Intelligent system and method for accurately controlling glue amount of pneumatic pump hot melt glue machine

Abstract

The invention relates to the technical field of industrial automation control, in particular to an intelligent system and method for accurately controlling the glue amount of a pneumatic pump hot melt glue machine, and the system comprises a sensing detection unit, a microcontroller main control unit, a pneumatic driving and adjusting unit and a man-machine interaction unit. Through multi-source parameter acquisition, rheological property compensation, high-temperature-resistant differential pressure detection and data fusion calibration, high-precision dynamic calculation of the actual glue discharge flow under the condition of no physical flowmeter is realized, accurate data support is provided for glue amount control, and the actual glue discharge flow is calculated based on the accurate data. A'prediction-correction 'double-closed-loop composite adjustment instruction is generated through self-adaptive algorithm weight adjustment and a first-order linear prediction method, dynamic adaptation of complex working conditions and precise regulation and control of the glue amount are achieved, and the control precision and stability of the glue output amount of the pneumatic pump hot melt glue machine are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, specifically to an intelligent system and method for precise control of adhesive quantity in a pneumatic pump hot melt adhesive machine. Background Technology

[0002] In bonding or sealing processes in packaging, electronics, furniture, textiles, and other industries, pneumatic pump hot melt adhesive machines are an indispensable piece of automated equipment. Specifically, this equipment uses compressed air as a power source. A pneumatic pump assembly heats and melts solid hot melt adhesive into a fluid state, precisely controlling the adhesive delivery and dispensing volume. Ultimately, the hot melt adhesive is applied to the target workpiece according to process requirements (such as coating or bonding). Its core feature is the stable delivery of adhesive through pneumatic drive. Furthermore, by controlling parameters such as air pressure and temperature, it can adapt to different types of hot melt adhesives and production scenarios, providing crucial support for efficient production across various industries.

[0003] With the increasing demands for product quality and production efficiency in industrial production, higher standards are being set for the accuracy and stability of glue dispensing control in pneumatic pump hot melt glue machines. However, in existing technologies, traditional pneumatic pump hot melt glue machines rely on physical flow meters, which are prone to blockage by high-temperature glue materials, leading to detection lag. They also have poor adaptability to complex operating conditions such as changes in glue type and temperature fluctuations, resulting in low glue dispensing control accuracy and insufficient stability.

[0004] Based on this, the present invention provides an intelligent system and method for precise control of adhesive quantity in a pneumatic pump hot melt adhesive machine, in order to solve the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent system and method for precise control of adhesive quantity in a pneumatic pump hot melt glue machine. This invention achieves high-precision dynamic estimation of the actual glue flow rate under conditions without a physical flow meter by acquiring multiple source parameters, compensating for rheological characteristics, detecting high-temperature differential pressure, and fusion calibration. This provides accurate data support for adhesive quantity control. Based on this accurate data, an adaptive algorithm weight adjustment and a first-order linear prediction method are used to generate a "prediction-correction" dual closed-loop composite adjustment command, which realizes dynamic adaptation to complex working conditions and precise control of adhesive quantity, significantly improving the control accuracy and stability of adhesive quantity in the pneumatic pump hot melt glue machine.

[0006] To achieve the above objectives, the present invention provides the following technical solution: This invention provides an intelligent system for precise glue quantity control of a pneumatic pump hot melt glue machine, comprising a sensing and detection unit, a microcontroller main control unit, a pneumatic drive and adjustment unit, and a human-machine interaction unit, wherein: The sensing and detection unit is used to dynamically calculate and calibrate the actual dispensing flow rate by fusing multi-source parameters and compensating for rheological characteristics, and combining it with a high-temperature differential pressure structure or actuator feedback mechanism, and transmit the calibrated data to the microcontroller main control unit. The microcontroller main control unit is used to dynamically regulate the amount of adhesive by adaptively fusing PID control and intelligent compensation algorithm with the multi-source operating condition parameters transmitted by the sensing and detection unit and combining the adhesive volume deviation trend prediction. The pneumatic drive and adjustment unit is used to adjust the pneumatic output according to the adjustment command, and drive the glue pump to output glue as needed. The human-computer interaction unit is used to provide an operation interface and status display, and supports parameter setting, mode switching and fault prompts.

[0007] The sensing and detection unit includes a multi-source parameter acquisition module, a rheological characteristic compensation module, a high-temperature differential pressure detection module, a data fusion and calibration module, and a data transmission module, wherein: The multi-source parameter acquisition module is used to acquire multi-dimensional basic parameters such as colloid temperature, system pressure, and execution timing. The rheological property compensation module dynamically corrects the viscosity-flowability relationship model of hot melt adhesive based on adhesive type and temperature change, and outputs a flowability compensation coefficient. The high-temperature differential pressure detection module is used to detect the pressure difference during the flow of the rubber compound using a differential pressure sensor with a high-temperature resistant structural design. The data fusion and calibration module is used to fuse multi-source acquired data and compensation results, dynamically calculate the actual glue flow rate in combination with the actuator feedback mechanism, and perform data calibration. The data transmission module is used to transmit the calibrated flow data to the microcontroller main control unit.

