Composite high pressure forming control method and system based on communication interaction

Abstract

This invention provides a control method and system for high-pressure composite forming based on communication interaction, relating to the field of control technology. The system involves collecting real-time data from multimodal sensors, a central controller calculating parameter deviations based on the data, adaptively adjusting the system using piecewise continuous functions and calculating compensation corrections in conjunction with a coupling influence matrix, establishing a priority dynamic scheduling strategy and an execution time table to generate coordinated control instructions, and automatically adjusting the execution status of subordinate controllers via a real-time communication bus. This invention improves the control accuracy and stability of the high-pressure composite forming process and enhances the reliability of multi-device collaborative operation.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a composite high-pressure forming control method and system based on communication interaction. Background Art

[0002] In the traditional high-pressure forming process of composite materials, preset process parameters are usually used for forming, and the equipment operates relatively independently. The adjustment of process parameters mainly relies on manual experience or a simple feedback control system.

[0003] During the high-pressure molding process of composite materials, process parameters such as temperature, pressure, and time have a decisive influence on the molding quality of the material. Traditional control methods often use fixed process parameter settings, lacking the ability to respond to parameter fluctuations during the molding process in real time. This makes it difficult to adapt to changes in material properties and the impact of environmental factors, resulting in unstable molding quality.

[0004] The defects and deficiencies of the existing technology are mainly reflected in the following aspects:

[0005] First, existing composite high-pressure forming control systems usually lack an effective multi-device collaborative control mechanism, and insufficient information exchange between devices makes it difficult to achieve overall system-level optimization. As a result, under complex process conditions, problems such as asynchronous progress and uncoordinated control parameters may occur between devices, affecting the stability of forming quality.

[0006] Secondly, traditional control methods often use simple PID control or fixed gain adjustment to adjust process parameters, which fails to consider the coupling effects between parameters and cannot achieve dynamic adaptive adjustment based on multimodal sensor data. When the process parameters deviate greatly or the operating conditions change suddenly, the adjustment effect is poor, which can easily lead to over-adjustment or under-adjustment. Summary of the Invention

[0007] The embodiments of the present invention provide a composite high pressure forming control method and system based on communication interaction, which can solve the problems in the prior art.

[0008] A first aspect of an embodiment of the present invention provides a composite high pressure forming control method based on communication interaction, comprising:

[0009] Collecting real-time data from multimodal sensors of composite high-pressure forming equipment and transmitting the real-time data to a central controller via industrial Ethernet;

[0010] The central controller calculates the process parameter deviation value of the composite high pressure forming process based on the real-time data. The central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the change trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of parameters.

[0011] Dynamically adjusting the process parameters in real time based on the compensation correction amount to generate compensation process parameters, establishing a priority dynamic scheduling strategy and a device execution schedule based on the compensation process parameters, and generating a multi-device collaborative control instruction by combining the priority dynamic scheduling strategy and the device execution schedule;

[0012] The central controller sends the multi-device collaborative control instructions to the lower controllers through the industrial Ethernet. The lower controllers exchange their respective execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the responding lower controller automatically adjusts the execution progress, and the other lower controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

[0013] The central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the variation trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of the parameters, including:

[0014] Performing a time series analysis on the process parameter deviation value, and calculating the deviation change rate of the process parameter deviation value within a plurality of consecutive sampling periods;

[0015] Based on the historical data of the process parameter deviation value, the weighted moving average prediction method is used to calculate the predicted deviation value of the next sampling period.

[0016] Calculating a dynamic adjustment coefficient according to a preset adaptive adjustment function, wherein the adaptive adjustment function adopts a piecewise continuous function form, adopts a linear growth mode of adjustment when the predicted deviation value is less than a first deviation threshold, adopts an exponential growth mode of adjustment when the predicted deviation value is greater than the first deviation threshold and less than a second deviation threshold, and adopts a saturation limit mode of adjustment when the predicted deviation value is greater than the second deviation threshold;

[0017] Calculating a proportional coefficient and a differential coefficient according to the dynamic adjustment coefficient, multiplying the proportional coefficient by the predicted deviation value to obtain a proportional term, and multiplying the differential coefficient by the deviation change rate to obtain a differential term;

[0018] Conduct cross-response tests on each process parameter to obtain the degree of mutual influence between the parameters and establish a coupling influence matrix. The diagonal elements of the coupling influence matrix are unity, and the off-diagonal elements range from zero to one, representing the coupling strength between different parameters.

[0019] The superposition value of the proportional term and the differential term is used to form a compensation vector, and the compensation vector is multiplied by the coupling influence matrix to obtain a final compensation amount considering the parameter coupling effect.

[0020] Based on the compensation correction amount, the process parameters are dynamically adjusted in real time to generate compensation process parameters, a priority dynamic scheduling strategy and a device execution time sequence table are established based on the compensation process parameters, and a multi-device collaborative control instruction is generated by combining the priority dynamic scheduling strategy and the device execution time sequence table.

[0021] Adding the compensation correction amount to the current process parameters respectively to obtain the compensated process parameters, wherein the compensated process parameters include the compensated molding cavity pressure parameter, the molding cavity temperature parameter, the pressure head displacement parameter and the material supply rate parameter;

[0022] establishing a multidimensional execution space coordinate system based on the compensation process parameters, mapping the control parameters of each execution device into an execution trajectory curve in the execution space coordinate system, calculating the interaction relationship between devices based on the execution trajectory curve, dividing a device combination with an interaction relationship strength greater than a first strength threshold into an execution unit, and establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit to determine the control timing of the execution devices;

[0023] Building a device execution timing table based on the compensation process parameters, wherein the device execution timing table divides the execution cycle into multiple control time slots, each control time slot is assigned to a different execution device, and a buffer interval is set between adjacent control time slots. The buffer interval is adaptively adjusted according to the dynamic response characteristics of the process parameters to eliminate mutual interference during device switching;

[0024] The priority dynamic scheduling strategy and the device execution timing table are combined to generate a multi-device collaborative control instruction.

