A method for real-time scoring of unmanned aerial vehicle flight skills based on remote controller joystick data

Abstract

The application relates to the technical field of unmanned aerial vehicle (UAV) entertainment flight and skill evaluation, and particularly discloses a UAV flight skill real-time scoring method based on remote controller joystick data, which comprises the following steps: step one, joystick data acquisition and preprocessing, wherein original joystick data of four control channels is acquired in real time through a remote controller; step two, action intensity scoring; step three, action continuity scoring; step four, counter hit cleanliness scoring; step five, rhythm control scoring; and step six, weighted total score calculation and weight configuration, wherein the action intensity score, the action continuity score, the counter hit cleanliness score and the rhythm control score are weighted and summed according to pre-configured weights to obtain a total score. The joystick data of the four main control channels is acquired and analyzed in real time at the remote controller end, real-time scoring is performed from four dimensions of action intensity, action continuity, counter hit cleanliness and rhythm control, and a configurable weight system is supported, so that the use effect is good.

Description

Technical Field

[0001] This invention relates to the field of drone recreational flight and skills assessment technology, and in particular to a real-time scoring method for drone flight skills based on remote controller joystick data. Background Technology

[0002] Racing drones (FPV drones) are small drones equipped with real-time image transmission systems, widely used in racing, aerobatic, and aerial photography entertainment. Unlike aerial photography drones that emphasize stability, racing drones prioritize agility and controllability. Pilots use the joystick on the remote controller to perform various flight maneuvers, such as rolls, flips, sudden stops, and rapid accelerations.

[0003] Unless otherwise specified, in the following descriptions, "drone" generally refers to racing drones and other consumer-grade drones; "joystick" refers to the control stick on the remote controller used to control the aircraft's attitude, which usually has four main channels: throttle (THR, controls elevator), aileron (AIL, controls roll), elevator (ELE, controls pitch), and yaw (RUD, controls yaw); "flying stunts" refers to FPV free flight aerobatic performances, in which the pilot executes various combinations of aerobatic maneuvers through continuous control.

[0004] In drone racing, assessing flight skills is always a crucial topic. Beginners want to understand their progress, experienced drone pilots want to compare their skill level with others, and event organizers desire objective scoring standards. However, current products and solutions on the market have the following problems in flight skill assessment:

[0005] (1) Subjective scoring scheme: Traditional flight skill assessment relies on the subjective judgment of judges or instructors, which has problems such as inconsistent standards and difficulty in quantification. For example, two judges may give different evaluations for the same flight segment.

[0006] (2) Video analysis solution: Some solutions use AI to analyze flight videos to evaluate skills. The problems are: they require post-processing and cannot provide real-time feedback; they depend on video quality and shooting angle; and the processing cost is high, making them difficult for ordinary users to use.

[0007] (3) Flight control data analysis scheme: Some professional software can export flight control black box data for analysis. The problems are: professional knowledge and tools are required; the data export and analysis process is cumbersome; and real-time scoring is not supported.

[0008] (4) Simple timing scheme: Some racing events use lap time as the judging standard. The problem is that it can only evaluate speed and cannot evaluate the aesthetics and style of flying skills; it is not suitable for non-racing scenarios such as freestyle flying.

[0009] (5) Built-in scoring scheme of simulator: Some drone simulator software has a simple built-in scoring function. The problems are: the scoring dimension is single; it can only be used in the simulator and cannot be used for real aircraft flight; the algorithm is not transparent and users do not know how to improve.

[0010] The common problems or disadvantages of the above-mentioned existing technologies are as follows:

[0011] (1) No real-time feedback: Users cannot understand their performance in real time during the flight and can only get the evaluation results after the flight ends, thus losing the opportunity to make timely adjustments.

[0012] (2) Single scoring dimension: Existing solutions usually only focus on a single indicator (such as speed, lap time), which cannot fully reflect multiple aspects of flying skills.

[0013] (3) Lack of playability: Without gamification elements, users lack the motivation and sense of accomplishment to continue practicing.

[0014] (4) High equipment dependence: It requires additional equipment (such as professional analysis software, high-definition recording equipment) or venues (such as professional tracks).

[0015] (5) Inability to quantify progress: Users have difficulty objectively understanding their progress and can only judge based on their feelings.

[0016] (6) Real aircraft and simulator are separated: The evaluation systems for simulator training and real aircraft flight are not unified, making it impossible to compare the training effects.

