Traffic law enforcement virtual simulation training method based on dynamic scene and cognitive evaluation

Abstract

The invention discloses a traffic law enforcement virtual simulation training method based on a dynamic scene and cognitive evaluation, and the method comprises the steps: generating an initialization parameter according to a trainee portrait and a training target, and driving an emergence type scene generation engine to synthesize a dynamic three-dimensional virtual training scene containing composite illegal elements; in scene operation, multi-modal interaction behaviors and physiological response data of the trainees are synchronously collected to form a data stream; through real-time analysis of the data flow, a cognitive behavior analysis report for evaluating attention, cognitive load and decision intention is generated; constructing a space-time knowledge graph based on the student operation sequence, performing automatic reasoning and contradiction detection in combination with formalized legal rules, and outputting an evidence chain logic verification result; and fusing the report and the result to carry out multi-dimensional weighted evaluation, and generating a comprehensive score and personalized feedback. According to the method, normal form transformation from preset process training to open cognitive evaluation is realized, and the authenticity, pertinence and evaluation depth of training are improved.

Description

Technical Field

[0001] This invention belongs to the field of traffic law enforcement virtual simulation training technology, and in particular, a traffic law enforcement virtual simulation training method based on dynamic scenarios and cognitive assessment. Background Technology

[0002] With the increasing complexity of road traffic safety, higher demands are being placed on traffic police in terms of law enforcement standardization, professionalism, and emergency response capabilities. Traditional on-the-job training models are limited by space, risk, cost, and repeatability, making it difficult to meet the needs of large-scale, high-frequency, and high-quality training. Therefore, training systems based on virtual simulation technology have become an important means of practical training for traffic police.

[0003] Currently, existing traffic enforcement virtual simulation systems mostly focus on reproducing and simulating the processes of single, typical violations. These systems typically use pre-designed fixed scenarios and linear scripts, resulting in relatively rigid training content. At the training and evaluation level, existing technologies primarily rely on comparing and scoring whether the trainee's operational steps are complete and in the correct order. The evaluation dimensions are singular, remaining at the level of superficial operational compliance assessment.

[0004] However, real-world law enforcement environments are highly uncertain, dynamic, and complex. Illegal acts are often intertwined and complex, and the enforcement process involves multiple tasks running concurrently, including procedural compliance, evidence collection, communication skills, risk assessment, and on-site control. This presents a comprehensive challenge to law enforcement officers' situational awareness, multitasking, rapid decision-making, and resilience under pressure. Existing technical solutions have significant shortcomings: First, statically preset scenarios cannot train trainees' dynamic response capabilities to emergencies and complex police situations; second, step-by-step comparison-based assessment methods cannot deeply quantify law enforcement officers' cognitive load, attention allocation, decision-making logic, and the completeness of the legal arguments behind the procedures; third, the system lacks the ability to personalize difficulty adjustments and provide intelligent guidance based on the trainee's real-time status, resulting in insufficient targeting and adaptability in the training process.

[0005] Therefore, there is an urgent need for a new generation of virtual simulation training methods and systems that can simulate complex and dynamic law enforcement environments, deeply integrate multimodal behavioral and physiological data to assess deep cognitive states, and achieve intelligent personalized adaptation, in order to fill the gaps in existing technologies in terms of training depth, assessment dimensions, and adaptive capabilities. Summary of the Invention

[0006] Purpose of the invention: In view of the above-mentioned problems of the prior art, this application provides a virtual simulation training method for traffic law enforcement based on dynamic scenes and cognitive assessment.

[0007] Technical solution: A virtual simulation training method for traffic law enforcement based on dynamic scenarios and cognitive assessment, comprising:

[0008] Initialization parameters are generated based on the trainee profile and training objectives, and an emergent scene generation engine is driven by the initialization parameters to synthesize a dynamic three-dimensional virtual training scene containing complex illegal elements.

[0009] During the operation of the dynamic 3D virtual training scene, multimodal interactive behavior data and physiological response data of trainees are collected simultaneously to obtain multimodal trainee behavior data stream;

[0010] Real-time analysis of multimodal learner behavior data streams is performed to calculate learners’ attention allocation characteristics, cognitive load levels and identify their law enforcement decision-making intentions, and generate cognitive behavior analysis reports.

[0011] Based on the structured operation sequence parsed from the multimodal learner behavior data stream, a spatiotemporal knowledge graph is constructed, and combined with formal legal logic rules, automatic reasoning and contradiction detection are performed to output the logical verification results of the evidence chain.

[0012] By integrating cognitive behavior analysis reports with evidence chain logic verification results, a multi-dimensional weighted evaluation is conducted to generate a comprehensive training score and personalized feedback report. Based on the evaluation results, adaptive intervention and adjustment are carried out on the dynamic three-dimensional virtual training scene.

[0013] Beneficial effects: This invention achieves a qualitative leap from simple operational simulation to comprehensive ability training by constructing dynamic composite violation scenarios that closely resemble real law enforcement situations and integrating multimodal data to conduct in-depth evaluation of trainees' cognitive state and law enforcement procedures; this invention can provide a personalized adaptive training closed loop, improving the authenticity of training, the scientific nature of evaluation, and efficiency. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the overall technical solution of the present invention.

[0015] Figure 2 This is a flowchart illustrating how the present invention generates initialization parameters based on student profiles and training objectives.

[0016] Figure 3 This is a flowchart illustrating the synthesis of dynamic 3D virtual training scenes based on an emergent scene generation engine driven by initialization parameters, as described in this invention.

[0017] Figure 4 The present invention provides a flowchart for simultaneously collecting multimodal interactive behavior data and physiological response data of trainees to obtain multimodal trainee behavior data stream.

[0018] Figure 5 The flowchart describes the construction of a spatiotemporal knowledge graph for this invention, combined with formalized legal logic rules for automatic reasoning.

[0019] Figure 6This is a flowchart illustrating the multi-dimensional weighted evaluation process of this invention, which integrates cognitive behavior analysis reports and evidence chain logic verification results. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] Example 1

[0022] like Figure 1 As shown, this embodiment provides an overall framework for a traffic enforcement virtual simulation training method based on dynamic scenarios and cognitive assessment. Specifically, the method of this embodiment includes the following steps:

[0023] Step S1: Generate initialization parameters based on student profiles and training objectives, and drive an emergent scene generation engine based on the initialization parameters to synthesize a dynamic 3D virtual training scene.

[0024] According to one aspect of this application, this embodiment is used to construct a highly personalized and unpredictable dynamic training environment for each trainee. Specifically, through a data-driven approach, a three-dimensional virtual scene containing complex illegal elements, possessing high realism and training relevance, is synthesized in real time. Its data processing flow is not a simple scene invocation, but an emergent generation process based on complex algorithms triggered by initialization parameters, specifically including:

[0025] The system collects and processes data to define the starting point of this training, including historical training records of trainees. This data is not simply a record of grades, but contains deep features such as response time, procedural completeness rate, typical error patterns, and decision-making preferences under pressure in past training regarding various illegal behaviors. The system mines and analyzes this historical data to extract trainee ability feature vectors that represent trainees' skill gaps and cognitive habits. Simultaneously, it collects explicit instruction data for the current training task, such as the training focus specified by the instructor (e.g., "communication and enforcement measures in drunk driving checks"), the preset difficulty level, and the legal provisions to be reinforced. After parsing, the task data is encoded into a structured training objective constraint vector. Furthermore, to introduce unpredictability, the system also collects a random seed number. Finally, these three types of data (ability feature vector, objective constraint vector, and random seed) are merged and encapsulated into a training initialization configuration package. This configuration package serves as the blueprint for all subsequent generation logic, ensuring that the scenario both meets the trainee's personalized training needs and possesses sufficient randomness.

[0026] In a preferred embodiment, the scene generation engine relies on a pre-built, rich resource library and rule library. The system collects large-scale historical real traffic violation case data, including accident reports, law enforcement recorder videos, interrogation transcripts, etc.; through natural language processing and computer vision technology, it extracts abstract patterns of illegal behaviors from these multimodal data, such as "drunk driving is often accompanied by slightly erratic vehicle trajectories" and "vehicles with license plate violations may hesitate and stop before checkpoints," etc.; these patterns are quantified into feature vectors of illegal behaviors, and the system calculates the correlation probability matrix of different types of violations (such as drunk driving, speeding, and vehicle modification) occurring together at specific times and locations; at the same time, the system collects high-precision urban 3D geographic information data (GIS) and dynamic traffic flow models to generate a basic environmental grid and background vehicle flow data stream that conform to physical laws.

[0027] In some preferred embodiments, the system further includes: after receiving the training initialization configuration package, driving an emergent scene generation engine based on a generative adversarial network architecture. The generator attempts to create a new scene based on a genetic blueprint and a resource library, while the discriminator judges its authenticity based on a real-world case library and physical traffic rules. For example, based on the correlation probability matrix, the engine decides to overlay drunk driving and illegal parking in an urban dining area at night; simultaneously, based on the trainee's historical records showing a lack of proficiency in handling crowds, the engine intelligently sets up a certain number of virtual pedestrians in the scene and assigns them behavioral scripts that change according to the development of the event, such as watching, taking pictures, or even discussing. According to a further improvement of this embodiment, each virtual agent in the scene (the illegal driver, passenger, and onlookers) has an initial emotional state and interaction logic based on a social dynamics model, making the entire scene not a static set, but a living, evolving social miniature test field. After multiple rounds of adversarial training and logical consistency verification, the engine finally outputs a complete and directly renderable dynamic 3D virtual training scene instance data. This instance includes a precise spatial layout, behavior trees of all characters, triggering conditions and evolution possibilities of illegal events, providing a scenario for subsequent immersive training.

