A computer-aided method for controlling deformation of electric light truck power battery box assembly

Abstract

The invention discloses a computer-aided electric light truck power battery box assembly deformation control method, which relates to the field of automobile manufacturing and intelligent assembly technology. The method comprises real-time synchronous transmission of logistics information based on data collected by sensors at each workstation of an assembly line, comparing material information at the current workstation with a standard process file through a central controller, generating control instructions, adjusting the material conveying path and sequence according to the control instructions, ensuring accurate matching of materials, and detecting the deformation of the battery box using three-dimensional scanning technology after assembly is completed; the computer-aided electric light truck power battery box assembly deformation control method realizes efficient management of logistics and assembly processes, helps promote the modernization and intelligent development of electric light truck power battery box assembly technology, accurately and synchronously controls the transmission of logistics information between each workstation of the assembly line, and solves the problem of safety hazards caused by material matching errors.

Description

Technical Field

[0001] The present invention relates to the field of automobile manufacturing and intelligent assembly technology, and in particular to a computer-aided electric light truck power battery box assembly deformation control method. Background Art

[0002] In the assembly process of the battery box of an electric light truck, the transmission of logistics information between the various workstations of the assembly line plays a key role in ensuring production efficiency and assembly quality. In the prior art, a material tracking system based on barcodes, QR codes or radio frequency identification technology is usually used in combination with traditional production management software to achieve the management of material and assembly information. However, these methods still have many problems in practical applications.

[0003] First, the existing system has a certain lag in the transmission and processing of logistics information. When multiple stations on the assembly line run in parallel, it is impossible to synchronize material information in real time and accurately, which easily leads to material matching errors between different stations. This error can cause assembly deformation of the battery box, mixed assembly of parts or assembly omissions, which will cause hidden dangers to vehicle safety performance. Secondly, the accuracy and real-time nature of logistics information in the existing assembly control method depends on the stability of the equipment and the standardization of manual operation. Once the barcode or QR code reading device fails, or the operator misses the material during the material scanning process, the transmission of logistics information will be interrupted, and errors in the assembly process cannot be corrected in time. In addition, when faced with complex material management needs in mass production, traditional systems lack the ability to dynamically regulate logistics information and cannot flexibly respond to temporary abnormal situations in the assembly line, such as material loss, replenishment delays or equipment failures. This limitation makes the entire assembly process less intelligent, affecting production efficiency and product quality. Summary of the invention

[0004] The purpose of the present invention is to provide a computer-aided electric light truck power battery box assembly deformation control method, which can accurately and synchronously control the logistics information transmission between various workstations on the assembly line to solve the safety hazards caused by material matching errors.

[0005] To achieve the above object, the present invention provides the following technical solution: a computer-aided electric light truck power battery box assembly deformation control method, the method comprising:

[0006] S1. Based on the data information collected by the sensors of each assembly line station, the material information of the current assembly station is transmitted synchronously in real time, including analyzing the material characteristics of the current station, comparing them with the target characteristics defined in the standard process file, counting the number of corresponding feature matches in the standard process file, and calculating the similarity between the material of the current station and the standard process file based on the number of feature matches and feature weights. The specific formula is: ;

[0007] Among them, S represents the similarity, N c Indicates the number of features in the current station material information that fully matches the standard process file, N t represents the total number of features defined in the standard process file, k represents the adjustment coefficient, and W c Represents the sum of the weights of the current matching features, W t Represents the total weight of all features in the standard process file;

[0008] S2. According to the similarity analysis results, adjust the material transportation path and priority to ensure that the material is accurately matched to the target station, and calculate the shortest transportation time of the material from the starting point to the target station. The specific formula is: ;

[0009] Where d represents the minimum time from the starting point to a certain workstation, min represents the minimum value, u represents the workstation where the current material is located, v represents the target workstation, d[u] represents the shortest path time from the starting workstation to the current workstation, d[v] represents the shortest path time for the material at the target workstation to reach the workstation from the starting point, and w(u,v) represents the actual transportation time of the material from u to v.

[0010] Adjust the working order of the conveying device according to the shortest path result to ensure that the materials are accurately matched to the correct workstation;

[0011] S3. According to the optimized material matching and conveying path, instruct the assembly equipment to perform precise assembly to ensure that the materials are correctly installed at the corresponding workstations. After the assembly is completed, real-time feedback data is generated and synchronously transmitted to the central controller for recording and archiving;

[0012] S4. Use 3D scanning technology to detect surface deformation of the assembled power battery box, generate a deformation data model, and compare it with the design standard data model to evaluate whether the deformation is within an acceptable range. If it exceeds the allowable range, output correction suggestions or rework instructions.

