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

Abstract

The invention provides a composite high-pressure forming control method and system based on communication interaction, and relates to the technical field of control, and the method comprises the steps: collecting the real-time data of a multi-modal sensor, calculating a parameter deviation value through a central controller according to the data, employing a segmented continuous function for adaptive adjustment, and combining a coupling influence matrix to calculate a compensation correction value, a priority dynamic scheduling strategy and an execution time sequence table are established to generate a cooperative control instruction, and the lower controllers interact execution states through a real-time communication bus and automatically adjust the execution states. The control precision and stability of the composite high-pressure forming process are improved, and the reliability of cooperative work of multiple devices is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a composite material 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] In the process of high-pressure molding 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, lack the ability to respond to parameter fluctuations in the molding process in real time, and are difficult to adapt to the impact of changes in material properties and environmental factors, resulting in unstable molding quality.

[0004] The defects and deficiencies of the existing technology are mainly reflected in the following aspects: First, the existing composite high-pressure forming control systems usually lack an effective multi-device collaborative control mechanism, and the information exchange between the devices is insufficient, making 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.

[0005] Secondly, traditional control methods mostly use simple PID control or fixed gain adjustment to adjust process parameters, which lacks consideration of 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

[0006] 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.

[0007] A first aspect of an embodiment of the present invention provides a composite high pressure forming control method based on communication interaction, comprising: 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 by 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 considering the mutual influence of parameters. Based on the compensation correction amount, the process parameters are adjusted in real time dynamically to generate compensated process parameters. A priority dynamic scheduling strategy and an equipment execution schedule are established respectively based on the compensated process parameters, and a multi-device collaborative control instruction is generated by integrating the priority dynamic scheduling strategy and the equipment execution schedule; The central controller sends the multi-device collaborative control instruction to the lower-level controllers respectively through the industrial Ethernet. The lower-level controllers interact their execution status information through a real-time communication bus. When it is detected that any execution status information exceeds a preset warning threshold, the corresponding lower-level controller automatically adjusts the execution progress, and other lower-level controllers synchronously adjust their control parameters according to the adjusted execution progress to ensure the synchronization of the multi-device execution progress.

[0008] The central controller calculates the dynamic adjustment coefficient by 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 by combining the coupling influence matrix considering the mutual influence of parameters, including: Conduct a timing analysis on the process parameter deviation value, and calculate the deviation change rate of the process parameter deviation value in multiple consecutive sampling periods; Based on the historical data of the process parameter deviation value, use the weighted moving average prediction method to calculate the predicted deviation value in the next sampling period, Calculate the dynamic adjustment coefficient according to a preset adaptive adjustment function. The adaptive adjustment function is in the form of a piecewise continuous function, and is adjusted in a linear growth manner when the predicted deviation value is less than the first deviation threshold, in an exponential growth manner when the predicted deviation value is greater than the first deviation threshold and less than the second deviation threshold, and in a saturation limiting manner when the predicted deviation value is greater than the second deviation threshold; Calculate the proportional coefficient and the differential coefficient respectively according to the dynamic adjustment coefficient, multiply the proportional coefficient by the predicted deviation value to obtain the proportional term, and multiply the differential coefficient by the deviation change rate to obtain the differential term; Conduct cross-response tests on each process parameter to obtain the degree of mutual influence between parameters, and establish a coupling influence matrix. The diagonal elements of the coupling influence matrix are ones, and the non-diagonal elements range from zero to one, representing the coupling strength between different parameters; Form a compensation vector with the superposition value of the proportional term and the differential term, and multiply the compensation vector by the coupling influence matrix to obtain the final compensation amount considering the parameter coupling effect.

[0009] Based on the compensation correction amount, the process parameters are adjusted in real time dynamically to generate compensated process parameters. Respectively, a priority dynamic scheduling strategy and an equipment execution time sequence table are established based on the compensated process parameters. Generating a multi-device collaborative control instruction by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table includes: Superimpose the compensation correction amount on the current process parameters respectively to obtain compensated process parameters, where the compensated process parameters include the compensated forming cavity pressure parameter, forming cavity temperature parameter, ram displacement parameter, and material supply rate parameter; Based on the compensated process parameters, a multi-dimensional execution space coordinate system is established. The control parameters of each execution device are mapped into execution trajectory curves in the execution space coordinate system. According to the execution trajectory curves, the interaction relationship between devices is calculated. Device combinations with interaction relationship strength greater than the first strength threshold are divided into one execution unit. A priority dynamic scheduling strategy is established based on the response characteristics of the execution unit to determine the control timing of the execution device; Based on the compensated process parameters, an equipment execution time sequence table is constructed. The equipment execution time sequence table divides the execution cycle into multiple control time slots, and each control time slot is assigned to a different execution device. A buffer interval time is set between adjacent control time slots, and the buffer interval time is adaptively adjusted according to the dynamic response characteristics of the process parameters to eliminate mutual interference during device switching; Generate a multi-device collaborative control instruction by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table.

[0010] Mapping the control parameters of each execution device into execution trajectory curves in the execution space coordinate system, calculating the interaction relationship between devices according to the execution trajectory curves, and performing hierarchical clustering analysis on the interaction relationship strength. Establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit includes: Construct an execution space coordinate system, map the change trend of the control parameters in the time dimension into an execution trajectory curve in the execution space coordinate system, calculate the minimum Euclidean distance between different execution trajectory curves, and use the minimum Euclidean distance as a quantization index of the interaction relationship strength between devices; Perform hierarchical clustering analysis on the interaction relationship strength. Combine device combinations with interaction relationship strength less than the first strength threshold into low-coupling execution units, combine device combinations with interaction relationship strength greater than the first strength threshold and less than the second strength threshold into medium-coupling execution units, and divide device combinations with interaction relationship strength greater than the second strength threshold into low-coupling execution units; Respectively collect the dynamic response characteristics of the devices in each execution unit. The dynamic response characteristics include response time, adjustment accuracy, and stabilization time, and perform weighted calculation on the dynamic response characteristics to obtain the comprehensive performance index of the execution unit; Allocate priority weights to each execution unit according to the comprehensive performance index, where the priority weight increases monotonically with the comprehensive performance index, and construct a dynamic scheduling strategy based on the priority competition mechanism.

