Interrupt response capability evaluation data processing method and system

Abstract

The invention relates to the technical field of human factor engineering and intelligent evaluation, in particular to an interruption response capability evaluation data processing method and system based on response time characteristics and task modeling. The method comprises the steps of constructing an experimental process scheduling structure comprising a main task and an interrupt task, collecting and constructing a complete behavior data stream, extracting time characteristics in a task switching and recovery process, executing standardization and disturbance labeling, performing factor analysis in combination with a task label, and generating a capability factor parameter set. And then matching a scoring template to calculate an individual interruption response capability score, generating a grade label according to the score, and completing task capability adaptation and recommendation. According to the method, the structured, quantitative and closed-loop feedback of capability evaluation is realized, and the evaluation accuracy and the task matching intelligent level are improved.

Description

Technical Field

[0001] The present invention relates to the cross - field of human factors engineering, cognitive psychology and artificial intelligence, and particularly to a method and system for processing evaluation data of interruption coping ability based on reaction time characteristics and task modeling. Background Art

[0002] In complex operation scenarios, personnel often face the situation of multi - task concurrency and high - frequency interruptions. An individual's coping ability with interruption information directly affects operation safety and system stability. Existing evaluation methods for interruption coping ability mainly rely on static questionnaire scales or single - task completion duration indicators, and it is difficult to truly reflect the response efficiency, recovery ability, and behavioral fluctuation characteristics during the dynamic task switching process.

[0003] In recent years, with the development of human - machine interaction and behavioral data collection technologies, researchers have begun to attempt to model the interruption coping ability through experimental task simulation and behavioral data stream analysis. However, the traditional methods still have the following deficiencies in terms of task structure design, standardization of index extraction, construction of ability dimensions, and personalized recommendation mechanisms: (1) Lack of a data modeling system for the whole process of main task - interruption - recovery task; (2) Lack of a quantitative mapping path between behavioral data and ability labels; (3) The evaluation results cannot be closed - loop feedback to subsequent task configurations, lacking the system's adaptive optimization ability. Summary of the Invention

[0004] The present invention provides a method and system for processing evaluation data of interruption coping ability based on reaction time characteristics and task modeling, so as to solve the problem of how to construct reaction time characteristic indicators based on the behavioral data stream during the task response process, combine with task labels to perform factor analysis modeling, and then realize the quantitative scoring of individual interruption coping ability and task matching recommendation.

[0005] To solve the above - mentioned technical problems, the present invention provides a method for processing evaluation data of interruption coping ability based on reaction time characteristics and task modeling, including: Construct a task structure, configure task switching logic and time control, and generate an experimental process scheduling structure; Present task content according to the experimental process scheduling structure, record user responses and mark stages, and construct a behavioral data stream; Calculate time indicators based on the behavioral data stream, perform data standardization processing, and generate perturbation annotation information and a set of characteristic indicators; Construct a mapping relationship by combining the set of characteristic indicators and experimental task labels, perform factor analysis processing, and generate a set of ability factor parameters; Match the scoring template, calculate the score for the ability to handle interruptions, generate labels based on the grade boundaries, and output the scoring recommendation result data. The expression of the scoring recommendation result data is as follows: Wherein, is the scoring recommendation result data; is the mapping structure function; is the label representing the interruption handling ability level; is the individual's interruption handling ability score; is the user's unique identifier; is the evaluation timestamp; is the experimental task number; is the summary of the ability dimension; is the ability-task adaptation identifier; Based on the scoring recommendation result data, complete the discrimination of task ability adaptation, generate the matching task vector, and feedback it to the task modeling process.

[0006] Furthermore, the steps for constructing the task structure include: Obtain the task template sets of the main task and the interruption task, construct the cross-task structure, and generate the task parameter configuration table; Based on the task parameter configuration table, set the interruption trigger timing and task switching logic, and generate the experimental process control table; Combine the experimental process control table to configure the task display order and time control logic, and output the experimental process scheduling structure.

[0007] Furthermore, the steps for constructing the behavior data stream include: Obtain the experimental process scheduling structure, execute the alternating presentation of the main task and the interruption task, and collect the user interaction response data; Mark the task stages and record the timestamps for the user interaction response data to generate the response time segmented data; Based on the response time segmented data, construct the behavior data streams of the main task, the interruption task, and the recovery task.

[0008] Furthermore, the steps for calculating the time metrics include: Obtain the behavior data stream, calculate the task switching time consumption, task recovery time consumption, and reaction delay time metrics; Perform data cleaning and standardization processing on the task switching time consumption, task recovery time consumption, and reaction delay time metrics; Based on the standardized processing results, identify the behavior fluctuation interval, and generate the perturbation annotation information and the feature metric set.

[0009] Furthermore, the steps for constructing the mapping relationship include: Obtain the set of characteristic indicators and perturbation annotation information, and construct the ability evidence structure and the ability mapping relationship table; Combine the ability evidence structure with the experimental task labels to establish an ability mapping relationship table between the ability dimension and the task performance.

[0010] Furthermore, the steps for generating the set of ability factor parameters include: Perform factor analysis on the ability mapping relationship table to generate the set of ability factor parameters.

[0011] Furthermore, the steps for calculating the ability score for interrupt handling include: Obtain the set of ability factor parameters, match the scoring template library, and calculate the individual's ability score for interrupt handling. The individual's ability score for interrupt handling is: where, is the individual's ability score for interrupt handling; is the total number of ability factor dimensions; is the ability factor loading; is the standardized ability characteristic indicator; is the th response function of the ability indicator; is the weighting factor for the corresponding time series factor; is the total task cycle time; reflects the dynamic change trend of the indicator within the task cycle.

