Petrochemical station intelligent inspection robot design method based on big data and QFD fusion
By combining big data analysis and QFD quality function development methods in the design of intelligent inspection robots in petrochemical stations, the system organizes and analyzes user needs, solving the problems of unclear requirements and difficult to obtain functional requirements, achieving improvements in design efficiency and accuracy, reducing costs and improving customer satisfaction.
Patent Information
- Application Number
- CN202411519223.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-03-14
AI Technical Summary
The existing technology has problems in the design of intelligent inspection robots for petrochemical stations with unclear requirements and difficult to obtain functional requirements, resulting in high R&D costs, long R&D cycles and fuzzy functions.
Using a design method based on the fusion of big data and QFD, a large database is formed by collecting historical parameters of petrochemical stations, and big data analysis is carried out to obtain work environment requirements and user needs. Combined with the KANO model, fuzzy clustering method and hierarchical analysis method, a systematic organization and analysis of user needs are determined to determine the importance of functional needs, and transform functional needs into engineering characteristics through QFD quality house.
It improves the efficiency and accuracy of product design, reduces design changes, reduces investment costs of petrochemical companies, and improves customer satisfaction.
Smart Images
Figure CN119623001A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of product design. More specifically, the present invention relates to a design method of an intelligent inspection robot for a petrochemical station based on the fusion of big data and QFD. Background Art
[0002] The basic idea of QFD (Quality Function Deployment) is to be customer- and market-oriented, and to reflect customer needs as accurately as possible in all aspects of product development, program design, process planning, and production manufacturing. The purpose is to accurately obtain the real needs of customers and transform such needs into technical characteristics of the design through the core tool of QFD, the quality house technology.
[0003] QFD also has its limitations. First, when QFD collects user needs, traditional sampling methods and surveys have great limitations. First, we cannot sample everything, so there must be a deviation in the replacement of the whole by a part. Second, users often fail to clearly and completely express their needs, so there are some deviations in the judgment of product needs. Due to the lack of focus on demand analysis, the R&D cost of intelligent inspection robots is high, the R&D cycle is long, and the functions are vague.
[0004] In the 21st century era of big data and the Internet of Things, the rise of big data has injected new vitality into QFD. Big data is not about sampling but about the population. All populations are samples. In this case, the statistics of data will be more accurate. Especially in the study of user needs, big data analysis can provide a more objective and accurate basis. However, there is currently no method that combines big data with QFD and applies it to the design of intelligent inspection robots in petrochemical sites. This application is based on this research. Summary of the invention
[0005] One object of the present invention is to provide a design method for an intelligent inspection robot for a petrochemical station based on the fusion of big data and QFD, which effectively improves the efficiency and accuracy of product design.
[0006] In order to solve the above technical problems, the present invention provides a design method of an intelligent inspection robot for a petrochemical station based on the fusion of big data and QFD, comprising the following steps: Step (1): Collect various historical parameters of the petrochemical site to form a big database, and perform big data analysis and processing to obtain the working environment requirements of the petrochemical site. Combined with the user's inspection goals, the relevant user needs of the intelligent inspection robot are obtained; Step (2): Use the KANO model to systematically organize and classify user needs, use the fuzzy clustering method to analyze user needs, use the analytic hierarchy process (AHP) to complete the relationship matrix of product user needs and calculate the importance, and obtain the corresponding weights of each user need by solving the normalized judgment matrix, and sort the user need weights from high to low; Step (3): Each user requirement corresponds to a functional requirement of the inspection robot. The functional requirements of the intelligent inspection robot are ranked by weight, and a scoring threshold of the functional requirements is set. When a functional requirement is greater than the threshold, it is a requirement for the functional development of the intelligent inspection robot. Step (4): Functional analysis of the intelligent inspection robot. Use the quality function deployment (QFD) method to classify the selected functional requirements, and then grade the requirements of each category to gradually expand and give the degree value of the functional requirements; Step (5): Sort the functional requirements according to their importance and create a QFD quality house; Step (6): Convert the functional requirements of QFD into engineering characteristics to complete the design of the intelligent selection robot.
[0007] Preferably, the various historical parameters of the Sinopec station in step (1) include climate environment, production data, gas composition, process flow, and safety accidents.
