Man-machine engineering evaluation method and system for commercial vehicle cab

Abstract

The invention relates to the technical field of man-machine engineering, in particular to a commercial vehicle cab man-machine engineering evaluation method and system, and the method comprises the steps: constructing a quantitative evaluation index system comprising three dimensions of view design, riding space and comfort, and operability, and obtaining each secondary index parameter value. A fuzzy analytic hierarchy process is used to construct and check a fuzzy consistency judgment matrix of each hierarchy index, and index weights are calculated. And performing multi-level fuzzy comprehensive operation based on the weight and the parameter value to generate a quantitative evaluation result. The result is input into a layout optimization system, a driving system automatically or semi-automatically adjusts geometric parameters or positions of parts such as an instrument desk and a seat in a cab three-dimensional model, a closed loop from quantitative evaluation to direct design optimization is achieved, and the man-machine performance and design efficiency of the cab are improved.

Description

Technical Field

[0001] This application relates to the field of human factors engineering technology, specifically to a method and system for evaluating the human factors engineering of a commercial vehicle cab. Background Technology

[0002] As the core space where drivers work for extended periods, the ergonomic performance of commercial vehicle cabs directly impacts driving safety, operational efficiency, and passenger comfort. Currently, the automotive industry's ergonomic analysis and evaluation of cabs primarily focuses on passenger vehicles, resulting in relatively mature theories and software tools. However, commercial vehicles differ significantly from passenger vehicles in functional layout, spatial composition, complexity of operating components, and usage scenarios. Their cabs must not only meet basic driving requirements but also accommodate long-distance rest, living quarters, and the operation of various specialized equipment.

[0003] Currently, the ergonomic design and evaluation of commercial vehicle cabs largely rely on designers' experience and judgment or scattered single-indicator tests, making it difficult to comprehensively weigh and quantitatively compare multi-dimensional performance. This makes it difficult to identify potential ergonomic defects in the design phase. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method and system for evaluating the ergonomics of commercial vehicle cabs. This method performs ergonomic analysis and optimization on the layout design of commercial vehicle cabs, improving various ergonomic performance aspects and further enhancing product competitiveness.

[0005] In a first aspect, the present invention provides a method for evaluating the ergonomics of a commercial vehicle cab, comprising the following steps: S1. Construct an evaluation index system, specifically including: establishing the target layer as improving the human-machine interface performance of commercial vehicle cabs; setting primary evaluation indicators, including visibility design, passenger space and comfort, and operability; and setting multiple secondary evaluation indicators under each primary evaluation indicator. The secondary evaluation indicators under the vision design include at least parameters for quantitatively evaluating the forward and downward field of vision and instrument visibility; the secondary evaluation indicators under the riding space and comfort include at least parameters for quantitatively evaluating riding space and sitting comfort; and the secondary evaluation indicators under the operability include at least parameters for quantitatively evaluating hand accessibility and ease of operation. S2. For each secondary evaluation indicator, obtain its corresponding quantitative parameter value; S3. Construct fuzzy consistent judgment matrices for the relative importance between the target layer and the first-level evaluation indicators, and between each first-level evaluation indicator and its subordinate second-level evaluation indicators. S4. Perform a consistency check on the fuzzy consistency judgment matrix; if the check passes, proceed to S5; if the check fails, return to S3 and adjust the scale values ​​of the elements in the fuzzy consistency judgment matrix. S5. Calculate the relative weights of each element in the fuzzy consistency judgment matrix; S6. Based on the quantitative parameter values ​​of the weights and secondary evaluation indicators, perform multi-level fuzzy comprehensive operations to generate a comprehensive evaluation result for quantitatively characterizing the human-machine performance level of the commercial vehicle cab.

[0006] As a preferred embodiment of the technical solution of the present invention, the parameter used to quantitatively evaluate the forward lower field of vision is the forward lower field of vision blind spot distance; the parameter used to quantitatively evaluate the instrument visibility is the instrument area visibility ratio; the parameters used to quantitatively evaluate the seating space include legroom distance, headroom distance, and footroom distance; the parameter used to quantitatively evaluate the sitting comfort is the overall sitting discomfort; the parameter used to quantitatively evaluate the hand accessibility is the hand function accessibility ratio; and the parameter used to quantitatively evaluate the ease of operation is the subjective rating of ease of operation.

[0007] As a preferred embodiment of the technical solution of this invention, the quantitative parameter values ​​of each secondary evaluation index are defined as follows: The forward lower field of vision blind spot distance is defined as the horizontal straight-line distance between the point on the ground projected by the driver's line of sight through the upper edge of the dashboard and the frontmost part of the vehicle. The visible ratio of the instrument area is defined as the proportion of the total area of ​​the instrument area that the driver can see directly or by slightly turning his eyes and head. Legroom, headroom, and footroom are defined as the shortest distances between the driver's legs and the dashboard, between the top of the head and the roof liner, and between the feet and the nearest obstacle, respectively. Overall discomfort in sitting posture is defined as a rating scale of 0-8, with lower scores indicating better comfort. The proportion of hand-accessible area is defined as the ratio of the area accessible to the driver's single hand or fingers for operating vehicle switches and controls to the total area of ​​the main operating area in front of the instrument panel. The subjective score for ease of operation is defined as the average score of subjective evaluations of ease of getting on and off the vehicle, passability inside the driver's cab, ease of access to the sleeper berth, and ease of cleaning the windshield. Evaluators will give subjective evaluations of ease of getting on and off the vehicle, passability inside the driver's cab, ease of access to the sleeper berth, and ease of cleaning the windshield on a scale of 1 to 7 according to the preset scoring rules, and calculate the average score.