[0028] Step S2: During the operation of the dynamic 3D virtual training scene, multimodal interactive behavior data and physiological response data of trainees are collected synchronously to obtain multimodal trainee behavior data stream.

[0029] After the dynamic 3D virtual training scenario instance begins operation, and trainees enter and begin law enforcement operations through interactive devices (such as VR headsets, data gloves, and force feedback steering wheels), the multimodal data synchronous acquisition phase begins. This embodiment aims not only to capture the trainees' external operational behaviors in the virtual world but also to simultaneously collect their internal physiological and cognitive response signals, thereby constructing a comprehensive, three-dimensional, and time-precise behavioral observation dataset. Specifically, this is a comprehensive measurement of the trainees' holistic physical and mental responses under stress tasks. This includes:

[0030] The system first collects explicit interactive behavior data from trainees, including their movement trajectories, head turning angles, and hand movements (such as grasping or using virtual law enforcement equipment, alcohol detectors, and police communication devices) in the virtual space, collected through a VR positioning system and operation logs. This raw coordinate and event log data undergoes semantic enhancement processing, transforming it into structured operation sequences with clear law enforcement semantics. For example, a series of coordinate movements and button clicks is identified as a complete, standardized law enforcement action: "retrieving cones from the trunk of the police car" and "placing cones 50 meters away in the direction of oncoming traffic."

[0031] Furthermore, to gain insight into the learner's cognitive attention allocation, the system uses an eye tracker integrated into the VR headset to collect the learner's raw gaze coordinates and pupil diameter variation data at high frequency. In one optional implementation, the raw gaze coordinates and pupil diameter variation data undergo complex preprocessing: first, filtering is performed to eliminate noise caused by blinking or rapid head movements; second, a pre-calibrated mapping model is used to accurately map the two-dimensional gaze coordinates onto specific objects in the three-dimensional virtual scene (such as vehicle license plates, driver's faces, and document details); finally, continuous and stable gaze focus trajectory data and pupil diameter time-series data reflecting changes in cognitive load are generated.

[0032] Meanwhile, the system collects the trainee's voice data throughout the entire law enforcement process using a high-fidelity microphone. This includes not only instructions issued to the virtual offending driver (such as "Please show your driver's license and vehicle registration"), but also reports to the virtual command center, and even self-talk under pressure. These audio streams are converted into text by a real-time speech recognition engine, and further analyzed using an emotion computing model to extract the voice-text instruction stream and emotional stress features.

[0033] Further improvements to this embodiment, to obtain more direct physiological stress indicators, trainees need to wear lightweight biosensors, such as ECG patches or wristband photoplethysmography (PPG) sensors. These devices continuously acquire raw ECG or photoplethysmography pulse wave signals. At the data processing end, the system first filters out power line interference and motion artifacts, then extracts the time-domain and frequency-domain features of heart rate variability from the pure heart rate signal. These features are the gold standard for assessing autonomic nervous system activity and quantifying psychological stress. Simultaneously acquired skin conductance activity signals are used to calculate the skin conductance response level, which directly reflects the level of sympathetic nerve excitation and is closely related to emotional arousal.

[0034] Finally, all the heterogeneous data streams from different sensors with different sampling rates are integrated into a high-precision time synchronization and data fusion process: using the system's high-precision clock as a reference, a unified timestamp is added to each frame of data, and spatial behavior (operations, gaze), language behavior (voice), and physiological responses (ECG, skin conductance) are aligned, interpolated, and encapsulated on the timeline. Preferably, the fusion process also adds scene stage markers to data segments based on the scene context (such as the event label "beginning confrontation with the party involved"). Ultimately, a well-structured, multi-channel synchronized multimodal trainee behavior data stream is output. This data stream, like a multi-track recording, fully records the trainees' multi-dimensional reactions, from external behavior to internal physiology, when responding to virtual law enforcement events, laying the data foundation for subsequent in-depth analysis.

[0035] Step S3: Perform real-time analysis on the multimodal learner behavior data stream, calculate learners' attention allocation characteristics and cognitive load levels, identify their law enforcement decision-making intentions, and generate a cognitive behavior analysis report.

[0036] After obtaining the synchronously fused multimodal learner behavior data stream, the system enters the real-time computation and analysis phase. This phase extracts key indicators from the massive parallel data that can quantitatively assess the learner's cognitive state and decision-making process. This embodiment constructs a cognitive cockpit dashboard to interpret the learner's attention focus, psychological load, and thought intentions in real time. Specifically, it is a complex computational process that transforms low-level sensor data into high-level cognitive semantics. This includes:

[0037] First, regarding the analysis of attention resource allocation, the system extracts gaze trajectory data from the data stream and combines it with a 3D object database of the virtual scene. This embodiment does not simply count gaze points, but performs advanced attention analysis: for example, the system divides the virtual law enforcement scene into multiple attention areas, such as "vehicle exterior," "driver and documents," "law enforcement equipment area," and "surrounding environment and threats." By calculating the frequency of the trainee's gaze switching between different areas and the percentage of time spent in each area, and applying information entropy theory, the entropy value of attention allocation can be calculated. An efficient law enforcement officer should maintain a longer, continuous gaze on key evidence areas (such as the driver's face and documents) while periodically scanning the environment; their entropy value will exhibit a specific healthy pattern. Conversely, an excessively high entropy value indicates inattentiveness, while an excessively low value indicates ignoring environmental threats. Simultaneously, the system accurately calculates the time elapsed from when a key piece of evidence (such as a forged driver's license) appears in the field of vision to when the trainee's gaze first stabilizes on it—the key evidence discovery delay—a direct indicator of observational acuity.

[0038] Furthermore, for assessing cognitive load, the system employs a multi-indicator fusion strategy. On one hand, it extracts time-series data on pupil diameter from the data stream. Since pupil diameter dilates with increased cognitive effort, the system calculates the rate of change in pupil diameter relative to baseline and uses this as a core load indicator. On the other hand, it extracts parameters such as the ratio of low-frequency to high-frequency power from heart rate variability characteristics; these parameters are highly correlated with psychological stress and working memory load. In a preferred embodiment, the system establishes a personalized baseline model, using the learner's physiological data in a calm state as a benchmark, and then compares it with real-time data to calculate a normalized cognitive load index. This index is a comprehensive scalar that dynamically reflects the instantaneous stress experienced by the learner's brain while processing the current task.

[0039] Beyond state assessment, this embodiment also focuses on understanding the trainee's decision-making intent: this is achieved by analyzing the voice-text command stream and structured operation sequence data, combined with the current context of the work scenario. The system incorporates a law enforcement process knowledge graph, defining standard steps and optional branches in the handling of different types of violations. Using natural language understanding technology, the system parses the trainee's voice commands in real time to determine whether their intent is "routine inspection," "request for cooperation in testing," or "preparation to take coercive measures." Simultaneously, it observes their operation sequence; for example, if a trainee directly retrieves handcuffs without completing document verification, this is identified as a "skipping step" or "procedural violation" decision-making tendency. By matching and comparing speech and behavior on a timeline with the standard process model, the system can generate a real-time decision intent label sequence and calculate its deviation from the optimal or standard decision path.

[0040] Ultimately, the calculated attention allocation entropy value, along with the key evidence discovery delay, dynamically changing cognitive load index, real-time decision-making intention labels, and their deviation, were summarized and formatted to generate a structured cognitive behavior analysis report. This report is no longer a simple score, but a diagnostic report containing a time curve, indicating that: "In the third minute of dealing with the party's violent resistance to law enforcement, the trainee's cognitive load reached the red alert threshold, and at the same time, their attention shifted completely from the party to the onlookers, resulting in the omission of the party's crucial action of attempting to destroy evidence." Such in-depth analysis provides insights for subsequent comprehensive evaluation and precise feedback.

[0041] Step S4: Based on the structured operation sequence parsed from the multimodal learner behavior data stream, construct a spatiotemporal knowledge graph, and combine it with formal legal logic rules to perform automatic reasoning and contradiction detection, and output the evidence chain logic verification result.

[0042] This embodiment is used to automatically construct and verify a legally logical digital evidence chain from the trainee's external structured operational sequence. Specifically, the system simulates the examination of the rigor of law enforcement procedures during post-event case review or court hearings, transforming subjective operational records into an objective knowledge system capable of formal logical reasoning. Specifically, it includes:

[0043] First, the system performs deep annotation and enhancement of the legal semantics of the original operation sequence, automatically identifying and extracting all legally significant action nodes from the sequence, such as "activating the law enforcement recorder," "showing police identification to prove identity," "verbally informing the perpetrator of the violation," "using equipment for testing," "making a scene record," and "seizing the items involved in the case." Each action node is transformed into a standardized legal procedural action tuple. Preferably, this tuple not only includes the action type but also precisely associates the timestamp of the action, its three-dimensional spatial coordinates in the virtual scene, the object entity involved (such as a specific vehicle or driver), and the index of the legal clause on which it is based. For example, an action can be described as <Action: Breathalyzer test, Time: t1, Object: Driver Zhang, Basis: Article XX of the Road Traffic Law>.

