Hydraulic integrated control system and method for automatic weighing machine

Abstract

This invention provides a hydraulic integrated control system and method for an automatic weighing machine, relating to the field of hydraulic control technology. The system includes: a data receiving module for receiving raw weight signal streams, raw position signal streams, and raw vibration spectrum streams; a clutter filtering module for adaptive cooperative clutter filtering to obtain corrected weight signal streams and corrected position signal streams; a deviation calculation module for calculating real-time feeding lag deviations; a decision module for making feeding speed switching decisions and outputting a feeding gate control command sequence; a feedback module for performing handling task trajectory interpolation feedback to obtain multi-axis cooperative motion commands; and a timing control module. This invention solves the technical problem that existing automatic weighing machines, when performing multi-axis cooperative motions such as handling and conveying, often rely on single-axis control and lack efficient multi-axis cooperative control schemes, leading to reduced equipment accuracy and overall work efficiency.

Description

Technical Field

[0001] This invention relates to the field of hydraulic control technology, and more specifically to an integrated hydraulic control system and method for automatic weighing machines. Background Technology

[0002] The core function of an automatic weighing machine is to measure the weight of materials in real time and accurately, and to perform corresponding operations according to set standards, such as feeding, handling, or distributing. This task requires the automatic weighing machine to be able to handle and adjust multiple interrelated control variables and provide efficient and precise operation in complex environments. Modern automatic weighing machines rely on hydraulic systems to drive actuators, while simultaneously using sensors and control systems to monitor and adjust weight, position, and speed in real time. However, in this process, existing technologies face many technical challenges that directly affect the accuracy, stability, and working efficiency of the automatic weighing machine.

[0003] In existing technologies, automatic weighing machines often rely on single-axis control systems when performing multi-axis coordinated movements such as handling and conveying. This lack of efficient multi-axis coordinated control solutions leads to asynchronous movements of different axes when multiple axes need to work in coordination, resulting in positional errors and unstable motion trajectories. Poor multi-axis coordination reduces equipment accuracy, increases displacement and deviation during material handling, lowers overall work efficiency, and may cause equipment damage or excessive wear, increasing maintenance costs. Summary of the Invention

[0004] This application provides a hydraulic integrated control system and method for an automatic weighing machine, aiming to solve the technical problem that existing automatic weighing machines often rely on the control of a single axis when performing multi-axis coordinated movements such as handling and conveying, lacking an efficient multi-axis coordinated control scheme, which leads to reduced equipment accuracy and reduced overall work efficiency.

[0005] The first aspect disclosed in this application provides a hydraulic integrated control system for an automatic weighing machine. The system includes: a data receiving module for receiving raw weight signal stream, raw position signal stream, and raw vibration spectrum stream transmitted back from a multimodal sensor network via a distributed industrial bus protocol; a clutter filtering module for introducing the raw vibration spectrum stream to adaptively and collaboratively filter the raw weight and raw position signals to obtain a corrected weight signal stream and a corrected position signal stream; a deviation calculation module for calculating real-time feeding lag deviation based on the feeding speed setting stream and the corrected weight signal stream; a decision module for making feeding speed switching decisions based on the real-time feeding lag deviation and the corrected weight signal stream, and outputting a feeding gate control command sequence; a feedback module for performing handling task trajectory interpolation feedback based on the corrected position signal stream to obtain multi-axis coordinated motion commands; and a timing control module for performing linkage start-stop timing control of the automatic weighing machine based on the feeding gate control command sequence when the gantry hydraulic actuator is driven by the multi-axis coordinated motion commands.

[0006] The second aspect of this application discloses a hydraulic integrated control method for an automatic weighing machine. This method is implemented through the aforementioned hydraulic integrated control system for the automatic weighing machine. The method includes: receiving raw weight signal stream, raw position signal stream, and raw vibration spectrum stream transmitted back from a multimodal sensor network via a distributed industrial bus protocol; introducing the raw vibration spectrum stream to perform adaptive cooperative clutter filtering on the raw weight signal and raw position signal to obtain a corrected weight signal stream and a corrected position signal stream; calculating the real-time feeding lag deviation based on the feeding speed setting stream and the corrected weight signal stream; making a feeding speed switching decision based on the real-time feeding lag deviation and the corrected weight signal stream, and outputting a feeding gate control command sequence; performing handling task trajectory interpolation feedback based on the corrected position signal stream to obtain a multi-axis cooperative motion command; and when using the multi-axis cooperative motion command to drive the gantry hydraulic actuator, performing linkage start-stop timing control of the automatic weighing machine according to the feeding gate control command sequence.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: During the operation of the automatic weighing machine, the data stream transmitted via a multimodal sensor network through a distributed industrial bus protocol, including the original weight signal stream, the original position signal stream, and the original vibration spectrum stream, enables multi-level information acquisition and monitoring, providing accurate real-time data for subsequent processing. By introducing vibration spectrum information for collaborative filtering, signal deviations caused by equipment vibration, external interference, or sensor errors can be effectively identified and eliminated in complex environments. This process significantly improves the accuracy and stability of weight and position data, providing a high-quality data foundation for subsequent control and decision-making. By calculating the feeding lag deviation in real time, deviations during the feeding process can be precisely monitored and adjusted, thereby improving weighing accuracy and material conveying stability. This feedback mechanism ensures precise control of the feeding process, avoiding errors and overfeeding caused by lag. Based on the real-time feeding lag deviation and the corrected weight signal stream, the machine makes feeding speed switching decisions and outputs the corresponding feeding gate. The control command sequence ensures that the system reacts promptly to deviations during dynamic feeding, avoiding overfeeding or underfeeding due to speed mismatch and maintaining the stability and accuracy of material flow. By correcting the position signal flow and performing trajectory interpolation feedback for the handling task, multi-axis coordinated motion commands are generated to ensure coordinated movement of each motion axis, completing the handling task. Multi-axis coordinated control enables mechanical equipment to maintain high precision in complex operations, adapting to various workloads and task requirements, and improving handling accuracy and efficiency. When using multi-axis coordinated motion commands to drive the gantry hydraulic actuator, the automatic weighing machine's start-stop timing is controlled according to the feeding gate control command sequence, ensuring coordinated operation of all parts. This control effectively improves system efficiency and reduces energy waste, especially in multi-task, multi-working states, ensuring smooth operational transitions. The linkage control can also quickly adjust the work rhythm according to different production needs, improving production flexibility and adaptability.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 A schematic diagram of the hydraulic integrated control system structure of the automatic weighing machine provided in the embodiments of this application.

