Evaluation method for subsequent search performance of multiple alert targets under optimized flight conditions

Abstract

The present invention discloses an evaluation method for subsequent search performance of multiple alert targets under optimized flight conditions, comprising: (1) establishing a screening library of coding elements of multiple alert targets under flight conditions; (2) performing flight state setting based on a flight simulator and conducting targeted coding element evaluation on the screening library in step (1); (3) proposing a comprehensive evaluation index for subsequent search performance based on the result of step (2); (3.1) screening relevant data; (3.2) processing the relevant data of step (3.1); (3.3) proposing a comprehensive evaluation index for subsequent search performance of multiple targets; (3.4) optimizing the search performance of multiple alert targets in a human-computer interaction interface; and (4) applying the evaluation index of step (3) to the generation of a multi-alert target perception interface in an aircraft cockpit. The proposed comprehensive evaluation index can balance the weights of various data, more directly and comprehensively reflect the change in search capability during the multi-target search process, and provide an optimized design method for the multi-alert target perception interface in the cockpit.

Description

Technical Field

[0001] The present invention relates to the field of subsequent search for multiple alert targets, and in particular to a method for evaluating the subsequent search performance for multiple alert targets in an optimized flight state. Background Art

[0002] Multi-target visual search is a common visual search task in real life, particularly in environments such as railway station security screening, hospital radiology inspections, and radar displays. Searchers must promptly and accurately detect all warning objects amidst a large number of interfering objects. However, the presence of already detected warning objects can affect the search performance for subsequent warning targets. This effect has been identified and named "subsequent search failure."

[0003] The impact of environmental complexity on attention and working memory resources is a key consideration in studying subsequent search errors, and researchers have extensively explored this topic through psychological experiments. After processing the first target, the visual system experiences visual search limitations similar to an attentional blink. Removing or highlighting already found targets from the search interface can improve subsequent search performance. Meanwhile, visual clutter and target motion can increase attentional resources and increase subsequent search errors.

[0004] The aforementioned research on subsequent search failures primarily used performance metrics such as subsequent search error rate and response time as indicators for evaluating subsequent search performance. However, a single performance evaluation metric cannot fully reflect subsequent search performance, especially in the unique environment of flight. The establishment of a comprehensive evaluation metric can provide a more comprehensive reference for evaluating subsequent search performance and more reasonably reflect the changes in pilots' search capabilities during the search process. Furthermore, how to quickly translate evaluation results into optimized design applications remains a challenge that needs further resolution. Summary of the Invention

[0005] The purpose of the present invention is to provide an evaluation method for the subsequent search performance of multiple alert targets in an optimized flight state, which can solve one or more of the above-mentioned technical problems.

[0006] In order to achieve the above object, the technical solution proposed by the present invention is as follows:

[0007] The evaluation method for subsequent search performance of multiple alert targets under optimized flight conditions includes:

[0008] (1) Establish a screening library of multiple warning target coding elements in the flight environment;

[0009] (2) Using a flight simulator to set flight conditions, conduct targeted coding element evaluation on the screening library in step (1);

[0010] The flight simulator primarily consists of a display system, a dynamic somatosensory system, an optical simulation system, a control system, and a data acquisition system. It simulates various flight conditions during flight, including ambient noise, flight attitude changes, and ambient light conditions. Subjects interact with the screen using the control device, while eye tracking equipment and the ErgoLAB human-machine-environment synchronization platform simultaneously collect various data.

[0011] (3) Propose a comprehensive evaluation index for subsequent search performance based on the results of step (2);

[0012] (3.1) Screening relevant data;

[0013] Obtain relevant data and screen them through the targeted evaluation in step (2);

[0014] The types of data used to evaluate multi-target search include performance data, physiological data, subjective data, etc. Performance evaluation data includes accuracy and reaction time.

[0015] For subsequent search performance, the performance data will focus on the second target search accuracy and second target response time.

[0016] Physiological data such as eye movements include blink frequency, return of interest area, saccades, scans, gazes, etc.

[0017] The evaluation of subsequent search performance will focus on data related to attention allocation and eye performance, such as the number of first-target look-backs.

[0018] (3.2) Processing of data related to step (3.1);

[0019] Normalize the relevant data screened in step (3.1) to eliminate the influence of search order and individual differences on data values ​​and balance the analysis weights of various types of data:

[0020] Taking the second target response time and the first target return data as an example, the corresponding data indicators after processing are the second target response time and the first target non-prominence:

[0021] According to the second target search time limit set in step (2), the second target response time is normalized and the second target response time is calculated:

[0022] Second target response time limit = (second target search time limit - second target response time) / second target search time limit.

