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

Abstract

The invention relates to the technical field of data analysis, in particular to a trailer axle welding positioning data processing system and method based on big data analysis, and the method comprises the steps: importing welding positioning data and traction data into a welding part traction influence analysis model, and analyzing the state of a welding part of a trailer axle under the action of traction; the welding positioning data and the load data are imported into a welding part load influence analysis model, and the welding part state of the trailer axle under the trailer load effect is analyzed; analyzing the state stability of the welding part of the trailer axle according to the state analysis result of the welding part of the trailer axle under the action of the traction force and the state analysis result of the welding part of the trailer axle under the action of the trailer load; and optimizing the welding process of the trailer axle according to the state stability analysis result of the welding part of the trailer axle. The production efficiency of the trailer axle can be improved, and meanwhile the structural strength of the trailer axle and the safety of a vehicle are guaranteed.

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 is a key component that connects the trailer and the tractor. Its welding quality is directly related to the safety and reliability of the entire vehicle transportation system. Therefore, precise control of every detail in the trailer axle welding process to ensure the strength and stability of the trailer axle is the core link in the trailer axle manufacturing process. Any minor welding defects may have a serious impact on the overall performance of the trailer axle, and then affect road safety. Therefore, how to achieve precise control of the trailer axle welding process has become an important challenge facing the manufacturing industry.

[0003] By introducing big data analysis technology, the key parameters of the trailer axle welding process can be fully monitored and analyzed in real time. This not only provides strong support for the optimization of the trailer axle welding process, but also lays a solid foundation for weld quality control. On this basis, big data analysis technology can further explore potential problems in the welding process, such as overheating and cold cracks, to achieve early warning and effectively prevent quality accidents.

[0004] However, the prior art only considers the impact of obvious defects in welds on the welding process of trailer axles in isolation when analyzing the welding of trailer axles, for example, by detecting the integrity of the welds, whether there are cracks, pores and other defects to evaluate the welding quality. Although this method can detect surface defects in welds, the forces on the trailer axle are dynamically changing during the driving of the trailer. Changes in slope, uneven road surface, and acceleration and deceleration of vehicles will lead to changes in load distribution and stress state on the trailer axle. The prior art cannot fully evaluate the dynamic performance of the welding parts of the trailer during actual driving, such as the impact of slope changes, dynamic changes in traction and load on the welding parts of the trailer axle. Therefore, it is difficult to fully evaluate the durability and reliability of the welding parts of the trailer axle. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present invention provides a trailer axle welding positioning data processing system and method based on big data analysis, which dynamically analyzes the influence of the trailer axle's traction force and load on the welding part state by acquiring the trailer axle welding positioning data and trailer traction data and load data. It can improve the production efficiency of the trailer axle while ensuring the structural strength of the trailer axle and the safety of the vehicle.

[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, and analyzing the state of the welding part of the trailer axle under the traction force;

[0010] S3, importing the welding positioning data and the load data into the welding position load influence analysis model, and analyzing the welding position state of the trailer axle under the trailer load;

[0011] S4. Analyze the state stability of the welding part of the trailer axle according to the state analysis results of the welding part of the trailer axle under the action of traction force and the state analysis results of the welding part of the trailer axle under the action of trailer load;

[0012] S5. According to 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, importing the welding positioning data of the trailer bridge 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 welding part of the trailer bridge; the traction force action state coefficient calculation formula is:

[0016]

[0017] Wherein, QY represents the traction force action state coefficient of the welding part of the trailer axle, 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] Wherein, 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 of the trailer under the experimental road conditions before leaving the factory in the traction data, respectively; 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 roughness and the minimum road roughness of the trailer under the experimental road conditions before leaving the factory in the traction data, respectively, and β represents the road slope of the trailer 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 welding positioning data of the trailer axle and load data of the trailer;

[0023] S32, substituting the welding positioning 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 welding part of the trailer axle; 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 compartment in the load data, Gq is the front weight of the trailer in the load data, L is the length of the trailer in the load data, and ΔL represents 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 welding part of the trailer axle;

[0031] S42, substituting the traction force state coefficient and the load state coefficient of the welding part of the trailer axle 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 force and 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 state of the trailer axle;

[0036] S52. A welding part state stability threshold is preset. When the welding part state stability coefficient of the trailer axle is less than the welding part state stability threshold, a welding process optimization notification is sent 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, a trailer axle welding qualification notification is sent 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, including:

[0038] The data acquisition module is used to acquire the welding positioning data of the trailer axle; and simultaneously acquire the traction data and load data of the trailer;

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

[0040] Welding part load influence analysis module, used to import welding positioning data and load data into welding part load influence analysis model, and analyze the welding part status of trailer axle under trailer load;

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

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

[0043] 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.

