A non-destructive welding device and welding method for connecting pins used in chip processing

Abstract

The present invention relates to the field of chip welding technology, specifically to a non-destructive welding device for connecting pins for chip processing and a welding method thereof, comprising the steps of: performing laser scanning processing on the chip pin area to obtain the three-dimensional spatial coordinate data of the chip, the three-dimensional spatial coordinate data including multiple data points; generating multiple connection nodes according to the spatial correlation relationship of the multiple data points, clustering the multiple connection nodes to obtain multiple regional node clusters; performing welding path optimization processing and heat flow distribution simulation processing on the multiple regional node clusters to obtain the temperature change curve of each path; performing data analysis processing and dynamic adjustment processing on the temperature change curve to obtain the final welding control strategy, and controlling the welding robot to weld the chip based on the final welding control strategy. The present application effectively solves the deficiencies of the existing welding technology in terms of accuracy, stability and adaptability by dynamically adjusting the control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of chip welding, and in particular to a non-destructive welding device and a welding method for connecting pins used for chip processing. Background Art

[0002] In the field of modern chip welding, as chip size continues to shrink and pin density continues to increase, existing welding methods have been unable to meet the growing precision and quality requirements. Especially in the pin area of ​​the chip, due to the small space and complex pin arrangement, the existing welding technology cannot accurately control every detail of the welding process, resulting in unstable welding results, which in turn affects the product's yield and long-term reliability. At present, most chip welding relies on experienced operators or equipment based on simple automated control, which makes it difficult to achieve precise control and global optimization of the welding process. This situation shows that the existing technology has significant deficiencies in welding path, temperature control and solder quantity, and fails to effectively solve the uncertainty and complexity of the welding process.

[0003] Existing chip welding technologies usually use fixed paths or regularized temperature control strategies, but these methods cannot adapt to the complex three-dimensional structure of the chip pin area and the dynamic changes during the welding process. They ignore the precise three-dimensional spatial data of the chip surface, resulting in the welding path not being optimized, which in turn affects the welding quality. The optimization of the welding path does not fully consider the distribution of heat flow and temperature changes during the welding process, which may cause overheating or insufficient cooling in the welding area, which not only affects the welding effect, but may also damage other parts of the chip. Summary of the invention

[0004] The main purpose of the present invention is to provide a non-destructive welding device and a welding method for chip processing pins, aiming to overcome the technical problem that the existing welding technology is unstable and affects the welding effect.

[0005] In order to achieve the above-mentioned invention problem, the present invention proposes a non-destructive welding method for connecting pins used in chip processing, the method comprising:

[0006] Performing laser scanning on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points;

[0007] Generating a plurality of connection nodes according to the spatial association relationship of the plurality of data points, and performing clustering processing on the plurality of connection nodes to obtain a plurality of regional node clusters;

[0008] Performing welding path optimization processing on the plurality of regional node clusters to obtain an optimal welding path from the center of each node cluster to a target welding point;

[0009] Performing heat flow distribution simulation processing on the optimal welding path to obtain a temperature change curve of each path;

[0010] Performing data analysis on the temperature variation curve to obtain a corresponding relationship between the welding temperature and the amount of solder;

[0011] The corresponding relationship between the welding temperature and the amount of solder is dynamically adjusted to obtain a final welding control strategy, and the welding robot is controlled to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes a welding path, a welding temperature and a solder amount.

[0012] Furthermore, the step of performing laser scanning on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points, comprises:

[0013] Scan the chip pin surface point by point based on a laser scanner to obtain spatial coordinate information of multiple sampling points, and generate preliminary three-dimensional point cloud data based on the spatial coordinate information;

[0014] Performing coordinate transformation processing on the preliminary three-dimensional point cloud data to obtain three-dimensional space coordinates;

[0015] Dividing the preliminary three-dimensional point cloud data into a plurality of grid units in the three-dimensional space coordinates based on a voxel grid method, each of the grid units including a data point;

[0016] Fitting the plurality of data points to generate a surface model, and segmenting the surface model according to the spatial coordinate information to obtain a plurality of sub-areas;

[0017] The coordinates of the plurality of sub-regions are spliced, and the coordinate data of the overlapping regions are adjusted by a weighted average method to obtain three-dimensional space coordinate data.

[0018] Furthermore, the step of generating a plurality of connection nodes according to the spatial association relationship of the plurality of data points, and clustering the plurality of connection nodes to obtain a plurality of regional node clusters includes:

[0019] Obtaining the spatial distance between multiple data points, and calculating the similarity between each data point and other data points, wherein if the spatial distance between two data points is less than a preset threshold, they are determined to be connected nodes;

[0020] Performing cluster analysis on the connection nodes based on a preset density threshold to obtain a preliminary node cluster;

[0021] Calculating the geometric center of each preliminary node cluster based on the average coordinate value of all data points in each preliminary node cluster to obtain the geometric center coordinates of each preliminary node cluster;

[0022] Performing spatial relative position analysis on the geometric center coordinates to obtain the relative position relationship between the preliminary node clusters and obtain spatial arrangement information of the preliminary node clusters;

[0023] The plurality of preliminary node clusters are optimized and sorted according to the spatial arrangement information to obtain the plurality of regional node clusters.

[0024] Furthermore, the step of performing welding path optimization processing on the plurality of regional node clusters to obtain an optimal welding path from the center of each node cluster to the target welding point includes:

[0025] Based on the geometric center coordinates of each of the regional node clusters, the spatial distances between the regional node clusters are acquired to obtain a distance matrix between the regional node clusters;

[0026] Performing a shortest path search on the plurality of regional node clusters according to the distance matrix to obtain a preliminary welding path from the center of each regional node cluster to a target welding point;

[0027] Performing path smoothing processing on the preliminary welding path to obtain a smoothing result of the preliminary welding path;

[0028] Obtain turning points in the preliminary welding path according to the smoothing result, and optimize the turning points with curvature greater than a preset curvature to obtain an optimized welding path;

[0029] The optimized welding path is subjected to local constraint processing, and the optimized welding path after multiple local constraint processing is subjected to path efficiency evaluation to obtain an optimal welding path.

[0030] Furthermore, the step of performing heat flow distribution simulation processing on the optimal welding path to obtain the temperature change curve of each path includes:

[0031] Calculating the heat source power distribution of each path segment of the optimal welding path to obtain the heat source distribution characteristics of each path segment, wherein the heat source distribution characteristics include the heat distribution of each path segment of the optimal welding path;

[0032] Calculating the temperature change rate of each path segment based on the heat distribution of each path segment, generating a heat conduction equation, and constructing a heat conduction model based on the heat conduction equation;

[0033] Numerically solving the heat conduction model to obtain the temperature distribution state of each path segment;

[0034] Perform a thermal flow dynamic simulation of the time step according to the temperature distribution state to obtain temperature change data at different time points;

[0035] Smoothing the temperature change data to obtain a smooth curve of temperature change over time;

[0036] The welding temperature change data is analyzed and processed based on the smooth curve to obtain a temperature change curve, wherein the welding temperature change data at least includes a temperature peak value, a stable value and a drop rate value.

