Method and device for analyzing and processing automatic assembly data of clamp

Abstract

The invention provides a hoop automatic assembly data analysis processing method and device, and belongs to the technical field of data analysis, and the method specifically comprises the steps: determining the assembly deviation types of different hoops according to the assembly deviation data of the different hoops in a consistent assembly scene, and determining the assembly deviation types of the different types of hoops according to the assembly deviation types of the different types of hoops and the distribution data; when the detection strategy of the clamp of the determined type does not belong to the real-time detection strategy, acquiring assembly deviation types of the clamps of different types and interval data of the clamps of the same type, and determining a detection target of the assembly deviation of the clamps of the type in combination with the interval data of the clamps corresponding to the real-time detection strategy; whether the assembly detection strategy needs to be adjusted or not is determined according to the detection data of the assembly deviation of the detection target, and the accuracy of assembly processing is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and particularly relates to a data analysis and processing method and device for automatic assembly of clamps. Background Art

[0002] In order to realize data analysis and processing in the assembly process, specifically in the patent application CN202510083565.9 "A Data Analysis System for Assembly Quality of Watch Products", the assembly quality index is judged and analyzed through an assembly repair management module, and corresponding assembly repair measures are taken, so as to accurately evaluate the tiny errors existing inside the watch after assembly, thereby ensuring the precision and stability of the watch product, and then comprehensively evaluating the product quality.

[0003] In the assembly process, the existing technical solutions ignore generating differentiated assembly detection strategies according to the clamp distribution data in the assembly process. For a large distribution density of clamps or a large difference in the distribution types of clamps, if the assembly detection and processing cannot be strengthened, the reliability of the assembly process cannot be guaranteed.

[0004] In view of the above technical problems, specifically, the present application provides a data analysis and processing method and device for automatic assembly of clamps. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions: In the first aspect, the present application provides a data analysis and processing method for automatic assembly of clamps, specifically including: S1 Taking the assembly processing target of the clamp, determining the distribution data of different types of clamps in the assembly processing target, and when it is determined that the assembly processing difficulty of the assembly processing target meets the requirements by using the distribution data, proceeding to the next step; S2 Taking the distribution data of different clamps, determining the adjacent clamps of different clamps, determining a consistent assembly scenario based on the distribution data of the adjacent clamps, and determining the assembly deviation types of different clamps according to the assembly deviation data under the consistent assembly scenario of different clamps; S3 When it is determined that the detection strategy of the clamp of the type does not belong to the real-time detection strategy according to the assembly deviation type and distribution data of the clamp in different types, obtaining the assembly deviation types of different clamps of the type and the interval data from the clamps of the same type, and 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 clamp of the type, and determining whether it is necessary to adjust the assembly detection strategy according to the detection data of the assembly deviation of the detection target.

[0006] The beneficial effects of the present invention are as follows: Based on the assembly deviation types and distribution data of the clamps in different types, determine whether the detection strategy for the clamps of the type belongs to a real-time detection strategy, taking into account not only the differences in the probabilities of the clamps having assembly deviations caused by the differences in the assembly deviation types of the clamps, but also the differences in the detection requirements of the clamps caused by the distribution dispersion degree among the clamps, realizing the screening of the types of clamps for the real-time detection strategy from multiple perspectives, and ensuring the accuracy of the detection and processing of the clamps with relatively high deviation probabilities and overly dispersed distributions.

[0007] Based on the detection data of the assembly deviation of the detection target, determine whether adjustment processing of the assembly detection strategy is required, thereby realizing the dynamic adjustment of the assembly detection strategy from the assembly deviation situation of the detection target, not only reducing the power consumption of the assembly detection processing in a relatively reliable assembly state, but also improving the reliability of the assembly detection processing in a relatively poor assembly state.

[0008] A further technical solution lies in that the types of the clamps are divided according to the sizes of the clamps.

