Intelligent schoolbag imbalance warning system and method based on dual-channel pressure induction

Abstract

The invention relates to the technical field of intelligent education equipment, and discloses an intelligent schoolbag imbalance warning system based on dual-channel pressure induction, which comprises a schoolbag body and two shoulder straps fixedly connected with the back surface of the schoolbag body, comprising a 3 * 6 matrix type flexible piezoresistive sensor, a dynamic sampling circuit, an environment compensation unit and an inertia measurement unit which are arranged in a shoulder strap on each side, and the sensing module is used for collecting double-shoulder pressure distribution data and backpack motion posture data. According to the intelligent schoolbag imbalance warning system and method based on dual-channel pressure induction, through cooperative work of matrix pressure sensors of shoulder straps on the two sides and a 9-axis IMU, the system can capture the pressure distribution difference of the two shoulders and the posture change of the schoolbag in real time; a time domain-frequency domain conjoint analysis algorithm and a Kalman filtering fusion technology are adopted, abnormal stress conditions under different gaits such as walking and running can be accurately recognized, and graded prompting is carried out through a three-color LED, vibration feedback and sound alarm.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent educational equipment, and in particular to an intelligent schoolbag imbalance warning system and method based on dual-channel pressure sensing. Background Art

[0002] In daily study and life, backpacks are essential items for students. Whether they are students, office workers or travelers, they often need to carry backpacks to load and carry items; especially during the teenage student period, they need to carry their study books and tools in backpacks, and as students' grades increase and the course content increases, the weight of books and tools will continue to increase; carrying a heavy backpack for a long time may cause discomfort to students' shoulders, neck and back, and even affect their physical health.

[0003] After searching, it was found that an adjustable backpack based on ergonomic carrying design disclosed in the invention patent with Chinese patent publication number "CN118806043A" includes: a backpack main body, a monitoring module and a feedback module, and the backpack main body is provided with a left shoulder strap, a right shoulder strap and a waist belt; the monitoring module includes a first pressure sensor, a second pressure sensor, a third pressure sensor and an IMU sensor; IMU sensors are respectively provided on the upper, middle and lower parts of the back of the backpack main body; the feedback module includes a data processing unit, a data analysis unit and a feedback unit; by setting up the monitoring module and the feedback module, the user's carrying posture and backpack weight are monitored in real time, the user's posture state is calculated and identified through the posture recognition algorithm, and IMU sensors are set in multiple positions on the back of the backpack to accurately monitor the tilt angle of the backpack. According to the set normal posture data comparison and analysis, the feedback unit outputs the analysis results and provides vibration and flashing prompts to help users adjust their posture and reduce the weight in time.

[0004] Although the device can monitor the carrying posture and backpack weight and provide real-time feedback, there are three major technical bottlenecks in backpack imbalance warning technology:

[0005] One-way monitoring flaws: Most products on the market use a single-point pressure trigger mechanism (such as an alarm for exceeding the total weight limit), which cannot identify the difference in force between the two shoulders;

[0006] Static detection limitations: Pressure analysis is only performed when the backpack is stationary, lacking the ability to dynamically monitor the backpack in motion.

[0007] Feedback delay: Traditional buzzer alarms have a response delay of more than 15 seconds, making real-time posture correction impossible.

[0008] Therefore, an intelligent backpack imbalance warning system and method based on dual-channel pressure sensing is proposed to solve the above-mentioned problems. Summary of the Invention

[0009] (1) Technical problems solved

[0010] In response to the shortcomings of the existing technology, the present invention provides an intelligent backpack imbalance warning system and method based on dual-channel pressure sensing, which has the advantages of real-time monitoring of the force difference between the two shoulders and dynamic correction of the carrier's posture. It solves the problems of the existing technology such as the inability to monitor the dynamic carrying status in real time, the lack of dual-channel pressure comparison analysis, and low warning feedback efficiency. Through precise monitoring and timely warnings, it helps the carrier maintain the correct carrying posture and prevent health problems.

