Intelligent optimization method for precision part machining process based on multi-modal data fusion

Abstract

The invention discloses an intelligent optimization method for a precision part machining process based on multi-modal data fusion, and belongs to the technical field of part machining supervision. The method is used for solving the technical problems of poor diversity and reliability of active processing and analysis of quality inspection data of different processing links of precision parts in an existing scheme. The method comprises the following steps: processing, analyzing and combining supervision index data of different abnormal manufacturing structures of different processing links of precision parts to obtain supervision index operation sequences and index supervision values corresponding to all supervision indexes of the different abnormal manufacturing structures; the method actively carries out diversified supervision and analysis on anomalies of existing supervision indexes of different abnormal manufacturing structures, and carries out diversified optimization management on the different abnormal manufacturing structures according to analysis results. Optimization management scheme data of different abnormal manufacturing structures are utilized to carry out active mining analysis of optimization implementation influence on the processing links to which the different abnormal manufacturing structures belong, and active optimization management is carried out on other non-abnormal manufacturing structures of the processing links in a self-adaptive manner.

Description

Technical Field

[0001] The present invention relates to the technical field of parts processing supervision, and in particular to a method for intelligent optimization of precision parts processing technology by multi-modal data fusion. Background Art

[0002] Precision parts processing technology refers to the process of manufacturing parts to meet high-precision and high-quality requirements, which usually involves complex operations and technologies to ensure that the dimensional accuracy, shape accuracy and surface finish of the final product meet the design requirements.

[0003] When implementing the existing precision parts processing technology optimization plan, it is impossible to conduct multi-dimensional supervision and negative impact assessment of the quality inspection results of different aspects of the existing parts processing products, and to adaptively implement targeted optimization management of the processing links and processing technologies of precision parts based on the negative impact assessment results of different dimensions. There are technical problems such as the diversity of active processing and analysis of quality inspection data of different processing links of precision parts and poor reliability. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent optimization method for precision parts processing technology based on multimodal data fusion, which is used to solve the technical problems of diversity and poor reliability of active processing and analysis of quality inspection data of different processing links of precision parts in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions: Intelligent optimization method for precision parts processing technology based on multi-modal data fusion, including: Actively monitor and process the quality inspection results of different processing links of precision parts, and conduct data analysis on the impact of abnormal manufacturing on all abnormal manufacturing structures obtained through corresponding supervision of different processing links. Dynamically mark different abnormal manufacturing structures in different processing links according to the analysis results, and implement targeted optimization management plans; The optimization management plan data of different abnormal manufacturing structures are used to actively mine and analyze the impact of optimization implementation on the corresponding processing links, and other non-abnormal manufacturing structures in the processing links are adaptively optimized and managed based on the analysis results.

[0006] Preferably, all quality inspection results corresponding to different processing links of each batch of precision parts are obtained, and all abnormal causes and abnormal manufacturing structures are determined based on all abnormal quality inspection results corresponding to different processing links; When performing data analysis on the impact of abnormal manufacturing for all abnormal manufacturing structures obtained through corresponding supervision of different processing links, obtain the supervision index values corresponding to all preset supervision indexes when the abnormal manufacturing structure processes precision components, and perform matching analysis on the supervision index values corresponding to different supervision indexes with the preset monitoring index range. Dynamically set the index reliability flag associated with the supervised index according to the analysis results; The dynamically set index reliability flag contains a value of 0 or 1.

[0007] Preferably, sort and combine the index reliability flags obtained by processing all preset supervision indexes of the abnormal manufacturing structure to obtain a supervision index operation sequence. Sum all the elements in the supervision index operation sequence and set it as the index supervision value ZJi corresponding to the abnormal manufacturing structure; i represents different abnormal manufacturing structures, i = 1, 2, 3,..., n; n is a positive integer; Perform data analysis on the index supervision value to determine that the abnormal execution status corresponding to the abnormal manufacturing structure is normal; If the index supervision value is 0, it is determined that the abnormal execution status corresponding to the abnormal manufacturing structure is normal, and it is marked as the first manufacturing structure.

