Intelligent dynamic weighing system for dual-circulation small materials based on Internet of Things technology

Abstract

This invention relates to the field of dynamic weighing and discloses a dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things (IoT) technology. This system addresses weighing inaccuracies caused by mechanical vibration, system delays, and temperature and humidity variations in highly dynamic industrial environments. The system includes synchronously acquiring hopper weight and vibration acceleration data; filtering dynamic interference in real-time through data fusion; extracting accurate material net weight and flow rate; dynamically predicting the amount of material falling from the air and calculating the early shut-off trigger point, using a feedforward and feedback collaborative algorithm to precisely control the feeding valve in stages; and automatically extracting data after each batch to update system action delays and optimize vibration filtering model parameters. This invention achieves high-precision, adaptive dynamic weighing of small materials under strong interference, effectively overcoming the accuracy drift phenomenon of traditional systems during long-cycle operation.

Description

Technical Field

[0001] This invention relates to the field of dynamic weighing, and more particularly to a dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things (IoT) technology. Background Technology

[0002] In many modern industrial production processes such as chemical engineering, pharmaceuticals, food processing, and new materials manufacturing, the precise proportioning of materials is a core factor determining the quality, consistency, and production cost of the final product. Especially for the weighing of small ingredients such as additives, catalysts, and trace elements, which constitute a small proportion but have a significant impact on the formulation, extremely high requirements are placed on the weighing accuracy, response speed, and stability of the system.

[0003] With the widespread adoption of Industrial Internet of Things (IIoT) technology and intelligent manufacturing, traditional automated weighing systems are evolving towards closed-loop control based on real-time sensing, data fusion, and intelligent decision-making. During dynamic weighing, the material is in a continuous flow state, requiring the system to complete weight acquisition, rate calculation, and precise control of the discharge valve's opening and closing within an extremely short time.

[0004] However, due to the complex industrial environment, inherent delays in mechanical structures, and variations in the physical properties of materials, achieving high-precision and highly adaptable weighing of small materials in a highly dynamic and disruptive production cycle has become a critical technological bottleneck that urgently needs to be addressed in the field of industrial automation. Although various automatic weighing systems are currently available on the market, the following significant technical defects and shortcomings still commonly exist in practical applications of dynamic weighing of small materials: In complex factory environments, the operation of motors, agitation, material impact, and vibrations from surrounding large equipment in the feeding equipment can directly couple to the weight signal, creating severe dynamic force interference. Traditional methods often use pure software algorithms such as moving average filtering to smooth the data, but this introduces significant signal lag, preventing the system from obtaining true real-time net weight and flow rate, thus missing the optimal control opportunity. Existing weighing controls mostly employ traditional pure PID feedback control. Because the weighing process is nonlinear and time-varying, pure feedback control can only adjust after an error occurs, making it difficult to balance the conflict between rapid material feeding and precise weighing. The lack of feedforward adjustment and staged control mechanisms easily leads to frequent valve actuation or system oscillation when approaching the target value, reducing weighing efficiency and accuracy. The flowability of powders or granular materials is highly susceptible to environmental factors. Increased humidity can cause materials to stick together and become less flowable. Existing systems typically ignore the impact of environmental factors on the material feeding rate and fail to incorporate environmental condition compensation mechanisms, resulting in significant fluctuations in weighing accuracy of the same set of parameters under different seasons or weather conditions.

[0005] Therefore, we propose a dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things (IoT) technology to solve the above problems. Summary of the Invention

[0006] This invention provides a dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things (IoT) technology, which is used to solve the weighing inaccuracy problem caused by mechanical vibration, system delay and temperature and humidity changes in a highly dynamic industrial environment.

