Passenger task completion time prediction and evaluation system and method based on clm-goms model

Abstract

The application provides a crew task completion time prediction and evaluation system and method based on a "Collaborative Level Model (CLM)-Goal, Operator, Method, Selection rules" (CLM-GOMS model). The system and method can realize the prediction and evaluation of the crew task completion time in a typical collaborative situation composed of individual operations and collaborative operations of the crew, have the characteristics of good ecological validity, strong applicability, high accuracy, etc., and can provide technical support for the task process optimization and human factor design of the crew cabin.

Description

Technical Field

[0001] This invention relates to a system and method for predicting and evaluating crew task completion time based on the CLM-GOMS model (Cooperative Level Model - Objective, Operation, Method, Selection Rule, CLM-GOMS model). Background Technology

[0002] Task completion time is a crucial metric for crew performance and a key area of ​​research in human factors engineering. It is not only related to task flow design but also closely linked to accidents and disasters that may occur during emergency operations. Numerous studies have shown that considering relatively short task completion times facilitates faster task completion, thereby improving task performance and ensuring operational safety.

[0003] Current crew task completion time assessment systems still have some shortcomings. First, most current task completion time prediction methods focus on single-person tasks, lacking consideration for multi-person collaborative scenarios. Second, current task completion time prediction methods mainly focus on the Keystroke-Level Model (KLM-GOMS) and Touch-Level Model (TLM-GOMS) GOMS methods, lacking consideration for collaborative operation units such as voice communication and visual search. Finally, human-in-the-loop experimental verification of the Collaborative Level Model (CLM-GOMS) GOMS method, including KLM-GOMS, TLM-GOMS, and collaborative factors, has not been fully carried out, especially in multi-person collaborative tasks.

[0004] Based on the above, this invention addresses the shortcomings by designing a crew task completion time prediction system based on the CLM-GOMS model. This system can be used to predict and compare crew task completion times under different collaborative task scenarios, thereby guiding the optimization of crew cabin task processes and human factors design. Summary of the Invention

[0005] The purpose of this invention is to provide a system for predicting and evaluating the task completion time of crew members in typical multi-person collaborative task scenarios consisting of multiple individual task operations and collaborative task operations. This system provides a description of the operational units for crew member task completion time in typical multi-person collaborative scenarios, focusing on the quantitative calculation of collaborative operation units and the risk level assessment of collaborative scenarios.

[0006] According to one aspect of the present invention, a crew task completion time prediction and evaluation system based on the CLM-GOMS model is provided, characterized by comprising:

[0007] The crew task input subsystem in the collaborative scenario is used to provide initial requirements for the crew task completion time prediction and determination system. It includes the individual operation input module, the collaborative operation input module, and the actual operation time input submodule, which are used to assign values ​​to design constants.

[0008] The crew task completion time calculation and evaluation subsystem based on the CLM-GOMS model includes a crew individual operation calculation submodule, a cooperative operation calculation submodule, a crew task completion time calculation submodule, a task completion time prediction effect judgment submodule, and a cooperative scenario task completion time risk level judgment submodule. It is used to execute the CLM-GOMS model-based task completion time algorithm calculation and risk level evaluation for the crew task input subsystem in the cooperative scenario.

[0009] The subsystem for outputting crew task completion time results in a collaborative scenario includes a submodule for outputting the trend of task completion time changes in a collaborative scenario, a submodule for outputting the prediction effect of task completion time in a collaborative scenario, and a submodule for outputting the risk level of task completion time in a collaborative scenario. These submodules provide output for the task completion time algorithm calculation and risk level assessment results based on the CLM-GOMS model.

[0010] in:

[0011] The individual operation input submodule includes:

[0012] The individual operation content input submodule is used to set the description of the individual operation content.

[0013] The individual operation content operation unit attribute input submodule is used to set the operation unit attributes of the individual operation content. The selectable operation unit attributes here include: type K, point P, return H, respond R, prepare finger E, tap T, quick move F, double tap TT, move F_M.

[0014] The collaborative operation input submodule includes:

[0015] The collaborative operation content input submodule is used to set the description of the collaborative operation content.

[0016] The submodule for inputting the operation unit attributes of collaborative operation content is used to set the operation unit attributes of collaborative operation content. The selectable collaborative task operation unit attributes here include communication preparation (M), communication waiting (W), communication preparation (C_P), voice communication (V_C), visual search (V_S), and situational understanding (U).

[0017] The submodule for inputting operation unit parameters for collaborative operations is used to set the operation unit parameters for collaborative operations. These parameters include the waiting time for communication (W), the number of initiators and recipients for communication preparation (C_P) instructions / reports, the number of words in the task instructions / reports for voice communication (V_C), the number of items in the operation interface of visual search (V_S), and the number of words in the text of instructions for situational understanding (U).

[0018] The actual operation time input submodule is used to set the actual operation time for multiple collaborative scenarios.

[0019] The individual operation calculation submodule is used according to equation (1):

[0020]

[0021] In equation (1), T individualtask is the total time of individual operations; i is the number of the individual operation unit, ranging from 1 to 9; a i For the attribute of the i-th individual operation unit; num{a i} represents the operation unit attribute 'a' in the individual operation content within this collaborative context. i Quantity; Δt i For a i Standard operating time

[0022] Among them, a i Standard operating time Δt i See Table 1.

[0023] Table 1. Standard operating time definition for individual operating units

[0024] Individual operation unit number i <![CDATA[Individual operation unit attribute a i > <![CDATA[Standard operation time Δt i (s)]]> 1 K 0.28 2 P 1.1 3 H 0.4 4 R 1 5 E 0.5 6 T 0.2 7 TT 0.4 8 F 0.4 9 F_M 0.7

[0025] The collaborative operation calculation submodule is used based on equation (2):

[0026]

[0027] In equation (2), T collaborativetask is the total time of the collaborative operation; j is the number of the collaborative operation unit, ranging from 1 to 6; c j For the attribute of the j-th cooperative operation unit; num{c j} represents the operation unit attribute c in the collaborative operation content within this collaborative context. j Quantity; Δt j For c j Standard operating time

[0028] Among them, c j Standard operating time Δt j See Table 2.

