Human-computer interaction simulation evaluation method and system

Abstract

The invention relates to the technical field of man-machine interaction evaluation, and particularly discloses a man-machine interaction simulation evaluation method and system. The method comprises the following steps: constructing a task structure chart based on task data and action parameters, and generating a rolling interaction instruction set; environment parameters are configured, a deception simulation environment is constructed, and a perception stimulation set is generated; analyzing the perceptual stimulation and task structure chart, recombining an interactive interface, and generating a configuration interactive structure chart; constructing a correlation model, simulating a trust induction process, and generating a state regulation factor; acquiring human factor data based on the state regulation factor, and generating a structured behavior tag sequence; and inference analysis is performed by combining the tag sequence and the task structure chart, an adjustment control strategy is generated, interaction configuration is updated, and feedback adjustment is completed. According to the method, dynamic modeling and interactive induction of trust state changes can be realized, and the perception ability and the adjustment accuracy of individual psychological cognition in the evaluation process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human-computer interaction intelligent evaluation and cognitive state modeling, and in particular to a human-computer interaction simulation evaluation method and system. Background Art

[0002] With the continuous development of multimodal interaction systems and immersive virtual simulation technology, dynamic identification of users' psychological states and trust level modeling in complex task environments have become key research directions in the field of human-computer interaction evaluation. Most existing technologies rely on linear analysis of static behavioral indicators or physiological data, lack the ability to jointly model the changes in interaction structure and the induced effects of the perceptual environment, and are difficult to effectively reflect the trust change trajectory and control state evolution process of individuals during task execution.

[0003] At the same time, traditional human factors assessment systems often have the following limitations: First, there is a lack of a structured linkage modeling mechanism between interactive content and perceptual stimulation, making it difficult to establish a causal relationship between interactive configuration and user psychological response; second, there is a lack of a unified task-interaction-state mapping model, making it impossible to achieve accurate mapping between behavioral labels and cognitive states; third, it is impossible to dynamically generate regulatory factors based on real-time trust fluctuations during the assessment process, making it difficult to support automatic adjustment and feedback updates of personalized interaction strategies.

[0004] Therefore, there is an urgent need for an intelligent evaluation method and system that integrates the configuration interaction structure diagram and the perceptual stimulus set for trust modeling, and generates state regulation factors through simulated evolution, so as to achieve dynamic perception and explainable prediction of the user's cognitive trust state, thereby providing a basis for subsequent interaction configuration optimization and personalized strategy update. Summary of the invention

[0005] The present invention provides a human-computer interaction simulation evaluation method and system to solve the problem of how to construct a trust-inducing factor association model and simulate the trust state evolution process based on the configuration interaction structure diagram and the perceptual stimulus set, so as to generate a state regulation factor to dynamically reflect the individual's psychological change trend and control state in a multimodal interaction task.

[0006] In order to solve the above technical problems, the present invention provides a human-computer interaction simulation evaluation method, comprising: Based on the flight mission data and motion control parameters, the mission structure diagram is constructed, the attitude requirements and operation instructions are extracted, and the roll interaction instruction set is generated; Based on the rolling interaction instruction set, configure simulation environment parameters, build a deception simulation environment, and generate a perception stimulus set; Parsing the perceptual stimulus set and the task structure diagram, constructing and reorganizing the interactive interface module, and generating a configuration interactive structure diagram; Based on the configuration interaction structure diagram and the perceptual stimulus set, an association model is constructed to simulate the induction process and generate a state regulation factor, wherein the state regulation factor is: in, is the set of state control factors; For the The time label corresponding to each stage; For the The control index value obtained based on the complex path integral at each stage; is the corresponding historical value of the trust inducing factor; is a set representation; is a natural number, indicating the position of the jth state control factor in the sequence; n is the total number of elements in the state control factor combination structure; Collecting human factor data according to the state control factor and the configuration interaction structure diagram, and processing and generating a structured behavior label sequence; The structured behavior label sequence is combined with the task structure diagram to perform reasoning analysis, generate a regulation control strategy, update the interaction configuration and complete feedback regulation.

[0007] Furthermore, constructing a task structure diagram includes: Obtain flight mission data and action control parameters, establish a mission structure diagram and perform module decomposition to form a mission phase sequence; Extracting posture change requirements and operation instruction information from the task phase sequence to construct an interactive control parameter set; The interactive control parameter set is dynamically mapped and converted into a rolling action to generate a rolling interactive instruction set.

[0008] Furthermore, constructing a deception simulation environment includes: Obtain the rolling interaction instruction set and match the environment simulation configuration such as lighting, sound effects and vibration; Constructing a multimodal deception simulation environment based on the environment simulation configuration, and generating illumination, noise and vibration simulation data; The light, noise and vibration simulation data are combined to generate a perceptual stimulus set.

[0009] Furthermore, constructing and reorganizing the interactive interface module includes: Acquire the perceptual stimulus set and the task structure diagram, and analyze the interaction load requirements and interface response characteristics; Based on the interface response characteristics and interaction load requirements, a set of configuration interaction interface modules is constructed; The configuration interaction interface module set is structurally reorganized to generate a configuration interaction structure diagram.

[0010] Furthermore, building a correlation model and generating state control factors include: The configuration interaction structure diagram and the perception stimulus set are obtained to establish a trust inducing factor association model, wherein the trust inducing factor association model is: in, For the Trust-inducing factors generated at each task stage; is the standardized function; For the The trust functional value calculated in each task stage; The trust inducing factor association model is simulated and evolved to generate trust state inducing process data.

[0011] Furthermore, key status indicators are extracted from the trust status inducing process data to generate status control factors.

[0012] Furthermore, the collection of human factors data includes: Obtaining the state control factor and the configuration interaction structure diagram, and performing synchronous collection of human factor data; The collected human factor data is cleaned and standardized to generate a set of human factor indicators.

[0013] Furthermore, the human factor indicator set is subjected to structure mapping and label encoding to generate a structured behavior label sequence.

[0014] Furthermore, the inference analysis includes: Acquire the structured behavior label sequence and the task structure diagram to construct a trust performance reasoning model; Perform task performance scoring and trust association analysis based on the trust performance reasoning model to generate a regulation control strategy; The regulation control strategy is applied to the configuration interaction structure diagram to update the interaction configuration and complete feedback regulation.

[0015] Furthermore, a human-computer interaction simulation evaluation system includes: A mission structure diagram building module is used to build a mission structure diagram and generate a roll interaction instruction set based on flight mission data and action control parameters; A deception simulation environment construction module, used to construct a deception simulation environment based on the rolling interaction instruction set and generate a perception stimulus set; An interactive interface configuration module, used to parse the perceptual stimulus set and the task structure diagram, and generate a configuration interactive structure diagram; A trust state modeling module, used for generating a state regulation factor based on the configuration interaction structure diagram and the perception stimulus set; A human factor data collection and processing module, used to generate a structured behavior label sequence according to the state control factor and the configuration interaction structure diagram; The interaction strategy updating module is used to generate a regulation control strategy and update the interaction configuration by combining the structured behavior label sequence with the task structure diagram.