[0007] The first aspect of this invention provides a dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things (IoT) technology. The system includes: an acquisition module for real-time synchronous acquisition of total weight measurement data and vibration acceleration data of the hopper; a filtering module for filtering vibration interference from the total weight measurement data based on the vibration acceleration data, generating real-time material net weight data and material flow rate data; a shutdown module for dynamically calculating the early shutdown trigger point for the current weighing process based on the material flow rate data and the currently stored system action delay time; a processing module for real-time adjustment of the action state of the feeding actuator based on the difference between the material net weight data and the early shutdown trigger point, combined with the material flow rate data, until a shutdown command is issued; and an update module for analyzing the changes in material net weight data and material flow rate data before and after the shutdown after a single weighing batch, updating the value of the system action delay time, and optimizing the model parameters on which vibration interference filtering depends.

[0008] Optionally, in a first implementation of the first aspect of the present invention, the method includes: calculating the dynamic force interference component caused by vibration based on the vibration acceleration data and a preset coupling coefficient; subtracting the dynamic force interference component from the total weight measurement data in real time to generate real-time material net weight data; performing differential processing on the real-time material net weight data to calculate its rate of change and generate real-time material mass flow rate data.

[0009] Optionally, in a second implementation of the first aspect of the present invention, the method includes: acquiring the material flow rate data and a pre-set weighing target value; calling the currently stored system action delay time and acquiring a safety margin coefficient determined based on historical weighing data; calculating the predicted air-drop mass based on the material flow rate data, the system action delay time, and the safety margin coefficient; and subtracting the predicted air-drop mass from the weighing target value to generate an early shutdown trigger point.

[0010] Optionally, in a third implementation of the first aspect of the present invention, the method includes: calculating the real-time weight deviation between the material net weight data and the early shutdown trigger point; generating a feedback control command based on the real-time weight deviation; generating a feedforward control command based on the material flow rate data and the early shutdown trigger point using a preset feedforward control model; generating a valve opening command for the feeding actuator by combining the feedback control command and the feedforward control command; issuing the valve opening command to the feeding actuator for execution, and determining in real time whether the material net weight data has reached the early shutdown trigger point; if so, generating and issuing a shutdown command.

[0011] Optionally, in the fourth implementation of the first aspect of the present invention, the ratio between the current net weight data of the material and the weighing target value is determined to determine whether the current stage is a rapid feeding stage or a precise weighing stage. If the rapid feeding stage is in progress, the first set of control parameters corresponding to this stage is invoked, and a first feedback control command is generated based on the first set of control parameters and the real-time weight deviation, while a first feedforward control command is generated based on the first set of control parameters and the material flow rate data. If the precise weighing stage is in progress, the second set of control parameters corresponding to this stage is invoked, and a second feedback control command is generated based on the second set of control parameters and the real-time weight deviation, while a second feedforward control command is generated based on the second set of control parameters and the material flow rate data.

[0012] Optionally, in the fifth implementation of the first aspect of the present invention, the method includes: after a single weighing batch is completed, extracting material net weight data and material flow rate data for a continuous period before and after the issuance of the shutdown command to form a shutdown process dataset; analyzing the changing trend of material net weight data in the shutdown process dataset to determine the time interval from the issuance of the shutdown command to the cessation of material net weight growth, as the measured system delay; processing the measured system delay with the currently stored system action delay time to generate an updated system action delay time; calculating the absolute deviation between the final material net weight data at the end of this weighing batch and the preset weighing target value, as the weighing accuracy error of this batch; fine-tuning the vibration coupling coefficient used in the vibration interference filtering process according to the direction and magnitude of the weighing accuracy error and a preset adjustment rule to generate an optimized vibration coupling coefficient; and loading the updated system action delay time and the optimized vibration coupling coefficient into the control system as initial parameters for subsequent weighing batches.

[0013] Optionally, in the sixth implementation of the first aspect of the present invention, a correction module is further included: collecting temperature and humidity data of the environment in which the hopper is located during the real-time control loop; querying a preset parameter correction table based on the temperature and humidity data to obtain a state compensation factor for the current environment; using the state compensation factor to correct the generated real-time material mass flow rate data to obtain environmentally compensated material flow rate data; and using the environmentally compensated material flow rate data to replace the original material flow rate data and participate in the calculation of the early shutdown trigger point.