[0044] In one alternative implementation, the system needs to construct a formalized legal logic rule base to encode the requirements of laws and regulations regarding the legality of evidence collection, the order of procedures, the relevance of evidence, and the obligation to inform, using first-order logic predicates, production rules, or descriptive logic. For example, a rule could be expressed as: "Before conducting an alcohol test (Action_Test), the obligation to inform (Action_Notify) must have been fulfilled and the party concerned must not refuse without a legitimate reason," or "The impounding of a vehicle (Action_Impound) must be supported by on-site photos (Action_Photo) and written records (Action_Record) as evidence."

[0045] Furthermore, the system utilizes the extracted legal procedural action tuples to dynamically construct a spatiotemporal knowledge graph based on their temporal and spatial relationships. In this graph, nodes represent legal actions, involved personnel, physical evidence, time points, and locations, while edges represent the relationships between them, such as "occurred at," "acts on," "proves," and "precedes." This graph visually presents the logical thread of the entire law enforcement event.

[0046] Further, the system enters the core automatic verification phase. It matches and reasons with the constructed spatiotemporal knowledge graph and a formalized legal logic rule base, checking whether the facts in the graph satisfy all the preconditions in the rule base. For example, the system automatically detects whether a "notification" node precedes a "detection" node; if not, it marks a "procedural inversion" vulnerability. The system also checks whether evidence nodes (such as transcripts, photos, and test reports) for the same illegal fact form a closed corroboration loop or whether there are breaks. Furthermore, the system simulates adversarial questioning: for example, if a trainee takes a picture of the vehicle's exterior but not a close-up of the vehicle identification number (VIN), the system infers a risk point of "insufficient probative value of physical evidence" based on the rule base.

[0047] Ultimately, the output is a structured logical verification result of the evidence chain, including at least: a quantitative evidence chain integrity score based on the covered necessary procedural nodes and evidence types; a detailed list of logical loopholes and procedural flaws, clearly indicating missing actions, the order of violations, and contradictions between pieces of evidence; and a reasoning path explanation, illustrating the logical basis for the system's conclusion. For example, the result might show: "Integrity score 75%. Major flaws: 1. The necessary procedural node of 'warning' was missing before taking compulsory subpoena; 2. The photos of the seized modified parts were not spatially correlated with the photos of the vehicle's overall appearance, resulting in a broken chain of physical evidence." This result elevates the operational record to the level of legal argumentation, providing trainees with invaluable training in procedural compliance.

[0048] Step S5: Integrate the cognitive behavior analysis report and the evidence chain logic verification results, conduct a multi-dimensional weighted evaluation, generate a comprehensive training score and personalized feedback report, and adaptively intervene and adjust the dynamic three-dimensional virtual training scene based on the evaluation results.

[0049] This embodiment integrates in-depth analysis results from cognitive and legal procedural dimensions to generate a comprehensive and multi-dimensional training evaluation report. Based on this evaluation, the training environment is adjusted in real time and intelligently to achieve personalized adaptive teaching. Specifically, this is a decision-making loop of data aggregation, value judgment, and system intervention.

[0050] First, the system performs multi-source data fusion and weighted evaluation. Based on further improvements in this embodiment, the system pre-defines a multi-dimensional evaluation model, which includes, but is not limited to, the following core dimensions: technical compliance (primarily based on evidence chain verification results), cognitive efficacy (based on attention and workload indicators), procedural legitimacy (based on the severity of procedural vulnerabilities), communication and law enforcement effectiveness (based on voice emotion analysis and the compliance of virtual parties), and situational judgment and ethical decision-making (based on choices made in complex situations such as elderly people breaking the law or children being present). Each dimension is assigned a corresponding weight, which can be dynamically adjusted according to the training phase objectives. For example, for new police officers, technical compliance and procedural legitimacy have higher weights; for advanced training, cognitive efficacy and situational judgment have higher weights. The system uses a weighted algorithm to aggregate the scores of each dimension into a comprehensive training score and generates a detailed multi-dimensional capability radar chart.

[0051] Furthermore, the system goes beyond simply scoring; it strives to generate actionable feedback. Based on a multi-dimensional capability radar chart and a specific list of vulnerabilities and cognitive indicator anomalies, the system activates an intelligent content matching engine. This engine connects to a vast teaching resource library, which stores structured resources such as detailed explanations of legal provisions, standard operating procedure videos, typical case analyses, error demonstration compilations, and psychological adjustment methods. The system automatically diagnoses the learner's weaknesses. For example, if the "procedural legitimacy" score is low and the specific vulnerability is "inappropriate notification," the system will not only point out the problem in the feedback report but also embed a link to a "standard video on administrative penalty notification procedures" and a summary of key legal provisions. If the "cognitive efficacy" score is low and the learner exhibits narrow attention span under high workload, the feedback will suggest relevant stress management techniques or attention allocation training. Ultimately, a personalized feedback and learning path suggestion report with illustrations and text, including specific improvement suggestions, is generated.

[0052] According to one aspect of this application, the system dynamically intervenes in the running dynamic 3D virtual training scene based on real-time evaluation results. In a preferred embodiment, this is embodied in a layered intervention strategy: for real-time cognitive overload (such as a persistently excessive cognitive load index), the system sends a "burden reduction instruction" to the scene engine, for example, temporarily reducing the density of background traffic, reducing the noise level of the virtual onlookers, or pausing the injection of new unexpected events (such as a sudden visit from a reporter), creating a brief breathing space for the trainee to refocus on the core task. For recurring procedural errors, the system triggers "instant teaching prompts," for example, when the system detects that the trainee is attempting to search a vehicle without a license again, a legal prompt box stating "A search warrant is required" will be highlighted in the virtual scene, or a reminder voice from a virtual commander will be played through the headphones. At a deeper level, at the training level, the system adaptively plans the difficulty curve of subsequent training scenes based on the trainee's overall training score trend. For example, after the trainee's procedural score stabilizes, the complexity of ethical conflicts or public opinion pressure elements in the scene are gradually increased to achieve personalized advanced training.

[0053] Example 2

[0054] This embodiment, based on Embodiment 1, describes in detail the data processing flow of a traffic enforcement virtual simulation training method based on dynamic scenarios and cognitive assessment, specifically including:

[0055] Step S1: Generate initialization parameters based on the student profile and training objectives, and drive the emergent scene generation engine based on the initialization parameters to synthesize a dynamic 3D virtual training scene, including:

[0056] S1.1: Student profile construction and training parameter initialization configuration.

[0057] Specifically, this embodiment establishes a highly personalized foundation for each training session. The system first collects the trainee's identification data and uses it as an index to retrieve the trainee's historical training records from the training profile database. These records contain detailed performance in various violation investigations, such as the order and time taken to complete operational steps, the type and frequency of procedural errors, emotional stability indicators in simulated adversarial situations, and attention distribution patterns reflected in eye-tracking. Further, the system collects training objective data issued by instructors or training managers, which clarifies the training focus (e.g., "investigation of combined violations involving license plates and certificates"), the core competencies to be strengthened (e.g., "awareness of evidence chain closure"), and the difficulty level. In a preferred embodiment, the system also introduces environmental random factors, such as a random seed generated based on system time. Subsequently, the system integrates and processes this multi-source data: using data analysis models to mine historical records and generate quantified trainee competency feature vectors; parsing and structurally encoding the training objectives to form training objective constraint vectors. Finally, the competency feature vectors, objective constraint vectors, and random seeds are fused, parameters are packaged and formatted, and personalized initialization configuration package data driving the entire training session is generated.

[0058] S1.2: Construction and loading of the illegal case feature library and the 3D environment material library.

[0059] According to a further improvement of this embodiment, a rich and realistic underlying data pool is a prerequisite for generating high-quality training scenarios. In this embodiment, the system collects and preprocesses basic materials from multiple data sources in parallel. First, the system accesses a database of real traffic violation cases that has been anonymized, containing tens of thousands of case texts, on-site photos, law enforcement recorder video clips, and penalty documents. Through multimodal artificial intelligence analysis, the system extracts key features of violations, such as the dynamic representation of violations (e.g., the driving trajectory characteristics of speeding vehicles), typical reaction patterns of the parties involved, and spatiotemporal correlation patterns between different types of violations. These features are then abstracted and encoded into a machine-understandable set of violation feature vectors. Simultaneously, the system calculates and stores the conditional probabilities and correlation matrices between different feature vectors. Second, the system collects high-precision 3D urban geographic information data, road network models, and traffic sign libraries. Combined with dynamic traffic flow simulation theory, it generates physically realistic basic 3D scene mesh data and a configurable set of background traffic flow parameters. These data together constitute the gene pool and canvas for scene generation.

[0060] S1.3: Emergent dynamic scene synthesis based on generative adversarial networks.

