Optimization method and device for man-machine cooperation assembly production line
Through the analysis of the work steps of the human-machine collaborative assembly production line and the evaluation of the criterion, the optimal human-machine collaborative assembly production line solution was determined, and the subjectivity and uncertainty in the optimization decision-making process in the existing technology were solved, and a more scientific and systematic evaluation method was achieved.
Patent Information
- Application Number
- CN202411482281.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-23
- Publication Date
- 2025-03-14
AI Technical Summary
When optimizing the human-machine collaborative assembly production line in the prior art, there is a lack of systematic and scientific evaluation methods, resulting in greater subjectivity and uncertainty in the decision-making process.
By analyzing the work steps of the human-machine collaborative assembly production line, the target work steps are determined, and multiple candidate human-machine collaboration plans are evaluated based on the predetermined criteria and weight values, and the final score of each plan is calculated to determine the optimal plan.
A more scientific and systematic evaluation method is realized, the subjectivity and uncertainty in the decision-making process is reduced, and the optimal human-machine collaborative assembly production line solution is selected.
Smart Images
Figure CN119620692A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of industrial automation control, and in particular to an optimization method and device for a human-machine collaborative assembly production line. Background Art
[0002] With the rapid development of industrial automation and intelligent manufacturing, human-machine collaboration technology has become a key technology to improve production efficiency, reduce costs, and enhance the flexibility of production systems. In modern manufacturing, human-machine collaboration can not only give full play to human flexibility and creativity, but also combine the high precision and strength of machines to achieve efficient completion of complex tasks.
[0003] In recent years, with the continuous advancement of robotics, sensor technology and artificial intelligence, human-machine collaborative systems have made significant progress in flexibility, safety and intelligence. Although human-machine collaborative technology has made certain progress, in actual production, how to scientifically evaluate and select the optimal human-machine collaborative assembly production system solution is still a challenge.
[0004] At present, many companies tend to rely on empirical judgment or simple cost-benefit analysis when optimizing production lines. They lack systematic and scientific evaluation methods, which leads to greater subjectivity and uncertainty in the decision-making process. Summary of the invention
[0005] In view of the problems existing in the prior art, the present invention provides an optimization method and device for a human-machine collaborative assembly production line to solve the technical defects existing in conventional technologies, that is, when optimizing the production line, it often relies on empirical judgment or simple cost-benefit analysis, lacks a systematic and scientific evaluation method, and leads to great subjectivity and uncertainty in the decision-making process.
[0006] The present invention provides an optimization method for a human-machine collaborative assembly production line, comprising: Analyze the work steps of the human-machine collaborative assembly production line to determine the target work step; wherein the target work step corresponds to multiple candidate solution options; Based on the multiple candidate solution options corresponding to the target process step, determine multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line; Based on the predetermined criteria, determine the index values corresponding to each candidate human-machine cooperation scheme under each criterion, perform normalization calculation based on the index values, and obtain the scores corresponding to each candidate human-machine cooperation scheme under each criterion; The final scores of the candidate human-machine cooperation schemes are obtained based on the scores of the candidate human-machine cooperation schemes under various criteria and the predetermined weight values corresponding to the various criteria, and the optimal scheme among the candidate human-machine cooperation schemes is determined based on the final scores.
[0007] According to the optimization method of the human-machine collaborative assembly production line provided by the present invention, the weight value corresponding to each criterion is calculated by the following method: Determine the relative importance of each of the criteria mentioned; Based on the relative importance of the criteria, a criterion judgment matrix is established, wherein each element A in the criterion judgment matrix ij Identify the importance of the criterion i relative to the criterion j; A consistency check is performed on the criterion judgment matrix. If the consistency check passes, the eigenvector of the criterion judgment matrix is calculated to obtain the weight value corresponding to each criterion.
[0008] According to the optimization method of the human-machine collaborative assembly production line provided by the present invention, the work steps of the human-machine collaborative assembly production line are analyzed to determine the target work steps, which specifically includes: analyzing the work steps of the human-machine collaborative assembly production line based on the process analysis method to determine the process information of each work step; analyzing based on the process information of the work step to determine the target work step.