[0017] The technical difficulty in solving the above problems lies in:

[0018] (1) How to assess flight skill level using only joystick data from the remote controller without relying on flight control data.

[0019] (2) How to design a multi-dimensional scoring system to comprehensively reflect different aspects of flying skills (intensity of movement, continuity, precision, rhythm, etc.).

[0020] (3) How to achieve real-time scoring so that users can get immediate feedback during the flight.

[0021] (4) How to design a reasonable scoring algorithm so that the scoring results are both discriminative and reflect the true skill level.

[0022] (5) How to unify the evaluation standards for real flight and simulator training. Summary of the Invention

[0023] The technical problem to be solved by this invention is to provide a real-time scoring method for drone flight skills based on remote controller joystick data, addressing the deficiencies in the existing technology. This method collects and analyzes joystick data from four main control channels in real time on the remote controller, and scores the drones in real time from four dimensions: action intensity, action continuity, counter-attack cleanliness, and rhythm control. It also supports a configurable weighting system and has good performance.

[0024] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0025] A real-time scoring method for drone flight skills based on remote controller joystick data includes the following steps:

[0026] Step 1: Joystick data acquisition and preprocessing. Raw joystick data from four control channels is acquired in real time via remote control. Standard joystick data is then output after preprocessing the raw joystick data.

[0027] Step 2: Motion intensity scoring. Based on the standard joystick data, the percentage of sampled frames with offsets exceeding the large joystick threshold is statistically analyzed, and the motion intensity score is calculated from the percentage of sampled frames using a piecewise linear function.

[0028] Step 3: Motion continuity scoring. Based on the standard joystick data, the number of motion segments and the average segment length are calculated, and the motion continuity score is calculated accordingly.

[0029] Step 4: Counter-attack cleanliness score. By detecting the counter-attack action and judging whether the counter-attack amplitude exceeds the effective counter-attack threshold, the number of clean counter-attacks and the total number of counter-attacks are counted, and the counter-attack cleanliness score is calculated accordingly.

[0030] Step 5: Rhythm control scoring. Peaks are identified by monitoring the upward and downward trends of the throttle channel, and the average absolute deviation and average peak height between adjacent peaks are calculated to determine the rhythm control score.

[0031] Step 6: Calculate the weighted total score and configure the weights. The action intensity score, action continuity score, counter-hit cleanliness score, and rhythm control score are weighted and summed according to the pre-configured weights to obtain the total score.

[0032] Preferably, the real-time scoring method further includes step seven: real-time scoring and cumulative statistics. Steps one to six are continuously executed according to the sampling period to display the total score, calculate the final score of the flight at the end of a single flight, and add it to the historical statistics.

[0033] Preferably, the real-time scoring method further includes step eight, flight mode recognition, which automatically identifies whether the current flight is a real aircraft flight or a simulator training by detecting the working status of the remote controller, and records the cumulative statistical data of the two modes respectively.

[0034] Preferably, in step one, the control channels include a throttle joystick channel, aileron joystick channel, elevator joystick channel, and directional joystick channel; the data preprocessing includes numerical range normalization, jitter filtering, and dead zone processing; after analog-to-digital conversion, the original joystick data outputs a numerical range of 0-1023, where 512 is the midpoint position.

[0035] Preferably, in step two, the stick quantity threshold is 60% of the travel. By detecting the stick positions of the aileron, elevator, and directional channels, if the offset of any one channel exceeds the stick quantity threshold, then that frame is counted as a stick quantity frame.

[0036] Preferably, in step three, when the joystick offset of any control channel exceeds the dead zone threshold, the system considers that the pilot has started to execute an action; when all channels return to within the dead zone threshold, the action segment ends.

[0037] Preferably, in step four, the cleanliness score of the counter-attack is based on the ratio of the number of clean counter-attacks to the total number of counter-attacks, and the cleanliness score of the counter-attack is calculated as follows: (number of clean counter-attacks / total number of counter-attacks) × 80 + number of clean counter-attacks.

[0038] Preferably, in step five, when the throttle value continues to rise and then begins to fall, and the peak value exceeds the midpoint, it is recorded as a valid peak value, and the time and height of the valid peak value are recorded, and the information of the most recent 32 peak values ​​is saved.

[0039] Preferably, in step six, the weight is configured as 25%, and the total score is calculated using the following formula:

[0040] Total score = Action intensity score × Action intensity weight + Action continuity score × Action continuity weight + Counter-attack cleanliness score × Counter-attack cleanliness weight + Rhythm control score × Rhythm control weight.