[0025] Mapping the control parameters of each execution device into an execution trajectory curve in the execution space coordinate system, calculating the interaction relationship between devices based on the execution trajectory curve, performing a hierarchical cluster analysis on the strength of the interaction relationship, and establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit includes:

[0026] Constructing an execution space coordinate system, mapping the change trend of the control parameter in the time dimension into an execution trajectory curve in the execution space coordinate system, calculating the minimum Euclidean distance between different execution trajectory curves, and using the minimum Euclidean distance as a quantitative indicator of the strength of the interaction relationship between devices;

[0027] Performing a hierarchical cluster analysis on the interaction relationship strength, grouping devices whose interaction relationship strength is less than a first strength threshold into a low-coupling execution unit, grouping devices whose interaction relationship strength is greater than the first strength threshold and less than a second strength threshold into a medium-coupling execution unit, and grouping devices whose interaction relationship strength is greater than the second strength threshold into a low-coupling execution unit;

[0028] Collecting dynamic response characteristics of devices in each execution unit respectively, the dynamic response characteristics including response time, adjustment accuracy and stabilization time, and weighting the dynamic response characteristics to calculate the comprehensive performance index of the execution unit;

[0029] A priority weight is allocated to each execution unit according to the comprehensive performance index, and the priority weight increases monotonically with the comprehensive performance index, thereby constructing a dynamic scheduling strategy based on a priority competition mechanism.

[0030] The responding lower-level controller automatically adjusts the execution progress. Other lower-level controllers synchronously adjust their control parameters based on the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices, including:

[0031] Calculating the ratio of the actual execution progress of each lower controller to the preset target progress to obtain a standardized execution progress value, calculating the average value of the standardized execution progress value, and taking the difference between the standardized execution progress value of each lower controller and the average value as the progress deviation;

[0032] When a progress deviation of a certain lower-level controller exceeds a first progress threshold, a progress compensation coefficient is calculated based on the progress deviation. The progress compensation coefficient is calculated using a linear relationship when the progress deviation is less than a second progress threshold, and is calculated using an exponential relationship when the progress deviation is greater than the second progress threshold. The progress compensation coefficient is multiplied by the current control parameter to obtain a compensated control parameter.

[0033] A progress adjustment instruction is generated based on the compensated control parameters. When the progress deviation is positive, the progress acceleration adjustment is achieved by reducing the control cycle and increasing the control gain value. When the progress deviation is negative, the progress deceleration adjustment is achieved by increasing the control cycle and reducing the control gain value, thereby ensuring the synchronization of the execution progress of multiple devices.

[0034] The lower controller includes at least one of a hydraulic system controller, a heating system controller, a displacement system controller, and a material supply system controller.

[0035] The hydraulic system controller controls the hydraulic system to adjust the molding pressure according to the molding cavity pressure parameter, the heating system controller adjusts the heating temperature according to the molding cavity temperature parameter, the displacement system controller adjusts the pressure head position according to the pressure head displacement parameter, and the material supply system controller adjusts the feeding speed according to the material supply rate parameter, thereby realizing coordinated control of the composite high-pressure forming process.

[0036] A second aspect of an embodiment of the present invention provides a composite high pressure forming control system based on communication interaction, comprising:

[0037] The first unit is used to collect real-time data from a multimodal sensor of a composite high-pressure forming equipment and transmit the real-time data to a central controller via industrial Ethernet;

[0038] The second unit is used for the central controller to calculate the process parameter deviation value of the composite high pressure forming process according to the real-time data. The central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the change trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of parameters.

[0039] A third unit is configured to dynamically adjust the process parameters in real time based on the compensation correction amount to generate compensation process parameters, establish a priority dynamic scheduling strategy and a device execution schedule based on the compensation process parameters, and generate a multi-device collaborative control instruction by combining the priority dynamic scheduling strategy and the device execution schedule;

[0040] The fourth unit is used for the central controller to send the multi-device collaborative control instructions to the lower controllers through the industrial Ethernet. The lower controllers exchange their respective execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the responding lower controller automatically adjusts the execution progress, and the other lower controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

[0041] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0042] processor;

[0043] a memory for storing processor-executable instructions;

[0044] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0045] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0046] The beneficial effects of this application are as follows:

[0047] The communication-interaction-based composite high-pressure forming control method provided by the present invention achieves precise control of the high-pressure forming process by collecting real-time data from multimodal sensors and dynamically adjusting process parameters, greatly improving the quality stability and forming accuracy of composite products, reducing product defective rates, and improving production efficiency.

[0048] By introducing an adaptive adjustment function in the form of a piecewise continuous function and a parameter coupling influence matrix, this method can comprehensively consider the mutual influence relationship between process parameters, avoid the system instability problem caused by single parameter adjustment, realize the coordinated optimization and control of multiple parameters under complex process conditions, and enhance the system's adaptability to different materials and process changes.

[0049] The method of the present invention establishes a multi-device collaborative control mechanism based on a priority dynamic scheduling strategy and an equipment execution schedule, and exchanges execution status information through a real-time communication bus between lower-level controllers, thereby realizing automatic coordination and synchronous adjustment between devices, significantly improving the stability and reliability of the entire forming system, and providing effective technical support for the intelligent and networked production of composite high-pressure forming. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of the flow of a composite high pressure forming control method based on communication interaction according to an embodiment of the present invention;

[0051] Figure 2 A diagram showing the performance comparison of progress synchronization on multiple devices. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0053] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0054] Figure 1 FIG. 1 is a flow chart of a composite high pressure forming control method based on communication interaction according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0055] Collecting real-time data from multimodal sensors of composite high-pressure forming equipment and transmitting the real-time data to a central controller via industrial Ethernet;

[0056] The central controller calculates the process parameter deviation value of the composite high pressure forming process based on the real-time data. The central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the change trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of parameters.

[0057] Dynamically adjusting the process parameters in real time based on the compensation correction amount to generate compensation process parameters, establishing a priority dynamic scheduling strategy and a device execution schedule based on the compensation process parameters, and generating a multi-device collaborative control instruction by combining the priority dynamic scheduling strategy and the device execution schedule;

[0058] The central controller sends the multi-device collaborative control instructions to the lower controllers through the industrial Ethernet. The lower controllers exchange their respective execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the responding lower controller automatically adjusts the execution progress, and the other lower controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

[0059] In an optional embodiment, the central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the variation trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of the parameters, including:

[0060] Performing a time series analysis on the process parameter deviation value, and calculating the deviation change rate of the process parameter deviation value within a plurality of consecutive sampling periods;

[0061] Based on the historical data of the process parameter deviation value, the weighted moving average prediction method is used to calculate the predicted deviation value of the next sampling period.

[0062] Calculating a dynamic adjustment coefficient according to a preset adaptive adjustment function, wherein the adaptive adjustment function adopts a piecewise continuous function form, adopts a linear growth mode of adjustment when the predicted deviation value is less than a first deviation threshold, adopts an exponential growth mode of adjustment when the predicted deviation value is greater than the first deviation threshold and less than a second deviation threshold, and adopts a saturation limit mode of adjustment when the predicted deviation value is greater than the second deviation threshold;

[0063] Calculating a proportional coefficient and a differential coefficient according to the dynamic adjustment coefficient, multiplying the proportional coefficient by the predicted deviation value to obtain a proportional term, and multiplying the differential coefficient by the deviation change rate to obtain a differential term;

[0064] Conduct cross-response tests on each process parameter to obtain the degree of mutual influence between the parameters and establish a coupling influence matrix. The diagonal elements of the coupling influence matrix are unity, and the off-diagonal elements range from zero to one, representing the coupling strength between different parameters.