[0011] (2) Technical solution

[0012] To achieve the above-mentioned real-time monitoring of the difference in force applied to both shoulders and the dynamic correction of the wearer's posture, the present invention provides the following technical solution: an intelligent backpack imbalance warning system based on dual-channel pressure sensing, comprising a backpack body and two shoulder straps fixedly connected to the back of the backpack body, including: a sensing module: the sensing module includes a 3×6 matrix flexible piezoresistive sensor built into each shoulder strap, a dynamic sampling circuit, an environmental compensation unit, and an inertial measurement unit, which is used to collect shoulder pressure distribution data and backpack motion posture data;

[0013] Intelligent Analysis Module: This module includes a dual-channel fusion processor and a dynamic threshold adjustment mechanism. The dual-channel fusion processor is used to perform a time-domain and frequency-domain joint analysis on the collected data to calculate the pressure difference between the two shoulders. The dynamic threshold adjustment mechanism is used to automatically adjust the pressure baseline value based on the weight and gait characteristics of the wearer.

[0014] Human-computer interaction module: The human-computer interaction module includes a three-color LED warning light ring, a tactile feedback unit, and a low-power Bluetooth module. It is used to provide light, vibration, and sound warning feedback according to the imbalance level, and realize data interaction with the mobile phone APP;

[0015] Lithium battery: The lithium battery is installed in the backpack body and is close to it.

[0016] Preferably, the three-color LED warning light ring is embedded in the annular groove at the top of the outer edge of the shoulder strap through a silicone sealing ring. The depth of the groove is 1.2 times the thickness of the light strip. The light ring is flush with the surface of the shoulder strap. The tactile feedback unit is a miniature piezoelectric ceramic vibrator arranged on the left and right shoulder straps, and a buzzer is installed on the surface of the backpack body.

[0017] Preferably, the dynamic sampling circuit is arranged in a waterproof box in the bottom interlayer of the backpack body, and is connected to the shoulder strap pressure-sensitive array through a flexible cable. The cable is routed along the inner side of the shoulder strap and covered with a TPU waterproof casing. The environmental compensation unit eliminates the interference of environmental factors on the pressure data through a temperature / humidity compensation algorithm.

[0018] Preferably, the 3×6 matrix flexible piezoresistive sensor has a thickness of 0.5 mm, and the sensor array is arranged laterally along the middle section of the inner side of the shoulder strap, with the long side parallel to the direction of the shoulder strap.

[0019] Preferably, the inertial measurement unit is a 9-axis sensor, which is fixed in the padding of the central area of ​​the back of the backpack body and is arranged in a triangular positioning layout with the shoulder strap pressure-sensitive array.

[0020] Preferably, the dual-channel fusion processor includes:

[0021] a first data processing unit configured to perform time domain feature extraction on the left shoulder strap pressure sensitive array data;

[0022] a second data processing unit configured to perform frequency domain feature extraction on the right shoulder strap pressure-sensitive array data;

[0023] The fusion decision unit is configured to perform Kalman filtering on the time domain and frequency domain features and output pressure difference data.

[0024] Preferably, the dynamic threshold adjustment mechanism includes:

[0025] An initial threshold calculation module is configured to calculate a reference threshold based on the inputted weight data of the carrier using the formula T0=k·W+b, where k=0.025, b=3.5, and W is the body weight;

[0026] The gait feature learning module is configured to identify three gaits: walking, running, and climbing stairs, using IMU data, and to establish a pressure fluctuation feature library for each gait;

[0027] The threshold adaptive adjustment module is configured to retrieve the corresponding threshold correction coefficient λ from the feature library according to the current gait, and the final threshold T = T0·λ.

[0028] Preferably, the time domain-frequency domain joint analysis algorithm includes:

[0029] Perform S-curve filtering on the original pressure data, and the filter coefficient α ranges from 0.3 to 0.5;

[0030] A 500ms sliding time window was used for short-time Fourier transform, and the frequency resolution was set to 10Hz;

[0031] The power spectrum density of pressure fluctuations in adjacent time windows is calculated, and the energy values ​​of characteristic frequency points are extracted.