[0041] Preferably, in step seven, the scoring mode includes real-time mode, current mode, and cumulative mode; in step eight, it also includes using a four-dimensional radar chart to visually display the scores in the four dimensions.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] (1) Real-time feedback: Users can see their performance score in real time during the flight of the drone without waiting for post-flight analysis. The score data is updated every 20 milliseconds, almost in sync with the joystick operation, which solves the problem of "inability to provide real-time feedback" in the existing technology.

[0044] (2) Multi-dimensional assessment: The flight skills are comprehensively assessed from four dimensions: action intensity, action continuity, counter-attack cleanliness, and rhythm control, rather than a single speed or lap time indicator; the four dimensions correspond to the core skills of racing drone flying, making the assessment results more comprehensive and meaningful, and solving the problem of "single scoring dimension" in the existing technology.

[0045] (3) Gamified experience: Through quantified scores, visualized radar charts, and cumulative achievement statistics, users are provided with a game-like progress experience; users can clearly see which dimension they need to improve in, as well as the progress curve over time. This solves the problem of "lack of playability" in existing technologies.

[0046] (4) Zero additional equipment: All scoring calculations are completed on the remote control, without the need for additional analysis software, recording equipment or professional venues; users can obtain skills assessments anytime and anywhere, solving the problem of "strong equipment dependence" in existing technologies.

[0047] (5) Quantifiable progress: The system saves complete historical statistics, allowing users to track their long-term progress curves and compare performance at different times; digital scoring makes progress visible and measurable, solving the problem of "inability to quantify progress" in existing technologies.

[0048] (6) Unification of real aircraft and simulator: The same scoring algorithm is used for real aircraft flight and simulator training. Users can practice on the simulator and then transfer their skills to the real aircraft. The scores are comparable, which solves the problem of "separation between real aircraft and simulator" in the existing technology.

[0049] (7) Personalized weights: Users can adjust the weights of the four dimensions according to their training goals. The system can also provide preset weight schemes (race mode, flower flying mode, etc.) to adapt to different usage scenarios.

[0050] (8) Scientific scoring algorithm: The scoring algorithm is designed based on the actual characteristics of racing drone flight. It adopts piecewise linear mapping to ensure that the scoring has reasonable discrimination and avoids the scoring being too concentrated or too dispersed.

[0051] (9) Low overhead implementation: The algorithm is highly efficient and requires very low computing resources to achieve real-time scoring without affecting other functions of the remote control. The sampling period is 20 milliseconds, which can capture all control actions without causing excessive computing burden.

[0052] (10) Perfect data persistence: Flight statistics are stored in the remote controller and will not be lost when the device is turned off; users can view their cumulative flight count, total flight time, high score count and other achievement data at any time. Attached Figure Description

[0053] Figure 1 This is a flowchart of the real-time scoring method for drone flight skills based on remote controller joystick data in this invention;

[0054] Figure 2 This is a schematic diagram of the data acquisition architecture in this invention;

[0055] Figure 3 This is a flowchart of the motion intensity data acquisition process in this invention;

[0056] Figure 4 This is a flowchart of the reverse detection process in this invention;

[0057] Figure 5 This is a schematic diagram of the flight lifecycle in this invention;

[0058] Figure 6 This is a flowchart of the flight mode recognition process in this invention. Detailed Implementation

[0059] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0060] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0061] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0062] like Figure 1-6 As shown, the real-time drone flight skill scoring method based on remote controller joystick data in this invention includes the following parts:

[0063] 1. Joystick data acquisition and preprocessing methods;

[0064] 2. Aggressiveness scoring method;

[0065] 3. Method for scoring the continuity of movement (Flow);

[0066] 4. Snap Cleanliness Scoring Method;

[0067] 5. Rhythm control scoring method;

[0068] 6. Weighted total score calculation and weight allocation method;

[0069] 7. Real-time scoring and cumulative statistical methods;

[0070] 8. A unified evaluation method for real aircraft flight training and simulator training;

[0071] The above method is associated according to the following steps:

[0072] Step 1: During flight, the remote controller collects raw joystick data from four channels (throttle, aileron, elevator, and directional) in real time at a fixed sampling period (e.g., 20 milliseconds). The data is then preprocessed, including normalization, jitter filtering, and dead zone handling, to output standardized joystick data that can be used for subsequent scoring calculations. This step uses Method 1.

