A method for generating an industrial chain quality map

Abstract

The present invention relates to the technical field of atlas data processing, and specifically discloses a method for generating an industrial chain quality atlas. By analyzing the internal correlation degree of each production factor in each production link belonging to the industrial chain, it helps to understand the mutual influence and dependence relationship between each production factor in each production link of the industrial chain. Thus, a relatively reliable industrial chain atlas is generated, and the influence weight index of each quality problem index of each production factor in each production link belonging to the industrial chain atlas is evaluated to accurately identify the key indicators of each quality problem that have a greater impact on the industrial chain. Finally, the key indicators of each quality problem of each production factor in each production link belonging to the industrial chain atlas are classified to generate an industrial chain quality atlas. By finely analyzing multi-dimensional parameters, focusing on the core problems of the industrial chain, guiding the generation of the industrial chain quality atlas, accurately examining the quality problems of the industrial chain, and providing strong data support for optimizing the industrial chain configuration.

Description

Technical Field

[0001] The present invention relates to the technical field of atlas data processing, and specifically to a method for generating an industrial chain quality atlas. Background Art

[0002] Atlases have played an important role in data analysis and intelligent decision-making support. However, traditional atlas generation methods usually rely on simple statistical analysis for drawing, without deeply exploring the complex relationships and dependencies among the elements involved in the atlas. At the same time, traditional atlas generation methods fail to effectively integrate and reveal the derivative problems of the elements belonging to the atlas, resulting in limitations in problem recognition and the formulation of solution strategies during the use of the atlas.

[0003] For example, the invention patent with the publication number CN111178940B, a method and system for automatically generating a sales talk atlas. Among them, the method includes: obtaining the conversation records of salespersons and customers; determining the conversation event sequence according to the conversation records; determining the causal relationship between different types of events and the corresponding causal probability according to the conversation event sequence; generating a sales talk atlas according to the causal relationship and causal probability. The above application embodiments can automatically generate a sales talk atlas according to sales records, without the need for sales experts to manually summarize sales experience, improving the accuracy and efficiency of sales evaluation. Salespersons can improve the order success rate based on the sales talk atlas.

[0004] For example, the invention patent with the publication number CN116432965B, which relates to a method for analyzing job capabilities based on a knowledge graph and a method for generating a tree diagram. The job capability analysis method includes crawling job information and company information; cleaning the job information and company information to obtain recruitment information; processing the recruitment information according to preset data processing rules to obtain entity information and relationship information; corresponding the entity information and relationship information to convert them into nodes and edges to construct a knowledge graph; identifying key job information according to the knowledge graph, and constructing a job capability model by using the key job information and the knowledge graph. Constructing a job capability model based on the knowledge graph can more accurately and systematically organize and summarize the key skills and ability requirements required for different jobs, providing guidance for the capabilities of relevant personnel.

[0005] However, in the process of implementing the embodiments of the present application, it is found that the above technologies have at least the following technical problems: In the process of constructing an atlas, the lack of clear atlas construction guiding principles will lead to deviations in information extraction and processing in the atlas, affecting the accuracy and integrity of the atlas, and further affecting the practicality and decision-making support effect of the generated atlas. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for generating an industrial chain quality map, which can effectively solve the problems involved in the above-mentioned background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for generating an industrial chain quality map, including: S1. Obtain the process parameters of each production factor in each production link of the industrial chain, analyze the internal correlation degree of each production factor in each production link of the industrial chain, and thus generate an industrial chain map; S2. Collect each quality problem index of each production factor in each production link of the industrial chain map, evaluate the influence weight index of each quality problem index of each production factor in each production link of the industrial chain map, and thus extract each quality problem key index of each production factor in each production link of the industrial chain map; S3. Classify each quality problem key index of each production factor in each production link of the industrial chain map, and thus generate an industrial chain quality map.

[0008] As a further method, the specific analysis process for extracting each quality problem key index of each production factor in each production link of the industrial chain map is as follows: Compare the influence weight index of each quality problem index of each production factor in each production link of the industrial chain map with the influence weight threshold stored in the industrial chain map database, and screen out each quality problem index of each production factor in each production link of the industrial chain map whose influence weight index is greater than the influence weight threshold, and mark it as each quality problem key index of each production factor in each production link of the industrial chain map.

[0009] As a further method, the specific classification process for each quality problem key index of each production factor in each production link of the industrial chain map is to analyze the problem keyword adaptation index between each quality problem key index of each production factor in each production link of the industrial chain map and each problem classification label, and thus classify each quality problem key index of each production factor in each production link of the industrial chain map.

[0010] As a further method, the specific analysis process for generating the industrial chain quality map is as follows: After classifying each quality problem key index of each production factor in each production link of the industrial chain map, automatically fill it into the corresponding problem classification label in the industrial chain map, and thus generate an industrial chain quality map.

[0011] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0012] (1) The present invention provides a method for generating an industrial chain quality map, analyzing the internal correlation degree of each production factor in each production link belonging to the industrial chain, which helps to understand the mutual influence and dependence relationship between each production factor in each production link of the industrial chain. Thus, a more reliable industrial chain map is generated, and the influence weight index of each quality problem index of each production factor in each production link belonging to the industrial chain map is evaluated to accurately identify the key quality problem indicators that have a greater impact on the industrial chain. Finally, the key quality problem indicators of each production factor in each production link belonging to the industrial chain map are classified to generate an industrial chain quality map. By finely analyzing multi-dimensional parameters, focusing on the core problems of the industrial chain, guiding the generation of the industrial chain quality map, accurately examining the quality problems of the industrial chain, and providing strong data support for optimizing the industrial chain configuration.

