Illegal behavior intelligent identification and evidence chain generation method for unmanned aerial vehicle license examination

Abstract

The invention provides an illegal behavior intelligent identification and evidence chain generation method for an unmanned aerial vehicle license test, and the method comprises the steps: carrying out the multi-dimensional collection and time alignment of heterogeneous data, such as an examinee operation video, an aircraft track, a remote control instruction, and the like, and generating a structured behavior sequence through attitude estimation and event segmentation; the method comprises the following steps: extracting a normalized rule unit from an examination rule document in combination with natural language processing, constructing a behavior-rule semantic graph, realizing cross-modal semantic alignment through a graph neural network and an attention mechanism, dynamically calculating the matching degree of behaviors and rules, judging violation events, and performing context correction in combination with a state machine model. And finally, outputting a violation judgment result with space-time positioning and an evidence chain. According to the method, the robustness and rule adaptability of behavior recognition are improved, and efficient, explainable and dynamically evolved violation intelligent judgment is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent unmanned aerial vehicle examination invigilation and irregular behavior identification, and particularly relates to an irregular behavior intelligent identification and evidence chain generation method for unmanned aerial vehicle license examination. BACKGROUND

[0002] In the current unmanned aerial vehicle license examination invigilation field, the detection of irregular behaviors of examinees during operation mainly relies on technical solutions based on traditional computer vision, pre-trained deep learning models or hand-written rule engine. In recent years, with the continuous development of artificial intelligence and multi-modal behavior identification technology, the industry has generally adopted action recognition, trajectory anomaly detection and action and rule matching based on video analysis to monitor the operation of examinees in real time, and automatically determine obvious irregular actions such as “illegal take-off” and “entering a no-fly zone”. Some systems further combine remote controller signal analysis and flight trajectory big data analysis to realize the fusion identification of multi-modal information, and improve the accuracy and real-time performance of irregular detection. The above methods rely on pre-set and rigid model training process of examination subjects and scoring rules, which can cover common basic operations and existing rule constraint scenarios, and are suitable for the invigilation and automatic scoring requirements of current standardized examination subjects.

[0003] However, the existing technology has significant technical limitations when facing changing examination rules and new complex irregular behaviors. On the one hand, the behavior identification technology based on traditional model training relies on historical scenarios and data samples, and has weak generalization ability for new types of irregular actions that have not been seen before, such as evasion detection behavior, combined irregular behavior and strategic irregular methods. Once the policy regulations or operation details change, it is often necessary to collect labeled data again, retrain the network model, resulting in long adaptation period and heavy maintenance burden of the system; on the other hand, the judgment logic based on static rule expression cannot capture the complex semantic relationships between behaviors, such as time sequence, causality or mutual exclusion, and it is difficult to analyze the semantic evolution and complex irregular strategies. In addition, when the rules are updated and new subjects are introduced, most existing systems cannot achieve flexible adaptation of the model without retraining, affecting the business response efficiency. SUMMARY

[0004] In order to solve the above-mentioned technical problems, the present application provides an irregular behavior intelligent identification and evidence chain generation method for unmanned aerial vehicle license examination.

[0005] The technical solution of the present application is implemented as follows: an irregular behavior intelligent identification and evidence chain generation method for unmanned aerial vehicle license examination, comprising: S1: Collect multimodal behavioral data in the drone license examination scenario, including candidate operation video stream, aircraft trajectory information and remote controller operation signals, and record the corresponding examination subjects and scoring rule texts as the raw input for behavioral semantic analysis and rule adaptation; S2: Perform attitude estimation processing on the collected video data, extract the motion sequence of the candidate's body key points, and combine the aircraft's GPS trajectory and remote control command log to generate a structured sequence of behavioral events, in which each event includes the attributes of action type, occurrence sequence, spatial location and duration. S3: Based on natural language processing technology, semantic parsing is performed on the examination rule document to identify the action constraints, regional restrictions and temporal condition elements, which are then transformed into standardized rule atomic expressions to build a rule knowledge base with computable semantics; S4: Based on the sequence of behavioral events and the atomic expression of rules, construct a behavioral semantic graph, where nodes represent specific behavioral units such as 'take-off', 'crossing a no-fly zone', or 'holding a remote control with one hand', and edges represent the temporal, causal, or mutually exclusive relationships between behaviors. Generate behavioral semantic embedding vectors through graph neural network encoding. S5: Input the behavior semantic embedding vector into the rule dynamic mapping module. This module performs semantic alignment calculation based on the attention mechanism to determine the matching degree score between the current behavior pattern and each rule atomic expression in the rule knowledge base, forming a rule fit distribution. S6: Determine whether the matching score of any rule atomic expression exceeds the preset threshold. If it does, trigger the corresponding violation judgment logic and generate a preliminary violation warning event with a timestamp and confidence score. S7: Combining the candidate's historical behavior trajectory maintained by the context memory mechanism, the state machine model is used to analyze continuous behavior patterns, identify situations of evading detection or cumulative violations, perform context correction on the initial violation warning events, and output the corrected violation judgment results. S8: Package the final violation judgment result with the corresponding behavioral event fragment, rule atomic expression and semantic matching path to generate a structured evidence chain. It supports online updating of the rule knowledge base and fine-tuning of semantic alignment parameters, so as to realize the dynamic adaptation of the recognition logic without retraining the backbone model.

[0006] The intelligent identification and evidence chain generation method for violations in drone license examinations provided by this invention has the following beneficial effects: (1) This invention constructs a two-layer structured representation system of behavioral semantic graph and rule knowledge base, and combines a semantic alignment algorithm based on attention mechanism to achieve the decoupling design of behavioral understanding and rule judgment. Compared with the existing technology that relies on a large amount of labeled data to retrain the model to adapt to new rules, this invention does not need to modify or retrain the backbone recognition network. It automatically converts the new examination rules into computable atomic expressions through natural language parsing and uses lightweight parameter fine-tuning to complete the dynamic mapping between rules and behavioral semantic space. This significantly improves the ability to respond quickly to new violations and the adaptability to cross-subject migration, and effectively overcomes the shortcomings of traditional methods in terms of long deployment cycle and high maintenance cost when facing rule updates. (2) This invention introduces a context memory mechanism and a state machine-driven sequence reasoning module, which can continuously track the candidate's historical behavior trajectory during the examination process and identify high-order violation patterns that are deceptive or continuous, such as "avoiding no-fly zones" and "segmented violation action splicing". By using a graph neural network to deeply encode the behavioral semantic graph, the causal, temporal, and mutual exclusion relationships between actions are extracted, enabling the system to not only have single-point behavior matching capabilities, but also to mine potential violation intentions from the overall behavior flow, greatly improving the accuracy and robustness of the judgment logic. At the same time, by integrating multi-source judgment results and outputting a transparent decision report with confidence scores and evidence fragment citations, the interpretability and authority of the judgment results are enhanced, meeting the needs of audit traceability and manual review in actual invigilation scenarios. Attached Figure Description

[0007] Figure 1 The flowchart shows the intelligent identification and evidence chain generation method for unmanned aerial vehicle license examinations according to the present invention. Figure 2 This is a sub-flowchart of the intelligent identification and evidence chain generation method for unmanned aerial vehicle (UAV) license examinations according to the present invention. Figure 3 This is another sub-flowchart of the intelligent identification and evidence chain generation method for unmanned aerial vehicle (UAV) license examinations according to the present invention. Detailed Implementation

[0008] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0009] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0010] like Figure 1 As shown, this invention provides a method for intelligent identification and evidence chain generation of violations in drone license examinations, specifically including: S1: Collect multimodal behavioral data in the drone license examination scenario, including candidate operation video stream, aircraft trajectory information and remote controller operation signals, and record the corresponding examination subjects and scoring rule texts as the raw input for behavioral semantic analysis and rule adaptation; S2: Perform attitude estimation processing on the collected video data, extract the motion sequence of the candidate's body key points, and combine the aircraft's GPS trajectory and remote control command log to generate a structured sequence of behavioral events, in which each event includes the attributes of action type, occurrence sequence, spatial location and duration. S3: Based on natural language processing technology, semantic parsing is performed on the examination rule document to identify the action constraints, regional restrictions and temporal condition elements, which are then transformed into standardized rule atomic expressions to build a rule knowledge base with computable semantics; S4: Based on the sequence of behavioral events and the atomic expression of rules, construct a behavioral semantic graph, where nodes represent specific behavioral units such as 'take-off', 'crossing a no-fly zone', or 'holding a remote control with one hand', and edges represent the temporal, causal, or mutually exclusive relationships between behaviors. Generate behavioral semantic embedding vectors through graph neural network encoding. S5: Input the behavior semantic embedding vector into the rule dynamic mapping module. This module performs semantic alignment calculation based on the attention mechanism to determine the matching degree score between the current behavior pattern and each rule atomic expression in the rule knowledge base, forming a rule fit distribution. S6: Determine whether the matching score of any rule atomic expression exceeds the preset threshold. If it does, trigger the corresponding violation judgment logic and generate a preliminary violation warning event with a timestamp and confidence score. S7: Combining the candidate's historical behavior trajectory maintained by the context memory mechanism, the state machine model is used to analyze continuous behavior patterns, identify situations of evading detection or cumulative violations, perform context correction on the initial violation warning events, and output the corrected violation judgment results. S8: Package the final violation judgment result with the corresponding behavioral event fragment, rule atomic expression and semantic matching path to generate a structured evidence chain. It supports online updating of the rule knowledge base and fine-tuning of semantic alignment parameters, so as to realize the dynamic adaptation of the recognition logic without retraining the backbone model.