[0012] Furthermore, the steps for generating the scoring recommendation result data include: Based on the ability score for interrupt handling, match the scoring grade boundary rules to generate the ability grade label. The grade label is: where, represents the ability grade label for interrupt handling; is the individual's ability score for interrupt handling; 、 are the grade scoring boundary thresholds; Perform a structural binding on the ability score for interrupt handling and the ability grade label to generate the scoring recommendation result data.

[0013] Furthermore, the steps for completing the discrimination of task ability adaptation include: Obtain the scoring recommendation result data, and perform a discrimination operation on task ability adaptation in combination with the task scenario label; According to the result of the discrimination operation on task ability adaptation, select the set of adapted task templates and generate the matching task vector; Use the matching task vector as the input parameter for the new round of task modeling and feedback it to the task modeling process.

[0014] Furthermore, a data processing system for evaluating the ability to cope with interruptions based on reaction time characteristics and task modeling includes: A task modeling and process control module for constructing a task structure, configuring task switching logic and time control, and generating an experimental process scheduling structure; A behavior acquisition and data construction module for presenting task content according to the experimental process scheduling structure, recording user responses, and constructing a behavior data stream; An index extraction and feature construction module for calculating time indexes based on the behavior data stream, performing data standardization processing, and generating a set of feature indexes; An ability modeling module for constructing a mapping relationship by combining the set of feature indexes and experimental task labels, performing factor analysis, and generating a set of ability factor parameters; A scoring and grading module for matching a scoring template, calculating an ability score and generating an ability level label, and outputting scoring recommendation result data; An adaptation recommendation module for completing task ability adaptation discrimination according to the scoring recommendation result data, generating a matching task vector, and feeding it back to the task modeling process.

[0015] The key innovation points of the present invention include: (1) A method for constructing an interruption response evaluation model based on a behavior data stream is proposed, which systematically integrates task switching response time and stage markers to support the whole-process evaluation modeling.

[0016] (2) A dynamic adaptation mechanism for scoring results and task templates is designed, and an adaptive closed-loop process of evaluation - recommendation - training is realized based on a label mapping and feedback mechanism.

[0017] The following are its main beneficial effects: (1) Improve the timeliness and accuracy of interruption response evaluation. By constructing an experimental process scheduling structure, the present invention automatically controls the presentation order and time logic of the main task and the interruption task, and records user response data in real time. Combining the behavior data stream, time indexes such as task switching time consumption, recovery time consumption, and reaction delay are calculated. Compared with the traditional method that relies on manual recording and subjective judgment, the system can achieve high-precision and standardized data acquisition and timing management, significantly improving the evaluation efficiency and accuracy.

[0018] (2) Form a closed-loop feedback mechanism for task modeling and ability evaluation. After the scoring result structure is bound, the present invention performs an ability adaptation discrimination operation according to the scoring recommendation result and the task scenario label, generates a matching task vector, and feeds it back to the interruption task modeling process to realize the automatic linkage between task generation and ability evaluation. This closed-loop structure effectively solves the problem of the disconnection between evaluation and recommendation in traditional systems and supports the design of continuous personalized training tasks. Description of the Drawings

[0019] Figure 1 It is a schematic flowchart of a method for processing evaluation data of interruption response ability based on reaction time characteristics and task modeling provided by an embodiment of the present application; Figure 2 It is a structural block diagram of a system for processing evaluation data of interruption response ability based on reaction time characteristics and task modeling provided by an embodiment of the present application. Detailed implementation manners

[0020] Embodiment 1: Refer to Figure 1 , which is a schematic flowchart of a method for processing evaluation data of interruption response ability based on reaction time characteristics and task modeling provided by an embodiment of the present invention. The process may at least include steps S100 - S600: S100. Construct a task structure based on the task template set, configure task switching logic and time control, and generate an experimental process scheduling structure; S200. Present task content according to the experimental process scheduling structure, record user responses and mark stages, and construct a complete behavior data stream; S300. Calculate time metrics based on the behavior data stream, complete data standardization processing, and generate perturbation annotation information and a set of feature metrics; S400. Combine the set of feature metrics with the experimental task labels to construct a mapping relationship, and perform factor analysis processing to generate a set of ability factor parameters; S500. Match the scoring template to calculate the interruption response ability score, generate labels based on the grade boundaries, and output scoring recommendation result data; S600. Based on the scoring recommendation result data, complete task ability adaptation discrimination, generate a matching task vector, and feedback it to the task modeling process.

[0021] Step S100 at least includes steps S110 - S130: S110. Obtain the task template sets of the main task and the interruption task, construct four types of cross - task structures, and generate a task parameter configuration table.

[0022] Specifically, in this step, the pre - constructed main task template and interruption task template are called from the task library. The task template set includes two types of tasks: operation - type tasks and memory - type tasks. Attribute parameters such as cognitive load level, duration, and task feedback mechanism are set for each type of task.

[0023] After obtaining the task template set, the system constructs the following four types of cross - task structures: operation - type main task and operation - type interruption task, operation - type main task and memory - type interruption task, memory - type main task and operation - type interruption task, memory - type main task and memory - type interruption task. The cross - task structure is used to simulate multi - type task switching behaviors in real - world work scenarios, covering different combinations of cognitive resource conflicts.

[0024] Furthermore, the system extracts task attribute parameters according to the task type, content length, task triggering method, and interruption insertion form, and generates a task parameter configuration table. The task parameter configuration table contains fields such as task identification, task classification label, execution duration, task interval time, prompt mechanism, feedback mode, etc., which are used as the input basis for subsequent experimental process control rules.

[0025] S120. Based on the task parameter configuration table, set the interruption trigger timing and task switching logic, and generate an experimental process control table.

