1. A human body three-dimensional imaging method comprising the following steps: step A: providing a control center and at least three interconnected control base stations and arranging the at least three control base stations on a circumference of a circle centered on a human body to form a measurement space completely covering the human body, with each control base station comprising two vertically arranged three-dimensional sensors and each three-dimensional sensor comprising a projector and two cameras, determining respective local three-dimensional geometric depth data by the control base stations in their respective local coordinate systems by performing depth computation for respective human body image information collected by the cameras upon projecting a coding pattern on the human body by the projector, wherein the two three-dimensional sensors of each control base station are respectively configured to acquire three-dimensional geometric depth data and texture data of an upper part and a lower part of the human body from the view angle of the control base station; step B: transforming the three-dimensional geometric depth data by each of the control base stations from its local coordinate systems to a global coordinate system; step C: obtaining texture data of human body colors by each of the control base stations by projecting white light to the human body and observing from its view angle, and sending the collected texture data together with the three-dimensional geometric depth data transformed in step B to the control center; and step D: determining a human body three-dimensional model by the control center by splicing the three-dimensional geometric depth data received from the control base stations in the global coordinate system, determining a fused human body three-dimensional model by removing redundancy for an integral model of the human body; determining fused texture data by performing a weighted operation for texture data of an overlapped portion of all human body colors received from the control base stations; and associating the fused human body three-dimensional model with the fused texture data; wherein the human body image information collected in step A is an image sequence formed of a multi-step phase-shift image and one pseudo random coding image, or an image sequence formed of a multi-step phase-shift image and a Grey coding image, or an image sequence formed of a multi-step phase-shift image and a temporal phase unwrapping image, wherein an upper body image and a lower body image collected by the two three-dimensional sensors of each of the control base stations are not overlapped, and are capable of being spliced into an integral human body image observed from the view angle of the control base station, and the cameras for collecting the upper body image and the lower body image are respectively defined as first cameras and second cameras; and the performing depth computation for the human body image information to obtain three-dimensional geometric depth data in step A comprises: step A1: performing epipolar rectification for the upper body image and the lower body image according to pre-calibrated parameters of the first and second cameras, such that the same y-coordinates of the upper body image and the lower image have an epipolar corresponding relationship; step A2: according to four-step phase-shifting algorithm, obtaining wrapped phases of the first and second cameras by means of computation with a phase-shift image subjected to epipolar rectification; step A3: traversing the j-th row of the second camera to obtain a pixel position p r (ir,j) having a minimum phase difference in each wrapping cycle, wherein a wrapped phase value at a pixel position p l (i,j) in the wrapped phase of the first camera is w(i,j), and these pixel positions p r are used as corresponding candidate points of the pixel position p l of the first camera; step A4: using pseudo random coding images of the first and second cameras as a target image and a to-be-matched image respectively, considering a matching window with p l as a center on the human body image has a size of (2w+1)×(2w+1), wherein a grey value of any pixel p l in the window is marked as p l (u l ,u l ), whereas a grey value of a corresponding point of the candidate point p r on the to-be-matched image is p r (u r ,u r ), and a normalized correlation measurement function N CC of the two windows is expressed by the following formula: Ncc ⁡ ( I l , I r ) = F ⁡ ( i , j , ir , j ) = { ∑ u = - w , w ⁢ ⁢ ∑ v = - w w ⁢ ⁢ ( p l ⁡ ( i + u , j + v ) - p l _ ) * ( p r ⁡ ( ir + u , j + ⁢ v ) - p r _ ) } 2 { ∑ u = - w , w ⁢ ⁢ ∑ v = - w w ⁢ ⁢ ( p l ⁡ ( i + u , j + v ) - p l _ ) } 2 * { ∑ u = - w , w ⁢ ⁢ ∑ v = - w w ⁢ ⁢ ( p r ⁡ ( ir + u , j + v ) - p r _ ) } 2 wherein p l and p r respectively represent an average image grey, u and v respectively represent coordinates in a selected matching window, p l (i+u,j+v) represents a grey value at a pixel position (i+u,j+v) in the window of the first camera, and p r (ir+u,j+v) represents a grey value at a pixel position (ir+u,j+v) in the window of the second camera; upon epipolar rectification, each candidate point has the same y-coordinate j; a correlation measurement function is obtained by traversing all the candidate points (ir,j) of the second camera and the correlated values of a position (i,j) of the first camera, wherein only the corresponding point has a higher correlation value, and pixel-level corresponding points of the first and second cameras are obtained from the candidate points by defining a threshold; and step A5: upon obtaining the pixel-level points, obtaining a corresponding relationship of subpixels according to the wrapped phase differences of the first and second cameras, and reconstructing three-dimensional geometric depth data in combination with pre-calibrated internal parameters and structural parameters of the first and second cameras.