[0073] Step 2: Based on the joystick data output in Step 1, count the percentage of sampled frames in the three channels of aileron, elevator, and directional whose offset exceeds the maximum stick amount threshold (60% travel). Calculate the action intensity score (Aggressiveness) based on this percentage using piecewise linear mapping. This step uses Method 2.

[0074] Step 3: Based on the joystick data output in Step 1, identify "action segments" according to the dead zone threshold (any channel exceeding the dead zone is the start of a segment, and returning to the dead zone is the end of a segment), count the number of action segments and the average segment length, and calculate the action continuity score (Flow) accordingly. This step uses method 3.

[0075] Step 4: Based on the joystick data output in Step 1, monitor the reversal of the joystick movement direction of the aileron and elevator channel respectively, detect counter-attack actions and determine whether the counter-attack amplitude exceeds the effective counter-attack threshold (40% travel), count the clean counter-attack and the total number of counter-attacks, and calculate the counter-attack cleanliness score (Snap) accordingly. This step adopts Method 4.

[0076] Step 5: Based on the joystick data output in Step 1, monitor the changes in the upward and downward trends of the throttle channel to identify peaks, calculate the stability of the interval between adjacent peaks (such as mean absolute deviation MAD) and the average peak height, and calculate the rhythm control score (Rhythm) accordingly. This step uses Method 5.

[0077] Step Six: The scores of the four dimensions output from Steps Two, Three, Four, and Five are weighted and summed according to the weights configured by the user to obtain the total score. The weights can be customized and the sum of the four is 100%. The system can provide preset schemes such as Balanced, Speed, Flower Flight, and Rhythm. This step adopts Method 6.

[0078] Step 7: During the flight, continue to execute steps 1 to 6 according to the sampling period of step 1 to achieve real-time score display; at the end of a single flight, calculate the final score of this flight and accumulate it into the historical statistics; support three display modes: real-time mode, current mode, and cumulative mode. This step adopts method 7.

[0079] Step 8: By detecting whether the remote controller is in USB / Bluetooth game controller mode and flight controller unlocked state, the system automatically identifies whether the current mode is real aircraft flight or simulator training. The real aircraft and simulator use the same scoring algorithm and weight configuration as in steps 1 to 6 to achieve a unified evaluation standard, but the cumulative statistical data of the two modes are recorded separately. This step uses method 8.

[0080] Step one above forms the data foundation for the entire method, and its output preprocessed joystick data is simultaneously used by steps two, three, four, and five. Steps two through five calculate scores across four dimensions in parallel, and the output is provided to step six. Step six executes after steps two through five are completed, and the output total score is provided to step seven for display and statistics. Step seven runs throughout the flight process, driving the cyclical execution and result output of steps one through six. Step eight identifies the flight mode at the process entry point and ensures that steps one through seven run consistently on both real aircraft and simulators. These steps form a complete data processing chain: raw joystick data → preprocessing → parallel scoring across four dimensions → weighted total score → real-time / current / cumulative display and statistics → unified evaluation on real aircraft and simulators.

[0081] Specifically, the above method is described in detail below.

[0082] Joystick Data Acquisition and Preprocessing Methods

[0083] 1.1 Data Acquisition Architecture

[0084] The system collects joystick data from four main control channels in real time on the remote controller, without needing to establish an additional data connection with the flight controller. The data acquisition architecture is as follows: Figure 2 As shown.

[0085] 1.2 Joystick Value Description

[0086] After analog-to-digital conversion, the remote control joystick outputs a value ranging from 0 to 1023, with 512 representing the midpoint position.

[0087] The meanings of each channel are shown in the table below:

[0088] aisle name Midpoint meaning Positive direction negative direction THR accelerator hovering power Accelerated rise Decelerate and descend AIL aileron No roll Roll to the right Roll to the left ELE Lifting No tilting nose lift Machine head down pressure RUD direction No yaw Yaw to the right Yaw to the left

[0089] 1.3 Key Threshold Definition: To accurately identify the pilot's control intentions, the system defines the following key thresholds (represented by midpoint offset):

[0090] Threshold name numerical values Full proportion of the trip Instructions for use Large rod threshold 307 60% Determined to be a large-scale operation Dead zone threshold 77 15% The joystick is determined to be in an inactive state. Effective counterattack threshold 205 40% The direction reversal operation was determined to be valid. Throttle trend threshold 20 4% Determine whether the throttle is rising or falling. jitter filter threshold 5 1% The slight vibration of the filter lever

[0091] 2. Aggressiveness scoring method

[0092] 2.1 Scoring Principles

[0093] Action intensity rating measures the intensity of a pilot's control, specifically the magnitude of the "stick movements." In racing and freestyle flying, large stick movements typically indicate a more aggressive flying style.