[0029] Table 2 Standard operating time definition for collaborative operation units

[0030] Cooperative operation unit number j <![CDATA[Collaborative operation unit attribute c j > <![CDATA[Standard time parameter Δt j (s)]]> 1 M 1.2 2 W(t) t 3 C_P <![CDATA[n total *M]]> 4 V_C <![CDATA[num vc *60 / 200]]> 5 V_S 1+N / 2 6 U <![CDATA[num u *60 / 300]]>

[0031] In Table 2:

[0032] t is the waiting time of W(t).

[0033] n total To prepare parameters for communication, n reporter To determine the number of people initiating the communication, n receiver To communicate the number of recipients, when n reporter or n receiver When there is more than 1 person, then n total =2; when n reporter and n receiver When all are 1 person, then n total =1,

[0034] num vc N represents the number of words in the voice communication, and N represents the number of targets in the visual search. u The number of characters entered for the task.

[0035] The crew mission completion time calculation submodule is used based on equation (3):

[0036] T total =T individualtask +T collaborativetask (3)

[0037] In equation (3), T total The crew member's task completion time is given by the total individual operation time T in equation (1). individualtask The total cooperative operation time T of equation (2) collaborativetask We obtain by summing.

[0038] The task completion time prediction effect judgment submodule is used based on equation (4):

[0039]

[0040] In equation (4),

[0041] x is the predicted task completion time. k This is the predicted value for the k-th collaborative scenario;

[0042] y represents the actual time it takes to complete the task. k This represents the actual task completion time for the k-th collaborative scenario.

[0043] This represents the mean of the predicted task completion times. The average of the actual task completion times;

[0044] r xy Let x be the correlation coefficient between x and y, and K be the total number of collaborative input scenarios, k = 1 to K.

[0045] Furthermore, at a confidence level of α = 0.05, a t-test was conducted on the predicted and actual task completion times for different collaborative scenarios, based on equation (5):

[0046]

[0047] In equation (5), t r The value of the test statistic; K-2 is the degrees of freedom;

[0048] Based on this, the significance p-value can be obtained according to the degrees of freedom. When p < 0.05, the correlation result is considered significant.

[0049] Furthermore, the model's predictive performance is used based on equation (6):

[0050]

[0051] In equation (6), P efficiency To predict the effectiveness of task completion time.

[0052] The collaborative task completion time risk level judgment submodule is used based on equation (7):

[0053] E k =|xy| / y (7)

[0054] In equation (7),

[0055] E k Let $\frac{k}{k}$ be the percentage of absolute error in the k-th cooperative scenario, where $k = 1 to $K$.

[0056] x represents the predicted task completion time in the collaborative scenario, and y represents the actual task completion time in the collaborative scenario.

[0057] Furthermore, the risk level determination of the completion time of collaborative task scenarios is based on equation (8):

[0058]

[0059] In equation (8), E k Let be the percentage of absolute error for the k-th cooperative scenario.

[0060] The task completion time trend output submodule in collaborative scenarios takes the results of the crew task completion time calculation submodule and the crew actual operation time input module as input, and presents the predicted values ​​x of task completion time in K collaborative scenarios in the form of a line graph.k The actual value of the task completion time y k In order to observe the corresponding trends of change,

[0061] The task completion time prediction effect output submodule in a collaborative context takes the result of the task completion time prediction effect judgment submodule as input and presents data including the Pearson correlation coefficient r. xy The predictive power of task completion time, including the significance level p.

[0062] The task completion time risk level output submodule in collaborative scenarios takes the results of the task completion time risk level judgment submodule in collaborative scenarios as input, and includes a risk scenario labeling presentation submodule and a risk scenario visualization presentation submodule.

[0063] in:

[0064] The risk scenario labeling and presentation module presents the absolute error percentage E for K collaborative scenarios. k The list of collaborative scenarios is marked in italics and bold, while those of medium risk are marked in bold. The collaborative scenarios of high, medium and low risk levels are output in sequence.

[0065] The risk scenario visualization module, based on the trend chart output by the task completion time trend chart output submodule, further marks high-risk and medium-risk collaborative scenarios in the form of rectangular dashed boxes and elliptical dashed boxes.

[0066] According to a further aspect of the present invention, a method for predicting and evaluating crew task completion time based on the CLM-GOMS model is provided, characterized by comprising:

[0067] The crew task input steps in a collaborative scenario are used to provide initial requirements for the prediction and determination of crew task completion time. These include individual operation input parameter operations, collaborative operation input parameter operations, and actual operation time input parameter operations for assigning values ​​to design constants.

[0068] The steps for calculating and evaluating crew task completion time based on the CLM-GOMS model include individual crew operation calculation steps, collaborative operation calculation steps, crew task completion time calculation steps, task completion time prediction effect judgment steps, and collaborative scenario task completion time risk level judgment steps. These steps are used to perform CLM-GOMS model-based task completion time algorithm calculation and risk level assessment for the crew task input subsystem in collaborative scenarios.

[0069] The steps for outputting the crew task completion time results in a collaborative scenario include steps for outputting the task completion time change trend, the task completion time prediction effect, and the task completion time risk level. These steps provide output for the task completion time algorithm calculation and risk level assessment results based on the CLM-GOMS model.

[0070] in:

[0071] Individual operation input sub-steps include:

[0072] The individual operation content input steps are used to set the description of the individual operation content.

[0073] The input steps for the operation unit attributes of individual operation content are used to set the operation unit attributes of individual operation content. The operation unit attributes that can be selected here include: type K, point P, return H, respond R, prepare finger E, tap T, quick move F, double tap TT, and move F_M.

[0074] The cooperative operation input sub-steps include:

[0075] The collaborative operation content input step is used to set the description of the collaborative operation content.

[0076] The step for inputting the operation unit attributes of collaborative operation content is used to set the operation unit attributes of the collaborative operation content. The selectable collaborative task operation unit attributes here include communication preparation (M), communication waiting (W), communication preparation (C_P), voice communication (V_C), visual search (V_S), and situational understanding (U).

[0077] The steps for inputting operation unit parameters for collaborative operations are used to set the operation unit parameters for collaborative operations. These parameters include the waiting time for communication (W), the number of initiators and recipients for communication preparation (C_P) instructions / reports, the number of words in the task instructions / reports for voice communication (V_C), the number of items within the operation interface of visual search (V_S), and the number of text words in the instructions for situational understanding (U).

[0078] The "Actual Operation Time Input" sub-step is used to set the actual operation time for multiple collaborative scenarios.

