A method and device for analyzing and processing data of automatic assembly of a clamp

Abstract

The application provides a clamp automatic assembly data analysis processing method and device, and belongs to the technical field of data analysis, and specifically comprises the following steps: determining the assembly deviation types of different clamps according to the assembly deviation data of the same assembly scene of the different clamps, determining the detection strategy of the clamps of the type according to the assembly deviation types and distribution data of the clamps in the different types, and when the detection strategy of the clamps of the type is not a real-time detection strategy, acquiring the assembly deviation types of the different clamps of the type and the interval data of the clamps of the same type, combining the interval data of the clamps corresponding to the real-time detection strategy to determine the detection target of the assembly deviation of the clamps of the type, and determining whether adjustment processing of the assembly detection strategy is needed according to the detection data of the assembly deviation of the detection target, so that the accuracy of assembly processing is improved.

Description

Technical Field

[0001] This invention belongs to the field of data analysis technology, and in particular relates to a method and apparatus for automatic assembly data analysis and processing of clamps. Background Technology

[0002] To achieve data analysis and processing during the assembly process, patent application CN202510083565.9, "A Data Analysis System for Assembly Quality of Watch Products," uses an assembly repair management module to judge and analyze the assembly quality index and take corresponding assembly repair measures. This allows for accurate assessment of minute internal errors in the assembled watch, ensuring the precision and stability of the watch product and thus comprehensively evaluating product quality.

[0003] In the assembly process, existing technical solutions neglect to generate differentiated assembly inspection strategies based on clamp distribution data during assembly. If the clamp distribution density is high or the clamp distribution types are significantly different, the reliability of the assembly process cannot be guaranteed if the assembly inspection process is not strengthened.

[0004] To address the aforementioned technical problems, this application specifically provides a method and apparatus for automatic assembly data analysis and processing of clamps. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Firstly, this application provides a method for analyzing and processing data related to the automatic assembly of clamps, specifically including: S1 takes the assembly processing target of clamps as the objective, determines the distribution data of different types of clamps in the assembly processing target, and uses the distribution data to determine that the assembly processing difficulty of the assembly processing target meets the requirements, then proceeds to the next step. S2 uses the distribution data of different clamps to determine the adjacent clamps of different clamps, determines the consistent assembly scenario based on the distribution data of the adjacent clamps, and determines the assembly deviation type of different clamps based on the assembly deviation data under the consistent assembly scenario of different clamps. S3 determines that when the detection strategy for a clamp of a certain type does not belong to the real-time detection strategy based on the assembly deviation type and distribution data of clamps of different types, it acquires the assembly deviation type of different clamps of that type and the interval data of clamps of the same type, and determines the detection target of the assembly deviation of the clamp of that type by combining the interval data of clamps corresponding to the real-time detection strategy. Based on the detection data of the assembly deviation of the detection target, it determines whether the assembly detection strategy needs to be adjusted.

[0006] The beneficial effects of this invention are as follows: Based on the assembly deviation types and distribution data of different types of clamps, it is determined whether the detection strategy for each type of clamp belongs to the real-time detection strategy. This not only takes into account the differences in the probability of assembly deviation caused by the differences in the types of clamp assembly deviations, but also the differences in the detection requirements of clamps caused by the dispersion of their distribution. This enables the screening of clamp types for real-time detection strategies from multiple perspectives, ensuring the accuracy of detection and processing of clamps with high deviation probabilities and overly dispersed distributions.

[0007] Based on the detection data of assembly deviations of the inspection target, it is determined whether the assembly inspection strategy needs to be adjusted. This enables dynamic adjustment of the assembly inspection strategy based on the assembly deviation of the inspection target, which not only reduces the power consumption of assembly inspection when the assembly is relatively reliable, but also improves the reliability of assembly inspection when the assembly is poor.

[0008] A further technical solution is that the type of clamp is classified according to the size of the clamp.

[0009] A further technical solution is that the distribution data of the clamps includes the distribution positions of different types of clamps within the same assembly process cycle and adjacent clamps.