[0094] The score is based on the "large stick distance frame ratio", which is the proportion of the total number of sampled frames in which the joystick deviates from the midpoint by more than 60% (large stick distance threshold) during the entire flight.

[0095] 2.2 Data Acquisition

[0096] The system detects the stick positions of the aileron, elevator, and directional channels within each sampling period (20 milliseconds). If the offset of any channel exceeds the large stick movement threshold (307), the frame is counted as a large stick movement frame. Figure 3 As shown.

[0097] 2.3 Scoring Algorithm

[0098] The mapping relationship between the proportion of high-scoring points and the score is achieved using a piecewise linear function to ensure that the scoring has a reasonable degree of discrimination.

[0099] Large rod volume ratio Score range illustrate ≥25% 100 points Top radical style 15%-25% 72-100 points radical style 8%-15% 40-72 points medium style 0%-8% 0-40 points conservative style

[0100] 3. Flow scoring method

[0101] 3.1 Scoring Principles

[0102] The motion continuity score is used to measure the smoothness of a pilot's movements. In professional racing drone articulation, excellent pilots can string together multiple movements into a coherent "motion segment," rather than performing individual movements in a disjointed manner.

[0103] The rating is based on two metrics:

[0104] Number of action segments: The fewer the number, the more fluid the action.

[0105] Average segment length: The longer the length, the more varied the movements within each segment.

[0106] 3.2 Definition of Action Segment

[0107] When the joystick offset of any control channel (AIL, ELE, RUD) exceeds the dead zone threshold (77%, or 15%), the system considers that the pilot has started to execute a move; the move segment ends when all channels return to the dead zone.

[0108] 3.3 Scoring Algorithm

[0109] The calculation of the continuity score takes into account two factors: the number of action segments and the average segment length.

[0110] Base score: 100 points;

[0111] Action Segment Quantity Penalty: 2 points are deducted for each additional action segment (the more segments there are and the more dispersed they are, the more points are deducted).

[0112] Average segment length bonus: The longer the average segment length, the greater the bonus (maximum 40 points).

[0113] Final score = 100 - number of action segments × 2 + average segment length bonus.

[0114] 4. Snap Cleanliness Scoring Method

[0115] 4.1 Scoring Principles

[0116] The counter-strike cleanliness score is used to measure the precision with which a pilot executes a counter-strike maneuver. Counter-strike is a core technique in racing drone articulation, referring to quickly and precisely switching to the opposite direction after performing a maneuver in one direction.

[0117] Excellent counter-attack characteristics:

[0118] The joystick can quickly and accurately move from one side to the other;

[0119] The counter-attack amplitude is large enough (more than 40% of the travel);

[0120] The movements were swift and decisive, without any hesitation or tremor in between.

[0121] 4.2 Reverse detection process

[0122] The system monitors the counter-strike actions of both the aileron (AIL) and elevator (ELE) channels and makes judgments, such as... Figure 4 As shown.

[0123] 4.3 Scoring Algorithm

[0124] The cleanliness score for a counter-attack is based on the ratio of the number of clean counter-attacks to the total number of counter-attacks:

[0125] Clean counter-attack percentage score: (Number of clean counter-attacks / Total number of counter-attacks) × 80;

[0126] Clean counter-attack bonus: 1 point for each clean counter-attack, up to a maximum of 20 points;

[0127] Final score = Clean counter-attack percentage score + Clean counter-attack number bonus.

[0128] 5. Rhythm Control Scoring Method

[0129] 5.1 Scoring Principles

[0130] The rhythm control score measures a pilot's sense of rhythm in throttle control. In racing drone stylized flight, rhythmic throttle control creates a melodious flight path and is an important indicator of high-level flying skills.

[0131] The rating is based on the regularity of throttle "peaks":

[0132] Stability of peak intervals (the more stable the interval, the better the rhythm);

[0133] Peak height (the higher the peak, the more powerful the movement).

[0134] 5.2 Throttle Peak Detection

[0135] The system identifies peak values ​​by monitoring the upward and downward trends of the throttle channel:

[0136] Peak detection rules:

[0137] 1. When the throttle value continues to rise and then begins to fall, and the peak value exceeds the midpoint (512), it is recorded as a valid peak value;

[0138] 2. Record the time and height of the peak;

[0139] 3. The system saves information on the 32 most recent peak values ​​for analysis.

