A method and system are disclosed for automatic replenishment of a retail enterprise store, and a computer-readable storage medium. In the method of the present disclosure, historical operational transaction data of at least one store of the same type as the retail enterprise store is used to obtain four indicators of each product of the at least one store, a plurality of target features having an impact on an indicator matrix composed of the four indicators are extracted to provide replenishment suggestions, and the indicator matrix composed of the four indicators is automatically adjusted to update a replenishment model. In the embodiments of the present disclosure, a set of algorithm models can be optimized and customized according to the historical operational transaction data of the store and external environments such as weather changes, business circle customer flow, discount events, etc., so that each store can be provided with SKU-level high-precision demand prediction and replenishment suggestions to generate replenishment suggestions, improving the processing efficiency of the server, and further realizing the artificially controllable intelligent replenishment decision-making function.

In a first step, a defined scope value is selected for each of a plurality of hydrodynamic input parameters. A simulated topographical result is generated using the selected scope values and a forward model. A detailed seismic interpretation is generated to represent specific seismic features or observed topography. A calculated a misfit value representing a distance between the simulated topographical result and a detailed seismic interpretation is minimized. An estimated optimized sand ratio and optimized hydrodynamic input parameters are generated. In a second step, a genetic algorithm is used to determine a proportion of each grain size in the estimated optimized sand ratio. A misfit value is used that is calculated from thickness and porosity data extracted from well data and a simulation result generated by the forward model to generate optimized components of different grain sizes. Optimized hydrodynamic input parameters and optimized components of different grain sizes are generated.

In a first step, a defined scope value is selected for each of a plurality of hydrodynamic input parameters. A simulated topographical result is generated using the selected scope values and a forward model. A detailed seismic interpretation is generated to represent specific seismic features or observed topography. A calculated a misfit value representing a distance between the simulated topographical result and a detailed seismic interpretation is minimized. An estimated optimized sand ratio and optimized hydrodynamic input parameters are generated. In a second step, a genetic algorithm is used to determine a proportion of each grain size in the estimated optimized sand ratio. A misfit value is used that is calculated from thickness and porosity data extracted from well data and a simulation result generated by the forward model to generate optimized components of different grain sizes. Optimized hydrodynamic input parameters and optimized components of different grain sizes are generated.

The present invention relates to a method for determining hydrocarbon production for a reservoir. The method comprises determining a projector matrix based on a Jacobian matrix function of the gridded model, then splitting the Jacobian matrix into subsets of consecutive lines. For each subset of consecutive lines, creating a respective square matrix based on said subset. A determining eigenvectors and respective eigenvalues associated with the respective square matrix and then determining relevant eigenvectors having respective eigenvalues below a predetermined threshold. The projector is determined as a concatenation of the extended eigenvectors ordered according to multiple criteria: the respective order value of the subset; and the respective eigenvalue of the relevant eigenvector.

The present invention relates to a method for determining hydrocarbon production for a reservoir. The method comprises determining a projector matrix based on a Jacobian matrix function of the gridded model, then splitting the Jacobian matrix into subsets of consecutive lines. For each subset of consecutive lines, creating a respective square matrix based on said subset. A determining eigenvectors and respective eigenvalues associated with the respective square matrix and then determining relevant eigenvectors having respective eigenvalues below a predetermined threshold. The projector is determined as a concatenation of the extended eigenvectors ordered according to multiple criteria: the respective order value of the subset; and the respective eigenvalue of the relevant eigenvector.

A method, for determining the real distance separating an object and an optical detection system, includes, from several so-called reported distances respectively less than or equal to individual reference distances dependent respectively on modulation frequencies: in a first step, determining an initial deviation coefficient between the reported distances and incrementing the smallest of the reported distances with the corresponding individual reference distance; then in a second step, determining a current deviation coefficient between the current distances obtained in the preceding step and incrementing the smallest of the current distances with the corresponding individual reference distance; and in a third step, repeating the second step until all the current distances exceed a common reference distance greater than the individual reference distances.

A method, for determining the real distance separating an object and an optical detection system, includes, from several so-called reported distances respectively less than or equal to individual reference distances dependent respectively on modulation frequencies: in a first step, determining an initial deviation coefficient between the reported distances and incrementing the smallest of the reported distances with the corresponding individual reference distance; then in a second step, determining a current deviation coefficient between the current distances obtained in the preceding step and incrementing the smallest of the current distances with the corresponding individual reference distance; and in a third step, repeating the second step until all the current distances exceed a common reference distance greater than the individual reference distances.

A processor of a computing device comprises: a rearrangement unit to rearrange a plurality of elements included in each of a Hessian matrix of an evaluation function and a coefficient matrix of the linear constraint; a generation unit to generate a simultaneous linear equation for finding the optimal solution, based on the evaluation function including the rearranged Hessian matrix and the linear constraint including the rearranged coefficient matrix; and a search unit to find the optimal solution using the simultaneous linear equation. The rearrangement unit rearranges the plurality of elements so as to gather a sparse element of the plurality of elements included in the Hessian matrix, and rearranges the plurality of elements so as to gather a sparse element of the plurality of elements included in the coefficient matrix.

A processor of a computing device comprises: a rearrangement unit to rearrange a plurality of elements included in each of a Hessian matrix of an evaluation function and a coefficient matrix of the linear constraint; a generation unit to generate a simultaneous linear equation for finding the optimal solution, based on the evaluation function including the rearranged Hessian matrix and the linear constraint including the rearranged coefficient matrix; and a search unit to find the optimal solution using the simultaneous linear equation. The rearrangement unit rearranges the plurality of elements so as to gather a sparse element of the plurality of elements included in the Hessian matrix, and rearranges the plurality of elements so as to gather a sparse element of the plurality of elements included in the coefficient matrix.

Disclosed herein are system, method, and computer program product embodiments that generally relate to systems and methods of removing systemic bias in a single datum and in data sets. Embodiments include systems and methods of data processing for data related to meter responses, and also to systems and methods of data processing of attitudinal data and/or analysis of questionnaire response(s), as well as systems and methods of processing and analysing statistical data, each of which can be affected by systemic bias.