A complex equipment assembly sequence deviation transmission analysis method and system

Abstract

This invention provides a method and system for analyzing the propagation of deviations in complex equipment assembly sequences. It acquires the dimensional attributes of components under each operation step, including the component dimensions involved in the corresponding operation, the upper deviation of the component dimensions, and the lower deviation. Based on the component dimensional attributes and the assembly sequence planning, it calculates the assembly deviations between components to construct a deviation propagation model. Following the time sequence planning of the assembly sequence, it calculates the operability of the corresponding operation steps based on the component dimensional attributes under each operation step, and then calculates the process complexity based on the operability, ultimately obtaining a process complexity sequence. The Lempel-Ziv algorithm is used to calculate the complexity of the process complexity sequence, and the optimal assembly sequence is determined based on the complexity. Specifically, this method effectively analyzes the degree of deviation propagation in complex equipment assembly sequences, providing theoretical support for optimizing path sequences in the assembly sequence planning stage.

Description

Technical Field

[0001] This invention belongs to the field of complex equipment assembly sequence deviation transmission analysis technology, and specifically relates to a method and system for complex equipment assembly sequence deviation transmission analysis. Background Technology

[0002] Assembly sequence planning is a crucial component in the manufacturing of complex mechanical equipment such as wind power generation equipment, offering significant advantages in improving production efficiency and reducing production costs. The more complex the product manufacturing process and the larger the assembly sequence, the greater the difficulty in ensuring overall assembly accuracy by controlling component tolerances during assembly. Manufacturing deviations of components and the stability of tooling fixtures in assembly sequence planning lead to randomness in the final product's part assembly. Sometimes, to ensure parts are assembled according to design requirements, forced assembly methods may be adopted, resulting in part deformation and additional assembly deviations. Accumulated deviations affect the quality of the final product, and increasingly complex assembly operations also increase the instability of the production process. Given the limitations of real-time measurement of assembly component accuracy, reducing the accumulation of deviation uncertainty from the perspective of deviation transmission in the assembly sequence can reduce operator error rates and contribute to the convergence of assembly accuracy.

[0003] Assembly operations exhibit a certain transferability within a sequential combination, particularly in the influence of preceding operations on subsequent operations. Accumulated assembly deviations reduce product assembly accuracy, and vibrations, deformations, and stresses caused by assembly errors hinder power transmission. Existing assembly accuracy analysis methods generally parameterize various properties of components and use statistical methods to conduct accuracy analysis to control assembly deviations.

[0004] The method of ensuring product quality by controlling the precision of components has reached a bottleneck. From the perspectives of product function, structure, and materials, the more complex the product, the higher the requirements for assembly process selection and parameters. This involves more complex pre-assembly relationships between processes, assembly time relationships between processes, and process method constraints related to processes. Different assembly sequence paths lead to significant differences in the accumulation of assembly deviations. Assembly sequences with low process complexity and small accumulation of assembly deviations result in a more concentrated distribution of product precision during implementation, less prone to dispersion. Summary of the Invention

[0005] Based on this, the present invention provides a method and system for analyzing the transmission of deviations in complex equipment assembly sequences, aiming to analyze the degree of transmission of deviations in complex equipment assembly sequences and provide theoretical support for targeted implementation of assembly sequence optimization management.

[0006] A first aspect of this invention provides a method for analyzing the propagation of deviations in complex equipment assembly sequences, the method comprising: Obtain the component size attributes under each operation step, including the component size involved in the corresponding operation, the upper deviation of the component size, and the lower deviation of the component size; Based on the component size attributes and assembly sequence planning, the assembly deviations between components are calculated to construct a deviation transmission model; According to the time sequence of the assembly sequence planning, the operability of the corresponding operation step is calculated based on the component size attributes under each operation step. Then, based on the operability, the process complexity is calculated, and finally the process complexity sequence is obtained. The complexity of the process complexity sequence is calculated using the Lempel-Ziv algorithm, and the optimal assembly sequence is determined based on the complexity.

