Group project dynamics energy efficiency evaluation system and method based on multi-sensor fusion

Abstract

The invention relates to a team project dynamics energy efficiency evaluation system and method based on multi-sensor fusion, and belongs to the technical field of sports aerodynamics and biomechanics. Comprising a multi-sensor synchronous acquisition and data transmission terminal, a high-precision power metering module, a high-precision laser ranging module, a micro differential pressure wind speed sensing module and a display module. According to the method, non-intrusive and high-sampling-rate data acquisition and multi-physical-field synchronous measurement are realized, a distance-energy-saving-pneumatic environment quantitative model is constructed, a riding state aerodynamic mathematical model is further established, and the law that the energy-saving effect is attenuated along with the distance is accurately described; therefore, a solid scientific foundation is provided for formulating an optimal car following strategy, and global dynamic monitoring and accurate on-site guidance are realized.

Description

Technical Field

[0001] This invention relates to a system and method for evaluating the dynamic energy efficiency of team sports based on multi-sensor fusion, belonging to the field of sports aerodynamics and biomechanics technology. Background Technology

[0002] In high-level track cycling competitions, especially in team sprints, pursuits, and team events, teamwork and tactical execution are key factors in determining victory. Among these, the aerodynamic rider and following technique are core team tactics that conserve energy. When an athlete closely follows another, their body enters the low-speed, low-pressure wake zone created by the preceding bike, significantly reducing air resistance. Studies show that under ideal conditions, closely following riders can save up to 30%–40% of power output. This energy saving allows the following rider to conserve energy for a later sprint or to rotate into the new aerodynamic rider, which is crucial for the team's overall speed and tactical flexibility.

[0003] Currently, research on the aerodynamic efficiency of team cyclists mainly relies on three methods: the first is wind tunnel testing, which involves fixing the bicycle and athlete, applying wind speed, and directly measuring drag using a force balance. For example, publication number CN115585981A, entitled "A Wind Tunnel Testing Method and Device for Measuring the Drag Coefficient of a Bicycle," describes a technical solution involving assembling a wind tunnel testing device, measuring the athlete's morphological parameters and calculating the frontal area, measuring the wind resistance of the bicycle and athlete in the wind tunnel, and measuring the incoming wind speed, thereby calculating the drag coefficient of the athlete and bicycle during cycling. The wind tunnel testing device includes an anemometer, a bracket, and measuring components. The bicycle is mounted on a bracket above a base plate, the base plate's tilt angle is adjusted by a flipping component to simulate the cycling state in corners, and a force measuring component is located at its bottom. The measuring component is used to measure the athlete's morphological parameters, and a damping cylinder at the rear of the bracket applies resistance to the bicycle's rear wheel. The force measuring component is used to detect the resistance experienced by the athlete and bicycle. This invention can accurately calculate the drag coefficient under actual cycling conditions, and is more meaningful for measuring the drag of rigid human models. This method offers high precision but is extremely expensive and cannot simulate real dynamic riding conditions and tactical coordination. The second method is computational fluid dynamics (CFD) simulation, which simulates mechanical changes in a flow field by creating three-dimensional digital models of the athlete and bicycle. While highly flexible and requiring no actual riding experiments, its accuracy is highly dependent on the model's precision and boundary condition settings, and it consumes significant computational resources, making it difficult to quickly use for monitoring and guiding riding techniques in daily training. The third method is empirical analysis based on power meters, which estimates energy-saving effects by comparing the power required for an athlete to maintain the same speed when riding alone versus following a bike. This is currently the most commonly used method, but its limitation is that it only provides an overall power difference, failing to reveal the specific aerodynamic causes of energy saving or accurately quantify the functional relationship between "energy saving effect" and "following distance." Existing power meter solutions typically neglect the synchronous, high-precision measurement of "distance" and "local wind speed," resulting in coarse data analysis and difficulty in building refined tactical models. In addition, accurately measuring the instantaneous distance between high-speed moving vehicles is also a technical challenge. Traditional video analysis or ordinary UWB ranging is not accurate enough and has high latency, making it difficult to meet the requirements of scientific research-level data synchronization.

[0004] In summary, existing technologies have two major flaws: First, the measurement methods are fragmented, making it difficult to achieve synchronous acquisition of key parameters such as "spacing - vehicle head velocity - power", resulting in a lack of data support for the correlation analysis between aerodynamic environment changes and energy consumption. Secondly, the accuracy of distance measurement and wind speed sensing is insufficient, and the data time is not synchronized between multiple nodes, which seriously affects the reliability and repeatability of the evaluation results.

[0005] Therefore, there is an urgent need to propose a system and method for evaluating the dynamic energy efficiency of group projects based on multi-sensor fusion in order to solve the above-mentioned technical problems. Summary of the Invention

[0006] To address the problems of fragmented measurement, high intrusiveness, and insufficient accuracy in existing bicycle aerodynamic efficiency evaluation methods, this paper provides a group project aerodynamic efficiency evaluation system and method based on multi-sensor fusion. A brief overview of this invention is provided below to offer a basic understanding of certain aspects of the invention. It should be understood that this overview is not an exhaustive summary of the invention. It is not intended to identify key or essential parts of the invention, nor is it intended to limit the scope of the invention.