[0014] Optionally, in the seventh implementation of the first aspect of the present invention, the method includes: performing a search operation in a preset parameter correction table based on the temperature and humidity data; if the temperature and humidity data are completely consistent with a set of pre-stored environmental condition data in the parameter correction table, then directly reading the state compensation factor corresponding to the set of pre-stored environmental condition data; if the temperature and humidity data are between multiple sets of pre-stored environmental condition data in the parameter correction table, then generating a state compensation factor that matches the current temperature and humidity data through linear interpolation.

[0015] Beneficial effects: By introducing vibration acceleration data and synchronizing it with timestamps, and through hardware-level data fusion and coupling coefficient calculation, dynamic force interference can be actively and in real time canceled out. Without sacrificing response speed, the true instantaneous net weight and flow rate of materials are restored, fundamentally solving the problem of measurement distortion in strong vibration industrial environments. By performing multi-dimensional nonlinear mapping of real-time flow rate, material flowability level, and temperature and humidity state compensation factor, the early shutdown trigger point for each weighing is dynamically calculated. This breaks the technical bias of setting dead zones based on experience, enabling the system to accurately predict and intercept falling materials in the air under sudden environmental changes or material batch differences, greatly reducing the deviation rate and significantly improving batch consistency. By extracting historical data before and after shutdown, the system can autonomously calculate the actual system action delay and fine-tune the vibration coupling coefficient for the next batch. The equipment has the ability to actively adapt to mechanical aging and long-term time drift, which greatly extends the high-precision life cycle of the system and significantly reduces the company's operation and maintenance costs. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an embodiment of the intelligent dynamic weighing system for dual-cycle small materials based on Internet of Things technology in this invention. Figure 2 This is a schematic diagram of another embodiment of the intelligent dynamic weighing system for dual-cycle small materials based on Internet of Things technology in this invention. Detailed Implementation

[0017] This invention provides a dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things (IoT) technology, designed to address weighing inaccuracies caused by mechanical vibration, system delay, and temperature and humidity variations in highly dynamic industrial environments. The terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the intelligent dynamic weighing system for dual-cycle small materials based on Internet of Things (IoT) technology in this invention includes: 101. Acquisition module, used to acquire the total weight measurement data and vibration acceleration data of the hopper in real time.

[0019] It is understood that the executing entity of this invention can be a dual-cycle intelligent dynamic weighing device for small materials based on Internet of Things technology, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0020] Specifically, the original weight signal representing the total weight of the hopper and its contents is obtained through a weighing sensor; the acceleration signal reflecting mechanical vibration is obtained through a vibration sensor installed on the hopper; a unified timestamp is assigned to the original weight signal and the acceleration signal to generate a time-synchronized sensor data sequence; the time-synchronized sensor data sequence is input into a preset vibration filtering model, and through data fusion processing, real-time material net weight data and real-time material mass flow rate data are output.

[0021] It should be noted that the following example illustrates the process of weighing antioxidant powder with a target value of 10,000 kg using an intelligent ingredient dispensing system (with a server as the executing entity) in a food additive production workshop: Three high-precision load cells installed at the bottom of the hopper collect raw weight signals in real time at a frequency of 1000 Hz. Assuming the initial empty weight of the hopper is 5.000 kg, during a rapid feeding phase, at a certain millisecond, due to mechanical resonance from adjacent mixing equipment in the workshop, the sensors are interfered with, resulting in a falsely high raw total weight signal, displaying 7.050 kg. Simultaneously, a triaxial vibration sensor fixed to the outside of the hopper, also operating at 1000 Hz, detects abnormal vertical vibrations, and the output acceleration signal shows a current amplitude of 0.2 standard gravitational accelerations.

[0022] The edge gateway uses a high-precision hardware clock to assign perfectly identical absolute timestamps to the two signals, generating a time-synchronized sensor data sequence. The server inputs this sequence into a preset vibration filtering model, which automatically filters out the 0.035 kg interference caused by vibration and deducts 5.000 kg of empty weight through feature extraction. Finally, the model outputs the real-time net weight data of the antioxidant as 2.015 kg, and, combined with the preceding millisecond-level data, synchronously outputs the real-time material mass flow rate data of 1.500 kg / s, indicating that the material is currently in a rapid feeding state.