[0032] The intelligent backpack imbalance warning method based on dual-channel pressure sensing includes the following steps:

[0033] S1: Data acquisition: The shoulder strap pressure-sensitive array and IMU sensor are used to collect shoulder pressure data and backpack body motion posture data in real time, and then processed by the dynamic sampling circuit and environmental compensation unit;

[0034] S2: Data processing: The dual-channel fusion processor performs time domain smoothing and time-domain-frequency domain joint analysis on the collected data to calculate the pressure difference between the two shoulders;

[0035] S3: Threshold determination: The calculated shoulder pressure difference is compared with the dynamic threshold automatically adjusted according to the weight and gait characteristics of the bearer to determine the imbalance level;

[0036] S4: Warning feedback: According to the imbalance level, light and vibration warnings are given through the three-color LED warning light ring and tactile feedback unit. The buzzer is activated when necessary, and the data is synchronized to the mobile phone APP through the low-power Bluetooth module.

[0037] Preferably, the imbalance level is divided into three levels: when the pressure difference is greater than 15%, a first-level warning is triggered, and the blue LED flashes slowly; when the pressure difference is greater than 25%, a second-level warning is triggered, the orange LED flashes quickly and the vibration plate vibrates briefly; when the pressure difference is greater than 35%, a third-level warning is triggered, the red LED is always on, the vibration plate continues to vibrate strongly and the buzzer alarm is activated.

[0038] (3) Beneficial effects

[0039] Compared with the prior art, the present invention provides a smart backpack imbalance warning system and method based on dual-channel pressure sensing, which has the following beneficial effects:

[0040] 1. This intelligent backpack imbalance warning system and method based on dual-channel pressure sensing uses matrix pressure sensors on both shoulder straps and a 9-axis IMU to work together to capture differences in pressure distribution on both shoulders and changes in backpack posture in real time. It uses a time-domain-frequency domain joint analysis algorithm and Kalman filter fusion technology to accurately identify abnormal stress conditions in different gaits, such as walking and running. It also provides graded prompts through three-color LEDs, vibration feedback, and sound alarms to help the wearer adjust their posture in a timely manner and prevent health problems such as scoliosis.

[0041] 2. This intelligent backpack imbalance warning system and method based on dual-channel pressure sensing is equipped with a dynamic threshold adjustment mechanism that can automatically calibrate the pressure baseline value based on the user's weight data (formula T0 = k·W + b) and real-time gait characteristics (identified through the HMM model). For example, a larger fluctuation threshold (λ = 1.2) is allowed when running, and the sensitivity is increased when stationary (λ = 0.8), realizing personalized imbalance judgment. This adaptive capability enables the system to maintain high-precision warnings in different usage scenarios, reduce the false alarm rate, and significantly improve the user experience and health protection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a front structural stereogram of the school bag body of the present invention;

[0043] Figure 2 This is a three-dimensional diagram of the back structure of the school bag body of the present invention;

[0044] Figure 3 This is a schematic diagram of the module flow of the intelligent backpack imbalance warning system based on dual-channel pressure sensing of the present invention;

[0045] Figure 4 This is a flow chart of the intelligent backpack imbalance warning method based on dual-channel pressure sensing of the present invention.

[0046] In the picture: 1. Backpack body; 2. Shoulder strap; 3. Sensor; 4. Micro piezoelectric ceramic vibrator; 5. Three-color LED warning light ring; 6. Dual-channel fusion processor; 7. Intelligent analysis module; 8. Bluetooth module; 9. Buzzer; 10. Lithium battery. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] Example 1

[0049] See also Figure 1-4 ,An intelligent backpack imbalance warning system based on dual-channel pressure sensing, including a sensing module;

[0050] Distributed pressure-sensitive array: A symmetrical 3×6 matrix-type flexible piezoresistive sensor 3 is built into the middle section of the inner side of each shoulder strap 2 of the backpack body 1. The sensor is only 0.5mm thick and the flexible material can closely fit the shoulder curve, covering the main stress points of the shoulder, and collecting real-time pressure distribution data in each area.

[0051] Dynamic sampling circuit: This circuit is located in a waterproof box in the bottom layer of the backpack body 1, close to the wiring endpoint of the sensor 3 in the shoulder strap 2. It uses a 50Hz high-frequency sampling chip with a measurement accuracy of ±2%. It can simultaneously collect pressure data from the left and right shoulder straps 2 and IMU attitude signals.