[0011] The corresponding lower-level controller automatically adjusts the execution progress, and other lower-level controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices, including: Calculate the ratio of the actual execution progress of each lower-level controller to the preset target progress to obtain a normalized execution progress value, calculate the average value of the normalized execution progress values, and use the difference between the normalized execution progress value of each lower-level controller and the average value as the progress deviation amount; When the progress deviation amount of a certain lower-level controller exceeds the first progress threshold, calculate a progress compensation coefficient according to the progress deviation amount. The progress compensation coefficient is calculated according to a linear relationship when the progress deviation amount is less than the second progress threshold, and is calculated according to an exponential relationship when the progress deviation amount is greater than the second progress threshold. Multiply the progress compensation coefficient by the current control parameter to obtain a compensated control parameter; Generate a progress adjustment instruction according to the compensated control parameter. When the progress deviation amount is positive, achieve progress acceleration adjustment by reducing the control period and increasing the control gain value. When the progress deviation amount is negative, achieve progress deceleration adjustment by increasing the control period and reducing the control gain value to ensure the synchronization of the execution progress of multiple devices.

[0012] The lower-level 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 forming pressure according to the forming cavity pressure parameter, the heating system controller adjusts the heating temperature according to the forming cavity temperature parameter, the displacement system controller adjusts the position of the punch according to the punch displacement parameter, and the material supply system controller adjusts the feeding speed according to the material supply rate parameter to achieve the coordinated control of the composite material high-pressure forming process.

[0013] In the second aspect of the embodiments of the present invention, a composite material high-pressure forming control system based on communication interaction is provided, including: The first unit is used to collect real-time data of multi-modal sensors of composite material high-pressure forming equipment and transmit the real-time data to the central controller through industrial Ethernet; The second unit is used for the central controller to calculate the process parameter deviation value of the composite material high-pressure forming process according to the real-time data. The central controller adopts an adaptive adjustment function in the form of a piecewise continuous function to calculate the dynamic adjustment coefficient according to the change trend of the process parameter deviation value, and calculates the compensation correction amount of each process parameter in combination with considering the coupling influence matrix of parameter mutual influence. The third unit is used to perform real-time dynamic adjustment on process parameters based on the compensation correction amount, generate compensated process parameters, establish a priority dynamic scheduling strategy and a device execution schedule respectively based on the compensated process parameters, and generate a multi-device collaborative control instruction by integrating 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 instruction to the lower controller respectively through the industrial Ethernet. The lower controllers interact their execution status information through a real-time communication bus. When it is detected that any execution status information exceeds a preset warning threshold, the corresponding lower controller automatically adjusts the execution progress, and other lower controllers synchronously adjust their control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

[0014] In the third aspect of the embodiments of the present invention, an electronic device is provided, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0015] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0016] The beneficial effects of this application are as follows: The composite material high-pressure forming control method based on communication interaction provided by the present invention realizes the precise control of the high-pressure forming process by collecting real-time data of multi-modal sensors and dynamically adjusting process parameters, greatly improves the quality stability and forming accuracy of composite material products, reduces the product defect rate, and improves the production efficiency.

[0017] 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 collaborative optimization control of multiple parameters under complex process conditions, and enhance the adaptability of the system to different material and process changes.

[0018] The method of the present invention establishes a multi-device collaborative control mechanism based on a priority dynamic scheduling strategy and a device execution schedule, and realizes the automatic coordination and synchronous adjustment between devices through the real-time communication bus interaction of execution status information between lower controllers, significantly improves the stability and reliability of the entire forming system, and provides effective technical support for the intelligent and networked production of composite material high-pressure forming. Description of the Drawings

[0019] Figure 1 This is a schematic flowchart of the composite material high-pressure forming control method based on communication interaction according to an embodiment of the present invention; Figure 2 This is a schematic diagram for comparing the performance of multi-device execution progress synchronization. Specific Embodiments

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0022] Figure 1 This is a schematic flowchart of the composite material high-pressure forming control method based on communication interaction according to an embodiment of the present invention, as Figure 1 shown. The method includes: Collect real-time data of multi-modal sensors of the composite material high-pressure forming equipment, and transmit the real-time data to the central controller through the industrial Ethernet; The central controller calculates the process parameter deviation value of the composite material high-pressure forming process according to the real-time data. The central controller calculates the dynamic adjustment coefficient by 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 considering the coupling influence matrix of the mutual influence of parameters. Based on the compensation correction amount, perform real-time dynamic adjustment on the process parameters to generate compensated process parameters. Establish a priority dynamic scheduling strategy and an equipment execution time sequence table respectively based on the compensated process parameters, and generate a multi-device collaborative control instruction by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table; The central controller sends the multi-device collaborative control instruction to the lower-level controller through the industrial Ethernet respectively. The lower-level controllers interact their execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the corresponding lower-level controller automatically adjusts the execution progress, and other lower-level controllers synchronously adjust their control parameters according to the adjusted execution progress to ensure the synchronization of the multi-device execution progress.

[0023] In an alternative embodiment, the central controller calculates a dynamic adjustment coefficient using an adaptive adjustment function in the form of a piecewise continuous function based on the changing trend of the process parameter deviation value, and calculates the compensation correction amount for each process parameter by considering the coupling influence matrix of the mutual influence of parameters, including: Conduct a time series analysis on the process parameter deviation value to calculate the deviation change rate of the process parameter deviation value within multiple consecutive sampling periods; Based on the historical data of the process parameter deviation value, use a weighted moving average prediction method to calculate the predicted deviation value for the next sampling period, Calculate the dynamic adjustment coefficient according to a preset adaptive adjustment function, where the adaptive adjustment function is in the form of a piecewise continuous function, and is adjusted in a linear growth manner when the predicted deviation value is less than the first deviation threshold, adjusted in an exponential growth manner when the predicted deviation value is greater than the first deviation threshold and less than the second deviation threshold, and adjusted in a saturation limiting manner when the predicted deviation value is greater than the second deviation threshold; Calculate the proportional coefficient and the differential coefficient respectively according to the dynamic adjustment coefficient, multiply the proportional coefficient by the predicted deviation value to obtain the proportional term, and multiply the differential coefficient by the deviation change rate to obtain the differential term; Conduct a cross-response test on each process parameter to obtain the degree of mutual influence between parameters, and establish a coupling influence matrix. The diagonal elements of the coupling influence matrix are unity, and the non-diagonal elements range from zero to one, representing the coupling strength between different parameters; Form a compensation vector with the superimposed values of the proportional term and the differential term, and multiply the compensation vector by the coupling influence matrix to obtain the final compensation amount considering the parameter coupling effect.

