Intelligent factory collaborative manufacturing management method and system based on AI

Abstract

The invention relates to the technical field of factory collaborative manufacturing management, and discloses an AI-based intelligent factory collaborative manufacturing management method and system, and the system comprises a collaborative manufacturing product information acquisition module, a collaborative manufacturing product process evaluation module, and a collaborative manufacturing product process simulation module. According to product production process productivity requirement text information, in combination with an intelligent search algorithm and target cost product production design process text information, a product process design productivity end accurately identifies product design process information; the product manufacturing process information is efficiently identified based on the production efficiency technical requirements in the product process design; product production process information is comprehensively and intelligently designed based on technical requirements, process design and manufacturing cost and process design productivity of products, and intelligence and quality of factory collaborative manufacturing management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of factory collaborative manufacturing management, and specifically to an AI-based intelligent factory collaborative manufacturing management method and system. Background Art

[0002] Collaborative manufacturing management is a production management model based on agile manufacturing, virtual manufacturing, network manufacturing, and global manufacturing. It breaks the constraints of time and space, and enables enterprises and partners in the entire supply chain to share customer, design, production and operation information through the Internet. It changes from the traditional serial working mode to the parallel working mode, thereby minimizing the time for new products to be launched, shortening the production cycle, quickly responding to customer needs, and improving the flexibility of design and production. Through process-oriented design, production-oriented design, cost-oriented design, and supplier-involved design, the product design level and manufacturability as well as the controllability of costs are improved. Collaborative manufacturing management includes product design, process design, tooling design, production preparation, material procurement, production manufacturing, sales and after-sales management; among them, the product process design of collaborative manufacturing management needs to consider the technical requirements, cost, production efficiency, and product quality of the product; the product process design in the existing collaborative manufacturing management cannot comprehensively and intelligently design the product production process information from the aspects of product manufacturability, cost control, and production efficiency, nor can it realize visual simulation of the product production process in combination with the production scenario, which reduces the accuracy and intelligence of factory collaborative manufacturing management.

[0003] A Chinese invention patent with announcement number CN114119268B discloses a collaborative manufacturing system for a printing and packaging production line. The information collection module collects historical manufacturing records of the SMT production line and sales information of various products, and combines the manufacturing analysis module to perform data analysis to obtain a manufacturing coefficient sequence. The sales analysis module performs data analysis based on the received product sales information to obtain a product sales coefficient sequence. The collaborative optimization module evaluates the product manufacturing coefficient sequence and sales coefficient sequence with timestamps in the database to obtain a product value optimization sequence. However, the above technical solutions cannot accurately design product process information based on the technical requirements, cost, and production efficiency of the product. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the problem that the product process design in the above-mentioned existing collaborative manufacturing management cannot comprehensively and intelligently design the product production process information from the aspects of product manufacturability, cost control, and production efficiency, and cannot realize visual simulation of the product production process in combination with the production scenario, thereby reducing the accuracy and intelligence of factory collaborative manufacturing management, the above purpose is to achieve the above-mentioned intelligent and accurate design of product production process information based on product manufacturability, cost control, and production efficiency, efficiently and accurately establish product production factory scene model information, and dynamically visually simulate product production process, so as to improve the intelligence and quality of product collaborative manufacturing management.

[0006] (II) Technical solution

[0007] The present invention is implemented by the following technical solution: an AI-based intelligent factory collaborative manufacturing management method, the method comprising the following steps:

[0008] S1. Collect text data on product technical requirements, product production process manufacturing cost requirements, and product production process productivity requirements;

[0009] S2. Analyze and process the technical requirements, production design and process information of the product production according to the product technical requirements text data and the product production design process text data, and generate target technical requirements product production design process text data;

[0010] S3, based on the product production process manufacturing cost requirement text data and the target technical requirement product production design process text data, the cost production design process information of the product production is screened and processed to generate the target cost product production design process text data;

[0011] S4, performing product production productivity design process information identification processing according to the product production process productivity requirement text data and the target cost product production design process text data, and generating target productivity product production design process text data;

[0012] S5, searching and processing the production line body 3D motion simulation information corresponding to the product production design process according to the target productivity product production design process text data and the product production design process line body 3D motion simulation data, and generating the target product production design process line body 3D motion simulation data;

[0013] S6. Collecting physical model data of product production factory scene;

[0014] S7. Construct and process the product production design process factory production scene simulation information based on the target product production design process line three-dimensional motion simulation data and the product production factory scene solid model data, generate the target product production design process factory production scene simulation data and execute the product production design process factory collaborative manufacturing scene simulation operation.

[0015] Preferably, the steps of collecting product technical requirement text data, product production process manufacturing cost requirement text data and product production process productivity requirement text data are as follows:

[0016] S11. Collect the technical requirements text information of the function, performance and structure of the target product design online through the material ERP management platform, and generate product technical requirements text data Q;

[0017] Collect product manufacturing cost requirement text information of the target product production design process online through the material ERP management platform, and generate product production process manufacturing cost requirement text data P, wherein the product production process manufacturing cost requirement text data represents the cost requirement information for each product produced by the production design process in the production design stage of the target product, wherein the unit of P is RMB per piece;

[0018] The product manufacturing productivity requirement text information of the target product production design process is collected online through the material ERP management platform, and the product production process productivity requirement text data J is generated. The product production process productivity requirement text data represents the productivity requirement information for the number of manufactured products per unit time of the production design process during the production design stage of the target product, wherein the unit of J is pieces per hour.

[0019] The present invention accurately collects the product technical requirements, product process design manufacturing cost requirements and product process design productivity requirements of the target product production process design online through the material ERP management platform, so as to achieve the effect of efficiently obtaining the manufacturability technical requirements, cost control technical requirements and production efficiency technical requirements of the product process design.