[0026] Specifically, the system loads the task parameter configuration table in the task logic engine, configures the task switching points according to the task duration field, and inserts the interrupted tasks into the main task timeline. The interruption trigger timing includes three forms: fixed time point trigger, random time period trigger, and task condition trigger. Set the interruption trigger point position of each main task through logical decision rules, mark the task conversion status, and embed the interrupted task entry.

[0027] In the process of constructing the task switching logic, the system defines the transfer paths of three types of stages: main tasks, interrupted tasks, and resumed tasks, and configures switching identifiers and behavior marking rules for each stage. The system synchronizes the decision trigger conditions with the time process, performs jumps from the main task to the interrupted task during operation, and then executes the recovery logic to return to the original task process.

[0028] Furthermore, after completing the above configuration, the system organizes and outputs the interruption trigger points, stage transfer identifiers, task jump paths, condition node lists, etc., to generate an experimental process control table. The experimental process control table will be used as the input content of the subsequent task scheduling control module, providing a basis for the specific scheduling of the task display order.

[0029] S130. Combine the experimental process control table to configure the task display order and time control logic, and output the experimental process scheduling structure.

[0030] Specifically, the system reads the task stage identifiers, task switching nodes, interruption trigger times, and task category order in the experimental process control table to establish a task display sequence. The task display sequence arranges the main task segment, interrupted task segment, and task recovery segment in chronological order, and defines the display duration, start and end timestamps, and time difference between adjacent task switches for each segment.

[0031] Furthermore, the system sets the time control logic to restrict the task presentation rhythm. The time control logic includes mechanisms such as the minimum interval time between tasks, the response time acquisition time window, the interruption duration limit, and the non-response timeout mark, ensuring that the user's behavioral responses are comparable and segmentable.

[0032] The system synchronously generates a task presentation control structure and a time axis structure based on the task display sequence and time control logic, and numbers and archives them. Finally, an experimental process scheduling structure is generated, which includes a task phase sequence, display time configuration, response synchronization identifier, and interruption handling rhythm, and is used to drive subsequent user interaction operations and response behavior acquisition processes.

[0033] Through the task structure modeling and process control steps from S110 to S130, the standardized configuration of task dimension design, parameter regulation, and behavior driving mechanism is realized, supporting the technical closed-loop of the whole process of subsequent multi-stage response behavior acquisition, index construction, and ability analysis. The constructed cross-task structure and interruption logic control strategy improve the accuracy and generality of the interruption response ability evaluation method, while ensuring controllable experimental scheduling, stable response characteristics, and unified behavior data structure, providing a traceable data basis and task label basis for the system scoring and feedback recommendation modules.

[0034] Step S200 includes at least steps S210 - S230: S210. Obtain the experimental process scheduling structure, alternately present the main task and the interruption task, and collect user interaction response data.

[0035] Specifically, in this step, the information of the main task segment, interruption task segment, and recovery task segment recorded in the experimental process scheduling structure is first read, and the task content is presented to the user under test in a visual or auditory form on the interaction platform. The task content is displayed in sequence control, and each task segment is bound with a unique identifier, content encoding, and corresponding timestamp interval.

[0036] During the task presentation process, the system control logic alternately switches the main task and the interruption task according to the timing and rhythm set in the scheduling structure, including but not limited to the alternation of operation tasks and memory tasks, and the switching of different task intensities. The tasks guide the user state change through explicit prompts or instantaneous jumps. During the task execution process, the user interacts with the system by means of mouse clicks, keyboard inputs, touch behaviors, or voice responses.

[0037] The system synchronously collects the interaction response data generated by the user in each task segment. The user interaction response data includes fields such as operation type, operation location, response time, task feedback information, and behavior sequence number. This data will be used to analyze the task execution rhythm and interruption reaction characteristics in subsequent steps.

[0038] S220. Mark the task phase and record the timestamp for the user interaction response data to generate response time segmented data.

[0039] Specifically, the system first extracts the timestamp of each response event from the user interaction response data, and divides all interaction behaviors into three categories: main task segment response, interrupt task segment response, and recovery task segment response, with reference to the start and end times of tasks set in the experimental process scheduling structure.

[0040] During the response time segmentation process, the system uses the task display order in the scheduling structure as the segmentation node, logically attributes the user interaction events according to the time sequence, and binds phase labels. The task phase markers include parameters such as task segment name, task number, task type, and task duration, so as to achieve the structural consistency of data segmentation labels.

[0041] Furthermore, the system calculates the first response time, average response interval, response quantity, and response distribution trend under each task segment, and the results constitute the response time segmentation data. The response time segmentation data will be used in step S230 to construct a complete behavior data stream.

[0042] During this process, the system filters out missing responses, duplicate responses, or non-task responses, and generates a response data quality report, providing a clean data source for subsequent task switching analysis and feature index extraction.

[0043] S230. Based on the response time segmentation data, construct a behavior data stream for the whole process of main task - interrupt task - recovery task.

[0044] Specifically, the system reads the task segment classification, response event sequence, time interval, and behavior label in the response time segmentation data, concatenates and combines the data of all task execution phases, and generates a behavior data stream composed of main task segments, interrupt task segments, and recovery task segments in chronological order.

[0045] The behavior data stream has a complete behavior event sequence structure, including fields such as phase number, task type, response time interval, switching point time mark, and phase start and end identifiers. Each phase data block contains a response behavior event chain, and the phases are connected through task switching nodes to form a complete user behavior execution trajectory.

[0046] Furthermore, the system constructs a task conversion record between behavior segments, including structures such as the jump path from the main task to the interrupt task and the regression path from the interrupt task to the recovery task, and embeds a task switching flag in the behavior data stream. This structure will be used in S310 to calculate metrics such as switching time consumption and recovery time.