[0024] In the specific implementation process, the central controller first obtains the real-time value and the target value of each process parameter, and calculates the process parameter deviation value. Taking the temperature parameter as an example, assume that the target temperature of a certain production equipment is 150 °C and the real-time measured temperature is 153 °C, then the temperature parameter deviation value is 3 °C. Similarly, the target value of the pressure parameter is obtained as 2.5 MPa, the real-time value is 2.3 MPa, and the deviation value is -0.2 MPa.

[0025] The central controller conducts a time series analysis on the process parameter deviation value and calculates the deviation change rate of the process parameter deviation value within multiple consecutive sampling periods. Specifically, the system continuously collects temperature parameter data at a sampling period of 500 milliseconds, and records the temperature deviation values of the last 10 sampling points as: 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, 4.2 °C. By calculating the difference between adjacent sampling point deviation values and dividing by the sampling time interval, the deviation change rate is obtained as 0.24 °C / second, indicating that the temperature deviation shows an upward trend.

[0026] Based on the historical data of process parameter deviation values, the central controller calculates the predicted deviation value for the next sampling period using the weighted moving average prediction method. The weighted moving average prediction assigns higher weights to recent data and lower weights to older data. In actual operation, weight coefficients can be assigned to the last 10 temperature deviation values, which are, from near to far: 0.25, 0.20, 0.15, 0.10, 0.08, 0.07, 0.06, 0.04, 0.03, 0.02. According to the weighted average calculation of these weight coefficients and the corresponding deviation values, the predicted temperature deviation value for the next sampling period is 4.32 °C.

[0027] Calculate the dynamic adjustment coefficient according to the preset adaptive adjustment function. This adaptive adjustment function adopts the form of a piecewise continuous function and uses different adjustment strategies according to the magnitude of the predicted deviation value. Suppose the preset first deviation threshold is 2.5 °C and the second deviation threshold is 5.0 °C. Since the temperature predicted deviation value of 4.32 °C is greater than the first deviation threshold and less than the second deviation threshold, exponential growth 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.

[0028] The central controller calculates the proportional coefficient and the differential coefficient respectively according to the dynamic adjustment coefficient. The proportional coefficient directly uses the dynamic adjustment coefficient, that 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. Multiply the proportional coefficient by the predicted deviation value to get the proportional term, that is, 2.51×4.32 = 10.84; multiply the differential coefficient by the deviation change rate to get the differential term, that is, 0.753×0.24 = 0.18.

[0029] The system conducts cross-response tests on each process parameter to obtain the degree of mutual influence between parameters and establish a coupling influence matrix. In a production system involving three process parameters of temperature, pressure, and flow rate, the coupling relationship between these three parameters is determined through experiments. The specific method is to change the set value of one parameter respectively and observe the response change amplitude of other parameters. The test results show that the influence coefficient of temperature change on pressure is 0.4 and on flow rate is 0.2; the influence coefficient of pressure change on temperature is 0.3 and on flow rate is 0.6; the influence coefficient of flow rate change on temperature is 0.1 and on pressure is 0.5. The coupling influence matrix constructed therefrom is as follows: temperature on temperature is 1, temperature on pressure is 0.4, temperature on flow rate is 0.2; pressure on temperature is 0.3, pressure on pressure is 1, pressure on flow rate is 0.6; flow rate on temperature is 0.1, flow rate on pressure is 0.5, flow rate on flow rate is 1.

[0030] The superimposed value of the proportional term and the derivative term forms a compensation vector. For the temperature parameter, the value of 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 rate parameter is 2.15. Multiply the compensation vector by the coupling influence matrix to obtain the final compensation amount considering the parameter coupling effect. The specific calculation process is to multiply the temperature compensation vector 11.02 by the influence coefficients of temperature on temperature, pressure, and flow rate respectively to obtain the compensation amounts of each parameter caused by temperature; similarly, calculate the compensation amounts of each parameter caused by pressure and flow rate, and finally add up all the compensation amounts of the same parameter to obtain the final compensation amount. After calculation, the final compensation amount for the temperature parameter is 10.21 °C, the final compensation amount for the pressure parameter is -1.47 MPa, and the final compensation amount for the flow rate parameter is -0.32 m³ / h.

[0031] Table 1: Record Table of Process Parameter Deviation Compensation Experiment Data:

[0032] As shown in Table 1, it shows the operation process and effect of the adaptive regulation system based on the piecewise continuous function. The table records the control process data of two key process parameters within 10 sampling periods. From the data, it can be seen that in the initial state, the deviation values of Process Parameter 1 and Process Parameter 2 are 5.20 and 3.15 respectively. As the control system operates, these deviation values gradually decrease and finally stabilize at around 0.10.

[0033] The system calculates the deviation change rate, predicts the deviation value in the next cycle, and combines the adaptive regulation function to dynamically adjust the control coefficient. When the deviation value is large (>4.5), the system adopts the saturation limiting method; when the deviation is in the middle region (2.5 - 4.5), the exponential growth method is adopted; when the deviation is small (<2.5), the linear regulation method is adopted. This strategy makes the regulation coefficient reach the peak value of 1.20 in the middle period and then gradually decreases as the deviation decreases.

[0034] The central controller applies the final compensation amount to the adjustment control of the process parameters, achieving precise compensation for each parameter. Through this method of adaptive regulation and considering the parameter coupling effect, the system can adjust each process parameter to the target value more quickly and accurately, improving the stability of the production process and the product quality.