[0020] Preferably, the steps of analyzing and processing the technical requirement production design process information of the product production according to the product technical requirement text data and the product production design process text data to generate the target technical requirement product production design process text data are as follows:

[0021] S21. Establish product production design process text data set A = (a 1 ,Λ,a m ,Λ,a ε ), m = 1, 2, 3, Λ, ε; where a mrepresents the product production design process text data corresponding to the mth product process design indicator requirement combination type, ε represents the maximum number of product process design indicator requirement combination types; the product process design indicator requirement combination type represents a data index type for searching product production design process information, which is mainly formed by a combination of product design technical requirements, product process design manufacturing cost requirements, and product process design productivity requirements. The product production design process text data represents the optimal product production process information set for the product production design requirements, and the product production process information includes the product production process step parameters and the production process step equipment information;

[0022] S22, the product technical requirement text data Q and the product production design process text data a in the product production design process text data set A are combined. m Perform product design technical requirement keyword matching to search for the product production design process text data a corresponding to the product technical requirement text data Q m , and generate the target technical requirement product production design process text data set B through data identification, and the specific operation steps for generating the target technical requirement product production design process text data set B are as follows:

[0023] S221, initialization, update the maximum number of iterations T, update the production process to search for the individual positions of the white shark population, the production process to search for the individual positions of the white shark population calculation formula is as follows: Among them, M i,j represents the position of the production process search white shark individual i in the j-dimensional space, that is, the position of the production process search white shark individual i in the search space of the product production design process text data set A with a spatial dimension of ε, σ and They represent the upper and lower limits of the product production design process text data set A, respectively, and R represents a random number between (0,1);

[0024] S222, speed update stage, the production process search white shark in the product production design process text data set A search space according to the product production design process text data a m The prey moves to sense its position and search for the product production design process text data a that matches the product technical requirement text data Q m And update its own speed. The calculation formula for updating its own speed is as follows:

[0025] Among them, H i,t+1 represents the speed of the production process search white shark individual i in the search space of the product production design process text data set A after t+1 iterations, H i,trepresents the speed of the production process search white shark individual i in the search space of the product production design process text data set A after t iterations, M best,t It represents the optimal position of the production process search white shark individual in the search space of the product production design process text data set A after t+1 iterations, M i,t represents the position of the production process search white shark individual i in the search space of the product production design process text data set A after t iterations; It represents the number of white shark individuals i searching for production process in the search space of the product production design process text data set A after t iterations and the speed H i,t The corresponding position; τ represents the algorithm shrinkage coefficient, υ 1 and 2 Respectively represent M best,t and The control coefficient, ω 1 and ω 2 Both represent random numbers between (0,1);

[0026] S223, position update stage, the production process search white shark updates its position in the search space of the product production design process text data set A by moving towards the optimal prey, and searches for the product production design process text data a that best matches or sub-optimally matches the product technical requirement text data Q in the search space of the product production design process text data set A. m Prey, production process search white shark position update calculation formula is as follows: Where M' i,t+1 It represents the position of the production process search white shark individual i in the search space of the product production design process text data set A after t+1 iterations in the position update phase. represents a bitwise operator, ξ is a logical vector, ψ and Φ are both binary vectors, It represents the attraction coefficient of the white shark approaching its prey, and Λ represents the wave frequency of the white shark sports product;

[0027] The production process search white shark moves towards the optimal production process search white shark position to get closer to the optimal position of the prey. The production process search white shark position update calculation formula is as follows:

[0028] M” i,t+1 =M best,t +r 1 ×Ξ×sgn(r 2 -0.5),r 3 <Γ, where M" i,t+1 express

[0029] Position update phase Search for the optimal production process Search for the production process of the white shark position Search for the position of the white shark individual i in the search space of the product production design process text data set A after t+1 iterations, r 1 、r 2 、r 3 All represent random numbers between (0,1), Ξ represents the distance between the white shark and the prey in the production process search, that is, the product production design process text data a that matches the product technical requirement text data Q is searched in the search space of the product production design process text data set A. m ; sgn represents the sign return function, Γ represents the production process to search for the olfactory and visual parameters of the white shark close to the best prey;

[0030] S224, in the stage of school behavior, the production process search white shark population retains the optimal production process search white shark position in the search space of the product production design process text data set A by feeding behavior in the position update stage, and updates the positions of other production process search white shark individuals according to the optimal production process search white shark position to obtain the product production design process text data a that matches the product technical requirement text data Q in the search space of the product production design process text data set A. m , the calculation formula for the production process search white shark individual position update is as follows: M"' i,t+1 =(M' i,t+1 +M” i,t+1 ) / 2R, where M'' i,t+1 Indicates the position of the white shark individual i in the search space of the product production design process text data set A after t+1 iterations in the fish school behavior stage to update other production processes;

[0031] S225. When the maximum number of iterations is met, output the product production design process text data a that matches the product technical requirement text data Q. m ;

[0032] S226: the product production design process text data a output in step S225 m After data identification, the target technical requirements product production design process text data set B is generated = (b m1 ,Λ,b m2 ), 1≤m1≤m≤m2≤ε, where b m1 and b m2 They respectively represent the m1th and m2th target technical requirement product production design process text data; the target technical requirement product production design process text data represents the product production process information that meets the product design technical requirements.

[0033] The present invention scientifically presets product production design process text information and combines the artificial intelligence White Shark optimization algorithm with product technical requirement text information to scientifically analyze product design process information at the product technical requirement end, thereby achieving the effect of intelligently matching product production and manufacturing process information based on manufacturability technical requirements in product process design.

[0034] Preferably, the steps of screening the cost production design process information of product production based on the product production process manufacturing cost requirement text data and the target technical requirement product production design process text data to generate the target cost product production design process text data are as follows:

[0035] S31, using the KMP search algorithm to compare the product production process manufacturing cost requirement text data P with the target technical requirement product production design process text data set B. m1 To b m2 Perform product process design and manufacturing cost character matching, search for the target technical requirement product production design process text data corresponding to the product production process manufacturing cost requirement text data P, and generate the target cost product production design process text data set B'=(b' m3 ,Λ,b' m4 ), m1≤m3≤m4≤m2, where b' m3 and b' m4 They respectively represent the m3th and m4th target cost product production design process text data; the target cost product production design process text data represents product production process information that meets product design technical requirements and product process design manufacturing cost requirements.

