Forklift structure health state evaluation method and system

Abstract

The present application belongs to the technical field of multiple variable measurement, and particularly relates to a forklift structure health state evaluation method and system. The method comprises: symmetrically installing multiple strain force sensors at left and right fork root portions of a forklift, the strain force sensors being used for real-time collection of force signal of the left and right forks in a three-dimensional space; and real-time collection of six-degree-of-freedom motion state data of the forklift by an inertial measurement unit installed on a main vehicle body of the forklift, the six-degree-of-freedom motion state data comprising linear acceleration along three orthogonal axes and angular velocity around three orthogonal axes. The system deploys a high-bandwidth strain sensor array at the fork root portions and synchronously acquires six-degree-of-freedom motion state of the whole vehicle, constructs an online decoupling mechanism based on a rigid-flexible coupling dynamics model, and fundamentally breaks through the dependence of traditional weighing technology on static working conditions.

Description

Technical Field

[0001] This invention belongs to the field of multivariate measurement technology, specifically relating to a method and system for assessing the structural health status of forklifts. Background Technology

[0002] With the continuous improvement of automation in modern logistics and warehousing, the operational safety and structural reliability of forklifts, as core material handling equipment, are receiving increasing attention. Forklifts frequently experience dynamic loads during operation, especially under conditions such as high-speed travel, sudden stops, or turns, where the force distribution on the forks is prone to instantaneous shifts. Traditional structural health monitoring methods primarily rely on static load cells, measuring loads when the vehicle is completely stationary, failing to capture real-time force changes during handling. These methods neglect the multidimensional mechanical response of forklifts under complex motion conditions, making it difficult to accurately assess risks such as uneven loading, torsion, or localized stress concentration in the forks, thus failing to effectively warn of potential structural instability or fatigue damage.

[0003] Dynamic assessment of the health status of forklift structures has become a key technological direction for ensuring the safe operation of forklifts. This direction aims to acquire multi-point force information of the forks during movement in real time through a sensing system, and combine this with an intelligent analysis model to make online judgments on the balance and rationality of the force distribution. Its core objective is to achieve continuous monitoring of the forklift structure status and early anomaly identification without interrupting normal operations, providing data support for preventing structural failures.

[0004] Although some high-end forklifts have integrated basic weighing functions, their sensing systems generally lack the ability to adapt to dynamic working conditions and have not established a mapping relationship between stress distribution and structural health. On the one hand, a single static sensor cannot resolve complex loads under multiple degrees of freedom; on the other hand, even if multiple sensors are deployed, without a lightweight, low-latency edge intelligent processing mechanism, massive amounts of dynamic data are difficult to transform into effective criteria for structural condition assessment.

[0005] In typical scenarios such as high-frequency handling, turning in confined spaces, or driving on uneven ground, existing solutions are unable to identify structural imbalance risks caused by off-center loading in a timely manner due to slow response, insufficient accuracy, or excessive computational overhead, resulting in long-term latent safety hazards. Summary of the Invention

[0006] This invention provides a method and system for assessing the structural health status of forklifts, aiming to solve the technical problem of the inability to identify and warn of structural imbalance risks caused by uneven loading of forklift forks during dynamic handling. In existing technologies, the weighing sensors equipped on forklifts can only measure weight when the vehicle is completely stationary. Their working principle relies on static mechanical equilibrium conditions. When the forklift is in dynamic conditions such as driving, turning, acceleration / deceleration, or on bumpy roads, the sensor output signal is severely contaminated by inertial forces, centrifugal forces, and vibration interference, failing to accurately reflect the actual load distribution of the forks and thus losing the ability to distinguish uneven force on the left and right forks. This technical limitation prevents operators from knowing whether the forks are in a dangerous uneven loading state, and long-term operation can easily lead to safety accidents such as mast deformation, fork breakage, or vehicle overturning.

