A welding control method and system for improving the strength of an engineering vehicle

Abstract

The present application relates to a kind of welding control method and system for improving the strength of engineering vehicle, it is related to welding control field, traverse multilayer welding area is carried out frequently mining and obtains the cold crack high-frequency trigger area of trigger frequency greater than or equal to trigger frequency threshold value;With k layer welding parameters and environmental information as background constraint, retrieve the interlayer temperature concentration value of the same type multilayer welding area of cold crack high-frequency trigger area;With k layer welding parameters, environmental information and interlayer temperature concentration value as background constraint, retrieve the cold crack trigger probability of the same type multilayer welding area;When probability is less than trigger probability threshold value, when k layer weld weld ends and the base material temperature value of multilayer welding area is monitored by temperature sensor, if same as interlayer temperature concentration value, execute k+1 layer weld weld control, solve the technical problem that multilayer welding process is difficult to accurately control interlayer welding interval, leading to unstable welding strength, the frequency of cold crack is high, and welding effect is difficult to accurately control.

Description

TECHNICAL FIELD

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

[0002] In the welding process of an engineering vehicle frame, multi-layer welding is a crucial process. However, the interval time control between welding layers is a long-standing technical problem. If the interval time is too long, cold cracks are likely to occur in the welding joint, which not only affects the welding quality, but also may adversely affect 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 welded metal may be reduced, which will also damage the welding effect. Currently, the interval time control of multi-layer welding in the welding scene mainly depends on the experience value of welders. Although welders will try to weld the subsequent layer as soon as possible to ensure the strength of the welding under the premise of ensuring the welding quality according to the demand of interlayer temperature, this method still has many shortcomings. On the one hand, it is difficult to accurately determine the appropriate interlayer interval time in different welding scenarios only by experience, which leads to unstable welding effect. On the other hand, the experience difference between different welders will also lead to uneven welding quality, further increasing the instability of the welding strength. Especially in the welding of engineering vehicle frames, since the welding demand is multi-linear, each demand scenario needs to configure the interval time of multi-layer welding according to experience, which not only increases the complexity of the welding process, but also makes it more difficult to predict the welding effect. This experience-dependent welding control method often cannot accurately predict the specific effect after welding, leaving potential risks to the overall strength of the engineering vehicle. SUMMARY

[0003] The present application provides a welding control method and system for improving the strength of an engineering vehicle to solve the technical problems that the interval time between layers cannot be accurately controlled in the multi-layer welding process in the prior art, the welding strength is unstable, the frequency of cold cracks is high, and the welding effect is difficult to accurately control.

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

[0005] In a first aspect, the present application provides a welding control method for improving the strength of an engineering vehicle, comprising: performing frequent mining on a multi-layer welding area of a target engineering vehicle frame to obtain a cold crack high-frequency triggering area with a triggering frequency greater than or equal to a triggering frequency threshold; retrieving an interlayer temperature central value of a same-type multi-layer welding area of the cold crack high-frequency triggering area as background constraints of k-layer welding parameters and environmental information, k being an integer greater than or equal to 1; retrieving a cold crack triggering probability of the same-type multi-layer welding area of the cold crack high-frequency triggering area as background constraints of the k-layer welding parameters, the environmental information, and the interlayer temperature central value; and when the cold crack triggering probability is less than a triggering probability threshold, performing k+1-layer welding control when k-layer welding is completed and a base material temperature value of the multi-layer welding area monitored by a temperature sensor is the same as the interlayer temperature central value.

[0006] In a second aspect, the present application provides a welding control system for improving the strength of an engineering vehicle, comprising: a region mining module configured to perform frequent mining on a multi-layer welding area of a target engineering vehicle frame to obtain a cold crack high-frequency triggering area with a triggering frequency greater than or equal to a triggering frequency threshold; a temperature retrieval module configured to retrieve an interlayer temperature central value of a same-type multi-layer welding area of the cold crack high-frequency triggering area as background constraints of k-layer welding parameters and environmental information, k being an integer greater than or equal to 1; a probability retrieval module configured to retrieve a cold crack triggering probability of the same-type multi-layer welding area of the cold crack high-frequency triggering area as background constraints of the k-layer welding parameters, the environmental information, and the interlayer temperature central value; and a welding control module configured to perform k+1-layer welding control when the cold crack triggering probability is less than a triggering probability threshold, k-layer welding is completed, and a base material temperature value of the multi-layer welding area monitored by a temperature sensor is the same as the interlayer temperature central value.