[0052] Environmental compensation unit: integrated with the dynamic sampling circuit, or placed on the top outside of the backpack body 1, with a built-in temperature / humidity sensor and supporting compensation algorithm to eliminate the interference of environmental factors on pressure data;

[0053] Inertial Measurement Unit (IMU): A 9-axis IMU sensor is installed in the padding of the central area of ​​the back of the backpack body 1, close to the human back, to collect backpack motion posture data.

[0054] Intelligent analysis module

[0055] Dual-channel fusion processor 6: Placed in the upper back layer of the backpack body 1 or in the waist and back support structure, it uses a low-power MCU chip, such as the ARM Cortex-M series, and has a built-in time-domain-frequency domain joint analysis algorithm to simultaneously process the pressure data of the left and right shoulder straps 2 and the IMU attitude data;

[0056] Dynamic threshold adjustment mechanism: The algorithm is integrated into the dual-channel fusion processor 6. The wearer's weight data is input through the external side button of the backpack body 1 or the mobile phone app. The pressure baseline value is automatically calibrated according to the weight and adaptively adjusted according to the user's gait characteristics.

[0057] Human-computer interaction module

[0058] Three-color LED warning light ring 5: A full-circle LED light strip with red, orange and blue colors, waterproof packaging, embedded in the annular groove at the top of the outer edge of the shoulder strap 2 through a silicone sealing ring, and presents different light warnings according to the imbalance level.

[0059] Tactile feedback unit: A micro piezoelectric ceramic vibrator 4 is integrated in the middle of the outer side of the left and right shoulder straps 2 or the connection between the back shoulder straps, and the warning level is distinguished by vibrations of different frequencies.

[0060] Bluetooth module 8: Located on the surface of the backpack body 1, it supports connection with the mobile phone APP to achieve data synchronization and personalized settings.

[0061] System hardware installation method:

[0062] Installation of sensor 3 in shoulder strap 2: A dedicated sensor 3 storage groove is pre-sewn in the middle section of the inner side of the left and right shoulder straps 2 of the backpack body 1, and the 3×6 matrix flexible piezoresistive sensor is flatly embedded to ensure that the sensor 3 has good contact with the shoulder and is connected to the dynamic sampling circuit inside the backpack body 1 through a flexible cable.

[0063] IMU sensor installation: If you choose to install it in the central area of ​​the back of the backpack body 1, reserve a groove in the back padding to fix the IMU sensor in it;

[0064] Circuit module installation: Circuit modules such as the dynamic sampling circuit, environmental compensation unit, and dual-channel fusion processor 6 are centrally installed in the interlayer above the back of the backpack body 1 or in the waist and back support structure, and signal connection between modules is achieved through reasonable wiring; the low-power Bluetooth module 8 is installed on the surface of the backpack body 1, and an antenna is extended to enhance signal transmission.

[0065] Installation of human-computer interaction components: Fix the three-color LED warning light ring 5 around the top edge of the outer edge of the shoulder strap 2 to ensure that the light strip is evenly distributed; stick the micro piezoelectric ceramic vibrator 4 at the connection of the back shoulder strap 2 and connect it to the drive circuit; install the set buzzer 9 on the surface of the backpack body 1 and in a position that is not easily squeezed.

[0066] Example 2

[0067] See also Figure 1-3 , the intelligent backpack imbalance warning system based on dual-channel pressure sensing includes:

[0068] 1: Dual-modal data fusion algorithm

[0069] 1. Synchronous collection of pressure and posture data

[0070] Timestamp alignment: The dynamic sampling circuit synchronously collects data from the pressure sensitive array (36 channels) and the IMU (9-axis data) at a frequency of 50 Hz, and generates a global timestamp T for each sampling cycle. n , ensure the pressure data P i,j (T n ) and posture data A(T n )(including acceleration and angular velocity) are strictly synchronized.

[0071] Spatial mapping model: Establish the mapping relationship between shoulder strap pressure distribution and backpack posture, define the shoulder strap coordinate system O-XYZ (the origin is located at the midpoint of both shoulders, the X-axis is horizontal to the right, and the Y-axis is vertically upward), and convert the pressure data into force components in the global coordinate system through the IMU attitude angle (pitch angle θ, roll angle φ, yaw angle ψ).