[0013] Preferably, S1 includes collecting current material data from sensors at each workstation, and setting the material supply quantity uploaded at the current workstation as Q 1 , the material demand required for the target station is Q 2 , judge Q 1 and Q 2 Are they equal? ​​When Q 1 >Q 2 When Q 1 <Q 2 When the material is transported, the transmission frequency of the material is increased.

[0014] Preferably, S4 includes:

[0015] Use 3D scanning technology to obtain the actual point cloud data of the battery box, extract the ideal coordinates from the standard battery box model, and calculate the offset distance of each point. The specific formula is:

[0016] ;

[0017] Where D represents the offset distance between the scanning point and the standard point, (x 1 ,y 1 , z 1 ) represents the coordinates of the 3D point cloud data obtained by scanning, (x 0 ,y 0 , z 0 ) represent the ideal coordinates of the corresponding points in the standard model.

[0018] Preferably, the sensors at each workstation of the assembly line in S1 include laser sensors, visual sensors and weight sensors, the laser sensors collect size information of materials, the visual sensors collect appearance information of materials, and the weight sensors collect weight information of materials.

[0019] Preferably, the real-time synchronous transmission of the logistics information in S1 is based on a wireless communication protocol, and the wireless communication protocol includes a Wi-Fi, Bluetooth or Zigbee protocol.

[0020] Preferably, the three-dimensional scanning technology in S4 acquires the actual three-dimensional structure of the battery box based on laser point cloud scanning.

[0021] Preferably, the result of the deformation detection in S4 is used to generate a deformation analysis report and mark the parts that exceed the design tolerance.

[0022] Preferably, the Q 1 and Q 2 The judgment is performed by a central controller, which dynamically compares the material supply and demand based on real-time collected data.

[0023] Preferably, the total weight W of all features in the standard process file in S1 is t The calculation formula is: ;

[0024] Among them, W t represents the total weight of all features in the standard process file, m represents the total number of features in the standard process file, and w i It represents the weight value of the i-th feature in the standard process file, where i represents the index number of the feature.

[0025] Preferably, the weight value w of the i-th feature in the standard process file isi The calculation formula is: ;

[0026] Among them, w i Represents the weight value of the i-th feature in the standard process file. Both i and j represent the index number of the feature. i represents the importance score of the i-th feature, R j represents the importance score of the jth feature, and m represents the total number of features in the standard process file.

[0027] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0028] The computer-aided electric light truck power battery box assembly deformation control method transmits logistics information in real time and synchronously based on data collected by sensors at each workstation of the assembly line. The central controller compares the current workstation material information with the standard process file, generates control instructions, and adjusts the material conveying path and sequence according to the control instructions to ensure accurate material matching. After assembly, the deformation of the battery box is detected using three-dimensional scanning technology, which effectively avoids material matching errors, mixed assembly of parts or assembly omissions caused by information lag, greatly improves assembly quality and production efficiency, and can ensure the continuity and accuracy of logistics information transmission through redundant data or dynamic adjustment strategies, reducing manual operation errors. Influence, flexibly respond to emergencies such as material loss and equipment failure, improve the intelligence level of the assembly process, can significantly reduce the risk of battery box assembly deformation due to material matching errors or information interruption, ensure the consistency of assembly quality, and thus improve the safety performance and reliability of the vehicle, can effectively reduce production delays caused by information lag or improper handling, achieve efficient management of logistics and assembly processes, improve the production efficiency and management level of the assembly line as a whole, help promote the modernization and intelligent development of the electric light truck power battery box assembly process, accurately and synchronously regulate the logistics information transmission between the various workstations on the assembly line, and solve the problem of safety hazards caused by material matching errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] like Figure 1As shown, the present invention provides a technical solution: a computer-aided electric light truck power battery box assembly deformation control method, the method comprising:

[0032] S1. Based on the data information collected by the sensors of each assembly line station, the material information of the current assembly station is transmitted synchronously in real time, including analyzing the material characteristics of the current station, comparing them with the target characteristics defined in the standard process file, counting the number of corresponding feature matches in the standard process file, and calculating the similarity between the material of the current station and the standard process file based on the number of feature matches and feature weights. The specific formula is: ;

