CN113919159A - Logistics space optimization method - Google Patents

Abstract

In order to solve the problems in the prior art, the invention provides a logistics space optimization method, which comprises the following steps: first, the load space virtual model M is obtained, and the load space bearing mass C0 is obtained. Then, the loading target is scanned sequentially to obtain a loading target virtual model Mn. And weighing to obtain the loading target mass Cn and acquiring the loading target requirement An. And finally, retracting the other side surfaces except the bottom surface of the M inwards in equal proportion for a preset distance to form a loading space virtual model M0, and sequentially fitting Mn into the M0. When M0 has insufficient space left to place the next Mn, the Mn in the current M0 is space optimized. The loading mass C is calculated, and when C exceeds C0, the current Mn is subtracted and the Mn in the current M0 is spatially optimized. And C is the accumulated sum of Cn. The invention can fit and put the most goods in the warehouse space or the carrying space to the maximum extent, thereby improving the utilization rate of the warehouse space or the carrying space.

Description

Logistics space optimization method

Technical Field

The invention relates to the field of logistics, in particular to a logistics space optimization method.

Background

Logistics (the english name: logistics) is originally intended as "physical distribution" or "goods distribution", is part of supply chain activities, and is a process of planning, implementing and controlling goods, service consumption and efficient, low-cost flow and storage of relevant information from a place of origin to a place of consumption in order to meet customer needs. The logistics takes storage as a center, and the production and the market are kept synchronous. Logistics is the whole process of planning, implementing and managing raw materials, semi-finished products, finished products and related information from the production place of commodities to the consumption place of the commodities by means of transportation, storage, distribution and the like at the lowest cost in order to meet the requirements of customers.

The logistics is composed of the links of the transportation, distribution, storage, packaging, carrying, loading and unloading, circulation and processing of commodities, related logistics information and the like. The key factor in determining logistics costs is the most efficient use of storage and shipping space. The prior art makes efficient use of storage and transport spaces, in particular transport spaces, generally based on the experience of logistics personnel. For example, when loading logistics vehicles, the loading is often performed based on experience of workers, and the utilization of the carrying space is often not utilized due to the fact that goods are placed unreasonably, so that unnecessary space waste is caused, and the logistics cost is increased.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a logistics space optimization method, which comprises the following steps:

first, the load space virtual model M is obtained, and the load space bearing mass C0 is obtained.

Then, the loading target is scanned sequentially to obtain a loading target virtual model Mn. And weighing to obtain the loading target mass Cn and acquiring the loading target requirement An.

And finally, retracting the other side surfaces except the bottom surface of the M inwards in equal proportion for a preset distance to form a loading space virtual model M0, and sequentially fitting Mn into the M0. When M0 has insufficient space left to place the next Mn, the Mn in the current M0 is space optimized. The loading mass C is calculated, and when C exceeds C0, the current Mn is subtracted and the Mn in the current M0 is spatially optimized. And C is the accumulated sum of Cn.

The space optimization method comprises the following steps:

(1) the cubic or cuboidal Mn in Mn is classified as Mn-A, the standard geometry-compliant Mn-B in the remaining Mn is classified as Mn-B, and the remaining Mn is classified as Mn-C.

(2) First, according to a loading target requirement An and a loading target mass Cn of Mn in Mn-A, Mn-A in which no non-withstand voltage characteristic is involved in An requirement is stacked and laid in order from the bottom layer to the upper layer of M0 with Cn heavier from below based on a volume collision rule to form a stacked body K1. Mn-A in which An is required to meet the non-pressure-resistant characteristics is then directly placed on the topmost layer of M0, and arranged on the basis of the volume collision rule, and when the topmost layer is fully arranged, Cn is moved to the next layer more largely, and arranged again on the basis of the volume collision rule, forming a stack K2.

(3) Mn — B was stacked on K1 based on the volume collision rule to form a stacked body K3. Mn — C were stacked on K3 based on the volume collision rule to form a stacked body K4.

(4) Calculating the space difference between K4 and K2, and on the basis of not exceeding C0, inserting the rest Mn-C, Mn-B and Mn-A with the volume less than or equal to the space difference from top to bottom in sequence on the basis of a volume collision rule until no Mn-C, Mn-B and Mn-A can be inserted, so as to obtain a K5 stack based on K4; stack K2 is adjusted to be above K5 based on the volume collision rule.

The volume collision rule is as follows: the virtual models can be attached to each other, but cannot be interpenetrated with each other.

Further, the method for acquiring the virtual model M0 of the load space includes:

firstly, matrix measurement is carried out on a loading space by adopting a rapid distance measuring device, and the distance L-n of a current measuring matrix point relative to the rapid distance measuring device is obtained, wherein n is the matrix point number of the current measuring matrix point. The angle J-n of the matrix point n with respect to the fast ranging device and the corresponding L-n are then recorded. And then obtaining the angle J- (N-N) and the corresponding L- (N-N) of the virtual matrix point N-N in the preset range vertically above the matrix point N and/or the preset range vertically below the matrix point N according to function calculation in a virtual angle endowing mode. And finally, integrating J-N, L-N, J- (N-N) and L- (N-N) of all matrix points N to form a loading space virtual model M0 based on a loading space, wherein the loading space virtual model M0 is a three-dimensional space formed by the matrix points N and the virtual matrix points N-N.

Further, the method for sequentially scanning the loading target to obtain the loading target virtual model Mn includes:

s1, performing matrix measurement on a bearing surface where an object to be measured is located through a measuring device, and converting the bearing surface where the object to be measured is located according to a conversion model to form a virtual background measuring space.

S2, selecting a virtual measuring surface in the virtual background measuring space, and forming correction parameters of each point array of the bearing surface where the target to be measured is located relative to the virtual measuring surface.

And S3, when the bearing surface with the target to be measured is identified, performing matrix measurement on the bearing surface with the target to be measured and the bearing surface with the target to be measured by using the measuring device, and converting the bearing surface with the target to be measured and the bearing surface with the target to be measured into a virtual target measuring space according to the conversion model of S1 and the correction parameters obtained in the step S2.

And S4, comparing the virtual background measurement space with the virtual target measurement space to obtain the virtual form of the target to be measured, which is positioned on the virtual measurement surface in the virtual background measurement space. The virtual form is constructed by virtual measuring points of the target to be measured which are arranged in a matrix. The virtual form is a loading target virtual model Mn.

Further, the measuring device includes: a fast distance measuring device. The fast distance measuring device has the functions of dot matrix projection and/or scanning distance measurement. Step S1, the method for forming the conversion parameters of each dot matrix according to the conversion model and converting the bearing surface on which the object to be measured is located to form the virtual background measurement space includes: firstly, the fast distance measuring device carries out matrix measurement on a bearing surface where a target to be measured is located, and the distance T-m of a current measurement matrix point relative to the fast distance measuring device is obtained, wherein m is the matrix point number of the current measurement matrix point. The angle G-m and the corresponding T-m of the matrix point m with respect to the fast ranging device are then recorded. And then, obtaining the angle G- (M-M) and the corresponding T- (M-M) of the virtual matrix point M-M in the preset range vertically above the matrix point M and/or the preset range vertically below the matrix point M by a virtual angle endowing mode according to function calculation. And finally, integrating G-M, T-M, G- (M-M) and T- (M-M) of all matrix points M to form a virtual background measuring space based on a bearing surface where the object to be measured is located, wherein the virtual background measuring space is a three-dimensional space formed by the matrix points M and the virtual matrix points M-M.