[0010] A further technical solution involves determining that the assembly processing difficulty of the assembly processing target meets the requirements, specifically including: Based on the distribution data of the assembly processing target, the number of clamps for the assembly processing target within the same assembly processing cycle is determined, and the number of clamps is used to determine the preset difficulty coefficient of the assembly processing target. Based on the distribution data of the clamps, the quantity of different types of clamps within the same assembly processing cycle is determined; Based on the proportion of different types of clamps and the preset difficulty coefficient of the assembly processing target, the assembly processing difficulty coefficient of the assembly processing target is determined, and based on the assembly processing difficulty coefficient, it is determined whether the assembly processing difficulty of the assembly processing target meets the requirements.

[0011] A further technical solution is that the method for determining the assembly difficulty coefficient of the assembly processing target is as follows: Based on the proportion of different types of clamps, determine the number of clamp types whose proportion is greater than a preset proportion, and determine the preset assembly dispersion coefficient of the assembly processing target based on the number of types. The assembly processing difficulty coefficient of the assembly processing target is determined based on the product of the preset difficulty coefficient and the preset assembly dispersion coefficient.

[0012] A further technical solution is that the assembly processing difficulty coefficient of the assembly processing target is between 0 and 1. When the assembly processing difficulty coefficient of the assembly processing target is greater than a preset difficulty coefficient threshold, it is determined that the assembly processing difficulty of the assembly processing template does not meet the requirements.

[0013] A further technical solution involves determining whether adjustments to the assembly and inspection strategy are necessary, specifically including: Based on the detection data of the assembly deviation of the detection targets, determine the proportion of the number of detection targets with assembly deviation of different types of clamps, and use the proportion of the number to determine the assembly deviation coefficient of different types of clamps. The weighting coefficients for different types of clamps are determined by the proportion of their quantities within the same assembly process cycle. Based on the assembly deviation coefficients and weighting coefficients of different types of clamps, the weighted sum of the assembly deviation coefficients is determined, and the weighted sum of the assembly deviation coefficients is used to determine whether the assembly inspection strategy needs to be adjusted.

[0014] A further technical solution is that when the weights of the assembly deviation coefficients are within a preset weight range, it is determined that no adjustment processing of the assembly inspection strategy is required.

[0015] A further technical solution is that when the weighted sum of the assembly deviation coefficients is not within the preset weight range, or when the weighted sum of the assembly deviation coefficients is less than the preset weight threshold, the assembly deviation coefficient is used as an adjustment factor, and the original detection frequency is adjusted by multiplying the adjustment factor by the original detection frequency. When the weighted sum of the assembly deviation coefficients is not less than the preset weight threshold, a real-time detection strategy is adopted for detection processing.

[0016] Secondly, this application provides an automatic clamp assembly data analysis and processing device, employing the aforementioned automatic clamp assembly data analysis and processing method, specifically including: Detection module, detection strategy adjustment module; The detection module is responsible for detecting and processing assembly deviations of different types of clamps. The detection strategy adjustment module is responsible for determining whether the assembly detection strategy needs to be adjusted based on the detection data of the assembly deviation of the detection target.

[0017] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 This is a flowchart of a data analysis and processing method for automatic clamp assembly; Figure 2 It is a flowchart that determines whether the assembly processing difficulty meets the requirements for the assembly processing target; Figure 3 This is a flowchart illustrating the method for determining the assembly deviation type of the clamp; Figure 4 The flowchart shows the detection strategy for a specific type of clamp, which is not part of the real-time detection strategy. Figure 5 This is a framework diagram of a data analysis and processing device for automatic clamp assembly. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0021] During the assembly process, it is necessary to generate differentiated assembly inspection strategies based on the distribution data of clamps during assembly. These strategies are based on the distribution density of different assembly processing targets to ensure the reliability of assembly processing.

[0022] The average distance is determined by the average of the interval distances between different adjacent clamps. When the average distance is not within the preset distance range, it is determined that the assembly processing difficulty of the assembly processing target does not meet the requirements. When the assembly processing difficulty of the assembly processing target does not meet the requirements, all clamps are subjected to real-time detection processing.