[0033] Among them, S represents the similarity, N c Indicates the number of features in the current station material information that fully matches the standard process file, N t represents the total number of features defined in the standard process file, k represents the adjustment coefficient, and W c Represents the sum of the weights of the current matching features, W t Represents the total weight of all features in the standard process file;

[0034] S2. According to the similarity analysis results, adjust the material transportation path and priority to ensure that the material is accurately matched to the target station, and calculate the shortest transportation time of the material from the starting point to the target station. The specific formula is: ;

[0035] Where d represents the minimum time from the starting point to a certain workstation, min represents the minimum value, u represents the workstation where the current material is located, v represents the target workstation, d[u] represents the shortest path time from the starting workstation to the current workstation, d[v] represents the shortest path time for the material at the target workstation to reach the workstation from the starting point, and w(u,v) represents the actual transportation time of the material from u to v.

[0036] Adjust the working order of the conveying device according to the shortest path result to ensure that the materials are accurately matched to the correct workstation;

[0037] S3. According to the optimized material matching and conveying path, instruct the assembly equipment to perform precise assembly to ensure that the materials are correctly installed at the corresponding workstations. After the assembly is completed, real-time feedback data is generated and synchronously transmitted to the central controller for recording and archiving;

[0038] S4. Use 3D scanning technology to detect surface deformation of the assembled power battery box, generate a deformation data model, and compare it with the design standard data model to evaluate whether the deformation is within an acceptable range. If it exceeds the allowable range, output correction suggestions or rework instructions.

[0039] The working principle of the above method is: the logistics information collected in real time by sensors at each workstation of the assembly line is compared one by one with the material characteristics defined in the standard process file, and the similarity calculation formula is used to calculate the material characteristics. Improve the matching accuracy and ensure that the characteristics of the current workstation materials are consistent with the requirements of the standard process documents. At the same time, the central controller dynamically adjusts the logistics delivery path, and uses the shortest path algorithm to ensure that the materials can be delivered to the target workstation in a timely and accurate manner, reducing logistics time and mismatching rate. After assembly is completed, the deformation of the battery box is fully detected through three-dimensional scanning technology to ensure product quality. This method accurately matches the workstation materials with the standard process documents through real-time data transmission and similarity calculation, reducing the errors that may be caused by manual intervention; optimizes the material delivery path through the shortest path algorithm, effectively reducing logistics time and costs; and uses three-dimensional scanning technology to detect the deformation of the battery box, which can quickly locate the problem and improve product quality control efficiency.

[0040] S1 includes collecting current material data from sensors at each workstation, and the data includes a set of material feature information at the current workstation. The feature set of the material at the current workstation is matched with the feature set in the standard process file, and the number of matching features and related weights are counted, and the similarity is calculated. Based on the similarity and control instructions, the material conveying path and sequence are optimized to ensure accurate matching and efficient transmission of materials. By comparing the feature data with the standard process file, a control instruction is generated to make the material supply quantity uploaded by the current workstation Q 1 , the material demand required for the target station is Q 2 , judge Q 1 and Q 2 Are they equal? ​​When Q 1 >Q 2 When Q 1 <Q 2 When the material is transported, the transmission frequency of the material is increased.

[0041] The formula compares the path time from the starting station to the current station (d[u]) plus the transportation time from the current station to the target station (w(u, v)), and takes the minimum time value among all possible paths as the final shortest path. Starting from the starting station, calculate the shortest path time (d[u]) to each intermediate station in turn, and add the transportation time from the current station to the target station (w(u, v)) to the known path time (d[u]). Compare the total time of all possible paths, select the path with the shortest time (min operation), and finally get the shortest path time d[v] from the starting station to the target station.

[0042] In the above embodiment, the current material data is collected based on the sensors at each workstation of the assembly line, and the material supply quantity Q uploaded by the workstation is monitored in real time. 1And the material demand Q required for the target workstation 2 , and through logical judgment Q 1 and Q 2 The material transmission strategy is adjusted dynamically based on the relationship between 1 >Q 2 By reducing the material transmission volume or lowering the material transmission frequency, we can avoid station congestion or waste of logistics resources caused by material overload; when Q 1 2 When the material is insufficient, the material transmission frequency is increased to ensure timely material supply and avoid affecting the assembly rhythm due to insufficient supply. This process achieves logistics balance and dynamic matching through automated control. This method effectively avoids the waste of logistics resources and transmission congestion caused by overload of workstations by real-time monitoring and dynamic adjustment of material transmission volume and frequency. It also solves the problem of production stagnation caused by insufficient supply, thereby improving the operating efficiency of the logistics system and the stability of the assembly line. The intelligent control mechanism of this solution not only reduces the need for manual intervention, but also improves the accuracy and response speed of material management.