[0035] In an alternative implementation manner, based on the compensation correction amount, the process parameters are adjusted dynamically in real time to generate compensated process parameters. A priority dynamic scheduling strategy and an equipment execution timing table are established respectively based on the compensated process parameters. The multi-device collaborative control instruction generated by integrating the priority dynamic scheduling strategy and the equipment execution timing table includes: Add the compensation correction amounts to the current process parameters respectively to obtain compensated process parameters, where the compensated process parameters include the compensated forming cavity pressure parameter, the forming cavity temperature parameter, the punch displacement parameter, and the material supply rate parameter; Based on the compensated process parameters, establish a multi-dimensional execution space coordinate system, map the control parameters of each execution device to the execution trajectory curve in the execution space coordinate system, calculate the interaction relationship between devices according to the execution trajectory curve, divide the device combinations with the interaction relationship strength greater than the first strength threshold into one execution unit, and establish a priority dynamic scheduling strategy based on the response characteristics of the execution unit to determine the control timing of the execution devices; Construct a device execution timing table based on the compensated process parameters. 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 time is set between adjacent control time slots. The buffer interval time is adaptively adjusted according to the dynamic response characteristics of the process parameters to eliminate the mutual interference during the device switching process; Generate a multi-device collaborative control instruction by integrating the priority dynamic scheduling strategy and the device execution timing table.

[0036] In this embodiment, the system makes real-time dynamic adjustments to the process parameters based on the compensation correction amounts, generates compensated process parameters, and respectively establishes a priority dynamic scheduling strategy and a device execution timing table based on the compensated process parameters, and finally generates a multi-device collaborative control instruction by integrating these two.

[0037] The system first adds the compensation correction amounts to the current process parameters respectively to obtain compensated process parameters. Specifically, the system compensates the forming cavity pressure parameter. For example, if the current pressure is 25 MPa and the compensation correction amount is +2 MPa, the compensated pressure parameter is 27 MPa; compensates the forming cavity temperature parameter. For example, if the current temperature is 180 °C and the compensation correction amount is -5 °C, the compensated temperature parameter is 175 °C; compensates the punch displacement parameter. For example, if the current displacement is 10 mm and the compensation correction amount is +0.3 mm, the compensated displacement parameter is 10.3 mm; compensates the material supply rate parameter. For example, if the current supply rate is 25 g / s and the compensation correction amount is -2 g / s, the compensated material supply rate is 23 g / s. These compensated process parameters will be used for the subsequent division of the execution unit and the construction of the timing table.

[0038] The system establishes a multi-dimensional execution space coordinate system based on compensation process parameters. This coordinate system uses temperature, pressure, displacement, and material supply rate as the respective coordinate axes, forming a four-dimensional space. The system maps the control parameters of each execution device into an execution trajectory curve in this execution space coordinate system. For example, the trajectory of the pressure control device in the coordinate system may be the change process from (180°C, 20 MPa, 10 mm, 25 g / s) to (175°C, 27 MPa, 10.3 mm, 23 g / s). The trajectory of the temperature control device in the coordinate system may be the change process from (180°C, 25 MPa, 10 mm, 25 g / s) to (175°C, 25 MPa, 10 mm, 25 g / s). Based on these execution trajectory curves, the system calculates the interaction relationship between devices. The strength of the interaction relationship is comprehensively calculated through the trajectory overlap degree, the correlation of parameter change rates, and the execution time overlap degree, with a value range of 0 to 1. For example, the strength of the interaction relationship between the pressure control device and the temperature control device is calculated as 0.85, exceeding the first strength threshold of 0.7 preset by the system. Therefore, these two devices are classified as one execution unit. The strength of the interaction relationship between the material supply device and the indenter displacement control device is calculated as 0.35, lower than the first strength threshold. Therefore, they are classified as different execution units.

[0039] Based on the response characteristics of the execution units, the system establishes a priority dynamic scheduling strategy. The response characteristics include parameters such as the response time, stabilization time, and overshoot of the execution units. For example, the response time of the temperature and pressure control execution unit is 2.5 seconds, the stabilization time is 4 seconds, and the overshoot is 3%; while the response time of the displacement control execution unit is 0.8 seconds, the stabilization time is 1.5 seconds, and the overshoot is 1%. The system assigns priorities to each execution unit according to these characteristics, and the execution unit with a shorter response time obtains a higher priority. In this example, the displacement control execution unit obtains priority 1, the temperature and pressure control execution unit obtains priority 2, and the material supply execution unit obtains priority 3. This priority assignment ensures that devices with fast response can be executed first, thereby improving the overall system response speed.

[0040] The system constructs an equipment execution time sequence table based on compensation process parameters. This time sequence table divides an execution cycle (such as 10 seconds) into multiple control time slots. For example, it divides it into 5 control time slots, each slot being 1.5 seconds, for a total of 7.5 seconds, and the remaining 2.5 seconds is used as the overall buffer time. According to 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 in the middle) 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 in the middle) to the material supply execution unit. The buffer interval time between adjacent control time slots is adaptively adjusted according to the dynamic response characteristics of the process parameters. For example, when the system detects that the pressure fluctuation caused by the change in the temperature parameter exceeds the expected value, the system increases the buffer interval time between temperature control and pressure control from 0.3 seconds to 0.5 seconds to eliminate mutual interference.

[0041] Finally, the system generates multi - device collaborative control instructions by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table. These instructions contain the specific actions and parameters that each execution device should execute at specific time points. For example, at the moment of 0 seconds, the system sends an instruction to the displacement control device to adjust the indenter displacement from 10 mm to 10.3 mm; at the moment of 1.8 seconds, the system sends an instruction to the temperature control device to reduce the chamber temperature from 180 °C to 175 °C; at the same time, it sends an instruction to the pressure control device to increase the chamber pressure from 25 MPa to 27 MPa; at the moment of 5.2 seconds, the system sends an instruction to the material supply device to reduce the supply rate from 25 g / s to 23 g / s. These collaborative control instructions ensure that each device works together according to the optimized time sequence and parameters, thereby improving the forming quality and efficiency.

