Personnel fatigue strength evaluation and analysis method for man-machine collaborative operation system

Abstract

A man-machine collaborative operation system personnel fatigue strength evaluation analysis method comprises the following steps: S1, establishing a state equation and an observation equation of a man-machine collaborative system, and determining the picking efficiency and fatigue state of a picker as state variables; s2, constructing a picker fatigue model, and comprehensively considering physiological and psychological factors including perceived workload, actual workload and shoulder and arm muscle load; s3, dynamically estimating the fatigue state of the picker by applying an extended Kalman filtering algorithm and combining actual order data; s4, designing a numerical experiment, setting parameters and simulation conditions, and analyzing sensitive factors such as sorting intensity and order input frequency to research the influence of the sensitive factors on system performance; and S5, analyzing a numerical experiment result, and evaluating system performance and picker fatigue states in different man-machine cooperation modes. According to the invention, the fatigue state of the picker can be accurately evaluated, the order picking process is optimized, the system efficiency is improved, and the body burden of the picker is reduced.

Description

Technical Field

[0001] The present invention relates to the field of human-machine collaborative systems and intelligent warehousing technology, and in particular to a method for evaluating and analyzing fatigue intensity of personnel in a human-machine collaborative operation system, which is used to evaluate and optimize the fatigue state of pickers in a human-machine collaborative order picking system. Background Art

[0002] With the rise of Industry 4.0, intelligent warehousing systems have gradually become a development trend in the logistics industry. In intelligent warehousing systems, human-machine collaborative order picking, as an emerging operation mode, effectively improves picking efficiency and accuracy through close cooperation between pickers and robots. However, pickers are prone to fatigue during long-term repetitive operations, which not only affects picking efficiency, but may also cause health problems such as musculoskeletal diseases. Therefore, how to scientifically evaluate the fatigue status of pickers and optimize the operation process accordingly has become an urgent problem to be solved in the field of intelligent warehousing.

[0003] Although existing research has focused on ergonomic issues in warehousing operations, most of the research focuses on purely manual picking systems, while relatively few studies have examined fatigue assessment and management strategies for human-machine collaborative systems. In addition, existing fatigue assessment methods mostly rely on questionnaires or subjective reports, which lack objectivity and real-time performance. For human-machine collaborative systems, a more accurate and automated fatigue assessment method is needed to achieve real-time monitoring and dynamic adjustment of the work status of pickers.

[0004] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0005] The main purpose of the present invention is to solve the problems existing in the above-mentioned background technology and to provide a method for evaluating and analyzing the fatigue strength of personnel in a human-machine collaborative operation system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for evaluating and analyzing fatigue intensity of personnel in a human-machine collaborative operation system comprises the following steps: S1, according to the characteristics of the human-machine collaborative system, establishing the state equation and observation equation of the system, and determining the picking efficiency and fatigue state of the picker as state variables; S2, based on the state variables determined in step S1, establishing a picker fatigue model, the model taking into account physiological and psychological factors, including actual workload and perceived workload, to quantify the fatigue degree of the picker; S3, based on the fatigue model constructed in step S2 and the state equation in step S1, using the extended Kalman filter algorithm, dynamically estimating the fatigue state of the picker according to actual order data; S4, under the guidance of the dynamic estimation result obtained in step S3, designing the parameters and simulation conditions of the numerical experiment, conducting sensitivity analysis, and studying the influence of different parameters such as picking intensity, order input frequency, etc. on the system performance and the fatigue state of the picker; S5, analyzing the numerical experiment results of step S4, and evaluating the system performance and the fatigue state of the picker under different human-machine collaborative modes.

[0008] Furthermore, in step S1, according to the characteristics of the human-machine collaborative order picking system, the state equation and observation equation of the system are first established. The state variable is set to the picking efficiency of the picker, because the picking efficiency directly reflects the operating status of the system and the working status of the picker. When constructing the state equation, considering that the picking efficiency decreases with the accumulation of fatigue index, a dynamic equation is used to describe this relationship. At the same time, the original order is used as the observation equation, and the state of the picking efficiency is estimated and updated in combination with the actual order completion status. This setting enables the system to reflect the changes in the working status of the picker in real time.

[0009] Furthermore, in the process of establishing the state equation and observation equation, the extended Kalman filter (EKF) model is used to deal with the nonlinear factors in the system. EKF linearizes the nonlinear model so that the traditional Kalman filter algorithm can be applied to this scenario. This processing method makes the system model closer to the actual situation and improves the accuracy of state estimation.