[0043] S4 includes using 3D scanning technology to obtain the actual point cloud data of the battery box, extracting the ideal coordinates from the standard battery box model, and calculating the offset distance of each point. The specific formula is:

[0044] ;

[0045] Where D represents the offset distance between the scanning point and the standard point, (x 1 ,y 1 , z 1 ) represents the coordinates of the 3D point cloud data obtained by scanning, (x 0 ,y 0 , z 0 ) represent the ideal coordinates of the corresponding points in the standard model.

[0046] In the above embodiment, the assembled battery box is scanned by a 3D scanner to obtain its actual point cloud data, and the data is preprocessed to remove noise and abnormal data. The cleaned actual point cloud data is matched with the 3D model of the standard battery box, and the actual coordinates (x 1 ,y 1 , z 1 ) and the ideal coordinates of the corresponding point in the standard model (x 0 ,y 0 , z 0 ), using the formula ​Calculate the offset distance of each point to evaluate the overall deformation of the battery case. By analyzing the offset data of all points, a deformation distribution map can be generated to quickly locate the deformation area, providing a basis for subsequent quality control and adjustment. This method greatly improves the accuracy and efficiency of battery case assembly deformation detection by introducing three-dimensional scanning technology and an accurate offset distance calculation model. Using point cloud data analysis, not only can the deformation area of ​​the case be quickly determined, but also quantitative offset values ​​can be provided to facilitate further correction and optimization. At the same time, this method reduces the manual dependence of traditional detection methods and improves the automation and reliability of the detection process.

[0047] The sensors at each workstation of the assembly line in S1 include a laser sensor, a visual sensor and a weight sensor. The laser sensor collects the size information of the material, the visual sensor collects the appearance information of the material, and the weight sensor collects the weight information of the material.

[0048] In the above embodiment, each workstation on the assembly line is equipped with a laser sensor, a visual sensor and a weight sensor to collect multi-dimensional data of the material in real time: Laser sensor: collects the size information of the material through the principle of laser ranging, and detects whether the length, width and height of the material meet the standard process requirements. Visual sensor: collects the appearance information of the material through image capture and processing technology, which is used to identify the surface defects of the material, such as cracks, scratches, and color, shape and other characteristics. Weight sensor: collects the weight information of the material through the principle of force-electric conversion, which is used to confirm whether the quality of the material is within the preset tolerance range. The data of the above sensors are integrated by the central controller to comprehensively judge whether the material meets the assembly requirements. If an abnormality is found, the system can automatically alarm and take corresponding measures, such as removing unqualified materials or suspending the conveying process. This multi-sensor fusion technology improves the accuracy and reliability of material detection. This embodiment can perform all-round detection of the size, appearance and weight of the material through the collaborative work of laser sensors, visual sensors and weight sensors, effectively avoiding the detection errors or omissions caused by the limitations of a single sensor. By collecting and feeding back data in real time, the accuracy and consistency of the materials at each workstation are ensured, thereby improving the quality stability of the battery box assembly process. This solution also reduces the workload of manual inspection and improves the automation level and production efficiency of the production line.

[0049] The real-time synchronous transmission of logistics information in S1 is based on a wireless communication protocol, which includes Wi-Fi, Bluetooth or Zigbee protocols. In the above implementation, the real-time synchronous transmission of logistics information relies on wireless communication protocols to achieve seamless data interaction between various workstations on the assembly line: Wi-Fi protocol: The logistics data collected by the workstation sensor is transmitted to the central controller through a high-speed wireless network connection, which is suitable for scenarios with large data volume and high bandwidth requirements. Bluetooth protocol: Logistics information is transmitted through short-range wireless communication, which is suitable for scenarios with power consumption sensitivity or short distances between workstations. Zigbee protocol: Logistics data is transmitted through a low-power, low-bandwidth mesh network structure, which is suitable for scenarios with a large number of nodes and high network stability requirements. The central controller integrates and processes data based on the logistics information transmitted by the workstation sensor, realizes real-time monitoring and dynamic regulation of the logistics status, and ensures accurate transportation and assembly of materials. This implementation method realizes real-time synchronous transmission of logistics information through the introduction of wireless communication protocols, gets rid of the limitations of traditional wired communications, and improves the flexibility and adaptability of data transmission. By selecting Wi-Fi, Bluetooth or Zigbee protocols according to different scenarios, you can flexibly balance the transmission rate, power consumption and network coverage to optimize communication performance. This method effectively reduces transmission delays, improves the real-time and accuracy of logistics information processing, simplifies the wiring complexity of assembly lines, and reduces installation and maintenance costs.