[0072] 2. Time-frequency domain joint analysis process

[0073] (1) Time domain feature extraction

[0074] Pressure gradient calculation: Calculate the transverse and longitudinal pressure gradients for each side of the 3×6 matrix sensor data:

[0075] lateral gradient

[0076] Vertical gradient

[0077] (Δx=20mm, Δy=15mm is the sensor spacing, i=1-3, j=1-6)

[0078] Dynamic Imbalance Factor: Definition Avoiding the denominator to be zero is used to characterize the asymmetry of pressure distribution on both shoulders.

[0079] (2) Frequency domain feature extraction

[0080] Gait frequency analysis: Perform fast Fourier transform (FFT) on the IMU acceleration data to extract the characteristic frequency f of the walking cycle w (normal cadence is about 1-2Hz), establish the correlation between pressure fluctuations and gait: (N=50 is the number of sampling points).

[0081] Abnormal vibration detection: Setting the frequency domain threshold T f =3f w , when the pressure fluctuation frequency exceeds T f When , it is determined to be non-periodic imbalance (such as a sudden tilt of a backpack).

[0082] (3) Data fusion formula

[0083] Fusion feature vector V = [F d ,max(P fft ),θ,φ], and generate the comprehensive imbalance index S through weighted summation:

[0084]

[0085] (The weights are optimized through machine learning training, and the objective function is the imbalanced grade classification accuracy).

[0086] 2. Implementation of Dynamic Threshold Adjustment Mechanism

[0087] 1. Initial threshold calculation

[0088] Weight benchmark formula: Based on ergonomic data, a linear relationship between the initial threshold and body weight W (kg) is established:

[0089] Level 1 threshold T1 = 0.15 + 0.002W

[0090] Secondary threshold T2 = 0.25 + 0.003W

[0091] Level 3 threshold T3=0.35+0.004W

[0092] (For example, for a child weighing 30 kg, the initial T1 is 0.21, which means the pressure difference is greater than 2196, triggering a.

[0093] 2. Adaptive gait correction

[0094] Gait classification model: Hidden Markov model (HMM) is used to identify gait of IMU data, and three states are defined: S1 (walking, transition probability matrix A1), S2 (running, A2), and S3 (stationary, A3). The current gait probability P(S) is calculated by Viterbi algorithm. k |A), take the state with the maximum probability as the current gait.

[0095] Threshold correction factor: adjust the threshold according to the gait type:

[0096] Walking state: λ = 1.0 (default coefficient)

[0097] Running state: λ = 1.2 (allowing greater pressure fluctuations)

[0098] Static state: λ = 0.8 (increases warning sensitivity) Final threshold T = T0·λ (T0 is the initial threshold).

[0099] 3. Implementation Effect Verification

[0100] Static testing

[0101] Test conditions: The subject was standing still with a backpack loaded with 5kg, and the pressure difference between the shoulders was manually adjusted.

[0102] Results: When the pressure difference reached 16%, the blue LED flashed slowly (level 1 warning). There was a 5% error with the theoretical threshold value T1 = 0.15 + 0.002 × 30 = 0.21 (21%), which was caused by the sensor zero drift. After correction by the environmental compensation algorithm, the error was <1%.

[0103] Dynamic testing

[0104] Test scenario: The subject walks at a speed of 1.5m / s and suddenly tilts his backpack to the left.

[0105] Data Records:

[0106] The IMU detects a roll angle of φ = 12°, and the sum of the pressure difference matrix D is 28%;

[0107] The system triggers a quick flash of the orange LED and vibration (secondary warning) within 180ms, and the feedback delay is lower than the design target of 200ms.