[0010] Furthermore, in the present invention, the establishment of the picker fatigue model takes into account multiple factors, including but not limited to the physical intensity of the picking task, the picker's psychological load, the working environment, and the picker's physiological and psychological state. By integrating these factors, the present invention can more comprehensively evaluate the picker's fatigue state.

[0011] Furthermore, in step S2, in order to comprehensively evaluate the fatigue status of the pickers, a fatigue model based on workload and muscle load is established. This model not only takes into account the actual workload, such as physical factors such as the weight and distance of the goods to be carried, but also adds the psychological dimension of perceived workload to comprehensively evaluate the fatigue level of the pickers during the operation. This comprehensive fatigue characterization method helps to more accurately understand the fatigue status of the pickers and provide a basis for subsequent fatigue management.

[0012] Furthermore, in step S3, the fatigue state of the picker is dynamically estimated using the extended Kalman filter algorithm. The algorithm updates the fatigue state of the picker in real time by continuously fusing system observation data (such as order completion status) and state equation prediction results. This method can effectively capture the dynamic changes of the picker's fatigue state and provide managers with real-time fatigue state information, thereby supporting more scientific decision-making.

[0013] Furthermore, the proposed method dynamically estimates the fatigue state of the picker through the extended Kalman filter algorithm, which can handle the nonlinear characteristics of the system. The time step length is the state transfer function The linearization of the observation function is performed to achieve the picking Accurate tracking of personnel fatigue status.

[0014] Furthermore, in step S3, the extended Kalman filter algorithm used in the present invention is particularly suitable for processing systems with nonlinear dynamic characteristics, such as human-machine collaborative order picking systems. The algorithm linearizes the state prediction model by calculating the Jacobian matrix, and then updates the state estimation at each time step, ensuring the accuracy and reliability of the state estimation.

[0015] Furthermore, when using the extended Kalman filter algorithm for fatigue state estimation, it is also necessary to adjust and verify the algorithm parameters in combination with actual order data. By comparing and analyzing the fatigue state estimation results under different order data, the algorithm performance can be further optimized and the accuracy and reliability of fatigue state estimation can be improved.

[0016] Furthermore, in step S4, the present invention designs a detailed numerical experiment scheme, and simulates different working conditions and system parameters through simulation experiments to study their effects on the fatigue state of the picker and the system performance. The experimental results will provide important data support for the design and optimization of the actual system.

[0017] Furthermore, in step S4, in order to study the impact of different parameters on system performance, the parameters and simulation conditions of the numerical experiment are designed. These parameters include picking intensity, order input frequency, picker input frequency, etc. Their different configurations will directly affect the operating efficiency of the system and the fatigue state of the pickers. Through simulation experiments, the impact of these parameters on system performance can be systematically evaluated, providing theoretical support for actual production management.

[0018] Furthermore, a sensitivity analysis of the experimental results is also included, by changing key parameters such as picking intensity, order input frequency, etc., to evaluate the specific impact of these parameter changes on the picker fatigue state and system performance. This helps to determine which factors are most critical to system performance, thereby providing direction for system optimization.

[0019] Furthermore, in step S5, by analyzing the results of numerical experiments, the system performance and the fatigue state of pickers under different human-machine collaborative modes can be evaluated. The experimental results show that different collaborative modes and system strategies have a significant impact on the fatigue state of pickers and the overall performance of the system. For example, the single-picker multi-robot mode performs well in reducing picker fatigue, while reasonable rest arrangements and order allocation strategies can also effectively reduce the fatigue level of pickers. These conclusions provide an important reference for optimizing the design and operation of human-machine collaborative order picking systems.

[0020] Furthermore, the present invention also proposes system optimization suggestions based on the simulation results, including adjusting the number ratio of pickers and robots, improving picking path planning, optimizing order allocation strategies, etc., so as to reduce picker fatigue and improve the overall efficiency of the system.

[0021] The present invention has the following beneficial effects:

[0022] This paper proposes a method for evaluating and analyzing the fatigue strength of personnel in a human-machine collaborative operation system. This method establishes a fatigue model for pickers, uses actual order data for dynamic estimation, and verifies the effectiveness of the method through numerical experiments. This method provides a new technical means for fatigue management of pickers in intelligent warehousing systems, which helps to improve the efficiency and safety of warehousing operations.

