Method and system for distributing the weight of a motor train unit

Abstract

The application discloses a motor train unit whole vehicle weight distribution method and system, which is used for double-plane equipment arrangement adjustment, realizes equipment longitudinal position adjustment and transverse position fine adjustment, realizes constraint-based equipment arrangement scheme design and equipment modular design, provides a hinged motor train unit weight calculation method according to a mathematical model, establishes a whole vehicle weight distribution model, establishes an equipment arrangement mathematical model based on an operational research planning method, and the development platform can automatically and quickly perform equipment arrangement optimization and realize automatic enumeration of an optimal scheme; the influence degree of input parameters on output parameters is analyzed, and key equipment is identified to participate in accurate design and quality design optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail vehicle equipment arrangement and weight management, in particular to a method and system for distributing the weight of a whole motor train unit. BACKGROUND

[0002] The articulated motor train unit is a three-car formation, including Mc1, T, and Mc2 cars, and the Mc car has a driver's cab. Each car has three cars, two powered bogies, and two non-powered bogies, and the two cars are connected by the non-powered bogies. The equipment is generally arranged directly under the car and on the roof, and the equipment in the car has less impact, which is not considered for the time being.

[0003] Considering the special nature of the articulated motor train unit, the equipment arrangement and weight management of the motor train unit formation (i.e., three-car formation) need to be considered as a whole. The distribution of equipment affects the weight distribution of the articulated motor train unit, and the distribution of equipment on the car and under the car also brings great difficulty to equipment arrangement adjustment and weight distribution. There is a large deviation between the designed weight and the weight distribution, which increases the difficulty of precise design of the weight distribution of the articulated motor train unit.

[0004] CN109446606A provides a method for optimizing the arrangement of under-car equipment of a rail vehicle, which virtually divides the rectangular area under the car, without considering the non-rectangular arrangement area on the car, resulting in limited optimization accuracy and inability to realize multi-car and multi-plane analysis of the roof, interior, and multiple cars. SUMMARY

[0005] The present application solves the technical problem of the prior art by providing a method and system for distributing the weight of a whole motor train unit, which combines multi-car and multi-plane spatial arrangement, optimizes equipment arrangement and weight distribution, and realizes precise design of axle load, wheel load, and their deviations.

[0006] To solve the above technical problems, the technical solution adopted by the present application is as follows: a method for distributing the weight of a whole motor train unit, comprising the following steps:

[0007] S1, determining the equipment arrangement area of the roof plane and the bottom plane of the motor train unit; for a rectangular arrangement area, dividing the rectangular arrangement area into N virtual positions; for a non-rectangular arrangement area, taking the tangent of the boundary curve of the non-rectangular arrangement area as the boundary of the non-rectangular arrangement area, and then dividing the non-rectangular arrangement area after replacing the boundary into N virtual positions;

[0008] S2, after placing all the equipment in the rectangular arrangement area and the non-rectangular arrangement area, determining the equipment arrangement and weight distribution constraint conditions, and calculating the total weight and center of gravity of the upper assembly of each car of the motor train unit with equipment arrangement, load, axle load, and wheel load;

[0009] S3, taking the axle load deviation, the longitudinal coordinate deviation of the center of gravity of the assembly on the vehicle from the longitudinal coordinate of the center of the vehicle body, and the lateral coordinate deviation of the center of gravity of the assembly on the vehicle from the lateral coordinate of the center of the vehicle body as the optimization target, a target function is determined; the target function is set as the weighted sum of the absolute value of the maximum axle load deviation of each bogie, the absolute value of the lateral coordinate deviation of the center of gravity of each vehicle from the center coordinate, and the absolute value of the longitudinal coordinate deviation of the center of gravity of each vehicle from the center coordinate; the target function is solved to obtain an optimized solution containing the longitudinal coordinate and lateral coordinate of the center of gravity of each device, the load information of the secondary spring group of each bogie, the axle load and axle load deviation of each bogie, and the wheel load and wheel load deviation of each bogie;

[0010] S4, the influence of the coordinate position of the device on the axle load deviation, the longitudinal coordinate deviation of the center of gravity of the assembly on the vehicle from the longitudinal coordinate of the center of the vehicle body, and the lateral coordinate deviation of the center of gravity of the assembly on the vehicle from the lateral coordinate of the center of the vehicle body is analyzed by using an optimization software, and key devices are identified;

[0011] S5, taking the arrangement parameters of the key devices as controllable factors, setting a noise factor, taking the controllable factors and the noise factor as inputs of the optimization software, and optimizing the optimized solution;

[0012] S6, taking the lateral coordinate and longitudinal coordinate of the arrangement parameters of the key devices as design variables, the lateral coordinate and longitudinal coordinate of the arrangement parameters of the key devices as design variables, the lateral distance of the primary spring, the lateral distance of the secondary spring, the longitudinal distance of the articulated bogie, the wheelbase of the articulated bogie, and the wheelbase of the power bogie as constants, the deviation between the design variables and the constants as a first set value, the variation coefficient as a second set value, setting the probability distribution type, setting the center of gravity of the assembly on the vehicle, the arrangement boundary of the device, the axle load, the axle load deviation, and the wheel load deviation as constraint conditions, defining the upper limit and lower limit of the variables and the lower limit of the constraint conditions, and taking the minimum wheel load deviation and axle load deviation as the optimization target, the lateral coordinate and longitudinal coordinate of the arrangement parameters of the key devices are adjusted by using an optimization method until the optimized wheel load deviation and axle load deviation meet the design requirements.

[0013] The application considers the characteristics of rectangular arrangement areas and non-rectangular arrangement areas, provides a device arrangement adjustment method in double planes (a vehicle top plane and a vehicle bottom plane), realizes device longitudinal position adjustment and lateral position fine adjustment, realizes constraint-based device arrangement scheme design, and optimizes the device arrangement scheme.

[0014] In step S1 of the application, the vehicle bottom plane includes two rectangular arrangement areas; and the vehicle top plane includes one rectangular arrangement area and two non-rectangular arrangement areas.

[0015] In step S1 of the application, the boundary curve related parameter calculation formula of the non-rectangular arrangement area is:

[0016] Wherein, θ is a linear approximation angle of the boundary curve, H is a center distance between two bogies of each car of the motor train unit, W is a maximum width of the non-rectangular arrangement area, W2 is a minimum distance of the curve boundary, R is a radius of the curve boundary, x B is an abscissa of a tangent point on the boundary curve, y B is an ordinate of the tangent point on the boundary curve.