[0007] The technical solution of this invention: A group project dynamics energy efficiency evaluation system based on multi-sensor fusion includes: Multi-sensor synchronous acquisition and data transmission terminal: responsible for integrating the data collected by all sensors, completing local storage, performing time synchronization of data, and transmitting data in real time; High-precision power metering module: The power meter serves as a real-time power standard input source, continuously monitoring the mechanical power output by the athlete during pedaling; the acquisition terminal aligns the power data with other sensor data on the time axis by analyzing its data signals; High-precision laser ranging module: Installed in the area in front of the steering axle of the vehicle, the laser beam is emitted horizontally forward, aiming at a high-reflectivity reflector fixed under the rear seat of the vehicle in front; the straight-line distance between the two vehicles is calculated by measuring the round-trip time of the laser. ; Miniature differential pressure wind speed sensing module: Enables measurement of wind speed in the flow field at the front of the athlete's vehicle; Display module: Supports data visualization and data analysis report export.

[0008] Preferably, the multi-sensor synchronous acquisition and data transmission terminal includes: a 5G networked data synchronization system that communicates with the foot-operated power meter via BLE and ANT+ communication protocols, and with the laser ranging module and the miniature differential pressure anemometer module via serial port and I2C wired communication protocols; the terminal automatically runs customized firmware, which is responsible for acquiring and storing the raw data of each module and marking it with a precise timestamp; the processed data is transmitted to the cloud database in real time via 5G.

[0009] Preferably, the high-precision power metering module uses a commercially available high-precision foot-operated power meter and wirelessly transmits power data via ANT+ or BLE protocol.

[0010] Preferably, the high-precision laser ranging module uses a lidar ranging sensor to dynamically measure the straight-line distance. for:

[0011] in: This refers to the distance from the laser sensor to the reflector of the vehicle in front. The horizontal distance from the reflector of the vehicle in front to the vertical tangent of the rear wheel of the vehicle in front; This is the horizontal distance from the laser sensor to the vertical tangent of the vehicle's front wheel.

[0012] Preferred: The high-precision laser ranging module needs to be calibrated before use. Use a precise tape measure to record laser ranging values ​​at multiple different distances and calibrate the laser ranging module.

[0013]

[0014] in, The calibration coefficients are used to correct for systematic errors in laser sensor measurements. To calibrate the number of times the experiment was performed, For the first The actual distance from the laser sensor to the reflector of the vehicle in front during the second calibration experiment. For the first The distance from the laser sensor to the reflector of the vehicle in front was tested in the second calibration experiment. This refers to the distance from the laser sensor to the reflector of the vehicle in front, measured during actual riding.

[0015] Preferably, the miniature differential pressure wind speed sensing module comprises a miniature differential pressure sensor and an external total pressure probe; the acquisition terminal reads the differential pressure output of the SDP, which is proportional to the local dynamic pressure and is used to characterize the wind speed in the wake. The degree of attenuation increases with increasing spacing;

[0016] in, To collect pressure in real time, This refers to air density.

[0017] Preferred method: Fix the device in a windless environment, use an adjustable blower and an anemometer to simultaneously collect data from the miniature differential pressure anemometer, record the differential pressure values ​​at different wind speeds multiple times, and calibrate the miniature differential pressure anemometer based on the anemometer data;

[0018]

[0019] in, The calibration coefficient is used to correct for systematic errors in SDP1000L sensor measurements. To calibrate the number of times the experiment was performed, For the first The second calibration experiment involved the actual applied wind speed of the SDP1000L sensor. For the first The wind speed measured by the sensor in the second calibration experiment. This refers to the wind speed measured by the sensor during actual cycling.

[0020] The group project dynamics energy efficiency evaluation method based on multi-sensor fusion, using the aforementioned group project dynamics energy efficiency evaluation system based on multi-sensor fusion, includes the following steps: S1, Data Acquisition System Calibration and Preparation: Before the formal test, all data acquisition units are calibrated. S2, Vehicle Preparation and Athlete Grouping: Assuming the selected number of athletes, have them wear their cycling gear in the usual manner, and prepare the competition vehicles corresponding to the number of athletes according to the standard competition configuration; install the power meter and data acquisition unit on the corresponding vehicles and turn on the power; ensure that all systems pass self-tests and the data indicator lights flash normally; Formation test execution: Athletes form a single file; a lead rider is assigned to test at high speed over a constant distance; after the system test program is started, the following athletes maintain stable riding at a specified following distance; each distance combination is continuously tested to obtain a sufficient number of steady-state data sets; the data acquisition terminal records all sensor data throughout the process; S3, Real-time Data Acquisition and Transmission: During the test, any acquisition unit automatically aligns with the timestamp and uses the 5G network to package and upload the athlete's real-time power, cadence, following distance, pressure, timestamp, and device ID to the cloud database. Data acquisition and uploading by other acquisition units are performed in accordance with this rule. S4, Real-time Data Monitoring and Export: Click to get data on the mobile APP, and the data will be automatically retrieved from the cloud database. You can check whether the key data streams of multiple athletes are normal in real time. Click to stop data acquisition, and the data of the entire cycling process will be automatically displayed. After the test ends, the system will automatically generate graph reports of "following distance and power", "following distance and wind speed" and "wind speed and power" throughout the test. It also supports exporting data and analysis reports collected by each sensor by ID category.

