Welding control method and system for improving strength of engineering vehicle

Abstract

The invention relates to a welding control method and system for improving the strength of an engineering vehicle, and relates to the field of welding control. A multi-layer welding area is traversed for frequent excavation to obtain a cold crack high-frequency trigger area with the trigger frequency larger than or equal to a trigger frequency threshold value; the interlayer temperature concentrated value of the same type of multi-layer welding area of the cold crack high-frequency trigger area is retrieved with the k-layer welding parameters and the environment information as background constraints; taking the k-layer welding parameters, the environment information and the interlayer temperature concentration value as background constraints to retrieve the cold crack triggering probability of the same type of multilayer welding areas; and when the probability is smaller than a triggering probability threshold value, when the welding of k layers of welding beads is finished and a base metal temperature value of a multi-layer welding area is monitored through a temperature sensor, if the base metal temperature value is the same as an interlayer temperature concentrated value, welding control of k + 1 layers of welding beads is executed, and the problems that the interlayer welding interval time is difficult to accurately control in the multi-layer welding process, the welding strength is unstable, and the welding quality is poor are solved. The occurrence frequency of cold cracks is high, and the welding effect is difficult to accurately control.

Description

Technical Field

[0001] The present invention relates to the field of welding control, and in particular to a welding control method and system for improving the strength of an engineering vehicle. Background Art

[0002] In the welding process of engineering vehicle frames, multi-layer welding is a crucial process. However, in multi-layer welding, the control of the interval time between welding layers is a long-standing technical problem. If the interval time is too long, cold cracks are prone to occur in the welded joint, which will not only affect the welding quality, but also have an adverse effect on the overall strength of the engineering vehicle; if the interval time is too short and the interlayer temperature is too high, the strength of the weld metal may be reduced, which will also damage the welding effect. At present, in welding scenarios, the interval time control of multi-layer welding mainly depends on the experience of the welder. Although welders will try their best to ensure the welding quality and weld the subsequent layer welds as early as possible to ensure the strength of the welding according to the requirements of the interlayer temperature, this method still has many shortcomings. On the one hand, it is difficult to accurately judge the appropriate interlayer interval time in different welding scenarios based on experience alone, resulting in unstable welding effects; on the other hand, the difference in experience between different welders will also lead to uneven welding quality, further increasing the instability of welding strength. Especially in the welding of engineering vehicle frames, due to the multi-linear welding requirements, each demand scenario needs to configure the interval length of multi-layer welding based on experience, which not only increases the complexity of the welding process, but also makes the prediction of welding results more difficult. This experience-based welding control method often makes it difficult to accurately predict the specific effect after welding, thus leaving potential risks to the overall strength of the engineering vehicle. Summary of the invention

[0003] The present invention aims to solve the technical problems in the prior art that it is difficult to accurately control the welding interval time between layers during multi-layer welding, resulting in unstable welding strength, high frequency of cold cracks, and difficulty in accurately controlling the welding effect. A welding control method and system for improving the strength of engineering vehicles are provided to solve the problem.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides a welding control method for improving the strength of an engineering vehicle, comprising: traversing the multi-layer welding area of ​​a target engineering vehicle frame for frequency mining to obtain a cold crack high-frequency triggering area whose trigger frequency is greater than or equal to a trigger frequency threshold; using k-layer welding parameters and environmental information as background constraints, retrieving the interlayer temperature concentration value of the same type of multi-layer welding area of ​​the cold crack high-frequency triggering area, k≥1, k is an integer; using the k-layer welding parameters, the environmental information and the interlayer temperature concentration value as background constraints, retrieving the cold crack triggering probability of the same type of multi-layer welding area of ​​the cold crack high-frequency triggering area; when the cold crack triggering probability is less than the trigger probability threshold, when the k-layer weld is completed, and the base material temperature value of the multi-layer weld area is monitored by a temperature sensor, if it is the same as the interlayer temperature concentration value, executing k+1 layer weld welding control.

[0006] In the second aspect, the present invention provides a welding control system for improving the strength of engineering vehicles, including: a regional mining module, which is used to traverse the multi-layer welding area of ​​the target engineering vehicle frame for frequency mining, and obtain a cold crack high-frequency triggering area with a trigger frequency greater than or equal to a trigger frequency threshold; a temperature retrieval module, which is used to retrieve the interlayer temperature concentration value of the same type of multi-layer welding area of ​​the cold crack high-frequency triggering area with k-layer welding parameters and environmental information as background constraints, k≥1, k is an integer; a probability retrieval module, which is used to retrieve the cold crack triggering probability of the same type of multi-layer welding area of ​​the cold crack high-frequency triggering area with the k-layer welding parameters, the environmental information and the interlayer temperature concentration value as background constraints; a welding control module, which is used to execute k+1 layer weld bead welding control when the cold crack trigger probability is less than the trigger probability threshold, when the k-layer weld is completed, and the base material temperature value of the multi-layer weld area is monitored by a temperature sensor, if it is the same as the interlayer temperature concentration value.

[0007] The beneficial effects of the present invention are as follows: by frequently mining, the high-frequency triggering area of ​​cold cracks is determined, and in combination with the k-layer welding parameters, environmental information and the interlayer temperature concentration value, the cold crack triggering probability of the same type of multi-layer welding area is accurately retrieved, and the next layer of weld is welded when the cold crack triggering probability is low and the base material temperature is consistent with the interlayer temperature concentration value, thereby effectively controlling the welding process, reducing the risk of cold cracks, and improving the strength of engineering vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a welding control method for improving the strength of an engineering vehicle provided by the present invention.

[0009] Figure 2 A structural schematic diagram of a welding control system for improving the strength of an engineering vehicle provided by the present invention.

[0010] Explanation of reference numerals: region mining module 11 , temperature retrieval module 12 , probability retrieval module 13 , welding control module 14 . DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0014] Embodiment 1:

[0015] like Figure 1 As shown, an embodiment of the present invention provides a welding control method for improving the strength of an engineering vehicle, comprising:

[0016] S10: traverse the multi-layer welding area of ​​the target engineering vehicle frame to perform frequent mining, and obtain a cold crack high-frequency triggering area with a triggering frequency greater than or equal to a triggering frequency threshold.

[0017] S20: Using k layers of welding parameters and environmental information as background constraints, searching for interlayer temperature concentration values ​​of multilayer welding areas of the same type as the cold crack high-frequency triggering zone, k≥1, where k is an integer.

[0018] S30: Retrieving the cold crack triggering probability of the same type of multi-layer welding area as the cold crack high-frequency triggering area by taking the k-layer welding parameters, the environmental information and the interlayer temperature concentration value as background constraints.