[0009] According to the optimization method of the human-machine collaborative assembly production line provided by the present invention, a normalized calculation is performed based on the index value to obtain the scores corresponding to each candidate human-machine collaborative solution under various criteria, specifically including: According to the index scores corresponding to each criterion of each candidate human-machine collaboration solution, a judgment matrix corresponding to each criterion is constructed respectively; each element B of the judgment matrix ij Identify the importance of the candidate human-machine cooperation solution i relative to the candidate human-machine cooperation solution j; The eigenvector of each judgment matrix is calculated using the sum-product method, wherein the eigenvector of each judgment matrix includes the score of each candidate human-machine cooperation solution under the criterion corresponding to the judgment matrix.
[0010] According to the optimization method of the human-machine collaborative assembly production line provided by the present invention, after constructing the judgment matrix corresponding to each criterion, the method also includes: performing a consistency check on the judgment matrix corresponding to each criterion, and if the consistency check passes, calculating the eigenvectors of each judgment matrix.
[0011] The present invention also provides an optimization device for a human-machine collaborative assembly production line, comprising: A work step analysis module is used to analyze the work steps of the human-machine collaborative assembly production line and determine the target work step; wherein the target work step corresponds to a plurality of candidate solution options; A candidate determination module, used to determine multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line based on multiple candidate solution options corresponding to the target process step; A score calculation module is used to determine the index value corresponding to each candidate machine-machine cooperation solution under each criterion based on a predetermined criterion, and perform normalization calculation based on the index value to obtain the score corresponding to each candidate machine-machine cooperation solution under each criterion; The optimal determination module is used to calculate the final scores of each candidate machine-machine cooperation plan according to the scores of each candidate machine-machine cooperation plan under various criteria and the weight values corresponding to the predetermined criteria, and determine the optimal plan among the candidate machine-machine cooperation plans according to the final scores.
[0012] The optimization device for the human-machine collaborative assembly production line provided by the present invention further includes a weight value determination module for: Determine the relative importance of each of the criteria mentioned; Based on the relative importance of the criteria, a criterion judgment matrix is established, wherein each element A in the criterion judgment matrix ij Identify the importance of the criterion i relative to the criterion j; A consistency check is performed on the criterion judgment matrix. If the consistency check passes, the eigenvector of the criterion judgment matrix is calculated to obtain the weight value corresponding to each criterion.
[0013] According to the optimization device of the human-machine collaborative assembly production line provided by the present invention, the score calculation module is specifically used to: construct a judgment matrix corresponding to each criterion according to the index score corresponding to each criterion of each candidate human-machine collaborative solution; each element B of the judgment matrix ij Identify the importance of the candidate human-machine cooperation solution i relative to the candidate human-machine cooperation solution j; calculate the eigenvectors of each judgment matrix using the sum-product method, wherein the eigenvector of each judgment matrix includes the score of each candidate human-machine cooperation solution under the criterion corresponding to the judgment matrix.
[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an optimization method for a human-machine collaborative assembly production line as described above is implemented.
[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for optimizing the human-machine collaborative assembly production line as described in any one of the above is implemented.
[0016] The optimization method and device of the human-machine collaborative assembly production line provided by the embodiment of the present invention analyzes the work steps of the human-machine collaborative assembly production line, determines the target work steps, and then determines multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line. Then, through predetermined criteria and corresponding weight values, it can scientifically evaluate multiple candidate human-machine collaborative solutions and calculate the final score of each solution, thereby determining the optimal solution. The method of the embodiment of the present invention overcomes the defects of large subjectivity and uncertainty in the selection of solutions in the prior art, and provides a more scientific and systematic evaluation method. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0018] Figure 1 This is one of the flow charts of the optimization method of the human-machine collaborative assembly production line provided by the present invention.
[0019] Figure 2 This is the second flow chart of the optimization method of the human-machine collaborative assembly production line provided by the present invention.
[0020] Figure 3 This is the third flow chart of the optimization method of the human-machine collaborative assembly production line provided by the present invention.
[0021] Figure 4 It is a schematic diagram of the sensor human-machine collaborative assembly production line provided by the present invention.
[0022] Figure 5 It is a result schematic diagram of the industrial engineering process analysis method provided by the present invention.
[0023] Figure 6 It is a structural schematic diagram of an optimization device for a human-machine collaborative assembly production line provided by the present invention.
