Balance and reliability analysis method of multi-variety variable batch production in flexible manufacturing production line

Abstract

The invention relates to the technical field of flexible manufacturing, and discloses a method for balancing and analyzing the production of multiple varieties and batches of flexible manufacturing production lines, comprising the following steps: constructing a physical model of the flexible manufacturing production line, obtaining the production relationship, availability analysis results, and production data between various process links in the flexible manufacturing system, and constructing a digital representation model based on an extended random Petri net; encoding the variable parameters of each process in the digital representation model to generate a new population, and determining a fitness function F; calculating the fitness of each individual in the new population, judging whether a maximum number of iterations is met, and if so, outputting an optimization result; if not, generating a new population, and then calculating the fitness value of each individual in the optimized new population according to the fitness function F, until the maximum number of iterations is reached; the invention adjusts the flexible manufacturing production line based on the analysis results of production balance, capacity reliability, and production balance reliability.

Description

Technical Field

[0001] The present invention relates to the field of flexible manufacturing technology, and more specifically to a flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method. Background Art

[0002] There are many research contents on production balance analysis, and the analysis process is roughly the same. Usually, the production balance problem of the flexible manufacturing production line is first converted into a mathematical model, and then the target manufacturing production line is optimized by using optimization methods such as genetic algorithms and hybrid optimization algorithms according to the goal of production balance optimization. Some studies also use multi-objective optimization methods to reduce material costs and labor costs at the same time. Innovations are mainly focused on algorithm optimization and new multi-objective optimization strategies. Some industrial software is used to compare the production balance before and after optimization to provide guidance for the operation of actual manufacturing production lines.

[0003] However, these studies mainly focus on the production balance problem under normal operation of the production link, and do not consider the failure of each production link. At the same time, the production cycle used in the study is a fixed value, which cannot well reflect the actual production situation. No one has conducted relevant research on production balance in terms of reliability. Summary of the invention

[0004] The purpose of the present invention is to provide a flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method, based on the analysis results of production balance, capacity reliability and production balance reliability, to adjust the flexible manufacturing production line.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method, comprising the following steps:

[0007] Construct a physical model of a flexible manufacturing production line to obtain the production relationship, availability analysis results, and production data between each process link in the flexible manufacturing system;

[0008] According to the production relationship between each process link, the availability analysis results, and the production data, a digital representation model based on the extended stochastic Petri net is constructed, and the digital representation model is an ESPN seven-tuple model;

[0009] The performance parameters of each process in the ESPN seven-tuple model are divided into variable parameters and invariable parameters, and the variable parameters are encoded to generate a new population;

[0010] Determine an adaptability function F for production balance optimization, wherein the optimization indicators of the adaptability function F include: a production smoothing index, a capacity reliability index, and a production balance reliability evaluation index;

[0011] The fitness value of each individual in the new population is calculated according to the fitness function F, and it is determined whether the maximum number of iterations is reached. If so, the optimization result is output; if not, the excellent ontology is selected according to the fitness value, and the optimized new population is solved by the genetic algorithm, and then the fitness value of each individual in the optimized new population is calculated according to the fitness function F until the maximum number of iterations is reached;

[0012] The optimization results include: production balance optimization results, production capacity reliability calculation results and production balance reliability calculation results.

[0013] Furthermore, the method for constructing the physical model of the flexible manufacturing production line is specifically as follows:

[0014] Based on the production line process flow of the flexible manufacturing system, the flexible manufacturing system is modularized to obtain each process module and obtain the performance parameters of each process module;

[0015] Based on the performance parameters of each process, the production relationship, production data and availability between each process module are analyzed.

[0016] Furthermore, the new population after optimization by genetic algorithm is specifically:

[0017] Perform crossover, mutation, and genetic operator operations to generate new populations, and use the average fitness to select populations with higher fitness.

[0018] Furthermore, the function representation of the ESPN seven-tuple model is specifically:

[0019]

[0020] in It is a finite set of places; is a finite set of transitions, , ; is the input function; is the output function; is the time parameter of any probability distribution or function; The upper limit of the storage capacity; Is a symbol, indicating the The first component is of a library number.

[0021] Furthermore, the library Number includes: Active Number and inactive Number, specifically:

[0022]

[0023] In the above formula, Indicates that the library is not activated number; Indicates the activated number.