[0042] In an alternative implementation, mapping the control parameters of each execution device to an execution trajectory curve in the execution space coordinate system, calculating the interaction relationship between devices according to the execution trajectory curve, and performing hierarchical clustering analysis on the intensity of the interaction relationship. Establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit includes: Construct an execution space coordinate system, map the change trend of the control parameters in the time dimension to an execution trajectory curve in the execution space coordinate system, calculate the minimum Euclidean distance between different execution trajectory curves, and use the minimum Euclidean distance as a quantitative index of the interaction relationship intensity between devices; Perform hierarchical clustering analysis on the intensity of the interaction relationship, combine the device groups with the interaction relationship intensity less than the first intensity threshold into low - coupling execution units, combine the device groups with the interaction relationship intensity greater than the first intensity threshold and less than the second intensity threshold into medium - coupling execution units, and divide the device groups with the interaction relationship intensity greater than the second intensity threshold into high - coupling execution units; Collect the dynamic response characteristics of the devices in each execution unit respectively. The dynamic response characteristics include response time, adjustment accuracy, and stabilization time, and calculate the comprehensive performance index of the execution unit by performing weighted calculation on the dynamic response characteristics. Allocate priority weights to each execution unit according to the comprehensive performance index. The priority weights increase monotonically with the comprehensive performance index, and construct a dynamic scheduling strategy based on the priority competition mechanism.

[0043] First, construct an execution space coordinate system. This coordinate system is multi-dimensional and can include multiple dimensions such as time, temperature, pressure, and flow rate. For example, in an industrial automation system, a three-dimensional execution space coordinate system can be constructed, where the X-axis represents time, the Y-axis represents temperature, and the Z-axis represents pressure. For each execution device, collect its control parameter data within a certain time period. For example, the temperature set value and actual temperature value of a certain heating device are recorded every 10 minutes within 24 hours. Map the change trend of these control parameters in the time dimension to the constructed execution space coordinate system to form a continuous execution trajectory curve. For example, the temperature control parameter of the heating device may gradually rise from 100°C to 150°C and then drop to 120°C, and this process forms a curve in the execution space.

[0044] 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 two execution trajectory curves, calculate their Euclidean distance in the execution space. In practical applications, 100 uniformly distributed sampling points can be selected to calculate the Euclidean distance at the corresponding time points of the two curves, and take the minimum value 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 interaction relationship strength between device A and device B is greater than that between device A and device C.

[0045] When performing hierarchical clustering analysis on the interaction relationship strength, the system classifies according to two preset 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 between the execution trajectory curves of two devices is less than 3.0 units, combine these two devices 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, combine these two devices into a medium-coupling execution unit. When the minimum Euclidean distance is greater than 7.0 units, combine these two devices into a low-coupling execution unit, indicating that the interaction relationship between them is weak.

[0046] In this way, in a system with 10 execution devices, it is possible to form 2 highly coupled execution units, 3 moderately coupled execution units, and 1 lowly 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, and they are also combined into a highly coupled execution unit; the minimum Euclidean distances of devices F, G, and H are between 3.0 and 7.0 units, and they are combined into three moderately coupled execution units; the minimum Euclidean distance of devices I and J is greater than 7.0 units, and they are combined into a lowly coupled execution unit.

[0047] For each formed execution unit, the system will separately collect the dynamic response characteristics of the internal devices. The dynamic response characteristics include three indicators: response time, adjustment accuracy, and stabilization time. The response time refers to the time from receiving a control instruction to starting execution, usually measured in milliseconds; the adjustment accuracy refers to the deviation degree between the actual execution parameter and the target parameter, usually expressed as a percentage; the stabilization time refers to the time required from the start of adjustment to reaching a stable state, usually measured in seconds.

[0048] In a specific example, the average response time of the highly coupled 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 the highly coupled 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 moderately coupled execution units are between those of the highly coupled and lowly coupled execution units; the average response time of the lowly coupled 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.

[0049] Perform a weighted calculation on these dynamic response characteristics to obtain the comprehensive performance index of the execution unit. The weighted calculation method is to take the reciprocal of the response time and multiply it by the weight of 0.3, multiply the adjustment accuracy by the weight of 0.4, take the reciprocal of the stabilization time and multiply it by the weight of 0.3, and then add the three together. For example, the comprehensive performance index of the highly coupled execution unit 1 is: 0.3 × (1 / 50) + 0.4 × 98.5% + 0.3 × (1 / 2.3) = 0.456; the comprehensive performance index of the highly coupled execution unit 2 is: 0.3 × (1 / 30) + 0.4 × 99.2% + 0.3 × (1 / 1.8) = 0.573; the comprehensive performance index of the lowly coupled execution unit is: 0.3 × (1 / 100) + 0.4 × 95.0% + 0.3 × (1 / 5.0) = 0.383.

[0050] Based on the calculated comprehensive performance indicators, priority weights are assigned to each execution unit. The priority weight is directly proportional to the comprehensive performance indicator, that is, the higher the comprehensive performance indicator, the greater the priority weight. In specific implementation, the comprehensive performance indicator can be directly normalized as the priority weight, or the comprehensive performance indicator can be mapped to a predefined weight range. For example, mapping the above calculated comprehensive performance indicator to an integer weight range from 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.

[0051] Based on these priority weights, a dynamic scheduling strategy is constructed. In the case of resource competition, the execution unit with a higher priority obtains computing and execution resources first. At the same time, the system dynamically adjusts the priority weights according to the real-time performance of the execution unit to ensure that the scheduling strategy can adapt to the changes of the system. For example, when the response time of the high-coupling execution unit 1 is improved from 50 milliseconds to 40 milliseconds, its comprehensive performance indicator will increase accordingly, and the priority weight may be increased from 8 to 9. Through this dynamic adjustment mechanism, the system can achieve more efficient resource allocation and task scheduling.