[0008] Preferably, when performing data analysis on the impact of abnormal manufacturing corresponding to the first manufacturing structure, obtain the total number ni´ of abnormal components generated abnormally during the processing corresponding to the first manufacturing structure, where i´ represents different first manufacturing structures, i´ = 1, 2, 3,..., m; m is a positive integer; and through the abnormal occurrence supervision formula Calculate the abnormal occurrence value YFi´ corresponding to the first manufacturing structure; in the formula, Ni´ is the total number of precision components processed in the corresponding batch of the first manufacturing structure; αi´ is the abnormal occurrence requirement value corresponding to the first manufacturing structure; If YFi´ < 1, it is determined that the abnormal occurrence impact of the production and processing corresponding to the first manufacturing structure is normal, and the mark of the first manufacturing structure is updated to a conventional manufacturing impact structure; Otherwise, it is determined that the abnormal occurrence impact of the production and processing corresponding to the first manufacturing structure is abnormal, and the mark of the first manufacturing structure is updated to a special manufacturing impact structure; Perform the first optimization management plan for the preset supervision indexes of all special manufacturing impact structures.

[0009] Preferably, if the index supervision value is not 0, it is determined that the abnormal execution status corresponding to the abnormal manufacturing structure is abnormal, and it is marked as the second manufacturing structure. Calculate the abnormal occurrence value corresponding to the second manufacturing structure through the abnormal occurrence supervision formula and analyze it.

[0010] Preferably, if the abnormal occurrence value corresponding to the second manufacturing structure is less than 1, it is determined that the abnormal occurrence of the production and processing corresponding to the second manufacturing structure affects the normal, and a second optimization management plan for the preset supervision index is carried out for it; On the contrary, it is determined that the abnormal occurrence of the production and processing corresponding to the second manufacturing structure affects abnormally, and a first optimization management plan and a second optimization management plan for the preset supervision index are carried out for it.

[0011] Preferably, count the first optimization total number M1 that only implements the first optimization management plan, the second optimization total number M2 that only implements the second optimization management plan, and the third optimization total number M3 that implements both the first optimization management plan and the second optimization management plan in the processing link, and pass the first optimization total number, the second optimization total number, and the third optimization total number of the processing link through the formula Calculate and obtain the corresponding optimization implementation influence value YYk; in the formula, k is 1, 2, 3, respectively representing only implementing the first optimization management plan, only implementing the second optimization management plan, and implementing both the first optimization management plan and the second optimization management plan; M0 is the total number of all manufacturing structures corresponding to the supervision in the processing link; βk is β1, β2, β3, which are the optimization implementation influence thresholds corresponding to implementing different optimization management plans respectively.

[0012] Preferably, when analyzing and managing the necessity of active optimization for other non-abnormal manufacturing structures in the processing link: If YYk≤0, it is prompted that the active optimization of the other non-abnormal manufacturing structures in the processing link by the corresponding optimization management plan is unnecessary; On the contrary, it is prompted that the active optimization of the other non-abnormal manufacturing structures in the processing link by the corresponding optimization management plan is necessary, and the other non-abnormal manufacturing structures in the processing link are actively optimized by using the corresponding optimization management plan.

[0013] Compared with the existing solution, the beneficial effects achieved by the present invention: By processing, analyzing, and combining the supervision index data of different abnormal manufacturing structures in different processing links of precision parts, the present invention obtains the supervision index operation sequence and index supervision value corresponding to all supervision indexes of different abnormal manufacturing structures, which can not only digitally represent the supervision status of all supervision indexes corresponding to different abnormal manufacturing, but also provide reliable data support for the subsequent analysis of the abnormal execution status corresponding to the abnormal manufacturing structure.

[0014] By actively carrying out diversified supervision and analysis on the existing abnormal supervision indexes of different abnormal manufacturing structures, and implementing diversified optimization management on different abnormal manufacturing structures according to the analysis results, the present invention improves the diversity of active processing and analysis of quality inspection data in different processing links of precision parts.

[0015] The present invention actively mines and analyzes the impact of optimizing management scheme data of different abnormal manufacturing structures on the optimization implementation of the corresponding processing links, and adaptively conducts active optimization management on other non-abnormal manufacturing structures in the processing links according to the analysis results, realizing the active optimization data analysis and management of other non-abnormal manufacturing structures in different processing links, and further improving the diversity of active processing and analysis of quality inspection data in different processing links of precision parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 It is a flow block diagram of the operation of the intelligent optimization method for the processing technology of precision parts with multi-modal data fusion of the present invention.

[0018] Figure 2 It is a flow block diagram of the active optimization analysis of the second manufacturing structure in the present invention.