[0023] 102. Filtering module, used to filter out vibration interference from the total weight measurement data based on vibration acceleration data, and generate real-time material net weight data and material flow rate data.

[0024] Specifically, based on the vibration acceleration data and the preset coupling coefficient, the dynamic force disturbance component caused by vibration is calculated; the dynamic force disturbance component is subtracted from the total weight measurement data in real time to generate real-time material net weight data; the real-time material net weight data is differentiated and its rate of change is calculated to generate real-time material mass flow rate data.

[0025] It should be noted that in the above data fusion process, the server specifically completes interference filtering and real data generation through the following calculation logic: The vibration coupling coefficient of the hopper was pre-set to 0.175 kg / standard gravitational acceleration in the database. The server extracts the vibration acceleration data of 0.2 standard gravitational acceleration at a specific millisecond, multiplies these two values, and obtains the dynamic force disturbance component caused by resonance as 0.035 kg.

[0026] From the total weight measurement data of 7.050 kg affected by the disturbance, the calculated dynamic force disturbance component of 0.035 kg was accurately subtracted, and the fixed empty weight of the hopper of 5.000 kg was further subtracted, successfully restoring the true real-time net weight data of the material at the current moment as 2.015 kg.

[0027] Retrieve the net weight data from the previous frame recorded 0.01 seconds ago from memory. Assuming the net weight 0.01 seconds ago was 2.000 kg, the system compares this to the current net weight of 2.015 kg, indicating an actual increase of 0.015 kg in 0.01 seconds. Dividing this weight increase by the time interval, the current rate of weight change, i.e., the real-time material mass flow rate, is calculated to be 1.500 kg / second.

[0028] 103. Shutdown module, used to dynamically calculate the early shutdown trigger point for this weighing process based on material flow rate data and the currently stored system action delay time.

[0029] Specifically, the process involves acquiring material flow rate data and a pre-set weighing target value; retrieving the currently stored system action delay time and obtaining a safety margin coefficient determined based on historical weighing data; calculating the predicted air-drop mass based on the material flow rate data, system action delay time, and safety margin coefficient; and subtracting the predicted air-drop mass from the weighing target value to generate an early shutdown trigger point. Further, the flowability level parameter of the current batch of materials is acquired; the material flow rate data, system action delay time, and flowability level parameter are input into a pre-set air-drop mass calculation model; the air-drop mass calculation model outputs the predicted air-drop mass based on the non-linear mapping relationship between the material flow rate data, system action delay time, and flowability level parameter.

[0030] It should be noted that the current feeding process is in the final stage of precise weighing, and the target weighing value is 10.000 kg. Simultaneously, the server retrieves the equivalent material mass flow rate data, calculated using an environmental compensation mechanism, as 0.147 kg / s, and obtains the currently stored system pneumatic valve action delay time as 0.200 seconds. Furthermore, the system obtains the current powder flowability grade parameter as 0.85, and a safety margin factor of 1.05 determined based on historical data.

[0031] The aforementioned flow rate, action delay time, and flowability level parameters are input into a preset nonlinear aerial material drop mass calculation model. The model comprehensively evaluates the parabolic resistance of the powder at the current flow rate and fine-tunes it by incorporating a safety margin coefficient, outputting a predicted aerial material drop mass of 0.032 kg. Finally, the server uses the preset target value of 10.000 kg to subtract the predicted aerial material drop mass of 0.032 kg, establishing the final early shutdown trigger point for this weighing as 9.968 kg.

[0032] 104. The processing module is used to adjust the action status of the feeding actuator in real time based on the difference between the net weight data of the material and the early shutdown trigger point, and in combination with the material flow rate data, until a shutdown command is issued.