[0065] The superposition value of the proportional term and the differential term is used to form a compensation vector, and the compensation vector is multiplied by the coupling influence matrix to obtain a final compensation amount considering the parameter coupling effect.

[0066] During implementation, the central controller first obtains the real-time and target values of each process parameter and calculates the process parameter deviation. For example, if the target temperature of a production device is 150°C and the real-time measured temperature is 153°C, the temperature parameter deviation is 3°C. Similarly, if the target value of the pressure parameter is 2.5 MPa and the real-time value is 2.3 MPa, the deviation is -0.2 MPa.

[0067] The central controller performs a time-series analysis of process parameter deviations, calculating the rate of change of these deviations over multiple consecutive sampling periods. Specifically, the system continuously collects temperature parameter data at a 500-millisecond sampling period, recording the temperature deviations for the ten most recent sampling points as follows: 3.0°C, 3.2°C, 3.3°C, 3.5°C, 3.6°C, 3.8°C, 3.9°C, 4.0°C, 4.1°C, and 4.2°C. By calculating the difference in deviations between adjacent sampling points and dividing it by the sampling interval, the resulting rate of change is 0.24°C / second, indicating an increasing temperature deviation trend.

[0068] Based on historical data on process parameter deviations, the central controller uses a weighted moving average prediction method to calculate the predicted deviation for the next sampling period. This method assigns higher weights to recent data and lower weights to more distant data. In practice, the ten most recent temperature deviations are assigned weight coefficients, from recent to recent: 0.25, 0.20, 0.15, 0.10, 0.08, 0.07, 0.06, 0.04, 0.03, and 0.02. The weighted average of these weight coefficients and the corresponding deviation values yields a predicted temperature deviation of 4.32°C for the next sampling period.

[0069] The dynamic adjustment coefficient is calculated according to the preset adaptive adjustment function. The adaptive adjustment function adopts the form of a piecewise continuous function and adopts different adjustment strategies according to the size of the predicted deviation value. Assume that the preset first deviation threshold is 2.5°C and the second deviation threshold is 5.0°C. Since the temperature prediction deviation value of 4.32°C is greater than the first deviation threshold and less than the second deviation threshold, an exponential growth method is used for adjustment. Specifically, the dynamic adjustment coefficient can be obtained by multiplying the base coefficient by the exponential function of the predicted deviation value. For example, the dynamic adjustment coefficient of the temperature parameter is calculated as 1.2×(4.32 / 2.5)^1.5=2.51.

[0070] The central controller calculates the proportional coefficient and differential coefficient based on the dynamic adjustment coefficient. The proportional coefficient directly uses the dynamic adjustment coefficient, which is 2.51. The differential coefficient is calculated by multiplying the dynamic adjustment coefficient by the preset differential gain factor of 0.3, which is 0.753. The proportional term is multiplied by the predicted deviation value, which is 2.51 × 4.32 = 10.84. The differential term is multiplied by the deviation change rate, which is 0.753 × 0.24 = 0.18.

[0071] The system conducts cross-response tests on various process parameters to determine the degree of mutual influence between them and establish a coupling influence matrix. In a production system involving three process parameters—temperature, pressure, and flow—the coupling relationships between these three parameters were determined experimentally. The specific method involved varying the setpoint of each parameter and observing the magnitude of the response changes in the other parameters. The experimental results show that the influence coefficient of temperature change on pressure is 0.4 and on flow is 0.2; the influence coefficient of pressure change on temperature is 0.3 and on flow is 0.6; and the influence coefficient of flow change on temperature is 0.1 and on pressure is 0.5. The resulting coupling influence matrix is as follows: temperature on temperature is 1, temperature on pressure is 0.4, and temperature on flow is 0.2; pressure on temperature is 0.3, pressure on pressure is 1, and pressure on flow is 0.6; flow on temperature is 0.1, flow on pressure is 0.5, and flow on flow is 1.

[0072] The compensation vector is formed by adding the proportional and differential terms. For the temperature parameter, the compensation vector is 10.84 + 0.18 = 11.02. Similarly, the compensation vector for the pressure parameter is calculated to be -3.06, and the compensation vector for the flow parameter is 2.15. Multiplying the compensation vector by the coupling influence matrix yields the final compensation value, which accounts for the parameter coupling effects. The specific calculation process involves multiplying the temperature compensation vector (11.02) by the temperature influence coefficients on temperature, pressure, and flow, respectively, to obtain the temperature-related compensation value for each parameter. Similarly, the compensation values for pressure and flow are calculated for each parameter. Finally, all compensation values for the same parameter are summed to obtain the final compensation value. The final compensation value for the temperature parameter is 10.21°C, the final compensation value for the pressure parameter is -1.47 MPa, and the final compensation value for the flow parameter is -0.32 m³ / h.

[0073] Table 1: Process parameter deviation compensation experimental data record table:

[0074]

[0075] Table 1 shows the operational process and results of the piecewise continuous function-based adaptive control system. The table records the control data for two key process parameters over 10 sampling periods. The data shows that, initially, the deviations for process parameters 1 and 2 were 5.20 and 3.15, respectively. As the control system operated, these deviations gradually decreased, ultimately stabilizing at around 0.10.

[0076] The system dynamically adjusts the control coefficient by calculating the rate of change of the deviation, predicting the deviation value for the next cycle, and incorporating an adaptive regulation function. When the deviation is large (>4.5), the system adopts saturation limiting; when the deviation is in the intermediate range (2.5-4.5), it adopts exponential growth; and when the deviation is small (<2.5), it adopts linear regulation. This strategy allows the regulation coefficient to peak at 1.20 in the medium term and then gradually decrease as the deviation decreases.

[0077] The central controller applies the final compensation value to adjust and control the process parameters, achieving precise compensation for each parameter. Through this adaptive adjustment method and consideration of parameter coupling effects, the system can adjust each process parameter to the target value more quickly and accurately, improving the stability of the production process and product quality.