[0011] Step S1: Collect multimodal behavioral data in the drone license examination scenario, including candidate operation video stream, aircraft trajectory information, and remote controller operation signals, and record the corresponding examination subject and scoring rule text as the raw input for behavioral semantic analysis and rule adaptation. Specifically, this includes: S1.1: Acquire multi-view high-definition video streams from the drone license examination site, perform frame-level alignment processing on multiple video signals based on the timestamp synchronization protocol to eliminate time offset between camera devices, generate spatiotemporally consistent original video datasets, and provide high-precision visual input for subsequent attitude estimation; The input condition is the raw video signal stream output by the multi-view high-definition camera equipment deployed at the drone license examination site. Each camera channel generates a frame sequence through an independent acquisition unit and includes a local acquisition timestamp. The system employs a timestamp synchronization protocol (parameters: the camera channel time source type is NTP, and the time synchronization accuracy requirement is less than 5ms) to achieve unified alignment of the acquisition time of multiple video signals. Furthermore, by using a frame-level synchronization algorithm (parameters: maximum frame shift compensation window is ±2 frames, interpolation method is bicubic interpolation), accurate registration of video frames between camera devices is achieved, and a temporally consistent multi-channel frame sequence is obtained. Furthermore, the external parameter mapping relationship of each video frame is calculated using the camera calibration matrix (parameters: the transformation matrix between the three-dimensional world coordinate system and each camera coordinate system is obtained by Zhang Zhengyou calibration method), to achieve spatial consistency correction and generate a set of multi-view video frames that have been unified to the standard reference coordinate system; Furthermore, a perspective distortion correction algorithm (parameters: the distortion model is radial plus tangential, and the maximum radial distortion coefficient does not exceed 0.01) is adopted to correct the geometric shape of the video frame and generate visual data consistent with the scale of the actual scene. Through the above synchronization and correction processing methods, the video signals output by each camera device are transformed into a raw high-definition video dataset with spatiotemporal consistency, high geometric accuracy, and can be directly loaded by subsequent pose estimation algorithms, thereby achieving the expected technical effect of high-precision visual input. For example, in a drone license examination scenario, four 4K resolution cameras were deployed: one each for forward-looking, left-looking, right-looking, and top-looking views. All cameras captured data at 60fps, and the time source was configured as a GPS-based NTP server. The timestamp synchronization protocol was set to a synchronization accuracy of 1ms, the frame-level synchronization algorithm window was ±1 frame, and bicubic interpolation was used to interpolate missing frames. Camera calibration adopted the Zhang Zhengyou calibration method, obtaining the intrinsic and extrinsic parameter matrices of each camera and completing spatial alignment under a unified reference coordinate system. Distortion correction considered both radial and tangential distortion coefficients, with a maximum radial distortion coefficient of 0.005 and a maximum tangential distortion coefficient of 0.002. After the above processing, the generated video dataset showed that the drone's position deviation did not exceed 2 mm when switching between any two viewpoints, meeting the accuracy requirements of subsequent attitude estimation input and effectively improving the accuracy of the judgment module in recognizing micro-movements and fine-grained spatial changes. S1.2: Obtain the positioning data stream output by the aircraft's onboard GPS module, and perform noise suppression and trajectory smoothing on the latitude and longitude coordinate sequence based on the Kalman filter algorithm to extract continuous and stable three-dimensional flight trajectory data, and generate aircraft motion trajectory segments with timestamps as the basis for spatial constraints of behavioral events; The positioning data stream output by the aircraft's onboard GPS module is acquired, and a serial communication protocol parser (parameters: supports multiple format protocols such as NMEA and RTCM, baud rate 9600-19200bps, preferably 19200bps) is used to parse the raw data message field by field, extract the longitude, latitude and altitude information of the current moment, and form a time-seriesd three-dimensional coordinate vector set as the basic input for trajectory calculation; Furthermore, through a coordinate system transformation algorithm (parameter: WGS) The 84 geographic coordinate system is converted to the ECEF Cartesian coordinate system, which maps latitude, longitude and altitude data into three-dimensional rectangular coordinates to eliminate distance calculation errors caused by spherical geometry and provide calculation consistency for trajectory smoothing. Furthermore, a Kalman filter algorithm (parameters: state transition matrix A is the three-dimensional position and velocity joint state, observation matrix H is the position observation vector, process noise covariance matrix Q is set according to the error characteristics of the GPS module, and observation noise covariance matrix R is estimated according to the on-site noise statistics) is used to perform dynamic noise suppression on the three-dimensional rectangular coordinate sequence, calculate the weighted fusion between the state prediction value and the observation value, and output the denoised continuous position sequence. Furthermore, for the denoised continuous position sequence, a trajectory smoothing interpolation algorithm (parameters: cubic spline interpolation, boundary condition: natural end condition) is used to perform curve fitting on the time series data to generate trajectory curves with higher spatial continuity, so as to eliminate the influence of position jumps on subsequent spatial constraint analysis. Furthermore, a unique temporal identifier is attached to each spatial location sample through a timestamp binding mechanism (parameter: high-precision system time source, synchronization accuracy 1ms), forming a set of aircraft motion trajectory segments with timestamps, providing basic constraints for spatial matching of behavioral events; By using the Kalman filter algorithm and trajectory smoothing interpolation, the original latitude, longitude and altitude data from the previous step are transformed into continuous, stable, and time-series-identified three-dimensional flight trajectory data, thereby achieving high-precision quantitative representation of spatial constraints. For example, in a drone license exam simulation scenario, the location message output by the onboard GPS module is NMEA. The parser is configured with a 0183 format, a baud rate of 19200bps, an update frequency of 10Hz, and extracts raw latitude and longitude coordinates to six decimal places with a height accuracy of 0.1 meters. WGS is used. 84. Transform to the ECEF coordinate system, where the Earth's radius is set to 6,378,137 meters, the flattening is 1 / 298.257223563, and the generated three-dimensional rectangular coordinate sequence contains 10 sampling points per second. The Kalman filter state vector is set to a six-dimensional quantity of position and velocity, and the process noise variance is set to... The observation noise variance is set to After iterative updates, the trajectory jitter amplitude was significantly reduced. The node spacing of the cubic spline interpolation was 0.1 seconds, the average curvature continuity of the fitted curve was significantly improved, and the timestamp synchronization accuracy stabilized at [value missing]. Within milliseconds. The final generated trajectory segment performed excellently in spatial continuity index measurement, and was able to effectively match the examinee's control actions with the actual positional changes of the aircraft in subsequent behavioral event spatial constraint analysis; S1.3: Obtain the instruction log data sent by the remote control operation terminal, and decode the PWM pulse width modulation signal and serial communication message based on the protocol parsing engine to restore the candidate's actual operation sequence and generate an operation event stream containing control variables such as throttle, rudder, and pitch angle to represent the candidate's subjective operation intention. Based on the wireless communication link between the remote controller and the ground receiver, the complete instruction log data stream generated during the examination is obtained. The data stream includes two types of raw control outputs: PWM pulse width modulation signals and serial communication messages. A protocol parsing engine (parameters: supports multiple remote control manufacturer protocols, sampling rate not less than 1kHz) is used to perform carrier demodulation and pulse width measurement processing on the PWM pulse width modulation signal, realize the duty cycle quantization output of each control channel, and generate the corresponding timestamp mark value; Furthermore, the serial communication message parsing module of the protocol parsing engine (parameters: baud rate 19200bps, frame format 8N1) performs frame boundary detection, check bit verification and field decomposition processing on the numerical control commands transmitted from the remote controller to the flight control system to obtain the original set of values ​​for joystick position encoding, mode switching commands and auxiliary function switch status. Furthermore, a signal normalization algorithm (parameter range: each channel value is mapped to an interval) is used to convert the pulse width value and serial command field into a unified standardized control variable to eliminate the scale difference of different remote control hardware outputs and ensure the consistency of subsequent data fusion. Furthermore, by using the motion parsing mapping table (parameters: throttle channel number CH3, rudder channel number CH4, pitch control channel number CH2), the standardized control variables are converted into semantic operation events. Combined with timestamp information, a continuous flow of operation events is generated. Each event includes the control variable name, current amplitude, rate of change, and corresponding execution time. Through the above protocol parsing and variable mapping processing, the original PWM and serial message commands are transformed into a structured operation event flow, realizing a standardized representation of the candidate's subjective operation intention, and providing accurate control data input for the spatiotemporal correlation analysis of subsequent actions and flight status. For example, during the test, the remote controller sends 50 frames of control signals per second, with the PWM signal sampling rate set to 2kHz. The protocol parsing engine detects the throttle channel pulse width, rudder pulse width, and pitch angle control pulse width. The mode switching field value in the serial command message is 2, and the auxiliary switch field value is 1. After pulse width signal normalization, the throttle variable, rudder variable, and pitch angle variable are converted into three operation events: "maintain neutral throttle," "right yaw," and "forward pitch," with an event interval of 20ms. Continuous recording generates an operation event stream containing 600 events, which are then used for subsequent time alignment and behavioral semantic analysis with the flight trajectory video stream. In this scenario, the parsing latency of the control data is significantly reduced, and the data fusion capability is greatly improved. S1.4: Obtain the electronic version of the scoring rules document for the current exam subject, and perform semantic unit segmentation on the natural language text based on regular expression matching and dependency parsing technology to identify key constraints such as action type, no-fly zone range, and time sequence window, and generate a preliminary set of rule elements to provide raw semantic materials for the construction of the rule knowledge base; S1.5: Align the video dataset, aircraft motion trajectory segments, operation event stream, and rule element set based on a unified time reference, and perform time synchronization and spatial normalization processing on multi-source heterogeneous data to generate a structured original input data packet with spatiotemporal correlation, ensuring that each modal data has fusionability and contextual consistency in subsequent semantic parsing.

