Abstract: A low-heat-loss operation method of a line-focusing heat collection system and the line-focusing heat collection system are provided. The method includes the following steps. Solar energy is utilized to preheat a collector tube in an empty tube state, so that the collector tube is in a preheating mode. After a set preheating temperature is reached, a heat transfer working medium is injected into the collector tube. In the injection process of the heat transfer working medium, an injection section of the collector tube is converted into a focusing mode from a preheating mode. After heat collection is finished, the circulation of the heat transfer working medium is stopped, and the focusing mode of the collector tube is kept. In the drainage process of the heat transfer working medium, an emptying section of the collector tube is converted into a light heat-tracing mode from a focusing mode.

Abstract: A method includes parsing a query to determine a plurality of data processing operations associated with the query and including an AS OF JOIN operation between first time series data in a first table and second time series data in a second table. A query plan of the query is generated. The query plan includes a plurality of nodes corresponding to the plurality of data processing operations. At least one of the plurality of nodes corresponding to the AS OF JOIN operation is modified to generate a modified query plan of the query. The modifying is based on applying a UNION operation on at least a first portion of column data in the first table and the second table to obtain a combined table. Execution of the query by at least one of a plurality of computing nodes is scheduled based on the modified query plan.

Abstract: A data sparse projection method, includes: randomly initializing a high-dimensional sparse two-dimensional matrix (S1); fixing the high-dimensional sparse two-dimensional matrix, and calculating an optimal output variable by using the high-dimensional sparse two-dimensional matrix (S2); fixing the optimal output variable, and calculating an optimal high-dimensional sparse two-dimensional matrix by using the optimal output variable (S3); and cyclically fixing the high-dimensional sparse two-dimensional matrix and the output variable until the optimal output variable is no longer increased when the high-dimensional sparse two-dimensional matrix is fixed (S4).

Abstract: A data sparse projection method, includes: randomly initializing a high-dimensional sparse two-dimensional matrix (S1); fixing the high-dimensional sparse two-dimensional matrix, and calculating an optimal output variable by using the high-dimensional sparse two-dimensional matrix (S2); fixing the optimal output variable, and calculating an optimal high-dimensional sparse two-dimensional matrix by using the optimal output variable (S3); and cyclically fixing the high-dimensional sparse two-dimensional matrix and the output variable until the optimal output variable is no longer increased when the high-dimensional sparse two-dimensional matrix is fixed (S4).