[0017] In step S1 of the present application, the method of placing all the devices in the rectangular arrangement area and the non-rectangular arrangement area comprises: arranging a C-shaped groove in a roof device arrangement area, mounting the roof device in the C-shaped groove, and keeping the mounting interfaces of the devices mounted on the roof plane consistent. Mounting the C-shaped groove long strip facilitates longitudinal adjustment of the devices.

[0018] The mounting interface is a waist-shaped hole. The waist-shaped hole facilitates lateral small displacement fine adjustment of the devices. For key devices with inconsistent spans, arranging the C-shaped groove and the waist-shaped hole at the mounting position facilitates in-plane position adjustment, and provides adjustment space for in-plane position optimization of the devices.

[0019] In step S2, the device arrangement and weight distribution constraint conditions comprise: the number of devices arranged in each car, the number and arrangement position of the hydraulic control units, the lateral position of the devices, the position of the auxiliary converter, the minimum gap in the longitudinal direction of the devices, the position of the main air cylinder, the orientation of the devices, the arrangement of the traction inverter and the high-voltage box in the same car, the arrangement of the auxiliary inverter and the low-voltage box in the same car, torque balance constraints, axle load and wheel load calculation constraints.

[0020] In step S3, when solving the objective function, the weights of each term of the objective function are all set to 1.

[0021] The present application solves the objective function by using the branch and bound method or the Monte Carlo simulation method.

[0022] In step S4, the optimization software is Isight software; the result data output by the Isight software is subjected to sensitivity analysis to obtain the contribution rate sorting of the normalized weight and coordinates of each device to the total weight Fcb of the device assembly on the car, the contribution rate sorting of the normalized weight and coordinates of each device to the coordinates Xcb of the center of gravity of the upper part of the car body, the contribution rate sorting of the normalized weight and coordinates of each device to the coordinates Ycb of the center of gravity of the upper part of the car body, the contribution rate sorting of the normalized weight and coordinates of each device to the coordinates Zcb of the center of gravity of the upper part of the car body, the contribution rate sorting of the normalized weight and coordinates of each device to the axle load deviation, the contribution rate sorting of the normalized weight and coordinates of each device to the wheel load deviation, and the key devices are determined by using the sorting.

[0023] In step S5, the noise factors comprise design conditions, the total weight of the assembly on the car, the load of the secondary spring, the center of gravity of the assembly on the car, the type of the vehicle, the lateral distance of the primary spring, the lateral distance of the secondary spring, the longitudinal distance of the articulated bogie, the wheelbase of the articulated bogie, and the wheelbase of the power bogie.

[0024] The application also provides a motor train unit whole vehicle weight distribution system, comprising a memory, a processor and a computer program stored in the memory; the processor executes the computer program to realize the steps of the above-mentioned method of the application.

[0025] Compared with the prior art, the application has the beneficial effects that:

[0026] 1) The method of the application is reliable, can simultaneously consider adjusting the weight of each component and the arrangement area, is convenient for adjusting the equipment on the roof and under the vehicle, establishes a global optimization strategy based on a mixed integer programming model, the prediction model can quickly obtain an optimized solution meeting the engineering requirements, maximally optimizes the total gravity center of the equipment on the vehicle and the air spring load distribution, effectively utilizes the mounting space on the roof of the vehicle body and the underframe, can quickly and automatically obtain an equipment arrangement optimization scheme, reduces the error of weight management, and improves the overall design efficiency of the vehicle;

[0027] 2) The method of the application can obtain an equipment arrangement optimization result and does not depend on an initial solution;

[0028] 3) The method of the application can quickly perform vehicle load distribution, and is convenient for adjusting the axle load deviation and the wheel load deviation during weighing;

[0029] 4) The application can realize accurate design of the axle load, the wheel load and the deviation thereof, and effectively realizes optimization of the quality design of weight distribution. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 Mc1, T, Mc2 three-section marshalling;

[0031] Figure 2 The figure is a schematic diagram of the equipment arrangement area in the embodiment of the application;

[0032] Figure 3 The figure is a schematic diagram of the rectangular equipment arrangement area in the embodiment of the application;

[0033] Figure 4 The figure is a device position virtual division scheme 1 of dividing the arrangement area into N virtual positions in the embodiment of the application;

[0034] Figure 5 The figure is a device position virtual division scheme 2 of dividing the arrangement area into N virtual positions in the embodiment of the application;

[0035] Figure 6 The figure is a device position virtual division scheme 3 of dividing the arrangement area into N virtual positions in the embodiment of the application;

[0036] Figure 7 The figure is a simplified device position virtual division 3 and a parameter schematic diagram thereof in the embodiment of the application;

[0037] Figure 8 Linear approximation function for the embodiment of the present application;

[0038] Figure 9 And Figure 10 Figure 2 is a schematic diagram of the installation of C-shaped grooves on both sides of the roof equipment installation of the embodiment of the present application;

[0039] Figure 11(a) is the weight distribution reference origin of the Mc car of the embodiment of the present application; Figure 11(b) is the weight distribution reference origin of the T car of the embodiment of the present application;

[0040] Figure 12 Figure 4 is a schematic diagram of 6 groups of spring loads defined according to the bogie bearing position of the train of the embodiment of the present application;

[0041] Figure 13 Figure 5 is a schematic diagram of special constraints of non-rectangular arrangement areas of the embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the embodiment of the present application more clear, the technical scheme of the embodiment of the present application will be clearly and completely explained below by combining the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiments of the present application, not all the embodiments. Based on the embodiment in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0043] In this document, the terms "first", "second", and other similar words are not intended to imply any order, number, and importance, but are only used to distinguish different elements. In this document, the terms "one", "a", and other similar words are not intended to mean that there is only one of the described things, but that the description is only directed to one of the described things, which can have one or more. In this document, the terms "include", "contain", and other similar words are intended to mean logical interrelation, and cannot be regarded as indicating spatial structural relation. For example, "A includes B" is intended to mean that B logically belongs to A, not that B is located inside A in space. In addition, the meaning of the terms "include", "contain", and other similar words should be regarded as open, not closed. For example, "A includes B" is intended to mean that B belongs to A, but B does not necessarily constitute all of A, and A can also include C, D, E and other elements.

