Electric locomotive static balance adjustment scheme screening method, electronic equipment and medium

Abstract

The invention discloses an electric locomotive static balance adjustment scheme screening method, electronic equipment and a medium. The method comprises the following steps: acquiring wheel load data including wheel load deviation and axle load deviation; recording the difference value of the wheel load deviation and the axle load deviation before and after the adjustment of each wheel position of the locomotive as a corresponding influence value; predicting adjusted wheel load deviation and axle load deviation according to the influence value and the adjustment value; and screening an adjustment scheme according to the adjusted wheel load deviation and axle load deviation. Through calculation and estimation of the static balance adjustment result of the electric locomotive, whether the adjustment result meets the specification or not can be visually seen, the overhaul period of the electric locomotive can be effectively shortened, the service life and performance of key parts of the electric locomotive are prolonged, occupation of public resources such as a transfer platform and an operation station and labor force of operators is reduced, and the maintenance cost is reduced. And the maintenance and operation quality of the locomotive is ensured.

Description

Technical Field

[0001] The present invention relates to the field of electric locomotive whole vehicle balance adjustment test, and more specifically, to an electric locomotive static balance adjustment scheme screening method, electronic equipment and medium. Background Art

[0002] After many repairs, the changes in the bogie wheel load and axle load of electric locomotives have a great impact on the running stability, safety and stress state of the track structure. If no adjustment is made, the axle load and wheel load in the vehicle weighing test after the locomotive is completed cannot meet the requirements specified in "GB / T3317-2006 General Technical Conditions for Electric Locomotives", that is, the axle load deviation cannot exceed ±2%, and the wheel load deviation cannot exceed 4%.

[0003] Therefore, the adjustment test of electric locomotive bogie wheel load and axle load has also received widespread attention. Some locomotive manufacturing plants have purchased various advanced bogie test equipment and carried out a lot of experimental research work. However, during the maintenance of electric locomotives, the vehicle weighing test process requires balance adjustment based on the full readiness of the locomotive.

[0004] In the actual production process, balance adjustment still relies heavily on the workers' production experience, resulting in low production efficiency and insufficient accuracy. During the adjustment process, multiple adjustments are often required to meet the specified requirements, resulting in a large waste of manpower, material resources and public resources, and high requirements for personnel quality. Multiple adjustments repeatedly stress the connecting parts of the electric locomotive running gear, such as bolts and nuts, which can easily reduce their tightness, shorten their service life, and cause the parts to shift and fall off, causing safety hazards in locomotive operation and increasing the risk control coefficient.

[0005] Therefore, it is necessary to develop a method, electronic equipment and medium for screening static balance adjustment schemes for electric locomotives.

[0006] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0007] The present invention proposes a method, electronic equipment and medium for screening static balance adjustment schemes for electric locomotives. By calculating and estimating the static balance adjustment results of the electric locomotives, it is intuitively seen whether the adjustment results meet the requirements, which can effectively shorten the maintenance cycle of the electric locomotives, improve the service life and performance of key components of the electric locomotives, reduce the occupation of public resources such as car transfer platforms and work stations and the labor of operating personnel, and ensure the maintenance and operation quality of the locomotives.

[0008] In a first aspect, an embodiment of the present disclosure provides a method for screening a static balance adjustment scheme for an electric locomotive, comprising:

[0009] Obtain wheel weight data, including wheel load deviation and axle load deviation;

[0010] Record the difference between the wheel load deviation and the axle load deviation before and after each wheel position adjustment of the locomotive as the corresponding impact value;

[0011] Predicting the wheel load deviation and axle load deviation after adjustment according to the impact value and the adjustment value;

[0012] Select the adjustment plan based on the adjusted wheel load deviation and axle load deviation.

[0013] Preferably, the wheel load deviation is:

[0014] Wheel load deviation = (wheel load - average wheel weight of the axle) / average wheel weight.

[0015] Preferably, the axle load deviation is:

[0016] Axle load deviation = (axle load - average axle weight) / average axle weight.

[0017] Preferably, the compression amount of the wheel spring is adjusted, and the wheel load deviation after adjustment is subtracted from the wheel load deviation before adjustment to obtain the corresponding wheel load influence value.

[0018] Preferably, the compression amount of the wheel spring is adjusted, and the axle load deviation after adjustment is subtracted from the axle load deviation before adjustment to obtain the corresponding axle load influence value.