[0023] A consistent assembly scenario is a historical assembly scenario that is consistent with the settings of the preset number of adjacent clamps.

[0024] The assembly deviation type is determined based on the preset deviation type corresponding to the average percentage of clamps with assembly deviations in a consistent assembly scenario. The assembly deviation types of clamps include Type I, Type II, and Type III deviation types.

[0025] Based on the spacing between adjacent clamps of the aforementioned type, the number of clamps with a spacing greater than a preset spacing is determined, and these are considered as distributed discrete clamps. By determining the assembly deviation type of the clamps, the proportion of clamps of different assembly deviation types is determined. When the average of the proportion of clamps of one type of deviation and the proportion of distributed discrete clamps is greater than 0.3, the detection strategy for the type of clamp is determined to be a real-time detection strategy.

[0026] The target for detecting assembly deviations is a type of clamp with a specific deviation.

[0027] When the proportion of assembly deviations in the inspection target is greater than 0.1, it is determined that the assembly inspection strategy needs to be adjusted.

[0028] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, in a first aspect, this application provides a method for automatic assembly data analysis and processing of clamps, specifically including: S1 takes the assembly processing target of clamps as the objective, determines the distribution data of different types of clamps in the assembly processing target, and uses the distribution data to determine that the assembly processing difficulty of the assembly processing target meets the requirements, then proceeds to the next step. Furthermore, the type of clamp is classified according to the size of the clamp.

[0029] Specifically, the distribution data of the clamps includes the distribution positions of different types of clamps within the same assembly process cycle, as well as adjacent clamps.

[0030] Specifically, such as Figure 2 As shown, determining that the assembly processing difficulty of the assembly processing target meets the requirements specifically includes: Based on the distribution data of the assembly processing target, the number of clamps for the assembly processing target within the same assembly processing cycle is determined, and the number of clamps is used to determine the preset difficulty coefficient of the assembly processing target. Based on the distribution data of the clamps, the quantity of different types of clamps within the same assembly processing cycle is determined; Based on the proportion of different types of clamps and the preset difficulty coefficient of the assembly processing target, the assembly processing difficulty coefficient of the assembly processing target is determined, and based on the assembly processing difficulty coefficient, it is determined whether the assembly processing difficulty of the assembly processing target meets the requirements.

[0031] It should be noted that the method for determining the assembly difficulty coefficient of the assembly processing target is as follows: Based on the proportion of different types of clamps, determine the number of clamp types whose proportion is greater than a preset proportion, and determine the preset assembly dispersion coefficient of the assembly processing target based on the number of types. The assembly processing difficulty coefficient of the assembly processing target is determined based on the product of the preset difficulty coefficient and the preset assembly dispersion coefficient.

[0032] Optionally, the preset assembly dispersion coefficient is determined based on the ratio of the number of types to the number of types of the assembly processing target.

[0033] Furthermore, the assembly processing difficulty coefficient of the assembly processing target is between 0 and 1. If the assembly processing difficulty coefficient of the assembly processing target is greater than the preset difficulty coefficient threshold, then it is determined that the assembly processing difficulty of the assembly processing template does not meet the requirements.

[0034] In another possible embodiment, determining that the assembly difficulty of the assembly process target meets the requirements specifically includes: Based on the distribution data of the clamps, the quantity of different types of clamps within the same assembly processing cycle is determined; Based on the quantity of different types of clamps, identify the types of clamps whose quantity exceeds a preset threshold and treat them as the key focus types. The assembly difficulty of the assembly process target is determined based on the number of the key focus types.

[0035] Furthermore, when the number of the key focus types is less than the preset number of key focus types, it is determined that the assembly processing difficulty of the assembly processing target meets the requirements.

[0036] Specifically, when the assembly difficulty of the assembly processing target does not meet the requirements, all clamps are subjected to real-time detection and processing.