[0047] Finally, the system outputs the behavior data stream for the whole process of main task - interrupt task - recovery task, as the basic data set for constructing metrics in the S300 phase, and as the behavior performance evidence in the S400 phase ability modeling.

[0048] By implementing steps S210, S220, and S230, the system realizes a full - process closed - loop from experimental task control to user behavior response collection, then to response behavior segmentation and behavior data stream construction, forming a continuous path structure of task - driven - behavior mapping - metric generation. The constructed response - time segmented data and behavior data stream provide a clean, structured, and traceable data basis for subsequent metric calculations, improving the accuracy and robustness of response feature extraction and ability modeling in interruption response ability assessment.

[0049] Step S300 includes at least steps S310 - S330: S310. Obtain the behavior data stream and calculate metrics for task - switching time consumption, task - recovery time consumption, and reaction delay time.

[0050] Specifically, the behavior data stream is constructed in step S230, which has completed the complete division of the main task segment, interruption task segment, and recovery task segment, and marked the task type, response time, task - switching nodes, and phase identifiers. The system first extracts the task - switching flags between each segment of tasks from the behavior data stream, confirms the first - switching event and recovery event between the main task and the interruption task, and identifies the timestamps of the task - switching points and recovery points.

[0051] After identifying the task - switching structure, the system calculates three types of time - related metrics based on the timestamps: task - switching time consumption, task - recovery time consumption, and reaction delay time metric. Among them, the task - switching time consumption refers to the interval time between the last valid response of the main task and the first response of the interruption task; the task - recovery time consumption refers to the interval time between the last response of the interruption task and the first response after the main task resumes; the reaction delay time metric refers to the delay time of the user's first behavior response relative to the start time of task display within each task segment.

[0052] The above three metrics are all based on behavior events, obtained through the time - series difference method, and appended to the behavior data stream structure, providing structured input for the subsequent data cleaning and standardization processing module.

[0053] S320. Perform data cleaning and standardization processing on the metrics of task - switching time consumption, task - recovery time consumption, and reaction delay time.

[0054] In this step, after receiving the three types of time - related metrics calculated in step S310, data quality filtering, error handling, and structural standardization conversion are performed to ensure the analysis stability, sample consistency, and statistical applicability of the metrics.

[0055] Specifically, the system first filters out the missing values, out-of-limit values, and duplicate values in the task switching time, task recovery time, and reaction delay time metrics. Among them, the missing values are filled in by interpolation, the out-of-limit values are removed according to the set threshold, and the duplicate records are merged by the task structure mapping rule.

[0056] The cleaned time-related metrics are re-archived in the form of a data sequence and standardized according to the task type and task segment markers. The standardization method adopts interval scaling and distribution alignment, so that the time-consuming metrics under different task types can be compared on a unified scale. The standardization process also includes steps such as data centering, variance normalization, in-task sequence sorting, and behavior state identification embedding.

[0057] The processed task switching time, task recovery time, and reaction delay time metrics will retain the task number, paragraph marker, and user identification, and output a structured metric sequence table, providing basic support for the identification of the behavior fluctuation interval and the construction of the feature metric set in S330.

[0058] S330. Based on the standardization result, identify the behavior fluctuation interval and generate perturbation annotation information and a set of feature metrics.

[0059] In this step, after receiving the standardized task switching time, task recovery time, and reaction delay time metrics, the system determines the behavior fluctuation interval through behavior change trend analysis and abnormal event identification, and outputs structured perturbation annotation information and a set of feature metrics.

[0060] Specifically, the system detects the internal behavior stability of the task according to the change curves of each response time in the standardized metric sequence. The system performs trend clustering and sliding window analysis on the upper and lower fluctuation intervals of consecutive response time intervals, and marks the scenarios such as a large increase in response time, a decrease in frequency, or response loss as the behavior fluctuation interval.

[0061] After identifying the behavior fluctuation interval, the system combines the task stage identifier and user mark to assign each behavior fluctuation interval to the main task segment, interrupted task segment, or recovery task segment, and marks the behavior perturbation source, including behavior feature labels such as device interference, sudden increase in task difficulty, and loss of attention.

[0062] The system constructs a set of feature metrics based on the perturbation annotation information. The set includes dimensions such as task switching time statistical features, recovery time trend slope, interval variance of reaction delay, perturbation duration distribution, and perturbation response fluctuation amplitude. The structure of the feature metric set contains metric number, metric type, associated task segment, and numerical field, which are used by the subsequent S400 module for ability modeling.

[0063] By implementing steps S310, S320, and S330, the system completes the process of layer-by-layer extraction from behavior response events to time-based metrics, and then to stability characteristics and perturbation labels. The constructed metrics of task switching time, task recovery time, and reaction delay time form the basis for quantitatively expressing an individual's interruption response behavior, while the behavior fluctuation range and perturbation annotation information further achieve the capture of the structural characteristics of cognitive stability and regulation efficiency, providing a solid data support and behavior interpretation basis for the construction of the ability evaluation model and the scoring strategy.

[0064] Step S400 includes at least steps S410 - S430: S410. Obtain the set of feature metrics and the perturbation annotation information, and construct an ability evidence structure and an ability mapping relationship table.

[0065] In the previous step S330, the set of feature metrics and the perturbation annotation information have been generated. The set of feature metrics includes standardized statistical metrics of task switching time, task recovery time, and reaction delay time, as well as behavior fluctuation characteristics such as perturbation duration, response variance, and response stability. The perturbation annotation information is the abnormal response marks identified in the behavior fluctuation range, including but not limited to identification fields such as loss of attention, high-time mutation, and frequency drop.

[0066] In this step, the system first obtains the above-mentioned structured data set, establishes a reference structure for ability mapping, and constructs an ability evidence structure. The ability evidence structure is used to express the parameter inheritance link from the behavior layer to the ability layer, specifically including: behavior index number, source task stage, behavior event characteristics, perturbation identification label, response trend category, and numerical expression form.