[0024] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION
[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.
[0026] Combine the following Figure 1-Figure 6 The optimization method and device of the human-machine collaborative assembly production line according to the embodiment of the present invention are described.
[0027] The embodiment of the present invention discloses a method for optimizing a human-machine collaborative assembly production line. Figure 1 ,include: 101. Analyze the work steps of the human-machine collaborative assembly production line to determine a target work step; wherein the target work step corresponds to a plurality of candidate solution options.
[0028] Specifically, step 101 includes: analyzing the process steps of the human-machine collaborative assembly production line based on a process analysis method to determine the process information of each process step; and analyzing the process information of the process step to determine the target process step.
[0029] In this embodiment, the ECRS principles of industrial engineering (including Eliminate, Combine, Rearrange, and Simplify) can be applied to analyze the work steps of the human-machine collaborative assembly production line in detail and determine the process information of each work step, including operating procedures, required equipment, staffing, etc.
[0030] Specifically, before analyzing the work steps, first collect detailed information about the production line through on-site observation, data recording, and employee interviews, etc. Record the execution time, required materials, equipment used, and operator skill requirements for each work step.
[0031] Utilize the collected data to analyze the process information of each work step, including operation flow, required equipment, staffing, etc. Identify waste, bottlenecks or non-value-added activities in the work step, which will be the focus of optimization.
[0032] Then, the ECRS principle is applied to analyze the work steps of the human-machine collaborative assembly production line in detail, including: Eliminate: Identify and eliminate steps that do not add value. For example, remove redundant inspection steps or simplify overly complex operations.
[0033] Combine: For steps that can be performed simultaneously or are logically adjacent, consider combining them to reduce changeover time and improve efficiency.
[0034] Rearrange: Rearrange the order of steps to optimize the process, for example, placing preparation activities at the beginning of the line and fine operations at the end.
[0035] Simplify: Simplify complex work steps to make them easier to perform, reduce errors and improve quality.
[0036] Based on ECRS analysis, key steps that have a significant impact on production efficiency and quality are identified as target steps. Target steps include those with high technical requirements, complex operations, or bottlenecks in the production process. For these target steps, since it is not certain whether they need automated assembly, each target step has multiple candidate options.
[0037] For example, for the interface assembly step in a human-machine collaborative assembly production line. If this step is automatically assembled by a collaborative robot, a special fixture needs to be customized, and the fixture circulation requirements must be met. At the same time, the component is an interference fit with pin holes for positioning, and the required automated assembly technology is more difficult than manual assisted assembly. If manual assembly is used, the above technical difficulties can be avoided, but the demonstration of the production line will be reduced and the labor cost will increase. Therefore, the candidate options include: manual assembly and automatic assembly.
[0038] 102. Based on the multiple candidate solution options corresponding to the target process step, determine multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line.
[0039] For example, a human-machine collaborative assembly production line includes five steps S1 to S5, and the five steps include two target steps S2 and S3. The candidate solution options corresponding to the target step S2 include S21 and S22, and the candidate solution options corresponding to the target step S3 include S31 and S32. The solution options corresponding to other non-target steps are unique, which are automatic assembly or manual assembly. Then, the multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line may include four types: S1-S21-S31-S4-S5, S1-S22-S31-S4-S5, S1-S21-S32-S4-S5, and S1-S22-S32-S4-S5.
[0040] 103. Based on predetermined criteria, determine the index value corresponding to each candidate human-machine collaboration solution under each criterion, perform normalization calculation based on the index value, and obtain the score corresponding to each candidate human-machine collaboration solution under each criterion.
[0041] In this embodiment, the criteria may be predetermined, for example, may include B1 cost, B2 degree of flexibility, B3 capital recovery period, B4 floor space and B5 production line exemplaryness.
[0042] Among them, B2 flexibility and B5 production line demonstration are qualitative criteria, B1 cost, B3 capital recovery period and B4 floor space are quantitative criteria.
[0043] For example, there are four candidate human-machine collaboration schemes, and the corresponding indicator values of the four candidate human-machine collaboration schemes under various criteria are determined. For example, the indicator values under B1 cost are 100, 110, 120, 130 and 140 respectively, and the indicator values under B2 flexibility are 6, 7, 8, 9 and 10 respectively.