In step S2, the correction parameters are: firstly, the distance difference TC-M between the matrix point M and the matrix point M or the virtual matrix point M-M on the selected measurement reference surface is calculated. And then forming a calculation conversion relation between the T-m and the TC-m through a function calculation formula, wherein the calculation conversion relation is the correction parameter.

Further, in step S3, the method for transforming the object to be measured and the bearing surface on which the object to be measured is located to form the virtual object measurement space according to the transformation model in step S1 and the correction parameters obtained in step S2 includes: firstly, the fast distance measuring device carries out matrix measurement on a bearing surface where a target to be measured is located, and the distance L-cm of a current measurement matrix point relative to the fast distance measuring device is obtained, wherein cm is the matrix point number of the current measurement matrix point when the target is measured. And then according to the position of the virtual matrix point M-M occupied by the measurement matrix point cm corresponding to the T-cm, correcting by adopting the correction parameters of the matrix point M corresponding to the virtual matrix point M-M to obtain the virtual measurement matrix point of the measurement point. And finally, integrating all virtual measurement matrix points to form a virtual target measurement space.

Further, the measuring device includes: an imaging device. In step S3, the method for identifying the object to be measured appearing on the carrying surface on which the object to be measured is located includes: firstly, the imaging device projects the bearing surface of the object to be measured to form a background projection surface. And then when the bearing surface of the object to be measured appears with the object to be measured, the background projection surface appears with a static object shadow, the contrast device performs contrast on the object to be measured at the moment, and a projection structure of the object to be measured is formed based on geometric calculation and based on a relative angle between the contrast device and the object to be measured.

Further, in step S4, the method for comparing the virtual background measurement space and the virtual target measurement space to obtain the virtual shape of the target to be measured in the virtual background measurement space background includes: and taking the part of the virtual target measurement space, which is overlapped with the original measurement point distance and angle, as an anchor point, and anchoring the virtual target measurement space into a virtual space which is overlapped with each virtual dot matrix of the virtual background measurement space. And then, data filling is carried out on the unmeasured surface of the measurement target according to the projection structure, so that the virtual form of the measurement target positioned on the virtual measurement surface in the virtual background measurement space is obtained.

Furthermore, when the virtual target measurement space is anchored to be a virtual space coinciding with each virtual dot matrix of the virtual background measurement space, firstly, the part of the virtual target measurement space coinciding with the original measurement point in the virtual background measurement space, where the distance and the angle of the original measurement point coincide, is taken as an anchor point, the virtual measurement matrix point in the virtual target measurement space is compared with the virtual matrix point at the corresponding position in the virtual background measurement space, the point position with the position error is generated, and the point position data of the virtual measurement matrix point is corrected based on the point position data of the virtual matrix point. And after the position data of all the virtual measurement matrix points are corrected, judging that the action is finished.

Further, the method for filling data into the unmeasured surface of the measurement target according to the projection structure comprises the following steps: firstly, in a virtual background measurement space, a virtual form model-measurement surface of a measurement target facing a quick distance measuring device is formed according to acquired virtual measurement matrix point data. And then, adjusting the projection structure to a position fitting the virtual form model-measuring surface according to the relative position relation between the projection device and the quick distance measuring device. And then adjusting the size of the projection structure to enable the projection structure to be superposed with the virtual form model-measuring surface to the maximum extent. And then, performing data point supplement by taking the structure boundary of the projection structure in the fitting state as a supplement point. And finally, obtaining the position data of the data point according to the position of the supplemented data point in the virtual background measurement space.

Further, the method for spatial optimization comprises:

(6) according to the MO carrier type and the safety requirement, an MO safety center of gravity point Z0 and a center of gravity point safety range zone Zc are set. The Zc comprises a column range formed by all fields of a vertical upper preset height and a vertical lower preset height in the range of a horizontal preset structure diagram of Z0. Calculating the mass emphasis Za of the currently stacked Mn, if Za is within the range of ZC, not performing emphasis adjustment, and if Za is outside the range of ZC, performing the following adjustment according to the relative position relationship between Za and ZC:

firstly, judging whether Za is higher than the height range of Zc, if so, moving the Mn-B object from large mass to small mass one by one to the position below the Mn-A object with a cross section Fa completely covering Fb, wherein Fa is the cross section shape of Mn-A, and Fb is the cross section shape of the current Mn-B to be moved. And (5) completing the movement once, performing primary space secondary optimization and calculating the optimized Za position until Za is located in the height range of Zc.

And then, judging whether the Za is positioned in the range of the Zc level preset structure diagram, if not, making a horizontal connecting line L1 of the nearest point of the Za and the Zc level and a horizontal connecting line L2 of the farthest point of the Za and the Zc level. And the following operations are carried out:

A. the minimum displacement difference W1 was obtained from the length of L1, and the maximum displacement difference W2 was obtained from the length of L2.

B. And selecting Mn-A similar to the three-dimensional data, and calculating the displacement difference W | [ Ma × Sa-Mb × Sb |, wherein Ma is the mass of the object on the left of the horizontal connecting line L, Sa is the distance between the object on the left of the horizontal connecting line L and L, Mb is the mass of the object on the right of the horizontal connecting line L, and Sb is the distance between the object on the right of the horizontal connecting line L and L. If the condition that W1 < W2 is met, exchanging the positions of two Mn-A according to two Mn-A selected during current W calculation, then carrying out primary space secondary optimization and calculating the optimized Za position, and repeating the step (6) until Za is located in the Zc range.

If the condition that W1 < W2 is not met, the following operations are carried out:

C. the mass-largest object in Mn-A was acquired as E1, and three-dimensional data of E1 was acquired. And judging whether three-dimensional data of a combined body E2 formed by adjacent Mn-As is close to three-dimensional data of E1 in Mn-As opposite to the L2 of the E1, if so, calculating W of E1 and E2 by taking the whole E2 as an object, and judging whether the conditions that W1 < W2 exist. If the position of E1 and E2 is adjusted, if the E2 does not exist or the condition that the mass is consistent with W1 < W2 does not exist, the object with the largest mass in Mn-A is selected as E1, the step C is repeated until the condition that the mass is consistent with E2 and the mass is consistent with W1 < W2 exists, the positions of the object and the object are adjusted according to E1 and E2 corresponding to the current W, then, the space secondary optimization is carried out for one time, the optimized Za position is calculated, and the step (6) is repeated until the Za is located in the Zc range.

D. If Mn-A is extracted, the corresponding E2 and W1 < W2 do not exist. Selecting the object with the maximum mass in Mn-A and an adjacent object thereof to form E3, acquiring three-dimensional data of E3, selecting E2 by taking E3 as a reference, repeating the steps C and D until the corresponding E2 exists and the condition that the W1 is more than W and less than W2 exists, adjusting the positions of the E3 and the E2 corresponding to the current W, performing primary space secondary optimization, calculating the optimized Za position, and repeating the steps (6) until the Za is located in the Zc range.