[0008] The rheological property compensation module dynamically corrects the viscosity-flowability relationship model of hot melt adhesive based on adhesive type and temperature changes, and outputs a flowability compensation coefficient. The specific operation is as follows: A1: Based on the input adhesive type signal, a benchmark relationship model matching the current adhesive type is retrieved from multiple viscosity-temperature relationship curves pre-stored in memory. The specific expression of the benchmark relationship model is as follows: ; In the formula, is the reference viscosity of the hot melt adhesive at temperature T, where T is the adhesive temperature, and a, b, and c are type-specific characteristic constants of the adhesive. A2: Receives colloid temperature data collected by the multi-source parameter acquisition module, inputs it into the benchmark relationship model, and calculates the theoretical viscosity value of the hot melt adhesive at the current temperature. The specific expression is as follows: ; In the formula, This is the theoretical viscosity value of the hot melt adhesive under the current operating conditions. The colloid temperature is acquired and converted in real time by the multi-source parameter acquisition module; A3: The theoretical viscosity value Input into the preset formula for converting the flowability compensation coefficient specific to each rubber type In the formula, , A conversion constant specific to each rubber type is used to calculate the flowability compensation coefficient K for correcting flow estimation, and then output it to the data fusion and calibration module.

[0009] The data fusion and calibration module is used to fuse multi-source acquired data and compensation results, dynamically calculate the actual glue dispensing flow rate by combining the actuator feedback mechanism, and perform data calibration. The specific operation is as follows: B1: Receives system pressure P and execution timing data transmitted from the multi-source parameter acquisition module. The flowability compensation coefficient K output by the rheological property compensation module and the rubber pressure difference output by the high temperature differential pressure detection module. The data is filtered and denoised. B2: Based on the pre-processed parameters, the initial glue discharge flow rate is calculated by flow rate estimation. The specific expression is as follows: ; In the formula, k is the system's inherent flow coefficient. The theoretical viscosity value of the hot melt adhesive output by the rheological property compensation module; B3: Receives the pump operating frequency f from the actuator feedback, and corrects the initial flow rate through calibration. The specific expression is as follows: ; In the formula, This is the reference operating frequency for the glue pump. For feedback calibration coefficients; B4: Calculate the actual dispensing flow rate after calibration. Transmitted to the data transmission module.

[0010] The microcontroller main control unit includes a data receiving and parsing module, an adaptive algorithm fusion module, and a glue volume prediction-correction module, wherein: The data receiving and parsing module is used to receive data transmitted by the sensing and detection unit, and to parse and extract multi-source operating parameters and actual glue flow information. The adaptive algorithm fusion module is used to dynamically adjust the weights of PID control and intelligent compensation algorithm based on operating parameters. The adhesive quantity prediction-correction module predicts future adhesive quantity fluctuations based on historical trends and real-time deviations, and generates a composite instruction of feedforward pre-adjustment and feedback correction.

[0011] The adhesive quantity prediction and correction module predicts future adhesive quantity fluctuations based on historical trends and real-time deviations, and generates a composite instruction of feedforward pre-adjustment and feedback correction. The specific operation is as follows: C1: Continuously cache historical glue output data and predict the trend and magnitude of glue volume deviation at a specific future time by analyzing the slope of the data sequence. C2: Based on the predicted deviation trend and magnitude, query the preset feedforward control rule table and generate feedforward pre-adjustment instructions; C3: Compare the actual dispensing flow rate fed back by the sensor detection unit with the target flow rate, and generate a real-time feedback correction command through the PID algorithm; C4: The feedforward pre-adjustment command and the real-time feedback correction command are weighted and superimposed to generate the final composite adjustment command, which is then output to the pneumatic drive and regulation unit.

[0012] The first-order linear prediction method used in C1 is to predict future glue quantity deviation. The specific expression for making a prediction is as follows: In the formula, The current flow deviation. This represents the flow deviation from the previous moment. These are the predicted gain coefficients obtained through system identification; The specific expression for weighted superposition in C4 is as follows: In the formula, This is a feedforward pre-adjustment command. To provide feedback on correction instructions, and This is a fusion weighting coefficient that can be dynamically adjusted according to operating conditions.

