Trailer axle welding positioning data processing system and method based on big data analysis

Abstract

The present invention relates to the field of data analysis technology, and in particular to a trailer axle welding positioning data processing system and method based on big data analysis. The system comprises: importing welding positioning data and traction data into a welding part traction force influence analysis model to analyze the state of the trailer axle welding part under the action of traction force; importing welding positioning data and load data into a welding part load influence analysis model to analyze the state of the trailer axle welding part under the action of trailer load; analyzing the state stability of the trailer axle welding part based on the analysis results of the state of the trailer axle welding part under the action of traction force and the analysis results of the state of the trailer axle welding part under the action of trailer load; and optimizing the trailer axle welding process based on the analysis results of the state stability of the trailer axle welding part. This system can improve the production efficiency of the trailer axle while ensuring the structural strength of the trailer axle and the safety of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a trailer axle welding positioning data processing system and method based on big data analysis. Background Art

[0002] The trailer axle, a key component connecting the trailer and tractor, has a welding quality that directly impacts the safety and reliability of the entire vehicle transportation system. Therefore, precise control of every detail in the trailer axle welding process to ensure its strength and stability is a core component of trailer axle manufacturing. Even the slightest welding defect can severely impact the overall performance of the trailer axle, and thus, road safety. Therefore, achieving precise control of the trailer axle welding process has become a major challenge facing the manufacturing industry.

[0003] By introducing big data analysis technology, comprehensive monitoring of key parameters in the trailer axle welding process is achieved, with real-time feedback analysis. This not only provides strong support for optimizing the trailer axle welding process but also lays a solid foundation for weld quality control. Furthermore, big data analysis technology can further identify potential welding problems, such as overheating and cold cracking, providing early warning and effectively preventing quality accidents.

[0004] However, when analyzing trailer axle welding, existing technologies only consider the impact of obvious defects in the welds on the trailer axle welding process in isolation. For example, the welding quality is evaluated by detecting the integrity of the welds and whether there are defects such as cracks and pores. Although this method can detect surface defects in the welds, the forces acting on the trailer axle are changing dynamically during the driving of the trailer. Changes in slope, uneven road surface, and acceleration and deceleration of the vehicle will all lead to changes in the load distribution and stress state on the trailer axle. Existing technologies are unable to comprehensively evaluate the dynamic performance of the welding parts of the trailer during actual driving, such as the impact of changes in slope, dynamic changes in traction and load on the welded parts of the trailer axle. Therefore, it is difficult to comprehensively evaluate the durability and reliability of the welding parts of the trailer axle. Summary of the Invention

[0005] To overcome the shortcomings and deficiencies of existing technologies, the present invention provides a trailer axle welding positioning data processing system and method based on big data analysis. By acquiring trailer axle welding positioning data as well as trailer traction and load data, the system dynamically analyzes the impact of the trailer axle's traction and load on the weld condition. This improves trailer axle production efficiency while ensuring the structural strength of the trailer axle and vehicle safety.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for processing trailer axle welding positioning data based on big data analysis, comprising the following steps:

[0008] S1. Obtain the welding positioning data of the trailer axle; and simultaneously obtain the traction data and load data of the trailer;

[0009] S2. Importing the welding positioning data and the traction data into the traction force influence analysis model of the welding part to analyze the state of the welding part of the trailer axle under the action of the traction force;

[0010] S3. Importing the welding position data and load data into the welding position load influence analysis model to analyze the welding position status of the trailer axle under the trailer load;

[0011] S4. Analyze the stability of the welding portion of the trailer axle based on the analysis results of the welding portion of the trailer axle under the action of traction force and the analysis results of the welding portion of the trailer axle under the action of trailer load;

[0012] S5. Based on the state stability analysis results of the trailer axle welding parts, the trailer axle welding process is optimized.

