Hot melt adhesive machine multi-path glue discharging control method, device, equipment and storage medium

Abstract

The present application relates to hot melt adhesive machine technical field, especially to hot melt adhesive machine multi-path glue output control method, device, equipment and storage medium, the present application obtains the preset glue output process target, based on the target, executes glue output and obtains each glue path actual glue output quality information, according to the information and preset target, generates the multi-path glue output state deviation matrix, the matrix is input to the multi-glue path coupling collaborative control model, and the collaborative control parameter of each glue path is obtained, finally according to the parameter, the collaborative control of each glue path actuator is carried out, to eliminate the glue output quality difference between each glue path, the present application realizes the high-precision collaborative control to the multi-path hot melt adhesive machine glue output quality by intelligent sensing global glue output state and accurate modeling compensation multi-path coupling relationship, effectively solves the systematic technical problem of multi-glue path glue output uneven, significantly improves the quality and consistency of packaging or coating product.

Description

Technical Field

[0001] This invention relates to the field of hot melt glue machine technology, and in particular to a method, apparatus, equipment and storage medium for controlling multiple glue dispensing channels in a hot melt glue machine. Background Technology

[0002] Hot melt adhesive machines, as highly efficient automated bonding and coating equipment, precisely apply solid adhesive materials to the surface of workpieces by heating and melting them. They are widely used in modern industrial fields such as electronic product assembly, automotive interiors, medical dressings, and packaging. To meet the extreme pursuit of efficiency in large-scale production, hot melt adhesive machines with multi-channel parallel dispensing capabilities have become the industry standard. They can complete the simultaneous coating of multiple adhesive lines within the same time, significantly improving work efficiency and adapting to the construction needs of complex planar trajectories.

[0003] However, the introduction of multi-path systems has also brought unprecedented technical challenges. In actual production, due to unavoidable manufacturing tolerances and wear over time in the mechanical actuators (such as precision gear pumps) of multiple adhesive paths, the dispensing volume per unit rotation experiences nonlinear drift. Even under the same control signal, the actual output of each pipeline will differ. Secondly, the independent heating modules and temperature sensors of each adhesive path, due to differences in their physical location, aging degree, and surrounding heat dissipation environment, cause slight differences between the actual heat received by the adhesive as it flows through each pipeline and its final temperature. Temperature directly affects the viscosity and flowability of the adhesive, thus causing instability in morphological parameters such as adhesive line width and height. Furthermore, multiple adhesive paths often share the same adhesive supply source. The fluid pressure coupling and mutual interference within the system further exacerbate the problem. Adjusting the parameters of a single adhesive path can trigger a redistribution of pressure throughout the system, thereby causing unpredictable disturbances to the remaining adhesive paths.

[0004] Therefore, how to achieve coordinated and precise control of the glue dispensing quality of multi-channel hot melt glue machines has become a technical problem that urgently needs to be solved in this industry. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, equipment, and storage medium for controlling the dispensing of hot melt glue in a multi-channel glue machine, aiming to solve the technical problem of how to achieve coordinated and precise control of the dispensing quality of multi-channel hot melt glue machines in the prior art.

[0006] To achieve the above objectives, the present invention provides a multi-channel glue dispensing control method for a hot melt glue machine, the method comprising the following steps:

[0007] Perform dispensing operations based on preset dispensing process targets, and obtain dispensing quality information for each glue path within a preset time period;

[0008] Based on the deviation between the glue dispensing quality information and the preset glue dispensing process target, collaborative control parameters are generated to compensate for each glue path.

[0009] The actuators of each glue path are controlled collaboratively according to the collaborative control parameters to eliminate the difference in glue quality between each glue path.

[0010] Optionally, before performing the dispensing operation based on a preset dispensing process target and obtaining the dispensing quality information of each glue path within a preset time, the method further includes:

[0011] Based on the preset glue dispensing process target, the temperature configuration information of each temperature zone is obtained, and each heating zone is controlled to heat independently based on the temperature configuration information;

[0012] When the actual temperature in the temperature zone is within the ready deviation range of the set temperature, the temperature zone is considered to have reached the set temperature.

[0013] A global ready signal is generated when all temperature zones have reached the set temperature.

[0014] Optionally, before the actual temperature in the temperature zone is within the ready deviation range of the set temperature, i.e., before the temperature zone is considered to have reached the set temperature, the method further includes:

[0015] When the actual temperature of any temperature zone exceeds the sum of its set temperature and the over-temperature deviation value, a first alarm signal is generated, and the heating control of the temperature zone and the dispensing operation of the whole machine are stopped.

[0016] When the temperature sensor feedback signal in any temperature zone indicates an electrical circuit fault, a second alarm signal is generated, the overall machine operation is locked to interrupt all operations, and a system shutdown requiring manual reset is triggered.

[0017] Optionally, before performing the dispensing operation based on a preset dispensing process target and obtaining the dispensing quality information of each glue path within a preset time, the method further includes:

[0018] Obtain glue dispensing task information, which includes the target total glue dispensing amount, target glue line width, target glue line height, and target glue temperature;

[0019] Based on the hardware identifiers and pipeline topology of each glue path in the hot melt glue machine, the glue dispensing task information is decomposed into sub-process objectives corresponding to each glue path to obtain the preset glue dispensing process objective.

[0020] Optionally, the step of generating collaborative control parameters for compensating each glue path based on the deviation between the glue dispensing quality information and the preset glue dispensing process target includes:

[0021] Based on the glue dispensing quality information and the preset glue dispensing process target, a multi-path glue dispensing state deviation matrix is ​​obtained;

[0022] The multi-path glue dispensing state deviation matrix is ​​input into the multi-path coupling and collaborative control model to obtain the collaborative control parameters of each glue path.

[0023] Optionally, obtaining the multi-path dispensing state deviation matrix based on the dispensing quality information and the preset dispensing process target includes:

[0024] The measured values ​​of each dimension in the glue dispensing quality information are calculated by subtracting the corresponding target values ​​of each dimension in the preset glue dispensing process target to obtain multiple single-dimensional deviation values. The single-dimensional deviation values ​​include at least glue dispensing amount deviation, glue line width deviation and glue line height deviation.

[0025] All single-dimensional deviation values ​​of the same adhesive path are combined in a predetermined order to form the single-tube deviation vector of the adhesive path.

[0026] Arrange the single-tube deviation vectors of all glue paths to construct the multi-path glue dispensing state deviation matrix.

[0027] Optionally, the step of inputting the multi-path glue dispensing state deviation matrix into the multi-path coupling and cooperative control model to obtain the cooperative control parameters of each glue path includes:

[0028] The multi-path glue dispensing state deviation matrix is ​​input into the multi-path glue coupling and cooperative control model to obtain the multi-path glue cooperative control parameter matrix;

[0029] The multi-path collaborative control parameter matrix and the multi-path dispensing state deviation matrix are compared one by one according to the dispensing path to obtain the dispensing compensation amount of each dispensing path.

[0030] Based on the glue discharge compensation amount, the glue discharge control parameters are mapped to obtain the corresponding collaborative control parameters for the glue path.

[0031] Furthermore, to achieve the above objectives, the present invention also proposes a multi-channel dispensing control device for a hot melt adhesive machine, the multi-channel dispensing control device comprising:

[0032] The data acquisition and monitoring module is used to execute the dispensing operation based on the preset dispensing process target and obtain the dispensing quality information of each glue path within a preset time.

[0033] The deviation analysis module is used to generate collaborative control parameters for compensating each glue path based on the deviation between the glue quality information and the preset glue process target.

[0034] The collaborative execution module is used to perform collaborative glue dispensing control on each glue path actuator according to the collaborative control parameters, so as to eliminate the glue dispensing quality difference between each glue path.

[0035] Furthermore, to achieve the above objectives, the present invention also proposes a multi-channel dispensing control device for a hot melt glue machine. The multi-channel dispensing control device for a hot melt glue machine includes: a memory, a processor, and a multi-channel dispensing control program for a hot melt glue machine stored in the memory and executable on the processor. The multi-channel dispensing control program for a hot melt glue machine is configured to implement the steps of the multi-channel dispensing control method for a hot melt glue machine as described above.

[0036] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a multi-channel dispensing control program for a hot melt glue machine. When the hot melt glue machine multi-channel dispensing control program is executed by a processor, it implements the steps of the hot melt glue machine multi-channel dispensing control method described above.