[0016] The key innovative features of the present invention include: (1) Configure the interaction structure diagram and the collaborative modeling mechanism of the perceptual stimulus set: Open up the structured modeling link between perceptual input, task structure and interface configuration to achieve a unified expression of the interaction space.

[0017] (2) Trust-inducing factor association model and state regulation function construction mechanism: Integrating topological mapping, state function simulation and psychological factor identification methods, innovatively establishing the "stimulus-response-trust" evolution channel.

[0018] (3) Feedback adjustment and strategy iteration mechanism based on behavior labels: The structured behavior label results are used in reverse for interactive configuration adjustment and task adaptation strategy generation, forming a complete "behavior identification-performance reasoning-configuration update" closed loop.

[0019] The following are its main beneficial effects: (1) The present invention constructs a linkage mechanism between the task structure diagram and the rolling interaction instruction set. By performing structured modeling on the flight mission data and action control parameters, the mapping accuracy between the task and the interaction instruction is significantly improved, and the control lag problem caused by the different granularity of task decomposition in the traditional scheme is avoided, which is beneficial to improving the timeliness of the interaction instruction and the task restoration degree.

[0020] (2) The present invention achieves synchronous linkage between environmental perception and user response by constructing a multimodal deception simulation environment and generating a set of perceptual stimuli, significantly enhancing the realism and interference control capability of the simulated interactive system, which is conducive to stimulating more representative human responses.

[0021] (3) This invention proposes for the first time a trust-inducing factor association modeling method based on a configuration interaction structure diagram and a perceptual stimulus set. Combining complex mathematical models such as topological analysis and function mapping mechanisms, a trust evolution function and a state control generation mechanism are constructed in the S400 module. This method can dynamically identify trust transfer and manipulation deviation phenomena in the cognitive process, which is conducive to high-precision reasoning about the evolution of an individual's psychological state.

[0022] (4) The present invention establishes a synchronous collection standard for multi-channel physiological and behavioral indicators through a human factors data collection and labeling processing mechanism driven by state control factors. Combined with standardized processing and label encoding technology, it can extract efficient structured behavioral labels from complex EEG, eye movement, ECG and posture data, which is conducive to the accurate identification and explainable prediction of cognitive behavioral states. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of a human-computer interaction simulation evaluation method provided in an embodiment of the present application; Figure 2 A structural block diagram of a human-computer interaction simulation evaluation system provided in an embodiment of the present application; Figure 3 The spherical rolling platform provided in the embodiment of the present application is a schematic diagram of an interactive module for simulating dynamic perception capabilities; Figure 4 A schematic diagram of an interactive module for a test pilot to simulate a typical mission scenario provided in an embodiment of the present application; Figure 5 Schematic diagram of an equivalent experiment for inducing human-computer interaction and trust state provided in an embodiment of the present application.

[0024] Figure 6 A schematic diagram of a quantitative evaluation of human-computer interaction features provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] Example 1: Reference Figure 1 , is a flow chart of a human-computer interaction simulation evaluation method provided by an embodiment of the present invention, and the flow chart may at least include steps S100-S600: S100, based on the flight mission data and action control parameters, construct a mission structure diagram, extract attitude requirements and operation instructions, and generate a rolling interaction instruction set; S200, configuring simulation environment parameters based on the rolling interaction instruction set, constructing a deception simulation environment, and generating a perception stimulus set; S300, parsing the perceptual stimulus set and the task structure diagram, constructing and reorganizing the interactive interface module, and generating a configuration interactive structure diagram; S400, constructing an association model based on the configuration interaction structure diagram and the perceptual stimulus set, simulating the induction process, and generating a state regulation factor; S500, collecting human factor data according to the state control factor and the configuration interaction structure diagram, and processing and generating a structured behavior label sequence; S600: Perform reasoning analysis based on the structured behavior tag sequence and the task structure diagram, generate an adjustment control strategy, update the interaction configuration and complete feedback adjustment.

[0026] Step S100 at least includes steps S110-S130: S110, obtaining flight mission data and action control parameters, establishing a mission structure diagram and performing module decomposition to form a mission phase sequence.

[0027] The system first obtains the flight mission data defined for the complex control mission process. The flight mission data includes structured information such as mission type, phase identification, space path requirements, attitude control expectations, etc., reflecting the operational objectives and time logic of the entire mission process.

[0028] At the same time, combined with the motion control parameters collected by the control device used by the pilot, the motion control parameters include input value records corresponding to the joystick, push rod, multi-dimensional knob, touch panel, etc., reflecting the user's control needs for the control platform at different stages.

[0029] Specifically, the system logically combines the flight mission data with the action control parameters, and establishes a mission structure diagram through timestamps, mission target identifiers, and input signal encoding rules. The mission structure diagram is a set of multi-node directed relationship graphs, each node represents a state transition stage in the mission, and the edge represents the logical flow path of the control instruction.

[0030] Furthermore, the task structure diagram is decomposed into modules, and several task phase units are formed according to the timeline sequence, operation complexity and control type. The task phase unit describes the operation requirements of the system at different control stages, including posture change, path response, state feedback, etc. The final task phase sequence is used as the execution basis of the entire task process and is called by the subsequent control parameter extraction step.

[0031] S120: extracting posture change requirements and operation instruction information from the task phase sequence, and constructing an interactive control parameter set.

[0032] After the task phase sequence is constructed, the system analyzes the control characteristics contained in each task phase unit item by item.

[0033] Specifically, for each task phase unit, the attitude change requirements are extracted, including indicators such as spatial rotation direction, angular velocity, pitch angle change rate, holding time, attitude stability requirements, etc. These indicators are used to describe the attitude adjustment tasks that the interactive platform should perform in this phase.

[0034] At the same time, the system extracts the operation instruction information corresponding to the above posture changes from the control action mapping rules. The operation instruction information includes the displacement amplitude of the joystick, input direction, operation mode (continuous / pulse), signal response priority, etc. The operation instruction information is constructed based on the mapping logic between the task and the platform to form an instruction pair between the control input and the platform response.

[0035] Based on the posture change requirements and operation instruction information, an interactive control parameter set is constructed. The interactive control parameter set is a multi-field structure, including an operation input field, a target state field, an execution constraint field, a mapping condition field, etc., which is used to characterize the control response behavior of the platform at a specific stage.

[0036] The interactive control parameter set serves as the basic input for generating rolling interactive control instructions. Its structure directly determines the instruction format, control dimension and platform interface coding logic of the next stage.

[0037] S130, dynamically mapping and converting the interactive control parameter set into a rolling action to generate a rolling interactive instruction set.