[0019] S40: When the cold crack trigger probability is less than the trigger probability threshold, when the k-layer weld is completed, and the base material temperature value of the multi-layer weld area is monitored by the temperature sensor, if it is the same as the interlayer temperature concentration value, the k+1-layer weld welding control is executed.

[0020] For example, in a specific multi-layer welding process, the welding is carried out layer by layer. The first layer of welding fills the welding material layer by layer according to the shape of the welding groove; after the first layer of welding is completed, the weld is cleaned, the welding slag and other impurities are removed, and the quality and continuity of the weld are checked using a magnifying glass or crack detection equipment. Then, the intermediate layer welding is carried out according to the established welding parameters to ensure that the width and height of the weld meet the design requirements; the cover layer welding is the last step of the multi-layer welding of the frame, and it is also welded according to the established welding parameters to ensure that the weld surface is flat and smooth. The multi-linear welding requirements of the engineering vehicle frame refer to the existence of multiple linear, or continuous, extended welding requirements or requirements in the frame welding process. These requirements may involve the layout of the weld, welding sequence, welding quality, welding strength and other aspects. Among them, the layout of the weld needs to take into account the overall structure and stress conditions of the frame to ensure that the weld can effectively transfer and disperse stress; the welding sequence is usually determined according to the assembly sequence of the frame and the distribution of the weld, showing a certain linear feature, that is, which parts are welded first and which parts are welded later, with a certain order and logic. The linear requirements for welding quality and strength mean that the weld needs to have sufficient strength and toughness to resist various forms of deformation and damage. At the same time, the surface quality of the weld also needs to meet certain standards, such as no defects such as pores, slag inclusions, and cracks. In summary, the multi-linear welding requirements in engineering frame welding refer to the linear characteristics and requirements of weld layout, welding sequence, welding quality, and strength. These requirements have an important impact on the selection of welding process, adjustment of welding parameters, control of welding deformation, and detection of welding quality. Therefore, these multi-linear welding requirements need to be fully considered during the frame welding process to ensure the welding quality and overall performance of the frame.

[0021] Therefore, in order to effectively control the welding quality, interlayer temperature control is considered. Specifically, in this scheme, it is necessary to have a comprehensive understanding of the target engineering frame, including the specific location of its multi-layer welding area, welding process, material properties, etc. Collect relevant data of the multi-layer welding area of ​​the frame, including welding records, weld quality inspection reports, stress analysis results, etc., which provide preliminary information about the welding process, weld quality, and frame stress conditions. Use data analysis tools and methods to conduct a preliminary analysis of the collected data. The purpose of this step is to find out areas that may have cold crack risks, that is, those areas with poor welding quality, stress concentration, or poor material properties. Fuzzy screening is performed based on the results of the preliminary analysis. This step does not involve precise calculations or measurements, but rather screens out areas that may have high-frequency cold crack triggering risks based on experience and expertise. These areas may be a large range or may contain multiple specific welds or components. The high-frequency triggering area is mined based on fuzzy screening. This step requires more precise data and methods, such as the use of non-destructive testing technologies such as ultrasonic testing and X-ray testing to conduct detailed inspections of the screened areas. At the same time, it is also necessary to collect stress distribution data of the frame during actual use to understand which areas are more susceptible to stress. The cold crack trigger frequency of each area is calculated by mining the data, which usually involves comprehensive consideration of multiple factors such as weld quality, stress distribution, and material properties. According to industry standards and experience, a trigger frequency threshold is set, and then the calculated trigger frequency is compared with the threshold to screen out those areas with a trigger frequency greater than or equal to the threshold as cold crack high-frequency trigger areas. In summary, the process of traversing the multi-layer weld area of ​​the target engineering frame for frequent mining to identify the cold crack high-frequency trigger area is a process from fuzzy sorting to precise identification. Through this process, potential problems in the frame welding process can be more effectively identified and solved, and the quality and reliability of the frame can be improved.

[0022] Optionally, the k-layer welding parameters may include welding current, voltage, welding speed, electrode type, preheating temperature, interlayer time, etc., which together determine the quality and characteristics of the k-layer welding. Environmental information may include room temperature, humidity, wind speed, etc. during welding, which may have a certain impact on the welding process. The process of retrieving the interlayer temperature concentration value of the same type of multilayer welding area in the cold crack high frequency triggering area using the k-layer welding parameters and environmental information as background constraints can be understood as a data retrieval and analysis process based on specific conditions. After clarifying the background constraints, the interlayer temperature concentration value of the same type of multilayer welding area in the cold crack high frequency triggering area is retrieved. The cold crack high frequency triggering area has been identified through data mining and is an area with a high probability of cold crack occurrence. The same type of multilayer welding area is an area similar to the cold crack high frequency triggering area in terms of welding type, material, structure, etc., and its interlayer temperature concentration value may have reference value. The interlayer temperature concentration value refers to the distribution of interlayer temperature, that is, the temperature between two adjacent layers of welds in the same type of multilayer welding area, especially those temperature values ​​with high frequency of occurrence. Based on the above background constraints and retrieval objectives, a retrieval strategy is constructed, that is, a database containing a large number of welding data of multi-layer welding areas of the same type is selected. These data should contain information such as welding parameters, environmental information, and interlayer temperature; retrieval conditions are set according to the background constraints, such as the range of welding parameters, the range of environmental information, etc., to ensure that the retrieved data are similar to the welding environment and parameters of the high-frequency triggering zone of cold cracks; appropriate data analysis methods are selected, such as statistical analysis, cluster analysis, etc., to extract the interlayer temperature concentration value from the retrieved data. Subsequently, the retrieval operation is performed according to the constructed retrieval strategy, and the interlayer temperature concentration value is extracted from the retrieved data. The retrieved data is preprocessed by cleaning and sorting to ensure the accuracy and consistency of the data. The selected data analysis method is applied to analyze the preprocessed data to extract the interlayer temperature concentration value, which may include calculating the mean, median, mode and other statistical quantities of the temperature, or finding the concentrated area of ​​the temperature distribution through cluster analysis and other methods. In summary, the process of retrieving the interlayer temperature concentration value of the same type of multilayer welding area in the high-frequency triggering zone of cold cracks based on the k-layer welding parameters and environmental information as the background constraints can more effectively utilize the existing data to provide strong support for optimizing the welding process and reducing the probability of cold cracks.