Further, the spatial quadratic optimization includes:

after the two objects are moved, whether the moving object and other surrounding objects are crossed or not is judged. If the intersection exists, the position of the mobile object with the intersection is adjusted on the basis of the volume collision rule, and if the position can be adjusted to the position without the intersection, the spatial quadratic optimization is completed.

And (II) if the adjustment can not be carried out until no intersection exists after the step (I), firstly, the height position of the moving object is adjusted until the side closest to the upper/lower boundary of the MO and the surrounding objects do not exist in an intersection. The horizontal position of the moving object is then adjusted to the point where the side closest to the MO left/right boundary does not intersect with surrounding objects. And finally, integrally adjusting the height positions and the horizontal positions of other objects based on a volume collision rule until Mn does not intersect, and finishing secondary optimization of the space.

And (III) if Mn exceeds the boundary of M after the step (II), integrally shifting the Mn until the Mn is completely positioned in the boundary of M. And (5) if the overall displacement can not enable Mn to be completely positioned in the boundary of M, restoring the displacement result and carrying out the step (6) again.

Further, the method for spatial optimization comprises:

(5) and (3) when stacking is carried out in the steps (2) to (4) to obtain K1-K5, calculating mass emphasis Zn of Mn finished by current stacking once each Mn is placed in the stack, and placing the next Mn at an opposite position according to the horizontal left and right positions of the mass emphasis Zn relative to Z0.

The invention has at least one of the following beneficial effects:

1. according to the invention, before the goods are filled in the warehouse space or the carrying space, the virtual model fitting mode is adopted, so that the most goods are fitted in the warehouse space or the carrying space to the maximum extent, and the virtual model fitting mode is adopted, so that the position can be conveniently adjusted to obtain the optimal solution.

2. The invention is based on the gravity center point constraint and the space constraint, and fully considers the bearing weight during carrying, thereby greatly improving the transportation safety.

3. The invention adopts special loading space measurement and cargo measurement technology, and can conveniently and rapidly acquire the three-dimensional virtual models of the loading space measurement and the cargo, thereby greatly improving the accuracy and the corresponding speed in space optimization.

Drawings

FIG. 1 is a schematic diagram of a process of forming a virtual background measurement space according to the present invention.

FIG. 2 is a schematic view of a field structure of the fast ranging apparatus for measuring a target object according to the present invention.

FIG. 3 is a schematic diagram of the process of supplementing data points according to the present invention.

FIG. 4 is a schematic view of a virtual model of a loading space according to the present invention.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

Example 1

A logistics space optimization method comprises the following steps:

first, the load space virtual model M is obtained, and the load space bearing mass C0 is obtained.

Then, the loading target is scanned sequentially to obtain a loading target virtual model Mn. And weighing to obtain the loading target mass Cn and acquiring the loading target requirement An.

And finally, retracting the other side surfaces except the bottom surface of the M inwards in equal proportion for a preset distance to form a loading space virtual model M0, and sequentially fitting Mn into the M0. When M0 has insufficient space left to place the next Mn, the Mn in the current M0 is space optimized. The loading mass C is calculated, and when C exceeds C0, the current Mn is subtracted and the Mn in the current M0 is spatially optimized. And C is the accumulated sum of Cn.

The space optimization method comprises the following steps:

(1) the cubic or cuboidal Mn in Mn is classified as Mn-A, the standard geometry-compliant Mn-B in the remaining Mn is classified as Mn-B, and the remaining Mn is classified as Mn-C.

(2) First, according to a loading target requirement An and a loading target mass Cn of Mn in Mn-A, Mn-A in which no non-withstand voltage characteristic is involved in An requirement is stacked and laid in order from the bottom layer to the upper layer of M0 with Cn heavier from below based on a volume collision rule to form a stacked body K1. Mn-A in which An is required to meet the non-pressure-resistant characteristics is then directly placed on the topmost layer of M0, and arranged on the basis of the volume collision rule, and when the topmost layer is fully arranged, Cn is moved to the next layer more largely, and arranged again on the basis of the volume collision rule, forming a stack K2.

(3) Mn — B was stacked on K1 based on the volume collision rule to form a stacked body K3. Mn — C were stacked on K3 based on the volume collision rule to form a stacked body K4.

(4) Calculating the space difference between K4 and K2, and on the basis of not exceeding C0, inserting the rest Mn-C, Mn-B and Mn-A with the volume less than or equal to the space difference from top to bottom in sequence on the basis of a volume collision rule until no Mn-C, Mn-B and Mn-A can be inserted, so as to obtain a K5 stack based on K4; stack K2 is adjusted to be above K5 based on the volume collision rule.

The prior art makes efficient use of loading spaces, such as storage spaces and transport spaces, in particular transport spaces, generally based on the experience of logistics personnel. For example, when loading logistics vehicles, the loading is usually performed based on experience of workers, and when the workers obtain a cargo, the placement position of the cargo is determined temporarily and quickly by manual loading, and when the cargo is standard, the space utilization rate is high. However, when the goods have special-shaped pieces or are not uniform in size, the carrying space is often not utilized due to unreasonable placement of the goods, and unnecessary space waste is caused, thereby increasing the logistics cost.

The invention constructs the loading space and the loading target (such as goods) into the virtual model, firstly obtains the target set which can load the goods by arranging and putting in the virtual model in advance, then distinguishes and stacks the target set based on the stacking mode of the invention, and inserts the rest loading goods into the gap. The space optimization process is carried out based on the virtual model, so that the goods are quickly inserted, taken out and adjusted and transformed in position, and the space utilization rate of the loading space can be greatly improved based on the space optimization process. Before the operator loads the goods, the optimal loading position of each piece of goods is marked in the virtual model MO, and the operator only needs to place the goods at the marked position according to the indication, so that the loading space can be utilized to the maximum extent to complete the loading of the goods. Compared with the manual loading mode in the prior art, under the condition that special-shaped pieces and non-standard pieces exist in the loading target, such as when express items are loaded, the single carrying capacity or storage capacity can be generally improved by more than 10%, and the logistics cost is remarkably reduced.

Example 2

Based on the logistics space optimization method in embodiment 1, the method for obtaining the virtual model M0 of the loading space includes:

firstly, matrix measurement is carried out on a loading space by adopting a rapid distance measuring device, and the distance L-n of a current measuring matrix point relative to the rapid distance measuring device is obtained, wherein n is the matrix point number of the current measuring matrix point. The angle J-n of the matrix point n with respect to the fast ranging device and the corresponding L-n are then recorded. And then obtaining the angle J- (N-N) and the corresponding L- (N-N) of the virtual matrix point N-N in the preset range vertically above the matrix point N and/or the preset range vertically below the matrix point N according to function calculation in a virtual angle endowing mode. And finally, integrating J-N, L-N, J- (N-N) and L- (N-N) of all matrix points N to form a loading space virtual model M0 based on a loading space, wherein the loading space virtual model M0 is a three-dimensional space formed by the matrix points N and the virtual matrix points N-N.