[0079] The individual operation calculation sub-steps are used according to equation (1):

[0080]

[0081] In equation (1), T individualtask is the total time of individual operations; i is the number of the individual operation unit, ranging from 1 to 9; a i For the attribute of the i-th individual operation unit; num{a i} represents the operation unit attribute 'a' in the individual operation content within this collaborative context. i Quantity; Δt i For a i Standard operating time

[0082] Among them, a i Standard operating time Δt i See Table 1.

[0083] Table 1. Standard operating time definition for individual operating units

[0084] Individual operation unit number i <![CDATA[Individual operation unit attribute a i > <![CDATA[Standard operation time Δt i (s)]]> 1 K 0.28 2 P 1.1 3 H 0.4 4 R 1 5 E 0.5 6 T 0.2 7 TT 0.4 8 F 0.4 9 F_M 0.7

[0085] The cooperative operation calculation sub-step is used according to equation (2):

[0086]

[0087] In equation (2), T collaborativetask is the total time of the collaborative operation; j is the number of the collaborative operation unit, ranging from 1 to 6; c j For the attribute of the j-th cooperative operation unit; num{c j} represents the operation unit attribute c in the collaborative operation content within this collaborative context. j Quantity; Δt j For c j Standard operating time

[0088] Among them, c j Standard operating time Δt j See Table 2.

[0089] Table 2 Standard operating time definition for collaborative operation units

[0090] Cooperative operation unit number j <![CDATA[Collaborative operation unit attribute c j > <![CDATA[Standard time parameter Δt j (s)]]> 1 M 1.2 2 W(t) t 3 C_P <![CDATA[n total *M]]> 4 V_C <![CDATA[num vc *60 / 200]]> 5 V_S 1+N / 2 6 U <![CDATA[num u *60 / 300]]>

[0091] In Table 2:

[0092] t is the waiting time of W(t).

[0093] n total To prepare parameters for communication, n reporter To determine the number of people initiating the communication, n receiver To communicate the number of recipients, when n reporter or n receiver When there is more than 1 person, then n total =2; when n reporter and n receiver When all are 1 person, then n total =1,

[0094] num vc N represents the number of words in the voice communication, and N represents the number of targets in the visual search.u The number of characters entered for the task.

[0095] The sub-step for calculating crew mission completion time is based on equation (3):

[0096] T total =T individualtask +T collaborativetask (3)

[0097] In equation (3), T total The crew member's task completion time is given by the total individual operation time T in equation (1). individualtask The total cooperative operation time T of equation (2) collaborativetask We obtain by summing.

[0098] The sub-step for judging the prediction effect of task completion time is based on equation (4):

[0099]

[0100] In equation (4),

[0101] x is the predicted task completion time. k This is the predicted value for the k-th collaborative scenario;

[0102] y represents the actual time it takes to complete the task. k This represents the actual task completion time for the k-th collaborative scenario.

[0103] This represents the mean of the predicted task completion times. The average of the actual task completion times;

[0104] r xy Let x be the correlation coefficient between x and y, and K be the total number of collaborative input scenarios, k = 1 to K.

[0105] Furthermore, at a confidence level of α = 0.05, a t-test was conducted on the predicted and actual task completion times for different collaborative scenarios, based on equation (5):

[0106]

[0107] In equation (5), t r The value of the test statistic; K-2 is the degrees of freedom;

[0108] Based on this, the significance p-value can be obtained according to the degrees of freedom. When p < 0.05, the correlation result is considered significant.

[0109] Furthermore, the model's predictive performance is used based on equation (6):

[0110]

[0111] In equation (6), P efficiency To predict the effectiveness of task completion time.

[0112] The sub-step for judging the risk level of collaborative task completion time is based on equation (7):

[0113] E k =|xy| / y (7)

[0114] In equation (7),

[0115] E k Let $\frac{k}{k}$ be the percentage of absolute error in the k-th cooperative scenario, where $k = 1 to $K$.

[0116] x represents the predicted task completion time in the collaborative scenario, and y represents the actual task completion time in the collaborative scenario.

[0117] Furthermore, the risk level determination of the completion time of collaborative task scenarios is based on equation (8):

[0118]

[0119] In equation (8), E k Let be the percentage of absolute error for the k-th cooperative scenario.

[0120] The output steps for the task completion time trend in collaborative scenarios take the results of the crew task completion time calculation submodule and the crew actual operation time input module as input, and present the predicted values ​​x of task completion time in K collaborative scenarios in the form of a line graph. k The actual value of the task completion time y k In order to observe the corresponding trends of change,

[0121] The task completion time prediction effect output sub-step in a collaborative context takes the result of the task completion time prediction effect judgment sub-module as input and presents the result including the Pearson correlation coefficient r. xy The predictive power of task completion time, including the significance level p.

[0122] The task completion time risk level output sub-step in a collaborative scenario takes the result of the collaborative scenario task completion time risk level judgment step as input, and includes a risk scenario labeling presentation step and a risk scenario visualization presentation step.

[0123] in:

[0124] Risk scenario labeling presentation steps present the absolute error percentage E of K collaborative scenarios. kThe list of collaborative scenarios is marked in italics and bold, while those of medium risk are marked in bold. The collaborative scenarios of high, medium and low risk levels are output in sequence.

[0125] The risk scenario visualization presentation step, based on the trend chart presented in the task completion time change trend output step, further marks high-risk and medium-risk collaborative scenarios in the form of rectangular dashed boxes and elliptical dashed boxes. Attached Figure Description

[0126] Figure 1 This is a schematic diagram of the overall structure of a crew task completion time prediction and evaluation system based on the CLM-GOMS model according to an embodiment of the present invention.

[0127] Figure 2 This is a diagram of the individual operation input submodule of the crew task input subsystem in a collaborative scenario according to an embodiment of the present invention.

[0128] Figure 3 This is a diagram of the cooperative operation input submodule of the crew task input subsystem in a cooperative scenario according to an embodiment of the present invention.

[0129] Figure 4 This is a diagram of the actual operation time input submodule of the crew task input subsystem in a collaborative scenario according to an embodiment of the present invention.

[0130] Figure 5 This describes the working process of a crew task completion time prediction and evaluation subsystem based on the CLM-GOMS model according to an embodiment of the present invention.

[0131] Figure 6 This is a diagram of the crew task completion time output submodule according to an embodiment of the present invention.