[0038] After constructing the interactive control parameter set, the system calls the dynamic mapping engine to perform platform adaptation processing on the operation requirements therein.

[0039] Specifically, the system first analyzes the target posture requirements and corresponding operation instructions in the interactive control parameter set based on the platform control model, and maps them into control signals that can be executed by the rolling simulation platform. The control signals include direction control instructions, angular velocity adjustment instructions, action start / end mark instructions, posture holding time, etc.

[0040] Furthermore, according to the stage time control information in the task structure diagram, the above control signals are dynamically serialized to form a rolling action scheduling list, which includes the operation start time, action sequence, execution window length, parameter correction coefficient, etc.

[0041] The system constructs a rolling interaction instruction set based on the rolling action scheduling list. The rolling interaction instruction set is a multidimensional data structure with complete time clues and instruction content, which meets the platform's real-time response requirements for dynamic instructions in the full-process task simulation.

[0042] The rolling interaction instruction set will serve as an important input for the construction of the simulation environment and the generation of perceptual stimuli in the subsequent steps. Specifically, it will serve as an environment configuration benchmark in step S200, and will be used in step S210 to control the trigger parameters of simulation factors such as lighting, sound effects, and vibration, so that the simulation platform has a phased dynamic task response capability and synchronous external feedback capability.

[0043] Through the three-stage task processing flow of S100 of the present invention, the structured decomposition of the control task, the accurate modeling of the control logic and the high-reduction instruction output of the execution action can be realized, which ensures the structured, continuous and real-time response capabilities of the simulation platform to complex interactive tasks. The generated rolling interaction instruction set provides basic input support for the construction of perceptual stimulation, the reconstruction of the interface layout and the formation of state feedback, realizing the closed loop closure of the system function from task intention to behavior feedback.

[0044] Step S200 at least includes steps S210-S230: S210, obtaining the rolling interaction instruction set, and matching the environment simulation configurations such as lighting, sound effects and vibration.

[0045] The system first obtains the rolling interaction instruction set, which is an action drive instruction sequence obtained by mapping the task structure diagram and the operation control parameters, including multiple action elements such as rolling direction, duration, target angle, rate type and holding state.

[0046] Specifically, the system constructs a simulation response table based on the time distribution and action characteristics in the rolling interaction instruction set. The simulation response table records the type of environmental feedback required for each interactive action, and divides it into a light channel, a sound effect channel, and a vibration channel according to the feedback channel.

[0047] Furthermore, predefined environmental feedback models are matched for different types of action units, such as slow posture transition, fast rolling response, long-term state maintenance, etc. The illumination model is used to simulate visual stimulus changes such as natural light intensity changes, glare interference, and light transition; the sound effect model is used to simulate auditory stimuli such as background noise, operation prompts, environmental audio, and feedback sound; the vibration model is used to simulate tactile simulations such as platform response, inertial feedback, and mechanical feedback.

[0048] The system constructs the initial environment simulation configuration parameters according to the three types of feedback models of illumination, sound effect and vibration matched by the simulation response table. The environment simulation configuration parameters are a set of structured environment response settings, including stimulus type, change cycle, output intensity, trigger window and synchronization flag, which are used to control the synchronization behavior of the multimodal feedback device.

[0049] The above-mentioned environment simulation configuration parameters serve as basic input for S220 to construct a multimodal simulation environment.

[0050] S220: construct a multimodal deception simulation environment based on the environment simulation configuration, and generate illumination, noise and vibration simulation data.

[0051] After obtaining the environmental simulation configuration parameters, the system constructs the lighting simulation environment, sound simulation environment and vibration simulation environment respectively according to the feedback channel classification.

[0052] Specifically, the lighting simulation environment simulates different light intensities, lighting angles, occlusion relationships and reflection change processes through programmable light sources, dynamic shading mechanisms and background layer control systems, and dynamically adjusts the degree of scene light interference and visual stimulation changes during task execution.

[0053] The sound simulation environment uses a three-dimensional spatial sound simulation system to load background sounds, voice broadcasts, operation prompt sounds and other elements analyzed from the environmental simulation configuration parameters, dynamically generate audio output clips that match the task phase, and fine-tune parameters such as sound pressure, frequency, and spatial directionality.

[0054] The vibration simulation environment uses a tactile feedback module or a multi-axis platform control module to set parameters according to the vibration type to generate a dynamic vibration feedback sequence. The vibration feedback can include multiple types of tactile stimulation modes such as uniform oscillation, pulse impact, and continuous shaking.

[0055] The system samples and caches the simulation data output by the illumination feedback module, the sound effect feedback module, and the vibration feedback module, respectively, to generate corresponding illumination simulation data, noise simulation data, and vibration simulation data. The simulation data is physical feedback drive data that has been aligned with the time window of the rolling interaction action, and has the characteristics of channel synchronization, intensity hierarchy, and time series consistency.

[0056] The above three types of simulated data serve as the original input of the S230 combined perceptual stimulus set.

[0057] S230: Combining the illumination, noise and vibration simulation data to generate a perceptual stimulus set.

[0058] After completing the generation of three types of simulation data: light, noise and vibration, the system enters the multimodal data fusion stage.

[0059] First, the system aligns the illumination simulation data, noise simulation data, and vibration simulation data in the order of the time axis according to the stage timestamp and synchronization mark of the rolling interaction instruction set. Through the channel mapping table, the data items on different channels are uniformly converted into time synchronization units based on the task execution stage.

[0060] Furthermore, the synchronized multi-channel data is structured and fused to construct a perceptual stimulation unit. Each perceptual stimulation unit consists of a set of time markers, light values, sound pressure values, vibration amplitudes, etc., and has a recognizable perceptual state and feedback trigger intensity.

[0061] Finally, the system combines all the perceptual stimulation units according to the stage task requirements and the interaction scene logic to generate a perceptual stimulation set. The perceptual stimulation set, as a structured multimodal feedback data set, will serve as the input basis for the interactive interface configuration and interface response regulation in S300, and as a stimulus input factor for the trust state association model in S400 to participate in modeling.

[0062] Through the three-stage processing flow of S200, the system generates a highly consistent multimodal perception stimulus set based on the rolling interaction instruction set, and builds a linkage input mechanism for the subsequent interaction interface reconstruction, state induced modeling, and behavioral decision reasoning. The generated perception stimulus set has the characteristics of complete environmental response structure, highly synchronized feedback channels, and dynamic matching of action logic, laying the foundation for the feedback channel to build a real perception environment for the human-computer interaction simulation system, and ensuring that the system achieves accurate response and process consistency in dynamic interaction.

[0063] Step S300 at least includes steps S310-S330: S310, obtaining the perceptual stimulus set and the task structure diagram, and analyzing the interaction load requirements and interface response characteristics.