[0036] The present invention achieves the effect of finely screening product production and manufacturing process information based on cost control technical requirements of product process design by combining KMP search algorithm with target technical requirements of product production design process text information to accurately screen product design process information at the product process design and manufacturing cost end.

[0037] Preferably, the steps of identifying the production design process information of the product production according to the product production process productivity requirement text data and the target cost product production design process text data to generate the target productivity product production design process text data are as follows:

[0038] S41, using Boyer-Moore search algorithm to compare the product production process productivity requirement text data J with the target cost product production design process text data b' in the target cost product production design process text data set B'. m3 to b'm4 Perform product process design productivity character matching, search for the target cost product production design process text data corresponding to the product production process productivity requirement text data J, and generate the target productivity product production design process text data set B"=(b" m5 ,Λ,b” m6 ), m3≤m5≤m6≤m4, where b” m5 and b” m6 They respectively represent the m5th and m6th target productivity product production design process text data; the target productivity product production design process text data represents product production process information that meets product design technical requirements, product process design manufacturing cost requirements and product process design productivity requirements.

[0039] The present invention accurately identifies product design process information at the product process design productivity end based on product production process productivity requirement text information combined with the Boyer-Moore search algorithm and target cost product production design process text information, thereby achieving the effect of efficiently identifying product production and manufacturing process information based on production efficiency technical requirements in product process design.

[0040] Preferably, the steps of searching and processing the production line body 3D motion simulation information corresponding to the product production design process according to the target productivity product production design process text data and the product production design process line body 3D motion simulation data to generate the target product production design process line body 3D motion simulation data are as follows:

[0041] S51. Establish product production design process line three-dimensional motion simulation data set C = (c 1 ,Λ,c m ,Λ,c ε ), where c m Represents the three-dimensional motion simulation data of the product production design process line corresponding to the mth product process design indicator requirement combination type, wherein the three-dimensional motion simulation data of the product production design process line represents the three-dimensional motion simulation data of the product production process of the process production manufacturing line corresponding to different types of product production design processes;

[0042] S52, using the Rabin-Karp search algorithm to search the target productivity product production design process text data set B" into the target productivity product production design process text data set B". m5 to b" m6 The product production design process line body three-dimensional motion simulation data c in the product production design process line body three-dimensional motion simulation data set C m Perform production design process keyword matching to search for the target productivity product production design process text data b"m5 to b" m6 The corresponding three-dimensional motion simulation data c of the product production design process line m , and construct the target product production design process line three-dimensional motion simulation data set C'=(c' m5 ,Λ,c' m6 ), where c' m5 and c' m6 Respectively represent the target productivity product production design process text data b" m5 and the target productivity product production design process text data b" m6 The corresponding target product production design process line body three-dimensional motion simulation data, when the generation of the target product production design process line body three-dimensional motion simulation data set C' is not completed, continue to execute the target product production design process line body three-dimensional motion simulation data set C' generation operation instruction.

[0043] The present invention achieves the effect of providing real data support for product process design visualization by efficiently matching product production design process line three-dimensional motion simulation data according to target productivity product production design process text parameters combined with the Rabin-Karp search algorithm and standard set product production design process line three-dimensional motion simulation parameters.

[0044] Preferably, the operation steps of collecting product production factory scene entity model data are as follows:

[0045] S61. When the target product production design process line three-dimensional motion simulation data set C' is generated, a three-dimensional laser scanner mounted on an unmanned aerial vehicle is used to collect the three-dimensional solid model data of the target design product's production manufacturing plant, and generate product production plant scene solid model data D.

[0046] The present invention uses a three-dimensional laser scanner mounted on an unmanned aerial vehicle to dynamically and accurately collect physical model information of product production factory scenes, thereby providing reliable data support for product process design simulation demonstration at the factory end.

[0047] Preferably, the steps of constructing and processing the product production design process factory production scene simulation information based on the target product production design process line body three-dimensional motion simulation data and the product production factory scene entity model data, generating the target product production design process factory production scene simulation data and executing the product production design process factory collaborative manufacturing scene simulation operation are as follows:

[0048] S71, the target product production design process line body three-dimensional motion simulation data c' in the target product production design process line body three-dimensional motion simulation data set C' m5 to c' m6In the 3D design software, the 3D motion simulation data is matched with the factory scene entity model data D of the product production plant in order according to the simulation data number, and the target product production design process factory production scene simulation data set E is generated. m5 ,Λ,e m6 ), where e m5 and e m6 Respectively represent the target product production design process line body three-dimensional motion simulation data c' m5 and the target product production design process line body three-dimensional motion simulation data c' m6 Corresponding target product production design process factory production scene simulation data; the target product production design process factory production scene simulation data represents the motion simulation data of the target product design process in the product manufacturing factory for the product production process;

[0049] S72, the target product production design process factory production scene simulation data set E of the target product production design process factory production scene simulation data set E m5 To e m6 In the 3D simulation software, the simulation data is displayed and output in order according to the simulation data number to execute the product production design process factory collaborative manufacturing scenario simulation operation.

[0050] The present invention intelligently constructs product process design factory production scene simulation information based on the three-dimensional motion simulation data of the target product production design process line in combination with three-dimensional design software and product production factory scene solid model data. At the same time, it combines the three-dimensional simulation software to autonomously and intuitively execute the product production design process factory collaborative manufacturing scene simulation operation, thereby achieving the effect of real simulation of the product process design process production and manufacturing line at the factory end.