[0007] Furthermore, in the step of calculating the assembly deviation between components based on the component size attributes and assembly sequence planning to construct a deviation transmission model, addition or subtraction operations are performed on the component size, upper deviation of component size and lower deviation involved in the corresponding operation according to the inclusion relationship of each component in the assembly sequence planning, so as to calculate the assembly deviation between components.

[0008] Furthermore, in the step of calculating the operability of a corresponding operation step based on the component size attributes under each operation step according to the time sequence planned by the assembly sequence, the operability calculation formula is as follows: ; in, To ensure the operability of component i in assembly. Let i be the dimension of component i. for The upper deviation, for The lower deviation.

[0009] Furthermore, in the step of calculating process complexity based on operability, the formula for calculating process complexity is: ; The process complexity of component i participating in the assembly.

[0010] Furthermore, the step of calculating the complexity sequence of the process complexity using the Lempel-Ziv algorithm and determining the optimal assembly sequence based on the complexity includes: Step S1: Coarsely divide the process complexity sequence into a [0,1] sequence and calculate the average complexity. Compare each element in the coarsely divided sequence with the average complexity. Assign 1 to elements with a complexity greater than the average complexity and 0 to elements with a complexity less than the average complexity. Re-encode to obtain a new sequence. Step S2: Based on the new sequence, construct two subsequences S and Q; Step S3: Construct an auxiliary sequence, wherein Q is appended to S to form a sequence SQ, and then the last element of sequence SQ is removed to obtain sequence SQP. Step S4: If Q is a substring of sequence SQP, keep S unchanged, expand Q, and update sequence SQP synchronously; if Q is a substring of non-sequence SQP, update S to sequence SQ, reset Q to the next new element, and the complexity count c(n) = c(n) + 1. Step S5: Repeat step S4 until S is traversed, and output the actual calculated c(n); Step S6: Compare the actual calculated c(n) with the baseline complexity to obtain the complexity.

[0011] Furthermore, the step of comparing the actual calculated c(n) with the baseline complexity to obtain the complexity includes: The information entropy and approximate entropy are calculated separately, and then weighted and fused according to the complexity, the information entropy and the approximate entropy to obtain a comprehensive complexity index. Assembly efficiency, assembly cost, and assembly accuracy are calculated separately. The analytic hierarchy process (AHP) is used to determine the target weights of the comprehensive complexity index, assembly efficiency, assembly cost, and assembly accuracy, and a fitness function for multi-objective optimization is constructed. The optimal assembly sequence is determined by solving the fitness function using a genetic algorithm.

[0012] Furthermore, the expression for the fitness function is: ; ; ; ; ; ; ; ; in, The weights of the overall complexity index are determined by... As a weight for assembly efficiency, As a weight of assembly cost, As a weight for assembly accuracy, , , These are the corresponding weights. To comprehensively measure complexity, Let C be assembly efficiency, P be assembly cost, and E be assembly accuracy. max ApEn is the maximum value of the information entropy, and ApEn is the approximate entropy. max It is the maximum value of the approximate entropy. Costs related to parts procurement and wear and tear. For the depreciation and calibration costs of assembly tools, For operator labor time costs, The cost of rework after deviation exceeds the standard. Let m be the probability of the l-th process complexity value appearing in the sequence, and m be the embedding dimension. The actual assembly deviation of the i-th component. Let n be the maximum allowable assembly deviation for the i-th component, and n be the total number of components. This represents the ratio of the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th m-dimensional subsequence to the total number of subsequences. This is the ratio of the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th (m+1)-th dimensional subsequence to the total number of subsequences. For the aforementioned complexity, Let be the baseline complexity.

[0013] A second aspect of this invention provides a system for analyzing the propagation of deviations in complex equipment assembly sequences, used to implement the method for analyzing the propagation of deviations in complex equipment assembly sequences as described in the first aspect, the system comprising: The acquisition module is used to acquire the component size attributes under each operation step. The component size attributes include the component size involved in the corresponding operation, the upper deviation of the component size, and the lower deviation of the component size. The first calculation module is used to calculate the assembly deviation between the parts based on the size attributes of the parts and the assembly sequence planning, so as to construct a deviation transmission model. The second calculation module is used to calculate the operability of the corresponding operation step according to the time sequence of the assembly sequence and the component size attributes under each operation step, and then calculate the process complexity based on the operability to finally obtain the process complexity sequence. The third calculation module is used to calculate the complexity of the process complexity sequence according to the Lempel-Ziv algorithm, and determine the optimal assembly sequence based on the complexity. A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for analyzing the transmission of deviations in complex equipment assembly sequences provided in the first aspect.