[0023] Furthermore, the focus is on the identified cold crack high-frequency triggering areas. These areas show high cold crack sensitivity due to the specific parameter combination, environmental conditions and interlayer temperature characteristics in the welding process. In order to more accurately quantify this risk, further analysis steps are taken. First, the background constraints of the analysis are clarified, namely the k-layer welding parameters, environmental information and interlayer temperature concentration values. These constraints constitute the benchmark for retrieval and analysis, ensuring that the data set has a high degree of similarity and comparability with the cold crack high-frequency triggering area. Using advanced database retrieval technology, multi-layer welding areas of the same type as the cold crack high-frequency triggering area are searched in the huge welding data set. These same-type areas are similar to the high-frequency triggering area in terms of welding process, material use, structural design, etc., so their cold crack triggering conditions have direct reference significance for the high-frequency triggering area. Among the retrieved multi-layer welding areas of the same type, data subsets that meet the background constraints are further screened. These data subsets are not only consistent with the high-frequency triggering area in welding parameters, environmental conditions and interlayer temperature, but also contain rich cold crack triggering records. Then, the data subsets were analyzed in depth using advanced statistical analysis and data mining techniques. The complex relationship between cold crack triggering and welding parameters, environmental conditions and interlayer temperature was gradually revealed by calculating the frequency of cold crack triggering, building a prediction model and conducting sensitivity analysis. Finally, the cold crack triggering probability of the same type of multilayer welding area in the cold crack high-frequency triggering area was obtained. This probability value not only reflects the possibility of cold crack occurrence under given conditions, but also provides valuable guidance for optimizing welding processes and reducing cold crack risks.

[0024] Finally, a cold crack trigger probability threshold is pre-set as an important basis for judging whether the welding quality meets the standard. This threshold is based on historical data and industry standards and reflects the acceptable risk level of cold cracks under given welding conditions. When the k-layer weld is completed, the next key step is immediately entered, which is to monitor the base material temperature value of the multi-layer weld area. The temperature sensor installed near the welding area can obtain the temperature data of the base material in real time to ensure the accuracy and timeliness of the data. At the same time, reviewing the interlayer temperature concentration value obtained by the above analysis, this concentration value represents the distribution of the interlayer temperature of the same type of multi-layer weld area, which is an important reference for optimizing the welding process and controlling the risk of cold cracks. The monitored base material temperature value is compared with the interlayer temperature concentration value. If the two are the same or very close, it means that the current welding conditions are highly consistent with the previous successful cases, which is conducive to reducing the probability of cold cracks. After confirming that the base material temperature value matches the interlayer temperature concentration value, the welding control of the k+1 layer weld is performed. This step includes selecting appropriate welding parameters, adjusting the welding speed, and ensuring the stability of the welding quality. Precise welding control aims to maintain the continuity and consistency of the welding process, thereby further improving the overall quality of the multi-layer welded structure. However, if the cold crack triggering probability exceeds the set threshold, even if the base material temperature value matches the interlayer temperature concentration value, the welding process needs to be suspended for more in-depth analysis and investigation. This may include checking the welding equipment, optimizing the welding process parameters, or taking other measures to reduce the risk of cold cracks. In general, real-time monitoring, data analysis, and precise control can ensure the stability and reliability of the welding process, thereby improving the overall performance and safety of the engineering frame.

[0025] In a preferred embodiment, the method also includes: when the cold crack trigger probability is greater than or equal to the trigger probability threshold, initializing the k-layer welding parameters based on the k-layer welding parameter constraint interval to obtain a k-layer welding parameter population; traversing the k-layer welding parameter population for analysis to obtain a cold crack trigger probability set; when the cold crack trigger probability sets are all greater than or equal to the trigger probability threshold, performing optimization on the k-layer welding parameters to generate k-layer target welding parameters with a cold crack trigger probability less than the trigger probability threshold, and replacing the k-layer welding parameters.

[0026] Specifically, when the cold crack trigger probability is greater than or equal to the trigger probability threshold, it is considered that the current welding parameter combination may not be ideal and needs to be adjusted. Next, the k-layer welding parameters are initialized based on the constraint interval of the k-layer welding parameters. This constraint interval is determined based on factors such as welding process requirements, material properties, and equipment capabilities, ensuring the rationality and feasibility of the welding parameters. A k-layer welding parameter population containing multiple possible welding parameter combinations is generated through initialization. Then, this k-layer welding parameter population is traversed and each parameter combination is analyzed in detail. A cold crack trigger probability set is obtained by simulating the welding process, calculating the cold crack trigger probability, and other steps. This set reflects the possibility of cold crack occurrence under different welding parameter combinations. However, if all values ​​in the cold crack trigger probability set are greater than or equal to the trigger probability threshold, it means that there is no suitable parameter combination in the current k-layer welding parameter population that can meet the requirements. In this case, it is necessary to perform an optimization operation on the k-layer welding parameters. The optimization operation is an iterative process that aims to reduce the cold crack trigger probability by continuously adjusting the welding parameters. Optimization methods such as genetic algorithms and particle swarm algorithms are used to search for the optimal solution within the constraint interval of the k-layer welding parameters. After multiple iterations and screening, a set of k-layer target welding parameters with a cold crack trigger probability less than the trigger probability threshold was finally generated. Finally, this set of target welding parameters replaced the original k-layer welding parameters to ensure that the subsequent welding process can meet the quality requirements. This series of steps not only reduces the probability of cold cracks, but also improves the stability and reliability of the welding process. In general, this process reflects the fine management of welding parameter optimization and strict control of cold crack risks. Real-time monitoring, data analysis and parameter adjustment can ensure the stability and reliability of the welding process, thereby improving the overall quality and safety of multi-layer welded structures.

[0027] In a preferred embodiment, the multi-layer welding area of ​​the target engineering vehicle frame is traversed for frequency mining to obtain a cold crack high-frequency triggering area with a trigger frequency greater than or equal to a trigger frequency threshold, including: according to the target engineering vehicle frame model, a plurality of cold crack detection logs are retrieved, wherein any one of the plurality of cold crack detection logs includes a multi-layer welding area number set; based on the multi-layer welding area number set, the plurality of cold crack detection logs are subjected to number frequency statistics to obtain a first area number triggering frequency up to an Nth area number triggering frequency; based on the first area number triggering frequency up to the Nth area number triggering frequency, the multi-layer welding area number with a trigger frequency greater than or equal to the trigger frequency threshold is extracted, and added into the cold crack high-frequency triggering area.