[0013] The pneumatic drive and regulation unit includes a high-precision pneumatic regulation module, a glue pump drive module, and an execution status feedback module, wherein: The high-precision air pressure regulation module is used to receive regulation commands and, through the electronically controlled proportional valve component, precisely adjust the magnitude and stability of the air supply pressure to match the target glue output. The glue pump drive module is used to convert the adjusted air pressure into mechanical driving force to drive the glue pump to output glue as needed. The execution status feedback module is used to detect the operating status and air pressure output value of the glue pump in real time and feed it back to the microcontroller main control unit.

[0014] The human-computer interaction unit includes a parameter setting module, a status display module, a mode switching module, and a fault prompt module, wherein: The parameter setting module is used to provide a visual operation interface and support users to preset the target value of adhesive volume and the threshold value of working condition parameters. The status display module is used to display key data such as system operating status, actual glue flow rate, air pressure value, and fault information in real time. The mode switching module is used to support manual / automatic control mode switching to adapt to different production scenario requirements. The fault indication module is used to receive fault signals from the microcontroller's main control unit and provide audible and visual alerts to the user.

[0015] This invention also proposes an intelligent method for precise control of adhesive quantity in a pneumatic pump hot melt adhesive machine, comprising the following steps: S1: By integrating colloid temperature, system air pressure, execution timing and differential pressure or actuator feedback signals, combined with rheological characteristic compensation model, the actual colloid flow rate under conditions without physical flow meter is dynamically calculated and calibrated. S2: Based on the calculated actual glue flow rate and multi-source operating parameters, it automatically identifies the current glue type and operating status, and dynamically adjusts the fusion weight of PID basic control and intelligent compensation algorithm. S3: Based on historical flow trends and real-time deviations, predict future glue volume fluctuations and generate a composite adjustment command that combines feedforward pre-adjustment and feedback correction. S4: Precisely regulates the output air pressure according to the composite adjustment command to drive the glue pump to dispense glue in a quantitative manner, and provides real-time feedback on the operating status; S5: Provides parameter configuration, mode switching, operation status visualization, and audible and visual fault prompts.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves high-precision dynamic estimation of actual glue flow rate under conditions without a physical flow meter by acquiring multiple parameters, compensating for rheological characteristics, detecting high-temperature differential pressure, and performing data fusion calibration. This provides accurate data support for glue quantity control. Based on this accurate data, an adaptive algorithm weight adjustment and a first-order linear prediction method are used to generate a "prediction-correction" dual closed-loop composite adjustment command. This enables dynamic adaptation to complex working conditions and precise control of glue quantity, significantly improving the control accuracy and stability of glue output of the pneumatic pump hot melt glue machine. Attached Figure Description

[0017] Figure 1 This is a system diagram of the intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine of the present invention.

[0018] Figure 2 This is a flowchart of the intelligent method for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine of the present invention.

[0019] Explanation of icon numbers: 100. Sensing and Detection Unit; 101. Multi-Source Parameter Acquisition Module; 102. Rheological Characteristic Compensation Module; 103. High Temperature Differential Pressure Detection Module; 104. Data Fusion and Calibration Module; 105. Data Transmission Module; 200. Microcontroller Main Control Unit; 201. Data Receiving and Parsing Module; 202. Adaptive Algorithm Fusion Module; 203. Adhesive Dosage Prediction-Correction Module; 300. Air Pressure Drive and Adjustment Unit; 301. High-Precision Air Pressure Adjustment Module; 302. Adhesive Pump Drive Module; 303. Execution Status Feedback Module; 400. Human-Machine Interaction Unit; 401. Parameter Setting Module; 402. Status Display Module; 403. Mode Switching Module; 404. Fault Indication Module. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: like Figure 1 As shown, this embodiment provides an intelligent system for precise glue quantity control of a pneumatic pump hot melt glue machine, including a sensor detection unit 100, a microcontroller main control unit 200, a pneumatic drive and adjustment unit 300, and a human-machine interaction unit 400. Specifically: the sensor detection unit 100 dynamically calculates and calibrates the actual glue flow rate by fusing multi-source parameters and compensating for rheological characteristics, combined with a high-temperature differential pressure structure or actuator feedback mechanism, and transmits the calibrated data to the microcontroller main control unit 200; the microcontroller main control unit 200 adaptively fuses PID control and intelligent compensation algorithms based on the multi-source operating parameters transmitted by the sensor detection unit 100, and generates a "prediction-correction" adjustment command based on glue quantity deviation trend prediction, dynamically regulating the glue quantity; the pneumatic drive and adjustment unit 300 adjusts the pneumatic pressure output according to the adjustment command, driving the glue pump to output glue as needed; and the human-machine interaction unit 400 provides an operation interface and status display, supporting parameter settings, mode switching, and fault prompts.