[0014] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the complex equipment assembly sequence deviation transmission analysis method provided in the first aspect.

[0015] This invention provides a method and system for analyzing the propagation of deviations in complex equipment assembly sequences. The method acquires the dimensional attributes of components under each operation step, including the component dimensions involved in the corresponding operation, the upper deviation of the component dimensions, and the lower deviation of the component dimensions. Based on the component dimensional attributes and the assembly sequence planning, the method calculates the assembly deviations between components to construct a deviation propagation model. Following the time sequence planning of the assembly sequence, the method calculates the operability of the corresponding operation steps based on the component dimensional attributes under each operation step, and then calculates the process complexity based on the operability, ultimately obtaining a process complexity sequence. The method calculates the complexity of the process complexity sequence using the Lempel-Ziv algorithm, and determines the optimal assembly sequence based on the complexity. Specifically, this method effectively analyzes the degree of deviation propagation in complex equipment assembly sequences, providing theoretical support for optimizing path sequences in the assembly sequence planning stage. Attached Figure Description

[0016] Figure 1 The flowchart illustrates the implementation of a method for analyzing the propagation of deviations in a complex equipment assembly sequence, as provided in Embodiment 1 of the present invention. Figure 2 This is a structural block diagram of a complex equipment assembly sequence deviation transmission analysis system provided in Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0017] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0018] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 According to an embodiment of the present invention, a method for analyzing the transmission of deviations in complex equipment assembly sequences is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0021] This first embodiment provides a method for analyzing the propagation of deviations in complex equipment assembly sequences, which can be used in electronic devices, such as computers. Please refer to... Figure 1 , Figure 1 The flowchart of the implementation of a method for analyzing the transmission of deviations in the assembly sequence of complex equipment provided in Embodiment 1 of the present invention is shown, specifically including steps S01 to S04.

[0022] Step S01: Obtain the component size attributes under each operation step. The component size attributes include the component size involved in the corresponding operation, the upper deviation of the component size, and the lower deviation of the component size.

[0023] Specifically, in the process of planning complex equipment assembly sequences, operational attributes for: ; in, For the first Each component. For the first Each component along The displacement of the axis translation. For the first Each component along The displacement of the axis translation. For the first The displacement of each component along the z-axis. For the first Each component is around The angle of rotation of the axis For the first Each component is around The angle of rotation of the axis For the first Each component is around The angle of rotation of the axis.

[0024] Component size attributes : ; Let i be the dimension of component i. for The upper deviation, for The lower deviation.

[0025] Step S02: Calculate the assembly deviation between components based on the component size attributes and assembly sequence planning to construct a deviation transmission model.

[0026] For example, suppose there are 5 operations, each of which involves displacement and rotation of a component, with the displacement and rotation referenced to an assembly reference. (Outer / Inner) indicates the inclusion relationship of parts in the dimensional chain; please refer to Table 1.

[0027] Table 1 List of Operation Attributes

[0028] Specifically, the operations in the assembly sequence have parameters such as movement, rotation, upper deviation, and lower deviation. Therefore, when an operation is assigned to a station, that station will form a dimensional chain. The assembly deviation range between parts can be calculated. Assume operations 1, 2, and 3 are completed in station 1, adding parts 1, 2, and 3 to station 1 respectively. Operations 4 and 5 are completed in station 2, adding parts 4 and 5 to station 2 respectively. At the first station, the product... After forming a dimensional chain with parts 1, 2, and 3, the product status is... . The parts will flow from station 1 to station 2.