[0013] In one implementation of the present invention, step S2 includes the following specific steps:

[0014] S21, extracting the welding positioning data of the trailer axle and the traction data of the trailer;

[0015] S22. Import the welding positioning data of the trailer axle and the traction data of the trailer into a traction force action state coefficient calculation formula to calculate the traction force action state coefficient of the trailer axle welding part; the traction force action state coefficient calculation formula is:

[0016]

[0017] Where QY represents the traction force action state coefficient of the trailer axle welding part, Kq represents the traction force influence coefficient, Lw represents the welding length of the welding part in the welding positioning data, Dw represents the diameter of the welding point on the welding part in the welding positioning data, and a represents the welding angle of the welding part in the welding positioning data.

[0018] In one implementation of the present invention, the calculation formula of the traction force influence coefficient is:

[0019]

[0020] Where Kq represents the traction force influence coefficient; Ft represents the maximum traction force of the trailer under the experimental road conditions before leaving the factory in the traction data; b and c represent the angles between the traction direction and the horizontal plane and the vertical direction corresponding to the maximum traction force under the experimental road conditions before leaving the factory in the traction data; E is the material elastic modulus of the weld in the welding positioning data, Tw represents the weld thickness of the weld in the welding positioning data; Ra and Ro represent the maximum and minimum road surface roughness of the trailer under the experimental road conditions before leaving the factory in the traction data, respectively; β represents the road surface slope under the experimental road conditions before leaving the factory in the traction data.

[0021] In one implementation of the present invention, step S3 includes the following specific contents:

[0022] S31, extracting the welding positioning data of the trailer axle and the load data of the trailer;

[0023] S32. Substitute the welding position data of the trailer axle and the load data of the trailer into the load action state coefficient calculation formula to calculate the load action state coefficient of the trailer axle welding part; the load action state coefficient calculation formula is:

[0024]

[0025] Where ZH represents the load action state coefficient of the trailer axle welding part, and Kz represents the load influence coefficient.

[0026] In one implementation of the present invention, the calculation formula of the load influence coefficient is:

[0027]

[0028] Where Kz represents the load influence coefficient, N is the number of trailer axles installed on the trailer in the load data, Gx is the weight of the trailer body in the load data, Gq is the head weight of the trailer in the load data, L is the length of the trailer in the load data, and ΔL is the average distance between the trailer axle and the center of gravity of the vehicle in the load data.

[0029] In one implementation of the present invention, step S4 includes the following specific steps:

[0030] S41. Obtaining the calculated traction force action state coefficient and load action state coefficient of the trailer axle welding portion;

[0031] S42. Substitute the traction force action state coefficient and the load action state coefficient of the trailer axle welding part into the welding part state stability coefficient calculation formula to calculate the welding part state stability coefficient of the trailer axle; the welding part state stability coefficient calculation formula is:

[0032] HW=a1×QY+a2×ZH;

[0033] Where HW represents the stability coefficient of the welding part of the trailer axle, a1 and a2 represent the proportion coefficient of traction effect and the proportion coefficient of load effect.

[0034] In one implementation of the present invention, step S5 includes the following specific steps:

[0035] S51. Obtaining the calculated stability coefficient of the welding position of the trailer axle;

[0036] S52. Preset a welding part state stability threshold. When the welding part state stability coefficient of the trailer axle is less than the welding part state stability threshold, send a welding process optimization notification to the trailer axle production workshop; when the welding part state stability coefficient of the trailer axle is greater than or equal to the welding part state stability threshold, send a trailer axle welding qualification notification to the trailer axle production workshop.

[0037] In a second aspect, an embodiment of the present invention further provides a trailer axle welding positioning data processing system based on big data analysis, comprising:

[0038] Data acquisition module, used to obtain the welding positioning data of the trailer axle; and simultaneously obtain the traction data and load data of the trailer;

[0039] The welding part traction force impact analysis module is used to import welding positioning data and traction data into the welding part traction force impact analysis model to analyze the welding part status of the trailer axle under the action of traction force;

[0040] The weld load impact analysis module is used to import weld positioning data and load data into the weld load impact analysis model to analyze the weld state of the trailer axle under the trailer load.