[0067] During the construction process, the system classifies and labels the position of each index in the task process according to the behavior segment attribution information of each index in the set of feature metrics, and performs structural fusion with the corresponding perturbation annotation information. The system establishes behavior segment attribution relationships, perturbation interaction relationships, and evaluation role identifications (such as switching ability, recovery efficiency, cognitive load tolerance, etc.) for each group of metrics.

[0068] Furthermore, the system corresponds the ability evidence structure with the fused structure to the behavior data record and generates an ability mapping relationship table. The ability mapping relationship table takes the behavior index as the source node and the ability label as the target node, and establishes a one-to-many or many-to-one mapping relationship. Each mapping relationship record includes: source index ID, target ability name, weight factor reference range, perturbation intervention influence degree, and task source mark.

[0069] S420. Combine the ability evidence structure with the experimental task label to establish an ability mapping relationship table between the ability dimension and the task performance.

[0070] After the ability mapping relationship table is constructed in S410, this step further combines this structure with the experimental task tags to complete the construction of the ability dimension and improve the ability mapping description in the task performance layer.

[0071] Specifically, this step first obtains the experimental task tags, which are generated during the construction of the task structure in S110–S130 and include structural fields such as task type (operational, memory-based), task segment attributes (main task, interruption task, recovery task), task difficulty coefficient, information interference category, etc.

[0072] The system performs joint structure modeling on the experimental task tags and the ability mapping relationship table, and constructs a hierarchical ability mapping table based on the task dimension. This ability mapping table consists of three parts: the ability dimension layer, the task tag layer, and the behavior index layer. The system classifies the ability indicators showing consistent behavior patterns under the same task tags into the same ability dimension, such as "cognitive flexibility", "attention switching ability", "interruption recovery ability".

[0073] During the modeling process, the system further performs classification processing on the nodes in the ability mapping relationship table and establishes a task mapping path. In the path, the source node is the feature index, the first-hop connection is the perturbation label, the second-hop is the task label, and the final hop is the ability dimension node. The system performs path integrity check and label consistency verification on this path, and eliminates the invalid nodes caused by task switching failure or incomplete data.

[0074] The final output is the ability dimension mapping structure table, which contains information such as the set of feature indicators associated with each ability dimension, the corresponding task segment number, the mapping path graph structure, and the number of nodes. This structure provides an input framework for the subsequent factor analysis model.

[0075] S430. Perform confirmatory factor analysis processing on the ability mapping relationship table to generate an ability factor parameter set.

[0076] After completing the mapping of the ability dimension and task performance, this step inputs the structured ability mapping relationship table into the modeling engine, performs confirmatory factor analysis processing, and outputs a quantifiable ability factor parameter set for individual ability difference modeling and scoring strategy matching.

[0077] Specifically, the system calls the confirmatory factor modeling module and imports the ability mapping relationship table and the experimental sample set. The system first performs dimension initialization processing, specifies the set of behavior indicators, the task tag structure, and the perturbation label status for each ability dimension, and performs parameter nesting processing.

[0078] Based on the structural consistency between the task label layer and the behavior metric layer, the system aggregates the metrics under the ability dimension to form a factor observation matrix. The observation matrix records the behavior performance scores of the user under each ability dimension, and combines with the previous standardization results to form a unified data scale.

[0079] Subsequently, the system performs model fitting and confirmatory factor analysis operations. Through the load coefficients of the behavior metrics under multiple ability dimensions, it confirms the factor contribution degree of each metric, and adjusts the model structure and path coefficients. The obtained set of ability factor parameters includes structural fields such as the ability dimension name, the set of core observation metrics, the average weight of the metrics, the load intensity, the factor variance explanation rate, and the factor correlation coefficient matrix.

[0080] The generated set of ability factor parameters is saved in the form of a standardized vector, and serves as the sole basis for the input of the S510 scoring model, which is used to match the scoring template library and calculate the interruption coping ability score.

[0081] By executing steps S410, S420, and S430, the system completes the complete conversion from the set of characteristic metrics and perturbation annotation information to the ability dimension modeling, and establishes an ability mapping network among multiple tasks, multiple metrics, and multiple labels. Based on the set of ability factor parameters extracted by the confirmatory factor analysis, it provides a structurally rigorous and numerically quantifiable evaluation basis for subsequent ability scoring, effectively improving the accuracy, interpretability, and generalization ability of the interruption coping ability modeling.

[0082] Step 500 includes at least steps S510 - S530: S510. Obtain the set of ability factor parameters, match the scoring template library, and calculate the individual interruption coping ability score.

[0083] In this step, the standardized ability metrics and their corresponding time - varying response functions are obtained from the set of ability factor parameters, and an ability comprehensive evaluation model containing the functional integral of the time derivative is designed.

[0084] The system first extracts the following input variables from the set of ability factor parameters output in the previous steps: Ability factor load Standardized characteristic metrics , such as from the standardized sequence of task switching time, task recovery time, and reaction delay time Dynamic response function , representing the th metric's response change during the evaluation period Functional integral weighting coefficient Based on the above data, the following original score function is constructed: ​① ① Wherein: : The individual's score for coping with interruptions; : The total number of dimensions of the ability factor; : The ability factor loading, derived from the factor analysis model; : The standardized ability characteristic index, originating from the feature aggregation of the behavioral data stream; : The th response function of the ability index, representing the change curve of the task switching reaction at different times; : The weighting factor corresponding to the timing factor; : The total task cycle time, in seconds (for example, the experimental duration is 120 seconds); : Reflecting the dynamic change trend of the index within the task cycle.