[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 positioning data and traction data into the welding part traction force influence analysis model to analyze the welding part state of the trailer bridge under the traction force; imports welding positioning data and load data into the welding part load influence analysis model to analyze the welding part state of the trailer bridge under the trailer load; analyzes the state stability of the trailer bridge welding part according to the analysis results of the welding part state of the trailer bridge under the traction force and the analysis results of the welding part state of the trailer bridge under the trailer load; optimizes the trailer bridge welding process according to the analysis results of the state stability of the trailer bridge welding part. The production efficiency of the trailer bridge can be improved, and the structural strength of the trailer bridge and the safety of the vehicle can be guaranteed at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0048] Figure 1 It is a 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 It 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. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides a method for processing trailer bridge 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, and analyzing the state of the welding part of the trailer axle under the traction force;

[0056] S3, importing the welding positioning data and the load data into the welding position load influence analysis model, and analyzing the welding position state of the trailer axle under the trailer load;

[0057] S4. Analyze the state stability of the welding part of the trailer axle according to the state analysis results of the welding part of the trailer axle under the action of traction force and the state analysis results of the welding part of the trailer axle under the action of trailer load;

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

[0059] In this embodiment, step S2 can more accurately evaluate the stress concentration of the welding part by considering geometric parameters such as welding length, welding point diameter and welding angle. At the same time, the traction influence coefficient is introduced to consider factors such as actual traction, traction direction angle, material elastic modulus, welding thickness and road conditions, so as to comprehensively evaluate the stress state of the welding part in actual use, improve the reliability and safety of the trailer axle, reduce maintenance costs and enhance 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, importing 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 welding part of the trailer axle; the traction force action state coefficient calculation formula is:

[0062]

[0063] In the formula, QY represents the traction state coefficient of the welding part of the trailer axle, Kq represents the traction 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. Among them, the ratio of the welding length Lw to the welding point diameter Dw reflects the influence of the geometric shape of the weld at the welding part on the stress concentration. A longer welding length and a smaller welding point diameter may produce greater stress concentration near the welding point, thereby affecting the state of the welding part under the action of traction.

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

[0065]

[0066] 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 respectively corresponding to the maximum traction of the trailer 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 roughness and minimum road roughness of the trailer under the experimental road conditions before leaving the factory in the traction data, and β represents the maximum road 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 direction angle of the traction 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 the stress concentration, and then the greater the impact on the stability of the welding part state.

[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 welding part of the trailer axle 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 compartment, 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. This embodiment adopts the load action state coefficient calculation formula to more accurately evaluate the stress distribution of the welding part under different load conditions, identify the potential high stress concentration area 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, and optimize the welding structure in the design and manufacturing stage of the trailer axle according to the welding part state stability coefficient, and 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 welding parts in long-term use, guide the maintenance and repair 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 welding positioning data of the trailer axle and load data of the trailer;

[0069] S32, substituting the welding positioning 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 welding part of the trailer axle; 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 compartment in the load data, Gq is the front weight of the trailer in the load data, L is the length of the trailer in the load data, and ΔL represents 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 welding part of the trailer axle;

[0077] S42. Substitute the traction force state coefficient and the load state coefficient of the welding part of the trailer axle 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 force and load effect.

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

[0081] S51, obtaining the calculated stability coefficient of the welding position state 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 qualified notification to the trailer axle production workshop. Among them, the value of the traction force proportion coefficient, the load effect proportion coefficient and the welding part state stability threshold is as follows: obtain 5000 sets of trailer axle welding positioning data and trailer load data; substitute the trailer axle welding positioning data and trailer load data into the welding part state stability coefficient calculation formula to calculate 5000 sets of trailer axle welding part state stability coefficients; obtain 5000 sets of trailer axle welding part state stability judgment results, import the trailer axle welding part state stability coefficient and the trailer axle welding part state stability judgment results into the fitting software, and output the corresponding traction force proportion coefficient, load effect proportion coefficient and welding part state stability threshold value that meet the highest welding part stability judgment accuracy.