[0012] Step S2: The collected video data undergoes attitude estimation processing to extract the motion sequence of the examinee's key body points. Combined with the aircraft's GPS trajectory and remote control command logs, a structured sequence of behavioral events is generated. Each event includes attributes such as action type, occurrence sequence, spatial location, and duration. Specifically, this includes: S2.1: Obtain the video stream of the candidate's operation in the drone license examination scenario, and perform frame-by-frame detection of human key points based on the human pose estimation model driven by convolutional neural network. Calculate the two-dimensional coordinate sequence of 17 joint points including head, shoulder, elbow, and wrist, and generate the original pose feature matrix as the basic input for subsequent action semantic parsing. The candidate's operation video stream, which is synchronously processed from the multi-view video data in step S1, is used as the visual input for pose estimation processing. A human pose estimation model driven by a convolutional neural network (parameters: 17 key points, input resolution 1920×1080, network structure is a stacked Hourglass network) is used to realize the detection and localization of human skeleton key points in each frame of image. Furthermore, heatmaps of candidate key points at different scales are extracted through a feature pyramid structure to improve detection accuracy in long-distance views or under partial occlusion conditions, and a two-dimensional coordinate set of 17 joints including head, shoulder, elbow, wrist, hip, knee, and ankle is obtained. Furthermore, a non-maximum suppression algorithm (parameters: window size = 3×3, suppression threshold = 0.1) is used to filter peaks in the key point heatmap, thereby achieving deduplication and precise localization of candidate key points and generating a key point coordinate sequence bound to timestamps; Furthermore, the spatial relationship between adjacent skeletal joints is verified using a human skeleton topological constraint model to eliminate false detection points that do not conform to human structure. At the same time, the key point information of each frame is arranged in time series to construct the original pose feature matrix, where the matrix row index corresponds to the frame number and the column index corresponds to the key point type and its two-dimensional position attribute value. Using the above algorithms and processing methods, the video frame data from the previous step is transformed into a raw pose feature matrix that can be used for action semantic analysis, thereby realizing a structured expression of the motion sequence of the candidate's key body points. For example, in the actual scenario of the drone license examination, the multi-view video acquisition system is configured with three high-definition cameras: one in front, one on the left, and one on the right. The video frame rate is set to 60fps, and the resolution is 1920×1080. The attitude estimation model inputs a single-frame image, scaled to 512×512. Convolutional feature extraction and deconvolutional localization are performed on each frame to generate heatmaps of 17 key points, each heatmap being 64×64 pixels in size. A non-maximum suppression algorithm is used to screen key points. In the detection of the right shoulder key point, the peak value of the heatmap is 0.85, and the intensity of adjacent pixels is all below 0.1, so it is retained as a valid point. During the skeleton topology verification process, the distance between the right elbow and the right wrist is detected to be 120 pixels in the pixel coordinate system, which conforms to the predefined length ratio range of human body structure (80~150 pixels). In the 30-second video segment of this examination sample (a total of 1800 frames), the original attitude feature matrix is ​​1800×34 in size, with 34 columns consisting of the x and y coordinates of the 17 key points. The matrix successfully captured the candidate's hand manipulation and turning observation actions. In the subsequent S2.2 sub-step, after using the matrix for spatiotemporal smoothing, the accuracy of action recognition was significantly improved. S2.2: Perform spatiotemporal smoothing filtering on the original pose feature matrix, use the Kalman filter algorithm to eliminate key point jitter noise caused by occlusion or lighting changes, optimize the continuity of key point trajectory, and output a denoised stable pose motion sequence to improve the robustness of action recognition. S2.3: Based on the denoised stable posture motion sequence, the motion segmentation mechanism based on sliding window is used to divide the motion segments, and the motion classification is performed in combination with a predefined motion dictionary. The temporal dynamic features of each motion segment are modeled using a bidirectional long short-term memory network, and a preliminary motion label sequence labeled with motion types such as 'take-off and landing', 'hand control', and 'turning and observing' is output. S2.4: Integrate the preliminary action tag sequence with the synchronously collected aircraft GPS trajectory information and remote control operation command log, realize the time synchronization of multi-source signals through the timestamp alignment mechanism, determine the coupling relationship between action and device status based on spatiotemporal association rules, and generate a state-enhanced behavior event unit with action type, start and end sequence, three-dimensional spatial coordinates and operation context. The initial action tag sequence output by S2.3, the aircraft GPS trajectory information and remote control operation command log obtained by S1.2 and S1.3 respectively, are compared with the data from S2.3. A multi-source signal synchronization algorithm based on timestamp alignment mechanism (parameters: unified time base UTC millisecond accuracy, maximum allowable deviation ±5ms) is used to achieve time domain consistency matching of cross-modal data. Furthermore, by using a three-dimensional spatial interpolation algorithm (parameters: cubic spline interpolation, sampling interval 0.1s), the GPS trajectory of the aircraft is completed with position vectors within the time window of the action, resulting in continuous three-dimensional spatial coordinate data that strictly corresponds to the action label; Furthermore, an operation signal parsing matching algorithm (parameters: command stream sampling frequency 50Hz, variable threshold setting based on the steady-state mean ±3σ range of each control channel) is adopted to map the control variable sequence of the remote control operation event stream within the action occurrence time window to the corresponding action label, forming an action-control variable alignment matrix; Furthermore, based on a predefined set of spatiotemporal association rules (rule types include state-dependent, duration-constrained, and spatially triggered types), a coupling relationship determination algorithm is executed (parameters: spatial proximity threshold 2m, time overlap threshold 0.5s) to calculate the correlation score between action tags and the spacecraft's spatial state and operation signals. Action state combinations that meet the coupling conditions are then filtered through the association matrix. By constructing state-enhanced events, the action type, start and end time sequence, three-dimensional spatial coordinates and operation context are structurally merged to generate state-enhanced behavioral event units with multimodal semantic support, thereby realizing the fusion representation of action recognition and device status. For example, in the test of drone takeoff, the initial action label sequence includes the 'takeoff' label, with a timestamp starting at 123456.7 seconds and ending at 123460.2 seconds; the number of sampling points of the aircraft's GPS trajectory within this time window is 35, which are completed by cubic spline interpolation to 35×5=175 isochronous sampling points, with spatial coordinates ranging from longitude 113.935 to 113.936, latitude 22.538 to 22.539, and altitude increasing from 0.0 meters to 15.3 meters; the remote controller operation command log shows that the throttle channel continuously increases based on the steady-state average, the pitch channel remains at zero, and the rudder channel fluctuates slightly. The spatiotemporal association rule set determines that the action and the aircraft's ascent motion have a temporal overlap of 0.98 seconds and a spatial proximity of 1.2 meters, with an association score of 0.87, which is higher than the coupling threshold of 0.8. The state-enhanced behavior event unit output by the event construction algorithm contains the following fields: action type = 'takeoff', start time = 123456.7 seconds, end time = 123460.2 seconds, three-dimensional coordinates = (113.935, 22.538, 0.0) to (113.936, 22.539, 15.3), and operation context = {throttle increase, pitch zero position, rudder micro-motion}. This event unit can be directly used for subsequent node construction and rule mapping processing of the behavior semantic graph. S2.5: Standardize and encapsulate the state-enhanced behavioral event unit according to a unified data structure to generate a structured behavioral event sequence. Each behavioral event includes action type, occurrence sequence, spatial location, duration, and associated device status fields, which are used to support the subsequent construction of behavioral semantic graphs and dynamic mapping analysis of rule knowledge base.