[0078] In an optional embodiment, the process parameters are dynamically adjusted in real time based on the compensation correction amount to generate compensation process parameters, a priority dynamic scheduling strategy and a device execution schedule are established based on the compensation process parameters, and the generation of multi-device collaborative control instructions by combining the priority dynamic scheduling strategy and the device execution schedule includes:

[0079] Adding the compensation correction amount to the current process parameters respectively to obtain the compensated process parameters, wherein the compensated process parameters include the compensated molding cavity pressure parameter, the molding cavity temperature parameter, the pressure head displacement parameter and the material supply rate parameter;

[0080] establishing a multidimensional execution space coordinate system based on the compensation process parameters, mapping the control parameters of each execution device into an execution trajectory curve in the execution space coordinate system, calculating the interaction relationship between devices based on the execution trajectory curve, dividing a device combination with an interaction relationship strength greater than a first strength threshold into an execution unit, and establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit to determine the control timing of the execution devices;

[0081] Building a device execution timing table based on the compensation process parameters, wherein the device execution timing table divides the execution cycle into multiple control time slots, each control time slot is assigned to a different execution device, and a buffer interval is set between adjacent control time slots. The buffer interval is adaptively adjusted according to the dynamic response characteristics of the process parameters to eliminate mutual interference during device switching;

[0082] The priority dynamic scheduling strategy and the device execution timing table are combined to generate a multi-device collaborative control instruction.

[0083] In this embodiment, the system dynamically adjusts the process parameters in real time based on the compensation correction amount to generate compensation process parameters, and establishes priority dynamic scheduling strategies and equipment execution timing tables based on the compensation process parameters, and finally combines the two to generate multi-device collaborative control instructions.

[0084] The system first adds the compensation correction amount to the current process parameters to obtain the compensated process parameters. Specifically, the system compensates for the molding cavity pressure parameters. For example, if the current pressure is 25MPa and the compensation correction amount is +2MPa, the compensated pressure parameter is 27MPa. It also compensates for the molding cavity temperature parameters. For example, if the current temperature is 180℃ and the compensation correction amount is -5℃, the compensated temperature parameter is 175℃. It also compensates for the pressure head displacement parameters. For example, if the current displacement is 10mm and the compensation correction amount is +0.3mm, the compensated displacement parameter is 10.3mm. It also compensates for the material feed rate parameters. For example, if the current feed rate is 25g / s and the compensation correction amount is -2g / s, the compensated material feed rate is 23g / s. These compensated process parameters will be used for the subsequent division of execution units and the construction of timing tables.

[0085] Based on the compensated process parameters, the system establishes a multidimensional execution space coordinate system. This coordinate system uses temperature, pressure, displacement, and material feed rate as coordinate axes, forming a four-dimensional space. The system maps the control parameters of each actuator into an execution trajectory curve within this execution space coordinate system. For example, the trajectory of a pressure control device in the coordinate system might change from (180°C, 20MPa, 10mm, 25g / s) to (175°C, 27MPa, 10.3mm, 23g / s). The trajectory of a temperature control device in the coordinate system might change from (180°C, 25MPa, 10mm, 25g / s) to (175°C, 25MPa, 10mm, 25g / s). Based on these execution trajectory curves, the system calculates the interaction relationships between the devices. The strength of the interaction relationship is calculated by combining trajectory overlap, parameter change rate correlation, and execution time overlap, with a value ranging from 0 to 1. For example, the calculated interaction strength between the pressure control device and the temperature control device is 0.85, exceeding the system's preset first strength threshold of 0.7. Therefore, these two devices are classified as one execution unit. However, the calculated interaction strength between the material supply device and the pressure head displacement control device is 0.35, below the first strength threshold, so they are divided into different execution units.

[0086] Based on the response characteristics of the execution units, the system establishes a priority dynamic scheduling strategy. Response characteristics include parameters such as the execution unit's response time, stabilization time, and overshoot. For example, the temperature and pressure control execution unit has a response time of 2.5 seconds, a stabilization time of 4 seconds, and an overshoot of 3%, while the displacement control execution unit has a response time of 0.8 seconds, a stabilization time of 1.5 seconds, and an overshoot of 1%. The system assigns a priority to each execution unit based on these characteristics, with execution units with shorter response times receiving higher priority. In this example, the displacement control execution unit receives priority 1, the temperature and pressure control execution unit receives priority 2, and the material supply execution unit receives priority 3. This priority assignment ensures that devices with faster responses receive priority execution, thereby improving the overall system response speed.

[0087] The system constructs a timetable for equipment execution based on the compensated process parameters. This table divides a 10-second execution cycle into multiple control time slots, for example, five control time slots, each 1.5 seconds long, for a total of 7.5 seconds, leaving 2.5 seconds for a buffer. Based on the previously determined priorities, the system allocates the first time slot (0-1.5 seconds) to the displacement control execution unit, the second and third time slots (1.8-4.8 seconds, with a 0.3-second buffer interval) to the temperature and pressure control execution units, and the fourth and fifth time slots (5.2-8.2 seconds, with a 0.4-second buffer interval) to the material supply execution unit. The buffer interval between adjacent control time slots is adaptively adjusted based on the dynamic response characteristics of the process parameters. For example, if the system detects that the pressure fluctuation caused by temperature parameter changes exceeds expectations, it increases the buffer interval between temperature and pressure control from 0.3 seconds to 0.5 seconds to eliminate mutual interference.

[0088] Finally, the system integrates the priority dynamic scheduling strategy and the device execution schedule to generate multi-device collaborative control instructions. These instructions contain the specific actions and parameters that each execution device should perform at a specific time point. For example, at time 0 seconds, the system sends an instruction to the displacement control device to adjust the ram displacement from 10mm to 10.3mm; at time 1.8 seconds, the system sends an instruction to the temperature control device to reduce the cavity temperature from 180°C to 175°C; at the same time, it sends an instruction to the pressure control device to increase the cavity pressure from 25MPa to 27MPa; and at time 5.2 seconds, the system sends an instruction to the material supply device to reduce the supply rate from 25g / s to 23g / s. These collaborative control instructions ensure that each device works together according to optimized timing and parameters, thereby improving molding quality and efficiency.

[0089] In an optional embodiment, mapping the control parameters of each execution device into an execution trajectory curve in the execution space coordinate system, calculating the interaction relationship between devices based on the execution trajectory curve, performing a hierarchical cluster analysis on the strength of the interaction relationship, and establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit includes:

[0090] Constructing an execution space coordinate system, mapping the change trend of the control parameter in the time dimension into an execution trajectory curve in the execution space coordinate system, calculating the minimum Euclidean distance between different execution trajectory curves, and using the minimum Euclidean distance as a quantitative indicator of the strength of the interaction relationship between devices;

[0091] Performing a hierarchical cluster analysis on the interaction relationship strength, grouping devices whose interaction relationship strength is less than a first strength threshold into a low-coupling execution unit, grouping devices whose interaction relationship strength is greater than the first strength threshold and less than a second strength threshold into a medium-coupling execution unit, and grouping devices whose interaction relationship strength is greater than the second strength threshold into a low-coupling execution unit;

[0092] Collecting dynamic response characteristics of devices in each execution unit respectively, the dynamic response characteristics including response time, adjustment accuracy and stabilization time, and weighting the dynamic response characteristics to calculate the comprehensive performance index of the execution unit;

[0093] A priority weight is allocated to each execution unit according to the comprehensive performance index, and the priority weight increases monotonically with the comprehensive performance index, thereby constructing a dynamic scheduling strategy based on a priority competition mechanism.