[0051] An AI-based smart factory collaborative manufacturing management system, used to implement the AI-based smart factory collaborative manufacturing management method, the system includes a collaborative manufacturing product information acquisition module, a collaborative manufacturing product process evaluation module, and a collaborative manufacturing product process simulation module;

[0052] The collaborative manufacturing product information acquisition module includes a product technical requirement information acquisition unit, a product production process manufacturing cost requirement information acquisition unit, and a product production process productivity requirement information acquisition unit;

[0053] The product technical requirement information collection unit collects product technical requirement text data through the material ERP management platform; the product production process manufacturing cost requirement information collection unit collects product production process manufacturing cost requirement text data through the material ERP management platform; the product production process productivity requirement information collection unit collects product production process productivity requirement text data through the material ERP management platform;

[0054] The collaborative manufacturing product process evaluation module includes a product production process information storage unit, a target technical requirement product production process analysis unit, a target cost requirement product production process screening unit, and a target productivity requirement product production process identification unit;

[0055] The product production process information storage unit is used to store product production design process text data; the target technical requirement product production process analysis unit performs analysis and processing of the technical requirement production design process information of product production based on the product technical requirement text data and the product production design process text data, and generates target technical requirement product production design process text data; the target cost requirement product production process screening unit performs screening and processing of the cost production design process information of product production based on the product production process manufacturing cost requirement text data and the target technical requirement product production design process text data, and generates target cost product production design process text data; the target productivity requirement product production process identification unit performs identification and processing of the productivity production design process information of product production based on the product production process productivity requirement text data and the target cost product production design process text data, and generates target productivity product production design process text data;

[0056] The collaborative manufacturing product process evaluation module includes a product production process line body three-dimensional motion simulation information storage unit, a target product production process line body three-dimensional motion simulation information search unit, a product production factory scene model acquisition unit, and a product production process factory scene simulation unit;

[0057] The product production process line three-dimensional motion simulation information storage unit is used to store the product production design process line three-dimensional motion simulation data; the target product production process line three-dimensional motion simulation information search unit searches for the production line three-dimensional motion simulation information corresponding to the product production design process according to the target productivity product production design process text data and the product production design process line three-dimensional motion simulation data, and generates the target product production design process line three-dimensional motion simulation data; the product production factory scene model acquisition unit collects the product production factory scene entity model data through a three-dimensional laser scanner mounted on an unmanned aerial vehicle; the product production process factory scene simulation unit constructs the product production design process factory production scene simulation information according to the target product production design process line three-dimensional motion simulation data combined with the three-dimensional design software and the product production factory scene entity model data, generates the target product production design process factory production scene simulation data and combines the three-dimensional simulation software to perform the product production design process factory collaborative manufacturing scene simulation operation.

[0058] (III) Beneficial effects

[0059] The present invention provides an AI-based intelligent factory collaborative manufacturing management method and system. It has the following beneficial effects:

[0060] 1. Accurately collect the product technical requirements, product process design manufacturing cost requirements and product process design productivity requirements of the target product production process design online through the material ERP management platform, so as to efficiently obtain the manufacturability technical requirements, cost control technical requirements and production efficiency technical requirements of the product process design, improve the efficiency and reliability of the target product production and manufacturing process matching, and improve the efficiency of factory collaborative manufacturing management.

[0061] 2. Scientifically analyze product design process information at the product technical requirement end by scientifically presetting product production design process information in combination with artificial intelligence recognition algorithms and product technical requirement text information, so as to realize product process design to intelligently match product production and manufacturing process information based on manufacturability technical requirements; accurately screen product design process information at the product process design and manufacturing cost end based on product production process manufacturing cost requirement text information combined with intelligent search algorithms and target technical requirements product production design process text information, so as to realize product process design to finely screen product production and manufacturing process information based on cost control technical requirements; accurately identify product design process information at the product process design productivity end based on product production process productivity requirement text information combined with intelligent search algorithms and target cost product production design process text information, so as to realize product process design to efficiently identify product production and manufacturing process information based on production efficiency technical requirements; realize comprehensive and intelligent design of product production process information based on product technical requirements, process design manufacturing cost and process design productivity, so as to improve the intelligence and quality of factory collaborative manufacturing management.

[0062] 3. Through the target productivity product production design process text parameters combined with intelligent search algorithms and standard set product production design process line 3D motion simulation parameters to efficiently match the product production design process line 3D motion simulation data, provide real data support for product process design visualization; through the use of drones equipped with 3D laser scanners to dynamically and accurately collect product production factory scene entity model information, provide reliable data support for product process design simulation demonstration at the factory end; based on the target product production design process line 3D motion simulation data combined with 3D design software and product production factory scene entity model data to intelligently construct product process design factory production scene simulation information, and at the same time combine 3D simulation software to autonomously and intuitively execute product production design process factory collaborative manufacturing scene simulation operations, realize the real simulation of product process design process production and manufacturing lines at the factory end, and improve the scientificity and effectiveness of factory collaborative manufacturing management. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of a module of an AI-based intelligent factory collaborative manufacturing management system provided by the present invention;

[0064] Figure 2 A flowchart of an AI-based smart factory collaborative manufacturing management method provided by the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] The embodiments of the AI-based intelligent factory collaborative manufacturing management method and system are as follows:

[0067] Embodiment 1:

[0068] See also Figure 1 - Figure 2 , an AI-based intelligent factory collaborative manufacturing management method, the method comprising the following steps:

[0069] S1. Collect text data on product technical requirements, product production process manufacturing cost requirements, and product production process productivity requirements;

[0070] S2. Analyze and process the technical requirements and production design process information of the product production according to the product technical requirements text data and the product production design process text data, and generate the target technical requirements product production design process text data;

[0071] S3. Screening and processing the cost production design process information of product production based on the product production process manufacturing cost requirement text data and the target technical requirement product production design process text data to generate the target cost product production design process text data;

[0072] S4. Performing product production productivity design process information identification and processing based on the product production process productivity requirement text data and the target cost product production design process text data to generate the target productivity product production design process text data;

[0073] S5, searching and processing the production line body 3D motion simulation information corresponding to the product production design process according to the target productivity product production design process text data and the product production design process line body 3D motion simulation data, and generating the target product production design process line body 3D motion simulation data;

[0074] S6. Collecting physical model data of product production factory scene;

[0075] S7. Construct and process the product production design process factory production scene simulation information based on the target product production design process line three-dimensional motion simulation data and the product production factory scene solid model data, generate the target product production design process factory production scene simulation data and execute the product production design process factory collaborative manufacturing scene simulation operation.