[0037] The one or more technical solutions proposed in this application have at least the following technical effects: First, the solution obtains the preset dispensing process target and collects the actual dispensing quality data, and constructs a multi-path dispensing state deviation matrix to quantify the output differences of each dispensing path; then, the matrix is ​​input into the multi-path coupling and collaborative control model, and the collaborative control parameters for each dispensing path are obtained through model calculation; finally, the actuators are adjusted in real time according to the parameters, thereby eliminating the differences between dispensing paths, effectively eliminating the problem of uneven dispensing caused by temperature fluctuations, mechanical errors and fluid characteristics, and significantly improving the quality and reliability of the encapsulation or coating process. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the first embodiment of the multi-channel glue dispensing control method for hot melt glue machines of the present invention;

[0041] Figure 2 This is a flowchart illustrating the second embodiment of the multi-channel glue dispensing control method for hot melt glue machines of the present invention;

[0042] Figure 3 This is a schematic diagram of the temperature setting interface of the second embodiment of the hot melt glue machine multi-channel glue dispensing control method of the present invention;

[0043] Figure 4This is a schematic diagram of the motor control interface of the second embodiment of the multi-channel glue dispensing control method for hot melt glue machine of the present invention;

[0044] Figure 5 This is a schematic diagram of the functional parameter interface of the second embodiment of the hot melt glue machine multi-channel glue dispensing control method of the present invention;

[0045] Figure 6 This is a schematic diagram of the alarm setting interface of the second embodiment of the multi-channel glue dispensing control method for hot melt glue machine of the present invention;

[0046] Figure 7 This is a schematic diagram of the system parameter interface of the second embodiment of the hot melt glue machine multi-channel glue dispensing control method of the present invention;

[0047] Figure 8 This is a schematic diagram of the IO status monitoring interface of the second embodiment of the hot melt glue machine multi-channel glue dispensing control method of the present invention;

[0048] Figure 9 This is a schematic diagram of the system parameter configuration interface of the second embodiment of the hot melt glue machine multi-channel glue dispensing control method of the present invention;

[0049] Figure 10 This is a schematic diagram of the glue dispensing parameter interface of the second embodiment of the hot melt glue machine multi-channel glue dispensing control method of the present invention;

[0050] Figure 11 This is a schematic diagram of the glue gun parameter setting interface of the second embodiment of the hot melt glue machine multi-channel glue dispensing control method of the present invention;

[0051] Figure 12 This is a flowchart illustrating the third embodiment of the multi-channel glue dispensing control method for hot melt glue machines of the present invention;

[0052] Figure 13 This is a flowchart illustrating the fourth embodiment of the multi-channel glue dispensing control method for hot melt glue machines of the present invention;

[0053] Figure 14 This is a structural block diagram of the first embodiment of the multi-channel glue dispensing control device for the hot melt glue machine of the present invention;

[0054] Figure 15 This is a schematic diagram of the structure of a multi-channel glue dispensing control device for a hot melt glue machine in the hardware operating environment of the embodiment of the present invention.

[0055] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0057] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0058] The main solution of this application embodiment is: to obtain the preset glue dispensing process target and collect the actual glue dispensing quality data, and to construct a multi-path glue dispensing state deviation matrix to quantify the output difference of each glue path; then input the matrix into the multi-path coupling and collaborative control model, and obtain the collaborative control parameters for each glue path through model calculation; finally, to adjust each actuator in real time according to the parameters, thereby eliminating the difference between glue paths.

[0059] Currently, in actual production, due to unavoidable manufacturing tolerances and wear over time in the mechanical actuators (such as precision gear pumps) of multiple adhesive lines, the dispensing volume per unit rotation experiences nonlinear drift, resulting in differences in the actual output of each pipeline even under the same control signal. Secondly, the independent heating modules and temperature sensors of each adhesive line, due to differences in their physical location, aging degree, and surrounding heat dissipation environment, cause slight differences between the actual heat received by the adhesive as it flows through each pipeline and its final temperature. Temperature directly affects the viscosity and flowability of the adhesive, leading to instability in morphological parameters such as adhesive line width and height. Furthermore, multiple adhesive lines often share the same adhesive supply source, and the fluid pressure coupling and mutual interference within the system further exacerbate the problem. Adjusting the parameters of a single adhesive line can trigger a redistribution of pressure throughout the system, causing unpredictable disturbances to other adhesive lines. Therefore, achieving coordinated and precise control of the dispensing quality of multi-channel hot melt adhesive machines is a pressing technical problem that needs to be solved.

[0060] It should be noted that the executing entity of this invention can be a multi-channel dispensing control device for a hot melt glue machine, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a thermal management device capable of realizing the above functions of a multi-channel dispensing control device for a hot melt glue machine. This embodiment does not specifically limit it in this way. The following uses a multi-channel dispensing control device for a hot melt glue machine as an example to describe this embodiment and the following embodiments.

[0061] Based on this, embodiments of this application provide a multi-channel glue dispensing control method for a hot melt glue machine, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the hot melt adhesive machine multi-channel dispensing control method of this application.

[0062] In this embodiment, the multi-channel glue dispensing control method for the hot melt glue machine includes steps S10~S30:

[0063] Step S10: Perform the dispensing operation based on the preset dispensing process target, and obtain the dispensing quality information of each glue path within a preset time.

[0064] Understandably, glue dispensing quality information is a multi-dimensional dataset that usually reflects the actual glue dispensing effect directly or indirectly. It mainly includes, but is not limited to: the instantaneous glue dispensing volume of each glue path, the cumulative glue dispensing volume, the actual glue temperature, the glue dispensing pressure, and the actual glue line width and height measured by a visual sensor.

[0065] In one embodiment, before performing the dispensing operation based on the preset dispensing process target and obtaining the dispensing quality information of each glue path within a preset time, the method further includes: obtaining dispensing task information, which includes the target total dispensing amount, target glue line width, target glue line height, and target glue temperature; and decomposing the dispensing task information into sub-process targets corresponding to each glue path according to the hardware identifier and pipeline topology relationship of each glue path of the hot melt glue machine, so as to constitute the preset dispensing process target.

[0066] It should be noted that the preset dispensing process target is the starting and benchmark stage for collaborative control. This target is usually set by upstream process requirements or users, and its core parameters include, but are not limited to, global indicators such as total dispensing volume, dispensing ratio of each adhesive path, dispensing rate, and adhesive temperature. These target parameters together constitute the ideal benchmark for the collaborative operation of the multi-path hot melt adhesive system, providing a basis for comparison in subsequent data acquisition and deviation calculation.

[0067] Understandably, the acquisition of this process target can be achieved through various means. It can be issued by a host computer management system or directly set and stored in the controller by the operator through a human-machine interface (HMI). For example, in photovoltaic module sealing coating applications, the target might be set to have four adhesive channels simultaneously and uniformly dispensing adhesive at a total rate of 300 grams per minute in a 1:1:1:1 ratio; while in electronic product packaging dispensing scenarios, the target might be for each adhesive channel to complete an asymmetric coating trajectory according to a specific timing and flow rate.

[0068] Understandably, after obtaining the specific targets for each adhesive path, it is also necessary to calculate and set initial control parameters for each specific actuator. This process involves pre-setting based on equipment calibration data and process models, aiming to ensure that each pipeline can quickly enter a stable operating range close to the target state when the adhesive dispensing operation starts.

[0069] In one embodiment, the step of performing the dispensing operation based on a preset dispensing process target and obtaining dispensing quality information of each adhesive path within a preset time period includes: initializing the initial control parameters of each adhesive path actuator according to the preset dispensing process target; driving the actuators based on the initial control parameters to enable multi-path coordinated dispensing; acquiring the colloid temperature and colloid shape data of the dispensing colloid in each adhesive path within a preset sampling time window; obtaining the dispensing amount of the adhesive path based on the colloid shape data; and using the colloid temperature, colloid shape data, and dispensing amount as the dispensing quality information.

[0070] It should be understood that after initialization, the system synchronously drives the actuators of all adhesive paths to begin coordinated dispensing. Within a preset sampling time window (such as a 50ms control cycle or the complete duration of a dispensing action), the system synchronously collects two types of key data through an integrated sensor network: first, the adhesive temperature obtained through a non-contact infrared temperature sensor; and second, the adhesive shape data, i.e., images of adhesive spots or lines, acquired through a vision system (such as a high-speed CCD camera). Subsequently, the image processing unit calculates the actual dispensing volume of the adhesive path within that time period by analyzing the product of the cross-sectional area and length of the adhesive line or the spreading area and height of the adhesive spot based on this shape data. Ultimately, the adhesive temperature, adhesive shape data, and the derived dispensing volume together constitute a complete information set for evaluating the dispensing quality during that time period.