[0044] Normalized values are usually in the range of 0 to 1, which simplifies mathematical calculations in multi-criteria decision analysis, such as weighted summation. The main purpose of normalization calculation is to convert the indicator values under different criteria into comparable scales so that these indicator values can fairly participate in the final scheme evaluation and decision-making process.
[0045] 104. Calculate the final scores of the candidate human-machine cooperation plans based on the scores of the candidate human-machine cooperation plans under various criteria and the predetermined weight values corresponding to the various criteria, and determine the optimal plan among the candidate human-machine cooperation plans based on the final scores.
[0046] Confirm the weight value of each criterion, which may be obtained through AHP analysis model, expert consultation or statistical analysis.
[0047] Through calculation, the final score of each candidate human-machine collaboration solution can be obtained, and the final score represents the quality of the candidate human-machine collaboration solution. By comparing the final scores, the best solution among the candidate human-machine collaboration solutions can be determined.
[0048] The optimization method of the human-machine collaborative assembly production line provided by the embodiment of the present invention analyzes the work steps of the human-machine collaborative assembly production line, determines the target work steps, and then determines multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line. Then, through predetermined criteria and corresponding weight values, it can scientifically evaluate multiple candidate human-machine collaborative solutions and calculate the final score of each solution, thereby determining the optimal solution. The method of the embodiment of the present invention overcomes the defects of large subjectivity and uncertainty in the selection of solutions in the prior art, and provides a more scientific and systematic evaluation method.
[0049] In this embodiment, the weight values corresponding to each criterion are obtained through statistical analysis of the AHP analysis model. Figure 2 , can be calculated by the following method: 201. Determine the relative importance of each criterion.
[0050] In this embodiment, the criteria of B1-B5 can be determined by constructing an AHP analysis model, which may include, for example, B1 cost, B2 degree of flexibility, B3 capital recovery period, B4 floor space and B5 production line demonstration.
[0051] 202. Based on the relative importance of the criteria, a criterion judgment matrix is established, wherein each element A in the criterion judgment matrix ij Identifies the importance of criterion i relative to criterion j.
[0052] 203. Perform a consistency check on the criterion judgment matrix. If the consistency check passes, calculate the eigenvector of the criterion judgment matrix to obtain the weight value corresponding to each criterion.
[0053] In this embodiment, a team of experts, including technicians, financial analysts, production managers, etc., can be organized to compare the criteria in pairs. Using Saaty's 1-9 scale, experts are asked to rate the relative importance of each pair of criteria. For example, if criterion B1 (cost) is considered to be 5 times more important than criterion B2 (flexibility), then element A 12 The value of (the importance of B1 relative to B2) is 5. Correspondingly, element A 21 The value of (the importance of B2 relative to B1) is 1 / 5.
[0054] According to the expert's rating, construct the criterion layer judgment matrix A. For example, if there are 5 criteria, construct a 5x5 matrix. Among them, it is necessary to ensure that the matrix is positive and negative, that is, if A ij = w, then A ji = 1 / w, where w is the importance score of criterion i relative to criterion j.
[0055] Perform consistency check: first calculate the maximum characteristic root of the criterion judgment matrix (λ max ), and then calculate the consistency index (CI): CI = (λ max −n) / (n−1), where n is the number of criteria. Then, the corresponding RI value is determined through the random consistency index (RI) table, and the consistency ratio (CR) is calculated: CR=RI / CI. If CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency and is acceptable; if CR is greater than or equal to 0.1, the judgment matrix needs to be readjusted until satisfactory consistency is achieved.
[0056] If the judgment matrix passes the consistency check, its eigenvector is calculated, which will give the weight value of each criterion. The eigenvector is normalized to ensure that the sum of the weight values is 1, thus obtaining the relative weight of each criterion.
[0057] Further, see Figure 3, step 103 comprises: 301. According to the index scores corresponding to the various criteria of each candidate human-machine collaboration solution, a judgment matrix corresponding to each criterion is constructed respectively; each element B of the judgment matrix ij The importance of the candidate human-machine cooperation solution i relative to the candidate human-machine cooperation solution j is identified.
[0058] After constructing the judgment matrices corresponding to each criterion, it is also necessary to perform a consistency check on the judgment matrices corresponding to each criterion. If the consistency check passes, the eigenvectors of each judgment matrix are calculated.