[0064] In this step, the system obtains the perceptual stimulus set output in S230 and the task structure diagram constructed in the S100 stage, and performs interactive content analysis.

[0065] Specifically, the perceptual stimulus set is the lighting, sound and vibration response data generated under the drive of the rolling interaction instruction set, which has been synchronized with the operation behavior timeline; the task structure diagram consists of multiple task stages, describing the task execution logic, operation requirements and feedback expectations.

[0066] Based on the synchronization relationship between task stages and perceptual stimuli, the system establishes a task-feedback mapping index table to mark the interface response type, feedback pressure level, and user interaction intensity level required for each task node.

[0067] On this basis, the interaction load requirements are extracted. The interaction load requirements are three types of indicators that need to be evaluated when the system performs structural adaptation on the interactive interface, including the number of interactive channels (such as touch, voice, buttons, etc.), the trigger frequency of each channel, the number and duration of simultaneously activated modules, and the pressure concentration on the focus area of ​​the interface under specific perceptual stimulation.

[0068] At the same time, the feedback type and intensity distribution of the perceptual stimulus set are combined to extract the interface response features. The interface response features are used to describe the adaptability of the interface under different perceptual inputs, including parameters such as module display and hiding mechanism, font dynamic reconstruction, color contrast switching, and interactive path contraction and expansion.

[0069] The above-mentioned interactive load requirements and interface response characteristics are used as parameter inputs for constructing an interactive interface module set at S320.

[0070] S320: Based on the interface response characteristics and the interaction load requirements, construct and configure an interaction interface module set.

[0071] After obtaining the interactive load requirements and interface response characteristics, the system constructs an interactive interface module set based on multi-dimensional adjustment rules.

[0072] Specifically, the system divides the task execution process into multiple interface control cycles according to the stage sequence of the task structure diagram, and configures a corresponding interface module requirement list for each cycle. The requirement list is modeled differently according to the required information density, number of user control paths, and perceived feedback intensity in the task stage.

[0073] Combined with the interface response characteristics, the system calls the preset interface module component library and selects appropriate interface module elements for instantiation. The interface module components include graphic display area, interactive control area, feedback prompt area, auxiliary navigation area, data chart area and status reminder area.

[0074] During the construction process, the perceptual adaptation strategy of each interface module is configured according to the channel type and intensity parameters provided by the perceptual stimulus set, such as increasing the interface contrast during the stage of drastic lighting changes, reducing text density and increasing voice output during the stage of strong vibrations, and reducing image animation dynamics during the multi-sound channel stage.

[0075] Furthermore, the interaction relationship between interface modules, focus switching logic and priority control strategy are uniformly encoded to form a structured set of configuration interaction interface modules. Each set unit represents the interface configuration state under a specific task cycle, including required modules, module layout, response mechanism, trigger boundary and module linkage mapping.

[0076] The configuration interaction interface module set is used as input for generating a configuration interaction structure diagram in S330.

[0077] S330: Structurally reorganize the configuration interaction interface module set to generate a configuration interaction structure diagram.

[0078] In this step, the system obtains the configuration interaction interface module set, performs a structured reorganization operation, and constructs a configuration interaction structure diagram for driving the interface interaction rendering and the interaction task logic linkage.

[0079] Specifically, the system semantically clusters the set of interface modules corresponding to each task stage, integrates multiple modules into module groups based on the relevance of interactive functions and the coordination of visual layout, and establishes logical control relationships between module groups.

[0080] Furthermore, based on the control sequence and stage trigger boundaries in the task structure diagram, the system nests the module groups into the structure diagram in the order of execution. Each structure node corresponds to a module group, which internally defines the display structure, input channel type, state response path and callback event chain.

[0081] At the same time, the system combines the stimulus timing information in the perceptual stimulus set to mark the interface deformation rules of each structural node in the multimodal feedback scenario, such as gradual display strategy, sliding rearrangement, module fading in and out, and mutually exclusive area arrangement.

[0082] Finally, the system divides the structure diagram of the entire module group into levels and analyzes the paths to generate a configuration interaction structure diagram. This structure diagram expresses the organizational relationship between the interface display and user interaction during the entire task process in a graph structure, including elements such as task stage index, module node distribution, node transfer path, and perception feedback response strategy.

[0083] The configuration interaction structure diagram will serve as the core input basis for the subsequent S400 to build the trust-inducing factor association model and state regulation factors.

[0084] Through the S300 three-stage process, the system can achieve semantic decoupling of the task structure diagram and the perceptual stimulus set, and the adaptive fusion of the interface modules, generating a complete configuration interaction structure diagram. This structure diagram has the ability to adapt to staged layout, module linkage logic and multi-modal feedback, providing basic data support for real-time interface response, structural deformation and behavior mapping in interactive tasks, and providing a scene input structure basis for the subsequent trust modeling module, thus enhancing the controllability and adaptability of the human-computer interaction system.

[0085] Step S400 at least includes steps S410-S430: S410: Acquire the configuration interaction structure diagram and the perception stimulus set, and establish a trust-inducing factor association model.

[0086] In this step, the system obtains the configuration interaction structure diagram from S330 and the perception stimulus set from S230 as inputs for building a trust-inducing factor association model.

[0087] Specifically, the configuration interaction structure diagram includes multi-stage interface module nodes and their response paths; the perceptual stimulus set includes channel information of light, sound and vibration at each interaction moment. The system needs to determine the multi-dimensional nonlinear correlation between stimulus intensity, interaction load and interface module complexity.

[0088] To this end, the following trust-inducing function model is constructed: ① Trust-induced functional model in: : Trust-induced function functional, which is used to describe the total impact of multi-channel stimulation and interaction structure on user trust formation; : The space-time integration domain defined by the interaction process (e.g., task execution time interval × control channel set); : The weight parameter of the environmental stimulus channel, corresponding to the importance of the light, sound, and vibration channels respectively; : Brightness response function of the light stimulation channel, is the time series of light intensity values ​​during the task cycle; : Sound pressure function of the sound effect stimulation channel, Indicates the sound pressure change value at the current stage; : Amplitude function of the vibration stimulation channel, is the sequence of vibration response in the time domain; : Interface complexity function, the input is: : Number of interface modules; : Interface module switching frequency; : focal area concentration coefficient; : In the space-time domain A tiny integrating unit on .

[0089] The trust-inducing functional model is used to describe the combined effect strength of multi-channel stimulation and interaction structure, and its result will serve as the input of the trust-inducing factor generation model.

[0090] ② Trust-inducing factor association model in: : No. Trust-inducing factors generated at each task stage; : Standardization function, often sigmoid function, that is , mapping the induced value to interval; : In the The trust functional value calculated in each task stage.

[0091] Generated The value is used as an input parameter of the simulation evolution module in S420.

[0092] S420: Simulate the evolution of the trust inducing factor association model to generate trust state inducing process data.