[0071] It should be noted that the target parameters (such as target flow rate, temperature, etc.) obtained from the aforementioned steps of the system decompose each adhesive path drive the corresponding metering pumps, heaters, etc. to start working, and within a pre-set time window, the sensor system collects the actual process data related to the adhesive quality in real time. The preset time can be a fixed sampling period or a complete time period for completing a specific process segment, the length of which depends on the process cycle and control accuracy requirements.

[0072] It should be understood that the purpose of this step is to obtain actual data that can characterize the actual state of the system and be compared with the preset dispensing process target. For example, the system drives four dispensing channels to dispense adhesive according to set values. During this process, the flow sensor continuously measures the actual dispensing weight per minute of each channel, the thermocouple monitors the real-time temperature of the adhesive at the nozzle, and after the coating of a sealing ring is completed, the line scan camera can capture an image of the adhesive residue and calculate the actual width and height of each adhesive line segment through image processing algorithms. All of these data, after being time-stamped, together constitute the dispensing quality information of each adhesive channel during that time period.

[0073] Step S20: Based on the deviation between the glue dispensing quality information and the preset glue dispensing process target, generate collaborative control parameters for compensating each glue path.

[0074] It's important to note that the core of this step lies in utilizing real-time dispensing quality information, such as the difference between parameters like glue line width, height, and dispensing volume, and the preset dispensing process target values. Through a systematic analysis method, a set of control parameters is generated that can collaboratively adjust the actuators of each dispensing path. This process is not simply about independently compensating for a single path, but rather about making comprehensive decisions based on the overall state of multiple dispensing paths. The aim is to eliminate multi-path coordination deviations caused by mechanical coupling, uneven thermal field distribution, or hydrodynamic interference. For example, when the system visually detects that the glue line width of the first path is too large while the dispensing volume of the second path is insufficient, it doesn't simply reduce the pressure of the first path or increase the motor speed of the second path. Instead, through coupling analysis, it may simultaneously fine-tune the temperature of the third path to suppress the indirect effects of heat conduction, thereby achieving coordinated and optimized control of multiple outputs.

[0075] Step S30: Perform coordinated glue dispensing control on each glue path actuator according to the coordinated control parameters to eliminate the glue dispensing quality differences between each glue path.

[0076] It should be noted that the system precisely adjusts the actuators of each glue path based on the collaborative control parameters obtained in the previous step. The aim is to eliminate the differences in glue dispensing quality between multiple glue paths through the collaborative control strategy and achieve the goal of process consistency. The actuators may include pressure valves, metering pumps, heating devices or motion motors, etc. They receive parameter compensation amounts from the control model (such as air pressure adjustment values, motor speed fine-tuning amounts or temperature correction values) and change the drive state in real time accordingly, thereby correcting the actual glue dispensing behavior of each glue path.

[0077] Understandably, the core of this step lies in coordinated control, which differs from independent adjustment in a single loop. It emphasizes systematic and synchronized adjustments in a multi-loop coupled context. For example, when the glue line width of a certain glue path is too large, the system may simultaneously lower the glue dispensing pressure of that path and fine-tune the temperature setting of adjacent glue paths to compensate for mutual interference caused by fluid interaction or heat conduction. This, in turn, suppresses the propagation and accumulation of deviations as a whole, and improves the consistency of multi-loop outputs.

[0078] This embodiment obtains a preset dispensing process target, performs dispensing operations based on the preset dispensing process target, and obtains dispensing quality information for each glue path within a preset time. Based on the dispensing quality information and the preset dispensing process target, a multi-path dispensing state deviation matrix is ​​obtained. The multi-path dispensing state deviation matrix is ​​input into a multi-path coupled collaborative control model to obtain collaborative control parameters for each glue path. Based on the collaborative control parameters, collaborative dispensing control is performed on the actuators of each glue path to eliminate dispensing quality differences between each glue path.

[0079] In summary, this technical solution significantly improves the overall control accuracy and process consistency of multi-channel hot melt adhesive machines under complex working conditions, reduces differences in glue output, glue line width, and height between different glue channels, and enhances the quality stability and production efficiency of glue application or packaging operations. At the same time, this method has strong adaptability and can continuously optimize control parameters based on real-time data, making it suitable for precision manufacturing fields with high requirements for glue output uniformity.

[0080] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S10 in the multi-channel glue dispensing control method for the hot melt glue machine, steps S001 to S003 are also included:

[0081] Step S001: Based on the preset glue dispensing process target, obtain the temperature configuration information of each temperature zone, and control each heating zone to heat independently based on the temperature configuration information.

[0082] Understandably, the multi-channel dispensing control system of this hot melt glue machine is composed of a hardware architecture and intelligent control logic. The core hardware includes a main controller, multiple independent temperature zone heating units, multiple glue path actuators, and a vision inspection unit. The main controller integrates a high-performance processor and storage unit, responsible for running the control algorithm and coordinating the work of various components. Each temperature zone is equipped with an independent temperature control unit, including a heater, a high-precision temperature sensor (such as PT100), and a PID control module, to achieve precise temperature control of different areas such as the throat, melting cylinder, and dispensing gun. The glue path actuator consists of a motor drive unit, a metering glue pump, and a dispensing gun head. The motor drive unit receives commands from the main controller and precisely controls the output of the glue pump by adjusting the speed of the servo or variable frequency motor. The vision inspection unit includes an industrial camera and a light source system, installed near the dispensing gun head, to capture images of the glue morphology online, providing real-time data for quality assessment. The human-machine interface uses a touch screen, integrating status indicator lights (heating, ready, alarm) and parameter setting functions, providing multiple interactive interfaces such as "temperature setting," "motor control," and "alarm setting," allowing users to configure process parameters and monitor system status.

[0083] like Figure 3 As shown, Figure 3 This is a schematic diagram of the temperature setting interface in Embodiment 2 of the present invention.

[0084] It should be noted that, Figure 3 The interface shown is used to set and monitor the temperature of each zone of the hot melt machine. Figure 3In this context, a temperature zone refers to an independent temperature control area for each load. Clicking the corresponding function key will change the status from "light blue" to "green" to activate that temperature zone; clicking the corresponding function key again will change the status from "green" to "light blue" to deactivate that temperature zone. The system supports 6 temperature control zone outputs, for a total of 14 temperature control zones. The temperature zones are arranged in the order of the AMP aviation sockets from top to bottom or from left to right. Additionally, each aviation socket's corresponding hose and connected gun body constitute one output group.

[0085] Understandable Figure 3 The temperature setting touch key needs to be set and input according to actual usage requirements and working conditions. The unit is degrees. The melting point of the hot melt adhesive should be used as the reference, and the melting point design needs to be combined with the specific type of hot melt adhesive and process requirements. Figure 3 The current temperature displayed refers to the actual temperature of the displayed temperature zone. When the temperature sensor is open-circuited or the load (hose or gun body) is disconnected, the system will be in alarm mode, and the red alarm indicator light on the control panel will illuminate. After the device is connected to the "hose and gun body" load, the current temperature field will display the actual temperature value. When the load is disconnected or not connected, the current temperature field will display the system default parameter value "0.0" (this value is an internal program value and not the actual temperature). In addition, in the "Function Parameters" interface, you can select to turn the alarm switch on or off as needed. When the temperature zone corresponding to the alarm switch function area is in the "on" state, if the load in the temperature zone is not connected, the device will default to an open-circuit state, and the alarm indicator light will illuminate.

[0086] like Figure 4 As shown, Figure 4 This is a schematic diagram of the motor control interface according to Embodiment 2 of the present invention.

[0087] It should be noted that, Figure 4 The interface shown is used for starting and stopping the motor of the hot melt machine, switching operating modes (manual / automatic), setting and monitoring speed, and supporting remote control and automatic speed adjustment via external signals or voltage. Figure 4 "Motor Control 1" refers to the number of frequency converters and their corresponding control areas; "Current Speed" displays the actual operating speed of the motor, in revolutions per minute (rpm); "Current Voltage" displays the current received external voltage value; "Current Status" allows switching between "Automatic" and "Manual" motor function states; "Encoder Speed" displays the actual speed value received from the external encoder in "Automatic" mode, in revolutions per minute (rpm); "Manual Speed" displays the speed value entered in "Manual" mode. Additionally, minimum and maximum speeds can be set as needed. Furthermore, clicking... Figure 4The motors in the list on the right can be configured differently for different motors, and the specific configuration logic is the same as described above.