For the virtual model M0 of the loading space, calibration may be performed, for example: an X-shaped standard transport vehicle, the carrying space of which is a standard X Y Z cube. The carrying space of a standard container is a standard X Y Z cube. An X-type aircraft has standard carrying spaces of S X Z columns and the like. However, sometimes the loading space is non-standard, or as shown in fig. 4, when a certain amount of reserved goods 9 are already loaded in the loading space 8, it is difficult to obtain an accurate MO by using a calibration method, and at this time, by using the method for obtaining the loading space virtual model M0 of the present invention, the loading space virtual model M0 can be quickly and accurately constructed, and it is avoided that space optimization due to M0 error cannot be effectively implemented.

Example 3

Based on the logistics space optimization method of embodiment 1, the method for sequentially scanning the loading target to obtain the loading target virtual model Mn comprises the following steps:

s1, performing matrix measurement on a bearing surface where an object to be measured is located through a measuring device, and converting the bearing surface where the object to be measured is located according to a conversion model to form a virtual background measuring space.

S2, selecting a virtual measuring surface in the virtual background measuring space, and forming correction parameters of each point array of the bearing surface where the target to be measured is located relative to the virtual measuring surface.

And S3, when the bearing surface with the target to be measured is identified, performing matrix measurement on the bearing surface with the target to be measured and the bearing surface with the target to be measured by using the measuring device, and converting the bearing surface with the target to be measured and the bearing surface with the target to be measured into a virtual target measuring space according to the conversion model of S1 and the correction parameters obtained in the step S2.

And S4, comparing the virtual background measurement space with the virtual target measurement space to obtain the virtual form of the target to be measured, which is positioned on the virtual measurement surface in the virtual background measurement space. The virtual form is constructed by virtual measuring points of the target to be measured which are arranged in a matrix. The virtual form is a loading target virtual model Mn.

As shown in fig. 1, the measuring apparatus includes: a fast ranging device 2 and an imaging device 3. The fast distance measuring device 2 has a dot matrix projection and/or scanning distance measuring function. Step S1, the method for forming the conversion parameters of each dot matrix according to the conversion model and converting the bearing surface on which the object to be measured is located to form the virtual background measurement space includes: firstly, the fast distance measuring device 2 carries out matrix measurement on a bearing surface 1 where a target to be measured is located, and the distance T-m of a current measuring matrix point relative to the fast distance measuring device 2 is obtained, wherein m is the matrix point number of the current measuring matrix point. The angle G-m and the corresponding T-m of the matrix point m with respect to the fast ranging device 2 are then recorded. And then, obtaining the angle G- (M-M) and the corresponding T- (M-M) of the virtual matrix point M-M in the preset range vertically above the matrix point M and/or the preset range vertically below the matrix point M by a virtual angle endowing mode according to function calculation. And finally, integrating G-M, T-M, G- (M-M) and T- (M-M) of all matrix points M to form a virtual background measuring space 5 based on the bearing surface 1 where the object to be measured is located, wherein the virtual background measuring space 5 is a three-dimensional space formed by the matrix points M and the virtual matrix points M-M.

In step S2, the correction parameters are: firstly, the distance difference TC-M between the matrix point M and the matrix point M or the virtual matrix point M-M on the selected measurement reference surface is calculated. And then forming a calculation conversion relation between the T-m and the TC-m through a function calculation formula, wherein the calculation conversion relation is the correction parameter.

As shown in fig. 2, the method for transforming the target to be measured and the bearing surface on which the target to be measured is located to form the virtual target measurement space according to the transformation model in step S1 and the correction parameters obtained in step S2 in step S3 includes: firstly, the fast distance measuring device 2 carries out matrix measurement on a bearing surface 1 where a target to be measured is located, and the distance L-cm of a current measurement matrix point relative to the fast distance measuring device 2 is obtained, wherein cm is the matrix point number of the current measurement matrix point when the target is measured. And then according to the position of the virtual matrix point M-M occupied by the measurement matrix point cm corresponding to the T-cm, correcting by adopting the correction parameters of the matrix point M corresponding to the virtual matrix point M-M to obtain the virtual measurement matrix point of the measurement point. And finally, integrating all virtual measurement matrix points to form a virtual target measurement space.

In step S3, the method for identifying the object to be measured appearing on the carrying surface on which the object to be measured is located includes: firstly, the contrast device 3 projects the bearing surface 1 of the object to be measured to form a background projection surface. Then when the bearing surface 1 where the target to be measured is located has the target 6 to be measured, the background projection surface has a static object shadow, the imaging device images the target 6 to be measured at the moment, and a projection structure of the target to be measured is formed based on geometric calculation and based on a relative angle between the imaging device 3 and the target 6 to be measured.

Step S4 is a method for obtaining a virtual shape of the target to be measured in the virtual background measurement space background by comparing the virtual background measurement space and the virtual target measurement space, including: and taking the part of the virtual target measurement space, which is overlapped with the original measurement point distance and angle, as an anchor point, and anchoring the virtual target measurement space into a virtual space which is overlapped with each virtual dot matrix of the virtual background measurement space. And then, data filling is carried out on the unmeasured surface of the measurement target according to the projection structure, so that the virtual form of the measurement target positioned on the virtual measurement surface in the virtual background measurement space is obtained.

The fast distance measuring device 2 can be a distance measuring device such as an infrared distance measuring device, a microwave distance measuring device and the like according to needs. The imaging device 3 generally comprises a projection device and a video device for acquiring projection variations.

The existing intelligent measurement method of a single device generally comprises the following steps: firstly, a target to be identified is marked or automatically identified, then the current two-dimensional identification data of the target to be identified is obtained, then the distance between the target to be identified and the quick distance measuring device 2 is measured through the quick distance measuring device 2, and the current two-dimensional identification data of the target to be identified is converted into two-dimensional measurement data in the same proportion based on the geometric principle of big-end-up and small-end-up, so that the scanning surface measurement data of the target to be measured is output. However, the volume data of the target to be recognized cannot be output. When the volume data of the target to be recognized needs to be measured, at least 3 point cloud scanning devices are often needed to recognize and analyze the target to be recognized from different angles, then the obtained data is constructed into a virtual model based on a digital-analog analysis method, and then the virtual model data of the target to be recognized is converted into measurement data in an equal proportion based on the geometric principle of big and small. The method comprises the steps of generally adopting a 3D point cloud scanning and 3D model construction technology in the process of constructing the virtual model, calibrating massive scanning points during point cloud scanning, comparing data acquired by 3 point cloud scanning devices during the process of constructing the 3D model, accurately splicing a part of virtual models of scanning targets constructed by the data of the 3 point cloud scanning devices to form a correct virtual model of the target to be measured, and covering the overlapped part during the splicing process. Therefore, the above-mentioned technology needs to acquire a large amount of scanning data, requires a computer to perform very complicated calculation, and based on a high-performance special computer, the final output measurement precision of the prior art is generally between 1.2 cm and 2.8cm, and the recognition speed is generally between 0.8 second and 1.5 seconds on the basis of adopting a measurement device with similar performance to the present invention. Meanwhile, because the output of the measurement result in the prior art needs to output the result after carrying out the same-scale amplification/reduction on the virtual model based on the near-far geometric principle, once the target to be measured is located on the inclined plane or the concave-convex surface, and the inclined plane or the concave-convex surface cannot be accurately identified in the model building process, an error of the judgment of the bottom surface position is inevitably generated, so that the output measurement data of the target to be measured, especially the great error value of the height and volume data is caused.

