Abstract: The present disclosure provides a touch driving method, a touch module and a display device, relating to the field of display technology. The display panel has M partitions, each partition includes independent touch control units, each touch control unit includes at least one common electrode block connected to each other; the driving method includes: a frame time includes a display time period and a touch time period, the display time period includes at least one display sub-time period, the touch time period includes at least one touch sub-time period; in each display sub-time period, applying a common electrode signal to all common electrode blocks; in each touch sub-time period, applying a touch driving signal to all touch units of N partitions and not applying the touch driving signal to the touch units of the remaining partitions, and sensing sense signals of the touch units of the N partitions.

Abstract: A display module and a touch display device are provided by the present application, which relates to the technical field of displaying. At least one driving chip is provided within the bonding region. The mainboard includes a power-supply chip. For a same driving chip, a resistance value between a first output terminal of the driving chip and the power-supply chip is substantially equal to a resistance value between a second output terminal of the driving chip and the power-supply chip, wherein the first output terminal refers to the output terminal that has the lowest distance to the first side edge, and the second output terminal refers to the output terminal that has the lowest distance to the second side edge. The display module is used to manufacture a touch display device.

Abstract: Charging and power supply optimization method and apparatus for a charging management system are provided. The method includes: obtaining information about power supply, transformation and distribution of a charging station, capability information of a charging facility, output information of a charging terminal, and charging demand information of an electric vehicle, determining a charging capability, power supply capability and an actual charging capacity of a charging facility system at the charging station, obtaining a model output result based on a pre-trained deep learning time series prediction algorithm model, generating a charging power allocation instruction in combination with an actual charging capacity of a charging facility system at the charging station, and a charging demand of a to-be-charged electric vehicle, and distributing electric energy to each charging facility.