[0013] (2) The present invention analyzes the number of improvement solutions, adjustability, and the connection degree of the corresponding production factor position nodes of each quality problem index of each production factor in each production link belonging to the industrial chain map within the production detection cycle, so as to accurately reflect the influence degree of each quality problem index on the overall operation of the industrial chain, thereby accurately identifying key quality problems, which helps to more reasonably and effectively guide the generation of the industrial chain quality map.

[0014] (3) The present invention analyzes the keyword co-occurrence degree and average correlation level between the key quality problem indicators of each production factor in each production link belonging to the industrial chain map and each problem classification label, and thus classifies the key quality problem indicators of each production factor in each production link belonging to the industrial chain map, providing a clearer and more definite decision-making basis for improving the operation efficiency of the industrial chain. According to the classification results, more targeted improvement measures and optimization strategies can be formulated for the quality problems of the industrial chain to improve the effectiveness of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0016] Figure 1 It is a schematic flow chart of the method of the present invention.

[0017] Figure 2 It is a schematic diagram (partial) of the integrated circuit industrial chain quality map involved in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Referring to Figure 1 As shown, the present invention provides a method for generating an industrial chain quality map, including: S1. Obtain the process parameters of each production factor in each production link of the industrial chain, analyze the internal correlation degree of each production factor in each production link of the industrial chain, and thus generate an industrial chain map; the above-mentioned obtaining of the process parameters of each production factor in each production link of the industrial chain can be extracted from the production logs recording each production factor in each production link.

[0020] In a specific embodiment, the present invention analyzes the keyword co-occurrence degree and average correlation level between each key quality index and each problem classification label of each production factor in each production link of the industrial chain map, and thus classifies each key quality index of each production factor in each production link of the industrial chain map, providing a clearer and more definite decision-making basis for improving the operation efficiency of the industrial chain. According to the classification results, more targeted improvement measures and optimization strategies can be formulated for the quality problems of the industrial chain, improving the effectiveness of decision-making.

[0021] Sort the internal correlation degrees of each production factor in each production link of the industrial chain in descending order to obtain the internal correlation order of each production factor in each production link of the industrial chain, and thus generate an industrial chain map; it should be noted that the greater the average internal correlation degree of each production link of the industrial chain, the greater the contribution degree of the production factor of this production link of the industrial chain to the industrial chain, usually meaning that the production factor of this production link of the industrial chain is in the upstream position of the industrial chain or has a greater overall impact on the industrial chain. Therefore, the production factor of this production link of the industrial chain is placed in a priority position in the industrial chain map. Sorting each production factor in each production link of the industrial chain means sorting the contribution degrees of each production factor in each production link of the industrial chain; for the above-mentioned generation of the industrial chain map, the map constructor can perform a generation process based on each production factor in each production link of the industrial chain that has been sorted by using graphic visualization software.

[0022] Furthermore, the specific analysis process of analyzing the internal correlation degree of each production factor in each production link of the industrial chain is as follows: According to the process parameters of each production factor in each production link of the industrial chain, extract the occurrence times of each production factor in each production link of the industrial chain, and the above-mentioned occurrence times are extracted from the production logs recording each production factor in each production link.

[0023] Match the data coupling degrees corresponding to each occurrence frequency interval stored in the industrial chain map database, thereby obtaining the data coupling degrees of each production factor in each production link to which the industrial chain belongs; the above-mentioned industrial chain map database refers to the database that stores the data required to generate the industrial chain quality map; the above-mentioned matching of the data coupling degrees corresponding to each occurrence frequency interval stored in the industrial chain map database means that each occurrence frequency interval corresponds to a data coupling degree. Find the occurrence frequency intervals that match the occurrence frequencies of each production factor in each production link to which the industrial chain belongs. Then, the data coupling degree corresponding to this occurrence frequency interval is the data coupling degree of each production factor in each production link to which the industrial chain belongs; the above-mentioned data coupling degree represents a value indicating the tightness of data transmission, which is formulated by the map constructor based on historical map data.

[0024] Locate the initial time points corresponding to each occurrence of each production factor in each production link to which the industrial chain belongs, and perform a difference processing with the end time points corresponding to each occurrence of each production factor in each production link to which the industrial chain belongs, so as to obtain the data support duration of each production factor in each production link to which the industrial chain belongs; the above-mentioned locating the initial time points corresponding to each occurrence of each production factor in each production link to which the industrial chain belongs means locating the time stamps of each occurrence of each production factor in each production link to which the industrial chain belongs from the production logs recording each production factor in each production link, and obtaining the occurrence time points in the time stamps, which is the initial time point corresponding to each occurrence of each production factor in each production link to which the industrial chain belongs; the method for obtaining the end time points corresponding to each occurrence of each production factor in each production link to which the industrial chain belongs is the same as the method for obtaining the initial time points corresponding to each occurrence of each production factor in each production link to which the industrial chain belongs.

[0025] Extract the product output of the production equipment corresponding to each production factor in each production link to which the industrial chain belongs during the production detection period from the process parameters of each production factor in each production link to which the industrial chain belongs, and perform a ratio processing with the duration corresponding to the production detection period, so as to obtain the production speed of the production equipment corresponding to each production factor in each production link to which the industrial chain belongs; the above-mentioned production detection period represents the period for detecting the performance of the industrial chain, and the duration of the period is formulated by the map constructor according to the actual situation; the above-mentioned obtaining the product output of the production equipment corresponding to each production factor in each production link to which the industrial chain belongs during the production detection period means extracting from the production logs of the production equipment.