[0041] A welding part state stability analysis module is used to analyze the state stability of the trailer axle welding part based on the analysis results of the trailer axle welding part state under traction force and the analysis results of the trailer axle welding part state under trailer load;

[0042] Trailer axle welding process optimization module, used to optimize the trailer axle welding process based on the state stability analysis results of the trailer axle welding parts;

[0043] A control module is used to control the operation of the data acquisition module, the welding part traction force influence analysis module, the welding part load influence analysis module, the welding part state stability analysis module and the trailer axle welding process optimization module.

[0044] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a trailer axle welding positioning data processing method based on big data analysis by calling the computer program stored in the memory.

[0045] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0046] The present invention imports welding position data and traction data into a welding part traction force influence analysis model to analyze the state of the trailer axle's welding parts under traction force. It also imports welding position data and load data into a welding part load influence analysis model to analyze the state of the trailer axle's welding parts under trailer load. Based on the analysis results of the trailer axle's welding part states under traction force and trailer load, the state stability of the trailer axle's welding parts is analyzed. Based on the analysis results of the trailer axle's welding part state stability, the trailer axle welding process is optimized. This can improve the production efficiency of trailer axles while ensuring the structural strength of the trailer axle and the safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0048] Figure 1 Schematic diagram of the overall process of the trailer axle welding positioning data processing method based on big data analysis of the present invention;

[0049] Figure 2 This is a schematic diagram of the electronic device structure of the trailer axle welding positioning data processing method based on big data analysis of the present invention;

[0050] Figure 3 It is a structural schematic diagram of the trailer axle welding positioning data processing system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides a method for processing trailer axle welding positioning data based on big data analysis, which specifically includes the following steps:

[0054] S1. Obtain the welding positioning data of the trailer axle; and simultaneously obtain the traction data and load data of the trailer;

[0055] S2. Importing the welding positioning data and the traction data into the traction force influence analysis model of the welding part to analyze the state of the welding part of the trailer axle under the action of the traction force;

[0056] S3. Importing the welding position data and load data into the welding position load influence analysis model to analyze the welding position status of the trailer axle under the trailer load;

[0057] S4. Analyze the stability of the welding portion of the trailer axle based on the analysis results of the welding portion of the trailer axle under the action of traction force and the analysis results of the welding portion of the trailer axle under the action of trailer load;

[0058] S5. Based on the state stability analysis results of the trailer axle welding parts, the trailer axle welding process is optimized.

[0059] In this embodiment, step S2 considers geometric parameters such as weld length, weld diameter, and weld angle to more accurately assess stress concentration at the weld. It also incorporates a traction influence coefficient, taking into account factors such as actual traction, traction direction angle, material elastic modulus, weld thickness, and road conditions. This allows for a comprehensive assessment of the stress state of the weld during actual use, improving the reliability and safety of the trailer axle while reducing maintenance costs and enhancing the user experience. Specifically, the following steps are included:

[0060] S21, extracting the welding positioning data of the trailer axle and the traction data of the trailer;

[0061] S22. Import the welding positioning data of the trailer axle and the traction data of the trailer into the traction force action state coefficient calculation formula to calculate the traction force action state coefficient of the trailer axle welding part; the traction force action state coefficient calculation formula is:

[0062]

[0063] Where QY represents the traction force coefficient for the trailer axle weld, Kq represents the traction force influence coefficient, Lw represents the weld length of the weld in the weld location data, Dw represents the diameter of the weld point in the weld location data, and a represents the weld angle in the weld location data. The ratio of weld length Lw to weld point diameter Dw reflects the effect of the weld geometry on stress concentration. Longer weld lengths and smaller weld point diameters can lead to greater stress concentration near the weld point, thus affecting the weld state under traction.