[0010] Figure 2 This is a schematic flowchart of the hydraulic integrated control method for an automatic weighing machine provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached diagram: Data receiving module 10, noise filtering module 20, deviation calculation module 30, decision module 40, feedback module 50, timing control module 60. Detailed Implementation

[0012] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0013] Example 1, as Figure 1 As shown in the embodiment of this application, a hydraulic integrated control system for an automatic weighing machine is provided, the system comprising: The data receiving module 10 receives the original weight signal stream, original position signal stream, and original vibration spectrum stream transmitted back from the multimodal sensor network via the distributed industrial bus protocol; the clutter filtering module 20 introduces the original vibration spectrum stream to perform adaptive cooperative clutter filtering of the original weight signal and original position signal, obtaining a corrected weight signal stream and a corrected position signal stream; the deviation calculation module 30 calculates the real-time feeding lag deviation based on the feeding speed setting stream and the corrected weight signal stream; the decision module 40 makes feeding speed switching decisions based on the real-time feeding lag deviation and the corrected weight signal stream, and outputs a feeding gate control command sequence; the feedback module 50 performs handling task trajectory interpolation feedback based on the corrected position signal stream, obtaining multi-axis cooperative motion commands; and the timing control module 60 performs linkage start-stop timing control of the automatic weighing machine based on the feeding gate control command sequence when the gantry hydraulic actuator is driven by the multi-axis cooperative motion commands.

[0014] Furthermore, the clutter filtering module 20 includes: The system includes a power spectral density analysis unit for performing power spectral density analysis on the original vibration spectrum stream and converting it into an output device vibration spectrum; a modal analysis unit for performing dominant interference mode analysis on the device vibration spectrum and locating multiple intermodulation interference frequency peaks; a parameter matching unit for extracting multi-dimensional frequency domain coupling features based on the multiple intermodulation interference frequency peaks and matching a collaborative filtering parameter set in an adaptive filter parameter library; and a collaborative clutter filtering unit for performing parameter reconstruction processing of the array filter bank using the collaborative filtering parameter set, inputting the original weight signal and the original position signal for collaborative clutter filtering to obtain the corrected weight signal stream and the corrected position signal stream.

[0015] Furthermore, the deviation calculation module 30 includes: The theoretical growth curve construction unit is used to construct the theoretical weight growth curve of the feed rate setting flow; the measured growth curve fitting unit is used to fit the measured weight growth curve based on the corrected weight signal flow; the alignment and comparison unit is used to align and compare the theoretical weight growth curve and the measured weight growth curve to extract the expected theoretical weight value and the measured stable weight value at the weight growth rate mutation point; the weight deviation calculation unit is used to calculate the real-time weight deviation between the expected theoretical weight value and the measured stable weight value; the feed rate setting extraction unit is used to extract the feed rate setting at the weight growth rate mutation point from the feed rate setting flow; and the hysteresis back-calculation unit is used to back-calculate the equivalent physical time hysteresis based on the real-time weight deviation and the feed rate setting, as the real-time feed hysteresis deviation output.

[0016] Furthermore, the theoretical growth curve construction unit includes: The system includes an interactive channel for obtaining the feed outlet cross-sectional area and feed flow characteristics of the feeding mechanism; a setpoint extraction channel for extracting the high-speed feed value, low-speed feed value, feed speed switching point, and mass setpoint from the feed speed setpoint flow; a linear growth segment construction channel for constructing high-speed and low-speed linear growth segments corresponding to the high-speed and low-speed feed values, based on the feed outlet cross-sectional area and feed flow characteristics as the basis for dynamic modeling; and a splicing channel for splicing the high-speed and low-speed linear growth segments with the mass setpoint as the target endpoint value and the feed speed switching point as the segment connection node, outputting the theoretical weight growth curve.

[0017] Furthermore, the decision module 40 includes: A dynamic correction switching point generation unit is used to generate a dynamic correction switching point by using the real-time feeding lag deviation to inversely compensate for the feeding speed switching point; a correction expected weight value extraction unit is used to extract the correction expected weight value from the theoretical weight growth curve based on the dynamic correction switching point; a measured stable weight value tracking unit is used to track the measured stable weight value based on the incremental update of the correction weight signal stream: S1: When the measured stable weight value is greater than the correction expected weight value, a high-speed to low-speed command is output; S2: When the measured stable weight value is greater than the target gate closing threshold, a feeding gate closing command is output, wherein the high-speed to low-speed command and the feeding gate closing command constitute the feeding gate control command sequence.

[0018] Furthermore, the data receiving module 10 also includes: The first deployment unit is used to deploy a weighing sensor array on the weighing bucket support point array of the automatic weighing machine, wherein the weighing sensor array is used to acquire the original weight signal stream; the second deployment unit is used to deploy a position encoder array on the motion axis drive node sequence of the automatic weighing machine, wherein the position encoder array is used to acquire the original position signal stream; the third deployment unit is used to deploy a vibration acceleration sensor array on the mechanical structure vibration sensitive node array of the automatic weighing machine, wherein the acceleration sensor array is used to acquire the original vibration spectrum stream in conjunction with spectrum analysis; wherein the weighing sensor array, the position encoder array, and the acceleration sensor array are connected to a deterministic communication network based on real-time industrial Ethernet through distributed I / O stations for timestamp alignment of acquisition.

[0019] Furthermore, the feedback module 50 includes: The pose state construction unit is used to construct the truss spatial pose state based on the corrected position signal stream; the trajectory model construction unit is used to locally call the transport task trajectory parameters to construct a theoretical spatial trajectory model; the trajectory deviation quantization unit is used to quantize the trajectory deviation of the theoretical spatial trajectory model based on the theoretical spatial trajectory model to obtain a spatial pose deviation vector; the kinematic decomposition unit block is used to perform trajectory interpolation on the spatial pose deviation vector, obtain a compensated trajectory point sequence, perform kinematic decomposition, and output the multi-axis cooperative motion command.