[0019] Preferably, the wheel load deviation after adjustment is predicted to be:

[0020]

[0021] Among them, K i represents the predicted wheel weight difference corresponding to the bogie at end i, k represents the original wheel weight difference, i∈{Ⅰ,Ⅱ}, Ⅰ represents the bogie at end I, Ⅱ represents the bogie at end II, and f p,q (b,c) represents the effect of the adjustment of the p-th axis q side of the i-end bogie on the b-th axis c side, a p,q represents the adjusted thickness corresponding to the qth side of the pth axle corresponding to the i-end bogie, b,p=1,…,P, P is the total number of axles corresponding to the bogie, c,q∈{1,2}, 1 represents the left side, and 2 represents the right side.

[0022] Preferably, the predicted adjusted axle load deviation is:

[0023]

[0024] Where M(d) represents the predicted axle weight difference for the dth axis, m represents the original axle weight difference, and x j (d) represents the impact of the adjustment of the j-th axis on the d-th axis, d,j = 1,…,J, J is the total number of axes, n j Indicates the adjustment value of the j-th axis.

[0025] Preferably, screening the adjustment scheme according to the adjusted wheel load deviation and axle load deviation includes:

[0026] The adjusted wheel load deviation and axle load deviation are predicted according to the adjustment plan. If the predicted wheel load deviation or axle load deviation is greater than a set threshold, the adjustment plan is deleted.

[0027] In a second aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0028] A memory storing executable instructions;

[0029] A processor runs the executable instructions in the memory to implement the electric locomotive static balance adjustment scheme screening method.

[0030] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for screening static balance adjustment schemes for electric locomotives is implemented.

[0031] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0033] Figure 1 A flow chart showing the steps of a method for screening static balance adjustment schemes for an electric locomotive according to an embodiment of the present invention.

[0034] Figure 2a and Figure 2b Schematic diagrams of wheel weight distribution and axle weight distribution of original data according to an embodiment of the present invention are respectively shown.

[0035] Figure 3a and Figure 3b Schematic diagrams of wheel weight distribution and axle weight distribution of predicted data according to an embodiment of the present invention are respectively shown. DETAILED DESCRIPTION

[0036] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0037] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0038] Example 1

[0039] Figure 1 A flow chart showing the steps of a method for screening static balance adjustment schemes for an electric locomotive according to an embodiment of the present invention.

[0040] like Figure 1 As shown, the electric locomotive static balance adjustment scheme screening method includes:

[0041] Step 101, obtaining wheel weight data, including wheel load deviation and axle load deviation;

[0042] Step 102, recording the difference between the front and rear wheel load deviations and the axle load deviations of each wheel position adjustment of the locomotive as corresponding impact values;

[0043] Step 103, predicting the adjusted wheel load deviation and axle load deviation according to the influence value and the adjustment value;

[0044] Step 104, selecting an adjustment solution according to the adjusted wheel load deviation and axle load deviation.

[0045] In one example, the wheel load deviation is:

[0046] Wheel load deviation = (wheel load - average wheel weight of the axle) / average wheel weight.

[0047] In one example, the axle load deviation is:

[0048] Axle load deviation = (axle load - average axle weight) / average axle weight.

[0049] In one example, the compression amount of the wheel spring is adjusted, and the wheel load deviation after the adjustment is subtracted from the wheel load deviation before the adjustment to obtain the corresponding wheel load influence value.

[0050] In one example, the compression amount of the wheel spring is adjusted, and the axle load deviation after the adjustment is subtracted from the axle load deviation before the adjustment to obtain the corresponding axle load impact value.

[0051] In one example, the predicted adjusted wheel load deviation is:

[0052]

[0053] Among them, K i represents the predicted wheel weight difference corresponding to the bogie at end i, k represents the original wheel weight difference, i∈{Ⅰ,Ⅱ}, Ⅰ represents the bogie at end I, Ⅱ represents the bogie at end II, and f p,q (b,c) represents the effect of the adjustment of the p-th axis q side of the i-end bogie on the b-th axis c side, a p,q represents the adjusted thickness corresponding to the qth side of the pth axle corresponding to the i-end bogie, b,p=1,…,P, P is the total number of axles corresponding to the bogie, c,q∈{1,2}, 1 represents the left side, and 2 represents the right side.

[0054] In one example, the predicted adjusted axle load deviation is:

[0055]

[0056] Where M(d) represents the predicted axle weight difference for the dth axis, m represents the original axle weight difference, and x j (d) represents the impact of the adjustment of the j-th axis on the d-th axis, d,j = 1,…,J, J is the total number of axes, n j Indicates the adjustment value of the j-th axis.