[0064] In this embodiment, the calculation formula of the traction force influence coefficient is:

[0065]

[0066] Where Kq represents the traction force influence coefficient; Ft represents the maximum traction force of the trailer under the experimental road conditions before leaving the factory in the traction data; b and c represent the angle between the traction direction and the horizontal plane and the angle between the traction direction and the vertical direction respectively corresponding to the maximum traction force under the experimental road conditions before leaving the factory in the traction data; E is the material elastic modulus of the welding part in the welding positioning data, Tw represents the welding thickness of the welding part in the welding positioning data; Ra and Ro represent the maximum road surface roughness and the minimum road surface roughness respectively under the experimental road conditions before leaving the factory in the traction data, and β represents the maximum road surface roughness of the trailer under the experimental road conditions before leaving the factory in the traction data. The road slope under the experimental road conditions before; Among them, the greater the traction, the greater the stress on the welding point, which may increase the stress concentration, thereby increasing the traction influence coefficient; At the same time, the traction direction angles b and c are the components of the traction in the horizontal and vertical directions, which will also affect the stress state of the welding point. When the traction direction angle is closer to the force direction of the welding part, the stress of the welding part will be more concentrated, and the higher road roughness and road slope will also cause the welding point that has been affected by the traction to bear more uneven stress, thereby increasing stress concentration, and thus having a greater impact on the stability of the welding part.

[0067] In this embodiment, step S3 extracts the welding positioning data of the trailer axle and the load data of the trailer, and substitutes these data into the load action state coefficient calculation formula, so as to comprehensively evaluate the stress state of the trailer axle welding part under the actual load. The load action state coefficient takes into account the load influence coefficient, which not only includes the number of trailer axles, the weight of the trailer body, the weight of the front of the vehicle, and the length of the vehicle, but also takes into account the average distance between the trailer axle and the center of gravity of the vehicle. The load action state coefficient calculation formula used in this embodiment can more accurately evaluate the stress distribution of the welding part under different load conditions, identify potential high stress concentration areas of the welding part, and thus calculate the welding part state stability coefficient of the trailer axle according to the subsequent welding part state stability coefficient calculation formula. Based on the welding part state stability coefficient, the welding structure is optimized during the design and manufacturing stage of the trailer axle to improve the reliability and durability of the welding part. At the same time, by comprehensively considering the load direction and distribution, this embodiment can also help predict the fatigue life and stability performance of the welded parts in long-term use, guide the maintenance and repair work of the trailer axle, reduce unnecessary repair costs, extend the service life of the trailer axle, and improve the driving safety of the trailer. S3 includes the following specific contents:

[0068] S31, extracting the welding positioning data of the trailer axle and the load data of the trailer;

[0069] S32. Substitute the welding position data of the trailer axle and the load data of the trailer into the load action state coefficient calculation formula to calculate the load action state coefficient of the trailer axle welding part; the load action state coefficient calculation formula is:

[0070]

[0071] Where ZH represents the load action state coefficient of the trailer axle welding part, and Kz represents the load influence coefficient.

[0072] In this embodiment, the calculation formula of the load influence coefficient is:

[0073]

[0074] Where Kz represents the load influence coefficient, N is the number of trailer axles installed on the trailer in the load data, Gx is the weight of the trailer body in the load data, Gq is the head weight of the trailer in the load data, L is the length of the trailer in the load data, and ΔL is the average distance between the trailer axle and the center of gravity of the vehicle in the load data.

[0075] In this embodiment, step S4 includes the following specific steps:

[0076] S41. Obtaining the calculated traction force action state coefficient and load action state coefficient of the trailer axle welding portion;

[0077] S42. Substitute the traction force action state coefficient and the load action state coefficient of the trailer axle welding part into the welding part state stability coefficient calculation formula to calculate the welding part state stability coefficient of the trailer axle; the welding part state stability coefficient calculation formula is:

[0078] HW=a1×QY+a2×ZH;

[0079] Where HW represents the stability coefficient of the welding part of the trailer axle, a1 and a2 represent the proportion coefficient of traction effect and the proportion coefficient of load effect.