[0008] As a preferred embodiment of the technical solution of the present invention, one or more of the following quantitative parameters—the blind spot distance of the lower front field of view, the visible ratio of the instrument area, the distance of each space, the overall discomfort of the sitting posture, and the ratio of the reachable range of hand functions—are achieved through the following simulation analysis steps: Establish a three-dimensional digital model of the commercial vehicle's cab; Based on the target population's human body size database, human body models at the 5th, 50th, and 95th percentiles were established as mannequins for analysis. The human body model is placed in the driver's cab model, and constraints are applied to its H-point, hand grip point and foot heel point. The standard driving posture is calculated by human factors engineering simulation software. Based on the standard driving posture and the analysis function of the simulation software, the corresponding quantitative parameter values ​​are obtained.

[0009] As a preferred embodiment of the technical solution of the present invention, the single quantitative parameter value of the secondary evaluation index obtained through simulation analysis is determined according to the following rules: The following values ​​were used to measure the distance to the lower frontal blind spot, leg space, head space, foot space, and overall discomfort of sitting posture: the simulation analysis results of the 95th percentile human body model. The visibility ratio of the instrument panel area and the reachability of hand functions are taken from the simulation analysis results of the 5th percentile human body model. As a preferred embodiment of the technical solution of the present invention, the comprehensive evaluation results are used to identify the weak links in the ergonomic design of the commercial vehicle cab and provide a quantitative basis for the optimization design of the cab layout; the comprehensive evaluation results are formatted as an ergonomic analysis report containing the scores of each level of indicators and the final evaluation level.

[0010] As a preferred embodiment of the technical solution of the present invention, when constructing the fuzzy consistency judgment matrix in S3, a scale of 0.1 to 0.9 is used to represent the relative importance between pairs of indicators; Fuzzy consistency judgment matrix The following conditions must be met simultaneously: ; ; ; in, Let be the order of the matrix. Indicates the first The first indicator is relative to the first The importance scale of each indicator; Constructing a fuzzy consistency judgment matrix includes the following steps: S31. Determine the reference row of the fuzzy consistency judgment matrix; wherein, the reference row is the first row of the fuzzy consistency judgment matrix; S32. Based on the scaling rules and the conditions of the fuzzy consistency judgment matrix, determine the scaling values ​​of other elements in the fuzzy consistency judgment matrix based on the baseline row.

[0011] As a preferred embodiment of the technical solution of the present invention, S5 involves hierarchical weight calculation, specifically including: For the fuzzy consistent judgment matrix constructed between the target layer and the first-level evaluation index, the weight vector of the first-level evaluation index is calculated. For each primary evaluation indicator and its subordinate secondary evaluation indicators, a fuzzy consistency judgment matrix is ​​constructed, and the local weight vector of each secondary evaluation indicator relative to its primary indicator is calculated. The formula for calculating the weights in the fuzzy consistency judgment matrix is:

[0012] in, For the first The weight of each indicator.

[0013] As a preferred embodiment of the technical solution of the present invention, in step S6, the multi-level fuzzy synthesis operation includes the following steps: S61. Determine the comment set V, wherein the comment set V contains multiple qualitative descriptive words used to characterize the evaluation level; S62. For each secondary evaluation indicator, obtain its corresponding fuzzy evaluation vector to the comment set V, where each component of the vector represents the degree to which the indicator belongs to the corresponding comment level. S63. For each primary evaluation indicator, combine the fuzzy evaluation vectors of all its subordinate secondary evaluation indicators into a fuzzy evaluation matrix for that primary indicator, and use the corresponding weight vectors calculated in S5 to perform a synthesis operation to obtain the fuzzy comprehensive evaluation result vector for that primary indicator. S64. Combine the fuzzy comprehensive evaluation result vectors of all first-level indicators into a fuzzy evaluation matrix of the target layer, and use the weight vectors of the first-level indicators calculated in S5 to perform a synthesis operation to obtain the comprehensive fuzzy evaluation result vector of the target layer A.

[0014] Secondly, the present invention also provides a human factors evaluation system for a commercial vehicle cab, comprising: The indicator system construction module is used to perform the following operations: Establish the target layer as improving the human-machine interface performance of commercial vehicle cabs; set primary evaluation indicators, including visibility design, passenger space and comfort, and operability; and set multiple secondary evaluation indicators under each primary evaluation indicator. Specifically, the secondary evaluation indicators under visibility design should include at least parameters for quantitatively evaluating forward and downward visibility and instrument visibility; the secondary evaluation indicators under passenger space and comfort should include at least parameters for quantitatively evaluating passenger space and seating comfort; and the secondary evaluation indicators under operability should include at least parameters for quantitatively evaluating hand accessibility and ease of operation. The parameter acquisition module is used to obtain the corresponding quantitative parameter value for each secondary evaluation indicator. The judgment matrix construction and verification module is used to construct fuzzy consistent judgment matrices for the relative importance between the target layer and the first-level evaluation indicators, as well as between each first-level evaluation indicator and its subordinate second-level evaluation indicators, and to perform consistency verification on the fuzzy consistent judgment matrices; if the verification fails, the scale values ​​of the elements in the fuzzy consistent judgment matrix are adjusted and the verification is repeated until it passes. The weight calculation module is used to calculate the relative weights of each element in the fuzzy consistent judgment matrix that has passed the consistency test. The fuzzy comprehensive operation module is used to perform multi-level fuzzy comprehensive operations based on the weights and the quantitative parameter values ​​of the secondary evaluation indicators to generate a comprehensive evaluation result for quantitatively characterizing the human-machine performance level of the commercial vehicle cab.