[0020] Furthermore, the pose state construction unit includes: The decomposition channel is used to decompose the corrected position signal stream to obtain multiple position feedback streams corresponding to multiple motion axis driving nodes in the motion axis driving node sequence; the axis real-time spatial trajectory construction channel is used to construct multiple axis real-time spatial trajectories based on the multiple position feedback streams; and the spatiotemporal fusion channel is used to spatiotemporally fuse the multiple axis real-time spatial trajectories to generate the truss spatial pose state.

[0021] Furthermore, the target closing threshold is calculated based on the air-dropping parameters and the mass setting value.

[0022] Through the detailed description of the hydraulic integrated control method for the automatic weighing machine that follows, those skilled in the art will clearly understand the hydraulic integrated control system for the automatic weighing machine in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to in the method section.

[0023] Example 2, based on the same inventive concept as the automatic weighing machine hydraulic integrated control system in the foregoing examples, such as... Figure 2 As shown in the embodiment of this application, a hydraulic integrated control method for an automatic weighing machine is provided, the method comprising: Receive the raw weight signal stream, raw position signal stream, and raw vibration spectrum stream transmitted back from the multimodal sensor network via the distributed industrial bus protocol.

[0024] The raw weight signal stream comes from the weighing sensor array, used to detect the weight of the material being weighed. The sensors generate signals based on changes in the material's weight. The raw position signal stream comes from the position encoder array, used to acquire the actual movement position and displacement of various components of the automatic weighing machine, such as conveyor belts or robotic arms. The raw vibration spectrum stream comes from the vibration acceleration sensor array, used to detect the vibration state of the equipment. The vibration spectrum reflects the health status of the equipment and abnormal fluctuations during operation. All these raw signals are transmitted to the system for real-time data processing via a distributed industrial bus protocol. Deterministic data transmission is performed using real-time industrial Ethernet to ensure timing synchronization and data accuracy.

[0025] The original vibration spectrum stream is introduced to perform adaptive cooperative clutter filtering on the original weight signal and the original position signal, resulting in a corrected weight signal stream and a corrected position signal stream.

[0026] Analyzing the spectral characteristics of vibration signals reveals that vibration data contains various interference information during equipment operation. Power spectral density analysis of the vibration signals identifies the main interference components within the frequency range. Modal analysis of the equipment's vibration spectrum locates the peak frequencies of the interference signals, i.e., the intermodulation interference frequencies. These frequencies originate from phenomena such as mechanical vibration, equipment collision, friction, or uneven loads. Based on the identified interference frequency peaks, multidimensional frequency domain coupling features are extracted. Based on these frequency domain features, optimal collaborative filtering parameters are matched in an adaptive filter parameter library. This process, by adjusting the filter parameters, ensures the best filtering effect on the original weight and position signals, effectively removing interference components. Finally, corrected weight and position signal streams are obtained. This process effectively eliminates signal interference caused by equipment vibration, noise, and other factors, thereby obtaining more accurate and true weight and position data.

[0027] The real-time feeding lag deviation is calculated based on the feed rate setting flow and the corrected weight signal flow.

[0028] The interactive process parameter configuration interface provides the feed rate setting stream, which is set according to the material flow rate requirements and includes information such as feed rate, feed quantity, and feed cycle. Based on the feed rate stream and the characteristics of the conveyed material, a theoretical weight growth curve is established, illustrating how the material weight should change over time with the feed rate. Based on the corrected weight signal stream, an actual weight growth curve is fitted, reflecting the weight change of the material during actual feeding. Comparing the theoretical and measured weight growth curves, abrupt changes in growth rate are extracted, indicating abnormalities such as feed lag or feed rate variations in the system. By comparing the theoretical and actual weight curves, the deviation between the expected theoretical weight value and the actual stable weight value is calculated, reflecting the lag phenomenon in the feeding process. Based on the real-time weight deviation, the physical time lag is calculated in reverse, representing the actual response delay of the feeding system, thus providing a basis for adjusting the feed rate and control system.

[0029] The feeding speed switching decision is made based on the real-time feeding lag deviation and the corrected weight signal stream, and the feeding gate control command sequence is output.

[0030] Based on real-time feeding lag deviation, reverse compensation is performed on the feeding speed switching point, that is, the switching time of the feeding speed is predicted and adjusted in advance to ensure that the switching occurs at the ideal time when lag occurs. After the feeding speed switching decision, a series of control commands are generated to instruct how the feeding gate should start, stop, or adjust. For example, when the measured weight value is close to or exceeds the target expected value, the feeding system will adjust to low speed operation to avoid excessive material accumulation; when the target weight is reached, a command to close the feeding gate is issued to stop further material supply. These command sequences directly control the operation of the feeding gate, ensuring that the system provides material on demand and avoids overfeeding or delayed feeding.

[0031] Based on the corrected position signal stream, the trajectory interpolation feedback of the transport task is performed to obtain multi-axis cooperative motion commands.

[0032] Trajectory interpolation refers to calculating the intermediate motion trajectory using known starting and target positions. Its purpose is to smooth the motion process and avoid jumps or discontinuities in equipment movement. Based on real-time corrected position signal flow, the trajectory is continuously corrected, and feedback control optimizes the equipment's motion trajectory. For example, if the current position of a robotic arm deviates from the predetermined trajectory, its movement path is adjusted to ensure it ultimately reaches the correct position. In automatic weighing machines, multiple axes are involved, such as multiple robotic arms or hydraulic actuators working collaboratively. These axes must move in a coordinated and synchronized manner to achieve precise material handling. Trajectory interpolation can calculate the motion trajectory of each axis based on the corrected position signal flow and generate multi-axis coordinated motion commands according to the motion requirements of each axis. These commands tell each motion axis how to coordinate with other axes to ensure the smooth completion of the overall task.