[0059] In this embodiment, if there are five criteria, five judgment matrices are generated accordingly, and the elements included in each judgment matrix are the importance of candidate human-machine cooperation solution i relative to candidate human-machine cooperation solution j under the criteria.
[0060] The importance can be calculated based on the index score of each candidate human-machine collaboration solution under the criterion.
[0061] 302. Calculate the eigenvector of each judgment matrix using the sum-product method, wherein the eigenvector of each judgment matrix includes the score of each candidate human-machine cooperation solution under the criterion corresponding to the judgment matrix.
[0062] Through steps 301 and 302, the scientificity, systematicness and transparency of the decision-making process are ensured, helping enterprises to select the best optimization strategy from multiple candidate solutions.
[0063] In order to facilitate understanding of the solution of this embodiment, a specific example is listed below to schematically illustrate the solution of the present invention.
[0064] The research object of this embodiment is a sensor human-machine collaborative assembly production line. The process flow chart is as follows: Figure 4 The main components of this product include shell, shell cover, interface, plastic core socket, circuit board, etc.
[0065] Use industrial engineering process analysis method to gradually analyze the optimization plan. The analysis results are as follows: Figure 5 As shown. Through process analysis, it can be found that there is a great controversy over whether to use automated processes for the interface assembly steps and the glue filling and drying stages. In addition to factors such as cost, capital recovery cycle, and floor space, there are also qualitative factors such as flexibility and production line demonstration, which cannot be directly quantitatively measured. This problem is common in the analysis of automated processes for human-machine collaborative production lines. In the prior art, in the process of enterprise practice, most decisions are made by discussing in meetings to solicit opinions or by the person in charge "shooting their heads". This decision-making method cannot scientifically evaluate the optimal process optimization solution.
[0066] In this embodiment, the AHP analysis model is constructed as follows: 1) Target layer: Select the best human-machine collaboration solution
[0067] 2) Standard level: B1 cost, B2 flexibility, B3 capital recovery period, B4 floor space and B5 production line demonstration.
[0068] Applying the Saaty 1-9 scaling method, the five indicators in the criterion layer are compared pairwise, and the criterion layer judgment matrix is constructed as follows:
[0069] Among them, the matrix A represents the judgment matrix of the criterion layer, and the matrix element A ij Identify the importance of criterion i relative to criterion j. The consistency test of matrix A using the sum-product method is shown in Table 1.
[0070] Table 1
[0071] The calculation results of the hierarchical analysis method show that the maximum characteristic root is 5.112. According to the RI table, the corresponding RI value is 1.11. Therefore, CR=CI / RI=0.025<0.1, which passes the one-time test.
[0072] 3) Solution layer: In addition to the other work steps that have been determined to require automated assembly, all current solutions mainly involve the differences in the human-machine collaborative automation solutions for two work steps: ① Interface assembly step. If this step is automatically assembled by a collaborative robot, a special fixture needs to be customized and the fixture circulation requirements must be met. However, this component is an interference fit and has pin holes for positioning, so manual assistance is required for assembly.
[0073] ② Glue filling and drying process. If this process adopts automatic assembly, because it involves epoxy glue filling, it is necessary to ensure that there is an exhaust gas collection and purification device in the glue filling process, and it is also necessary to equip it with drying equipment.
[0074] There are currently three candidate human-machine collaboration solutions: C1: Automatic interface assembly and automatic glue filling and drying; C2: Manual assembly of interfaces, automatic glue filling and drying; C3: The interface is manually assembled, and the product is manually transported to the glue pouring room for manual glue pouring and waiting for drying.
[0075] The indicators of the three candidate human-machine collaboration schemes are shown in Table 2.
[0076] Table 2
[0077] Among them, the cost index is scored as the sum of all construction investments after the construction of each plan and the costs required for annual operation and maintenance; the flexibility index is scored as the types of products that can be produced by the product; the capital recovery cycle index is the cost recovery cycle after considering the saved labor costs and production line investment costs; the floor space index is the actual floor space of the production line; for the demonstration of the production line, this indicator is a qualitative indicator, and the score comes from the "Delphi method", which is scored back-to-back by process experts and automation experts familiar with the process using the Saaty1-9 scaling method, and the consistency is verified by the sum-product method, and finally the average method is used to obtain the score value of the indicator.