[0044] Embodiment 1

[0045] Since the articulated motor train vehicle equipment is mainly installed on the roof and the underframe of the car body, the influence of the equipment in the car is small. The vertical coordinates generally do not change with the change of the equipment position, so only the horizontal plane related parameters of the roof and the bottom of the car are counted for the equipment weight parameters.

[0046] Embodiment 1 of the present invention is achieved through the following technical solution, including the following steps:

[0047] Step 1: Determine the equipment layout areas and layout methods on the roof and bottom of the articulated EMU

[0048] like Figure 1 In the three-car formation of Mc1, T, and Mc2 shown in the figure, the equipment is arranged on the roof of the Mc car, and some equipment is arranged on the roof of the T car and the bottom area of ​​the Mc car. The arrangement area has two planes: the roof and the bottom. Figure 2 shown.

[0049] Given a layout area and equipment, and considering the specific constraints of equipment layout, an operations research planning model is established to determine the specific location of the equipment in the layout area, thereby minimizing the wheel weight and axle weight deviation of the vehicle. The equipment is abstracted into a rectangle based on its outer contour, and there will be problems with the orientation of the equipment door during layout. Figure 3 shown.

[0050] For a conventional rectangular layout area, consider that there are K devices that need to be placed in the layout area. According to the device layout requirements and constraints, the layout area is divided into N virtual positions (N>=K). The division is shown in the virtual division scheme 1 of the device position, as shown in FIG. Figure 4 As shown in the figure, the origin of the coordinate system is selected as the lower left end point of the layout area.

[0051] For conventional rectangular areas divided into multiple blocks, such as the case (considering the layout of equipment on and under the vehicle, the vehicle is divided into two rectangular layout areas, the vehicle is divided into one rectangular layout area and two non-rectangular layout areas), the Mc car roof, T car roof, and Mc car bottom area required in this case are both multi-block area layout and multi-plane layout. The division is shown in the virtual division scheme 2 of the equipment position, as shown in Figure 5 shown.

[0052] For irregular layout areas, since the boundary is a curve, in order to build a linear model for efficient solution, the model performs a linear approximation on the layout area and replaces the original curve boundary with the tangent of the curve. Then, the irregular layout area is divided. The division is shown in the virtual division scheme 3 of the equipment position, as shown in Figure 6 shown.

[0053] Virtual partitioning scheme 1 and virtual partitioning scheme 2 are both regular rectangular layout areas. The boundary constraints of the equipment layout are only linear relationships. The boundary points of the layout area are fixed values ​​relative to the coordinate origin. However, the equipment layout area of ​​virtual partitioning scheme 3 is a curve boundary. When the rail vehicle is running, the minimum curve radius R needs to be designed. Therefore, the equipment is arranged within the envelope of the curve radius. The simplification of the virtual partitioning scheme 3 of the equipment position and its parameters are as follows: Figure 7As shown; W is the maximum width in the curve arrangement area, W1 is the minimum width in the curve arrangement area; the curve boundary is a circular arc with R as the radius, and the linear approximation function θ (as shown) is adopted in this embodiment. This boundary approximation is suitable for fast equipment arrangement in operational research planning, and compared with a nonlinear function, this linear approximation reduces the calculation specification and improves the calculation speed. Figure 8 As shown; W is the maximum width in the curve arrangement area, W1 is the minimum width in the curve arrangement area; the curve boundary is a circular arc with R as the radius, and the linear approximation function θ (as shown) is adopted in this embodiment. This boundary approximation is suitable for fast equipment arrangement in operational research planning, and compared with a nonlinear function, this linear approximation reduces the calculation specification and improves the calculation speed.

[0054] The relevant functions are as follows:

[0055]

[0056]

[0057]

[0058] θ is the linear approximation angle of the boundary; W is the center distance of the bogie; W2 is the narrowest distance of the curve boundary; R is the radius of the curve boundary; x B is the X coordinate of the curve boundary B point;

[0059] y B is the Y coordinate of the curve boundary B point.

[0060] The final goal of the model needs to determine which position of the K devices is placed in the N virtual positions of the three schemes, and determine the relative coordinates of the device to make the weighted sum of the axle load deviation and the wheel load deviation of the whole vehicle minimum. That is, considering that there are K devices to be placed, assuming that the first row has N / 2 positions and the second row also has N / 2 positions, the next step is to place the K devices in the N positions, and finally make the gravity center of the whole vehicle and the center as close as possible.

[0061] Step 2, providing a device mounting method for facilitating device arrangement adjustment

[0062] Determine the device arrangement space and weight parameters of each mounting device in the roof arrangement area.

[0063] As shown in Figure 9 and Figure 10 , the installation C-shaped groove long strip is provided on both sides of the roof device mounting to facilitate longitudinal adjustment of the device, and the mounting interface span of the main roof device such as the driver's cabin air conditioner, air source module, battery box, traction transformer and vacuum circuit breaker is kept consistent; the installation C-shaped groove is provided on the side beam of the chassis to facilitate longitudinal adjustment of the device, and the mounting interface span of the main roof device is kept consistent; the mounting interface of the main device is designed as a waist-shaped hole to facilitate small transverse displacement fine adjustment of the device.

[0064] For key devices with inconsistent spans, C-shaped grooves and waist-shaped holes are provided at the installation positions to facilitate in-plane position adjustment and provide adjustment space for in-plane position optimization of the device.

[0065] Step 3, determine the equipment arrangement and weight distribution constraints, realize the constraint-based equipment arrangement scheme design and equipment modular design

[0066] 1) The number of equipment arrangement constraints per car: at least 4 equipment arrangements per car, excluding the hydraulic control unit.

[0067] 2) Hydraulic control unit constraint: 3 hydraulic control units per vehicle, arranged at both ends of each car, close to the end of the arrangement area.

[0068] 3) Equipment transverse constraint: all equipment is arranged transversely in the center with a deviation of 100.

[0069] 4) Auxiliary converter constraint: auxiliary converter is arranged transversely in the center (deviation 100) across left and right areas.

[0070] 5) The minimum gap in the longitudinal direction of the equipment is uniformly adjusted to 300.

[0071] 6) Main air cylinder constraint: main air cylinder is assembled at both ends to connect with the bogie.

[0072] 7) Equipment orientation constraint: equipment doors should face outward for easy maintenance.