[0033] When the truss hydraulic actuator is driven by the multi-axis coordinated motion command, the automatic weighing machine is controlled by the linkage start and stop sequence according to the feed gate control command sequence.

[0034] The operation of the feed gate, such as its on / off control, must be coordinated with the movement of the gantry hydraulic actuator. In other words, when the feed gate opens, the hydraulic system needs to begin the material handling task; when the feed gate closes, the hydraulic actuator needs to stop moving. Timing control ensures the correct sequence of operations for both. The feed gate control command sequence has already been generated; these commands serve as inputs to control the start and stop of the hydraulic actuator. For example, when the feed gate closes, a stop command is sent to the hydraulic actuator to ensure the system no longer handles material. This coordinated control ensures that the equipment in all stages—feeding, weighing, and handling—works in a coordinated manner throughout the entire operation of the automatic weighing machine, avoiding conflicting, delayed, or inconsistent operations, thereby improving the efficiency and accuracy of the entire system.

[0035] Furthermore, the original vibration spectrum stream is introduced to perform adaptive cooperative clutter filtering on the original weight signal and the original position signal to obtain a corrected weight signal stream and a corrected position signal stream. The method includes: Power spectral density analysis is performed on the original vibration spectrum stream to convert and output the device vibration spectrum; dominant interference mode analysis is performed on the device vibration spectrum to locate multiple intermodulation interference frequency peaks; multi-dimensional frequency domain coupling features are extracted based on the multiple intermodulation interference frequency peaks, and a collaborative filtering parameter group is matched in the adaptive filter parameter library; after parameter reconstruction processing of the array filter group using the collaborative filtering parameter group, the original weight signal and the original position signal are input for collaborative clutter filtering to obtain the corrected weight signal stream and the corrected position signal stream.

[0036] Power spectral density analysis is a signal processing method used to represent the frequency components and power distribution of a signal. In this step, the original vibration signal is converted to the frequency domain. By calculating the power spectral density of the signal, the energy distribution of the signal in each frequency band is analyzed. The power spectral density analysis results can reveal the intensity distribution of the frequency components in the vibration signal, which is used for subsequent identification of interference signals and screening of effective signals. Through this step, the vibration spectrum of the equipment is obtained, which shows the frequency range of the equipment vibration, including the main frequency modes, vibration intensity and possible frequency interference components during equipment operation.

[0037] By analyzing the vibration spectrum of the equipment, dominant interference modes are identified. These interference modes refer to the frequency components that dominate the equipment during operation. They originate from mechanical vibration, resonance, friction, or other abnormal factors. These interference modes are manifested as frequency peaks in the spectrum. These frequencies can be periodic interference generated by the equipment or generated by the interaction between the equipment and the environment. Intermodulation interference refers to new frequency components generated when different frequency components interact. These components are unrelated to the original signal and affect signal quality. By analyzing the equipment vibration spectrum, multiple intermodulation interference frequency peaks are located. These frequency peaks represent interference signals generated by multiple signal sources during equipment operation.

[0038] Multidimensional frequency domain coupling feature extraction refers to understanding the interaction of interfering signals by analyzing the coupling relationships between different frequency components. This step extracts the correlation features between interfering signals, providing information about their mutual influence, enabling filters to accurately suppress interfering components during design. In adaptive filters, filter parameters are dynamically adjusted based on the characteristics of the input signal. To effectively remove interfering signals, the optimal set of collaborative filtering parameters is selected from an adaptive filter parameter library based on the extracted frequency domain coupling features. This library contains multiple different filter parameters that can be adjusted according to different signal characteristics, allowing the filtering process to adapt to dynamically changing interference patterns.

[0039] In adaptive filters, filter parameters are dynamically adjusted as the characteristics of the input signal change. By matching and optimizing the collaborative filtering parameter set, the filter parameters are reconstructed. This step involves the joint optimization of multiple filters, thereby improving the performance of the entire filter bank. The reconstructed filter is then used to perform collaborative clutter filtering on the original signal. This means that the filter combines the characteristics of the original vibration spectrum stream, the original weight signal stream, and the original position signal stream, processing them synchronously. Through this process, clutter, noise, and other interference components in the signal can be effectively removed. Here, the original weight signal and the original position signal are corrected, resulting in a more accurate corrected weight signal stream and a corrected position signal stream.

[0040] Furthermore, the method for calculating real-time feed lag deviation based on the feed rate set flow and the corrected weight signal flow includes: Construct the theoretical weight growth curve of the feed rate setting flow; fit the measured weight growth curve based on the corrected weight signal flow; align and compare the theoretical weight growth curve and the measured weight growth curve to extract the expected theoretical weight value and the measured stable weight value at the weight growth rate abrupt change point; calculate the real-time weight deviation between the expected theoretical weight value and the measured stable weight value; extract the feed rate setting at the weight growth rate abrupt change point from the feed rate setting flow; and back-calculate the equivalent physical time lag based on the real-time weight deviation and the feed rate setting as the real-time feed lag deviation output.

[0041] The feed rate setpoint flow is the feed rate set based on process requirements or the automatic control system. It includes parameters such as feed rate, feed volume, and time, and describes the ideal speed of material conveying. Under ideal conditions of no lag and no disturbance, assuming that the velocity and mass distribution of the material flowing into the weighing hopper during the feeding process are stable and uniform, the ideal weight growth curve of the material in the weighing hopper can be calculated based on the feed rate setpoint flow, that is, the expected trajectory of the material weight change over time.

[0042] The corrected weight signal stream is the true weight signal after removing interference and noise. Compared with the original signal, this signal is closer to the actual material weight. Based on the corrected weight signal stream, a measured weight growth curve is fitted. This curve describes the change of material weight over time during the actual feeding process. This curve can be affected by factors such as equipment performance, material characteristics, or control system response.

[0043] Aligning and comparing the theoretical weight growth curve with the measured weight growth curve ensures that the two curves are correctly matched on the time axis for accurate comparison. Abrupt change points refer to the moments when the material feed rate changes significantly; these points can be instantaneous changes caused by adjustments to the feeding system, equipment switching speeds, or other factors. The expected theoretical weight value corresponding to the abrupt change point is extracted from the theoretical curve, representing the material weight that should be reached at that point under ideal conditions. The measured stable weight value corresponding to the abrupt change point is extracted from the measured curve, representing the final stable weight of the material in the actual system.