[0078] 4) Construct the judgment matrix of each criterion based on the scheme as follows: B1= ; B2= ; B3= ; B4= ; B5=
[0079] In the above matrix, matrix B k (k=1,2,3,4,5) means B i The judgment matrix of the indicator. Among them, the matrix element bk ij Represents the candidate human-machine collaboration solution C i Relative to candidate human-machine collaboration solution C j The importance of bk ij =C i / C j The maximum eigenvalue, normalized relative importance vector and consistency index (CI value) of the four judgment matrices were calculated using the sum-product method.
[0080] The consistency test table of the judgment matrix of the three human-machine collaboration schemes is shown in Table 3.
[0081] Table 3
[0082] The scoring calculation of the optimal human-machine collaboration solution is shown in Table 4 below: Table 4
[0083] The results show that C2 is better among the three solutions, and manual assembly of interfaces and automatic glue filling and drying are the optimal human-machine collaboration solutions. Through the three solutions, the indicators of each solution were comprehensively evaluated from five indicators: cost, flexibility, capital recovery cycle, floor space and production line demonstration. The various factors of concern faced by enterprises when optimizing automation processes were fully incorporated into the evaluation index system. This index evaluation system is applicable to the automation upgrade and transformation of production lines in various industries. At the same time, the industrial engineering process analysis method was applied to select scientific and reasonable solutions for the problem of automation process optimization, and the characteristics of each solution were comprehensively scored to complete the solution selection, achieve scientific and reasonable decision-making, and provide a scientific and reasonable decision-making method for the upgrade and transformation of human-machine collaboration production lines.
[0084] 1) Compared with the prior art, the method of the embodiment of the present invention has wider and more complex application scenarios. The method adopts the industrial engineering process analysis method to select the optimization plan for the human-machine collaborative production line, which is simple and clear for process design and more operational. The analysis of process optimization projects is simple and intuitive.
[0085] 2) The evaluation method of AHP applied in the embodiment of the present invention has wider applicability for the sum evaluation of qualitative and quantitative indicators. By constructing a hierarchical AHP model, abstract problems are concretized, and the weights of various factors are quantified using the Delphi method, and the optimization strategy of the human-machine collaborative assembly production system is studied, aiming to provide enterprises with a scientific and systematic optimization method.
[0086] The optimization device for the human-machine collaborative assembly production line provided by the present invention is described below. The optimization device for the human-machine collaborative assembly production line described below and the optimization method for the human-machine collaborative assembly production line described above can be referenced to each other.
[0087] Figure 6 Schematic diagram of the structure of the optimization device of the human-machine collaborative assembly production line provided by the present invention. Figure 6 As shown, the device comprises: The work step analysis module 601 is used to analyze the work steps of the human-machine collaborative assembly production line and determine the target work step; wherein the target work step corresponds to a plurality of candidate solution options; A candidate determination module 602 is used to determine a plurality of candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line based on a plurality of candidate solution options corresponding to the target process step; The score calculation module 603 is used to determine the index value corresponding to each candidate machine-machine cooperation solution under each criterion based on the predetermined criterion, and perform normalization calculation based on the index value to obtain the score corresponding to each candidate machine-machine cooperation solution under each criterion; The optimal determination module 604 is used to calculate the final scores of each candidate machine-machine cooperation plan according to the scores of each candidate machine-machine cooperation plan under various criteria and the predetermined weight values corresponding to each criterion, and determine the optimal plan among the candidate machine-machine cooperation plans according to the final scores.
[0088] Optionally, the device further includes a weight value determination module, configured to: Determine the relative importance of each of the criteria mentioned; Based on the relative importance of the criteria, a criterion judgment matrix is established, wherein each element A in the criterion judgment matrix ij Identify the importance of the criterion i relative to the criterion j; A consistency check is performed on the criterion judgment matrix. If the consistency check passes, the eigenvector of the criterion judgment matrix is calculated to obtain the weight value corresponding to each criterion.
[0089] Optionally, the work step analysis module 601 is specifically used to: analyze the work steps of the human-machine collaborative assembly production line based on a process analysis method to determine the process information of each work step; and determine the target work step based on the analysis of the process information of the work step.