[0007] The present application has the following beneficial effects: the cold crack high-frequency triggering area is determined through frequent mining, and the cold crack triggering probability of the same-type multi-layer welding area is accurately retrieved in combination with the k-layer welding parameters, the environmental information, and the interlayer temperature central value, so that the next layer of welding is performed when the cold crack triggering probability is low and the base material temperature is consistent with the interlayer temperature central value, thereby effectively controlling the welding process, reducing the risk of cold crack generation, and improving the strength of the engineering vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 FIG. 1 is a flowchart of a welding control method for improving the strength of an engineering vehicle according to the present application.

[0009] Figure 2 FIG. 2 is a structural schematic diagram of a welding control system for improving the strength of an engineering vehicle according to the present application.

[0010] Reference signs: area excavation module 11, temperature retrieval module 12, probability retrieval module 13, welding control module 14. DETAILED DESCRIPTION

[0011] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0012] In the description of the present application, the terms "first", "second" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0013] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope of principles and features disclosed.

[0014] Embodiment one:

[0015] As shown in the figure, the welding control method for improving the strength of the engineering vehicle provided by the embodiments of the present application comprises: Figure 1 S10: traversing the multi-layer welding area of the target engineering vehicle frame to perform frequent excavation, and obtaining a cold crack high-frequency triggering area with a triggering frequency greater than or equal to a triggering frequency threshold.

[0016] S20: taking the k-layer welding parameters and environmental information as background constraints, retrieving the interlayer temperature central value of the same type of multi-layer welding area of the cold crack high-frequency triggering area, k>1, k is an integer.

[0017]

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

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

[0020] Exemplarily, in a specific multi-layer welding process, the first layer welding fills the welding material layer by layer according to the shape of the welding groove; after the first layer welding is completed, the weld is cleaned to remove slag and other impurities, and a magnifying glass or crack detection equipment is used to check the quality and continuity of the weld, and then the intermediate layer welding is performed according to the predetermined 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 is also performed according to the predetermined welding parameters to ensure that the weld surface is smooth and smooth. The multi-line welding requirement of the engineering vehicle frame refers to the existence of multiple lines, or continuous and extended welding requirements or demands in the frame welding process. These requirements may involve the layout of the weld, the welding sequence, the welding quality, the welding strength, and other aspects. Among them, the layout of the weld needs to consider the overall structure and stress condition 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 certain linear characteristics, i.e., which components are welded first, which components are welded next, and there is a certain sequence and logic. The linear requirements of welding quality and strength mean that the weld needs to have sufficient strength and toughness to resist various forms of deformation and damage, and at the same time, the surface quality of the weld also needs to reach a certain standard, such as no pores, slag inclusions, cracks, etc. In summary, the multi-line welding requirement in the engineering frame welding refers to the linear characteristics and requirements of the weld layout, welding sequence, welding quality, and strength, etc. These requirements have an important influence on the selection of welding process, the adjustment of welding parameters, the control of welding deformation, and the detection of welding quality. Therefore, these multi-line welding requirements need to be fully considered in the frame welding process to ensure the welding quality and overall performance of the frame.

[0021] Therefore, to effectively control welding quality, interlayer temperature control is considered. Specifically, in this solution, a comprehensive understanding of the target vehicle frame is first required, including the specific location of its multi-layer weld areas, welding process, and material properties. Relevant data on the multi-layer weld areas of the frame is collected, including welding records, weld quality inspection reports, and stress analysis results. This data provides preliminary information on the welding process, weld quality, and the stress conditions of the frame. Data analysis tools and methods are then used to conduct a preliminary analysis of the collected data. This step aims to identify areas at risk for cold cracking, namely those with poor weld quality, stress concentration, or poor material properties. Based on the results of this preliminary analysis, fuzzy screening is performed. This step does not involve precise calculations or measurements, but rather relies on experience and expertise to screen out areas at risk for high-frequency cold crack triggering. These areas may be large, or they may encompass multiple specific welds or components. Based on this fuzzy screening, high-frequency triggering areas are identified. This step requires more precise data and methods, such as detailed inspection of the screened areas using non-destructive testing techniques such as ultrasonic testing and X-ray testing. 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 a 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 cold crack high-frequency trigger areas 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 resolved, thereby improving the quality and reliability of the frame.