[0044] By comparing the expected theoretical weight value with the measured stable weight value, the real-time weight deviation is calculated. This deviation reflects the actual deviation during the feeding process, caused by equipment performance, control response delay, or other factors. If the deviation is large, it indicates that there is a lag or other problem in the response of the feeding system, and the control strategy needs to be adjusted; if the deviation is small, it indicates that the feeding system is operating within the ideal range.

[0045] Extract the feed rate setting value corresponding to the mutation point from the feed rate setting stream. The feed rate setting value reflects the expected feed rate during the feeding process and provides a basis for subsequent adjustments.

[0046] By analyzing real-time weight deviation and feed rate setpoints, the physical time lag in the feeding process is deduced. Lag refers to the time difference between an adjustment made by the feeding system and the actual change in material quantity. This lag is caused by factors such as equipment response time, material flow characteristics, and control system processing delays. Deducing the lag helps to understand and quantify its impact. Based on the calculated physical lag, a real-time feeding lag deviation output is generated. This output value can serve as a feedback signal to adjust the control system's response speed and strategy, ensuring a smoother feeding process and avoiding overfeeding or delayed feeding.

[0047] Furthermore, the method for constructing the theoretical weight growth curve of the feed rate set flow includes: The feeding mechanism's outlet cross-sectional area and feeding flow characteristics are obtained interactively; the high-speed feeding value, low-speed feeding value, feeding speed switching point, and mass setting value are extracted from the feeding speed setting flow; based on the feeding outlet cross-sectional area and feeding flow characteristics as the basis for dynamic modeling, high-speed linear growth segments and low-speed linear growth segments corresponding to the high-speed feeding value and low-speed feeding value are constructed; using the mass setting value as the target endpoint value and the feeding speed switching point as the segment connection node, the high-speed linear growth segments and low-speed linear growth segments are spliced ​​together to output the theoretical weight growth curve.

[0048] The outlet cross-sectional area of ​​the feeding mechanism refers to the area of ​​the material outlet point, expressed in square meters (e.g., square meters). This parameter directly affects the feeding velocity and material flow rate. The larger the outlet cross-sectional area, the greater the amount of material flowing out per unit time. This cross-sectional area is a design parameter of the feeding mechanism or can be obtained through on-site measurement. Feeding flow characteristics refer to the flow behavior of the material during the feeding process. Different materials exhibit different flow characteristics, such as fluidity, viscosity, particle size, and moisture content.

[0049] The high-speed feeding value is the maximum feeding speed during the feeding process. In the initial stage of production, the feeding system quickly feeds the material into the weighing hopper to complete the feeding task as soon as possible. The low-speed feeding value is the minimum feeding speed during the feeding process. Near the end of the feeding process, the system slows down to avoid overfeeding. The feeding speed switching point is the time node or material quantity node from high-speed feeding to low-speed feeding. Determining the switching point is crucial for maintaining system accuracy and avoiding overfeeding. Usually, the switching point is adjusted based on the material weight approaching the target value. The quality setpoint is the target quality value of the feeding task, that is, the expected final material quality. The feeding speed is adjusted based on this value to ensure that the feeding process is completed accurately.

[0050] Two linear growth segments for the feeding stages were constructed using a kinetic model to describe the material weight changes during the high-speed and low-speed feeding stages, respectively. For the high-speed linear growth segment, at the start of feeding, the system rapidly feeds the material into the weighing hopper at a high feeding rate (high-speed value). During this stage, the material weight increases linearly over time. The linear growth model for this stage is based on the feeding rate and the kinetic characteristics of the feeding mechanism, including the outlet cross-sectional area and flow characteristics, and can be used to predict the material weight change over time. For the low-speed linear growth segment, the feeding rate slows down (low-speed value) as the material approaches the target weight. To avoid overfeeding, the system fine-tunes at a lower rate. During this stage, the material weight increase is generally still linear, but at a slower rate.

[0051] The ultimate goal of the theoretical weight growth curve is the setpoint, i.e., the desired final material weight. It's crucial to ensure the feeding process smoothly stops when this endpoint is reached. At this point, the feeding speed switches from high to low speed; therefore, the theoretical weight growth curve needs to inflection at this point, smoothly transitioning from the high-speed to the low-speed phase. By splicing the high-speed linear growth segments with the low-speed linear growth segments, creating a seamless connection at the feeding speed switching point, the spliced ​​theoretical weight growth curve represents the ideal weight change throughout the feeding process, demonstrating a smooth transition from high to low speed.

[0052] Furthermore, the method includes making a feeding speed switching decision based on the real-time feeding lag deviation and the corrected weight signal stream, and outputting a feeding gate control command sequence. The real-time feeding lag deviation is used to compensate for the feeding speed switching point, generating a dynamic correction switching point; based on the dynamic correction switching point, the corrected expected weight value is extracted from the theoretical weight growth curve; based on the incremental update of the corrected weight signal stream, the measured stable weight value is tracked: S1: when the measured stable weight value is greater than the corrected expected weight value, a high-speed to low-speed command is output; S2: when the measured stable weight value is greater than the target gate closing threshold, a feeding gate closing command is output, wherein the high-speed to low-speed command and the feeding gate closing command constitute the feeding gate control command sequence.

[0053] Reverse compensation refers to adjusting the feed rate switching point in advance based on real-time feed lag deviation. This ensures an early response during the feeding process, compensating for errors caused by lag. Through this compensation, the switching timing during the feeding process can be dynamically adjusted to avoid overfeeding or underfeeding. The dynamically corrected switching point obtained after compensation represents the new feed rate switching time point that needs to be adjusted in actual operation. This switching point is dynamically calculated based on the current lag deviation, aiming to enable the system to respond in real time according to the actual situation.