[0094] First, an execution space coordinate system is constructed. This multidimensional coordinate system can include multiple dimensions such as time, temperature, pressure, and flow. For example, in an industrial automation system, a three-dimensional execution space coordinate system can be constructed, with the X-axis representing time, the Y-axis representing temperature, and the Z-axis representing pressure. For each execution device, control parameter data is collected over a specific time period. For example, for a heating device, the set temperature and actual temperature values are recorded every 10 minutes over 24 hours. The temporal trends of these control parameters are mapped to the constructed execution space coordinate system, forming a continuous execution trajectory curve. For example, the temperature control parameter of a heating device may gradually increase from 100°C to 150°C and then decrease to 120°C. This process forms a curve in the execution space.

[0095] In order to calculate the minimum Euclidean distance between different execution trajectory curves, the system samples the curves in the execution space to obtain a discrete point set. For each pair of sampling points of the two execution trajectory curves, their Euclidean distance in the execution space is calculated. In practical applications, 100 evenly distributed sampling points can be selected to calculate the Euclidean distance of the corresponding time points of the two curves, and the minimum value is taken as the minimum Euclidean distance between the two curves. For example, in a test scenario, the minimum Euclidean distance between the execution trajectory curves of device A and device B is 2.5 units, while the minimum Euclidean distance between device A and device C is 8.7 units, which indicates that the strength of the interaction relationship between device A and device B is greater than the strength of the interaction relationship between device A and device C.

[0096] When performing hierarchical cluster analysis on the strength of the interaction relationship, the system classifies according to two pre-set thresholds. In practical applications, the first strength threshold can be set to 3.0 units, and the second strength threshold can be set to 7.0 units. When the minimum Euclidean distance of the execution trajectory curves of two devices is less than 3.0 units, the two devices are combined into a high-coupling execution unit, indicating that there is a strong interaction relationship between them. When the minimum Euclidean distance is greater than 3.0 units but less than 7.0 units, the two devices are combined into a medium-coupling execution unit. When the minimum Euclidean distance is greater than 7.0 units, the two devices are combined into a low-coupling execution unit, indicating that the interaction relationship between them is weak.

[0097] In this way, a system containing 10 execution devices may form two highly coupled execution units, three moderately coupled execution units, and one low-coupled execution unit. For example, the minimum Euclidean distances of devices A, B, and C are all less than 3.0 units, so they are combined into a highly coupled execution unit. The minimum Euclidean distance of devices D and E is 2.8 units, so they are also combined into a highly coupled execution unit. The minimum Euclidean distance of devices F, G, and H is between 3.0 and 7.0 units, so they are combined into three moderately coupled execution units. The minimum Euclidean distance of devices I and J is greater than 7.0 units, so they are combined into a low-coupled execution unit.

[0098] For each execution unit, the system collects the dynamic response characteristics of its internal devices. Dynamic response characteristics include three indicators: response time, adjustment accuracy, and stabilization time. Response time refers to the time from receiving a control command to the start of execution, typically measured in milliseconds; adjustment accuracy refers to the degree of deviation between the actual execution parameters and the target parameters, typically expressed as a percentage; and stabilization time refers to the time from the start of adjustment to reaching a stable state, typically measured in seconds.

[0099] In a specific example, the average response time of high-coupling execution unit 1 (including devices A, B, and C) is 50 milliseconds, the average adjustment accuracy is 98.5%, and the average stabilization time is 2.3 seconds; the average response time of high-coupling execution unit 2 (including devices D and E) is 30 milliseconds, the average adjustment accuracy is 99.2%, and the average stabilization time is 1.8 seconds; the average dynamic response characteristics of the medium-coupling execution unit are between the high-coupling and low-coupling execution units; the average response time of the low-coupling execution unit (including devices I and J) is 100 milliseconds, the average adjustment accuracy is 95.0%, and the average stabilization time is 5.0 seconds.

[0100] These dynamic response characteristics are weighted to obtain the comprehensive performance index of the execution unit. This weighted calculation is performed by multiplying the inverse of the response time by a weight of 0.3, multiplying the adjustment accuracy by a weight of 0.4, and multiplying the inverse of the settling time by a weight of 0.3. The three are then added together. For example, the comprehensive performance index of high-coupling execution unit 1 is: 0.3 × (1 / 50) + 0.4 × 98.5% + 0.3 × (1 / 2.3) = 0.456; the comprehensive performance index of high-coupling execution unit 2 is: 0.3 × (1 / 30) + 0.4 × 99.2% + 0.3 × (1 / 1.8) = 0.573; and the comprehensive performance index of the low-coupling execution unit is: 0.3 × (1 / 100) + 0.4 × 95.0% + 0.3 × (1 / 5.0) = 0.383.

[0101] According to the calculated comprehensive performance index, a priority weight is assigned to each execution unit. The priority weight is proportional to the comprehensive performance index, that is, the higher the comprehensive performance index, the greater the priority weight. In a specific implementation, the comprehensive performance index can be directly normalized as the priority weight, or the comprehensive performance index can be mapped to a predefined weight range. For example, the comprehensive performance index calculated above is mapped to an integer weight range of 1 to 10, the priority weight of the high-coupling execution unit 2 is 10, the priority weight of the high-coupling execution unit 1 is 8, and the priority weight of the low-coupling execution unit is 6.

[0102] Based on these priority weights, a dynamic scheduling strategy is constructed. In the event of resource competition, execution units with higher priorities are given priority for computing and execution resources. At the same time, the system dynamically adjusts priority weights based on the real-time performance of the execution units, ensuring that the scheduling strategy can adapt to system changes. For example, if the response time of highly coupled execution unit 1 increases from 50 milliseconds to 40 milliseconds, its overall performance index will also improve accordingly, and its priority weight may increase from 8 to 9. This dynamic adjustment mechanism enables more efficient resource allocation and task scheduling.