[0022] Optionally, the k-layer welding parameters can include welding current, voltage, welding speed, electrode type, preheating temperature, interpass time, etc., which collectively determine the quality and characteristics of the k-layer welding. The environmental information can include room temperature, humidity, wind speed, etc. during welding, which can have some impact on the welding process. The process of retrieving the interpass temperature central value of the same type of multi-layer welding area in the cold crack high-frequency trigger zone as the background constraint of the k-layer welding parameters and environmental information can be understood as a data retrieval and analysis process based on specific conditions. After the background constraint is defined, the interpass temperature central value of the same type of multi-layer welding area in the cold crack high-frequency trigger zone is retrieved. The cold crack high-frequency trigger zone has been identified through data mining and is a region with a higher probability of cold crack occurrence. The same type of multi-layer welding area is a region similar to the cold crack high-frequency trigger zone in terms of welding type, material, structure, etc., and its interpass temperature central value can have reference value. The interpass temperature central value refers to the distribution of interpass temperature, i.e. the temperature between adjacent two layers of weld, especially those with higher frequency, in the same type of multi-layer welding area. Based on the above background constraints and retrieval targets, a retrieval strategy is constructed, i.e. selecting a database containing a large amount of welding data of the same type of multi-layer welding area, which should include welding parameters, environmental information and interpass temperature, etc. 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 is similar to the welding environment and parameters of the cold crack high-frequency trigger zone. Appropriate data analysis methods, such as statistical analysis, clustering analysis, etc. are selected to extract the interpass temperature central value from the retrieved data. Then, the retrieval operation is performed according to the constructed retrieval strategy, and the interpass temperature central value is extracted from the retrieved data. The retrieved data is preprocessed, such as cleaning and sorting, to ensure the accuracy and consistency of the data. The preprocessed data is analyzed by applying the selected data analysis method to extract the interpass temperature central value, which can include calculating the mean, median, mode, etc. of the temperature, or finding the central region of the temperature distribution through clustering analysis, etc. In summary, the process of retrieving the interpass temperature central value of the same type of multi-layer welding area in the cold crack high-frequency trigger zone as the background constraint of the k-layer welding parameters and environmental information can more effectively utilize existing data to provide strong support for optimizing welding process and reducing the probability of cold crack occurrence.

[0023] Further, the focus is on the identified cold crack high frequency trigger areas. These areas show higher cold crack sensitivity due to specific parameter combinations, environmental conditions and interpass temperature characteristics during the welding process. In order to quantify this risk more accurately, further analysis steps are taken. First, the background constraints of the analysis are defined, i.e. the k-layer welding parameters, environmental information and interpass temperature concentration values, which form the basis for the search and analysis, ensuring that the data set has a high degree of similarity and comparability with the cold crack high frequency trigger area. Using advanced database search techniques, the multi-layer welding area of the same type as the cold crack high frequency trigger area is searched in the vast welding data set. These same type areas are similar to the high frequency trigger area in terms of welding process, material use, structure design, etc., so their cold crack trigger conditions have direct reference significance for the high frequency trigger area. The data subsets that meet the background constraints are further selected from the searched same type multi-layer welding areas. These data subsets not only maintain consistency with the high frequency trigger area in terms of welding parameters, environmental conditions and interpass temperature, but also contain rich cold crack trigger records. Then advanced statistical analysis and data mining techniques are used to conduct in-depth analysis of these data subsets. Through calculating the frequency of cold crack trigger, building prediction models and conducting sensitivity analysis, the complex relationship between cold crack trigger and welding parameters, environmental conditions and interpass temperature is gradually revealed. Finally, the cold crack trigger probability of the same type multi-layer welding area of the cold crack high frequency trigger area is obtained. This probability value not only reflects the possibility of cold crack occurrence under given conditions, but also provides valuable guidance for optimizing the welding process and reducing the cold crack risk.

[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, reflecting the acceptable risk level of cold crack occurrence under given welding conditions. When the k-layer welding is completed, the next critical step is to monitor the base metal temperature value in the multi-layer welding area. By installing temperature sensors near the welding area, real-time temperature data of the base metal can be obtained, ensuring the accuracy and timeliness of the data. At the same time, review the aforementioned interlayer temperature central value obtained by analysis. This central value represents the distribution of interlayer temperature in the same type of multi-layer welding area and is an important reference for optimizing welding process and controlling cold crack risk. Compare the monitored base metal temperature value with the interlayer temperature central value. If they are the same or very close, it means that the current welding conditions are highly consistent with previous successful cases, which is conducive to reducing the probability of cold crack occurrence. After confirming that the base metal temperature value matches the interlayer temperature central value, perform k+1 layer welding control. This step includes selecting appropriate welding parameters, adjusting welding speed, and ensuring the stability of welding quality. Through precise welding control, the continuity and consistency of the welding process are maintained, further improving the overall quality of the multi-layer welding structure. However, if the cold crack trigger probability exceeds the set threshold, even if the base metal temperature value matches the interlayer temperature central value, the welding process needs to be paused for further analysis and investigation. This may include checking welding equipment, optimizing welding process parameters, or taking other measures to reduce the risk of cold crack. In summary, through real-time monitoring, data analysis, and precise control, the stability and reliability of the welding process can be ensured, thereby improving the overall performance and safety of the engineering vehicle frame.