[0054] Because of dynamic changes during the feeding process, the actual weight will have some error. Therefore, after considering the dynamic correction switching point, the expected weight value extracted from the theoretical curve will be adjusted. This corrected expected weight value can be used as the target weight of the system at the current moment to guide subsequent feeding operations.

[0055] The corrected weight signal stream reflects the system's real-time feedback on material weight. Through incremental updates, it can continuously obtain the latest weight data and perform subsequent operations based on this data. The measured stable weight value is the stable value in the corrected weight signal stream. It is the stable data output by the system when the material is close to the target weight during the feeding process, and it represents the stable weight of the material during the current actual feeding process.

[0056] If the actual measured weight of the material exceeds the corrected expected weight value, meaning the material is close to the target, it indicates that the feeding process needs to enter the next stage, namely the low-speed stage. This judgment condition helps identify that the material feeding process is nearing its end and prepares to reduce the feeding speed. At this time, a high-speed to low-speed command is issued, prompting the feeding speed to transition from high speed to low speed. This command is to avoid overfeeding and ensure a smooth end to the feeding process.

[0057] The target shut-off threshold refers to the threshold at which feeding should stop when the material weight reaches a target or predetermined value. This threshold is slightly lower than the actual target weight to allow for overfeeding. When the measured stable weight value exceeds the target shut-off threshold, a feed gate closing command is output to instruct the feed gate to close, stopping material flow and completing the feeding task, thereby ensuring accurate material feeding.

[0058] Through steps S1 and S2, a complete sequence of control instructions for the feed gate is generated. This sequence of instructions includes instructions to switch from high speed to low speed and instructions to close the feed gate, ensuring that the feeding task is completed on time and accurately, while avoiding overfeeding or underfeeding.

[0059] Furthermore, the method also includes: A weighing sensor array is deployed at the support point array of the weighing hopper of the automatic weighing machine, wherein the weighing sensor array is used to acquire the original weight signal stream; a position encoder array is deployed at the motion axis drive node sequence of the automatic weighing machine, wherein the position encoder array is used to acquire the original position signal stream; a vibration acceleration sensor array is deployed at the mechanical structure vibration sensitive node array of the automatic weighing machine, wherein the acceleration sensor array is used to acquire the original vibration spectrum stream in conjunction with spectrum analysis; wherein the weighing sensor array, the position encoder array, and the acceleration sensor array are connected to a deterministic communication network based on real-time industrial Ethernet through distributed I / O stations for timestamp alignment of acquisition.

[0060] An array of load cells is installed on a support point array beneath the weighing hopper of an automatic weighing machine to collect the raw weight signal stream. The load cell array consists of multiple load cells, using force sensors or strain gauges, to measure weight changes within the weighing hopper as material passes through. These sensors convert the dynamically sensed weight changes into electrical signals, providing real-time weight data of the material. The raw weight signal stream, captured by the load cells, serves as system feedback for subsequent processing and control decisions.

[0061] Position encoder arrays are deployed on the drive nodes of the motion axes of an automated weighing machine to acquire raw position signal streams. These arrays consist of multiple position encoders (also known as linear encoders or rotary encoders) mounted on the key drive axes of the automated weighing machine to measure motion positions in real time. The encoders can accurately track the position of each motion axis, ensuring the equipment moves along a predetermined trajectory. The raw position signal stream reflects the real-time position information of each motion axis. Converted into digital position data by the electrical pulse signals provided by the encoders, these position signal streams provide accurate equipment position and movement status, providing the foundational data for subsequent control and trajectory interpolation.

[0062] A vibration acceleration sensor array is deployed at the vibration-sensitive nodes of the automatic weighing machine's mechanical structure. Combined with spectral analysis, the raw vibration spectrum stream is acquired. The vibration acceleration sensor array consists of multiple acceleration sensors used to monitor the vibration of the mechanical structure. The acceleration sensors utilize the piezoelectric or capacitive effects to sense changes in vibration acceleration and convert them into electrical signals. The raw vibration spectrum stream can be converted into a frequency domain signal through spectral analysis to analyze the frequency components of the vibration. The vibration spectrum stream can reveal the operating status of the mechanical equipment and help determine whether there are mechanical faults, loosening, or abnormal vibrations.

[0063] The weighing sensor array, position encoder array, and vibration acceleration sensor array are connected to a deterministic communication network based on real-time industrial Ethernet via distributed I / O stations. The distributed I / O stations are responsible for converting the analog signals from each sensor into digital signals and transmitting them to the central control system or data processing unit via the network. Employing a real-time Ethernet communication protocol ensures that the data acquired by the sensors can be transmitted with low latency and high accuracy, meeting the stringent real-time requirements of industrial applications. To ensure synchronization between multiple sensor data, a timestamp is added to each acquired signal, and all sensor data streams are aligned on the time axis to ensure consistent time references for all signal sources. This allows data from different sensors to be precisely matched at the same point in time, ensuring that subsequent analysis, calculations, and control are based on a consistent data time reference.

[0064] Furthermore, the method involves performing trajectory interpolation feedback for the transport task based on the corrected position signal stream to obtain multi-axis cooperative motion commands, and includes: Based on the corrected position signal flow, the truss spatial pose state is constructed; the trajectory parameters of the transport task are locally called to construct a theoretical spatial trajectory model; the trajectory deviation of the theoretical spatial trajectory model is quantized based on the theoretical spatial trajectory model to obtain a spatial pose deviation vector; the spatial pose deviation vector is interpolated to obtain a sequence of compensated trajectory points, and then kinematic decomposition is performed to output the multi-axis cooperative motion command.

[0065] In mechanical systems, especially truss structures such as hydraulically driven multi-axis handling machinery, pose state refers to the spatial position and orientation (i.e., direction) of a robotic arm or platform. Pose state includes not only position coordinates, such as X, Y, and Z coordinates, but also orientation parameters, such as roll, pitch, and yaw angles. This information constitutes a complete description of the object or mechanical structure in three-dimensional space. By mapping the corrected position signal stream into a spatial model, the actual pose state of the current mechanical system can be accurately obtained.