[0015] As can be seen from the above technical solutions, this application has the following advantages: By constructing an indicator system covering three key dimensions—visual design, seating space and comfort, and operability—and performing multi-level comprehensive calculations based on fuzzy hierarchical analysis, complex and multi-factor human-machine performance issues can be transformed into quantifiable and comparable comprehensive evaluation results. This helps to identify and optimize human-machine layout defects during the design phase, reduce design rework after physical prototype production, shorten the development cycle, and improve the overall human-machine performance level and market competitiveness of the product. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention.

[0018] Figure 2 A block diagram of a system provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this application and in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0021] like Figure 1 As shown, this embodiment of the invention provides a method for evaluating the ergonomics of a commercial vehicle cab, including the following steps: S1. Construct an evaluation index system, specifically including: establishing the target layer as improving the human-machine interface performance of commercial vehicle cabs; setting primary evaluation indicators, including visibility design, passenger space and comfort, and operability; and setting multiple secondary evaluation indicators under each primary evaluation indicator. Specifically, the secondary evaluation indicators under visibility design should include at least parameters for quantitatively evaluating forward and downward visibility and instrument visibility; the secondary evaluation indicators under passenger space and comfort should include at least parameters for quantitatively evaluating passenger space and seating comfort; and the secondary evaluation indicators under operability should include at least parameters for quantitatively evaluating hand accessibility and ease of operation. In this embodiment of the invention, the parameter used to quantify the assessment of the forward lower field of vision is the forward lower field of vision blind spot distance; the parameter used to quantify the assessment of instrument visibility is the instrument area visibility ratio; the parameters used to quantify the assessment of seating space include legroom distance, headroom distance, and footroom distance; the parameter used to quantify the assessment of sitting comfort is the overall sitting discomfort; the parameter used to quantify the assessment of hand accessibility is the hand function accessibility ratio; and the parameter used to quantify the assessment of operational convenience is the subjective rating of operational convenience.

[0022] S2. For each secondary evaluation indicator, obtain its corresponding quantitative parameter value; The quantitative parameter values ​​for each secondary evaluation indicator are defined as follows: The forward lower field of vision blind spot distance is defined as the horizontal straight-line distance between the point on the ground projected by the driver's line of sight through the upper edge of the dashboard and the frontmost part of the vehicle. The visible ratio of the instrument area is defined as the proportion of the total area of ​​the instrument area that the driver can see directly or by slightly turning his eyes and head. Legroom, headroom, and footroom are defined as the shortest distances between the driver's legs and the dashboard, between the top of the head and the roof liner, and between the feet and the nearest obstacle, respectively. Overall discomfort in sitting posture is defined as a rating scale of 0-8, with lower scores indicating better comfort. The proportion of hand-accessible area is defined as the ratio of the area accessible to the driver's single hand or fingers for operating vehicle switches and controls to the total area of ​​the main operating area in front of the instrument panel. The subjective score for ease of operation is defined as the average score of subjective evaluations of ease of getting on and off the vehicle, passability inside the driver's cab, ease of access to the sleeper berth, and ease of cleaning the windshield. Evaluators will give subjective evaluations of ease of getting on and off the vehicle, passability inside the driver's cab, ease of access to the sleeper berth, and ease of cleaning the windshield on a scale of 1 to 7 according to the preset scoring rules, and calculate the average score.

[0023] It should be noted that one or more of the following quantitative parameters—the forward lower field of vision blind spot distance, the visible ratio of the instrument area, the distances in various spaces, the overall discomfort of the sitting posture, and the ratio of the reachable range of hand functions—were achieved through the following simulation analysis steps: Establish a three-dimensional digital model of the commercial vehicle's cab; Based on the target population's human body size database, human body models at the 5th, 50th, and 95th percentiles were established as mannequins for analysis. The human body model is placed in the driver's cab model, and constraints are applied to its H-point, hand grip point and foot heel point. The standard driving posture is calculated by human factors engineering simulation software. Based on the standard driving posture and the analysis function of the simulation software, the corresponding quantitative parameter values ​​are obtained. The final single quantitative parameter value used for evaluation of the secondary evaluation indicators obtained through simulation analysis is determined according to the following rules: The following values ​​were used to measure the distance to the lower frontal blind spot, leg space, head space, foot space, and overall discomfort of sitting posture: the simulation analysis results of the 95th percentile human body model. The visual scale of the instrument area and the scale of the reach of hand functions are taken from the simulation analysis results of the 5th percentile human body model.

[0024] S3. Construct fuzzy consistent judgment matrices for the relative importance between the target layer and the first-level evaluation indicators, and between each first-level evaluation indicator and its subordinate second-level evaluation indicators. In this step, when constructing the fuzzy consistent judgment matrix, a scale of 0.1 to 0.9 is used to represent the relative importance between pairs of indicators; fuzzy consistent judgment matrix The following conditions must be met simultaneously: ; ; ; in, Let be the order of the matrix. Indicates the first The first indicator is relative to the first The importance scale of each indicator; Constructing a fuzzy consistency judgment matrix includes the following steps: S31. Determine the reference row of the fuzzy consistency judgment matrix; wherein, the reference row is the first row of the fuzzy consistency judgment matrix; S32. Based on the scaling rules and the conditions of the fuzzy consistency judgment matrix, determine the scaling values ​​of other elements in the fuzzy consistency judgment matrix based on the baseline row.