[0007] To overcome the aforementioned shortcomings, this invention proposes a method and system for assessing the structural health status of forklifts based on multi-source dynamic sensing and adaptive mechanical decoupling. This method abandons the traditional weighing logic that relies on static equilibrium assumptions, instead constructing a closed-loop assessment system that integrates high-frequency mechanical sensing, motion state identification, and real-time dynamic modeling. The system symmetrically arranges high-bandwidth strain gauge force sensor arrays at the roots of the left and right forks and simultaneously collects six-degree-of-freedom motion state data of the entire forklift. It utilizes an established rigid-flexible coupling dynamic model of the forklift to decouple dynamic disturbance forces online, thereby accurately separating the purely static component caused by uneven loading of the cargo. This enables millisecond-level quantitative assessment of the force difference between the left and right forks, and on this basis, generates a structural health index and safety warning signals.

[0008] This invention provides a method for assessing the structural health status of a forklift, comprising: symmetrically installing multiple strain gauge force sensors at the root of the left and right forks of the forklift, wherein the strain gauge force sensors are used to collect force signals of the left and right forks in three-dimensional space in real time; collecting six-degree-of-freedom motion state data of the forklift in real time through an inertial measurement unit installed on the main body of the forklift, wherein the six-degree-of-freedom motion state data includes linear acceleration along three orthogonal axes and angular velocity about three orthogonal axes; and based on a preset rigid-flexible coupling dynamic model of the forklift. Using the six-degree-of-freedom motion state data as input, the inertial disturbance force component acting on the root of the fork under the current dynamic working condition is calculated; the corresponding inertial disturbance force component is subtracted from the force signals of the left and right forks respectively to obtain the net load component of the left fork and the net load component of the right fork; the fork off-center load ratio is calculated based on the net load components of the left and right forks; the fork off-center load ratio is compared with a preset safety threshold, and when the fork off-center load ratio exceeds the safety threshold, a structural health warning signal is triggered.

[0009] As one embodiment of the present invention, the symmetrical installation of multiple strain gauge force sensors at the root of the left and right forks of the forklift specifically includes: installing a strain gauge in each of the four key stress concentration areas at the root of each fork: the upper flange, the lower flange, the inner side of the web, and the outer side of the web. Each strain gauge constitutes one arm of a Wheatstone bridge, forming a full-bridge measurement circuit to simultaneously sense the composite strain caused by bending moment, shear force, and torque.

[0010] In one embodiment of the present invention, the inertial measurement unit is fixedly installed at the center of the lower crossbeam of the forklift mast, and its sampling frequency is not less than 500 Hz. Its linear acceleration measurement range is ±10 times the gravitational acceleration, and its angular velocity measurement range is ±200 radians per second.

[0011] As one embodiment of the present invention, the process of establishing the rigid-flexible coupling dynamic model of the forklift includes: simplifying the forklift mast system into a two-degree-of-freedom transmission system composed of a rigid main beam and flexible forks; defining the generalized coordinates as the vertical displacement of the fork's center of mass and the pitch angle around the mast hinge point; and establishing a set of dynamic differential equations including the fork mass, moment of inertia, suspension stiffness, damping coefficient, and external excitation terms according to the Lagrange equations; wherein, the external excitation terms are determined by coordinate transformation of the linear acceleration and angular velocity output by the inertial measurement unit.

[0012] As one embodiment of the present invention, the calculation of the inertial disturbance force component acting on the root of the fork under the current dynamic working condition specifically includes: converting the linear acceleration data output by the inertial measurement unit to the local coordinate system of the root of the fork; calculating the translational inertial force by combining the mass distribution parameters of the fork; obtaining the angular acceleration by differentiating the angular velocity data with respect to time, and calculating the rotational inertial torque by combining the moment of inertia of the fork about the root; projecting the translational inertial force and the rotational inertial torque onto the measurement direction of the strain gauge force sensor to obtain the inertial disturbance force component corresponding to each sensor.

[0013] As one embodiment of the present invention, the formula for calculating the fork off-center load rate is: the fork off-center load rate is equal to the absolute value of the difference between the net load component of the left fork and the net load component of the right fork divided by the sum of the two, and then multiplied by 100%.

[0014] As one embodiment of the present invention, the preset safety threshold is dynamically adjusted according to the rated lifting capacity and fork length of the forklift, and its value ranges from 15% to 25%.

[0015] This invention provides a forklift structural health status assessment system, which includes:

[0016] The fork force sensing module is used to symmetrically install multiple strain gauge force sensors at the root of the left and right forks of the forklift to collect the force signals of the left and right forks in three-dimensional space in real time.