Compared with the prior art, the invention uses the projection device to identify whether the object to be measured 6 appears on the bearing surface 1 of the object to be measured, the identification method can be that the object to be measured is determined as the object to be measured according to the stable time of the foreign object shadow appearing on the background projection surface when the stable time of the foreign object shadow appearing on the background projection surface exceeds the threshold value. The stabilization time is as follows: the time at which the foreign object shadow is no longer displaced. The identification method may also be: when the foreign matter moves continuously, the foreign matter forms an independent and complete shadow structure on the bearing surface 1 where the object to be measured is located. Then, based on the rapid distance measuring device 2 and the method of the invention, the virtual form of the measuring target is constructed on the virtual measuring surface of the virtual background measuring space, and the construction process only needs to obtain the distance data and the projection structure data of the target to be measured, so the data acquisition process is very rapid, the bandwidth required by data transmission is much smaller, and the real-time data transmission requirement of the invention can be met even if short-distance transmission technologies such as USB, Bluetooth and the like or mobile networks of more than 2G are adopted. The operation functions related to the virtual model building process are mainly trigonometric functions and proportional transfer functions, and the calculation amount is far lower than that of a 3D point cloud and 3D model building technology, so that the calculation performance requirement on a processor device is much lower than that of the processor adopting the 3D point cloud and 3D model building technology. In addition, the method has the setting of correction parameters in the virtual modeling process, and the setting enables the target to be measured to be virtually placed on the virtual measuring surface of the absolute plane for analysis and acquisition of morphological data, so that the problem of overlarge measurement data error caused by inclination or depression of the bearing surface 1 where the target to be measured is located is effectively avoided. In the case of existing low-performance hardware, such as: acconeer's A111-001-TR infrared range sensor, overhead miniature camera, DLP infrared projector, and low performance processor, such as: based on Intel Pentium G2020, the accuracy error of the measured target shape data is 0.4-0.6cm, and the time for measurement and analysis is increased to 0.005-0.01 second. If the device with the same or similar functions as the device is adopted to measure on a basically horizontal smooth single color measuring surface, the target measurement is carried out by the existing 3D point cloud scanning and 3D model construction technology, the error is about 2.2-3.5cm, and the measurement time is about 1-15 seconds according to the complexity of the target structure. If the device with the same or similar functions as the device is adopted, the measurement is carried out on a basically horizontal smooth single color measurement surface, the target measurement is carried out by the existing structured light technology, the error is about 0.8-1.0cm, and the measurement time is about 0.5-3 seconds according to the complexity of the target structure. Compared with the prior art, the invention has remarkable progress in hardware requirements, measurement speed, measurement precision and the like, and as the invention has lower requirements on the hardware requirements and the bearing surface 1 of the target to be measured, as long as the target to be measured 6 can be placed, even if the measurement surfaces such as an inclined surface, a concave-convex surface and the like are all capable of realizing rapid and accurate measurement, the invention can be combined and applied in various occasions without the structural limitation of a fixed device. When more advanced hardware and a processor are selected, although the cost is increased, the accuracy error of the measurement target form data can be improved to about 0.04cm, and the time for measurement and analysis is improved to 0.001 second. Theoretically, as the performance of hardware continues to improve, the measurement accuracy and measurement time can be further improved.

On the basis of the space optimization method, the processes of cargo measurement, space optimization operation and actual loading generally exist in the logistics loading process. By adopting the method, the virtual model construction can be rapidly carried out on the loading target, so that the cargo can be measured without stopping, the cargo transferring efficiency is improved, enough calculation time is provided for space optimization, the waiting time between cargo measurement, space optimization calculation and actual loading is reduced, and the loading efficiency of logistics is greatly improved. Especially in the fields of express delivery centralized sorting bases, airport loading, train loading, seaport loading and the like, transfer links such as measurement, weighing and the like exist between the goods from a warehouse to a delivery vehicle, so that certain transfer time is needed. By adopting the cargo measurement and space optimization method, the cargo measurement and loading space optimization can be completed in the cargo transferring process, the cargo transferring time is effectively utilized again, and the logistics efficiency is improved.

Example 4

Based on the logistics space optimization method described in embodiment 3, when anchoring the virtual target measurement space to a virtual space coinciding with each virtual dot matrix of the virtual background measurement space, first, taking a part of the virtual target measurement space coinciding with the original measurement point in the virtual background measurement space, where the distance and angle of the original measurement point coincide, as an anchor point, comparing the virtual measurement matrix point in the virtual target measurement space with the virtual matrix point at the corresponding position in the virtual background measurement space, where a position error occurs, and correcting the position data of the point of the virtual measurement matrix point based on the position data of the virtual matrix point. And after the position data of all the virtual measurement matrix points are corrected, judging that the action is finished.

The method is used for further correcting the position data information of the virtual measurement matrix point, the scanning precision error of the rapid scanning device 2 sometimes causes systematic or partial point data error, and the position data error of the virtual measurement matrix point caused by the scanning error can be obviously reduced after the correction by the method, so that the measurement data precision during output is further improved, and the existing low-performance and low-performance hardware is adopted, such as: acconeer's A111-001-TR infrared range sensor, overhead miniature camera, DLP infrared projector, and low performance processor, such as: compared with the embodiment 1, the method can reduce the measurement accuracy error to about 0.2cm on the basis of Intel Pentium G2020.

The method for filling the data of the unmeasured surface of the measurement target according to the projection structure comprises the following steps: firstly, in a virtual background measurement space, a virtual form model-measurement surface of a measurement target facing a quick distance measuring device is formed according to acquired virtual measurement matrix point data. And then, adjusting the projection structure to a position fitting the virtual form model-measuring surface according to the relative position relation between the projection device and the quick distance measuring device. And then adjusting the size of the projection structure to enable the projection structure to be superposed with the virtual form model-measuring surface to the maximum extent. And then, performing data point supplement by taking the structure boundary of the projection structure in the fitting state as a supplement point. And finally, obtaining the position data of the data point according to the position of the supplemented data point in the virtual background measurement space.

As shown in fig. 2, when the object 6 to be measured appears on the bearing surface 1 where the object to be measured is located, due to the shielding effect of the object 6 to be measured, the scanning field formed by the fast distance measuring device 2 forms a scanning surface 601 and a shielding surface 602 on the object 6 to be measured, and at the same time, forms a shielding space 7 between the bearing surface 1 where the object to be measured is located and the object 6 to be measured. If the virtual model is directly established without data supplementation, the finally displayed form of the virtual model is only the scanning surface 601, or the accumulation of the target 6 to be measured and the shielded space 7 can cause huge errors in the measured data. Therefore, the present invention adopts the data supplement method as shown in fig. 3, that is: first, as shown in a in fig. 3, a virtual model of the scanning surface 601 is constructed on the virtual measuring surface based on the scanning data of the fast ranging apparatus 2. The projection structure 3 is then fitted to a virtual model of the scan surface 601, as shown at B in fig. 3, to form a basis for rectification. Finally, as shown in C in fig. 3, the virtual model of the occlusion surface 602 is added to the virtual model of the scanning surface 601, so as to form a final virtual model of the target to be measured.

