowledge graph for crop variety management. The method includes: constructing a field ontology for crop variety management according to a subject word dictionary for crop variety management; acquiring corresponding crop variety management data according to the field ontology, and Knowledge extraction is performed on the variety management data; the extracted knowledge is represented by triples, and a composite index is established; the triples are expanded through knowledge reasoning; according to user interest characteristics, user input behavior characteristics and browsing behavior characteristics, The compound index searches the triplet data set, and generates a different knowledge graph data display interface for each user. The method reduces the degree of manual participation in the process of knowledge graph construction, improves the level of automated construction, helps to solve the problems of low efficiency, low degree of automation, and poor scalability in the construction of knowledge graphs for crop variety management, and provides intuitive and convenient knowledge organization, management and presentation.

The invention discloses a method for evaluating the accuracy and completeness of a three-dimensional reconstruction point cloud. The specific steps include the following steps: (1) performing spatial point cloud registration on the ground truth point cloud model G and the reconstructed point cloud model R; (2) according to the The relative scale unit ε is obtained by calculating the true value point cloud model G; (3) taking the relative scale unit ε as the measurement unit and the distance coefficient as d, the evaluation accuracy P(d) and completeness C(d) and composite indicator F(d). The present invention provides a method for evaluating the accuracy and completeness of the three-dimensional reconstruction point cloud by objectively and quantitatively evaluating the accuracy and completeness of the point cloud model obtained from the three-dimensional reconstruction to promote the further development of the reconstruction technology.

The invention discloses a forecasting method, device, equipment and medium based on capital flow forecasting rate of return. The forecasting method includes: acquiring sampled macro-fund flow variables and the monthly data sequence of the return rate of the Shanghai Stock Exchange Index. The macro-fund flow variables include money supply and Bank overnight lending rate; carry out unit root test on the monthly data sequence, and determine whether the unit root characteristic of the monthly data sequence after the unit root test meets the grace condition of the multivariate cointegration test; the unit root characteristic is that the monthly data sequence is N i Order integrated sequence, N i ≥0; when the unit root characteristics meet the grace conditions of the multivariate cointegration test, the Johansen maximum likelihood estimation method is used to determine the co-integration relationship between the monthly data series of the macro-fund flow variable and the monthly data series of the Shanghai Stock Exchange index yield; according to The VECM model established based on the co-integration relationship predicts the return rate of the Shanghai stock index. The forecasting method of the invention fills in the gap that there is still a lack of macro capital flow indicators to forecast the Chinese stock index.

The invention discloses a multi-dimensional data query method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: establishing a dimension retrieval table according to a fact table and a dimension table related to the fact table, and adding a composite index of the dimension retrieval table; according to at least one dimension query condition in the query request, performing query in a dimension retrieval table by utilizing the composite index, and determining a target data primary key; and according to the target dataprimary key, inquiring the fact table to obtain data needing to be inquired in the inquiry request. According to the embodiment, a dimension retrieval table is adopted; according to the technical scheme, the method has the advantages that the data can be quickly retrieved only by creating a small amount of indexes in the dimension retrieval table, the number of the indexes is not increased alongwith the increase of the number of dimensions, the dimension retrieval table can be used for uniformly managing the dimension data, and the dimension management complexity is reduced.

The embodiment of the invention discloses a generating method and device of a compound index key value. The method includes the steps of determining a precedent line according to filtering conditions, establishing the key value of the precedent line according to the numerical value of the precedent line, and combining the key value of the precedent line with the filtering conditions to generate the key value of a compound index. By means of the method and device, by combining the value of the precedent line with the filtering conditions, the compound index key value is generated. Condition scanning is conducted by means of the generated compound key value, and there is no need to scan all indexes first and then screen the indexes according to conditions, the computational amount can be effectively reduced, scanning duration is shortened, and scanning efficiency is improved.

The invention discloses a method for designing an index-type agricultural insurance product and its products. The method includes: S1 obtaining data required for designing an index-type agricultural insurance product in a research area; S2: constructing an ideal scenario and simulating a disaster scenario; S3: Carry out the localization of the site-level crop growth model, and then input the ideal scenario and the simulated disaster scenario in step S2 into the localized crop growth model to obtain the output data corresponding to each scenario at each research site; S4: According to the yield Loss calculation insurance premium rate of agricultural insurance products; S5: Select multiple alternative indicators and form a composite index, use the yield loss rate and composite index of each simulated disaster scenario to construct a vulnerability that reflects the response between the yield loss rate and the composite index Vulnerability model; S6: Determine how to make compensation according to the vulnerability model and the insurance premium rate.