[0088] It should be understood that, in Figure 4 In the interface, under "Automatic Mode," the encoder speed can be adjusted to an analog speed, meaning the "Analog 0-10V" function can be selected in Automatic Mode. This function aims to directly convert the currently received external voltage into a motor speed value, essentially calculating the speed automatically based on the external voltage.

[0089] like Figure 5 As shown, Figure 5 This is a schematic diagram of the functional parameter interface of Embodiment 2 of the present invention.

[0090] It should be noted that, Figure 5 The interface shown is used to perform graded start-up and heat preservation control of the temperature zone of the hot melt machine, and provides PID parameter setting, saving, reading and self-tuning functions to achieve precise and automated temperature management. Figure 5 The "staged start" function allows for separate start-up of different temperature zones. "Stage temperature" indicates that other temperature zones only begin heating when the melting cylinder reaches the input temperature. In the heat preservation function section, "Heat preservation temperature" displays the input heat preservation temperature value. Clicking the "Enter Heat Preservation" button in the heat preservation settings section puts the system into standby mode at the set temperature, and the button displays a green indicator. Clicking the "Cancel Heat Preservation" button puts the system into operation mode, and the button displays a light blue indicator to alert the operator.

[0091] It should be noted that by clicking Figure 5 The "PID Parameters" button allows for precise configuration of PID control parameters for each independent temperature zone within the system after correctly entering the pre-set password. Users can input the PID value for the corresponding temperature zone in the specific PID settings interface and save it to the system by clicking the "Write" button, or click the "Read" button to display the real-time PID values ​​for each temperature zone. Additionally, this interface allows for self-tuning by clicking the "Auto-tuning" option for the corresponding temperature zone and then clicking the "Start" button. This function automatically analyzes the temperature zone characteristics and calculates a set of optimal PID parameters, greatly simplifying the complex manual debugging process and serving as a crucial step in achieving precise temperature control.

[0092] like Figure 6 As shown, Figure 6 This is a schematic diagram of the alarm setting interface according to Embodiment 2 of the present invention.

[0093] It should be noted that, Figure 6The interface shown allows users to set thresholds for "over-temperature deviation" and "readiness deviation" to define the conditions under which the system issues alarm and readiness signals. Users can also independently enable or disable the alarm function for each temperature zone, achieving differentiated control. By monitoring temperature status (over-temperature, not ready, sensor open circuit, etc.), the system issues alarms and stops the entire unit in case of abnormalities, providing safety protection. Over-temperature deviation refers to the actual temperature of the temperature zone exceeding the upper limit of the "set temperature." When the actual temperature exceeds "set temperature" + "upper limit," the system issues an alarm signal. The normal setting for this parameter is between 15 and 30. Readiness deviation refers to the actual temperature of the temperature zone reaching the upper or lower limit of the "set temperature." When the actual temperature is within the range of "set temperature" ± "upper and lower limits," the system issues a readiness signal. The normal setting for this parameter is between 10 and 20, and should be lower than the "over-temperature deviation" setting. Furthermore, the alarm function for each temperature zone can be enabled or disabled differentiatedly by clicking the alarm on / off switch.

[0094] like Figure 7 As shown, Figure 7 This is a schematic diagram of the system parameter interface in Embodiment 2 of the present invention.

[0095] It should be noted that, Figure 7 The interface shown is used to monitor the status of all hardware signals and perform global function configuration. Figure 7 In the system parameter settings interface, clicking the "Filter" button will take you to the filter data reading interface for each melting cylinder, hose, and cavity. Clicking the "Read" button in the reading interface will display the filter values ​​for each temperature zone, or you can enter the filter values ​​for the corresponding temperature zone and then click the "Write" button to save them into the system.

[0096] like Figure 8 As shown, Figure 8 This is a schematic diagram of the IO status monitoring interface according to Embodiment 2 of the present invention.

[0097] It should be noted that, Figure 12 The interface shown is designed to monitor the real-time status of all input / output (IO) signals of the hot melt machine, including the operating status of the motor, glue gun, sensors, and alarm devices. Clicking the "System" - "IO" - "IO Screen" button in the system parameter settings interface will take you to the system settings module from the main menu. Then, specifically accessing the "Input / Output" submodule will open an interface that monitors the real-time status of all IO points. These IO points include: motor-related fault signals, motor signals, start / stop signals, etc.; glue dispensing control-related connection signals and dispensing signals for each glue gun; operating condition detection signals such as liquid level detection signals, low liquid level alarm signals, low air pressure alarm signals, etc.; and various status indicators and alarm light signals.

[0098] like Figure 9 As shown, Figure 9 This is a schematic diagram of the system parameter configuration interface for Embodiment 2 of the present invention.

[0099] It should be noted that, Figure 9 The interface shown allows you to set equipment specifications, safety parameters, and enable other advanced functions. Clicking the "System" - "System Configuration" - "System Configuration" button in the system parameter settings interface and entering the pre-set password will take you to the core system configuration menu. Here, you can globally configure the most important hardware specifications and core functions of the machine. For example, you can set the quantity of critical equipment such as the melting cylinder temperature zone, glue hoses, glue guns, and motors; configure safety parameters such as over-temperature alarm thresholds; and authorize or disable advanced functions such as encoder tracking, analog control, and glue level detection. Additionally, the core system configuration menu allows you to select the corresponding sensor type, including both resistance temperature detectors (RTDs) and thermocouples.

[0100] like Figure 10 As shown, Figure 10 This is a schematic diagram of the glue application parameter interface in Embodiment 2 of the present invention.

[0101] Understandable Figure 10 The interface shown allows you to configure the glue application mode, calibrate the correspondence between pulses and displacement, and correct positional deviations during high-speed glue application to achieve precise control. Within the aforementioned core system configuration menu, clicking the "Glue Application Parameters" button will take you to the process parameter setting interface. Here, you can select either "segmented" or "continuous" glue application modes, enabling independent manual glue dispensing control for different glue guns. You can also manually set the pulse length and the encoder's maximum pulse count. Based on the external encoder pulses, precise distance measurement and calibration are performed. This parameter establishes the correspondence between pulses and actual displacement. It also provides a glue position compensation ratio function to correct glue position deviations caused by system response delays during high-speed glue application, thereby achieving high-precision glue application across the entire speed range.

[0102] like Figure 11 As shown, Figure 11 This is a schematic diagram of the glue gun parameter setting interface according to Embodiment 2 of the present invention.

[0103] It should be noted that, Figure 11The interface shown allows you to set the movement and glue dispensing parameters of a single glue gun in a set spraying mode. In the system parameter setting interface, click the "Glue Gun" - "Spraying Application" - "Parameter Setting" button to set specific glue gun parameters, including the following: "Glue Gun Function Switch" to turn the corresponding glue gun's spraying function on or off; "Signal to Spraying Start Point" refers to the distance from the system-received signal to the spraying start point; "Spraying Length" refers to the length of the glue gun's spray; "Glue Position Interval" refers to the interval between two glue sprays; and "Number of Sprays" refers to the number of sprays per signal cycle. These settings are not available in "Normal: Continuous Glue Dispensing" mode.

[0104] Step S002: When the actual temperature of the temperature zone is within the ready deviation range of the set temperature, the temperature zone is considered to have reached the set temperature.

[0105] It should be noted that the system's control logic follows the principles of hierarchical decision-making and closed-loop regulation. First, a thermal preparation phase is executed: the main controller analyzes the temperature configuration of each temperature zone based on the preset dispensing process target and instructs each temperature control unit to heat independently. The system continuously compares the actual temperature with the set value. If the temperature enters the ready deviation range, the temperature zone is marked as ready. If overheating is detected (exceeding the sum of the set temperature and the overheat deviation value), the first alarm is triggered, and heating and dispensing are stopped. If an electrical fault such as an open circuit in the temperature sensor is detected, a second alarm requiring manual reset is triggered, and the machine is locked. After all temperature zones are ready, a global ready signal is generated, allowing entry into the dispensing phase. Subsequently, the dispensing control closed loop is entered: multi-dimensional quality data such as glue line width and height are extracted from the glue image collected by the visual inspection unit and compared with the preset target value to generate a deviation matrix. The collaborative control parameters of each glue path are calculated through a multi-glue path coupling and collaborative control model (such as a multivariable decoupling algorithm). Finally, precise collaborative control of the dispensing quantity is achieved by adjusting the motor speed, eliminating differences between multiple paths and ensuring consistent dispensing quality. The entire process ensures the system's safety and adaptability through alarm management, status monitoring, and parameter configurability.