[0050] The three-dimensional scanning technology in S4 obtains the actual three-dimensional structure of the battery box based on laser point cloud scanning. In the above embodiment, the actual three-dimensional structure of the battery box is accurately measured by laser point cloud scanning technology: the laser scanner emits a laser beam to illuminate the surface of the battery box, receives the reflected light, measures the laser round-trip time or phase difference, and obtains the spatial position coordinates of the surface points of the battery box. The scanned points are integrated into point cloud data to form the actual three-dimensional structure of the battery box. The scanned point cloud data is processed, including denoising, resampling and point cloud alignment operations, and compared with the standard three-dimensional model. By calculating the offset data of the actual point cloud and the standard three-dimensional model, the possible deformation area of ​​the battery box is identified, and the degree of deformation is quantified to support subsequent adjustment or correction work. This process relies on the high-precision characteristics of laser point cloud scanning technology, and can quickly and efficiently complete the three-dimensional structure acquisition and deformation evaluation of the battery box. This embodiment can accurately obtain the actual three-dimensional structure of the battery box through laser point cloud scanning technology, significantly improving the resolution and accuracy of deformation detection. The high-density characteristics of point cloud data enable subtle deformations to be effectively captured and quantified, thereby providing a reliable basis for optimizing assembly quality. Laser point cloud scanning technology has the advantages of non-contact and fast response, avoiding the potential damage to the surface of the battery box caused by traditional contact detection, while improving detection efficiency and applicability.

[0051] The result of deformation detection in S4 is used to generate a deformation analysis report and mark the parts that exceed the design tolerance. In the above embodiment, based on the result of deformation detection, a deformation analysis report is generated and the parts that exceed the design tolerance are marked by the following steps: Data acquisition and calculation: Use three-dimensional scanning technology to obtain the point cloud data of the battery box, calculate the offset value between the actual point cloud and the standard model, and identify the deformation area and its offset. Comparison with design tolerance: Compare the offset data with the preset design tolerance range point by point to determine whether it exceeds the allowable range. Generate a deformation analysis report: According to the comparison results, a deformation analysis report is automatically generated, which includes statistical analysis of the offset data, such as the maximum offset value, the average offset value, the standard deviation, etc., a three-dimensional visualization chart of the deformation area, and the parts that exceed the design tolerance and their specific coordinate information. Deformation area marking: Mark the parts that exceed the tolerance in the report by color marking or graphic symbols, such as using red to highlight the severely deformed area to intuitively reflect the deformation distribution. The deformation analysis report can be used as a basis for subsequent correction and optimization of the assembly process, and supports the continuous improvement of assembly quality. This embodiment generates a deformation analysis report to make the deformation detection results more readable and practical. The automatically generated report not only saves manual analysis time, but also provides quantitative deformation assessment data, providing a scientific basis for assembly process optimization and quality control. At the same time, the intuitive marking of out-of-tolerance parts significantly improves the efficiency of problem location, supports rapid decision-making and correction, and improves the overall operation efficiency and product quality of the production line.