[0073] 8) Traction inverter, high-voltage box arranged with the same car, auxiliary inverter, low-voltage box arranged with the same car.

[0074] 9) Model constraints usually include torque balance constraints, axle load and wheel load calculation constraints, equipment and arrangement area constraints, and special constraints (including special equipment constraints or special requirements).

[0075] 10) Torque balance constraint: used to calculate the lateral and longitudinal center of gravity and torque of the vehicle

[0076] 11) Axle load and wheel load calculation constraint: used to calculate the weight on the secondary spring of the vehicle, wheel load, axle load, etc.

[0077] Step 4, determine the weight distribution method of the articulated motor train set

[0078] 1) After the equipment arrangement is completed, calculate the Mc1, T, Mc2 car containing the equipment arrangement upper assembly weight and center of gravity, define the coordinate system x axis to the right, and select the lower left endpoint of the arrangement area as the coordinate system origin, as shown in Figure 2 .

[0079] 2) As shown in Fig. 11(a) and Fig. 11(b), define the upper assembly weight of the Mc1, T, Mc2 car with equipment arrangement as Gmc1, Gt, Gmc2 respectively, the longitudinal coordinate as x1, x2, x3 respectively, the lateral coordinate as y1, y2, y3 respectively, L as the distance of the vehicle, l0 as the longitudinal distance of the secondary spring of the articulated bogie, Fjb as the weight of the articulated bogie, Fb as the weight of the non-articulated bogie.

[0080] 3) As shown in Fig. 12(a) and Fig. 12(b), define the six groups of spring loads according to the bogie bearing position of the train, and define each group of loads as F1, F2, F3, F4, F5, F6. Figure 12

[0081] 4) Calculate F1, F2, F3, F4, F5, F6.

[0082] F1 = (L - l0 - 2x1) * Gmc1 / (2L - l0);

[0083] F2 = (L + 2x1) * Gmc1 / (2L - l0);

[0084] F3 = (0.5 - (x2 / (L - l0))) * Gt;

[0085] F4 = (0.5 + (x2 / (L - l0))) * Gt;

[0086] F5 = (L - l0 + 2x3) * Gmc2 / (2L - l0);

[0087] F6 = (L - 2x3) * Gmc2 / (2L - l0);

[0088] 5) Axle load calculation

[0089] Define the wheel pair distance of the articulated bogie as L1, the wheel pair distance of the non-articulated bogie as L2, the longitudinal center of gravity of the articulated bogie as xjb, and the longitudinal center of gravity of the non-articulated bogie as xb.

[0090] Fw1 = (F1 / 2) + (Fb * (L2 + 2xb + L) / L2 / 2);

[0091] Fw2 = (F1 / 2) + (Fb * (L2 - 2xb - L) / L2 / 2);

[0092] Fw3 = [(F3(L1 - l0) + F2(L1 + l0) + Fjb * (L1 + 2xjb - L)] / L1 / 2;

[0093] Fw4 = [(F3(L1 + l0) + F2(L1 - l0) + Fjb * (L1 - 2xjb + L)] / L1 / 2;

[0094] ​Fw5=[(F5(L1+l0)+F4(L1-l0)+Fjb*(L1-2xjb+L)] / L1 / 2;

[0095] Fw6=[(F5(L1-l0)+F4(L1+l0)+Fjb*(L1+2xjb-L)] / L1 / 2;

[0096] Fw7=(F6 / 2)+(Fb*(L2-2xb-L) / L2 / 2);

[0097] Fw8=(F6 / 2)+(Fb*(L2+2xb+L) / L2 / 2);

[0098] 6) Wheel weight calculation

[0099] The distance between the wheel and rail contact points is dg.

[0100] Fwl1=Fw1*(y1 / dg+0.5);

[0101] Fwl2=Fw1*(-y1 / dg+0.5);

[0102] Fwl3=Fw2*(y1 / dg+0.5);

[0103] Fwl4=Fw2*(-y1 / dg+0.5);

[0104] Fwl5=Fw3*(y1 / dg+0.5);

[0105] Fwl6=Fw3*(-y1 / dg+0.5);

[0106] Fwl7=Fw4*(y2 / dg+0.5);

[0107] Fwl8=Fw4*(-y2 / dg+0.5);

[0108] Fwl9=Fw5*(y2 / dg+0.5);

[0109] Fwl10=Fw5*(-y2 / dg+0.5);

[0110] Fwl11=Fw6*(y3 / dg+0.5);

[0111] Fwl12=Fw6*(-y3 / dg+0.5);

[0112] Fwl13=Fw7*(y3 / dg+0.5);

[0113] Fwl14=Fw7*(-y3 / dg+0.5);

[0114] Fwl15 = Fw8 * (y3 / dg + 0.5);

[0115] Fwl16 = Fw8 * (-y3 / dg + 0.5).

[0116] Step 5, based on the mathematical model of equipment layout

[0117] Based on the plane optimization problem, the layout area and layout method specified in step 1 are used to plan and mathematically model the equipment, and the planning method is used to develop a fast and automatic optimization platform for the equipment layout of the articulated motor train unit (including roof equipment, indoor equipment, and undercarriage equipment). The platform describes the position relationship of the equipment, boundary conditions, constraint conditions, optimization objectives, and encapsulates the weight calculation method determined in step 4.

[0118] The optimization objective of the model is the weighted sum of the relative wheel weight deviation and the axle weight deviation.

[0119]

[0120] When the relative wheel weight or the relative axle weight cannot be linearly expressed, the optimization objective of the model becomes the absolute deviation of the wheel weight and the axle weight or the absolute deviation of the vehicle center of gravity and the center. At this time, the relative deviation value can be represented in the model constraints.