[0093] In order to further describe the dynamic change trend of the induced process, this step describes the trust propagation state.

[0094] ③ Trust evolution state field model (tensor form) in: : Trust state tensor component, used to describe a point in the trust state space Local curvature properties in direction; : Affine connection, that is, the change direction tensor of the trust-inducing factor, represents the In the stimulus dimension, The interaction module and The affine connection tensor formed by the interaction of the perceptual stimulus factors; : It is the trust-inducing factor field, in the and The input dimension direction causes the The coupling influence of the trust status output dimension : Partial derivative symbol, indicating the local differential operation on the variable; : Indicates affine connection Relative to stimulus input dimensions The partial derivative of is used to characterize the disturbance effect of stimulus input changes on the trust transfer direction in this direction.

[0095] , : Stimulus input dimensions (e.g. feedback time, channel type, task phase number, etc.); : is the product term between affine connection tensors, which is used to describe the “composite deformation trend of connection” in the trust state space, indicating the Channel Passage Joint drive in The propagation process on the channel; : is the product term between affine connection tensors, which is used to describe the “composite deformation trend of connection” in the trust state space, indicating the Channel Passage Linkage Channel-constrained perturbations; all :All from S410 The tensor tension function constructed by deriving the trust factor field reflects the propagation and deformation trends induced by trust in the state space.

[0096] This formula is used to simulate the non-Euclidean state shift trajectory in the trust induction process under complex multi-channel interaction conditions.

[0097] ④ State-induced evolution function in: : At time point The cumulative trust status level of : In the Trust-inducing factors generated in each stage (derived from S410); : No. The stimulus response intensity coefficient of each task stage; : No. The trust decay coefficient of each task stage is used to characterize the time weakening of the trust influence; : No. The start time of each task phase; : Exponential decay term, reflecting the decreasing trend of induced influence over time; : Time microelement.

[0098] The function is used to describe the dynamic accumulation and attenuation trend of the trust level. The discrete sampling result of the function will enter S430 as the basis for extracting the state control factor.

[0099] S430: extract key status indicators from the trust status inducing process data to generate status control factors.

[0100] This step extracts status indicators that have practical regulatory significance for subsequent human factor data collection based on the evolution results of S420.

[0101] Specifically, identify the inflection points, mutation areas and saturation areas in the trust induction process.

[0102] ⑤ State control index extraction function in: : No. A state regulation index, indicating the influence of the state circuit in psychological induction; : No. The induced path loop of each task state in the complex plane (for example, the closed stimulation trajectory of repeated vibration + highlight interface combination in a certain period); : complex function, the real part is the trust evolution value , the imaginary part is the perturbation term of the perceived stimulus; :Indicates that in a closed path Integrate on : small variable increment on the complex plane; : Indicates the absolute value of the induced intensity under this path.

[0103] This integral form is used to measure whether a certain interface combination and stimulus sequence forms a "psychologically induced closed loop" mechanism in a continuous task state.

[0104] ⑥ State control factor combination structure in: : A set of state control factors; : No. The time label corresponding to each stage; : In the The control index value obtained based on the complex path integral at each stage; : Corresponding historical value of trust inducing factor (output from S410); : Set representation, indicating that the regulatory factors are composed of a state number index structure; is a natural number, indicating the position of the jth state control factor in the sequence; n is the total number of elements in the state control factor combination structure.

[0105] This structure will be passed to S500 as the control signal source in the human factors data collection process, driving the dynamic configuration of parameters such as the collection window, feedback interval, and data cleaning threshold.

[0106] Instructions for closed-loop connection between previous and subsequent steps: S410 is generated by the perception stimulus set and the configuration interaction structure diagram The value is the kernel function term in the S420 evolution model; Generated in S420 The function result serves as the basis for S430 to extract control indicators; S430 final output state control factor , which will be directly used in the regulation mechanism of the human factors data acquisition module (S510–S530) in S500; The entire S400 module completes the logical closed loop from interactive perception feedback → trust factor establishment → state simulation → parameter extraction.

[0107] Through the trust-induced evolution modeling method designed by the S400 module that integrates functional analysis, differential geometry and complex analysis theory, the system can accurately identify the comprehensive impact of the combination of perceptual stimulation and interface structure on the trust state under multi-channel conditions, and extract key state control factors based on the evolution model to achieve precise regulation of subsequent human factors data collection paths and label determination strategies. The established association model has the advantages of mathematical stability, consistency of input indicators and continuity of the induced process, which significantly enhances the system's structured modeling capabilities for user responses in complex interactive scenarios.

[0108] Step 500 at least includes steps S510-S530: S510: Acquire the state control factor and the configuration interaction structure diagram, and perform synchronous collection of human factor data.

[0109] In this step, the system obtains the state control factor from S430 and the configuration interaction structure diagram from S330, and jointly drives the synchronous collection process of human factors data. The state control factor is a dynamic psychological response control parameter calculated based on the multi-channel perception stimulation and interaction structure induced model, which is used to guide the sampling window, channel selection and data accuracy setting of the human factors collection process; the configuration interaction structure diagram is used to provide the human-computer interface layout structure, focus distribution path and interaction node information under the task stage, ensuring that the collection is highly aligned with the behavior context.

[0110] Specifically, the system dynamically adjusts the data acquisition window according to the task timing parameters and stimulus synchronization flags in the state control factor. In the high-load interaction state, the sampling window is shortened and the synchronization accuracy is improved to capture the rapidly changing human response; in the low-intensity interaction state, regular periodic sampling is used to ensure data continuity and integrity.

[0111] At the channel level, the system enables a multi-source parallel acquisition mechanism, covering but not limited to the following data channels: ECG signal acquisition channel: records the user's heart rate variability and ECG waveform structure during the interaction process; EEG signal acquisition channel: obtains electrical activity data in the head area to reflect the user's concentration and cognitive load level; Eye tracking channel: Identify the user's gaze path, pupil contraction rate and gaze rest point during interface interaction; Posture motion channel: Captures body posture adjustment, micro-motion response, and head and shoulder movement sequences based on inertial sensor units; Facial electromyography channel (if adapted): records the user's micro-expression reactions and eyelid and mouth corner muscle activity trajectories; Hand micro-motion channel (if adapted): captures hand pressure, click rhythm and stagnation characteristics during input operations.

[0112] All of the above channels are triggered for collection according to the dynamic frequency set by the state control factor, and combined with the mapping relationship between the interaction nodes and controls identified in the configuration interaction structure diagram, a data-event synchronization record table is established to achieve precise alignment of the task behavior chain and the physiological response chain.

[0113] S520. Clean and standardize the collected human factor data to generate a human factor indicator set.

[0114] After completing the synchronous collection of multi-channel human factors data, the system enters the data processing stage to clean, filter, standardize and extract indicators for the original human factors data to form a human factors indicator set in a unified format.