[0061] In one optional implementation, the system receives personalized initialization configuration package data and calls the loaded set of illegal behavior feature vectors, correlation matrix, basic 3D scene mesh data, and background traffic flow parameter set. Then, an emergent scene generation engine based on a conditional generative adversarial network framework is launched. The generator receives the initialization configuration as conditional input and first intelligently selects one or more illegal behaviors for organic combination based on the correlation matrix and training objectives (e.g., combining "drunk driving" with "running a checkpoint"), and determines the time and location of the occurrence (e.g., late at night in a restaurant district). Further, on the basic 3D scene mesh, based on physical laws and common law enforcement practices, the initial positions of police officers, the status of illegal vehicles, surrounding pedestrians and vehicles are automatically deployed, and the initial behavior scripts and interaction logic for all virtual characters are generated. Simultaneously, the discriminator, based on the distribution learned from massive amounts of real-world case data and built-in traffic regulations and common-sense rules, rigorously evaluates the authenticity, rationality, and logical consistency of the generated scene. Both undergo multiple rounds of adversarial training and iterative optimization until a candidate scene that satisfies both personalized training conditions and passes authenticity verification is generated. Furthermore, the system adds random details (such as weather changes, vehicle colors, and pedestrian clothing) to the scene based on random seeds to ensure the uniqueness of each generation. Finally, the engine outputs a dynamic 3D virtual training scene instance data package containing a complete semantic description, physical parameters, character behavior tree, and event triggering logic. This data package can be directly driven by the graphics rendering engine and the physics engine to form a virtual world waiting for trainees to intervene.

[0062] Step S2: During the operation of the dynamic 3D virtual training scene, multimodal interactive behavior data and physiological response data of trainees are collected synchronously to obtain a multimodal trainee behavior data stream, including:

[0063] S2.1: Semantic parsing and structuring of virtual environment interaction operation logs.

[0064] Specifically, once the learner is immersed in the virtual scene and begins to interact, the system first collects low-level operation log data from all input devices. This includes six-DOF pose data from the VR headset, button events and spatial trajectory data from the controllers, and tactile interaction signals from force feedback devices. The system performs real-time parsing and semantic enhancement on these raw, low-level signal streams. For example, by combining continuous controller spatial movement trajectories with object collision detection results in the virtual scene, it identifies compound actions such as "picking up a breathalyzer" and "approaching the driver for testing." Furthermore, the system incorporates a law enforcement operation knowledge graph, aggregating a series of atomic operations into legally meaningful law enforcement action units, such as recognizing "approaching the vehicle - signaling to stop - showing identification" as "stopping for inspection." Each identified high-level semantic action is recorded along with its precise timestamp and the ID of the virtual object involved, forming a structured law enforcement operation sequence data with rich context.

[0065] S2.2: Calibration, mapping, and feature extraction of visual attention channel data.

[0066] Furthermore, to gain insight into the learner's cognitive focus, the system collects raw gaze coordinates and pupil image data using eye-tracking. This data undergoes a rigorous preprocessing process: filtering algorithms are applied to eliminate noise caused by violent head movements or blinking; a personal eye-tracking model established using a nine-point calibration method is used to spatially correct the gaze data. Subsequently, the corrected two-dimensional gaze points are precisely projected onto the surface of specific objects in the three-dimensional virtual space using an eye-tracking-virtual scene coordinate mapping model, for example, determining whether the learner is looking at "the birth date field on a driver's license" or "the driver's blinking eyes." This process generates high-precision three-dimensional gaze focus sequence data and pupil diameter change time-series data. Simultaneously, the system can calculate heatmaps and transition matrices of attention allocation in real time based on the dwell time and switching frequency of the gaze in different functional areas of the scene (evidence area, threat area, control area).

[0067] S2.3: Synchronous acquisition and preliminary analysis of multi-channel physiological signals and speech data.

[0068] According to a further improvement of this embodiment, the system synchronously collects the physiological and speech data of trainees through a biosensor array and audio equipment. ECG and skin conductance sensors worn on the trainee continuously collect raw physiological electrical signals. At the ECG signal processing end, the system first performs baseline drift correction and power line interference filtering, then uses a QRS wave detection algorithm to identify the heartbeat cycle, and subsequently calculates a series of time-domain and frequency-domain features of heart rate variability, such as SDNN and the LF / HF ratio, which are key indicators for assessing autonomic nervous activity and psychological load. For skin conductance signals, peak detection and trend analysis are used to extract the skin conductance response level and frequency characteristics. At the speech processing end, the audio stream collected by the directional microphone is segmented into utterances by speech activity detection, converted into a command text stream by automatic speech recognition, and simultaneously processed by an emotional speech analysis model to extract acoustic features such as pitch, speech rate, and energy, outputting an emotional state probability distribution (such as the likelihood values ​​for confidence, tension, and anger).

[0069] S2.4: Time synchronization, alignment and fusion encapsulation of multi-source heterogeneous data streams.

[0070] In a preferred embodiment, the data from each of the aforementioned channels have different time bases, sampling rates, and data formats. This embodiment constructs a data view under a unified spatiotemporal reference frame: the system employs a high-precision time synchronization mechanism based on a hardware clock or network time protocol to timestamp each original data sample with microsecond-level precision. For non-uniformly sampled data (such as operational events), the timestamp is made when the event is triggered; for continuously sampled data (such as physiological signals and gaze data), the timestamp is made at the sampling moment. Subsequently, the data fusion engine uses the channel with the highest frequency as the reference (usually eye movement or physiological signals) to perform time-aligned interpolation on the data from all other channels, ensuring that at any analysis moment, each modality of data strictly corresponds to the same physical instant. Finally, the engine encapsulates the time-aligned structured operational sequence, three-dimensional gaze and pupil data, physiological feature vectors, speech text, and emotion tags by time frame, generating a standardized, multi-channel, synchronous multimodal learner behavior data stream, providing complete and consistent input for subsequent deep cognitive analysis.

[0071] According to another aspect of this application, the time synchronization, alignment, and fusion encapsulation of multi-source heterogeneous data streams includes:

[0072] S2.4.1: Establishment of a unified spatiotemporal reference based on a hardware clock source.

[0073] Specifically, to achieve multimodal data synchronization with nanosecond-level precision, the system first establishes a global, unified spatiotemporal reference. In this embodiment, the system uses an independent, highly stable hardware clock source (such as a GPS disciplined clock or a high-precision temperature-controlled crystal oscillator) as the master clock. This master clock generates a continuously increasing global microsecond-level timestamp sequence. All data acquisition terminals, including VR positioning systems, eye trackers, biosensors, and audio acquisition cards, periodically synchronize and calibrate with this master clock through a precise time protocol (such as PTP, Precision Time Protocol) to ensure that the deviation of each terminal's local clock is limited to within microseconds. Furthermore, the system defines an absolute simulation world time for the virtual simulation world, bound to the master clock, and the occurrence time of all virtual events is based on this time.

[0074] S2.4.2: Timestamp marking and buffered queued reception of multi-channel raw data.

[0075] In one alternative implementation, each data acquisition module, upon capturing a raw data sample or event, immediately reads the current time from a synchronized local clock and binds this high-precision acquisition timestamp to the raw data packet, forming a stamped raw data unit. For example, a pupil diameter sampling point, an ECG R-wave peak event, or a button press event on a handheld device all have acquisition timestamps accurate to microseconds. These stamped data units are then sent to the corresponding receive buffer of the central processing server via a high-speed, low-latency data bus (such as USB 3.0 or fiber optic). Each data channel (eye-tracking, physiological, operational, audio) corresponds to a timestamped first-in-first-out (FIFO) data queue, and the system continuously reads data from the head of each queue in an asynchronous, non-blocking manner for subsequent processing.

[0076] S2.4.3: Alignment window sliding and data interpolation fusion based on reference clock.

[0077] Furthermore, the central processing engine initiates a sliding time window processing mechanism. The system uses the channel with the highest sampling rate (usually eye-tracking or physiological signals, such as 1000Hz) as the reference time axis. For each reference time point, the engine creates a multimodal data alignment window. The algorithm traverses the data queues of all other channels, searching for all data units whose timestamps fall within the current alignment window. For event-driven data (such as operational actions), it is directly associated with the window; for continuously sampled signals but channels with sampling rates lower than the reference (such as 30Hz speech features), the system uses cubic spline interpolation or linear interpolation algorithms to calculate the estimated value at the reference time point based on the samples adjacent to the window. Through this implementation, the system uniformly resamples and aligns multiple originally discrete, multi-frequency data streams to the same set of high-frequency reference time point sequences, generating a series of time-aligned multimodal data frames.

[0078] S2.4.4: Encapsulation, compression, and persistent storage of structured fusion data packets.

[0079] In a further improvement to this embodiment, after time alignment, the system performs structured encapsulation of each frame of data. Each time-aligned multimodal data frame is organized into a standardized data structure, whose fields include at least: a global timestamp, 3D head pose, 3D coordinates of the gaze focus and pupil diameter, structured operation semantic labels, multi-dimensional physiological feature vectors, speech recognition text and sentiment labels, and a snapshot index of the current virtual scene. To further improve processing efficiency and save storage space, the system uses differential encoding for slowly changing data (such as scene state) in consecutive frames and lossy or lossless compression algorithms for high-dimensional data such as physiological signals. Finally, the system pushes the encapsulated and compressed synchronous multimodal data stream to the downstream cognitive analysis steps in real time, and writes it into the training process holographic video database in an efficient columnar storage format (such as Apache Parquet) for post-event review, model training, and deep analysis, realizing a complete closed loop of data from acquisition, synchronization, fusion to application and archiving.

[0080] Step S3: Perform real-time analysis of the multimodal learner behavior data stream, calculate learners' attention allocation characteristics and cognitive load levels, identify their law enforcement decision-making intentions, and generate a cognitive behavior analysis report, including:

[0081] S3.1: Pattern recognition and quantitative evaluation of attention resource allocation.