[0013] like Figure 2 As shown, step S3 involves: semantically parsing the examination rule document using natural language processing technology to identify action constraints, regional restrictions, and temporal condition elements, transforming them into standardized rule atomic expressions, and constructing a rule knowledge base with computable semantics. Specifically, this includes: S3.1: Obtain the original rule text file corresponding to the drone license examination subjects, and preprocess the text based on the sentence segmentation and word segmentation algorithms in natural language processing technology to generate a standardized sentence unit sequence as the basic input for subsequent semantic element extraction; S3.2: Based on the dependency parsing algorithm, perform syntactic structure parsing on the normalized statement unit sequence, identify the subject-verb-object structure and modification relationship, extract the triplet of action subject, operation action, and target, and form a preliminary action constraint semantic unit; Based on the normalized sentence unit sequence generated by step S3.1, a dependency parsing algorithm (parameters: arc-based dependency parser model, part-of-speech tagging weight factor 0.85) is used to parse the syntactic structure of each sentence unit to obtain the dependency relationship matrix between words. Furthermore, by using the subject-verb-object relation extraction method (parameters: dependency relation type filtering rule set R1, predicate recognition threshold 0.75), the grammatical nodes in the dependency relation matrix that satisfy the predicate center word condition are identified, and the preliminary combination of subject nodes, predicate nodes and object nodes in the sentence is obtained; Furthermore, a modification relation aggregation algorithm (parameters: adjective modification weight α=0.3, adverb modification weight β=0.2) is adopted to aggregate the sets of modifiers attached to the subject node and the object node, and to perform semantic enhancement annotation on the verb adverbs attached to the predicate node to ensure the semantic integrity of the action subject and the object of action; Furthermore, functions are constructed using triples. (Parameters: Triple combination rule set R2, missing component filling strategy is "context inference"), to integrate subject node, predicate node and object node and their modification information into a structured triple in the format of <action subject, operation action, target>, and to fill in the corresponding element with context information when any component is missing; By using dependency parsing and ternary combination processing, the normalized sentence units from the previous step are transformed into preliminary action constraint semantic units, realizing action-level semantic extraction of rule texts and providing a semantic basis for spatial and temporal constraint recognition in S3.3. For example, a standardized sentence unit in the drone license examination rules document is "Candidates must complete the aircraft self-check before takeoff." The dependency parsing algorithm is configured to use a Biaffine-based dependency parsing model with a part-of-speech weight factor set to 0.85. The parsing process outputs a dependency relation matrix. In the matrix, there is a subject-verb relationship between "candidate" and "complete," a verb-object relationship between "complete" and "self-check," and a noun-head relationship between "aircraft" and "self-check." The subject-verb-object extraction method identifies the subject node as "candidate," the verb node as "complete," and the object node as "self-check" under a threshold of 0.75, and adds the modification relation "aircraft" to the object node. The modification relation aggregation algorithm integrates the additional information with an adjective weight of 0.3 and an adverb weight of 0.2, enhancing the semantics of the object to "aircraft self-check." A triplet construction function is used. Under the rule set R2, a triple {"Action Subject":"Examinee","Operation Action":"Completion","Target":"Aircraft Self-Check"} is formed. In this embodiment, missing components do not need to be filled in, and the final output action constraint semantic unit accurately describes the action layer instructions of the rule. The application effect is that the subsequent named entity recognition in S3.3 can directly deduce the temporal condition "before takeoff" and the spatial condition "empty" from this semantic unit, thereby significantly improving the accuracy and processing efficiency of rule semantic parsing. S3.3: Use the named entity recognition model to locate and classify the spatial and temporal expressions in the semantic unit, identify geofence elements such as no-fly zones, height restrictions, and take-off and landing ranges, and time constraints such as time sequence windows and duration, to generate structured semantic units of regional restrictions and time sequence conditions. S3.4: The action constraints, region restrictions and temporal condition semantic units are transformed into rule atomic expressions in a unified format through a rule template matching algorithm. Each rule atomic expression contains a condition item, an operation item and a logical connector. The output is a set of standardized rule units that can be calculated and compared by the system. Based on the action constraint semantic units generated by S3.2 and the region restriction semantic units and temporal condition semantic units generated by S3.3, a rule template matching algorithm is adopted (parameter: the template set is designed according to the examination rule meta-model, including action template, spatial template and temporal template) to realize the structured mapping of multiple types of semantic units to rule atomic expressions; Furthermore, through the action template matching algorithm (parameters: action type label, subject category, operation object identifier), the action constraint semantic unit is converted into an action rule segment containing condition items and operation items. The condition items clarify the operation subject and the preceding state, and the operation items define the specific actions that should be performed or prohibited. Logical connectors are generated to combine with other rule segments according to requirements. Furthermore, through a spatial template matching algorithm (parameters: region category code, geographic coordinate range, height limit value), the region restriction semantic unit is transformed into a standardized spatial condition item. Coordinate normalization processing is used to transform the geofence into a spatial index expression that can be compared by the system, ensuring that it forms a one-to-one mapping relationship with the spatial coordinate field of the behavior event. Furthermore, by using a time template matching algorithm (parameters: start and end of time window, duration, sequence dependency flag), the temporal condition semantic unit is converted into a logical time constraint term. Millisecond-level timestamp precision is used to ensure the temporal alignment consistency of subsequent matching calculations. Logical connectors are combined with actions and spatial condition terms to form a complete atomic rule structure. Furthermore, a rule item combination algorithm is adopted to branch and merge the above action rule segments, spatial condition items and time constraint items according to the logical operation priority, and generate a unified format rule atomic expression composed of condition items (CONDITION), operation items (ACTION) and logical connectors (LOGIC). Each expression satisfies the characteristics of machine parsing and computability, and the output is a standardized rule unit set. By using rule template matching and combination algorithms, the results of the previous step are transformed into a set of rule atomic expressions, realizing the standardized expression of rule semantic elements under a unified calculation format, and providing a structured data foundation for the construction and retrieval of rule knowledge bases; For example, in a drone testing scenario, the input conditions are the action constraint semantic unit "the examinee takes off without performing a self-check", the area restriction semantic unit "the flight altitude does not exceed 50 meters", and the temporal condition semantic unit "the self-check action must be completed within 10 seconds before takeoff". The action template matching algorithm maps the action constraints to the condition item CONDITION={subject: examinee, previous state: no self-check} and the operation item ACTION={action: takeoff, restriction: prohibited}; the spatial template matching algorithm maps the area restriction to the spatial condition item CONDITION={altitude restriction: The time template matching algorithm maps time series conditions to time constraints CONDITION={time window: The rule combination algorithm combines the above condition items and operation items according to the logical template (L1∧L2)∧L3 to generate the rule atomic expression: IF(subject: examinee ∧pre-state: no self-check ∧flight altitude ≤ 50 meters ∧self-check time meets -10 to 0 second window) THEN prohibit takeoff. This expression is stored as a standardized rule unit in the rule knowledge base. The system can directly perform matching calculations and retrieve the behavior patterns that meet this logic from real-time behavior data to realize the immediate judgment and semantic tracing of violations. S3.5: Construct a rule knowledge base based on the standardized rule unit set, use a graph database to store the semantic tags of each rule atomic expression and their relationships, and establish an indexing mechanism to support semantic alignment calculation based on the attention mechanism in the subsequent rule dynamic mapping module, so as to ensure that the rule knowledge base has searchability and scalability.