[0023] The picker fatigue analysis method based on extended Kalman filtering in the present invention effectively solves the deficiencies of picker fatigue assessment and management in the existing human-machine collaborative order picking system, improves picking efficiency, and reduces the physical and psychological fatigue of pickers caused by long working hours, as well as the health risks associated therewith. By accurately assessing the fatigue state of pickers and dynamically adjusting work and rest plans, the present invention can optimize the operation process of the human-machine collaborative system, improve the overall performance of the system, and meet the high requirements for immediacy, efficiency, and employee well-being in modern intelligent warehousing systems.

[0024] By establishing the state equation and observation equation of the human-machine collaborative order picking system and taking the picking efficiency of the picker as the state variable, the present invention can reflect the working status of the picker and the robot in real time and accurately, thereby improving the picking efficiency and accuracy. At the same time, combined with the picker fatigue model, considering physiological and psychological factors, the fatigue state of the picker is further dynamically estimated by extending the Kalman filter algorithm, providing a scientific basis for system optimization. These measures help reduce errors caused by fatigue, improve overall operational efficiency, and optimize warehouse management.

[0025] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to an embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of a linear Kalman filter according to an embodiment of the present invention;

[0028] Figure 3 It is a schematic diagram of the extended Kalman filter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0030] like Figure 1 As shown, the embodiment of the present invention provides a method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system, comprising the following steps S1 to S5:

[0031] S1. According to the characteristics of the human-machine collaborative system, establish the system's state equation and observation equation, and determine the state variables.

[0032] The state equation is used to describe the dynamic change of the picker's fatigue state over time, as well as the relationship between picking efficiency and fatigue state. The observation equation is used to correct and update the picker's state estimation based on the actual order data. The specific steps include S11~S12:

[0033] S11. First, based on the characteristics of the human-machine collaborative system, the state equation and observation equation of the system are established. The state equation describes the dynamic change of the picker's fatigue state over time, as well as the relationship between picking efficiency and fatigue state. The observation equation uses actual order data to correct and update the picker's state estimation.

[0034] The state equation can be expressed as: k+1 =δ k-α·δ k Workload k +w k Among them, α is the fatigue influence factor, and Workload k is the workload of stage k, w k is process noise. The observation equation relates picking efficiency to actual order fulfillment: k =H·δ k +∈ k , H is the observation matrix, Z k is the observed order completion, ∈ k is the observation noise.

[0035] S12. Determine the following state variables: 1. Picking efficiency (δ k ) : Indicates the picker's ability to complete orders per unit time, and this efficiency varies with the picker's fatigue state. 2. Fatigue state: Quantifies the degree of physical and mental fatigue accumulated by the picker due to long working hours. This can be assessed through physiological measurements (such as heart rate, electromyography) and psychological questionnaires. 3. Rest need: Dynamically adjust the rest time according to the fatigue state and picking efficiency to maintain the picker's work performance and health. The rest need can be expressed by the following formula: Rest k+1 =Rest k +β·(δ k -Threshold) where β is the rest adjustment factor, Threshold

[0036] is the preset fatigue threshold. Specifically, it includes the following steps:

[0037] S121. Define initial conditions for state variables, such as initial fatigue level and initial work efficiency, which are based on the state of the picker before the start of work; S122. According to the characteristics of the picking task and the work performance of the picker, establish a mathematical model of the change of state variables over time; S123. Use appropriate mathematical tools and algorithms to solve the state equation to ensure that the fatigue state and picking efficiency of the picker can be tracked and predicted in real time; S124. Use the observation equation to combine the actual order data with the state estimate, and use the Kalman filter algorithm to correct the state of the picker in real time to improve the accuracy of the state estimate; S125. Consider special requirements and constraints such as workstation layout, picking path and order processing flow to ensure that the state equation and observation equation can truly reflect the working conditions of the human-machine collaborative system.

[0038] S2. Construct a comprehensive picker fatigue assessment model, which quantifies and analyzes the picker's fatigue status at work through two main aspects.