[0019] Figure 3 It is a flow block diagram of the necessity analysis for actively optimizing other non-abnormal manufacturing structures in the processing links in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] As Figure 1 shown, the present invention is an intelligent optimization method for the processing technology of precision parts with multi-modal data fusion, including: Actively supervising and processing data on the quality inspection results of different processing links of precision parts, and conducting data analysis on the abnormal manufacturing impacts of all abnormal manufacturing structures obtained through corresponding supervision of different processing links. Dynamically marking different abnormal manufacturing structures in different processing links according to the analysis results, and implementing targeted optimization management schemes; including: Obtaining all the quality inspection results corresponding to different processing links of each batch of precision parts, and determining all the abnormal causes and abnormal manufacturing structures based on all the abnormal quality inspection results corresponding to different processing links; It should be noted that different processing links are classified according to the processing requirements of actual precision parts, or can also be classified according to processing equipment; In addition, different processing links are equipped with several manufacturing structure supervisions, and different manufacturing structures are preset with several supervision indicators; specifically, the manufacturing structure can be the spindle in the processing machine tool, and the preset supervision indicators are the spindle radial runout and the spindle axial displacement; It needs to be explained that the spindle in the processing machine tool has the influence of spindle radial runout and spindle axial movement; Spindle radial runout: Spindle bearing wear or assembly error can cause the tool rotation center to shift; for example, when the spindle radial runout exceeds 2μm, the coaxiality error of the machined hole can reach more than 5μm; Spindle axial movement: Spindle axial displacement during processing (such as thermal expansion) will cause depth dimension deviation; in precision processing, 0.5μm of movement can magnify the deep hole processing error to 3μm; There is no limit on the number of manufacturing structures set up in different processing links, and the specific number of regulatory indicators preset in different manufacturing structures, which can be carried out according to the existing precision parts processing and production requirements; In addition, all abnormal causes and abnormal manufacturing structures are determined based on abnormal quality inspection results. This can be achieved based on existing fault automation supervision technology or manual supervision, or both. The specific determination scheme is not limited here. When performing data analysis on the impact of abnormal manufacturing on all abnormal manufacturing structures obtained through corresponding supervision of different processing links, obtain the supervisory indicator values ​​corresponding to all preset supervisory indicators when processing precision parts with abnormal manufacturing structures, and match and analyze the supervisory indicator values ​​corresponding to different supervisory indicators with the preset monitoring indicator ranges; Among them, all preset regulatory indicators of different manufacturing structures are preset with a corresponding monitoring indicator range. The monitoring indicator range corresponding to the regulatory indicator can be determined based on the design requirement data of precision parts processing, or based on the historical processing and optimization adjustment data of precision parts. The specific value of the monitoring indicator range is not limited; If the regulatory indicator values ​​corresponding to the regulatory indicators all belong to the pre-determined monitoring indicator range, the indicator reliability flag associated with the corresponding regulatory indicator is set to 0; If the value of the supervision indicator corresponding to the supervision indicator does not belong to the preset monitoring indicator range, and active processing control is generated when the supervision indicator value does not belong to the preset monitoring indicator range, the indicator reliability flag associated with the corresponding supervision indicator is set to 0; this can be understood as the supervision indicator of the manufacturing structure is abnormal, but the existing fault automation supervision technology actively intervenes to handle it, such as controlling the manufacturing structure to suspend operation; If the regulatory indicator value corresponding to the regulatory indicator does not fall within the preset monitoring indicator range, and no active processing control is generated when the regulatory indicator value does not fall within the preset monitoring indicator range, the indicator reliability flag associated with the regulatory indicator is set to 1; this can be understood as an anomaly in the regulatory indicator of the manufacturing structure, but the existing fault automatic monitoring technology does not actively intervene for processing, such as not actively pausing the operation of the manufacturing structure. Sort and combine the indicator reliability flags obtained by processing all the preset regulatory indicators of the abnormal manufacturing structure to obtain a regulatory indicator operation sequence. Sum all the elements in the regulatory indicator operation sequence and set it as the indicator regulatory value ZJi corresponding to the abnormal manufacturing structure; i represents different abnormal manufacturing structures, i = 1, 2, 3, …, n; n is a positive integer representing the total number of abnormal manufacturing structures. In the embodiments of the present invention, by processing, analyzing, and combining the regulatory indicator data of different abnormal manufacturing structures in different processing links of precision components, the regulatory indicator operation sequence and the indicator regulatory value corresponding to all regulatory indicators of different abnormal manufacturing structures are obtained. This can not only digitally represent the monitoring status of all regulatory indicators corresponding to different abnormal manufacturing, but also provide reliable data support for the subsequent analysis of the abnormal execution status corresponding to the abnormal manufacturing structure.