[0057] In one example, screening the adjustment scheme according to the adjusted wheel load deviation and axle load deviation includes:

[0058] The adjusted wheel load deviation and axle load deviation are predicted according to the adjustment plan. If the predicted wheel load deviation or axle load deviation is greater than a set threshold, the adjustment plan is deleted.

[0059] Specifically, the core principle of adjustment is based on Hooke's law, that is, the elongation of the spring is proportional to the external force it receives. In electric locomotives, the locomotive suspension spring system includes primary springs and secondary springs, which play a supporting and buffering role. By adjusting the compression of the spring (that is, adjusting the number of gaskets), the force conditions of each support point of the locomotive can be changed, thereby adjusting the distribution of wheel (axle) weight. In the elastic suspension structure, the primary spring stiffness is usually greater than that of the secondary spring, and the adjustment effect is more obvious than that of the secondary spring, and the wheel load and axle load can be directly adjusted at the same time.

[0060] In actual operation, the spring adjustment process requires precise calculation and there are many interfering factors. Since the weights of the wheels (axles) of the locomotive are coupled with each other, adjusting one spring may affect the weights of other wheels (axles). Therefore, it is necessary to establish a spring adjustment model to simulate the calculation method of the primary spring padding amount to provide accurate data support for spring adjustment.

[0061] Taking a six-axle locomotive as an example, the wheel weight data is obtained. The wheel weight data is the reading of the wheel weight of each wheel by the weighing equipment sensor during the electric locomotive weighing experiment, including total weight, wheel load deviation, axle load, average axle weight, and axle load deviation.

[0062] Total weight = sum of all wheel weights

[0063] Wheel load deviation = (wheel load - average wheel weight of the axle) / average wheel weight

[0064] Axle load = sum of wheel loads on both sides of the same axis

[0065] Average axle weight = total locomotive weight / 6

[0066] Axle load deviation = (axle load - average axle weight) / average axle weight

[0067] The wheel (axle) weight deviation calculation formula is the basis for forming the static balance data model of the electric locomotive. It is used to analyze the load distribution of the locomotive under static load and provide a basis for adjustment and optimization.

[0068] In the actual production process, the adjustment objects are the 12 springs in the first series of the whole vehicle. The height of the support point basically remains in a straight line when the locomotive is in a static load state, so the spring adjustment is regarded as a linear programming problem, and the simplex method is used to solve the linear programming, which consumes the least process time.

[0069] The average difference between the wheel load and axle load before and after each wheel position adjustment of 1mm of the locomotive is recorded, and the recorded data is statistically recorded using normal distribution to obtain the influence value f of each wheel position adjustment of 1mm on other wheel positions of the same bogie (i.e. the difference between the wheel load deviation after adjustment and the wheel load deviation before adjustment) as shown in Table 1 and Table 2, and the influence value x of each axle position adjustment of 1mm on other axle positions (i.e. the difference between the axle load deviation after adjustment and the axle load deviation before adjustment) as shown in Table 3.

[0070] Table 1 Adjustment f value of bogie at end I (%)

[0071]

[0072]

[0073] Table 2 Adjustment f value of the bogie at the II end (%)

[0074]

[0075]

[0076] Table 3 Adjustment x value of each axis (%)

[0077]

[0078] According to the statistical analysis of Table 1 and Table 2, the influence of adjustment of the axle (including one, two and three) in the bogie at end I of the same locomotive on axle four, five and six, as well as the influence of adjustment of the axle (including four, five and six) in the bogie at end II on axle one, two and three are both less than or equal to 0.15%, and the coupling correlation is not strong (the sensor error of the weighing equipment is ≤0.2%), and the wheel weight difference can be calculated independently for the three-axle and six-wheel positions of a single bogie.

[0079] The single bogie wheel weight difference prediction formula is:

[0080]

[0081] Among them, K i represents the predicted wheel weight difference corresponding to the bogie at end i, k represents the original wheel weight difference, i∈{Ⅰ,Ⅱ}, Ⅰ represents the bogie at end I, Ⅱ represents the bogie at end II, and f p,q (b,c) represents the effect of the adjustment of the p-th axis q side of the i-end bogie on the b-th axis c side, a p,q Represents the adjusted thickness corresponding to the q side of the p-th axis corresponding to the i-end bogie, b,p=1,…,P, P is the total number of axles corresponding to the bogie, for this embodiment, P=3, c,q∈{1,2}, 1 represents the left side, and 2 represents the right side.

