Excavator Stress-Strain Monitoring Method, Control Method, System, Equipment, Storage Medium, and Excavator Based on Multi-Sensor Fusion

Abstract

This invention discloses a multi-sensor fusion-based method, control method, system, equipment, storage medium, and excavator for excavator stress and strain monitoring, relating to the field of engineering machinery control technology. The monitoring method includes: S1 sensor array data acquisition; S2 noise reduction processing; S3 fusion modeling and state assessment; and S4 load path analysis. The control method specifically includes: A1 graded threshold setting, A2 control strategy implementation, and A3 control strategy library matching. The system includes: a sensor array assembly, a data processing module, and a control module. This invention overcomes existing limitations through multi-dimensional sensing technology. By integrating multi-source data such as structural stress, hydraulic load, and operating posture, and employing a noise reduction fusion algorithm to eliminate environmental interference, it overcomes the limitations and data distortion problems of single-sensor monitoring, achieving real-time monitoring of the stress state of key excavator components and significantly improving the reliability of monitoring results under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of engineering machinery control technology, specifically to a method, control method, system, equipment, storage medium, and excavator for stress and strain monitoring based on multi-sensor fusion. Background Technology

[0002] As a core piece of equipment in earthmoving construction, the structural health and operational efficiency of excavators directly impact project safety and progress. With the development of intelligent technologies, real-time monitoring of the stress and strain status of key excavator components and precise control have become crucial for improving equipment reliability and reducing maintenance costs. Traditional monitoring methods often rely on single-type sensors (such as strain gauges and inclinometers), making it difficult to comprehensively cover dynamic load changes under complex working conditions. Intelligent control technology based on multi-sensor fusion, by integrating multi-dimensional sensing data and combining it with adaptive algorithms, provides a new technical approach to solving this problem.

[0003] Currently, excavator stress and strain monitoring and control technology faces multiple challenges. Single sensors are susceptible to environmental interference (such as vibration and dust), leading to data distortion. For example, traditional strain gauges are prone to drift errors under high-frequency vibration conditions, while wire sensors may experience reduced accuracy due to mechanical wear after prolonged use. On the other hand, existing control systems are mostly based on preset rules or simple PID algorithms, making it difficult to adapt to complex working conditions. For instance, when excavation resistance changes abruptly, traditional control strategies may lead to engine overload or decreased hydraulic system efficiency. Furthermore, distributed sensor layouts not only increase hardware costs but also suffer from data fusion lag, failing to meet real-time decision-making requirements. For example, some existing solutions require disassembling components to obtain self-weight stress data, causing interruptions in the monitoring process. Summary of the Invention

[0004] The purpose of this invention is to provide a method, control method, system, device, storage medium, and excavator for stress and strain monitoring of excavators based on multi-sensor fusion, so as to solve the problems currently in the market mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the stress and strain of excavators based on multi-sensor fusion, comprising:

[0006] S1 Sensor Array Data Acquisition: By deploying a sensor array in the stress concentration area of ​​the excavator, in conjunction with the pressure sensor in the hydraulic circuit and the body IMU module, the sensor feedback data from various parts is collected and a multi-dimensional dataset is formed.

[0007] S2 noise reduction: High-frequency vibration noise in the multi-dimensional dataset obtained from S1 is filtered out while retaining the effective stress fluctuation characteristics;

[0008] S3 Fusion Modeling and Condition Assessment: Establish a multi-source data fusion model, automatically allocate sensor weights according to working conditions, and calculate the equivalent stress and strain of key components in real time;

[0009] S4 Load Path Analysis: Combining sensor data and hydraulic pressure gradient, it is determined whether the load is due to hydraulic overload or external impact.

[0010] If a high-stress area is accompanied by a sharp rise in hydraulic pressure, it is determined to be a hydraulic overload; if there is no hydraulic abnormality in the high-stress area, it is determined to be an external impact.