[0140] 5.3 Scoring Algorithm

[0141] The tempo score is calculated based on the stability of the peak interval (measured using mean absolute deviation (MAD)) and the average peak height:

[0142] MAD calculation method: First, calculate the average value, then calculate the sum of the absolute deviations of each value from the average value, and finally calculate the mean absolute deviation value.

[0143] Base score: 100 points;

[0144] Interval stability penalty: The higher the MAD value, the greater the penalty (maximum deduction of 40 points).

[0145] Peak Height Bonus: The higher the average peak height, the greater the bonus (up to 40 points).

[0146] Final score = 100 - interval stability penalty + peak height bonus.

[0147] 6. Weighted total score calculation and weight allocation method

[0148] 6.1 Calculation of Weighted Total Score

[0149] The scores from the four dimensions are weighted to obtain the total score, with the default weighting set at 25% for each dimension.

[0150] Dimension Default weight illustrate Action intensity (Aggr) 25% Measuring the intensity of control Flow of movement 25% Measuring the smoothness of movement Counter-attack cleanliness (Snap) 25% Measuring the accuracy of counter-attacks Rhythm control 25% Measure the rhythm of throttle

[0151] Total score calculation formula:

[0152] Total score = Aggr score × Aggr weight + Flow score × Flow weight + Snap score × Snap weight + Rhythm score × Rhythm weight.

[0153] 6.2 Weight Configuration Method

[0154] The system supports user-defined weights for four dimensions to accommodate different flight style preferences:

[0155] Preset weighting scheme (Note: the sum of the four weights must equal 100%):

[0156] Solution Name Aggr Flow Snap Rhythm Applicable Scenarios Balanced mode 25% 25% 25% 25% General Racing Mode 40% 30% 20% 10% Emphasis on speed and radicalism Flower Flying Mode 15% 35% 35% 15% Emphasizing the beauty of movement Rhythm Mode 20% 20% 20% 40% Emphasis on musical rhythm

[0157] 7. Real-time scoring and cumulative statistical methods

[0158] 7.1 Three scoring models

[0159] The system offers three scoring modes to meet different usage scenarios:

[0160] model illustrate Use cases Real-time mode The score is updated every 20ms during flight. Check current performance during flight This mode Final score after a single flight Single flight summary Cumulative mode Average score across multiple flights Long-term progress tracking

[0161] 7.2 Flight Life Cycle

[0162] Flight lifecycle such as Figure 5 As shown.

[0163] 7.3 Minimum Flight Duration Requirements

[0164] To ensure the validity of the scoring and prevent accidental operation, the system sets a minimum flight time (default 3 minutes):

[0165] Flight duration ≥ 3 minutes: calculated based on actual score;

[0166] Flight duration < 3 minutes: Score is calculated proportionally based on flight duration (e.g., a flight of 1.5 minutes will receive 50% of the score).

[0167] Flight duration < 3 seconds: This flight will not be included in the statistics to avoid accidental operation.

[0168] 7.4 Cumulative Statistical Data

[0169] The system persistently stores the following statistics:

[0170] Data Items illustrate Total number of flights All valid flights Number of real aircraft flights Number of times using real aircraft for flight Simulator flight count Number of times the simulator was used Total playtime on real devices Total flight time of the real aircraft Simulator cumulative time Total flight time in simulator High score attempts (on a real machine) Number of times a real aircraft flight score is ≥95 High score attempts (simulator) Number of times the simulator score is ≥95

[0171] 8. Unified evaluation method for real aircraft flight and simulator training

[0172] 8.1 Flight Mode Recognition

[0173] The system automatically identifies whether it is currently in real flight or simulator training by detecting the working status of the remote controller. Figure 6 As shown.

[0174] 8.2 Standardized Scoring Criteria

[0175] The real flight and simulator training use the exact same scoring algorithm to ensure that the scores are comparable. However, the system records statistical data for both modes separately, allowing users to track their progress in different environments.

[0176] 8.3 Score Display

[0177] The system uses a four-dimensional radar chart to visually display the scores in the four dimensions.

[0178] Compared with the prior art, the present invention has the following advantages:

[0179] (1) Real-time feedback: Users can see their performance score in real time during the flight of the drone without waiting for post-flight analysis. The score data is updated every 20 milliseconds, almost in sync with the joystick operation, which solves the problem of "inability to provide real-time feedback" in the existing technology.