[0026] From the above comprehensive analysis, the internal correlation degree of each production factor in each production link of the industrial chain is obtained. In this embodiment, the internal correlation degree of each production factor in each production link of the above industrial chain is obtained through the comprehensive analysis of the data coupling degree, data support duration, and production speed of the corresponding production equipment of each production factor in each production link of the industrial chain, which is a numerical value used to determine the internal correlation degree of each production factor in each production link of the industrial chain. The specific expression is:

[0027]

[0028] Among them, σ D ab is the internal correlation degree of the b-th production factor in the a-th production link of the industrial chain. The longer the data support duration, the higher the degree of dependence of the industrial chain on the data of this production factor in this production link, and at the same time, the higher the data coupling degree of this production factor in this production link. At the same time, if the production speed of the production equipment corresponding to a certain production factor in a certain production link is faster, it indicates that this production factor in this production link is the infrastructure in the industrial chain and has a significant and powerful impact on the overall operation and efficiency of the industrial chain, jointly improving the internal correlation degree. Therefore, by smoothly analyzing the three parameters, the operation mechanism and data dependence relationship of the industrial chain can be deeply understood, the data circulation path of the industrial chain can be optimized, and a precise data management strategy can be provided for the generation of the industrial chain map.

[0029] CS ab is the data coupling degree of the b-th production factor in the a-th production link of the industrial chain. A high data coupling degree means that the data of this production factor is frequently exchanged and shared in other production links of the industrial chain, and other production links have a strong dependence on this data.

[0030] DSD ab is the data support duration of the b-th production factor in the a-th production link of the industrial chain. The longer the data support duration, the more deeply this production factor participates in the formulation and implementation of many important decisions in the industrial chain. Therefore, this production factor has an important position in the industrial chain.

[0031] IF ab is the production speed of the production equipment corresponding to the b-th production factor in the a-th production link of the industrial chain. The faster the production speed, the relatively simpler and more efficient the operation process of this production factor in the industrial chain, thus being the cornerstone for the stable operation of the industrial chain and having a significant impact on the overall operation and efficiency of the industrial chain.

[0032] a is the number of each production link, a = {1, 2, 3,..., t}, where t is the total number of production links, and b is the number of each production factor, b = {1, 2, 3,..., p}, where p is the total number of production factors.

[0033] zg1 is the influence factor corresponding to the predefined data coupling degree unit value in the industrial chain map database, representing the value of the influence degree of the data coupling degree unit value on the internal correlation degree. The fitting curve corresponding to the data coupling degree is obtained by fitting the relationship between the historical data importance index, the historical data flow frequency and the data coupling degree. According to the real-time data importance index and the data flow frequency, substituting them into the fitting curve corresponding to the data coupling degree, the influence factor corresponding to the data coupling degree unit value in this example is obtained. In this example, the value range of this value is from 0 to 1.

[0034] zg2 is the influence factor corresponding to the predefined data support duration unit value in the industrial chain map database, representing the value of the influence degree of the data support duration unit value on the internal correlation degree. The fitting curve corresponding to the data support duration is obtained by fitting the relationship between the historical data exception handling time, the historical data response time and the data support duration. According to the real-time data exception handling time and the data response time, substituting them into the fitting curve corresponding to the data support duration, the influence factor corresponding to the data support duration unit value in this example is obtained. In this example, the value range of this value is from 0 to 1.

[0035] zg3 is the influence factor corresponding to the predefined production speed unit value in the industrial chain map database, representing the value of the influence degree of the production speed unit value on the internal correlation degree. The fitting curve corresponding to the production speed is obtained by fitting the relationship between the historical market demand order quantity, the historical raw material cleanliness and the production speed. According to the real-time market demand order quantity and the raw material cleanliness, substituting them into the fitting curve corresponding to the production speed, the influence factor corresponding to the production speed unit value in this example is obtained. In this example, the value range of this value is from 0 to 1.

[0036] S2. Collect each quality problem index of each production factor in each production link to which the industrial chain map belongs, evaluate the influence weight index of each quality problem index of each production factor in each production link to which the industrial chain map belongs, and thus extract each key quality problem index of each production factor in each production link to which the industrial chain map belongs; the above-mentioned collection of each quality problem index of each production factor in each production link to which the industrial chain map belongs refers to that the map constructor collects each quality problem index of each production factor in each production link to which the industrial chain map belongs through channels such as industry forums and official websites based on the historical problems of each production factor in each production link to which the industrial chain map belongs.

[0037] In a specific embodiment, the present invention analyzes the number of improvement solutions, adjustability, and the connection degree of the corresponding production factor position nodes of each quality problem index of each production factor in each production link of the industrial chain map within the production detection cycle, so as to accurately reflect the impact degree of each quality problem index on the overall operation of the industrial chain, thereby accurately identifying key quality problems, which helps to more reasonably and effectively guide the generation of the industrial chain quality map.