[0037] In another possible embodiment, determining that the assembly difficulty of the assembly process target meets the requirements specifically includes: Based on the distribution data of the assembly processing target, the number of clamps of the assembly processing target in the same assembly processing cycle is determined. When the number of clamps in the same assembly processing cycle is less than the preset number of clamps, it is determined that the assembly processing difficulty of the assembly processing target meets the requirements. When the number of clamps in the same assembly processing cycle is not less than the preset number of clamps: Based on the different intervals between adjacent clamps, the number of clamps with intervals less than a preset interval is determined. The clamps with intervals less than the preset interval are considered as clustered clamps. When the number of clustered clamps is greater than a preset clustering threshold, it is determined that the assembly processing difficulty of the assembly processing target does not meet the requirements. When the number of cluster clamps is not greater than the preset cluster quantity threshold: Based on the distribution data of the clamps, the number of different types of clamps in the same assembly processing cycle is determined. When there is a type with a number of clamps greater than a preset number threshold, the type with a number of clamps greater than the preset number threshold is taken as the type of concern. When the number of the type of concern is greater than the preset number threshold of the type of concern, it is determined that the assembly processing difficulty of the assembly processing target does not meet the requirements. When the number of attention types is not greater than the preset threshold for the number of attention types: The type distribution dispersion coefficient is determined by the number of attention types and the number of clamps of different attention types. When the type distribution dispersion coefficient does not meet the requirements, it is determined that the assembly processing difficulty of the assembly processing target does not meet the requirements. When there is no type with a number of clamps greater than the preset number threshold or the type distribution dispersion coefficient meets the requirements, and when the ratio of the number of clamps to the length of the assembly processing target within the same assembly processing cycle meets the requirements, then the assembly processing difficulty of the assembly processing target is determined to meet the requirements. When the ratio of the number of clamps to the length of the assembly target within the same assembly cycle does not meet the requirements: The number of clamps is used to determine the preset difficulty coefficient of the assembly processing target. Based on the proportion of different types of clamps and the preset difficulty coefficient of the assembly processing target, the assembly processing difficulty coefficient of the assembly processing target is determined. Based on the assembly processing difficulty coefficient, it is determined whether the assembly processing difficulty of the assembly processing target meets the requirements.

[0038] S2 uses the distribution data of different clamps to determine the adjacent clamps of different clamps, determines the consistent assembly scenario based on the distribution data of the adjacent clamps, and determines the assembly deviation type of different clamps based on the assembly deviation data under the consistent assembly scenario of different clamps. Furthermore, the consistent assembly scenario refers to a historical assembly scenario that is consistent with the setting of a preset number of adjacent clamps.

[0039] Specifically, such as Figure 3 As shown, the method for determining the assembly deviation type of the clamp is as follows: Based on the assembly deviation data of the clamp under different consistent assembly scenarios, the consistent assembly scenarios with assembly deviations are determined and taken as the deviation assembly scenarios. The type of assembly deviation of the clamp is determined based on the proportion of the deviation assembly scenarios to the number of consistent assembly scenarios.

[0040] Furthermore, based on the proportion of the deviation assembly scenarios to the number of consistent assembly scenarios, the assembly deviation type of the clamp is determined, specifically including: The proportion of the number of deviation assembly scenarios in the consistent assembly scenarios is taken as the deviation quantity proportion. Based on the preset deviation type corresponding to the deviation quantity proportion, the assembly deviation type of the clamp is determined.

[0041] It should be noted that the assembly deviation types of the clamps include Type I deviation, Type II deviation, and Type III deviation.

[0042] S3 determines that when the detection strategy for a clamp of a certain type does not belong to the real-time detection strategy based on the assembly deviation type and distribution data of clamps of different types, it acquires the assembly deviation type of different clamps of that type and the interval data of clamps of the same type, and determines the detection target of the assembly deviation of the clamp of that type by combining the interval data of clamps corresponding to the real-time detection strategy. Based on the detection data of the assembly deviation of the detection target, it determines whether the assembly detection strategy needs to be adjusted.