[0076] For further information, see Figure 1 - Figure 2 The steps for collecting product technical requirements text data, product production process manufacturing cost requirements text data and product production process productivity requirements text data are as follows:

[0077] S11. Collect the technical requirements text information of the function, performance and structure of the target product design online through the material ERP management platform, and generate product technical requirements text data Q;

[0078] Collect the product manufacturing cost requirement text information of the target product production design process online through the material ERP management platform, and generate the product production process manufacturing cost requirement text data P, which represents the cost requirement information of each product produced by the production design process in the production design stage of the target product, where the unit of P is RMB per piece;

[0079] The product manufacturing productivity requirement text information of the target product production design process is collected online through the material ERP management platform, and the product production process productivity requirement text data J is generated. The product production process productivity requirement text data represents the productivity requirement information for the number of manufactured products per unit time of the production design process during the production design stage of the target product, where the unit of J is pieces per hour.

[0080] Through the mutual cooperation among the product technical requirement information collection unit, the product production process manufacturing cost requirement information collection unit and the product production process productivity requirement information collection unit, the material ERP management platform is used to accurately collect the product technical requirements, product process design manufacturing cost requirements and product process design productivity requirements of the target product production process design online, so as to efficiently obtain the manufacturability technical requirements, cost control technical requirements and production efficiency technical requirements of the product process design, improve the efficiency and reliability of the matching of the target product production and manufacturing process, and enhance the efficiency of factory collaborative manufacturing management.

[0081] For further information, see Figure 1 - Figure 2 , the steps for analyzing and processing the technical requirements and production design process information of product production according to the product technical requirements text data and the product production design process text data, and generating the target technical requirements product production design process text data are as follows:

[0082] S21. Establish product production design process text data set A = (a 1 ,Λ,a m ,Λ,a ε ), m = 1, 2, 3, Λ, ε; where a mrepresents the product production design process text data corresponding to the mth product process design indicator requirement combination type, ε represents the maximum number of product process design indicator requirement combination types; the product process design indicator requirement combination type represents a data index type mainly formed by a combination of product design technical requirements, product process design manufacturing cost requirements, and product process design productivity requirements for searching product production design process information, the product production design process text data represents the optimal product production process information set for product production design requirements, and the product production process information includes the product production process step parameters and the production process step equipment information;

[0083] S22, combine the product technical requirement text data Q with the product production design process text data set A. m Perform keyword matching of product design technical requirements and search for product production design process text data a corresponding to product technical requirement text data Q m , and generate the target technical requirements product production design process text data set B through data identification. The specific steps for generating the target technical requirements product production design process text data set B are as follows:

[0084] S221, initialization, update the maximum number of iterations T, update the production process to search for the individual positions of the white shark population, the production process to search for the individual positions of the white shark population calculation formula is as follows: Where Mi,j represents the position of the production process search white shark individual i in the j-dimensional space, that is, the position of the production process search white shark individual i in the search space of the product production design process text data set A with a spatial dimension of ε, σ and They represent the upper and lower limits of the product production design process text data set A, respectively, and R represents a random number between (0,1);

[0085] S222, speed update stage, the production process search white shark in the product production design process text data set A search space according to the product production design process text data a m The prey moves to sense its position and search for the product production design process text data a that matches the product technical requirements text data Q m And update its own speed. The calculation formula for updating its own speed is as follows:

[0086] Among them, H i,t+1 represents the speed of the production process search white shark individual i in the search space of the product production design process text data set A after t+1 iterations, H i,t represents the speed of the production process search white shark individual i in the search space of the product production design process text data set A after t iterations, M best,trepresents the optimal position of the white shark individual in the search space of the product production design process text data set A after t+1 iterations, M i,t It represents the position of the production process search white shark individual i in the search space of the product production design process text data set A after t iterations; It represents the search result of the white shark individual i in the product production design process text data set A after t iterations and the speed H i,t The corresponding position; τ represents the algorithm shrinkage coefficient, υ 1 and 2 Respectively represent M best,t and The control coefficient, ω 1 and ω 2 Both represent random numbers between (0,1);

[0087] S223, position update stage, the production process search white shark updates its position in the product production design process text data set A search space by moving towards the optimal prey, and searches for the product production design process text data a that best matches or sub-optimally matches the product technical requirement text data Q in the product production design process text data set A search space. m Prey, production process search white shark position update calculation formula is as follows: Where M' i,t+1 It represents the position of the production process search white shark individual i in the search space of the product production design process text data set A after t+1 iterations in the position update phase. represents a bitwise operator, ξ is a logical vector, ψ and Φ are both binary vectors, It represents the attraction coefficient of the white shark approaching its prey, and Λ represents the wave frequency of the white shark sports product;

[0088] The production process search white shark moves towards the optimal production process search white shark position to get closer to the optimal position of the prey. The production process search white shark position update calculation formula is as follows:

