Intelligent self-learning multi-interface grab bucket driving current weighing system and method
The intelligent self-learning multi-interface grab bucket crane current weighing system collects motor current in real time and automatically optimizes parameters, solving the problems of high cost and environmental susceptibility of traditional weighing methods, and achieving high-precision, stable and low-cost weighing results.
Patent Information
- Application Number
- CN202511678783.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-02-10
AI Technical Summary
Traditional grab bucket crane weighing methods are costly, complex to install, and easily affected by the environment. Existing current weighing technology lacks self-learning ability, resulting in accuracy being affected by changes in working conditions.
The intelligent self-learning multi-interface grab bucket crane current weighing system includes a current acquisition module, a processing unit, and a self-learning module. By acquiring the motor current in real time, the self-learning module automatically optimizes parameters to compensate for errors, achieving high-precision weighing.
It improves weighing accuracy and stability, reduces installation and maintenance costs, adapts to different working conditions, has strong compatibility, high real-time performance, and reduces the risk of manual intervention and equipment failure.
Smart Images
Figure CN121493792A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of grab bucket crane technology, specifically to an intelligent self-learning multi-interface grab bucket crane current weighing system and method. Background Technology
[0002] Grab bucket cranes are widely used for material handling in ports, mines, and other locations. Traditional weighing methods, such as load cells or pressure sensors, suffer from drawbacks such as high cost, complex installation, and susceptibility to shock and environmental influences. Current-based weighing technology estimates the load by measuring motor current, but existing technologies are limited and lack self-learning capabilities, resulting in accuracy being affected by changes in operating conditions (such as grab bucket wear and changes in material properties). Therefore, a current-based weighing system capable of automatically learning and compensating for errors is needed. Summary of the Invention
[0003] To address the aforementioned technical problems, this invention proposes an intelligent self-learning multi-interface grab bucket crane current weighing system, which can accurately estimate the weight of materials through current signals and automatically optimize parameters.
[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0005] A smart self-learning multi-interface grab bucket crane current weighing system, characterized in that it includes a current acquisition module, a processing unit, and a self-learning module, wherein the current acquisition module and the self-learning module are respectively connected to the processing unit, wherein:
[0006] Current acquisition module: Real-time acquisition of the rising current I of the grab bucket motor. Up and closed current I Close ;
[0007] Processing unit: Receives real-time current data from the current acquisition module and processes it according to the built-in adjustable parameters. and Calculate the weight of the grab bucket and the weight of the material inside it;
[0008] Self-learning module: When the material weight is available, compare the predicted material weight with the actual material weight and update it using an optimization algorithm. and To achieve long-term accuracy.
[0009] As a preferred embodiment of the present invention, the current acquisition module is provided with an analog current signal interface and a digital current signal interface.
[0010] An analog signal path is formed based on the analog current signal interface. When the analog current signal is input, it is processed by the current sensor, signal amplifier, low-pass filter and converter before being input to the processing unit.
[0011] A digital signal path is formed based on the current signal interface. When a digital current signal is input, it is directly input to the processing unit after being parsed via the 485 interface and the Modbus protocol.
[0012] As a preferred embodiment of the present invention, the processing unit is one of an embedded microprocessor, a PLC, or an industrial computer.
[0013] As a preferred technical solution of the present invention, the optimization algorithm used by the self-learning module when updating parameters includes, but is not limited to, gradient descent or least squares method.
[0014] A smart self-learning multi-interface grab bucket crane current weighing method, characterized by the following steps:
[0015] S1, Dry-grab calibration:
[0016] Collecting the current of the empty grab bucket Calculate the weight of the grab bucket The calculated grab weight is compared with the actual grab weight to achieve empty grab calibration;
[0017] S2. Material weighing:
[0018] Collect current UC current value Calculate the weight of materials ;
[0019] S3, Self-learning:
[0020] When the material weight m_actual is known, the calculation error is... = m_actual- and adjust and To minimize .
[0021] As a preferred technical solution of the present invention, step S1 is as follows:
[0022] S11. Dry grab operation, collect the current of the dry grab bucket:
[0023] Perform or invoke an empty grab operation, and during this process, collect the sum of the rising and closing currents of the empty grab bucket, and record it as the empty grab current. Based on the empty current and initial parameters Through formula The weight of the grab bucket was calculated. The calculated weight of the grab bucket The empty grab calibration is achieved by comparing the actual weight of the grab bucket with its own weight.