[0021] Preferably, in S1, system calibration includes: a) Zeroing the power meter and calibrating the torque; b) Calibrate the laser rangefinder sensor and adjust the line of sight to ensure that the laser radar matrix is ​​aligned with the center of the reflector of the vehicle in front; c) Static zero point and dynamic calibration of differential pressure sensors; at the same time, check the battery level, real-time communication function and internal storage space of all devices.

[0022] Preferred method: The group project dynamic energy efficiency evaluation method based on multi-sensor fusion is applied to the aerodynamic energy efficiency evaluation of the bicycle group project.

[0023] The present invention has the following beneficial effects: 1. This invention achieves non-invasive, high-sampling-rate data acquisition and simultaneous multi-physics field measurement: Through a specially customized sensor layout, a miniature differential pressure anemometer, a laser rangefinder, and a 5G communication module are integrated and installed at the front end of the carbon fiber rod extending from the intersection of the handlebars and steering fork. A pedal-mounted power meter is normally mounted on the crank, ensuring that all devices operate stably without altering the rider's posture, affecting control safety, or damaging the bicycle's original aerodynamic shape. This guarantees the authenticity and representativeness of the collected data. Based on this, high-precision synchronous acquisition of multiple physical field parameters—"distance-wind speed-power"—is achieved: the laser rangefinder dynamically measures the real-time distance between riders in front and behind with centimeter-level accuracy; a front-mounted total pressure probe, in conjunction with a miniature differential pressure sensor, non-contactly captures local dynamic pressure changes in the front area, effectively characterizing the trend of wake intensity variation with following distance; simultaneously, the pedal-mounted power meter accurately records the athlete's output power; the system further integrates multi-device time synchronization technology, ensuring sub-second time alignment of all sensor data distributed across multiple independent acquisition units. This fundamentally solves the data misalignment problem caused by asynchronous acquisition across multiple nodes, providing a solid data foundation for subsequently building a highly reliable aerodynamic energy-saving quantitative model.

[0024] 2. This invention constructs a quantitative model of "distance-energy saving-aerodynamic environment": Through systematic platooning riding experiments, multi-dimensional data were collected at different following distances. Based on this data, curves of "following distance - power saving rate" and "following distance - local dynamic pressure" can be plotted, thereby establishing an aerodynamic mathematical model of riding conditions to accurately describe the law of energy saving effect decay with distance; this provides a solid scientific foundation for formulating optimal following strategies.

[0025] 3. The real-time data visualization system of this invention: The system integrates a 5G network system, which packages and uploads data such as the athlete's real-time power, following distance, and airflow speed at the front of the vehicle to the cloud database every 100 ms. The system can automatically obtain data from the cloud database through a mobile APP, and can support real-time viewing of data such as the athlete's real-time power, following distance, and aerodynamic efficiency, so as to realize full-domain dynamic monitoring and precise on-site guidance. Attached Figure Description

[0026] Appendix Figure 1 This is a schematic diagram of the overall composition of the group project dynamic energy efficiency evaluation system based on multi-sensor fusion described in this invention.

[0027] Appendix Figure 2 This is a schematic diagram of the multi-sensor synchronous acquisition terminal function described in this invention.

[0028] Appendix Figure 3 This is a schematic diagram of the hardware structure of the multi-sensor synchronous acquisition terminal described in this invention.

[0029] Appendix Figure 4 This is a schematic diagram of the hardware installation of the multi-sensor synchronous acquisition terminal described in this invention.

[0030] In the diagram: ①—Lead bike data acquisition and transmission unit; ②—Second bike data acquisition and transmission unit; ③—Third bike data acquisition and transmission unit; ④—Fourth bike data acquisition and transmission unit; ⑤—Lead bike distance measuring reflective sign; ⑥—Second bike distance measuring reflective sign; ⑦—Third bike distance measuring reflective sign; ⑧—Fourth bike distance measuring reflective sign.

[0031] Appendix Figure 5 This is a flowchart of the aerodynamic energy efficiency evaluation method for bicycle team projects described in this invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is described below with reference to specific embodiments shown in the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0033] Specific implementation method one: Combining Figure 1-4 This embodiment describes a multi-sensor fusion-based group project dynamics energy efficiency evaluation system, which includes: Multi-sensor synchronous acquisition and data transmission terminal: This module is the core of the entire system. It is responsible for integrating the data collected by all sensors, completing local storage, performing time synchronization of the data, and transmitting it in real time. High-precision power metering module: In this system, the power meter serves as a real-time power standard input source, continuously monitoring the mechanical power output by the athlete during pedaling; since the power meter itself has a timestamp, the acquisition terminal can align the power data with other sensor data on the time axis by parsing its data signal; High-precision laser ranging module: The sensor is integrated and securely mounted in the area in front of the vehicle's steering axle. Figure 4 (①②③④) The firing direction is horizontally forward, aiming at the high-reflectivity reflector fixed under the rear seat of the adjacent vehicle. Figure 4 (⑤⑥⑦⑧); By measuring the round-trip time of the laser, the straight-line distance between the two vehicles can be accurately calculated. The data is transmitted to the acquisition terminal in real time via the I2C interface. Miniature differential pressure wind speed sensor module: This module is key to achieving accurate measurement of wind speed in the flow field at the front of the athlete's vehicle; Real-time cycling aerodynamic performance display module: This module is based on a cloud database; the mobile APP connects to the network in real time and automatically obtains real-time aerodynamic performance and sports biomechanics data of multiple athletes from the cloud database, supporting data visualization and export of data analysis reports; This invention proposes a testing method and system capable of operating in real, dynamic cycling environments. For the first time, it integrates high-precision laser ranging, miniature differential pressure anemometers, high-sampling-rate power meters, and multi-device precise time synchronization technology to construct a lightweight, non-invasive multi-sensor synchronous data acquisition system that can be installed on racing bicycles. This facilitates cost control. Within a standard timeframe, the system can simultaneously acquire the precise distance to the following rider, local wind speed, and real-time power output. With its non-invasive, high-precision, and highly synchronized characteristics, it comprehensively integrates multi-dimensional data such as distance, local aerodynamic environment, and power output to achieve a comprehensive evaluation of the aerodynamic efficiency of team cycling events.