[0115] First, the system performs anomaly detection and noise filtering: Perform R wave peak recognition and artifact removal on ECG signals to remove spikes caused by sensor shedding or noise interference; The EEG signal is identified by high-frequency oscillation within a sliding window, myoelectric interference is removed, and artifact channel shielding is performed; Extract fixation points and saccade segments from eye movement data, and interpolate and complete saccade events and defocus drift points; Perform three-axis filtering on the posture data, smooth the posture turning boundaries, and unify the movement direction and speed dimensions; The EMG and hand data were processed to remove baseline drift, and the input pressure and micro-movement amplitude were normalized.

[0116] Then, the system slices the behavior intervals of each channel data according to the interface node sequence and interaction behavior number identified in the interaction structure diagram, and extracts the corresponding indicator dimensions. The indicators include but are not limited to: Heart rate variability, average heart rate, peak response time, heart rate recovery rate; EEG frequency domain energy distribution (α, β, θ bands), attention index, cognitive load index; Gaze concentration, target area fixation ratio, and eye-jump frequency; Postural stability coefficient, rate of change of motion amplitude, asymmetric body excursion index; Facial electromyographic response amplitude, mouth corner lifting angle, blinking frequency; Hand stability coefficient, operation rhythm stability, input point pressure sequence average value, etc.

[0117] The above indicators are uniformly encapsulated into human factor indicator units, and the numerical expressions of each channel and each stage are recorded in key-value pairs. The system constructs a human factor indicator set structure with a unified structure, and each structural unit aligns the task stage number, interaction node code, data sampling time, indicator field list and original interval index.

[0118] Finally, the system inputs the complete set of human factor indicators into S530 for use in behavior label mapping.

[0119] S530: Structural mapping and label encoding are performed on the human factor indicator set to generate a structured behavior label sequence.

[0120] After the human factor indicator set is constructed, the system performs the construction and encoding of structured behavioral labels. The goal of this step is to structure and map the abstract numerical responses in the physiological data into semantic labels with behavioral meanings for subsequent policy reasoning and adjustment strategy generation stages.

[0121] The system first calls the behavior label rule library and loads the corresponding label mapping template according to the task type, interaction stage and interface structure definition. The template defines the observable behavior label types in each task stage (such as concentration, high load, stress response, loss, confusion, no response, etc.), and sets the indicator threshold and combination conditions.

[0122] Specifically, the system processes the human factor indicator set in each task stage as follows: Match the combination of "heart rate sudden increase + HRV sudden drop" in the ECG indicators and mark it as "stress response" label; Specifically, the definition of "heart rate surge" : Indicates the time Heart rate at the moment, in beats per minute; : Indicates the rate of change of heart rate, usually defined as: in, is the time interval between two measurements.

[0123] when (threshold), it is marked as a “heart rate surge” event.

[0124] Definition of HRV Dip : represents heart rate variability, which is the standard deviation or frequency domain characteristics of the RR interval in a short time window; : represents the change in heart rate variability, which can be defined as: like , then it represents the "HRV drop" phenomenon.

[0125] Combination judgment: when both and , a “stress response” label is generated.

[0126] Match the combination of "high beta wave power + alpha wave suppression" in the EEG indicators and mark it as "high cognitive load" label; Specifically, "high beta wave energy" is defined as: : represents the energy power of the EEG beta wave (13–30 Hz) frequency band, which comes from frequency domain transformation (such as FFT); if (set energy threshold), the beta wave is considered active.

[0127] Alpha Suppression Definition: : represents the energy of the alpha wave (8–12 Hz) frequency band; :α wave power change: like , that is, the α wave decreases significantly, it is judged as α wave suppression.

[0128] Combination judgment: when both and , generates a "high cognitive load" label.

[0129] Match the "non-target area fixation ratio > threshold" in the eye movement index and mark it as the "attention deviation" label; Match the "rapid action changes + balance fluctuations" in the posture indicators and mark them as "instability" labels; Match the "operation rhythm interruption + pressure drop" in the hand indicator and mark it as "operation hesitation"; Other labels such as “low-motion response”, “high concentration”, “stable execution”, etc. are generated based on rules.

[0130] The system creates a bidirectional index between the above behavior labels and their corresponding indicator structures, and sorts them in time series order to build a structured behavior label sequence. Each label sequence unit contains the following fields: Timestamp; The task phase and interaction node number; Tag category; Tag trigger source indicator set reference; Confidence score (calculated based on historical matching frequency and indicator variation).

[0131] This structured behavior label sequence serves as the reasoning input basis of S600 and runs through the subsequent task performance modeling and strategy adjustment modules.

[0132] Through the three-stage processing flow of this step, the system can accurately collect multi-dimensional human response data of users under the joint guidance of structured interface diagrams and state control factors, forming a highly consistent, multi-channel, and highly semantic structured behavior label sequence, and building a clear and controllable individual behavior portrait for subsequent performance analysis and strategy adjustment. The system has the integrated capabilities of collection-processing-mapping-output, realizing the structural level conversion of physiological signals to cognitive behavior labels, and effectively enhancing the interpretability of the human-computer interaction evaluation system for individual responses under complex dynamic tasks and the ability to formulate response strategies.

[0133] Step S600 at least includes steps S610-S630: S610: Acquire the structured behavior label sequence and the task structure diagram to construct a trust performance reasoning model.

[0134] In this step, the system obtains the structured behavior label sequence output by S530 and the task structure diagram information output by S130, and fuses them to build a trust performance reasoning model. The structured behavior label sequence is a set of temporal behavior labels displayed by the user during the task, including label type, associated indicators, stage identifiers, interaction nodes, and label confidence; the task structure diagram includes the associated mapping of the logical order of the task stages, action control parameters, and posture change requirements.

[0135] Specifically, the system builds a task flow diagram based on the stage number and behavior path in the task structure diagram, maps the structured behavior labels to the nodes of each stage in the task flow, and forms a label-node comparison table. Based on this, the system calculates the following inference input vector: Node behavior label density vector: counts the label frequency and label type distribution on each task node; Average node behavior confidence: Calculate the average confidence of each type of label in each task node; Label cross-stage propagation path: Identify similar label paths that appear continuously in multiple task nodes; Task stage transfer context: Extract the order and triggering conditions between stages in the task structure diagram to provide support for subsequent causal modeling.

[0136] On the basis of the above, the system builds a trust performance reasoning model based on the task behavior label graph. The model adopts a graph structure reasoning framework, takes the task node as the graph node, builds the stage transfer relationship as the edge weight channel, and embeds the corresponding label density, confidence and behavior type distribution information in the graph node.

[0137] The system further establishes a weight function for clustering nodes within the graph to identify areas with significant trust fluctuations in the task. The model automatically extracts the following core reasoning features: High frequency tag coupling area; Low confidence fluctuation frequency band; Non-target path drift label marker segment; Multi-channel conflict trigger paths, etc.