[0038] Specifically, each quality problem index of each production factor in each production link of the industrial chain map specifically includes each occurrence time point of each quality problem index of each production factor in each production link of the industrial chain map within the production detection cycle; the above-mentioned each occurrence time point is extracted from the production logs recording each production factor in each production link, and each quality problem index can occur 0 times or several times within the production detection cycle. For example, the problem index that the packaging materials required for high-end chips such as 5G rely on imports appears once within the production detection cycle, and there is no corresponding solved time point within the production detection cycle.

[0039] Specifically, the specific analysis process for extracting the key quality problem indexes of each production factor in each production link of the industrial chain map is as follows: compare the influence weight index of each quality problem index of each production factor in each production link of the industrial chain map with the influence weight threshold stored in the industrial chain map database, and screen out each quality problem index of each production factor in each production link of the industrial chain map whose influence weight index is greater than the influence weight threshold, and mark it as the key quality problem index of each production factor in each production link of the industrial chain map; the above-mentioned influence weight threshold refers to the minimum value set during the evaluation of the influence weight index, which is a value determined by the map constructor based on historical map construction work; it should be noted that for the above-mentioned each quality problem index of each production factor in each production link of the industrial chain map whose influence weight index is greater than the influence weight threshold, if the influence weight index of a certain quality problem index of each production factor in each production link of the industrial chain map is greater than the influence weight threshold, it indicates that the solution difficulty of this quality problem index is relatively large, and it may also be a historical legacy problem, and it is difficult to find a solution in a short time. Therefore, this quality problem index needs to be screened out and put into the industrial chain map to guide the improvement direction of the industrial chain. If the influence weight index of a certain quality problem index of each production factor in each production link of the industrial chain map is less than or equal to the influence weight threshold, it indicates that this quality problem index can be solved in a short time and has a relatively small impact on the industrial chain. Therefore, it can be not screened out.

[0040] Locate and count the number of post-production factor position nodes and the number of post-production factor position node types corresponding to each quality problem indicator of each production factor in each production link to which the industrial chain map belongs. Multiply them respectively with the connection degree factor corresponding to the preset number of post-production factor position nodes and the connection degree factor corresponding to the number of post-production factor position node types in the industrial chain map database, and then perform cumulative processing to obtain the production factor position node connection degree corresponding to each quality problem indicator of each production factor in each production link to which the industrial chain map belongs. It should be noted that the production factor position node connection degree corresponding to each quality problem indicator of each production factor in each production link to which the industrial chain map belongs refers to the result of multiplying the number of post-production factor position nodes of each production factor position node in each production link to which the industrial chain map belongs by the connection degree factor corresponding to the number of post-production factor position nodes, plus the result of multiplying the number of post-production factor position node types by the connection degree factor corresponding to the number of post-production factor position node types. For example, the number of post-production factor position nodes of a certain production factor position node is 4, and the result of multiplying it by the connection degree factor 0.09 corresponding to the number of post-production factor position nodes is 0.36. The number of post-production factor position node types of this production factor position node is 2, and the result of multiplying it by the connection degree factor 0.05 corresponding to the number of post-production factor position node types is 0.1. Then the connection degree of this production factor position node is the sum of 0.36 and 0.1, which is 0.46. The connection degree factor corresponding to the above-mentioned number of post-production factor position nodes and the connection degree factor corresponding to the number of post-production factor position node types are formulated by the map constructor based on historical map construction work experience. For example Figure 2 As shown in the Quality Map of the Integrated Circuit Industrial Chain (Partial), which shows the production link of integrated circuit manufacturing (wafer processing). The post-production factor position nodes of the photoresist coating node include exposure, etching, doping deposition, detection, and those not listed. Counting the number of these post-production factor position nodes is the number of post-production factor position nodes of the photoresist coating position node in the integrated circuit manufacturing (wafer processing) production link to which the industrial chain map belongs. Exposure, etching, doping deposition, and detection are all action plans, and the number of types of these four post-production factor position nodes is 1. The types of post-production factor position nodes also include problem judgment types, resource supply types, and quality control types, etc.

[0041] Count the number of occurrence time points of each quality problem index of each production factor in each production link of the statistical industrial chain map within the production inspection cycle, and perform a ratio process with the number of resolved time points of each quality problem index of each production factor in each production link of the obtained industrial chain map within the production inspection cycle, to obtain the adjustable degree of each quality problem index of each production factor in each production link of the industrial chain map; the number of resolved time points of each quality problem index of each production factor in each production link of the above industrial chain map is extracted from the production log recording each production factor in each production link. If there is no number of resolved time points for a certain quality problem index of each production factor in each production link of the industrial chain map within the production inspection cycle, the adjustable degree of this quality problem index within the production inspection cycle is 0.

[0042] Obtain the number of improvement plans for each quality problem index of each production factor in each production link of the industrial chain map within the production inspection cycle, and comprehensively evaluate from this to obtain the influence weight index of each quality problem index of each production factor in each production link of the industrial chain map; the number of improvement plans for each quality problem index of each production factor in each production link of the above industrial chain map can be extracted from the report recording the improvement plans of the quality problem index.