[0034] Specific Implementation Method Two: Combining Figure 1-4 This embodiment describes a multi-sensor fusion-based group project dynamic energy efficiency evaluation system. The multi-sensor synchronous acquisition and data transmission terminal includes: its main body is a 5G networked data synchronization system, which communicates with a pedal-operated power meter via BLE and ANT+ communication protocols, and with a laser ranging module and a miniature differential pressure anemometer module via serial port and I2C wired communication protocols. This protocol is widely applicable in bicycle motion data acquisition equipment, possessing low power consumption and real-time communication capabilities, sufficient to support high-speed collection and transmission of multi-channel data; the terminal automatically runs customized firmware, responsible for acquiring the raw data from each module. The data is stored and accurately timestamped; the organized data is transmitted to the cloud database in real time via 5G; the customized firmware includes a differential pressure sensor, a laser rangefinder, a power meter, an MCU (microcontroller unit), 5G and ANT+ / BLE communication. The differential pressure sensor and laser rangefinder are arranged at one end of the housing, and the power meter, MCU (microcontroller unit), 5G and ANT+ / BLE communication are arranged inside the housing. The above components are powered by a battery inside the housing. The differential pressure sensor and laser rangefinder are connected to the MCU, the power meter is connected to the MCU via ANT+ / BLE communication, and the MCU is connected to the cloud database via 5G.

[0035] Specific implementation method three: Combining Figure 1-4 This embodiment describes a multi-sensor fusion-based team project dynamic energy efficiency evaluation system. The high-precision power metering module uses a commercially available high-precision pedal-operated power meter, such as the SRMPowerMeter or Garmin series. The power meter is used in professional cycling and has a measurement accuracy of ±1%. It can also wirelessly transmit power data via ANT+ or BLE protocols.

[0036] Specific implementation method four: Combination Figure 1-4 This embodiment describes a multi-sensor fusion-based group project dynamic energy efficiency evaluation system. The high-precision laser ranging module uses a miniaturized, high-frequency laser radar ranging sensor, such as the Aeye4Sight series, with a range of up to 30 meters, static accuracy better than ±1.5cm, and a response frequency as high as 100Hz. This fully meets the requirements for dynamic distance measurement of bicycles, specifically the linear distance measured during dynamic ranging. for:

[0037] in: This refers to the distance from the laser sensor to the reflector of the vehicle in front. The horizontal distance from the reflector of the vehicle in front to the vertical tangent of the rear wheel of the vehicle in front; This is the horizontal distance from the laser sensor to the vertical tangent of the vehicle's front wheel.

[0038] Specific Implementation Method Five: Combining Figure 1-4 This embodiment describes a multi-sensor fusion-based group project dynamics energy efficiency evaluation system. Before use, the high-precision laser ranging module needs to be calibrated. A precise measuring tape is used to record laser ranging values ​​at multiple different distances, and the laser ranging module is calibrated.

[0039]

[0040] in, The calibration coefficients are used to correct for systematic errors in laser sensor measurements. To calibrate the number of times the experiment was performed, For the first The actual distance from the laser sensor to the reflector of the vehicle in front during the second calibration experiment. For the first The distance from the laser sensor to the reflector of the vehicle in front was tested in the second calibration experiment. This refers to the distance from the laser sensor to the reflector of the vehicle in front, measured during actual riding.

[0041] Specific Implementation Method Six: Combination Figure 1-4This embodiment describes a group project dynamic energy efficiency evaluation system based on multi-sensor fusion. The miniature differential pressure wind speed sensing module comprises a miniature differential pressure sensor (Sensirion SDP1000-L series) and an external total pressure probe. The SDP1000-L is a MEMS-based digital differential pressure sensor with extremely high sensitivity (adjustable acquisition frequency 10-100Hz, selectable range ±500Pa), fast response, and excellent long-term stability. The acquisition terminal reads the differential pressure output from the SDP (Service Discovery Protocol), a value proportional to the local dynamic pressure, which can be used to characterize the wind speed in the wake. The degree of attenuation increases with increasing spacing;

[0042] in, Real-time pressure acquisition for SDP1000-L This refers to air density.