[0037] Furthermore, the step of performing data analysis on the temperature change curve to obtain the corresponding relationship between the welding temperature and the solder amount includes:

[0038] Fitting the temperature change curve to obtain a mathematical model of temperature change over time;

[0039] Calculate the welding time at different temperatures according to the mathematical model to obtain the relationship data between welding time and temperature;

[0040] According to the welding time and temperature relationship data, the temperature is segmented and analyzed to obtain several temperature ranges, and the solder amount is analyzed based on the several temperature ranges to obtain the solder amount change law corresponding to each temperature range;

[0041] Performing curve fitting processing on the variation law of the solder amount to obtain a fitting equation of the solder amount and temperature, and performing data interpolation processing on the fitting equation to obtain solder amount prediction data under different temperature conditions;

[0042] The error analysis of solder quantity prediction data was performed to obtain the corresponding relationship between solder quantity and temperature.

[0043] Furthermore, the corresponding relationship between the welding temperature and the amount of solder is dynamically adjusted to obtain a final welding control strategy, and the welding robot is controlled to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes the steps of welding path, welding temperature and solder amount, including:

[0044] Acquiring temperature fluctuation data in the temperature change curve, performing trend analysis on the temperature fluctuation data, and obtaining a time series model of temperature fluctuation;

[0045] Calculate the change trend of the solder amount during the welding process according to the time series model to obtain adjustment data of the solder amount;

[0046] Performing fuzzy control processing on the adjustment data of the solder quantity to obtain an optimal adjustment strategy for the solder quantity;

[0047] The welding temperature is adjusted in real time according to the optimal adjustment strategy to obtain optimal welding temperature data;

[0048] The adjustment results of the welding path, the optimal welding temperature and the solder amount are collaboratively optimized to obtain a final welding control strategy.

[0049] The present invention also provides a non-destructive welding device for connecting pins used in chip processing, comprising:

[0050] A scanning module, used for performing laser scanning processing on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points;

[0051] A generating module, used for generating a plurality of connection nodes according to the spatial association relationship of the plurality of data points, and performing clustering processing on the plurality of connection nodes to obtain a plurality of regional node clusters;

[0052] An optimization module, used for performing welding path optimization processing on the plurality of regional node clusters to obtain an optimal welding path from the center of each node cluster to a target welding point;

[0053] A simulation module, used to perform heat flow distribution simulation processing on the optimal welding path to obtain a temperature change curve of each path;

[0054] An analysis module is used to perform data analysis on the temperature change curve to obtain a corresponding relationship between the welding temperature and the amount of solder;

[0055] The adjustment module is used to dynamically adjust the corresponding relationship between the welding temperature and the solder amount to obtain a final welding control strategy, and control the welding robot to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes a welding path, a welding temperature and a solder amount.

[0056] The present invention further provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0057] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented.

[0058] Beneficial effects:

[0059] The present application proposes a non-destructive welding device and a welding method for connecting pins for chip processing, which effectively solves the shortcomings of existing welding technology in terms of accuracy, stability and adaptability by introducing three-dimensional spatial coordinate data and dynamic adjustment control strategy. The accurate three-dimensional coordinate data of the chip pin area is obtained by laser scanning processing, which can fully reflect the complex three-dimensional structure of the chip surface, and then generate multiple regional node clusters through clustering processing, ensuring that the welding path can be optimized according to the specific needs of each area. When optimizing the welding path, based on the heat flow distribution and temperature change during the welding process, the temperature change curve obtained by simulation analysis can better control the temperature fluctuation during the welding process, avoid overheating or insufficient cooling, thereby effectively reducing the occurrence of welding defects to achieve non-destructive welding. In addition, by dynamically adjusting the corresponding relationship between the welding temperature and the amount of solder, the method can achieve precise control of the welding process, avoid the limitations of traditional fixed paths or regularized control strategies, make the welding process more flexible and adaptable, significantly improve the welding quality and stability, and then improve the yield rate and long-term reliability of chip products, and meet the needs of modern chip welding for high precision and high quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the steps of a non-destructive welding method for connecting pins used in chip processing in one embodiment of the present invention;

[0061] Figure 2 It is a schematic block diagram of the structure of a non-destructive welding device for connecting pins used for chip processing according to an embodiment of the present invention;

[0062] Figure 3 is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention;

[0063] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is said to be "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0066] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0067] Reference Figure 1 The embodiment of the present invention provides a non-destructive welding method for connecting pins used in chip processing, comprising the following steps:

[0068] S1: performing laser scanning processing on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points;

[0069] In step S1, step S1 involves laser scanning the chip pin area to obtain the three-dimensional spatial coordinate data of the chip. Specifically, the chip pin area is usually a very small and complex area. The chip surface is irradiated with a high-precision laser beam, and the reflected laser beam is received by the sensor and converted into a data signal, thereby obtaining the three-dimensional spatial coordinate data of the chip surface. In this process, the laser scanning system first emits a laser beam. After the laser beam is irradiated on the surface of the chip, the reflected laser beam will produce time delays at different positions depending on the surface morphology and material. The laser scanner uses this time delay information to calculate the specific position of the laser beam reflection, thereby accurately determining the three-dimensional coordinates of each point on the chip surface. In this way, the shape of the chip surface and the spatial position of each pin can be accurately reconstructed to form a set of three-dimensional spatial coordinate data including multiple data points. The three-dimensional spatial coordinate data of the chip pin area obtained by laser scanning technology is composed of many discrete data points, each of which represents the position coordinates of a point on the chip surface and contains detailed geometric information of the pin area on the chip surface.