The applicant researches and discovers that especially when a measurement target is in a motion state, due to continuous motion of the measurement target, boundary blurring of an output virtual model is likely to exist in the existing 3D point cloud scanning +3D model construction technology or structured light measurement technology, namely the virtual model is output after target models of a plurality of time points are overlapped and forms a significant difference with an actual target to be measured, and therefore huge errors occur in the output target form and measurement data. Therefore, the 3D point cloud scanning +3D model building technology or the structured light measurement technology generally requires that the measurement target is stationary. However, the above problems can be effectively solved by the method of the present embodiment. On one hand, by adopting the technology of the embodiment, the problem of measurement errors caused by insufficient construction of the virtual model or loading of the shielding space 7 or mutual shielding among multiple targets can be solved. On the other hand, the technology of the embodiment can solve the problem of accuracy of motion measurement, because the invention distinguishes and completes the target virtual model formed by scanning based on the projection structure 3, and does not completely depend on scanning measurement to form the virtual model. The method effectively solves the problem of effectively defining the actual boundary of the target when the target moves. Compared with the prior art, the method can realize accurate measurement of the moving target and can accurately output the measured target data, and compared with the prior art which cannot realize accurate measurement of the moving target, the method realizes a significant breakthrough in the technology.

Example 5

Based on the logistics space optimization method of the embodiment 1, the space optimization method comprises the following steps:

(6) according to the MO carrier type and the safety requirement, an MO safety center of gravity point Z0 and a center of gravity point safety range zone Zc are set. The Zc comprises a column range formed by all fields of a vertical upper preset height and a vertical lower preset height in the range of a horizontal preset structure diagram of Z0. Calculating the mass emphasis Za of the currently stacked Mn, if Za is within the range of ZC, not performing emphasis adjustment, and if Za is outside the range of ZC, performing the following adjustment according to the relative position relationship between Za and ZC:

firstly, judging whether Za is higher than the height range of Zc, if so, moving the Mn-B object from large mass to small mass one by one to the position below the Mn-A object with a cross section Fa completely covering Fb, wherein Fa is the cross section shape of Mn-A, and Fb is the cross section shape of the current Mn-B to be moved. And (5) completing the movement once, performing primary space secondary optimization and calculating the optimized Za position until Za is located in the height range of Zc.

And then, judging whether the Za is positioned in the range of the Zc level preset structure diagram, if not, making a horizontal connecting line L1 of the nearest point of the Za and the Zc level and a horizontal connecting line L2 of the farthest point of the Za and the Zc level. And the following operations are carried out:

A. the minimum displacement difference W1 was obtained from the length of L1, and the maximum displacement difference W2 was obtained from the length of L2.

B. And selecting Mn-A similar to the three-dimensional data, and calculating the displacement difference W | [ Ma × Sa-Mb × Sb |, wherein Ma is the mass of the object on the left of the horizontal connecting line L, Sa is the distance between the object on the left of the horizontal connecting line L and L, Mb is the mass of the object on the right of the horizontal connecting line L, and Sb is the distance between the object on the right of the horizontal connecting line L and L. If the condition that W1 < W2 is met, exchanging the positions of two Mn-A according to two Mn-A selected during current W calculation, then carrying out primary space secondary optimization and calculating the optimized Za position, and repeating the step (6) until Za is located in the Zc range.

If the condition that W1 < W2 is not met, the following operations are carried out:

C. the mass-largest object in Mn-A was acquired as E1, and three-dimensional data of E1 was acquired. And judging whether three-dimensional data of a combined body E2 formed by adjacent Mn-As is close to three-dimensional data of E1 in Mn-As opposite to the L2 of the E1, if so, calculating W of E1 and E2 by taking the whole E2 as an object, and judging whether the conditions that W1 < W2 exist. If the position of E1 and E2 is adjusted, if the E2 does not exist or the condition that the mass is consistent with W1 < W2 does not exist, the object with the largest mass in Mn-A is selected as E1, the step C is repeated until the condition that the mass is consistent with E2 and the mass is consistent with W1 < W2 exists, the positions of the object and the object are adjusted according to E1 and E2 corresponding to the current W, then, the space secondary optimization is carried out for one time, the optimized Za position is calculated, and the step (6) is repeated until the Za is located in the Zc range.

D. If Mn-A is extracted, the corresponding E2 and W1 < W2 do not exist. Selecting the object with the maximum mass in Mn-A and an adjacent object thereof to form E3, acquiring three-dimensional data of E3, selecting E2 by taking E3 as a reference, repeating the steps C and D until the corresponding E2 exists and the condition that the W1 is more than W and less than W2 exists, adjusting the positions of the E3 and the E2 corresponding to the current W, performing primary space secondary optimization, calculating the optimized Za position, and repeating the steps (6) until the Za is located in the Zc range.

The spatial quadratic optimization comprises:

after the two objects are moved, whether the moving object and other surrounding objects are crossed or not is judged. If the intersection exists, the position of the mobile object with the intersection is adjusted on the basis of the volume collision rule, and if the position can be adjusted to the position without the intersection, the spatial quadratic optimization is completed.

And (II) if the adjustment can not be carried out until no intersection exists after the step (I), firstly, the height position of the moving object is adjusted until the side closest to the upper/lower boundary of the MO and the surrounding objects do not exist in an intersection. The horizontal position of the moving object is then adjusted to the point where the side closest to the MO left/right boundary does not intersect with surrounding objects. And finally, integrally adjusting the height positions and the horizontal positions of other objects based on a volume collision rule until Mn does not intersect, and finishing secondary optimization of the space.

And (III) if Mn exceeds the boundary of M after the step (II), integrally shifting the Mn until the Mn is completely positioned in the boundary of M. And (5) if the overall displacement can not enable Mn to be completely positioned in the boundary of M, restoring the displacement result and carrying out the step (6) again.

Space optimization often considers the extreme use of the loading space, but when the loading space is a vehicle, the improper position of the center of gravity is likely to cause safety hazards in transportation. For example: flying vehicles require that the center of gravity should be located near the center of the vehicle, and vehicle, train, ship vehicles require that the center of gravity be as low as possible and near the centerline. Adopt this embodiment space optimization, on the basis that satisfies space utilization, optimized the regulation to the focus position of goods for the goods focus is located the preset safety range of carrier, has effectively avoided because the improper transportation safety risk that leads to of goods focus.

Example 6

Based on the logistics space optimization method of the embodiment 5, the space optimization method comprises the following steps:

(5) and (3) when stacking is carried out in the steps (2) to (4) to obtain K1-K5, calculating mass emphasis Zn of Mn finished by current stacking once each Mn is placed in the stack, and placing the next Mn at an opposite position according to the horizontal left and right positions of the mass emphasis Zn relative to Z0.