[0052] In an alternative implementation, the subordinate controller in response automatically adjusts the execution progress, and other subordinate controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices, including: Calculate the ratio of the actual execution progress of each subordinate controller to the preset target progress to obtain the normalized execution progress value, calculate the average value of the normalized execution progress values, and use the difference between the normalized execution progress value of each subordinate controller and the average value as the progress deviation amount; When the progress deviation amount of a certain subordinate controller exceeds the first progress threshold, calculate the progress compensation coefficient according to the progress deviation amount. The progress compensation coefficient is calculated according to a linear relationship when the progress deviation amount is less than the second progress threshold, and is calculated according to an exponential relationship when the progress deviation amount is greater than the second progress threshold. Multiply the progress compensation coefficient by the current control parameter to obtain the compensated control parameter; Generate a progress adjustment instruction according to the compensated control parameter. When the progress deviation amount is positive, achieve progress acceleration adjustment by reducing the control period and increasing the control gain value. When the progress deviation amount is negative, achieve progress deceleration adjustment by increasing the control period and reducing the control gain value to ensure the synchronization of the execution progress of multiple devices.

[0053] The system architecture includes multiple lower-level controllers, and each controller is responsible for the operation control of one device. The central control module monitors the execution status of each lower-level controller in real time and sends adjustment instructions according to the preset synchronization strategy. In actual implementation, the control network can adopt the RS-485 bus or Ethernet, and the communication protocol supports Modbus RTU or TCP / IP to ensure the real-time and reliability of data transmission.

[0054] 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 certain 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, and the preset target progress is 50% for all. The system divides the actual progress by the target progress to obtain the standardized execution progress values, which are 0.96, 1.04, and 1.00 respectively. The system calculates the average value of these standardized values, which is 1.00. Subsequently, it calculates the differences between the standardized progress values of each controller and the average value, which are -0.04, 0.04, and 0.00 respectively, and these differences are the progress deviation amounts.

[0055] The progress compensation system adopts a two-stage adjustment strategy. The system presets two key thresholds: the first progress threshold is set to 0.02, and the second progress threshold is set to 0.05. When the absolute value of the progress deviation amount of a certain controller exceeds the first threshold (0.02), the system starts the compensation mechanism. For example, the progress deviation amount of the first device is -0.04, which exceeds the first threshold and needs to be compensated; the progress deviation amount of the second device is 0.04, which also needs to be compensated; the third device does not need adjustment.

[0056] The calculation of the compensation coefficient is divided into two regions. When the absolute value of the progress deviation amount is less than the second threshold (0.05), a linear compensation relationship is adopted. The system multiplies the progress deviation amount by the preset linear compensation factor 10 to obtain the compensation coefficient. Taking the first device as an example, its progress deviation amount is -0.04, and the calculated compensation coefficient is -0.04×10 = -0.4, that is, 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, that is, the current control parameters need to be adjusted by 1 + 0.4 = 1.4 times.

[0057] When the absolute value of the progress deviation is greater than the second threshold (0.05), the compensation coefficient is calculated using an exponential relationship to achieve a stronger adjustment. The exponential relationship can be expressed as: subtract the second threshold from the absolute value of the progress deviation to obtain the excess, then multiply the portion by the exponential base (set to 20), and finally add the maximum compensation value of the linear region, 0.5. For example, if the progress deviation of a device is 0.07, which exceeds the second threshold of 0.05, the excess is 0.02, the exponential compensation coefficient is 0.5+0.02×20=0.9, and the final compensation coefficient is 1+0.9=1.9 times.

[0058] The adjustment of control parameters adopts different strategies according to the positive and negative nature of the progress deviation. When the progress deviation is a negative value (such as -0.04 for the first device), it indicates that the execution speed is too slow, and the system slows down the device by increasing the control cycle and reducing the control gain value. In the specific implementation, the control cycle is increased from the original 100 milliseconds to 100÷0.6=167 milliseconds, and the control gain is reduced from the original value of 1.2 to 1.2×0.6=0.72. When the progress deviation is a positive value (such as 0.04 for the second device), it indicates that the execution speed is too fast, and the system speeds up the device operation by reducing the control cycle and increasing the control gain value. The control cycle of the second device is reduced from 100 milliseconds to 100÷1.4=71 milliseconds, and the control gain is increased from 1.2 to 1.2×1.4=1.68.

[0059] During the implementation of the system, a smooth adjustment mechanism is also used to prevent the compensation action from being too drastic and causing system oscillation. Smooth adjustment introduces the moving average of historical compensation values ​​and performs a weighted average of the currently calculated compensation coefficient and the historical compensation coefficient, with a weight 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.

[0060] Through continuous real-time monitoring and dynamic adjustment, the system can maintain the execution progress synchronization of multiple devices under different working conditions and load changes. Practice has shown that after adopting this method, the progress synchronization deviation of the three devices has been reduced from the original ±4% to within ±1%, significantly improving the system's collaborative work efficiency and product quality stability.

[0061] Figure 2It is a schematic diagram for comparing the progress synchronization performance of multiple devices. As can be seen from the figure, the method in this paper shows the lowest progress deviation rate in all test scenarios, with its value range being 0.7% to 1.3%; the basic process synchronization method ranks second, with the 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%. Especially in the batch material synchronization scenario, the progress deviation rate of the method in this paper is only 1.3%, which is about 82% lower than 7.1% of the traditional method, fully demonstrating that the technical solution of the adaptive adjustment algorithm combined with the parameter coupling influence matrix proposed by the present invention has significant technical effects.

[0062] This technology has been applied in multiple automated production lines and is particularly suitable for scenarios requiring high-precision collaborative operations, such as industrial scenarios like multi-robot collaborative welding, multi-axis linkage machining, and precision assembly.

[0063] In an optional implementation manner, 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 forming pressure according to the forming cavity pressure parameter, the heating system controller adjusts the heating temperature according to the forming cavity temperature parameter, the displacement system controller adjusts the position of the punch according to the punch displacement parameter, and the material supply system controller adjusts the feeding speed according to the material supply rate parameter to achieve the coordinated control of the composite material high-pressure forming process.