[0108] Example 3

[0109] See also Figure 1-4 The intelligent backpack imbalance warning method based on dual-channel pressure sensing includes the following steps:

[0110] S1: Data acquisition: The shoulder strap 2 pressure-sensitive array and IMU sensor are used to collect shoulder pressure data and the movement posture data of the backpack body 1 in real time, and then processed by the dynamic sampling circuit and environmental compensation unit;

[0111] S2: Data processing: The dual-channel fusion processor 6 performs time domain smoothing and time domain-frequency domain joint analysis on the collected data to calculate the pressure difference between the two shoulders;

[0112] S3: Threshold determination: The calculated shoulder pressure difference is compared with the dynamic threshold automatically adjusted according to the weight and gait characteristics of the bearer to determine the imbalance level;

[0113] S4: Warning feedback: According to the imbalance level, light and vibration warnings are given through the three-color LED warning light ring 5 and the tactile feedback unit. The buzzer 9 is activated when necessary, and the data is synchronized to the mobile phone APP through the low-power Bluetooth module 8.

[0114] The imbalance level is divided into three levels: when the pressure difference is greater than 15%, the first level alarm is triggered and the blue LED flashes slowly; when the pressure difference is greater than 25%, the second level alarm is triggered, the orange LED flashes quickly and the vibrator vibrates briefly; when the pressure difference is greater than 35%, the third level alarm is triggered, the red LED is always on, the vibrator vibrates continuously and strongly and the buzzer is activated to alarm.

[0115] In summary, the intelligent backpack imbalance warning system and method based on dual-channel pressure sensing, through the collaborative work of the matrix pressure sensors of the bilateral shoulder straps 2 and the 9-axis IMU, the system can capture the differences in pressure distribution on both shoulders and changes in the backpack posture in real time; using the time domain-frequency domain joint analysis algorithm and Kalman filter fusion technology, it can accurately identify abnormal force conditions in different gaits such as walking and running, and through three-color LED, vibration feedback and sound alarm graded prompts, help the carrier to adjust the posture in time and prevent health problems such as scoliosis.

[0116] In addition, the system is equipped with a dynamic threshold adjustment mechanism, which can automatically calibrate the pressure reference value based on the user's weight data formula T0=k·W+b and real-time gait characteristics through HMM model identification. For example, a larger fluctuation threshold λ=1.2 is allowed when running, and the sensitivity is increased to λ=0.8 when stationary, to achieve personalized imbalance judgment. This adaptive capability enables the system to maintain high-precision warnings in different usage scenarios, reduce false alarm rates, and significantly improve user experience and health protection effects.

[0117] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0118] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent backpack imbalance warning system based on dual-channel pressure sensing, comprising a backpack body (1), and two shoulder straps (2) fixedly connected to the back of the backpack body (1), characterized in that: include: Sensing module: The sensing module includes a 3×6 matrix flexible piezoresistive sensor (3) built into each shoulder strap (2), a dynamic sampling circuit, an environmental compensation unit, and an inertial measurement unit, and is used to collect shoulder pressure distribution data and backpack movement posture data; Intelligent analysis module (7): The intelligent analysis module (7) includes a dual-channel fusion processor (6) and a dynamic threshold adjustment mechanism. The dual-channel fusion processor (6) is used to perform a time domain-frequency domain joint analysis on the collected data to calculate the pressure difference between the two shoulders. The dynamic threshold adjustment mechanism is used to automatically adjust the pressure reference value according to the weight and gait characteristics of the bearer. Human-computer interaction module: The human-computer interaction module includes a three-color LED warning light ring (5), a tactile feedback unit and a low-power Bluetooth module (8), which is used to provide light, vibration and sound warning feedback according to the imbalance level and realize data interaction with the mobile phone APP; Lithium battery (10): The lithium battery (10) is installed in and close to the backpack body (1).

2. The intelligent backpack imbalance warning system based on dual-channel pressure sensing according to claim 1 is characterized by: The three-color LED warning light ring (5) is embedded in the annular groove at the top of the outer edge of the shoulder strap (2) through a silicone sealing ring. The depth of the groove is 1.2 times the thickness of the light belt. The light ring is flush with the surface of the shoulder strap (2). The tactile feedback unit is a micro piezoelectric ceramic vibration piece (4) arranged on the left and right shoulder straps (2). A buzzer (9) is installed on the surface of the backpack body (1).