When steps (2) to (4) are performed to obtain K1 to K5, the center of gravity of Mn can be stabilized within a preset safety range with a high probability based on the stacking addition rule of this embodiment, so that the amount of calculation in performing step (6) is significantly reduced and the space optimization response speed is improved by reducing the possibility of performing the position change of step (6), lowering the number of steps in performing step (6), and reducing the number of rounds in performing step (6).

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (10)

1. A logistics space optimization method is characterized by comprising the following steps:

firstly, acquiring a loading space virtual model M and acquiring loading space bearing mass C0;

then, scanning the loading target in sequence to obtain a loading target virtual model Mn; weighing to obtain a loading target mass Cn, and acquiring a loading target requirement An;

finally, retracting the other side surfaces except the bottom surface of the M inwards in equal proportion for a preset distance to form a loading space virtual model M0, and sequentially fitting Mn into the M0; when M0 has insufficient space left to place the next Mn, performing space optimization on the Mn in the current M0; calculating the loading mass C, and when C exceeds C0, deducting the current Mn, and carrying out space optimization on Mn in the current M0; c is the cumulative sum of Cn;

the space optimization method comprises the following steps:

(1) classifying cubic or cube-like Mn in Mn as Mn-A, classifying other Mn which meets the standard geometric structure as Mn-B, and classifying the rest Mn as Mn-C;

(2) firstly, according to a loading target requirement An and a loading target mass Cn of Mn in Mn-A, sequentially stacking and paving Mn-A from a bottom layer to An upper layer of M0 with Cn heavier from the bottom layer to the upper layer according to a volume collision rule to form a stacked body K1, wherein the Mn-A is not related to non-pressure-resistant characteristics in the requirement An; then, Mn-A, wherein An is required to meet the non-pressure-resistant characteristic, is directly positioned on the topmost layer of M0 and is arranged based on a volume collision rule, after the topmost layer is fully arranged, Cn is moved to the next layer in a larger mode and is arranged again based on the volume collision rule to form a stacked body K2;

(3) stacking Mn-B on K1 based on a volume collision rule to form a stacked body K3; stacking Mn-C on K3 based on a volume collision rule to form a stacked body K4;

(4) calculating the space difference between K4 and K2, and on the basis of not exceeding C0, inserting the rest Mn-C, Mn-B and Mn-A with the volume less than or equal to the space difference from top to bottom in sequence on the basis of a volume collision rule until no Mn-C, Mn-B and Mn-A can be inserted, so as to obtain a K5 stack based on K4; stack K2 is adjusted to be above K5 based on the volume collision rule.

2. The logistics space optimization method of claim 1, wherein the method for obtaining the virtual model M0 of the loading space comprises:

firstly, carrying out matrix measurement on a loading space by adopting a rapid distance measuring device to obtain the distance L-n of a current measurement matrix point relative to the rapid distance measuring device, wherein n is the matrix point number of the current measurement matrix point; then recording the angle J-n of the matrix point n relative to the rapid distance measuring device and the corresponding L-n; then, an angle J- (N-N) and a corresponding L- (N-N) of a virtual matrix point N-N within a preset range vertically above and/or within a preset range vertically below the matrix point N are obtained according to function calculation in a virtual angle endowing mode; and finally, integrating J-N, L-N, J- (N-N) and L- (N-N) of all matrix points N to form a loading space virtual model M0 based on a loading space, wherein the loading space virtual model M0 is a three-dimensional space formed by the matrix points N and the virtual matrix points N-N.

3. The logistics space optimization method of claim 1, wherein the method for sequentially scanning the loading targets to obtain the loading target virtual model Mn comprises:

s1, performing matrix measurement on a bearing surface where an object to be measured is located through a measuring device, and converting the bearing surface where the object to be measured is located according to a conversion model to form a virtual background measuring space;

s2, selecting a virtual measuring surface in the virtual background measuring space to form correction parameters of each point array of a bearing surface where a target to be measured is located relative to the virtual measuring surface;

s3, when the bearing surface with the target to be measured is identified, performing matrix measurement on the bearing surface with the target to be measured and the bearing surface with the target to be measured through the measuring device, and converting the bearing surface with the target to be measured and the bearing surface with the target to be measured to form a virtual target measuring space according to the conversion model S1 and the correction parameters obtained in the step S2;

s4, comparing the virtual background measurement space with the virtual target measurement space to obtain a virtual form of the target to be measured, which is positioned on a virtual measurement surface in the virtual background measurement space; the virtual form is constructed by virtual measuring points of the target to be measured which are arranged in a matrix; the virtual form is a loading target virtual model Mn.

4. The logistics space optimization method of claim 3, wherein the measuring device comprises: a fast ranging device; the rapid distance measuring device has the functions of dot matrix projection and/or scanning distance measurement;

step S1, the method for forming the conversion parameters of each dot matrix according to the conversion model and converting the bearing surface on which the object to be measured is located to form the virtual background measurement space includes: firstly, carrying out matrix measurement on a bearing surface where a target to be measured is located by a rapid distance measuring device to obtain the distance T-m of a current measurement matrix point relative to the rapid distance measuring device, wherein m is the matrix point number of the current measurement matrix point; then recording the angle G-m and the corresponding T-m of the matrix point m relative to the rapid distance measuring device; then, an angle G- (M-M) and a corresponding T- (M-M) of a virtual matrix point M-M in a preset range vertically above and/or a preset range vertically below the matrix point M are obtained through function calculation in a virtual angle endowing mode; finally, integrating G-M, T-M, G- (M-M) and T- (M-M) of all matrix points M to form a virtual background measuring space based on a bearing surface where an object to be measured is located, wherein the virtual background measuring space is a three-dimensional space formed by the matrix points M and the virtual matrix points M-M;

in step S2, the correction parameters are: firstly, calculating the distance difference TC-M between a matrix point M and a matrix point M or a virtual matrix point M-M on a selected measurement reference surface; and then forming a calculation conversion relation between the T-m and the TC-m through a function calculation formula, wherein the calculation conversion relation is the correction parameter.

5. The logistics space optimization method of claim 4, wherein the step S3 of transforming the object to be measured and the bearing surface on which the object to be measured is located into the virtual object measurement space according to the transformation model of S1 and the correction parameters obtained in the step S2 comprises: firstly, carrying out matrix measurement on a bearing surface where a target to be measured is located by a rapid distance measuring device to obtain the distance L-cm of a current measurement matrix point relative to the rapid distance measuring device, wherein cm is the matrix point number of the current measurement matrix point when the target is measured; then, according to the position of a virtual matrix point M-M occupied by a measurement matrix point cm corresponding to the T-cm, correcting by adopting a correction parameter of a matrix point M corresponding to the virtual matrix point M-M to obtain a virtual measurement matrix point of the measurement point; and finally, integrating all virtual measurement matrix points to form a virtual target measurement space.