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

[0010] A further technical solution lies 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 target, determine the number of clamps of the assembly processing target within the same assembly processing cycle, and use the number of the clamps to determine the preset difficulty coefficient of the assembly processing target; Based on the distribution data of the clamps, determine the number of different types of clamps within the same assembly processing cycle; According to the proportion of the number of different types of clamps and the preset difficulty coefficient of the assembly processing target, determine the assembly processing difficulty coefficient of the assembly processing target, and based on the assembly processing difficulty coefficient, determine whether the assembly processing difficulty of the assembly processing target meets the requirements.

[0011] A further technical solution lies in that the method for determining the assembly processing difficulty coefficient of the assembly processing target is: Based on the proportion of the number of different types of clamps, determine the number of types of clamps with a proportion greater than the preset proportion, and based on the number of types, determine the preset assembly dispersion coefficient of the assembly processing target; Based on the product of the preset difficulty coefficient and the preset assembly dispersion coefficient, determine the assembly processing difficulty coefficient of the assembly processing target.

[0012] A further technical solution lies in that the value range of 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 the 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 lies in determining whether adjustment processing of the assembly detection strategy is required, specifically including: According to the detection data of the assembly deviation of the detection target, determine the proportion of the number of detection targets with assembly deviation for different types of clamps, and use the proportion to determine the assembly deviation coefficient of different types of clamps; Determine the weight coefficient of different types of clamps based on the proportion of the number of different types of clamps in the same assembly processing cycle; According to the assembly deviation coefficient and weight coefficient of different types of clamps, determine the weighted sum of the assembly deviation coefficients, and use the weighted sum of the assembly deviation coefficients to determine whether adjustment processing of the assembly detection strategy is required.

[0014] A further technical solution lies in that when the weighted sum of the assembly deviation coefficients is within the preset weight range, it is determined that adjustment processing of the assembly detection strategy is not required.

[0015] A further technical solution lies in that when the weighted sum of the assembly deviation coefficients is not within the preset weight range, when the weighted sum of the assembly deviation coefficients is less than the preset weight threshold, use the assembly deviation coefficient as an adjustment factor, and multiply the adjustment factor by the original detection frequency to adjust the original detection frequency. When the weighted sum of the assembly deviation coefficients is not less than the preset weight threshold, adopt a real-time detection strategy for detection processing.

[0016] In a second aspect, the present application provides a clamp automatic assembly data analysis and processing device, adopting the above-mentioned clamp automatic assembly data analysis and processing method, specifically including: A detection module and a detection strategy adjustment module; Wherein the detection module is responsible for performing detection processing on the detection targets of the assembly deviations of different types of clamps; The detection strategy adjustment module is responsible for determining whether adjustment processing of the assembly detection strategy is required according to the detection data of the assembly deviations of the detection targets.

[0017] Other features and advantages will be described in the subsequent specification. The objectives and other advantages of the present invention are realized and obtained by the structures specifically pointed out in the specification and the drawings.

[0018] To make the above-mentioned objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. Brief Description of the Drawings

[0019] The above and other features and advantages of the present invention will become more apparent by describing in detail its exemplary embodiments with reference to the accompanying drawings; Figure 1 is a flowchart of a method for data analysis and processing of automatic clamp assembly; Figure 2 is a flowchart for determining that the assembly processing difficulty of the assembly processing target meets the requirements; Figure 3 is a flowchart of a method for determining the type of assembly deviation of the clamp; Figure 4 is a flowchart for determining that the detection strategy of the clamp of a certain type does not belong to the real-time detection strategy; Figure 5 is a framework diagram of a device for data analysis and processing of automatic clamp assembly. Detailed Embodiments

[0020] In order 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 in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0021] During the assembly process, it is necessary to generate a differentiated assembly detection strategy according to the clamp distribution data during the assembly process, and generate a differentiated assembly detection strategy according to the distribution density of different assembly processing targets, so as to ensure the reliability of the assembly processing during the assembly process.