[0082] Specifically, this embodiment extracts a three-dimensional gaze focus sequence from a synchronous multimodal behavioral data stream and combines it with semantic segmentation information of the virtual scene (i.e., the category and importance label of each virtual object). The system first models the trainee's visual behavior as an exploration process in a visual information space, calculating a series of attention metrics in real time: including region-weighted gaze duration (giving higher weight to gazes on key evidence regions such as faces and documents), entropy and fractal dimension of the visual scanning path (measuring the randomness and complexity of the scanning pattern), and the contrast between machine learning-based saliency prediction and the trainee's actual gaze (assessing whether their attention is captured by irrelevant visual saliencies). Furthermore, the system precisely calculates the delay from the occurrence of a preset key information cue event (such as abnormal hand movement by the driver) to the trainee's first eye lock onto the relevant region, i.e., the context-aware response delay. These metrics together constitute a dynamic attention performance evaluation report.

[0083] S3.2: Real-time calculation of cognitive load and emotional arousal by integrating multiple physiological dimensions.

[0084] In a further improvement to this embodiment, the assessment of cognitive and emotional states relies on the fusion analysis of multiple physiological signals. The system extracts heart rate variability features, skin conductance response features, and pupil diameter changes from the data stream. In an optional implementation, the system employs a fusion estimation model trained on a large amount of psychophysiological experimental data. This model takes standardized and sliding window-processed physiological features as input, while considering both the baseline workload of the task (determined by the complexity of the scenario) and the individual's historical baseline. The model outputs two core real-time indicators: a cognitive load index, which comprehensively reflects the degree of working memory occupancy and information processing intensity; and an emotional arousal index, which characterizes the excitation level of the sympathetic nervous system and is associated with tension, anxiety, or excitement. The system continuously monitors these indicators; when the cognitive load index exceeds the individual's adaptive threshold, it indicates a decline in decision-making quality; and when the emotional arousal is abnormally high, it suggests the occurrence of a stressful or conflicting event.

[0085] S3.3: Law enforcement strategies and intent recognition based on multimodal input.

[0086] Furthermore, the system strives to understand trainees' decision-making logic and action plans in complex situations by integrating and analyzing voice command text streams, structured operation sequences, and the current virtual scenario state. The system incorporates a hierarchical law enforcement strategy knowledge base, encompassing standard and variant forms ranging from macro-tactics (e.g., "control-style inspection" vs. "appeasement-style communication") to micro-action sequences. Through natural language understanding, it analyzes the strategic intent behind trainees' verbal commands in real time; through action sequence analysis, it identifies whether their operational patterns follow standard procedures or employ innovative or unethical responses. For example, when the system detects that a trainee has prepared police equipment in advance without issuing a verbal warning, and physiological data shows increased arousal, the system infers that the trainee has a potential intent to "anticipate escalation and prepare for coercive action." The system compares this intent with the objective threat assessment of the current scenario, generating a decision intent identification result and its conformity score with the standard / optimal strategy.

[0087] S3.4: Generate integrated, interpretable cognitive behavioral analysis and diagnostic reports.

[0088] Finally, this embodiment integrates, interprets, and visualizes all the above analysis results, rather than simply listing data, to generate a cognitive behavioral analysis report with diagnostic significance. The report presents the change curves of key cognitive indicators in a timeline format, aligned with key events in the scenario (such as "the party began to resist law enforcement" or "new evidence was discovered"). The report highlights key findings, such as: "During the escalation phase, the trainee's cognitive load reached its peak, and their attention was completely focused on a single threat source, causing them to miss the crucial clue that a bystander behind them was raising their phone to take pictures, and their decision-making intention quickly shifted from 'communication control' to 'coercive suppression'." The report not only points out "what happened" but also attempts to explain "why it happened," establishing a causal relationship between external behavior and internal cognitive state, providing in-depth evidence for accurate feedback and intervention.

[0089] According to another aspect of this application, the real-time cognitive load and emotional arousal calculation based on the fusion of multiple physiological dimensions includes:

[0090] S3.2.1: Real-time preprocessing and artifact correction of physiological signals,

[0091] Specifically, the raw physiological signals (ECG, ductus skin function, pupillary light) extracted from the synchronous multimodal data stream contain a large amount of noise and motion artifacts, which must be cleaned online. For ECG signals, the system first applies an adaptive filtering algorithm to remove power line interference and baseline drift. Next, a heartbeat detection algorithm based on wavelet transform or machine learning is used to robustly identify each QRS complex from the noise and mark its occurrence time. For signal distortion (motion artifacts) caused by body movement, the system synchronously references accelerometer data from the VR device and uses blind source separation techniques (such as independent component analysis, ICA) to attempt to separate and remove motion-related signal components. For ductus skin function signals, the system performs low-pass filtering to extract slowly changing ductus skin function levels, while simultaneously performing differentiation processing to capture rapid ductus skin function response peaks. For pupil diameter data, median filtering is used to remove transient abrupt changes caused by blinking. Finally, clean, time-aligned physiological signal time-series data is output, ready for feature extraction.

[0092] S3.2.2: Multi-dimensional extraction of time-domain, frequency-domain and nonlinear lattice features.

[0093] Furthermore, the system performs multi-dimensional feature engineering on clean physiological signals within a sliding time window (e.g., every 60 seconds, sliding in 1-second increments). For ECG signals, it calculates time-domain features of heart rate variability, such as the standard deviation of the RR interval and RMSSD; and frequency-domain features, such as obtaining low-frequency power, high-frequency power, and their ratios through Fast Fourier Transform. In addition, it extracts nonlinear features, such as Poincaré scatter plot indices and sample entropy, to characterize the complexity of heart rate dynamics. For EDS signals, it extracts the mean, standard deviation, number, and average amplitude of EDS response peaks within the window. For pupil diameter, it calculates its mean, coefficient of variation, and the expansion amplitude and latency related to scene event locking. Finally, each time window generates a multi-physiological feature vector containing dozens of dimensions.

[0094] S3.2.3: Fusing cognitive state modeling based on personalized calibration and task context.

[0095] In a preferred embodiment, the system employs a deep learning model pre-trained on a large-scale psychophysiological dataset as the core evaluator. In another preferred embodiment, to accommodate individual differences, the system performs a brief (e.g., 3-minute) personalized baseline calibration before each training session: the learner rests in a static, low-load virtual environment, and the system collects their resting physiological data to establish a personal resting physiological baseline. During real-time evaluation, the model receives the multi-physiological feature vector of the current window, the personal resting physiological baseline, and the current task context vector (e.g., scene complexity score, current task type encoding) as input. The model performs non-linear fusion through a deep neural network, outputting two core, normalized continuous indices: a cognitive load index, reflecting a continuous state from relaxation to information processing overload; and an emotional arousal index, reflecting a continuous state from calm to high excitement / tension. The model can also output the confidence level of its predictions.

[0096] S3.2.4: Trend analysis and threshold early warning of state changes.

[0097] Further improvements to this embodiment focus not only on instantaneous indicators but also on their dynamic trends. The system continuously tracks the changes in the cognitive load index and emotional arousal index over time, calculating their first derivative (rate of change) and second derivative (acceleration of change). When a rapid increase in the load index is detected within a short period (e.g., the slope exceeds a threshold), the system generates a warning event for "accelerated increase in cognitive load." Simultaneously, the system maintains personally adaptive dynamic thresholds: when the load index consistently exceeds the individual's historical percentile (e.g., 85%) for a certain duration, or when the arousal index exceeds a safe operating threshold, the system triggers a high-level "cognitive state warning." These trend analyses and warning events are encapsulated in real-time into the cognitive state stream, providing crucial information for subsequent intervention decisions, enabling the system to anticipate rather than merely respond to cognitive crises that have already occurred.

[0098] According to another aspect of this application, law enforcement strategies and intent recognition based on multimodal input include:

[0099] S3.3.1: Alignment, extraction and intermediate representation generation of multimodal features.

[0100] Specifically, this embodiment extracts intent-related features from a synchronous multimodal data stream and projects them into a unified intermediate semantic space. The system extracts the speech recognition text stream and transforms it into a semantic feature vector containing word vector sequences and dialogue behavior labels using a natural language processing model. Simultaneously, it extracts the action type, object, and temporal relationships between actions from the structured operation sequence, forming an operation mode feature vector. Furthermore, the system acquires a snapshot of the current virtual scene state, including the emotional state of the parties involved, the density of onlookers, and the list of discovered evidence, encoding this as a scene context feature vector. These feature vectors from different modalities are fed into a cross-modal attention network, which learns which speech words, operations, and scene elements are more critical for intent inference in a specific scenario and outputs a fixed-dimensional joint semantic representation vector that integrates multimodal information.

[0101] S3.3.2: Intent classification and policy matching based on hierarchical policy knowledge base.

[0102] Furthermore, the system maintains a hierarchical law enforcement strategy and intent knowledge graph. The top layer of this graph represents macro-level tactical objectives (such as "control and deterrence," "appeasement and communication," and "prioritized evidence collection"), while the lower layers contain associated specific strategy patterns (such as "standard procedures for single-officer checks," "SOPs for handling adversarial parties," and "multi-person collaborative investigation and control formations"). The leaf nodes represent a series of standard operating procedures for implementing these strategies. The system inputs the joint semantic representation vector obtained in the previous step into an intent classification model. Based on the knowledge graph structure, this model first predicts the high-level tactical intent category currently pursued by the trainee and its probability. Then, under the selected tactical category, the system further performs dynamic time-warped matching between the trainee's real-time operational sequence and various strategy patterns in the knowledge graph, identifying the reference strategy pattern with the highest matching degree and calculating the matching similarity score.

