Complex product assembly sequence planning problem solving method considering human factors

Abstract

The invention relates to a complex product assembly sequence planning problem solving method considering human factors, and the method comprises the steps: comprehensively introducing human factors such as cognition, physiology, psychology, skills and organization in a complex product assembly sequence planning process, and carrying out the optimization solving of an assembly sequence based on genetic programming. By effectively deciding the human factor data acquisition mode, obtaining the state data of the workers and combining with the constraint of human factors to adjust the assembly sequence, the optimal assembly sequence planning considering the states of the workers is finally realized, so that the efficiency and reliability of the assembly process are improved. Therefore, the problems that in the prior art, manual planning depends on experiences of engineers, human factors are difficult to comprehensively consider, and modern complex product manufacturing requirements cannot be met are solved.

Description

Technical Field

[0001] This application relates to the technical field of complex product assembly, and particularly to a method for solving the problem of complex product assembly sequence planning considering human factor Background Art

[0002] Complex products refer to a type of products with complex customer requirements, complex system composition, complex product technology, complex manufacturing process, complex test and maintenance, complex project management, and complex working environment, such as spacecraft, aircraft, ships, complex electromechanical products, etc. Due to the large variety of components and complex structure in the assembly process of complex products, extremely high requirements are put forward for the assembly sequence planning.

[0003] Traditional assembly sequence planning usually relies on the experience of engineers for manual planning. This method is both time-consuming and easily affected by subjective factors, making it difficult to achieve efficient assembly. In addition, the state changes of workers during the assembly process (such as fatigue, skill differences, etc.) have a significant impact on the assembly efficiency and quality, but traditional methods often ignore these human factors, resulting in instability and potential safety hazards during the assembly process. With the continuous improvement of the requirements for the assembly accuracy and efficiency of complex products in the modern industrial market, and the increasing complexity of assembly, the traditional manual planning method is difficult to meet the high standards of modern complex product manufacturing.

[0004] In summary, the existing technology relies on the experience of engineers for manual planning, is difficult to comprehensively consider human factors, and cannot meet the requirements of modern complex product manufacturing, which urgently needs to be solved. Summary of the Invention

[0005] This application provides a method for solving the problem of complex product assembly sequence planning considering human factor, so as to solve problems such as the existing technology relying on the experience of engineers for manual planning, being difficult to comprehensively consider human factors, and not meeting the requirements of modern complex product manufacturing.

[0006] The first aspect of the embodiments of the present application provides a method for solving the problem of complex product assembly sequence planning considering human factor, including the following steps: Based on a preset human factor identification framework, establish a human factor library for the target complex product assembly field, and construct a priority evaluation model according to a preset hierarchical analysis strategy, and use the priority evaluation model to calculate the comprehensive score of each human factor in the human factor library, where the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors; Based on a pre-constructed data collection method library, collect human factor data corresponding to each human factor, and use the human factor data to perform mathematical modeling on each human factor to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field; Judge the start surface situation of the complex product assembly sequence planning model. If the start surface situation is a single start surface situation, then based on a preset heuristic algorithm, solve the complex product assembly sequence planning model in the single start surface situation to obtain a corresponding target assembly sequence; If the start surface situation is a multi-start surface situation, then determine the assembly time, assembly cost, each human factor, and multiple constraints of the complex product assembly sequence planning model in the multi-start surface situation, and construct a fitness function according to the assembly time, the assembly cost, each human factor, and the multiple constraints, so as to solve the complex product assembly sequence planning model in the multi-start surface situation through the comprehensive score, the fitness function, and a preset hybrid genetic programming algorithm to obtain a corresponding target assembly sequence.

[0007] Optionally, in an embodiment of the present application, the step of establishing a human factor library for the target complex product assembly field based on a preset human factor identification framework, constructing a priority evaluation model according to a preset hierarchical analysis strategy, and using the priority evaluation model to calculate the comprehensive score of each human factor in the human factor library includes: Based on the actual work requirements of the target complex product assembly field and a preset plurality of factor identification theories, construct the human factor identification framework, and establish the human factor library for the target complex product assembly field according to the human factor identification framework, where the plurality of factor identification theories include human factors engineering theory, work design theory, SHEL model, operation risk management theory, and organizational behavior theory; Perform relative importance scoring on each pair of human factors in the human factor library to construct a judgment matrix, and calculate the maximum eigenvalue and consistency index of the judgment matrix; Based on the maximum eigenvalue and the consistency index, calculate the consistency ratio, and perform consistency test on each pair of human factors according to the consistency ratio to obtain a corresponding consistency test result; Normalize each column of data of the judgment matrix to calculate the weight of each human factor, and based on the weight and the consistency test result, determine the comprehensive score of each human factor in the human factor library.

[0008] Optionally, in an embodiment of the present application, collecting the human factor data corresponding to each human factor based on the pre-constructed data collection method library includes: respectively determining the cognitive factor data collection method, physiological factor data collection method, psychological factor data collection method, skill factor data collection method, and organizational factor data collection method corresponding to the cognitive factor, physiological factor, psychological factor, skill factor, and organizational factor; and constructing the data collection method library according to the cognitive factor data collection method, physiological factor data collection method, psychological factor data collection method, skill factor data collection method, and organizational factor data collection method.

[0009] Optionally, in an embodiment of the present application, using the human factor data to perform mathematical modeling on each human factor to obtain the complex product assembly sequence planning model corresponding to the target complex product assembly field includes: quantifying the worker's attention, memory ability, and decision-making ability during the execution of the complex product assembly task in the target complex product assembly field, and constructing the cognitive factor mathematical model according to the quantified worker's attention, memory ability, and decision-making ability; obtaining the working hours, rest hours, and at least one physiological index of the worker during the execution of the complex product assembly task, determining the fatigue degree of the worker according to the working hours and the rest hours, and estimating the physical fitness state of the worker through the at least one physiological index, and constructing the physiological factor mathematical model according to the fatigue degree and the physical fitness state; quantifying the emotional state of the worker during the execution of the complex product assembly task through a preset emotion rating scale, obtaining at least one physiological parameter of the worker, and quantifying the stress level of the worker using the at least one physiological parameter and a preset subjective report, and constructing the psychological factor mathematical model based on the quantified emotional state and stress level; obtaining the average time and the benchmark time for the worker to complete the same complex product assembly task, and calculating the skill level of the worker according to the average time and the benchmark time; obtaining the number of participations of the worker in the assembly tasks that meet the preset similarity requirements with the complex product assembly task, and constructing the skill factor mathematical model according to the skill level and the number of participations; determining the training degree, rule compliance degree, and team collaboration efficiency of the worker, and constructing the organizational factor mathematical model according to the training degree, the rule compliance degree, and the team collaboration efficiency.

[0010] Optionally, in an embodiment of the present application, determining the assembly time, assembly cost, each human factor, and multiple constraints of the complex product assembly sequence planning model in the case of multiple work surfaces, and constructing a fitness function according to the assembly time, the assembly cost, each human factor, and the multiple constraints, so as to solve the complex product assembly sequence planning model in the case of multiple work surfaces through the comprehensive score, the fitness function, and a preset hybrid genetic programming algorithm, to obtain a corresponding target assembly sequence, includes: reading and parsing multiple constraints of the parallel assembly sequence planning, where the multiple constraints include assembly process constraints, resource constraints, space constraints, the maximum number of work surfaces, and human factor constraints; determining the factor scores and weight coefficients of each important human factor corresponding to each complex product assembly task, and constructing a human factor evaluation function through the factor scores and the weight coefficients, so as to construct the fitness function according to the assembly time, the assembly cost, the human factor evaluation function, and the multiple constraints, and determining the expression form of the assembly sequence planning result through the fitness function, and performing a neighborhood search operation on the assembly sequence planning result to obtain a corresponding initial population; based on the hybrid genetic programming algorithm, screening out multiple individuals that meet the preset fitness requirements from the initial population, and performing a crossover operation on each individual in the multiple individuals with the parental individuals in the initial population corresponding to each individual according to a preset crossover strategy to obtain multiple new offspring individuals; based on a preset mutation probability, performing a mutation operation on the multiple new offspring individuals, and performing a fitness evaluation on the multiple new offspring individuals after the mutation operation to obtain the fitness function values corresponding to each new offspring individual in the multiple new offspring individuals, and iteratively executing the screening, crossover, mutation, and fitness evaluation operations until a preset termination condition is met, so as to obtain a target assembly sequence that meets the preset fitness value requirements.

[0011] Optionally, in an embodiment of the present application, after solving the complex product assembly sequence planning model in the case of multiple work surfaces through the comprehensive score, the fitness function, and a preset hybrid genetic programming algorithm to obtain a corresponding target assembly sequence, it further includes: obtaining the current human factor data of the worker during the execution of the complex product assembly task based on the data collection method library; dynamically adjusting the human factor evaluation function in the fitness function according to the current human factor data, so as to solve the complex product assembly sequence planning model in the case of multiple work surfaces by using the dynamically adjusted human factor evaluation function.

[0012] Optionally, in an embodiment of the present application, the mathematical expression of the fitness function:

[0013] Fitness(X) = θ·T total(X) + β·C total (X) + γ·F human (X) + λ·Penalty(X)

[0014] Among them, X represents the assembly sequence plan; T total (X) represents the assembly time of the assembly sequence plan X; C total (X) represents the assembly cost; F human (X) represents the human factor evaluation function; Penalty(X) represents the penalty term for violating the constraint conditions; θ represents the weight coefficient corresponding to the assembly time; β represents the weight coefficient corresponding to the assembly cost; γ represents the weight coefficient corresponding to the human factor evaluation function; λ represents the weight coefficient corresponding to the penalty term.

