Embodiments of the present application provide a method, device, storage medium, and electronic device for controlling flight equipment, wherein the method includes: acquiring positioning position information from a positioning system deployed on a target flight equipment; detecting an operating state of the positioning system according to positioning position information, wherein the operating state comprises: normal state and abnormal state; when the operating state is an abnormal state, controlling the target flight equipment to return to a ground control terminal according to the relative position information between the target flight equipment and the ground control terminal of the target flight equipment.

Grids representing predictions of wind vectors (or velocities) over a ground region are obtained from a model and downscaled to a greater level of resolution based on surface roughness metrics of the ground region. Observations of wind vectors over the ground region are determined from ground-based or airborne systems and assimilated into the grids. Subsequently, grids representing the assimilated wind vectors at an initial time, and the predictions of wind vectors at future times, are provided as inputs to a machine learning model, such as a gradient-boosted decision trees algorithm, along with the surface roughness metrics and other relevant features. A continuous representation of predicted wind vectors over the ground region generated based on outputs received from the model is utilized to make operational decisions regarding an aerial vehicle.

Grids representing predictions of wind vectors (or velocities) over a ground region are obtained from a model and downscaled to a greater level of resolution based on surface roughness metrics of the ground region. Observations of wind vectors over the ground region are determined from ground-based or airborne systems and assimilated into the grids. Subsequently, grids representing the assimilated wind vectors at an initial time, and the predictions of wind vectors at future times, are provided as inputs to a machine learning model, such as a gradient-boosted decision trees algorithm, along with the surface roughness metrics and other relevant features. A continuous representation of predicted wind vectors over the ground region generated based on outputs received from the model is utilized to make operational decisions regarding an aerial vehicle.

A scene identification method, a control device, a movable platform and a computer-readable storage medium are provided, the method includes: controlling a probing device to emit probing light to detect a current scene; the probing device including an area-array photoelectric sensor; when the area-array photoelectric sensor receives a reflected echo of the probing light, obtaining multiple signals output by multiple photoelectric units of the area-array photoelectric sensor; identifying whether the current scene is a target scene based on signal parameters of the multiple signals, where the target scene at least includes a water surface scene. The scene identification steps are simple, facilitating rapid acquisition of scene identification results.

A scene identification method, a control device, a movable platform and a computer-readable storage medium are provided, the method includes: controlling a probing device to emit probing light to detect a current scene; the probing device including an area-array photoelectric sensor; when the area-array photoelectric sensor receives a reflected echo of the probing light, obtaining multiple signals output by multiple photoelectric units of the area-array photoelectric sensor; identifying whether the current scene is a target scene based on signal parameters of the multiple signals, where the target scene at least includes a water surface scene. The scene identification steps are simple, facilitating rapid acquisition of scene identification results.