[0103] In an optional embodiment, the responding lower-level controller automatically adjusts the execution progress, and the other lower-level controllers synchronously adjust their respective control parameters according to the adjusted execution progress, thereby ensuring the synchronization of the execution progress of multiple devices. The steps include:

[0104] Calculating the ratio of the actual execution progress of each lower controller to the preset target progress to obtain a standardized execution progress value, calculating the average value of the standardized execution progress value, and taking the difference between the standardized execution progress value of each lower controller and the average value as the progress deviation;

[0105] When a progress deviation of a certain lower-level controller exceeds a first progress threshold, a progress compensation coefficient is calculated based on the progress deviation. The progress compensation coefficient is calculated using a linear relationship when the progress deviation is less than a second progress threshold, and is calculated using an exponential relationship when the progress deviation is greater than the second progress threshold. The progress compensation coefficient is multiplied by the current control parameter to obtain a compensated control parameter.

[0106] A progress adjustment instruction is generated based on the compensated control parameters. When the progress deviation is positive, the progress acceleration adjustment is achieved by reducing the control cycle and increasing the control gain value. When the progress deviation is negative, the progress deceleration adjustment is achieved by increasing the control cycle and reducing the control gain value, thereby ensuring the synchronization of the execution progress of multiple devices.

[0107] The system architecture comprises multiple lower-level controllers, each responsible for controlling the operation of a single device. The central control module monitors the execution status of each lower-level controller in real time and issues adjustment instructions based on pre-set synchronization strategies. In practical implementation, the control network can utilize an RS-485 bus or Ethernet, with communication protocols supporting Modbus RTU or TCP / IP to ensure real-time and reliable data transmission.

[0108] The core of execution progress synchronization lies in the calculation and comparison of standardized progress values. The system obtains the actual execution progress of each lower-level controller, such as the percentage of workpieces completed by a processing device, or the number of trajectory points completed by a robotic arm. Suppose the actual progress of three devices is 48%, 52%, and 50%, respectively, while the preset target progress is 50%. The system divides the actual progress by the target progress to obtain standardized execution progress values of 0.96, 1.04, and 1.00, respectively. The system calculates the average of these standardized values, which is 1.00. The difference between the standardized progress value and the average value of each controller is then calculated, which is -0.04, 0.04, and 0.00, respectively. These differences are the progress deviation.

[0109] The progress compensation system employs a two-stage adjustment strategy. The system presets two key thresholds: the first progress threshold is set at 0.02, and the second progress threshold is set at 0.05. When the absolute value of a controller's progress deviation exceeds the first threshold (0.02), the system initiates the compensation mechanism. For example, if the progress deviation of the first device is -0.04, exceeding the first threshold, compensation is required; the progress deviation of the second device is 0.04, also requiring compensation; the third device does not require adjustment.

[0110] The calculation of the compensation coefficient is divided into two areas. When the absolute value of the progress deviation is less than the second threshold (0.05), a linear compensation relationship is adopted. The system multiplies the progress deviation by the preset linear compensation factor of 10 to obtain the compensation coefficient. Taking the first device as an example, its progress deviation is -0.04, and the calculated compensation coefficient is -0.04×10=-0.4, which means that the current control parameters need to be adjusted by 1-0.4=0.6 times. The compensation coefficient of the second device is 0.04×10=0.4, which means that the current control parameters need to be adjusted by 1+0.4=1.4 times.

[0111] When the absolute value of the progress deviation exceeds the second threshold (0.05), an exponential relationship is used to calculate the compensation coefficient, achieving a stronger adjustment force. The exponential relationship can be expressed as follows: subtract the second threshold from the absolute value of the progress deviation to obtain the excess, then multiply this excess by the exponential base (set to 20), and finally add the maximum compensation value in the linear region, 0.5. For example, if the progress deviation of a device is 0.07, exceeding the second threshold of 0.05, the excess is 0.02, and the exponential compensation coefficient is 0.5 + 0.02 × 20 = 0.9, resulting in a final compensation coefficient of 1 + 0.9 = 1.9 times.

[0112] Control parameter adjustments employ different strategies depending on the positive or negative nature of the progress deviation. When the progress deviation is negative (e.g., -0.04 for the first device), it indicates slow execution. The system slows down the device by increasing the control cycle and decreasing the control gain. Specifically, the control cycle was increased from 100 milliseconds to 100 ÷ 0.6 = 167 milliseconds, and the control gain was reduced from the original value of 1.2 to 1.2 × 0.6 = 0.72. When the progress deviation is positive (e.g., 0.04 for the second device), it indicates fast execution. The system speeds up the device by decreasing the control cycle and increasing the control gain. The control cycle for the second device was reduced from 100 milliseconds to 100 ÷ 1.4 = 71 milliseconds, and the control gain was increased from 1.2 to 1.2 × 1.4 = 1.68.

[0113] During system implementation, a smoothing adjustment mechanism was also implemented to prevent system oscillations caused by overly drastic compensation. This smoothing adjustment incorporates a moving average of historical compensation values, taking a weighted average of the currently calculated compensation coefficient and the historical compensation coefficients, with a weighting ratio of 7:3. For example, if the historical compensation coefficient is 0.8 and the currently calculated compensation coefficient is 0.6, the final applied compensation coefficient is 0.6 × 0.7 + 0.8 × 0.3 = 0.66.

[0114] Through continuous real-time monitoring and dynamic adjustments, the system can maintain synchronization of the execution schedules of multiple devices under varying operating conditions and loads. Practice has shown that this approach has reduced the schedule synchronization deviation of the three devices from ±4% to within ±1%, significantly improving the system's collaborative efficiency and product quality stability.

[0115] Figure 2 The following figure shows a comparison of the progress synchronization performance of multiple devices. As can be seen, our method exhibits the lowest progress deviation rate in all test scenarios, ranging from 0.7% to 1.3%. The basic process synchronization method is second, with a progress deviation rate between 2.3% and 3.9%. The traditional control method has the highest progress deviation rate, ranging from 4.2% to 7.1%. In particular, in the batch material synchronization scenario, our method achieves a progress deviation rate of only 1.3%, an approximately 82% reduction compared to the 7.1% of the traditional method. This fully demonstrates the significant technical effectiveness of our proposed adaptive adjustment algorithm combined with the parameter coupling influence matrix.

[0116] This technology has been applied in many automated production lines and is particularly suitable for scenarios requiring high-precision collaborative operations, such as multi-robot collaborative welding, multi-axis linkage processing, and precision assembly.

[0117] In an optional embodiment, the lower controller includes at least one of a hydraulic system controller, a heating system controller, a displacement system controller, and a material supply system controller.

[0118] The hydraulic system controller controls the hydraulic system to adjust the molding pressure according to the molding cavity pressure parameter, the heating system controller adjusts the heating temperature according to the molding cavity temperature parameter, the displacement system controller adjusts the pressure head position according to the pressure head displacement parameter, and the material supply system controller adjusts the feeding speed according to the material supply rate parameter, thereby realizing coordinated control of the composite high-pressure forming process.