[0022] It should be noted that the sensing and detection unit 100 senses and calibrates the dispensing flow rate in real time and transmits the data to the microcontroller main control unit 200. The microcontroller main control unit 200 generates a "prediction-correction" dual closed-loop adjustment command based on multi-source operating conditions, which drives the pneumatic drive and adjustment unit 300 to accurately output the adhesive material. At the same time, parameter configuration, status monitoring and fault feedback are realized through the human-machine interaction unit 400.

[0023] In this embodiment, it should also be noted that the sensing and detection unit 100 includes a multi-source parameter acquisition module 101, a rheological property compensation module 102, a high-temperature differential pressure detection module 103, a data fusion and calibration module 104, and a data transmission module 105. Specifically: the multi-source parameter acquisition module 101 is used to acquire multi-dimensional basic parameters such as colloid temperature, system air pressure, and execution timing; the rheological property compensation module 102 dynamically corrects the viscosity-flowability relationship model of the hot melt adhesive based on the type of adhesive and temperature changes, and outputs a flowability compensation coefficient. The specific operation is as follows: A1: Based on the input adhesive type signal, a reference relationship model matching the current adhesive type is called from multiple viscosity-temperature relationship curves pre-stored in the memory. The specific expression of the reference relationship model is: ; In the formula, A1: The reference viscosity of the hot melt adhesive at temperature T, where T is the colloid temperature, and a, b, and c are type-specific characteristic constants; A2: Receives colloid temperature data collected by the multi-source parameter acquisition module 101, inputs it into the reference relationship model, and calculates the theoretical viscosity value of the hot melt adhesive at the current temperature. The specific expression is as follows: ; In the formula, This is the theoretical viscosity value of the hot melt adhesive under the current operating conditions. The colloid temperature is collected and converted in real time by the multi-source parameter acquisition module 101; A3: The theoretical viscosity value is... Input into the preset formula for converting the flowability compensation coefficient specific to each rubber type In the formula, , A conversion constant specific to each rubber type is used to calculate the flow compensation coefficient K for correcting flow rate estimation, and this coefficient is output to the data fusion and calibration module 104. The high-temperature differential pressure detection module 103 uses a differential pressure sensor with a high-temperature resistant structure to detect the pressure difference during rubber flow. The data fusion and calibration module 104 fuses multi-source acquired data and compensation results, dynamically calculates the actual dispensing flow rate based on the actuator feedback mechanism, and performs data calibration. Specific operations are as follows: B1: Receives the system air pressure P and execution timing transmitted by the multi-source parameter acquisition module 101. The flowability compensation coefficient K output by the rheological property compensation module 102 and the pressure difference of the rubber compound output by the high temperature differential pressure detection module 103 are also mentioned. B2: Based on the preprocessed parameters, the initial dispensing flow rate is calculated by flow rate estimation, as shown in the following expression: ; In the formula, k is the system's inherent flow coefficient. B3: The theoretical viscosity value of the hot melt adhesive output by the rheological property compensation module 102; B4: The pump operating frequency f received from the actuator, which is used to correct the initial flow rate through calibration. The specific expression is as follows: ; In the formula, This is the reference operating frequency for the glue pump. For feedback calibration coefficient; B4: The actual dispensing flow rate after calibration. The data is transmitted to the data transmission module 105. The data transmission module 105 is used to transmit the calibrated flow data to the microcontroller main control unit 200.

[0024] It should be noted that the multi-source parameter acquisition module 101 acquires the colloid temperature, system air pressure, and execution timing; the rheological property compensation module 102 dynamically calculates the viscosity based on the colloid type and real-time temperature and outputs the flowability compensation coefficient; and the high-temperature differential pressure detection module 103 provides the colloid pressure difference value. The data from these three sources are integrated into the data fusion and calibration module 104. This module integrates the parameters, compensation results, and actuator feedback, calculates and calibrates the actual colloid flow rate through a physical model, and finally, the data transmission module 105 uploads the calibrated flow rate data to the microcontroller main control unit 200.

[0025] Furthermore, it should be noted that in A3... , For conversion constants specific to certain types of adhesives (such as EVA adhesive) , PUR glue , ), pre-stored in memory.

[0026] In B1, a moving average filtering algorithm is used to process the data, with the filtering window size set to 5-10 sampling periods (50ms per period) to eliminate high-frequency interference.

[0027] B3 medium-strength rubber pump reference operating frequency The preset frequency is 50Hz, and the feedback calibration coefficient is [not specified]. The specific range of values ​​is 0.01-0.03.

[0028] The high-temperature differential pressure detection module 103 features a differential pressure sensor with a measurement range of 0-0.5MPa, an accuracy of ±0.5%FS, and an operating temperature of 80-200℃, making it suitable for high-temperature hot melt adhesive applications.

[0029] The data transmission module 105 uses the RS485 communication protocol to transmit data at a baud rate of 115200bps and a transmission delay of ≤10ms to ensure data real-time performance.