[0080] In this embodiment, step S5 includes the following specific steps:

[0081] S51. Obtaining the calculated stability coefficient of the welding position of the trailer axle;

[0082] S52: Preset a welding part state stability threshold. When the welding part state stability coefficient of the trailer axle is less than the welding part state stability threshold, send a welding process optimization notification to the trailer axle production workshop. When the welding part state stability coefficient of the trailer axle is greater than or equal to the welding part state stability threshold, send a trailer axle welding qualification notification to the trailer axle production workshop. The values of the traction force ratio coefficient, the load force ratio coefficient, and the welding part state stability threshold are determined by: obtaining 5,000 sets of trailer axle welding positioning data and trailer load data; substituting the trailer axle welding positioning data and trailer load data into the welding part state stability coefficient calculation formula to calculate 5,000 sets of trailer axle welding part state stability coefficients; obtaining 5,000 sets of trailer axle welding part state stability judgment results, importing the trailer axle welding part state stability coefficients and the trailer axle welding part state stability judgment results into the fitting software, and outputting the corresponding traction force ratio coefficient, load force ratio coefficient, and welding part state stability threshold values that meet the highest welding part stability judgment accuracy.

[0083] Example 2

[0084] like Figure 3 As shown, this embodiment provides a trailer axle welding positioning data processing system based on big data analysis, including:

[0085] Data acquisition module, used to obtain the welding positioning data of the trailer axle; and simultaneously obtain the traction data and load data of the trailer;

[0086] The welding part traction force impact analysis module is used to import welding positioning data and traction data into the welding part traction force impact analysis model to analyze the welding part status of the trailer axle under the action of traction force;

[0087] The weld load impact analysis module is used to import weld positioning data and load data into the weld load impact analysis model to analyze the weld state of the trailer axle under the trailer load.

[0088] A welding part state stability analysis module is used to analyze the state stability of the trailer axle welding part based on the analysis results of the trailer axle welding part state under traction force and the analysis results of the trailer axle welding part state under trailer load;

[0089] Trailer axle welding process optimization module, used to optimize the trailer axle welding process based on the state stability analysis results of the trailer axle welding parts;

[0090] The control module is used to control the operation of the data acquisition module, the welding part traction force influence analysis module, the welding part load influence analysis module, the welding part state stability analysis module and the trailer axle welding process optimization module.

[0091] The above-mentioned parameters and steps for each unit module to realize the corresponding functions in the trailer axle welding positioning data processing system based on big data analysis of the present invention can refer to the parameters and steps in the embodiment of the trailer axle welding positioning data processing method based on big data analysis above, and will not be repeated here.

[0092] Example 3

[0093] like Figure 2 As shown, an electronic device according to an embodiment of the present invention includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a trailer axle welding positioning data processing method based on big data analysis by calling the computer program stored in the memory. It should be noted that all computer programs of the trailer axle welding positioning data processing method based on big data analysis are implemented in C language, wherein the data acquisition module, welding part traction force impact analysis module, welding part load impact analysis module, welding part state stability analysis module, trailer axle welding process optimization module, and control module are all controlled by a remote server.