[0180] (2) Multi-dimensional assessment: The flight skills are comprehensively assessed from four dimensions: action intensity, action continuity, counter-attack cleanliness, and rhythm control, rather than a single speed or lap time indicator; the four dimensions correspond to the core skills of racing drone flying, making the assessment results more comprehensive and meaningful, and solving the problem of "single scoring dimension" in the existing technology.

[0181] (3) Gamified experience: Through quantified scores, visualized radar charts, and cumulative achievement statistics, users are provided with a game-like progress experience; users can clearly see which dimension they need to improve in, as well as the progress curve over time. This solves the problem of "lack of playability" in existing technologies.

[0182] (4) Zero additional equipment: All scoring calculations are completed on the remote control, without the need for additional analysis software, recording equipment or professional venues; users can obtain skills assessments anytime and anywhere, solving the problem of "strong equipment dependence" in existing technologies.

[0183] (5) Quantifiable progress: The system saves complete historical statistics, allowing users to track their long-term progress curves and compare performance at different times; digital scoring makes progress visible and measurable, solving the problem of "inability to quantify progress" in existing technologies.

[0184] (6) Unification of real aircraft and simulator: The same scoring algorithm is used for real aircraft flight and simulator training. Users can practice on the simulator and then transfer their skills to the real aircraft. The scores are comparable, which solves the problem of "separation between real aircraft and simulator" in the existing technology.

[0185] (7) Personalized weights: Users can adjust the weights of the four dimensions according to their training goals. The system can also provide preset weight schemes (race mode, flower flying mode, etc.) to adapt to different usage scenarios.

[0186] (8) Scientific scoring algorithm: The scoring algorithm is designed based on the actual characteristics of racing drone flight. It adopts piecewise linear mapping to ensure that the scoring has reasonable discrimination and avoids the scoring being too concentrated or too dispersed.

[0187] (9) Low overhead implementation: The algorithm is highly efficient and requires very low computing resources to achieve real-time scoring without affecting other functions of the remote control. The sampling period is 20 milliseconds, which can capture all control actions without causing excessive computing burden.

[0188] (10) Perfect data persistence: Flight statistics are stored in the remote controller and will not be lost when the device is turned off; users can view their cumulative flight count, total flight time, high score count and other achievement data at any time.

[0189] Understandably, this invention is rationally designed, uniquely constructed, and possesses the following key technical features:

[0190] 1. A method for evaluating flight skills using remote control joystick data (rather than flight control data), including the acquisition and preprocessing of data from four channels: throttle, ailerons, elevator, and yaw.

[0191] 2. The design of the four-dimensional scoring system includes the definition and evaluation methods of the four dimensions: Aggressiveness, Flow, Snap, and Rhythm.

[0192] 3. Motion intensity scoring method, including the definition of the large stroke threshold (60% travel), the statistics of the proportion of large stroke frames, and the scoring algorithm of piecewise linear mapping;

[0193] 4. A method for scoring motion coherence, including the definition of motion segments (based on dead zone threshold), statistics on the number of motion segments and average segment length, and a comprehensive scoring algorithm based on these two indicators;

[0194] 5. Counter-attack cleanliness scoring method, including the counter-attack action detection algorithm (based on the reversal of the joystick movement direction), the definition of the effective counter-attack threshold (40% travel), the distinction criteria between clean counter-attack and dirty counter-attack, and the scoring algorithm based on the clean counter-attack ratio;

[0195] 6. Rhythm control scoring methods, including throttle peak detection algorithms (based on the trend change from rising to falling), peak interval stability measurement methods (mean absolute deviation MAD), and comprehensive scoring algorithms combining interval stability and peak height;

[0196] 7. A configurable weighting system, including the constraint that the sum of the weights of the four dimensions must equal 100%, and the design of preset weighting schemes (balanced mode, racing mode, flower flying mode, rhythm mode);

[0197] 8. The design of three scoring modes, including a switching mechanism for real-time mode (updated in real time during flight), current mode (score at the end of a single flight), and cumulative mode (average score across multiple flights);

[0198] 9. The method for handling the relationship between flight time and score, including the setting of the minimum flight time requirement (3 minutes) and the score conversion mechanism when the flight time is insufficient;

[0199] 10. A unified evaluation method for real aircraft flight and simulator training, including a mechanism for automatically identifying flight modes through the working status of the remote controller, and a design that uses the same scoring algorithm for both modes but performs separate statistics;

[0200] 11. Definition of key threshold parameters, including the setting of values ​​for large lever volume threshold (60%), dead zone threshold (15%), effective counter-attack threshold (40%), throttle trend threshold (4%), and jitter filtering threshold (1%);

[0201] 12. The selection of the sampling period (20 milliseconds) and its balance between computational efficiency and accuracy.