[0030] In this embodiment, it should also be noted that the microcontroller main control unit 200 includes a data receiving and parsing module 201, an adaptive algorithm fusion module 202, and a glue quantity prediction-correction module 203. Specifically: the data receiving and parsing module 201 receives data transmitted from the sensing unit 100 and parses and extracts multi-source operating parameters and actual glue flow information; the adaptive algorithm fusion module 202 dynamically adjusts the weights of PID control and intelligent compensation algorithms based on operating parameters; and the glue quantity prediction-correction module 203 predicts future glue quantity fluctuations based on historical trends and real-time deviations, generating a composite instruction for feedforward pre-adjustment and feedback correction. The specific operation is as follows: C1: Continuously caches historical glue flow data and predicts the glue quantity deviation trend and magnitude at a specific future time by analyzing the slope of the data sequence; C1 uses a first-order linear prediction method to predict future glue quantity deviations. The specific expression for making a prediction is as follows: In the formula, The current flow deviation. This represents the flow deviation from the previous moment. C1: The predicted gain coefficient obtained through system identification; C2: Based on the predicted deviation trend and magnitude, the preset feedforward control rule table is queried to generate a feedforward pre-adjustment command; C3: The actual dispensing flow rate fed back by the sensing unit 100 is compared with the target flow rate, and a real-time feedback correction command is generated through a PID algorithm; C4: The feedforward pre-adjustment command and the real-time feedback correction command are weighted and superimposed to generate the final composite adjustment command, which is then output to the pneumatic drive and adjustment unit 300. The specific expression for the weighted superposition in C4 is as follows: In the formula, This is a feedforward pre-adjustment command. To provide feedback on correction instructions, and This is a fusion weighting coefficient that can be dynamically adjusted according to operating conditions.

[0031] It should be noted that the data receiving and parsing module 201 extracts the operating parameters and calibration flow rate transmitted from the sensing and detection unit 100 in real time. The adaptive algorithm fusion module 202 dynamically adjusts the fusion weight of PID and intelligent compensation algorithm accordingly to realize online optimization of control strategy. The glue quantity prediction-correction module 203 predicts future deviations based on historical flow trends using a first-order linear prediction method, generates feedforward pre-adjustment instructions, and generates feedback correction instructions in combination with the current error. Through dynamic weighted fusion, a "prediction-correction" dual closed-loop composite adjustment instruction is formed and finally output to the pneumatic drive unit 300.

[0032] Furthermore, it should be noted that C1 continuously caches historical glue discharge flow data for the most recent 30 control cycles (total duration 1.5s), and calculates the slope of the data sequence using linear regression. To assist in judging the trend of deviation; predict the gain coefficient. The value is set between 0.6 and 1.0, determined through system identification experiments to ensure that the prediction deviation is ≤3%. The feedforward control rule table in C2 contains 5 sets of corresponding relationships. For example, when the deviation amplitude is ≤2%, the feedforward command strength is 30% of the target value; when the deviation amplitude is >5%, the feedforward command strength is 70% of the target value. The proportional gain in the PID algorithm in C3... Integral coefficient To avoid system overshoot or response lag. (C4) When the operating condition fluctuation is ≤5% , (Focus on feedback); When fluctuation > 5% , (Focusing on feedforward).

[0033] In this embodiment, it should also be noted that the pneumatic drive and adjustment unit 300 includes a high-precision pneumatic pressure adjustment module 301, a glue pump drive module 302, and an execution status feedback module 303, wherein: the high-precision pneumatic pressure adjustment module 301 is used to receive adjustment commands and, through an electronically controlled proportional valve component, precisely adjust the magnitude and stability of the air supply pressure to match the target glue output; the glue pump drive module 302 is used to convert the adjusted air pressure into mechanical driving force to drive the glue pump to output glue as needed; the execution status feedback module 303 is used to detect the glue pump operating status and air pressure output value in real time and feed them back to the microcontroller main control unit 200.

[0034] It should be noted that the high-precision air pressure regulation module 301, based on the composite regulation command issued by the microcontroller main control unit 200, uses an electronically controlled proportional valve to precisely regulate the air supply pressure. The glue pump drive module 302 converts this air pressure into a stable mechanical driving force, driving the glue pump to output glue material in a quantitative manner as needed. At the same time, the execution status feedback module 303 collects the glue pump's operating status and actual air pressure value in real time and sends the feedback signal back to the microcontroller main control unit 200.

[0035] Furthermore, it should be noted that the electronically controlled proportional valve in the high-precision air pressure regulation module 301 has a response time of ≤5ms and a pressure regulation accuracy of ±0.01MPa, ensuring rapid air pressure adaptation to commands. The execution status feedback module 303 uses a sampling frequency of 100Hz, synchronized with the sensing unit 100, and provides feedback data including the glue pump speed and actual air pressure value, forming a control closed loop.