[0103] S3.3.3: Calculation of Strategy Deviation and Reasoning of Potential Risks.

[0104] In one alternative implementation, after identifying the matching strategy, the system conducts a deep analysis of subtle deviations between the trainee's behavior and the standard / optimal strategy. The algorithm not only compares "what was done," but also analyzes "what wasn't done" and "the timing of the actions." For example, the standard strategy requires clear notification of rights and obligations before questioning; if this step is missing from the trainee's operational sequence, even if subsequent questioning matches, the system will mark it as "procedural deviation: missing notification." Simultaneously, the system combines cognitive state flow (e.g., high arousal) and contextual information (e.g., the trainee's hands are hidden) to perform risk inference on the deviating behavior: the system generates a strategy deviation report listing key deviations, a preliminary assessment of their compliance and effectiveness, and potential law enforcement risk warnings (e.g., "potential exposure to lateral risks due to failure to observe the environment beforehand").

[0105] S3.3.4: Intent-State-Scene Consistency and Anomaly Detection.

[0106] Finally, the system performs a high-level consistency check, comparing the identified tactical intent, the calculated real-time cognitive and emotional state, and the objective threat level of the virtual scenario in a three-dimensional comparison. For example, if the system identifies the trainee's intent as "escalating force control," but their cognitive state is shown as "high load, low arousal" (possibly in a state of confusion or hesitation), and the scenario threat level is assessed as low, this inconsistency will trigger an "intent-capability-situation mismatch" anomaly flag. Conversely, if the intent is "communication to resolve the conflict," but arousal is extremely high and the scenario threat is increasing, it indicates that the trainee is suppressing a stress response. This consistency analysis provides a key perspective for understanding the deeper logic behind trainees' decisions (whether it is a rational choice or a stress response). The results of this consistency analysis, together with the aforementioned intent labels, strategy matching degree, and deviation reports, constitute a complete conclusion on law enforcement strategy and intent identification.

[0107] Step S4: Based on the structured operation sequence parsed from the multimodal learner behavior data stream, construct a spatiotemporal knowledge graph, and combine it with formal legal logic rules for automatic reasoning and contradiction detection, outputting the evidence chain logic verification results, including:

[0108] S4.1: Extraction and formalization of facts from behavioral sequences to legal procedures.

[0109] Specifically, this embodiment converts the trainee's structured operation sequence into a set of legal facts that can be logically reasoned by the machine. The system uses a detailed and scalable law enforcement procedure ontology to annotate each action in the operation sequence with legal semantics. This ontology defines the legal attributes, necessary preconditions, and expected outputs of various law enforcement actions (such as "identifying oneself," "informing about rights and obligations," "collecting physical evidence," and "making a statement"). The system matches the operation actions with the concepts of the ontology and extracts the relevant spatiotemporal and object context to generate standardized legal procedure fact triples. For example, an operation can be formalized as (law enforcement subject: trainee A, action performed: breathalyzer test, object of action: driver B, time: t, location: L, associated evidence ID: E001). Simultaneously, the system identifies and links relevant legal citations from the voice command text stream, using them as the legal basis attributes for the fact.

[0110] S4.2: Knowledge representation and construction of a formal legal logic rule base.

[0111] Further improvements to this embodiment require a precise and unambiguous set of legal logic rules for automated verification. This embodiment addresses the engineering transformation of legal knowledge. The system extracts clauses concerning enforcement procedures, rules of evidence, and scope of authority from legal databases and departmental regulations. Through collaboration between legal experts and knowledge engineers, or by employing advanced legal text extraction and formalization techniques, these natural language provisions are transformed into formalized logical rules. These rules exist in the form of production rules, first-order logic predicates, or descriptive logic axioms. For example, a rule can be expressed as: "MandatoryAction(AlcoholTest) ∧ HasPrerequisite(AlcoholTest,InformingRights)" (Conducting an alcohol test is a mandatory action, and is contingent upon informing the user of their rights). The rule base is organized by modules, covering all aspects such as jurisdiction, recusal, investigation, evidence, decision-making, and execution, forming a machine-readable and reasonable formalized legal logic rule knowledge graph.

[0112] S4.3: Spatiotemporal knowledge graph construction and rule-based automatic logical reasoning.

[0113] In a preferred embodiment, the system dynamically constructs a spatiotemporal knowledge graph of the current law enforcement event using extracted legal procedural fact triples as nodes and edges. This graph not only contains fact nodes but also completes the implicit relationships between facts through reasoning, such as causal, sequential, and corroborative relationships. Subsequently, the system launches a logical reasoning engine to match this fact graph with a formalized legal logic rule knowledge graph. The reasoning engine performs a series of checks: temporal legality checks (e.g., checking whether "seizure" occurred after "approval"), procedural integrity checks (e.g., checking whether "interrogation" corresponds to "the signature of the person being interrogated"), evidentiary sufficiency checks (e.g., checking whether "determination of speeding" is supported by both "speed camera calibration records" and "speeding photos"), and authority compliance checks (e.g., whether a single person can execute a "search"). The reasoning process generates a logical proof trajectory, recording the basis for each step of reasoning.

[0114] S4.4: Generation of Evidence Chain Quality Assessment and Legal Risk Diagnosis Report.

[0115] Finally, the reasoning engine summarizes all the inspection results and generates a deep evidence chain logic verification and risk assessment report. The report's core outputs include: 1) Evidence chain integrity index: a quantitative score calculated based on rule satisfaction and evidence closure; 2) Procedural defect list: detailing each violation of procedural regulations, including the specific rules violated, the factual basis, and the potential diminishment of evidentiary value or procedural illegal consequences; 3) Legal risk warning: highlighting high-risk defects (such as defects that may lead to the exclusion of key evidence or invalidity of administrative actions); 4) Visual evidence relationship diagram: graphically displaying the constructed evidence chain and highlighting its breaks and weaknesses. This report, like the opinion of a rigorous legal auditor, enables trainees to clearly understand the true legal implications and potential risks of their actions.

[0116] According to another aspect of this application, spatiotemporal knowledge graph construction and rule-based automated logical reasoning include:

[0117] S4.3.1: Spatiotemporal graph database storage and index construction of legal fact triples.

[0118] Specifically, the system continuously writes legal procedural fact triples extracted from the operation sequence into a graph database. Each triple serves as an edge with attributes, connecting entity nodes such as the law enforcement subject and the action object. The graph database not only stores static relationships but, more importantly, stores the temporal and spatial coordinate attributes of each fact. The system establishes an efficient joint index for timestamps and spatial coordinates. When new fact triples are added, the graph database automatically inserts them as new nodes and edges and connects them with the existing graph, for example, associating the action of "taking a photo of a vehicle" with the physical evidence node of "vehicle VIN code". This embodiment dynamically constructs a continuously growing spatiotemporal knowledge graph of this law enforcement event, with a timeline and spatial relationships as its framework.

[0119] S4.3.2: Contextualized activation of formal rules and matching of inference antecedents.

[0120] Furthermore, the system loads rules from a formalized legal logic rule knowledge graph. The inference engine does not blindly apply all rules, but rather employs a contextual activation mechanism. The engine analyzes the latest state of the spatiotemporal knowledge graph in real time, especially the most recently inserted fact types. For example, when a new "search" action node is added to the graph, the engine activates all rules whose premises or conclusions contain "search." These activated rules constitute a current set of rules to be inferred. Then, the engine iterates through each rule in the current set of rules to be inferred, performing pattern matching between the logical predicates in its IF part (antecedent) and existing nodes and edges in the spatiotemporal knowledge graph. It checks whether the facts required by the antecedent are already established in the graph and records the specific fact nodes matched as the rule antecedent matching binding set.

[0121] S4.3.3: Implicit fact derivation and contradiction detection based on logical reasoning chains.

[0122] In a preferred embodiment, for rules where the antecedent is fully satisfied, the inference engine performs the derivation of the THEN part (consequence), generating new, implicit knowledge. For example, the rule "If an alcohol test was conducted (Action_AlcoholTest) and the result exceeded the legal limit (Evidence_Result_Positive), then the illegal fact of 'suspected drunk driving' (Inferred_Violation_DUI) can be deduced." The engine adds such derived implicit fact nodes as inference conclusions to the spatiotemporal knowledge graph, connecting them with dotted lines to indicate the basis for the derivation. Simultaneously, this is a crucial step in contradiction detection: when the derivation conclusion of a rule directly and logically conflicts with existing facts in the graph (or the conclusion of another rule) (e.g., both deducing "right to search" and the fact of "lack of reasonable grounds"), the engine immediately identifies a logical contradiction pair and records the fact nodes of both conflicting parties and the rule that caused the conflict.

[0123] S4.3.4: Generation of Reasoning Path Backtracking and Evidence Chain Completeness / Legality Assessment Report.