[0029] Suppose that 3 parts have been added to the product at workstation 1. The status is... The assembly deviations of the products are as follows:

[0030] state The assembly deviations of the products are as follows:

[0031] If the assembly sequence has h stations, then the assembly deviation of the product is: Furthermore, it can calculate the assembly deviations at each workstation. Analyzing assembly deviations using material flow is feasible and can provide theoretical support for product quality control and process optimization.

[0032] Step S03: According to the time sequence of the assembly sequence, the operability of the corresponding operation step is calculated based on the component size attributes under each operation step. Then, the process complexity is calculated based on the operability, and finally the process complexity sequence is obtained.

[0033] It should be noted that the operability calculation formula is as follows: ; in, To ensure the operability of component i in assembly. Let i be the dimension of component i. for The upper deviation, for The lower deviation.

[0034] The formula for calculating process complexity is: ; The process complexity of component i participating in the assembly.

[0035] Step S04: Calculate the complexity of the process complexity sequence according to the Lempel-Ziv algorithm, and determine the optimal assembly sequence based on the complexity.

[0036] In this embodiment of the invention, step S1 involves coarsening the process complexity sequence into a [0,1] sequence and calculating the average complexity. Each element in the coarsened sequence is compared with the average complexity. Elements with a complexity greater than the average complexity are assigned the value 1, and elements with a complexity less than the average complexity are assigned the value 0. The sequence is then re-encoded to obtain a new sequence. Step S2: Based on the new sequence, construct two subsequences S and Q; Step S3: Construct an auxiliary sequence, wherein Q is appended to S to form a sequence SQ, and then the last element of sequence SQ is removed to obtain sequence SQP. Step S4: If Q is a substring of sequence SQP, keep S unchanged, expand Q, and update sequence SQP synchronously; if Q is a substring of non-sequence SQP, update S to sequence SQ, reset Q to the next new element, and the complexity count c(n) = c(n) + 1. Step S5: Repeat step S4 until S has been traversed, and output the actual calculated c(n), where, , Let S represent the different types of elements in S. , , Take the upper limit, and also, ; Step S6: Ratio the actual calculated c(n) with the baseline complexity to obtain the complexity. Understandably, the optimal assembly sequence is determined based on the minimum value of the complexity.

[0037] In other embodiments of the present invention, in order to make the optimal assembly sequence more in line with the actual needs of the industry and improve the practicality and feasibility of the solution, the information entropy and approximate entropy are calculated separately, and a weighted fusion is performed based on the complexity, the information entropy and the approximate entropy to obtain a comprehensive complexity index. Assembly efficiency, assembly cost, and assembly accuracy are calculated separately. The analytic hierarchy process (AHP) is used to determine the target weights for the overall complexity index, assembly efficiency, assembly cost, and assembly accuracy. A fitness function for multi-objective optimization is then constructed, and its expression is as follows: ; ; ; ; ; ; ; ; in, The weights of the overall complexity index are determined by... As a weight for assembly efficiency, As a weight of assembly cost, As a weight for assembly accuracy, , , These are the corresponding weights. To comprehensively measure complexity, Let C be assembly efficiency, P be assembly cost, and E be assembly accuracy. max ApEn is the maximum value of the information entropy, and ApEn is the approximate entropy. max It is the maximum value of the approximate entropy. Costs related to parts procurement and wear and tear. For the depreciation and calibration costs of assembly tools, For operator labor time costs, The cost of rework after deviation exceeds the standard. Let m be the probability of the l-th process complexity value appearing in the sequence, and m be the embedding dimension. In this embodiment of the invention, m=2. The actual assembly deviation of the i-th component. Let n be the maximum allowable assembly deviation for the i-th component, and n be the total number of components. This represents the ratio of the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th m-dimensional subsequence to the total number of subsequences. Let r be the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th (m+1)-th dimensional subsequence, and the ratio of this number to the total number of subsequences, where r is the similarity tolerance. For the aforementioned complexity, The baseline complexity is... The optimal assembly sequence is determined by solving the fitness function using a genetic algorithm. The specific steps involved in solving the fitness function using a genetic algorithm are as follows: Encoding: Encode the assembly sequence into integer chromosomes (e.g., chromosomes [1,2,3,4,5,6,7,8] corresponding to assembly steps 1-8); Initialize the population: Randomly generate N chromosomes (assembly sequences), N is recommended to be 50-100; Selection: Using a roulette wheel method, chromosomes with higher fitness values ​​(F) have a greater probability of being selected for the next generation; Crossover: Randomly select two chromosomes, exchange some genes (assembly step), and generate a new chromosome; Mutation: Randomly select a gene in the chromosome and replace it with another assembly step to avoid local optima; Iteration Termination: When the number of iterations reaches a preset value (e.g., 100 generations), or the fitness value tends to stabilize (fluctuation less than 0.001), the chromosome with the highest fitness value is output, which is the optimal assembly sequence.