[0025] In a preferred embodiment, the method further comprises: 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 set is 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 according to welding process requirements, material properties, and equipment capabilities, etc., ensuring the reasonableness and feasibility of the welding parameters. Through initialization, a k-layer welding parameter population containing multiple possible welding parameter combinations is generated. Then, the k-layer welding parameter population is traversed, and each parameter combination is analyzed in detail. Through steps such as simulating the welding process and calculating the cold crack trigger probability, a set of cold crack trigger probabilities is obtained, which reflects the possibility of cold crack occurrence under different welding parameter combinations. However, if all values in the set of cold crack trigger probabilities 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 to meet the requirements. In this case, optimization operation needs to be performed on the k-layer welding parameters. Optimization operation is an iterative process aimed at reducing the cold crack trigger probability by continuously adjusting the welding parameters. Optimization methods such as genetic algorithm and particle swarm algorithm 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 cold crack trigger probability less than the trigger probability threshold is finally generated. Finally, the original k-layer welding parameters are replaced with the target welding parameters to ensure that the subsequent welding process meets the quality requirements. Through this series of steps, not only the probability of cold crack occurrence is reduced, but also the stability and reliability of the welding process are improved. Overall, this process embodies the meticulous management of welding parameter optimization and the strict control of cold crack risk. Through real-time monitoring, data analysis, and parameter adjustment, the stability and reliability of the welding process can be ensured, thereby improving the overall quality and safety of multi-layer welded structures.

[0027] In a preferred embodiment, the cold crack high-frequency trigger area with a trigger frequency greater than or equal to the trigger frequency threshold is obtained by traversing the multi-layer welding area of the target engineering vehicle frame for frequent mining, including: retrieving a plurality of cold crack detection logs according to the target engineering vehicle frame model, wherein any one of the plurality of cold crack detection logs includes a set of multi-layer welding area numbers; based on the set of multi-layer welding area numbers, performing number frequency statistics on the plurality of cold crack detection logs to obtain a first area number trigger frequency to an Nth area number trigger frequency; based on the first area number trigger frequency to the Nth area number trigger frequency, extracting a multi-layer welding area number with a trigger frequency greater than or equal to the trigger frequency threshold and adding it to the cold crack high-frequency trigger area.

[0028] Optionally, according to the model of the target engineering vehicle frame, a plurality of cold crack detection logs related to the model are retrieved from the database, which record the cold crack information detected in the welding process of vehicle frames of different batches at different times. Each cold crack detection log contains a set of multi-layer welding area numbers, which correspond to specific welding areas on the vehicle frame. Next, the frequency of the numbers in the cold crack detection logs is counted. Specifically, the number of times each multi-layer welding area number appears in the logs, i.e., the cold crack trigger frequency of the area, is counted. In this way, complete statistical results from the first area number trigger frequency to the Nth area number trigger frequency are obtained. After obtaining the cold crack trigger frequency of each area, a trigger frequency threshold is set as a basis for determining whether an area is a cold crack high-frequency trigger area. This threshold is determined based on historical data, industry standards, and our tolerance for cold crack risk, etc. Then, according to the trigger frequency threshold, the multi-layer welding area numbers with trigger frequencies greater than or equal to the threshold are extracted from the statistical results. The areas corresponding to these numbers are the cold crack high-frequency trigger areas, which have a higher risk of cold crack occurrence during the welding process of the vehicle frame and therefore require special attention. Finally, the numbers of these high-frequency trigger areas are added to a special list, i.e., the cold crack high-frequency trigger 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 cold crack high-frequency trigger areas in the multi-layer welding structure of the target engineering vehicle frame are successfully identified, which not only improves the identification of cold crack risk, but also provides strong data support for quality improvement work.