[0023] The highest number of return glances for the first target in the valid eye movement data in this step is used as the return benchmark. The number of return glances for the first target is normalized to calculate the non-salience of the first target:

[0024] The non-prominence of the first target = (the highest number of revisits - the first target number of revisits) / the highest number of revisits.

[0025] (3.3) Proposing comprehensive evaluation indicators for multi-target subsequent search performance;

[0026] For specific flight scenarios, specific data of evaluation indicators are added or reduced to balance the weights of various data, form a comprehensive evaluation of subsequent search performance, and form key coding elements for screening;

[0027] A comprehensive evaluation index is proposed for the subsequent search performance of multiple targets, such as subsequent search performance = second target accuracy + second target response time + first target non-prominence. The specific data of this evaluation index can be added or reduced according to the independent variables proposed in step (2). The purpose is to study the changes in subsequent search performance for specific flight scenarios. The indicators that can be added include but are not limited to the time difference between the first and second target searches, blink frequency stability, and related fixation time. This index can balance the weights of various data, form a comprehensive evaluation of subsequent search performance, and help screen key coding elements.

[0028] (3.4) Optimization of multi-alert target search performance in the human-computer interaction interface;

[0029] Based on the comprehensive evaluation indicators of subsequent search performance proposed in (3.3), key coding elements are selected; relevant data selected at different levels of independent variables are analyzed and tested, and the specific relationship between variables and subsequent search performance is explored; a mapping relationship between multi-alert target coding elements and subsequent search performance is established to optimize the multi-alert target search performance of the human-computer interaction interface;

[0030] (4) Apply the evaluation indicators of step (3) to the generation of the multi-alert target perception interface in the aircraft cockpit.

[0031] Preferably, the step (1) includes the following sub-steps:

[0032] (1.1) Organize and compile statistics on aircraft cockpit interface areas, information levels, and graphic symbols;

[0033] (1.2) Organize and compile statistics on special flight factors such as flight environment, flight attitude, and flight mission;

[0034] (1.3) Using the statistical results of steps (1.1) and (1.2), a coding element screening library for flight status is established.

[0035] Preferred: Filter independent variables from the coding factor screening library and determine the level of independent variables, combine with the multi-objective search paradigm, carry out ergonomic evaluation based on the flight simulation device, and collect relevant data.

[0036] Preferably, step (3.1) is screening through physiological experiments.

[0037] Preferably: the optimization process in step (3.4) is based on the human-computer interaction interface designed by Vue, as follows:

[0038] (3.4.1) In the human-computer interaction interface rapid construction tool, select the flight icons and interface coding components that meet the evaluation results;

[0039] (3.4.2) Using the components selected in (3.4.1), conduct rapid redesign by selecting, placing, and combining components in the human-computer interaction interface rapid construction tool, automatically generate interface code, and output the generated design results;

[0040] (3.4.3) If (3.4.2) outputs multiple sets of optimized design results, the design results can be iteratively evaluated using the above evaluation method to complete the screening of the design results, and finally output a multi-alert target perception interface code that can be directly used.

[0041] The technical effects of the present invention are:

[0042] The data types that can be used to evaluate multi-target search in this invention include performance data, physiological data, subjective data, and other types. A comprehensive evaluation index for the performance of subsequent multi-target searches is proposed. Compared to simply evaluating subsequent search performance based on various types of data separately, the proposed comprehensive evaluation index can balance the weights of various data, forming a comprehensive evaluation of subsequent search performance, more comprehensively reflecting the changes in search capabilities during the multi-target search process, and helping to select key coding elements. Especially in special search environments such as flight operations, multi-dimensional comprehensive evaluation can comprehensively consider the pilot's multi-target search capabilities from multiple aspects such as operational performance, attention allocation, and fatigue level.

[0043] This evaluation method establishes a rapid redesign path from "results to design and application" for evaluating subsequent multi-target search performance. Using a Vue-based rapid human-computer interaction interface construction tool, components of the multi-alert target perception interface are screened and redesigned, enabling simultaneous page preview and code generation. Further evaluation and iteration can be conducted for multiple optimization solutions, resulting in directly applicable interface design solutions and code output.

[0044] Aiming at the special environment of flight, the present invention applies comprehensive evaluation indicators to analyze subsequent search performance. This evaluation method solves the problem of scattered and single dimensions in the current subsequent search performance evaluation. The proposed comprehensive evaluation indicators can balance the weights of various data, more directly and comprehensively reflect the changes in search capabilities during multi-target search, and provide an optimized design method for the cockpit multi-alert target perception interface. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings in the specification, which constitute a part of this application, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0046] In the attached figure:

[0047] Figure 1 It is a flow chart of the evaluation method and optimization application of the present invention.