[0011] Preferably, the sensor feedback data collected in the S1 sensor array data acquisition are structural strain, hydraulic pressure and pitch / rollover angle;

[0012] The resulting multidimensional dataset includes multidimensional data on mechanics, hydraulics, and kinematics.

[0013] Preferably, the S2 noise reduction process uses wavelet packet decomposition for noise reduction, specifically as follows:

[0014] By selecting an appropriate wavelet basis and decomposition level, the noisy signal is decomposed into multiple sub-bands of different frequencies. The processed wavelet packet coefficients are then used for inverse decomposition to reconstruct the denoised signal.

[0015] The optimization is achieved through Kalman filtering, specifically as follows:

[0016] The system's state equation and observation equation are determined, initial state estimates and covariance matrices are set, and a state-space model is established. The denoised signal is used as the observation, and the signal is further optimized through iterative calculations following the prediction and update steps of Kalman filtering. The state-space model formula is as follows:

[0017] ;

[0018] In the formula, X is the equivalent stress of the state variable, U is the hydraulic load of the input variable, the process noise is W~N(0, σ²), and the root mean square error RMSE after iteration is ≤5%.

[0019] Preferably, in S3 fusion modeling and state assessment, the multi-source data fusion model is established based on the weighted least squares method to create a three-dimensional mapping model with stress, load, and attitude as the mapping relationship, as shown in the following formula:

[0020] ;

[0021] In the formula, the weight coefficient ω is obtained through training with working condition data;

[0022] The calculation of equivalent stress and strain of key components specifically includes:

[0023] Based on the relevant geometric parameters, material properties, and boundary condition information obtained from the three-dimensional mapping model, select an appropriate strength theory formula;

[0024] Substitute the relevant parameters into the formula to calculate the equivalent stress value inside the structure or material.

[0025] A stress threshold-based control method identifies hydraulic overload scenarios and adaptively controls the excavator based on the multi-sensor fusion-based excavator stress-strain monitoring method; specifically as follows:

[0026] A1 Classification Threshold Setting: Based on the excavator structural component design standards, three levels of stress safety thresholds are preset. The threshold parameters can be adjusted for working conditions through the equipment's local configuration module.

[0027] A2 control strategy implementation: Input the load path results, and when the stress value exceeds the stress safety threshold at each level, gradually increase the control restrictions on the excavator's operation;

[0028] A3 Control Strategy Library Matching: Through a multi-condition control strategy library, the optimal power adjustment parameters are automatically matched based on the fused stress data, hydraulic load, and equipment posture.

[0029] Preferably, in the A1 graded threshold setting, the stress safety threshold is divided into a first-level early warning threshold of 80%, a second-level protection threshold of 100%, and a third-level protection threshold of 120%.

[0030] Preferably, when the A2 control strategy is determined to be an external shock, the external shock control strategy is executed.

[0031] When hydraulic overload is detected, a three-level stress safety threshold control strategy is implemented.

[0032] Preferably, the multi-condition control strategy library matched with the A3 control strategy library includes:

[0033] The three-level stress safety threshold control strategy is as follows:

[0034] When the stress exceeds the first-level warning threshold, an alarm is triggered on the instrument interface to alert relevant personnel of the abnormal stress situation.

[0035] After the instrument panel triggers an alarm, if the stress exceeds the secondary warning threshold, the control system will reduce the engine speed and adjust the action sequence. It will control the engine ECU via CAN bus signals to reduce the engine operating speed and increase engine torque output, and will execute the unloading action sequence in the following priority order:

[0036] The boom retracts by 5%-8% of its stroke to reduce bucket resistance;

[0037] Raise the boom by 8°-12° to change the angle of force application;

[0038] If the limits are continuously exceeded, micro-vibration of the bucket will be triggered;

[0039] If the stress exceeds the level 2 warning threshold and then exceeds the level 3 warning threshold, the control handle cannot control the working device to perform forward operation and can only operate in reverse to exit the current working condition.