[0038] In summary, the complex equipment assembly sequence deviation propagation analysis method in the above embodiments of the present invention obtains the component size attributes under each operation step, including the component size involved in the corresponding operation, the upper deviation of the component size, and the lower deviation of the component size; calculates the assembly deviation between components based on the component size attributes and the assembly sequence planning to construct a deviation propagation model; calculates the operability of the corresponding operation step according to the time sequence planning of the assembly sequence, based on the component size attributes under each operation step, and then calculates the process complexity based on the operability, finally obtaining the process complexity sequence; calculates the complexity of the process complexity sequence according to the Lempel-Ziv algorithm, and determines the optimal assembly sequence based on the complexity. Specifically, it can effectively analyze the degree of deviation propagation in complex equipment assembly sequences and provide theoretical support for optimizing the path sequence in the assembly sequence planning stage.

[0039] Example 2 Please see Figure 2 , Figure 2This is a structural block diagram of a complex equipment assembly sequence deviation transmission analysis system 200 provided in Embodiment 2 of the present invention. This complex equipment assembly sequence deviation transmission analysis system 200 is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0040] Specifically, the complex equipment assembly sequence deviation transmission analysis system 200 includes: an acquisition module 21, a first calculation module 22, a second calculation module 23, and a third calculation module 24, wherein: The acquisition module 21 is used to acquire the component size attributes under each operation step. The component size attributes include the component size involved in the corresponding operation, the upper deviation of the component size, and the lower deviation of the component size. The first calculation module 22 is used to calculate the assembly deviation between parts based on the size attributes of the parts and the assembly sequence planning, so as to construct a deviation transmission model. According to the inclusion relationship of each part in the assembly sequence planning, it performs addition or subtraction operations on the size of the parts involved in the corresponding operation, the upper deviation of the size of the parts, and the lower deviation of the size of the parts, so as to calculate the assembly deviation between parts. The second calculation module 23 is used to calculate the operability of the corresponding operation steps according to the time sequence planned by the assembly sequence, based on the component size attributes under each operation step, and then calculate the process complexity based on the operability, finally obtaining the process complexity sequence. The formula for calculating operability is: ; in, To ensure the operability of component i in assembly. Let i be the dimension of component i. for The upper deviation, for The lower deviation; The formula for calculating process complexity is: ; The process complexity of component i participating in the assembly; The third calculation module 24 is used to calculate the complexity of the process complexity sequence according to the Lempel-Ziv algorithm, and determine the optimal assembly sequence based on the complexity.

[0041] Furthermore, in some optional embodiments of the present invention, the third computing module 24 includes: The comparison unit is used to coarsely divide the process complexity sequence into a [0,1] sequence, calculate the average complexity, compare each element in the coarsely divided sequence with the average complexity, assign 1 to elements with a complexity greater than the average complexity, assign 0 to elements with a complexity less than the average complexity, and re-encode to obtain a new sequence. The building unit is used to construct two subsequences S and Q based on the new sequence; A construction unit is used to construct an auxiliary sequence, wherein Q is appended to S to form a sequence SQ, and then the last element of the sequence SQ is removed to obtain the sequence SQP. The decision unit is used to determine if Q is a substring of the sequence SQP, then keep S unchanged, expand Q, and update the sequence SQP synchronously; if Q is a substring of the sequence SQP, then update S to the sequence SQ, reset Q to the next new element, and the complexity count c(n) = c(n) + 1. The unit is traversed repeatedly to make judgments until S is completely traversed, and then the actual calculated c(n) is output. The first computational unit is used to compare the actual calculated c(n) with the baseline complexity to obtain the complexity.