[0082] Therefore, according to the italic data in Table 1, the predicted value of the load deviation of the left wheel of the first axle of the bogie at end I is:

[0083] K(Ⅰ,1,1)=k+(1.34×a 1,1 )+(-1.04×a 2,1 )+(0.37×a 3,1 )+(-1.62

[0084] ×a 1,2 )+(0.78×a 2,2 )+(-0.4×a 3,2 )

[0085] Similarly, according to the data in Table 1, the predicted values ​​of other unknown wheel load deviations of the bogie at end I are:

[0086] Prediction of load deviation of the left wheel on the second axle:

[0087] K(Ⅰ,2,1)=k+(-1.06×a 1,1)+(1.82×a 2,1 )+(-0.83×a 3,1 )+(0.66

[0088] ×a 1,2 )+(-2.06×a 2,2 )+(0.67×a 3,2 );

[0089] Prediction of load deviation of the left wheel of the three axles:

[0090] K(Ⅰ,3,1)=k+(0.35×a 1,1 )+(-0.87×a 2,1 )+(1.03×a 3,1 )+(-0.24

[0091] ×a 1,2 )+(0.47×a 2,2 )+(-1.53×a 3,2 );

[0092] Prediction value of load deviation of right wheel on axle:

[0093] K(Ⅰ,1,2)=k+(-1.34×a 1,1 )+(1.04×a 2,1 )+(-0.37×a 3,1 )+(1.62

[0094] ×a 1,2 )+(-0.78×a 2,2 )+(0.4×a 3,2 );

[0095] Prediction of load deviation of right wheel on second axle:

[0096] K(Ⅰ,2,2)=k+(1.06×a 1,1 )+(-1.82×a 2,1 )+(0.83×a 3,1 )+(-0.66

[0097] ×a 1,2 )+(2.06×a 2,2 )+(-0.67×a 3,2 );

[0098] Prediction of right wheel load deviation on three axles:

[0099] K(Ⅰ,3,2)=k+(-0.35×a 1,1 )+(0.87×a 2,1 )+(-1.03×a 3,1 )+(0.24

[0100] ×a 1,2 )+(-0.47×a 2,2 )+(1.53×a 3,2 ).

[0101] Similarly, according to the data in Table 2, the predicted value of wheel load deviation corresponding to the bogie at end II can also be obtained.

[0102] Vehicle axle load deviation prediction formula:

[0103]

[0104] Where M(d) represents the predicted axle weight difference for the dth axis, m represents the original axle weight difference, and x j (d) represents the influence of the adjustment of the j-th axis on the d-th axis, d, j = 1, ..., J, J is the total number of axes, for this embodiment, J = 6, n j Indicates the adjustment value of the j-th axis.

[0105] According to the italic data in Table 3, the predicted value of the axle load deviation is:

[0106] M(1)=m+(0.23×n1)+(-0.19×n2)+(0.14×n3)+(-0.03×n4)+

[0107] (-0.01×n5)+(-0.02×n6)

[0108] Similarly, the predicted value of the two-axis axle load deviation is:

[0109] M(2)=m+(-0.24×n1)+(0.43×n2)+(-0.33×n3)+(-0.02×n4)+

[0110] (-0.02×n5)+(-0.01×n6);

[0111] The predicted values ​​of the three-axis axle load deviation are:

[0112] M(3)=m+(0.13×n1)+(-0.17×n2)+(0.38×n3)+(-0.04×n4)+

[0113] (-0.04×n5)+(-0.05×n6);

[0114] The predicted values ​​of the four-axis axle load deviation are:

[0115] M(4)=m+(-0.05×n1)+(-0.02×n2)+(-0.07×n3)+(0.34×n4)+

[0116] (-0.16×n5)+(-0.18×n6);

[0117] The predicted values ​​of the five-axis load deviation are:

[0118] M(5)=m+(-0.02×n1)+(-0.02×n2)+(-0.06×n3)+(-0.37×n4)

[0119] +(0.49×n5)+(-0.34×n6);

[0120] Final value of six-axis load deviation:

[0121] M(6)=m+(-0.04×n1)+(-0.03×n2)+(-0.06×n3)+(0.14×n4)+

[0122] (-0.26×n5)+(0.24×n6).

[0123] Figure 2a and Figure 2b Schematic diagrams of wheel weight distribution and axle weight distribution of original data according to an embodiment of the present invention are respectively shown.

[0124] The static load adjustment of electric locomotive is composed of 12 equations of the same type (6 for each of the two bogies) of the wheel load deviation value algorithm and 6 equations of the same type of axle load deviation value algorithm, based on the original data shown in Table 4 (k, m values), where the bold data are data exceeding the preset threshold, and the wheel load distribution and axle load distribution are as follows Figure 2a , Figure 2b As shown. Adjust the pad thickness n as a variable value to obtain the K and M values.