[0014] like Figure 3 As shown, step S4 involves constructing a behavioral semantic graph based on the behavioral event sequence and the rule atomic expression. Nodes represent specific behavioral units such as 'takeoff,' 'crossing a no-fly zone,' or 'holding a remote control with one hand,' and edges represent temporal, causal, or mutually exclusive relationships between behaviors. A behavioral semantic embedding vector is then generated using a graph neural network encoding. Specifically, this includes: S4.1: Based on the structured behavioral event sequence output by S2 and the rule atomic expression constructed by S3, extract the mappable behavioral semantic units, label each behavioral event with semantic tags, and generate a set of behavioral nodes with semantic labels, where each node corresponds to a specific operation action or state, such as 'take off', 'crossing a no-fly zone' or 'holding the remote controller with one hand', as the topological basis of the behavioral semantic graph; S4.2: Perform temporal alignment processing on each node in the set of behavioral nodes. Based on the occurrence time and duration attributes of behavioral events, identify the order between adjacent behaviors, construct a directed temporal edge set to represent the temporal dependency between behaviors, and generate a preliminary graph structure topology framework. A timeline sorting process is performed on the set of behavior nodes with semantic identifiers. An event timestamp parsing algorithm (parameter: accuracy ±1ms) is used to rearrange the order of each node under a unified time base to ensure the time consistency of subsequent relationship construction. Furthermore, by using a duration calculation algorithm (parameter: based on the start and end time fields of the behavior event), the duration difference between adjacent nodes is obtained, and the time interval data between every two consecutive behavior events is obtained, which is used to determine the sequential dependency relationship; Furthermore, a temporal dependency determination model (parameter: minimum time interval threshold Δt=0.5s) is adopted to realize the logical determination of the order of occurrence between nodes and generate a sequential relationship label matrix, where the matrix element values ​​represent the direct temporal dependency between the starting node and the target node; Furthermore, by using a directed edge set generation algorithm (parameter: edge weight is set to the inverse ratio of duration difference), the sequential relationship label matrix is ​​transformed into a directed edge set, forming a temporal connection structure between nodes, and the edge weights are standardized for subsequent graph encoding calculations; By using the graph structure topology construction method (parameter: node set size N≤500), the directed temporal edge set and the behavior node set from the previous step are merged to generate a preliminary graph structure topology framework, realizing the graphical expression of temporal dependencies between behaviors, and providing a structural foundation for subsequent causal and mutual exclusion relationships. For example, in a drone license exam scenario, the set of behavior nodes includes three nodes: takeoff (timestamp: 1023.500s, duration: 3.000s), crossing a no-fly zone (timestamp: 1030.200s, duration: 5.000s), and holding the remote controller with one hand (timestamp: 1036.500s, duration: 2.000s). Using an event timestamp parsing algorithm, the nodes are rearranged in chronological order, resulting in the sequence: takeoff → crossing a no-fly zone → holding the remote controller with one hand. The duration calculation formula is as follows:

[0015] in, The time interval between the starting behavior node i and the target behavior node j. Let be the start timestamp of the target node j. Let be the end timestamp of the starting node i. Substituting the parameters into the formula, Δt ≈ 3.200s between takeoff and crossing the no-fly zone, and Δt ≈ 1.300s between crossing the no-fly zone and holding the remote controller with one hand, both greater than the set threshold of 0.5s. Therefore, directed edges are established for takeoff → crossing the no-fly zone and crossing the no-fly zone → holding the remote controller with one hand, with edge weights set to 1 / 3.200 and 1 / 1.300 respectively, which are 0.29 and 0.71 after standardization. Finally, a preliminary graph structure topology framework containing three nodes and two directed temporal edges is generated, realizing the structured expression of temporal dependencies between behavioral events and providing the input structure for the encoding stage of the graph neural network. S4.3: Based on the exam rules logic and domain knowledge base, analyze the logical constraint relationship between behavioral units, identify behavioral pairs with causal relationships or mutually exclusive conditions, such as 'taking off without completing self-check' constitutes a causal violation, and 'both hands simultaneously detach from the remote control' is mutually exclusive with 'stable control'. Based on this, add causal edges and mutually exclusive edges to the graph structure to improve the multi-dimensional relationship expression of the behavioral semantic graph. Based on the preliminary temporal topology framework generated in step S4.2 and the rule knowledge base constructed in S3, the rule logic mapping algorithm (parameters: set of rule atomic expressions, domain knowledge graph index) is used to retrieve and extract the potential logical constraint relationships between behavioral nodes. Furthermore, by using a causal relationship identification method (parameters: start and end time of behavioral events, device status field), the system locates behavioral pairs that have sequential dependencies and violate rules, and generates causal relationship edges that conform to semantic constraints. In this process, when it is detected that the completion time of node A is earlier than the start time of node B and there is a "must be preceding" logical entry in the rule knowledge base, a directed causal edge from A to B is automatically established, and the violation attribute is marked. Furthermore, through the mutual exclusion condition determination algorithm (parameters: node state label set, rule mutual exclusion matrix), the identification of combinations of behaviors that occur within the same time window and cannot coexist according to the rules is realized, and mutual exclusion relationship edges are generated; for example, for the two types of state labels of both hands simultaneously removing from the remote control and stable operation, they are determined to be mutually exclusive pairs after matching with the mutual exclusion matrix, and bidirectional mutual exclusion edges are established. Furthermore, a constraint conflict detection algorithm (parameters: established causal edge set and mutually exclusive edge set) is adopted to perform conflict analysis on the intersection region of causal and mutually exclusive relationships, and to mark the overlapping relationship of causal violation and mutually exclusive violation for the multi-relation encoding of the graph neural network. The algorithm adjusts its conflict weight parameter to a weighted value when a certain behavior node has both a preceding causal violation edge and a parallel mutually exclusive edge, so as to improve the discriminability of subsequent embeddings. By supplementing the logical relationships, the results of the previous step are transformed into multi-dimensional semantic relationship edge set data, realizing a complete topological expression of the behavioral semantic graph in three dimensions: temporal dependence, causal constraints, and mutual exclusion conditions. For example, for the structured behavioral event sequence of the "Complex Takeoff and Landing and Area Crossing" subject in the drone license examination, the rule knowledge base defines the pre-causal rules between "self-check" and "takeoff," as well as the mutual exclusion rules between "two-handed control" and "free observation." During implementation, the behavioral event sequence records the start and end times of node A (self-check) as 10-15 seconds, and the start time of node B (takeoff) as 14 seconds. A causal relationship identification method is used to match the pre-rule entries in the rule base, and a causal violation edge from A to B is established. Simultaneously, at the 20-second timestamp, concurrent occurrences of node C (two-handed control) and node D (free observation) are detected; mutual exclusion condition judgment matches the mutual exclusion matrix entries, establishing... The mutually exclusive edges. For node B, its associated equipment status has an abnormal throttle signal at takeoff, which conflicts with the equipment constraint rules; the constraint conflict detection algorithm superimposes the causal violation and mutual exclusion violation weights of B to generate a conflict weight value. This serves as the edge weight input for subsequent graph neural network embedding. The output in this embodiment includes a causal edge set {A→B} and a mutually exclusive edge set {...}. The application of the multi-relation topology graph with conflict-weighted edges {B} and the graph structure with significantly rich relational dimensions shows that the subsequent semantic encoding has a greatly improved ability to identify illegal patterns. S4.4: Input the behavioral semantic graph into the graph neural network model, use the graph convolution algorithm based on the attention mechanism to perform neighborhood aggregation calculation on the nodes, perform multi-layer nonlinear transformation on the initial feature vector of each behavioral node, fuse the semantic information of its first-order and high-order neighboring nodes, and generate a high-dimensional behavioral semantic embedding vector with context awareness. S4.5: Perform normalization and dimensionality reduction optimization on the high-dimensional behavioral semantic embedding vector, use principal component analysis algorithm to extract the most discriminative low-dimensional semantic feature subspace, and output a compact behavioral semantic embedding representation as the input vector for semantic alignment calculation in the rule dynamic mapping module in S5, so as to ensure the efficiency and computability of semantic representation.