[0070] S2: generating a plurality of connection nodes according to the spatial association relationship of the plurality of data points, and performing clustering processing on the plurality of connection nodes to obtain a plurality of regional node clusters;

[0071] In step S2, multiple connection nodes are generated according to the spatial coordinate data using a preset algorithm (such as a nearest neighbor algorithm, a distance-based clustering algorithm, etc.). A connection node can be understood as a collection of points with certain similarities or associations in space, representing an important characteristic area of ​​the chip surface structure. In the process of generating connection nodes, the data points are classified, and the distance, direction or other features between the data points are calculated to identify which data points have a closer relationship and which belong to a more dispersed area. After the connection nodes are generated, the nodes are clustered, and the nodes are grouped according to their spatial positions and features to form multiple regional node clusters. The nodes in these node clusters are relatively concentrated in space and have a small distance between each other, and can be considered as a local area or functional module of the chip pin area. Through the clustering algorithm, the differences between different areas can be identified, and it is determined which areas need to be focused on and which areas can be classified into the same category. Taking an actual chip welding process as an example, it is assumed that the pin area of ​​the chip is composed of multiple tiny pins, and these pins are not evenly distributed in three-dimensional space. Through step S1, the spatial coordinate data of each pin is obtained. If these coordinate data are used directly for welding path planning, the path will be too complicated or the calculation will be too large. Through the clustering processing of step S2, pins with similar spatial positions can be divided into a cluster, and then centralized processing is performed within each cluster to optimize the planning of the welding path and make the welding process more efficient. For example, for a smaller node cluster, it is decided to use a shorter welding path, while for a larger cluster, a more sophisticated path optimization strategy may be required to adapt to the physical characteristics of different regions. It is worth noting that different clustering algorithms, such as K-means, DBSCAN, hierarchical clustering, etc., may have different clustering results. Therefore, in practical applications, a specific clustering algorithm is selected according to the actual situation.

[0072] S3: performing welding path optimization processing on the plurality of regional node clusters to obtain an optimal welding path from the center of each node cluster to a target welding point;

[0073] In step S3, the regional node clusters obtained by clustering in S2 are used as the starting point for welding path optimization. Each node cluster represents a chip pin area or a functional unit related to welding, and the cluster center is the most representative position in the area. The point with the smallest average distance from all nodes in the area is selected. A variety of path optimization algorithms can be used, such as heuristic algorithms, genetic algorithms, simulated annealing, etc., to find the optimal or approximately optimal path through certain rules or objective functions. In actual operation, the objective function is first set, and this objective function can include multiple objectives such as minimizing path length, shortest time, welding temperature control, and welding quality assurance. For minimizing path length, the shortest path from the center of one node cluster to the center of another node cluster can be calculated by the shortest path algorithm (such as Dijkstra algorithm or A* algorithm). When performing path optimization, the distance and time of robot movement can be reduced through reasonable path planning. The method adopted can be to first weld areas with relatively close positions, and then gradually weld to distant areas.

[0074] S4: performing heat flow distribution simulation processing on the optimal welding path to obtain a temperature change curve of each path;

[0075] In step S4, the heat flux distribution simulation aims to predict the temperature changes on different paths during the welding process through numerical simulation. The optimal welding path is used as input data for further heat flux simulation. Through the heat flux distribution simulation, the temperature change trend of each point on the welding path can be accurately predicted. First, a thermal model of the welding area is established. Specifically, factors such as the thermal characteristics of the chip, the thermal conductivity properties of the welding point, and the thermal conductivity coefficient of the welding material are all included in the model. By discretizing the welding path, the three-dimensional structure of the chip is divided into multiple small units, and the heat conduction calculation of each unit is performed. The heat conduction process of these small units will be dynamically adjusted according to parameters such as temperature gradient and heat flux density, so as to accurately simulate the heat flux distribution during the entire welding process. Through heat flow simulation, not only can temperature changes be predicted, but also the impact of different path selection on welding time can be evaluated. For example, if the heat flux distribution of a certain path is more uniform, the welding process may be more efficient, while another path may take more time to reach the required welding temperature. By comparing different paths, simulation can help select the optimal welding route, thereby improving the overall efficiency of the welding process. The results of heat flux distribution are presented through the temperature change curve, which can intuitively show the trend of temperature change over time at each point on the welding path, reflecting the thermal changes in the welding process.

[0076] S5: performing data analysis and processing on the temperature variation curve to obtain a corresponding relationship between the welding temperature and the solder amount;

[0077] In step S5, by processing these temperature change curves, a mathematical model is established to describe the relationship between the welding temperature and the amount of solder. This process can be based on statistical analysis methods or machine learning techniques, using a large number of temperature change curves and welding experimental data to fit the law between temperature and solder amount. For example, a regression analysis method can be used to compare the data points in the temperature change curve with the actual solder amount to obtain the optimal solder amount corresponding to the temperature within a certain range. Through these data analyses, the optimal solder amount can be provided for different welding conditions (such as different temperature ranges, different welding times, etc.) to ensure the quality of the welding process. Through data analysis, the coordination of welding temperature and solder amount can be accurately controlled on each welding path to avoid welding defects caused by temperature fluctuations or inappropriate solder amount. Specifically, by analyzing the temperature curve, the solder amount can be dynamically adjusted so that the optimal range of solder amount can be maintained under different welding conditions. For example, during the soldering process of some complex chips, the temperature in some areas changes rapidly, which may cause local overheating. The system automatically adjusts the amount of solder by analyzing these temperature change curves to ensure that even in high-temperature areas, the amount of solder can adapt to the needs of local temperature changes, avoiding overflow or failure caused by excessive solder.

[0078] S6: Dynamically adjust the corresponding relationship between the welding temperature and the amount of solder to obtain a final welding control strategy, and control the welding robot to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes a welding path, a welding temperature and a solder amount.

[0079] In step S6, the dynamic adjustment process is based on the feedback of real-time data, which comes from the operation process of the welding robot, including parameters such as welding temperature, solder amount, welding speed, etc. These data are collected in real time by sensors and enter the control system for analysis. The control system will continuously compare the difference between the current welding state and the ideal state (i.e., the corresponding relationship between the welding temperature and the solder amount obtained in S5) to determine whether it is necessary to adjust certain parameters in the welding process. For example, if the welding temperature is high and the solder amount is insufficient at some welding points, the control system will increase the solder amount in real time to ensure the welding quality; on the contrary, if the welding temperature is too low and the solder amount is too much, the solder amount will be reduced to prevent excessive solder accumulation at the welding point, thereby avoiding circuit short circuit or loose solder joints. In another embodiment, the optimization process based on historical data and model prediction continuously optimizes the welding control strategy as the welding process proceeds according to the results of the previous weldings and the collected temperature and solder amount data. By learning and analyzing a large amount of welding data, the prediction accuracy of the welding process can be gradually improved, and the control strategy can be adjusted based on historical data to achieve the best welding effect. For example, in some special chip pin areas, the distribution of soldering temperature may change due to different surface materials or shapes. By collecting and analyzing these data, the system can predict these changes and adjust the soldering path, soldering temperature and solder amount in real time to ensure the stability and consistency of the soldering process.