[0017] The motion state sensing module is used to collect the six-degree-of-freedom motion state data of the forklift in real time through an inertial measurement unit installed on the main body of the forklift.

[0018] The dynamic disturbance decoupling module is used to calculate the inertial disturbance force component acting on the root of the fork under the current dynamic working condition based on the preset rigid-flexible coupling dynamic model of the forklift, taking the six-degree-of-freedom motion state data as input, and subtracting the corresponding inertial disturbance force component from the force signals of the left and right forks respectively to obtain the net load component of the left fork and the net load component of the right fork.

[0019] The off-center load assessment module is used to calculate the off-center load rate of the forks based on the net load components of the left and right forks.

[0020] The safety warning module is used to compare the fork off-center load rate with a preset safety threshold. When the fork off-center load rate exceeds the safety threshold, a structural health warning signal is triggered.

[0021] As one embodiment of the present invention, the strain gauge force sensor in the fork force sensing module adopts a temperature self-compensating foil strain gauge with a nominal resistance of 120 ohms, a sensitivity coefficient of 2.0, and an operating temperature range of -40℃ to +80℃.

[0022] In one embodiment of the present invention, the dynamic interference decoupling module is deployed in the forklift on-board controller, which adopts a dual-core processor architecture, wherein the core is dedicated to running the dynamic decoupling algorithm, and the algorithm execution cycle is 2 milliseconds.

[0023] In one embodiment of the present invention, the safety warning module outputs a warning signal through an audible and visual alarm device in the forklift cab. The audible and visual alarm device includes a red flashing warning light and a buzzer. When the fork offset rate exceeds 80% of the safety threshold, a yellow warning is activated, and when it exceeds the safety threshold, a red emergency warning is activated.

[0024] As one embodiment of the present invention, the system further includes a data storage unit for recording the net load component of the forks, the fork off-center load rate, and the timestamp of the warning event during each operation, forming a structural health history archive for subsequent fatigue life analysis and preventive maintenance decisions.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] This invention constructs an online decoupling mechanism for dynamic disturbance forces based on a rigid-flexible coupling dynamic model by deploying a high-bandwidth strain sensor array at the root of the forks and simultaneously acquiring the six-degree-of-freedom motion state of the entire vehicle. This fundamentally breaks through the dependence of traditional weighing technology on static working conditions.

[0027] This invention can accurately and in real time separate the purely static load component caused by uneven loading of goods in complex dynamic scenarios such as forklift driving, turning, acceleration and deceleration, and traversing uneven road surfaces, thereby achieving millisecond-level quantitative assessment of uneven force on the left and right forks. Based on this assessment result, the generated structural health index and graded early warning signals can effectively prevent major safety accidents such as mast plastic deformation, fork root crack propagation, and even vehicle instability and overturning caused by long-term uneven loading operation.

[0028] The system's recorded structural health history provides a high-value data foundation for forklift preventative maintenance and remaining life prediction, significantly improving the safety, reliability, and lifecycle management efficiency of forklift operation. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall technical architecture of the forklift structural health status assessment method and system proposed in this invention;

[0030] Figure 2 This is a schematic diagram of the core principle framework of adaptive decoupling of dynamic disturbance force based on rigid-flexible coupling dynamic model in this invention;

[0031] Figure 3 This is a flowchart of the multi-source dynamic sensing logic framework for the synchronous acquisition of fork force sensing and motion status in this invention.

[0032] Figure 4 This is a flowchart illustrating the data processing logic framework for the online calculation of inertial disturbance force components and the separation of net load in this invention.

[0033] Figure 5 This is a flowchart illustrating the safety assessment logic framework for calculating fork offset loading rate, comparing thresholds, and generating graded early warnings in this invention.