[0121] When calculating the wheel weight deviation, a nonlinear constraint appears, so the maximum lateral center and gravity deviation is used to replace the optimization, and the minimum longitudinal center and gravity deviation is added to the objective function to balance the stress inside each car. At this time, the optimization objective of this mathematical model is improved as follows:

[0122]

[0123] Since the above formula considers the influence of each axle weight deviation, the longitudinal coordinate of the vehicle assembly center of gravity, the influence of the weight contained in the longitudinal moment of the vehicle assembly, the lateral coordinate of the vehicle assembly center of gravity, and the influence of the weight contained in the lateral moment of the vehicle assembly. If the weight is not an optimization objective, the optimization objective of the above formula is simplified in actual application to evaluate the minimum absolute value of the maximum axle weight deviation of each bogie, the absolute value of the deviation between the lateral center of gravity coordinate and the center coordinate of each car, and the absolute value of the deviation between the longitudinal center of gravity coordinate and the center coordinate of each car. Simplified formula as follows:

[0124] min∈ α +λ1a T +λ2b T ;

[0125] ∈ α : maximum axle weight deviation;

[0126] a j: The absolute value of the deviation between the lateral center of gravity coordinate and the center coordinate of the j-th vehicle;

[0127] a T :a j The maximum value of

[0128] b j : The absolute value of the deviation between the longitudinal center of gravity coordinate and the center coordinate of the j-th vehicle;

[0129] b T :b j The maximum value of

[0130] λ1 is the weight coefficient of the deviation between the lateral center and the center of gravity (determined according to the impact of the target importance);

[0131] λ2 is the weight coefficient of the deviation between the longitudinal center and the center of gravity (determined according to the impact of the target importance);

[0132] Layout area and device boundary constraints:

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] Special constraints for non-rectangular layout areas (such as Figure 13 shown):

[0145]

[0146] Y1 cu ≤y B ≤Y1 cd ;

[0147] U1\k fz1,k sg1 : U1 is the device numbered as fz1 and the device numbered as sg1 in the first arrangement area.

[0148] N1 is the number of positions that can be arranged in the first arrangement area.

[0149] Here, Ns is the number of positions that can be arranged in the s-th arrangement area (see the virtual division diagram), and in the embodiment of the present application, the articulated motor train set with 3 cars contains 5 arrangement areas including the roof and the undercarriage, so the interpretation of s is from 1 to 5.

[0150] Because Ns is the number of positions that can be arranged in the s-th arrangement area, the interpretation of i is from 1 to Ns.

[0151] k is the k-th device; U1 is the first arrangement area; k fz1 : the k-th device is numbered as fz1; k sg1: the k-th device is numbered as sg1; Us is the s-th arrangement area; H(W): the maximum length (width) in the horizontal (vertical) direction in the arrangement area; l k (w k ): the length (width) of the k-th device.

[0152] 0,1 variable, in the s-th arrangement area, when the k-th device is placed in the i-th position, it is 1, otherwise it is 0.

[0153] 0,1 variable, in the first arrangement area, when the k-th device is placed in the i-th position, it is 1, otherwise it is 0.

[0154] 0,1 variable, this is a decision function, used to determine whether the device is in the i-th position.

[0155] In the first arrangement area, when the device numbered as k fg1 (k fz1 ) is placed in the i-th position , it is 1, otherwise it is 0.

[0156] In the first arrangement area, when the device numbered as k fz1 (k fg1 ) is placed in the i-th position, the x-coordinate.

[0157] In the first arrangement area, when the device numbered as k fz1 (k fg1 ) is placed in the i-th position, the length of the device.

[0158] Left (right) end point horizontal coordinate of the s-th arrangement area.

[0159] Left (right) end point horizontal coordinate of the s-th arrangement area.

[0160] Upper (lower) end point vertical coordinate of the 1-st arrangement area.

[0161] Upper (lower) end point vertical coordinate of the s-th arrangement area.

[0162] δ w : Minimum distance between the equipment and the arrangement area boundary [value range: 20-100].

[0163] Axle load deviation constraint:

[0164]

[0165] ∈ α : Maximum axle load deviation

[0166] Fw1, Fw2, Fw3, Fw4 are the weights of the 1st, 2nd, 3rd, 4th wheels of the bogie.

[0167] In the process of establishing the planning model, the main contents are as follows: 1) The objective function is the weighted sum of the maximum axle load deviation and the maximum wheel load deviation tending to zero. According to the left end point coordinate and the lower end point coordinate of each equipment, the overall moment of the arranged equipment of each vehicle is calculated. The arrangement center of gravity of each vehicle, the spring load of each vehicle, the axle load of each vehicle, the average axle load of each bogie, the maximum axle load deviation, the maximum lateral deviation of the center of gravity from the center of gravity in the vehicle, and the maximum longitudinal deviation of the center of gravity from the center of gravity in the vehicle are calculated.

[0168] The mathematical model of the operational research planning is as follows:

[0169] min∈ α +λ1a T +λ2b T ;

[0170] s.t.

[0171] Calculation of the center of gravity of the equipment assembly arranged in each vehicle:

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178] Each section of vehicle on-board assembly gravity center calculation (including other system weight not involved in the arrangement):

[0179]

[0180]

[0181] Each group of spring weight calculation:

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188] Each axle axle weight calculation:

[0189] Fw1 = (F1 / 2) + (Fb*(L2+2xb+L) / L2 / 2);

[0190] Fw2 = (F1 / 2) + (Fb*(L2-2xb-L) / L2 / 2);

[0191] Fw3 = [(F3(L1-l0) + F2(L1+l0) + Fjb*(L1+2xjb-L)] / L1 / 2;

[0192] Fw4 = [(F3(L1+l0) + F2(L1-l0) + Fjb*(L1-2xjb+L)] / L1 / 2;

[0193] Fw5 = [(F5(L1+l0) + F4(L1-l0) + Fjb*(L1-2xjb+L)] / L1 / 2;

[0194] Fw6 = [(F5(L1-l0) + F4(L1+l0) + Fjb*(L1+2xjb-L)] / L1 / 2;

[0195] Fw7 = (F6 / 2) + (Fb*(L2-2xb-L) / L2 / 2);

[0196] Fw8 = (F6 / 2) + (Fb*(L2+2xb+L) / L2 / 2);

[0197] Average Axle Load Calculation:

[0198]

[0199] Axle Load Deviation Calculation:

[0200]

[0201]

[0202]

[0203]

[0204] Vehicle Assembly Center of Gravity Horizontal Coordinate Deviation Calculation:

[0205]

[0206]

[0207]

[0208] Vehicle Assembly Center of Gravity Vertical Coordinate Deviation Calculation:

[0209]

[0210]

[0211]

[0212] Regarding the door orientation, in addition to the special requirement that the device's door faces down, the rest of the device's door faces up, expressed as v k represents the model as a parameter input.