[0138] The above model will serve as the decision basis for scoring and strategy generation in S620, and has self-learning capabilities, which can continuously optimize reasoning accuracy in multiple rounds of tasks.

[0139] S620: Perform task performance scoring and trust association analysis based on the trust performance reasoning model to generate a regulation control strategy.

[0140] This step is based on the trust performance reasoning model built in S610, performs task performance scoring and trust association analysis, and finally generates a regulatory control strategy. This process takes the task node as the smallest analysis unit, integrates the structured behavior label and the node state characteristics in the task execution path, and evaluates the performance and trust level of individuals in each stage of the task.

[0141] Specifically, the system first performs performance scoring on each task node. The scoring input is: The number and distribution type of node behavior labels; Label confidence; Whether the task action parameters are fully executed; Node stay time and interactive interface response information.

[0142] According to the scoring template, the system outputs the following scoring results for each node: Behavior completion score; Operational consistency rating; Attention stability score; Stress response sensitivity score.

[0143] Then, the system constructs a trust association graph within the task based on the sequential logic between nodes and the temporal characteristics of the behavior labels. The graph is formed based on the following rules: If multiple consecutive nodes have the label of "attention deviation", a weak trust connection segment is formed; If a node shows "high cognitive load", but the node immediately following it shows "high stability", a trust recovery segment is formed; If "operational hesitation" and "high concentration" frequently alternate in multiple stages, it will be marked as a trust fluctuation segment.

[0144] Based on the above results, the system identifies the overall trust status trend of the task and aggregates the scoring data to generate a regulatory control strategy structure. The strategy structure includes: Identification of mission phases that require priority intervention; Recommended adjustment of interactive interface elements; Recommended additional stimulation channel change types; Recommend slowing down or speeding up task pacing parameters; Recommend reduced or enhanced interaction complexity parameters.

[0145] The above regulation control strategy will serve as the input of S630 and directly drive the update behavior of the interaction configuration.

[0146] S630: Apply the adjustment control strategy to the configuration interaction structure diagram, update the interaction configuration and complete feedback adjustment.

[0147] In this step, the system applies the regulation control strategy output by S620 to the configuration interaction structure diagram output by S330, completes the dynamic update of the interaction configuration, and establishes a feedback regulation link to achieve closed-loop control.

[0148] Specifically, the system locates and configures the corresponding interface node structure in the interaction structure diagram according to the target task stage identified in the regulation control strategy, and performs the following processing on the node parameters: Interaction complexity adjustment: Based on the complexity coefficient indicated in the strategy, the system can increase or decrease the number of interface controls, display logic, and number of labels; Stimulus channel update: If the strategy recommends increasing the intensity of perceived stimulation, the system can add light, vibration or sound simulation signals at the corresponding task stage; Interaction frequency adjustment: If the strategy recommends slowing down the pace, the system inserts a waiting mark, interaction confirmation prompt, or auxiliary guidance information on the corresponding node; Interface focus reconstruction: The system reconstructs the interface control layout based on the focus area identified by the user in the previous gaze and action response data to reduce the user's operation load; Feedback loop embedding: The system embeds real-time feedback sensor trigger points in all modified nodes, recording the new response data generated by the user after the configuration is updated.

[0149] All update operations perform parameter replacement and module update without changing the task structure logic to ensure the integrity of the task closed loop.

[0150] The system further encapsulates the above update information into an interactive configuration update log, and marks its corresponding task stage, adjustment trigger source, policy version and user status identifier. This log can be used as an evolutionary training data source in subsequent evaluations to support the continuous optimization learning of the inference model.

[0151] The system will eventually reload the updated configuration interaction structure diagram into the task engine and restart the task simulation to achieve dynamic verification and effect feedback of the adjustment control strategy under the current user behavior state.

[0152] Through the execution of this module, the system can build a trust performance reasoning model with context-awareness based on structured behavior labels and task structure diagrams, explore the implicit trust changes and performance characteristics in user behavior, further generate dynamic interaction adjustment strategies and feed them back to the configuration structure diagram in real time, and realize intelligent adaptation and personalized adjustment between task configuration and user status. This module has causal reasoning modeling capabilities, multi-label evaluation capabilities, and configuration closed-loop feedback capabilities, effectively improving the intelligence, adaptability, and dynamic evolution level of human-computer interaction simulation evaluation.

[0153] Embodiment 2: Figure 2 FIG. 2 shows a structural block diagram of a human-computer interaction simulation evaluation system according to an embodiment of the present invention. Figure 2 As shown, the structure may include: The task structure diagram building module 10 is used to build a task structure diagram and generate a rolling interaction instruction set based on the flight task data and action control parameters.

[0154] Obtain flight mission data and motion control parameters; Create a task structure diagram and break it down into a sequence of task phases; Extract posture change requirements and operation instruction information, and build a set of interactive control parameters; The interactive control parameter set is dynamically mapped and converted into a rolling action to generate a rolling interactive instruction set.

[0155] This module realizes the mapping of task space structure to instruction space, providing a unified control baseline for environment simulation and interaction construction.

[0156] The deception simulation environment construction module 20 is used to configure simulation environment parameters based on the rolling interaction instruction set, construct a multimodal deception simulation environment, and generate a perceptual stimulus set.

[0157] Get the rolling interaction instruction set, call the environment configuration library, and match the lighting, sound effects, and vibration parameters; Build a simulation environment, including lighting changes, noise simulation, and vibration interference generation; The simulation output results are combined into a set of perceptual stimuli, which serve as the input context for interface interaction.

[0158] This module enables the simulation system to present a highly complex and realistic interactive environment and supports multi-source interference stimulus output.

[0159] The interactive interface configuration module 30 is used to parse the perceptual stimulus set and the task structure diagram, construct and reorganize the interactive interface module, and generate a configuration interactive structure diagram.

[0160] Extract interaction load and response features from the perceptual stimulus set and task structure diagram; Build a collection of interactive interface modules and identify interface control distribution and behavior nodes; A structural reorganization operation is performed on the interface module set to generate a configuration interaction structure diagram.

[0161] This module realizes dynamic interface construction and personalized interaction design based on perceptual input and task objectives.

[0162] The trust state modeling module 40 is used to generate state control factors based on the configuration interaction structure diagram and the perception stimulus set.

[0163] Obtain the configuration interaction structure diagram and the perceptual stimulus set, and establish a trust-inducing factor association model; Simulate the trust induction process based on user behavior characteristics and stimulus response; Extract key state indicators (such as attention stability, task responsiveness, etc.) and generate state control factors.

[0164] This module constructs the psychological state evolution mechanism during the human-computer interaction process, and is the core support for subsequent human factor evaluation and behavior label reasoning.