[0066] The trajectory parameters for a transport task are provided by the system's control strategy or task planning system. These parameters include the starting point, target point, velocity, acceleration limits, and motion mode. These parameters determine the object's path from the starting point to the destination. Based on these trajectory parameters, an ideal theoretical spatial trajectory model is generated. This model can be a straight line, a curved path, or a more complex path. This trajectory model, based on physical laws and task requirements, describes how the mechanical equipment can move smoothly in three-dimensional space.

[0067] By comparing the ideal trajectory with the actual trajectory, the differences between the two are identified. These differences are caused by factors such as errors, mechanical precision, and external disturbances. Quantifying the deviation aims to convert these differences into specific numerical values ​​for subsequent correction. This is represented as a spatial pose deviation vector, where each component represents the deviation in a certain direction in space, such as the deviation of the X, Y, and Z coordinates, as well as the orientation deviation. This vector represents the gap between the theoretical trajectory and the actual trajectory; it is a multi-dimensional vector describing the position and attitude errors of an object. By calculating these errors, appropriate adjustments can be made to ensure more accurate subsequent motion.

[0068] Trajectory interpolation corrects the spatial pose deviation vector, gradually approximating the ideal trajectory to the actual motion trajectory. Through interpolation algorithms, a new sequence of compensated trajectory points is generated. These compensated points represent every position the mechanical structure should traverse on the adjusted trajectory. The interpolation process involves calculating parameters such as position, velocity, and acceleration at each moment, ensuring a smooth transition without abrupt changes. Kinematic decomposition decomposes the multidimensional motion of the entire mechanical system (multi-axis robotic arm or multiple coordinating motion components) into independent sub-tasks. By combining the compensated trajectory point sequence with the kinematic model of the mechanical structure, precise motion parameters for each axis, such as angular velocity and acceleration, are calculated. Finally, based on the kinematic decomposition results, multi-axis coordinated motion commands are generated for each motion axis. These commands are sent to the drive system to guide the motion of each axis, ensuring that all axes complete the task in a coordinated manner.

[0069] Furthermore, the method for constructing the truss spatial pose state based on the corrected position signal stream includes: The corrected position signal stream is decomposed to obtain multiple position feedback streams corresponding to multiple motion axis driving nodes in the motion axis driving node sequence; multiple axis real-time spatial trajectories are constructed based on the multiple position feedback streams; the multiple axis real-time spatial trajectories are spatiotemporally fused to generate the truss spatial pose state.

[0070] Automatic weighing machines have multiple motion axes, each controlled by a drive node. The drive node is the junction of sensors, actuators, and the control system, responsible for the actual movement of the driven axis. Position information for each motion axis is extracted from the corrected position signal stream and converted into a position feedback stream. The feedback stream for each axis represents its spatial position at a given moment. Through these feedback streams, the position changes of each motion axis can be monitored in real time.

[0071] Based on multiple position feedback streams, the real-time spatial trajectory of each axis is calculated. The trajectory is not only a continuous record of position but also includes dynamic information such as velocity and acceleration. The constructed real-time spatial trajectory describes the motion path and behavior of each axis in space. This trajectory can accurately reflect the real-time performance of each axis during task execution, providing basic data for subsequent trajectory correction and synchronization.

[0072] Spatiotemporal fusion combines the real-time spatial trajectories of multiple axes to ensure coordinated operation between the various motion axes. In this process, by combining time (the progress of motion of each axis) and space (the specific position and orientation of each axis), the state of the entire system at each moment is obtained. By fusing the trajectories of all axes, the overall truss spatial pose state is obtained. This pose state not only describes the position of the truss structure in space but also includes the orientation of its individual parts, i.e., the orientation of each axis and part. The truss spatial pose state provides the necessary feedback for multi-axis coordinated motion, ensuring that the various components cooperate correctly in space.

[0073] Furthermore, the target closing threshold is calculated based on the air-dropping parameters and the mass setting value.

[0074] Aerial drop parameters refer to the motion parameters of materials falling through the air, such as velocity and acceleration. These parameters are determined by factors such as the material's gravity and density. Proper control of the aerial drop process is crucial to ensure accurate material entry into the feeding system and prevent deviations. The mass setpoint is the predetermined target material mass, representing the ideal or target mass during the feeding process. The target gate closing threshold is the condition under which the system decides to close the feeding gate; when the material reaches this threshold, the feeding gate should automatically close to stop material flow. This value is jointly determined by the aerial drop parameters and the mass setpoint, aiming to ensure precise stopping of material flow when the target mass value is reached, avoiding overfeeding or underfeeding.

[0075] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An automatic weighing machine hydraulic integrated control system, characterized in that, The system includes: The data receiving module is used to receive the raw weight signal stream, raw position signal stream, and raw vibration spectrum stream transmitted back by the multimodal sensor network through the distributed industrial bus protocol. The clutter filtering module is used to introduce the original vibration spectrum stream to perform adaptive collaborative clutter filtering of the original weight signal and the original position signal, so as to obtain the corrected weight signal stream and the corrected position signal stream. The deviation calculation module is used to calculate the real-time feeding lag deviation based on the feed rate setting flow and the corrected weight signal flow. The decision module is used to make feeding speed switching decisions based on the real-time feeding lag deviation and the corrected weight signal stream, and output the feeding gate control command sequence. The feedback module is used to perform material handling trajectory interpolation feedback based on the corrected position signal stream to obtain multi-axis cooperative motion commands; The timing control module is used to perform linkage start-stop timing control of the automatic weighing machine according to the feed gate control command sequence when the truss hydraulic actuator is driven by the multi-axis coordinated motion command.

2. The automatic weighing machine hydraulic integrated control system as described in claim 1, characterized in that, The clutter filtering module includes: The power spectral density analysis unit is used to perform power spectral density analysis on the original vibration spectrum stream and convert it to output the device vibration spectrum. The modal analysis unit is used to perform dominant interference mode analysis on the vibration spectrum of the equipment and locate multiple intermodulation interference frequency peaks; The parameter matching unit is used to extract multi-dimensional frequency domain coupling features based on the multiple intermodulation interference frequency peaks and match the collaborative filtering parameter group in the adaptive filter parameter library. The cooperative clutter filtering unit is used to perform parameter reconstruction processing of the array filter bank using the cooperative filtering parameter group, and then input the original weight signal and the original position signal for cooperative clutter filtering to obtain the corrected weight signal stream and the corrected position signal stream.