[0025] S4. Perform a consistency check on the fuzzy consistency judgment matrix; if the check passes, proceed to S5; if the check fails, return to S3 and adjust the scale values ​​of the elements in the fuzzy consistency judgment matrix. S5. Calculate the relative weights of each element in the fuzzy consistency judgment matrix; calculate the weights hierarchically, specifically including: For the fuzzy consistent judgment matrix constructed between the target layer and the first-level evaluation index, the weight vector of the first-level evaluation index is calculated. For each primary evaluation indicator and its subordinate secondary evaluation indicators, a fuzzy consistency judgment matrix is ​​constructed, and the local weight vector of each secondary evaluation indicator relative to its primary indicator is calculated. The fuzzy consistency judgment matrix Weight of each indicator The calculation formula is:

[0026] In the formula, This is about the matrix. Summing all elements in the row. It represents the sum of the elements in the first row. The sum of the importance of this indicator relative to all other indicators. The larger this value, the more important the indicator is.

[0027] This is a fixed correction term related to the matrix size. Its purpose is to ensure all weights are correct. The sum is strictly equal to 1.

[0028] This is a normalization factor that ensures the calculated... It is a value between 0 and 1, ultimately all The sum equals 1.

[0029] S6. Based on the weights and secondary evaluation index parameter values, perform multi-level fuzzy comprehensive operations to generate a comprehensive evaluation result for quantitatively characterizing the human-machine performance level of the commercial vehicle cab; Performing multi-level fuzzy synthesis operations includes the following steps: S61. Determine the comment set V, which contains multiple qualitative descriptive words used to characterize the evaluation level; the comment set V is: V = {poor, poor, average, good, relatively good}; S62. For each secondary evaluation indicator, obtain its corresponding fuzzy evaluation vector to the comment set V, where each component of the vector represents the degree to which the indicator belongs to the corresponding comment level. The fuzzy evaluation vector is obtained through expert evaluation. This involves multiple evaluation experts determining the rating level of each secondary evaluation indicator based on its actual parameter values ​​and calculating the selection ratio. The selection ratio of each level is then used as the corresponding component in the fuzzy evaluation vector.

[0030] S63. For each primary evaluation indicator, combine the fuzzy evaluation vectors of all its subordinate secondary evaluation indicators into a fuzzy evaluation matrix for that primary indicator, and use the corresponding weight vectors calculated in S5 to perform a synthesis operation to obtain the fuzzy comprehensive evaluation result vector for that primary indicator. S64. Combine the fuzzy comprehensive evaluation result vectors of all first-level indicators into a fuzzy evaluation matrix of the target layer, and use the weight vectors of the first-level indicators calculated in S5 to perform a synthesis operation to obtain the comprehensive fuzzy evaluation result vector of the target layer A.

[0031] The composition operation in S63 and S64 is fuzzy matrix multiplication, specifically: multiplying the weight vector by the corresponding fuzzy evaluation matrix, as shown in the following formula: If the weight vector is The corresponding fuzzy evaluation matrix is The result vector of the synthesis operation ,in Furthermore, the result vector is usually normalized to make it... .

[0032] The specific process for generating the comprehensive evaluation result is as follows: based on the comprehensive fuzzy evaluation result vector of the target layer A obtained in S64, and according to the principle of maximum membership, the evaluation level corresponding to the component with the largest value in the vector is selected as the final qualitative evaluation conclusion of the human-machine performance level of the commercial vehicle cab.

[0033] The comprehensive evaluation result also includes a comprehensive score value A, which is calculated using the following formula:

[0034] in, This is the weight vector of the first-level indicators. This represents the local weight vector of the secondary indicators under each primary indicator. These are the standardized values ​​of the secondary evaluation index parameters.

[0035] In practice, a complete digital model of the commercial vehicle cab to be evaluated is created using 3D modeling software. This model should include all components related to human-machine interaction, such as the dashboard, steering wheel, seat, pedals, various control buttons, gear shift lever, and sleeper berth.

[0036] Using the human-computer interaction simulation software RAMSIS, mannequin models representing the target user group are created based on set standards. Typically, three mannequin models need to be created: the 5th percentile (P5, representing short women), the 50th percentile (P50, representing average men), and the 95th percentile (P95, representing tall men).

[0037] The dummy model was placed into the digital model of the driver's cab, and constraints were applied: the dummy's H-point was constrained within the range of the seat's designed H-point travel; the hands' grip points were constrained to the 3 and 9 o'clock positions on the steering wheel; the heels were constrained to the floor; the right foot's pedal point was constrained to 1 / 3 of the accelerator pedal's travel; and the line-of-sight angle was set to 5-10°. The standard driving posture under these constraints was calculated using RAMSIS software.

[0038] Based on this standard driving posture, the quantitative parameter values ​​of each secondary evaluation indicator are obtained: Front lower field of vision blind spot distance C11: In the software, simulate the driver's line of sight (focusing on the P95 dummy), project it through the upper edge of the dashboard (or the lower edge of the windshield) onto the virtual ground, and measure the horizontal distance between this projection point and the frontmost bumper point of the vehicle.