[0106] Understandably, this step defines the core logic for the system to determine whether a temperature zone is "ready." It doesn't require the actual temperature to be exactly equal to the setpoint temperature, but rather introduces an acceptable readiness deviation range (for example, if the setpoint temperature is 200°C and the readiness deviation range is ±5°C, then 195°C to 205°C are considered ready). This design avoids frequent system state switching caused by small temperature fluctuations near the setpoint, enhancing control stability and practicality. When the actual temperature of a temperature zone stably falls within this range, the main controller marks that temperature zone as "ready."

[0107] In one embodiment, the step of considering the temperature zone as having reached the set temperature before the actual temperature of the temperature zone is within the ready deviation range of the set temperature further includes: generating a first alarm signal and stopping the heating control of the temperature zone and the dispensing operation of the whole machine when the actual temperature of any temperature zone exceeds the sum of its set temperature and the over-temperature deviation value; generating a second alarm signal and locking the whole machine's operating state to interrupt all operations when the temperature sensor feedback signal of any temperature zone indicates an electrical circuit fault, and triggering a system shutdown that requires manual reset.

[0108] It should be noted that the over-temperature alarm (first alarm) occurs when the actual temperature in any temperature zone exceeds the sum of its "set temperature" and "over-temperature deviation value" (for example, if the set temperature is 200°C and the over-temperature deviation value is set to 10°C, then exceeding 210°C will trigger the alarm). The main controller will immediately generate a first alarm signal. The system response is to immediately stop the heating control of the abnormal temperature zone and simultaneously stop all dispensing operations to prevent the adhesive from carbonizing due to overheating and to protect the heating components. The hardware fault alarm (second alarm) occurs when the temperature sensor feedback signal in any temperature zone indicates an electrical circuit fault (such as an open circuit, short circuit, or abnormal signal loss). The main controller will generate a higher-level second alarm signal. This alarm will lock the entire machine's operating state, interrupt all operations including heating, and trigger a system shutdown that must be confirmed and handled by an operator on-site. This shutdown requires manual reset to be lifted. This design aims to prevent blind heating in case of inaccurate detection, which could lead to serious safety accidents.

[0109] Step S003: When all temperature zones have reached the set temperature, generate a global ready signal.

[0110] It should be noted that the control interface uses a touchscreen as the core of the human-machine interface, and has three global status indicator lights on the top: HEATING: A yellow indicator light indicates that the system is powered on and each temperature zone is in the process of heating up. READY: A green indicator light indicates that the actual temperature of all temperature zones has reached the allowable deviation range of the set value, the whole machine is ready, and the motor can be started. ALARM: A red indicator light indicates that the system has encountered an abnormality (such as temperature zone over-temperature, sensor failure, etc.), and will display the specific alarm status of all available temperature zones.

[0111] Understandably, users can switch between different function interfaces by clicking buttons on the screen to set parameters and monitor performance. For example, the temperature setting interface is the core interface for temperature control, and the system supports temperature control for up to 14 independent temperature zones (divided into 6 output groups). Users can click the on / off button for each temperature zone ("light blue" for off, "green" for on) and set the target temperature (unit: °C). The interface displays the current actual temperature of each temperature zone in real time. If no load (hose or gun body) is connected or the sensor malfunctions, the value will display as "0.0" and trigger an alarm. The motor control interface is used to control the motor driving the glue pump. It supports multiple motors, and users can switch between "automatic" and "manual" operating modes. In automatic mode, the motor speed can be controlled by an external signal, supporting either "encoder linear tracking" or "analog 0-10V" modes, and displaying the speed signal from the external source in real time. In manual mode, users can directly set the motor speed, and also control the motor's start / stop and enable / disable the remote start function.

[0112] It should be understood that, in addition to the above, the control interface also includes a function parameter interface, which is an advanced settings menu containing multiple sub-interfaces. The alarm setting interface allows independent setting of over-temperature deviation values ​​and ready deviation ranges for each temperature zone, and enables or disables the alarm function for that zone individually. The PID parameter area, after entering a password, allows tuning and writing PID control parameters for each temperature zone to optimize temperature control. The system configuration interface, after entering a password, allows setting the system hardware scale (such as the number of temperature zones and motors), selecting the signal type (encoder or 0-10V analog signal), and setting the maximum over-temperature alarm temperature. The glue application parameter interface sets parameters related to the spraying application, such as spray length, glue interval, number of sprays, and the important glue compensation ratio, used to eliminate positional deviations during high-speed glue spraying.

[0113] In this embodiment, precise dispensing is achieved through a main controller coordinating independent temperature control and feedback adjustment across multiple temperature zones. The system first controls the heating of each temperature zone according to the process objectives, intelligently determining temperature compliance based on the ready deviation range, and is equipped with dual safety alarms for over-temperature and sensor malfunction. Once all temperature zones are ready, the dispensing stage begins. An industrial camera monitors the colloid morphology in real time, and image analysis generates multiple collaborative control signals to dynamically adjust the motor speed, ensuring uniform dispensing. The entire system is monitored and parameters configured via a touchscreen interface.

[0114] In summary, this embodiment achieves intelligent and high-precision multi-channel glue dispensing in a hot melt glue machine by integrating multi-zone independent temperature control, visual feedback closed-loop control, and intelligent safety protection mechanisms. Real-time image analysis dynamically adjusts multi-channel glue dispensing parameters, effectively eliminating the uneven glue dispensing problem common in traditional equipment and significantly improving product quality. A dual safety alarm mechanism proactively prevents overheating and hardware failure risks, greatly enhancing equipment operational safety. An intuitive human-machine interface makes operation simple and flexible, greatly improving the equipment's adaptability to different processes and overall production efficiency, ultimately achieving efficient, stable, and reliable multi-channel glue dispensing production.

[0115] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 12 Step S20 in the multi-channel glue dispensing control method for the hot melt glue machine includes steps A10 to A20:

[0116] Step A10: Based on the glue dispensing quality information and the preset glue dispensing process target, obtain the multi-channel glue dispensing state deviation matrix.

[0117] It should be noted that this step involves systematically and quantitatively comparing the measured glue quality information of each glue path with the preset process sub-targets, and constructing a multi-path glue output state deviation matrix based on the deviation values ​​of each glue path in various quality parameters.

[0118] Understandably, the multi-path dispensing state deviation matrix is ​​a formalized and structured mathematical representation of the overall system state. Its rows typically represent different dispensing path numbers, while its columns represent key process parameters monitored in different dimensions (such as flow rate deviation, temperature deviation, width deviation, height deviation, etc.). Each element of this matrix is ​​a specific deviation value, calculated as the difference (or relative error) between the measured value (or target setpoint) of the corresponding dispensing path for a certain parameter.

[0119] Understandably, this deviation matrix is ​​constructed to determine the overall difference and distribution between the actual dispensing state and the ideal state of the entire multi-channel system under the current system conditions, after directly inputting the original setting parameters. For example, for a four-pipe system, its deviation matrix might show a temperature deviation of +3°C in one pipe and a flow deviation of -5% in another. This allows the control system to determine the deviations distributed across multiple pipes and multiple parameters.

[0120] It should be understood that, to summarize the core idea of ​​this embodiment, for a hot melt glue machine used for a long time, the deviation between the actual glue dispensing results and preset parameters during the coordinated glue dispensing process can be evaluated based on the measured data obtained from multiple glue dispensing experiments. These deviations are integrated into a matrix according to the glue dispensing pipeline and quality parameters to form a training dataset, and machine learning methods are used to establish a predictive model between the device's set parameters and the actual glue dispensing results. After the model training is completed, the control parameters of each pipeline are continuously fine-tuned so that the model output gradually approaches the actual required glue dispensing target. Finally, the fine-tuning matrix corresponding to achieving the target is determined as the actual control parameters.

[0121] In one embodiment, step A10 includes steps A11 to A13.

[0122] Step A11: Calculate the difference between the measured values ​​of each dimension in the glue dispensing quality information and the corresponding target values ​​of each dimension in the preset glue dispensing process target to obtain multiple single-dimensional deviation values. The single-dimensional deviation values ​​include at least glue dispensing amount deviation, glue line width deviation, and glue line height deviation.

[0123] Step A12: Combine all single-dimensional deviation values ​​of the same adhesive path in a predetermined order to form the single-tube deviation vector of the adhesive path.