[0165] The human factor data collection and processing module 50 is used to collect human factor data according to the state control factor and the configuration interaction structure diagram, and process and generate a structured behavior label sequence.

[0166] Perform synchronous collection of human factors data such as ECG, eye movement, EEG, and posture; Clean, filter and normalize the original human factor data to extract a standardized human factor indicator set; Map the human factor indicator set to the structured behavior tag library to generate a behavior tag sequence to mark the user behavior status and deviation characteristics.

[0167] This module realizes the transformation from underlying physiological and behavioral signals to identifiable cognitive behavioral labels.

[0168] The interaction strategy updating module 60 is used to combine the structured behavior label sequence with the task structure diagram, generate an adjustment control strategy, and update the interaction configuration to complete the feedback adjustment.

[0169] Obtain structured behavior label sequences and task structure diagrams to build a trust performance reasoning model; Conduct performance scoring and trust dynamic analysis on the task process to identify problem stages and behavioral fluctuations; Generate a regulation control strategy based on the reasoning results and feed it back to the configuration interaction structure diagram; Adjust interface complexity, stimulation intensity, and interaction frequency to achieve real-time task interaction adaptation and personalized optimization.

[0170] This module forms a system intelligent feedback loop, realizing the adaptive evolutionary logic of configuration-collection-analysis-adjustment.

[0171] This embodiment has the following beneficial effects: (1) A highly integrated task-interaction-human factor full-link modeling is achieved. By integrating flight mission parameters, simulation environment and human factor data into the structured modeling system, the separation problem between the traditional task simulation system and the psychological behavior assessment system is solved.

[0172] (2) A reasonable and adjustable cognitive state recognition mechanism is established. Through the trust-inducing factor association model and structured behavior label sequence, the system can identify the trust changes and operational instability of individuals during the interaction process, and reversely adjust the interaction configuration to achieve dynamic adaptation.

[0173] (3) Supports traceable behavior data mapping and intelligent strategy update closed loop. The structured behavior label sequence and adjustment control strategy build a full-process evaluation-feedback-re-evaluation closed loop logic, supporting rapid iteration and personalized customization of interaction strategies.

[0174] (4) The system module boundaries are clear and the data interface is unified, which is easy to integrate and expand. The modules exchange data based on standardized structures, support the access of multiple sensor channels and simulation output forms, and have good system adaptability and scalability.

[0175] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A human-computer interaction simulation evaluation method, characterized in that: The following steps are involved: Based on the flight mission data and motion control parameters, the mission structure diagram is constructed, the attitude requirements and operation instructions are extracted, and the roll interaction instruction set is generated; Based on the rolling interaction instruction set, configure simulation environment parameters, build a deception simulation environment, and generate a perception stimulus set; Parsing the perceptual stimulus set and the task structure diagram, constructing and reorganizing the interactive interface module, and generating a configuration interactive structure diagram; Based on the configuration interaction structure diagram and the perceptual stimulus set, an association model is constructed to simulate the induction process and generate a state regulation factor, wherein the state regulation factor is: in, is the set of state control factors; For the The time label corresponding to each stage; For the The control index value obtained based on the complex path integral at each stage; is the corresponding historical value of the trust-inducing factor; is a set representation; is a natural number, indicating the position of the jth state control factor in the sequence; n is the total number of elements in the state control factor combination structure; Collecting human factor data according to the state control factor and the configuration interaction structure diagram, and processing and generating a structured behavior label sequence; The structured behavior label sequence is combined with the task structure diagram to perform reasoning analysis, generate a regulation control strategy, update the interaction configuration and complete feedback regulation.

2. The method according to claim 1, characterized in that The task structure diagram includes: Obtain flight mission data and action control parameters, establish a mission structure diagram and perform module decomposition to form a mission phase sequence; Extracting posture change requirements and operation instruction information from the task phase sequence to construct an interactive control parameter set; The interactive control parameter set is dynamically mapped and converted into a rolling action to generate a rolling interactive instruction set.

3. The method according to claim 1, characterized in that Building a deception simulation environment includes: Obtain the rolling interaction instruction set and match the environment simulation configuration such as lighting, sound effects and vibration; Constructing a multimodal deception simulation environment based on the environment simulation configuration, and generating illumination, noise and vibration simulation data; The light, noise and vibration simulation data are combined to generate a perceptual stimulus set.

4. The method according to claim 1, characterized in that: Constructing and reorganizing the interactive interface modules include: Acquire the perceptual stimulus set and the task structure diagram, and analyze the interaction load requirements and interface response characteristics; Based on the interface response characteristics and interaction load requirements, a set of configuration interaction interface modules is constructed; The configuration interaction interface module set is structurally reorganized to generate a configuration interaction structure diagram.

5. The method according to claim 1, characterized in that Building an association model and generating state control factors include: The configuration interaction structure diagram and the perception stimulus set are obtained to establish a trust inducing factor association model, wherein the trust inducing factor association model is: in, For the Trust-inducing factors generated at each task stage; is the standardization function; For the The trust functional value calculated in each task stage; The trust inducing factor association model is simulated and evolved to generate trust state inducing process data.

6. The method according to claim 5, characterized in that Key state indicators are extracted from the trust state inducing process data to generate state control factors.

7. The method according to claim 1, characterized in that Human factors data collected include: Obtaining the state control factor and the configuration interaction structure diagram, and performing synchronous collection of human factor data; The collected human factor data is cleaned and standardized to generate a set of human factor indicators.

8. The method according to claim 7, characterized in that The human factor indicator set is subjected to structural mapping and label encoding to generate a structured behavior label sequence.

9. The method according to claim 1, characterized in that: Conducting inference analysis includes: Acquire the structured behavior label sequence and the task structure diagram to construct a trust performance reasoning model; Perform task performance scoring and trust association analysis based on the trust performance reasoning model to generate a regulation control strategy; The regulation control strategy is applied to the configuration interaction structure diagram to update the interaction configuration and complete feedback regulation.

10. A human-computer interaction simulation evaluation system, applied to the human-computer interaction simulation evaluation method according to any one of claims 1 to 9, characterized in that: include: A mission structure diagram building module is used to build a mission structure diagram and generate a roll interaction instruction set based on flight mission data and action control parameters; A deception simulation environment construction module, used to construct a deception simulation environment based on the rolling interaction instruction set and generate a perception stimulus set; An interactive interface configuration module, used to parse the perceptual stimulus set and the task structure diagram, and generate a configuration interactive structure diagram; A trust state modeling module, used for generating a state regulation factor based on the configuration interaction structure diagram and the perception stimulus set; A human factor data collection and processing module, used to generate a structured behavior label sequence according to the state control factor and the configuration interaction structure diagram; The interaction strategy updating module is used to generate a regulation control strategy and update the interaction configuration by combining the structured behavior label sequence with the task structure diagram.

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