[0034] Figure 6 This is a schematic diagram of the multi-level interaction relationship and data flow between the fork force sensing module and the vehicle controller in this invention. Detailed Implementation

[0035] Please refer to Figures 1 to 6This invention provides a method and system for assessing the structural health status of forklifts, aiming to solve the technical problem of the inability to identify and warn of structural imbalance risks caused by uneven loading of goods during dynamic handling operations. In existing technologies, the weighing sensors installed on forklifts can only measure weight when the vehicle is completely stationary, and their working principle relies on static mechanical equilibrium conditions. When the forklift is in dynamic conditions such as driving, turning, accelerating, decelerating, or traversing uneven road surfaces, the sensor output signal is severely contaminated by inertial forces, centrifugal forces, and vibration interference, failing to accurately reflect the actual load distribution of the forks and thus losing the ability to distinguish uneven force on the left and right forks. This technical limitation prevents operators from knowing whether the forks are in a dangerous uneven loading state, and long-term operation can easily lead to safety accidents such as mast plastic deformation, fork root crack propagation, and even vehicle overturning.

[0036] To overcome the aforementioned shortcomings, this invention proposes a forklift structural health status assessment method based on multi-source dynamic sensing and adaptive mechanical decoupling. This method abandons the traditional weighing logic that relies on static equilibrium assumptions and constructs a closed-loop assessment system integrating high-frequency mechanical sensing, motion state identification, and real-time dynamic modeling. The system symmetrically arranges high-bandwidth strain gauge force sensor arrays at the roots of the left and right forks and simultaneously collects six-degree-of-freedom motion state data of the entire forklift. It utilizes an established rigid-flexible coupling dynamic model of the forklift to decouple dynamic disturbance forces online, thereby accurately separating the purely static component caused by uneven loading of the cargo. This enables millisecond-level quantitative assessment of the force difference between the left and right forks, and on this basis, generates a structural health index and safety warning signals.

[0037] The forklift structural health status assessment method includes the following steps:

[0038] S1, Multiple strain gauge force sensors are symmetrically installed at the root of the left and right forks of the forklift. The strain gauge force sensors are used to collect the force signals of the left and right forks in three-dimensional space in real time.

[0039] S2, The inertial measurement unit installed on the main body of the forklift collects the six degrees of freedom motion state data of the forklift in real time. The six degrees of freedom motion state data includes linear acceleration along three orthogonal axes and angular velocity about three orthogonal axes.

[0040] S3, based on the preset rigid-flexible coupling dynamic model of the forklift, the six-degree-of-freedom motion state data is used as input to calculate the inertial disturbance force component acting on the root of the fork under the current dynamic working condition;

[0041] S4, subtract the corresponding inertial disturbance force components from the force signals of the left fork and the right fork respectively to obtain the net load components of the left fork and the net load components of the right fork.

[0042] S5. Calculate the fork off-center load rate based on the net load components of the left and right forks.

[0043] S6, compare the fork off-center load rate with a preset safety threshold, and trigger a structural health warning signal when the fork off-center load rate exceeds the safety threshold.

[0044] In step S1, multiple strain gauge force sensors are symmetrically installed at the root of the left and right forks of the forklift. One strain gauge is installed in each of the four key stress concentration areas at the root of each fork: the upper flange, lower flange, inner side of the web, and outer side of the web. Each strain gauge forms one arm of a Wheatstone bridge, creating a full-bridge measurement circuit to simultaneously sense the combined strain caused by bending moment, shear force, and torque. The strain gauge force sensors are temperature-compensated foil strain gauges with a nominal resistance of 120 ohms, a sensitivity coefficient of 2.0, and an operating temperature range of -40℃ to +80℃. All strain gauges are connected to a signal conditioning module via shielded twisted-pair cables. The signal conditioning module integrates a constant current excitation source, a low-pass anti-aliasing filter, and a programmable gain amplifier, with a sampling frequency set to 2000 Hz to ensure complete capture of the high-frequency dynamic response of the forks under bumpy road conditions or sharp turns.

[0045] In step S2, the inertial measurement unit (IMU) is fixedly installed at the center of the lower crossbeam of the forklift mast. Its sampling frequency is no less than 500 Hz, the linear acceleration measurement range is ±10 times the gravitational acceleration, and the angular velocity measurement range is ±200 radians per second. The IMU includes a triaxial MEMS accelerometer and a triaxial MEMS gyroscope, both sharing the same reference coordinate system and undergoing six-sided calibration and temperature drift compensation before leaving the factory. The data stream output by the IMU is synchronized with the data stream from the strain gauge force sensor in the time domain via a timestamp synchronization module, with the time synchronization error controlled within 50 microseconds. The timestamp synchronization module uses a hardware triggering mechanism, with the main controller issuing a unified sampling enable signal to ensure strict consistency of multi-source sensor data in physical time.