[0008] Preferably, the big data analysis and processing in step (1) includes data collection, data processing, and data analysis. Data collection includes first collecting a large amount of data, then reasonably labeling the data according to the design requirements, and finally classifying and storing the data; data processing includes data cleaning, data integration, and data transformation; data analysis includes converting various historical parameters of the petrochemical site into working environment requirements.
[0009] Preferably, user requirements include safety, material, weight, environmental protection, controllability, mobility, noise, battery life, and inspection function.
[0010] Preferably, the specific method of using the KANO model is: List multiple user requirements; According to the Kano KANO model, the demand is divided into basic demand M, expected demand P and exciting demand E; based on the analysis of big data statistics and the historical data of the station, the membership of multiple demands to the basic demand M, expected demand P and exciting demand E in the Kano KANO model is obtained respectively; The user demand membership matrix is obtained, and the correlation coefficient method is used to transform it to obtain the fuzzy similarity matrix; Select appropriate thresholds for dynamic clustering; According to the results of membership and fuzzy clustering classification, user needs are classified with the three needs in the Kano model to form three kinds of subordinate relationships; Through qualitative analysis of user needs and petrochemical station environmental needs, the analytic hierarchy process (AHP) is used to determine the weights of each factor at the same level relative to the previous level indicators by comparing them two by two; and the comprehensive weights of each user need are obtained; The obtained comprehensive weight table of user needs is used as the basis for ranking the importance of customer needs in the process of building the quality house.
[0011] Preferably, basic needs are used. If such needs are not met or are poorly performed, user dissatisfaction will increase dramatically, and when such needs are met, user dissatisfaction can be eliminated, but user satisfaction cannot be increased. Expectation needs are used. If they are met or performed well, user satisfaction will increase significantly. When such needs are not met or are poorly performed, user dissatisfaction will also increase significantly. Excitatory needs are used. When their characteristics are insufficient and insignificant, users do not care. When products meet such needs, users will be very satisfied with the products.
[0012] The present invention has at least the following beneficial effects: 1. The integrated application of big data and quality function deployment (QFD) in the present invention overcomes the shortcomings of unclear requirements and difficult to obtain functional requirements in the product design process, improves design reliability, effectively reduces later design changes, reduces the investment cost of petrochemical enterprises, and improves customer satisfaction.
[0013] 2. The present invention adds big data processing, mainly including data collection, data preprocessing, data analysis and mining, etc., which can analyze user needs more intuitively and accurately, add the KANO model to systematically organize and analyze user needs, adopt the fuzzy clustering method to analyze customer needs, and finally use the hierarchical analysis method to calculate the importance of user needs; the design method based on the fusion of big data and QFD can tailor products for petrochemical users in a targeted manner.
[0014] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 Design a relationship diagram for the intelligent inspection robot of the present invention; Figure 2 It is the technical roadmap of the present invention; Figure 3 It is a schematic diagram of the QFD quality house of the present invention. DETAILED DESCRIPTION
[0016] In order to better understand the purpose, structure and function of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement it according to the description.
[0017] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials are commercially available unless otherwise specified; in the description of the present invention, the terms "lateral", "longitudinal", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.
[0018] like Figures 1 to 3 As shown, the present invention provides a design method for an intelligent inspection robot for a petrochemical station based on the fusion of big data and QFD, which performs big data analysis on historical parameters such as the climate environment, explosion-proof environment, production data, gas composition, process flow, and safety accidents of the petrochemical station; adopts quality function deployment (QFD) to complete the user's functional requirements for the intelligent inspection robot; builds a quality house to convert user requirements into engineering characteristics of the product, thereby completing the design of the inspection robot. Specifically, it includes the following steps: Step 1: Collect various historical parameters of the petrochemical site, such as climate environment, production data, gas composition, process flow, safety accidents, etc. to form a big database, and perform big data analysis and processing to obtain the working environment requirements of the petrochemical site. Combined with the user's inspection goals, the relevant user needs of the intelligent inspection robot are obtained; big data analysis and processing include data collection, data processing, and data analysis. Data collection includes first collecting massive amounts of data, then reasonably labeling according to design requirements, and finally classifying and storing the data; data processing includes data cleaning, data integration, and data transformation; data analysis includes converting various historical parameters of the petrochemical site into working environment requirements, such as climate environment corresponding to materials; production data corresponding to environmental protection, weight, and endurance; gas composition corresponding to safety and environmental protection; process flow corresponding to mobility, controllability, and noise; safety accidents to safety and controllability, etc.