[0043] It is understandable that, such as Figure 4 As shown, the detection strategy for this type of clamp is not a real-time detection strategy, specifically including: Based on the distribution data of the clamps of the aforementioned type, the interval distance between adjacent clamps of the aforementioned type is determined, and the number of clamps with an interval distance greater than a preset interval distance is determined, and these are taken as distributed discrete clamps; By determining the assembly deviation type of the clamps of the aforementioned type, the proportion of clamps with different assembly deviation types is determined; By using the proportion of clamps of a certain type of deviation, and the average proportion of the proportion of discrete clamps, it is determined whether the detection strategy for the type of clamp belongs to the real-time detection strategy.

[0044] Furthermore, when the average proportion of the number of clamps of the aforementioned deviation type and the proportion of the number of discrete clamps is greater than a preset proportion threshold, it is determined that the detection strategy for the aforementioned type of clamp belongs to the real-time detection strategy.

[0045] In another embodiment, the detection strategy for determining that the type of clamp is not a real-time detection strategy specifically includes: Based on the distribution data of the clamps of the aforementioned type, the spacing distance between adjacent clamps of the aforementioned type is determined, and based on the spacing distance between adjacent clamps, the spacing dispersion coefficient of different clamps of the aforementioned type is determined; Based on the assembly deviation type of the clamp of the aforementioned type, the preset deviation coefficient of the clamp for different assembly deviation types is determined; Whether the detection strategy for the type of clamp belongs to the real-time detection strategy is determined by the average of the product of the preset deviation coefficient and the interval dispersion coefficient of different clamps.

[0046] Optionally, the detection strategy for determining the type of clamp is not a real-time detection strategy, specifically including: S21 uses the distribution data of the clamps of the aforementioned type to determine the spacing distance between adjacent clamps of the aforementioned type, and determines the distribution dispersion coefficient of the clamps of the aforementioned type based on the average value of the spacing distance between different adjacent clamps; S22 determines the proportion of clamps of different assembly deviation types by identifying the assembly deviation types of the clamps of that type, and determines the deviation coefficient of the clamps of that type by using the proportion of clamps of different assembly deviation types. S23 determines the detection requirement coefficient of the type of clamp based on the deviation coefficient and the distribution dispersion coefficient, and uses the detection requirement coefficient to determine whether the detection strategy of the type of clamp belongs to the real-time detection strategy.

[0047] Optionally, step S21 above includes the following: S211 uses the distribution data of the clamps of the type to determine the interval distance between adjacent clamps of the type. When there is a clamp with an interval distance greater than a preset interval distance between adjacent clamps of the type, proceed to step S212. When there is no clamp with an interval distance greater than a preset interval distance between adjacent clamps of the type, proceed to step S214. S212 defines clamps with an interval greater than a preset interval as distributed discrete clamps. When the proportion of distributed discrete clamps in the number of clamps of this type is greater than the preset proportion of clamps, it is determined that the detection strategy of this type of clamp belongs to the real-time detection strategy. When the proportion of distributed discrete clamps in the number of clamps of this type is not greater than the preset proportion of clamps, proceed to step S213. S213 Based on the number of the distributed discrete clamps and the proportion of the number of clamps of the type, determine the screening discrete coefficient. When the screening discrete coefficient is greater than the preset discrete coefficient threshold, it is determined that the detection strategy of the type of clamp belongs to the real-time detection strategy. When the screening discrete coefficient is not greater than the preset discrete threshold, proceed to step S214. S214 determines the distribution dispersion coefficient of the clamp of the type based on the average value of the interval distance between different adjacent clamps, and proceeds to step S22.