[0043] Specific implementation method seven: Combination Figure 1-4 This embodiment describes a group project dynamics energy efficiency evaluation system based on multi-sensor fusion. The system is fixed in a windless environment and uses an adjustable blower and an anemometer to simultaneously collect data from a miniature differential pressure anemometer module. The differential pressure values ​​at different wind speeds are recorded multiple times, and the miniature differential pressure anemometer module is calibrated based on the anemometer data.

[0044]

[0045] in, The calibration coefficient is used to correct for systematic errors in SDP1000L sensor measurements. To calibrate the number of times the experiment was performed, For the first The second calibration experiment involved the actual applied wind speed of the SDP1000L sensor. For the first The wind speed measured by the sensor in the second calibration experiment. This refers to the wind speed measured by the sensor during actual cycling.

[0046] Specific implementation method eight: Combination Figure 1-5 This embodiment describes a group project dynamics energy efficiency evaluation method based on multi-sensor fusion, which uses the group project dynamics energy efficiency evaluation system based on multi-sensor fusion described in embodiments one through seven, and includes the following steps: S1, Data Acquisition System Calibration and Preparation: Before the formal test, all data acquisition units are calibrated. S2, Vehicle Preparation and Athlete Grouping: Assuming the selected number of athletes, have them wear their cycling gear in the usual manner, and prepare the competition vehicles corresponding to the number of athletes according to the standard competition configuration; install the power meter and data acquisition unit on the corresponding vehicles and turn on the power; ensure that all systems pass self-tests and the data indicator lights flash normally; Formation test execution: Athletes form a single file; a lead rider (#1) is assigned to test at high speed over a constant distance (e.g., 200m in a sprint race); after the system test program starts, the following athletes (#2, #3, #4…) maintain stable riding at designated following distances (e.g., 0.6m, 1.0m, 2.0m); each distance combination (e.g., #2 at 0.6m, #3 at 2m) is continuously tested to obtain a sufficient number of steady-state data sets; the data acquisition terminal records all sensor data throughout the process. S3, Real-time Data Acquisition and Transmission: During the test, any acquisition unit automatically aligns with the timestamp and uses the 5G network to package and upload the athlete's real-time power, cadence, following distance, pressure, timestamp, and device ID to the cloud database. Data acquisition and uploading by other acquisition units are performed in accordance with this rule. S4, Real-time Data Monitoring and Export: Click to obtain data on the mobile APP, and the data will be automatically retrieved from the cloud database. You can check in real time whether the key data streams of multiple athletes (such as real-time power and real-time distance changes) are normal. Click to stop data acquisition, and the data of the entire cycling process will be automatically displayed. After the test ends, the system will automatically generate graph reports of "following distance and power", "following distance and wind speed" and "wind speed and power" throughout the test. It also supports exporting data and analysis reports collected by each sensor by ID category.

[0047] Specific Implementation Method Nine: Combining Figure 1-5 This embodiment describes a multi-sensor fusion-based method for evaluating the dynamic energy efficiency of group projects. In step S1, system calibration includes: a) Zeroing the power meter and calibrating the torque; b) Calibrate the laser rangefinder sensor and adjust the line of sight to ensure that the laser radar matrix is ​​aligned with the center of the reflector of the vehicle in front; c) Static zero point and dynamic calibration of differential pressure sensors; at the same time, check the battery level, real-time communication function and internal storage space of all devices.

[0048] Specific Implementation Method Ten: Combining Figure 1-5This embodiment describes a multi-sensor fusion-based method for evaluating the aerodynamic energy efficiency of team events. The overall architecture of this invention consists of multiple independent acquisition units with identical functions. Each unit is installed on a racing bicycle, together forming a team formation test system. Each acquisition unit contains 5 core modules. The multi-sensor fusion-based system and method for evaluating the aerodynamic energy efficiency of team events in cycling is applied to the evaluation of aerodynamic energy efficiency in cycling team events. The present invention aims to solve the technical problem that existing testing methods are difficult to accurately quantify the relationship between "distance-wind speed-power" between following athletes under real cycling conditions, thereby providing a reliable basis for the scientific optimization of team cycling tactics; The technical solution of the present invention mainly includes the following implementation steps: (1) Constructing a high-precision, lightweight multi-sensor synchronous data acquisition and transmission terminal. The pedal-type high-precision power meter, laser range sensor, micro differential pressure wind speed probe, high-precision time synchronization module and 5G data transmission module are integrated into a single acquisition unit to form a set of non-intrusive data acquisition and transmission terminals that can be installed on track bicycles, and transmit the data of multiple acquisition units to the cloud database in real time. This device achieves real-time acquisition and wireless transmission of key parameters without changing the athlete's equipment and riding posture. The laser range sensor dynamically obtains the real-time distance between the front and rear riders, and the micro differential pressure probe extended at the front end measures the local dynamic pressure in the front area of ​​the bike in a non-contact manner to reflect the changes in the aerodynamic environment. The power meter synchronously records the output power during the riding process, thereby realizing the accurate measurement of multi-physical field data of "distance-wind speed-power". All sensor data are globally aligned based on a unified time reference to ensure that the multi-source signals are highly synchronized in the time dimension, providing reliable timing guarantee for subsequent data analysis. (2) Real-time aerodynamic energy efficiency display system. By using Wi-Fi or the internet to transmit data from a cloud database back to the app, the system automatically displays the athlete's real-time power, speed, following distance, and wind speed at the front of the bike. Based on multimodal data, the system evaluates the athlete's real-time aerodynamic efficiency. Compared to existing technologies, this invention can simultaneously acquire aerodynamic and biomechanical parameters with high precision and reliability in real-world cycling scenarios, effectively overcoming the limitations of traditional wind tunnel or simulation experiments that fail to capture actual motion conditions. The system possesses excellent deployability and practicality, significantly improving the accuracy of aerodynamic efficiency evaluation in team cycling events, and providing direct and scientific data support for competitive training and tactical decision-making.