[0132] Figure 7 This is a flowchart of an individual operation input submodule according to an embodiment of the present invention.

[0133] Figure 8 This is a flowchart of a collaborative operation input submodule according to an embodiment of the present invention.

[0134] Figure 9 This is a schematic diagram of the user interface of a crew task completion time prediction and evaluation system based on the CLM-GOMS model according to an embodiment of the present invention.

[0135] Figure 10 This is a user manual display page for a crew task completion time prediction and evaluation system based on the CLM-GOMS model according to an embodiment of the present invention.

[0136] Figure 11This is the output page of a crew task completion time prediction and evaluation system based on the CLM-GOMS model according to an embodiment of the present invention. Detailed Implementation

[0137] According to one aspect of the present invention, a crew task completion time prediction and evaluation system based on the CLM-GOMS model is provided, comprising:

[0138] The crew task input subsystem in the collaborative scenario is used to provide initial requirements for the crew task completion time prediction and determination system. It includes the individual operation input module, the collaborative operation input module, and the actual operation time input submodule, which are used to assign values ​​to design constants.

[0139] The crew task completion time calculation and evaluation subsystem based on the CLM-GOMS model includes a crew individual operation calculation submodule, a cooperative operation calculation submodule, a crew task completion time calculation submodule, a task completion time prediction effect judgment submodule, and a cooperative scenario task completion time risk level judgment submodule. It is used to execute the CLM-GOMS model-based task completion time algorithm calculation and risk level evaluation for the crew task input subsystem in the cooperative scenario.

[0140] The subsystem for outputting crew task completion time results in a collaborative scenario includes a submodule for outputting the trend of task completion time changes in a collaborative scenario, a submodule for outputting the prediction effect of task completion time in a collaborative scenario, and a submodule for outputting the risk level of task completion time in a collaborative scenario. These submodules are used to provide outputs for the task completion time algorithm calculation and risk level assessment results based on the CLM-GOMS model.

[0141] According to a further embodiment of the present invention, the crew task input module in a cooperative context includes three sub-modules: an individual operation input sub-module, a cooperative operation input sub-module, and an actual operation time input sub-module. The individual operation input sub-module is used to input individual operation content and the operation unit attributes of the individual operation content. The cooperative operation input sub-module is used to input cooperative operation content and the operation unit attributes of the cooperative operation content, and to assign values ​​to the parameters of the operation unit attributes of the cooperative operation content.

[0142] According to another further embodiment of the present invention, the crew task completion time calculation and evaluation subsystem based on the CLM-GOMS model includes a crew individual operation calculation submodule, a collaborative operation calculation submodule, a crew task completion time calculation submodule, a task completion time prediction effect judgment submodule, and a collaborative scenario task completion time risk level judgment submodule. The individual operation calculation submodule is used to calculate the total time of individual operations in a collaborative scenario, based on the sum of the products of the number of attributes of each operation unit and their standard operation time. The collaborative operation calculation submodule is used to calculate the total time of collaborative operations in a collaborative scenario, based on the sum of the products of the number of attributes of each operation unit and their standard operation time. The crew task completion time calculation submodule is used to calculate the task completion time in a typical collaborative scenario, based on the sum of the total time of individual operations and the total time of collaborative operations in that scenario. The task completion time prediction effect judgment submodule is used to judge the prediction effect of task completion time, based on the correlation coefficient and significance level between the predicted and actual values ​​of task completion time. The collaborative scenario task completion time risk level judgment submodule is used to judge the risk level of task completion time in a typical collaborative scenario, based on the percentage of absolute error in a typical collaborative scenario.

[0143] like Figure 1 As shown, an embodiment of the CLM-GOMS model-based crew task completion time prediction and evaluation system according to the present invention includes three modules: a crew task input subsystem in a collaborative scenario, a crew task completion time calculation and evaluation subsystem based on the CLM-GOMS model, and a crew task completion time result output subsystem in a collaborative scenario. The crew task input subsystem in a collaborative scenario includes an individual operation input module, a collaborative operation input module, and an actual operation time input submodule. The crew task completion time calculation and evaluation subsystem based on the CLM-GOMS model includes a crew individual operation calculation submodule, a collaborative operation calculation submodule, a crew task completion time calculation submodule, a task completion time prediction effect judgment submodule, and a collaborative scenario task completion time risk level judgment submodule.

[0144] like Figure 2 As shown, the individual operation input submodule of the crew task input subsystem in a cooperative context according to an embodiment of the present invention includes:

[0145] The individual operation content input submodule is used to set the description of the individual operation content;

[0146] The individual operation content operation unit attribute input submodule is used to set the operation unit attributes of the individual operation content. The selectable operation unit attributes include key (K), pointing (P), homing (H), response (R), prep (E), tap (T), flick (F), double tap (TT), and finger move (F_M).

[0147] like Figure 3 As shown, the cooperative operation input submodule of the crew task input subsystem in a cooperative context according to an embodiment of the present invention includes:

[0148] The collaborative operation content input submodule is used to set the description of the collaborative operation content;

[0149] The submodule for inputting operation unit attributes of collaborative operation content is used to set the operation unit attributes of collaborative operation content. The selectable collaborative task operation unit attributes include communication preparation (M), communication waiting (W), communication preparation (C_P), voice communication (V_C), visual search (V_S), and situational understanding (U).

[0150] The submodule for inputting operation unit parameters for collaborative operation content is used to set the operation unit parameters for collaborative operation content. The operation unit parameters that need to be set include the waiting time for communication wait (W), the number of initiators and recipients of the instruction / report for communication preparation (C_P), the number of words in the task instruction / report for voice communication (V_C), the number of items in the operation interface for visual search (V_S), and the number of words in the text of the instruction for situational understanding (U).

[0151] Figure 4 The actual operation time input submodule of the crew task input subsystem in a collaborative scenario according to an embodiment of the present invention is shown, which is used to set the actual operation time of multiple collaborative scenarios.

[0152] Figure 5 The following describes the working process of a crew task completion time prediction and evaluation subsystem based on the CLM-GOMS model according to an embodiment of the present invention. After setting the parameters in the crew task input subsystem under a collaborative scenario, which includes an individual operation input module, a collaborative operation input module, and an actual operation time input submodule, the obtained measurement results are substituted into the following eight equations:

[0153] The individual operation calculation submodule is based on equation (1).