[0040] Preferably, the multi-condition control strategy library matched with the A3 control strategy library also includes:

[0041] The external shock control strategy is as follows:

[0042] Once the impact is determined to be external, an external impact warning will be displayed on the instrument panel.

[0043] Continuously monitor and count the number of times the stress value exceeds the external impact threshold;

[0044] When the number of cycles is greater than or equal to the calibrated value, the flow acceleration of the main pump output is reduced, and the maximum output displacement of the main pump is limited to limit the power output of the main pump.

[0045] A system, characterized in that it comprises:

[0046] Sensor array components are distributed and installed at key load-bearing parts of the excavator to collect data on structural components.

[0047] The data processing module, based on the aforementioned excavator stress-strain monitoring method based on multi-sensor fusion, is used to receive and process feedback data from the monitoring sensor array components.

[0048] The control module is based on the stress threshold-based control method and is located in the instrument for controlling the adaptive control of the control module to be turned on and off.

[0049] Preferably, the sensor array assembly includes:

[0050] Triaxial strain gauges are installed in stress concentration areas of critical load-bearing components of excavators.

[0051] Temperature sensor, installed 10-20cm away from the triaxial strain gauge, is used to compensate for temperature drift;

[0052] Pressure sensor, the pressure sensor collects hydraulic pressure;

[0053] The nine-axis IMU module is fixed at the center of gravity of the fuselage to collect pitch angle, roll angle and three-axis acceleration in real time.

[0054] The sensor array assembly is connected to the data processing module via a CAN bus to provide real-time data feedback.

[0055] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the excavator stress-strain monitoring method based on multi-sensor fusion and / or the control method based on stress threshold.

[0056] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the excavator stress-strain monitoring method based on multi-sensor fusion and / or the control method based on stress threshold.

[0057] An excavator equipped with the aforementioned system.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] This invention overcomes existing limitations through multi-dimensional sensing technology. By integrating multi-source data such as structural stress, hydraulic load, and operating posture, and employing a noise reduction fusion algorithm to eliminate environmental interference, it overcomes the limitations and data distortion issues of single-sensor monitoring, achieving real-time monitoring of the stress state of key excavator components and significantly improving the reliability of monitoring results under complex working conditions.

[0060] The system design achieves rapid response from stress monitoring to control execution by identifying load paths and designing different control methods based on different load states. Simultaneously, the combination of a multi-condition strategy library and control algorithms enables the equipment to dynamically optimize the overall control strategy according to real-time operating conditions, ensuring no impact on overall machine operability. Furthermore, while guaranteeing structural safety, it effectively reduces operational efficiency losses due to overload protection, enhancing the control system's dynamic adaptability to complex operating conditions. Attached Figure Description

[0061] The accompanying drawings, as part of this invention, are provided to further illustrate the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation thereof. Clearly, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0062] Figure 1 This is a flowchart of a stress-strain monitoring method for excavators based on multi-sensor fusion according to the present invention.

[0063] Figure 2 This is a flowchart of a stress threshold-based control method according to the present invention;

[0064] Figure 3 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Example 1

[0067] Please see Figure 1 A method for monitoring the stress and strain of excavators based on multi-sensor fusion includes: S1 sensor array data acquisition, S2 noise reduction processing, S3 fusion modeling and state assessment, and S4 load path analysis. The specific steps are as follows:

[0068] S1 Sensor Array Data Acquisition: By deploying a sensor array in the stress concentration area of ​​the excavator, in conjunction with the pressure sensor in the hydraulic circuit and the machine body IMU module, the sensor feedback data from various parts is collected and a multi-dimensional dataset is formed.

[0069] As further explanation: the sensor feedback data collected from various parts includes structural strain, hydraulic pressure, and pitch / rollover angles; the resulting multi-dimensional dataset includes multi-dimensional data from mechanics, hydraulics, and kinematics.