[0089] M” i,t+1 =M best,t +r 1 ×Ξ×sgn(r 2 -0.5),r 3 <Γ, where M" i,t+1 represents the position update phase, searching for the optimal production process, searching for the production process of the white shark position, and searching for the position of the white shark individual i in the search space of the product production design process text data set A after t+1 iterations, r 1 、r 2 、r 3Both represent random numbers between (0,1), Ξ represents the distance between the white shark and the prey in the production process search, that is, search for product production design process text data a that matches the product technical requirement text data Q in the search space of the product production design process text data set A. m ; sgn represents the sign return function, Γ represents the production process to search for the olfactory and visual parameters of the white shark close to the best prey;

[0090] S224, in the stage of school behavior, the production process search white shark population retains the optimal production process search white shark position in the product production design process text data set A search space in the update stage, and updates the positions of other production process search white shark individuals according to the optimal production process search white shark position to obtain the product production design process text data a that matches the product technical requirement text data Q in the product production design process text data set A search space. m , the calculation formula for the production process search white shark individual position update is as follows: M"' i,t+1 =(M' i,t+1 +M” i,t+1 ) / 2R, where M'' i,t+1 It indicates that the fish school behavior stage updates other production processes to search for the position of the white shark individual i in the search space of the product production design process text data set A after t+1 iterations;

[0091] S225. When the maximum number of iterations is met, the product production design process text data a matching the product technical requirement text data Q is output. m ;

[0092] S226, the product production design process text data a output in step S225 m After data identification, the target technical requirements product production design process text data set B is generated = (b m1 ,Λ,b m2 ), 1≤m1≤m≤m2≤ε, where b m1 and b m2 They respectively represent the m1th and m2th target technical requirement product production design process text data; the target technical requirement product production design process text data represents the product production process information that meets the product design technical requirements.

[0093] Based on the product production process manufacturing cost requirement text data and the target technical requirement product production design process text data, the cost production design process information of product production is screened and processed to generate the target cost product production design process text data as follows:

[0094] S31, using the KMP search algorithm to compare the product production process manufacturing cost requirement text data P with the target technical requirement product production design process text data set B in the target technical requirement product production design process text data b. m1 To b m2 Perform product process design and manufacturing cost character matching, search for the target technical requirement product production design process text data corresponding to the product production process manufacturing cost requirement text data P, and generate the target cost product production design process text data set B'=(b' m3 ,Λ,b' m4 ), m1≤m3≤m4≤m2, where b' m3 and b' m4 They respectively represent the m3th and m4th target cost product production design process text data; the target cost product production design process text data represents the product production process information that meets the product design technical requirements and product process design and manufacturing cost requirements.

[0095] According to the product production process productivity requirement text data and the target cost product production design process text data, the production productivity production design process information of the product production is identified and processed, and the operation steps of generating the target productivity product production design process text data are as follows:

[0096] S41, using Boyer-Moore search algorithm to compare the product production process productivity requirement text data J with the target cost product production design process text data set B' of the target cost product production design process text data b' m3 to b' m4 Perform product process design productivity character matching, search for the target cost product production design process text data corresponding to the product production process productivity requirement text data J, and generate the target productivity product production design process text data set B"=(b" m5 ,Λ,b” m6 ), m3≤m5≤m6≤m4, where b” m5 and b” m6 They respectively represent the m5th and m6th target productivity product production design process text data; the target productivity product production design process text data represents the product production process information that meets the product design technical requirements, product process design manufacturing cost requirements and product process design productivity requirements.

[0097] Through the cooperation of the product production process information storage unit and the target technical requirement product production process analysis unit, the product production design process text information is scientifically preset, combined with the artificial intelligence recognition algorithm and the product technical requirement text information to scientifically analyze the product design process information on the product technical requirement side, so as to realize the intelligent matching of product production and manufacturing process information based on the manufacturability technical requirements of product process design; the target cost requirement product production process screening unit, based on the product production process manufacturing cost requirement text information combined with the intelligent search algorithm and the target technical requirement product production design process text information, accurately screens the product design process information on the product process design manufacturing cost side, so as to realize the refined screening of product production and manufacturing process information based on the cost control technical requirements of product process design; the target productivity requirement product production process identification unit, based on the product production process productivity requirement text information combined with the intelligent search algorithm and the target cost product production design process text information, accurately identifies the product design process information on the product process design productivity side, so as to realize the efficient identification of product production and manufacturing process information based on the production efficiency technical requirements of product process design; realizes the comprehensive and intelligent design of product production process information based on the product technical requirements, process design manufacturing cost and process design productivity, and improves the intelligence and quality of factory collaborative manufacturing management.

[0098] For further information, see Figure 1 - Figure 2 According to the target productivity product production design process text data and the product production design process line body 3D motion simulation data, the production line body 3D motion simulation information corresponding to the product production design process is searched and processed, and the operation steps for generating the target product production design process line body 3D motion simulation data are as follows:

[0099] S51. Establish product production design process line three-dimensional motion simulation data set C = (c 1 ,Λ,c m ,Λ,c ε ), where c m The three-dimensional motion simulation data of the product production design process line corresponding to the m-th product process design index requirement combination type is represented. The three-dimensional motion simulation data of the product production design process line represents the three-dimensional motion simulation data of the product production process of the process production manufacturing line corresponding to the production design processes of different types of products;

[0100] S52, using the Rabin-Karp search algorithm, the target productivity product production design process text data set B" in the target productivity product production design process text data set B" m5 to b" m6 The product production design process line body three-dimensional motion simulation data set C in the product production design process line body three-dimensional motion simulation data c mPerform production design process keyword matching to search for target productivity product production design process text data b" m5 to b" m6 Corresponding product production design process line 3D motion simulation data c m , and construct the target product production design process line three-dimensional motion simulation data set C'=(c' m5 ,Λ,c' m6 ), where c' m5 and c' m6 Respectively represent the target productivity product production design process text data b" m5 and target productivity product production design process text data b" m6 The corresponding target product production design process line body three-dimensional motion simulation data, when the target product production design process line body three-dimensional motion simulation data set C' is not generated, continue to execute the target product production design process line body three-dimensional motion simulation data set C' generation operation instruction.