[0028] Optionally, according to the model of the target engineering vehicle frame, multiple cold crack detection logs related to the model are retrieved from the database. These logs record in detail the cold crack information detected during the welding process of the vehicle frame at different times and different batches. Each cold crack detection log contains a set of multi-layer welding area numbers, which correspond to specific welding areas on the frame. Next, the number frequency statistics of these cold crack detection logs are performed. Specifically, the number of times each multi-layer welding area number appears in multiple logs is counted, that is, the cold crack trigger frequency of the area. In this way, the complete statistical results from the trigger frequency of the first area number to the trigger frequency of the Nth area number are obtained. After obtaining the cold crack trigger frequency of each area, a trigger frequency threshold is set as the basis for judging whether an area is a high-frequency trigger area of ​​cold cracks. This threshold is determined based on historical data, industry standards, and our tolerance for cold crack risks. Then, according to the trigger frequency threshold, the multi-layer welding area numbers whose trigger frequency is greater than or equal to the threshold are extracted from the statistical results. The areas corresponding to these numbers are high-frequency trigger areas of cold cracks. These areas have a high risk of cold cracks during the welding process of the frame, so they need special attention. Finally, the numbers of these high-frequency triggering areas are added to a special list, namely the cold crack high-frequency triggering area list. This list will become an important basis for subsequent welding process optimization, quality control improvement and risk warning. Through this series of steps, the high-frequency triggering area of ​​cold cracks in the multi-layer welded structure of the target engineering frame was successfully identified. This process not only improves the ability to identify cold crack risks, but also provides strong data support for quality improvement work.

[0029] In a preferred embodiment, taking the k-layer welding parameters and environmental information as background constraints, the interlayer temperature concentration value of the same type of multilayer welding area as the cold crack high-frequency triggering area is retrieved, including: constructing a first background constraint function based on the k-layer welding parameters and the environmental information, and constructing a foreground constraint function according to the cold crack high-frequency triggering area; retrieving the first multilayer welding sample area that satisfies both the first background constraint function and the foreground constraint function, and counting the set of interlayer temperature record values ​​of the first multilayer welding sample area; performing concentration value evaluation on the set of interlayer temperature record values ​​to obtain the interlayer temperature concentration value.

[0030] Furthermore, the first background constraint function is constructed based on the k-layer welding parameters and environmental information. This function comprehensively considers the key parameters in the welding process (such as current, voltage, welding speed, etc.) and external environmental factors (such as temperature, humidity, wind speed, etc.), which together constitute important background conditions that affect welding quality and the probability of cold crack occurrence. Assuming that the k-layer welding parameters include welding current I, welding voltage V, welding speed S, etc., and the environmental information includes temperature T, humidity H, etc., the first background constraint function can be expressed as: f1(I, V, S, T, H) = constraint conditions; where the constraint conditions may include the reasonable range of welding parameters, the acceptable range of environmental information, etc. Then, the foreground constraint function is constructed based on the identified cold crack high-frequency triggering area. This function focuses on those welding areas where cold cracks frequently appear in historical data, which have similar structural characteristics, material composition and welding requirements. Assuming that the cold crack high-frequency triggering area is numbered A, the foreground constraint function can be expressed as: f2(A) = similar characteristics; where similar characteristics may include similarities in welding materials, welding structures, welding processes, etc. By constructing the foreground constraint function, the multilayer welding sample areas of the same type as the high-frequency triggering area can be screened out to further analyze their interlayer temperature characteristics. Then, the first multilayer welding sample areas that meet both the first background constraint function and the foreground constraint function are retrieved. These sample areas are not only similar to the high-frequency triggering area in welding parameters and environmental conditions, but also comparable in structural characteristics, material composition, etc. The interlayer temperature record value set of these sample areas can be obtained by searching. After obtaining the interlayer temperature record value set, a centralized value evaluation is performed on it. Centralized value evaluation is a statistical analysis method used to determine the most typical or common value in a set of data. In this process, the mean, median or other appropriate statistics of the interlayer temperature record values ​​are calculated as the interlayer temperature centralized value. This centralized value reflects the general level of interlayer temperature of the same type of multilayer welding area under given welding parameters and environmental conditions. Finally, the interlayer temperature centralized value of the same type of multilayer welding area in the high-frequency triggering area of ​​cold crack is obtained. This value not only provides important information about the interlayer temperature characteristics, but also provides strong data support for subsequent welding parameter adjustment and optimization of welding process. Through this series of steps, the welding process can be controlled more accurately, the probability of cold cracks can be reduced, and the overall quality and safety of multi-layer welded structures can be improved.

[0031] In a preferred embodiment, based on the k-layer welding parameters and the environmental information, a first background constraint function is constructed, and according to the cold crack high-frequency triggering zone, a foreground constraint function is constructed, including: the first background constraint function construction step includes: constructing a statistical sample k-layer welding parameter and a k-layer welding deviation parameter of the k-layer welding parameter, and calculating a first calculation formula for the proportion of the number of attributes whose k-layer welding deviation parameter is greater than or equal to the welding parameter deviation threshold of the corresponding attribute; constructing a statistical sample environmental information and an environmental deviation parameter of the environmental information, and calculating a second calculation formula for the proportion of the number of attributes whose environmental deviation parameter is greater than or equal to the environmental parameter deviation threshold of the corresponding attribute; when the output value of the first calculation formula is less than the first output threshold, and the output value of the second calculation formula is less than the second When the output threshold is reached, the first background constraint function is deemed to be satisfied; the foreground constraint function construction step includes: obtaining the joint structure, parent material structure, parent material and the first k-layer weld structure of the cold crack high-frequency triggering zone; respectively constructing a third calculation formula for statistically analyzing the structural similarity between the sample joint structure and the joint structure, a fourth calculation formula for statistically analyzing the structural similarity between the sample parent material structure and the parent material structure, a fifth calculation formula for statistically analyzing the material intersection ratio between the sample parent material and the parent material, and a sixth calculation formula for statistically analyzing the structural similarity between the first k-layer weld structure of the sample and the first k-layer weld structure; when the third calculation formula, the fourth calculation formula, the fifth calculation formula until the sixth calculation formula are respectively greater than the corresponding output thresholds, the foreground constraint function is deemed to be satisfied.

[0032] Preferably, in the welding process of the multi-layer welding structure, in order to optimize the welding process and reduce the probability of cold cracks, two key constraint functions need to be constructed: a first background constraint function and a foreground constraint function. These two functions will screen out multi-layer welding sample areas with similar characteristics to the high-frequency triggering area of ​​cold cracks under given welding parameters and environmental conditions. First, a batch of statistical samples are collected, and these samples contain k-layer welding parameters of the multi-layer welding structure. For a given k-layer welding parameter, the deviation of each welding parameter in the statistical sample from the given parameter is calculated to obtain the k-layer welding deviation parameter. For each welding parameter, a welding parameter deviation threshold is set. The proportion of the number of attributes greater than or equal to the threshold in the k-layer welding deviation parameter is calculated, and the first calculation formula is used. Similarly, the deviation of each environmental information in the statistical sample from the given environmental information is calculated to obtain the environmental deviation parameter. For each environmental information, an environmental parameter deviation threshold is set, and the proportion of the number of attributes greater than or equal to the threshold in the environmental deviation parameter is calculated, and the second calculation formula is used. If the output value of the first calculation formula is less than the first output threshold, and the output value of the second calculation formula is less than the second output threshold, it is considered that the statistical sample satisfies the first background constraint function. As for the foreground constraint function, key information such as joint structure, parent material structure, parent material and the first k-layer weld structure is obtained from the cold crack high-frequency triggering zone. For the joint structure, the third calculation formula is constructed to calculate the structural similarity between the statistical sample joint structure and the given joint structure; for the parent material structure, the fourth calculation formula is constructed to calculate the structural similarity between the statistical sample parent material structure and the given parent material structure; for the parent material, the fifth calculation formula is constructed to calculate the material intersection ratio between the statistical sample parent material and the given parent material; for the first k-layer weld structure, the sixth calculation formula is constructed to calculate the structural similarity between the first k-layer weld structure of the statistical sample and the given first k-layer weld structure. If the output values ​​of the third calculation formula, the fourth calculation formula, the fifth calculation formula and the sixth calculation formula are respectively greater than the corresponding output thresholds, it is considered that the statistical sample meets the foreground constraint function. Through this series of steps, the first background constraint function and the foreground constraint function are successfully constructed. These two functions will screen out the multi-layer weld sample area with similar characteristics to the cold crack high-frequency triggering zone under given welding parameters and environmental conditions, providing strong data support for subsequent research and analysis.