[0070] S2 noise reduction: High-frequency vibration noise in the multi-dimensional dataset obtained from S1 is filtered out, while the effective stress fluctuation characteristics are preserved.

[0071] As further explanation: Denoising is achieved through wavelet packet decomposition, specifically as follows:

[0072] By selecting an appropriate wavelet basis and decomposition level, the noisy signal is decomposed into multiple sub-bands of different frequencies. The processed wavelet packet coefficients are used for inverse decomposition. Noise components in each sub-band are removed by methods such as thresholding. The processed sub-band signals are then reconstructed to obtain the denoised signal.

[0073] The optimization is achieved through Kalman filtering, specifically as follows:

[0074] The system's state equation and observation equation are determined, initial state estimates and covariance matrices are set, and a state-space model is established. The denoised signal is used as the observation, and the signal is further optimized by iterative calculation following the prediction and update steps of Kalman filtering to obtain a smoother and more accurate signal. The state-space model formula is shown below:

[0075] ;

[0076] In the formula, X is the equivalent stress of the state variable, U is the hydraulic load of the input variable, the process noise is W~N(0, σ²), and the root mean square error RMSE after iteration is ≤5%.

[0077] S3 Fusion Modeling and Condition Assessment: Establish a multi-source data fusion model, automatically allocate sensor weights according to operating conditions, and calculate the equivalent stress and strain of key components in real time.

[0078] As further explanation: The multi-source data fusion model is established based on the weighted least squares method to create a three-dimensional mapping model with stress, load, and attitude as the mapping relationships, supporting automatic identification of working conditions such as excavation, hoisting, and rotation. The formula is shown below:

[0079] ;

[0080] In the formula, the weight coefficient ω is obtained through training with working condition data.

[0081] The calculation of equivalent stress and strain of key components specifically includes:

[0082] Equivalent stress calculation: Based on the relevant geometric parameters, material properties (such as elastic modulus, Poisson's ratio, etc.) and boundary condition information obtained from the 3D mapping model, a suitable strength theory formula is selected. The relevant parameters are then substituted into the formula to calculate the equivalent stress value inside the structure or material.

[0083] S4 Load Path Analysis: Combining sensor data and hydraulic pressure gradient, the system determines whether the load is due to hydraulic overload or external impact. Specifically, if a high-stress area is accompanied by a sharp rise in hydraulic pressure, it is determined to be hydraulic overload; if there is no hydraulic anomaly in the high-stress area, it is determined to be external impact.

[0084] In one example, a hydraulic overload scenario: During hard rock excavation, if the stick cylinder pressure increases from 20MPa to 32MPa (within 2 seconds), and at the same time, the stick root strain increases from 800με to 1200με, the system determines that it is a hydraulic overload.

[0085] When the bucket collides with the rock, the strain at the root of the bucket changes from 500με to 1500με (within 0.2 seconds), while the main pump pressure remains at 28MPa±1MPa.

[0086] Based on the load path and the calculated equivalent stress, the response is executed in Example 2.

[0087] Example 2

[0088] Please see Figure 2 As shown, a stress threshold-based control method, based on the multi-sensor fusion-based excavator stress-strain monitoring method of Embodiment 1, identifies hydraulic overload scenarios and adaptively controls the excavator; specifically as follows:

[0089] A1 Classification Threshold Setting: Based on the excavator structural component design standards, three levels of stress safety thresholds are preset. The threshold parameters can be adjusted for working conditions through the equipment's local configuration module.

[0090] As further explanation: the stress safety threshold is divided into a first-level warning threshold of 80%, a second-level protection threshold of 100%, and a third-level protection threshold of 120%.

[0091] It should be noted that the threshold parameters can be adjusted according to working conditions through the device's local configuration module (such as different modes like soft soil excavation and hard rock breaking), and are not limited to the thresholds mentioned above. Those skilled in the art can make appropriate settings according to the actual working conditions.