[0036] In this embodiment, it should also be noted that the human-machine interaction unit 400 includes a parameter setting module 401, a status display module 402, a mode switching module 403, and a fault prompting module 404, wherein: the parameter setting module 401 is used to provide a visual operation interface, supporting users to preset the glue volume target value and working condition parameter threshold; the status display module 402 is used to display key data such as system operating status, actual glue flow rate, air pressure value, and fault information in real time; the mode switching module 403 is used to support manual / automatic control mode switching to adapt to different production scenario requirements; and the fault prompting module 404 is used to receive fault signals from the microcontroller main control unit 200 and prompt the user with sound and light.

[0037] It should be noted that the parameter setting module 401 and the mode switching module 403 are responsible for receiving user operation instructions and transmitting them to the microcontroller main control unit 200. The status display module 402 provides real-time feedback on key system operation data, and the fault prompt module 404 receives fault signals from the microcontroller main control unit 200 and triggers audible and visual prompts.

[0038] Furthermore, it should be noted that the parameter setting module 401 supports the storage of 10 sets of process parameters (such as target flow rates and temperature thresholds for different adhesive types), which can be recalled with a single click. The status display module 402 displays a refresh rate of 10Hz, and key data retains two decimal places for accurate monitoring. The fault indication module 404 provides fault classification indications: minor faults (such as parameters exceeding thresholds) are indicated only by text; serious faults (such as abnormal air pressure or sensor failure) are simultaneously triggered by a 2kHz buzzer and a flashing red indicator light to prevent production accidents.

[0039] Example 2: like Figure 2 As shown in this embodiment, the intelligent method for precise control of adhesive quantity in a pneumatic pump hot melt adhesive machine specifically includes the following steps: S1. System initialization and parameter preloading: S1.1. After the system is powered on, it performs a self-test to confirm that all components and communication links are normal; S1.2. Users select the type of adhesive and target process (such as target adhesive output) required for current production through the operation interface. S1.3. The system automatically retrieves a complete set of exclusive parameters matching the type of adhesive from its internal memory, including viscosity-temperature characteristic constant, flowability compensation parameters, adhesive pump reference frequency, etc. S2. Real-time acquisition of multi-source data and dynamic traffic estimation: S2.1. Real-time acquisition of multi-dimensional basic operating parameters such as colloid temperature, system air pressure, and execution timing; S2.2. Based on the selected type of adhesive and the real-time temperature, the theoretical viscosity value is calculated according to the pre-stored benchmark relationship model, and further combined with the adhesive type-specific conversion formula, the flowability compensation coefficient used to correct the flow rate calculation is obtained. S2.3. Real-time detection of the pressure difference generated when the rubber compound flows in the flow channel; S2.4. The collected data is preprocessed by moving average filtering to eliminate high-frequency noise. Then, the initial dispensing flow rate is calculated by combining the pressure difference, system air pressure, theoretical viscosity and flowability compensation coefficient through the built-in physical model. Finally, the feedback signal of the actual operating frequency of the glue pump is introduced to calibrate the initial flow rate and finally obtain a high-precision actual dispensing flow rate value. S2.5. Package the calibrated actual dispensing flow rate and related operating parameters, and upload them to the main controller in real time via RS485 communication protocol; S3, Intelligent Decision Making and "Prediction-Correction" Instruction Generation: S3.1. Receive and parse the uploaded data packet, and extract the key actual glue discharge flow rate and multi-source operating condition information; S3.2. Based on real-time operating conditions (such as the magnitude and trend of deviation), dynamically adjust the fusion weights of PID control and intelligent compensation algorithm to achieve online adaptive optimization of the control strategy; S3.3. Execute the "predictive-corrective" control algorithm: Ⅰ. Continuously cache the historical glue flow rate data for the most recent 1.5 seconds (30 control cycles), and use the first-order linear prediction method to predict the future glue volume deviation trend and magnitude; II. Based on the predicted deviation results, query the preset control rule table and generate a feedforward pre-adjustment command with anticipatory adjustment function; III. Compare the target flow rate with the actual flow rate, and use the PID algorithm (proportional coefficient) to... Integral coefficient Generate real-time feedback correction instructions; IV. Based on the current fluctuation level of the operating conditions, dynamically allocate the weights of the feedforward and feedback commands, and then weight and merge the two to generate the final comprehensive adjustment command. S4, Precise air pressure drive and closed-loop status feedback: S4.1. Receives compound adjustment commands and adjusts the output air pressure precisely and quickly through an electronically controlled proportional valve with a response time of ≤5ms, with its accuracy controlled within ±0.01MPa; S4.2. The adjusted stable air pressure is converted into mechanical driving force to drive the glue pump to output glue material in a quantitative manner according to the target requirements; S4.3. Real-time acquisition of the operating status (such as operating frequency) and actual output air pressure value of the glue pump, and real-time transmission of this status information back to the main controller; S5. Human-Computer Interaction and System Monitoring: S5.1. The display interface updates and displays the system operating status in real time with a high refresh rate, and displays key data such as actual glue flow rate, air pressure value, and temperature in real time, which is convenient for users to monitor. S5.2. The system continuously performs self-diagnosis. Once an abnormality is detected, it immediately provides a prompt based on the severity of the fault: for minor abnormalities, a text warning is displayed on the interface; for serious faults that may affect production, an audible and visual alarm is activated simultaneously to alert the operators.