[0046] In step S3, based on the preset rigid-flexible coupling dynamics model of the forklift, the six-degree-of-freedom motion state data is used as input to calculate the inertial disturbance force components acting on the root of the forks under the current dynamic working condition. The process of establishing the rigid-flexible coupling dynamics model of the forklift includes: simplifying the forklift mast system into a two-degree-of-freedom transmission system composed of a rigid main beam and flexible forks; defining the generalized coordinates as the vertical displacement of the fork's center of mass and the pitch angle around the mast hinge point; and establishing a set of dynamic differential equations including the fork mass, moment of inertia, suspension stiffness, damping coefficient, and external excitation terms according to the Lagrange equations; wherein, the external excitation terms are determined by the linear acceleration and angular velocity output by the inertial measurement unit after coordinate transformation.

[0047] Specifically, let the total mass of the forks be... The horizontal distance from the center of mass of the forks to the mast hinge point is The moment of inertia of the forks about the hinge point is The equivalent suspension stiffness of the gantry is The equivalent damping coefficient is Define a generalized coordinate vector. ,in This represents the vertical displacement of the fork's center of gravity. Let be the pitch angle of the forks about the hinge point. Then the Lagrangian function of the system is:

[0048]

[0049] Consider small angle approximation And introduce a damping dissipation function We can obtain the system of differential equations of motion:

[0050]

[0051] in, and The external excitation force and torque are calculated from the vehicle acceleration and angular acceleration measured by the inertial measurement unit (IMU) after coordinate transformation. Let the linear acceleration of the vehicle body measured by the IMU be... angular velocity is The translational acceleration of the fork root in the local coordinate system is... for:

[0052]

[0053] in, Let be the rotation matrix from the vehicle body coordinate system to the local coordinate system at the root of the fork. The vector from the mounting point of the inertial measurement unit to the root of the fork. Angular acceleration. Calculated using numerical differentiation. Substituting these values ​​into the above equation, the instantaneous acceleration field at the root of the forks can be obtained. Combined with the mass distribution parameters of the forks, the translational inertial force can be calculated. The angular velocity data is differentiated with respect to time to obtain angular acceleration. Combined with the moment of inertia of the forks about the root, the rotational inertial torque is calculated. Finally, the translational inertial force and rotational inertial torque are projected onto the sensitive directions of each strain gauge force sensor to obtain the inertial disturbance force component corresponding to each sensor.

[0054] In step S4, the corresponding inertial disturbance force components are subtracted from the force signals of the left and right forks respectively to obtain the net load components of the left and right forks. The force signal of each fork is output by a full-bridge circuit composed of four strain gauges, converted into a voltage signal after signal conditioning, and then deduced into a three-dimensional force vector through a calibration coefficient matrix. and Since the forks primarily bear vertical loads, therefore... and As the original load signal. Correspondingly, the vertical component of the inertial disturbance force calculated from the dynamic model is: and Then the net load component of the left fork Net load component of the right fork To improve robustness, a sliding window mid-range filter with a window length of 20 milliseconds is applied to the net load component to suppress residual high-frequency noise.

[0055] In step S5, the fork off-center load ratio is calculated based on the net load components of the left and right forks. The formula for calculating the fork off-center load ratio is:

[0056]

[0057] This formula ensures that the eccentricity ratio varies continuously between 0 and 100%. The eccentricity ratio is 0 when the loads on the left and right sides are completely equal, and reaches 100% when one side is completely unloaded while the other side bears the full weight. During the calculation, if... If the load is less than 5% of the rated lifting capacity, it is determined to be in a state of no effective load, and the off-center load rate is set to 0 to avoid false alarms caused by no-load shaking.