[0024] S12, Storage Parameters:
[0025] After successful calibration, the no-load current will be applied. , Grab weight and initial parameters , It is stored in non-volatile memory.
[0026] As a preferred technical solution of the present invention, step S2 is as follows:
[0027] S21. Normal material gripping current value acquisition:
[0028] The rising current I of the grab bucket motor during the material grabbing process is collected in real time by the current acquisition module. Up and closed current I Close And calculate the UC current value I1, I1 = I Up +I Close ;
[0029] S22. Calculate the weight according to the formula:
[0030] The processing unit uses formulas Calculate the predicted material weight
[0031] S23, Output weight result:
[0032] Calculate the material weight Output to the self-learning module.
[0033] As a preferred technical solution of the present invention, step S3 is as follows:
[0034] S31. Obtain the actual weight:
[0035] The actual weight of the material is obtained through a weight detection device;
[0036] S32, Read the predicted weight:
[0037] Read the predicted weight corresponding to the actual weight from the processing unit. ;
[0038] S33, Calculation error:
[0039] Calculate the error between the actual weight and the predicted weight. ;
[0040] S34. Determine if the error exceeds the threshold:
[0041] If the parameter does not exceed the limit, it is retained and remains unchanged.
[0042] If the threshold is exceeded, the parameters are updated as follows: The self-learning module uses a preset optimization algorithm based on the error... Calculate the parameters and Adjust the amount to generate new parameters. and The system uses and Overwrite the old parameters stored to complete the parameter update.
[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0044] High precision and long-term stability:
[0045] By dynamically adjusting key parameters through a self-learning module, the system can compensate in real time for errors caused by grab wear, changes in motor characteristics, or differences in material properties, significantly improving weighing accuracy. Experimental data shows that after self-learning optimization, the error between predicted and actual weight can be controlled within a low range (e.g., ...). Figure 6 As shown in the figure, the system can maintain stable performance during long-term operation.
[0046] Strong compatibility and flexibility:
[0047] The current acquisition module supports both analog and digital interfaces, making it compatible with grab bucket cranes of different eras and control methods (such as traditional relay control or modern frequency converter control). This multi-interface compatibility allows for rapid system deployment without modifying existing hardware, reducing upgrade costs and complexity.
[0048] Real-time performance and high efficiency:
[0049] The processing unit uses an embedded microprocessor, PLC, or industrial computer to collect current data and calculate material weight in real time. The weighing process does not interrupt the workflow, improving material handling efficiency. Meanwhile, the self-learning process can be executed automatically in the background without affecting normal operation.
[0050] Reduce maintenance costs:
[0051] Traditional weighing systems require regular sensor calibration and maintenance, while this invention automatically calibrates parameters through a self-learning mechanism, reducing the frequency of manual intervention and maintenance. Furthermore, the system eliminates the need for additional physical weighing sensors, avoiding the risk of malfunctions caused by sensor damage or environmental factors (such as humidity and impact).
[0052] Intelligent and adaptive capabilities:
[0053] The self-learning module employs optimization algorithms (such as gradient descent or least squares) to continuously optimize model parameters based on historical error data, enabling the system to adapt to diverse operating conditions (such as different material densities and gripping methods). This intelligent design makes the system more robust in complex industrial environments.
[0054] Economic efficiency:
[0055] Compared to traditional weighing solutions such as load cells and pressure sensors, this invention utilizes existing motor current signals for weighing, eliminating the need to purchase high-cost sensors and significantly reducing initial investment and operating costs. Furthermore, the system is easy to install and debug, further saving time and human resources. Attached Figure Description
[0056] Figure 1 This is a framework diagram of an intelligent self-learning multi-interface grab bucket crane current weighing system.
[0057] Figure 2 This is a schematic diagram of the current acquisition module.
[0058] Figure 3 This is a flowchart of the processing unit.
[0059] Figure 4 This is a flowchart of the self-learning module.
[0060] Figure 5 This is the result of an electric current weighing experiment.
[0061] Figure 6 It is a line graph comparing the current weighing with the actual accurate weight. Detailed Implementation
[0062] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.