[0024] Furthermore, the triggering rules of the transition and The activation rules of the number are as follows:

[0025] The triggering of the transition includes the following rules:

[0026] Only when When, and ,change can be enabled, where The upper limit of the storage capacity;

[0027] like In the logo Enabled, new identifiers are generated according to the following activation rules :

[0028]

[0029]

[0030]

[0031] Said The activation of numbers includes the following rules:

[0032] , Any place; the place will be inactive The number is activated and a new identifier is generated according to the following rules :

[0033]

[0034]

[0035]

[0036] Furthermore, the adaptability function F for production balance optimization is determined, and the optimization index of the adaptability function F includes: production smoothing index, capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production smoothing index is specifically as follows:

[0037] The total number of products processed in each process link ;

[0038]

[0039] In the above formula, m is the total product types, i=1,2,3...,m;

[0040] The total time for completing the simulated production of products in each process link Run_time ;

[0041]

[0042] Assume that there are S process links in the flexible manufacturing production line. The expression of the total time vector T of each process link is as follows:

[0043]

[0044] Total time spent on a single link The expression is:

[0045]

[0046] in: It takes time to load the workpiece. Time consuming for failures. Time-consuming troubleshooting. Because the processing is time-consuming, It takes time to unload the material;

[0047] productivity P 0i The calculation formula is:

[0048]

[0049]

[0050] Where i=1,2,...,m;

[0051] The corresponding production loss rate IR of productivity is:

[0052]

[0053] Production Smoothing Index SI for:

[0054]

[0055]

[0056] Where i=1,2,...,m.

[0057] Furthermore, the adaptability function F for production balance optimization is determined, and the optimization index of the adaptability function F includes: production smoothing index, production capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production capacity reliability index is specifically as follows:

[0058] The total number of products processed in each process link ;

[0059]

[0060] Total time to complete the processing simulation production in each process link Run_time ;

[0061]

[0062] Then the expression of the capacity reliability index of the i-th process is:

[0063]

[0064] in: is the time for the i-th process to complete the processing simulation production, TT is the production cycle of the flexible manufacturing production line under ideal conditions without any failure, i=1,2,...,m;

[0065] The expression of the production capacity reliability index of all products is:

[0066] .

[0067] Furthermore, the adaptability function F for production balance optimization is determined, and the optimization index of the adaptability function F includes: production smoothing index, capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production balance reliability evaluation index is specifically as follows:

[0068]

[0069] in: Run_time_1 is the total time when the fault occurs, N_1 is the total number of products when the failure occurred, Run_ time_2 is the total time from when the failure occurs to when the repair is completed, N_2 is the total number of products when the failure occurred and was repaired. TT The production rhythm of the flexible manufacturing production line under ideal conditions without any failure;

[0070] Assume that the failure rate of each process link is

[0071] The expression of the production balance reliability evaluation index of the kth product is:

[0072]

[0073] Where: k = 1, 2, ..., m;

[0074] Then the expression of the production balance reliability evaluation index of all products is:

[0075]

[0076] Furthermore, the expression of the adaptability function F of the production balance optimization is specifically:

[0077]

[0078] Among them: a, b, c are constants, is the capacity reliability index, is the production balance reliability evaluation index, is the production smoothing index.

[0079] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0080] The present invention constructs a physical model of a flexible manufacturing production line, obtains production relations, availability analysis results, and production data between various process links in the flexible manufacturing system, and constructs a digital representation model based on an extended random Petri net; encodes variable parameters of various processes in the digital representation model to generate a new population, and determines a fitness function; calculates the fitness of each individual in the new population, and obtains an optimization result; the present invention can predict the performance of various process flows and optimize the production process by constructing a Petri net model, can search for the optimal solution in a large range through a genetic algorithm, and is helpful to find the optimal configuration of production balance; can reduce production fluctuations and achieve a stable production rhythm by optimizing a production smoothing index; can help balance the loads of various process modules by optimizing the reliability of production balance, extend the life of equipment, reduce maintenance costs, and can also improve production efficiency and reduce the risk of being unable to produce due to failures or unexpected downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0082] The following further describes the flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method of the present invention in conjunction with the accompanying drawings;

[0083] Figure 1 It is a schematic diagram of the overall process of the flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method provided by the present invention;

[0084] Figure 2 It is a schematic diagram of a complex component manufacturing production line in a flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method provided by the present invention;

[0085] Figure 3 It is a schematic diagram of the ESPN model in the flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method provided by the present invention;

[0086] Figure 4 It is a schematic diagram of an adaptability function for determining production balance optimization based on the ESPN model in the flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method provided by the present invention. DETAILED DESCRIPTION

[0087] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0088] In order to better understand the purpose, structure and function of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings.