3. The intelligent backpack imbalance warning system based on dual-channel pressure sensing according to claim 1 is characterized by: The dynamic sampling circuit is arranged in a waterproof box in the bottom interlayer of the backpack body (1), and is connected to the pressure-sensitive array of the shoulder strap (2) via a flexible cable. The cable is routed along the inner side of the shoulder strap (2) and is covered with a TPU waterproof sleeve. The environmental compensation unit eliminates interference of environmental factors on pressure data through a temperature / humidity compensation algorithm.

4. The intelligent backpack imbalance warning system based on dual-channel pressure sensing according to claim 1 is characterized by: The 3×6 matrix-type flexible piezoresistive sensors (3) have a thickness of 0.5 mm, and the sensor (3) array is arranged transversely along the middle section of the inner side of the shoulder strap (2), with the long sides parallel to the direction of the shoulder strap (2).

5. The intelligent backpack imbalance warning system based on dual-channel pressure sensing according to claim 1 is characterized by: The inertial measurement unit (IMU) is a 9-axis sensor, fixed in the padding of the central area of ​​the back of the backpack body (1), and arranged in a triangular positioning with the pressure-sensitive array of the shoulder strap (2).

6. The intelligent backpack imbalance warning system based on dual-channel pressure sensing according to claim 1 is characterized by: The dual-channel fusion processor (6) includes: a first data processing unit configured to perform time domain feature extraction on the left shoulder strap pressure sensitive array data; a second data processing unit configured to perform frequency domain feature extraction on the right shoulder strap pressure-sensitive array data; The fusion decision unit is configured to perform Kalman filtering on the time domain and frequency domain features and output pressure difference data.

7. The intelligent backpack imbalance warning system based on dual-channel pressure sensing according to claim 1 is characterized by: The dynamic threshold adjustment mechanism includes: an initial threshold calculation module configured to calculate a reference threshold based on the inputted weight data of the carrier using the formula T0=k·W+b, where k=0.025, b=3.5, and W is the body weight (kg); The gait feature learning module is configured to identify three gaits: walking, running, and climbing stairs, using IMU data, and to establish a pressure fluctuation feature library for each gait; The threshold adaptive adjustment module is configured to retrieve the corresponding threshold correction coefficient λ from the feature library according to the current gait, and the final threshold T = T0·λ.

8. The intelligent backpack imbalance warning system based on dual-channel pressure sensing according to claim 1 is characterized by: The time domain-frequency domain joint analysis algorithm includes: Perform S-curve filtering on the original pressure data, and the filter coefficient α ranges from 0.3 to 0.5; A 500ms sliding time window was used for short-time Fourier transform, and the frequency resolution was set to 10Hz; The power spectrum density of pressure fluctuations in adjacent time windows is calculated, and the energy values ​​of characteristic frequency points are extracted.

9. A smart backpack imbalance warning method based on dual-channel pressure sensing, applied to the smart backpack imbalance warning system based on dual-channel pressure sensing as described in claims 1-8, characterized in that: The following steps are involved: S1: Data acquisition: The shoulder strap (2) pressure sensitive array and IMU sensor are used to collect the shoulder pressure data and the movement posture data of the backpack body (1) in real time, and then processed by the dynamic sampling circuit and the environmental compensation unit; S2: Data processing: The dual-channel fusion processor (6) performs time domain smoothing and time domain-frequency domain joint analysis on the collected data to calculate the pressure difference between the two shoulders; S3: Threshold determination: The calculated shoulder pressure difference is compared with the dynamic threshold automatically adjusted according to the weight and gait characteristics of the bearer to determine the imbalance level; S4: Warning feedback: According to the imbalance level, light and vibration warnings are given through the three-color LED warning light ring (5) and the tactile feedback unit. The buzzer (9) is activated when necessary, and the data is synchronized to the mobile phone APP through the low-power Bluetooth module (8).

10. The smart backpack imbalance warning method based on dual-channel pressure sensing according to claim 9, characterized in that: The imbalance level is divided into three levels: when the pressure difference is greater than 15%, a first-level warning is triggered, and the blue LED flashes slowly; when the pressure difference is greater than 25%, a second-level warning is triggered, and the orange LED flashes quickly and the vibrating plate vibrates briefly; when the pressure difference is greater than 35%, a third-level warning is triggered, and the red LED is always on, the vibrating plate vibrates continuously and strongly, and the buzzer (9) is activated to alarm.

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