[0039] First, based on the workload fatigue characterization method, the perceived workload and actual completed workload of the pickers are quantified through questionnaires, behavioral observations, and physiological indicator monitoring, thereby evaluating their overall workload. Secondly, based on the fatigue model of muscle load, physiological measurement techniques such as electromyography (EMG) are used to collect activity data of shoulder and arm muscles during the picking process, analyze the relationship between muscle load and fatigue level, and establish a corresponding mathematical model. In addition, the effects of the working environment, workstation design, and picking task characteristics on muscle load are considered, and a real-time monitoring and adjustment algorithm is developed to ensure that the pickers work within a safe fatigue threshold. Finally, the two fatigue models are combined to provide a comprehensive method for evaluating the fatigue status of pickers, which provides a basis for formulating reasonable work arrangements and rest schedules. The program provides a scientific basis for optimizing human-machine collaborative systems Workflow, improve efficiency and ensure The health of the pickers. The specific steps include S21~S22:

[0040] S21. The following detailed methods are used to characterize the fatigue of pickers based on workload: Definition of perceived workload: The subjective feelings of pickers about work tasks, including task difficulty, complexity, time pressure, etc., are quantified into numerical indicators through questionnaire surveys; Quantification of actual workload: Record the number of orders actually completed and the weight of goods handled by pickers within a certain period of time, which are automatically collected through the data recording system of the workstation; Quantification method: Combine questionnaire surveys, behavioral observations and physiological indicators (such as heart rate, blood pressure) monitoring, and use statistical methods to convert perceived workload and actual workload into comparable numerical values; Establishment of relationship model: Apply linear regression analysis to establish a relationship model between perceived workload and actual workload, for example: Fatigue Index =a·Perceived Workload+b·Actual Workload+c, where a, b, and c are regression coefficients obtained by data fitting.

[0041] S22. Use the following methods to establish a fatigue model based on muscle load: Application of physiological measurement technology: Use electromyography (EMG) equipment to collect shoulder and arm muscle activity data during the picking process and analyze muscle force; Muscle load analysis: Evaluate the muscle load when lifting, carrying and placing goods, considering the frequency and duration of muscle contraction; Construction of mathematical model: Establish a mathematical model between muscle load and fatigue level, considering the maximum contraction force (MVC), continuous working time and recovery time, for example: Muscle Fatigue (t) = MVC (1-e -λ·t ), where λ is the fatigue growth rate and t is the working time; Biomechanical simulation: Use biomechanical principles to simulate the muscle fatigue accumulation process, predict the muscle fatigue threshold, and ensure that the work is arranged within a safe range. The specific steps include S221~S224:

[0042] S221. Consider the impact of the following factors on picker fatigue: Workstation design: Evaluate how shelf height, picking tools, workstation ergonomics, etc. affect the picker's muscle load.

[0043] S222. Adjust the muscle load model to reflect actual working conditions: Consider factors such as order diversity, cargo weight distribution, picking path layout, etc., and adjust the muscle load model to more accurately reflect actual working conditions.

[0044] S223. Develop an algorithm to monitor and adjust the picker's muscle load in real time: Develop an algorithm to monitor muscle load in real time to ensure that the picker works within a safe fatigue threshold.

[0045] S224. Combining two fatigue models, a method for comprehensively evaluating the fatigue status of pickers is provided: combining the fatigue characterization based on workload and the fatigue model results based on muscle load to provide a scientific basis for work arrangements and rest plans.

[0046] S3, such as Figure 2 , Figure 3 As shown, by initializing the fatigue state and error covariance matrix of the picker, an extended Kalman filter algorithm is established and used to dynamically predict and update the fatigue state of the picker. The specific steps include S31 to S310, and the symbols of the Kalman filter model are shown in Table 1:

[0047] Table 1 Kalman filter model symbol description

[0048]

[0049] S31, setting the initial estimated value δ0 of the fatigue state of the picker and the initial error covariance matrix P0. These initial values ​​are based on the state of the picker before the start of work, such as the initial fatigue level and initial work efficiency.

[0050] S32. Construct a mathematical model to describe the evolution of the fatigue state of the picker over time. The model considers the impact of the picking task on fatigue and the effect of rest on fatigue relief. The model takes the following form: k|k-1 =δ k-1 -α·Workload k-1 +Rest k-1 Among them, α is the fatigue influence factor, and Workload k-1 is the workload of the last moment, Rest k-1 It is the amount of relief from fatigue caused by rest.

[0051] S33, using the state of the previous moment to estimate δ k-1 and prediction model to calculate the fatigue state prediction value δ at the current moment k|k-1This step makes predictions based only on previous information and does not involve actual observed data.