[0015] In the second aspect of the embodiments of the present application, a device for solving the complex product assembly sequence planning problem considering human factors is provided, including: a modeling module, configured to establish a human factor library for the target complex product assembly field based on a preset human factor identification framework, construct a priority evaluation model according to a preset hierarchical analysis strategy, and calculate the comprehensive score of each human factor in the human factor library by using the priority evaluation model, where the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors; a collection module, configured to collect human factor data corresponding to each human factor based on a pre-constructed data collection method library, and perform mathematical modeling on each human factor by using the human factor data to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field; a first solving module, configured to judge the start surface situation of the complex product assembly sequence planning model. If the start surface situation is a single start surface situation, then solve the complex product assembly sequence planning model in the single start surface situation based on a preset heuristic algorithm to obtain a corresponding target assembly sequence; a second solving module, configured to if the start surface situation is a multi-start surface situation, determine the assembly time, assembly cost, each human factor, and multiple constraint conditions of the complex product assembly sequence planning model in the multi-start surface situation, and construct a fitness function according to the assembly time, the assembly cost, each human factor, and the multiple constraint conditions, so as to solve the complex product assembly sequence planning model in the multi-start surface situation by using the comprehensive score, the fitness function, and a preset hybrid genetic programming algorithm to obtain a corresponding target assembly sequence.

[0016] Optionally, in an embodiment of the present application, the modeling module includes: a first construction unit, configured to construct the human factor identification framework based on the actual work requirements in the target complex product assembly field and a plurality of preset factor identification theories, so as to establish a human factor library in the target complex product assembly field according to the human factor identification framework, where the plurality of factor identification theories include human factors engineering theory, work design theory, SHEL model, operational risk management theory, and organizational behavior theory; a scoring unit, configured to score the relative importance of each pair of human factors in the human factor library to construct a judgment matrix, and calculate the maximum eigenvalue and consistency index of the judgment matrix; a consistency test unit, configured to calculate a consistency ratio based on the maximum eigenvalue and the consistency index, and perform a consistency test on each pair of human factors according to the consistency ratio to obtain a corresponding consistency test result; a normalization unit, configured to normalize each column of data in the judgment matrix to calculate the weight of each human factor, and determine the comprehensive score of each human factor in the human factor library based on the weight and the consistency test result.

[0017] Optionally, in an embodiment of the present application, the acquisition module includes: a determination unit, configured to respectively determine the cognitive factor data acquisition method, physiological factor data acquisition method, psychological factor data acquisition method, skill factor data acquisition method, and organizational factor data acquisition method corresponding to the cognitive factor, the physiological factor, the psychological factor, the skill factor, and the organizational factor; a second construction unit, configured to construct the data acquisition method library according to the cognitive factor data acquisition method, the physiological factor data acquisition method, the psychological factor data acquisition method, the skill factor data acquisition method, and the organizational factor data acquisition method.

[0018] Optionally, in an embodiment of the present application, the acquisition module further includes: a quantization unit, configured to quantify the attention, memory ability, and decision-making ability of workers during the execution of complex product assembly tasks in the target complex product assembly field, so as to construct the cognitive factor mathematical model according to the quantified attention, memory ability, and decision-making ability of workers; an estimation unit, configured to obtain the working hours, rest hours, and at least one physiological index of workers during the execution of the complex product assembly tasks, determine the fatigue degree of the workers according to the working hours and the rest hours, and estimate the physical fitness state of the workers through the at least one physiological index, so as to construct the physiological factor mathematical model according to the fatigue degree and the physical fitness state; a first acquisition unit, configured to quantify the emotional state of the workers during the execution of the complex product assembly tasks through a preset emotion rating scale, obtain at least one physiological parameter of the workers, and quantify the stress level of the workers by using the at least one physiological parameter and a preset subjective report, so as to construct the psychological factor mathematical model based on the quantified emotional state and stress level; a calculation unit, configured to obtain the average time and the reference time for the workers to complete the same complex product assembly tasks, and calculate the skill level of the workers according to the average time and the reference time; a second acquisition unit, configured to obtain the number of participations of the workers in assembly tasks that meet the preset similarity requirements with the complex product assembly tasks, and construct the skill factor mathematical model according to the skill level and the number of participations; a third construction unit, configured to determine the training degree, rule compliance degree, and teamwork efficiency of the workers, and construct the organizational factor mathematical model according to the training degree, the rule compliance degree, and the teamwork efficiency.

[0019] Optionally, in an embodiment of the present application, the second solution module includes: a reading unit, configured to read and parse multiple constraint conditions of parallel assembly sequence planning, where the multiple constraint conditions include assembly process constraints, resource constraints, space constraints, the maximum number of starting surfaces, and human factor constraints; a neighborhood search unit, configured to determine the factor scores and weight coefficients of each important human factor corresponding to each complex product assembly task, and construct a human factor evaluation function through the factor scores and the weight coefficients, so as to construct the fitness function according to the assembly time, the assembly cost, the human factor evaluation function, and the multiple constraint conditions, and determine the expression form of the assembly sequence planning result through the fitness function, and perform a neighborhood search operation on the assembly sequence planning result to obtain a corresponding initial population; an algorithm processing unit, configured to screen out multiple individuals that meet the preset fitness requirements from the initial population based on the hybrid genetic programming algorithm, and perform a crossover operation on each individual in the multiple individuals and the parent individuals in the initial population corresponding to each individual according to a preset crossover strategy to obtain multiple new offspring individuals; an iteration unit, configured to perform a mutation operation on the multiple new offspring individuals based on a preset mutation probability, and perform a fitness evaluation on the multiple new offspring individuals after the mutation operation to obtain the fitness function value corresponding to each new offspring individual in the multiple new offspring individuals, and iteratively execute the screening, crossover, mutation, and fitness evaluation operations until a preset termination condition is met, so as to obtain a target assembly sequence that meets the preset fitness value requirements.

[0020] Optionally, in an embodiment of the present application, it further includes: a data acquisition module, configured to obtain the current human factor data of the worker during the execution of the complex product assembly task based on the data acquisition method library after solving the complex product assembly sequence planning model in the case of multiple starting surfaces through the comprehensive score, the fitness function, and a preset hybrid genetic programming algorithm to obtain a corresponding target assembly sequence; a dynamic adjustment module, configured to dynamically adjust the human factor evaluation function in the fitness function according to the current human factor data, so as to solve the complex product assembly sequence planning model in the case of multiple starting surfaces by using the dynamically adjusted human factor evaluation function.

[0021] Optionally, in an embodiment of the present application, the mathematical expression of the fitness function:

[0022] Fitness(X) = θ·T total (X) + β·C total (X) + γ·F human (X) + λ·Penalty(X)

[0023] where X represents the assembly sequence scheme; T total(X) represents the assembly time of the assembly sequence plan X; C total (X) represents the assembly cost; F human (X) represents the human factor evaluation function; Penalty(X) represents the penalty term for violating the constraint conditions; θ represents the weight coefficient corresponding to the assembly time; β represents the weight coefficient corresponding to the assembly cost; γ represents the weight coefficient corresponding to the human factor evaluation function; λ represents the weight coefficient corresponding to the penalty term.

[0024] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the method for solving the problem of complex product assembly sequence planning considering human factors as described in the above embodiments.

[0025] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the method for solving the problem of complex product assembly sequence planning considering human factors as described above.

[0026] Therefore, the embodiments of the present application have the following beneficial effects:

[0027] Embodiments of the present application can establish a human factor library for the target complex product assembly field based on a preset human factor recognition framework, construct a priority evaluation model according to a preset hierarchical analysis strategy, and calculate the comprehensive score of each human factor in the human factor library by using the priority evaluation model. Among them, the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors; based on a pre-constructed data collection method library, collect human factor data corresponding to each human factor, and use the human factor data to perform mathematical modeling on each human factor to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field; based on a preset heuristic algorithm, solve the complex product assembly sequence planning model in the case of a single starting surface to obtain a corresponding target assembly sequence; determine the assembly time, assembly cost, each human factor, and multiple constraints of the complex product assembly sequence planning model in the case of multiple starting surfaces, and construct a fitness function according to the assembly time, assembly cost, each human factor, and multiple constraints, so as to solve the complex product assembly sequence planning model in the case of multiple starting surfaces by using the comprehensive score, fitness function, and a preset hybrid genetic programming algorithm to obtain a corresponding target assembly sequence. The present application is an assembly sequence planning method that can efficiently and comprehensively consider human factors, and can realize the planning of the optimal assembly sequence under complex constraint conditions, thereby improving the assembly efficiency, reducing costs, and ensuring the assembly quality and worker safety. Thus, it solves the problems in the prior art that rely on the experience of engineers for manual planning, are difficult to comprehensively consider human factors, and cannot meet the requirements of modern complex product manufacturing, etc.