[0019] Step 2: Use the KANO model to systematically organize and classify user needs, use the fuzzy clustering method to analyze user needs, use the hierarchical analysis method AHP to complete the relationship matrix determination and importance calculation of product user needs, and obtain the corresponding weights of each user need after solving the normalized judgment matrix, and sort the user need weights from high to low; user needs include safety, material, weight, environmental protection, controllability, mobility, noise, endurance, and inspection function, as shown in Table 1 below.
[0020] According to the Kano model, needs are divided into basic needs M, expected needs P and excitatory needs E.
[0021] Basic needs, also known as the pain points of user needs, if such needs are not met or performed poorly, user dissatisfaction will increase sharply, and after such needs are met, user dissatisfaction can be eliminated, but it cannot bring about an increase in user satisfaction.
[0022] Expected needs: If they are met or performed well, user satisfaction will increase significantly. When such needs are not met or performed poorly, user dissatisfaction will also increase significantly.
[0023] For excitatory needs, when the characteristics are insufficient and insignificant, users do not care. When a product meets such needs, users will be very satisfied with the product.
[0024] Table 1 User requirements correspondence table Conduct relevant analysis based on user needs: Assume U={x1, x2, …, x9}, where x1: safety; x2: material; x3: weight; x4: environmental protection; x5: Controllability; x6: Sportiness; x7: Noise; x8: Endurance; x9: Inspection function.
[0025] Taking a petrochemical site in southwest China as an example, based on the analysis of big data statistics and the historical data of the site, the membership of 9 demands to the basic demand M, expected demand P, and exciting demand E in the Kano model was obtained.
[0026] x1={0.6,0.9,0.2}; x2={0.9,0.2,0.1}; x3={0.8,0.6,0.3}; x4={0.6,0.9,0.2}; x5={0.3,0.3,0.9}; x6={0.2,0.3,0.9}; x7={0.9,0.3,0.1}; x8={0.9,0.5,0.2}; x9={0.4,0.3,0.8}.
[0027] Get the user demand membership matrix: ; In order to further classify these demand information, the correlation coefficient method is used to transform them and obtain the fuzzy similarity matrix: ; Solve the transitive closure t(R): ; In the obtained fuzzy equivalence matrix, to determine whether certain features belong to the same category, we only need to select an appropriate threshold λ∈[0, 1], perform dynamic clustering according to the λ-intercept relationship, and set the value of λ from 1 to 0, which is: When λ=0.980, U is divided into three categories: {x1, x4}, {x2, x3, x7, x8}, {x5, x6, x9}. The intercept matrix is: ; According to the results of membership and fuzzy clustering classification, the subordinate relationship between specific user needs and the three needs can be described as: {exciting needs E} = {material, weight, noise, battery life}; {expected needs P} = {safety, environmental protection}; {basic needs M} = {control, movement, inspection function}, as shown in Table 2 below.
[0028] Table 2 The relationship between user needs and three needs Through qualitative analysis of petrochemical enterprise user needs and site environment information, with user satisfaction as the design goal, the hierarchical analysis method AHP is used to determine the weights of each factor at the same level by comparing them two by two. The customer demand weights shown in Tables 4 to 7 are obtained through calculation and data processing: Table 3 Customer satisfaction M P E CR 0.136 0.323 0.541 0.005 Table 4 Excitement needs X1 X2 X3 X4 CR 0.293 0.250 0.182 0.275 0.008 Table 5 Expected demand P X5 X6 CR 0.512 0.488 0 Table 6 Basic requirements M X7 X8 X9 CR 0.425 0.323 0.252 0.006 Table 7 Comprehensive weights of user requirements User needs X1 X2 X3 X4 X5 X6 X7 X8 X9 Comprehensive weight 0.159 0.135 0.135 0.098 0.165 0.158 0.058 0.044 0.034 The obtained comprehensive weight table of user needs is used as the basis for ranking the importance of customer needs in the process of building the quality house.
[0029] Step 3: Each user requirement corresponds to a functional requirement of the inspection robot. The functional requirements of the intelligent inspection robot are ranked by weight and a scoring threshold of the functional requirements is set. When a functional requirement is greater than the threshold, it is the requirement for the functional development of the intelligent inspection robot. Step 4: Functional analysis of the intelligent inspection robot. Use the quality function deployment (QFD) method to classify the selected functional requirements, and then grade the requirements of each category to gradually expand and give the degree value of the functional requirements; Step 5: Sort the functional requirements according to their importance and create the QFD quality house; Step 6: Convert the functional requirements of QFD into engineering characteristics to complete the design of the intelligent selection robot.