[0080] In steps S1-S6, the pin area of ​​the chip is laser scanned to obtain the three-dimensional spatial coordinate data of the chip. These data represent multiple data points on the surface of the chip and can accurately describe the geometric shape of the chip surface and the spatial distribution of the pins. Based on these data points, their mutual relationship in space is analyzed to generate multiple connection nodes, and then these nodes are classified into multiple regional node clusters through clustering processing. These clusters help to simplify the path planning in the welding process. The welding path of these node clusters is optimized, and the optimal welding path from the center of each regional node cluster to the target solder joint is calculated to ensure the efficiency and accuracy of the welding operation. The heat flow distribution of these optimal paths is simulated to simulate the temperature change during the welding process, and the temperature change curve of each path is obtained, which helps to analyze the heat distribution during the welding process, thereby avoiding chip damage caused by overheating or uneven temperature. The temperature change curve is analyzed, and the corresponding relationship between the welding temperature and the amount of solder is obtained, which provides a basis for the next step of the welding process. Based on the relationship between the temperature and the amount of solder obtained by S5, dynamic adjustment processing is performed, that is, the temperature and the amount of solder in the welding process are adjusted in real time to meet the needs of different welding points, and finally a comprehensive welding control strategy is generated. This strategy not only includes the specific parameters of the welding path, welding temperature and solder amount, but can also be adjusted according to real-time feedback to ensure the stability and high quality of welding. Through such steps, from the scanning of chip pins, data analysis to the real-time control of the welding process, the present invention realizes an accurate and efficient non-destructive welding process, which is suitable for the processing of high-precision chips, such as the welding of miniaturized electronic products or complex integrated circuits.

[0081] In one embodiment, the step of performing laser scanning on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points, comprises:

[0082] Scan the chip pin surface point by point based on a laser scanner to obtain spatial coordinate information of multiple sampling points, and generate preliminary three-dimensional point cloud data based on the spatial coordinate information;

[0083] Performing coordinate transformation processing on the preliminary three-dimensional point cloud data to obtain three-dimensional space coordinates;

[0084] Dividing the preliminary three-dimensional point cloud data into a plurality of grid units in the three-dimensional space coordinates based on a voxel grid method, each of the grid units including a data point;

[0085] Fitting the plurality of data points to generate a surface model, and segmenting the surface model according to the spatial coordinate information to obtain a plurality of sub-areas;

[0086] The coordinates of the plurality of sub-regions are spliced, and the coordinate data of the overlapping regions are adjusted by a weighted average method to obtain three-dimensional space coordinate data.

[0087] In the above embodiment, the chip pin area is scanned point by point by a laser scanner to obtain the spatial coordinate information of each sampling point. The laser scanner emits a laser beam and measures the time it takes for it to be reflected back, thereby accurately determining the three-dimensional position of each point on the surface, and then generating preliminary three-dimensional point cloud data. The spatial coordinate information of each point reflects the geometric characteristics of different positions on the chip surface. The preliminary three-dimensional point cloud data is subjected to coordinate transformation processing, and the collected point cloud data is unified into a standard three-dimensional coordinate system. After obtaining these three-dimensional coordinate data, the voxel grid method is used for further processing. The voxel grid method is a method of dividing a three-dimensional space into small cubic grid units, which is used to simplify complex three-dimensional data into a form that is easier to process. In this embodiment, the voxel grid method divides the preliminary three-dimensional point cloud data into multiple small grid units, each grid unit contains a data point, thereby dividing the entire chip surface into multiple discrete areas. In this way, the complexity of the data can be reduced, making subsequent processing more efficient while maintaining the representativeness and accuracy of the data. Fit multiple data points in each grid unit to generate a surface model. Through mathematical methods (such as surface fitting), discrete data points are converted into a smooth continuous surface model to accurately reflect the true shape of the chip pin surface. In the fitting process, a curve or surface fitting algorithm can be used to make the fitting result consistent with the actual point cloud data, thereby ensuring the accuracy of the surface model. According to the spatial coordinate information, the fitted surface model is segmented to obtain multiple sub-areas, and the chip pin area is divided into several smaller parts. Each sub-area represents an independent area on the chip surface. These sub-areas can be divided according to the arrangement, shape or other geometric features of the chip pins. The coordinate data of multiple sub-areas are spliced, and the coordinate data of the overlapping area is adjusted by the weighted average method. The weighted average method is a method of adjusting the coordinates according to the importance of the data points. The coordinate points of the overlapping area will differ due to the fitting deviation of different sub-areas. The weighted average method can ensure that the final spliced ​​three-dimensional space coordinate data is more accurate and smooth, and finally the three-dimensional space coordinate data is obtained.

[0088] In one embodiment, the step of generating a plurality of connection nodes according to the spatial association relationship of the plurality of data points, and clustering the plurality of connection nodes to obtain a plurality of regional node clusters includes:

[0089] Obtaining the spatial distance between multiple data points, and calculating the similarity between each data point and other data points, wherein if the spatial distance between two data points is less than a preset threshold, they are determined to be connected nodes;

[0090] Performing cluster analysis on the connection nodes based on a preset density threshold to obtain a preliminary node cluster;

[0091] Calculating the geometric center of each preliminary node cluster based on the average coordinate value of all data points in each preliminary node cluster to obtain the geometric center coordinates of each preliminary node cluster;

[0092] Performing spatial relative position analysis on the geometric center coordinates to obtain the relative position relationship between the preliminary node clusters and obtain spatial arrangement information of the preliminary node clusters;

[0093] The plurality of preliminary node clusters are optimized and sorted according to the spatial arrangement information to obtain the plurality of regional node clusters.

[0094] In the above embodiment, the spatial distance between multiple data points is obtained. These data points come from the three-dimensional coordinate data generated by laser scanning. Each data point represents the coordinates of a specific position on the chip surface. By calculating the spatial distance between these data points, the similarity between them can be evaluated, and then which data points belong to adjacent areas in space can be determined. If the spatial distance between two data points is less than a preset threshold, the two data points are considered to be nodes connected to each other. This process is actually looking for points on the chip surface that are close to each other and have similar geometric features. Based on these connected nodes, a cluster analysis is performed using a preset density threshold. The purpose of cluster analysis is to combine connected nodes that are close to each other into larger regional node clusters. These clusters represent different parts of the chip pin area with certain spatial correlation. Each preliminary node cluster consists of multiple similar connected nodes, representing a specific area on the chip pin surface. Based on the spatial coordinates of all data points in each preliminary node cluster, the geometric center of each cluster is calculated. This step is to extract a representative point from each cluster, called the geometric center, which is the average coordinate value of all nodes in the cluster, perform spatial relative position analysis on these geometric centers, and further obtain the arrangement information of the node cluster in three-dimensional space. By analyzing the relative position of the geometric center, it is clear which node clusters are adjacent to each other and which clusters are far apart, which helps to select the most appropriate welding sequence and path planning when optimizing the welding path. Finally, these preliminary node clusters are optimized and sorted based on the spatial arrangement information. The purpose of the optimization sorting is to determine the optimal welding path sequence based on the relative position relationship between the node clusters. For example, if a node cluster is located near another cluster, when welding, the robot should first process these adjacent clusters to avoid excessive idle time and unnecessary movement, thereby improving welding efficiency.