[0089] The technical problem studied in the present invention is to optimize the production balance for cost reduction and efficiency improvement in a flexible manufacturing production line with multiple varieties and variable batches, analyze the production capacity reliability for completing production tasks within a specified time, and analyze the production balance reliability of the impact of fault repair in each production link on the production level of the manufacturing production line.

[0090] Among them, multi-variety variable batch refers to the flexible manufacturing production line dynamically adjusting the production plan according to the actual production tasks. Multi-variety refers to the production of diversified products of the same type and different models in the production line at the same time, and variable batch refers to the change in the production ratio of multiple products.

[0091] Production balance refers to the balance of material input and product output in each production link of the flexible manufacturing production line during the production process, which will not cause a large amount of backlog in the logistics system due to different production rhythms, and some links are too slow and some links are too relaxed. The goal of production balance optimization is to reduce the backlog problem in the existing production relationship and balance the workload of each link. The balance of the production line can ensure that the workload of each link is relatively uniform, thereby reducing the idleness of each link and reducing the storage demand of the logistics system to achieve the purpose of reducing costs and increasing efficiency.

[0092] Capacity reliability refers to the ability of flexible manufacturing production to complete the specified production task within the specified time. The calculation method is to divide the ideal production cycle by the actual production time to obtain a result between [0,1]. The capacity reliability of the flexible manufacturing production line needs to consider the failure of all links at the same time, while the production balance reliability sensitivity analysis of each link is that the flexible manufacturing production line only considers the capacity reliability of the current link failure.

[0093] The production balance reliability is composed of a comprehensive evaluation of the capacity reliability analysis results of each production link. The impact analysis of production balance reliability refers to the ability of the flexible manufacturing production line to complete the specified production tasks within the specified time during the repair period of a production link failure in the flexible manufacturing production line. The specific calculation method is that after a link fails, the link stops outputting products and relies on the logistics storage system to continue to complete the tasks of the production link. By calculating the production capacity of the flexible manufacturing production line from the occurrence of the link failure to the completion of the failure, the production balance reliability sensitivity analysis of the link (capacity reliability under the failure) can be obtained. After obtaining the sensitivity analysis of each link, combined with relevant evaluation methods, the production balance reliability evaluation index is formed.

[0094] Based on the technical issues studied above, such as Figure 1 The present invention provides a flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method, comprising the following steps:

[0095] Construct a physical model of a flexible manufacturing production line to obtain the production relationship, availability analysis results, and production data between each process link in the flexible manufacturing system;

[0096] According to the production relationship between each process link, the availability analysis results, and the production data, a digital representation model based on the extended stochastic Petri net is constructed, and the digital representation model is an ESPN seven-tuple model;

[0097] The performance parameters of each process in the ESPN seven-tuple model are divided into variable parameters and invariable parameters, and the variable parameters are encoded to generate a new population;

[0098] Determine an adaptability function F for production balance optimization, wherein the optimization indicators of the adaptability function F include: a production smoothing index, a capacity reliability index, and a production balance reliability evaluation index;

[0099] The fitness value of each individual in the new population is calculated according to the fitness function F, and it is determined whether the maximum number of iterations is reached. If so, the optimization result is output; if not, the excellent ontology is selected according to the fitness value, and the optimized new population is solved by the genetic algorithm, and then the fitness value of each individual in the optimized new population is calculated according to the fitness function F until the maximum number of iterations is reached;

[0100] The optimization results include: production balance optimization results, production capacity reliability calculation results and production balance reliability calculation results.

[0101] The method for constructing the physical model of the flexible manufacturing production line is specifically as follows:

[0102] Based on the production line process flow of the flexible manufacturing system, the flexible manufacturing system is modularized to obtain each process module and obtain the performance parameters of each process module;

[0103] Based on the performance parameters of each process, the production relationship, production data and availability between each process module are analyzed.