[0154]

[0155] Equation (2) is the cooperative operation computation submodule:

[0156]

[0157] Equation (3) is the submodule for calculating crew task completion time:

[0158] T total =T individualtask +T collaborativetask (3)

[0159] The task completion time prediction effect judgment submodule includes equations (4) to (6):

[0160] Equation (4) is used to calculate the correlation coefficient between the predicted and actual task completion times:

[0161]

[0162] Equation (5) is a t-test of the predicted and actual task completion times for different collaborative scenarios at a confidence level of α = 0.05:

[0163]

[0164] Equation (6) is the criterion for the model's prediction performance.

[0165]

[0166] The collaborative task completion time risk level judgment submodule includes equations (7) to (8):

[0167] Equation (7) calculates the percentage absolute error between the predicted and actual task completion times.

[0168] E k =|xy| / y (7)

[0169] Equation (8) is the criterion for determining the risk level of collaborative task completion time:

[0170]

[0171] In equation (1), T individualtask is the total time of individual operations; i is the number of the individual operation unit, ranging from 1 to 9; a i For the attribute of the i-th individual operation unit; num{a i} represents the operation unit attribute 'a' in the individual operation content within this collaborative context. i Quantity; Δt i For a i Standard operating time

[0172] Among them, a i Standard operating time Δt i See Table 1.

[0173] Table 1. Standard operating time definition for individual operating units

[0174] Individual operation unit number i <![CDATA[Individual operation unit attribute a i > <![CDATA[Standard operation time Δt i (s)]]> 1 K 0.28 2 P 1.1 3 H 0.4 4 R 1 5 E 0.5 6 T 0.2 7 TT 0.4 8 F 0.4 9 F_M 0.7

[0175] In equation (2), T collaborativetask is the total time of the collaborative operation; j is the number of the collaborative operation unit, ranging from 1 to 6; c j For the attribute of the j-th cooperative operation unit; num{c j} represents the operation unit attribute c in the collaborative operation content within this collaborative context. j Quantity; Δt j For c j The standard operating time, where c j Standard operating time Δt j See Table 2.

[0176] Table 2 Standard operating time definition for collaborative operation units

[0177] Cooperative operation unit number j <![CDATA[Collaborative operation unit attribute c j > <![CDATA[Standard time parameter Δt j (s)]]> 1 M 1.2 2 W(t) t 3 C_P <![CDATA[n total *M]]> 4 V_C <![CDATA[num vc *60 / 200]]> 5 V_S 1+N / 2 6 U <![CDATA[num u *60 / 300]]>

[0178] In Table 2:

[0179] t is the waiting time of W(t).

[0180] n total To prepare parameters for communication, n reporter To determine the number of people initiating the communication, n receiver To communicate the number of recipients, when n reporter or n receiver When there is more than 1 person, then n total =2; when n reporter and n receiver When all are 1 person, then n total =1,

[0181] num vc N represents the number of words in the voice communication, and N represents the number of targets in the visual search. u The number of characters entered for the task.

[0182] In equation (3), T total The crew member's task completion time is given by the total individual operation time T in equation (1). individualtaskThe total cooperative operation time T of equation (2) collaborativetask We obtain the result by adding them together.

[0183] In equation (4), x is the predicted time to complete the task. k y is the predicted value for the k-th collaborative scenario; y is the actual value of the task completion time. k This represents the actual task completion time for the k-th collaborative scenario. This represents the mean of the predicted task completion times. r is the average of the actual times the task was completed; xy Let x be the correlation coefficient between x and y, and K be the total number of collaborative input scenarios, k = 1 to K.

[0184] In equation (5), t r To determine the value of the test statistic; K-2 represents the degrees of freedom.

[0185] In equation (6), P efficiency The effect of predicting task completion time.

[0186] In equation (7), E k Let be the percentage of absolute error for the k-th collaborative scenario, where k = 1 to K; x be the predicted task completion time for the collaborative scenario; and y be the actual task completion time for the collaborative scenario.

[0187] In equation (8), E k This represents the percentage of absolute error for the k-th cooperative scenario.

[0188] Figure 6 This diagram illustrates a submodule for outputting occupant task completion time results in a collaborative scenario, according to an embodiment of the present invention. After receiving the calculation results from the occupant task completion time calculation submodule, the collaborative scenario task completion time risk level judgment submodule, and the task completion time prediction effect judgment submodule, as well as the input from the occupant actual operation time input module, it presents the results from three submodules: the collaborative scenario task completion time change trend output submodule, the collaborative scenario task completion time prediction effect output submodule, and the collaborative scenario task completion time risk level output submodule.

[0189] in:

[0190] The task completion time trend output submodule in collaborative scenarios takes the results of the crew task completion time calculation submodule and the crew actual operation time input module as input, and presents the predicted values ​​x of task completion time in K collaborative scenarios in the form of a line graph. k (Triangles mark the model predictions for each collaborative scenario, connected by dashed lines) and the actual task completion time y. k(Square marks represent the actual measured values ​​for each collaborative scenario, connected by solid lines), with the horizontal axis representing the collaborative scenario and the vertical axis representing the task completion time, thus allowing observation of the corresponding trends.

[0191] The task completion time prediction effect output submodule in a collaborative context takes the result of the task completion time prediction effect judgment submodule as input and presents data including the Pearson correlation coefficient r. xy The predictive power of task completion time, including the significance level p.

[0192] The task completion time risk level output submodule in collaborative scenarios takes the result of the task completion time risk level judgment submodule in collaborative scenarios as input, and includes a risk scenario labeling presentation submodule and a risk scenario visualization presentation submodule.

[0193] in:

[0194] The risk scenario labeling and presentation submodule presents the absolute error percentage E of K collaborative scenarios. k The list

[0195] The table uses italics and bold to indicate high-risk collaborative scenarios, and bold to indicate medium-risk collaborative scenarios.

[0196] In the same scenario, collaborative scenarios with three risk levels—high, medium, and low—are output sequentially.

[0197] The risk scenario visualization submodule outputs the trend of task completion time.

[0198] Based on the trend chart, high-risk areas are further marked using rectangular and elliptical dashed boxes.

[0199] Medium-risk collaborative scenarios.