[0048] Optionally, step S22 above includes the following: S221 determines the proportion of clamps of different assembly deviation types based on the assembly deviation type of the clamps of the aforementioned type. When the proportion of clamps of a certain type of deviation is greater than the preset deviation proportion, it is determined that the detection strategy of the clamps of the aforementioned type belongs to the real-time detection strategy. When the proportion of clamps of a certain type of deviation is not greater than the preset deviation proportion, proceed to step S222. S222 When the number of clamps of a certain type of deviation meets the requirements, proceed to step S224; when the number of clamps of the certain type of deviation does not meet the requirements, proceed to step S223. S223 determines the basic deviation coefficient based on the number and proportion of clamps of the first type of deviation. When the basic deviation coefficient does not meet the requirements, it is determined that the detection strategy of the clamp of the first type belongs to the real-time detection strategy. When the basic deviation coefficient meets the requirements, proceed to step S224. S224 determines the deviation coefficient of the clamp type by using the proportion of the number of clamps of different assembly deviation types. When the deviation coefficient of the clamp type does not meet the requirements, it is determined that the detection strategy of the clamp type belongs to the real-time detection strategy. When the deviation coefficient of the clamp type meets the requirements, proceed to step S23.

[0049] Specifically, the method for determining the detection target of the assembly deviation of the aforementioned type of clamp is as follows: Based on the distribution data of the clamps of the aforementioned type, the quantity of clamps of that type is determined, and using the quantity of clamps of that type, the quantity of detection targets for the clamps is determined; Based on the assembly deviation types of different clamps of the aforementioned type, a preset deviation coefficient for each clamp under the aforementioned assembly deviation type is determined. Using the interval data between the clamp and clamps of the same type, the interval distance with adjacent clamps of the same type is determined. And using the average value of the interval distance with adjacent clamps of the same type, the distribution dispersion coefficient of the clamp is determined. Based on the interval data of the clamps corresponding to the real-time detection strategy, the interval distance of the clamps corresponding to the adjacent real-time detection strategies is determined, and the preset detection requirement coefficient of the clamps at the interval distance is determined using the interval distance. Based on the average value of the preset detection requirement coefficient, the distribution dispersion coefficient, and the preset deviation coefficient, the detection priority coefficient of the clamp is determined. Based on the detection priority coefficient and the number of detection targets of the clamp, the detection target of the assembly deviation of the clamp of the type is determined.

[0050] Furthermore, the distribution dispersion coefficient is determined by multiplying the average distance between adjacent clamps of the same type and a preset scaling factor.

[0051] It is understood that, based on the detection processing priority coefficient and the number of detection targets for the clamps, the detection targets for assembly deviations of this type of clamp are determined, specifically including: Using the number of detection targets for the clamps as a constraint, and based on the detection processing priority coefficients of different clamps from large to small, the detection targets for assembly deviations of the clamps of the aforementioned type are determined.

[0052] Optionally, the method for determining the detection target of the assembly deviation of the clamp of the aforementioned type is as follows: Based on the different types of clamp assembly deviations, when the type of clamp assembly deviation is a type of deviation, the clamp is determined to be the target of the detection of the type of clamp assembly deviation. When the assembly deviation type of the clamp does not belong to a single type of deviation: When the assembly deviation type of the clamp belongs to the second type of deviation: Using the spacing data of the clamp and clamps of the same type, the spacing distance with adjacent clamps of the same type is determined. When the spacing distance with adjacent clamps of the same type is greater than the preset spacing distance, the clamp is determined to be the detection target of the assembly deviation of the clamp of the type. When the distance between the clamp and an adjacent clamp of the same type is not greater than the preset distance, or when the assembly deviation type of the clamp belongs to the third type of deviation: Based on the distribution data of the clamps of the aforementioned type, the quantity of clamps of that type is determined, and using the quantity of clamps of that type, the quantity of detection targets for the clamps is determined; Based on the assembly deviation types of different clamps of the aforementioned type, a preset deviation coefficient for each clamp under the aforementioned assembly deviation type is determined. Using the interval data between the clamp and clamps of the same type, the interval distance with adjacent clamps of the same type is determined. And using the average value of the interval distance with adjacent clamps of the same type, the distribution dispersion coefficient of the clamp is determined. Based on the interval data of the clamps corresponding to the real-time detection strategy, the interval distance of the clamps corresponding to the adjacent real-time detection strategies is determined, and the preset detection requirement coefficient of the clamps at the interval distance is determined using the interval distance. Based on the average value of the preset detection requirement coefficient, the distribution dispersion coefficient, and the preset deviation coefficient, the detection priority coefficient of the clamp is determined. Based on the detection priority coefficient and the number of detection targets of the clamp, the detection target of the assembly deviation of the clamp of the type is determined.