[0213] v k : 0,1 variable, the door orientation of device k, 1 for the front facing up (door facing up), and 0 otherwise

[0214] Continuous variable, the horizontal (vertical) moment of all devices of the jth section vehicle relative to the coordinate origin

[0215] k∈U1,U4: the kth device is arranged in the 1st arrangement area or the 4th arrangement area

[0216] The distance of the center of gravity of the kth device when the front is facing up (door facing up) from the left (bottom) endpoint

[0217] The distance between the center of gravity of the kth device when it is upright (door facing down) and the left (lower) end point

[0218] m k : the weight of the kth device

[0219] The horizontal (vertical) coordinate of the left end point when the kth device is placed in the ith position in the st arrangement area

[0220] Continuous variable: the horizontal (vertical) coordinate of the center of gravity of all devices and the car body (including the weight of other systems not involved in the arrangement) of the jth car

[0221] M j : the weight of the jth car (excluding devices and bogies)

[0222] The horizontal (vertical) coordinate of the jth car body

[0223] G j : the weight of the jth car assembly (including devices arranged on the roof and under the car, excluding bogies)

[0224] The average axle load on the tth bogie

[0225] According to the above planning model calculation formula, the weight parameters of the objective function in the model solving process are all 1, and they are equally important. According to the design requirements, the center of gravity of the assembly, the axle load and the wheel load have index requirements (generally 2% deviation of axle load and 4% deviation of wheel load), and the optimization calculation is performed with the index or higher requirements (generally 1.5% deviation of axle load and 3% deviation of wheel load) as constraint conditions, so that the automatic enumeration of the optimal scheme of device arrangement can be quickly completed.

[0226] In terms of model solving, a program is developed or a commercial solver (such as CPLEX) is used to solve the operations research planning model by programming in a software (such as Visual Studio) environment. The program takes a txt file as input parameter, and the device coordinates and the final axle load and wheel load related parameters are output to two different txt files respectively.

[0227] The branch and bound method is commonly used to solve operations research planning models. Branching is to divide the feasible region successively, and bounding is to calculate the lower bound of the optimal solution of the original problem (the minimum value optimization problem in this model) for each branch. These lower bounds are used to determine non-optimal points in the solving process, avoid complete enumeration, and improve the solving efficiency.

[0228] Optional solution method is Monte Carlo simulation method to solve the operational planning model, this method is suitable for nonlinear programming model, from the constraints of each variable range, then from the above range with a random number of experimental points, and verify whether they meet all the constraints, if meet, will be divided into feasible group, then find the optimal solution from the feasible group function.

[0229] The parameters contained in the optimal solution are the barycentric coordinates and transverse coordinates of each arrangement device, the load information of the secondary spring group of the bogie, the axle load and axle load deviation of each bogie, the wheel load and wheel load deviation of each bogie, etc. The optimal solution takes the minimum axle load deviation, the deviation between the barycentric longitudinal coordinate of the vehicle assembly and the longitudinal coordinate of the vehicle body center, and the deviation between the barycentric transverse coordinate of the vehicle assembly and the transverse coordinate of the vehicle body center as the optimization target, and the optimal solution is determined by the minimum weighted optimization target and the most stable rule.

[0230] Step 6, analyze the influence degree of input parameters on output parameters, identify key devices and influence factors

[0231] In order to further explore the device arrangement design space, a mathematical model is established with the optimization target and constraint conditions determined in step 5, the key device arrangement parameters affecting the axle load, wheel load, axle load deviation and wheel load deviation are found through experimental design method and parameter sensitivity analysis, and the importance of the influence on the optimization target is sorted, such as contribution rate diagram.

[0232] The experimental design method uses optimization software platforms such as Isight to analyze the impact of input parameters on output parameters and calculate all sampled test samples within the equipment layout design space. The input parameters here are primarily the equipment coordinate positions. In Isight software, the optimal Latin hypercube algorithm is used to perform experimental design on the application in the task model. The range of design variables is determined, including the initial value and boundaries of the component weight, the initial value and boundaries of the component x-coordinate, the initial value and boundaries of the component y-coordinate, and the initial value and boundaries of the component z-coordinate. The total number of test points is selected, and the corresponding sample point matrix is ​​generated. The range of the constraint variables and optimization variables is then defined. Experimental design calculations are performed to obtain the resulting data. Sensitivity analysis is then performed on the resulting data to determine the contribution rankings of the normalized weights and coordinates of each device to the vehicle equipment assembly weight Fcb, the contribution rankings of the normalized weights and coordinates of each device to the vehicle body upper center of gravity coordinate Xcb, the contribution rankings of the normalized weights and coordinates of each device to the vehicle body upper center of gravity coordinate Ycb, the contribution rankings of the normalized weights and coordinates of each device to the vehicle body upper center of gravity coordinate Zcb, the contribution rankings of the normalized weights and coordinates of each device to axle load deviation, and the contribution rankings of the normalized weights and coordinates of each device to wheel load deviation. By analyzing the impact of each device's coordinate position on the optimization objective, the importance of the impact of axle load deviation, wheel weight deviation, and the center of gravity of the vehicle assembly equipment can be ranked. This contribution ranking is used to identify controllable variables (factors) within the design space, namely, key equipment parameters with significant influence. The rankings are then presented as Pareto cloud charts for post-experimental design processing, which are used for accurate and robust design of axle weights, wheel weights, and their deviations.

[0233] Step 7: Develop an optimization platform based on robust design methods to achieve accurate design of axle load, wheel load and their deviation

[0234] This study focuses on EMU vehicle weight management, with axle weight deviation, wheel weight deviation, axle weight, and wheel weight as research objectives. The study identifies controllable factors and noise factors in weight management, particularly weight distribution. Robust design methods incorporate noise factors into the design process. By minimizing their impact ("Minimize Variation"), they determine a "robust control factor level" and strive to maintain system performance (response) close to the ideal response ("Mean-on-Target"), thereby achieving quality improvement.

[0235] Through sensitivity analysis of the 6th step experiment design, the weight of each device and the device coordinates that significantly affect the optimization target are obtained. These devices with prominent effects are temporarily referred to as key devices. The main layout parameters of these key devices are used as controllable factors (shown in Table 1), and the design conditions, the total weight of the vehicle assembly, the load of the secondary spring, the center of gravity of the vehicle assembly, the vehicle category, the lateral distance of the primary spring, the lateral distance of the secondary spring, the longitudinal distance of the articulated bogie, the wheelbase of the articulated bogie, the wheelbase of the power bogie, etc. are used as noise factors. The signal-to-noise ratio analysis of controllable factors and noise factors is realized through a program (such as the Isight optimization software). It provides a P-diagram parameter system to realize data input, and the factors are set according to the requirements of the maximum quality characteristic, the minimum quality characteristic, the large quality characteristic, and the asymmetric quality loss function.