[0052] S34. Calculate the Jacobian matrix F for the nonlinear state prediction model k To perform linearization. The Jacobian matrix contains the first-order partial derivative of the state prediction model with respect to the state variable, and its calculation formula is as follows:

[0053] S35. Establish an observation model based on the actual order data to associate the fatigue state of the picker with the observable order completion status. The observation model can be in the following form: k =h(δ k )+∈ k Among them, h is the observation function, Z k is the observed order completion, ∈ k is the observation noise.

[0054] S36, according to the current prediction error covariance P k|k-1 The Kalman gain K is calculated by using the error covariance R of the observation model k The Kalman gain determines the weight of the observation data in the state update, and its calculation formula is as follows: K k =P k|k-1 H T (HP k|k-1 H T +R) -1 .

[0055] S37, combined with the predicted fatigue state δ k|k-1 and the actual order data Z k , through the Kalman gain K k Correct the predicted state to obtain the updated fatigue state estimate δ k :δ k =δ k|k-1 +K k (Z k -Hδ k|k-1 ).

[0056] S38. Update the error covariance matrix P k To reflect the uncertainty of the new state estimate: P k =(IK k H)P k|k-1 .

[0057] S39, evaluating the prediction accuracy of the model and adjusting the model parameters as needed to improve the accuracy of fatigue state estimation. This may include adjusting the fatigue influence factor α, the observation function h, or the observation noise covariance R.

[0058] S310, after each time step or after a certain number of orders are completed, steps S32 to S39 are repeatedly executed. This process realizes dynamic tracking and estimation of the fatigue state of the picker, ensuring the continuity and real-time nature of the estimation.

[0059] S4. Design and implement numerical experiments to evaluate the impact of picker fatigue in human-machine collaborative order picking systems. This includes determining the experimental objectives, setting up the simulation environment, selecting key parameters (such as the number of pickers and robots, order characteristics, work intensity, etc.), initializing the simulation model, running the simulation process, and collecting and recording key performance data. Through these data, analyze the system performance and the fatigue status of the pickers under different parameter configurations to provide a basis for system optimization. The specific steps include S41~S45:

[0060] S41. Design a numerical experiment plan based on the characteristics of the human-machine collaborative system. This includes clarifying the purpose of the experiment, such as evaluating the impact of different parameters on picker fatigue and system performance; setting up the simulation environment, determining the system states and variables to be simulated in the experiment; and establishing evaluation criteria for expected results to measure whether the experimental results meet the expected goals.

[0061] S42. Set the key parameters in the simulation experiment based on the experimental design. These parameters include the number of pickers, which simulates the needs of different scales of operations; the number of robots, which affects the effectiveness of human-machine collaborative operations; rate and fatigue level; order arrival pattern, The workload that affects the system; and the complexity of the picking task Complexity reflects the diversity and difficulty of order processing.

[0062] S43, using the state equation and observation equation established in step S1, and the fatigue model proposed in step S2, the simulation model is initialized. This includes setting the initial fatigue state and picking efficiency of the picker, providing a realistic starting point for the simulation experiment.

[0063] S44, start the simulation software or the custom simulation program, and run the simulation process of the human-machine collaborative order picking system according to the set parameters and initialization conditions. This step is the core of the experimental design, which simulates the actual system operation through simulation.

[0064] S45. Collect key performance indicator data in real time during the simulation process. These data include the fatigue status of the picker, order completion status, system throughput, etc., providing a basis for subsequent data analysis and result evaluation.

[0065] The parameters in step S4 include: the number of pickers: set the number of pickers in the experiment to simulate the work requirements of different scales; the number of robots: determine the number of robots working with the pickers, which will affect the work efficiency and the fatigue level of the pickers; order characteristics: including the number, type, size, etc. of orders, which affect the complexity and workload of the picking tasks; work intensity: the amount of work completed by the pickers in a unit time, which affects the speed of fatigue accumulation; order input frequency: the speed at which new orders enter the system, which affects the work rhythm and rest time arrangement of the pickers; picker input frequency: the frequency of pickers participating in tasks, which is related to workload and rest time; fatigue parameters: set according to the physiological and psychological state of the pickers, used to evaluate the impact of fatigue on work efficiency; rest strategy: determine the length and frequency of the rest time after the pickers complete the task, which affects the fatigue recovery effect; partition strategy: if applicable, including the division method of the picking area and the distribution of pickers' work in different areas.