[0042] Furthermore, in some optional embodiments of the present invention, the third computing module 24 further includes: The second calculation unit is used to calculate the information entropy and the approximate entropy respectively, and to perform weighted fusion based on the complexity, the information entropy and the approximate entropy to obtain a comprehensive complexity index. The third calculation unit is used to calculate assembly efficiency, assembly cost, and assembly accuracy respectively. It uses the analytic hierarchy process (AHP) to determine the target weights for the comprehensive complexity index, assembly efficiency, assembly cost, and assembly accuracy, and constructs a fitness function for multi-objective optimization. The expression of the fitness function is as follows: ; ; ; ; ; ; ; ; in, The weights of the overall complexity index are determined by... As a weight for assembly efficiency, As a weight of assembly cost, As a weight for assembly accuracy, , , These are the corresponding weights. To comprehensively measure complexity, Let C be assembly efficiency, P be assembly cost, and E be assembly accuracy. max ApEn is the maximum value of the information entropy, and ApEn is the approximate entropy. max It is the maximum value of the approximate entropy. Costs related to parts procurement and wear and tear. For the depreciation and calibration costs of assembly tools, For operator labor time costs, The cost of rework after deviation exceeds the standard. Let m be the probability of the l-th process complexity value appearing in the sequence, and m be the embedding dimension. The actual assembly deviation of the i-th component. Let n be the maximum allowable assembly deviation for the i-th component, and n be the total number of components. This represents the ratio of the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th m-dimensional subsequence to the total number of subsequences. This is the ratio of the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th (m+1)-th dimensional subsequence to the total number of subsequences. For the aforementioned complexity, The baseline complexity is... The determination unit is used to solve the fitness function using a genetic algorithm to determine the optimal assembly sequence.

[0043] Example 3 In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The image shows an electronic device according to Embodiment 3 of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the complex equipment assembly sequence deviation transmission analysis method as described above.

[0044] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0045] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0046] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0047] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for analyzing the transmission of deviations in complex equipment assembly sequences.

[0048] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0049] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0050] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0051] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0052] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for analyzing the propagation of deviations in complex equipment assembly sequences, characterized in that, The method includes: Obtain the component size attributes under each operation step, including the component size involved in the corresponding operation, the upper deviation of the component size, and the lower deviation of the component size; Based on the component size attributes and assembly sequence planning, the assembly deviations between components are calculated to construct a deviation transmission model; According to the time sequence of the assembly sequence planning, the operability of the corresponding operation step is calculated based on the component size attributes under each operation step. Then, based on the operability, the process complexity is calculated, and finally the process complexity sequence is obtained. The complexity of the process complexity sequence is calculated using the Lempel-Ziv algorithm, and the optimal assembly sequence is determined based on the complexity.

2. The method for analyzing the propagation of deviations in complex equipment assembly sequences according to claim 1, characterized in that, In the step of calculating the assembly deviation between components based on the component size attributes and assembly sequence planning to construct a deviation transmission model, addition or subtraction operations are performed on the component size, upper deviation and lower deviation of the component size involved in the corresponding operation according to the inclusion relationship of each component in the assembly sequence planning to calculate the assembly deviation between components.

3. The method for analyzing the propagation of deviations in complex equipment assembly sequences according to claim 2, characterized in that, In the step of calculating the operability of a corresponding operation step based on the component size attributes under each operation step according to the time sequence planned by the assembly sequence, the operability calculation formula is as follows: ； in, To ensure the operability of component i in assembly. Let i be the dimension of component i. for The upper deviation, for The lower deviation.

4. The method for analyzing the propagation of deviations in complex equipment assembly sequences according to claim 3, characterized in that, In the step of calculating process complexity based on operability, the formula for calculating process complexity is: ； The process complexity of component i participating in the assembly.