[0124] Step A13: Arrange the single-tube deviation vectors of all glue paths to construct the multi-path glue dispensing state deviation matrix.

[0125] Understandably, all deviation values ​​of a single adhesive path are combined into a vector in a preset order (such as adhesive quantity, width, and height), i.e., a single-path deviation vector. This vector mathematically describes the overall process status of that adhesive path. Furthermore, arranging the deviation vectors corresponding to all adhesive paths in the system by rows or columns forms the final multi-path adhesive dispensing status deviation matrix. This matrix presents the overall deviation distribution of the entire system across multiple quality dimensions in a structured manner, providing a quantitative basis for subsequent multi-path collaborative compensation control.

[0126] Step A20: Input the multi-path glue dispensing state deviation matrix into the multi-path coupling and collaborative control model to obtain the collaborative control parameters of each glue path.

[0127] It should be noted that the multi-path coupling and collaborative control model is a multi-input multi-output model pre-trained using machine learning or system identification methods. It comprehensively analyzes the coupling relationships and deviation states between multiple adhesive paths and outputs control parameters with collaborative compensation capabilities. The model's input is the multi-path dispensing state deviation matrix constructed in previous steps, which integrates deviation information for each adhesive path in key process dimensions (such as dispensing quantity, adhesive line width, and height). Its output consists of collaborative control parameters for each adhesive path, such as temperature compensation values, pressure adjustments, or motor speed corrections. These parameters aim to collaboratively adjust multiple actuators to reduce overall dispensing deviation. Theoretically, by identifying the transmission characteristics and coupling relationships of each adhesive path actuator, the model can accurately describe the dynamic behavior of the system. In actual model training, sufficient sample data can be obtained by collecting historical production data or designing system identification experiments to train or fit the control model, enabling it to accurately reflect the complex mapping relationship between system inputs (control parameters) and outputs (dispensing quality). For example, the model can learn coupling patterns such as "how increasing the dispensing pressure of adhesive path A will affect the adhesive line width of adhesive path B", and thus compensate for this in control decisions.

[0128] Understandably, the core function of this control model is to address the mutual interference caused by factors such as mechanical structure, hydrodynamics, or thermal field distribution when multiple adhesive channels operate simultaneously. It no longer treats each adhesive channel as an independent control object, but rather takes a holistic system perspective, using coupling analysis to calculate an optimized solution that allows the output of each channel to reapproach the target value. For example, when the adhesive line width of a certain channel exhibits a positive deviation, the model may not only suggest reducing the pressure of that channel but also fine-tune the temperature of adjacent adhesive channels to suppress the negative coupling effect caused by thermal diffusion.

[0129] It should be understood that the collaborative control parameters will ultimately be sent to the lower-level actuators (such as servo motors, heaters, pressure valves, etc.) to complete the closed-loop control from state perception and intelligent decision-making to precise execution. The effectiveness of this model relies on training with a large amount of historical data, and its output can significantly improve the overall control accuracy and process consistency under multi-coupling conditions.

[0130] In this embodiment, after acquiring the actual quality information of multiple glue outlets through a vision unit, it is precisely compared with the preset process target. A multi-channel glue outlet state deviation matrix is ​​generated by subtraction calculation, covering all glue outlets and all key quality dimensions. This matrix structurally expresses the difference between the overall system output and the ideal state. Subsequently, this deviation matrix is ​​input into a pre-trained multi-glue-channel coupled collaborative control model. This model analyzes the global deviation state and collaboratively calculates a set of collaborative control parameters for each glue outlet. Finally, by adjusting the corresponding actuators, it intelligently offsets system errors and inter-channel interference, thereby achieving high-precision, high-consistency collaborative control of multiple glue outlets.

[0131] In summary, this embodiment achieves systematic and intelligent multi-path collaborative compensation by introducing a multi-path dispensing state deviation matrix and a multi-path coupling collaborative control model. This effectively counteracts the complex inter-path influences caused by mechanical coupling, thermal interference, and fluid fluctuations, fundamentally solving the problems of uneven dispensing and poor coordination among multiple paths. This significantly improves product appearance consistency and packaging reliability. Furthermore, this data-driven control method possesses strong adaptability and anti-interference capabilities, enabling it to cope with process drift caused by changes in adhesive properties and actuator aging. This reduces reliance on manual adjustments and enhances the long-term stability and process reproducibility of the equipment.

[0132] Based on the first embodiment of this application, in the fourth embodiment of this application, the content that is the same as or similar to that in the third embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 13 Before step A20 in the multi-channel glue dispensing control method for the hot melt glue machine, steps A001 to A005 are also included:

[0133] Step A001: Obtain historical production data, which includes multiple sets of measured values ​​of quality parameters and corresponding process parameter settings under multi-channel glue dispensing processes.

[0134] It should be noted that the core purpose of this step is to systematically collect historical data reflecting the equipment's operating status and process effectiveness from the actual production process. This data is organized in groups, each containing two key components: first, the specific process parameter settings issued by the control system to each adhesive path actuator during a particular process run, such as temperature settings, pressure settings, and motor speed for each path; and second, the actual adhesive quality parameters measured by various sensors (such as flow meters, thermocouples, and vision inspection systems) after the process is completed, such as the actual adhesive output, adhesive line width, adhesive line height, and adhesive temperature for each path.

[0135] Understandably, these historical datasets record the input and output responses of the equipment under various operating conditions. The amount, coverage, and quality of this data directly determine the accuracy and generalization ability of the subsequently trained control model. For example, to train a model suitable for a four-tube dispensing device, it is necessary to collect dispensing data from multiple runs under different total dispensing volume requirements, different dispensing ratios, and different ambient temperatures. Each run constitutes a set of data, where the setpoints represent the control inputs, and the measured values ​​characterize the overall output performance of the system. Together, they form a complete "state-result" sample pair.

[0136] Step A002: Based on the historical production data, obtain the historical glue discharge state deviation matrix.

[0137] It's important to note that the core of this step is processing the acquired raw historical production data, transforming it into a structured data format that represents the difference between the actual and desired states of the multi-channel glue dispensing system—namely, the historical glue dispensing state deviation matrix. Each row of this matrix represents an independent historical process operation record, while each column represents a specific deviation dimension. The specific value of this dimension is calculated from the measured values ​​of quality parameters collected during that operation and their corresponding process parameter settings, such as glue dispensing volume deviation, glue line width deviation, and glue line height deviation. By constructing this matrix, a large amount of scattered, multi-dimensional historical data is integrated into a unified analytical object that is easy for mathematical models to process.

[0138] Understandably, the generation of this deviation matrix is ​​a systematic quantitative calculation process. For each set of data in the historical dataset, the system calculates the deviation value of each quality parameter item by item. For example, in one record, the target glue output of glue path 1 is 50 grams, and the measured value is 48 grams, so its glue output deviation is -2 grams; the target glue line width is 2 millimeters, and the measured value is 2.1 millimeters, so the width deviation is +0.1 millimeters. Arranging all such deviation values ​​of all glue paths in a fixed order (such as glue path number first, then parameter type) into a vector constitutes a single deviation vector representing the running state of that operation. Finally, stacking the deviation vectors corresponding to all historical records row by row forms a complete historical glue output state deviation matrix.

[0139] It should be understood that by analyzing the covariance of deviation values ​​between different lines or different historical operating conditions, the coupling mechanism and interference path between multiple glue lines can be inferred in reverse. For example, if historical data generally shows that whenever the pressure setpoint of glue line 2 is increased, not only does its own glue output deviation increase, but glue output of glue line 1 also shows a negative deviation, then this stable covariance relationship will be recorded in the deviation matrix, thus providing a direct learning target for subsequent training of a control model that can understand and compensate for this coupling effect.

[0140] Step A003: Construct a label dataset based on each row vector in the historical glue dispensing state deviation matrix and its corresponding actual process parameter adjustment amount.

[0141] It should be noted that the purpose of this step is to construct a standard dataset, i.e., a labeled dataset, using historical data for training supervised learning models. This dataset consists of feature vectors and label vectors. The feature vectors are derived from the row vectors of the historical glue dispensing state deviation matrix, representing the comprehensive deviation state of the multi-glue system at a specific historical moment. The label vectors correspond to the effective process parameter adjustments verified in actual production under this deviation state, such as temperature compensation values, pressure correction values, or motor speed adjustment values ​​for each glue path.