[0049] Example 1: This invention proposes a multi-sensor fusion-based system and method for evaluating the dynamic energy efficiency of team cycling events. The aim is to construct a precise quantitative relationship between spacing, wind speed, and power, promoting a shift in team cycling tactics research from experience-based judgment to a data-driven scientific paradigm. The ultimate goal is to provide a reliable and practical evaluation system that elevates the study of aerodynamic energy-saving effects in team cycling events from qualitative description to quantitative modeling and predictability. This provides coaches with a solid quantitative basis for developing refined training programs and competition tactics, thereby driving the sport of competitive cycling towards a higher level of technological advancement. Specifically, this includes: like Figure 2 As shown, the implementation of the multi-sensor data acquisition and synchronous data transmission terminal is as follows: The hardware platform of the data acquisition terminal is based on the STM32F4 series. Its MCU handles multiple tasks. A specific real-time clock synchronization system is run; this module uses 5G networking and automatically performs a time synchronization upon power-on. The MCU manages three independent data acquisition tasks: the first uses ANT+ / BLE to connect to the power meter, receiving real-time power, cadence, and timestamp data and writing it to the built-in storage module; the second uses the built-in wired serial port protocol to read real-time data from the laser rangefinder sensor; and the third uses the I2C wired protocol to read the real-time differential pressure value of the SDP1000L sensor. The MCU automatically synchronizes the acquired real-time distance using the built-in time synchronization RTC module. and real-time pressure Accurate timestamp tags are added and written to the built-in storage module. The MCU manages the real-time data packaging and transmission task: aligning the power, cadence, and timestamp data collected by the power meter with the laser ranging data and real-time differential pressure value, using high-frequency data to align to low-frequency data (the real-time data collected by the power meter is 10Hz, so it follows the power meter timestamp), averaging the real-time distance and real-time differential pressure within the corresponding timestamp, packaging the processed data sequentially according to timestamp, power, cadence, real-time distance, and real-time differential pressure, and transmitting it to the cloud database in real time via 5G communication.

[0050] like Figure 1-2 As shown, the high-precision power metering module is integrated: This module uses a Garmin RallyRK210 high-precision pedal power meter to transmit real-time power, cadence, and timestamp data packets via its native protocol (ANT+ or BLE). The acquisition terminal has built-in ANT+ or BLE to automatically identify the specific ID of the power meter from this bike and parse the power value (watts), cadence (RPM), and timestamp from the data packet. The system uses the MCU's internal time synchronization mechanism to map the received power data onto a global precise timeline, ensuring synchronization with data from other sensors.

[0051] like Figure 3 The deployment of the high-precision laser ranging module is shown below: This module's laser rangefinder sensor (such as the Aeye 4Sight) is fixed to the center axis of the bogie at the front of the vehicle using a 3D-printed bracket, with its emitting surface facing directly forward of the vehicle, emitting a laser radar array. A circular, high-reflectivity reflector (such as the 3M series) needs to be attached to the area under the seat of the vehicle in front. This arrangement ensures good visibility on both straight and curved roads. The sensor is set to continuous measurement mode, transmitting real-time distance data to the acquisition terminal via wired transmission at a frequency of 100 Hz. The system MCU automatically performs simple filtering on the distance data, eliminating special outliers caused by sensor acquisition instability. The module needs to be calibrated before use. Use a precise measuring tape to record laser rangefinder values ​​at multiple different distances and calibrate the laser rangefinder module.

[0052]

[0053]

[0054] in The calibration coefficients are used to correct for systematic errors in laser sensor measurements. To calibrate the number of times the experiment was performed, For the first The actual distance from the laser sensor to the reflector of the vehicle in front during the second calibration experiment. For the first The distance from the laser sensor to the reflector of the vehicle in front was tested in the second calibration experiment. This refers to the distance from the laser sensor to the reflector of the vehicle in front, measured during actual riding.

[0055] like Figure 4 The deployment of the miniature differential pressure anemometer module is shown below: This module uses the SDP1000L miniature differential pressure sensor, with a data acquisition frequency of 100Hz. To minimize interference with the rider and avoid directly exposing the sensor to the complex flow field of the bike frame, the miniature differential pressure anemometer sensor required specialized modification. One end of a carbon fiber extension rod is used to fix the integrated acquisition unit to the fork steering shaft via a high-strength plastic base. The other end is glued with a 2mm diameter miniature stainless steel tube as the total pressure probe. The probe tip is polished smooth, with the opening perpendicular to the rod axis. A silicone tube connects the probe to the SDP1000L sensor. The tube is kept as short and straight as possible to reduce signal acquisition delay. The static pressure end of the SDP1000L is connected to the cavity inside the integrated data acquisition and transmission device through a microporous filter, ensuring complete sealing of the external gaps to obtain a relatively stable static pressure reference. The system needs to be calibrated before use: fix the vehicle in a windless environment, use an adjustable blower and an anemometer to collect data from the SDP1000L sensor at the same time, record the differential pressure values ​​at different wind speeds multiple times, and calibrate the SDP1000L sensor based on the anemometer data.