6. The logistics space optimization method of claim 5, wherein the measuring device comprises: an imaging device; in step S3, the method for identifying the object to be measured appearing on the carrying surface on which the object to be measured is located includes: firstly, the radiography device projects to a bearing surface where a target to be measured is located to form a background projection surface; then when the bearing surface where the target to be measured is located appears the target to be measured, the background projection surface appears a static object shadow, the contrast device performs contrast on the target to be measured at the moment, and a projection structure of the target to be measured is formed based on geometric calculation and based on a relative angle between the contrast device and the target to be measured;

step S4 is a method for obtaining a virtual shape of the target to be measured in the virtual background measurement space background by comparing the virtual background measurement space and the virtual target measurement space, including: taking the part of the virtual target measurement space, which is coincident with the original measurement point distance and angle, and the virtual background measurement space as an anchor point, and anchoring the virtual target measurement space into a virtual space which is coincident with each virtual dot matrix of the virtual background measurement space; and then, data filling is carried out on the unmeasured surface of the measurement target according to the projection structure, so that the virtual form of the measurement target positioned on the virtual measurement surface in the virtual background measurement space is obtained.

7. The logistics space optimization method of claim 6, wherein when anchoring the virtual target measurement space to a virtual space coinciding with each virtual dot matrix of the virtual background measurement space, first taking the part of the virtual target measurement space coinciding with the original measurement point in distance and angle as an anchor point, comparing the virtual measurement matrix point in the virtual target measurement space with the virtual matrix point at the corresponding position in the virtual background measurement space, and correcting the point location data of the virtual measurement matrix point based on the virtual matrix point location data; after the position data of all the virtual measurement matrix points are corrected, judging that the action is finished;

the method for filling the data of the unmeasured surface of the measurement target according to the projection structure comprises the following steps: firstly, forming a virtual form model-measuring surface of a measuring target facing a quick distance measuring device according to acquired virtual measuring matrix point data in a virtual background measuring space; then, adjusting the projection structure to a position where a virtual form model is fitted with a measurement surface according to the relative position relation between the projection device and the rapid distance measuring device; then adjusting the size of the projection structure to enable the projection structure to be superposed with the virtual form model-measuring surface to the maximum extent; then, data point supplement is carried out by taking the structure boundary of the projection structure in the fitting state as a supplement point; and finally, obtaining the position data of the data point according to the position of the supplemented data point in the virtual background measurement space.

8. The logistics space optimization method of claim 1, wherein the space optimization method comprises:

(6) setting an MO safety center of gravity point Z0 and a center of gravity point safety range zone Zc according to the type of the MO carrier and the safety requirements; the Zc comprises a column range formed by all fields of a vertical upper preset height and a vertical lower preset height in the range of a horizontal preset structure diagram of Z0; calculating the mass emphasis Za of the currently stacked Mn, if Za is within the range of ZC, not performing emphasis adjustment, and if Za is outside the range of ZC, performing the following adjustment according to the relative position relationship between Za and ZC:

firstly, judging whether Za is higher than the height range of Zc, if so, moving the Mn-B object from large mass to small mass one by one to the position below the Mn-A object with a cross section Fa completely covering Fb, wherein Fa is the cross section shape of Mn-A, and Fb is the cross section shape of the current Mn-B to be moved; after the movement is completed once, performing primary space secondary optimization and calculating the optimized Za position until Za is located in the height range of Zc;

then, judging whether Za is located in a ZC level preset structure chart range or not, if not, making a horizontal connecting line L1 of the nearest point of the Za and the ZC level and a horizontal connecting line L2 of the farthest point of the Za and the ZC level; and the following operations are carried out:

A. obtaining a minimum displacement difference W1 according to the length of L1 and a maximum displacement difference W2 according to the length of L2;

B. selecting Mn-A similar to three-dimensional data, and calculating the displacement difference W | Ma Sa-Mb Sb |, wherein Ma is the mass of an object on the left of a horizontal connecting line L, Sa is the distance between the object on the left of the horizontal connecting line L and L, Mb is the mass of an object on the right of the horizontal connecting line L, and Sb is the distance between the object on the right of the horizontal connecting line L and L; if the condition that W1 is more than W and less than W2 is met, exchanging the positions of two Mn-A according to two Mn-A selected during the current W calculation, then carrying out primary space secondary optimization and calculating the optimized Za position, and repeating the step (6) until Za is located in the range of ZC;

if the condition that W1 < W2 is not met, the following operations are carried out:

C. acquiring an object with the maximum mass in Mn-A as E1, and acquiring three-dimensional data of E1; judging whether three-dimensional data of a combined body E2 formed by adjacent Mn-As is close to three-dimensional data of E1 exists in Mn-As opposite to L2 of E1, if so, calculating W of E1 and E2 by taking the whole E2 as an object, and judging whether conditions that W1 < W2 exist; if the two objects exist, the positions of E1 and E2 are adjusted, if the E2 does not exist or the condition that the W1 is more than W and less than W2 does not exist, the object with the largest mass in Mn-A is selected as E1, the step C is repeated until the condition that the E2 which accords with the M is present and the condition that the W1 is more than W and less than W2 accords with the M is present, the positions of the E1 and the E2 which correspond to the current W are adjusted, then, the space secondary optimization is carried out for the first time, the optimized Za position is calculated, and the step (6) is repeated until the Za is located in the Zc range;

D. if the Mn-A is selected, the conforming E2 does not exist and the condition that the W1 < W2 does not exist; selecting the object with the maximum mass in Mn-A and an adjacent object thereof to form E3, acquiring three-dimensional data of E3, selecting E2 by taking E3 as a reference, repeating the steps C and D until the corresponding E2 exists and the condition that the W1 is more than W and less than W2 exists, adjusting the positions of the E3 and the E2 corresponding to the current W, performing primary space secondary optimization, calculating the optimized Za position, and repeating the steps (6) until the Za is located in the Zc range.

9. The logistics space optimization method of claim 8, wherein the spatial quadratic optimization comprises:

after the two objects are moved, judging whether the moving object and other surrounding objects are crossed or not; if the intersection exists, the position of the mobile object with the intersection is adjusted on the basis of the volume collision rule, and if the position of the mobile object with the intersection can be adjusted to the position without the intersection, the space secondary optimization is completed;

if the adjustment can not be carried out until no intersection exists after the step (one) is carried out, firstly, the height position of the moving object is adjusted until one side closest to the upper/lower boundary of the MO does not exist the intersection with the surrounding objects; then adjusting the horizontal position of the moving object to ensure that one side of the moving object closest to the MO left/right boundary does not intersect with the surrounding objects; finally, integrally adjusting the height positions and the horizontal positions of other objects based on a volume collision rule until no intersection exists between Mn, and finishing secondary optimization of space;

thirdly, if Mn exceeds the boundary of M after the step (II), wholly shifting Mn until Mn is completely positioned in the boundary of M; and (5) if the overall displacement can not enable Mn to be completely positioned in the boundary of M, restoring the displacement result and carrying out the step (6) again.

10. The logistics space optimization method of claim 8, wherein the space optimization method comprises:

(5) and (3) when stacking is carried out in the steps (2) to (4) to obtain K1-K5, calculating mass emphasis Zn of Mn finished by current stacking once each Mn is placed in the stack, and placing the next Mn at an opposite position according to the horizontal left and right positions of the mass emphasis Zn relative to Z0.

Images