[0092] A2 control strategy implementation: Input the load path results, and when the stress value exceeds the stress safety threshold at each level, gradually increase the control restrictions on the excavator's operation.

[0093] As a further explanation: when it is determined to be an external shock, the external shock control strategy is implemented.

[0094] When hydraulic overload is detected, a three-level stress safety threshold control strategy is implemented.

[0095] A3 Control Strategy Library Matching: Through a multi-condition control strategy library, the optimal power adjustment parameters are automatically matched based on the fused stress data, hydraulic load, and equipment posture, ensuring structural safety while reducing the loss of work efficiency caused by sudden power drops.

[0096] As further explanation: The multi-condition control strategy library includes a three-level stress safety threshold control strategy and an external impact control strategy.

[0097] The three-level stress safety threshold control strategy is as follows:

[0098] When the stress exceeds the first-level warning threshold, an alarm will be triggered on the instrument interface, including but not limited to "suggest reducing the excavation depth", to alert relevant personnel to the abnormal stress situation.

[0099] After the instrument panel triggers an alarm, if the stress exceeds the secondary warning threshold, the control system will reduce the engine speed and adjust the action sequence. It will control the engine ECU via CAN bus signals to reduce the engine operating speed and increase engine torque output, and will execute the unloading action sequence in the following priority order:

[0100] The stick retracts by 5%-8% of its stroke to reduce bucket resistance.

[0101] Raise the boom by 8°-12° to change the angle of force application.

[0102] If the limits are continuously exceeded, micro-vibration of the bucket will be triggered.

[0103] If the stress exceeds the level 2 warning threshold and then exceeds the level 3 warning threshold, the control handle cannot control the working device to perform forward operation and can only operate in reverse to exit the current working condition.

[0104] The external shock control strategy is as follows:

[0105] Once the impact is determined to be external, an external impact warning will be displayed on the instrument panel.

[0106] Continuously monitor and count the number of times the stress value exceeds the external impact threshold.

[0107] When the number of cycles is greater than or equal to the calibrated value, the flow acceleration of the main pump output is reduced, and the maximum output displacement of the main pump is limited to limit the power output of the main pump.

[0108] Example 3

[0109] Please see Figure 3 As shown, a system includes:

[0110] Sensor array components are distributed and installed at key load-bearing parts of the excavator to collect data on structural components.

[0111] The data processing module is based on the multi-sensor fusion-based excavator stress and strain monitoring method of Embodiment 1, and is used to receive and process feedback data from the monitoring sensor array component.

[0112] The control module is based on the stress threshold-based control method of Embodiment 2, and the control module is located in the instrument for controlling the adaptive control of the control module to be turned on and off.

[0113] As further explained: The sensor array assembly includes a triaxial strain gauge, a temperature sensor, a pressure sensor, and a nine-axis IMU module. The triaxial strain gauge is installed in the stress concentration areas of the excavator's critical load-bearing components; the temperature sensor is installed 10-20cm away from the triaxial strain gauge to compensate for temperature drift; the pressure sensor collects hydraulic pressure; the nine-axis IMU module is fixed at the machine's center of gravity to collect pitch angle, roll angle, and triaxial acceleration in real time; the sensor array assembly is connected to the data processing module via a CAN bus to provide real-time data feedback.

[0114] It should be noted that key load-bearing components include, but are not limited to, stress concentration areas such as the boom web and upper and lower flaps of the stick of the excavator. The installation positions are determined through finite element simulation, and data on structural stress and strain, hydraulic pressure, and working tilt angle are collected synchronously with pressure sensors in the hydraulic circuit and the IMU module in the machine body.

[0115] The control module controls the activation and deactivation of adaptive control, referring to the monitoring in Embodiment 1 and / or the control in Embodiment 2.