[0104] The new population after optimization by genetic algorithm is specifically:

[0105] Perform crossover, mutation, and genetic operator operations to generate new populations, and use the average fitness to select populations with higher fitness.

[0106] The method for constructing the ESPN seven-tuple model is specifically as follows:

[0107]

[0108] in It is a finite set of places; is a finite set of transitions, , ; is the input function; is the output function; is the time parameter of any probability distribution or function; The upper limit of the storage capacity; An identifier, indicating the The first component is of a library number.

[0109] The library Number includes: Active Number and inactive Number, specifically:

[0110]

[0111] In the above formula, Indicates that the library is not activated number; Indicates the activated number.

[0112] The triggering rules of the transition and The activation rules of the number are as follows:

[0113] The triggering of the transition includes the following rules:

[0114] Only when When, and ,change can be enabled, where The upper limit of the storage capacity;

[0115] like In the logo The following activation rules are used to generate new identifiers: :

[0116]

[0117]

[0118]

[0119] Said The activation of numbers includes the following rules:

[0120] , Any place; the place will be inactive The number is activated and a new identifier is generated according to the following rules :

[0121]

[0122]

[0123] .

[0124] The adaptive function F for production balance optimization is determined, and the optimization index of the adaptive function F includes: production smoothing index, capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production smoothing index is specifically as follows:

[0125] The total number of products processed in each process link ;

[0126]

[0127] The total time for completing the simulated production of products in each process link Run_time ;

[0128]

[0129] Assume that there are S process links in the flexible manufacturing production line. The expression of the total time vector T of each process link is as follows:

[0130]

[0131] Total time spent on a single link The expression is:

[0132]

[0133] in: It takes time to load the workpiece. Time consuming for failures. Time-consuming troubleshooting. Because the processing is time-consuming, It takes time to unload the material;

[0134] productivity P 0i The calculation formula is:

[0135]

[0136]

[0137] Where i=1,2,...,m;

[0138] The corresponding production loss rate IR of productivity is:

[0139]

[0140] Production Smoothing Index SI for:

[0141]

[0142]

[0143] Where i=1,2,...,m.

[0144] The adaptive function F for production balance optimization is determined, and the optimization index of the adaptive function F includes: production smoothing index, production capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production capacity reliability index is specifically as follows:

[0145] The total number of products processed in each process link ;

[0146]

[0147] Total time to complete the processing simulation production in each process link Run_time ;

[0148]

[0149] Then the expression of the capacity reliability index of the i-th process is:

[0150]

[0151] in: is the time for the i-th process to complete the processing simulation production, TT is the production cycle of the flexible manufacturing production line under ideal conditions without any failure, i=1,2,...,m;

[0152] The expression of the production capacity reliability index of all products is:

[0153] .

[0154] The adaptive function F for production balance optimization is determined, and the optimization index of the adaptive function F includes: production smoothing index, capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production balance reliability evaluation index is specifically as follows:

[0155]

[0156] in: Run_time_1 is the total time when the fault occurs, N_1 is the total number of products when the failure occurred, Run_ time_2 is the total time from when the failure occurs to when the repair is completed, N_2 is the total number of products when the failure occurred and was repaired. TT The production rhythm of the flexible manufacturing production line under ideal conditions without any failure;

[0157] Assume that the failure rate of each process link is ;

[0158] The expression of the production balance reliability evaluation index of the kth product is:

[0159]

[0160] Where: k = 1, 2, ..., m;

[0161] Then the expression of the production balance reliability evaluation index of all products is:

[0162] .

[0163] The expression of the adaptability function F of the production balance optimization is specifically:

[0164]

[0165] Among them: a, b, c are constants, is the capacity reliability index, is the production balance reliability evaluation index, is the production smoothing index.

[0166] Example 1

[0167] The present invention takes a complex component manufacturing production line as an example. Figure 2 As shown, the complex component is assembled from component 1 and component 2. The production of component 1 has multiple production process lines, and some production links have different processes for replacement. The flexible manufacturing production line can select different process methods for production according to different product models.