[0033] Furthermore, the first calculation formula for calculating the proportion of the number of attributes whose k-layer welding deviation parameters are greater than or equal to the welding parameter deviation threshold of the corresponding attribute may be: in, is the value of the i-th welding parameter in the statistical sample, is the value of the given i-th welding parameter; is the deviation threshold of the ith welding parameter, and n is the total number of welding parameters. The expression in the brackets indicates whether the deviation of the ith welding parameter is greater than or equal to the deviation threshold. If so, the result is 1 (true), otherwise 0 (false). Finally, add all items with a result of 1 and divide by the total number n to obtain the welding parameter deviation ratio. The second calculation formula can be: in, is the value of the jth environmental information in the statistical sample, is the value of the given j-th environmental information, is the deviation threshold of the jth environmental information, and m is the total number of environmental information. The calculation method of this formula is similar to that of the first calculation formula. The third calculation formula can be: joint structure similarity = number of common features / total number of given joint structure features, which calculates the similarity by comparing the ratio of the number of common features between the statistical sample joint structure and the given joint structure to the total number of given joint structure features. The fourth calculation formula can be: parent material structure similarity = number of common structure features / total number of given parent material structure features, which is similar to the third calculation formula, but is used to compare parent material structures. The fifth calculation formula can be: material intersection ratio = number of common elements between the statistical sample parent material and the given parent material / total number of given parent material elements, which evaluates the material intersection ratio by calculating the ratio of the number of common elements between the statistical sample parent material and the given parent material to the total number of given parent material elements. The sixth calculation formula can be: similarity of the first k-layer weld bead structure = number of common weld bead features / total number of given first k-layer weld bead structure features, which is similar to the third calculation formula, but is used to compare the first k-layer weld bead structure.

[0034] In a preferred embodiment, the k-layer welding parameters, the environmental information and the interlayer temperature concentration value are used as background constraints to retrieve the cold crack trigger probability of the same type of multi-layer welding area as the cold crack high-frequency triggering area, including: constructing a second background constraint function based on the k-layer welding parameters, the environmental information and the interlayer temperature concentration value; retrieving the second multi-layer welding sample area that satisfies both the second background constraint function and the foreground constraint function, and counting the proportion of the number of cold crack trigger samples in the second multi-layer welding sample area, which is set as the cold crack trigger probability.

[0035] Exemplarily, a second background constraint function is constructed based on given k-layer welding parameters (these parameters may include welding current, voltage, welding speed, etc.), environmental information (such as humidity, temperature, wind speed, etc.) and interlayer temperature concentration value (i.e., the temperature distribution between layers during welding). This function is a mathematical model that evaluates whether a multilayer welding area meets specific background conditions based on the input parameter values. The second background constraint function is used in combination with the defined foreground constraint function to retrieve the multilayer welding sample database. The goal is to find those sample areas that meet both constraint functions at the same time. These areas are called second multilayer welding sample areas. After determining these second multilayer welding sample areas, the number of samples triggered by cold cracks in these areas is counted. This is done by checking the historical records or experimental results of each sample area. All samples that trigger cold cracks are recorded and their proportion in the entire second multilayer welding sample area is calculated. This proportion, that is, the ratio of the number of cold crack triggering samples to the total number of second multilayer welding sample areas, is set as the cold crack triggering probability. This probability value provides a quantitative indicator for evaluating the possibility of triggering cold cracks in a multilayer welding area under given background constraints and foreground constraints. In general, this process combines background constraints and foreground constraints, and predicts the high-frequency triggering area of ​​cold cracks in the multi-layer welding area by building a mathematical model, retrieving a sample database, and performing statistical analysis, thereby improving the accuracy and reliability of the prediction.

[0036] In a preferred embodiment, based on the k-layer welding parameters, the environmental information and the interlayer temperature concentration value, a second background constraint function is constructed, including: the second background constraint function construction step includes: building a seventh calculation formula for the temperature deviation parameter between the statistical sample interlayer temperature and the interlayer temperature concentration value, and calculating the standard deviation between the temperature deviation parameter and the temperature parameter deviation threshold; when the output value of the first calculation formula is less than the first output threshold, and the output value of the second calculation formula is less than the second output threshold, and the output value of the seventh calculation formula is less than the third output threshold, it is deemed that the second background constraint function is satisfied.

[0037] Furthermore, one of the key steps in constructing the second background constraint function is to build a temperature deviation parameter for the interlayer temperature of the statistical sample and the concentrated value of the interlayer temperature. This parameter is used to quantify the difference between the actual value and the concentrated value of the interlayer temperature, which reflects the stability and consistency of the temperature control during the welding process. The temperature deviation parameter of each sample can be calculated by collecting and analyzing the data of a large number of welding samples. Next, it is necessary to calculate the standard deviation between this temperature deviation parameter and the preset temperature parameter deviation threshold, which is completed by the seventh calculation formula. This standard deviation measures the degree of discreteness between the temperature deviation parameter and the deviation threshold, which provides additional information about the temperature control accuracy. The smaller the standard deviation, the more stable the temperature control during the welding process, and vice versa. With the first calculation formula (for evaluating welding parameters), the second calculation formula (for evaluating environmental information) and the seventh calculation formula (for evaluating the standard deviation between the temperature deviation parameter and the deviation threshold), in order to determine whether a welding sample meets the second background constraint function, it is necessary to check whether the output values ​​of these three formulas are all less than their respective preset output thresholds. Specifically, if the output value of the first calculation formula is less than the first output threshold, it means that the welding parameters are within an acceptable range; if the output value of the second calculation formula is less than the second output threshold, it means that the environmental information also meets the specific requirements; if the output value of the seventh calculation formula is less than the third output threshold, it indicates that the standard deviation between the temperature deviation parameter and the deviation threshold is small, that is, the temperature control is relatively stable. Only when these three conditions are met at the same time can the welding sample be considered to meet the second background constraint function. Such a screening process ensures that only those welding samples that meet strict conditions are used for further analysis and research, thereby improving the accuracy of the prediction of the cold crack triggering probability.