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

[0030] Further, a first background constraint function is constructed based on the k-layer welding parameters and environmental information. This function considers 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, a foreground constraint function is constructed based on the identified cold crack high-frequency trigger area. This function focuses on welding areas that frequently appear with cold cracks in historical data, which have similar structural characteristics, material composition, and welding requirements. Assuming that the cold crack high-frequency trigger area is numbered as A, the foreground constraint function can be expressed as: f2(A) = similar characteristics; where the similar characteristics may include similarities in welding materials, welding structure, welding process, etc. By constructing the foreground constraint function, sample areas of multi-layer welding of the same type as the high-frequency trigger area can be screened out for further analysis of their interlayer temperature characteristics. Then, the first multi-layer welding sample area that satisfies both the first background constraint function and the foreground constraint function is retrieved. These sample areas not only have similar welding parameters and environmental conditions to the high-frequency trigger area, but also have comparable structural characteristics and material composition. Through retrieval, the set of interlayer temperature record values of these sample areas can be obtained. After obtaining the set of interlayer temperature record values, centralized value evaluation is performed. Centralized value evaluation is a statistical analysis method used to determine the most typical or most common value in a set of data. In this process, the mean, median, or other appropriate statistical quantity of the interlayer temperature record values is calculated as the interlayer temperature centralized value. This centralized value reflects the general level of interlayer temperature in the same type of multi-layer welding area under given welding parameters and environmental conditions. Finally, the interlayer temperature centralized value of the same type of multi-layer welding area of the cold crack high-frequency trigger area 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 welding process optimization. Through this series of steps, the welding process can be more accurately controlled, the probability of cold crack occurrence can be reduced, and the overall quality and safety of multi-layer welding 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 a foreground constraint function is constructed according to the cold crack high-frequency trigger area, including: the first background constraint function construction step includes: building a k-layer welding parameter and a k-layer welding deviation parameter of the k-layer welding parameter of the statistical sample, and calculating a first calculation formula of the attribute number proportion of the k-layer welding deviation parameter greater than or equal to the welding parameter deviation threshold of the corresponding attribute; build a statistical sample environmental information and an environmental deviation parameter of the environmental information, and calculate a second calculation formula of the attribute number proportion of the environmental deviation parameter greater than or equal to the environmental parameter deviation threshold of the corresponding attribute; when the first calculation formula output value is less than the first output threshold, and the second calculation formula output value is less than the second output threshold, it is considered that the first background constraint function is satisfied; the foreground constraint function construction step includes: obtaining the joint structure, the base material structure, the base material and the front k-layer welding structure of the cold crack high-frequency trigger area; respectively constructing a third calculation formula of the structure similarity of the statistical sample joint structure and the joint structure, a fourth calculation formula of the structure similarity of the statistical sample base material structure and the base material structure, a fifth calculation formula of the material intersection proportion of the statistical sample base material and the base material, and a sixth calculation formula of the structure similarity of the statistical sample front k-layer welding structure and the front k-layer welding structure; when the third calculation formula, the fourth calculation formula, the fifth calculation formula to the sixth calculation formula are greater than the corresponding output threshold, it is considered that the foreground constraint function is satisfied.

[0032] Preferably, in the welding process of multi-layer welding structure, in order to optimize the welding process and reduce the probability of cold crack occurrence, two key constraint functions need to be constructed: the first background constraint function and the foreground constraint function, which will filter out the multi-layer welding sample area 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 is collected, which contains k-layer welding parameters of multi-layer welding structure. Calculate the deviation of each welding parameter in the statistical sample from the given parameter to obtain the k-layer welding deviation parameter. For each welding parameter, set a welding parameter deviation threshold. Calculate the proportion of the number of attributes greater than or equal to the threshold in the k-layer welding deviation parameter, using the first calculation formula. Similarly, calculate the deviation of each environmental information in the statistical sample from the given environmental information to obtain the environmental deviation parameter. For each environmental information, set an environmental parameter deviation threshold, and calculate the proportion of the number of attributes greater than or equal to the threshold in the environmental deviation parameter, using the second calculation formula. 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. And for the foreground constraint function, the key information such as joint structure, base material structure, base material and the first k-layer welding structure is obtained from the high-frequency triggering area of cold cracks. For the joint structure, a third calculation formula is constructed to calculate the structural similarity of the statistical sample joint structure and the given joint structure; for the base material structure, a fourth calculation formula is constructed to calculate the structural similarity of the statistical sample base material structure and the given base material structure; for the base material, a fifth calculation formula is constructed to calculate the material intersection proportion of the statistical sample base material and the given base material; for the first k-layer welding structure, a sixth calculation formula is constructed to calculate the structural similarity of the statistical sample first k-layer welding structure and the given first k-layer welding structure. If the output values of the third calculation formula, the fourth calculation formula, the fifth calculation formula and the sixth calculation formula are greater than the corresponding output thresholds, respectively, it is considered that the statistical sample satisfies the foreground constraint function. Through this series of steps, the first background constraint function and the foreground constraint function are successfully constructed, which will filter out the multi-layer welding sample area with similar characteristics to the high-frequency triggering area of cold cracks under given welding parameters and environmental conditions, providing strong data support for subsequent research and analysis.