[0028] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Brief Description of the Drawings

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, in which:

[0030] Figure 1 It is a flowchart of a method for solving the problem of complex product assembly sequence planning considering human factors according to an embodiment of the present application;

[0031] Figure 2 It is a schematic diagram of the output of a problem-solving example considering the maximum starting surface of 1 worker provided by an embodiment of the present application;

[0032] Figure 3 It is a schematic diagram of the output of a problem-solving example considering the maximum starting surface of 5 assembly workers provided by an embodiment of the present application;

[0033] Figure 4An exemplary diagram of a device for solving the problem of complex product assembly sequence planning considering human factors according to an embodiment of the present application;

[0034] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0035] Among them, 10 - a device for solving the problem of complex product assembly sequence planning considering human factors; 100 - a modeling module, 200 - a collection module, 300 - a first solution module, 400 - a second solution module; 501 - a memory, 502 - a processor, 503 - a communication interface. Detailed implementation manners

[0036] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals indicate the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0037] The following describes a method for solving the problem of complex product assembly sequence planning considering human factors in the embodiments of the present application with reference to the accompanying drawings. Aiming at the problems mentioned in the above background art, the present application provides a method for solving the problem of complex product assembly sequence planning considering human factors. In this method, based on a preset human factor identification framework, a human factor library in the target complex product assembly field is established, and a priority evaluation model is constructed according to a preset hierarchical analysis strategy. The comprehensive score of each human factor in the human factor library is calculated using the priority evaluation model, where the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors; based on a pre-constructed data collection method library, human factor data corresponding to each human factor is collected, and mathematical modeling is performed on each human factor using the human factor data to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field; based on a preset heuristic algorithm, the complex product assembly sequence planning model in the case of a single starting surface is solved to obtain the corresponding target assembly sequence; the assembly time, assembly cost, each human factor, and multiple constraint conditions in the case of multiple starting surfaces of the complex product assembly sequence planning model are determined, and a fitness function is constructed according to the assembly time, assembly cost, each human factor, and multiple constraint conditions, so as to solve the complex product assembly sequence planning model in the case of multiple starting surfaces through the comprehensive score, fitness function, and a preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence. The present application can efficiently and comprehensively consider the assembly sequence planning method of human factors, and can realize the planning of the optimal assembly sequence under complex constraint conditions, thereby improving the assembly efficiency, reducing the cost, and ensuring the assembly quality and worker safety. Thus, the problems in the prior art that rely on the experience of engineers for manual planning, are difficult to comprehensively consider human factors, and cannot meet the requirements of modern complex product manufacturing are solved.

[0038] Specifically, Figure 1 FIG. is a flowchart of a method for solving the problem of complex product assembly sequence planning considering human factors provided by an embodiment of the present application.

[0039] As Figure 1 shown, the method for solving the problem of complex product assembly sequence planning considering human factors includes the following steps:

[0040] In step S101, based on a preset human factor identification framework, a human factor library in the target complex product assembly field is established, and a priority evaluation model is constructed according to a preset hierarchical analysis strategy. The comprehensive score of each human factor in the human factor library is calculated using the priority evaluation model to obtain multiple important human factors in the human factor library through the comprehensive score, where the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors.

[0041] Different from traditional assembly sequence planning methods, in order to scientifically and systematically identify all human factors that need to be considered in the field of complex product assembly, embodiments of the present application can systematically incorporate multi-dimensional human factors such as cognition, physiology, psychology, skills, and organization into a priority evaluation model, so that the assembly sequence is more in line with the actual capabilities and states of workers, ensuring the comprehensive identification and analysis of the above human factors.

[0042] Optionally, in an embodiment of the present application, based on a preset human factor identification framework, a human factor library for the target complex product assembly field is established, and a priority evaluation model is constructed according to a preset hierarchical analysis strategy. The comprehensive score of each human factor in the human factor library is calculated using the priority evaluation model, including: constructing a human factor identification framework based on the actual work requirements of the target complex product assembly field and a preset plurality of factor identification theories, so as to establish a human factor library for the target complex product assembly field according to the human factor identification framework. Among them, the plurality of factor identification theories include human factors engineering theory, job design theory, SHEL model, operational risk management theory, and organizational behavior theory; performing a relative importance score on each pair of human factors in the human factor library to construct a judgment matrix, and calculating the maximum eigenvalue and consistency index of the judgment matrix; calculating a consistency ratio based on the maximum eigenvalue and consistency index, and performing a consistency test on each pair of human factors according to the consistency ratio to obtain the corresponding consistency test result; normalizing each column of data in the judgment matrix to calculate the weight of each human factor, and determining the comprehensive score of each human factor in the human factor library based on the weight and the consistency test result.

[0043] It should be noted that in the process of identifying human factors, embodiments of the present application first need to establish a comprehensive framework to guide the systematic identification of factors. Embodiments of the present application can use human factors engineering (HFE), job design theory (JCM), SHEL model (Software, Hardware, Environment, Liveware), operational risk management theory, and organizational behavior theory as the basic theories for factor identification, and combine the actual work of complex product assembly to construct a complete framework from cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors, and evaluate the impact of human factors on complex product assembly from multiple angles. The various human factors are described as follows:

[0044] 1. Cognitive factors: such as the cognitive load of work tasks, the understanding of the assembly process, and decision-making ability;

[0045] 2. Physiological factors: such as fatigue state, work load, physiological comfort, etc.;

[0046] 3. Psychological factors: including stress, motivation, satisfaction, and mental health;

[0047] 4. Skill factors: the degree of matching between workers' skills and the complexity of assembly tasks;

[0048] 5. Organizational factors: such as the organization's rules and regulations, skill training, management methods, etc.

[0049] This overall framework can be iterated and tailored according to the actual situation of different organizations. The overall purpose is to systematically and comprehensively establish a library of human factors that need to be considered in this field.

[0050] After that, the embodiments of this application can classify all identified human factor elements and evaluate their priorities according to their influence degrees on the assembly process; the embodiments of this application can establish an evaluation model through the Analytic Hierarchy Process (AHP) to score and rank different factors, and determine the key human factor elements that need to be most concerned about and optimized according to the resource constraints and specific objectives of specific assembly tasks. The specific process is as follows:

[0051] 1. Construct a judgment matrix:

[0052] For each pair of factors, use the scaling method to score the relative importance and construct a judgment matrix (A) as shown in the following formula:

[0053]

[0054] For example, for the importance score (a ij ) of factors (i) and (j), if (a ij = 3) indicates that factor (i) is more important than factor (j), then

[0055] 2. Consistency test:

[0056] Calculate the maximum eigenvalue (λ max ) and the consistency index (CI) of the judgment matrix:

[0057]

[0058] Among them, (n) is the number of factors, and then calculate the consistency ratio (CR):

[0059]

[0060] Among them, (RI) is the random consistency index; if (CR < 0.1), the consistency is acceptable;

[0061] 3. Calculate the weights:

[0062] By normalizing each column of the judgment matrix, the weights of various factors are calculated; the weight (w i ) can be obtained by summing the elements of each row and then normalizing;

[0063] 4. Comprehensive scoring:

[0064] The weights of each level are combined to obtain the comprehensive scores of each factor for the target finally.

[0065] Thus, the embodiments of the present application can provide a set of steps and tools for identifying and evaluating human factors in the assembly process of complex products, thereby ensuring that the human factor library can ensure that the most important factors are considered when resources are limited or the assembly tasks are targeted.

[0066] In step S102, based on the pre-constructed data collection method library, multiple important human factor data corresponding to each important human factor among multiple important human factors are collected, and mathematical modeling is performed on each important human factor using the multiple important human factor data to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field.

[0067] After the identification of human factors is completed, furthermore, the embodiments of the present application also need to further enrich and improve the list of human factors through data collection and investigation. Since there is a suitable collection method for human factors in each dimension, therefore, the embodiments of the present application need to construct the general characteristics for each category of human factors to establish a data collection method library.

[0068] Optionally, in an embodiment of the present application, based on the pre-constructed data collection method library, multiple important human factor data corresponding to each important human factor among multiple important human factors are collected, including: respectively determining the cognitive factor data collection method, physiological factor data collection method, psychological factor data collection method, skill factor data collection method, and organizational factor data collection method corresponding to cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors; constructing a data collection method library according to the cognitive factor data collection method, physiological factor data collection method, psychological factor data collection method, skill factor data collection method, and organizational factor data collection method.

[0069] In the embodiments of the present application, the data collection methods corresponding to cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors can be determined respectively as follows:

[0070] 1. Data collection of cognitive factors:

[0071] Cognitive factors mainly involve the understanding, decision-making, and cognitive load of workers when performing assembly tasks. The embodiments of the present application can adopt the following methods to collect cognitive factor data:

[0072] (1)Questionnaire and cognitive load test tools: Use standardized questionnaires, such as NASA - TLX (Task Load Index), to evaluate the subjective cognitive load of workers during task execution;

[0073] (2)Cognitive Task Analysis (CTA): Through detailed interviews and observations of workers, understand their thinking processes and cognitive strategies during task execution;

[0074] (3)Laboratory simulation tests: By simulating the assembly environment, measure the information - processing speed and working - memory capacity of workers. The tests can include reaction - time tasks and memory tasks to evaluate workers' cognitive abilities;

[0075] (4)Eye - tracking: Use eye - tracking devices to collect data on the fixation points and fixation times of workers during assembly tasks to judge the information - processing load and attention allocation.

[0076] 2. Data collection on physiological factors:

[0077] Physiological factors include workers' fatigue status, workload, and physiological comfort, etc. Data can be collected through the following methods:

[0078] (1)Wearable physiological sensors: Use wearable devices, such as heart - rate monitors, skin - conductance response sensors, wristbands, etc., to continuously monitor physiological indicators of workers, such as heart - rate variability (HRV) and skin - electrical activity, for evaluating fatigue status and stress levels;

[0079] (2)Work - environment monitoring devices: Use devices such as thermometers, noise meters, and illuminometers to collect physical parameters of the work environment to evaluate the impact of the environment on workers' physiological comfort;

[0080] (3)Motion - tracking systems: Through motion - capture technology, record the postures and motion frequencies of workers during work to judge workload and fatigue levels.

[0081] 3. Data collection on psychological factors:

[0082] Psychological factors involve aspects such as workers' mental health, work stress, motivation, and morale. This data can be collected through the following methods:

[0083] (1)Mental - health scales: Adopt standardized mental - health assessment tools, such as GHQ (General Health Questionnaire) or PHQ - 9 (Patient Health Questionnaire), to evaluate workers' mental - health status, including anxiety, depression, etc.;

[0084] (2) Pressure assessment questionnaire: Use scales such as the Perceived Stress Scale (PSS) to assess the stress level of workers at work, and verify the questionnaire results in combination with physiological data (such as heart rate and galvanic skin response);

[0085] (3) Motivation and morale interview: Through in-depth interviews, understand the motivation level of workers and their attitudes towards work. Questionnaires in the Self-Determination Theory (SDT) can be used to evaluate the intrinsic motivation and extrinsic motivation of workers;

[0086] (4) Emotion recognition technology: Through facial expression analysis and voice emotion recognition technology, monitor the emotional state of workers in real time to evaluate the impact of their mental health and emotional fluctuations on work.