[0063] like Figure 1-6 As shown, the intelligent self-learning multi-interface grab bucket crane current weighing system proposed in this invention includes a current acquisition module, a processing unit, and a self-learning module. The current acquisition module and the self-learning module are respectively connected to the processing unit, wherein:
[0064] Current acquisition module: Real-time acquisition of the rising current I of the grab bucket motor. Up and closed current I Close ;
[0065] Processing unit: Receives real-time current data from the current acquisition module and processes it according to the built-in adjustable parameters. and Calculate the weight of the grab bucket and the weight of the material inside it;
[0066] Self-learning module: When the material weight is available, compare the predicted material weight with the actual material weight and update it using an optimization algorithm. and To achieve long-term accuracy.
[0067] The system of the present invention consists of three core modules: a current acquisition module, a processing unit, and a self-learning module.
[0068] The current acquisition module serves as the system's data input, responsible for real-time acquisition of current signals from the hoisting motor and the switching motor of the grab trolley. A key feature of this module is its multi-interface compatibility, enabling it to simultaneously or selectively process analog current signals (e.g., 4-20mA standard signals converted by current transformers and transmitters) and digital current signals (e.g., current values read directly from the frequency converter via an RS485 interface according to industrial communication protocols such as Modbus). This design allows the system to flexibly adapt to grab trolley equipment of different eras and control methods.
[0069] The processing unit is the computational core of the system, implemented using an embedded microprocessor, PLC, or industrial computer. It connects to the current acquisition module, receives raw current data, and executes the core weight calculation algorithm. The processing unit internally stores the idle current value. Grab weight G, and key adjustable parameters and Its primary task is to calculate the UC current value. (That is, the sum of the rising current Up and the closing current Close), then according to the formula Calculate material weight The results are then output to a display device or a higher-level management system.
[0070] The self-learning module is key to the system's intelligence and high precision. It works in conjunction with the processing unit 102 to form a closed-loop control system. The self-learning module is activated when an actual weight (e.g., the true weight value obtained after material is unloaded onto the weighbridge, which can be manually input or automatically transmitted via a communication interface) is available. It acquires the actual weight. and the predicted weight calculated by the processing unit By comparison, the error can be obtained. Based on this error, the self-learning module automatically adjusts the parameters using built-in optimization algorithms (such as gradient descent or least squares). and The optimized parameters are then fed back to the processing unit for subsequent weight calculations, thereby continuously reducing system errors and adapting to changes in operating conditions.
[0071] like Figure 2The diagram shown is a schematic of the current acquisition module in this invention.
[0072] The current acquisition module is equipped with an analog current signal interface and a digital current signal interface.
[0073] An analog signal path is formed based on the analog current signal interface. When the analog current signal is input, it is processed by the current sensor, signal amplifier, low-pass filter and converter before being input to the processing unit.
[0074] A digital signal path is formed based on the current signal interface. When a digital current signal is input, it is directly input to the processing unit after being parsed via the 485 interface and the Modbus protocol.
[0075] Analog Current Acquisition Path: This path is used to process traditional analog current signals. The current signal from the motor's power line is first detected by a current sensor (such as a Hall effect sensor) and converted into a corresponding small voltage signal. This signal then enters the signal conditioning circuit for amplification and filtering to improve the signal-to-noise ratio and adjust it to a level suitable for sampling. The processed analog signal is then sampled and quantized by an analog-to-digital converter (ADC) to convert it into a digital signal, which is finally sent to the processing unit.
[0076] Digital Current Acquisition Path: This path is used to directly acquire high-precision digital current values from modern frequency converters. The system establishes a physical connection with the frequency converter via a digital communication interface (such as RS485 or Ethernet). Then, according to a preset communication protocol (such as Modbus-RTU) frame format, a query command is sent to the frequency converter to read the real-time motor current values (including rising and closing currents) stored in its internal registers. The parsed data is directly used by the processing unit.
[0077] This dual-path design ensures the system's applicability in different hardware environments and the reliability of data acquisition.
[0078] like Figure 3 The diagram shown is a flowchart of the process unit calculating the weight of materials in this invention.
[0079] In the step "No-load grab operation, collect no-load grab current", the system executes or calls a no-load grab operation once, and during this process, collects the sum of the rising and closing currents at this time, and records it as the no-load grab current. This value serves as the baseline for subsequent calculations.
[0080] In the step "Store Parameters", the grab's self-weight will be calculated. and will , and initial parameters , It is securely stored in non-volatile memory to prevent loss in the event of power failure.
[0081] When the actual material grabbing operation is performed, the process enters the step "Normal Material Grabbing Current Value Acquisition", which collects the UC current value during the current material grabbing process in real time. .