[0064] Exemplarily, the hydraulic system controller is used to control the hydraulic system to adjust the forming pressure according to the forming cavity pressure parameter. The hydraulic system controller receives the forming cavity pressure parameter sent by the upper controller, and this parameter defines the pressure curve that needs to be applied during the composite material high-pressure forming process. For example, during the forming process of a carbon fiber composite material sheet, the pressure curve can be set to increase at a rate of 5 MPa / minute to 15 MPa in the initial stage, maintain for 30 minutes, then increase at a rate of 3 MPa / minute to 25 MPa and maintain for 60 minutes. The hydraulic system controller precisely adjusts the pressure applied to the forming cavity by controlling the output flow of the hydraulic pump and the opening degree of the pressure regulating valve. The hydraulic system controller is also equipped with a pressure feedback sensor to continuously monitor the actual pressure in the forming cavity. When it detects a deviation between the actual pressure and the target pressure, the controller will automatically adjust the working parameters of the hydraulic system to eliminate the deviation and ensure the precise control of the forming pressure. The pressure control accuracy can reach ±0.2 MPa.

[0065] The heating system controller adjusts the heating temperature according to the temperature parameters of the molding cavity. The heating system controller receives the temperature parameters sent by the upper controller, which defines the temperature curve during the composite material 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 per minute, hold for 20 minutes, then rise to 180°C at a rate of 2°C per minute, hold for 90 minutes, and finally drop to room temperature at a rate of 1°C per 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 installed inside the molding cavity to monitor the temperature at different positions in real time. The heating system controller adopts the PID control algorithm and dynamically adjusts the power output of the heaters in each area according to the temperature feedback information to ensure the temperature uniformity in the molding cavity, and the temperature control accuracy can reach ±2°C.

[0066] The displacement system controller adjusts the position of the indenter according to the indenter displacement parameters. The displacement system controller receives the indenter displacement parameters sent by the upper controller and controls the movement trajectory and speed of the indenter. In the preforming stage of the composite material, it is necessary to control the indenter to gradually descend at a speed of 0.5 mm per second until it touches the surface of the material; in the compaction stage, the indenter needs to continue to press down to the predetermined position at a speed of 0.2 mm per second; in the curing stage, the position of the indenter needs to be finely adjusted according to the shrinkage of the material to maintain a constant pressure. The displacement system controller uses a servo motor and a high-precision displacement sensor to achieve precise control of the indenter position, and the displacement control accuracy can reach ±0.05 mm. The displacement system controller is also equipped with a force sensor to monitor the force applied by the indenter in real time, judge the state change of the material during the molding process according to the relationship between force and displacement, and adjust the control strategy in a timely manner.

[0067] 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 sent by the upper controller and controls the conveying speed and quantity of the material. For example, in the resin transfer molding process, the injection rate of the resin can be set to be injected at a rate of 5 ml per minute in the initial stage, reduced to 3 ml per minute when the mold filling reaches 50%, and further reduced to 1 ml per minute when the filling reaches 80% to reduce bubbles and ensure uniform filling. The material supply system controller achieves precise control of the material flow through a precision metering pump and a flow sensor, and the flow control accuracy can reach ±0.1 ml per 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 according to these parameters.

[0068] The upper controller, as the center of the entire system, is responsible for coordinating the work of each lower controller. The upper controller formulates a forming plan based on the type of composite material, the requirements of the forming process, and the expected product quality, generates control parameters for each subsystem, and sends these parameters to the corresponding lower controller. During the forming process, the upper controller receives in real-time the feedback information uploaded by each lower controller, including data such as pressure, temperature, displacement, and material supply, evaluates the state of the forming process based on this data, and adjusts the control parameters if necessary to ensure that the forming process proceeds as expected.

[0069] Through the collaborative work of the above system controllers, the composite material high-pressure forming system can achieve precise control of the forming process, ensuring the quality stability and consistency of composite material products. This control method is applicable to various types of composite material high-pressure forming processes, including hot pressing, resin transfer molding, prepreg molding, etc., and can effectively improve production efficiency and product quality.

[0070] The composite material high-pressure forming control system based on communication interaction in the embodiments of the present invention includes: The first unit is used to collect real-time data of multi-modal sensors of composite material high-pressure forming equipment and transmit the real-time data to the central controller through the industrial Ethernet; The second unit is used for the central controller to calculate the process parameter deviation value of the composite material 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 considering the coupling influence matrix of parameter mutual influence. The third unit is used to perform real-time dynamic adjustment of process parameters based on the compensation correction amount, generate compensation process parameters, establish a priority dynamic scheduling strategy and an equipment execution time sequence table respectively based on the compensation process parameters, and generate a multi-device collaborative control instruction by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table; The fourth unit is used for the central controller to send the multi-device collaborative control instruction to the lower controller respectively through the industrial Ethernet. The lower controllers interact their execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the corresponding lower controller automatically adjusts the execution progress, and other lower controllers synchronously adjust their control parameters according to the adjusted execution progress to ensure the synchronization of the multi-device execution progress.

[0071] In the third aspect of the embodiments of the present invention, an electronic device is provided, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0072] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0073] 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, on which computer-readable program instructions for executing various aspects of the present invention are loaded.

[0074] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A composite material high-pressure forming control method based on communication interaction, characterized in that Including: Collecting real-time data of multi-modal sensors of the composite high-pressure forming equipment, and transmitting the real-time data to a central controller through an 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 a dynamic adjustment coefficient by 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 considering the mutual influence of parameters; Based on the compensation correction amount, the process parameters are adjusted dynamically in real time to generate compensated process parameters. A priority dynamic scheduling strategy and an equipment execution timing table are established respectively based on the compensated process parameters. A multi-device collaborative control instruction is generated by integrating the priority dynamic scheduling strategy and the equipment execution timing table; The central controller sends the multi-device collaborative control instruction to the lower-level controllers respectively through the industrial Ethernet. The lower-level controllers exchange their respective execution status information through a real-time communication bus. When it is detected that any execution status information exceeds a preset warning threshold, the corresponding lower-level controller automatically adjusts the execution progress, and other lower-level controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the multi-device execution progress.