[0085] Formula ① introduces the change rate of behavioral characteristics in the time domain, enabling the index score to reflect the user's dynamic response ability during the task interruption - recovery process and making up for the timing blind spot of static scoring. The weight values of each factor are as follows: , , originating from the maximum likelihood estimation of historical samples.

[0086] S520. Based on the above - mentioned score for coping with interruptions, match the rating level boundary rules to generate ability level labels.

[0087] To achieve the labeling process of ability levels, the system, according to the score generated in step S510, matches the rating level boundary rules composed of multi - level piece - wise functions to generate three types of level labels.

[0088] ② ② Wherein: : Represents the ability level label for coping with interruptions; , : Are the rating boundary thresholds, and the recommended values are , , which can be dynamically adjusted according to the task complexity; If the difficulty of the task template is marked as "high", then can be adjusted up to 70 to meet the requirements of higher scoring accuracy.

[0089] The mapping function can be automatically generated according to the label distribution in the training set. Its purpose is to map continuous scores to discrete levels for subsequent task recommendation matching and visualization display.

[0090] S530. Structurally bind the interruption response ability score and the ability level label to generate score recommendation result data.

[0091] The system constructs the following mapping structure function , and binds the score and the level label to generate a complete score recommendation result structure: ③ ③ Where: : Score recommendation result data; : User unique identifier; : Evaluation timestamp; : Experimental task number; : Ability dimension summary, such as "Reaction stability: 89, Switching agility: 82"; : Ability-task adaptation identifier, used to judge the matching degree in S600.

[0092] The system also performs the following normalization operations on the output results: Compress the factor summary in into a dimension structure table; Round to 1 decimal place; Add a field verification mechanism to ensure that each score result structure has complete fields.

[0093] This structure will be used as the input source in the subsequent task adaptation discrimination module (S600) to drive the logic engine for task template pushing.

[0094] Explanation of the connection with the previous and subsequent steps Connection with the previous step: The set of ability factor parameters output by S430 is used as the basic input of formula ①; The output by formula ① is used as the input of formula ②; The output by formula ② and are simultaneously referenced by ③ to complete the structural binding.

[0095] Transfer to the subsequent step: Score recommendation result data As the input of S610, it is used for task ability matching and vector generation; Field Will participate in the similarity calculation process and compare with the task scenario label Comparison; Field Is used for the visual rendering of the personalized ability growth path.

[0096] By introducing an original mathematical model that integrates functional analysis, higher-order derivative integration, and set mapping structure, S510–S530 successfully constructs a high-dimensional transformation path from ability factors to scores, scores to labels, and labels to structures. The formula not only retains the quantitative differences of the original indicators but also reflects their time-domain dynamic characteristics during the task execution process, realizing a static–dynamic combined ability evaluation system. This mechanism improves the evaluation accuracy, enhances the sensitivity to behavior fluctuations and ability responses, is significantly superior to the traditional average weighted score model, and has outstanding innovation and practical value.

[0097] Step S600 includes at least steps S610 - S630: S610. Obtain the scoring recommendation result data, combine it with the task scenario label, and perform a task ability adaptation discrimination operation.

[0098] This step performs an ability adaptation discrimination operation based on the scoring recommendation result data and the task scenario label to achieve a closed-loop conversion from scoring output to task recommendation structure.

[0099] Specifically, the system first calls the scoring recommendation result data output by the S530 structure binding module, which includes fields: individual interruption response ability score, ability level label, ability dimension summary, and task matching identifier. This scoring recommendation result data has been structured and encapsulated in the system, with a unique identifier and traceability.

[0100] Furthermore, the system calls the preset task scenario label set in the task label database and compares this set with the task matching identifier field in the scoring recommendation result data. The task scenario labels include task complexity label, switching frequency label, attention allocation requirement label, and response accuracy requirement label, which respectively correspond to the structure parameter sets of the main task and interruption task templates.

[0101] The system performs a mapping discrimination of ability–scenario labels through a structure comparison method, specifically including: performing a matching discrimination logic on each ability dimension summary field in the scoring recommendation result data to determine whether the ability score structure of the current individual meets the threshold criteria set in the candidate task scenario label. For example, when the "recovery efficiency" is lower than the set threshold and the task scenario label requires "high-intensity continuous recovery performance", it is determined that the task template is not suitable.

[0102] Finally, the system outputs a set of discriminative operation results for task ability adaptation, which includes an adaptation task label list, a non - adaptation label list, and a task recommendation confidence weight field, providing a decision basis for generating the subsequent matching task template set.

[0103] S620. According to the discriminative operation results of the task ability adaptation, select the matching task template set and generate a matching task vector.

[0104] This step is based on the discriminative operation results of the task ability adaptation, filters the matching task template set from the task template library, and generates a matching task vector accordingly.

[0105] Specifically, the system extracts all task template sets with complete structure identifiers from the task template library. This set includes the main task template structure and the interrupt task template structure. Each task template contains five - dimensional fields: structure encoding, task requirement labels, interaction process, switching logic, and response ability requirement labels.

[0106] The system performs a structural mapping between the adaptation label list in the discriminative operation results of the task ability adaptation and the task requirement labels in the template library, filters out the task template sets with a matching rate higher than the threshold, and forms the current individual ability - task adaptation set.

[0107] On this basis, the system performs parameter vectorization processing on each filtered task template, extracts its core parameters such as task structure encoding, switching complexity parameter, recovery rhythm intensity factor, and interaction requirement intensity factor, and performs sequence encoding according to a unified rule to form a matching task vector.

[0108] Each dimension in the matching task vector corresponds to a first - level structure variable in the task modeling parameters, which will be referenced in the subsequent task structure generation and display scheduling. The generated matching task vector will be used as the core input item of the interrupt task modeling parameter set to implement the model - driven ability - task reconstruction logic.