[0090] Optionally, the score calculation module 603 is specifically used to: According to the index scores corresponding to the various criteria of each candidate human-machine cooperation scheme, a judgment matrix corresponding to each criterion is constructed respectively; each element Bij of the judgment matrix indicates the importance of the candidate human-machine cooperation scheme i relative to the candidate human-machine cooperation scheme j; The eigenvector of each judgment matrix is calculated using the sum-product method, wherein the eigenvector of each judgment matrix includes the score of each candidate human-machine cooperation solution under the criterion corresponding to the judgment matrix.
[0091] Optionally, the score calculation module 603 is specifically used to: after constructing the judgment matrix corresponding to each criterion, perform a consistency check on the judgment matrix corresponding to each criterion, and if the consistency check passes, calculate the eigenvector of each judgment matrix.
[0092] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7As shown, the electronic device may include: a processor (processor) 710 , a communication interface (Communications Interface) 720 , a memory (memory) 730 and a communication bus 740 , wherein the processor 710 , the communication interface 720 , and the memory 330 communicate with each other via the communication bus 740 . The processor 710 can call the logic instructions in the memory 730 to execute the optimization method of the human-machine collaborative assembly production line, which method includes: analyzing the work steps of the human-machine collaborative assembly production line to determine the target work step; wherein the target work step corresponds to multiple candidate solution options; based on the multiple candidate solution options corresponding to the target work step, determine the multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line; based on predetermined criteria, determine the indicator value corresponding to each candidate human-machine collaborative solution under each criterion, perform normalization calculation based on the indicator value, and obtain the score corresponding to each candidate human-machine collaborative solution under each criterion; calculate according to the score of each candidate human-machine collaborative solution under each criterion, and the weight value corresponding to each predetermined criterion, respectively obtain the final score of each candidate human-machine collaborative solution, and determine the optimal solution among each candidate human-machine collaborative solution according to the final score.
[0093] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.
[0094] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the optimization method of the human-machine collaborative assembly production line provided by the above methods, the method including: parsing the work steps of the human-machine collaborative assembly production line to determine the target work step; wherein the target work step corresponds to a plurality of candidate solution options; based on the plurality of candidate solution options corresponding to the target work step, determining a plurality of candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line; based on predetermined criteria, determining the index value corresponding to each candidate human-machine collaborative solution under each criterion, performing normalization calculation based on the index value, and obtaining the score corresponding to each candidate human-machine collaborative solution under each criterion; calculating according to the score of each candidate human-machine collaborative solution under each criterion and the predetermined weight value corresponding to each criterion, respectively obtaining the final score of each candidate human-machine collaborative solution, and determining the optimal solution among each candidate human-machine collaborative solution according to the final score.
[0095] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to perform the optimization method of the human-machine collaborative assembly production line provided by the above methods, the method comprising: The work steps of the human-machine collaborative assembly production line are analyzed to determine the target work step; wherein the target work step corresponds to a plurality of candidate solution options; based on the plurality of candidate solution options corresponding to the target work step, a plurality of candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line are determined; based on predetermined criteria, the index values corresponding to each candidate human-machine collaborative solution under each criterion are determined, and normalization calculations are performed based on the index values to obtain the scores corresponding to each candidate human-machine collaborative solution under each criterion; calculations are performed based on the scores of each candidate human-machine collaborative solution under each criterion and the predetermined weight values corresponding to each criterion to obtain the final scores of each candidate human-machine collaborative solution, and the optimal solution among each candidate human-machine collaborative solution is determined based on the final scores.
[0096] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.
[0097] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.
[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for optimizing a human-machine collaborative assembly production line, characterized in that: include: Analyze the work steps of the human-machine collaborative assembly production line to determine the target work step; wherein the target work step corresponds to multiple candidate solution options; Based on the multiple candidate solution options corresponding to the target process step, determine multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line; Based on the predetermined criteria, determine the index values corresponding to each candidate human-machine cooperation scheme under each criterion, perform normalization calculation based on the index values, and obtain the scores corresponding to each candidate human-machine cooperation scheme under each criterion; The final scores of the candidate human-machine cooperation schemes are obtained based on the scores of the candidate human-machine cooperation schemes under various criteria and the predetermined weight values corresponding to the various criteria, and the optimal scheme among the candidate human-machine cooperation schemes is determined based on the final scores.