[0022] The average distance is determined by the average value of the interval distances between different adjacent clamps. When the average distance is not within the preset distance interval, 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, real-time detection processing is performed on all the clamps.

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

[0024] According to the preset deviation type corresponding to the average value of the proportion of the number of clamps with assembly deviations in the consistent assembly scenario, the type of assembly deviation is determined. The types of assembly deviation of the clamp include a type I deviation type, a type II deviation type, and a type III deviation type.

[0025] Determine the number of clamps with an interval distance greater than the preset interval distance based on the interval distance between adjacent clamps of the said type, and take them as the distributed discrete clamps. Determine the proportion of the number of clamps of different assembly deviation types according to the assembly deviation type of the clamps. When the average value of the proportion of the number of clamps of a certain deviation type and the proportion of the number of distributed discrete clamps is greater than 0.3, it is determined that the detection strategy for the type of clamps belongs to the real-time detection strategy.

[0026] The detection target of the assembly deviation is the clamps of a certain deviation type.

[0027] When the proportion of the number of assembly deviations of the detection target is greater than 0.1, it is determined that adjustment processing of the assembly detection strategy is required.

[0028] Embodiment 1 To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, a first aspect is provided. The present application provides a data analysis and processing method for automatic clamp assembly, specifically including: S1 Based on the assembly processing target of the clamps, determine the distribution data of different types of clamps in the assembly processing target, and when it is determined that the assembly processing difficulty of the assembly processing target meets the requirements by using the distribution data, proceed to the next step; Furthermore, the types of the clamps are divided according to the sizes of the clamps.

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

[0030] Specifically, as Figure 2 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, determine the number of clamps in the assembly processing target within the same assembly processing cycle, and use the number of the clamps to determine the preset difficulty coefficient of the assembly processing target; Based on the distribution data of the clamps, determine the number of different types of clamps within the same assembly processing cycle; According to the proportion of the number of different types of clamps and the preset difficulty coefficient of the assembly processing target, determine the assembly processing difficulty coefficient of the assembly processing target, and based on the assembly processing difficulty coefficient, determine 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 processing difficulty coefficient of the assembly processing target is: Based on the proportion of the number of different types of clamps, determine the number of types of clamps with a proportion greater than the preset proportion, and determine the preset assembly dispersion coefficient of the assembly processing target based on the number of types; Based on the product of the preset difficulty coefficient and the preset assembly dispersion coefficient, determine the assembly processing difficulty coefficient of the assembly processing target.

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

[0033] Further, the value range of 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 the preset difficulty coefficient threshold, 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 processing difficulty of the assembly processing target meets the requirements specifically includes: Based on the distribution data of the clamps, determine the number of different types of clamps in the same assembly processing cycle; Based on the number of different types of clamps, determine the types of clamps with a number greater than the preset number threshold, and use them as the key attention types; Determine whether the assembly processing difficulty of the assembly processing target meets the requirements according to the number of the key attention types.

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

[0036] Specifically, when the assembly processing difficulty of the assembly processing target does not meet the requirements, real-time detection processing is performed on all the clamps.

[0037] In another possible embodiment, 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, determine the number of clamps of the assembly processing target in the same assembly processing cycle. 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 spacing distances between different adjacent clamps, determine the number of clamps with a spacing distance less than a preset spacing distance. Consider the clamps with a spacing distance less than the preset spacing distance as aggregated clamps. When the number of aggregated clamps is greater than a preset aggregation quantity threshold, it is determined that the assembly processing difficulty of the assembly processing target does not meet the requirements; When the number of aggregated clamps is not greater than the preset aggregation quantity threshold: Based on the distribution data of the clamps, determine the number of different types of clamps within the same assembly processing cycle. When there is a type with the number of clamps greater than a preset quantity threshold, consider the type with the number of clamps greater than the preset quantity threshold as the concerned type. When the number of concerned types is greater than a preset concerned type quantity threshold, it is determined that the assembly processing difficulty of the assembly processing target does not meet the requirements; When the number of concerned types is not greater than the preset concerned type quantity threshold: Based on the number of concerned types and the number of clamps of different concerned types, determine the type distribution dispersion coefficient. 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 the number of clamps greater than the preset quantity threshold or the type distribution dispersion coefficient meets the requirements, and when the ratio of the number of clamps within the same assembly processing cycle to the length of the assembly processing target meets the requirements, it is determined that the assembly processing difficulty of the assembly processing target meets the requirements; When the ratio of the number of clamps within the same assembly processing cycle to the length of the assembly processing target does not meet the requirements: Use the number of clamps to determine the preset difficulty coefficient of the assembly processing target. Based on the proportion of the number of different types of clamps and the preset difficulty coefficient of the assembly processing target, determine the assembly processing difficulty coefficient of the assembly processing target, and based on the assembly processing difficulty coefficient, determine whether the assembly processing difficulty of the assembly processing target meets the requirements.