[0039] Instrument Panel Visibility Ratio C12: Under standard driving posture, this parameter simulates the driver's eye position and analyzes the situation where the instrument panel area is obstructed by components such as the steering wheel and handles. The ratio of the area of ​​the instrument panel that the driver can directly observe within the normal driving head rotation range to the total designed area of ​​the instrument panel is the visibility ratio.

[0040] The passenger space distances include the shortest distance from the legs to the dashboard (C21), the shortest distance from the top of the head to the headliner (C22), and the shortest distance from the feet to the nearest obstacle (C23): the shortest distances between the P95 dummy's knees and the lower dashboard panel, the shortest distances between the top of the head and the headliner, and the shortest distances between the P5 dummy's heels and the nearest obstacle are measured respectively.

[0041] Overall discomfort in sitting posture (C24): Using the comfort analysis module of RAMSIS software, a biomechanical analysis was performed on a dummy in the current standard driving posture. The software outputs an overall discomfort score from 0 to 8. The lower the value, the better. Discomfort in different body parts includes discomfort sensations in the neck, shoulders, back, hips, legs, and arms. The score ranges from 0 to 8. A score less than 4 is ideal, 4 to 5 is acceptable but requires improvement, and a score higher than 5 is unacceptable.

[0042] Hand Accessibility Ratio C31: Using the driver's shoulder as the center, a hand reach interface is generated in RAMSIS. The main operating area on the dashboard is defined; in this embodiment, it is a rectangular area 600mm wide and 300mm deep, with the steering wheel center as a reference. The area of ​​buttons within this area that can be reached by a single finger of the P5 dummy and by the entire hand of the P95 dummy is analyzed. The ratio of this accessible area to the total area of ​​the main operating area is the accessibility ratio.

[0043] Subjective rating for ease of operation (C32): A group of experienced drivers or evaluation experts were invited to rate the ease of getting on and off the vehicle, the cab's internal accessibility, the sleeper berth accessibility, and the windshield cleaning ease of the vehicle, based on design drawings, digital models, or physical models, according to the scoring rules in Table 1. The average of the four scores was calculated as the parameter value for this indicator.

[0044] Table 1 Subjective Scoring Rules

[0045] Establish the hierarchical structure of the evaluation index system as shown in Table 2.

[0046] Table 2 Hierarchical Structure of Evaluation Index System

[0047] Target layer element A is the same as the element in the next layer (Level I indicator). ... If n=1, 2, 3... are related, then the fuzzy matrix R is represented as shown in Table 3.

[0048] Table 3 Representation of the fuzzy matrix R

[0049] Scale The actual meaning is element and elements The degree of importance compared to the scale The numerical meanings are shown in Table 4.

[0050] Table 4 Scale Numerical meaning

[0051] Taking the primary indicator as an example, while ensuring basic safety (visibility and operation), comfort is particularly important for commercial vehicles engaged in long-distance driving. Therefore, a judgment matrix is ​​constructed for target layer A on B1, B2, and B3. Using the first row (B1 row) as the benchmark, the judgment is: B1 is equally important as B1 itself (…). =0.5); B1 (view of view) is slightly more important than B2 (comfort). =0.6); B1 and B3 (operations) are equally important ( =0.5). According to the property of fuzzy consistent matrix ( , , , ), and further deduced and The final judgment matrix A is obtained. Then, a consistency check is performed to ensure that the difference between corresponding elements in any two rows is a constant.

[0052] For the judgment matrix A that passes the test, use the weight calculation formula. Perform the calculation. Assume the calculated weight vector for the primary indicator is... This represents the importance weights of B1, B2, and B3 to the overall goal. Similarly, by constructing and calculating the judgment matrices of B1, B2, and B3 to their subordinate secondary indicators, a local weight vector is obtained, as shown below. .

[0053] Determine the set of comments V = {Poor (V1), Poor (V2), Average (V3), Good (V4), Fairly Good (V5)}.

[0054] Based on the acquired secondary indicator parameter values, and considering the current cab design, each indicator is determined to belong to a specific level within the evaluation set V. The selection percentages are statistically analyzed to form a fuzzy evaluation vector for each secondary indicator. For example, for C11, if 10% consider it "Average (V3)", 40% consider it "Good (V4)", and 50% consider it "Fairly Good (V5)", then its evaluation vector is... .

[0055] According to the formula Calculate the fuzzy evaluation results for each primary indicator. .in, For fuzzy synthesis operators, here it is a weighted average.

[0056] Finally, the comprehensive evaluation vector of target layer A is calculated. Assuming the result is A = (0.05, 0.10, 0.20, 0.35, 0.30), according to the principle of maximum membership, 0.35 corresponds to "Good (V4)", thus the overall evaluation of the cab's human-machine interface performance can be concluded as "Good". Simultaneously, a detailed analysis report can be generated, indicating that the proportion of "Poor" memberships in the footspace distance C23 is relatively high, representing a weak point requiring focused optimization.

[0057] In some embodiments, the method further includes: S7. Input the comprehensive evaluation results into the cab layout optimization system. Based on the evaluation results, the system automatically or semi-automatically adjusts the geometric parameters or relative positions of one or more components in the three-dimensional digital model of the commercial vehicle cab. These components include the dashboard, seat, steering column, or control button panel. The comprehensive evaluation results are used to identify weaknesses in the ergonomic design of the commercial vehicle cab and provide quantitative basis for optimizing the cab layout. The comprehensive evaluation results are formatted as an ergonomic analysis report containing scores for each level of indicators and the final evaluation level, used to guide the design adjustments of relevant cab components, including the dashboard, seat, steering mechanism, or control button layout. Input the above comprehensive evaluation results and weakness analysis report into the cab layout optimization system integrated with this invention. This system can be a plug-in deeply integrated with CAD software or a standalone platform.