[0109] S630. Use the matching task vector as the input parameter for the new round of interrupt task modeling, and feedback it in a closed - loop manner to the interrupt scenario task modeling and experimental process control.

[0110] This step uses the matching task vector as the input parameter for the new round of interrupt task modeling to realize the closed - loop construction of the experimental process.

[0111] Specifically, the system first calls the interrupt task modeling parameter generator set in the task modeling module, and injects the matching task vector into the task modeling parameter configuration interface. The field structure of the matching task vector is consistent with the task template set structure in S110, and the fields include task identifier, switching trigger condition, interaction response requirement, and timing rhythm control factor.

[0112] After the system inputs this task vector, according to the parameter processing logic in the original modeling process, it reconstructs the experimental task structure. This includes: automatically constructing a task parameter configuration table, resetting the interrupt trigger timing and switching rhythm rules, updating the experimental process control table, and generating a new experimental process scheduling structure.

[0113] Meanwhile, the system performs parameter integrity verification and task template compatibility verification to ensure the deployability and data consistency of the new round of task structure.

[0114] Finally, the formed closed-loop experimental process scheduling structure will enter the task execution path again, driving subsequent task display, response collection, data calculation, ability modeling, and adaptation update, forming a continuously evolving intelligent adaptation evaluation loop.

[0115] By implementing the sub-steps of S610–S630, the system realizes the conversion logic from the interrupt response ability structure scoring result to the individualized task recommendation structure, and completes the closed-loop adaptation of the ability–task structure body. This module constructs a dynamically updatable task matching mechanism, and based on the scoring result and task tags, realizes the adaptive ability modeling logic, with high adaptability, high responsiveness, and iterative optimization ability, providing the system with continuous evolution ability and task reconstruction ability, and significantly improving the application value of the interrupt response ability evaluation result in actual task scheduling.

[0116] Embodiment 2: Figure 2 The structural block diagram of the interrupt response ability evaluation data processing system based on reaction time characteristics and task modeling according to an embodiment of the present invention is shown. As Figure 2 shown, this structure may include: The task modeling and process control module 10 is used to construct a task structure based on the task template set, configure the task switching logic and time control, and generate an experimental process scheduling structure. Specifically, this module obtains the template sets of the main task and the interrupt task from the task template database, constructs a cross-task structure and generates a task parameter configuration table; further, according to the task parameter configuration table, sets the interrupt trigger timing and switching logic of the task, and generates an experimental process control table; finally, combines the experimental process control table to configure the task display sequence and time control logic, and outputs a complete and executable experimental process scheduling structure, providing structural guidance for the execution of subsequent experimental tasks and the collection of user interaction response data.

[0117] The Behavior Collection and Data Construction Module 20 is used to present task content according to the experimental process scheduling structure, record user responses and mark phases, and construct a complete behavioral data stream. Specifically, this module alternately presents the main task and the interruption task in sequence according to the generated experimental process scheduling structure, and real-time collects the user's interaction response data, including reaction duration, response method, and task phase completion status; then performs task phase marking and timestamp recording processing on the collected user interaction response data to form response time segmented data; finally, based on the response time segmented data, constructs a continuous behavioral data stream covering the whole process of the main task, interruption task, and recovery task, providing a complete data basis for subsequent data analysis.

[0118] The Index Extraction and Feature Construction Module 30 is used to calculate time indexes based on the behavioral data stream, complete data standardization processing, and generate perturbation annotation information and a set of feature indexes. Specifically, this module extracts key time indexes such as task switching time, task recovery time, and reaction delay time from the behavioral data stream; further performs data cleaning and standardization processing on the extracted time indexes to eliminate abnormal data and scale differences; then identifies the individual's behavioral fluctuation range according to the standardized data, and generates accurate perturbation annotation information and the corresponding set of feature indexes, providing data input for subsequent ability dimension modeling.

[0119] The Ability Modeling Module 40 is used to construct a mapping relationship by combining the set of feature indexes and the experimental task labels, perform factor analysis processing, and generate a set of ability factor parameters. Specifically, this module uses the set of feature indexes and the perturbation annotation information to construct an ability evidence structure and an ability mapping relationship table, and further clarifies the corresponding relationship between the ability dimension and the task performance in combination with the experimental task labels; then performs confirmatory factor analysis processing on the ability mapping relationship table to obtain a clear, definite, and stable set of ability factor parameters, providing a reliable factorized data basis for subsequent ability score calculation and label generation.

[0120] The Scoring and Grading Module 50 is used to match the scoring template library, calculate the individual's interruption coping ability score, and generate an ability level label according to the grade boundary, and output the scoring recommendation result data. Specifically, this module matches the preset scoring template library according to the set of ability factor parameters, and performs the score calculation of the individual's interruption coping ability; further calls the built-in scoring grade boundary rule according to the calculated ability score value to generate a structured ability level label; finally, structurally binds the individual ability score and the corresponding level label to form a unified scoring recommendation result data, providing a scoring basis for subsequent task ability adaptation decision-making.

[0121] The adaptation recommendation module 60 is used to complete the discrimination of task ability adaptation based on the scoring recommendation result data, generate a matching task vector and feedback it to the task modeling process. Specifically, this module first obtains the scoring recommendation result data, combines it with the task scenario tags, performs the adaptation discrimination operation of ability and task scenario, and determines the task characteristics suitable for the individual to execute; subsequently, according to the result of the adaptation discrimination, it accurately selects a set of adapted task templates from the preset task template library, and generates a structured matching task vector based on the selected templates; finally, it closes the loop and feedbacks the matching task vector to the task modeling and process control module, driving the re-modeling of the new round of interrupted task structure and the control of the experimental process, forming a continuously optimized evaluation feedback loop.