[0200] Figure 7 A flowchart of an individual operation input submodule according to an embodiment of the present invention is shown. Its operation process is as follows: First, the individual operation content 1 in collaborative scenario 1 is input. The input order is: individual operation content input submodule, followed by individual operation content operation unit attribute input submodule. First, a description of individual operation content 1 is input. Only after the description of individual operation content 1 is completed can the operation unit attribute of that individual operation content be selected. Only after the operation unit attribute of individual operation content 1 is set can the next individual operation content be input. Only after the description of individual operation content and the selection of operation unit attributes in collaborative scenario 1 are completed can the input of collaborative scenario 2 be performed.

[0201] Figure 8A flowchart of a collaborative operation input submodule according to an embodiment of the present invention is shown. Its operation process is as follows: First, the collaborative operation content 1 in collaborative scenario 1 is input. The input order is as follows: collaborative operation content input submodule, collaborative operation content operation unit attribute input submodule, and collaborative operation content operation parameter input submodule. First, a description of collaborative operation content 1 is input. Only after completing the input of the content description of collaborative operation content 1 can the operation unit attribute of the collaborative operation content be selected. Next, the parameters of the operation unit attribute of the collaborative operation content need to be set. Only after completing the parameter setting of the operation unit attribute of collaborative operation content 1 can the next collaborative operation content be input. Only after completing the collaborative operation content description, operation unit attribute selection, and operation unit attribute parameter setting in collaborative scenario 1 can the input of collaborative scenario 2 be performed.

[0202] Figure 9 This diagram illustrates the user interface of a crew task completion time prediction and evaluation system based on the CLM-GOMS model, according to a specific example of an embodiment of the present invention. After the system enters the CLM-GOMS model-based crew task completion time prediction and evaluation system, its main interface is as follows: Figure 9 As shown in the image, this interface contains four buttons: "Instructions for Use," "Collaborative Context Input," "Start Assessment," and "Exit System." After entering this interface, the user first clicks the "Instructions for Use" button to access the instructions interface, as shown below. Figure 10 As shown. Users read the system's user manual to understand how to use the system for crew task completion time prediction and assessment. After reading the manual, they click the "Return to Homepage" button to return to the main interface. When conducting an assessment, the user clicks the "Start Assessment" button to enter the assessment system interface. First, the individual operation input submodule interface is accessed, as shown... Figure 2 As shown, users need to input a description of the individual operation and select the operation unit attribute for that individual operation; then they will enter the collaborative operation input submodule interface, as shown below. Figure 3 As shown, users need to input a description of the collaborative operation content, select the operation unit attribute of the collaborative operation content, and set the parameters of the operation unit attribute of the collaborative operation content; finally, they also need to input the actual operation time of multiple collaborative scenarios. After submitting the above three parts of information, clicking the "Start Evaluation" interface will cause the system to substitute the above input parameters into the crew task completion time prediction and evaluation algorithm based on the CLM-GOMS model described in Equations 1-8 for calculation, thereby outputting the crew task completion time prediction and evaluation result, such as... Figure 11 As shown.

[0203] Figure 10 This document illustrates the usage instructions for a crew task completion time prediction and evaluation system based on the CLM-GOMS model, in a specific example according to an embodiment of the present invention.

[0204] Figure 11 This document displays the output of a crew task completion time prediction and assessment system based on the CLM-GOMS model, in a specific example according to an embodiment of the present invention. The output includes the task completion time trend under collaborative scenarios, the prediction effect of task completion time under collaborative scenarios, and the risk level of task completion time under collaborative scenarios. The prediction effect of task completion time under collaborative scenarios displays the corresponding correlation coefficient, p-value, and model prediction effect. The risk level of task completion time under collaborative scenarios includes two parts: risk scenario labeling and risk scenario visualization. The risk scenario labeling displays collaborative scenario numbers indicating high, medium, and low risk.

[0205] Advantages and benefits of the present invention

[0206] The advantages and / or beneficial effects of the present invention specifically include:

[0207] (1) A crew task input subsystem in a collaborative context is provided, including an individual operation input submodule, a collaborative operation input submodule and an actual operation time input submodule, which can simulate the task completion time of crew members in typical collaborative contexts and provide an input interface for actual operation time;

[0208] (2) A crew task completion time prediction and evaluation subsystem based on CLM-GOMS model is provided, which can predict and evaluate crew task completion time after inputting individual operation, collaborative operation and actual operation time in typical collaborative scenarios.

[0209] (3) A subsystem for predicting and evaluating crew task completion time based on CLM-GOMS model is provided to output the evaluation results of multiple task completion times. It can output the trend of task completion time changes and the prediction effect of task completion time. It can also determine the risk level of the corresponding collaborative situation by marking and graphically presenting the risk situation.

[0210] (4) Compared with traditional crew completion time assessment system methods, the present invention can predict crew completion time in typical collaborative scenarios, thereby conducting human factor assessment and identifying existing problems in the early stages of crew cabin mission process optimization and human factor design. It can also be used to optimize training strategies in later crew training, thereby helping to reduce design costs and ensure crew safety.