[0083] Example 2

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

[0085] The data acquisition module is used to acquire the welding positioning data of the trailer axle; and simultaneously acquire the traction data and load data of the trailer;

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

[0087] Welding part load influence analysis module, used to import welding positioning data and load data into welding part load influence analysis model, and analyze the welding part status of trailer axle under trailer load;

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

[0089] Trailer axle welding process optimization module, used to optimize the trailer axle welding process according to 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 bridge 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 the trailer bridge 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 bridge welding positioning data processing method based on big data analysis are implemented in C language, wherein 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, the trailer bridge welding process optimization module and the 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that 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 displayed 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 may 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform 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 codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

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

Claims

1. A method for processing trailer bridge welding positioning data based on big data analysis, characterized in that: The steps include: S1. Obtain the welding positioning data of the trailer axle; and 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, and analyzing the state of the welding part of the trailer axle under the traction force; S3, importing the welding positioning data and the load data into the welding position load influence analysis model, and analyzing the welding position state of the trailer axle under the trailer load; S4. Analyze the state stability of the welding part of the trailer axle according to the state analysis results of the welding part of the trailer axle under the action of traction force and the state analysis results of the welding part of the trailer axle under the action of trailer load; S5. According to the state stability analysis results of the trailer axle welding parts, the trailer axle welding process is optimized.

2. The method for processing trailer axle welding positioning data based on big data analysis according to claim 1, characterized in that: The step S2 comprises the following specific steps: S21, extracting the welding positioning data of the trailer axle and the traction data of the trailer; S22, importing the welding positioning data of the trailer bridge 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 welding part of the trailer bridge; the traction force action state coefficient calculation formula is: Wherein, QY represents the traction force action state coefficient of the welding part of the trailer axle, 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.

3. The method for processing trailer axle welding positioning data based on big data analysis according to claim 2 is characterized in that: The calculation formula of the traction force influence coefficient is: Wherein, 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 of the trailer under the experimental road conditions before leaving the factory in the traction data, respectively; 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 roughness and the minimum road roughness of the trailer under the experimental road conditions before leaving the factory in the traction data, respectively, and β represents the road slope of the trailer under the experimental road conditions before leaving the factory in the traction data.

4. The method for processing trailer axle welding positioning data based on big data analysis according to claim 3 is characterized in that: The step S3 includes the following specific contents: S31, extracting welding positioning data of the trailer axle and load data of the trailer; S32, substituting the welding positioning 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 welding part of the trailer axle; the load action state coefficient calculation formula is: Where ZH represents the load action state coefficient of the trailer axle welding part, and Kz represents the load influence coefficient.

5. The method for processing trailer axle welding positioning data based on big data analysis according to claim 4 is characterized in that: 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 compartment in the load data, Gq is the front weight of the trailer in the load data, L is the length of the trailer in the load data, and ΔL represents the average distance between the trailer axle and the center of gravity of the vehicle in the load data.

6. The method for processing trailer bridge welding positioning data based on big data analysis according to claim 5 is characterized in that: The step S4 comprises the following specific steps: S41, obtaining the calculated traction force action state coefficient and load action state coefficient of the welding part of the trailer axle; S42, substituting the traction force state coefficient and the load state coefficient of the welding part of the trailer axle 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 force and load effect.

7. The method for processing trailer axle welding positioning data based on big data analysis according to claim 6, characterized in that: The step S5 comprises the following specific steps: S51, obtaining the calculated stability coefficient of the welding position state of the trailer axle; S52. A welding part state stability threshold is preset. When the welding part state stability coefficient of the trailer axle is less than the welding part state stability threshold, a welding process optimization notification is sent 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, a trailer axle welding qualification notification is sent to the trailer axle production workshop.

8. A trailer bridge welding positioning data processing system based on big data analysis, used to implement the trailer bridge welding positioning data processing method based on big data analysis as described in any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to acquire the welding positioning data of the trailer axle; and simultaneously acquire the traction data and load data of the trailer; The welding part traction force influence analysis module is used to import the welding positioning data and traction data into the welding part traction force influence analysis model to analyze the welding part status of the trailer axle under the traction force; Welding part load influence analysis module, used to import welding positioning data and load data into welding part load influence analysis model, and analyze the welding part status of trailer axle under trailer load; A welding part state stability analysis module is used to analyze the state stability of the welding part of the trailer axle according to the state analysis results of the welding part of the trailer axle under the action of traction force and the state analysis results of the welding part of the trailer axle under the action of trailer load; Trailer axle welding process optimization module, used to optimize the trailer axle welding process according to the state stability analysis results of the trailer axle welding parts; 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.

9. 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 7 by calling the computer program stored in the memory.

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