[0038] S2 Based on the distribution data of different clamps, determine the adjacent clamps of different clamps. Based on the distribution data of the adjacent clamps, determine a consistent assembly scenario. According to the assembly deviation data of different clamps under the consistent assembly scenario, determine the assembly deviation types of different clamps; Furthermore, the consistent assembly scenario is a historical assembly scenario that is consistent with the setting situation of a preset number of adjacent clamps.

[0039] Specifically, as Figure 3 shown, the method for determining the assembly deviation types of the clamps is: Based on the assembly deviation data of the clamps under different consistent assembly scenarios, determine the consistent assembly scenarios with assembly deviations and consider them as deviation assembly scenarios; Determine the assembly deviation type of the clamp according to the proportion of the number of deviation assembly scenarios in the number of consistent assembly scenarios.

[0040] Further, determine the assembly deviation type of the clamp according to the proportion of the number of deviation assembly scenarios in the number of consistent assembly scenarios, specifically including: Take the proportion of the number of deviation assembly scenarios in the number of consistent assembly scenarios as the proportion of deviation quantity, and determine the assembly deviation type of the clamp according to the preset deviation type corresponding to the proportion of deviation quantity.

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

[0042] S3 When it is determined that the detection strategy of the clamp of the type does not belong to the real-time detection strategy according to the assembly deviation type and distribution data of the clamp in different types, obtain the assembly deviation type of different clamps of the type and the interval data of the clamps of the same type, and combine the interval data of the clamps corresponding to the real-time detection strategy to determine the detection target of the assembly deviation of the clamp of the type. According to the detection data of the assembly deviation of the detection target, determine whether it is necessary to adjust the assembly detection strategy.

[0043] It can be understood that as Figure 4 shown, determining that the detection strategy of the clamp of the type does not belong to the real-time detection strategy specifically includes: Based on the distribution data of the clamps of the type, determine the interval distance between adjacent clamps of the type, and determine the number of clamps with an interval distance greater than the preset interval distance, and take it as the distributed discrete clamps; Through the assembly deviation type of the clamps of the type, determine the proportion of the number of clamps of different assembly deviation types; Use the proportion of the number of clamps of type I deviation type and the average value of the proportion of the number of distributed discrete clamps to determine whether the detection strategy of the clamps of the type belongs to the real-time detection strategy.

[0044] Further, when the average value of the proportion of the number of clamps of type I deviation type and the proportion of the number of distributed discrete clamps is greater than the preset proportion threshold, it is determined that the detection strategy of the clamps of the type belongs to the real-time detection strategy.

[0045] In another embodiment, determining that the detection strategy of the clamp of the type does not belong to the real-time detection strategy specifically includes: Based on the distribution data of the clamps of the type, determine the interval distance between adjacent clamps of the type, and based on the interval distance between adjacent clamps, determine the interval dispersion coefficient of different clamps of the type; Determine the preset deviation coefficients of the clamps of different assembly deviation types according to the assembly deviation types of the clamps of the said type; Use the average value of the product of the preset deviation coefficients of different clamps and the interval discrete coefficients to determine whether the detection strategy of the clamps of the said type belongs to a real-time detection strategy.