[0082] Next, in the step "Calculate weight according to formula", the processing unit calls the core algorithm and uses the formula. Calculate the predicted material weight .
[0083] Finally, in the step "Output Weight Result", the calculated weight result will be displayed. Output.
[0084] The process can be ended, or you can return to "Start" and wait for the next material grab.
[0085] like Figure 4 The diagram shown is a flowchart of the self-learning module updating parameters in this invention.
[0086] This flowchart details how the system achieves parameter self-optimization through a learning mechanism:
[0087] This process can be triggered after each actual weight is obtained, or it can be executed periodically, starting from the beginning of the learning process.
[0088] In the step "Obtain Actual Weight Value", the system obtains a reliable actual weight value. .
[0089] The step "Read Predicted Weight" reads the predicted weight corresponding to the actual weight from the processing unit. And calculate the error between the two. .
[0090] The step "Does the error exceed the threshold?" is a decision point, determining whether the absolute value of the current error exceeds a preset threshold. If it does not exceed the threshold ("no" branch), it means the current accuracy is within an acceptable range, and the process jumps to the step "Retain parameters," ending this learning session, with the parameters remaining unchanged. If the threshold is exceeded ("yes" branch), the core parameter update step "Update parameters" is then initiated.
[0091] The self-learning module uses a pre-defined optimization algorithm to optimize the learning process based on error. Calculate the parameters and Adjust the amount to generate new parameters. and .
[0092] Subsequently, the system used and Overwrite the stored old parameters to complete the knowledge update. Process ends.
[0093] Through this continuous "measurement-comparison-correction" cycle, the system can gradually eliminate systematic errors caused by factors such as mechanical wear and changes in material properties, achieving long-term stable high-precision weighing.
[0094] The intelligent self-learning multi-interface grab bucket crane current weighing method proposed in this invention includes the following steps:
[0095] S1, Dry-grab calibration:
[0096] Collecting the current of the empty grab bucket Calculate the weight of the grab bucket The calculated grab weight is compared with the actual grab weight to achieve empty grab calibration.
[0097] Specifically as follows:
[0098] S11. Dry grab operation, collect the current of the dry grab bucket:
[0099] Perform or invoke an empty grab operation, and during this process, collect the sum of the rising and closing currents of the empty grab bucket, and record it as the empty grab current. Based on the empty current and initial parameters Through formula The weight of the grab bucket was calculated. The calculated weight of the grab bucket The empty grab calibration is achieved by comparing the actual weight of the grab bucket with its own weight.
[0100] S12, Storage Parameters:
[0101] After successful calibration, the no-load current will be applied. , Grab weight and initial parameters , It is stored in non-volatile memory.
[0102] S2. Material weighing:
[0103] Collect current UC current value Calculate the weight of materials .
[0104] Specifically as follows:
[0105] S21. Normal material gripping current value acquisition:
[0106] The rising current I of the grab bucket motor during the material grabbing process is collected in real time by the current acquisition module. Up and closed current I Close And calculate the UC current value I1, I1 = I Up +I Close ;
[0107] S22. Calculate the weight according to the formula:
[0108] The processing unit uses formulas Calculate the predicted material weight
[0109] S23, Output weight result:
[0110] Calculate the material weight Output to the self-learning module.
[0111] S3, Self-learning:
[0112] When the material weight m_actual is known, the calculation error is... = m_actual- and adjust and To minimize .
[0113] Specifically as follows:
[0114] S31. Obtain the actual weight:
[0115] The actual weight of the material is obtained through a weight detection device;
[0116] S32, Read the predicted weight:
[0117] Read the predicted weight corresponding to the actual weight from the processing unit. ;
[0118] S33, Calculation error:
[0119] Calculate the error between the actual weight and the predicted weight. ;
[0120] S34. Determine if the error exceeds the threshold:
[0121] If the parameter does not exceed the limit, it is retained and remains unchanged.
[0122] If the threshold is exceeded, the parameters are updated as follows: The self-learning module uses a preset optimization algorithm based on the error... Calculate the parameters and Adjust the amount to generate new parameters. and The system uses and Overwrite the old parameters stored to complete the parameter update.
[0123] The specific implementation of the method is as follows:
[0124] Torque is a parameter used to represent the rotational performance of an electric motor; it is the moment of force (force × radius of rotation) that produces rotation. Torque T is generated by force F at a rotation radius r and is represented by "F × r". For the same motor, r is the same.