[0142] Understandably, the construction process essentially involves the data-driven encapsulation of historical control strategies. For example, when the deviation vector indicates insufficient glue dispensing in one glue path while overflowing in another, and the overall glue line width exceeds the standard range, querying the historical records reveals that the control system actually implemented a combination of actions: increasing the heating temperature of the insufficient glue path, reducing the conveying pressure of the overflowing glue path, and fine-tuning the overall temperature setting. Subsequent production confirmed that this adjustment effectively eliminated the deviation. In this case, this comprehensive deviation state serves as the input feature, and the corresponding validated sequence of parameter adjustments serves as the output label, together forming a training sample.

[0143] Step A004: Based on the labeled dataset, supervised training is performed using a neural network model to learn the nonlinear mapping relationship between the deviation of the glue dispensing state in multiple glue paths and the adjustment amount of the process parameters of each glue path, thereby obtaining an initial multi-glue path control model.

[0144] It should be noted that during training, the historical dispensing state deviation vectors from the labeled dataset are used as input features, and the corresponding actual process parameter adjustments are used as target labels. Supervised learning is performed using a neural network model. The model calculates predicted values ​​through forward propagation and uses a loss function to measure the difference between the predicted values ​​and the true labels. Then, the backpropagation algorithm is used to calculate the gradient of the loss function with respect to the network weights, and the gradient descent optimization algorithm is used to iteratively update the network weights, continuously reducing the prediction error. Through multiple rounds of iterative training, the model gradually learns the complex nonlinear mapping relationship between multi-dimensional deviation states and multi-process parameter adjustments, ultimately obtaining an initial multi-path control model capable of accurately predicting adjustment amounts based on real-time deviations. This neural network model can automatically learn and internalize the complex nonlinear mapping relationship between deviation states and correction actions in a multi-path dispensing system. With its multi-layered sensing structure and nonlinear activation function, the neural network can effectively capture complex patterns and coupling effects existing in the high-dimensional feature space, which is difficult to achieve with traditional linear control models. During training, the network takes the historical deviation vector as input and the verified effective adjustment amount as the target output. It continuously optimizes its internal weight parameters through backpropagation algorithm. The ultimate goal is to enable the model to accurately predict the adjustment amount of each glue path process parameter to be applied based on the real-time deviation of the input.

[0145] Understandably, this training process essentially involves the machine simulating and learning the decision-making process of human experts. For example, when the training data repeatedly shows a simultaneous occurrence of "negative deviation in glue output from glue path A and positive deviation in glue line width from glue path B," the corresponding label is usually "increase the temperature of glue path A and decrease the pressure of glue path B." By analyzing thousands of such samples, the neural network autonomously discovers the statistical correlation between this combination of deviations and the adjustment action, and encodes it into the model's connection weights. The finally trained model, when encountering similar real-time combinations of deviations, can automatically infer corresponding, historically validated, and effective adjustment strategies.

[0146] It should be understood that the initial multi-path control model obtained through training is not simply a memorization of historical operations, but rather a non-linear functional relationship between the dispensing deviation and adjustment amount of the multi-path hot melt glue machine obtained through machine learning. It can not only handle the deviation scenarios that have been trained, but also has a certain generalization ability, and can reasonably infer similar deviation states that have not been seen before, laying the core foundation for subsequent real-time online applications and fine-tuning of dispensing parameters.

[0147] Step A005: Based on the multi-path coupling constraint, the loss function of the initial multi-path control model is regularized, and the model parameters are trained through collaborative optimization to obtain the multi-path coupling collaborative control model.

[0148] It should be noted that the core of this step is to regularize the loss function of the initial model by introducing constraints that reflect the physical coupling relationship between multiple adhesive paths, so as to improve its collaborative control performance in complex coupling environments.

[0149] Understandably, multi-path coupling constraints stem from actual dispensing systems. For example, the maximum total power of the heating system limits the simultaneous and significant increase in temperature across different dispensing paths, or the total flow rate of the pressure supply system determines a competitive relationship where the dispensing volume of each path increases at one rate. These prior physical constraints are embedded mathematically (e.g., inequalities or equality constraints) into the model's optimization objective. These constraints, combined with the original prediction error loss term (e.g., mean squared error), form a new loss function, guiding the model during training to learn adjustment strategies that are not only precise but also physically achievable.

[0150] It should be understood that this regularization and co-optimization process essentially integrates domain knowledge (physical constraints) with the data-driven model, preventing the model from outputting idealized but infeasible adjustment schemes that violate the actual system's operational constraints. For example, without constraints, the model might suggest adjusting the heating power of all adhesive circuits to the maximum to quickly correct the negative bias of a certain adhesive circuit, but this would exceed the system's total power limit and lead to overheating risks. Through regularization, the new loss function penalizes such predictions that violate the total power constraint, forcing the model to explore the trade-offs between the parameters of each adhesive circuit during training. It learns how to coordinately allocate resources (such as temperature and pressure) under the system's global constraints, thereby outputting a set of optimal or near-optimal adjustment values ​​that both alleviate local biases and satisfy global constraints.

[0151] In this embodiment, historical production data is systematically acquired to construct a historical deviation matrix reflecting the deviation state of the multi-glue path system, and a labeled training dataset is generated accordingly. Subsequently, a neural network is used to train an initial control model from the data through supervised learning, which can map the deviation of the glue dispensing state to the adjustment amount of the process parameters. Finally, the model loss function is regularized by introducing constraints that characterize the physical coupling relationship of the multi-glue path, and after collaborative optimization training, the multi-glue path coupling collaborative control model is finally obtained.

[0152] In summary, this embodiment employs a historical production data-driven approach to train the neural network model. This model can automatically learn and internalize the complex, nonlinear coupling relationships and deviation compensation strategies in a multi-path glue dispensing system, overcoming the shortcomings of traditional mechanistic modeling, which suffers from high difficulty and low accuracy, thereby improving the accuracy and adaptability of control. Secondly, by introducing multi-path coupling constraints for regularization and collaborative optimization during the model training phase, the final control model's decision logic deeply integrates the physical limitations and global operating boundaries of the actual system. It can automatically generate parameter adjustment schemes for collaborative optimization of each glue path under global resource constraints (such as total power and total flow), effectively avoiding control failures caused by parameter conflicts or system over-limits. This ensures the synergy and feasibility of the output control signal in principle, ultimately achieving improved consistency and stability of multi-path glue dispensing quality.

[0153] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the multi-channel glue dispensing control method of the hot melt glue machine of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0154] This application also provides a multi-channel dispensing control device for a hot melt adhesive machine; please refer to [reference needed]. Figure 14 The multi-channel dispensing control device for the hot melt glue machine includes:

[0155] The data acquisition and monitoring module 10 is used to perform the dispensing operation based on the preset dispensing process target and to obtain the dispensing quality information of each glue path within a preset time.

[0156] Deviation analysis module 20 is used to generate collaborative control parameters for compensating each glue path based on the deviation between the glue quality information and the preset glue process target;

[0157] The collaborative execution module 30 is used to perform collaborative glue dispensing control on each glue path actuator according to the collaborative control parameters, so as to eliminate the glue dispensing quality difference between each glue path.

[0158] This embodiment acquires a preset dispensing process target and collects actual dispensing quality data. Based on this, a multi-path dispensing state deviation matrix is ​​constructed to quantify the output differences of each dispensing path. This matrix is ​​then input into a multi-path coupling and collaborative control model. The model solves the model to obtain the collaborative control parameters for each dispensing path. Finally, based on these parameters, each actuator is adjusted in real time to eliminate differences between dispensing paths. Because this solution uses a multi-path dispensing state deviation matrix, the system can accurately perceive and quantify the collaborative deviation of multiple dispensing paths, overcoming the limitations of traditional single-path control in dealing with coupling interference. Secondly, by solving the matrix through the multi-path coupling and collaborative control model, the coupling relationship between multiple actuators can be dynamically analyzed, and globally optimal compensation parameters can be generated, thus achieving a leap from independent control to collaborative optimization at the control level. Finally, by coordinating the actuators based on the compensation parameters, high-precision closed-loop control of the dispensing quality of each dispensing path is achieved, effectively eliminating the problem of uneven dispensing caused by temperature fluctuations, mechanical errors, and fluid characteristics, significantly improving the quality and reliability of the encapsulation or coating process.