[0046] Optionally, determining that the detection strategy of the clamps of the said type does not belong to a real-time detection strategy specifically includes: S21 Use the distribution data of the clamps of the said type to determine the interval distance between adjacent clamps of the said type, and based on the average value of the interval distances between different adjacent clamps, determine the distribution discrete coefficient of the clamps of the said type; S22 Determine the proportion of the number of clamps of different assembly deviation types according to the assembly deviation types of the clamps of the said type, and use the proportion of the number of clamps of different assembly deviation types to determine the deviation coefficient of the clamps of the said type; S23 Based on the deviation coefficient and the distribution discrete coefficient, determine the detection requirement coefficient of the clamps of the said type, and use the detection requirement coefficient to determine whether the detection strategy of the clamps of the said type belongs to a real-time detection strategy.

[0047] Optionally, the above step S21 includes the following content: S211 Use the distribution data of the clamps of the said type to determine the interval distance between adjacent clamps of the said type. When there is a clamp whose interval distance from the adjacent clamp of the said type is greater than the preset interval distance, then go to step S212; when there is no clamp whose interval distance from the adjacent clamp of the said type is greater than the preset interval distance, then go to step S214; S212 Take the clamp with an interval distance greater than the preset interval distance as a distribution discrete clamp. When the proportion of the distribution discrete clamps in the number of clamps of the said type is greater than the preset clamp number proportion, then determine that the detection strategy of the clamps of the said type belongs to a real-time detection strategy; when the proportion of the distribution discrete clamps in the number of clamps of the said type is not greater than the preset clamp number proportion, go to step S213; S213 Based on the number of the distribution discrete clamps and their proportion in the number of clamps of the said type, determine the screening discrete coefficient. When the screening discrete coefficient is greater than the preset discrete coefficient threshold, then determine that the detection strategy of the clamps of the said type belongs to a real-time detection strategy; when the screening discrete coefficient is not greater than the preset discrete coefficient threshold, go to step S214; S214 Based on the average value of the interval distances between different adjacent clamps, determine the distribution discrete coefficient of the clamps of the said type, and go to step S22.

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

[0049] Specifically, the method for determining the detection target of the assembly deviation of the clamps of the said type is as follows: Based on the distribution data of the clamps of the said type, determine the number of the clamps of the said type, and use the number of the clamps of the said type to determine the number of the detection targets of the clamps; Based on the assembly deviation types of different clamps of the said type, determine the preset deviation coefficients of different clamps under the said assembly deviation type. Use the interval data between the clamps and the clamps of the same type to determine the interval distance from the adjacent clamps of the same type, and use the average value of the interval distances from the adjacent clamps of the same type to determine the distribution dispersion coefficient of the clamps; According to the interval data of the clamps corresponding to the real-time detection strategy, determine the interval distance from the adjacent clamps corresponding to the real-time detection strategy, and use the said interval distance to determine the preset detection requirement coefficient of the clamps under the said interval distance; Based on the average value of the preset detection requirement coefficient, the distribution dispersion coefficient and the preset deviation coefficient, determine the detection processing priority coefficient of the clamps. According to the detection processing priority coefficient and the number of the detection targets of the clamps, determine the detection target of the assembly deviation of the clamps of the said type.

[0050] Furthermore, the distribution dispersion coefficient is determined by the product of the average value of the interval distances from the adjacent clamps of the same type and the preset proportional factor.

[0051] It can be understood that, according to the detection processing priority coefficient and the number of detection targets of the clamp, determining the detection target of the assembly deviation of the clamp of this type specifically includes: Taking the number of detection targets of the clamp as a constraint condition, and based on the detection processing priority coefficients of different clamps from large to small, determining the detection target of the assembly deviation of the clamp of this type.