[0236] By selecting factors and determining the orthogonal table experiment design, the signal-to-noise ratio (SN ratio) is output. The influence of controllable factors and noise factors on the optimization target can be obtained through the signal-to-noise ratio program, which realizes the reduction of the variation of the target value (i.e. reduces the influence of noise factors on the target value). The control factor level is determined, and the robustness of the weight distribution design is enhanced to ensure the robustness and reproducibility of the weight distribution.

[0237] Table 1 Device layout factors and levels

[0238]

[0239]

[0240] Step 8, based on the 6Sigma method to develop an optimization platform to realize the optimization of weight distribution quality design

[0241] The device layout scheme that best meets the requirements of axle weight deviation and wheel weight deviation quality is solved by the 6Sigma method. The lateral coordinates and longitudinal coordinates of the key device layout parameters are determined as design variables; the lateral distance of the primary spring, the lateral distance of the secondary spring, the longitudinal distance of the articulated bogie, the wheelbase of the articulated bogie, and the wheelbase of the power bogie are constants; the deviation of the design variables and constants is a certain value, temporarily set as 1%; the coefficient of variation is a certain value, temporarily set as 0.01; the probability distribution is normal distribution, which can be selected from lognormal distribution, Weibull distribution, exponential distribution, etc.; the center of gravity of the vehicle assembly, the device layout boundary, the axle weight, the axle weight deviation, and the wheel weight deviation are set as constraint conditions; the reliability of the weight distribution is obtained, and the relationship between the reliability and the failure probability is obtained.

[0242] HOOKE-JEEVES, or adaptive simulated annealing (ASA), NCGA multi-objective genetic algorithm, etc. are selected. The upper and lower limits of the design variables are defined, the lower limit of the constraint condition is defined (as 4.5 Sigma level is set here to ensure 99.999% reliability), the optimization target is selected to minimize the axis weight deviation and wheel weight deviation, and the average value and standard deviation of the axis weight deviation and wheel weight deviation in the 6 Sigma result are also minimized. In this way, 6 Sigma quality design can be implemented for weight management and weight distribution.

[0243] The calculation flow is based on the Isight optimization platform.

[0244] Taking the equipment arrangement of a certain articulated motor train unit as an example, as the equipment arrangement can be simplified as a two-dimensional plane problem, only the length and width of all parameters are given, and the height parameter is not given here.

[0245] The equipment arrangement weight is shown in Tables 2-3.

[0246] Table 2 Mc car roof equipment

[0247]

[0248]

[0249] Table 3 T car roof equipment

[0250]

[0251] Table 4 Mc car under equipment

[0252]

[0253] 1) The Mc car and T car contour profile are shown in Figure 2 , the arrangement boundary line and equipment should be arranged within the contour curve.

[0254] 2) The additional air cylinder 1 and the additional air cylinder 2 are arranged along the two sides to achieve boundary arrangement, and the traction converter is arranged on the right side.

[0255] 3) The vacuum circuit breaker, the bow lifting air cylinder, and the pantograph packing arrangement are arranged at the non-driver room end of the Mc car, and the pantograph is arranged at the edge of the non-driver room end. The vacuum circuit breaker and the bow lifting air cylinder are arranged in the same column, and the longitudinal gap between the vacuum circuit breaker and the pantograph is 300-500.

[0256] 4) The air source module, the air cylinder, and the auxiliary air source are arranged at the driver room end of the Mc car, and the auxiliary air source and the air cylinder are arranged in the same column.

[0257] 5) The passenger room air conditioner and the air source module leave at least 500 mm of maintenance and heat dissipation space on the left and right sides, and the constraint can be modified in the form of a variable.

[0258] 6) Driver's room air conditioning arrangement Driver's room end, one side does not arrange equipment, the other side arranges equipment, and at least 500 maintenance and heat dissipation space is left on the equipment arrangement side, which is constrained to be input in a variable manner.

[0259] 7) T car passenger room air conditioning should be arranged in the middle, considering a single section T car, the longitudinal central arrangement deviation is 100, which is constrained to be input in a variable manner.

[0260] 8) At least 900 maintenance and heat dissipation space is left on the right side of the traction transformer, which is constrained to be input in a variable manner.

[0261] 9) Equipment transverse constraint: the transverse central arrangement deviation of the equipment single arrangement is 100.

[0262] 10) The minimum gap of the equipment in the longitudinal direction is uniformly adjusted to 300.

[0263] 11) Equipment orientation constraint: the equipment door should be oriented outward to facilitate maintenance.

[0264] 12) All related data inputs are input in a variable manner.

[0265] According to the above equipment arrangement mathematical model and weight distribution method, the secondary spring load and its axle load and wheel load deviation are as follows:

[0266] Table 5 Secondary spring group load

[0267]

[0268] Table 6 Axle load and its deviation

[0269]

[0270] Table 7 Wheel load and its deviation

[0271]

[0272] The optimization calculation process can be completed in a few minutes, and during the model solving process, each weight is set to λ=1, the axle load deviation is about 0.03%, and the wheel load deviation is about 0.012%.

[0273] Through subsequent precise design of axle load, wheel load and its deviation, weight distribution quality design optimization, the axle load deviation and the wheel load deviation are further reduced, and the optimization result tends to be 0%.

[0274] Example 2

[0275] Embodiment 2 of the present application provides a weight distribution optimization system corresponding to the above-mentioned embodiment 1, comprising a memory, a processor and a computer program stored in the memory; the processor executes the computer program stored in the memory to realize the steps of the method of the above-mentioned embodiment 1.

[0276] In some implementations, the memory can be a high-speed random access memory (RAM), and can also include a non-volatile memory, such as at least one disk memory.

[0277] In other implementations, the processor can be a central processing unit (CPU), a digital signal processor (DSP), or various types of general-purpose processors, without limitation.

[0278] Although the preferred embodiments of the present application have been described, those skilled in the art who have the benefit of the present disclosure can make additional changes and modifications to these embodiments without departing from the spirit and scope of the present application. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0279] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and variations.