[0048] Figure 2 It is a flowchart for constructing comprehensive evaluation indicators of multi-target subsequent search performance of the present invention.

[0049] Figure 3 This is the result-design-application path diagram of the human-computer interaction interface rapid construction tool based on Vue design of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0051] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0052] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0053] To validate the effectiveness of a method for evaluating the subsequent search performance of multiple alert targets in various flight environments, a targeted evaluation was conducted using the coding factors of multiple alert targets in the head-up display as an example. Target similarity and target feedback intensity were selected from the design coding factor screening library as research targets, and a multi-factor evaluation was conducted and the results analyzed.

[0054] First, the design coding elements of multiple alert targets in the flight situation monitoring interface are refined.

[0055] In the context of joint operations, this paper focuses on the information complexity of target symbols in the situation monitoring interface in terms of type, quantity, model, etc., as well as the effects of subsequent search errors on target perception sets and resource depletion. Two key coding elements, target similarity and target feedback intensity, are selected to carry out subsequent work efficiency evaluation.

[0056] Subsequently, the extracted target similarity and target feedback strength were used as independent variables to evaluate the subsequent search performance.

[0057] Target similarity refers to the similarity between the two targets to be searched for, with two levels: similar and dissimilar. Target feedback intensity refers to the salience of the interface's visual feedback on the found targets, with three levels: low, medium, and high. In this experiment, this specifically refers to three feedback mechanisms: marking, highlighting, and flashing. This assessment will utilize a 2x3 within-group experiment, with each experimental condition consisting of 15 trials, for a total of 90 trials (2x3x15). Each trial begins with a 500ms blank screen to eliminate residual visual perception. A black "+" symbol then appears in the center of the screen, directing the subject's gaze to the center of the screen. A 2s visual cue interface for the search task appears, informing the subject of the two targets to be searched. This interface then enters a 15s situational search interface, requiring the subject to search for and click on the two designated targets. Correctly clicking on the targets will result in a valid feedback mechanism: marking, highlighting, or flashing. Upon successful completion of the search task or reaching the 15s search time, the interface automatically advances to the next trial. The 90 trials were randomly divided into 6 groups, each containing 15 trials. After completing each group of experiments, the subjects would take a 1-minute break to relax their eyes before continuing the experiment.

[0058] Based on target similarity and target feedback strength, the second target accuracy, second target response time, and first target look-back times were selected as key data, and the subsequent search performance was processed and calculated and analyzed.

[0059] After eliminating invalid and extreme data from the experiment, SPSS Statistics software was used to analyze subsequent search performance, with a significance level of P = 0.05. Second-target response time and first-target lookback times were normalized, and "subsequent search performance was calculated as second-target accuracy + second-target response time + first-target non-salience." An analysis of variance was conducted on subsequent search performance, and the results are shown in Table 1. The results showed no significant differences among the three target feedback intensities (F = 1.176, P = 0.309 > 0.05), while there was a significant difference between the two target similarities (F = 9.929, P = 0.003 < 0.05). This indicates that in dual-target search, the similarity of the target's graphical encoding significantly influences subsequent search performance.

[0060]

[0061] SPSS Statistics software was used to further analyze the second target search accuracy, second target response time, and first target return data, with a significance level of P = 0.05. The results are shown in Tables 2, 3, and 4.

[0062] The results showed that there was no significant difference among the three target feedback intensities (F=0.757, P=0.470>0.05), while there was a significant difference between the two target similarities (F=5.728, P=0.017<0.05). That is, in dual-target search, the similarity of the search targets in graphic encoding has a significant impact on the subjects' second target search accuracy.

[0063]

[0064] A further analysis of variance was conducted on the second target response time, and the results are shown in Table 2. The results showed no significant differences among the three target feedback intensities (F = 0.436, P = 0.674 > 0.05), nor between the two target similarities (F = 3.051, P = 0.081 > 0.05). In other words, in dual-target search, neither the feedback intensity nor the similarity in graphical encoding had a significant effect on the subjects' second target response time, which may be due to the high difficulty of the experimental material.

[0065]

[0066] We further conducted an ANOVA on the number of returns to the first target region of interest, and the results are shown in Table 4. The results showed significant differences between the three target feedback intensities (F = 3.304, P = 0.037 < 0.05), and a significant difference between the two target similarities (F = 14.496, P = 0.000 < 0.05). This indicates that in dual-target search, the feedback intensity and graphical encoding similarity of the search targets significantly influenced the subjects' returns to the first target region of interest.

[0067]

[0068] Combined with the subsequent comprehensive evaluation of search performance and specific analysis of screening data, the following optimization design directions are provided for the design of multiple warning targets in the flight head-up interface.

