2results about How to "Achieve direct conversion" patented technology

This invention discloses an intelligent prediction method for deformation caused by deep foundation pit construction based on multi-source data fusion, belonging to the field of multi-source data fusion technology. By acquiring the corresponding measured deformation sequence Obs and comparing it with the deformation prediction sequence Pre, a prediction deviation process that changes over time is constructed. The prediction deviation is segmented to form a deviation segment set Seg, and morphological features describing the change mode of the prediction deviation are further extracted to construct a deviation morphology vector Mor. This realizes the quantitative expression of the evolution characteristics of the prediction deviation over time. Based on the deviation morphology vector Mor, the category of the evolution mode of the prediction deviation is determined and a morphology category set Cls is formed. Different prediction deviation evolution modes are mapped to clear deformation risk levels, forming a risk level set Grd and outputting the corresponding early warning result set Alt. This method can be used even when the deformation value has not significantly exceeded the limit but the prediction deviation has shown continuous amplification or rapid instability evolution.

The application relates to the technical field of intelligent agricultural digital platforms, and discloses an intelligent agricultural data processing method and an intelligent agricultural digital platform, the method comprising the following steps: pre-processing target field image data, extracting a first parameter data set representing macroscopic growth trends of seedlings, extracting weak high-frequency texture information of early diseases and insect pests in a noise residual image of the image data as a second parameter data set representing local pathological conditions, and quantifying a pathological risk parameter through the second parameter data set; combining agricultural operation record data, performing seedling growth analysis, and outputting planting growth analysis results; and determining an agricultural operation strategy adjustment result according to the planting growth analysis results. The application is beneficial to solving the technical problem that it is difficult to give field data management and prospective prediction based on existing monitoring data in the existing agricultural technology.