[0087] 4. Data collection on skill factors:

[0088] The skill factor involves whether the skill level of workers matches the task requirements. The data collection of this type can be carried out in the following ways:

[0089] (1) Skill assessment test: Design a standardized skill assessment test to examine the specific skill level of workers in the assembly process, such as part recognition, tool use, fine operation, etc.;

[0090] (2) Work performance record: Analyze the performance records of workers in specific assembly tasks, collect data related to skill matching degree, and the work sampling method can be used to evaluate the time and accuracy required for workers to complete tasks;

[0091] (3) Training record: Collect the skill training records that workers have participated in the past to understand their skill background; evaluate the performance after training to verify the training effect.

[0092] 5. Data collection on organizational factors:

[0093] The organizational factor involves the impact of rules and regulations, skill training, management methods, etc. on workers' performance. The data collection methods of this type include:

[0094] (1) Rules and regulations and management interview: Conduct an interview with the management to understand the organization's rules and regulations, work processes and management strategies; analyze how these organizational factors affect workers' behaviors and performances;

[0095] (2) Training effect evaluation questionnaire: After workers receive skill training, use a questionnaire to evaluate the training effect, including the mastery of new skills and the application in work;

[0096] (3) Employee satisfaction survey: Evaluate the satisfaction of workers with management methods, working environment, and rules and regulations through questionnaires. Employee satisfaction is an important indicator for evaluating the impact of organizational factors;

[0097] (4) Performance data analysis: Collect performance data such as workers' productivity, work quality, and task completion time to quantify the impact of organizational factors on work performance.

[0098] After that, the embodiments of the present application can form the following library table of specific human factors and corresponding data collection methods, as shown in Table 1:

[0099] Table 1

[0100]

[0101]

[0102] Therefore, the final data collection method in the embodiments of the present application needs to be comprehensively considered and selected in combination with factors such as data accuracy requirements, impact on workers' work, cost, and feasibility.

[0103] In step S103, judge the starting surface situation of the complex product assembly sequence planning model. If the starting surface situation is a single starting surface situation, then based on a preset heuristic algorithm, solve the complex product assembly sequence planning model in the single starting surface situation to obtain the corresponding target assembly sequence.

[0104] In step S104, if the starting surface situation is a multi-starting surface situation, then determine the assembly time, assembly cost, multiple important human factors, and multiple constraint conditions of the complex product assembly sequence planning model in the multi-starting surface situation, and construct a fitness function according to the assembly time, assembly cost, multiple important human factors, and multiple constraint conditions, so as to solve the complex product assembly sequence planning model in the multi-starting surface situation through the fitness function and a preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence.

[0105] In complex product assembly sequence planning, it is crucial to quantitatively express human factors and incorporate them into the operations research optimization model. Therefore, in order to comprehensively and accurately describe these factors, the following embodiments of the present application will elaborate on the method of mathematically expressing the five dimensions of cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors.

[0106] After mathematically expressing human factors and performing mathematical modeling, for the problem model of complex product assembly sequence planning (ASP), the embodiments of the present application can consider the following two situations:

[0107] I. For the case of a single working surface (i.e., only one worker on the assembly line performs the linear assembly task caozuo1), heuristic algorithms such as genetic algorithms can be used to solve the problem.

[0108] II. For the problem of multiple working surfaces (i.e., multiple workers on the assembly line carry out assembly tasks in parallel).

[0109] It is particularly necessary to carry out parallel assembly sequence planning. Therefore, in the model solving section, the embodiment of the present application proposes a special solving method for the design of parallel working surfaces. In the traditional linear assembly mode, each assembly task depends on the completion of the previous task, which often leads to production bottlenecks and time delays. The parallel assembly mode allows multiple assembly tasks to be carried out simultaneously, which can effectively improve the flexibility and response speed of the production line. Especially in the face of diversified and personalized market demands, the parallel assembly method can better adapt to the rapidly changing production environment, thereby achieving higher production efficiency and shorter delivery cycles. In the actual assembly process of complex products, the required material resources are often over-allocated, but the constraints of human resources are widespread, that is, the number of assembly personnel available at each assembly workstation often determines the maximum number of working surfaces that can be started.

[0110] When solving the above assembly sequence planning problem, Hybrid Genetic Programming (HGP), as a powerful optimization algorithm, has good adaptability and global search ability. Compared with traditional heuristic algorithms, HGP combines the global search advantages of genetic algorithms and the flexibility of programming, and can effectively handle complex assembly sequence planning problems; by introducing the consideration of personnel constraints, HGP can not only optimize resource allocation in assembly tasks, but also ensure the reasonable use of human resources, thereby improving the overall assembly efficiency.

[0111] To solve the optimal assembly sequence planning problem under the current maximum working surface limit, the embodiment of the present application adopts the method of Genetic Programming (GP). Genetic programming is an optimization algorithm based on the idea of evolution, which is suitable for solving complex combinatorial optimization problems. Through genetic programming, the optimal or approximate optimal assembly sequence can be found under the consideration of various constraints and objectives.

[0112] Optionally, in an embodiment of the present application, mathematical modeling is performed on each important human factor using multiple important human factor data to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field, including: quantifying the worker's attention, memory ability, and decision-making ability during the execution of complex product assembly tasks in the target complex product assembly field, and constructing a cognitive factor mathematical model based on the quantified worker's attention, memory ability, and decision-making ability; obtaining the working hours, rest hours, and at least one physiological index of the worker during the execution of complex product assembly tasks, determining the fatigue degree of the worker according to the working hours and rest hours, and estimating the physical fitness state of the worker through at least one physiological index, and constructing a physiological factor mathematical model based on the fatigue degree and physical fitness state; quantifying the emotional state of the worker during the execution of complex product assembly tasks through a preset emotion rating scale, obtaining at least one physiological parameter of the worker, and quantifying the stress level of the worker using at least one physiological parameter and a preset subjective report, and constructing a psychological factor mathematical model based on the quantified emotional state and stress level; obtaining the average time and benchmark time for the worker to complete the same complex product assembly task, and calculating the skill level of the worker according to the average time and benchmark time; obtaining the number of participations of the worker in assembly tasks that meet the preset similarity requirements with complex product assembly tasks, and constructing a skill factor mathematical model according to the skill level and the number of participations; determining the training degree, rule compliance degree, and teamwork efficiency of the worker, and constructing an organizational factor mathematical model according to the training degree, rule compliance degree, and teamwork efficiency.

[0113] It should be noted that the process of mathematical modeling of cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors in the embodiments of the present application is as follows:

[0114] 1. Mathematical expression of cognitive factors:

[0115] Cognitive factors include the worker's attention, memory ability, decision-making ability, etc. during task execution, and these factors will affect the accuracy and efficiency of the worker in completing assembly tasks; the embodiments of the present application can be quantified in the following ways:

[0116] 1. Attention level (A t ):

[0117] Attention can be reflected by the number of work errors within a specific time period. The higher the attention level, the fewer the error numbers, indicating that the worker's attention is more concentrated during this time period.

[0118] Let the attention level at time t be A t , which is inversely proportional to the number of errors E t , and can be expressed as:

[0119] At = 1 / (1 + k 1 * E t )

[0120] where (E t ) represents the number of errors that occur during the time period (t), and (k 1 ) is a weighting factor used to adjust the degree of influence of errors on attention; this formula indicates that as the number of errors increases, the attention level decreases;

[0121] 2. Decision-making ability (D c ):

[0122] Decision-making ability can be represented by the correct decision-making rate of workers when performing multiple tasks; the higher the decision-making ability, the more correct decisions workers can make in a multi-task environment.

[0123] D c = n correct / n total

[0124] where (n correct ) is the number of correct decisions, and (n total ) is the total number of decisions; the larger this ratio, the stronger the decision-making ability of the worker.

[0125] 2. Mathematical expressions of physiological factors:

[0126] Physiological factors mainly include fatigue level, physical fitness, etc. These factors have a significant impact on the efficiency of workers in completing tasks, as described below:

[0127] 1. Fatigue level (F t ):

[0128] The fatigue level can be measured by the length of the worker's working hours and rest time. The fatigue level increases with the increase in working hours and decreases with the increase in rest time.

[0129] F t = F (t-1) + k 2 * W t - k 3 * R t

[0130] where F (t-1) represents the fatigue level in the previous time period, W t represents the length of working hours at time t, R t represents the length of rest time at time t, k 2 and k 3Weight factors for fatigue growth and recovery respectively; this formula indicates that as working hours increase, the degree of fatigue rises, while rest time helps with fatigue recovery.

[0131] 2. Physical fitness status (P s ): The physical fitness status can be estimated through indicators such as the worker's heart rate and blood pressure, and is usually represented by a standardized physical fitness index:

[0132] P s = (HR max - HR rest ) / HR max

[0133] where HR max is the maximum heart rate and HR rest is the resting heart rate. This formula shows that the physical fitness status is related to the difference in heart rate, and the greater the difference, the better the worker's physical fitness status.

[0134] 3. Mathematical expression of psychological factors:

[0135] Psychological factors include emotional state, stress level, etc., which will affect the worker's concentration and task completion quality, as described below:

[0136] 1. Emotional state (M e ):

[0137] In the embodiments of the present application, it can be quantified by the score measured by an emotional rating scale, ranging from -1 (extremely negative) to 1 (extremely positive):

[0138] M e ∈ [-1, 1]

[0139] where the higher the value of M e , the more positive the emotional state, and a negative value indicates a negative emotional state.