[0159] The multi-channel dispensing control device for hot melt glue machines provided in this application adopts the multi-channel dispensing control method for hot melt glue machines described in the above embodiments, and can solve the technical problem of how to achieve coordinated and precise control of the dispensing quality of multiple hot melt glue machines. Compared with the prior art, the beneficial effects of the multi-channel dispensing control device for hot melt glue machines provided in this application are the same as those of the multi-channel dispensing control method for hot melt glue machines provided in the above embodiments, and other technical features in the multi-channel dispensing control device for hot melt glue machines are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0160] This application provides a multi-channel dispensing control device for a hot melt glue machine. The multi-channel dispensing control device for a hot melt glue machine includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the multi-channel dispensing control method for a hot melt glue machine in the above embodiment 1.

[0161] The following is for reference. Figure 15This document illustrates a structural schematic diagram of a multi-channel dispensing control device for a hot melt glue machine suitable for implementing embodiments of this application. The multi-channel dispensing control device for a hot melt glue machine in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 15 The hot melt glue machine multi-channel glue dispensing control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0162] like Figure 15 As shown, the multi-channel dispensing control device for a hot melt glue machine may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the multi-channel dispensing control device for the hot melt glue machine. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the hot melt glue machine multi-dispensing control device to wirelessly or wiredly communicate with other devices to exchange data. Although the figure shows a hot melt glue machine multi-dispensing control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0163] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0164] The multi-channel dispensing control device for hot melt glue machines provided in this application, employing the multi-channel dispensing control method for hot melt glue machines described in the above embodiments, can solve the technical problem of how to achieve coordinated and precise control of the dispensing quality of multiple hot melt glue machines. Compared with the prior art, the beneficial effects of the multi-channel dispensing control device for hot melt glue machines provided in this application are the same as those of the multi-channel dispensing control method for hot melt glue machines provided in the above embodiments, and other technical features in this multi-channel dispensing control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0165] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0167] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the hot melt glue machine multi-channel glue dispensing control method in the above embodiments.

[0168] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0169] The aforementioned computer-readable storage medium may be included in the multi-channel dispensing control device of the hot melt glue machine; or it may exist independently and not be assembled into the multi-channel dispensing control device of the hot melt glue machine.

[0170] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the multi-channel dispensing control device of the hot melt glue machine, the multi-channel dispensing control device of the hot melt glue machine causes the following: it acquires a preset dispensing process target; it performs dispensing operations based on the preset dispensing process target, acquiring dispensing quality information for each glue path within a preset time; it obtains a multi-channel dispensing state deviation matrix based on the dispensing quality information and the preset dispensing process target; it inputs the multi-channel dispensing state deviation matrix into a multi-glue path coupled collaborative control model to obtain collaborative control parameters for each glue path; and it performs collaborative dispensing control on the actuators of each glue path based on the collaborative control parameters to eliminate dispensing quality differences between each glue path.

[0171] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0172] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0173] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described multi-channel dispensing control method for a hot melt glue machine. This solves the technical problem of how to achieve coordinated and precise control of the dispensing quality of multiple hot melt glue machines. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the multi-channel dispensing control method for hot melt glue machines provided in the above embodiments, and will not be repeated here.

[0174] The computer program product provided in this application can solve the technical problem of multi-channel glue dispensing control in hot melt glue machines. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the multi-channel glue dispensing control method for hot melt glue machines provided in the above embodiments, and will not be repeated here.

[0175] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A hot melt adhesive machine multi-path glue discharging control method, characterized in that, The hot melt adhesive machine multi-path glue output control method comprises the following steps: Based on the preset glue output process target, the glue output operation is performed, and the glue output quality information of each glue path within a preset time is obtained; According to the deviation of the glue output quality information and the preset glue output process target, the collaborative control parameters for compensating each glue path are generated; The deviation of the glue output quality information and the preset glue output process target, the collaborative control parameters for compensating each glue path are generated, which comprises: According to the glue output quality information and the preset glue output process target, a multi-path glue output state deviation matrix is obtained; The multi-path glue output state deviation matrix is input into the multi-glue path coupling collaborative control model to obtain the collaborative control parameters of each glue path; The multi-path glue output state deviation matrix is input into the multi-glue path coupling collaborative control model to obtain the collaborative control parameters of each glue path, which comprises: The multi-path glue output state deviation matrix is input into the multi-glue path coupling collaborative control model to obtain the multi-glue path collaborative control parameter matrix; The multi-glue path collaborative control parameter matrix and the multi-path glue output state deviation matrix are compared one by one according to the glue path to obtain the glue output compensation amount of each glue path; According to the glue output compensation amount, the glue output control parameter mapping is performed to obtain the collaborative control parameters corresponding to the glue path; According to the collaborative control parameters, the collaborative glue output control of each glue path actuator is performed to eliminate the glue output quality difference between each glue path.

2. The hot melt adhesive machine multi-path glue discharging control method according to claim 1, characterized in that, Before the step of based on the preset glue output process target, performing the glue output operation, and obtaining the glue output quality information of each glue path within a preset time, the method further comprises the following steps: According to the preset glue output process target, the temperature configuration information of each temperature zone is obtained, and each heating zone is independently heated based on the temperature configuration information; When the actual temperature of the temperature zone is within the ready deviation range of the set temperature, it is considered that the temperature zone reaches the set temperature; When all temperature zones reach the set temperature, a global readiness signal is generated.

3. The hot melt adhesive machine multi-path glue discharging control method according to claim 2, characterized in that, Before the step of when the actual temperature of the temperature zone is within the ready deviation range of the set temperature, it is considered that the temperature zone reaches the set temperature, the method further comprises the following steps: When the actual temperature of any temperature zone exceeds the sum of the set temperature and the over-temperature deviation value, a first alarm signal is generated, and the heating control of the temperature zone and the glue output operation of the whole machine are stopped; When the temperature sensor feedback signal of any temperature zone indicates an electrical line fault, a second alarm signal is generated, and the system is locked to interrupt all operations and trigger a manual reset system shutdown.

4. The hot melt adhesive machine multi-path glue discharging control method according to claim 1, characterized in that, Before the step of based on the preset glue output process target, performing the glue output operation, and obtaining the glue output quality information of each glue path within a preset time, the method further comprises the following steps: Obtain the glue output task information, which includes the target total glue output, the target glue line width, the target glue line height, and the target glue temperature; According to the current hardware identification and pipeline topological relationship of each glue path of the hot melt adhesive machine, the glue output task information is decomposed into sub-process targets corresponding to each glue path to obtain the preset glue output process target.

5. The hot melt adhesive machine multi-path glue discharging control method according to claim 1, characterized in that, The step of according to the glue output quality information and the preset glue output process target, obtaining a multi-path glue output state deviation matrix, comprises: The single-dimension deviation value at least includes a glue output amount deviation, a glue line width deviation, and a glue line height deviation. The single-pipe deviation vectors of all glue paths are arranged to construct the multi-path glue output state deviation matrix. The hot melt glue machine multi-path glue output control device comprises:

6. A hot melt adhesive machine multi-path glue discharging control device, characterized in that, The data acquisition and monitoring module is configured to perform glue output operations based on the preset glue output process target and acquire glue output quality information of each glue path within a preset time. The deviation analysis module is configured to generate a collaborative control parameter for compensating each glue path according to deviations between the glue output quality information and the preset glue output process target. The deviation analysis module is further configured to obtain a multi-path glue output state deviation matrix according to the glue output quality information and the preset glue output process target, input the multi-path glue output state deviation matrix into a multi-glue path coupling collaborative control model, and obtain the collaborative control parameter of each glue path. The deviation analysis module is further configured to input the multi-path glue output state deviation matrix into the multi-glue path coupling collaborative control model to obtain a multi-glue path collaborative control parameter matrix, compare the multi-glue path collaborative control parameter matrix and the multi-path glue output state deviation matrix according to glue paths, obtain a glue output compensation amount of each glue path, perform glue output control parameter mapping according to the glue output compensation amount, and obtain the collaborative control parameter corresponding to the glue path. The collaborative execution module is configured to perform collaborative glue output control on each glue path actuator according to the collaborative control parameter to eliminate glue output quality differences between the glue paths. The hot melt glue machine multi-path glue output control device comprises a memory, a processor, and a hot melt glue machine multi-path glue output control program stored on the memory and executable on the processor, and the hot melt glue machine multi-path glue output control program is configured to implement the steps of the hot melt glue machine multi-path glue output control method according to any one of claims 1 to 5.

7. A hot melt adhesive machine multi-path glue discharging control device, characterized in that, The storage medium stores a hot melt glue machine multi-path glue output control program, and the hot melt glue machine multi-path glue output control program is executed by the processor to implement the steps of the hot melt glue machine multi-path glue output control method according to any one of claims 1 to 5.

8. A storage medium, characterized by ​

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