[0094] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. On the other hand, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0096] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0097] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A trailer axle welding positioning data processing method based on big data analysis is characterized in that: The steps include: S1. Obtain the welding positioning data of the trailer axle; simultaneously obtain the traction data and load data of the trailer; S2. Importing the welding positioning data and the traction data into the traction force influence analysis model of the welding part to analyze the state of the welding part of the trailer axle under the action of the traction force; S3. Importing the welding position data and load data into the welding position load influence analysis model to analyze the welding position status of the trailer axle under the trailer load; S4. Analyze the stability of the welding portion of the trailer axle based on the analysis results of the welding portion of the trailer axle under the action of traction force and the analysis results of the welding portion of the trailer axle under the action of trailer load; S5. Optimize the trailer axle welding process based on the state stability analysis results of the trailer axle welding parts; The step S2 includes the following specific steps: S21, extracting the welding positioning data of the trailer axle and the traction data of the trailer; S22. Import the welding positioning data of the trailer axle and the traction data of the trailer into a traction force action state coefficient calculation formula to calculate the traction force action state coefficient of the trailer axle welding part; the traction force action state coefficient calculation formula is: Where QY represents the traction force action state coefficient of the trailer axle welding part, Kq represents the traction force influence coefficient, Lw represents the welding length of the welding part in the welding positioning data, Dw represents the diameter of the welding point on the welding part in the welding positioning data, and a represents the welding angle of the welding part in the welding positioning data; The calculation formula of the traction force influence coefficient is: Where Kq represents the traction influence coefficient; Ft represents the maximum traction of the trailer under the experimental road conditions before leaving the factory in the traction data; b and c represent the angle between the traction direction and the horizontal plane and the angle between the traction direction and the vertical direction corresponding to the maximum traction under the experimental road conditions before leaving the factory in the traction data; E represents the material elastic modulus of the weld part in the welding positioning data, Tw represents the weld thickness of the weld part in the welding positioning data; Ra and Ro represent the maximum and minimum road surface roughness under the experimental road conditions before leaving the factory in the traction data, respectively; β represents the road surface slope under the experimental road conditions before leaving the factory in the traction data; The step S3 includes the following specific contents: S31, extracting the welding positioning data of the trailer axle and the load data of the trailer; S32. Substitute the welding position data of the trailer axle and the load data of the trailer into the load action state coefficient calculation formula to calculate the load action state coefficient of the trailer axle welding part; the load action state coefficient calculation formula is: Where ZH represents the load action coefficient of the trailer axle welding part, and Kz represents the load influence coefficient; The calculation formula of the load influence coefficient is: Where Kz represents the load influence coefficient, N is the number of trailer axles installed on the trailer in the load data, Gx is the weight of the trailer body in the load data, Gq is the head weight of the trailer in the load data, L is the length of the trailer in the load data, and ΔL is the average distance between the trailer axle and the center of gravity of the vehicle in the load data.

2. The trailer axle welding positioning data processing method based on big data analysis according to claim 1 is characterized in that: The step S4 includes the following specific steps: S41. Obtaining the calculated traction force action state coefficient and load action state coefficient of the trailer axle welding portion; S42. Substitute the traction force action state coefficient and the load action state coefficient of the trailer axle welding part into the welding part state stability coefficient calculation formula to calculate the welding part state stability coefficient of the trailer axle; the welding part state stability coefficient calculation formula is: HW=a1×QY+a2×ZH; Where HW represents the stability coefficient of the welding part of the trailer axle, a1 and a2 represent the proportion coefficient of traction effect and the proportion coefficient of load effect.

3. The trailer axle welding positioning data processing method based on big data analysis according to claim 2 is characterized in that: The step S5 includes the following specific steps: S51. Obtaining the calculated stability coefficient of the welding position of the trailer axle; S52. Preset a welding part state stability threshold. When the welding part state stability coefficient of the trailer axle is less than the welding part state stability threshold, send a welding process optimization notification to the trailer axle production workshop; when the welding part state stability coefficient of the trailer axle is greater than or equal to the welding part state stability threshold, send a trailer axle welding qualification notification to the trailer axle production workshop.

4. A trailer axle welding positioning data processing system based on big data analysis, used to implement the trailer axle welding positioning data processing method based on big data analysis according to any one of claims 1 to 3, characterized in that: The system comprises: Data acquisition module, used to obtain the welding positioning data of the trailer axle; and simultaneously obtain the traction data and load data of the trailer; The welding part traction force impact analysis module is used to import welding positioning data and traction data into the welding part traction force impact analysis model to analyze the welding part status of the trailer axle under the action of traction force; The weld load impact analysis module is used to import weld positioning data and load data into the weld load impact analysis model to analyze the weld state of the trailer axle under the trailer load. A welding part state stability analysis module is used to analyze the state stability of the trailer axle welding part based on the analysis results of the trailer axle welding part state under traction force and the analysis results of the trailer axle welding part state under trailer load; Trailer axle welding process optimization module, used to optimize the trailer axle welding process based on the state stability analysis results of the trailer axle welding parts; A control module is used to control the operation of the data acquisition module, the welding part traction force influence analysis module, the welding part load influence analysis module, the welding part state stability analysis module and the trailer axle welding process optimization module.

5. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the trailer axle welding positioning data processing method based on big data analysis as described in any one of claims 1 to 3 by calling the computer program stored in the memory.

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