Claims

1. A system for predicting and evaluating crew task completion time based on the CLM-GOMS model, characterized in that... include: A) Crew task input subsystem in collaborative scenarios, used to provide initial requirements for the crew task completion time prediction and determination system, and to assign values ​​to design constants, including: A1) Individual operation input submodule, including: The individual operation content input submodule is used to set the description of the individual operation content. The individual operation content operation unit attribute input submodule is used to set the operation unit attributes of the individual operation content. The selectable operation unit attributes include: type K, point P, return H, respond R, prepare finger E, tap T, quick move F, double tap TT, and move F_M. A2) The collaborative operation input submodule includes: The collaborative operation content input submodule is used to set the description of the collaborative operation content. The submodule for inputting operation unit attributes of collaborative operation content is used to set the operation unit attributes of collaborative operation content. The selectable collaborative task operation unit attributes include communication preparation (M), communication waiting (W), communication preparation (C_P), voice communication (V_C), visual search (V_S), and situational understanding (U). The submodule for inputting operation unit parameters for collaborative operation content is used to set the parameters of the operation unit attributes of collaborative operation content. The operation unit parameters that need to be set include: the waiting time for communication waiting (W), the number of initiators and recipients for communication preparation (C_P) instructions / reports, the number of words in the task instructions / reports for voice communication (V_C), the number of items in the operation interface of visual search (V_S), and the number of words in the text of the instructions for situational understanding (U). A3) Actual operation time input submodule, used to set the actual operation time for multiple collaborative scenarios. B) A subsystem for calculating and assessing crew task completion time based on the CLM-GOMS model, used to perform task completion time calculation and risk level assessment based on the CLM-GOMS model for the crew task input subsystem in cooperative scenarios, including: B1) Individual operation calculation submodule, used to determine according to equation (1): T individualtask is the total time for individual operations; i is the number of the individual operation unit, ranging from 1 to 9; a i For the attribute of the i-th individual operation unit; num{a i } represents the operation unit attribute 'a' in the individual operation content within this collaborative context. i Quantity; Δt i For a i Standard operating time B2) Cooperative operation calculation submodule, used to determine according to equation (2): T collaborativetask is the total time of the collaborative operation; j is the number of the collaborative operation unit, ranging from 1 to 6; c j For the attribute of the j-th cooperative operation unit; num{c j } represents the operation unit attribute c in the collaborative operation content within this collaborative context. j Quantity; Δt j For c j Standard operating time B3) Crew mission completion time calculation submodule, used to determine according to formula (3): T total =T individualtask +T collaborativetask (3) T total The crew member's task completion time is given by the total individual operation time T in equation (1). individualtask The total cooperative operation time T of equation (2) collaborativetask We obtain by summing. B4) Task completion time prediction effect judgment submodule, used to determine based on equation (4): x is the predicted task completion time. k This is the predicted value for the k-th collaborative scenario; y represents the actual time it takes to complete the task. k This represents the actual task completion time for the k-th collaborative scenario. This represents the mean of the predicted task completion times. The average of the actual task completion times; r xy Let x be the correlation coefficient between x and y, and K be the total number of collaborative input scenarios, k = 1 to K. Furthermore, at a confidence level of α = 0.05, a t-test was conducted on the predicted and actual task completion times for different collaborative scenarios to determine the time based on equation (5): t r The value of the test statistic; K-2 is the degrees of freedom; Based on this, the significance p-value is obtained according to the degrees of freedom. When p < 0.05, the correlation result is considered significant. Furthermore, the model's predictive performance is determined based on equation (6): In equation (6), P efficiency To predict the effectiveness of task completion time. B5) Collaborative task completion time risk level judgment submodule, used to determine based on formula (7): AND k =|xy| / y (7), In equation (7), E k Let $\frac{k}{k}$ be the percentage of absolute error in the k-th cooperative scenario, where $k = 1 to $K$. x represents the predicted task completion time in the collaborative scenario, and y represents the actual task completion time in the collaborative scenario. Among them, the risk level of the collaborative task completion time is determined according to formula (8): In equation (8), E k Let be the percentage of absolute error for the k-th cooperative scenario. C) Subsystem for outputting crew task completion time results in collaborative scenarios, used to provide output for task completion time calculation based on the CLM-GOMS model and risk level assessment results, including: C1) The task completion time trend output submodule in collaborative scenarios takes the results of the crew task completion time calculation submodule and the crew actual operation time input module as input, and presents the predicted values ​​x of task completion time in K collaborative scenarios in the form of a line graph. k The actual value of the task completion time y k In order to observe the corresponding trends of change, C2) The task completion time prediction effect output submodule in a collaborative scenario takes the result of the task completion time prediction effect judgment submodule as input and presents the result including the Pearson correlation coefficient r. xy The predictive power of task completion time, including the significance level p. C3) Task Completion Time Risk Level Output Submodule in Collaborative Contexts: This submodule takes the results of the Task Completion Time Risk Level Judgment Submodule in Collaborative Contexts as input and includes a Risk Context Marking Presentation Submodule and a Risk Context Visualization Presentation Submodule. in: The risk scenario labeling and presentation submodule presents the absolute error percentage E of K collaborative scenarios. k The list of collaborative scenarios is marked in italics and bold, while those of medium risk are marked in bold. The collaborative scenarios of high, medium and low risk levels are output in sequence. The Risk Scenario Visualization Submodule, building upon the trend chart presented by the Task Completion Time Trend Output Submodule in collaborative scenarios, further marks high-risk and medium-risk collaborative scenarios using rectangular and elliptical dashed boxes. a i Standard operating time Δt i The value is: c j Standard operating time Δt j The value is: in: t is the waiting time of W(t). n total To prepare parameters for communication, n reporter To determine the number of people initiating the communication, n receiver To communicate the number of recipients, when n reporter or n receiver When there is more than 1 person, then n total =2; when n reporter and n receiver When all are 1 person, then n total =1, num vc N represents the number of words in the voice communication, and N represents the number of targets in the visual search. u The number of characters entered for the task.

2. The individual operation input submodule and the collaborative operation input submodule according to claim 1, characterized in that: The working process of the individual operation input submodule is as follows: First, the individual operation content 1 in collaborative scenario 1 is input. The input order is: individual operation content input submodule, then operation unit attribute input submodule. In the individual operation content input submodule, a description of the individual operation content is entered. After completing the description, the operation unit attribute input submodule is entered to select the operation unit attribute for that individual operation content. The individual operation unit attribute library includes: K, P, H, R, E, T, F, TT, and F_M. Only after completing the selection of the operation unit attribute for an individual operation content can the next operation content be inputted. Only after completing the description of the individual operation content and the selection of the operation unit attribute in collaborative scenario 1 can the input for collaborative scenario 2 be performed. The working process of the collaborative operation input submodule is as follows: First, the collaborative operation content in collaborative scenario 1 is input. The input order is as follows: collaborative operation content input submodule, collaborative operation content operation unit attribute input submodule, and collaborative operation content operation parameter input submodule. In the collaborative operation content input submodule, the collaborative operation content description is entered. After completing the content description input, the collaborative operation content operation unit attribute input submodule is entered to select the collaborative operation content operation unit attribute. The collaborative operation unit attribute library includes: M, W, V_C, C_P, U, V_S. After completing the collaborative operation content operation unit attribute selection, the collaborative operation content operation parameter input submodule is entered to set the collaborative operation content operation unit parameters. Only after the collaborative operation content operation unit parameter setting is completed can the next collaborative operation content be input. Only after completing the collaborative operation content description, operation unit attribute selection, and operation unit attribute parameter setting in collaborative scenario 1 can the input of collaborative scenario 2 be performed.