[0122] The beneficial effects of the above embodiments are as follows: The interruption response ability evaluation data processing system based on reaction time characteristics and task modeling provided by the present invention completely covers the whole process from the construction of the experimental task structure to the individual ability evaluation and task adaptation recommendation, significantly improving the structured level of the evaluation data and the accuracy of analysis; the system effectively improves the recognition accuracy and reliability of the interruption response ability through the accurate collection of task interaction response data, the accurate extraction of characteristic indicators and factor analysis modeling; further, through the closed-loop feedback mechanism, it realizes the dynamic update of the individual task adaptation ability and the real-time reconstruction of the task structure, forming a continuously evolving evaluation and training closed-loop, ensuring the sustainable optimization and long-term stable operation of the system; at the same time, the intuitive user interface and the perfect real-time monitoring guarantee mechanism improve the user experience and decision-making efficiency, ensuring the practicality and reliability of the system in actual applications, and having significant technical advantages and promotion value.

Claims

1. A method for processing interruption response capability assessment data based on reaction time characteristics and task modeling, characterized in that: The following steps are involved: Build the task structure, configure the task switching logic and time control, and generate the experimental process scheduling structure; Present the task content according to the experimental process scheduling structure, record user responses and mark the stages, and build the behavior data flow; Calculate the time index based on the behavior data stream, perform data standardization, and generate disturbance annotation information and feature index sets; Combine the characteristic indicator set with the experimental task label to build a mapping relationship, perform factor analysis, and generate a capability factor parameter set; Match the scoring template, calculate the interruption response capability score, generate labels according to the level boundary, and output the scoring recommendation result data. The expression of the scoring recommendation result data is: in, Recommend result data for scoring; is the mapping structure function; To indicate the level of interruption response capability label; To score the individual's ability to cope with interruptions; Unique identifier for the user; Timestamp for the evaluation; Number the experimental tasks; is a summary of the capability dimensions; To identify the capability-task adaptation; Based on the score recommendation result data, the task capability adaptation judgment is completed, the matching task vector is generated and fed back to the task modeling process.

2. The method according to claim 1, characterized in that The steps to build a task structure include: Obtain the task template set of the main task and interrupt task, build the cross-task structure, and generate the task parameter configuration table; Set the interrupt trigger timing and task switching logic based on the task parameter configuration table to generate the experiment process control table; Combine the experimental process control table to configure the task display sequence and time control logic, and output the experimental process scheduling structure.

3. The method according to claim 1, characterized in that The steps to build a behavioral data flow include: Obtain the experimental process scheduling structure, perform alternating presentation of main tasks and interrupt tasks, and collect user interaction response data; Mark the task phase and timestamp the user interaction response data to generate response time segment data; The behavioral data streams of the main task, interruption task and recovery task are constructed based on the response time segmentation data.

4. The method according to claim 1, characterized in that: The steps to calculate the time metric include: Obtain behavioral data streams and calculate task switching time, task recovery time, and response delay time indicators; Perform data cleaning and standardization on indicators of task switching time, task recovery time, and response delay time; Based on the standardized processing results, the behavioral fluctuation range is identified and the disturbance annotation information and feature indicator set are generated.

5. The method according to claim 1, characterized in that The steps to build a mapping relationship include: Obtain feature indicator sets and disturbance annotation information, and construct capability evidence structure and capability mapping relationship table; Combining the capability evidence structure with the experimental task labels, an capability mapping relationship table between capability dimensions and task performance is established.

6. The method according to claim 1, characterized in that The steps to generate a capability factor parameter set include: Perform factor analysis on the capability mapping relationship table to generate a capability factor parameter set.

7. The method according to claim 1, characterized in that The steps to calculate the Disruption Capability score include: Obtain the capability factor parameter set, match the scoring template library, and calculate the individual interruption response capability score, where the individual interruption response capability score is: in, To score the individual's ability to cope with interruptions; is the total number of capability factor dimensions; is the capability factor loading; To standardize capability characteristic indicators; For the The response function of a capability indicator; is the weighting factor corresponding to the timing factor; is the total task cycle time; Reflects the dynamic change trend of indicators during the task cycle.

8. The method according to claim 1, characterized in that The steps for generating rating recommendation result data include: Based on the interruption response capability score, the rating level boundary rules are matched to generate capability level labels, which are: in, A label indicating the level of interruption coping capability; To score the individual's ability to cope with interruptions; , is the grade scoring boundary threshold; The interruption response capability score is structurally bound to the capability level label to generate score recommendation result data.

9. The method according to claim 1, characterized in that: The steps to complete task capability adaptation determination include: Obtain the score recommendation result data, combine it with the task scenario label, and perform task capability adaptation and judgment operations; According to the task capability adaptation judgment operation results, a set of adaptation task templates is selected to generate a matching task vector; The matching task vector is used as the input parameter of a new round of task modeling and fed back to the task modeling process.

10. A data processing system for evaluating interruption response capability based on reaction time characteristics and task modeling, characterized in that: include: Task modeling and process control module, used to build task structure, configure task switching logic and time control, and generate experimental process scheduling structure; The behavior collection and data construction module is used to present the task content according to the experimental process scheduling structure, record user responses, and construct the behavior data stream; The indicator extraction and feature construction module is used to calculate the time indicator based on the behavior data stream, perform data standardization processing, and generate a set of feature indicators; The capability modeling module is used to build a mapping relationship between the feature indicator set and the experimental task label, perform factor analysis, and generate a capability factor parameter set; Scoring and grading module, used to match scoring templates, calculate capability scores and generate capability level labels, and output scoring recommendation result data; The adaptation recommendation module is used to complete the task capability adaptation judgment based on the score recommendation result data, generate the matching task vector and feed it back to the task modeling process.

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