[0043] Furthermore, the influence weight index of each quality problem index of each production factor in each production link of the industrial chain map. In this embodiment, the influence weight index of each quality problem index of each production factor in each production link of the industrial chain map is obtained through comprehensive analysis of the number of improvement plans, adjustable degree, and the connection degree of the corresponding production factor position nodes of each quality problem index of each production factor in each production link of the industrial chain map within the production inspection cycle. It is a numerical value used to comprehensively determine the influence degree of each quality problem index of each production factor in each production link of the industrial chain map on the industrial chain. The specific expression is:

[0044]

[0045] Among them, μ abnIt is the influence weight index of the nth quality problem index of the bth production factor in the ath production link of the industrial chain map. If the number of improvement solutions for a certain problem index is small, the adjustability of this quality problem index will be small, indicating that it is more difficult to adjust the quality problem index during the production detection cycle. Therefore, the influence weight index of this problem index is increased. If the connection degree of the production factor position node corresponding to a certain quality problem index is large, it indicates that the production factor position where this quality problem index belongs occupies a relatively core or key position in the industrial chain map, and the influence of this quality problem index on the production chain is also relatively significant. Therefore, by integrating and quantitatively analyzing the above parameters, the key points of industrial chain problems can be accurately identified, limited resources can be invested in key problems and important links, the efficient utilization of resources can be achieved, which helps to optimize the industrial chain process flow and promote the progress of the industrial chain.

[0046] AJ abn It is the connection degree of the production factor position node corresponding to the nth quality problem index of the bth production factor in the ath production link of the industrial chain map. Production factors with a high connection degree of production factor position nodes can quickly spread the negative impact of quality problem indexes to multiple links of the industrial chain.

[0047] NP abn It is the adjustability of the nth quality problem index of the bth production factor in the ath production link of the industrial chain map during the production detection cycle. A smaller adjustability indicates that it is difficult to quickly respond to and solve this quality problem index, increasing the risk of industrial chain interruption and losses.

[0048] AT abn It is the number of improvement solutions for the nth quality problem index of the bth production factor in the ath production link of the industrial chain map during the production detection cycle. A smaller number of improvement solutions indicates that the root cause of this quality problem index is relatively complex and it is difficult to directly find an effective solution. Therefore, the influence weight index is increased.

[0049] a is the number of each production link, a = {1, 2, 3,..., t}, t is the total number of production links; b is the number of each production factor, b = {1, 2, 3,..., p}, p is the total number of production factors; n is the number of each quality problem index, n = {1, 2, 3,..., m}, m is the total number of quality problem indexes.

[0050] Mk1 is the influence factor corresponding to the unit value of the connection degree of the production factor position node predefined in the industrial chain map database, representing the numerical value of the influence degree of the unit value of the connection degree of the production factor position node on the influence weight index. The fitting curve corresponding to the connection degree of the production factor position node is obtained by fitting the relationship between the number of historical subsequent production factor position nodes, the number of historical subsequent production factor position types, and the connection degree of the production factor position node. And according to the real-time number of subsequent production factor position nodes and the number of subsequent production factor position types, substituting them into the fitting curve corresponding to the connection degree of the production factor position node, thus obtaining the influence factor corresponding to the unit value of the connection degree of the production factor position node in this example. In this example, the value range of this numerical value is from 0 to 1.

[0051] Mk2 is the influence factor corresponding to the unit value of the adjustable degree predefined in the industrial chain map database, representing the numerical value of the influence degree of the unit value of the adjustable degree on the influence weight index. The fitting curve corresponding to the adjustable degree is obtained by fitting the relationship between the solution rate of the historical quality problem index, the occurrence rate of the historical quality problem index, and the adjustable degree. And according to the real-time solution rate of the quality problem index and the occurrence rate of the quality problem index, substituting them into the fitting curve corresponding to the adjustable degree, thus obtaining the influence factor corresponding to the unit value of the adjustable degree in this example. In this example, the value range of this numerical value is from 0 to 1.

[0052] Mk3 is the influence factor corresponding to the unit value of the number of improvement plans predefined in the industrial chain map database, representing the numerical value of the influence degree of the unit value of the number of improvement plans on the influence weight index. The fitting curve corresponding to the number of improvement plans is obtained by fitting the relationship between the historical output speed of improvement plans, the historical success rate of improvement plans, and the number of improvement plans. And according to the real-time output speed of improvement plans and the success rate of improvement plans, substituting them into the fitting curve corresponding to the number of improvement plans, thus obtaining the influence factor corresponding to the unit duration of the number of improvement plans in this example. In this example, the value range of this numerical value is from 0 to 1.

[0053] In the embodiment of this example, the change table of the influence weight index of each quality problem index of the bth production factor in the ath production link to which the above industrial chain map belongs and its corresponding parameters is shown in Table 1:

[0054] Table 1 Change table of the influence weight index of each quality problem index of the bth production factor in the ath production link to which the industrial chain map belongs and its corresponding parameters

[0055]

[0056] In this exemplary embodiment, the value of the influencing factor corresponding to the unit value of the connection degree of the production factor position node is 0.7, the value of the influencing factor corresponding to the unit value of the adjustable degree is 0.8, and the value of the influencing factor corresponding to the unit value of the number of improvement solutions is 0.9. Table 1 also reflects the positive correlation between the adjustable degree and the number of improvement solutions, meaning that when the adjustable degree is relatively high, there are usually more improvement solutions to choose from. The influence weight index of the fourth quality problem indicator is 117%, which is greater than the influence weight threshold, indicating that this quality problem indicator has a greater impact on the industrial chain and needs to be marked as a key quality problem indicator.

[0057] S3. Classify the key quality problem indicators of each production factor in each production link of the industrial chain map, thereby generating an industrial chain quality map.