[0053] Further, determine whether adjustments to the assembly inspection strategy are necessary, specifically including: Based on the detection data of the assembly deviation of the detection targets, determine the proportion of the number of detection targets with assembly deviation of different types of clamps, and use the proportion of the number to determine the assembly deviation coefficient of different types of clamps. The weighting coefficients for different types of clamps are determined by the proportion of their quantities within the same assembly process cycle. Based on the assembly deviation coefficients and weighting coefficients of different types of clamps, the weighted sum of the assembly deviation coefficients is determined, and the weighted sum of the assembly deviation coefficients is used to determine whether the assembly inspection strategy needs to be adjusted.

[0054] It should be noted that when the weights of the assembly deviation coefficients are within the preset weight range, it is determined that no adjustment of the assembly inspection strategy is required.

[0055] It is understood that when the weighted sum of the assembly deviation coefficients is not within the preset weight range, or when the weighted sum of the assembly deviation coefficients is less than the preset weight threshold, the assembly deviation coefficient is used as an adjustment factor, and the original detection frequency is adjusted by multiplying the adjustment factor by the original detection frequency. When the weighted sum of the assembly deviation coefficients is not less than the preset weight threshold, a real-time detection strategy is adopted for detection.

[0056] Example 2 Secondly, such as Figure 5 As shown, this application provides an automatic clamp assembly data analysis and processing device, which employs the aforementioned automatic clamp assembly data analysis and processing method, specifically including: Detection module, detection strategy adjustment module; The detection module is responsible for detecting and processing assembly deviations of different types of clamps. The detection strategy adjustment module is responsible for determining whether the assembly detection strategy needs to be adjusted based on the detection data of the assembly deviation of the detection target.

[0057] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0058] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0059] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for analyzing and processing data from automatic clamp assembly, characterized in that, Specifically, it includes: Based on the assembly processing target of the clamp, determine the distribution data of different types of clamps in the assembly processing target, and use the distribution data to determine when the assembly processing difficulty of the assembly processing target meets the requirements, then proceed to the next step; Based on the distribution data of different clamps, the adjacent clamps of different clamps are determined. Based on the distribution data of the adjacent clamps, a consistent assembly scenario is determined. Based on the assembly deviation data of different clamps under the consistent assembly scenario, the assembly deviation type of different clamps is determined. Based on the assembly deviation types and distribution data of clamps of different types, when it is determined that the detection strategy of the clamp of the type does not belong to the real-time detection strategy, the assembly deviation types of different clamps of the type and the interval data of clamps of the same type are obtained, and the detection target of the assembly deviation of the clamp of the type is determined by combining the interval data of the clamps corresponding to the real-time detection strategy. Based on the detection data of the assembly deviation of the detection target, it is determined whether the assembly detection strategy needs to be adjusted. Determine whether adjustments to the assembly inspection strategy are necessary, specifically including: Based on the detection data of assembly deviation of the detection targets, determine the proportion of the number of detection targets with assembly deviation of different types of clamps, and use the proportion of the number to determine the assembly deviation coefficient of different types of clamps. The weighting coefficients for different types of clamps are determined by the proportion of their quantities within the same assembly process cycle. Based on the assembly deviation coefficients and weighting coefficients of different types of clamps, the weighted sum of the assembly deviation coefficients is determined, and the weighted sum of the assembly deviation coefficients is used to determine whether the assembly inspection strategy needs to be adjusted. The method for determining the detection target of the assembly deviation of the clamp of the aforementioned type is as follows: Based on the distribution data of the clamps of the aforementioned type, the quantity of clamps of that type is determined, and using the quantity of clamps of that type, the quantity of detection targets for the clamps is determined; Based on the assembly deviation types of different clamps of the aforementioned type, a preset deviation coefficient for each clamp under the aforementioned assembly deviation type is determined. Using the interval data between the clamp and clamps of the same type, the interval distance with adjacent clamps of the same type is determined. And using the average value of the interval distance with adjacent clamps of the same type, the distribution dispersion coefficient of the clamp is determined. Based on the interval data of the clamps corresponding to the real-time detection strategy, the interval distance of the clamps corresponding to the adjacent real-time detection strategies is determined, and the preset detection requirement coefficient of the clamps at the interval distance is determined using the interval distance. Based on the average value of the preset detection requirement coefficient, the distribution dispersion coefficient, and the preset deviation coefficient, the detection priority coefficient of the clamp is determined. Based on the detection priority coefficient and the number of detection targets of the clamp, the detection target of the assembly deviation of the clamp of the type is determined.