[0101] The steps for collecting the physical model data of the product production factory scene are as follows:

[0102] S61. When the target product production design process line three-dimensional motion simulation data set C' is generated, a three-dimensional laser scanner mounted on an unmanned aerial vehicle is used to collect the three-dimensional solid model data of the target design product's production manufacturing plant, and generate product production plant scene solid model data D.

[0103] The steps for constructing and processing the product production design process factory production scene simulation information based on the target product production design process line body 3D motion simulation data and the product production factory scene entity model data, generating the target product production design process factory production scene simulation data and executing the product production design process factory collaborative manufacturing scene simulation operation are as follows:

[0104] S71, the target product production design process line body three-dimensional motion simulation data c' in the target product production design process line body three-dimensional motion simulation data set C' m5 to c' m6 In the 3D design software, the 3D motion simulation data is matched with the factory scene entity model data D in order according to the simulation data number, and the target product production design process factory production scene simulation data set E is generated. m5 ,Λ,e m6 ), where e m5 and e m6 Respectively represent the target product production design process line three-dimensional motion simulation data c' m5 and the target product production design process line body 3D motion simulation data c' m6The corresponding target product production design process factory production scene simulation data; the target product production design process factory production scene simulation data represents the motion simulation data of the target product design process in the product manufacturing factory;

[0105] S72, target product production design process factory production scenario simulation data set E of the target product production design process factory production scenario simulation data set E m5 To e m6 In the 3D simulation software, the simulation data is displayed and output in order according to the simulation data number to execute the product production design process factory collaborative manufacturing scenario simulation operation.

[0106] Through the target product production process line 3D motion simulation information search unit, the product production design process line 3D motion simulation data is efficiently matched according to the target productivity product production design process text parameters combined with the intelligent search algorithm and the standard set product production design process line 3D motion simulation parameters, providing real data support for product process design visualization; the product production factory scene model acquisition unit dynamically and accurately collects product production factory scene entity model information through the drone equipped with a 3D laser scanner, providing reliable data support for the simulation demonstration of product process design at the factory end; the product production process factory scene simulation unit, based on the target product production design process line 3D motion simulation data combined with 3D design software and product production factory scene entity model data, intelligently constructs product process design factory production scene simulation information, and at the same time combines the 3D simulation software to autonomously and intuitively execute product production design process factory collaborative manufacturing scene simulation operations, realizing the real simulation of product process design process production and manufacturing lines at the factory end, and improving the scientificity and effectiveness of factory collaborative manufacturing management.

[0107] Embodiment 2:

[0108] See also Figure 1 - Figure 2 , an AI-based smart factory collaborative manufacturing management system, used to implement an AI-based smart factory collaborative manufacturing management method, the system includes a collaborative manufacturing product information acquisition module, a collaborative manufacturing product process evaluation module, and a collaborative manufacturing product process simulation module;

[0109] The collaborative manufacturing product information acquisition module includes a product technical requirement information acquisition unit, a product production process manufacturing cost requirement information acquisition unit, and a product production process productivity requirement information acquisition unit;

[0110] The product technical requirement information collection unit collects product technical requirement text data through the material ERP management platform; the product production process manufacturing cost requirement information collection unit collects product production process manufacturing cost requirement text data through the material ERP management platform; the product production process productivity requirement information collection unit collects product production process productivity requirement text data through the material ERP management platform;

[0111] The collaborative manufacturing product process evaluation module includes a product production process information storage unit, a target technical requirement product production process analysis unit, a target cost requirement product production process screening unit, and a target productivity requirement product production process identification unit;

[0112] A product production process information storage unit is used to store product production design process text data; a target technical requirement product production process analysis unit is used to analyze and process the technical requirement production design process information of product production based on the product technical requirement text data and the product production design process text data, and generate the target technical requirement product production design process text data; a target cost requirement product production process screening unit is used to screen and process the cost production design process information of product production based on the product production process manufacturing cost requirement text data and the target technical requirement product production design process text data, and generate the target cost product production design process text data; a target productivity requirement product production process identification unit is used to identify and process the productivity production design process information of product production based on the product production process productivity requirement text data and the target cost product production design process text data, and generate the target productivity product production design process text data;

[0113] The collaborative manufacturing product process evaluation module includes a product production process line three-dimensional motion simulation information storage unit, a target product production process line three-dimensional motion simulation information search unit, a product production factory scene model acquisition unit, and a product production process factory scene simulation unit;

[0114] A product production process line body three-dimensional motion simulation information storage unit is used to store product production design process line body three-dimensional motion simulation data; a target product production process line body three-dimensional motion simulation information search unit searches for and processes the production line body three-dimensional motion simulation information corresponding to the product production design process according to the target productivity product production design process text data and the product production design process line body three-dimensional motion simulation data, and generates target product production design process line body three-dimensional motion simulation data; a product production factory scene model acquisition unit collects product production factory scene entity model data through a three-dimensional laser scanner mounted on an unmanned aerial vehicle; a product production process factory scene simulation unit constructs and processes product production design process factory production scene simulation information according to the target product production design process line body three-dimensional motion simulation data combined with three-dimensional design software and product production factory scene entity model data, generates target product production design process factory production scene simulation data, and combines three-dimensional simulation software to perform product production design process factory collaborative manufacturing scene simulation operations.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI-based intelligent factory collaborative manufacturing management method, characterized in that: The method comprises the following steps: S1. Collect text data on product technical requirements, product production process manufacturing cost requirements, and product production process productivity requirements; S2. Analyze and process the production design process information of the technical requirements for product production to generate text data of the production design process of the target technical requirements product; S3, screening and processing the cost production design process information of product production to generate target cost product production design process text data; S4, performing identification and processing of the production design process information of the product production, and generating the production design process text data of the target productivity product; S5, searching and processing the production line body 3D motion simulation information corresponding to the product production design process, and generating the target product production design process line body 3D motion simulation data; S6. Collecting physical model data of product production factory scene; S7. Carry out product production design process factory production scenario simulation information construction and processing, generate target product production design process factory production scenario simulation data and execute product production design process factory collaborative manufacturing scenario simulation operation.