[0168] Taking the laser cutting process as an example, the classic ESPN model was adjusted accordingly to make it more suitable for multi-level characterization model components. Specifically:

[0169] Define ESPN as a seven-tuple model:

[0170]

[0171] 1) It is a finite set of places;

[0172] 2) is a finite set of transitions; and at the same time satisfies , ;

[0173] 3) is the input function;

[0174] 4) is the output function;

[0175] 5) is the time parameter of any probability distribution or function

[0176] 6) The upper limit of the storage capacity

[0177] 7) is a logo, the first The first component is of a library The present invention will It is divided into two categories: activated and inactivated. ,in Indicates that the library is not activated number, Indicates the activated Use place activated instead of inactive in the description place. Change to active state.

[0178] Change trigger and The activation rules are as follows:

[0179] 1) If In the logo The following activation rules are used to generate new identifiers: :

[0180]

[0181]

[0182]

[0183] 2) Only when When, and .change When it can be enabled.

[0184] 3) If , is any place. Then the place will not be activated Activate and generate a new identifier according to the following rules ;

[0185]

[0186]

[0187]

[0188] The improved ESPN of the present invention is different from the classic ESPN in the following aspects:

[0189] 1) The inhibition function is cancelled and replaced with the upper limit of the place capacity, which simplifies the Petri net model diagram.

[0190] 2) Both places and transitions can be timed, providing conditions for mutual transformation between multi-level models.

[0191] 3) In the library Divided into activated and inactivated states, activated state Only then can the change be activated.

[0192] 4) Triggering rules and The activation rules are more specific.

[0193] After converting each link of the flexible manufacturing production line into a digital representation model according to the above method, the production process can be simulated and analyzed. The digital modeling process can be simplified through the library and transition, so that the simplified Petri net structure has the ability to calculate reliability. Figure 3 Table 1 is used as an example:

[0194]

[0195] It can be seen from the above digital representation model that failures include processing unit equipment failures and process system failures. Processing unit equipment failures refer to the situation where the equipment fails during the production process and stops production, making it impossible to continue production; process system failures refer to the situation where the product processing quality does not meet the standards due to improper process methods or process parameters, and ultimately produce defective products. Figure 3 After the device is in the operating state, it can be transferred to the fault state by a fault transition, or it can work normally.

[0196] For a variable batch multi-product production line, assume that m product categories. Since the entire Petri net model was huge in the past, Figure 3 An example is given to illustrate the general structure of the Petri net model of multiple varieties and variable batches. From left to right, there are Petri net process flow charts of multiple products; among them, P j,k K in the figure represents the equipment of the kth process; P j,s It represents the sth logistics transfer system. Product numbers from 1 to m represent multiple varieties of products, and variable batches can be achieved according to the final product output ratio.

[0197] like Figure 4 As shown, the production smoothing index, capacity reliability and production balance reliability evaluation indicators are analyzed respectively, including the following steps:

[0198] 1. Production balance analysis (beat analysis)

[0199] 1) The total number of products processed in each process link ;

[0200]

[0201] 2) Total time for product simulation production to complete each process step Run_time ;

[0202]

[0203] 3) Assume that there are S process links in the flexible manufacturing production line. The expression of the total time vector T of each process link is:

[0204]

[0205] Total time spent on a single link The expression is:

[0206]

[0207] in: It takes time to load the workpiece. Time consuming for failures. Time-consuming troubleshooting. Because the processing is time-consuming, It takes time to unload the material;

[0208] productivity P 0i The calculation formula is:

[0209]

[0210]

[0211] Where i=1,2,...,m;

[0212] The corresponding production loss rate IR of productivity is:

[0213]

[0214] Production Smoothing Index SI for:

[0215]

[0216]

[0217] Where i=1,2,...,m.