2. The automatic assembly data analysis and processing method for clamps as described in claim 1, characterized in that, The types of clamps are classified according to their dimensions.

3. The automatic assembly data analysis and processing method for clamps as described in claim 1, characterized in that, The distribution data of the clamps includes the distribution positions of different types of clamps within the same assembly process cycle, as well as adjacent clamps.

4. The automatic assembly data analysis and processing method for clamps as described in claim 1, characterized in that, Determining that the assembly processing difficulty of the assembly processing target meets the requirements specifically includes: Based on the distribution data of the assembly processing targets, the number of clamps for the assembly processing targets within the same assembly processing cycle is determined, and the number of clamps is used to determine the preset difficulty coefficient of the assembly processing targets. Based on the distribution data of the clamps, the quantity of different types of clamps within the same assembly processing cycle is determined; Based on the proportion of different types of clamps and the preset difficulty coefficient of the assembly processing target, the assembly processing difficulty coefficient of the assembly processing target is determined, and based on the assembly processing difficulty coefficient, it is determined whether the assembly processing difficulty of the assembly processing target meets the requirements.

5. The automatic assembly data analysis and processing method for clamps as described in claim 4, characterized in that, The method for determining the assembly difficulty coefficient of the assembly processing target is as follows: Based on the proportion of different types of clamps, determine the number of clamp types whose proportion is greater than a preset proportion, and determine the preset assembly dispersion coefficient of the assembly processing target based on the number of types. The assembly processing difficulty coefficient of the assembly processing target is determined based on the product of the preset difficulty coefficient and the preset assembly dispersion coefficient.

6. The automatic assembly data analysis and processing method for clamps as described in claim 4, characterized in that, The assembly difficulty coefficient of the assembly processing target is between 0 and 1. If the assembly difficulty coefficient of the assembly processing target is greater than the preset difficulty coefficient threshold, then the assembly difficulty of the assembly processing target is determined to be unsatisfactory.

7. The automatic assembly data analysis and processing method for clamps as described in claim 1, characterized in that, When the weights of the assembly deviation coefficients are within a preset weight range, it is determined that no adjustment of the assembly inspection strategy is required.

8. The automatic assembly data analysis and processing method for clamps as described in claim 7, characterized in that, When the weighted sum of the assembly deviation coefficients is not within the preset weight range, or when the weighted sum of the assembly deviation coefficients is less than the preset weight threshold, the assembly deviation coefficient is used as an adjustment factor, and the original detection frequency is adjusted by multiplying the adjustment factor by the original detection frequency. When the weighted sum of the assembly deviation coefficients is not less than the preset weight threshold, a real-time detection strategy is adopted for detection.

9. A data analysis and processing device for automatic clamp assembly, employing the data analysis and processing method for automatic clamp assembly as described in any one of claims 1-8, characterized in that, Specifically, it includes: Detection module, detection strategy adjustment module; The detection module is responsible for detecting and processing assembly deviations of different types of clamps. The detection strategy adjustment module is responsible for determining whether the assembly detection strategy needs to be adjusted based on the detection data of the assembly deviation of the detection target.

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