[0095] In one embodiment, the step of performing welding path optimization processing on the plurality of regional node clusters to obtain an optimal welding path from the center of each node cluster to the target welding point includes:

[0096] Based on the geometric center coordinates of each of the regional node clusters, the spatial distances between the regional node clusters are acquired to obtain a distance matrix between the regional node clusters;

[0097] Performing a shortest path search on the plurality of regional node clusters according to the distance matrix to obtain a preliminary welding path from the center of each regional node cluster to a target welding point;

[0098] Performing path smoothing processing on the preliminary welding path to obtain a smoothing result of the preliminary welding path;

[0099] Obtain turning points in the preliminary welding path according to the smoothing result, and optimize the turning points with curvature greater than a preset curvature to obtain an optimized welding path;

[0100] The optimized welding path is subjected to local constraint processing, and the optimized welding path after multiple local constraint processing is subjected to path efficiency evaluation to obtain an optimal welding path.

[0101] In the above embodiment, the spatial distance is obtained based on the geometric center coordinates of each regional node cluster. The geometric center of each node cluster is obtained by calculating the average coordinates of all data points in the cluster. The geometric center of each cluster represents an overall position of the node in the cluster. By calculating the spatial distances between these geometric centers, a distance matrix can be obtained. This matrix records the relative position relationship between each regional node cluster. This distance matrix is ​​used to search for the shortest path, thereby obtaining a preliminary welding path from the center of each node cluster to the target welding point. After obtaining the preliminary welding path, the path is smoothed to make the path smoother. The turning in the path is reduced by, for example, curve fitting or path optimization algorithms. The results of the smoothed path are further analyzed to find out whether there is a turning point in the path. Specifically, the tangent direction of the path at each point is calculated. The direction of the tangent can be approximately represented by the connecting line of two adjacent points on the path. For each pair of adjacent tangent vectors in the path, the angle between them can be calculated to identify the turning point. The turning point refers to the point where the direction of the path suddenly changes. During the welding process, these points will cause the welding robot to have a large change in direction, thereby affecting the welding accuracy and efficiency. For turning points, turning points with a curvature greater than the preset curvature need to be optimized. The preset curvature means that in the path, when the turning angle exceeds a certain threshold, the curvature of the point is considered to be too large, which may cause the robot to move unsmoothly or the welding quality to decrease. The turning points with large curvature will be optimized through the algorithm. The path angle can be adjusted to reduce sharp turns, or a smoother transition can be achieved by introducing intermediate transition points, and finally an optimized welding path is obtained. The optimized welding path is processed with local constraints to ensure the rationality and operability of the path. For example, there may be some physical constraints, such as spatial limitations in the welding area or limitations in the working range of the robot, which may affect the actual operability of the welding path. Local constraint processing is to add these constraints to the path planning to ensure that the path is not only optimal, but also can be accurately executed in actual operation to avoid the path exceeding the working range of the robot or interfering with other equipment. Finally, the optimized welding path after multiple local constraint processing is evaluated for path efficiency. The overall efficiency of the path is comprehensively considered, including factors such as path length, number of turns, welding accuracy, etc., and the optimal welding path is finally obtained. Through evaluation, it can be determined which path can complete the welding task with the minimum time and maximum accuracy while meeting all constraints, thereby improving the overall welding efficiency and quality.

[0102] In one embodiment, the step of performing heat flux distribution simulation processing on the optimal welding path to obtain the temperature change curve of each path includes:

[0103] Calculating the heat source power distribution of each path segment of the optimal welding path to obtain the heat source distribution characteristics of each path segment, wherein the heat source distribution characteristics include the heat distribution of each path segment of the optimal welding path;

[0104] Calculating the temperature change rate of each path segment based on the heat distribution of each path segment, generating a heat conduction equation, and constructing a heat conduction model based on the heat conduction equation;

[0105] Numerically solving the heat conduction model to obtain the temperature distribution state of each path segment;

[0106] Perform a thermal flow dynamic simulation of the time step according to the temperature distribution state to obtain temperature change data at different time points;

[0107] Smoothing the temperature change data to obtain a smooth curve of temperature change over time;

[0108] The welding temperature change data is analyzed and processed based on the smooth curve to obtain a temperature change curve, wherein the welding temperature change data at least includes a temperature peak value, a stable value and a drop rate value.

[0109] In the above embodiment, the heat source distribution characteristics of each path segment are determined according to the specific geometry of the welding path and the heat generated during the welding process. In each path segment, by simulating the distribution of the heat source during the welding process, the heat distribution characteristics of these path segments can be obtained, so as to accurately calculate the heat input of each path segment. Based on the heat distribution of each path segment, the temperature change rate of each path segment is calculated, and the rate of temperature change over time is derived by the power of the heat source and the heat conduction characteristics of the path segment, thereby providing the necessary parameters for the establishment of the heat conduction equation. The heat conduction equation is a mathematical model that describes how heat diffuses in the material, and its function is to simulate how the temperature is distributed along the path segment during the welding process. Based on these equations, a heat conduction model is constructed and numerically solved to obtain the temperature distribution state of each path segment. In this process, finite element analysis (FEA) or other numerical methods can be used to obtain the temperature distribution diagram of each path segment at different time points through numerical solution. The temperature change during the welding process is simulated by dynamic simulation. The simulation process can be based on the time step. The temperature change is calculated once for each time step, and the temperature change data of the entire welding process is gradually generated. Through this dynamic simulation, the temperature state at different time points during the welding process can be observed in real time. The temperature change data is smoothed to remove the noise in the simulation process, making the curve of temperature change over time smoother and more realistic. The smoothing process can include moving average method, spline interpolation method, etc. The welding temperature change data is analyzed based on the smoothed temperature change curve to obtain key parameters such as the peak temperature, stable value and drop rate of the temperature during the welding process. For example, the temperature peak represents the highest temperature in the welding process, which is closely related to the heat input of the welding. The stable value is the temperature equilibrium state in the welding process, and the drop rate reflects the speed at which heat is dissipated after the welding is completed.