[0016] Step S5: The behavioral semantic embedding vector is input to the rule dynamic mapping module. This module performs semantic alignment calculation based on an attention mechanism to determine the matching degree score between the current behavioral pattern and each rule atomic expression in the rule knowledge base, forming a rule fit distribution. Specifically, this includes: S5.1: Obtain the behavioral semantic embedding vector generated by the graph neural network. This vector is the high-dimensional semantic representation result output by step S4, containing deep semantic features of behavioral units such as 'take-off and landing actions' and 'crossing no-fly zones'. Construct a semantic alignment calculation framework based on a multi-head attention mechanism. Use the behavioral semantic embedding vector as the input of the query and the semantic vector after encoding each rule atomic expression in the rule knowledge base as the key and value. Perform attention weight calculation across the modal semantic space to capture the fine-grained semantic correspondence between behavioral patterns and potential violation rules. The high-dimensional behavioral semantic embedding vector generated by the graph neural network is input to the rule dynamic mapping module as the query vector input in the semantic alignment calculation framework to realize cross-modal semantic retrieval function; A semantic alignment computation architecture is constructed using a multi-head attention mechanism (parameters: number of attention heads h≥8, query vector dimension dq and rule vector dimension dk are aligned). The semantic vector of the encoded atomic expression of the rule in the rule knowledge base is used as the key and value. Multi-head parallel attention weight calculation is performed to capture the fine-grained correspondence between behavior patterns and potential violation rules. Furthermore, the scaling dot product attention algorithm (parameter: scaling factor) is used. The cross product of the query vector and the key vector is calculated to obtain the initial relevance score matrix, thereby realizing the similarity measurement across modal spaces. Furthermore, the aforementioned correlation matrix is ​​input into the masking mechanism processing module, and negative infinity masking values ​​are applied to the positions of inapplicable or missing rule semantic vectors. The masking operation is achieved through matrix addition, thereby suppressing the influence of invalid rules in the subsequent softmax normalization. Furthermore, by weighted summation, the weight coefficients of each attention head are multiplied by the corresponding value vector to form a set of multi-path context matching vectors, which are then concatenated along the attention head dimension to generate a multi-head fused semantic matching context representation. Through the multi-head attention processing described above, the behavioral semantic embedding vector from the previous step is transformed into a cross-modal rule context matching representation, achieving high-precision alignment between behavioral patterns and rule semantics; For example, in the drone license examination scenario, the input behavioral semantic embedding vector has a dimension of 64, the rule knowledge base contains 120 rule atomic expressions, the encoded semantic vector also has a dimension of 64, and the attention mechanism is set to have 8 attention heads, each with a dimension of 8, and a scaling factor of [missing value]. In the relevance calculation stage, matrix multiplication is used to multiply the 64-dimensional query vector and the 64-dimensional key vector. The result is then divided by a scaling factor to obtain 120 relevance scores. After masking irrelevant rules, softmax normalization is used to obtain the weight distribution along the rule dimension. Subsequently, the weights are element-wise multiplied with the value vectors and summed to form eight 8-dimensional context matching vectors, which are then concatenated into a 64-dimensional fusion representation. In actual operation, this configuration, in the newly added "nighttime flight altitude restriction" rule test scenario, can accurately activate the matching path corresponding to the rule without retraining the backbone model. The output fusion representation, after subsequent matching measurement stages, significantly improves the recognition accuracy and adaptability for complex violations. S5.2: The attention weight matrix calculated in S5.1 is normalized, and the softmax function is used to generate a normalized semantic alignment distribution along the rule dimension. Each element represents the local correlation strength between the current behavior semantic embedding vector and a certain rule atomic expression in the semantic space. Based on this distribution, the semantic vectors of the rule atomic expressions are weighted and aggregated to generate a context-aware rule alignment embedding vector, which serves as the basic representation for the matching metric calculation in the next stage. S5.3: Based on the cosine similarity calculation method, the similarity between the rule-aligned embedding vector generated in S5.2 and the original semantic vector of each rule atomic expression is measured to obtain a set of scalar semantic matching scores. This score is a quantitative indicator of the global consistency between behavioral semantics and rule semantics, which is used to reflect the semantic fit between the current behavioral event sequence and each rule atomic expression. S5.4: Perform a threshold comparison operation on the semantic matching score set output by S5.3, filter out the atomic expressions of rules that are higher than the preset semantic matching threshold, and form a preliminary matching rule candidate set; at the same time, retain the matching scores of all rules to form a complete rule fit distribution vector, which serves as the direct input basis for triggering the subsequent violation judgment logic, and supports priority sorting and conflict resolution in scenarios with concurrent activation of multiple rules. Based on the semantic matching score set output in step S5.3, a threshold comparison algorithm (parameter: preset semantic matching threshold τ) is used to determine the matching degree of each rule atomic expression. Furthermore, a Boolean filtering mechanism (parameter: matching degree ≥ τ) is used to generate a candidate rule set and obtain preliminary candidate set data containing atomic expressions of rules with high matching degree. Furthermore, by utilizing the index-preserving algorithm (parameter: bidirectional mapping between rule identifier and matching score), the matching score of all rule atomic expressions is fully preserved, and a rule fit distribution vector is generated to maintain the global accessibility of the judgment results. Furthermore, a priority queue is constructed within the candidate set using a priority sorting algorithm (parameters: matching score weight w, rule importance coefficient c), and a sorted rule candidate output queue is generated. Furthermore, a conflict resolution algorithm is employed (parameter: regular mutual exclusion matrix). This enables conflict path suppression in scenarios with concurrent activation of multiple rules, and yields a conflict-optimized rule candidate set structure. Through the above chain processing, the matching score set of the previous step is transformed into a rule fit distribution vector after high confidence screening, priority optimization and conflict resolution, thereby achieving a dual improvement in the accuracy and relevance of the rule judgment input data. For example, in a drone license examination scenario, let the preset semantic matching threshold τ be... The matching score set contains 8 rule atomic expressions, with corresponding scores as follows: , , , , , , , Use a Boolean filtering mechanism to filter scores ≥ According to the rules, the candidate set is obtained. Construct a complete fit distribution vector using the index-preserving algorithm. The rules are stored in a bidirectional mapping structure between rule identifiers and scores. In the priority ranking algorithm, the weight w is set as the matching score, and the importance coefficient c is set to values ​​{1.2, 1.1, 1.0, 1.3, 1.0} based on the severity level of the violation represented by the rule, to calculate the priority. The sorted queue is obtained. The conflict resolution algorithm uses a mutual exclusion matrix. R4 and R6 are mutually exclusive, and R8 and R2 are mutually exclusive. After conflict suppression, the final candidate set is output. Its fit distribution vector is This vector will then enter the subsequent differentiable decision gating module to enhance noise resistance and semantic focus characteristics; S5.5: Input the rule fit distribution vector generated in S5.4 into the differentiable decision gating module, suppress low confidence matching paths based on the gating mechanism, enhance the output weight of high matching rules, and optimize the final rule activation distribution; output the calibrated rule fit distribution as a precondition for triggering the violation judgment logic in step S6, ensuring that the judgment process has the ability to resist noise interference and semantic focus characteristics; Obtain the rule fit distribution vector output by step S5.4 as the input signal of the differentiable decision gating module. This vector contains the semantic matching degree value of each rule atomic expression and its relative weight information in the global distribution. A differentiable soft gate gating mechanism (parameters: Sigmoid gate function type, temperature coefficient τ=0.3) is employed to compress and expand the confidence response curve of the input fit distribution vector. During this process, each fit value v is processed by... The gating output is calculated, where Sensitive thresholds for dynamically adjusted text rules; Furthermore, a matching degree gradient weighting algorithm (parameters: α=0.6, β=0.4) is used to enhance the weights of high-matching-degree rules. The weighted result is calculated by combining the g of the gated output vector g with the matching degree vector v. This yields a new weight distribution; Furthermore, the weight vector w is mapped to the rule activation probability space by a normalized redistribution algorithm (parameter: L1 norm is selected for normalization), where the probability vector p satisfies the condition that the sum is 1 and reflects the proportion of rule activation intensity. Furthermore, the probability vector p is smoothed by a noise suppression filter (method: mean filtering based on a sliding window, window length = 3) to eliminate activation anomalies caused by low confidence fluctuations and obtain a structurally stable calibration activation distribution; By using the above-mentioned gating mechanism and weight enhancement, normalization and noise suppression processing methods, the rule fit distribution of the previous step is transformed into an optimized rule activation distribution, so as to achieve the expected technical effects of high confidence, low noise and semantic focus in the input signal of the violation judgment logic. For example, in a drone license examination scenario, the input rule fit distribution vector is [0.15, 0.82, 0.67, 0.34]. The differentiable decision gating module uses the Sigmoid function and sets the temperature coefficient τ = 0.3, with a threshold value of [missing value]. The threshold is set to 0.5. When calculating the gating output, the gating value is obtained for the second rule (match degree 0.82). The probability vector is approximately 0.78. Gradient weighting calculation yields a weight value w = 0.6 × 0.78 + 0.4 × 0.82 = 0.796. L1 normalization is applied to all rule weights, resulting in a probability vector p ≈ [0.073, 0.324, 0.27, 0.133]. Finally, a moving average filter of length 3 is applied to smooth the probability vector, avoiding false triggers caused by instantaneous fluctuations in matching degree. In this embodiment, the activation probability of the second rule is significantly improved and its stability enhanced. Subsequent violation detection modules, upon receiving this distribution, can focus on highly relevant rules for logical triggering, reducing the false alarm rate and improving the robustness of identification.