[0140] 2. Stress level (S t ):

[0141] The stress level can be quantified by subjective reports combined with physiological parameters (such as skin conductance, heart rate variability):

[0142] S t = k 4 * HRV + k 5 * GSR

[0143] where HRV is heart rate variability, GSR is skin conductance, and k 4 and k 5is a weighting factor used to adjust the influence of heart rate variability and skin conductance on the stress level. Changes in heart rate variability and skin conductance can reflect the psychological stress level of workers.

[0144] 4. Mathematical expression of skill factors:

[0145] Skill factors include the professional skill level, experience, etc. of workers, and have a direct impact on the efficiency and quality of assembly tasks, as described below:

[0146] 1. Skill level (S k ):

[0147] In the embodiments of the present application, the skill level can be represented by the ratio of the average time for a worker to complete the same task to the benchmark time:

[0148] S k =T baseline / T worker

[0149] Wherein, T baseline is the benchmark time, representing the time to complete the task under ideal conditions, and T worker is the average time for the worker to actually complete the task. The larger this value, the higher the skill level of the worker, and the more efficiently the task can be completed.

[0150] 2. Experience (E x ):

[0151] Experience can be quantified by the number of times a worker participates in similar tasks.

[0152] E x =n task

[0153] Wherein, n task is the number of times the worker participates in similar tasks. The more times, the richer the experience of the worker.

[0154] 5. Mathematical expression of organizational factors:

[0155] Organizational factors include training, degree of rule compliance, teamwork, etc. These factors will affect the overall efficiency and cooperation effect of the team, as described below:

[0156] 1. Degree of training (T r ):

[0157] In the embodiments of the present application, it can be represented by the number of training courses a worker participates in and their grades:

[0158] T r =∑(C i *G i), where \(i = 1, 2,\cdots, n\)

[0159] Among them, \(C\) i is the weight of the \(i\)-th course, indicating the importance of this course, and \(G\) i is the score of the worker in the \(i\)-th course, and \(n\) is the number of training courses; this formula is used to evaluate the overall training level of the worker.

[0160] 2. Degree of rule compliance (\(C\) r ):

[0161] The degree of rule compliance can be quantified by the number of violations of the worker during work:

[0162] \(C\) r = 1 - (\(n\) vio / \(n\) checks )

[0163] Among them, \(n\) vio is the number of violations, and \(n\) checks is the total number of inspections. The closer this value is to 1, the higher the degree of rule compliance and the fewer the number of violations.

[0164] 3. Team collaboration efficiency (\(C\) e ):

[0165] Team collaboration can be represented by the ratio of the time taken by the team to complete a task to the ideal time:

[0166] \(C\) e = \(T\) iaeal / \(T\) actual

[0167] Among them, \(T\) ideal is the ideal task completion time, indicating the time when the team should complete the task under ideal circumstances, and \(T\) actual is the actual time taken by the team to complete the task. The larger this value is, the higher the team collaboration efficiency.

[0168] Optionally, in an embodiment of the present application, the assembly time, assembly cost, multiple important human factor elements, and multiple constraint conditions of the complex product assembly sequence planning model in the case of multiple working surfaces are determined, and a fitness function is constructed based on the assembly time, assembly cost, multiple important human factor elements, and multiple constraint conditions, so as to solve the complex product assembly sequence planning model in the case of multiple working surfaces through the fitness function and a preset hybrid genetic programming algorithm to obtain a corresponding target assembly sequence, including: reading and parsing multiple constraint conditions of the parallel assembly sequence planning, where the multiple constraint conditions include assembly process constraints, resource constraints, space constraints, the maximum number of working surfaces, and human factor constraints; determining the factor scores and weight coefficients of each important human factor corresponding to each complex product assembly task, and constructing a human factor evaluation function through the factor scores and weight coefficients, so as to construct a fitness function based on the assembly time, assembly cost, human factor evaluation function, and multiple constraint conditions, and determining the expression form of the assembly sequence planning result through the fitness function, and performing a neighborhood search operation on the assembly sequence planning result to obtain a corresponding initial population; based on the hybrid genetic programming algorithm, screening multiple individuals that meet the preset fitness requirements from the initial population, and performing a crossover operation on each individual in the multiple individuals and the parental individuals in the initial population corresponding to each individual according to a preset crossover strategy to obtain multiple new offspring individuals; performing a mutation operation on the multiple new offspring individuals based on a preset mutation probability, and performing a fitness evaluation on the multiple new offspring individuals after the mutation operation to obtain the fitness function values corresponding to each new offspring individual in the multiple new offspring individuals, and iteratively executing the screening, crossover, mutation, and fitness evaluation operations until a preset termination condition is met, so as to obtain a target assembly sequence that meets the preset fitness value requirements.

[0169] Specifically, the embodiment of the present application first needs to read and parse various constraint conditions of the parallel assembly sequence planning, and these constraints mainly include assembly process constraints, resource constraints, space constraints, and human factor constraints, etc. Among them, the maximum number of working surfaces is a key limiting factor, indicating the maximum number of assembly operations that can be carried out simultaneously.

[0170] In the embodiment of the present application, the assembly task set is set as A = {a 1 ,a 2 ,...,a N}, where N is the total number of assembly tasks; each assembly task has a pre-task and a post-task, constituting the sequential relationship of the assembly process; the process constraints between assembly tasks can be expressed as a set of relationships:

[0171] a j ∈A, a i <a j means a i must be before aj completed previously

[0172] Resource constraints include the finiteness of human resources, equipment resources, etc., and it is necessary to ensure that at any moment, the allocated resources do not exceed their total available amount; space constraints involve the space limitations of the assembly site, and ensure that within the same time period, the number of working surfaces does not exceed the maximum limit P:

[0173]

[0174] where, δ k，t indicates whether task a k is executed at time t (δ k，t = 1 indicates execution, 0 indicates non-execution), and T is the entire assembly time range.

[0175] Secondly, in order to evaluate the advantages and disadvantages of different assembly sequences, the embodiments of the present application also need to establish a fitness function, which comprehensively considers assembly time, assembly cost, human factor, and the degree of satisfaction with constraint conditions.

[0176] Optionally, in an embodiment of the present application, the mathematical expression of the fitness function:

[0177] Fitness(X) = θ·T total (X)+β·C total (X)+γ·F human (X)+λ·Penalty(X)

[0178] where, X represents the assembly sequence scheme; T total (X) represents the assembly time of the assembly sequence scheme X; C total (X) represents the assembly cost; F human (X) represents the human factor evaluation function; Penalty(X) represents the penalty term for violating the constraint conditions; θ represents the weight coefficient corresponding to the assembly time; β represents the weight coefficient corresponding to the assembly cost; γ represents the weight coefficient corresponding to the human factor evaluation function; λ represents the weight coefficient corresponding to the penalty term.

[0179] In the embodiments of the present application, the fitness function for evaluating the assembly sequence is shown in the following formula:

[0180] Fitness(X) = θ·T total (X)+β·C total (X)+γ·F human (X)+λ·Penalty(X)

[0181] where, x represents an assembly sequence scheme; T total (X) is the total assembly time of the assembly sequence scheme X; C total(X) is the total assembly cost; F human (X) is the evaluation function of human factors, measuring the impact of the assembly sequence on workers; Penalty(X) is the penalty term for violating the constraints; θ, β, γ, λ are weight coefficients used to balance the importance of various indicators.

[0182] It should be noted that the above total assembly time T total (X) can be obtained by scheduling and simulating the assembly sequence to calculate the latest time point when all tasks are completed; the total assembly cost C total (X) includes labor costs, equipment usage costs, etc.

[0183] The human factor evaluation function F human (X) can be defined as:

[0184]

[0185] Among them, H cog (a i ) is the cognitive factor score of task a i ; H phys (a i ) is the physiological factor score of task a i ; H psych (a i ) is the psychological factor score of task a i ; H skill (a i ) is the skill factor score of task a i ; H org (a i ) is the organizational factor score of task a i ; w cog , w phys , w psych , w skill , w org are the weight coefficients of the corresponding factors.

[0186] In the actual implementation process, the embodiments of the present application can perform the following corresponding mathematical processing on human factors:

[0187] 1. Cognitive factor score H cog (a i ):

[0188] Considering the cognitive load of the worker when performing task a i and the understanding of the assembly process, the embodiments of the present application can evaluate through the task complexity C task (a i ) and the worker experience level E(a i ):

[0189]

[0190] Among them, the higher the task complexity, the lower the experience level, and the higher the cognitive load score.

[0191] 2. Physiological factor score H phys (a i ):

[0192] Embodiments of the present application consider the fatigue state, workload, and physiological comfort of workers, and calculate the physiological factor score according to the cumulative working time t work (a i ) and rest time t rest (a i ) of the workers:

[0193] H phys (a i ) = φ · (t work (a i ) - t rest (a i ))

[0194] Among them, φ is the physiological load coefficient.

[0195] 3. Psychological factor score H psych (a i ):

[0196] Embodiments of the present application can evaluate the stress, motivation, and satisfaction of workers when performing task a i , and measure the psychological factor score through the psychological stress index P(a i ) and motivation level M(a i ):

[0197] H psych (a i ) = P(a i ) - θ · M(a i )

[0198] Among them, θ is the motivation influence coefficient.

[0199] 4. Skill factor score H skill (a i ):

[0200] Embodiments of the present application can measure the matching degree between the skills of workers and the complexity of assembly tasks, and calculate the skill factor score using the skill matching degree S(a i ):

[0201]

[0202] Among them, when the skill matching degree is low, a high score indicates an adverse effect on fitness.