[0119] Exemplarily, the hydraulic system controller is used to control the hydraulic system to adjust the molding pressure according to the molding cavity pressure parameters. The hydraulic system controller receives the molding cavity pressure parameters sent by the upper controller, which define the pressure curve that needs to be applied during the high-pressure molding process of the composite material. For example, in the molding process of carbon fiber composite material sheets, the pressure curve can be set to increase to 15MPa at a rate of 5MPa / minute in the initial stage, maintain for 30 minutes, and then increase to 25MPa at a rate of 3MPa / minute and maintain for 60 minutes. The hydraulic system controller accurately adjusts the pressure applied to the molding cavity by controlling the output flow of the hydraulic pump and the opening of the pressure regulating valve. The hydraulic system controller is also equipped with a pressure feedback sensor that can monitor the actual pressure in the molding cavity in real time. When a deviation between the actual pressure and the target pressure is detected, the controller will automatically adjust the working parameters of the hydraulic system to eliminate the deviation and ensure precise control of the molding pressure. The pressure control accuracy can reach ±0.2MPa.

[0120] The heating system controller adjusts the heating temperature according to the molding cavity temperature parameters. The heating system controller receives the temperature parameters sent by the upper controller, which defines the temperature curve during the composite molding process. Taking epoxy resin-based composite materials as an example, the temperature curve can be set to rise from room temperature to 120°C at a rate of 3°C / minute, hold for 20 minutes, then rise to 180°C at a rate of 2°C / minute, hold for 90 minutes, and finally drop to room temperature at a rate of 1°C / minute. The heating system controller achieves precise control of the temperature in different areas of the molding cavity by controlling the power output of the multi-zone heater. Multiple temperature sensors are set inside the molding cavity to monitor the temperature at different locations in real time. The heating system controller adopts a PID control algorithm to dynamically adjust the power output of the heaters in each area according to the temperature feedback information to ensure temperature uniformity in the molding cavity. The temperature control accuracy can reach ±2°C.

[0121] The displacement system controller adjusts the position of the ram according to the ram displacement parameters. The displacement system controller receives the ram displacement parameters sent by the upper controller and controls the movement trajectory and speed of the ram. During the preforming stage of the composite material, the ram needs to be controlled to gradually descend at a speed of 0.5mm / second until it contacts the surface of the material; during the compaction stage, the ram needs to maintain a speed of 0.2mm / second and continue to press down to the predetermined position; during the curing stage, the ram position needs to be fine-tuned according to the shrinkage of the material to maintain constant pressure. The displacement system controller uses a servo motor and a high-precision displacement sensor to achieve precise control of the ram position, and the displacement control accuracy can reach ±0.05mm. The displacement system controller is also equipped with a force sensor, which can monitor the force applied by the ram in real time, judge the state changes of the material during the molding process based on the relationship between force and displacement, and adjust the control strategy in time.

[0122] The material supply system controller adjusts the feeding speed according to the material supply rate parameters. The material supply system controller receives the material supply rate parameters from the upper controller and controls the material delivery speed and quantity. For example, in the resin transfer molding process, the resin injection rate can be set to inject at a rate of 5ml / minute in the initial stage, then reduced to 3ml / minute when the mold is 50% full, and further reduced to 1ml / minute when the mold is 80% full to reduce bubbles and ensure filling uniformity. The material supply system controller uses precision metering pumps and flow sensors to achieve precise control of material flow, with a flow control accuracy of up to ±0.1ml / minute. At the same time, the material supply system controller also monitors the temperature and viscosity of the material and dynamically adjusts the feeding strategy based on these parameters.

[0123] The upper-level controller serves as the central hub of the entire system, coordinating the work of each lower-level controller. Based on the type of composite material, molding process requirements, and expected product quality, the upper-level controller formulates a molding plan, generates control parameters for each subsystem, and transmits these parameters to the corresponding lower-level controllers. During the molding process, the upper-level controller receives real-time feedback from each lower-level controller, including data such as pressure, temperature, displacement, and material supply. Based on this data, the upper-level controller assesses the molding process status and adjusts control parameters as necessary to ensure the molding process proceeds as expected.

[0124] By integrating these system controllers, the composite high-pressure forming system achieves precise control of the forming process, ensuring the stability and consistency of composite product quality. This control method is applicable to various composite high-pressure forming processes, including hot pressing, resin transfer molding, and prepreg molding, effectively improving production efficiency and product quality.

[0125] The composite high pressure forming control system based on communication interaction according to an embodiment of the present invention includes:

[0126] The first unit is used to collect real-time data from a multimodal sensor of a composite high-pressure forming equipment and transmit the real-time data to a central controller via industrial Ethernet;

[0127] The second unit is used for the central controller to calculate the process parameter deviation value of the composite high pressure forming process according to the real-time data. The central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the change trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of parameters.

[0128] A third unit is configured to dynamically adjust the process parameters in real time based on the compensation correction amount to generate compensation process parameters, establish a priority dynamic scheduling strategy and a device execution schedule based on the compensation process parameters, and generate a multi-device collaborative control instruction by combining the priority dynamic scheduling strategy and the device execution schedule;

[0129] The fourth unit is used for the central controller to send the multi-device collaborative control instructions to the lower controllers through the industrial Ethernet. The lower controllers exchange their respective execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the responding lower controller automatically adjusts the execution progress, and the other lower controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

[0130] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0131] processor;

[0132] a memory for storing processor-executable instructions;