5. The method for analyzing the propagation of deviations in complex equipment assembly sequences according to claim 4, characterized in that, The step of calculating the complexity sequence of the process complexity using the Lempel-Ziv algorithm and determining the optimal assembly sequence based on the complexity includes: Step S1: Coarsely divide the process complexity sequence into a [0,1] sequence and calculate the average complexity. Compare each element in the coarsely divided sequence with the average complexity. Assign 1 to elements with a complexity greater than the average complexity and 0 to elements with a complexity less than the average complexity. Re-encode to obtain a new sequence. Step S2: Based on the new sequence, construct two subsequences S and Q; Step S3: Construct an auxiliary sequence, wherein Q is appended to S to form a sequence SQ, and then the last element of sequence SQ is removed to obtain sequence SQP. Step S4: If Q is a substring of sequence SQP, keep S unchanged, expand Q, and update sequence SQP synchronously; if Q is a substring of non-sequence SQP, update S to sequence SQ, reset Q to the next new element, and the complexity count c(n) = c(n) + 1. Step S5: Repeat step S4 until S is traversed, and output the actual calculated c(n); Step S6: Compare the actual calculated c(n) with the baseline complexity to obtain the complexity.

6. The method for analyzing the propagation of deviations in complex equipment assembly sequences according to claim 5, characterized in that, The step of comparing the actual calculated c(n) with the baseline complexity to obtain the complexity includes: The information entropy and approximate entropy are calculated separately, and then weighted and fused according to the complexity, the information entropy and the approximate entropy to obtain a comprehensive complexity index. Assembly efficiency, assembly cost, and assembly accuracy are calculated separately. The analytic hierarchy process (AHP) is used to determine the target weights of the comprehensive complexity index, assembly efficiency, assembly cost, and assembly accuracy, and a fitness function for multi-objective optimization is constructed. The optimal assembly sequence is determined by solving the fitness function using a genetic algorithm.

7. The method for analyzing the propagation of deviations in complex equipment assembly sequences according to claim 6, characterized in that, The expression for the fitness function is: ； ； ； ； ； ； ； ； in, The weights of the overall complexity index are determined by... As a weight for assembly efficiency, As a weight of assembly cost, As a weight for assembly accuracy, , , These are the corresponding weights. To comprehensively measure complexity, Let C be assembly efficiency, P be assembly cost, and E be assembly accuracy. max ApEn is the maximum value of the information entropy, and ApEn is the approximate entropy. max It is the maximum value of the approximate entropy. Costs related to parts procurement and wear and tear. For the depreciation and calibration costs of assembly tools, For operator labor time costs, The cost of rework after deviation exceeds the standard. Let m be the probability of the l-th process complexity value appearing in the sequence, and m be the embedding dimension. The actual assembly deviation of the i-th component. Let n be the maximum allowable assembly deviation for the i-th component, and n be the total number of components. This represents the ratio of the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th m-dimensional subsequence to the total number of subsequences. This is the ratio of the number of subsequences in the process complexity sequence that have a similarity greater than r to the l-th (m+1)-th dimensional subsequence to the total number of subsequences. For the aforementioned complexity, Let be the baseline complexity.

8. A system for analyzing the transmission of deviations in the assembly sequence of complex equipment, characterized in that, The system is used to implement the complex equipment assembly sequence deviation propagation analysis method as described in any one of claims 1-7, the system comprising: The acquisition module is used to acquire the component size attributes under each operation step. The component size attributes include the component size involved in the corresponding operation, the upper deviation of the component size, and the lower deviation of the component size. The first calculation module is used to calculate the assembly deviation between the parts based on the size attributes of the parts and the assembly sequence planning, so as to construct a deviation transmission model. The second calculation module is used to calculate the operability of the corresponding operation step according to the time sequence of the assembly sequence and the component size attributes under each operation step, and then calculate the process complexity based on the operability, and finally obtain the process complexity sequence. The third calculation module is used to calculate the complexity of the process complexity sequence according to the Lempel-Ziv algorithm, and determine the optimal assembly sequence based on the complexity.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for analyzing the propagation of deviations in complex equipment assembly sequences as described in any one of claims 1-7.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the complex equipment assembly sequence deviation transmission analysis method as described in any one of claims 1-7.

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