[0038] In a preferred embodiment, when the cold crack trigger probability set is greater than or equal to the trigger probability threshold, the k-layer welding parameters are optimized to generate k-layer target welding parameters whose cold crack trigger probability is less than the trigger probability threshold, and the k-layer welding parameters are replaced, including: based on the cold crack trigger probability set from small to large, the k-layer welding parameter population is sorted to obtain the first k-layer welding parameter individual, the second k-layer welding parameter individual and the third k-layer welding parameter individual; taking the welding parameter dimension as the multidimensional coordinate element, calculating the mean of the same-dimensional coordinate elements of the first k-layer welding parameter individual, the second k-layer welding parameter individual and the third k-layer welding parameter individual, and obtaining the target migration point; taking the target migration point as the migration target, the k-layer welding parameter population is migrated to a close position to obtain the updated k-layer welding parameter; when the cold crack trigger probability of the updated k-layer welding parameter is less than the trigger probability threshold, the updated k-layer welding parameter is set to the k-layer target welding parameter; when the cold crack trigger probability of the updated k-layer welding parameter is greater than or equal to the trigger probability threshold, an optimization cycle is performed.

[0039] Specifically, the k-layer welding parameter population is sorted according to the existing cold crack trigger probability set. This sorting process is based on the order of cold crack trigger probability from small to large, so that three representative k-layer welding parameter individuals are selected: the first k-layer welding parameter individual (lowest trigger probability), the second k-layer welding parameter individual (medium trigger probability) and the third k-layer welding parameter individual (high trigger probability). Next, these three k-layer welding parameter individuals are used to calculate the target migration point. Specifically, the dimensions of the welding parameters (such as current, voltage, welding speed, etc.) are used as elements of the multidimensional coordinates, and the mean values ​​of the coordinate elements of the three individuals in the same dimension are calculated respectively. This mean point is the target point to be migrated, that is, the target migration point. After the target migration point is obtained, the k-layer welding parameter population is migrated close to the position. This means that the parameter values ​​of each individual in the population will be adjusted so that they gradually approach the target migration point. This process involves multiple iterative steps until a new set of k-layer welding parameters is obtained, that is, the updated k-layer welding parameters. Then, the cold crack trigger probability of this set of updated k-layer welding parameters is evaluated. If the trigger probability is less than the trigger probability threshold, then the target welding parameters that meet the requirements have been found and can be replaced with the original k-layer welding parameters. If the trigger probability is still greater than or equal to the trigger probability threshold, then it is necessary to continue the optimization cycle and repeat the above steps of sorting, calculating the target migration point, moving the position close to the migration, and evaluating the trigger probability until the k-layer target welding parameters that meet the requirements are found. In general, this process is an iterative optimization process, which continuously adjusts the value of the k-layer welding parameters and evaluates the corresponding cold crack trigger probability, and finally finds a set of k-layer target welding parameters that can significantly reduce the cold crack trigger probability.

[0040] A welding control method for improving the strength of an engineering vehicle provided by an embodiment of the present invention has at least the following technical effects:

[0041] 1. By traversing the multi-layer welding area of ​​the target engineering frame, the frequency mining technology is used to accurately identify the high-frequency trigger area of ​​cold cracks with a trigger frequency greater than or equal to the trigger frequency threshold, which not only improves the recognition accuracy of cold crack risks, but also provides a clear target area for subsequent welding parameter optimization and control. At the same time, combined with the cold crack detection log and number frequency statistics, the accuracy of positioning is further enhanced, so that preventive measures can be implemented more targeted.

[0042] 2. When searching for the cold crack triggering probability of the same type of multi-layer weld area in the cold crack high-frequency triggering area, multi-dimensional background constraints are used, including k-layer welding parameters, environmental information, and interlayer temperature concentration values. The construction of this multi-dimensional background constraint makes the retrieval results more in line with the actual situation and improves the accuracy and reliability of the cold crack triggering probability prediction. By constructing the first background constraint function and the second background constraint function, combined with the foreground constraint function, the accurate screening of the multi-layer weld sample area is achieved, providing strong support for the accurate evaluation of the cold crack triggering probability.

[0043] 3. When the cold crack trigger probability is greater than or equal to the trigger probability threshold, the k-layer welding parameters are optimized using an intelligent optimization algorithm to generate k-layer target welding parameters with a cold crack trigger probability less than the trigger probability threshold. This not only improves the optimization efficiency of the welding parameters, but also makes the welding process more stable and controllable, effectively reducing the risk of cold crack triggering. Intelligent adjustment and optimization of welding parameters are achieved by constructing a k-layer welding parameter population and sorting it, calculating the target migration point, and migrating the position close to it. At the same time, the optimization process is executed cyclically to ensure that the k-layer target welding parameters finally obtained have the optimal cold crack trigger probability performance.

[0044] Embodiment 2:

[0045] like Figure 2 As shown, based on the same inventive concept of a welding control method for improving the strength of an engineering vehicle provided in Embodiment 1, an embodiment of the present invention further provides a welding control system for improving the strength of an engineering vehicle, comprising:

[0046] The area mining module 11 is used to traverse the multi-layer welding area of ​​the target engineering vehicle frame to perform frequent mining and obtain a cold crack high-frequency triggering area with a triggering frequency greater than or equal to a triggering frequency threshold.

[0047] The temperature retrieval module 12 is used to retrieve the interlayer temperature concentration value of the same type of multilayer welding area in the cold crack high-frequency triggering area with k layers of welding parameters and environmental information as background constraints, k≥1, and k is an integer.

[0048] The probability retrieval module 13 is used to retrieve the cold crack triggering probability of the same type of multi-layer welding area as the cold crack high-frequency triggering area, taking the k-layer welding parameters, the environmental information and the interlayer temperature concentration value as background constraints.

[0049] The welding control module 14 is used to execute k+1 layer weld control when the cold crack trigger probability is less than the trigger probability threshold, when the k-layer weld is completed, and the base material temperature value of the multi-layer weld area is monitored by the temperature sensor, if it is the same as the interlayer temperature concentration value.