[0058] Based on the conclusion that the foot space distance C23 score is low, the system automatically locates relevant constraint features in the 3D digital model, such as the foot pedal envelope, floor height, and wheel arch shape. Guided by a preset adjustment rule library (e.g., "If foot space is insufficient, prioritize adjusting the pedal position in the Y direction or modifying the local floor shape"), the system can automatically generate multiple adjustment schemes or assist designers in semi-automatic modifications through highlighted prompts and parameter suggestions. After adopting the suggestions and modifying the model, the designer can rerun S1-S6 for iterative evaluation until a satisfactory comprehensive human-machine performance evaluation result is obtained.

[0059] like Figure 2 As shown, this embodiment of the invention provides a human factors evaluation system for a commercial vehicle cab, comprising: The indicator system construction module is used to perform the following operations: Establish the target layer as improving the human-machine interface performance of commercial vehicle cabs; set primary evaluation indicators, including visibility design, passenger space and comfort, and operability; and set multiple secondary evaluation indicators under each primary evaluation indicator. Specifically, the secondary evaluation indicators under visibility design should include at least parameters for quantitatively evaluating forward and downward visibility and instrument visibility; the secondary evaluation indicators under passenger space and comfort should include at least parameters for quantitatively evaluating passenger space and seating comfort; and the secondary evaluation indicators under operability should include at least parameters for quantitatively evaluating hand accessibility and ease of operation. The parameter acquisition module is used to obtain the corresponding quantitative parameter value for each secondary evaluation indicator. The judgment matrix construction and verification module is used to construct fuzzy consistent judgment matrices for the relative importance between the target layer and the first-level evaluation indicators, as well as between each first-level evaluation indicator and its subordinate second-level evaluation indicators, and to perform consistency verification on the fuzzy consistent judgment matrices; if the verification fails, the scale values ​​of the elements in the fuzzy consistent judgment matrix are adjusted and the verification is repeated until it passes. The weight calculation module is used to calculate the relative weights of each element in the fuzzy consistent judgment matrix that has passed the consistency test. The fuzzy comprehensive operation module is used to perform multi-level fuzzy comprehensive operations based on the weights and the quantitative parameter values ​​of the secondary evaluation indicators to generate a comprehensive evaluation result for quantitatively characterizing the human-machine performance level of the commercial vehicle cab. In some embodiments, the parameter acquisition module includes: The simulation analysis unit is configured to perform the following steps to obtain at least some of the quantitative parameter values: establishing a three-dimensional digital model of the commercial vehicle cab; establishing human body models at the 5th, 50th, and 95th percentiles as dummy models for analysis based on a target population anthropometric database; placing the dummy model in the cab model and applying constraints to its H-point, hand grip point, and heel point, and calculating a standard driving posture using ergonomic simulation software; and obtaining quantitative parameter values ​​for one or more of the following based on the standard driving posture and the analysis function of the simulation software: forward lower field of vision blind spot distance, instrument area visibility ratio, leg space distance, head space distance, foot space distance, overall sitting discomfort, and hand function access range ratio. The subjective evaluation processing unit is used to obtain the quantitative parameter value of the subjective score of ease of operation, including: receiving the 1-7 score input by the evaluator according to the preset scoring rules for ease of getting on and off the vehicle, passability inside the driver's cab, ease of getting in and out of the sleeper berth, and ease of cleaning the windshield, and calculating the average score.

[0060] In some embodiments, the simulation analysis unit is configured to determine the final single parameter value used for evaluation according to the following rules when the acquired quantified parameter values ​​correspond to multiple percentile human body models: The following values ​​were taken from the simulation analysis results of the 95th percentile human body model: forward lower visual field blind spot distance, leg space distance, head space distance, foot space distance, and overall sitting discomfort. The visible scale of the instrument area and the scale of the reachable range of hand functions are taken from the simulation analysis results of the 5th percentile human body model.

[0061] In some embodiments, the judgment matrix construction and verification module is configured as follows: A fuzzy consistency judgment matrix is ​​constructed by using a scale of 0.1 to 0.9 to represent the relative importance between pairs of indicators. And simultaneously meet the following conditions: ; ; ; in, Let be the order of the matrix. Indicates the first The first indicator is relative to the first The importance scale of each indicator; The first row of the fuzzy consistency judgment matrix is ​​used as the base row, and the scale values ​​of other elements in the matrix are determined according to the scaling rules and matrix conditions.

[0062] In some embodiments, the weight calculation module is configured to: calculate the weight of the i-th indicator in the fuzzy consistency judgment matrix using a formula, where n is the matrix order and is the matrix element; the weight calculation is performed hierarchically, and the weight vector of the first-level evaluation indicator and the local weight vector of each second-level evaluation indicator relative to its respective first-level indicator are calculated.