[0058] Specifically, the classification of the key quality problem indicators of each production factor in each production link of the industrial chain map is specifically to analyze the problem keyword adaptation index between the key quality problem indicators of each production factor in each production link of the industrial chain map and each problem classification label, thereby classifying the key quality problem indicators of each production factor in each production link of the industrial chain map; it should be explained that the specific classification process is as follows: sort the problem keyword adaptation index between a key quality problem indicator of a certain production factor in a certain production link of the industrial chain map and each problem classification label in ascending order, and extract the problem classification label corresponding to the first-ranked problem key index. Then, the key quality problem indicator of a certain production factor in a certain production link of the industrial chain map belongs to this problem classification label.

[0059] Specifically, the generation of the industrial chain quality map has the following specific analysis process: after classifying the key quality problem indicators of each production factor in each production link of the industrial chain map, automatically fill them into the corresponding problem classification labels in the industrial chain map, thereby generating an industrial chain quality map; it should be explained that the above-mentioned automatic filling into the corresponding problem classification labels in the industrial chain map means filling the key quality problem indicators after classification processing into the reserved problem classification label area in the industrial chain map through an automatic filling algorithm, such as Figure 2 Schematic diagram of the integrated circuit industrial chain quality map (partial), which is a part of the finally generated integrated circuit industrial chain quality map. If the problem classification label area is not filled, it is a schematic diagram of the integrated circuit industrial chain map (partial).

[0060] Further, the problem keyword adaptation index between the key quality problem indicators of each production factor in each production link of the industrial chain map and each problem classification label is analyzed as follows: Extract the keywords of each problem classification label from the industrial chain map database. The keywords of each problem classification label mentioned above refer to collecting the definitions of each problem classification label from the document materials related to the industrial chain. Each problem classification label may have several definitions, and then extract the keywords of each problem classification label from the definitions of each problem classification label through the term frequency-inverse document frequency method; the above-mentioned key quality problem indicator names refer to the words describing the key quality problem indicators.

[0061] At the same time, locate the keywords of the key quality problem indicator names of each production factor in each production link of the industrial chain map, and respectively compare the keywords with the keywords of each problem classification label for keyword overlap. Count the number of keyword overlaps between the keywords of the key quality problem indicator names of each production factor in each production link of the industrial chain map and the keywords of each problem classification label, and perform a ratio process with the total number of keywords of each problem classification label to obtain the keyword co-occurrence degree between the key quality problem indicators of each production factor in each production link of the industrial chain map and each problem classification label; it should be noted that the above-mentioned comparison of keyword overlap with the keywords of each problem classification label refers to using the set operation method in the programming language to convert the keywords of each problem classification label into a set, and also convert the keywords of the key quality problem indicator names of each production factor in each production link of the industrial chain map into a set, and calculate the intersection of the keyword sets of the key quality problem indicator names of each production factor in each production link of the industrial chain map and the keyword sets of each problem classification label respectively, so as to obtain the overlapping words between the keywords of each problem classification label and the key quality problem indicator names of each production factor in each production link of the industrial chain map.

[0062] Match the association level between the keywords of the key quality problem indicator names of each production factor in each production link of the industrial chain map and the keywords of each problem classification label from the industrial chain map database, and perform an average process to obtain the average association level between the key quality problem indicators of each production factor in each production link of the industrial chain map and each problem classification label; the above-mentioned association level refers to the map builder judging the semantic similarity of words based on the method based on the semantic dictionary in the semantic similarity calculation, and then formulating the association level based on the actual situation. For example, if the semantic similarity is 80%, the association level is 8. The higher the association level, the closer the meanings expressed by the two words are.

[0063] Thus, through comprehensive analysis, the problem keyword adaptation index between the key quality problem indicators of each production factor in each production link of the industrial chain map and the problem keywords of each problem classification label is obtained. In this embodiment, the above problem keyword adaptation index is obtained through comprehensive analysis of the co-occurrence degree of the keywords of the key quality problem indicators of each production factor in each production link of the industrial chain map and each problem classification label, as well as the average association level, and is a numerical value used to evaluate the adaptation degree between the key quality problem indicators of each production factor in each production link of the industrial chain map and the problem keywords of each problem classification label. The specific expression is:

[0064]

[0065] Among them, Φ abs_v is the problem keyword adaptation index between the s-th key quality problem indicator of the b-th production factor in the a-th production link of the industrial chain map and the v-th problem classification label. A low average association level indicates a large semantic difference between the key quality problem indicator and the problem classification label, resulting in a weak correlation between the two, thus reducing the co-occurrence degree of keywords and jointly reducing the problem keyword adaptation index. By quantitatively analyzing the problem keyword adaptation index, the co-occurrence degree of keywords, and the average association level, the accuracy and reliability of the classification of quality problem indicators are improved, and subjective assumptions and misjudgments in the classification process can be reduced.

[0066] C_MK abs_v is the co-occurrence degree of the keywords between the s-th key quality problem indicator of the b-th production factor in the a-th production link of the industrial chain map and the v-th problem classification label, indicating the coincidence ratio of the keywords of the quality problem indicator and the problem classification label. If the co-occurrence degree of keywords is high, it means that the meanings expressed by the two are similar.

[0067] E_NS abs_v is the average association level between the s-th key quality problem indicator of the b-th production factor in the a-th production link of the industrial chain map and the v-th problem classification label. The average association level is a quantitative value of the similarity degree between keywords. If two keywords are very similar semantically or often appear in the same context, their average association level may be very high.