[0022] Perform data analysis on the indicator regulatory value to determine that the abnormal execution status corresponding to the abnormal manufacturing structure is normal. If the indicator regulatory value is 0, it is determined that the abnormal execution status corresponding to the abnormal manufacturing structure is normal, and it is marked as the first manufacturing structure. When performing data analysis on the abnormal manufacturing impact corresponding to the first manufacturing structure, obtain the total number of abnormal components ni´ generated by the processing corresponding to the first manufacturing structure, where i´ represents different first manufacturing structures, i´ = 1, 2, 3, …, m; m is a positive integer representing the total number of first manufacturing structures; and through the abnormal occurrence monitoring formula Calculate the abnormal occurrence value YFi´ corresponding to the first manufacturing structure; in the formula, Ni´ is the total number of precision components processed in the corresponding batch of the first manufacturing structure; αi´ is the abnormal occurrence requirement value corresponding to the first manufacturing structure, which can be determined according to the existing processing industry design requirement data of the first manufacturing structure, or the preliminary test data before the first manufacturing structure is put into production. It should be explained that the abnormal occurrence value is used to process and calculate all the regulatory indicator processing data of the first manufacturing structure to digitally represent the abnormal occurrence impact on the production and processing corresponding to the first manufacturing structure; the larger the abnormal occurrence value, the greater the abnormal occurrence impact on the production and processing corresponding to the first manufacturing structure. If YFi´ < 1, it is determined that the abnormal occurrence of the production and processing corresponding to the first manufacturing structure affects the normal situation, and the mark of the first manufacturing structure is updated to a conventional manufacturing influence structure; Otherwise, it is determined that the abnormal occurrence of the production and processing corresponding to the first manufacturing structure affects abnormally, and the mark of the first manufacturing structure is updated to a special manufacturing influence structure; Perform the first optimization management plan for the preset supervision indicators of all special manufacturing influence structures; Among them, the first optimization management plan is specifically to supplement and increase all existing supervision indicators of the first manufacturing structure to solve the incomplete coverage of the existing supervision indicators; In the embodiment of the present invention, when the abnormal execution state corresponding to the abnormal manufacturing structure is normal, data analysis is performed on the abnormal manufacturing influence corresponding to the first manufacturing structure, and dynamic optimization management is performed on the preset supervision indicators of different first manufacturing structures according to the analysis results, improving the active supervision and optimization effect of the preset supervision indicators of different manufacturing structures in different processing links.

[0023] Such as Figure 2 As shown, if the index supervision value is not 0, it is determined that the abnormal execution state corresponding to the abnormal manufacturing structure is abnormal, and it is marked as the second manufacturing structure. The abnormal occurrence value corresponding to the second manufacturing structure is calculated through the abnormal occurrence supervision formula and analyzed; If the abnormal occurrence value corresponding to the second manufacturing structure is less than 1, it is determined that the abnormal occurrence of the production and processing corresponding to the second manufacturing structure affects normally, and the second optimization management plan for the preset supervision indicators is performed on it; Otherwise, it is determined that the abnormal occurrence of the production and processing corresponding to the second manufacturing structure affects abnormally, and the first optimization management plan and the second optimization management plan for the preset supervision indicators are performed on it; Among them, the second optimization management plan is specifically to add, delete, and modify the supervision rules and supervision content of all existing supervision indicators of the second manufacturing structure to improve the reliability of the existing supervision indicators; It can be understood that when the abnormal execution state corresponding to the abnormal manufacturing structure is abnormal, there are two problems. One is the abnormal operation of the existing preset supervision indicators of the second manufacturing structure, and the other is the abnormal operation of the existing preset supervision indicators of the second manufacturing structure, and the coverage of the existing preset supervision indicators of the second manufacturing structure is incomplete, which can no longer meet the requirements of the quality supervision of the second manufacturing structure; It should be noted that different from the existing technical solutions, which only stay in the processing, analysis, and prompting of the supervision data of the conventional supervision indicators, the embodiment of the present invention actively conducts diversified supervision and analysis on the abnormalities existing in the existing supervision indicators of different abnormal manufacturing structures, and implements diversified optimization management on different abnormal manufacturing structures according to the analysis results, improving the diversity of the active processing and analysis of the quality inspection data in different processing links of precision parts.