[0218] 2. Capacity reliability indicators:

[0219] Simulation analysis results of flexible manufacturing production line based on ESPN

[0220] 1) The total number of products processed in each process ;

[0221]

[0222] 2) Total time to complete the processing simulation production in each process link Run_time ;

[0223]

[0224] Then the expression of the capacity reliability index of the i-th process is:

[0225]

[0226] in: is the time for the i-th process to complete the processing simulation production, TT is the production cycle of the flexible manufacturing production line under ideal conditions without any failure, i=1,2,...,m;

[0227] 3) The expression of the production capacity reliability index of all products is:

[0228]

[0229] 3. Production balance reliability analysis indicators:

[0230] Based on the simulation analysis results of ESPN on the flexible manufacturing production line, the production balance reliability evaluation index is carried out. At this time, the simulation analysis should only include uncertain factors such as the failure rate of a certain analysis link, and the calculation steps need to be large enough to ensure that the uncertain factors are included in the calculation results. Simulate m different products separately. The simulation analysis results of the flexible manufacturing production line include:

[0231] 1) Total time when the fault occurs Run_time_1 ;

[0232] 2) Total number of products when the failure occurred N_1 ;

[0233] 3) The total time from when the failure occurred to when the repair was completed Run_time_2 ;

[0234] 4) Total number of products when the failure occurred and was repaired N_2 ;

[0235] 5) Ideally, the production cycle of a flexible manufacturing production line without any failure TT ;

[0236] At this time, the impact of this link on the reliability of production balance is analyzed as follows:

[0237]

[0238] According to this method, the impact analysis of each link on the production balance reliability is obtained. Because the impact analysis is related to the failure of each link, the failure rate of each link is set to P ={p t1 , p t2 ,,… p ts}.

[0239] The production balance reliability evaluation index for the kth product

[0240]

[0241] Then the production balance reliability evaluation index of all products is

[0242]

[0243] 4. Production balance optimization

[0244] The production balance optimization of the present invention is a multi-objective optimization, that is, a lower production smoothness index and a higher production balance reliability evaluation index. Thus, the fitness function F of the production balance optimization can be obtained:

[0245]

[0246] Among them, a, b, and c are constants, which need to be adjusted according to the production smoothing index, capacity reliability, and production reliability balance index. If a good production smoothing index is needed, c will be larger, and so on.

[0247] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method, characterized in that: The following steps are involved: Construct a physical model of a flexible manufacturing production line to obtain the production relationship, availability analysis results, and production data between each process link in the flexible manufacturing system; According to the production relationship between each process link, the availability analysis results, and the production data, a digital representation model based on the extended stochastic Petri net is constructed, and the digital representation model is an ESPN seven-tuple model; The performance parameters of each process in the ESPN seven-tuple model are divided into variable parameters and invariable parameters, and the variable parameters are encoded to generate a new population; Determine an adaptability function F for production balance optimization, wherein the optimization indicators of the adaptability function F include: a production smoothing index, a capacity reliability index, and a production balance reliability evaluation index; The fitness value of each individual in the new population is calculated according to the fitness function F, and it is determined whether the maximum number of iterations is reached. If so, the optimization result is output; if not, the excellent ontology is selected according to the fitness value, and the optimized new population is solved by the genetic algorithm, and then the fitness value of each individual in the optimized new population is calculated according to the fitness function F until the maximum number of iterations is reached; The optimization results include: production balance optimization results, capacity reliability calculation results and production balance reliability calculation results; The function representation of the ESPN seven-tuple model is specifically: N=(P,T,I,O,F,H,M), Where P = {p1, p2, L, p n }n≥1 is a finite set of places; T={t1,t2,L,t s }s≥1 is a finite set of transitions, I: P×T→N is the input function; O: T×P→N is the output function; F={F(p1),L,F(p n ),F(t1),L,F(t s )} is the time parameter of any probability distribution or function; H is the upper limit of the place capacity; M is an identifier, indicating that the i-th component in the identifier is the number of tokens in the i-th place; The number of tokens in the place includes: the number of activated tokens and the number of unactivated tokens, specifically: M=unenable_M+enable_M In the above formula, unenable_M represents the number of unactivated tokens in the place; enable_M represents the number of activated tokens in the place; The triggering rules of the transition and the activation rules of the number of tokens are specifically as follows: The triggering of the transition includes the following rules: Only when When t∈T, enable_M'(p)≥I(p,t), and M'(p)≤H(p), the transition t∈T can be enabled, where H is the upper limit of the place capacity; If t∈T is enabled under the flag M, a new flag M' is generated according to the following activation rule: unenable_M'=unenable_M+O(p,t) enable_M'=enable_M-I(p,t) M'=unenable_M'+enable_M' The activation of the token number includes the following rules: unenable_M'(i)≥1, i is any place; the place activates the unactivated tokens and generates a new identifier M' according to the following rules: unenable_M'(i)=unenable_M(i)-1 enable_M'(i)=enable_M(i)+1 M'=unenable_M'+enable_M'.