[0050] Furthermore, the welding control module 14 is also used to perform the following steps:

[0051] When the cold crack trigger probability is greater than or equal to the trigger probability threshold, the k-layer welding parameters are initialized based on the k-layer welding parameter constraint interval to obtain a k-layer welding parameter population; the k-layer welding parameter population is traversed for analysis to obtain a cold crack trigger probability set; when the cold crack trigger probability sets are all greater than or equal to the trigger probability threshold, optimization is performed on the k-layer welding parameters to generate k-layer target welding parameters whose cold crack trigger probability is less than the trigger probability threshold, and the k-layer welding parameters are replaced.

[0052] Furthermore, the region mining module 11 is further configured to perform the following steps:

[0053] According to the target engineering vehicle frame model, multiple cold crack detection logs are retrieved, wherein any one of the multiple cold crack detection logs includes a multi-layer weld area number set; based on the multi-layer weld area number set, the number frequency statistics of the multiple cold crack detection logs are performed to obtain the first area number trigger frequency until the Nth area number trigger frequency; based on the first area number trigger frequency until the Nth area number trigger frequency, the multi-layer weld area number whose trigger frequency is greater than or equal to the trigger frequency threshold is extracted, and added into the cold crack high-frequency trigger area.

[0054] Furthermore, the temperature retrieval module 12 is further configured to perform the following steps:

[0055] Based on the k-layer welding parameters and the environmental information, a first background constraint function is constructed, and according to the cold crack high-frequency triggering area, a foreground constraint function is constructed; a first multi-layer welding sample area that satisfies both the first background constraint function and the foreground constraint function is retrieved, and a set of interlayer temperature record values ​​of the first multi-layer welding sample area is counted; a concentration value evaluation is performed on the set of interlayer temperature record values ​​to obtain the interlayer temperature concentration value.

[0056] Furthermore, the temperature retrieval module 12 is further configured to perform the following steps:

[0057] The first background constraint function construction step includes: constructing a first calculation formula for the k-layer welding parameters of the statistical sample and the k-layer welding deviation parameters of the k-layer welding parameters, and calculating the proportion of the number of attributes whose k-layer welding deviation parameters are greater than or equal to the welding parameter deviation threshold of the corresponding attribute; constructing a second calculation formula for the environmental deviation parameters of the statistical sample environmental information and the environmental deviation parameters of the environmental information, and calculating the proportion of the number of attributes whose environmental deviation parameters are greater than or equal to the environmental parameter deviation threshold of the corresponding attribute; when the output value of the first calculation formula is less than the first output threshold, and the output value of the second calculation formula is less than the second output threshold, it is considered that the first background constraint function is satisfied; the foreground constraint function construction step The steps include: obtaining the joint structure, parent material structure, parent material and the first k-layer weld bead structure of the cold crack high-frequency trigger zone; respectively constructing a third calculation formula for statistically analyzing the structural similarity between the sample joint structure and the joint structure, a fourth calculation formula for statistically analyzing the structural similarity between the sample parent material structure and the parent material structure, a fifth calculation formula for statistically analyzing the material intersection ratio between the sample parent material and the parent material, and a sixth calculation formula for statistically analyzing the structural similarity between the first k-layer weld bead structure of the sample and the first k-layer weld bead structure; when the third calculation formula, the fourth calculation formula, the fifth calculation formula until the sixth calculation formula are respectively greater than the corresponding output thresholds, it is deemed that the prospect constraint function is satisfied.

[0058] Furthermore, the probability retrieval module 13 is also used to perform the following steps:

[0059] Based on the k-layer welding parameters, the environmental information and the interlayer temperature concentration value, a second background constraint function is constructed; a second multi-layer welding sample area that satisfies both the second background constraint function and the foreground constraint function is retrieved, and the proportion of the number of cold crack triggering samples in the second multi-layer welding sample area is counted and set as the cold crack trigger probability.

[0060] Furthermore, the probability retrieval module 13 is also used to perform the following steps:

[0061] The step of constructing the second background constraint function includes: building a temperature deviation parameter between the statistical sample interlayer temperature and the interlayer temperature concentration value, and calculating the seventh calculation formula for the standard deviation of the temperature deviation parameter and the temperature parameter deviation threshold; when the output value of the first calculation formula is less than the first output threshold, and the output value of the second calculation formula is less than the second output threshold, and the output value of the seventh calculation formula is less than the third output threshold, it is considered that the second background constraint function is satisfied.

[0062] Furthermore, the welding control module 14 is also used to perform the following steps:

[0063] Based on the cold crack trigger probability set from small to large, the k-layer welding parameter population is sorted to obtain the first k-layer welding parameter individual, the second k-layer welding parameter individual and the third k-layer welding parameter individual; taking the welding parameter dimension as the multidimensional coordinate element, calculating the mean of the same-dimensional coordinate elements of the first k-layer welding parameter individual, the second k-layer welding parameter individual and the third k-layer welding parameter individual, to obtain the target migration point; taking the target migration point as the migration target, the k-layer welding parameter population is migrated to a close position to obtain the updated k-layer welding parameter; when the cold crack trigger probability of the updated k-layer welding parameter is less than the trigger probability threshold, the updated k-layer welding parameter is set to the k-layer target welding parameter; when the cold crack trigger probability of the updated k-layer welding parameter is greater than or equal to the trigger probability threshold, executing an optimization cycle.

[0064] Through the above-mentioned detailed description of a welding control method for improving the strength of an engineering vehicle, those skilled in the art can clearly understand a welding control system for improving the strength of an engineering vehicle in this embodiment. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0065] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A welding control method for improving the strength of an engineering vehicle, characterized in that: include: Traverse the multi-layer welding area of ​​the target engineering vehicle frame to conduct frequent mining, and obtain the high-frequency triggering area of ​​cold cracks with a triggering frequency greater than or equal to the triggering frequency threshold; Taking k layers of welding parameters and environmental information as background constraints, retrieve the interlayer temperature concentration value of the same type of multilayer welding area in the cold crack high-frequency triggering zone, k≥1, k is an integer; Using the k-layer welding parameters, the environmental information and the interlayer temperature concentration value as background constraints, retrieving the cold crack triggering probability of the same type of multi-layer welding area as the cold crack high-frequency triggering area; When the cold crack trigger probability is less than the trigger probability threshold, when the k-layer weld is completed and the base material temperature value of the multi-layer weld area is monitored by the temperature sensor, if it is the same as the interlayer temperature concentration value, the k+1-layer weld welding control is executed.

2. The method according to claim 1, characterized in that Also includes: When the cold crack trigger probability is greater than or equal to the trigger probability threshold, the k-layer welding parameters are initialized based on the k-layer welding parameter constraint interval to obtain a k-layer welding parameter population; Traversing the k-layer welding parameter population for analysis to obtain a cold crack triggering probability set; When the cold crack trigger probability sets are all greater than or equal to the trigger probability threshold, optimization is performed on the k-layer welding parameters to generate k-layer target welding parameters whose cold crack trigger probability is less than the trigger probability threshold, and the k-layer welding parameters are replaced.