[0024] Proactively mine and analyze the impact of optimizing the implementation of the data of the optimization management plan using different abnormal manufacturing structures on the corresponding processing links, and adaptively proactively optimize and manage other non-abnormal manufacturing structures in the processing links according to the analysis results; including: Statistically calculate the first optimization total number M1 that only implements the first optimization management plan, the second optimization total number M2 that only implements the second optimization management plan, and the third optimization total number M3 that implements both the first optimization management plan and the second optimization management plan in the processing link, and sequentially pass the first optimization total number, the second optimization total number, and the third optimization total number of the processing link through the formula Calculate and obtain the corresponding optimization implementation impact value YYk; in the formula, k is 1, 2, 3, respectively representing only implementing the first optimization management plan, only implementing the second optimization management plan, and implementing both the first optimization management plan and the second optimization management plan; M0 is the total number of all manufacturing structures corresponding to the supervision of the processing link; βk is β1, β2, β3, which are the optimization implementation impact thresholds corresponding to implementing different optimization management plans, and can be determined according to the existing processing industry design requirement data of different processing links, or the preliminary test data before the processing link is put into production; It should be explained that the optimization implementation impact value is used to process and calculate the data of implementing different optimization management plans for the processing link, so as to digitally represent the impact of different optimization management plans on other non-abnormal manufacturing structures in the processing link; the larger the optimization implementation impact value, the greater the impact of the corresponding optimization management plan on other non-abnormal manufacturing structures in the processing link; As Figure 3 shown, when conducting proactive optimization necessity analysis and management on other non-abnormal manufacturing structures in the processing link: If YYk ≤ 0, it is prompted that the proactive optimization of the corresponding optimization management plan for other non-abnormal manufacturing structures in the processing link is unnecessary; On the contrary, it is prompted that the proactive optimization of the corresponding optimization management plan for other non-abnormal manufacturing structures in the processing link is necessary, and the corresponding optimization management plan is used to proactively optimize other non-abnormal manufacturing structures in the processing link; It can be understood that the larger the optimization implementation impact value, the more effectively the proactive optimization of other non-abnormal manufacturing structures in the processing link using the corresponding optimization management plan can reduce the processing quality impact generated by other non-abnormal manufacturing structures in the processing link, improve the proactive processing impact prevention effect of different non-abnormal manufacturing structures, and the targeted implementation effect of different optimization management plans.

[0025] In the embodiments of the present invention, active mining and analysis are performed on the optimization implementation impact of the data of different abnormal manufacturing structures on the corresponding processing links, and according to the analysis results, the active optimization management of other non-abnormal manufacturing structures in the processing links is adaptively carried out, realizing the data analysis and management of the active optimization of other non-abnormal manufacturing structures in different processing links, and further improving the diversity of the active processing and analysis of the quality inspection data in different processing links of precision components.

[0026] In several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the described embodiments of the invention are merely illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0027] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0028] In addition, in each embodiment of the present invention, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0029] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. Intelligent optimization method for precision parts processing technology based on multi-modal data fusion, characterized in that: include: Actively monitor and process the quality inspection results of different processing links of precision parts, and conduct data analysis on the impact of abnormal manufacturing on all abnormal manufacturing structures obtained through corresponding supervision of different processing links. Dynamically mark different abnormal manufacturing structures in different processing links according to the analysis results, and implement targeted optimization management plans; The optimization management plan data of different abnormal manufacturing structures are used to actively mine and analyze the impact of optimization implementation on the corresponding processing links, and other non-abnormal manufacturing structures in the processing links are adaptively optimized and managed based on the analysis results.

2. The intelligent optimization method for precision parts processing technology based on multimodal data fusion according to claim 1 is characterized in that: Obtain all quality inspection results corresponding to different processing links of each batch of precision parts, and determine all abnormal causes and abnormal manufacturing structures based on all abnormal quality inspection results corresponding to different processing links; When performing data analysis on the impact of abnormal manufacturing on all abnormal manufacturing structures obtained by corresponding supervision of different processing links, obtain the regulatory indicator values ​​corresponding to all preset regulatory indicators when processing precision parts with abnormal manufacturing structures, and match and analyze the regulatory indicator values ​​corresponding to different regulatory indicators with the preset monitoring indicator ranges, and dynamically set the indicator reliability identifier associated with the corresponding regulatory indicator according to the analysis results; Dynamically set indicators reliably contain values ​​of 0 or 1.