[0017] Step S6: Determine whether the matching score of any rule atomic expression exceeds a preset threshold. If it does, trigger the corresponding violation judgment logic and generate a preliminary violation warning event with a timestamp and confidence score. Specifically, this includes: S6.1: Obtain the rule fit distribution generated by the rule dynamic mapping module based on the attention mechanism, which includes the semantic alignment matching score between the current behavior semantic embedding vector and each rule atomic expression in the rule knowledge base, as the input basis for violation judgment; S6.2: Based on a preset dynamic judgment threshold, the matching scores of each item in the rule fit distribution are traversed and compared to identify the set of rule atomic expressions with matching scores greater than or equal to the threshold, so as to filter out potentially applicable violation judgment rules. S6.3: For each rule atomic expression that meets the threshold condition, call the violation judgment logic script bound to it, perform condition verification and behavior pattern consistency analysis, generate the corresponding preliminary violation type label, and calculate the initial confidence score; S6.4: The initial violation type label, the triggered rule atomic expression identifier, the matching degree score and the timestamp information of the corresponding behavior semantic embedding vector are encapsulated in a structured manner to generate an initial violation warning event with temporal location and semantic source tracing attributes; S6.5: Output all generated preliminary violation warning events to the context memory mechanism module, so that the state machine model can combine historical behavior trajectories to identify continuous violation patterns and correct false alarms, ensuring that subsequent judgment results have contextual coherence and logical consistency. Based on the structured and encapsulated preliminary violation warning event set as the input object, the event tag and timestamp index matching method (parameters: time resolution Δt=0.02s, event index length L=128) is used to achieve unique identification and order preservation of the event sequence, which is used to ensure the index consistency of subsequent context references; Furthermore, through a multimodal event metadata mapping algorithm (parameters: including action type encoding, spatial coordinate encoding, and rule atom identifier encoding), the attribute association of the same event under different data modalities is realized, and a unified event mapping table is generated to provide a directly indexable multimodal consistency interface for data writing for the context memory mechanism; Furthermore, by using a circular buffer write control method (parameters: buffer capacity N=1024, overwrite update strategy is FIFO), the initial violation warning events are enqueued in the context memory module in real time and old events are automatically dequeued, resulting in an event cache queue with finite length but full fidelity. Furthermore, an event semantic compression coding algorithm (parameters: based on semantic embedding dimensionality reduction dimension d=32, using principal component analysis PCA method) is adopted to achieve dimensionality reduction processing of high-dimensional semantic embedding vectors in events, and the compressed low-dimensional representation is bound to the original event metadata, reducing the space occupied by context memory while maintaining the integrity of semantic features; By using timestamp synchronization and state frame index mapping methods, the event sequence in the circular buffer is transformed into a context event matrix with strict temporal consistency, so as to achieve the expected technical effect that the input data sent to the state machine model satisfies the temporal continuity and semantic relevance. For example, in the scenario of Drone License Examination Subject 3, the preliminary violation warning event includes the type label "No-fly zone crossing", rule atom identifier R_15, matching score 0.892, action type code 05, three-dimensional spatial coordinates (12.5, -8.3, 102.7), and timestamp t=153.24s. The ring buffer capacity is set to 1024 event units, the time resolution Δt is set to 0.02s, and the buffer coverage strategy is FIFO. After the event is established by the multimodal mapping table and synchronized with the video frame ID and remote controller command log ID, it is written to the context memory mechanism buffer index position 415. The original dimension of the action semantic embedding is 256, which is reduced to 32 dimensions by PCA, and the cumulative contribution rate of the main components of the semantic features is significantly improved. The following event "One-handed remote controller holding", under the condition that the position coordinates and timestamp meet the spatiotemporal proximity condition, together with the aforementioned "No-fly zone crossing" event, constitutes the context input matrix for state machine continuity analysis. When the output data matrix is ​​transmitted to the state machine model module, it maintains the consistency of time sequence and multimodal attributes, effectively enhancing the feasibility of persistent violation pattern recognition.

[0018] Step S7: Combining the candidate's historical behavioral trajectory maintained by the contextual memory mechanism, using a state machine model to analyze continuous behavioral patterns, identifying evasion of detection or cumulative violations, performing contextual correction on preliminary violation warning events, and outputting the corrected violation judgment result. Specifically, this includes: S7.1: Based on the context memory mechanism, obtain the candidate's historical behavior trajectory sequence in the current examination stage. The historical behavior trajectory is composed of the previously generated behavior event sequence, which is continuously written into the circular buffer after time alignment and state encoding. The sliding time window is used to extract the most recent N behavior events as context input to retain the behavior context information with limited length but complete semantics. S7.2: Perform state abstraction transformation on the captured historical behavior trajectory. Based on the predefined state mapping rule set, each behavior event is transformed into a corresponding state code, where the state code represents discrete state categories such as 'normal operation', 'suspected occlusion', and 'instruction interruption', generating a state sequence that can be processed by the state machine, providing formal input for subsequent state transition analysis; S7.3: Input the state sequence into a pre-built finite state machine model. The state machine defines legal state transition paths according to the behavioral logic constraints in the examination rules and sets abnormal transition edges to identify potential evasion behaviors. By advancing the state machine frame by frame to execute state transitions, detect whether there are illegal paths or loop evasion patterns that violate the preset transition rules. S7.4: When the state machine detects an illegal transition or entry into a preset cumulative violation state node, it generates a context violation marker and calculates the time alignment and semantic relevance score between the marker and the initial violation warning event. If the two meet the conditions of spatiotemporal proximity and behavioral consistency, the initial warning is determined to be part of a persistent or strategic violation. Based on the abnormal state transition data detected in S7.3 using the finite state machine model, a context violation labeling generation algorithm (parameters: illegal transition edge set, cumulative violation state node set) is adopted to identify potential persistent or strategic violation patterns. Furthermore, by using a time alignment calculation method (parameters: initial violation warning event timestamp, context violation marker timestamp, maximum allowable time deviation Δt), the relative positions of the two types of events on the time coordinate system are matched, and a time proximity score is obtained; where the time proximity score is defined as:

[0019] in, This is the timestamp for the initial violation warning event. Add a timestamp to the context violation. This is the time deviation threshold; Furthermore, a semantic relevance calculation method (parameters: initial violation event semantic embedding vector E1, context violation marker semantic embedding vector E2) is used to measure the similarity between the two in a high-dimensional semantic space and obtain a relevance score; the relevance score is calculated using the cosine similarity formula:

[0020] In this context, both the vector inner product and the modulus are calculated in the unified and normalized semantic space; Furthermore, a dual-condition judgment mechanism (parameters: temporal proximity score threshold θt, semantic relevance score threshold θs) is used to classify and judge preliminary violation warning events; if and only if the temporal proximity score is greater than θt and the semantic relevance score is greater than θs, the preliminary warning event is judged to be a component of persistent or strategic violation behavior. The violation classification results output by the condition judgment are logically bound to the context violation mark of the previous step and the preliminary violation warning event, and transformed into violation pattern data confirmed by the context, so as to achieve the expected technical effect of accurate classification of violation type and subsequent confidence optimization. For example, during a drone license exam, a finite state machine detected an illegal transition path in the candidate's state sequence, from "normal control" to "command interruption" and then to "takeoff." The corresponding context violation timestamp was 125.6s, the initial violation warning event timestamp was 126.0s, and the maximum allowable time deviation Δt was configured to 2.0s. Using the time alignment formula, the time proximity score was calculated as follows: =0.833, satisfying the criterion θt=0.8. The cosine similarity between the event semantic embedding vector and the context violation marker vector in the 512-dimensional semantic space is calculated as follows: =0.92, satisfying the judgment condition of θs=0.85. Since both conditions are met, the system classifies the initial violation warning event as part of a strategic violation, triggering subsequent confidence enhancement processes and violation pattern archiving. In this embodiment, by introducing a dual judgment mechanism of temporal proximity and semantic relevance, the accuracy and robustness of violation event classification are significantly improved, and the contextual consistency of violation pattern recognition is ensured. S7.5: Based on the contextual violation label, the confidence of the initial violation warning event is reassessed and the classification is corrected. If the original warning lacks contextual support, its confidence is lowered and it is marked as an isolated event. If state machine evidence is obtained, its confidence is increased and it is classified as a cumulative or avoidance violation. The final violation judgment result after context correction is output. Based on the context violation label input to the confidence reassessment module, a weighted fusion algorithm (parameters: context relevance score, time alignment, initial confidence) is used to correct the confidence value of the initial violation warning event. Furthermore, a Bayesian update method (parameters: prior confidence, likelihood function based on state machine evidence strength) is used to perform posterior correction of the original confidence based on contextual evidence, and a set of confidences enhanced by context is obtained. Furthermore, a classification mapping algorithm (parameters: state machine violation type label, context violation label category) is adopted to reclassify the violation types of warning events and generate standard category labels such as "isolated event", "cumulative violation" and "avoidance violation"; Furthermore, the re-evaluated confidence set is bound to the reclassification label execution logic. Through a rule-driven label update mechanism, consistency between confidence and violation categories is maintained, and a final violation judgment entry with consistency constraints is generated. By encapsulating the output, the final violation judgment item from the previous step is transformed into real-time judgment data with context correction attributes, thereby achieving high reliability and business interpretability of the judgment result in the evidence chain generation stage. For example, in a scenario of a drone license test (Part 3), the initial confidence level of the preliminary violation warning event is: The context relevance score is Time alignment is The weighted fusion algorithm sets the weight parameters as follows: The formula for calculating the fusion confidence is: ,in As the initial confidence level, To score context relevance, For time alignment. The fusion confidence score was calculated. for Subsequently, a Bayesian update method was used, with the fusion confidence level set as the prior. = Based on the likelihood value of the evidence strength of the state machine = The formula for calculating posterior confidence is: ,in Let be the likelihood value. = and = Substituting into the formula, the posterior confidence level is calculated as follows: According to the state machine violation label mapping rules, the event is revised from the "initial suspected violation" category to "cumulative violation", and the confidence level is output in the final violation judgment entry. With the revised classification labels, the detection results are significantly enhanced and the stability is improved.