[0116] First, multiple types of sensors are used to collect multi-source data such as structural stress, hydraulic load, and working posture in real time. Data fusion is then achieved through algorithms such as Kalman filtering, significantly improving monitoring accuracy and anti-interference capabilities. Furthermore, control parameters can be dynamically adjusted based on real-time analysis and calculations.

[0117] Example 4

[0118] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the excavator stress-strain monitoring method based on multi-sensor fusion of Embodiment 1 and / or the control method based on stress threshold of Embodiment 2.

[0119] Example 5

[0120] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the excavator stress-strain monitoring method based on multi-sensor fusion of Embodiment 1 and / or the stress threshold-based control method of Embodiment 1.

[0121] Example 6

[0122] An excavator equipped with the system of Embodiment 3.

[0123] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for monitoring stress and strain in excavators based on multi-sensor fusion, characterized in that, include: S1 Sensor Array Data Acquisition: By deploying a sensor array in the stress concentration area of ​​the excavator, in conjunction with the pressure sensor in the hydraulic circuit and the body IMU module, the sensor feedback data from various parts is collected and a multi-dimensional dataset is formed. S2 noise reduction: High-frequency vibration noise in the multi-dimensional dataset obtained from S1 is filtered out while retaining the effective stress fluctuation characteristics; S3 Fusion Modeling and Condition Assessment: Establish a multi-source data fusion model, automatically allocate sensor weights according to working conditions, and calculate the equivalent stress and strain of key components in real time; S4 Load Path Analysis: Combining sensor data and hydraulic pressure gradient, it is determined whether the load is due to hydraulic overload or external impact. If a high-stress area is accompanied by a sharp rise in hydraulic pressure, it is determined to be a hydraulic overload; if there is no hydraulic abnormality in the high-stress area, it is determined to be an external impact.

2. The excavator stress-strain monitoring method based on multi-sensor fusion according to claim 1, characterized in that: The S1 sensor array data acquisition process collects feedback data from various parts of the sensor, including structural strain, hydraulic pressure, and pitch / rollover angles. The resulting multidimensional dataset includes multidimensional data on mechanics, hydraulics, and kinematics.

3. The excavator stress-strain monitoring method based on multi-sensor fusion according to claim 1 or 2, characterized in that: In S2 noise reduction, wavelet packet decomposition is used for noise reduction, specifically as follows: By selecting an appropriate wavelet basis and decomposition level, the noisy signal is decomposed into multiple sub-bands of different frequencies. The processed wavelet packet coefficients are then used for inverse decomposition to reconstruct the denoised signal. The optimization is achieved through Kalman filtering, specifically as follows: The system's state equation and observation equation are determined, initial state estimates and covariance matrices are set, and a state-space model is established. The denoised signal is used as the observation, and the signal is further optimized through iterative calculations following the prediction and update steps of Kalman filtering. The state-space model formula is as follows: ； In the formula, X is the equivalent stress of the state variable, U is the hydraulic load of the input variable, the process noise is W~N(0, σ²), and the root mean square error RMSE after iteration is ≤5%.

4. The excavator stress-strain monitoring method based on multi-sensor fusion according to claim 3, characterized in that: In S3 fusion modeling and state assessment, a multi-source data fusion model is established based on the weighted least squares method to create a three-dimensional mapping model with stress, load, and attitude as the mapping relationship. The formula is as follows: ； In the formula, the weight coefficient ω is obtained through training with working condition data; The calculation of equivalent stress and strain of key components specifically includes: Based on the relevant geometric parameters, material properties, and boundary condition information obtained from the three-dimensional mapping model, select an appropriate strength theory formula; Substitute the relevant parameters into the formula to calculate the equivalent stress value inside the structure or material.