[0063] In some embodiments, the fuzzy synthesis module is configured to perform the following operations: Determine the comment set V = {Poor, Poor, Average, Good, Fair}; Obtain the fuzzy evaluation vector corresponding to the comment set V for each secondary evaluation indicator; For each primary evaluation indicator, the fuzzy evaluation vectors of its subordinate secondary evaluation indicators are combined into a fuzzy evaluation matrix, and then synthesized with the corresponding weight vector to obtain the fuzzy comprehensive evaluation result vector of the primary indicator. The fuzzy comprehensive evaluation result vectors of all first-level indicators are combined into a target-level fuzzy evaluation matrix, and then synthesized with the weight vectors of the first-level indicators to obtain the target-level comprehensive fuzzy evaluation result vector. Based on the target layer comprehensive fuzzy evaluation result vector, the final evaluation level is determined according to the principle of maximum membership.

[0064] In some embodiments, the system further includes a design optimization execution module, configured to generate optimization instructions for the ergonomic design of the commercial vehicle cab based on the comprehensive evaluation results, and drive adjustments to the ergonomic layout parameters in the three-dimensional design model or physical prototype of the cab according to the instructions. The design optimization execution module includes: The instruction generation unit is used to parse the comprehensive evaluation results, identify design weaknesses, and generate optimization instructions that include specific modification points and parameter adjustment suggestions. A drive interface unit is used to convert the optimization instructions into commands executable by the target design environment; wherein the target design environment is a computer-aided design software or a control system for adjusting a physical prototype tooling.

[0065] The output module is configured to format the comprehensive evaluation results into an ergonomic analysis report containing scores for each level of indicators and the final evaluation level, and to output optimization suggestions for guiding the design of relevant components of the cab, including the dashboard, seat, steering mechanism, or layout of control buttons.

[0066] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or other media capable of storing program code. It includes several instructions to cause a computer terminal (which may be a personal computer, server, or a second terminal, network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0067] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0068] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0070] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the ergonomics of a commercial vehicle cab, characterized in that, Includes the following steps: S1. Construct an evaluation index system, specifically including: establishing the target layer as improving the human-machine interface performance of commercial vehicle cabs; setting primary evaluation indicators, including visibility design, passenger space and comfort, and operability; and setting multiple secondary evaluation indicators under each primary evaluation indicator. The secondary evaluation indicators under the vision design include at least parameters for quantitatively evaluating the forward and downward field of vision and instrument visibility; the secondary evaluation indicators under the riding space and comfort include at least parameters for quantitatively evaluating riding space and sitting comfort; and the secondary evaluation indicators under the operability include at least parameters for quantitatively evaluating hand accessibility and ease of operation. S2. For each secondary evaluation indicator, obtain its corresponding quantitative parameter value; S3. Construct fuzzy consistent judgment matrices for the relative importance between the target layer and the first-level evaluation indicators, and between each first-level evaluation indicator and its subordinate second-level evaluation indicators. S4. Perform a consistency check on the fuzzy consistency judgment matrix; if the check passes, proceed to S5; if the check fails, return to S3 and adjust the scale values ​​of the elements in the fuzzy consistency judgment matrix. S5. Calculate the relative weights of each element in the fuzzy consistency judgment matrix; S6. Based on the quantitative parameter values ​​of the weights and secondary evaluation indicators, perform multi-level fuzzy comprehensive operations to generate a comprehensive evaluation result for quantitatively characterizing the human-machine performance level of the commercial vehicle cab.

2. The ergonomic evaluation method for commercial vehicle cabs according to claim 1, characterized in that, The parameter used to quantify the downward field of vision is the blind spot distance; the parameter used to quantify the visibility of the instrument panel is the visible ratio of the instrument panel area; the parameters used to quantify the seating space include legroom, headroom, and footroom; the parameter used to quantify the comfort of sitting posture is the overall discomfort of sitting posture; the parameter used to quantify the accessibility of the hands is the proportion of the range of reachable hand functions; and the parameter used to quantify the ease of operation is the subjective rating of ease of operation.

3. The ergonomic evaluation method for commercial vehicle cabs according to claim 2, characterized in that, The quantitative parameter values ​​for each secondary evaluation indicator are defined as follows: The forward lower field of vision blind spot distance is defined as the horizontal straight-line distance between the point on the ground projected by the driver's line of sight through the upper edge of the dashboard and the frontmost part of the vehicle. The visible ratio of the instrument area is defined as the proportion of the total area of ​​the instrument area that the driver can see directly or by slightly turning his eyes and head. Legroom, headroom, and footroom are defined as the shortest distances between the driver's legs and the dashboard, between the top of the head and the roof liner, and between the feet and the nearest obstacle, respectively. Overall discomfort in sitting posture is defined as a rating scale of 0-8, with lower scores indicating better comfort. The proportion of hand-accessible area is defined as the ratio of the area accessible to the driver's single hand or fingers for operating vehicle switches and controls to the total area of ​​the main operating area in front of the instrument panel. The subjective score for ease of operation is defined as the average score of subjective evaluations of ease of getting on and off the vehicle, passability inside the driver's cab, ease of access to the sleeper berth, and ease of cleaning the windshield. Evaluators will give subjective evaluations of ease of getting on and off the vehicle, passability inside the driver's cab, ease of access to the sleeper berth, and ease of cleaning the windshield on a scale of 1 to 7 according to the preset scoring rules, and calculate the average score.