[0033] Specifically, the real-time weight deviation between the material's net weight data and the early shutdown trigger point is calculated; based on the real-time weight deviation, a feedback control command is generated using a proportional-integral-derivative control algorithm; based on the material flow rate data and the early shutdown trigger point, a feedforward control command is generated using a preset feedforward control model; the feedback control command and the feedforward control command are combined to generate the final valve opening command for the feeding actuator; the valve opening command is sent to the feeding actuator for execution, and it is determined in real time whether the material's net weight data has reached the early shutdown trigger point; if it has, a shutdown command is generated and sent. Furthermore, the ratio between the current net weight of the material and the target weighing value is determined to identify whether the current stage is rapid feeding or precise weighing. If the rapid feeding stage is in progress, the first set of control parameters corresponding to this stage is invoked, and a first feedback control command is generated based on the first set of control parameters and the real-time weight deviation. Simultaneously, a first feedforward control command is generated based on the first set of control parameters and the material flow rate data. If the precise weighing stage is in progress, the second set of control parameters corresponding to this stage is invoked, and a second feedback control command is generated based on the second set of control parameters and the real-time weight deviation. Simultaneously, a second feedforward control command is generated based on the second set of control parameters and the material flow rate data.

[0034] It should be noted that after the server establishes the early shutdown trigger point, it dynamically adjusts and precisely shuts down the feeding actuator: During the feeding process, the system monitors the status at high frequency. When the real-time net weight of the material transmitted by the sensor reaches 9.500 kg, the system calculates that 95% of the target amount has been reached. This triggers the switching critical point rule, and the server determines that the rapid feeding phase has ended and the system has entered the precise weighing phase. The actual physical flow rate of the powder also drops significantly from the previous 1.500 kg / s to approximately 0.150 kg / s. The system automatically calls upon a second set of control parameters specifically designed for the precise weighing phase, which provides a gentler feel.

[0035] During the precise weighing phase, the server subtracts the current net weight of 9.500 kg from the pre-closing trigger point of 9.968 kg to obtain a real-time weight deviation of 0.468 kg. This deviation is input into the proportional-integral-derivative (PID) control algorithm, combined with the second set of parameters, to generate a second feedback control command representing a 12% valve opening. Simultaneously, the system retrieves the current actual flow rate and inputs it into the feedforward control model to generate a second feedforward control command representing a 3% valve opening.

[0036] The two commands are combined, and a 15% valve opening command is issued. The valve quickly contracts to maintain a trickle flow. When the real-time net weight is detected to accurately reach 9.968 kg, the system immediately issues a full closure shut-off command, completing the dynamic weighing.

[0037] 105. Update module, used to analyze the changes in material net weight data and material flow rate data before and after the shutdown after a single weighing batch is completed, update the value of system action delay time, and optimize the model parameters on which vibration interference filtering depends for weighing control of subsequent batches.

[0038] Specifically, after a single weighing batch is completed, the net weight data and flow rate data of the material are extracted within a continuous period before and after the shutdown command is issued, forming a shutdown process dataset. The changing trend of the net weight data in the shutdown process dataset is analyzed to determine the time interval from the issuance of the shutdown command to the cessation of the net weight increase, which is used as the measured system delay. The measured system delay is weighted and fused with the currently stored system action delay time to generate an updated system action delay time. The absolute deviation between the final net weight data of the material at the end of this weighing batch and the preset weighing target value is calculated as the weighing accuracy error of this batch. Based on the direction and magnitude of the weighing accuracy error, the vibration coupling coefficient used in the vibration interference filtering process is fine-tuned according to the preset adjustment rules to generate an optimized vibration coupling coefficient. The updated system action delay time and the optimized vibration coupling coefficient are loaded into the control system as the initial parameters for subsequent weighing batches.

[0039] It should be noted that after a single weighing of a 10,000 kg target is completed, adaptive learning of the system parameters is performed: Analysis of the extracted shutdown process dataset revealed that 0.210 seconds elapsed from the issuance of the shutdown command to the complete cessation of powder weight gain. The system weighted and fused the previously stored 0.200-second delay with the measured 0.210-second delay (70% historical, 30% measured) to arrive at an updated system action delay of 0.203 seconds, accurately compensating for the slight fatigue delay of the mechanical valve.