2. The flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method according to claim 1 is characterized in that: The method for constructing the physical model of the flexible manufacturing production line is specifically as follows: Based on the production line process flow of the flexible manufacturing system, the flexible manufacturing system is modularized to obtain each process module and obtain the performance parameters of each process module; Based on the performance parameters of each process, the production relationship, production data and availability between each process module are analyzed.

3. The method for balancing and analyzing the reliability of multi-variety and variable-batch production of a flexible manufacturing production line according to claim 1 is characterized in that: The new population after optimization by genetic algorithm is specifically: Perform crossover, mutation, and genetic operator operations to generate new populations, and use the average fitness to select populations with higher fitness.

4. The flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method according to claim 1 is characterized in that: The adaptive function F for production balance optimization is determined, and the optimization index of the adaptive function F includes: production smoothing index, capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production smoothing index is specifically as follows: The total number of products N that have been processed in each process link i ; N i =(N1,N2,…,N m ) In the above formula, m is the total product types, i = 1, 2, 3..., m; The total Run time of each process link to complete the simulated production of products; Run_time i =(Run_time1,Run_time2,..,Run_time m ) Assume that there are S process links in the flexible manufacturing production line. The expression of the total time vector T of each process link is as follows: Total time of a single link t ij The expression is: t ij =t ij01 +t ij02 +t ij03 +t ij04 +t ij05 Where: t ij01 The time taken to load the workpiece, t ij02 is the time taken for the failure to occur, t ij03 The time taken to repair the fault, t ij04 is the processing time, t ij05 It takes time to unload the material; Productivity 0i The calculation formula is: P 0i =(P 01 ,P 02 ,…,P 0m ) Where, i = 1, 2, ..., m; The corresponding production loss rate IR of productivity is: AND=1-P 0i The production smoothing index SI is: SI i =(SI1,SI2,…,SI m ) Wherein, i=1,2,...,m.

5. The method for balancing and analyzing the reliability of multi-variety and variable-batch production of a flexible manufacturing production line according to claim 1 is characterized in that: The adaptive function F for production balance optimization is determined, and the optimization index of the adaptive function F includes: production smoothing index, production capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production capacity reliability index is specifically as follows: The total number of products processed in each process link N i ; N i =(N1,N2,…,N m ) The total time Run_time for each process link to complete the processing simulation production; Run_time i =(Run_time1,Run_time2,..,Run_time m ) Then the expression of the capacity reliability index of the i-th process is: Where: Run_time i is the time for the i-th process to complete the processing simulation production, TT is the production cycle of the flexible manufacturing production line under ideal conditions without any failure, i = 1, 2, ..., m; The expression of the production capacity reliability index of all products is: P=(P 1_1 ,P 1_2 ,…,P 1_m )。 6. The flexible manufacturing production line multi-variety variable batch production balance and reliability analysis method according to claim 1 is characterized in that: The adaptive function F for production balance optimization is determined, and the optimization index of the adaptive function F includes: production smoothing index, capacity reliability index and production balance reliability evaluation index; wherein the calculation method of the production balance reliability evaluation index is specifically as follows: Where: Run_time_1 is the total time when the fault occurs, N_1 is the total number of products when the fault occurs, Run_time_2 is the total time when the fault occurs and is repaired, N_2 is the total number of products when the fault occurs and is repaired, and TT is the production cycle of the flexible manufacturing production line in an ideal state without any faults; Assume that the failure rate of each process link is p tj ={p t1 ,p t2 ,,…p ts }; The expression of the production balance reliability evaluation index of the kth product is: Where: k = 1, 2, ..., m; Then the expression of the production balance reliability evaluation index of all products is: P 2_i =(P 2_1 ,P 2_2 ,…,P 2_m )。 7. The method for balancing and analyzing the reliability of multi-variety and variable-batch production of a flexible manufacturing production line according to claim 1 is characterized in that: The expression of the adaptability function F of the production balance optimization is specifically: Among them: a, b, c are constants, P 1_i is the capacity reliability index, P 2_i is the production balance reliability evaluation index, SI i is the production smoothing index.

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