4. The ergonomic evaluation method for commercial vehicle cabs according to claim 3, characterized in that, The quantitative parameters of one or more of the following—forward lower field of vision blind spot distance, instrument area visibility ratio, distances in various spaces, overall discomfort of sitting posture, and proportion of hand function access range—are achieved through the following simulation analysis steps: Establish a three-dimensional digital model of the commercial vehicle's cab; Based on the target population's human body size database, human body models at the 5th, 50th, and 95th percentiles were established as mannequins for analysis. The human body model is placed in the driver's cab model, and constraints are applied to its H-point, hand grip point and foot heel point. The standard driving posture is calculated by human factors engineering simulation software. Based on the standard driving posture and the analysis function of the simulation software, the corresponding quantitative parameter values ​​are obtained.

5. The ergonomic evaluation method for commercial vehicle cabs according to claim 4, characterized in that, The secondary evaluation indicators obtained through simulation analysis are ultimately used to determine the single quantitative parameter value for evaluation according to the following rules: The following values ​​were used to measure the distance to the lower frontal blind spot, leg space, head space, foot space, and overall discomfort of sitting posture: the simulation analysis results of the 95th percentile human body model. The visual scale of the instrument area and the scale of the reach of hand functions are taken from the simulation analysis results of the 5th percentile human body model.

6. The ergonomic evaluation method for commercial vehicle cabs according to claim 1, characterized in that, The comprehensive evaluation results are used to identify weaknesses in the ergonomic design of commercial vehicle cabs and provide quantitative basis for optimizing the cab layout. The comprehensive evaluation results are formatted as an ergonomic analysis report containing scores for each level of indicators and the final evaluation level.

7. The ergonomic evaluation method for commercial vehicle cabs according to claim 1, characterized in that, When constructing the fuzzy consistent judgment matrix in S3, a scale of 0.1 to 0.9 is used to represent the relative importance between pairs of indicators; Fuzzy Consistency Judgment Matrix The following conditions must be met simultaneously: ； ； ； in, Let be the order of the matrix. Indicates the first The first indicator is relative to the first The importance scale of each indicator; Constructing a fuzzy consistency judgment matrix includes the following steps: S31. Determine the reference row of the fuzzy consistency judgment matrix; wherein, the reference row is the first row of the fuzzy consistency judgment matrix; S32. Based on the scaling rules and the conditions of the fuzzy consistency judgment matrix, determine the scaling values ​​of other elements in the fuzzy consistency judgment matrix based on the baseline row.

8. The ergonomic evaluation method for a commercial vehicle cab according to claim 7, characterized in that, In S5, weights are calculated hierarchically, specifically including: For the fuzzy consistent judgment matrix constructed between the target layer and the first-level evaluation index, the weight vector of the first-level evaluation index is calculated. For each primary evaluation indicator and its subordinate secondary evaluation indicators, a fuzzy consistency judgment matrix is ​​constructed, and the local weight vector of each secondary evaluation indicator relative to its primary indicator is calculated. The formula for calculating the weights in the fuzzy consistency judgment matrix is: in, For the first The weight of each indicator.

9. The ergonomic evaluation method for a commercial vehicle cab according to claim 8, characterized in that, In S6, performing multi-level fuzzy synthesis operations includes the following steps: S61. Determine the comment set V, wherein the comment set V contains multiple qualitative descriptive words used to characterize the evaluation level; S62. For each secondary evaluation indicator, obtain its corresponding fuzzy evaluation vector to the comment set V, where each component of the vector represents the degree to which the indicator belongs to the corresponding comment level. S63. For each primary evaluation indicator, combine the fuzzy evaluation vectors of all its subordinate secondary evaluation indicators into a fuzzy evaluation matrix for that primary indicator, and use the corresponding weight vectors calculated in S5 to perform a synthesis operation to obtain the fuzzy comprehensive evaluation result vector for that primary indicator. S64. Combine the fuzzy comprehensive evaluation result vectors of all first-level indicators into a fuzzy evaluation matrix of the target layer, and use the weight vectors of the first-level indicators calculated in S5 to perform a synthesis operation to obtain the comprehensive fuzzy evaluation result vector of the target layer A.

10. A human factors evaluation system for a commercial vehicle cab, characterized in that, include: The indicator system construction module is used to perform the following operations: establish the target layer as improving the human-machine performance of commercial vehicle cabs; and set up primary evaluation indicators, including visibility design, passenger space and comfort, and operability. Each primary evaluation indicator is further divided into multiple secondary evaluation indicators. The secondary evaluation indicators under the vision design include at least parameters for quantitatively evaluating the forward and downward field of vision and instrument visibility. The secondary evaluation indicators under the riding space and comfort include at least parameters for quantitatively evaluating the riding space and sitting comfort. The secondary evaluation indicators under the operability include at least parameters for quantitatively evaluating hand accessibility and ease of operation. The parameter acquisition module is used to obtain the corresponding quantitative parameter value for each secondary evaluation indicator. The judgment matrix construction and verification module is used to construct fuzzy consistent judgment matrices for the relative importance between the target layer and the first-level evaluation indicators, as well as between each first-level evaluation indicator and its subordinate second-level evaluation indicators, and to perform consistency verification on the fuzzy consistent judgment matrices; if the verification fails, the scale values ​​of the elements in the fuzzy consistent judgment matrix are adjusted and the verification is repeated until it passes. The weight calculation module is used to calculate the relative weights of each element in the fuzzy consistent judgment matrix that has passed the consistency test. The fuzzy comprehensive operation module is used to perform multi-level fuzzy comprehensive operations based on the quantitative parameter values ​​of weights and secondary evaluation indicators, and generate comprehensive evaluation results for quantitatively characterizing the human-machine performance level of the commercial vehicle cab.

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