[0052] Optionally, the method for determining the detection target of the assembly deviation of the clamp of this type is: Based on the assembly deviation types of different clamps of this type, when the assembly deviation type of the clamp is a type I deviation type, then determining that the clamp belongs to the detection target of the assembly deviation of the clamp of this type; When the assembly deviation type of the clamp does not belong to the type I deviation type: When the assembly deviation type of the clamp belongs to the type II deviation type: Using the interval data between the clamp and the clamps of the same type, determining the interval distance from the adjacent clamps of the same type. When the interval distance from the adjacent clamps of the same type is greater than the preset interval distance, then determining that the clamp belongs to the detection target of the assembly deviation of the clamp of this type; When the interval distance from the adjacent clamps of the same type is not greater than the preset interval distance or the assembly deviation type of the clamp belongs to the type III deviation type: Based on the distribution data of the clamps of this type, determining the number of clamps of this type, and using the number of clamps of this type to determine the number of detection targets of the clamp; Based on the assembly deviation types of different clamps of this type, determining the preset deviation coefficients of different clamps under the assembly deviation type, using the interval data between the clamp and the clamps of the same type to determine the interval distance from the adjacent clamps of the same type, and using the average value of the interval distances from the adjacent clamps of the same type to determine the distribution dispersion coefficient of the clamp; According to the interval data of the clamps corresponding to the real-time detection strategy, determining the interval distance from the adjacent clamps corresponding to the real-time detection strategy, and using the interval distance to determine the preset detection requirement coefficient of the clamp under the interval distance; Based on the average value of the preset detection requirement coefficient, the distribution dispersion coefficient and the preset deviation coefficient, determining the detection processing priority coefficient of the clamp, and according to the detection processing priority coefficient and the number of detection targets of the clamp, determining the detection target of the assembly deviation of the clamp of this type.

[0053] Furthermore, determining whether it is necessary to perform adjustment processing on the assembly detection strategy specifically includes: Based on the detection data of the assembly deviation of the detection target, determine the proportion of the number of detection targets with assembly deviation for different types of clamps, and use the said proportion to determine the assembly deviation coefficient of different types of clamps; Determine the weight coefficient of different types of clamps according to the proportion of the number of different types of clamps in the same assembly processing cycle; According to the assembly deviation coefficient and the weight coefficient of different types of clamps, determine the weighted sum of the assembly deviation coefficient, and use the said weighted sum of the assembly deviation coefficient to determine whether it is necessary to perform adjustment processing on the assembly detection strategy.

[0054] It should be noted that when the weighted sum of the assembly deviation coefficient is within the preset weight range, it is determined that there is no need to perform adjustment processing on the assembly detection strategy.

[0055] It can be understood that when the weighted sum of the assembly deviation coefficient is not within the preset weight range, when the weighted sum of the assembly deviation coefficient is less than the preset weight threshold, use the said assembly deviation coefficient as the adjustment factor, and multiply the adjustment factor by the original detection frequency to perform adjustment processing on the original detection frequency. When the weighted sum of the assembly deviation coefficient is not less than the preset weight threshold, adopt a real-time detection strategy for detection processing.

[0056] Embodiment 2 In a second aspect, as Figure 5 shown, the present application provides a data analysis and processing device for automatic clamp assembly, which adopts the above-mentioned data analysis and processing method for automatic clamp assembly, and specifically includes: A detection module and a detection strategy adjustment module; Wherein the detection module is responsible for performing detection processing on the detection targets of the assembly deviations of different types of clamps; The detection strategy adjustment module is responsible for determining whether it is necessary to perform adjustment processing on the assembly detection strategy according to the detection data of the assembly deviations of the detection targets.

[0057] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key points of each embodiment are the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0058] The specific embodiments of the present specification have been described above. 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 in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] The above description is only for one or more embodiments of the present specification and is not intended to limit the present specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of the present specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more embodiments of the present specification shall be included within the scope of the claims of the present specification.