[0069] In multi-alert target search, the mechanism by which target feedback intensity influences subsequent search performance is as follows. The average number of look-backs for the three different intensities of interactive feedback, flashing, highlighting, and marking, in the multi-target search task was ≤2. However, high-intensity feedback significantly increased the number of look-backs for the found target, which occupies more attentional resources and thus affects subsequent target search performance. Therefore, this should be considered as a key design element in the design of flight situational interface interactions that require high attention.

[0070] In multi-target search, the mechanism by which target similarity influences subsequent search performance is as follows. Target similarity significantly influences response time to the second target and the number of glances back to the first target. Search targets with similar graphical encoding characteristics can reduce the attentional and memory resources consumed by the already found target, keeping the number of glances below two, optimizing cognitive resource allocation during subsequent searches, and improving subsequent search performance.

[0071] Target similarity and target feedback strength are key coding factors influencing the design of multi-target search and recognition in heads-up monitoring interfaces. Target graphic encoding requires a rational classification and layering design to ensure effective information differentiation and rapid memorization. Interactive encoding should be appropriate and balanced, ensuring clear differentiation without excessive attention consumption. This optimizes subsequent search performance in complex multi-target search and recognition interfaces.

[0072] In summary, this study provides guidance on interactive coding and graphic coding for optimizing the presentation of multiple warning target information in the flight head-up interface, and provides an important basis for further results-design-application.

[0073] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the subsequent search performance of multiple alert targets in an optimized flight state, characterized by: include (1) Establish a screening library of multiple warning target coding elements in the flight environment; The step (1) includes the following sub-steps: (1.1) Organize and compile statistics on aircraft cockpit interface areas, information levels, and graphic symbols; (1.2) Organize and compile statistics on special flight factors such as flight environment, flight attitude, and flight mission; (1.3) Using the statistical results of steps (1.1) and (1.2), establish a coding element screening library for flight status; (2) Using a flight simulator to set flight conditions, conduct targeted coding element evaluation on the screening library in step (1); (3) Propose comprehensive evaluation indicators for subsequent search performance based on the results of step (2); (3.1) Screening relevant data; Obtain relevant data and screen them through the targeted evaluation in step (2); (3.2) Processing of data related to step (3.1); Normalize the relevant data screened in step (3.1) to eliminate the influence of search order and individual differences on data values ​​and balance the analysis weights of various types of data: (3.3) Proposing comprehensive evaluation indicators for multi-target subsequent search performance; For specific flight scenarios, specific data of evaluation indicators are added or reduced to balance the weights of various data, form a comprehensive evaluation of subsequent search performance, and form key coding elements for screening; (3.4) Optimization of multi-alert target search performance in the human-computer interaction interface; Based on the comprehensive evaluation indicators of subsequent search performance proposed in (3.3), key coding elements are selected; relevant data selected at different independent variable levels are analyzed and tested, and a mapping relationship between multi-alert target coding elements and subsequent search performance is established to optimize the multi-alert target search performance of the human-computer interaction interface; The subsequent search performance = second target accuracy + second target response time + first target non-prominence. The independent variable can add or subtract specific data of this evaluation indicator. The purpose is to study the changes in subsequent search performance for specific flight scenarios. Indicators such as blink frequency stability and related fixation time can be added. This indicator can balance the weights of various data to form a comprehensive evaluation of subsequent search performance and help select key coding elements. (4) Apply the evaluation indicators of step (3) to the generation of the multi-alert target perception interface in the aircraft cockpit.

2. The method for evaluating subsequent search performance of multiple alert targets in an optimized flight state according to claim 1, characterized in that: Independent variables are selected from the coding factor screening library and their levels are determined. Combined with the multi-objective search paradigm, ergonomic evaluation is carried out based on the flight simulation device to collect relevant data.

3. The method for evaluating subsequent search performance of multiple alert targets in an optimized flight state according to claim 1, characterized in that: Step (3.1) is screening through physiological experiments.

4. The method for evaluating subsequent search performance of multiple alert targets in an optimized flight state according to claim 1, characterized in that: The optimization process in step (3.4) is based on the human-computer interaction interface designed by Vue, as follows: (3.4.1) In the human-computer interaction interface rapid construction tool, select the flight icons and interface coding components that meet the evaluation results; (3.4.2) Using the components selected in (3.4.1), conduct rapid redesign by selecting, placing, and combining components in the human-computer interaction interface rapid construction tool, automatically generate interface code, and output the generated design results; (3.4.3) If (3.4.2) outputs multiple sets of optimized design results, the design results can be iteratively evaluated using the above evaluation method to complete the screening of the design results, and finally output a multi-alert target perception interface code that can be directly used.

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