[0056]

[0057]

[0058] in, The calibration coefficient is used to correct for systematic errors in SDP1000L sensor measurements. To calibrate the number of times the experiment was performed, For the first The second calibration experiment involved the actual applied wind speed of the SDP1000L sensor. For the first The wind speed measured by the sensor in the second calibration experiment. This refers to the wind speed measured by the sensor during actual cycling.

[0059] like Figure 1 As shown, the real-time cycling aerodynamic performance display module: This module, based on an internet communication module, is primarily responsible for visualizing and calculating the aerodynamic efficiency of cycling data collected by onboard sensors from multiple athletes in real time. The mobile device connects to a cloud database via the internet. The app adds start / stop data acquisition buttons. Based on app operation needs, it automatically acquires data from the cloud database in 10-second intervals, categorizing it by device ID address and using the acquisition timeline as the standard. It then updates and visualizes the real-time pedaling power, cadence, distance, and velocity data of multiple athletes. After data acquisition ends, it automatically calculates and displays the aerodynamic efficiency of multiple athletes throughout the entire cycling session.

[0060] like Figure 5 As shown, the aerodynamic efficiency evaluation method for team cycling events is as follows: S1: Data Acquisition System Calibration and Preparation. Before the formal test, all three acquisition units are calibrated. This includes: a) zeroing the power meter and calibrating the torque; b) calibrating the laser rangefinder sensor's range and adjusting the line of sight to ensure the lidar matrix is ​​aligned with the center of the reflector on the vehicle ahead; c) calibrating the differential pressure sensor's static zero point and dynamic calibration; simultaneously, check the battery level, real-time communication function, and internal storage space of all devices.

[0061] S2: Vehicle Preparation and Athlete Grouping. Assume four athletes are selected and equipped with cycling gear as usual. Prepare four racing bicycles according to the standard race configuration. Install the power meter and data acquisition unit onto the corresponding bicycle and turn on the power. Ensure all systems pass self-tests and the data indicator lights flash normally.

[0062] Formation testing was conducted. Four athletes formed a single file. The lead rider (#1) was assigned to ride at high speed over a constant distance (e.g., 200m in a sprint). After the system test program started, the following athletes (#2, #3, #4) maintained stable riding at designated following distances (e.g., 0.6m, 1.0m, 2.0m). Each distance combination (e.g., #2 at 0.6m, #3 at 2m) was continuously tested to obtain a sufficient number of steady-state data sets. All sensor data was recorded throughout the test.

[0063] S3: Real-time data acquisition and transmission. During the test, any acquisition unit automatically aligns with the timestamp and uses the 5G network to package and upload the athlete's real-time power, cadence, following distance, pressure, timestamp, and device ID to the cloud database. Data acquisition and uploading by other acquisition units follow the same rules.

[0064] S4: Real-time Data Monitoring and Export. The mobile app automatically retrieves data from the cloud database upon clicking "Get Data," allowing real-time monitoring of key data streams from multiple athletes (such as real-time power and distance changes) to ensure they are normal. Clicking "End Data Acquisition" automatically displays all data from the entire cycling session. After the test ends, the app automatically generates graphs and reports for "Following Distance and Power," "Following Distance and Wind Speed," and "Wind Speed ​​and Power" throughout the test. It supports exporting data and analysis reports from various sensors, categorized by ID.

[0065] It should be noted that in the above embodiments, as long as the technical solutions are not contradictory, they can be permuted and combined. Those skilled in the art can exhaust all possibilities based on the mathematical knowledge of permutation and combination. Therefore, the present invention will not describe the technical solutions after permutation and combination one by one, but it should be understood that the technical solutions after permutation and combination have been disclosed by the present invention.

[0066] 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 principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A group project dynamics energy efficiency evaluation system based on multi-sensor fusion, characterized in that: include: Multi-sensor synchronous acquisition and data transmission terminal: responsible for integrating the data collected by all sensors, completing local storage, performing time synchronization of data, and transmitting data in real time; High-precision power metering module: The power meter serves as a real-time power standard input source, continuously monitoring the mechanical power output by the athlete during pedaling; the acquisition terminal aligns the power data with other sensor data on the time axis by analyzing its data signals; High-precision laser ranging module: Installed in the area in front of the steering axle of the vehicle, the laser beam is emitted horizontally forward, aiming at a high-reflectivity reflector fixed under the rear seat of the vehicle in front; the straight-line distance between the two vehicles is calculated by measuring the round-trip time of the laser. ; Miniature differential pressure wind speed sensing module: Enables measurement of wind speed in the flow field at the front of the athlete's vehicle; Display module: Supports data visualization and data analysis report export.

2. The group project dynamics energy efficiency evaluation system based on multi-sensor fusion according to claim 1, characterized in that: The multi-sensor synchronous acquisition and data transmission terminal includes: a 5G networked data synchronization system that communicates with the foot-operated power meter via BLE and ANT+ communication protocols, and with the laser ranging module and the miniature differential pressure wind speed sensor module via serial port and I2C wired communication protocols; the terminal automatically runs customized firmware, which is responsible for acquiring and storing the raw data of each module and marking it with a precise timestamp; the processed data is transmitted to the cloud database in real time via 5G.