Claims

1. A method for analyzing and processing clamp automatic assembly data, characterized in that: Specifically include: Determine the distribution data of different types of clamps in the assembly processing target based on the assembly processing target of the clamp, and use the distribution data to determine that the assembly processing difficulty of the assembly processing target meets the requirements, and then proceed to the next step; Determine adjacent clamps of different clamps based on distribution data of different clamps, determine consistent assembly scenarios based on the distribution data of the adjacent clamps, and determine assembly deviation types of different clamps based on assembly deviation data of the consistent assembly scenarios of different clamps; When it is determined that the detection strategy of the type of clamp does not belong to the real-time detection strategy based on the assembly deviation types and distribution data of the clamps of different types, the assembly deviation types of the different types of clamps and the spacing data of the clamps of the same type are obtained, and the detection target of the assembly deviation of the type of clamp is determined in combination with the spacing data of the clamps corresponding to the real-time detection strategy, and whether it is necessary to adjust the assembly detection strategy based on the detection data of the assembly deviation of the detection target.

2. The clamp automatic assembly data analysis and processing method according to claim 1, characterized in that: The types of the clamps are divided according to the size of the clamps.

3. The clamp automatic assembly data analysis and processing method according to claim 1, characterized in that: The distribution data of the clamps include the distribution positions of different types of clamps in the same assembly process cycle and adjacent clamps.

4. The clamp automatic assembly data analysis and processing method according to claim 1, characterized in that: Determining that the assembly processing difficulty of the assembly processing target meets the requirements specifically includes: Determine the number of clamps of the assembly processing target in the same assembly processing cycle based on the distribution data of the assembly processing target, and determine the preset difficulty coefficient of the assembly processing target based on the number of clamps; Determining the number of different types of clamps in a same assembly process cycle based on the distribution data of the clamps; According to 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 clamp automatic assembly data analysis and processing method according to claim 4, characterized in that: The method for determining the assembly process difficulty coefficient of the assembly process target is: Based on the quantity ratio of different types of clamps, determining the number of types of clamps whose quantity ratio is greater than a preset quantity ratio, and determining a preset assembly discrete coefficient of the assembly processing target based on the number of types; The assembly process difficulty coefficient of the assembly process target is determined based on the product of the preset difficulty coefficient and the preset assembly dispersion coefficient.

6. The clamp automatic assembly data analysis and processing method according to claim 4, characterized in that: The value range of 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.

7. The clamp automatic assembly data analysis and processing method according to claim 1, characterized in that: Determine whether the assembly inspection strategy needs to be adjusted, including: According to the detection data of the assembly deviation of the detection target, determining the number ratio of the detection targets of different types of clamps that have assembly deviations, and determining the assembly deviation coefficients of the different types of clamps using the number ratio; Determine the weight coefficients of different types of clamps according to the quantity ratio of different types of clamps in the same assembly process cycle; According to the assembly deviation coefficients and weight coefficients of different types of clamps, the weight sum of the assembly deviation coefficients is determined, and the weight sum of the assembly deviation coefficients is used to determine whether an adjustment process of the assembly detection strategy is required.

8. The clamp automatic assembly data analysis and processing method according to claim 7, characterized in that: When the weight sum of the assembly deviation coefficients is within a preset weight range, it is determined that there is no need to adjust the assembly detection strategy.

9. The clamp automatic assembly data analysis and processing method according to claim 8, characterized in that: When the weight sum of the assembly deviation coefficients is not within the preset weight range, when the weight 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 with the original detection frequency. When the weight sum of the assembly deviation coefficients is not less than the preset weight threshold, a real-time detection strategy is used for detection.

10. A clamp automatic assembly data analysis and processing device, using a clamp automatic assembly data analysis and processing method according to any one of claims 1 to 9, characterized in that: Specifically include: Detection module, detection strategy adjustment module; The detection module is responsible for detecting the detection target of assembly deviation of different types of clamps; The detection strategy adjustment module is responsible for determining whether it is necessary to adjust the assembly detection strategy according to the detection data of the assembly deviation of the detection target.

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