3. A method for predicting and evaluating crew task completion time based on the CLM-GOMS model, characterized in that... include: The crew task input steps in a collaborative scenario are used to provide initial requirements for the prediction and determination of crew task completion time. These include individual operation input parameter operations, collaborative operation input parameter operations, and actual operation time input parameter operations for assigning values ​​to design constants. The steps for calculating and evaluating crew task completion time based on the CLM-GOMS model include individual crew operation calculation steps, collaborative operation calculation steps, crew task completion time calculation steps, task completion time prediction effect judgment steps, and collaborative scenario task completion time risk level judgment steps. These steps are used to perform CLM-GOMS model-based task completion time algorithm calculation and risk level assessment for the crew task input subsystem in collaborative scenarios. The steps for outputting the crew task completion time results in a collaborative scenario include steps for outputting the task completion time change trend, the task completion time prediction effect, and the task completion time risk level. These steps provide output for the task completion time algorithm calculation and risk level assessment results based on the CLM-GOMS model. in: Individual operation input sub-steps include: The individual operation content input steps are used to set the description of the individual operation content. The input steps for the operation unit attributes of individual operation content are used to set the operation unit attributes of individual operation content. The selectable operation unit attributes here include key (K), pointing (P), homing (H), response (R), prep (E), tap (T), flick (F), double tap (TT), and finger move (F_M). The cooperative operation input sub-steps include: The collaborative operation content input step is used to set the description of the collaborative operation content. The step for inputting the operation unit attributes of collaborative operation content is used to set the operation unit attributes of the collaborative operation content. The selectable collaborative task operation unit attributes here include Communication Preparation (M), Communication Waiting (W), Communication Preparation (C_P), Verbal Communication (V_C), Visual Search (V_S), and Situational Understanding (U). The steps for inputting operation unit parameters for collaborative operations are used to set the operation unit parameters for collaborative operations. These parameters include the waiting time for communication wait (W), the number of initiators and recipients for the instruction / report in communication preparation (C_P), the number of words in the task instruction / report for voice communication (V_C), the number of items in the operation interface for visual search (V_S), and the number of words in the instruction text for situational understanding (U). The actual operation time input sub-step is used to set the actual operation time for multiple collaborative scenarios, and the individual operation calculation sub-step is used based on equation (1): In equation (1), T individualtask is the total time of individual operations; i is the number of the individual operation unit, ranging from 1 to 9; a i For the attribute of the i-th individual operation unit; num{a i } represents the operation unit attribute 'a' in the individual operation content within this collaborative context. i Quantity; Δt i For a i Standard operating time The cooperative operation calculation sub-step is used according to equation (2): In equation (2), T collaborativetask is the total time of the collaborative operation; j is the number of the collaborative operation unit, ranging from 1 to 6; c j For the attribute of the j-th cooperative operation unit; num{c j } represents the operation unit attribute c in the collaborative operation content within this collaborative context. j Quantity; Δt j For c j Standard operating time The sub-step for calculating crew mission completion time is based on equation (3): T total =T individualtask +T collaborativetask (3) In equation (3), T total The crew member's task completion time is given by the total individual operation time T in equation (1). individualtask The total cooperative operation time T of equation (2) collaborativetask The summation is used to determine the sub-step for judging the prediction effect of task completion time, based on equation (4): In equation (4), x is the predicted task completion time. k This is the predicted value for the k-th collaborative scenario; y represents the actual time it takes to complete the task. k This represents the actual task completion time for the k-th collaborative scenario. This represents the mean of the predicted task completion times. The average of the actual task completion times; r xy Let x be the correlation coefficient between x and y, and K be the total number of collaborative input scenarios, k = 1 to K. Furthermore, at a confidence level of α = 0.05, a t-test was conducted on the predicted and actual task completion times for different collaborative scenarios, based on equation (5): In equation (5), t r The value of the test statistic; K-2 is the degrees of freedom; Based on this, the significance p-value can be obtained according to the degrees of freedom. When p < 0.05, the correlation result is considered significant. Furthermore, the model's predictive performance is used based on equation (6): In equation (6), P efficiency To predict the effectiveness of task completion time. The sub-step for judging the risk level of collaborative task completion time is based on equation (7): AND k =|xy| / y (7) In equation (7), E k Let $\frac{k}{k}$ be the percentage of absolute error in the k-th cooperative scenario, where $k = 1 to $K$. x represents the predicted task completion time in the collaborative scenario, and y represents the actual task completion time in the collaborative scenario. Furthermore, the risk level determination of the completion time of collaborative task scenarios is based on equation (8): In equation (8), E k Let be the percentage of absolute error for the k-th cooperative scenario. The sub-step for outputting the trend of task completion time changes in collaborative scenarios takes the results of the crew task completion time calculation sub-module and the crew actual operation time input module as input, and presents the predicted values ​​x of task completion time in K collaborative scenarios in the form of a line graph. k The actual value of the task completion time y k In order to observe the corresponding trends of change, The task completion time prediction effect output sub-step in a collaborative context takes the result of the task completion time prediction effect judgment sub-module as input and presents the result including the Pearson correlation coefficient r. xy The predictive power of task completion time, including the significance level p. The task completion time risk level output sub-step in a collaborative scenario takes the result of the collaborative scenario task completion time risk level judgment step as input, and includes a risk scenario labeling presentation step and a risk scenario visualization presentation step. in: Risk scenario labeling presentation steps present the absolute error percentage E of K collaborative scenarios. k The list of collaborative scenarios is marked in italics and bold, while those of medium risk are marked in bold. The collaborative scenarios of high, medium and low risk levels are output in sequence. The risk scenario visualization presentation steps, based on the trend chart of task completion time changes in collaborative scenarios, further mark high-risk and medium-risk collaborative scenarios with rectangular and elliptical dashed boxes. a i Standard operating time Δt i The value is: ， c j Standard operating time Δt j The value is: in: t is the waiting time of W(t). n total To prepare parameters for communication, n reporter To determine the number of people initiating the communication, n receiver To communicate the number of recipients, when n reporter or n receiver When there is more than 1 person, then n total =2; when n reporter and n receiver When all are 1 person, then n total =1, num vc N represents the number of words in the voice communication, and N represents the number of targets in the visual search. u The number of characters entered for the task.

4. A computer-readable storage medium storing a computer program that enables a processor to execute the crew task completion time prediction and evaluation method according to claim 3.

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