[0124] Finally, the engine summarizes all triggered inferences and generates an interpretable verification report. For each important legal conclusion (such as "punishment is established"), the engine backtracks the reasoning path, generating a logical proof chain from the original operational facts to the conclusion, clearly demonstrating the basis for each step of the deduction. Based on the graph structure and reasoning results, the system calculates a series of evaluation indicators: for example, the evidentiary support of key illegal fact nodes (how many independent sources of evidence support it), the completeness of the procedural chain (whether all necessary procedural nodes appear in the correct order), and the number and severity of contradictions and conflicts. Combining the results of all rule checks, the deduced facts, the detected contradictions, and the calculated indicators, the system generates a final evidence chain logic verification and risk assessment report. This report clearly indicates whether the enforcement process is logically consistent, whether the procedure is complete, and whether the evidence is sufficient, and classifies any defects according to their potential for diminished evidentiary value or procedural illegality risk.

[0125] Step S5: Integrate the cognitive behavioral analysis report with the evidence chain logic verification results, conduct a multi-dimensional weighted evaluation, generate a comprehensive training score and personalized feedback report, and adaptively intervene and adjust the dynamic 3D virtual training scene based on the evaluation results, including:

[0126] S5.1: Weighted fusion of multi-dimensional capability models and generation of comprehensive capability profiles.

[0127] Specifically, this embodiment is used to conduct a panoramic and comprehensive evaluation of trainees' performance. The system receives cognitive behavior analysis reports from the cognitive dimension and evidence chain logic verification reports from the legal dimension as core inputs. A multi-dimensional law enforcement capability assessment model is pre-loaded or dynamically loaded. This model includes, but is not limited to, the following core dimensions and their dynamic weights: legal procedure standardization (based on the completeness of the evidence chain), tactical skill proficiency (based on operational efficiency and accuracy), cognitive decision-making effectiveness (based on attention and workload indicators), communication and public relations skills (based on voice emotion, reactions of virtual parties and bystanders), emergency response and risk management capabilities (based on awareness of responding to emergencies and safety protection), and law enforcement ethics and humanistic care (based on choices made under conflicts of law, reason, and emotion). The system calculates sub-scores for each dimension and then generates a comprehensive capability score through a weighted fusion algorithm. At the same time, the scores of each dimension are visualized in the form of radar charts, etc., forming a comprehensive capability profile of the trainee in this training, clearly showing the strengths and weaknesses of their capability structure.

[0128] S5.2: Personalized feedback and adaptive learning path planning based on data mining.

[0129] Furthermore, the value of the assessment lies in guiding improvement. The system conducts in-depth analysis of the comprehensive competency profile, identifying the root causes of low scores, such as "low scores in legal procedure compliance, mainly due to systematic omissions in the notification procedure." Subsequently, the system connects to a structured, tagged knowledge base of teaching resources, which includes micro-courses on hundreds of common law enforcement issues, typical case analyses, detailed explanations of laws and regulations, error demonstration videos, psychological training modules, etc. The system automatically matches and assembles the most relevant learning resources for the learner, generating a personalized diagnostic feedback and learning plan report. The report not only points out the problems but also provides solutions, such as: "You have flaws in the 'search' procedure. It is recommended that you first study the course 'Statutory Procedures for Administrative Inspection and Search,' then focus on analyzing the evidence collection stage of the 'Case of Li's Illegal Vehicle Modification' in the case library, and finally complete the targeted situational test."

[0130] S5.3: Dynamic intervention and adjustment of micro-scenes based on real-time status.

[0131] According to further improvements in this embodiment, the system can intervene in real-time and intelligently during training. This relies on continuous monitoring of synchronous multimodal behavioral data streams and real-time cognitive state indicators. The system has preset a variety of intervention strategies: when the cognitive load index of the trainee is detected to be persistently high, a "stress reduction mode" is triggered, sending instructions to the scene engine to dynamically reduce environmental complexity, such as reducing background traffic, simplifying irrelevant AI characters, or temporarily "freezing" the development of secondary storylines. When the system detects that the trainee is about to repeat a typical programming error that has been marked, a "preventive prompt" is triggered, such as making the correct law enforcement equipment glow slightly in the virtual environment, or transmitting a reminder voice through a virtual radio. When the system detects that the trainee's communication tone is out of control due to high stress, the system will temporarily adjust the AI ​​of the virtual subject to make it slightly more compliant, giving the trainee positive feedback to stabilize emotions. These interventions are subtle and contextualized, aiming to keep the training in the zone of proximal development.

[0132] S5.4: Macro-level training path optimization and "AI coach" decision-making based on long-term archives.

[0133] In a preferred embodiment, the system possesses long-term planning and evolution capabilities. The comprehensive competency profile, detailed process data, and feedback reports for each training session are encrypted and stored in the trainee's long-term personal digital profile. The system utilizes machine learning algorithms to analyze the time-series data in the profile, identifying the trainee's long-term progress trends, learning plateaus, competency transfer patterns, and optimal incentive models. Based on these macro-level analyses, the "AI coach" can make more advanced decisions: for example, automatically recommending higher-level training modules such as "Handling Stability-Related Incidents" or "Media and Public Opinion Response" to trainees who have mastered basic procedures; designing a progressive stress-resistance training package for trainees prone to errors under pressure; and even generating group-based teaching focus suggestions for instructors based on the common weaknesses of the entire training team. In this way, the system achieves a closed loop from single training evaluation to periodic individual development planning and then to group training strategy optimization, becoming a smart brain that enhances the overall effectiveness of law enforcement training.

[0134] According to another aspect of this application, dynamic intervention and adjustment of micro-scenes based on real-time status includes:

[0135] S5.3.1: Real-time diagnosis of multi-dimensional status monitoring and intervention needs.

[0136] Specifically, the triggering of dynamic intervention relies on continuous monitoring and diagnosis of high-frequency synchronous multimodal data streams, real-time cognitive state streams, and law enforcement intent recognition conclusions. The system runs a lightweight state diagnosis engine with several pre-defined non-ideal state modes. For example, the cognitive overload mode is characterized by a cognitive load index that consistently exceeds a threshold and an increasing operational error rate; the emotional outburst mode is characterized by a spike in emotional arousal accompanied by negative vocal emotion; and the strategy rigidity mode is characterized by singular intent recognition and repetitive, ineffective operational sequences. The diagnosis engine matches and calculates the trainee's streaming data against these modes in real time. Once the matching degree exceeds a set threshold, a clear intervention need diagnosis event is generated, which includes the diagnosed problem pattern, severity level, and relevant contextual data.

[0137] S5.3.2: Matching and parameterization command generation for the hierarchical intervention strategy library.

[0138] Furthermore, the system maintains a structured, hierarchical intervention strategy library. Interventions are divided into multiple levels: L1 - cue level (e.g., visual cues, voice reminders), L2 - assist level (e.g., simplifying tasks, providing options), and L3 - takeover level (e.g., forced pause, narration). Each intervention strategy in the strategy library defines its applicable problem pattern, triggering conditions, execution content, and expected goals. Upon receiving an intervention request diagnosis event, the intervention scheduler selects one or more of the most suitable intervention strategies from the strategy library based on the severity of the problem and the training stage. Then, the scheduler injects the specific parameters of the current scenario (e.g., the party's ID, vehicle location) into the strategy template to generate an executable, parameterized, specific intervention instruction. For example, for the specific error of "missing to check the trunk," the generated instruction is "In the virtual view, make the vehicle's trunk area appear as a semi-transparent, bright flashing light for 5 seconds."

[0139] S5.3.3: Lossless coupling adjustment of instruction distribution and scene engine / AI behavior.

[0140] In a preferred embodiment, the generated specific intervention instructions are distributed to different components of the virtual simulation system for execution, with the aim of minimizing disruption to the training immersion in a lossless coupling manner. Visual cue instructions are sent directly to the graphics rendering engine, which renders the cue elements in the overlay. Instructions requiring adjustments to the virtual agent's behavior are sent to the AI ​​behavior engine. For example, when diagnosing an escalation of confrontation due to poor communication, the instruction might request that "the AI ​​agent reduce its confrontational probability by 10% and add a guided dialogue option." The AI ​​behavior engine dynamically inserts or adjusts nodes in its current behavior tree to achieve a smooth transition, rather than an abrupt switch. The execution of all intervention instructions is recorded and correlated back to the original diagnostic event.

[0141] S5.3.4: Real-time evaluation of intervention effects and adaptive optimization of strategies.

[0142] Finally, the system forms an intervention closed loop. Within a preset timeframe after the intervention command is executed, the state diagnosis engine continuously monitors changes in the learner's key indicators and evaluates the intervention's effectiveness. For example, after implementing the "reduce environmental complexity" intervention, it observes whether the learner's cognitive load index has effectively decreased and whether operational accuracy has rebounded. The system records the entire "diagnosis-intervention-response" process of this intervention as a sample in the intervention case library. In the long term, the system utilizes reinforcement learning or statistical analysis to evaluate the effectiveness of different intervention strategies in different contexts and dynamically optimizes the triggering conditions, parameters, or execution content of each strategy in the intervention strategy library. It can even personalize the intervention preferences for specific learners based on their learning characteristics, making the system's AI coach role increasingly intelligent and precise, realizing the evolution of intervention from rule-based to data-driven optimization.