5. A control method based on stress threshold, characterized in that, The excavator stress-strain monitoring method based on multi-sensor fusion, as described in any one of claims 1-4, identifies hydraulic overload scenarios and adaptively controls the excavator; specifically as follows: A1 Classification Threshold Setting: Based on the excavator structural component design standards, three levels of stress safety thresholds are preset. The threshold parameters can be adjusted for working conditions through the equipment's local configuration module. A2 control strategy implementation: Input the load path results, and when the stress value exceeds the stress safety threshold at each level, gradually increase the control restrictions on the excavator's operation; A3 Control Strategy Library Matching: Through a multi-condition control strategy library, the optimal power adjustment parameters are automatically matched based on the fused stress data, hydraulic load, and equipment posture.

6. The control method based on stress threshold according to claim 5, characterized in that: In the A1 graded threshold setting, the stress safety threshold is divided into a first-level early warning threshold of 80%, a second-level protection threshold of 100%, and a third-level protection threshold of 120%.

7. The control method based on stress threshold according to claim 6, characterized in that: When the A2 control strategy is determined to be an external shock, the external shock control strategy is executed. When hydraulic overload is detected, a three-level stress safety threshold control strategy is implemented.

8. The control method based on stress threshold according to claim 7, characterized in that: The multi-condition control strategy library matched by the A3 control strategy library includes: The three-level stress safety threshold control strategy is as follows: When the stress exceeds the first-level warning threshold, an alarm is triggered on the instrument interface to alert relevant personnel of the abnormal stress situation. After the instrument panel triggers an alarm, if the stress exceeds the secondary warning threshold, the control system will reduce the engine speed and adjust the action sequence. It will control the engine ECU via CAN bus signals to reduce the engine operating speed and increase engine torque output, and will execute the unloading action sequence in the following priority order: The boom retracts by 5%-8% of its stroke to reduce bucket resistance; Raise the boom by 8°-12° to change the angle of force application; If the limits are continuously exceeded, micro-vibration of the bucket will be triggered; If the stress exceeds the level 2 warning threshold and then exceeds the level 3 warning threshold, the control handle cannot control the working device to perform forward operation and can only operate in reverse to exit the current working condition.

9. The stress threshold-based control method according to claim 7 or 8, characterized in that: The multi-condition control strategy library matched by the A3 control strategy library also includes: The external shock control strategy is as follows: Once the impact is determined to be external, an external impact warning will be displayed on the instrument panel. Continuously monitor and count the number of times the stress value exceeds the external impact threshold; When the number of cycles is greater than or equal to the calibrated value, the flow acceleration of the main pump output is reduced, and the maximum output displacement of the main pump is limited to limit the power output of the main pump.

10. A system, characterized in that, include: Sensor array components are distributed and installed at key load-bearing parts of the excavator to collect data on structural components. The data processing module, based on the excavator stress and strain monitoring method based on multi-sensor fusion as described in any one of claims 1-4, is used to receive and process feedback data from the monitoring and processing sensor array components. A control module, which is based on the stress threshold-based control method according to any one of claims 5-9, and the control module is located in the instrument for controlling the on and off of the adaptive control of the control module.

11. The system according to claim 10, characterized in that, The sensor array assembly includes: Triaxial strain gauges are installed in stress concentration areas of critical load-bearing components of excavators. Temperature sensor, installed 10-20cm away from the triaxial strain gauge, is used to compensate for temperature drift; Pressure sensor, the pressure sensor collects hydraulic pressure; The nine-axis IMU module is fixed at the center of gravity of the fuselage to collect pitch angle, roll angle and three-axis acceleration in real time. The sensor array assembly is connected to the data processing module via a CAN bus to provide real-time data feedback.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the excavator stress and strain monitoring method based on multi-sensor fusion as described in any one of claims 1-4 and / or the stress threshold-based control method as described in any one of claims 5-9.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the excavator stress and strain monitoring method based on multi-sensor fusion as described in any one of claims 1-4 and / or the stress threshold-based control method as described in any one of claims 5-9.

14. An excavator, characterized in that: The excavator is equipped with the system described in claim 10 or 11.

Images