[0040] The final stable net weight of the material after this weighing was read as 10.005 kg. Compared with the target value, the absolute deviation is a positive overweight of 0.005 kg. To prevent the next batch from being overweight again, the system must prompt the trigger command to be issued earlier. According to the adjustment rules, the system fine-tunes the vibration coupling coefficient, reducing the original value from 0.175 to 0.172. After the coefficient is reduced, the amount of interference deducted by the system will be smaller, and the calculated real-time net weight will be closer to the larger actual value, thus reaching the threshold more quickly to complete the early shutdown. See Table 1 below: Table 1 Please see Figure 2 In addition to the steps described above, another embodiment of the dual-cycle small-material intelligent dynamic weighing system based on Internet of Things technology in this invention further includes: 106. Correction module, used to collect temperature and humidity data of the environment in which the hopper is located during real-time control cycle; Specifically, based on temperature and humidity data, a preset parameter correction table is consulted to obtain the state compensation factor for the current environment. The generated real-time material mass flow rate data is then corrected using this state compensation factor to obtain environmentally compensated material flow rate data. This environmentally compensated material flow rate data replaces the original material flow rate data and is used in the calculation of the early shutdown trigger point. Further, based on the temperature and humidity data, a search operation is performed in the preset parameter correction table. If the temperature and humidity data completely match a set of pre-stored environmental condition data in the parameter correction table, the state compensation factor corresponding to that set of pre-stored environmental condition data is directly read. If the temperature and humidity data fall between multiple sets of pre-stored environmental condition data in the parameter correction table, a state compensation factor matching the current temperature and humidity data is generated through linear interpolation.

[0041] It should be noted that, considering the powder's flowability is easily affected by temperature and humidity, the system uses environmental compensation as a pre-processing step within each extremely short control cycle to correct subsequent material drop predictions and avoid redundant compensation for already performed physical calculations. Sensors installed above the hopper collect real-time data showing the current micro-environment temperature at 25 degrees Celsius and relative humidity at 65%. The server compares this data with a pre-set parameter correction table. Since the temperature matches perfectly and the relative humidity falls between the pre-stored 60% and 70% nodes in the table, the system triggers linear interpolation calculations, extracting the intermediate value between 1.00 and 0.96, and generating a current matching state compensation factor of 0.98.

[0042] To avoid redundant interference with objective physical facts, the system does not tamper with the actual flow velocity measured at the underlying level (i.e., 0.150 kg / s in step 104). Instead, it uses a compensation factor to convert it, multiplying 0.150 kg / s by 0.98 to obtain an equivalent predicted flow velocity of 0.147 kg / s after environmental compensation. This equivalent value is directly input into the aerial material drop prediction model in step 103, specifically for more accurately predicting the material drop trend in the next few milliseconds. See Table 2: Table 2 The present invention also provides a dual-cycle intelligent dynamic weighing device for small materials based on Internet of Things (IoT) technology. The dual-cycle intelligent dynamic weighing device for small materials based on IoT technology includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the dual-cycle intelligent dynamic weighing system for small materials based on IoT technology in the above embodiments.

[0043] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the dual-cycle small material intelligent dynamic weighing system based on Internet of Things technology.

[0044] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0045] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0046] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things (IoT) technology, characterized in that, include: The acquisition module is used to acquire the total weight measurement data and vibration acceleration data of the hopper in real time. The filtering module is used to filter out vibration interference from the total weight measurement data based on the vibration acceleration data, and generate real-time material net weight data and material flow rate data. The shutdown module is used to dynamically calculate the early shutdown trigger point for this weighing process based on the material flow rate data and the currently stored system action delay time. The processing module is used to adjust the action state of the feeding actuator in real time based on the difference between the net weight data of the material and the early shutdown trigger point, and in combination with the material flow rate data, until a shutdown command is issued. The update module is used to analyze the changes in material net weight data and material flow rate data before and after the shutdown after a single weighing batch is completed, update the value of the system action delay time, and optimize the model parameters on which vibration interference filtering depends.