[0021] Step S8: Package the final violation determination result with the corresponding behavioral event fragment, rule atomic expression, and semantic matching path to generate a structured evidence chain. This supports online updates to the rule knowledge base and fine-tuning of semantic alignment parameters, enabling dynamic adaptation of the recognition logic without retraining the backbone model. Specifically, this includes: S8.1: Obtain the final violation judgment result output from the context correction module, the behavior event fragment generated by the behavior semantic graph, the rule atomic expression in the rule knowledge base, and the semantic matching path data output by the rule dynamic mapping module as input data sources for the construction of the structured evidence chain, so as to ensure that the evidence elements are complete and traceable; S8.2: The input data source is structurally encapsulated based on the evidence chain template definition language, and the violation judgment result is semantically annotated using JSON-LD format to generate a standardized judgment record containing attributes such as 'judgment subject', 'violation behavior type', 'occurrence timestamp', 'spatial location', and 'confidence score', which serves as the core metadata unit of the evidence chain; S8.3: Perform video keyframe extraction and compression encoding on behavioral event segments, use the H.265 encoding algorithm to generate lightweight multimedia evidence packages, and hash-bind them with the corresponding semantic embedding vectors to form a 'behavior-feature' consistency verification mechanism to prevent evidence tampering and ensure the integrity of the original data; S8.4: The atomic expression of the rule is associated with the attention weight distribution triggered in the semantic matching path and stored together to generate a rule applicability proof subchain, which includes a matching score curve, a key alignment node sequence and a semantic similarity heatmap, for visualizing the logical fit between the rule and the behavior. S8.5: Calculate the digital fingerprint of the entire evidence chain and write it into the blockchain evidence storage system, while opening the API interface for regulatory authorities to call and verify; when a new examination rule text is received, start the incremental update process of the rule knowledge base, and perform gradient fine-tuning of the attention parameters of the semantic alignment module based on a small number of labeled samples, so as to realize the online evolution and closed-loop optimization of the recognition logic without reconstructing the backbone model.

[0022] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0023] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and rules of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent identification and evidence chain generation of violations in drone license examinations, characterized in that, Includes the following steps: S1: Collect multimodal behavioral data in the drone license examination scenario, including candidate operation video stream, aircraft trajectory information and remote controller operation signals, and record the corresponding examination subjects and scoring rule texts as the raw input for behavioral semantic analysis and rule adaptation; S2: Perform attitude estimation processing on the candidate's operation video stream, extract the motion sequence of the candidate's key body points, and combine the aircraft's GPS trajectory and remote control command log to generate a structured sequence of behavioral events; S3: Based on natural language processing technology, semantic parsing of examination rule documents is performed to identify action constraints, regional restrictions and temporal condition elements, which are then transformed into standardized rule atomic expressions to build a rule knowledge base; S4: Construct a behavioral semantic graph based on the behavioral event sequence and the rule atomic expression, and generate behavioral semantic embedding vectors through graph neural network encoding; S5: Input the behavior semantic embedding vector into the rule dynamic mapping module. The rule dynamic mapping module performs semantic alignment calculation based on the attention mechanism to determine the matching degree score between the current behavior pattern and each rule atomic expression in the rule knowledge base, forming a rule fit distribution. S6: Determine whether the matching score of any rule atomic expression exceeds the preset threshold. If it does, trigger the corresponding violation judgment logic and generate a preliminary violation warning event.

2. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 1, characterized in that, Following step S6, the following is also included: S7: Combining the candidate's historical behavior trajectory maintained by the context memory mechanism, using the state machine model to analyze continuous behavior patterns, performing context correction on the preliminary violation warning event, and outputting the corrected violation judgment result; S8: Package the violation determination result with the corresponding behavioral event fragment, rule atomic expression and semantic matching path to generate a structured evidence chain.

3. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 1, characterized in that, Step S1 specifically includes: The system acquires multi-view high-definition video streams from the drone license examination site, performs frame-level alignment processing on multiple video signals based on a timestamp synchronization protocol, and generates a spatiotemporally consistent original video dataset. The positioning data stream output by the onboard GPS module of the aircraft is acquired. Based on the Kalman filter algorithm, noise suppression and trajectory smoothing are performed on the latitude and longitude coordinate sequence. Continuous and stable three-dimensional flight trajectory data is extracted to generate aircraft motion trajectory segments with timestamps. The system acquires command log data sent by the remote control terminal, decodes the PWM pulse width modulation signal and serial communication message based on the protocol parsing engine, and generates an operation event stream. Obtain the electronic version of the scoring rules document for the current exam subject, and perform semantic unit segmentation on the natural language text based on regular expression matching and dependency parsing techniques to generate a preliminary set of rule elements; Based on a unified time reference, the original video dataset, the aircraft motion trajectory segments, the operation event stream, and the rule element set are aligned, and time synchronization and spatial normalization processing of multi-source heterogeneous data are performed to generate structured original input data packets.

4. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 3, characterized in that, Step S1 further includes acquiring the original video signal stream through multi-view high-definition camera equipment deployed at the drone license examination site, with each camera channel generating a frame sequence through an independent acquisition unit and attaching a local acquisition timestamp.

5. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 1, characterized in that, Step S2 specifically includes: The system obtains video streams of candidates' operations in the drone license examination scenario, performs frame-by-frame detection of key points of the human body based on a human pose estimation model driven by a convolutional neural network, calculates the two-dimensional coordinate sequence of the joints, and generates the original pose feature matrix. The original attitude feature matrix is ​​subjected to spatiotemporal smoothing filtering to output a denoised stable attitude motion sequence; Based on the denoised stable posture motion sequence, a sliding window-based action segmentation mechanism is used to divide the action segments, and a predefined action dictionary is used to perform action classification. A bidirectional long short-term memory network is used to model the temporal dynamic features of each action segment, and a preliminary action label sequence is output. By integrating the preliminary action tag sequence with the synchronously collected aircraft GPS trajectory information and remote control operation command logs, time synchronization of multi-source signals is achieved through a timestamp alignment mechanism. Based on spatiotemporal association rules, the coupling relationship between actions and device states is determined, and state-enhanced behavioral event units are generated. The state-enhanced behavioral event units are standardized and encapsulated according to a unified data structure to generate a structured sequence of behavioral events.

6. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 1, characterized in that, Step S3 specifically includes: Obtain the original rule text file corresponding to the drone license examination subjects, preprocess the text based on sentence segmentation and word segmentation algorithms in natural language processing technology, and generate a standardized sentence unit sequence; The normalized sentence unit sequence is parsed using a dependency parsing algorithm to identify the subject-verb-object structure and modification relations, and to extract the triplet of action subject, operation action, and target object to form a preliminary action constraint semantic unit. The named entity recognition model is used to locate and classify the spatial and temporal expressions in the action constraint semantic unit, identify geofence elements and temporal constraints, and generate structured regional restriction semantic units and temporal condition semantic units. The action constraint semantic unit, the region restriction semantic unit, and the temporal condition semantic unit are transformed into a unified format rule atomic expression through a rule template matching algorithm, and a standardized rule unit set is output. A rule knowledge base is constructed based on the standardized rule unit set. A graph database is used to store the semantic tags of each rule atomic expression and their relationships, and an indexing mechanism is established.

7. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 6, characterized in that, Each rule atomic expression in the rule atomic expression contains a condition item, an operation item, and a logical connector.

8. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 1, characterized in that, Step S4 specifically includes: Based on the output structured behavioral event sequence and the constructed rule atomic expression, the mappable behavioral semantic units are extracted, each behavioral event is semantically labeled, and a set of behavioral nodes with semantic identifiers is generated. Perform temporal alignment processing on each node in the set of behavioral nodes. Based on the occurrence time and duration attributes of behavioral events, identify the order between adjacent behaviors, construct a directed temporal edge set, and generate a preliminary graph structure topology framework. Based on the exam rules logic and domain knowledge base, the logical constraint relationship between behavioral units is analyzed, and behavioral pairs with causal relationships or mutually exclusive conditions are identified. Causal edges and mutually exclusive edges are added to the graph structure accordingly to obtain a behavioral semantic graph containing temporal, causal and mutually exclusive relationships. The behavioral semantic graph is input into a graph neural network model. A graph convolution algorithm based on attention mechanism is used to perform neighborhood aggregation calculation on the nodes. The initial feature vector of each behavioral node is subjected to multi-layer nonlinear transformation. The semantic information of its first-order and high-order neighboring nodes is fused to generate a high-dimensional behavioral semantic embedding vector. Normalization and dimensionality reduction optimization are performed on the high-dimensional behavioral semantic embedding vector. The principal component analysis algorithm is used to extract the most discriminative low-dimensional semantic feature subspace and output a compact behavioral semantic embedding representation.

9. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 8, characterized in that, In the set of behavioral nodes, each node corresponds to a specific operation or state.

10. The method for intelligent identification and evidence chain generation of violations for drone license examinations according to claim 1, characterized in that, The rule dynamic mapping determination module adopts a multi-head attention semantic alignment mechanism, with the number of heads being greater than or equal to 8, and the query vector dimension being aligned with the rule vector dimension.

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