[0110] In one embodiment, the algorithm expression of the above embodiment is: , It represents the temperature at time tt and position rr, that is, the objective function to be solved, which represents the temperature of the welding point at a given time and spatial position, and determines the heat transfer and distribution during the welding process. It is the total heat source power, which indicates the total heat input during the welding process and is related to the laser power, welding current or the power of the heating source. It is the density of the material, indicating the material density of the welding object (such as chip pins, welding metal, etc.), and the unit is kg / m³. It is the specific heat capacity of the material, which indicates the specific heat capacity per unit mass of the material, in J / (kg·K), that is, the amount of heat required to heat 1 kg of material by 1 degree. N represents the number of path segments, indicating how many heat source areas or path segments there are on the welding path. Represents the heat source attenuation factor of path segment i, which is the heat source attenuation rate of each path segment, representing the degree to which heat decays rapidly as the distance from the path segment increases. It is a negative exponential attenuation factor. is the current position of path segment i, is the position vector of the path segment, and represents the spatial coordinates of a specific position on the welding path. It is the center position of the path segment, that is, the geometric center of the path segment, which is used to calculate the distance from each path segment to the center point. is the smoothing parameter of path segment i. is the heat conduction factor of the path point, which indicates the relationship between temperature attenuation and spatial position. This function describes the influence of the distance from the nearest point of the welding path on the temperature. This function can be designed according to the actual situation, such as an attenuation function that is inversely proportional to the square of the distance. It is the time-dependent adjustment factor of the welding process. It is a dynamic adjustment factor that represents the effect of time changes on temperature during the welding process. The temperature tolerance range adjustment factor is used to limit temperature changes and ensure that the temperature changes during welding are within a certain tolerance range. , the welding process will be adjusted appropriately. is the maximum radius of the path segment. Temperature tolerance refers to the maximum temperature deviation allowed during the welding process, which is used to ensure that the chip or welding area will not be affected by excessive or low heat during the welding process. In practical applications, it is assumed that there are multiple path segments on the welding path, and the heat input of each path segment is different. For example, the heat source attenuation factor of the first path segment is higher, while the attenuation factor of the second path segment is lower. According to the attenuation term in the formula and the position of the path segment, the welding process of the path segment is dynamically adjusted, and finally a smooth curve of the temperature change over time of each path segment is obtained. Through this dynamic optimization process, overheating damage to the chip can be avoided while controlling the welding quality.

[0111] In one embodiment, the step of performing data analysis on the temperature change curve to obtain the corresponding relationship between the welding temperature and the amount of solder includes:

[0112] Fitting the temperature change curve to obtain a mathematical model of temperature change over time;

[0113] Calculate the welding time at different temperatures according to the mathematical model to obtain the relationship data between welding time and temperature;

[0114] According to the welding time and temperature relationship data, the temperature is segmented and analyzed to obtain several temperature ranges, and the solder amount is analyzed based on the several temperature ranges to obtain the solder amount change law corresponding to each temperature range;

[0115] Performing curve fitting processing on the variation law of the solder amount to obtain a fitting equation of the solder amount and temperature, and performing data interpolation processing on the fitting equation to obtain solder amount prediction data under different temperature conditions;

[0116] The error analysis of solder quantity prediction data was performed to obtain the corresponding relationship between solder quantity and temperature.

[0117] In the above embodiment, the temperature change curve is fitted to generate a mathematical model of temperature change over time. The purpose of this fitting process is to obtain a function model that can accurately describe the temperature change trend by digitally processing the actually measured temperature change data. Polynomial fitting, exponential fitting or other more complex mathematical models can be selected, depending on the distribution of experimental data. After obtaining the mathematical model of temperature change, the welding time at different temperatures is calculated based on the model, and then the relationship data between welding time and temperature are obtained. This can be achieved by interpolation or numerical integration. Based on these welding time and temperature relationship data, the temperature is segmented and analyzed to divide multiple temperature ranges. These temperature ranges can be set according to the melting point of different materials, the characteristics of the welding process or experimental results. Each temperature range usually corresponds to different welding behaviors or solder amount change trends. Subsequently, in each temperature range, the solder amount is analyzed to establish the law of solder amount change with temperature, and the change of solder amount at different temperatures is observed through experimental data. For example, at low temperatures, the solder amount may be less, while at higher temperatures, the solder amount will increase. Through these data, we can get the change rules of solder quantity corresponding to each temperature range, and further perform curve fitting on these rules to get the fitting equation between solder quantity and temperature. Through data interpolation, we can get the solder quantity prediction data under different temperature conditions, so that the solder quantity can be dynamically adjusted with the change of temperature. Finally, by performing error analysis on the solder quantity prediction data, we can evaluate the prediction accuracy of the model, correct the deviation, and finally get the accurate correspondence between solder quantity and temperature.

[0118] In one embodiment, the corresponding relationship between the welding temperature and the amount of solder is dynamically adjusted to obtain a final welding control strategy, and a welding robot is controlled to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes the steps of welding path, welding temperature and solder amount, including:

[0119] Acquiring temperature fluctuation data in the temperature change curve, performing trend analysis on the temperature fluctuation data, and obtaining a time series model of temperature fluctuation;

[0120] Calculate the change trend of the solder amount during the welding process according to the time series model to obtain adjustment data of the solder amount;

[0121] Performing fuzzy control processing on the adjustment data of the solder quantity to obtain an optimal adjustment strategy for the solder quantity;

[0122] The welding temperature is adjusted in real time according to the optimal adjustment strategy to obtain optimal welding temperature data;

[0123] The adjustment results of the welding path, the optimal welding temperature and the solder amount are collaboratively optimized to obtain a final welding control strategy.