[0040] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0041] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A precise adhesive quantity control intelligent system for a pneumatic pump hot melt adhesive machine, characterized in that: It includes a sensing and detection unit (100), a microcontroller main control unit (200), a pneumatic drive and regulation unit (300), and a human-machine interaction unit (400), wherein: The sensing and detection unit (100) is used to dynamically calculate and calibrate the actual dispensing flow rate by multi-source parameter fusion and rheological characteristic compensation, combined with high temperature differential pressure structure or actuator feedback mechanism, and transmit the calibrated data to the microcontroller main control unit (200). The microcontroller main control unit (200) is used to dynamically regulate the amount of adhesive by adaptively fusing PID control and intelligent compensation algorithm with the multi-source working condition parameters transmitted by the sensing and detection unit (100) and combining the trend prediction of adhesive deviation. The pneumatic drive and adjustment unit (300) is used to adjust the pneumatic output according to the adjustment command and drive the glue pump to output glue as needed. The human-computer interaction unit (400) is used to provide an operation interface and status display, and supports parameter setting, mode switching and fault prompts.

2. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 1, characterized in that, The sensing and detection unit (100) includes a multi-source parameter acquisition module (101), a rheological property compensation module (102), a high-temperature differential pressure detection module (103), a data fusion and calibration module (104), and a data transmission module (105), wherein: The multi-source parameter acquisition module (101) is used to acquire multi-dimensional basic parameters such as colloid temperature, system pressure, and execution timing. The rheological property compensation module (102) dynamically corrects the viscosity-flowability relationship model of hot melt adhesive based on the type of adhesive and temperature change, and outputs the flowability compensation coefficient. The high-temperature differential pressure detection module (103) is used to detect the pressure difference during the flow of the rubber compound through a differential pressure sensor designed with a high-temperature structure. The data fusion and calibration module (104) is used to fuse multi-source acquired data and compensation results, dynamically calculate the actual glue discharge flow rate in combination with the actuator feedback mechanism, and perform data calibration. The data transmission module (105) is used to transmit the calibrated flow data to the microcontroller main control unit (200).

3. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 2, characterized in that, The rheological property compensation module (102) dynamically corrects the viscosity-flowability relationship model of hot melt adhesive based on the type of adhesive and temperature changes, and outputs the flowability compensation coefficient. The specific operation is as follows: A1: Based on the input adhesive type signal, a benchmark relationship model matching the current adhesive type is retrieved from multiple viscosity-temperature relationship curves pre-stored in memory. The specific expression of the benchmark relationship model is as follows: ； In the formula, is the reference viscosity of the hot melt adhesive at temperature T, where T is the adhesive temperature, and a, b, and c are type-specific characteristic constants of the adhesive. A2: Receives the colloid temperature data collected by the multi-source parameter acquisition module (101), inputs it into the reference relationship model, and calculates the theoretical viscosity value of the hot melt adhesive at the current temperature. The specific expression is as follows: ； In the formula, This is the theoretical viscosity value of the hot melt adhesive under the current operating conditions. The colloid temperature is collected and converted in real time by the multi-source parameter acquisition module (101); A3: The theoretical viscosity value Input into the preset formula for converting the flowability compensation coefficient specific to each rubber type In the formula, , The conversion constant is used for rubber type specific conversion. The flowability compensation coefficient K used to correct flow estimation is obtained and output to the data fusion and calibration module (104).

4. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 3, characterized in that, The data fusion and calibration module (104) is used to fuse multi-source acquired data and compensation results, dynamically calculate the actual glue dispensing flow rate by combining the actuator feedback mechanism, and perform data calibration. The specific operation is as follows: B1: Receives system air pressure P and execution timing transmitted by the multi-source parameter acquisition module (101). The flowability compensation coefficient K output by the rheological property compensation module (102) and the pressure difference of the rubber compound output by the high temperature differential pressure detection module (103) are also mentioned. The data is filtered and denoised. B2: Based on the pre-processed parameters, the initial glue discharge flow rate is calculated by flow rate estimation. The specific expression is as follows: ； In the formula, k is the system's inherent flow coefficient. The theoretical viscosity value of the hot melt adhesive output by the rheological property compensation module (102); B3: Receives the pump operating frequency f from the actuator feedback, and corrects the initial flow rate through calibration. The specific expression is as follows: ； In the formula, This is the reference operating frequency for the glue pump. For feedback calibration coefficients; B4: Calculate the actual dispensing flow rate after calibration. Transmitted to data transmission module (105).

5. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 1, characterized in that, The microcontroller main control unit (200) includes a data receiving and parsing module (201), an adaptive algorithm fusion module (202), and a glue volume prediction-correction module (203), wherein: The data receiving and parsing module (201) is used to receive data transmitted by the sensing and detection unit (100), and to parse and extract multi-source operating parameters and actual glue flow information; The adaptive algorithm fusion module (202) is used to dynamically adjust the weights of PID control and intelligent compensation algorithm according to the operating parameters; The adhesive quantity prediction-correction module (203) predicts future adhesive quantity fluctuations based on historical trends and real-time deviations, and generates a composite instruction of feedforward pre-adjustment and feedback correction.

6. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 5, characterized in that, The adhesive quantity prediction-correction module (203) predicts future adhesive quantity fluctuations based on historical trends and real-time deviations, and generates a composite instruction of feedforward pre-adjustment and feedback correction. The specific operation is as follows: C1: Continuously cache historical glue output data and predict the trend and magnitude of glue volume deviation at a specific future time by analyzing the slope of the data sequence. C2: Based on the predicted deviation trend and magnitude, query the preset feedforward control rule table and generate feedforward pre-adjustment instructions; C3: Compare the actual dispensing flow rate fed back by the sensing unit (100) with the target flow rate, and generate a real-time feedback correction command through the PID algorithm; C4: The feedforward pre-adjustment command and the real-time feedback correction command are weighted and superimposed to generate the final composite adjustment command, which is then output to the pneumatic drive and regulation unit (300).

7. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 6, characterized in that, The first-order linear prediction method used in C1 is to predict future glue quantity deviation. The specific expression for making a prediction is as follows: In the formula, The current flow deviation. This represents the flow deviation from the previous moment. These are the predicted gain coefficients obtained through system identification; The specific expression for weighted superposition in C4 is as follows: In the formula, This is a feedforward pre-adjustment command. To provide feedback on correction instructions, and This is a fusion weighting coefficient that can be dynamically adjusted according to operating conditions.

8. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 1, characterized in that, The pneumatic drive and regulation unit (300) includes a high-precision pneumatic regulation module (301), a glue pump drive module (302), and an execution status feedback module (303), wherein: The high-precision air pressure regulation module (301) is used to receive regulation commands and, through the electronically controlled proportional valve component, precisely adjust the magnitude and stability of the air supply pressure to match the target glue output. The glue pump drive module (302) is used to convert the adjusted air pressure into mechanical driving force to drive the glue pump to output glue as needed. The execution status feedback module (303) is used to detect the operating status and air pressure output value of the glue pump in real time and feed it back to the microcontroller main control unit (200).

9. The intelligent system for precise control of adhesive quantity in the pneumatic pump hot melt adhesive machine according to claim 1, characterized in that, The human-computer interaction unit (400) includes a parameter setting module (401), a status display module (402), a mode switching module (403), and a fault prompt module (404), wherein: The parameter setting module (401) is used to provide a visual operation interface and support users to preset the target value of adhesive amount and the threshold value of working condition parameters. The status display module (402) is used to display key data such as system operating status, actual glue flow rate, air pressure value, and fault information in real time. The mode switching module (403) is used to support manual / automatic control mode switching to adapt to different production scenario requirements. The fault indication module (404) is used to receive fault signals from the microcontroller main control unit (200) and provide audible and visual prompts to the user.

10. An intelligent method for precise control of adhesive quantity in a pneumatic pump hot melt adhesive machine, as described in claims 1-9, characterized in that... Includes the following steps: S1: By integrating colloid temperature, system air pressure, execution timing and differential pressure or actuator feedback signals, combined with rheological characteristic compensation model, the actual colloid flow rate under conditions without physical flow meter is dynamically calculated and calibrated. S2: Based on the calculated actual glue flow rate and multi-source operating parameters, it automatically identifies the current glue type and operating status, and dynamically adjusts the fusion weight of PID basic control and intelligent compensation algorithm. S3: Based on historical flow trends and real-time deviations, predict future glue volume fluctuations and generate a composite adjustment command that combines feedforward pre-adjustment and feedback correction. S4: Precisely regulates the output air pressure according to the composite adjustment command to drive the glue pump to dispense glue in a quantitative manner, and provides real-time feedback on the operating status; S5: Provides parameter configuration, mode switching, operation status visualization, and audible and visual fault prompts.

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