[0030] It is understood that the present invention is described by some embodiments, and those skilled in the art are aware that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation mode, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, other modifications can be easily implemented, so without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the illustrations shown and described here.
Claims
1. A design method for an intelligent inspection robot for petrochemical stations based on the fusion of big data and QFD, characterized in that: The steps include: Step (1): Collect various historical parameters of the petrochemical site to form a big database, and perform big data analysis and processing to obtain the working environment requirements of the petrochemical site. Combined with the user's inspection goals, the relevant user needs of the intelligent inspection robot are obtained; Step (2): Use the KANO model to systematically organize and classify user needs, use the fuzzy clustering method to analyze user needs, use the analytic hierarchy process (AHP) to complete the relationship matrix of product user needs and calculate the importance, and obtain the corresponding weights of each user need by solving the normalized judgment matrix, and sort the user need weights from high to low; Step (3): Each user requirement corresponds to a functional requirement of the inspection robot. The functional requirements of the intelligent inspection robot are ranked by weight, and a scoring threshold of the functional requirements is set. When a functional requirement is greater than the threshold, it is a requirement for the functional development of the intelligent inspection robot. Step (4): Functional analysis of the intelligent inspection robot. Use the quality function deployment (QFD) method to classify the selected functional requirements, and then grade the requirements of each category to gradually expand and give the degree value of the functional requirements; Step (5): Sort the functional requirements according to their importance and create a QFD quality house; Step (6): Convert the functional requirements of QFD into engineering characteristics to complete the design of the intelligent selection robot.
2. The design method of the petrochemical station intelligent inspection robot based on the fusion of big data and QFD as claimed in claim 1 is characterized in that: In step (1), various historical parameters of Sinopec stations include climate environment, production data, gas composition, process flow, and safety accidents.
3. The design method of the petrochemical station intelligent inspection robot based on the fusion of big data and QFD as claimed in claim 1 is characterized in that: The big data analysis and processing in step (1) includes data collection, data processing, and data analysis. Data collection includes first collecting a large amount of data, then reasonably labeling the data according to the design requirements, and finally classifying and storing the data; data processing includes data cleaning, data integration, and data transformation; data analysis includes converting various historical parameters of the petrochemical site into working environment requirements.
4. The design method of the petrochemical station intelligent inspection robot based on the fusion of big data and QFD as claimed in claim 1 is characterized in that: User requirements include safety, material, weight, environmental protection, controllability, mobility, noise, battery life, and inspection functions.
5. The design method of the petrochemical station intelligent inspection robot based on the fusion of big data and QFD as claimed in claim 1 is characterized in that: The specific way to use the KANO model is: List multiple user needs; According to the Kano KANO model, the demand is divided into basic demand M, expected demand P and exciting demand E; based on the analysis of big data statistics and the historical data of the station, the membership of multiple demands to the basic demand M, expected demand P and exciting demand E in the Kano KANO model is obtained respectively; The user demand membership matrix is obtained, and the correlation coefficient method is used to transform it to obtain the fuzzy similarity matrix; Select appropriate thresholds for dynamic clustering; According to the results of membership and fuzzy clustering classification, user needs are classified with the three needs in the Kano model to form three kinds of subordinate relationships; Through qualitative analysis of user needs and petrochemical station environmental needs, the analytic hierarchy process (AHP) is used to determine the weights of each factor at the same level relative to the indicators at the previous level by comparing them two by two; and the comprehensive weights of each user need are obtained; The obtained comprehensive weight table of user needs is used as the basis for ranking the importance of customer needs in the process of building the quality house.
6. The design method of the petrochemical station intelligent inspection robot based on the fusion of big data and QFD as claimed in claim 5 is characterized in that: Basic needs: if such needs are not met or performed poorly, user dissatisfaction will increase sharply, and when such needs are met, user dissatisfaction can be eliminated, but user satisfaction cannot be increased; expectation needs: if met or performed well, user satisfaction will increase significantly, and when such needs are not met or performed poorly, user dissatisfaction will also increase significantly; excitatory needs: when the characteristics are insufficient and insignificant, users do not care, and when the product meets such needs, users will be very satisfied with the product.