[0068] a is the number of each production link, a = {1, 2, 3,..., t}, where t is the total number of production links; b is the number of each production factor, b = {1, 2, 3,..., p}, where p is the total number of production factors; s is the number of each key quality problem indicator, s = {1, 2, 3,..., g}, where g is the total number of key quality problem indicators; v is the number of each problem classification label, v = {1, 2, 3,..., d}, where d is the total number of problem classification labels.

[0069] zl1 is the influence factor corresponding to the predefined co-occurrence degree unit value in the industrial chain map database, which represents the numerical value of the influence degree of the co-occurrence degree unit value of keywords on the problem keyword adaptation index. The fitting curve corresponding to the co-occurrence degree of keywords is obtained by fitting the relationship between the historical keyword co-occurrence frequency, the historical keyword co-occurrence quantity and the co-occurrence degree of keywords. And according to the real-time keyword co-occurrence frequency and keyword co-occurrence quantity, substituting them into the fitting curve corresponding to the co-occurrence degree of keywords, thus obtaining the influence factor corresponding to the co-occurrence degree unit value of keywords in this example. In this example, the value range of this numerical value is from 0 to 1.

[0070] zl2 is the influence factor corresponding to the predefined average association level unit value in the industrial chain map database, which represents the numerical value of the influence degree of the average association level unit value of keywords on the problem keyword adaptation index. The fitting curve corresponding to the average association level is obtained by fitting the relationship between the historical direct association quantity of keywords, the historical indirect association quantity of keywords and the average association level. And according to the real-time direct association quantity of keywords and the indirect association quantity of keywords, substituting them into the fitting curve corresponding to the average association level, thus obtaining the influence factor corresponding to the average association level unit value of keywords in this example. In this example, the value range of this numerical value is from 0 to 1.

[0071] In the embodiment of this example, the change table of the problem keyword adaptation index between the key index of the s-th quality problem of the b-th production factor in the a-th production link to which the above industrial chain map belongs and each problem classification label and its corresponding parameters is shown in Table 2:

[0072] Table 2 Change table of the problem keyword adaptation index between the key index of the s-th quality problem of the b-th production factor in the a-th production link to which the industrial chain map belongs and each problem classification label and its corresponding parameters

[0073]

[0074]

[0075] In the embodiment of this example, the numerical value of the influence factor corresponding to the co-occurrence degree unit value of keywords is 0.5, and the numerical value of the influence factor corresponding to the average association level unit value of keywords is 0.4. The co-occurrence degree of keywords and the average association level are positively correlated. The problem keyword adaptation index between the key index of the s-th quality problem of the b-th production factor in the a-th production link to which the industrial chain map belongs and the 4th problem classification label is the highest. Then the key index of the s-th quality problem of the b-th production factor in the a-th production link to which the industrial chain map belongs belongs to the 4th problem classification label.

[0076] In a specific embodiment, the present invention provides a method for generating an industrial chain quality map, analyzes the internal correlation of each production factor in each production link of the industrial chain, helps to understand the mutual influence and dependence between each production factor in each production link in the industrial chain, thereby generating an industrial chain map with strong reliability, and evaluates the influence weight index of each quality problem indicator of each production factor in each production link of the industrial chain map, accurately identifies each key indicator of quality problems that have a greater impact on the industrial chain, and finally classifies and processes each key indicator of quality problems of each production factor in each production link of the industrial chain map, thereby generating an industrial chain quality map, focusing on the core issues of the industrial chain through refined analysis of multi-dimensional parameters, guiding the generation of the industrial chain quality map, accurately examining the quality problems of the industrial chain, and providing strong data support for optimizing the configuration of the industrial chain.

[0077] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for generating an industrial chain quality map, characterized in that: include: S1. Obtain the process parameters of each production factor in each production link of the industrial chain, analyze the internal correlation of each production factor in each production link of the industrial chain, and generate an industrial chain map; S2. Collect the quality problem indicators of each production factor in each production link of the industrial chain map, evaluate the impact weight index of each quality problem indicator of each production factor in each production link of the industrial chain map, and extract the key indicators of each quality problem of each production factor in each production link of the industrial chain map; S3. Classify and process the key indicators of quality issues of each production factor in each production link of the industrial chain map, thereby generating an industrial chain quality map; The analysis of the internal correlation of each production factor in each production link of the industrial chain is as follows: According to the process parameters of each production factor in each production link of the industrial chain, the occurrence times of each production factor in each production link of the industrial chain are extracted, and matched with the data coupling degrees corresponding to each occurrence frequency interval stored in the industrial chain map database, thereby obtaining the data coupling degrees of each production factor in each production link of the industrial chain; Locate the initial time point corresponding to each occurrence of each production factor in each production link of the industrial chain, and perform difference processing with the end time point corresponding to each occurrence of each production factor in each production link of the industrial chain to obtain the data support duration of each production factor in each production link of the industrial chain; The product output of the production equipment corresponding to each production factor in each production link of the industrial chain within the production inspection cycle is extracted from the process parameters of each production factor in each production link of the industrial chain, and the ratio is processed with the duration corresponding to the production inspection cycle to obtain the production speed of the production equipment corresponding to each production factor in each production link of the industrial chain. Based on this comprehensive analysis, the internal correlation of each production factor in each production link of the industrial chain is obtained.

2. The method for generating an industrial chain quality map according to claim 1, characterized in that: The specific analysis process of generating the industrial chain map is as follows: The internal correlation of each production factor in each production link of the industrial chain is sorted in order from large to small, and the internal correlation order of each production factor in each production link of the industrial chain is obtained, thereby generating an industrial chain map.