[0143] Compared with existing technologies, the traffic enforcement virtual simulation training method based on dynamic scenarios and cognitive assessment provided by this invention has the following significant advantages:

[0144] Traditional systems only train the hands (operational steps), while this invention, by simultaneously collecting and analyzing multimodal data such as eye movements, speech, and physiological data, achieves, for the first time in virtual training, a deep assessment and training of the trainee's brain (attention, cognitive load, decision-making intention) and mind (emotional stress). This overcomes the bottleneck of traditional methods' inability to quantify internal cognitive states and stress responses, enabling training to move beyond superficial behavioral compliance and delve into the core cognitive abilities that influence law enforcement effectiveness.

[0145] By using an emergent scene generation engine based on generative adversarial networks, this invention can synthesize non-preset, dynamically evolving scenes that include complex illegal activities, unexpected events, and social interactions. This breaks the limitations of fixed scripts, enhances the unpredictability and realism of training scenarios, and effectively trains trainees' dynamic situational awareness, rapid judgment, and flexible handling capabilities in complex and uncertain environments.

[0146] This invention combines formal legal logic verification with multimodal cognitive analysis. It not only automatically reviews the compliance of law enforcement procedures and the logical closure of the evidence chain (legal dimension), but also simultaneously assesses the cognitive effectiveness and decision-making quality of the execution process (cognitive dimension). This fusion assessment can accurately pinpoint the root cause of problems, such as a lack of legal knowledge, weak procedural awareness, or inappropriate allocation of cognitive resources under high pressure, thus providing unprecedented diagnostic depth.

[0147] The system can dynamically adjust the difficulty of the scenario, provide tiered prompts and interventions based on the learner's real-time cognitive state and operational performance, and plan subsequent personalized learning paths. This makes training feel like having a tireless AI coach, which can always maintain the training intensity within the learner's zone of proximal development, achieving precise training tailored to individual needs and improving training efficiency and conversion rates.

[0148] All multimodal data, evaluation results, and intervention cases generated during the training process are recorded by the system, forming a rich training big data dataset. By analyzing this data, the system can continuously optimize the scene generation algorithm, refine the evaluation model, and verify and improve the intervention strategy. This allows the entire system to learn and evolve continuously in practice, like a digital instructor, with its training scientific rigor and effectiveness constantly increasing over time.

[0149] In conclusion, this invention, through technological innovation, constructs a new paradigm for law enforcement training that is more realistic, profound, and intelligent. It has significant application value and promising prospects for comprehensively improving traffic police officers' legal literacy, tactical skills, cognitive resilience, and overall combat capabilities.

[0150] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A virtual simulation training method for traffic law enforcement based on dynamic scenarios and cognitive assessment, characterized in that, include: Initialization parameters are generated based on student profiles and training objectives, and an emergent scene generation engine is driven by the initialization parameters to synthesize dynamic 3D virtual training scenes. During the operation of the dynamic 3D virtual training scene, multimodal interactive behavior data and physiological response data of trainees are collected simultaneously to obtain multimodal trainee behavior data stream; Real-time analysis of multimodal learner behavior data streams is performed to calculate learners’ attention allocation characteristics, cognitive load levels and identify their law enforcement decision-making intentions, and generate cognitive behavior analysis reports. Based on the structured operation sequence parsed from the multimodal learner behavior data stream, a spatiotemporal knowledge graph is constructed, and combined with formal legal logic rules, automatic reasoning and contradiction detection are performed to output the logical verification results of the evidence chain. By integrating cognitive behavior analysis reports with evidence chain logic verification results, a multi-dimensional weighted evaluation is conducted to generate a comprehensive training score and personalized feedback report. Based on the evaluation results, adaptive intervention and adjustment are carried out on the dynamic three-dimensional virtual training scene.

2. The method according to claim 1, characterized in that, Initialization parameters are generated based on student profiles and training objectives, including: Collect trainees' historical training records, and perform data mining analysis on the operation sequences, error patterns, and performance indicators contained therein to generate quantified trainee ability feature vectors. Collect the target configuration data for this training, parse and encode the specified training focus, capability dimension and difficulty coefficient, and generate a structured training target constraint vector; By integrating the student's ability feature vector with the training objective constraint vector and introducing an environmental random factor, a personalized initialization configuration package for driving the scenario is generated.

3. The method according to claim 1, characterized in that, Based on an emergent scene generation engine driven by initialization parameters, dynamic 3D virtual training scenes are synthesized, including: Call a pre-built feature library of illegal cases, which contains a set of feature vectors of illegal behaviors extracted from real cases and a probability matrix describing the relationship between different types of illegal acts; With a personalized initialization configuration package as a condition, a scene generator based on generative adversarial networks is driven. The generator intelligently combines illegal elements according to a probability matrix and generates an initial scene layout and character behavior script on a three-dimensional environment model. The discriminator verifies and iteratively optimizes the authenticity and logical consistency of the initial scene layout and character behavior scripts, ultimately rendering a complete and interactive dynamic 3D virtual training scene instance.

4. The method according to claim 1, characterized in that, Simultaneously collect multimodal interaction behavior data and physiological response data of trainees to obtain a multimodal trainee behavior data stream, including: The system collects students' interactive operation logs in the virtual environment, and after semantic parsing and timestamp marking, it forms structured operation sequence data. The visual fixation point and pupil diameter data of the trainees are collected by an eye tracker. After calibration filtering and three-dimensional spatial mapping, the gaze trajectory data and pupil change data are obtained. Voice recordings of law enforcement processes are collected via microphone, and then processed through speech recognition and sentiment analysis to obtain speech-text data and sentiment analysis data. ECG and skin conductance signals were collected using biosensors, and after denoising and feature extraction, heart rate variability and skin conductance response characteristics were obtained. All the above data are synchronized and aligned with high precision, and then encapsulated to obtain a synchronized multimodal learner behavior data stream.

5. The method according to claim 4, characterized in that, Calculate the learners' attention allocation characteristics, including: By combining semantic importance tags of objects in the virtual scene, gaze trajectory data is extracted from the synchronous multimodal learner behavior data stream; Calculate the weighted dwell time of trainees' gaze in different importance areas, the entropy value of the scanning path, and the visual discovery delay of key evidence targets; Based on gaze trajectory data, weighted dwell time, entropy of scanning path, and visual discovery delay of key evidence targets, an attention allocation feature set that quantifies the efficiency and acuity of attention resource distribution is calculated.

6. The method according to claim 1, characterized in that, Constructing a spatiotemporal knowledge graph and combining it with formalized legal logic rules for automated reasoning, including: From the structured operation sequence, identify and extract legally significant procedural actions to form a legal procedural fact triple containing the subject, type, object, time, and space of the action; By connecting the legal procedural fact triplets according to timeline and spatial relationship, a spatiotemporal knowledge graph representing the entire law enforcement process is dynamically constructed; The legal logic rules concerning procedural legality, relevance of evidence, and sufficiency of proof, which are pre-coded in formal logic, will be matched and automatically reasoned with the spatiotemporal knowledge graph. The output includes the evidence chain integrity score, a list of logical vulnerabilities, and a program risk warning.

7. The method according to claim 1, characterized in that, Integrating cognitive behavioral analysis reports with logical verification results of the evidence chain, a multi-dimensional weighted evaluation is conducted, including: Five assessment dimensions are defined: technology compliance, cognitive effectiveness, procedural legitimacy, communication effectiveness, and ethical decision-making, and weight coefficients are assigned to each dimension. Scores for technical compliance and procedural legitimacy are calculated based on the results of evidence chain logic verification. The cognitive efficacy dimension score is calculated based on the attention allocation characteristics and cognitive load level in the cognitive behavior analysis report. The communication effectiveness score is calculated based on the voice emotion data and feedback from virtual characters in the multimodal learner behavior data stream. Based on the choices made by trainees when faced with preset ethical dilemmas in virtual scenarios, scores for the ethical decision dimension are calculated using an ethical decision tree model. The scores from the five dimensions are weighted and summed to generate a comprehensive training score.

8. The method according to claim 1, characterized in that, Adaptive intervention and adjustment of dynamic 3D virtual training scenarios, including: The cognitive load level in the cognitive behavior analysis report is monitored in real time. If it continues to exceed the personal adaptive threshold, adjustment instructions to reduce the complexity of the scene environment are generated and executed. When the system identifies that a trainee is about to repeat a specific type of programming error, it triggers a preventative prompt intervention, providing guiding visual or auditory prompts in a non-intrusive manner in the virtual scene; Based on the phased trend of the comprehensive training score, the combination complexity of illegal elements and the adversarial nature of the agent in subsequent training modules are dynamically adjusted to achieve adaptive planning of training difficulty.

9. The method according to claim 1, characterized in that, Generate personalized feedback reports, including: By combining specific loopholes in the logical verification results of the evidence chain with abnormal indicators in the cognitive behavior analysis report, we can analyze the weak points in the scores of each dimension of the comprehensive training score. Match learning resources corresponding to weak areas and specific problems from the structured teaching resource database; The matched learning resources are assembled to generate a personalized feedback report that includes problem diagnosis, explanation of principles, improvement suggestions, and recommended learning paths.

10. The method according to claim 8, characterized in that, Adaptive intervention regulation also includes a closed-loop optimization step: After the intervention instructions are implemented, the trainees' subsequent behavioral data and cognitive status indicators are continuously monitored to assess the effectiveness of the intervention measures. Data including problem diagnosis, intervention measures, and effect feedback will be used as samples and recorded in the intervention case library; Based on intervention case data, machine learning is used to optimize the triggering conditions and execution parameters of intervention strategies, thereby achieving adaptive evolution of the intervention strategy library.

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