[0124] In the above embodiment, the temperature fluctuation data in the temperature change curve is obtained. These data reflect the temperature change during the welding process. The temperature fluctuation data is subjected to trend analysis. Specifically, the regularity of temperature change can be identified by the time series analysis method, and a time series model of temperature fluctuation can be established. This model can predict the future change trend of temperature during the welding process, especially the heating and cooling process of the welding area, by analyzing the fluctuation trend of temperature in different time periods, and then deduce the change relationship between the amount of solder and temperature. Based on this time series model, the change trend of the amount of solder during the welding process is further calculated to obtain the adjustment data of the amount of solder. There is a certain nonlinear relationship between the amount of solder and the temperature. Through simulation calculation, the reasonable range and the best adjustment method of the amount of solder under temperature change are obtained. Fuzzy control processing is performed on the adjustment data of the amount of solder. Through fuzzy control theory, the adjustment of the amount of solder is adjusted in real time to cope with uncertain factors in the welding process, such as temperature fluctuations, material differences and environmental changes. Fuzzy control can formulate the optimal adjustment strategy of the amount of solder according to the current welding conditions and the required welding quality, that is, the delivery amount of solder is automatically adjusted at different temperatures and time points to ensure that the amount of solder in the welding process is always maintained at the optimal level. The welding temperature is adjusted in real time according to the solder amount adjustment strategy. By optimizing the welding temperature, the temperature is always in the most favorable range throughout the welding process to avoid the influence of too high or too low temperature on the welding quality. Finally, the adjustment results of the welding path, the optimal welding temperature and the solder amount are coordinated and optimized to obtain an optimal welding control strategy. This strategy can coordinate the temperature, solder amount and welding path throughout the welding process to ensure the accuracy and consistency of each step of the welding operation, and to avoid welding defects caused by mismatched welding conditions, such as cold soldering, over-soldering or insufficient soldering, to the greatest extent.

[0125] Reference Figure 2 , a non-destructive welding device for connecting pins used in chip processing, comprising:

[0126] The scanning module 100 is used to perform laser scanning on the chip pin area to obtain the three-dimensional space coordinate data of the chip, wherein the three-dimensional space coordinate data includes a plurality of data points;

[0127] A generating module 200 is used to generate a plurality of connection nodes according to the spatial association relationship of the plurality of data points, and to perform clustering processing on the plurality of connection nodes to obtain a plurality of regional node clusters;

[0128] The optimization module 300 is used to perform welding path optimization processing on the plurality of regional node clusters to obtain an optimal welding path from the center of each node cluster to a target welding point;

[0129] A simulation module 400 is used to perform heat flow distribution simulation processing on the optimal welding path to obtain a temperature change curve of each path;

[0130] The analysis module 500 is used to perform data analysis on the temperature change curve to obtain the corresponding relationship between the welding temperature and the amount of solder;

[0131] The adjustment module 600 is used to dynamically adjust the corresponding relationship between the welding temperature and the solder amount to obtain a final welding control strategy, and control the welding robot to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes a welding path, a welding temperature and a solder amount.

[0132] In this embodiment, the pin area of ​​the chip is laser scanned to obtain the three-dimensional spatial coordinate data of the chip. These data represent multiple data points on the surface of the chip and can accurately describe the geometric shape of the chip surface and the spatial distribution of the pins. Based on these data points, their mutual relationship in space is analyzed to generate multiple connection nodes, and then these nodes are classified into multiple regional node clusters through clustering processing. These clusters help to simplify the path planning in the welding process. The welding path of these node clusters is optimized, and the optimal welding path from the center of each regional node cluster to the target solder joint is calculated to ensure the efficiency and accuracy of the welding operation. The heat flow distribution of these optimal paths is simulated to simulate the temperature change during the welding process, and the temperature change curve of each path is obtained, which helps to analyze the heat distribution during the welding process, thereby avoiding chip damage caused by overheating or uneven temperature. The temperature change curve is analyzed, and the corresponding relationship between the welding temperature and the amount of solder is obtained, which provides a basis for the next step of the welding process. Based on the relationship between the temperature and the amount of solder obtained in S5, dynamic adjustment processing is performed, that is, the temperature and the amount of solder in the welding process are adjusted in real time to meet the needs of different welding points, and finally a comprehensive welding control strategy is generated. This strategy not only includes specific parameters of welding path, welding temperature and solder amount, but also can be adjusted according to real-time feedback to ensure the stability and high quality of welding. Through such steps, from scanning of chip pins, data analysis to real-time control of welding process, the present invention realizes an accurate and efficient non-destructive welding process, which is suitable for the processing of high-precision chips, such as welding of miniaturized electronic products or complex integrated circuits.

[0133] Reference Figure 3 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as a database of non-destructive welding methods for connecting pins for chip processing. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a non-destructive welding method for connecting pins for chip processing is implemented.

[0134] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a non-destructive welding method for connecting pins for chip processing is implemented, including the following steps: performing laser scanning processing on a chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes multiple data points; generating multiple connection nodes according to the spatial correlation relationship between the multiple data points, clustering the multiple connection nodes to obtain multiple regional node clusters; performing welding path optimization processing on the multiple regional node clusters to obtain an optimal welding path from the center of each node cluster to a target solder point; performing heat flow distribution simulation processing on the optimal welding path to obtain a temperature change curve of each path; performing data analysis processing on the temperature change curve to obtain a corresponding relationship between the welding temperature and the amount of solder; dynamically adjusting the corresponding relationship between the welding temperature and the amount of solder to obtain a final welding control strategy, and controlling a welding robot to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes a welding path, a welding temperature and an amount of solder.

[0135] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0136] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A non-destructive soldering method for pins used in chip processing, characterized in that: include: Performing laser scanning on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points; Generating a plurality of connection nodes according to the spatial association relationship of the plurality of data points, and clustering the plurality of connection nodes to obtain a plurality of regional node clusters, specifically includes: Obtaining the spatial distance between multiple data points, and calculating the similarity between each data point and other data points, wherein if the spatial distance between two data points is less than a preset threshold, they are determined to be connected nodes; Performing cluster analysis on the connection nodes based on a preset density threshold to obtain a preliminary node cluster; Calculating the geometric center of each preliminary node cluster based on the average coordinate value of all data points in each preliminary node cluster to obtain the geometric center coordinates of each preliminary node cluster; Performing spatial relative position analysis on the geometric center coordinates to obtain the relative position relationship between the preliminary node clusters and obtain spatial arrangement information of the preliminary node clusters; performing an optimization sorting process on the plurality of preliminary node clusters according to the spatial arrangement information to obtain a plurality of regional node clusters; Performing welding path optimization processing on the plurality of regional node clusters to obtain an optimal welding path from the center of each node cluster to the target welding point specifically includes: Based on the geometric center coordinates of each of the regional node clusters, the spatial distances between the regional node clusters are acquired to obtain a distance matrix between the regional node clusters; Performing a shortest path search on the plurality of regional node clusters according to the distance matrix to obtain a preliminary welding path from the center of each regional node cluster to a target welding point; Performing path smoothing processing on the preliminary welding path to obtain a smoothing result of the preliminary welding path; Obtain turning points in the preliminary welding path according to the smoothing result, and optimize the turning points with curvature greater than a preset curvature to obtain an optimized welding path; Performing local constraint processing on the optimized welding path, and evaluating the path efficiency of the optimized welding path after multiple local constraint processing to obtain an optimal welding path; Performing heat flow distribution simulation processing on the optimal welding path to obtain a temperature change curve of each path; Performing data analysis on the temperature variation curve to obtain a corresponding relationship between the welding temperature and the amount of solder; The corresponding relationship between the welding temperature and the amount of solder is dynamically adjusted to obtain a final welding control strategy, and the welding robot is controlled to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes a welding path, a welding temperature and a solder amount.