3. The method for generating an industrial chain quality map according to claim 1, characterized in that: The quality problem indicators of each production factor in each production link of the industrial chain map specifically include each occurrence time point of each quality problem indicator of each production factor in each production link of the industrial chain map within the production inspection cycle.

4. The method for generating an industrial chain quality map according to claim 3, characterized in that: The impact weight index of each quality problem indicator of each production factor in each production link of the industrial chain map is evaluated, and the specific evaluation process is as follows: Locate and count the number of post-production factor position nodes and the number of post-production factor position node types corresponding to each quality problem indicator of each production factor in each production link of the industrial chain map, and the connectivity factor corresponding to the number of post-production factor position nodes and the connectivity factor corresponding to the number of post-production factor position node types preset in the industrial chain map database, perform product analysis respectively, and accumulate and process to obtain the production factor position node connectivity corresponding to each quality problem indicator of each production factor in each production link of the industrial chain map; Count the number of time points at which each quality problem indicator of each production factor in each production link of the industrial chain map appears within the production inspection cycle, and perform ratio processing with the number of time points at which each quality problem indicator of each production factor in each production link of the industrial chain map is resolved within the production inspection cycle, so as to obtain the adjustability of each quality problem indicator of each production factor in each production link of the industrial chain map within the production inspection cycle; The number of improvement plans for each quality problem indicator of each production factor in each production link of the industrial chain map within the production inspection cycle is obtained, and the impact weight index of each quality problem indicator of each production factor in each production link of the industrial chain map is obtained by comprehensive evaluation.

5. The method for generating an industrial chain quality map according to claim 4, characterized in that: The influence weight index of each quality problem indicator of each production factor in each production link of the industrial chain map is specifically expressed as follows: Among them, μ abn AJ is the impact weight index of the nth quality problem indicator of the bth production factor in the ath production link of the industrial chain map, abn is the node connectivity of the production factor position corresponding to the nth quality problem indicator of the bth production factor in the ath production link of the industrial chain graph, NP abn AT is the adjustability of the nth quality problem indicator of the bth production factor in the ath production link of the industrial chain map within the production inspection cycle, abn is the number of improvement plans for the nth quality problem indicator of the bth production factor of the ath production link in the industrial chain map within the production inspection cycle, mk1 is the impact factor corresponding to the unit value of the production factor position node connectivity predefined in the industrial chain map database, mk2 is the impact factor corresponding to the unit value of the adjustable degree predefined in the industrial chain map database, mk3 is the impact factor corresponding to the unit value of the number of improvement plans predefined in the industrial chain map database, a is the number of each production link, a={1,2,3,...,t}, t is the total number of production links, b is the number of each production factor, b={1,2,3,...,p}, p is the total number of production factors, n is the number of each quality problem indicator, n={1,2,3,...,m}, m is the total number of quality problem indicators.

6. The method for generating an industrial chain quality map according to claim 5, characterized in that: The specific analysis process of extracting the key indicators of each quality problem of each production factor in each production link of the industrial chain map is as follows: The influence weight index of each quality problem indicator of each production factor in each production link of the industrial chain map is compared with the influence weight threshold stored in the industrial chain map database, and the quality problem indicators of each production factor in each production link of the industrial chain map whose influence weight index is greater than the influence weight threshold are screened out and marked as the key indicators of each quality problem of each production factor in each production link of the industrial chain map.

7. The method for generating an industrial chain quality map according to claim 1, characterized in that: The described classification processing of the key indicators of quality problems of each production factor in each production link belonging to the industrial chain map is specifically to analyze the problem keyword adaptation index between the key indicators of quality problems of each production factor in each production link belonging to the industrial chain map and each problem classification label, thereby classifying and processing the key indicators of quality problems of each production factor in each production link belonging to the industrial chain map.

8. The method for generating an industrial chain quality map according to claim 7, characterized in that: The problem keyword adaptation index between the key indicators of each quality problem of each production factor in each production link of the industrial chain map and each problem classification label is analyzed in detail as follows: Extract each keyword of each problem classification label from the industrial chain map database, and locate each keyword of each key indicator name of each quality problem of each production factor in each production link of the industrial chain map at the same time, and compare with each keyword of each problem classification label for keyword overlap, count the number of keyword overlaps between each keyword of each key indicator name of each quality problem of each production factor in each production link of the industrial chain map and each keyword of each problem classification label, and perform ratio processing with the total number of keywords of each problem classification label to obtain the keyword co-occurrence degree between each key indicator of each quality problem of each production factor in each production link of the industrial chain map and each problem classification label; The correlation levels between the keywords of the key indicators of each quality problem of each production factor in each production link of the industrial chain map and the keywords of each problem classification label are matched from the industrial chain map database, and the average correlation level between the key indicators of each quality problem of each production factor in each production link of the industrial chain map and each problem classification label is obtained by mean processing. Based on this, the adaptation index of the key indicators of each quality problem of each production factor in each production link of the industrial chain map and the problem keywords of each problem classification label is obtained through comprehensive analysis.

9. The method for generating an industrial chain quality map according to claim 8, characterized in that: The specific analysis process of generating the quality map of the industrial chain is as follows: After classifying the key indicators of quality problems of each production factor in each production link of the industrial chain map, they are automatically filled into the corresponding problem classification labels in the industrial chain map, thereby generating an industrial chain quality map.

Images