[0058] In step S6, the fork off-center load rate is compared with a preset safety threshold. When the fork off-center load rate exceeds the safety threshold, a structural health warning signal is triggered. The preset safety threshold is dynamically adjusted based on the forklift's rated lifting capacity and fork length, and its value ranges from 15% to 25%. Specifically, the safety threshold... Determined by the following formula:

[0059]

[0060] in, The base threshold is set at 15%. This is the effective length of the currently installed forks; The standard fork length is typically 1.2 meters. The length correction factor is set to 10%. Therefore, for a two-meter-long extended fork, the safety threshold is 25%. The system has a two-level warning mechanism: when the fork offset rate exceeds 80% of the safety threshold, a yellow warning is activated, and the yellow warning light in the cab remains on, prompting the operator to adjust the cargo position; when the fork offset rate exceeds the safety threshold, a red emergency warning is activated, and the red flashing warning light and buzzer are activated simultaneously, forcing the operator to immediately stop work and reload.

[0061] The forklift structural health assessment system includes a fork force sensing module, a motion state sensing module, a dynamic disturbance decoupling module, an off-center load assessment module, and a safety warning module. The fork force sensing module symmetrically installs multiple strain gauge force sensors at the root of the left and right forks to collect three-dimensional force signals in real time. The motion state sensing module collects six-degree-of-freedom motion state data through an inertial measurement unit. The dynamic disturbance decoupling module is deployed in the forklift's onboard controller, which uses a dual-core processor architecture, with the core dedicated to running the dynamic decoupling algorithm, which has an execution cycle of 2 milliseconds. The off-center load assessment module calculates the off-center load rate based on the decoupled net load components. The safety warning module outputs tiered warning signals through an audible and visual alarm device. The system also includes a data storage unit to record the fork net load components, off-center load rate, and timestamps of warning events during each operation, forming a structural health history archive for subsequent fatigue life analysis and preventative maintenance decisions. The data storage unit uses non-volatile flash memory with a storage capacity of 8 gigabytes, supports cyclic overwrite, and retains complete operation records for the most recent 360 days.

[0062] The entire system operates as follows: After power-on initialization, each sensor completes self-testing and zero-point calibration; after operation begins, the fork force sensing module and motion status sensing module synchronously collect data; the dynamic interference decoupling module performs decoupling calculations every 2 milliseconds and outputs the net load component; the off-center load assessment module updates the off-center load rate in real time; the safety warning module continuously compares thresholds and drives the alarm device; the data storage unit records key parameters at a second-level granularity. All modules are interconnected via a controller area network bus with a communication baud rate of 500 kilobits per second, ensuring the real-time performance and reliability of data transmission.

[0063] Through the above methods and systems, this invention realizes real-time monitoring of dynamic off-center load under all working conditions of forklifts, solves the fundamental defect that traditional static weighing technology cannot be applied to mobile scenarios, and significantly improves the intelligence level of forklift structural safety monitoring and accident prevention capabilities.

Claims

1. A method for evaluating the health state of a forklift structure, characterized by, The method comprises the following steps: A plurality of strain force sensors are symmetrically installed at the left fork root and the right fork root of the forklift, and the strain force sensors are used to collect force signals of the left fork and the right fork in three-dimensional space in real time; A six-degree-of-freedom motion state data of the forklift is collected in real time by an inertial measurement unit installed on the main body of the forklift, and the six-degree-of-freedom motion state data includes linear acceleration along three orthogonal axes and angular velocity around three orthogonal axes; Based on a preset rigid-flexible coupling dynamics model of the forklift, the six-degree-of-freedom motion state data is taken as input to calculate an inertial interference force component acting on the fork root under a current dynamic working condition; The inertial interference force component is subtracted from the force signals of the left fork and the right fork respectively to obtain a left fork net load component and a right fork net load component; The left fork net load component and the right fork net load component are used to calculate a fork load deviation rate; The fork load deviation rate is compared with a preset safety threshold, and a structure health warning signal is triggered when the fork load deviation rate exceeds the safety threshold.

2. The method of assessing the structural health of a forklift truck according to claim 1, wherein A plurality of strain force sensors are symmetrically installed at the left fork root and the right fork root of the forklift, and the method comprises the following steps: One strain gauge is installed at each of four key stress concentration areas of the upper flange, the lower flange, the inner side of the web and the outer side of the web of each fork root, each strain gauge constitutes one arm of a Wheatstone bridge, and a full-bridge measurement circuit is formed to simultaneously sense the composite strain caused by the bending moment, the shear force and the torque.