[0125] Table 4

[0126]

[0127] Based on the unchanged total weight of the locomotive (the total mass of the locomotive is about 138t-150t, the mass of the 1mm adjustment pad is about 0.3Kg and can be ignored), the weight data of each wheel can be deduced, thereby estimating the adjustment result.

[0128] Figure 3a and Figure 3b Schematic diagrams of wheel weight distribution and axle weight distribution of predicted data according to an embodiment of the present invention are respectively shown.

[0129] In the production process, the original data of the adjustment program is the wheel (axle) weight data and parameters before the locomotive weighing test adjustment. After entering the adjustment plan, the simulation results can be obtained by real-time calculation, as shown in Table 5. The wheel weight distribution and axle weight distribution are as follows: Figure 3a , Figure 3bshown.

[0130] Table 5

[0131]

[0132] Modification of the adjustment plan When the simulation results meet the requirements specified in GB / T3317-2006 General Technical Conditions for Electric Locomotives, the adjustment plan is derived as shown in Table 6, and the adjustment operation is implemented to achieve a single adjustment that meets the specified requirements.

[0133] Table 6

[0134]

[0135] Example 2

[0136] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the above-mentioned electric locomotive static balance adjustment scheme screening method.

[0137] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0138] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0139] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0140] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0141] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0142] Example 3

[0143] An embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for screening static balance adjustment schemes for electric locomotives is implemented.

[0144] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.

[0145] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0146] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0147] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for screening static balance adjustment schemes for electric locomotives, characterized in that: include: Obtain wheel weight data, including wheel load deviation and axle load deviation; Record the difference between the wheel load deviation and the axle load deviation before and after each wheel position adjustment of the locomotive as the corresponding impact value; Predicting the wheel load deviation and axle load deviation after adjustment according to the impact value and the adjustment value; Select the adjustment plan based on the adjusted wheel load deviation and axle load deviation.

2. The method for selecting a static balance adjustment scheme for an electric locomotive according to claim 1, wherein: The wheel load deviation is: Wheel load deviation = (wheel load - average wheel weight of the axle) / average wheel weight.

3. The method for selecting static balance adjustment schemes for electric locomotives according to claim 1, wherein: The axle load deviation is: Axle load deviation = (axle load - average axle weight) / average axle weight.

4. The method for selecting static balance adjustment schemes for electric locomotives according to claim 1, wherein: The compression amount of the wheel spring is adjusted, and the wheel load deviation after adjustment is subtracted from the wheel load deviation before adjustment to obtain the corresponding wheel load impact value.

5. The method for selecting static balance adjustment schemes for electric locomotives according to claim 1, wherein: The compression amount of the wheel spring is adjusted, and the axle load deviation after adjustment is subtracted from the axle load deviation before adjustment to obtain the corresponding axle load impact value.

6. The method for selecting static balance adjustment schemes for electric locomotives according to claim 1, wherein: The predicted adjusted wheel load deviation is: Among them, K i represents the predicted wheel weight difference corresponding to the bogie at end i, k represents the original wheel weight difference, i∈{Ⅰ,Ⅱ}, Ⅰ represents the bogie at end I, Ⅱ represents the bogie at end II, and f p,q (b,c) represents the effect of the adjustment of the p-th axis q side of the i-end bogie on the b-th axis c side, a p,q represents the adjusted thickness corresponding to the qth side of the pth axle corresponding to the i-end bogie, b,p=1,…,P, P is the total number of axles corresponding to the bogie, c,q∈{1,2}, 1 represents the left side, and 2 represents the right side.

7. The method for selecting static balance adjustment schemes for electric locomotives according to claim 1, wherein: The predicted adjusted axle load deviation is: Where M(d) represents the predicted axle weight difference for the dth axis, m represents the original axle weight difference, and x j (d) represents the impact of the adjustment of the j-th axis on the d-th axis, d,j=1,…,J, J is the total number of axes, n j Indicates the adjustment value of the j-th axis.

8. The method for selecting static balance adjustment schemes for electric locomotives according to claim 1, wherein: The adjustment solutions screened based on the adjusted wheel load deviation and axle load deviation include: The adjusted wheel load deviation and axle load deviation are predicted according to the adjustment plan. If the predicted wheel load deviation or axle load deviation is greater than a set threshold, the adjustment plan is deleted.

9. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the method for screening static balance adjustment schemes for electric locomotives according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for screening static balance adjustment schemes for electric locomotives according to any one of claims 1 to 8 is implemented.

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