2. The method according to claim 1, characterized in that The central controller calculates a dynamic adjustment coefficient by 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 considering the mutual influence of parameters, including: Performing a timing analysis on the process parameter deviation value, and calculating the deviation change rate of the process parameter deviation value within a continuous plurality of sampling periods; Based on the historical data of the process parameter deviation value, a weighted moving average prediction method is used to calculate the predicted deviation value of the next sampling period; Calculating a dynamic adjustment coefficient according to a preset adaptive adjustment function. The adaptive adjustment function is in the form of a piecewise continuous function, and is adjusted in a linear growth manner when the predicted deviation value is less than a first deviation threshold, adjusted in an exponential growth manner when the predicted deviation value is greater than the first deviation threshold and less than a second deviation threshold, and adjusted in a saturation limiting manner when the predicted deviation value is greater than the second deviation threshold; Calculating a proportional coefficient and a differential coefficient respectively 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; Performing a cross-response test on each process parameter to obtain the degree of mutual influence between parameters, and establishing a coupling influence matrix. The diagonal elements of the coupling influence matrix are unity, and the non-diagonal elements range from zero to one, representing the coupling strength between different parameters; Combining the superposition value of the proportional term and the differential term to form a compensation vector, and multiplying the compensation vector by the coupling influence matrix to obtain the final compensation amount considering the parameter coupling effect.

3. The method according to claim 1, characterized in that Based on the compensation correction amount, the process parameters are adjusted in real time dynamically to generate compensated process parameters. Based on the compensated process parameters, a priority dynamic scheduling strategy and an equipment execution time sequence table are established respectively. Generating a multi-device collaborative control instruction by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table includes: Superimposing the compensation correction amount on the current process parameters respectively to obtain compensated process parameters, where the compensated process parameters include the compensated forming cavity pressure parameter, the forming cavity temperature parameter, the punch displacement parameter, and the material supply rate parameter; Establishing a multi-dimensional execution space coordinate system based on the compensated 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 according to the execution trajectory curve, and dividing the device combinations with the interaction relationship strength greater than the first strength threshold into one execution unit. Establishing a priority dynamic scheduling strategy based on the response characteristics of the execution unit to determine the control timing of the execution device; Constructing an equipment execution time sequence table based on the compensated process parameters, where the equipment execution time sequence 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 time is set between adjacent control time slots. The buffer interval time is adaptively adjusted according to the dynamic response characteristics of the process parameters to eliminate the mutual interference during the device switching process; Generating a multi-device collaborative control instruction by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table.

4. The method according to claim 3, wherein 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 according to the execution trajectory curve, performing hierarchical clustering analysis on the interaction relationship strength, 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 parameters 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 quantization index of the interaction relationship strength between devices; Performing hierarchical clustering analysis on the interaction relationship strength, combining the device combinations with the interaction relationship strength less than the first strength threshold into a low-coupling execution unit, combining the device combinations with the interaction relationship strength greater than the first strength threshold and less than the second strength threshold into a medium-coupling execution unit, and dividing the device combinations with the interaction relationship strength greater than the second strength threshold into a high-coupling execution unit; Collecting the dynamic response characteristics of the devices in each execution unit respectively, where the dynamic response characteristics include response time, adjustment accuracy, and stabilization time, and performing weighted calculation on the dynamic response characteristics to obtain the comprehensive performance index of the execution unit; Assigning priority weights to each execution unit according to the comprehensive performance index, where the priority weights increase monotonically with the comprehensive performance index, and constructing a dynamic scheduling strategy based on the priority competition mechanism.

5. The method according to claim 1, wherein The corresponding lower-level controller automatically adjusts the execution progress, and other lower-level controllers synchronously adjust their respective control parameters according to the adjusted execution progress to ensure the synchronization of the multi-device execution progress includes: Calculate the ratio of the actual execution progress of each lower-level controller to the preset target progress to obtain a standardized execution progress value, calculate the average value of the standardized execution progress values, and use the difference between the standardized execution progress value of each lower-level controller and the average value as the progress deviation amount; When the progress deviation amount of a certain lower-level controller exceeds the first progress threshold, calculate a progress compensation coefficient according to the progress deviation amount. The progress compensation coefficient is calculated according to a linear relationship when the progress deviation amount is less than the second progress threshold, and is calculated according to an exponential relationship when the progress deviation amount is greater than the second progress threshold. Multiply the progress compensation coefficient by the current control parameter to obtain a compensated control parameter; Generate a progress adjustment instruction according to the compensated control parameter. When the progress deviation amount is positive, achieve progress acceleration adjustment by reducing the control period and increasing the control gain value. When the progress deviation amount is negative, achieve progress deceleration adjustment by increasing the control period and decreasing the control gain value to ensure the synchronization of the execution progress of multiple devices.

6. The method according to claim 1, wherein The lower-level 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 forming pressure according to the forming cavity pressure parameter, the heating system controller adjusts the heating temperature according to the forming cavity temperature parameter, the displacement system controller adjusts the punch position according to the punch displacement parameter, and the material supply system controller adjusts the feeding speed according to the material supply rate parameter to achieve coordinated control of the composite material high-pressure forming process.

7. A composite material high-pressure forming control system based on communication interaction, used to implement the method described in any one of claims 1-6, characterized in that, Comprising: A first unit for collecting real-time data of multi-modal sensors of a composite material high-pressure forming device and transmitting the real-time data to a central controller through an industrial Ethernet; A second unit for the central controller to calculate the process parameter deviation value of the composite material high-pressure forming process according to the real-time data. The central controller adopts an adaptive adjustment function in the form of a piecewise continuous function to calculate the dynamic adjustment coefficient 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 considering the mutual influence of parameters; A third unit for dynamically adjusting the process parameters in real time based on the compensation correction amount to generate compensated process parameters, establishing a priority dynamic scheduling strategy and an equipment execution time sequence table respectively based on the compensated process parameters, and generating a multi-device coordinated control instruction by integrating the priority dynamic scheduling strategy and the equipment execution time sequence table; A fourth unit for the central controller to send the multi-device coordinated control instruction to the lower-level controller through the industrial Ethernet respectively. The lower-level controllers interact their execution status information through a real-time communication bus. When it is detected that any execution status information exceeds the preset warning threshold, the corresponding lower-level controller automatically adjusts the execution progress, and other lower-level controllers synchronously adjust their control parameters according to the adjusted execution progress to ensure the synchronization of the execution progress of multiple devices.

8. An electronic device, characterized in that, Comprising: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

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