[0125] In electrical machinery, torque, torque, and torque are the same concept. They are the basic load forms of transmission shafts in various working machines, and are closely related to factors such as the working capacity, energy consumption, efficiency, service life, and safety performance of power machinery. In electric motors, torque is usually inversely proportional to the motor speed; that is, under the condition of constant power, the higher the speed, the lower the torque, and vice versa.
[0126] For AC motors, the rated torque can be calculated using formula (1):
[0127] (1);
[0128] Where T is torque, measured in N·m, and rated power. The unit is kilowatt (kW), and the rated speed is... The unit is revolutions per minute (r / min). For DC motors, the speed is directly proportional to the armature voltage and inversely proportional to the excitation voltage. The torque is directly proportional to the excitation flux and the armature current.
[0129] In the study of motor drive systems, based on physical principles, by analyzing the change in current during motor operation and combining it with the motor's power formula, the force acting on an object can be deduced, and thus the object's weight can be calculated. During the lifting and lowering process of a motor-driven grab bucket, the power p is constant, and the motor's operating voltage U is a fixed 220V AC. By measuring the distance the grab bucket travels from its lowest point h to its highest point H and the time t required, and combining this with the force F acting on the grab bucket, the following formula can be used for calculation:
[0130] (2);
[0131] (3);
[0132] Where P represents the motor power, t represents the time required for the grab bucket to rise from its lowest point to its highest point, and H and h represent the heights of the highest and lowest points of the grab bucket, respectively. This formula allows for the accurate calculation of the force acting on the grab bucket, and thus the weight of the object.
[0133] In a motor drive system, by analyzing the relationship between current and force, a series of formulas can be derived to calculate the weight of an object. First, based on formulas (2) and (3), we can derive formula (4):
[0134] (4);
[0135] This formula further reveals the relationship between current I and force F, namely formula (5).
[0136] (5);
[0137] Where I can be further calculated using formula (6):
[0138] (6);
[0139] Where k is a constant, and T is the torque of the motor itself, in N / m (Newtons per meter). When the motor current is less than the rated current, the current is proportional to the torque. When the current exceeds the rated current and the iron core becomes magnetically saturated, further increases in current will not increase the torque. Therefore, formula (5) can be adjusted to formula (7):
[0140] (7);
[0141] Where C is a constant value, equal to U / k. At time t, the actual weight of the grabbed material can be calculated by subtracting the weight of the grab itself, from the total weight F of the grab bucket. That is, formula (8):
[0142] (8);
[0143] In each complete grab bucket operation, a complete motion process is required, including crane operation, grabbing at the designated point, lifting after grabbing, and unloading at the designated point. The current changes continuously throughout the process, making it crucial to select an appropriate range of current values. Through comparative studies of multiple experiments, it can be found from the experimental data that during the ore discharge process, the current is stable, and the value under the interaction of the parameters is better and closer to the true value. Among them, the uc current is the combined value of the current when up rises and the current when close closes. It is listed as a separate variable as the uc current value, thus we have formula (9):
[0144] (9);
[0145] Where G is the weight of the grab bucket. Given the current of the grab bucket under no-load conditions, based on these two known quantities, we can deduce... This constant value, if the weight of the material being grasped at this time is... Then we have formula (10):
[0146] (10);
[0147] in, To compensate for the parameters, it was found through multiple experiments that... It is a constant. Due to factors such as the instability and jumpiness of current, The value needs to be continuously optimized using multiple sets of experimental data.
[0148] For optimizing the coefficients, since the data and the uc value exhibit a certain linear relationship, but to obtain accurate coefficients, a nonlinear regression least squares method can be used. This method is a parameter estimation method that estimates the parameters of a nonlinear static model by minimizing the sum of squared errors. Let the model of the nonlinear system be... =f( , )+ The specific formula is shown in (11):
[0149] (11);
[0150] In formula (11) The current value is the value when the grab bucket does not grab any material. Based on experimental data, the final correct weight is used as the result for iterative fitting of correlation coefficients and compensation, so that the calculated current weighing value is closer to the true value.
[0151] Field experiment:
[0152] like Figure 5 The data shown represents the data from the first experiment. b, s, and h represent the coordinates of the main trolley and the auxiliary trolley during operation, as well as the height of the grab bucket above the ground, all in millimeters. The weight in pounds (g) represents the accurate on-site weighing result, and the current (g) represents the weight calculated based on the parameters preliminarily determined using the aforementioned model and algorithm.