Claims

1. A method for distributing the weight of an EMU vehicle, characterized in that: The following steps are involved: S1. Determine equipment layout areas on the roof and bottom planes of the EMU; for a rectangular layout area, divide the rectangular layout area into N virtual positions; for a non-rectangular layout area, use a tangent line of a boundary curve of the non-rectangular layout area as the boundary of the non-rectangular layout area, and then divide the non-rectangular layout area after replacing the boundary into N virtual positions; S2. After placing all equipment in the rectangular layout area and the non-rectangular layout area, determine the equipment layout and weight distribution constraints, and calculate the weight and center of gravity of the upper assembly including the equipment layout, load, axle weight, and wheel weight of each car of the EMU; S3. Determine an objective function with the optimization objective of minimizing axle load deviation, the deviation between the longitudinal coordinate of the center of gravity of the onboard assembly and the longitudinal coordinate of the vehicle body center, and the deviation between the transverse coordinate of the center of gravity of the onboard assembly and the transverse coordinate of the vehicle body center; the objective function is set as the weighted sum of the absolute value of the maximum axle load deviation of each bogie, the absolute value of the deviation between the transverse center of gravity coordinate and the center coordinate of each vehicle section, and the absolute value of the deviation between the longitudinal center of gravity coordinate and the center coordinate of each vehicle section; solve the objective function to obtain an optimized solution containing the longitudinal and transverse coordinates of the center of gravity of each device, the load information of the bogie secondary spring group, the axle weight and axle load deviation of each bogie, and the wheel weight and wheel weight deviation of each bogie; S4. Use optimization software to analyze the impact of equipment coordinate position on axle load deviation, deviation between the vertical coordinate of the vehicle assembly center of gravity and the vertical coordinate of the vehicle body center, and deviation between the horizontal coordinate of the vehicle assembly center of gravity and the horizontal coordinate of the vehicle body center, and identify key equipment; S5. Using the key equipment layout parameters as controllable factors, setting noise factors, and using the controllable factors and noise factors as inputs to the optimization software to optimize the optimization solution; S6. Take the transverse coordinates and longitudinal coordinates of the key equipment layout parameters as design variables, the transverse coordinates and longitudinal coordinates of the key equipment layout parameters as design variables, the transverse spacing of the primary spring, the transverse spacing of the secondary spring, the longitudinal spacing of the articulated bogie, the wheelbase of the articulated bogie, and the wheelbase of the power bogie as constants, the deviation between the design variables and the constants as the first set value, the coefficient of variation as the second set value, set the probability distribution type, set the optimized center of gravity of the vehicle assembly, equipment layout boundary, axle weight, axle weight deviation, and wheel weight deviation as constraints, define the upper and lower limits of the variables involved, and the lower limit of the constraints, take the minimization of wheel weight deviation and axle weight deviation as the optimization goal, and use the optimization method to adjust the transverse coordinates and longitudinal coordinates of the key equipment layout parameters until the optimized wheel weight deviation and axle weight deviation meet the design requirements.

2. The method for distributing the weight of an EMU according to claim 1, characterized in that: In step S1 , the vehicle bottom plane includes two rectangular arrangement areas; the vehicle roof plane includes one rectangular arrangement area and two non-rectangular arrangement areas.

3. The method for distributing the weight of an EMU train set according to claim 1, characterized in that: In step S1, the boundary curve-related parameters of the non-rectangular layout area are calculated using the following formula: ; ; ; where θ is the linear approximation angle of the boundary curve, H is the center distance between the two bogies of each car of the EMU, W is the maximum width of the non-rectangular layout area, W2 is the narrowest distance of the curve boundary, and R is the radius of the curve boundary. is the horizontal coordinate of the tangent point on the boundary curve, is the ordinate of the tangent point on the boundary curve.

4. The method for distributing the weight of an EMU train set according to claim 1, characterized in that: In step S1, the method of placing all equipment in the rectangular layout area and the non-rectangular layout area includes: setting a C-shaped groove in the roof equipment layout area, installing the roof equipment in the C-shaped groove, and keeping the installation interface span of the equipment installed on the roof plane consistent.

5. The method for distributing the weight of an EMU train according to claim 4, characterized in that: The installation interface is a waist-shaped hole.

6. The method for distributing the weight of an EMU vehicle according to claim 1, characterized in that: In step S2, the equipment layout and weight distribution constraints include: the number of equipment arranged in each car, the number and layout position of hydraulic control units, the lateral position of the equipment, the position of the auxiliary converter, the minimum longitudinal clearance of the equipment, the position of the main air cylinder, the orientation of the equipment, the arrangement of the traction inverter and the high-voltage box on the same car, the arrangement of the auxiliary inverter and the low-voltage box on the same car, the torque balance constraint, and the axle weight and wheel weight calculation constraints.

7. The method for distributing the weight of an EMU train set according to claim 1, characterized in that: In step S3, when solving the objective function, the weights of each item of the objective function are all set to 1.

8. The method for distributing the weight of an EMU according to claim 7, characterized in that: The objective function is solved by using a branch and bound method or a Monte Carlo simulation method.

9. The method for distributing the weight of an EMU train set according to claim 1, characterized in that: In step S4, the optimization software is Isight software; a sensitivity analysis is performed on the result data output by the Isight software to obtain the ranking of the contribution rate of the normalized weight and coordinate of each device to the weight of the equipment assembly Fcb on the vehicle, the ranking of the normalized contribution rate of the weight and coordinate of each device to the upper center of gravity coordinate Xcb of the vehicle body, the ranking of the normalized contribution rate of the weight and coordinate of each device to the upper center of gravity coordinate Ycb of the vehicle body, the ranking of the normalized contribution rate of the weight and coordinate of each device to the upper center of gravity coordinate Zcb of the vehicle body, the ranking of the normalized contribution rate of the weight and coordinate of each device to the axle weight deviation, and the ranking of the normalized contribution rate of the weight and coordinate of each device to the wheel weight deviation, and use the ranking to determine the key equipment.

10. The method for distributing the weight of an EMU train set according to claim 1, characterized in that: In step S5, the noise factors include design operating conditions, vehicle assembly weight, secondary spring load, vehicle assembly center of gravity, vehicle type, primary spring lateral spacing, secondary spring lateral spacing, articulated bogie longitudinal spacing, articulated bogie wheelbase, and power bogie wheelbase.

11. A vehicle weight distribution system for an EMU, comprising a memory, a processor, and a computer program stored in the memory; characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 10.

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