3. The intelligent optimization method for precision parts processing technology based on multimodal data fusion according to claim 2 is characterized in that: The reliable indicator identifiers obtained by processing all preset supervision indicators of the abnormal manufacturing structure are sorted and combined to obtain a supervision indicator operation sequence, and all elements in the supervision indicator operation sequence are summed up and set as the indicator supervision value ZJi corresponding to the abnormal manufacturing structure; i is a different abnormal manufacturing structure, i=1, 2, 3, ..., n; n is a positive integer; Perform data analysis on the indicator supervision value to determine whether the abnormal execution status corresponding to the abnormal manufacturing structure is normal; If the indicator supervision value is 0, it is determined that the abnormal execution state corresponding to the abnormal manufacturing structure is normal, and it is marked as the first manufacturing structure.

4. The intelligent optimization method for precision parts processing technology based on multimodal data fusion according to claim 3 is characterized in that: When performing data analysis on the abnormal manufacturing impact corresponding to the first manufacturing structure, the total number of abnormal parts ni´ generated by abnormal processing corresponding to the first manufacturing structure is obtained, where i´ is a different first manufacturing structure, i´=1, 2, 3, ..., m; m is a positive integer; And through the exception supervision formula Calculate and obtain the abnormal occurrence value YFi´ corresponding to the first manufacturing structure; where Ni´ is the total number of precision parts processed in the batch corresponding to the first manufacturing structure; αi´ is the abnormal occurrence requirement value corresponding to the first manufacturing structure; If YFi´<1, it is determined that the abnormality of the production and processing corresponding to the first manufacturing structure has a normal impact, and the mark of the first manufacturing structure is updated to a normal manufacturing impact structure; Otherwise, it is determined that the abnormality corresponding to the production and processing of the first manufacturing structure has an impact abnormality, and the mark of the first manufacturing structure is updated to a special manufacturing impact structure; The first optimization management plan for all special manufacturing impact structures with preset regulatory indicators.

5. The intelligent optimization method for precision parts processing technology based on multimodal data fusion according to claim 4 is characterized in that: If the indicator supervision value is not 0, the abnormal execution state corresponding to the abnormal manufacturing structure is determined to be abnormal, and it is marked as the second manufacturing structure. The abnormal occurrence value corresponding to the second manufacturing structure is calculated and analyzed through the abnormal occurrence supervision formula.

6. The intelligent optimization method for precision parts processing technology based on multimodal data fusion according to claim 5 is characterized in that: If the abnormal occurrence value corresponding to the second manufacturing structure is less than 1, it is determined that the abnormal occurrence impact of the production and processing corresponding to the second manufacturing structure is normal, and a second optimization management plan with preset supervision indicators is implemented for it; On the contrary, it is determined that the abnormality of the second manufacturing structure corresponding to the production and processing has an impact on the abnormality, and the first optimization management plan and the second optimization management plan with preset supervision indicators are applied to it.

7. The intelligent optimization method for precision parts processing technology based on multimodal data fusion according to claim 6 is characterized in that: The total number of first optimizations M1 in which only the first optimization management scheme is implemented, the total number of second optimizations M2 in which only the second optimization management scheme is implemented, and the total number of third optimizations M3 in which both the first optimization management scheme and the second optimization management scheme are implemented are counted, and the total number of first optimizations, the total number of second optimizations, and the total number of third optimizations in the processing link are calculated in turn through the formula Calculate and obtain the corresponding optimization implementation impact value YYk; where k is 1, 2, and 3, respectively indicating the implementation of only the first optimization management plan, only the implementation of the second optimization management plan, and the implementation of both the first optimization management plan and the second optimization management plan; M0 is the total number of all manufacturing structures supervised in the processing link; βk is β1, β2, and β3, which are the optimization implementation impact thresholds corresponding to the implementation of different optimization management plans.

8. The intelligent optimization method for precision parts processing technology based on multimodal data fusion according to claim 7 is characterized in that: When actively optimizing the necessity analysis and management of other non-abnormal manufacturing structures in the processing link: If YYk≤0, it indicates that the optimization management scheme is unnecessary for the active optimization of other non-abnormal manufacturing structures in the processing link; Otherwise, it is suggested that the optimization management scheme is necessary to actively optimize other non-abnormal manufacturing structures in the processing link, and the optimization management scheme is used to actively optimize other non-abnormal manufacturing structures in the processing link.

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