2. The AI-based intelligent factory collaborative manufacturing management method according to claim 1, characterized in that: The S1 comprises the following steps: S11. Collect the technical requirements text information of the function, performance and structure of the target product design online through the material ERP management platform, and generate product technical requirements text data Q; Collect the product manufacturing cost requirement text information of the target product production design process online through the material ERP management platform, and generate the product production process manufacturing cost requirement text data P, where the unit of P is yuan per piece; The product manufacturing productivity requirement text information of the target product production design process is collected online through the material ERP management platform, and the product production process productivity requirement text data J is generated, where the unit of J is pieces per hour.

3. The AI-based intelligent factory collaborative manufacturing management method according to claim 2 is characterized by: The S2 comprises the following steps: S21. Establish product production design process text data set A = (a1, Λ, a m ,Λ,a ε ), m = 1, 2, 3, Λ, ε; where a m represents the product production design process text data corresponding to the mth product process design index requirement combination type, and ε represents the maximum number of product process design index requirement combination types; S22, compare the Q with the a in A m Perform keyword matching of product design technical requirements and search for the a corresponding to the Q m , and generate the target technical requirement product production design process text data set B through data identification, and the specific operation steps for generating the B are as follows: S221, initialization, updating the maximum number of iterations T, updating the production process to search for individual positions of white shark populations; S222, speed update stage, the production process search white shark in the A search space according to the a m The prey moves to sense its position and search for the a that matches the Q m And update its own speed; S223, position update stage, the production process search white shark updates its position in the A search space by moving towards the optimal prey, and searches for the a that best matches or suboptimally matches the Q in the A search space. m Prey; The production process search white shark moves towards the optimal production process search white shark position to get closer to the optimal position of the prey; S224, in the stage of school behavior, the production process search white shark population retains the optimal production process search white shark position in the A search space in the position update stage through feeding behavior, and updates the positions of other production process search white shark individuals according to the optimal production process search white shark position to obtain the product production design process text data a that matches the Q in the A search space m ; S225. When the maximum number of iterations is met, output the a that matches the Q m ; S226: the product production design process text data a output in step S225 m After data identification, the target technical requirements product production design process text data set B is generated = (b m1 ,Λ,b m2 ), 1≤m1≤m≤m2≤ε, where b m1 and b m2 They respectively represent the m1th and m2th target technical requirements product production design process text data.

4. The AI-based intelligent factory collaborative manufacturing management method according to claim 3 is characterized by: The S3 comprises the following steps: S31, using KMP search algorithm to compare the P with the b in B m1 To b m2 Perform product process design and manufacturing cost character matching, search for the target technical requirement product production design process text data corresponding to P, and generate the target cost product production design process text data set B'=(b' m3 ,Λ,b' m4 ), m1≤m3≤m4≤m2, where b' m3 and b' m4 They respectively represent the production design process text data of the m3th and m4th target cost products.

5. The AI-based intelligent factory collaborative manufacturing management method according to claim 4 is characterized by: The S4 comprises the following steps: S41, using the Boyer-Moore search algorithm to compare the J with the b' in the B' m3 to b' m4 Perform product process design productivity character matching, search for the target cost product production design process text data corresponding to J, and generate the target productivity product production design process text data set B"=(b" m5 ,Λ,b” m6 ), m3≤m5≤m6≤m4, where b” m5 and b” m6 They respectively represent the production design process text data of the m5th and m6th target productivity products.

6. The AI-based intelligent factory collaborative manufacturing management method according to claim 5, characterized in that: The S5 comprises the following steps: S51. Establish product production design process line three-dimensional motion simulation data set C = (c1, Λ, c m ,Λ,c ε ), where c m Represents the three-dimensional motion simulation data of the product production design process line corresponding to the mth product process design index requirement combination type; S52, using the Rabin-Karp search algorithm to find the b" in B" m5 to b" m6 The same as the C in c m Perform production design process keyword matching and search for the b" m5 to b" m6 The corresponding c m , and construct the target product production design process line three-dimensional motion simulation data set C'=(c' m5 ,Λ,c' m6 ), where c' m5 and c' m6 Respectively represent the b" m5 and the b" m6 The corresponding target product production design process line body three-dimensional motion simulation data, when the C' generation is not completed, continue to execute the C' generation operation instruction.

7. The AI-based intelligent factory collaborative manufacturing management method according to claim 6 is characterized by: The S6 comprises the following steps: S61. When the generation of C' is completed, a 3D laser scanner mounted on an unmanned aerial vehicle is used to collect 3D solid model data of the manufacturing plant of the target design product, and to generate product manufacturing plant scene solid model data D.

8. The AI-based intelligent factory collaborative manufacturing management method according to claim 7, characterized in that: The S7 comprises the following steps: S71, the c' in the C' m5 to c' m6 In the 3D design software, the 3D motion simulation data is matched with the factory scene entity model data in order according to the simulation data number, and the target product production design process factory production scene simulation data set E is generated. m5 ,Λ,e m6 ), where e m5 and e m6 Respectively represent the c′ m5 and the c′ m6 The corresponding target product production design process factory production scenario simulation data; S72, the e in the E m5 To e m6 In the 3D simulation software, the simulation data is displayed and output in order according to the simulation data number to execute the product production design process factory collaborative manufacturing scenario simulation operation.

9. An AI-based smart factory collaborative manufacturing management system, used to implement an AI-based smart factory collaborative manufacturing management method according to any one of claims 1 to 8, characterized in that: The system includes a collaborative manufacturing product information acquisition module, a collaborative manufacturing product process evaluation module, and a collaborative manufacturing product process simulation module.

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