[0202] Understandably, in practical applications, the main difference between this invention and the prior art lies in:

[0203] Comparison items Existing technology This invention Data source Relying on flight control black box data or video recordings Data using only the remote control joystick Timing of scoring Post-event analysis Real-time rating Timing of scoring Single dimension (such as speed, lap time) Four-dimensional comprehensive score Rating Dimensions Professional analysis software or recording equipment is required. No additional equipment required Scope of application Real device only or emulator only Unified for real devices and emulators Personalization Fixed scoring criteria Supports weight configuration Feedback methods Digital Report Visualized radar charts + digital reports

[0204] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A real-time scoring method for drone flight skills based on remote controller joystick data, characterized in that: Includes the following steps: Step 1: Joystick data acquisition and preprocessing. Raw joystick data from four control channels is acquired in real time via remote control. Standard joystick data is then output after preprocessing the raw joystick data. Step 2: Motion intensity scoring. Based on the standard joystick data, the percentage of sampled frames with offsets exceeding the large joystick threshold is statistically analyzed, and the motion intensity score is calculated from the percentage of sampled frames using a piecewise linear function. Step 3: Motion continuity scoring. Based on the standard joystick data, the number of motion segments and the average segment length are calculated, and the motion continuity score is calculated accordingly. Step 4: Counter-attack cleanliness score. By detecting the counter-attack action and judging whether the counter-attack amplitude exceeds the effective counter-attack threshold, the number of clean counter-attacks and the total number of counter-attacks are counted, and the counter-attack cleanliness score is calculated accordingly. Step 5: Rhythm control scoring. Peaks are identified by monitoring the upward and downward trends of the throttle channel, and the average absolute deviation and average peak height between adjacent peaks are calculated to determine the rhythm control score. Step 6: Calculate the weighted total score and configure the weights. The action intensity score, action continuity score, counter-hit cleanliness score, and rhythm control score are weighted and summed according to the pre-configured weights to obtain the total score.

2. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 1, characterized in that: It also includes step seven, real-time scoring and cumulative statistics. Steps one to six are continuously executed according to the sampling period to display the total score. At the end of a single flight, the final score of the flight is calculated and added to the historical statistics.

3. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 2, characterized in that: It also includes step eight, flight mode recognition, which automatically identifies whether the current flight is a real aircraft flight or a simulator training by detecting the working status of the remote controller, and records the cumulative statistics of the two modes respectively.

4. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 1, characterized in that: In step one, the control channels include the throttle joystick channel, aileron joystick channel, elevator joystick channel, and directional joystick channel. The data preprocessing includes numerical range normalization, jitter filtering, and dead zone processing. After analog-to-digital conversion, the original joystick data outputs a numerical range of 0-1023, where 512 is the midpoint position.

5. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 1, characterized in that: In step two, the stick quantity threshold is 60% of the travel. By detecting the stick positions of the aileron, elevator, and directional channels, if the offset of any channel exceeds the stick quantity threshold, the frame is counted as a stick quantity frame.

6. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 1, characterized in that: In step three, when the joystick offset of any control channel exceeds the dead zone threshold, the system considers that the pilot has started to execute an action; when all channels return to within the dead zone threshold, the action segment ends.

7. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 1, characterized in that: In step four, the cleanliness score of the counter-attack is based on the ratio of the number of clean counter-attacks to the total number of counter-attacks. The cleanliness score of the counter-attack is calculated as follows: (number of clean counter-attacks / total number of counter-attacks) × 80 + number of clean counter-attacks.

8. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 1, characterized in that: In step five, when the throttle value continues to rise and then begins to fall, and the peak value exceeds the midpoint, it is recorded as a valid peak value. The time and height of the valid peak value are recorded, and the information of the most recent 32 peak values ​​is saved.

9. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 1, characterized in that: In step six, the weight is configured as 25%, and the total score is calculated using the following formula: Total score = Action intensity score × Action intensity weight + Action continuity score × Action continuity weight + Counter-attack cleanliness score × Counter-attack cleanliness weight + Rhythm control score × Rhythm control weight.

10. The real-time scoring method for drone flight skills based on remote controller joystick data according to claim 3, characterized in that: In step seven, the scoring modes include real-time mode, current mode, and cumulative mode; in step eight, a four-dimensional radar chart is used to visually display the scores in the four dimensions.

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