3. The method of assessing the structural health of a forklift truck according to claim 2, wherein, The inertial measurement unit is fixedly installed at the center position of the lower crossbeam of the forklift mast, the sampling frequency is not less than 500 Hz, the linear acceleration measurement range is ±10 times of the gravity acceleration, and the angular velocity measurement range is ±200 rad / s.

4. The method of assessing the structural health of a forklift truck according to claim 3, wherein The establishment process of the rigid-flexible coupling dynamics model of the forklift comprises the following steps: The forklift mast system is simplified into a two-degree-of-freedom transmission system composed of a rigid main beam and a flexible fork; A generalized coordinate is defined as the vertical displacement of the mass center of the fork and the pitch angle around the hinge point of the mast; According to the Lagrange equation, a set of differential equations of dynamics is established, which includes the mass of the fork, the moment of inertia, the suspension stiffness, the damping coefficient and an external excitation term; The external excitation term is determined by the linear acceleration and the angular velocity output by the inertial measurement unit after coordinate transformation.

5. The method of assessing the structural health of a forklift truck according to claim 4, wherein, The inertial interference force component acting on the fork root under the current dynamic working condition is calculated, and the method comprises the following steps: The linear acceleration data output by the inertial measurement unit is converted to the local coordinate system of the fork root; The translational inertia force is calculated in combination with the mass distribution parameters of the fork; The angular acceleration is obtained by differentiating the angular velocity data with respect to time, and the rotational inertia moment is calculated in combination with the moment of inertia of the fork around the root; The translational inertia force and the rotational inertia moment are projected to the measurement direction of the strain force sensor to obtain the corresponding inertial interference force component of each sensor.

6. The method of assessing the structural health of a forklift truck according to claim 5, wherein, The left fork net load component and the right fork net load component are obtained, and the method comprises the following steps: The force signals of the left fork and the right fork are respectively converted into three-dimensional force vectors; The vertical component of the three-dimensional force vector is extracted as an original load signal; The left fork net load component and the right fork net load component are obtained by subtracting the vertical component of the corresponding inertial interference force from the original load signal respectively. The left and right fork net load components are subjected to sliding window median filtering processing.

7. The method of assessing the structural health of a forklift truck according to claim 6, wherein, The formula for calculating the fork load imbalance rate is: The fork load imbalance rate is equal to the absolute value of the difference between the left and right fork net load components divided by the sum of the two, multiplied by 100%. When the sum of the left and right fork net load components is less than 5% of the forklift's rated load, the fork load imbalance rate is set to 0.

8. The method of assessing the structural health of a forklift truck according to claim 7, wherein, The preset safety threshold is dynamically adjusted according to the forklift's rated load and fork length, with a value range of 15% to 25%. When the fork load imbalance rate exceeds 80% of the safety threshold, a yellow pre-warning signal is triggered. When the fork load imbalance rate exceeds the safety threshold, a red emergency pre-warning signal is triggered.

9. A forklift structure health status evaluation system characterized by, It includes: A fork force sensing module for symmetrically installing multiple strain force sensors at the left and right fork roots of the forklift to real-time collect force signals of the left and right forks in three-dimensional space; A motion state sensing module for real-time collecting six-degree-of-freedom motion state data of the forklift through an inertial measurement unit installed on the forklift's main vehicle body; A dynamic interference decoupling module for calculating the inertial interference force components acting on the fork roots under the current dynamic working condition based on a preset forklift rigid-flex coupling dynamics model, taking the six-degree-of-freedom motion state data as input, and subtracting the corresponding inertial interference force components from the force signals of the left and right forks respectively to obtain the left and right fork net load components; An imbalance evaluation module for calculating the fork load imbalance rate according to the left and right fork net load components; A safety pre-warning module for comparing the fork load imbalance rate with a preset safety threshold and triggering a structural health pre-warning signal when the fork load imbalance rate exceeds the safety threshold.

10. The forklift structural health state assessment system according to claim 9, wherein, The strain force sensors in the fork force sensing module use temperature self-compensating foil strain gauges with a nominal resistance of 120 ohms, a sensitivity coefficient of 2.0, and a working temperature range of -40°C to +80°C.

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