3. The method according to claim 1, characterized in that Traverse the multi-layer welding area of ​​the target engineering frame for frequent mining to obtain the high-frequency triggering area of ​​cold cracks with a trigger frequency greater than or equal to the trigger frequency threshold, including: Retrieving a plurality of cold crack detection logs according to a target engineering vehicle frame model, wherein any one of the plurality of cold crack detection logs includes a multi-layer weld area number set; Based on the multi-layer welding area number set, performing number frequency statistics on the plurality of cold crack detection logs to obtain the first area number trigger frequency until the Nth area number trigger frequency; Based on the trigger frequency of the first region number to the trigger frequency of the Nth region number, the multilayer welding region numbers whose trigger frequency is greater than or equal to the trigger frequency threshold are extracted and added into the cold crack high frequency trigger zone.

4. The method according to claim 1, characterized in that Taking the k-layer welding parameters and environmental information as background constraints, the interlayer temperature concentration value of the same type of multilayer welding area of ​​the cold crack high-frequency triggering zone is retrieved, including: Based on the k-layer welding parameters and the environmental information, a first background constraint function is constructed, and according to the cold crack high-frequency triggering area, a foreground constraint function is constructed; Retrieving a first multi-layer welding sample area that satisfies both the first background constraint function and the foreground constraint function, and counting a set of interlayer temperature record values ​​of the first multi-layer welding sample area; A concentration value evaluation is performed on the interlayer temperature record value set to obtain the interlayer temperature concentration value.

5. The method according to claim 4, characterized in that Based on the k-layer welding parameters and the environmental information, a first background constraint function is constructed, and according to the cold crack high-frequency triggering zone, a foreground constraint function is constructed, including: The first background constraint function construction step comprises: Constructing a first calculation formula for statistically sampling k-layer welding parameters and k-layer welding deviation parameters of the k-layer welding parameters, and calculating the proportion of the number of attributes whose k-layer welding deviation parameters are greater than or equal to the welding parameter deviation threshold of the corresponding attribute; Constructing a second calculation formula for calculating the environmental deviation parameter between the statistical sample environmental information and the environmental information, and calculating the proportion of the number of attributes whose environmental deviation parameter is greater than or equal to the environmental parameter deviation threshold of the corresponding attribute; When the output value of the first calculation formula is less than the first output threshold, and the output value of the second calculation formula is less than the second output threshold, it is considered that the first background constraint function is satisfied; The foreground constraint function construction step comprises: Obtaining the joint structure, parent metal structure, parent metal material and the first k-layer weld bead structure of the cold crack high-frequency triggering zone; A third calculation formula for calculating the structural similarity between the joint structure of the statistical sample and the joint structure, a fourth calculation formula for calculating the structural similarity between the parent material structure of the statistical sample and the parent material structure, a fifth calculation formula for calculating the material intersection ratio between the parent material of the statistical sample and the parent material, and a sixth calculation formula for calculating the structural similarity between the first k layers of the sample weld bead structure and the first k layers of the weld bead structure are respectively constructed; When the third calculation formula, the fourth calculation formula, the fifth calculation formula, and the sixth calculation formula are respectively greater than the corresponding output thresholds, it is considered that the foreground constraint function is satisfied.

6. The method according to claim 4, characterized in that Taking the k-layer welding parameters, the environmental information and the interlayer temperature concentration value as background constraints, retrieving the cold crack triggering probability of the same type of multi-layer welding area of ​​the cold crack high-frequency triggering area, including: Constructing a second background constraint function based on the k-layer welding parameters, the environmental information and the interlayer temperature concentration value; A second multi-layer welding sample area that satisfies both the second background constraint function and the foreground constraint function is retrieved, and a proportion of the number of cold crack triggering samples in the second multi-layer welding sample area is counted, which is set as the cold crack triggering probability.

7. The method according to claim 6, characterized in that Based on the k-layer welding parameters, the environmental information and the interlayer temperature concentration value, a second background constraint function is constructed, including: The second background constraint function construction step comprises: Constructing a seventh calculation formula for calculating the temperature deviation parameter between the statistical sample interlayer temperature and the interlayer temperature concentration value, and calculating the standard deviation between the temperature deviation parameter and the temperature parameter deviation threshold value; When the output value of the first calculation formula is less than the first output threshold, the output value of the second calculation formula is less than the second output threshold, and the output value of the seventh calculation formula is less than the third output threshold, it is considered that the second background constraint function is satisfied.

8. The method according to claim 2, characterized in that When the cold crack trigger probability sets are all greater than or equal to the trigger probability threshold, optimizing the k-layer welding parameters to generate k-layer target welding parameters whose cold crack trigger probability is less than the trigger probability threshold, and replacing the k-layer welding parameters, including: Based on the cold crack trigger probability set from small to large, the k-layer welding parameter population is sorted to obtain a first k-layer welding parameter individual, a second k-layer welding parameter individual and a third k-layer welding parameter individual; Taking the welding parameter dimension as a multidimensional coordinate element, calculating the mean of the same-dimensional coordinate elements of the first k-layer welding parameter individuals, the second k-layer welding parameter individuals and the third k-layer welding parameter individuals, and obtaining the target migration point; Taking the target migration point as the migration target, performing position close migration on the k-layer welding parameter population to obtain updated k-layer welding parameters; When the cold crack triggering probability of the updated k-layer welding parameter is less than the triggering probability threshold, setting the updated k-layer welding parameter to the k-layer target welding parameter; When the cold crack triggering probability of the updated k-layer welding parameters is greater than or equal to the triggering probability threshold, an optimization cycle is executed.

9. A welding control system for improving the strength of an engineering vehicle, characterized in that: A welding control method for improving the strength of an engineering vehicle for implementing any one of claims 1 to 8, comprising: The regional mining module is used to traverse the multi-layer welding area of ​​the target engineering vehicle frame for frequent mining, and obtain the high-frequency triggering area of ​​cold cracks with a trigger frequency greater than or equal to the trigger frequency threshold; A temperature retrieval module is used to retrieve the interlayer temperature concentration value of the same type of multilayer welding area in the cold crack high-frequency triggering area with k layers of welding parameters and environmental information as background constraints, k ≥ 1, k is an integer; A probability retrieval module, used to retrieve the cold crack triggering probability of the same type of multi-layer welding area of ​​the cold crack high-frequency triggering area by taking the k-layer welding parameters, the environmental information and the interlayer temperature concentration value as background constraints; The welding control module is used to execute k+1 layer weld control when the cold crack trigger probability is less than the trigger probability threshold, when the k-layer weld is completed, and the base material temperature value of the multi-layer weld area is monitored by a temperature sensor, if it is the same as the interlayer temperature concentration value.

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