[0066] S5. Perform sensitivity analysis and statistical processing on the data collected from the simulation experiment to evaluate the impact of changes in key parameters on the fatigue status of pickers and system performance. This step aims to identify the factors with the greatest impact, extract optimization strategies, and form practical conclusions that help improve system efficiency and protect the welfare of pickers. The specific steps include S51~S52:

[0067] S51. Conduct sensitivity analysis to identify the impact of key parameters on system performance and picker fatigue. This includes changing parameters such as order input intensity and picker rest frequency, and observing how these changes affect picking efficiency, order completion time, and the accumulated fatigue of pickers. The purpose of this step is to determine which factors are most sensitive to the system, thereby providing optimization direction for system design and operation.

[0068] S52. Conduct in-depth statistical analysis on the data collected from the simulation experiment. This includes calculating statistical indicators such as mean, standard deviation, and probability distribution to evaluate the effects of different strategies on system efficiency (such as throughput). quantity, order processing time) and picker health (e.g. fatigue level, need for rest). Based on these analyses, we have drawn conclusions that are helpful for optimizing actual systems, such as the best human-machine ratio, optimal rest frequency, etc.

[0069] In summary, the method for evaluating and analyzing fatigue intensity of personnel in a human-machine collaborative operation system of the present invention proposes an innovative solution to the needs of fatigue management of pickers in intelligent warehousing systems. This method captures the picking efficiency and fatigue status of pickers in real time by establishing state equations and observation equations, providing a data basis for accurate evaluation. Furthermore, by constructing a fatigue model that comprehensively considers physiological and psychological factors, the present invention can quantify the fatigue level of pickers, thereby more comprehensively understanding the impact of fatigue on work efficiency and health.

[0070] By using the extended Kalman filter algorithm, the present invention realizes the dynamic estimation of the fatigue state of the picker. This method not only improves the accuracy of the evaluation, but also enhances the real-time and automation level of the evaluation, effectively solving the shortcomings of the traditional method that relies on questionnaires or subjective reports. By designing numerical experiments and sensitivity analysis, the present invention further studies the impact of key parameters such as picking intensity and order input frequency on system performance and the fatigue state of the picker, providing a scientific basis for system optimization.

[0071] The present invention can monitor and dynamically adjust the work status of pickers in real time, optimize the operation process of the human-machine collaborative system, and significantly improve the picking efficiency and accuracy. At the same time, by accurately evaluating and dynamically adjusting the work and rest plan, the physical and mental fatigue of the pickers is reduced, the health risks associated with it are reduced, and the high requirements of modern intelligent warehousing systems for immediacy, efficiency and employee well-being are met. In addition, the application of the present invention is not limited to intelligent warehousing, and its principles and methods also have the potential to be extended to other fields, with broad practical application value and social benefits.

[0072] The above content is a further detailed description of the present invention in combination with specific / preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, they can also make several substitutions or modifications to these described embodiments, and these substitutions or modifications should be regarded as belonging to the scope of protection of the present invention. In the description of this specification, the reference terms "one embodiment", "some embodiments", "preferred embodiments", "examples", "specific examples", or "some examples" and the like are intended to refer to the specific features, structures, materials or characteristics described in combination with the embodiment or example included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. Without contradiction, technicians in this field can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples. Although it has been described in detail, Describes The embodiments of the present invention and their advantages, but it should be understood that without departing from the protection of the patent application Various changes, substitutions, and alterations can be made herein without departing from the scope thereof.

Claims

1. A method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system, characterized in that: The following steps are involved: S1. According to the characteristics of the human-machine collaborative system, the state equation and observation equation of the system are established, and the picking efficiency and fatigue state of the picker are determined as state variables; S2. Based on the state variables determined in step S1, a picker fatigue model is established, wherein the model takes into account physiological and psychological factors, including actual workload and perceived workload, to quantify the picker's fatigue level; S3, based on the fatigue model constructed in step S2 and the state equation in step S1, using the extended Kalman filter algorithm, dynamically estimate the fatigue state of the picker according to the actual order data; S4. Under the guidance of the dynamic estimation results obtained in step S3, design the parameters and simulation conditions of the numerical experiment, conduct sensitivity analysis, and study the impact of different parameters such as picking intensity and order input frequency on system performance and picker fatigue status; S5. Analyze the numerical experimental results of step S4 and evaluate the system performance and picker fatigue status under different human-machine collaboration modes.

2. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to claim 1, characterized in that: Step S1 specifically includes: S11. According to the characteristics of the human-machine collaborative system, the state equation and observation equation of the system are established, wherein the state equation is used to describe the dynamic change of the picker's fatigue state over time, and the relationship between the picking efficiency and the fatigue state; the observation equation is used to correct and update the picker's state estimation according to the actual order data; S12. Determine state variables, including: The picking efficiency of the picker indicates the ability to complete orders per unit time, which varies with the picker's fatigue state; The fatigue status of the pickers, which quantifies the degree of physical and mental fatigue accumulated by the pickers due to long working hours; The picker's rest needs are dynamically adjusted according to fatigue status and picking efficiency to maintain the picker's work performance and health.

3. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to claim 2, characterized in that: Step S12 specifically includes: S121, further determining the initial conditions of the state variables based on the state of the picker before the start of work, including the initial fatigue level and the initial work efficiency; S122, defining the evolution rules of the state variables, and establishing a mathematical model of the change of the state variables over time according to the characteristics of the picking task and the work performance of the picker; S123. Use appropriate mathematical tools and algorithms to solve the state equation to ensure that the fatigue state and picking efficiency of the pickers can be tracked and predicted in real time; S124, combining the actual order data with the state estimation using the observation equation, and correcting the state of the picker in real time through the Kalman filter algorithm to improve the accuracy of the state estimation; S125. Consider the special requirements and constraints of the system, including workstation layout, picking path and order processing flow, to ensure that the state equation and observation equation can truly reflect the working conditions of the human-machine collaborative system.

4. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to any one of claims 1 to 3, characterized in that: Step S2 specifically includes: S21. Perform workload-based fatigue characterization, including: Define perceived workload, which is the subjective feeling of the picker on the work task, including the difficulty and complexity of the task and the perception of time pressure; Define the actual workload completed, which is the number of orders actually completed by the picker within a certain period of time and the weight of goods handled; Quantify the perceived and actual workload of pickers through questionnaires, behavioral observations, or physiological indicator monitoring; Using statistical analysis methods, a relationship model between perceived workload and actual workload is established to evaluate the overall workload of pickers. S22. Establish a fatigue model based on muscle load, with special attention to the load on the shoulder and arm muscles during picking. The model includes: Through electromyography (EMG) and other physiological measurement technologies, the activity data of the pickers' shoulder and arm muscles during the picking process are collected; Analyze the impact of picking actions on muscle load, including muscle effort when lifting, carrying and placing goods; Establish a mathematical model between muscle load and fatigue level, taking into account the maximum contraction force, continuous working time and recovery time of the muscle; Using the principles of biomechanics, the accumulation process of muscle fatigue of pickers in different working conditions is simulated, and the threshold of muscle fatigue is predicted.

5. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to claim 4, characterized in that: Step S22 specifically includes: S221. Consider the impact of the work environment and workstation design on picker fatigue, including shelf height, use of picking tools, and ergonomic design of workstations; S222. Consider the characteristics of the picking task, including the diversity of orders, the weight distribution of goods, and the layout of the picking path, and adjust the muscle load model to more accurately reflect the actual working conditions; S223. Real-time monitoring and adjustment of muscle load models to ensure that pickers work within a safe fatigue threshold and reduce the risk of musculoskeletal disorders; S224. Combining the workload-based fatigue characterization and the muscle load-based fatigue model, a method for comprehensively evaluating the fatigue status of pickers is developed to provide a basis for the formulation of work arrangements and rest plans.

6. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to any one of claims 1 to 5, characterized in that: In step S3, the Kalman filter algorithm includes a state space model and an observation model; the state space model is used to describe the evolution of the system state over time. In the state space model, the state of the system is defined by a set of first-order differential equations, and the equations describe the dynamic behavior of the system state; The observation model cooperates with the state space model to define how to obtain actual observation data from the system state. The observation model contains observation noise, which reflects the deviation between the actual observation and the ideal state. The working process of the Kalman filter includes: Prediction step: At the beginning of each time step, based on the current state estimate and the known system dynamic model, the state at the next moment is predicted. This step does not rely on new observations but is based on previous information. Update step: Once new observation data is obtained, the Kalman filter compares the data with the predicted state, calculates the residual, and updates the state estimate through an optimal estimate, the Kalman gain, which minimizes the sum of squares of the prediction errors; The extended Kalman filter (EKF) makes the Kalman filter applicable to nonlinear systems by linearizing the nonlinear function at each time step; Among them, the linearization processing of nonlinear functions includes: EKF converts the nonlinear problem into a linear problem by calculating the Jacobian matrix, that is, the first-order partial derivatives of the state transfer function and the observation function with respect to the state variable, thereby allowing the filter to update its estimate at each time step to adapt to the nonlinear characteristics of the system.

7. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to claim 6, characterized in that: Step S3 includes: S31, initialization state: setting the initial estimated value of the picker's fatigue state and the initial error covariance matrix, wherein the initial estimated value is based on the state of the picker before the start of work, including the initial fatigue level and the initial work efficiency; S32. Prediction model establishment: Construct a mathematical model to describe the evolution of the picker's fatigue status over time. The model takes into account the impact of the picking task on the picker's fatigue and the effect of rest on alleviating fatigue. S33, state prediction: using the state estimation and prediction model of the previous moment, calculate the fatigue state prediction value of the current moment. This step does not involve actual observation data, and only predicts based on previous information; S34, calculating Jacobian matrix: for the nonlinear state prediction model, calculating Jacobian matrix for linearization processing, the Jacobian matrix includes the first-order partial derivative of the state prediction model with respect to the state variable; S35, observation model establishment: based on the actual order data, an observation model is established, which associates the fatigue state of the picker with the observable order completion status; S36, Kalman gain calculation: Calculate the Kalman gain based on the current prediction error covariance and the error covariance of the observation model. The Kalman gain determines the weight of the observation data in the state update. S37, state update: combining the predicted fatigue state and the actual order data, correcting the predicted state through the Kalman gain to obtain an updated fatigue state estimate; S38, Error covariance update: Update the error covariance matrix to reflect the uncertainty of the new state estimate; S39, Model verification and adjustment: Evaluate the prediction accuracy of the model and adjust the model parameters as needed to improve the accuracy of fatigue state estimation; S310, loop execution: at each time step or after completing a certain number of orders, steps S32 to S39 are repeatedly executed to achieve dynamic tracking and estimation of the fatigue state of the picker.

8. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to any one of claims 1 to 7, characterized in that: The specific process of step S4 includes: S41. Experimental design: Design numerical experimental schemes according to the characteristics of human-computer collaborative systems, including experimental objectives, simulation environment settings, and evaluation criteria for expected results; S42, parameter setting: based on the experimental design, set the key parameters in the simulation experiment, including the number of pickers, the number of robots, the order arrival pattern, and the complexity of the picking task; S43, model initialization: using the state equation and observation equation established in step S1, and the fatigue model established in step S2, the simulation model is initialized, and the initial fatigue state and picking efficiency of the picker are set; S44, simulation operation: starting the simulation software or the custom simulation program, and running the simulation process of the human-machine collaborative order picking system according to the set parameters and initialization conditions; S45. Data collection: During the simulation process, key performance indicators of pickers are collected in real time, including fatigue status data, order completion status, and system throughput.

9. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to claim 8, characterized in that: The parameters in step S4 include: Number of pickers: The number of pickers set in the experiment is used to simulate the operation requirements of different scales; Number of robots: The number of robots working with pickers affects the efficiency and fatigue level of human-machine collaborative work; Order characteristics: including the quantity, type, and size of the order. Order characteristics affect the complexity of the picking task and the workload of the picker; Work intensity: the amount of work that a picker completes per unit time, which affects the rate at which the picker's fatigue accumulates; Order input frequency: The speed at which new orders enter the system affects the work rhythm and rest time arrangement of the pickers; Picker input frequency: the frequency with which pickers participate in picking tasks, which is related to the picker’s workload and rest time; Fatigue parameters: parameters set according to the physiological and psychological state of the picker to evaluate the impact of fatigue on work efficiency; Rest strategy: The length and frequency of rest that pickers take after completing a task affects the effectiveness of fatigue recovery; Zoning strategy: includes how the picking areas are divided and how the pickers are assigned work in different areas.

10. The method for evaluating and analyzing fatigue strength of personnel in a human-machine collaborative operation system according to any one of claims 1 to 9, characterized in that: Step S5 includes: S51. Sensitivity analysis: By changing key parameters, including order input intensity and picker rest frequency, observe the impact of changes in key parameters on picker fatigue status and system performance; S52. Result processing: Conduct statistical analysis on the data collected from the simulation experiment, evaluate the impact of different strategies on the efficiency of the human-machine collaborative system and the health of pickers, and draw conclusions that are helpful for optimizing the actual system.

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