[0052] Q 1 and Q 2 The judgment is performed by the central controller, which dynamically compares the material supply and demand based on the real-time collected data. 1 , and the demand Q 2 The specific process is as follows: Data collection: The material supply quantity Q of the current station is collected in real time through sensors at each station of the assembly line. 1 and the material demand Q of the target workstation 2 Dynamic comparison: The central controller inputs the collected data into the built-in algorithm to calculate Q in real time. 1 and Q 2 The difference: When Q 1 >Q 2 When Q 1 2 ​When the material is insufficient, the controller issues a command to increase the material transmission frequency or increase the transmission volume to ensure that the material supply meets the demand. Closed-loop control: The central controller dynamically adjusts the operating parameters of the material conveying equipment, such as transmission speed or frequency, through periodic comparison and feedback to maintain the balance between supply and demand and avoid the impact on assembly efficiency due to overload or insufficient supply. This embodiment can accurately control the balance of material supply and demand and realize intelligent management of material transportation through the dynamic comparison function of the central controller. The real-time judgment and closed-loop control mechanism of the central controller effectively reduce the risk of overload or insufficient supply, improve the accuracy of material distribution, and thus optimize the operating efficiency of the assembly line. In addition, the automated control of the system reduces the need for manual intervention and improves the stability and reliability of the overall assembly process.

[0053] The total weight W of all features in the standard process file in S1 t The calculation formula is: ;

[0054] Among them, W t represents the total weight of all features in the standard process file, m represents the total number of features in the standard process file, and w i It represents the weight value of the i-th feature in the standard process file, where i represents the index number of the feature.

[0055] In the above embodiment, the total feature weight W of the standard process file is t It is a key parameter used to measure the overall importance of material characteristics. The calculation steps are as follows: Feature weight assignment: In the standard process file, a weight value w is assigned to each material feature. i The size of the weight value is determined by the importance of the feature. For example, the weight value of the key feature is larger, while the weight value of the secondary feature is smaller. Weight value accumulation: The weight value w of all features in the standard process file is i Accumulate and calculate the total feature weight W t , and its calculation formula is: , where m represents the total number of features, ensuring that the weight of each feature is accurately included in the calculation. Dynamic adjustment mechanism: When adding or modifying features in the standard process file, the weight value w can be dynamically adjusted. i And recalculate W t, ensuring that the sum of weights is always consistent with the actual situation. Through this weight calculation formula, accurate basic data can be provided for the similarity calculation formula, so as to more accurately judge the degree of match between the current material information and the standard process file. This implementation method can effectively quantify the importance of features through the accurate calculation of the feature weights of the standard process file, provide a scientific basis for the similarity calculation formula, and improve the accuracy and flexibility of matching judgments. The dynamic adjustment weight mechanism ensures the real-time and adaptability of the weight value allocation, and can adapt to different process conditions or changes in demand. In addition, this method simplifies the weight calculation process by accumulating weight values, which is convenient for rapid application in large-scale feature data.

[0056] The weight value w of the i-th feature in the standard process file i The calculation formula is: ;

[0057] Among them, w i Represents the weight value of the i-th feature in the standard process file. Both i and j represent the index number of the feature. i represents the importance score of the i-th feature, R j represents the importance score of the jth feature, and m represents the total number of features in the standard process file.

[0058] In the above embodiment, the weight value w of each feature in the standard process file is calculated by the following steps: i :Feature importance score: According to the importance of each feature in the process, an importance score R is assigned i The scoring basis may include the impact of the feature on assembly accuracy, stability or functional realization. The higher the score, the more important the feature. Weight normalization: The importance score R of each feature is i The sum of all feature scores Divide and calculate the weight value w of each feature i This normalization process ensures that the sum of the weights is 1, making it applicable to feature sets of different sizes. Dynamic adjustment: When the importance of features or process requirements change, the new score R can be used to adjust the feature set. i Recalculate the weight value w i , to ensure the flexibility and adaptability of weight distribution. The weight value w calculated by the above method iIt is possible to quantify the importance of each feature and provide an accurate weight basis for related calculations, such as similarity calculations. This implementation method scientifically quantifies the importance of each feature by combining importance scoring and normalization calculations. The normalization method ensures that the sum of the weight values ​​is fixed and is not affected by the number of features or the scoring range, which enhances the applicability and stability of the weight calculation. In addition, by dynamically adjusting the scoring and weight distribution, it is possible to flexibly respond to process changes and improve the intelligence level of the production process. Ultimately, this method can support more accurate feature matching and assembly quality control.