2. The dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things technology according to claim 1, characterized in that, include: Based on the vibration acceleration data and the preset coupling coefficient, the dynamic force disturbance component caused by the vibration is calculated. The dynamic force disturbance component is subtracted from the total weight measurement data in real time to generate real-time material net weight data; The real-time material net weight data is differentiated to calculate its rate of change and generate real-time material mass flow rate data.

3. The dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things technology according to claim 1, characterized in that, include: Acquire the material flow rate data and the preset weighing target value; Call the currently stored system action delay time and obtain a safety margin coefficient determined based on historical weighing data; Based on the material flow rate data, the system action delay time, and the safety margin coefficient, the predicted air-drop material mass is calculated. The predicted mass of the material falling from the air is subtracted from the target weighing value to generate an early shutdown trigger point.

4. The dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things technology according to claim 1, characterized in that, include: Calculate the real-time weight deviation between the net weight data of the material and the early shutdown trigger point; Feedback control commands are generated based on the real-time weight deviation; Based on the material flow rate data and the early shutdown trigger point, a feedforward control command is generated through a preset feedforward control model. By combining the feedback control command and the feedforward control command, a valve opening command for the feeding actuator is generated; The valve opening command is sent to the feeding actuator for execution, and the net weight data of the material is judged in real time to see if it reaches the early shutdown trigger point. If it does, a shutdown command is generated and sent.

5. The dual-cycle small-material intelligent dynamic weighing system based on Internet of Things technology according to claim 4, characterized in that, Determine the ratio between the current net weight data of the material and the target weighing value to determine whether the current stage is rapid feeding or precise weighing. If it is in the rapid feeding stage, the first set of control parameters corresponding to this stage is called, and a first feedback control command is generated based on the first set of control parameters and the real-time weight deviation. At the same time, a first feedforward control command is generated based on the first set of control parameters and the material flow rate data. If the weighing stage is in progress, the second set of control parameters corresponding to that stage is invoked, and a second feedback control command is generated based on the second set of control parameters and the real-time weight deviation. At the same time, a second feedforward control command is generated based on the second set of control parameters and the material flow rate data.

6. The dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things technology according to claim 1, characterized in that, include: After a single weighing batch is completed, extract the net weight data and material flow rate data of the material during a continuous period before and after the shutdown command is issued to form a shutdown process dataset. Analyze the changing trend of the net weight data of materials in the dataset during the shutdown process, and determine the time interval from the issuance of the shutdown command to the cessation of the net weight increase of materials, which is used as the measured system delay. The measured system delay is processed together with the currently stored system action delay time to generate an updated system action delay time; The absolute deviation between the final net weight of the material at the end of this weighing batch and the preset weighing target value is calculated as the weighing accuracy error of this batch. Based on the direction and magnitude of the weighing accuracy error, and according to the preset adjustment rules, the vibration coupling coefficient used in the vibration interference filtering process is fine-tuned to generate an optimized vibration coupling coefficient. The updated system action delay time and the optimized vibration coupling coefficient are loaded into the control system as initial parameters for subsequent weighing batches.

7. The dual-cycle intelligent dynamic weighing system for small materials based on Internet of Things technology according to claim 1, characterized in that, It also includes a correction module: The temperature and humidity data of the environment in which the hopper is located are collected in real time during the control cycle; Based on the temperature and humidity data, query the preset parameter correction table to obtain the state compensation factor for the current environment; The generated real-time material mass flow rate data is corrected using the aforementioned state compensation factor to obtain environmentally compensated material flow rate data. The environmentally compensated material flow rate data is used to replace the original material flow rate data and participate in the calculation of the early shutdown trigger point.

8. The dual-cycle small-material intelligent dynamic weighing system based on Internet of Things technology according to claim 7, characterized in that, include: Based on the temperature and humidity data, a lookup operation is performed in the preset parameter correction table; If the temperature and humidity data are completely consistent with a set of pre-stored environmental condition data in the parameter correction table, then the state compensation factor corresponding to that set of pre-stored environmental condition data is directly read. If the temperature and humidity data are between multiple sets of pre-stored environmental condition data in the parameter correction table, a state compensation factor matching the current temperature and humidity data is generated through linear interpolation.

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