[0153] Preliminary experiments have shown that... The value is 0.15. The value is 1.07. The results obtained by substituting this parameter into the curve are shown below. Figure 6 As shown in the curve, the two values are quite close, indicating a good fit. Preliminary experiments have confirmed this. Value and value
[0154] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any modifications or equivalent changes made based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.
Claims
1. An intelligent self-learning multi-interface grab bucket overhead crane current weighing system, characterized in that, It includes a current acquisition module, a processing unit, and a self-learning module. The current acquisition module and the self-learning module are respectively connected to the processing unit, wherein: Current acquisition module: Real-time acquisition of the rising current I of the grab bucket motor. Up and closed current I Close ; Processing unit: Receives real-time current data from the current acquisition module and processes it according to the built-in adjustable parameters. and Calculate the weight of the grab bucket and the weight of the material inside it; Self-learning module: When the material weight is available, compare the predicted material weight with the actual material weight and update it using an optimization algorithm. and To achieve long-term accuracy.
2. The intelligent self-learning multi-interface grab bucket crane current weighing system according to claim 1, characterized in that, The current acquisition module is equipped with an analog current signal interface and a digital current signal interface. An analog signal path is formed based on the analog current signal interface. When the analog current signal is input, it is processed by the current sensor, signal amplifier, low-pass filter and converter before being input to the processing unit. A digital signal path is formed based on the current signal interface. When a digital current signal is input, it is directly input to the processing unit after being parsed via the 485 interface and the Modbus protocol.
3. The intelligent self-learning multi-interface grab bucket crane current weighing system according to claim 1, characterized in that, The processing unit is one of an embedded microprocessor, a PLC, or an industrial computer.
4. The intelligent self-learning multi-interface grab bucket crane current weighing system according to claim 1, characterized in that, The optimization algorithms used by the self-learning module when updating parameters include, but are not limited to, gradient descent or least squares.
5. The intelligent self-learning multi-interface grab bucket crane current weighing method according to any one of claims 1-4, characterized in that, Includes the following steps: S1, Dry-grab calibration: Collecting the current of the empty grab bucket Calculate the weight of the grab bucket The calculated grab weight is compared with the actual grab weight to achieve empty grab calibration; S2, Material weighing: Collect current UC current value Calculate the weight of materials ; S3, Self-learning: When the material weight m_actual is known, the calculation error is... = m_actual- and adjust and To minimize .
6. The intelligent self-learning multi-interface grab bucket crane current weighing method according to claim 5, characterized in that, Step S1 is as follows: S11. Empty grab operation, collect the current of the empty grab bucket: Perform or invoke an empty grab operation, and during this process, collect the sum of the rising and closing currents of the empty grab bucket, and record it as the empty grab current. Based on the empty current and initial parameters Through formula The weight of the grab bucket was calculated. The calculated weight of the grab bucket The empty grab calibration is achieved by comparing the actual weight of the grab bucket with its own weight. S12, Storage Parameters: After successful calibration, the no-load current will be applied. , Grab weight and initial parameters , It is stored in non-volatile memory.
7. The intelligent self-learning multi-interface grab bucket crane current weighing method according to claim 5, characterized in that, Step S2 is as follows: S21. Normal material gripping current value acquisition: The rising current I of the grab bucket motor during the material grabbing process is collected in real time by the current acquisition module. Up and closed current I Close And calculate the UC current value I1, I1 = I Up +I Close ; S22. Calculate the weight according to the formula: The processing unit uses formulas Calculate the predicted material weight ; S23, Output weight result: Calculate the material weight Output to the self-learning module.
8. The intelligent self-learning multi-interface grab bucket crane current weighing method according to claim 5, characterized in that, Step S3 is as follows: S31. Obtain the actual weight: The actual weight of the material is obtained through a weight detection device; S32, Read the predicted weight: Read the predicted weight corresponding to the actual weight from the processing unit. ; S33, Calculation error: Calculate the error between the actual weight and the predicted weight. ; S34. Determine if the error exceeds the threshold: If the parameter does not exceed the limit, it is retained and remains unchanged. If the threshold is exceeded, the parameters are updated as follows: The self-learning module uses a preset optimization algorithm based on the error... Calculate the parameters and Adjust the amount to generate new parameters. and The system uses and Overwrite the old parameters stored to complete the parameter update.