3. The automatic weighing machine hydraulic integrated control system as described in claim 1, characterized in that, The deviation calculation module includes: Theoretical growth curve construction unit is used to construct the theoretical weight growth curve of the feed rate set flow; The measured growth curve fitting unit is used to fit the measured weight growth curve based on the corrected weight signal stream. The alignment and comparison unit is used to align and compare the theoretical weight growth curve and the measured weight growth curve to extract the expected theoretical weight value and the measured stable weight value at the point of sudden change in weight growth rate. The weight deviation calculation unit is used to calculate the real-time weight deviation between the expected theoretical weight value and the measured stable weight value. A feed rate setting extraction unit is used to extract the feed rate setting of the weight increase abrupt point from the feed rate setting stream; The hysteresis back-calculation unit is used to back-calculate the equivalent physical time hysteresis based on the real-time weight deviation and feeding speed, and output it as the real-time feeding hysteresis deviation.

4. The automatic weighing machine hydraulic integrated control system as described in claim 3, characterized in that, The theoretical growth curve construction unit includes: Interactive channel, used to interactively obtain the feed outlet cross-sectional area and feed flow characteristics of the feeding mechanism; The setpoint extraction channel is used to extract the high-speed feed value, low-speed feed value, feed speed switching point and quality setpoint from the feed speed setting stream. The linear growth segmentation channel is used to construct high-speed linear growth segments and low-speed linear growth segments corresponding to the high-speed and low-speed feed values, based on the feed outlet cross-sectional area and feed flow characteristics as the basis for dynamic modeling. The splicing channel is used to splice the high-speed linear growth segment and the low-speed linear growth segment with the mass setting value as the target endpoint value and the feed speed switching point as the segment connection node, and outputs the theoretical weight growth curve.

5. The automatic weighing machine hydraulic integrated control system as described in claim 4, characterized in that, The decision-making module includes: The dynamic correction switching point generation unit is used to use the real-time feeding lag deviation to reverse compensate the feeding speed switching point and generate a dynamic correction switching point. The corrected expected weight value extraction unit is used to extract the corrected expected weight value from the theoretical weight growth curve based on the dynamic correction switching point. The measured stable weight value tracking unit is used to track the measured stable weight value based on the incremental updates of the corrected weight signal stream. S1: When the measured stable weight value is greater than the corrected expected weight value, output a high-speed to low-speed command; S2: When the measured stable weight value is greater than the target closing threshold, a feeding gate closing command is output, wherein the high-speed to low-speed command and the feeding gate closing command constitute the feeding gate control command sequence.

6. The hydraulic integrated control system for the automatic weighing machine as described in claim 1, characterized in that, The data receiving module further includes: The first deployment unit is used to deploy a weighing sensor array on the scale bucket support point array of the automatic weighing machine, wherein the weighing sensor array is used to collect the raw weight signal stream; The second deployment unit is used to deploy a position encoder array in the motion axis drive node sequence of the automatic weighing machine, wherein the position encoder array is used to acquire the original position signal stream; The third deployment unit is used to deploy a vibration acceleration sensor array on the mechanical structure vibration sensitive node array of the automatic weighing machine, wherein the acceleration sensor array is used to collect the original vibration spectrum stream in combination with spectrum analysis; The weighing sensor array, position encoder array, and acceleration sensor array are connected to a deterministic communication network based on real-time industrial Ethernet via distributed I / O stations for timestamp alignment.

7. The automatic weighing machine hydraulic integrated control system as described in claim 6, characterized in that, The feedback module includes: The pose state construction unit is used to construct the truss spatial pose state based on the modified position signal stream. The trajectory model construction unit is used to locally call the trajectory parameters of the transport task and construct a theoretical spatial trajectory model. The trajectory deviation quantization unit is used to quantize the trajectory deviation of the theoretical spatial trajectory model based on the theoretical spatial trajectory model to obtain the spatial pose deviation vector. The kinematic decomposition unit block is used to perform trajectory interpolation on the spatial pose deviation vector, obtain a sequence of compensated trajectory points, perform kinematic decomposition, and output the multi-axis cooperative motion command.

8. The automatic weighing machine hydraulic integrated control system as described in claim 7, characterized in that, The pose state construction unit includes: The decomposition channel is used to decompose the corrected position signal stream to obtain multiple position feedback streams corresponding to multiple motion axis drive nodes in the motion axis drive node sequence; A real-time spatial trajectory construction channel for axes is used to construct multiple real-time spatial trajectories based on the multiple position feedback streams; The spatiotemporal fusion channel is used to spatiotemporally fuse the real-time spatial trajectories of the multiple axes to generate the spatial pose state of the truss.

9. The automatic weighing machine hydraulic integrated control system as described in claim 5, characterized in that, The target closing threshold is calculated based on the air-dropping parameters and the mass setting value.

10. A hydraulic integrated control method for an automatic weighing machine, characterized in that, The method, implemented based on the automatic weighing machine hydraulic integrated control system according to any one of claims 1-9, comprises: Receive the raw weight signal stream, raw position signal stream, and raw vibration spectrum stream transmitted back from the multimodal sensor network via the distributed industrial bus protocol; The original vibration spectrum stream is introduced to perform adaptive cooperative clutter filtering on the original weight signal and the original position signal, resulting in a corrected weight signal stream and a corrected position signal stream. Calculate the real-time feeding lag deviation based on the feed rate set flow and the corrected weight signal flow; Based on the real-time feeding lag deviation and the corrected weight signal stream, a feeding speed switching decision is made, and a feeding gate control command sequence is output. Based on the corrected position signal stream, the material handling task trajectory interpolation feedback is performed to obtain multi-axis cooperative motion commands; When the truss hydraulic actuator is driven by the multi-axis coordinated motion command, the automatic weighing machine is controlled by the linkage start and stop sequence according to the feed gate control command sequence.

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