3. The group project dynamics energy efficiency evaluation system based on multi-sensor fusion according to claim 1, characterized in that: The high-precision power metering module uses a commercially available high-precision foot-operated power meter and wirelessly transmits power data via ANT+ or BLE protocol.

4. The group project dynamics energy efficiency evaluation system based on multi-sensor fusion according to claim 1, characterized in that: The high-precision laser ranging module uses a laser radar ranging sensor to dynamically measure the straight-line distance. for: in: This refers to the distance from the laser sensor to the reflector of the vehicle in front. The horizontal distance from the reflector of the vehicle in front to the vertical tangent of the rear wheel of the vehicle in front; This is the horizontal distance from the laser sensor to the vertical tangent of the vehicle's front wheel.

5. The group project dynamics energy efficiency evaluation system based on multi-sensor fusion according to claim 4, characterized in that: The high-precision laser ranging module needs to be calibrated before use. Use a precise tape measure to record the laser ranging values ​​at multiple different distances and then calibrate the laser ranging module. in, The calibration coefficients are used to correct for systematic errors in laser sensor measurements. To calibrate the number of times the experiment was performed, For the first The actual distance from the laser sensor to the reflector of the vehicle in front during the second calibration experiment. For the first The distance from the laser sensor to the reflector of the vehicle in front was tested in the second calibration experiment. This refers to the distance from the laser sensor to the reflector of the vehicle in front, measured during actual riding.

6. The group project dynamics energy efficiency evaluation system based on multi-sensor fusion according to claim 1, characterized in that: The miniature differential pressure wind speed sensing module comprises a miniature differential pressure sensor and an external total pressure probe; the acquisition terminal reads the differential pressure output of the SDP, which is proportional to the local dynamic pressure and is used to characterize the wind speed in the wake. The degree of attenuation increases with increasing spacing; in, To collect pressure in real time, This refers to air density.

7. The group project dynamics energy efficiency evaluation system based on multi-sensor fusion according to claim 6, characterized in that: The device is fixed in a windless environment. An adjustable blower and an anemometer are used to collect data from the miniature differential pressure anemometer module. The differential pressure values ​​at different wind speeds are recorded multiple times. The miniature differential pressure anemometer module is calibrated based on the data from the anemometer. in, The calibration coefficient is used to correct for systematic errors in SDP1000L sensor measurements. To calibrate the number of times the experiment was performed, For the first The second calibration experiment involved the actual applied wind speed of the SDP1000L sensor. For the first The wind speed measured by the sensor in the second calibration experiment. This refers to the wind speed measured by the sensor during actual cycling.

8. A method for evaluating the dynamic energy efficiency of group projects based on multi-sensor fusion, characterized in that: The group project dynamic energy efficiency evaluation system based on multi-sensor fusion as described in any one of claims 1-7 includes the following steps: S1, Data Acquisition System Calibration and Preparation: Before the formal test, all data acquisition units are calibrated. S2, Vehicle Preparation and Athlete Grouping: Assuming the selected number of athletes, have them wear their cycling gear in the usual manner, and prepare the competition vehicles corresponding to the number of athletes according to the standard competition configuration; install the power meter and data acquisition unit on the corresponding vehicles and turn on the power; ensure that all systems pass self-tests and the data indicator lights flash normally; Formation test execution: Athletes form a single file; a lead rider is assigned to test at high speed over a constant distance; after the system test program is started, the following athletes maintain stable riding at a specified following distance; each distance combination is continuously tested to obtain a sufficient number of steady-state data sets; the data acquisition terminal records all sensor data throughout the process; S3, Real-time Data Acquisition and Transmission: During the test, any acquisition unit automatically aligns with the timestamp and uses the 5G network to package and upload the athlete's real-time power, cadence, following distance, pressure, timestamp, and device ID to the cloud database. Data acquisition and uploading by other acquisition units are performed in accordance with this rule. S4, Real-time Data Monitoring and Export: Click to get data on the mobile APP, and the data will be automatically retrieved from the cloud database. You can check whether the key data streams of multiple athletes are normal in real time. Click to stop data acquisition, and the data of the entire cycling process will be automatically displayed. After the test ends, the system will automatically generate graph reports of "following distance and power", "following distance and wind speed", and "wind speed and power" throughout the entire test. It also supports exporting data and analysis reports collected by each sensor by ID category.

9. The method for evaluating the dynamic energy efficiency of group projects based on multi-sensor fusion according to claim 1, characterized in that: In S1, system calibration includes: Power meter zeroing and torque calibration; Laser rangefinder sensor ranging calibration and line-of-sight adjustment ensure that the lidar matrix is ​​aligned with the center of the reflector of the vehicle in front; Perform static zero-point and dynamic calibration of the differential pressure sensor; simultaneously, check the battery level, real-time communication function, and internal storage space of all devices.

10. The method for evaluating the dynamic energy efficiency of group projects based on multi-sensor fusion according to claim 1, characterized in that: A multi-sensor fusion-based method for evaluating the dynamic energy efficiency of team projects is applied to the aerodynamic energy efficiency evaluation of team cycling projects.

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