[0033] Further, the first calculation formula for calculating the proportion of the number of welding parameters greater than or equal to the corresponding attribute welding parameter deviation threshold in the k-layer welding deviation parameter can be: wherein, 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 i-th welding parameter, and n is the total number of welding parameters. The expression in the parentheses represents a judgment on whether the deviation of the i-th welding parameter is greater than or equal to the deviation threshold, and if so, the result is 1 (true), otherwise 0 (false). Finally, the sum of all items with the result of 1 is added and divided by the total number n to obtain the proportion of welding parameter deviation. The second calculation formula can be: wherein, is the value of the j-th environmental information in the statistical sample, is the value of the given j-th environmental information, is the deviation threshold of the j-th environmental information, and m is the total number of environmental information. The calculation method of this formula is similar to 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 proportion of the number of common features between the statistical sample joint structure and the given joint structure and the total number of features of the given joint structure. The fourth calculation formula can be: base material structure similarity = number of common structure features / total number of given base material structure features, which has a similar calculation method to the third calculation formula, but is used to compare the base material structure. The fifth calculation formula can be: material intersection proportion = number of common elements between statistical sample base material and given base material / total number of elements of given base material, which evaluates the material intersection proportion by calculating the proportion of the number of common elements between the statistical sample base material and the given base material and the total number of elements of the given base material. The sixth calculation formula can be: top k-layer weld structure similarity = number of common weld features / total number of given top k-layer weld structure features, which has a similar calculation method to the third calculation formula, but is used to compare the top k-layer weld structure.

[0034] In a preferred embodiment, based on the k-layer welding parameters, the environmental information and the interlayer temperature central value, a second background constraint function is constructed, and a second multi-layer welding sample area that satisfies both the second background constraint function and the foreground constraint function is searched, and the proportion of the number of cold crack trigger samples in the second multi-layer welding sample area is counted and set as the cold crack trigger probability of the same type of multi-layer welding area in the cold crack high-frequency trigger area.

[0035] For example, a second background constraint function is constructed based on given k-layer welding parameters (which can include welding current, voltage, welding speed, etc.), environmental information (such as humidity, temperature, wind speed, etc.), and inter-layer temperature central value (i.e. the temperature distribution between layers during the welding process). This function is a mathematical model that evaluates whether a multi-layer welding area meets specific background conditions based on input parameter values. Using the second background constraint function, combined with the defined foreground constraint function, the multi-layer welding sample database is searched, and the goal is to find those sample areas that meet both constraint functions. These areas are referred to as second multi-layer welding sample areas. After determining these second multi-layer welding sample areas, the number of samples that trigger 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 multi-layer welding sample area is calculated. This proportion, i.e. the ratio of the number of cold crack triggering samples to the total number of second multi-layer welding sample areas, is set as the cold crack triggering probability. This probability value provides a quantitative indicator for assessing the likelihood of cold crack triggering in a multi-layer welding area under given background and foreground constraint conditions. In summary, this process combines background and foreground constraints to predict high-frequency triggering areas of cold cracks in multi-layer welding areas through steps such as building mathematical models, searching sample databases, and statistical analysis, 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 inter-layer temperature central value, a second background constraint function is constructed, including: the second background constraint function construction step includes: building a temperature deviation parameter of the statistical sample inter-layer temperature and the inter-layer temperature central value, and calculating a seventh calculation formula of the standard deviation of the temperature deviation parameter and the temperature parameter deviation threshold; when the first calculation formula output value is less than the first output threshold value, and the second calculation formula output value is less than the second output threshold value, and the seventh calculation formula output value is less than the third output threshold value, it is considered that the second background constraint function is satisfied.

[0037] Further, one of the key steps in constructing the second background constraint function is to build a temperature deviation parameter that quantifies the difference between the actual and the central value of the interpass temperature for each sample. This parameter reflects the stability and consistency of the temperature control during the welding process. By collecting and analyzing a large number of welding samples, the temperature deviation parameter for each sample can be calculated. Next, the standard deviation between this temperature deviation parameter and a pre-set temperature parameter deviation threshold needs to be calculated, which is done through the seventh calculation formula. This standard deviation measures the degree of dispersion between the temperature deviation parameter and the deviation threshold, providing additional information about the accuracy of the temperature control. 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), 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 the respective pre-set 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 indicates that the environmental information also meets certain requirements; if the output value of the seventh calculation formula is less than the third output threshold, it means that the standard deviation between the temperature deviation parameter and the deviation threshold is small, i.e., the temperature control is relatively stable. Only when all three conditions are met 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 cold crack trigger probability prediction.

[0038] In a preferred embodiment, when the set of cold crack trigger probabilities 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 with a cold crack trigger probability less than the trigger probability threshold, and the k-layer welding parameters are replaced, including: sorting the k-layer welding parameter population from small to large based on the set of cold crack trigger probabilities to obtain a first k-layer welding parameter individual, a second k-layer welding parameter individual, and a third k-layer welding parameter individual; 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 with the welding parameter dimension as the multi-dimensional coordinate element to obtain a target migration point; performing position-close migration on the k-layer welding parameter population with the target migration point as the migration target to obtain updated k-layer welding parameters; when the cold crack trigger probability of the updated k-layer welding parameters is less than the trigger probability threshold, setting the updated k-layer welding parameters as the k-layer target welding parameters; and when the cold crack trigger probability of the updated k-layer welding parameters is greater than or equal to the trigger probability threshold, performing an optimization loop.