[0203] 5. Score H of organizational factors org (a i ):

[0204] Embodiments of the present application consider the impact of organizational rules and regulations, skill training, and management methods on workers performing task a i and can evaluate the score of organizational factors through organizational support degree O(a i ):

[0205]

[0206] Among them, the higher the organizational support degree, the lower the score, and the smaller the negative impact on fitness.

[0207] In addition, the penalty term Penalty(X) in the embodiments of the present application can be used to impose penalties on assembly sequences that violate the constraint conditions to guide genetic programming to search in the feasible solution space. This penalty term can be defined as the total degree of violation of the constraint conditions:

[0208]

[0209] Among them, M is a relatively large positive number, Δ 资源 and Δ 空间 respectively represent the degrees to which resource and space limitations are exceeded.

[0210] Thus, embodiments of the present application can more comprehensively evaluate the advantages and disadvantages of assembly sequences by designing a fitness function that comprehensively considers assembly time, assembly cost, human factor, and constraint conditions, so as to improve the quality of the optimization results.

[0211] Again, embodiments of the present application can determine the expression form of the assembly sequence planning result; specifically, the result of the assembly sequence planning needs to be expressed in a clear and easy-to-operate form. In the embodiments of the present application, a tree structure can be used to represent the assembly sequence, and each node represents an assembly task, and the connection between nodes represents the sequence of tasks.

[0212] Embodiments of the present application assume that the assembly sequence is represented as a directed acyclic graph (Directed Acyclic Graph, DAG), denoted as:

[0213] G=(V, E),

[0214] where V is the set of nodes, that is, the assembly tasks; E is the set of directed edges, representing the sequence of tasks.

[0215] It can be understood that the above representation method can not only clearly express the assembly process flow, but also facilitate operations such as crossover and mutation in genetic programming.

[0216] After that, the embodiments of the present application can use the neighborhood search module to initialize the solution.

[0217] Specifically, in order to improve the search efficiency of genetic programming, the embodiments of the present application can first use the neighborhood search module to initialize the solution; neighborhood search is an optimization method based on local search, which searches for a better solution by making small adjustments to the initial solution.

[0218] The generation of the initial solution can be based on the topological sorting of the assembly tasks. On the premise of meeting the process constraints, several feasible assembly sequences are randomly generated as the initial population, and these initial solutions need to cover different regions of the solution space to increase diversity.

[0219] In neighborhood search, for each initial solution X 0 , its neighborhood solution set N(X 0 ) is generated through operations such as swapping, inserting, and deleting; then, the solution with a higher fitness is selected from the neighborhood solutions as the initial population of genetic programming.

[0220] Specifically, the neighborhood search operation includes the following steps:

[0221] Step 1, swapping operation: Swap the positions of two non-directly related tasks in the assembly sequence;

[0222] Step 2, insertion operation: Insert a certain task into another position, provided that the process constraints are not violated;

[0223] Step 3, reverse operation: Reverse a certain subsequence to change its execution order.

[0224] Thus, through neighborhood search, the embodiments of the present application can obtain a batch of feasible solutions with higher quality in the initial stage, thereby laying a foundation for the subsequent optimization of genetic programming.

[0225] Subsequently, after obtaining the initial population, the embodiments of the present application can use the genetic programming method to solve the problem, that is, use the genetic programming method to optimize the assembly sequence. This genetic programming mainly includes processes such as selection, crossover, mutation, and fitness evaluation, which are specifically described as follows:

[0226] 1. Selection operation:

[0227] In the embodiments of the present application, the selection operation aims to select individuals with higher fitness from the current population to enter the next generation. The embodiments of the present application can adopt methods such as roulette wheel selection and tournament selection. Assume that the population size is Np and the fitness value is Fitness(X i ), then the selection probability pi can be defined as:

[0228]

[0229] where N p is the population size, and Fitness(X i ) is the fitness value of individual X i .

[0230] It should be noted that this method can ensure that individuals with higher fitness (i.e., smaller Fitness(X i )) have a greater probability of being selected.

[0231] 2. Crossover operation:

[0232] In the embodiments of the present application, the crossover operation can generate new offspring individuals by exchanging part of the structures of two parent individuals; since the assembly sequence is represented in the form of a tree or a graph, the crossover operation needs to ensure that the generated offspring still satisfy the process constraints; the specific process of the crossover method adopted in the embodiments of the present application is as follows:

[0233] (1) Randomly select two parent individuals X parent1 and X parent2 ;

[0234] (2) Randomly select a subtree (subgraph) in their respective assembly sequences;

[0235] (3) Exchange these two subtrees to generate two new offspring individuals X child1 and X child2 ;

[0236] (4) Check whether the offspring individuals satisfy the constraint conditions. If not, repair or discard them.

[0237] 3. Mutation operation:

[0238] In the embodiments of the present application, the mutation operation makes minor random modifications to individuals to increase the diversity of the population. This mutation operation needs to be carefully designed to avoid destroying the feasibility of the assembly sequence; the specific process of this mutation operation is as follows:

[0239] (1) Node replacement: Replace a certain assembly task with another alternative task;

[0240] (2) Subtree recombination: Rearrange a certain subtree to change the order of its internal tasks;

[0241] (3) Task insertion / deletion: Insert or delete an assembly task without violating process constraints.

[0242] In the specific implementation process, the mutation probability is usually set low to maintain the stability of the population.

[0243] 4. Fitness evaluation:

[0244] In the embodiments of the present application, the fitness function value Fitness(X) of the newly generated offspring individuals can be calculated. If the offspring individuals violate the constraint conditions, their fitness values will become poor due to the existence of penalty terms and will be eliminated in the selection process.

[0245] 5. Iterative operation:

[0246] In the embodiments of the present application, the above processes of selection, crossover, mutation, and fitness evaluation can be repeated until the termination condition is met. The termination condition can be reaching the preset maximum number of iterations G max , or the fitness value has not been significantly improved in several consecutive generations.

[0247] It should be noted that in the embodiments of the present application, the goal of genetic programming is to solve the following optimization problem:

[0248]

[0249] Among them, is the set of all feasible assembly sequences.

[0250] Furthermore, the embodiments of the present application can summarize the above detailed steps in each step to obtain a general process for solving the assembly sequence planning problem using the genetic programming algorithm:

[0251] 1. Initialization: Generate an initial population using neighborhood search

[0252] 2. Fitness evaluation: Calculate the fitness value of each individual in the initial population;

[0253] 3. Iterative loop (for g = 1 to G max ):

[0254] (1) Selection: Select individuals from the population according to the fitness value ;

[0255] (2) Crossover: Perform a crossover operation on the selected individuals to generate offspring individuals;

[0256] (3) Mutation: Perform a mutation operation on the offspring individuals;

[0257] (4) Fitness evaluation: Calculate the fitness value of the new individual;

[0258] (5) Population update: Generate a new population

[0259] 5. Output: After the termination condition is met, output the assembly sequence with the optimal fitness value.

[0260] In the assembly sequence planning, the embodiments of the present application need to ensure that the number of assembly tasks carried out simultaneously does not exceed the maximum start-up surface limit P; in the fitness evaluation and constraint check of genetic programming, it is necessary to count the number of assembly tasks in progress at each time point t:

[0261]

[0262] where, δ i，t indicates whether task a i is in progress at time t.

[0263] It can be understood that the embodiments of the present application use the genetic programming method to solve the assembly sequence, so as to have advantages such as strong global search ability and high adaptability, so as to be able to find the optimal or approximate optimal solution under the consideration of various constraints and objectives.

[0264] Optionally, in an embodiment of the present application, after solving the complex product assembly sequence planning model with multiple start-up surfaces through the fitness function and the preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence, it further includes obtaining the current human factor data of the worker during the execution of the complex product assembly task; dynamically adjusting the human factor evaluation function in the fitness function according to the current human factor data, so as to use the dynamically adjusted human factor evaluation function to solve the complex product assembly sequence planning model with multiple start-up surfaces.

[0265] In addition, since human factors are dynamic during the assembly process, for example, the fatigue degree of workers will increase with time. Therefore, the embodiments of the present application can use digital thread and sensor technology to obtain the status data of workers in real time, so that during the genetic programming process, the F human (X) in the fitness function can be dynamically adjusted according to the latest human factor data.

[0266] As a feasible implementation method, the genetic programming method in the embodiments of the present application can be programmed and implemented in the python tool, and the corresponding effects can be obtained according to the input number of workers / maximum start-up surface limit, such as Figure 2 and Figure 3 shown; in the specific implementation process, the example implemented this time uses an assembly with 23 assembly tasks for display, Figure 2Showing the situation with only one worker, a linear assembly task sequence is obtained; Figure 3 Showing the situation where 5 workers can start assembling simultaneously, the algorithm result output is a hierarchical display form with the assembly tasks included in each starting surface.

[0267] In summary, in the embodiment of the present application, by comprehensively introducing human factors such as cognition, physiology, psychology, skills, and organization in the process of complex product assembly sequence planning, optimizing and solving the assembly sequence based on genetic programming, and obtaining the status data of workers through an effective decision-making human factor data collection method, adjusting the assembly sequence in combination with the constraints of human factors, finally realizing the optimal assembly sequence planning considering the worker status, thereby improving the efficiency and reliability of the assembly process.

[0268] According to the method for solving the complex product assembly sequence planning problem considering human factors proposed in the embodiment of the present application, by considering human factors in the assembly process, including cognitive, physiological, psychological, skills, and organizational factors, etc., and comprehensively using genetic programming technology to find the optimal or approximate optimal assembly sequence, so as to ensure efficient assembly under the maximum starting surface limit; in addition, the present application covers the whole process method of identifying key human factors, data collection and analysis, operational research optimization modeling, and solving using genetic programming in the complex product assembly scenario, providing an efficient and reliable assembly sequence planning solution for complex product systems.