[0133] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0134] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0135] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A composite high pressure forming control method based on communication interaction, characterized in that: include: Collecting real-time data from multimodal sensors of composite high-pressure forming equipment and transmitting the real-time data to a central controller via industrial Ethernet; The central controller calculates the process parameter deviation value of the composite high pressure forming process according to the real-time data. The central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the change trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of parameters, including: Performing a time series analysis on the process parameter deviation value, calculating the deviation change rate of the process parameter deviation value in a plurality of consecutive sampling periods; and calculating the predicted deviation value of the next sampling period using a weighted moving average prediction method based on the historical data of the process parameter deviation value. Calculating a dynamic adjustment coefficient according to a preset adaptive adjustment function, wherein the adaptive adjustment function adopts a piecewise continuous function form, adopts a linear growth mode of adjustment when the predicted deviation value is less than a first deviation threshold, adopts an exponential growth mode of adjustment when the predicted deviation value is greater than the first deviation threshold and less than a second deviation threshold, and adopts a saturation limit mode of adjustment when the predicted deviation value is greater than the second deviation threshold; A proportional coefficient and a differential coefficient are calculated based on the dynamic adjustment coefficient, the proportional coefficient is multiplied by the predicted deviation value to obtain a proportional term, and the differential coefficient is multiplied by the deviation change rate to obtain a differential term; a cross-response test is performed on each process parameter to obtain the degree of mutual influence between the parameters, and a coupling influence matrix is established, wherein the diagonal elements of the coupling influence matrix are unity and the non-diagonal elements have a value range of zero to one, representing the coupling strength between different parameters; the superposition value of the proportional term and the differential term is used to form a compensation vector, and the compensation vector is multiplied by the coupling influence matrix to obtain a final compensation amount that takes into account the parameter coupling effect; Dynamically adjusting the process parameters in real time based on the compensation correction amount to generate compensation process parameters, establishing a priority dynamic scheduling strategy and a device execution schedule based on the compensation process parameters, and generating a multi-device collaborative control instruction by combining the priority dynamic scheduling strategy and the device execution schedule; The central controller sends the multi-device collaborative control instructions to the lower controllers through the industrial Ethernet. The lower controllers exchange their respective execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the responding lower controller automatically adjusts the execution progress, and the other lower controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

2. The method according to claim 1, characterized in that Based on the compensation correction amount, the process parameters are dynamically adjusted in real time to generate compensation process parameters, a priority dynamic scheduling strategy and a device execution time sequence table are established based on the compensation process parameters, and a multi-device collaborative control instruction is generated by combining the priority dynamic scheduling strategy and the device execution time sequence table. Adding the compensation correction amount to the current process parameters respectively to obtain the compensated process parameters, wherein the compensated process parameters include the compensated molding cavity pressure parameter, the molding cavity temperature parameter, the pressure head displacement parameter and the material supply rate parameter; establishing a multidimensional execution space coordinate system based on the compensation process parameters, mapping the control parameters of each execution device into an execution trajectory curve in the execution space coordinate system, calculating the interaction relationship between devices based on the execution trajectory curve, dividing a device combination with an interaction relationship strength greater than a first strength threshold into an execution unit, and establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit to determine the control timing of the execution devices; Building a device execution timing table based on the compensation process parameters, wherein the device execution timing table divides the execution cycle into multiple control time slots, each control time slot is assigned to a different execution device, and a buffer interval is set between adjacent control time slots. The buffer interval is adaptively adjusted according to the dynamic response characteristics of the process parameters to eliminate mutual interference during device switching; The priority dynamic scheduling strategy and the device execution timing table are combined to generate a multi-device collaborative control instruction.

3. The method according to claim 2, characterized in that Mapping the control parameters of each execution device into an execution trajectory curve in the execution space coordinate system, calculating the interaction relationship between devices based on the execution trajectory curve, performing a hierarchical cluster analysis on the strength of the interaction relationship, and establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit includes: Constructing an execution space coordinate system, mapping the change trend of the control parameter in the time dimension into an execution trajectory curve in the execution space coordinate system, calculating the minimum Euclidean distance between different execution trajectory curves, and using the minimum Euclidean distance as a quantitative indicator of the strength of the interaction relationship between devices; Performing a hierarchical cluster analysis on the interaction relationship strength, grouping devices whose interaction relationship strength is less than a first strength threshold into a low-coupling execution unit, grouping devices whose interaction relationship strength is greater than the first strength threshold and less than a second strength threshold into a medium-coupling execution unit, and grouping devices whose interaction relationship strength is greater than the second strength threshold into a low-coupling execution unit; Collecting dynamic response characteristics of devices in each execution unit respectively, the dynamic response characteristics including response time, adjustment accuracy and stabilization time, and weighting the dynamic response characteristics to calculate the comprehensive performance index of the execution unit; A priority weight is allocated to each execution unit according to the comprehensive performance index, and the priority weight increases monotonically with the comprehensive performance index, thereby constructing a dynamic scheduling strategy based on a priority competition mechanism.

4. The method according to claim 1, wherein The responding lower-level controller automatically adjusts the execution progress. Other lower-level controllers synchronously adjust their control parameters based on the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices, including: Calculating the ratio of the actual execution progress of each lower controller to the preset target progress to obtain a standardized execution progress value, calculating the average value of the standardized execution progress value, and taking the difference between the standardized execution progress value of each lower controller and the average value as the progress deviation; When a progress deviation of a certain lower-level controller exceeds a first progress threshold, a progress compensation coefficient is calculated based on the progress deviation. The progress compensation coefficient is calculated using a linear relationship when the progress deviation is less than a second progress threshold, and is calculated using an exponential relationship when the progress deviation is greater than the second progress threshold. The progress compensation coefficient is multiplied by the current control parameter to obtain a compensated control parameter. A progress adjustment instruction is generated based on the compensated control parameters. When the progress deviation is positive, the progress acceleration adjustment is achieved by reducing the control cycle and increasing the control gain value. When the progress deviation is negative, the progress deceleration adjustment is achieved by increasing the control cycle and reducing the control gain value, thereby ensuring the synchronization of the execution progress of multiple devices.

5. The method according to claim 1, wherein The lower controller includes at least one of a hydraulic system controller, a heating system controller, a displacement system controller, and a material supply system controller. The hydraulic system controller controls the hydraulic system to adjust the molding pressure according to the molding cavity pressure parameter, the heating system controller adjusts the heating temperature according to the molding cavity temperature parameter, the displacement system controller adjusts the pressure head position according to the pressure head displacement parameter, and the material supply system controller adjusts the feeding speed according to the material supply rate parameter, thereby realizing coordinated control of the composite high-pressure forming process.

6. A composite high pressure forming control system based on communication interaction, used to implement the method according to any one of claims 1 to 5, characterized in that: include: The first unit is used to collect real-time data from a multimodal sensor of a composite high-pressure forming equipment and transmit the real-time data to a central controller via industrial Ethernet; The second unit is used for the central controller to calculate the process parameter deviation value of the composite high pressure forming process according to the real-time data. The central controller calculates the dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function according to the change trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with a coupling influence matrix that considers the mutual influence of parameters. A third unit is configured to dynamically adjust the process parameters in real time based on the compensation correction amount to generate compensation process parameters, establish a priority dynamic scheduling strategy and a device execution schedule based on the compensation process parameters, and generate a multi-device collaborative control instruction by combining the priority dynamic scheduling strategy and the device execution schedule; The fourth unit is used for the central controller to send the multi-device collaborative control instructions to the lower controllers through the industrial Ethernet. The lower controllers exchange their respective execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the responding lower controller automatically adjusts the execution progress, and the other lower controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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