2. The optimization method of the human-machine collaborative assembly production line according to claim 1, characterized in that: The weight values corresponding to the above criteria are calculated by the following method: Determine the relative importance of each of the criteria mentioned; Based on the relative importance of the criteria, a criterion judgment matrix is established, wherein each element A in the criterion judgment matrix ij Identify the importance of the criterion i relative to the criterion j; A consistency check is performed on the criterion judgment matrix. If the consistency check passes, the eigenvector of the criterion judgment matrix is calculated to obtain the weight value corresponding to each criterion.
3. The optimization method of the human-machine collaborative assembly production line according to claim 1, characterized in that: Analyze the work steps of the human-machine collaborative assembly production line and determine the target work steps, including: Analyze the process steps of the human-machine collaborative assembly production line based on the process analysis method to determine the process information of each step; The target step is determined by analyzing the process information of the step.
4. The optimization method of the human-machine collaborative assembly production line according to claim 1, characterized in that: Based on the index values, normalized calculations are performed to obtain the scores of each candidate human-machine collaboration solution under various criteria, including: According to the index scores corresponding to each criterion of each candidate human-machine collaboration solution, a judgment matrix corresponding to each criterion is constructed respectively; each element B of the judgment matrix ij Identify the importance of the candidate human-machine cooperation solution i relative to the candidate human-machine cooperation solution j; The eigenvector of each judgment matrix is calculated using the sum-product method, wherein the eigenvector of each judgment matrix includes the score of each candidate human-machine cooperation solution under the criterion corresponding to the judgment matrix.
5. The optimization method of the human-machine collaborative assembly production line according to claim 4, characterized in that: After constructing the judgment matrix corresponding to each criterion, the method further includes: A consistency check is performed on the judgment matrices corresponding to the various criteria. If the consistency check passes, the eigenvectors of the judgment matrices are calculated.
6. An optimization device for a human-machine collaborative assembly production line, characterized in that: include: A work step analysis module is used to analyze the work steps of the human-machine collaborative assembly production line and determine the target work step; wherein the target work step corresponds to a plurality of candidate solution options; A candidate determination module, used to determine multiple candidate human-machine collaborative solutions corresponding to the human-machine collaborative assembly production line based on multiple candidate solution options corresponding to the target process step; A score calculation module is used to determine the index value corresponding to each candidate machine-machine cooperation solution under each criterion based on a predetermined criterion, and perform normalization calculation based on the index value to obtain the score corresponding to each candidate machine-machine cooperation solution under each criterion; The optimal determination module is used to calculate the final scores of each candidate machine-machine cooperation plan according to the scores of each candidate machine-machine cooperation plan under various criteria and the weight values corresponding to the predetermined criteria, and determine the optimal plan among the candidate machine-machine cooperation plans according to the final scores.
7. The optimization device for a human-machine collaborative assembly production line according to claim 6, characterized in that: It also includes a weight value determination module, which is used to: Determine the relative importance of each of the criteria mentioned; Based on the relative importance of the criteria, a criterion judgment matrix is established, wherein each element A in the criterion judgment matrix ij Identify the importance of the criterion i relative to the criterion j; A consistency check is performed on the criterion judgment matrix. If the consistency check passes, the eigenvector of the criterion judgment matrix is calculated to obtain the weight value corresponding to each criterion.
8. The optimization device for a human-machine collaborative assembly production line according to claim 6, characterized in that: The score calculation module is specifically used for: According to the index scores corresponding to each criterion of each candidate human-machine collaboration solution, a judgment matrix corresponding to each criterion is constructed respectively; each element B of the judgment matrix ij Identify the importance of the candidate human-machine cooperation solution i relative to the candidate human-machine cooperation solution j; The eigenvector of each judgment matrix is calculated using the sum-product method, wherein the eigenvector of each judgment matrix includes the score of each candidate human-machine cooperation solution under the criterion corresponding to the judgment matrix.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the optimization method of the human-machine collaborative assembly production line as described in any one of claims 1 to 5 is implemented.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the optimization method of the human-machine collaborative assembly production line as described in any one of claims 1 to 5.