[0269] Secondly, a device for solving the complex product assembly sequence planning problem considering human factors proposed in the embodiment of the present application is described with reference to the accompanying drawings.

[0270] Figure 4 It is a block diagram of the device for solving the complex product assembly sequence planning problem considering human factors in the embodiment of the present application.

[0271] As Figure 4 shown, the device 10 for solving the complex product assembly sequence planning problem considering human factors includes: a modeling module 100, a collection module 200, a first solution module 300, and a second solution module 400.

[0272] Among them, the modeling module 100 is used to establish a human factor library for the target complex product assembly field based on a preset human factor identification framework, construct a priority evaluation model according to a preset hierarchical analysis strategy, and calculate the comprehensive score of each human factor in the human factor library by using the priority evaluation model, where the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors.

[0273] The acquisition module 200 is configured to acquire the human factor data corresponding to each human factor based on a pre-constructed data acquisition method library, and perform mathematical modeling on each human factor using the human factor data, so as to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field.

[0274] The first solving module 300 is configured to determine the starting surface situation of the complex product assembly sequence planning model. If the starting surface situation is a single starting surface situation, then based on a preset heuristic algorithm, solve the complex product assembly sequence planning model in the single starting surface situation to obtain the corresponding target assembly sequence.

[0275] The second solving module 400 is configured to, if the starting surface situation is a multi-starting surface situation, determine the assembly time, assembly cost, each human factor, and multiple constraint conditions of the complex product assembly sequence planning model in the multi-starting surface situation, and construct a fitness function according to the assembly time, assembly cost, each human factor, and multiple constraint conditions, so as to solve the complex product assembly sequence planning model in the multi-starting surface situation through comprehensive scoring, the fitness function, and a preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence.

[0276] Optionally, in an embodiment of the present application, the modeling module 100 includes: a first construction unit, a scoring unit, a consistency checking unit, and a normalization unit.

[0277] Among them, the first construction unit is configured to construct a human factor identification framework based on the actual work requirements of the target complex product assembly field and a preset plurality of factor identification theories, so as to establish a human factor library of the target complex product assembly field according to the human factor identification framework, where the plurality of factor identification theories include human factors engineering theory, work design theory, SHEL model, operation risk management theory, and organizational behavior theory.

[0278] The scoring unit is configured to perform relative importance scoring on each pair of human factors in the human factor library to construct a judgment matrix, and calculate the maximum eigenvalue and consistency index of the judgment matrix.

[0279] The consistency checking unit is configured to calculate a consistency ratio based on the maximum eigenvalue and the consistency index, and perform consistency checking on each pair of human factors according to the consistency ratio to obtain the corresponding consistency checking result.

[0280] The normalization unit is configured to normalize each column of data of the judgment matrix to calculate the weight of each human factor, and determine the comprehensive score of each human factor in the human factor library based on the weight and the consistency checking result.

[0281] Optionally, in an embodiment of the present application, the acquisition module 200 includes: a determination unit and a second construction unit.

[0282] Among them, a determination unit is configured to respectively determine the cognitive factor data collection method, physiological factor data collection method, psychological factor data collection method, skill factor data collection method, and organizational factor data collection method corresponding to cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors.

[0283] A second construction unit is configured to construct a data collection method library according to the cognitive factor data collection method, physiological factor data collection method, psychological factor data collection method, skill factor data collection method, and organizational factor data collection method.

[0284] Optionally, in an embodiment of the present application, the acquisition module 200 further includes: a quantization unit, an estimation unit, a first acquisition unit, a calculation unit, a second acquisition unit, and a third construction unit.

[0285] Among them, the quantization unit is configured to quantify the worker's attention, worker's memory ability, and worker's decision-making ability during the execution of complex product assembly tasks in the target complex product assembly field, so as to construct a cognitive factor mathematical model according to the quantified worker's attention, worker's memory ability, and worker's decision-making ability.

[0286] The estimation unit is configured to obtain the working hours, rest hours, and at least one physiological index of the worker during the execution of complex product assembly tasks, determine the fatigue degree of the worker according to the working hours and rest hours, and estimate the physical fitness state of the worker through at least one physiological index, so as to construct a physiological factor mathematical model according to the fatigue degree and physical fitness state.

[0287] The first acquisition unit is configured to quantify the emotional state of the worker during the execution of complex product assembly tasks through a preset emotion rating scale, obtain at least one physiological parameter of the worker, and quantify the stress level of the worker by using at least one physiological parameter and a preset subjective report, so as to construct a psychological factor mathematical model based on the quantified emotional state and stress level.

[0288] The calculation unit is configured to obtain the average time and the reference time for the worker to complete the same complex product assembly task, and calculate the skill level of the worker according to the average time and the reference time.

[0289] The second acquisition unit is configured to obtain the number of participations of the worker in assembly tasks that meet the preset similarity requirements with complex product assembly tasks, and construct a skill factor mathematical model according to the skill level and the number of participations.

[0290] The third construction unit is configured to determine the training degree, rule compliance degree, and teamwork efficiency of the worker, and construct an organizational factor mathematical model according to the training degree, rule compliance degree, and teamwork efficiency.

[0291] Optionally, in an embodiment of the present application, the second solution module 400 includes: a reading unit, a neighborhood search unit, an algorithm processing unit, and an iteration unit.

[0292] Among them, the reading unit is configured to read and parse multiple constraint conditions of parallel assembly sequence planning, where the multiple constraint conditions include assembly process constraints, resource constraints, space constraints, the maximum number of starting surfaces, and human factor constraints.

[0293] The neighborhood search unit is configured to determine the factor scores and weight coefficients of each important human factor corresponding to each complex product assembly task, and construct a human factor evaluation function through the factor scores and weight coefficients, so as to construct a fitness function based on the assembly time, assembly cost, human factor evaluation function, and multiple constraint conditions, and determine the expression form of the assembly sequence planning result through the fitness function, and perform a neighborhood search operation on the assembly sequence planning result to obtain a corresponding initial population.

[0294] The algorithm processing unit is configured to, based on the hybrid genetic programming algorithm, screen out multiple individuals that meet the preset fitness requirements from the initial population, and perform a crossover operation on each individual in the multiple individuals with the parental individuals in the initial population corresponding to each individual according to the preset crossover strategy to obtain multiple new offspring individuals.

[0295] The iteration unit is configured to perform a mutation operation on the multiple new offspring individuals based on the preset mutation probability, and perform a fitness evaluation on the multiple new offspring individuals after the mutation operation to obtain the fitness function values corresponding to each new offspring individual in the multiple new offspring individuals, and iteratively execute the screening, crossover, mutation, and fitness evaluation operations until the preset termination condition is met, so as to obtain a target assembly sequence that meets the preset fitness value requirements.

[0296] Optionally, in an embodiment of the present application, the device 10 for solving the problem of complex product assembly sequence planning considering human factors in the embodiments of the present application further includes: a data acquisition module and a dynamic adjustment module.

[0297] Among them, the data acquisition module is configured to, after solving the complex product assembly sequence planning model in the case of multiple starting surfaces through comprehensive scoring, a fitness function, and the preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence, acquire the current human factor data of the worker during the execution of the complex product assembly task based on the data acquisition method library.

[0298] The dynamic adjustment module is configured to dynamically adjust the human factor evaluation function in the fitness function according to the current human factor data, so as to solve the complex product assembly sequence planning model in the case of multiple starting surfaces by using the dynamically adjusted human factor evaluation function.

[0299] Optionally, in an embodiment of the present application, the mathematical expression of the fitness function is:

[0300] Fitness(X) = θ·T total (X) + β·C total (X) + γ·F human (X) + λ·Penalty(X)

[0301] where X represents the assembly sequence plan; T total (X) represents the assembly time of the assembly sequence plan X; C total (X) represents the assembly cost; F human (X) represents the human factor evaluation function; Penalty(X) represents the penalty term for violating the constraint conditions; θ represents the weight coefficient corresponding to the assembly time; β represents the weight coefficient corresponding to the assembly cost; γ represents the weight coefficient corresponding to the human factor evaluation function; λ represents the weight coefficient corresponding to the penalty term.

[0302] It should be noted that the foregoing explanation of the embodiment of the solution method for the problem of complex product assembly sequence planning considering human factors also applies to the device for solving the problem of complex product assembly sequence planning considering human factors in this embodiment, and will not be elaborated here.

[0303] A problem-solving device for complex product assembly sequence planning considering human factors according to an embodiment of the present application includes a modeling module, which is used to establish a human factor library for the target complex product assembly field based on a preset human factor identification framework, construct a priority evaluation model according to a preset hierarchical analysis strategy, and calculate the comprehensive score of each human factor in the human factor library by using the priority evaluation model. Among them, the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors, and organizational factors; a collection module, which is used to collect human factor data corresponding to each human factor based on a pre-constructed data collection method library, and perform mathematical modeling on each human factor by using the human factor data to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field; a first solution module, which is used to solve the complex product assembly sequence planning model in the case of a single starting surface based on a preset heuristic algorithm to obtain a corresponding target assembly sequence; a second solution module, which is used to determine the assembly time, assembly cost, each human factor, and multiple constraints of the complex product assembly sequence planning model in the case of multiple starting surfaces, and construct a fitness function according to the assembly time, assembly cost, each human factor, and multiple constraints, so as to solve the complex product assembly sequence planning model in the case of multiple starting surfaces by using the comprehensive score, fitness function, and a preset hybrid genetic programming algorithm to obtain a corresponding target assembly sequence. The present application can provide an assembly sequence planning method that efficiently and comprehensively considers human factors, and can realize the planning of the optimal assembly sequence under complex constraint conditions, thereby improving the assembly efficiency, reducing the cost, and ensuring the assembly quality and worker safety.