[0059] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A computer-aided electric light truck power battery box assembly deformation control method, characterized in that: The method comprises: S1. Based on the data information collected by the sensors of each assembly line station, the material information of the current assembly station is transmitted synchronously in real time, including analyzing the material characteristics of the current station, comparing them with the target characteristics defined in the standard process file, counting the number of corresponding feature matches in the standard process file, and calculating the similarity between the material of the current station and the standard process file based on the number of feature matches and feature weights. The specific formula is: ; Among them, S represents the similarity, N c Indicates the number of features in the current station material information that fully matches the standard process file, N t represents the total number of features defined in the standard process file, k represents the adjustment coefficient, and W c Represents the sum of the weights of the current matching features, W t Represents the total weight of all features in the standard process file; S2. According to the similarity analysis results, adjust the material transportation path and priority to ensure that the material is accurately matched to the target station, and calculate the shortest transportation time of the material from the starting point to the target station. The specific formula is: ; Where d represents the minimum time from the starting point to a certain workstation, min represents the minimum value, u represents the workstation where the current material is located, v represents the target workstation, d[u] represents the shortest path time from the starting workstation to the current workstation, d[v] represents the shortest path time for the material at the target workstation to reach the workstation from the starting point, and w(u,v) represents the actual transportation time of the material from u to v. Adjust the working order of the conveying device according to the shortest path result to ensure that the materials are accurately matched to the correct workstation; S3. According to the optimized material matching and conveying path, instruct the assembly equipment to perform precise assembly to ensure that the materials are correctly installed at the corresponding workstations. After the assembly is completed, real-time feedback data is generated and synchronously transmitted to the central controller for recording and archiving; S4. Use 3D scanning technology to detect surface deformation of the assembled power battery box, generate a deformation data model, and compare it with the design standard data model to evaluate whether the deformation is within an acceptable range. If it exceeds the allowable range, output correction suggestions or rework instructions.

2. A computer-aided electric light truck power battery box assembly deformation control method according to claim 1, characterized in that: The S1 includes collecting current material data from sensors at each workstation, assuming that the material supply uploaded by the current workstation is Q1, and the material demand required by the target workstation is Q2, and judging whether Q1 and Q2 are equal. When Q1>Q2, reducing material transmission or reducing frequency to avoid overload, and when Q1<Q2, increasing the material transmission frequency.

3. According to claim 1, a computer-aided electric light truck power battery box assembly deformation control method is characterized in that: The S4 includes: Use 3D scanning technology to obtain the actual point cloud data of the battery box, extract the ideal coordinates from the standard battery box model, and calculate the offset distance of each point. The specific formula is: ； Where D represents the offset distance between the scanned point and the standard point, (x1, y1, z1) represents the coordinates of the 3D point cloud data obtained by scanning, and (x0, y0, z0) represents the ideal coordinates of the corresponding point in the standard model.

4. A computer-aided electric light truck power battery box assembly deformation control method according to claim 1, characterized in that: The sensors at each workstation of the assembly line in S1 include a laser sensor, a visual sensor and a weight sensor. The laser sensor collects size information of the material, the visual sensor collects appearance information of the material, and the weight sensor collects weight information of the material.

5. A computer-aided electric light truck power battery box assembly deformation control method according to claim 1, characterized in that: The real-time synchronous transmission of the logistics information in S1 is based on a wireless communication protocol, which includes a Wi-Fi, Bluetooth or Zigbee protocol.

6. A computer-aided electric light truck power battery box assembly deformation control method according to claim 1, characterized in that: The three-dimensional scanning technology in S4 obtains the actual three-dimensional structure of the battery box based on laser point cloud scanning.

7. A computer-aided electric light truck power battery box assembly deformation control method according to claim 1, characterized in that: The result of the deformation detection in S4 is used to generate a deformation analysis report and mark the parts that exceed the design tolerance.

8. A computer-aided electric light truck power battery box assembly deformation control method according to claim 2, characterized in that: The determination of Q1 and Q2 is performed by a central controller, which dynamically compares the material supply and demand based on real-time collected data.

9. A computer-aided electric light truck power battery box assembly deformation control method according to claim 1, characterized in that: The total weight W of all features in the standard process file in S1 t The calculation formula is: ; Among them, W t represents the total weight of all features in the standard process file, m represents the total number of features in the standard process file, and w i It represents the weight value of the i-th feature in the standard process file, where i represents the index number of the feature.

10. A computer-aided electric light truck power battery box assembly deformation control method according to claim 9, characterized in that: The weight value w of the i-th feature in the standard process file i The calculation formula is: ; Among them, w i Represents the weight value of the i-th feature in the standard process file. Both i and j represent the index number of the feature. i represents the importance score of the i-th feature, R j represents the importance score of the jth feature, and m represents the total number of features in the standard process file.

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