[0039] Specifically, the k-layer welding parameter population is sorted according to the existing set of cold crack trigger probability. This sorting process is based on the order of cold crack trigger probability from small to large, thereby selecting three representative k-layer welding parameter individuals: the first k-layer welding parameter individual (lowest trigger probability), the second k-layer welding parameter individual (moderate trigger probability), and the third k-layer welding parameter individual (higher trigger probability). Next, the target migration point is calculated using these three k-layer welding parameter individuals. Specifically, taking each dimension of the welding parameters (such as current, voltage, welding speed, etc.) as the elements of the multi-dimensional coordinate, the mean of the coordinate elements of these three individuals in the same dimension is calculated. This mean point is the target point to which the migration is expected, i.e., the target migration point. After obtaining the target migration point, the k-layer welding parameter population is subjected to position-approaching migration. This means that the parameter values of each individual in the population are adjusted so that they gradually approach the target migration point. This process involves multiple iteration steps until a new set of k-layer welding parameters is obtained, i.e., the updated k-layer welding parameters. Then, the cold crack trigger probability of this set of updated k-layer welding parameters is evaluated. If its trigger probability is less than the trigger probability threshold, the target welding parameters that meet the requirements are found, which can replace the original k-layer welding parameters. If its trigger probability is still greater than or equal to the trigger probability threshold, the optimization loop needs to be continued, and the steps of sorting, calculating the target migration point, position-approaching migration, and evaluating the trigger probability are repeated until the k-layer target welding parameters that meet the requirements are found. In summary, this process is an iterative optimization process, which continuously adjusts the values of the k-layer welding parameters and evaluates their corresponding cold crack trigger probabilities, ultimately finding a set of k-layer target welding parameters that can significantly reduce the cold crack trigger probability.

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

[0041] 1. By traversing the multi-layer welding area of the target engineering vehicle frame, the cold crack high-frequency trigger area with a trigger frequency greater than or equal to the trigger frequency threshold is accurately identified using the frequent mining technology. This not only improves the identification accuracy of the cold crack risk, 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 the number frequency statistics, the positioning accuracy is further enhanced, so that the preventive measures can be more targeted.

[0042] 2. In the retrieval of cold crack high-frequency trigger area of the same type of multi-layer welding area, multi-dimensional background constraints are adopted, including k-layer welding parameters, environmental information and interlayer temperature concentration value, etc. The construction of such multi-dimensional background constraints makes the retrieval results more in line with the actual situation, and improves the accuracy and reliability of the cold crack trigger probability prediction. By constructing the first background constraint function and the second background constraint function, and combining the foreground constraint function, the precise screening of the multi-layer welding sample area is realized, which provides strong support for the accurate evaluation of the cold crack trigger probability.

[0043] 3. When the cold crack trigger probability is greater than or equal to the trigger probability threshold, an intelligent optimization algorithm is used to optimize the k-layer welding parameters 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. By constructing the k-layer welding parameter population and performing sorting, calculating the target migration point, and position close migration, the intelligent adjustment and optimization of the welding parameters are realized. At the same time, the optimization process is executed in a loop to ensure that the final obtained k-layer target welding parameters have the optimal cold crack trigger probability performance.

[0044] Embodiment Two:

[0045] As shown in Figure 2 Based on the same inventive concept as the welding control method for improving the strength of the engineering vehicle provided in Embodiment One, the present embodiment also provides a welding control system for improving the strength of the engineering vehicle, comprising:

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

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

[0048] The probability retrieval module 13 is used to retrieve the cold crack trigger probability of the same type of multi-layer welding area of the cold crack high-frequency trigger area with 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 welding control when the cold crack trigger probability is less than the trigger probability threshold, when the k-layer welding is completed, and the base metal temperature value of the multi-layer welding area is monitored by the temperature sensor, and if it is the same as the interlayer temperature concentration value.

[0050] Further, the welding control module 14 is further configured 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 and analyzed to obtain a cold crack trigger probability set; when the cold crack trigger probability set is all greater than or equal to the trigger probability threshold, the k-layer welding parameters are optimized to generate k-layer target welding parameters with a cold crack trigger probability less than the trigger probability threshold, and the k-layer welding parameters are replaced.

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

[0053] 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 region number set; based on the multi-layer welding region number set, the number frequency of the plurality of cold crack detection logs is counted to obtain a first region number trigger frequency to an Nth region number trigger frequency; based on the first region number trigger frequency to the Nth region number trigger frequency, a multi-layer welding region number with a trigger frequency greater than or equal to a trigger frequency threshold is extracted and added to the cold crack high-frequency trigger area.