[0304] Figure 5 The structural schematic diagram of the electronic device provided by the embodiment of the present application. The electronic device may include:

[0305] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0306] When the processor 502 executes the program, it implements the method for solving the problem of complex product assembly sequence planning considering human factors provided in the above embodiment.

[0307] Furthermore, the electronic device further includes:

[0308] A communication interface 503, which is used for communication between the memory 501 and the processor 502.

[0309] The memory 501 is used to store a computer program executable on the processor 502.

[0310] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0311] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity, Figure 5 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0312] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0313] The processor 502 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0314] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for solving the problem of complex product assembly sequence planning considering human factors as described above is implemented.

[0315] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0316] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0317] Any process or method description represented in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present application includes additional implementations in which functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0318] The logic and / or steps represented in a flowchart or described otherwise herein, for example, may be considered a sequenced list of executable instructions for implementing a logical function and may be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or N wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise appropriate processing if necessary, and then stored in a computer memory.

[0319] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0320] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0321] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0322] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for solving complex product assembly sequence planning problems considering human factors, characterized in that: The following steps are involved: Based on the preset human factor identification framework, a human factor library in the target complex product assembly field is established, and a priority evaluation model is constructed according to the preset hierarchical analysis strategy, and the priority evaluation model is used to calculate the comprehensive score of each human factor in the human factor library, wherein the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors and organizational factors; Based on a pre-built data collection method library, human factor data corresponding to each human factor are collected, and mathematical modeling is performed on each human factor using the human factor data to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field; Determine the start-up face situation of the complex product assembly sequence planning model. If the start-up face situation is a single start-up face situation, solve the complex product assembly sequence planning model under the single start-up face situation based on a preset heuristic algorithm to obtain a corresponding target assembly sequence; If the start-up surface situation is a multiple start-up surface situation, the assembly time, assembly cost, each human factor and multiple constraints of the complex product assembly sequence planning model under the multiple start-up surfaces situation are determined, and a fitness function is constructed according to the assembly time, the assembly cost, each human factor and the multiple constraints, so as to solve the complex product assembly sequence planning model under the multiple start-up surfaces situation through the comprehensive score, the fitness function and the preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence.

2. The method according to claim 1, characterized in that The human factor factor library in the target complex product assembly field is established based on the preset human factor identification framework, and a priority evaluation model is constructed according to the preset hierarchical analysis strategy, and the priority evaluation model is used to calculate the comprehensive score of each human factor in the human factor library, including: Based on the actual work requirements of the target complex product assembly field and the preset multiple factor identification theories, the human factor identification framework is constructed, so as to establish a human factor library in the target complex product assembly field according to the human factor identification framework, wherein the multiple factor identification theories include human factor engineering theory, job design theory, SHEL model, operational risk management theory and organizational behavior theory; Score the relative importance of each pair of human factors in the human factor library to construct a judgment matrix, and calculate the maximum eigenvalue and consistency index of the judgment matrix; Based on the maximum eigenvalue and the consistency index, a consistency ratio is calculated, and a consistency test is performed on each pair of human factors according to the consistency ratio to obtain a corresponding consistency test result; Each column of data in the judgment matrix is ​​normalized to calculate the weight of each human factor, and based on the weight and the consistency test result, a comprehensive score of each human factor in the human factor library is determined.

3. The method according to claim 1, characterized in that The collecting of human factor data corresponding to each human factor based on the pre-built data collection method library includes: Determine the cognitive factor data collection method, the physiological factor data collection method, the psychological factor data collection method, the skill factor data collection method and the organizational factor data collection method corresponding to the cognitive factor, the physiological factor, the psychological factor, the skill factor and the organizational factor respectively; The data collection method library is constructed according to the cognitive factor data collection method, the physiological factor data collection method, the psychological factor data collection method, the skill factor data collection method and the organizational factor data collection method.

4. The method according to claim 1, characterized in that: The method of using the human factor data to mathematically model each human factor to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field includes: Quantifying the worker's attention, memory and decision-making ability during the execution of the complex product assembly task in the target complex product assembly field, so as to construct the cognitive factor mathematical model according to the quantified worker's attention, memory and decision-making ability; Acquiring the working hours, resting hours and at least one physiological index of the worker during the execution of the complex product assembly task, determining the fatigue degree of the worker according to the working hours and the resting hours, and estimating the physical state of the worker through the at least one physiological index, so as to construct the mathematical model of physiological factors according to the fatigue degree and the physical state; quantifying the emotional state of the worker during the execution of the complex product assembly task by a preset emotional rating scale, obtaining at least one physiological parameter of the worker, and quantifying the stress level of the worker by using the at least one physiological parameter and a preset subjective report, so as to construct the psychological factor mathematical model based on the quantified emotional state and stress level; Obtaining the average time and benchmark time for the worker to complete the same complex product assembly task, and calculating the worker's skill level based on the average time and the benchmark time; Obtaining the number of times the worker participates in the assembly task that meets a preset similarity requirement with the complex product assembly task, and constructing the skill factor mathematical model according to the skill level and the number of times the worker participates; The workers' training degree, rule compliance degree and teamwork efficiency are determined, and the organizational factor mathematical model is constructed based on the training degree, rule compliance degree and teamwork efficiency.

5. The method according to claim 1, characterized in that The step of determining the assembly time, assembly cost, each human factor and multiple constraints of the complex product assembly sequence planning model under the condition of multiple opening surfaces, and constructing a fitness function according to the assembly time, the assembly cost, each human factor and the multiple constraints, so as to solve the complex product assembly sequence planning model under the condition of multiple opening surfaces through the comprehensive score, the fitness function and a preset hybrid genetic programming algorithm, so as to obtain a corresponding target assembly sequence, includes: Read and parse multiple constraints of parallel assembly sequence planning, wherein the multiple constraints include assembly process constraints, resource constraints, space constraints, maximum number of work surfaces, and human factor constraints; Determine the factor score and weight coefficient of each important human factor corresponding to each complex product assembly task, and construct a human factor evaluation function through the factor score and the weight coefficient, so as to construct the fitness function according to the assembly time, the assembly cost, the human factor evaluation function and the multiple constraints, and determine the expression form of the assembly sequence planning result through the fitness function, and perform a neighborhood search operation on the assembly sequence planning result to obtain a corresponding initial population; Based on the hybrid genetic programming algorithm, a plurality of individuals that meet preset fitness requirements are selected from the initial population, and a crossover operation is performed on each of the plurality of individuals with a parent individual in the initial population corresponding to each individual according to a preset crossover strategy to obtain a plurality of new offspring individuals; Based on a preset mutation probability, a mutation operation is performed on the multiple new offspring individuals, and fitness evaluation is performed on the multiple new offspring individuals after the mutation operation to obtain a fitness function value corresponding to each new offspring individual in the multiple new offspring individuals, and screening, crossover, mutation and fitness evaluation operations are iteratively performed until a preset termination condition is met to obtain a target assembly sequence that meets the preset fitness value requirements.

6. The method according to claim 5, characterized in that After solving the complex product assembly sequence planning model under the multi-opening surface condition by using the comprehensive score, the fitness function and the preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence, the method further includes: Based on the data collection method library, obtaining the current human factor data of the workers in the process of performing the complex product assembly task; The human factor evaluation function in the fitness function is dynamically adjusted according to the current human factor data, so as to solve the complex product assembly sequence planning model in the case of multiple working surfaces by using the dynamically adjusted human factor evaluation function.

7. The method according to claim 1, characterized in that The mathematical expression of the fitness function is: Fitness(X)=α·T total (X)+β·C total (X)+γ·F human (X)+λ·Penalty(X) Where X represents the assembly sequence scheme; T total (X) represents the assembly time of assembly sequence scheme X; C total (X) represents assembly cost; F human (X) represents the human factor evaluation function; Penalty(X) represents the penalty item for violating the constraint condition; α represents the weight coefficient corresponding to the assembly time; β represents the weight coefficient corresponding to the assembly cost; γ represents the weight coefficient corresponding to the human factor evaluation function; λ represents the weight coefficient corresponding to the penalty item.

8. A device for solving complex product assembly sequence planning problems considering human factors, characterized in that: include: A modeling module, for establishing a human factor library in the target complex product assembly field based on a preset human factor identification framework, and constructing a priority evaluation model according to a preset hierarchical analysis strategy, and using the priority evaluation model to calculate a comprehensive score of each human factor in the human factor library, wherein the human factor library includes cognitive factors, physiological factors, psychological factors, skill factors and organizational factors; A collection module, for collecting human factor data corresponding to each human factor based on a pre-built data collection method library, and using the human factor data to perform mathematical modeling on each human factor, so as to obtain a complex product assembly sequence planning model corresponding to the target complex product assembly field; A first solving module is used to determine the start-up face situation of the complex product assembly sequence planning model. If the start-up face situation is a single start-up face situation, then based on a preset heuristic algorithm, the complex product assembly sequence planning model under the single start-up face situation is solved to obtain a corresponding target assembly sequence; The second solving module is used to determine the assembly time, assembly cost, each human factor and multiple constraints of the complex product assembly sequence planning model under the multi-starting surface situation if the starting surface situation is a multi-starting surface situation, and construct a fitness function according to the assembly time, the assembly cost, each human factor and the multiple constraints, so as to solve the complex product assembly sequence planning model under the multi-starting surface situation through the comprehensive score, the fitness function and the preset hybrid genetic programming algorithm to obtain the corresponding target assembly sequence.

9. An electronic device, characterized in that: include: 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 a method for solving complex product assembly sequence planning problems taking human factors into consideration as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a method for solving a complex product assembly sequence planning problem taking human factors into consideration as described in any one of claims 1 to 7.

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