2. The non-destructive soldering method for connecting pins used in chip processing according to claim 1, characterized in that: The step of performing laser scanning on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points, comprises: Scan the chip pin surface point by point based on a laser scanner to obtain spatial coordinate information of multiple sampling points, and generate preliminary three-dimensional point cloud data based on the spatial coordinate information; Performing coordinate transformation processing on the preliminary three-dimensional point cloud data to obtain three-dimensional space coordinates; Dividing the preliminary three-dimensional point cloud data into a plurality of grid units in the three-dimensional space coordinates based on a voxel grid method, each of the grid units including a data point; Fitting the plurality of data points to generate a surface model, and segmenting the surface model according to the spatial coordinate information to obtain a plurality of sub-areas; The coordinates of the plurality of sub-regions are spliced, and the coordinate data of the overlapping regions are adjusted by a weighted average method to obtain three-dimensional space coordinate data.

3. The non-destructive soldering method for connecting pins used in chip processing according to claim 1, characterized in that: The step of performing heat flow distribution simulation processing on the optimal welding path to obtain the temperature change curve of each path includes: Calculating the heat source power distribution of each path segment of the optimal welding path to obtain the heat source distribution characteristics of each path segment, wherein the heat source distribution characteristics include the heat distribution of each path segment of the optimal welding path; Calculating the temperature change rate of each path segment based on the heat distribution of each path segment, generating a heat conduction equation, and constructing a heat conduction model based on the heat conduction equation; Numerically solving the heat conduction model to obtain the temperature distribution state of each path segment; Perform a thermal flow dynamic simulation of the time step according to the temperature distribution state to obtain temperature change data at different time points; Smoothing the temperature change data to obtain a smooth curve of temperature change over time; The welding temperature change data is analyzed and processed based on the smooth curve to obtain a temperature change curve, wherein the welding temperature change data at least includes a temperature peak value, a stable value and a drop rate value.

4. The non-destructive soldering method for connecting pins used in chip processing according to claim 1, characterized in that: The step of performing data analysis on the temperature change curve to obtain the corresponding relationship between the welding temperature and the amount of solder comprises: Fitting the temperature change curve to obtain a mathematical model of temperature change over time; Calculate the welding time at different temperatures according to the mathematical model to obtain the relationship data between welding time and temperature; According to the welding time and temperature relationship data, the temperature is segmented and analyzed to obtain several temperature ranges, and the solder amount is analyzed based on the several temperature ranges to obtain the solder amount change law corresponding to each temperature range; Performing curve fitting processing on the variation law of the solder amount to obtain a fitting equation of the solder amount and temperature, and performing data interpolation processing on the fitting equation to obtain solder amount prediction data under different temperature conditions; The error analysis of solder quantity prediction data was performed to obtain the corresponding relationship between solder quantity and temperature.

5. The non-destructive soldering method for connecting pins used in chip processing according to claim 1, characterized in that: The corresponding relationship between the welding temperature and the amount of solder is dynamically adjusted to obtain a final welding control strategy, and a welding robot is controlled to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes steps of welding path, welding temperature and solder amount, including: Acquiring temperature fluctuation data in the temperature change curve, performing trend analysis on the temperature fluctuation data, and obtaining a time series model of temperature fluctuation; Calculate the change trend of the solder amount during the welding process according to the time series model to obtain adjustment data of the solder amount; Performing fuzzy control processing on the adjustment data of the solder quantity to obtain an optimal adjustment strategy for the solder quantity; The welding temperature is adjusted in real time according to the optimal adjustment strategy to obtain optimal welding temperature data; The adjustment results of the welding path, the optimal welding temperature and the solder amount are collaboratively optimized to obtain a final welding control strategy.

6. A non-destructive soldering device for pins used in chip processing, characterized in that: include: A scanning module, used for performing laser scanning processing on the chip pin area to obtain three-dimensional spatial coordinate data of the chip, wherein the three-dimensional spatial coordinate data includes a plurality of data points; A generation module is used to generate multiple connection nodes according to the spatial association relationship of the multiple data points, and cluster the multiple connection nodes to obtain multiple regional node clusters, specifically: obtaining the spatial distance between the multiple data points, calculating the similarity between each data point and other data points, wherein if the spatial distance between two data points is less than a preset threshold, they are determined to be connection nodes; Performing cluster analysis on the connection nodes based on a preset density threshold to obtain a preliminary node cluster; Calculating the geometric center of each preliminary node cluster based on the average coordinate value of all data points in each preliminary node cluster to obtain the geometric center coordinates of each preliminary node cluster; Performing spatial relative position analysis on the geometric center coordinates to obtain the relative position relationship between the preliminary node clusters and obtain spatial arrangement information of the preliminary node clusters; performing an optimization sorting process on the plurality of preliminary node clusters according to the spatial arrangement information to obtain a plurality of regional node clusters; The optimization module is used to perform welding path optimization processing on the plurality of regional node clusters to obtain the optimal welding path from the center of each node cluster to the target welding point, specifically: based on the geometric center coordinates of each regional node cluster, the spatial distance between the regional node clusters is obtained to obtain the distance matrix between the regional node clusters; Performing a shortest path search on the plurality of regional node clusters according to the distance matrix to obtain a preliminary welding path from the center of each regional node cluster to a target welding point; Performing path smoothing processing on the preliminary welding path to obtain a smoothing result of the preliminary welding path; Obtain turning points in the preliminary welding path according to the smoothing result, and optimize the turning points with curvature greater than a preset curvature to obtain an optimized welding path; Performing local constraint processing on the optimized welding path, and evaluating the path efficiency of the optimized welding path after multiple local constraint processing to obtain an optimal welding path; A simulation module, used to perform heat flow distribution simulation processing on the optimal welding path to obtain a temperature change curve of each path; An analysis module is used to perform data analysis on the temperature change curve to obtain a corresponding relationship between the welding temperature and the amount of solder; The adjustment module is used to dynamically adjust the corresponding relationship between the welding temperature and the amount of solder to obtain a final welding control strategy, and control the welding robot to weld the chip based on the final welding control strategy, wherein the final welding control strategy at least includes a welding path, a welding temperature and a solder amount.

7. A computer device, characterized in that: It comprises a processor, a memory and a computer program stored in the memory and running on the processor, and the processor implements the welding method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the welding method according to any one of claims 1 to 5 is implemented.

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