[0054] Further, 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 based on the cold crack high-frequency trigger area, a foreground constraint function is constructed; a first multi-layer welding sample region that satisfies the first background constraint function and the foreground constraint function at the same time is retrieved, and a set of interlayer temperature record values of the first multi-layer welding sample region is counted; a central value evaluation is performed on the set of interlayer temperature record values to obtain the interlayer temperature central value.

[0056] Further, 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 of 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 of 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 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 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 to 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 further configured 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 further configured 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 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 considered that the second background constraint function is satisfied.

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

[0063] Sort the k-layer welding parameter population from small to large based on the set of cold crack trigger probabilities, to obtain a first k-layer welding parameter individual, a second k-layer welding parameter individual, and a third k-layer welding parameter individual; take the welding parameter dimension as a multi-dimensional coordinate element, calculate the same-dimensional coordinate element mean 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 a target migration point; take the target migration point as a migration target, perform position-close migration on the k-layer welding parameter population, to obtain updated k-layer welding parameters; when the cold crack trigger probability of the updated k-layer welding parameters is less than a trigger probability threshold, set the updated k-layer welding parameters as the k-layer target welding parameters; when the cold crack trigger probability of the updated k-layer welding parameters is greater than or equal to the trigger probability threshold, perform an optimization cycle.

[0064] The foregoing description of the method for improving the strength of the engineering vehicle by welding control enables those skilled in the art to clearly understand the welding control system for improving the strength of the engineering vehicle in the 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 part can be referred to the method part.

[0065] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the 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 weld area of ​​the target engineering vehicle frame and conduct 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; Using 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; Retrieving the cold crack triggering probability of the same type of multi-layer weld area as the cold crack high-frequency triggering area using the k-layer welding parameters, the environmental information, and the interlayer temperature concentration value as background constraints; When the cold crack trigger probability is less than the trigger probability threshold, when the k-th 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 control is executed; 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 with a cold crack trigger probability less than the trigger probability threshold, and the k-layer welding parameters are replaced.

2. The method according to claim 1, wherein Traverse the multi-layer weld area of ​​the target engineering vehicle frame for frequent mining to obtain the cold crack high-frequency triggering area 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 region number set; Based on the multi-layer weld region number set, performing number frequency statistics on the plurality of cold crack detection logs to obtain a first region number trigger frequency up to an Nth region number trigger frequency; Based on the first region number trigger frequency to the Nth region number trigger frequency, 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.

3. The method according to claim 1, wherein Using k-layer welding parameters and environmental information as background constraints, retrieve the interlayer temperature concentration value of the same type of multi-layer welding area in the cold crack high-frequency triggering zone, 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; Performing a centralized value evaluation on the interlayer temperature record value set to obtain the interlayer temperature centralized value; Wherein, 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 of k-layer welding parameters and k-layer welding deviation parameters of the k-layer welding parameters, and calculating a first calculation formula for 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 includes: 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; Constructing respectively a third calculation formula for calculating the structural similarity between a statistical sample joint structure and the joint structure, a fourth calculation formula for calculating the structural similarity between a statistical sample parent material structure and the parent material structure, a fifth calculation formula for calculating the material intersection ratio between a statistical sample parent material and the parent material, and a sixth calculation formula for calculating the structural similarity between a statistical sample first k-layer weld bead structure and the first k-layer weld bead structure; When the third calculation formula, the fourth calculation formula, the fifth calculation formula, and the sixth calculation formula are respectively greater than corresponding output thresholds, it is considered that the foreground constraint function is satisfied.

4. The method according to claim 3, wherein Retrieving the cold crack triggering probability of the same type of multi-layer weld region as the cold crack high-frequency triggering region using the k-layer welding parameters, the environmental information, and the interlayer temperature concentration value as background constraints, 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 weld sample region that satisfies both the second background constraint function and the foreground constraint function is retrieved, and a proportion of cold crack triggering samples in the second multi-layer weld sample region is counted, which is set as the cold crack triggering probability.

5. The method according to claim 4, wherein 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: Constructing a seventh calculation formula for calculating a temperature deviation parameter between the statistical sample interlayer temperature and the interlayer temperature concentration value, and calculating a 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, 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.

6. The method according to claim 1, wherein 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 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, sorting is performed from the k-layer welding parameter population 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 layers of welding parameter individuals, the second k layers of welding parameter individuals, and the third k layers of welding parameter individuals to obtain a target migration point; Taking the target migration point as the migration target, performing positional 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.

7. 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 according to any one of claims 1 to 6, comprising: The regional mining module is used to traverse the multi-layer weld area of ​​the target engineering vehicle frame to conduct 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 multi-layer welding area in the cold crack high-frequency triggering area based on k-layer welding parameters and environmental information as background constraints, where k≥1 and k is an integer; A probability retrieval module 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 by using 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 the temperature sensor, if it is the same as the interlayer temperature concentration value.

Images