AI-based forming process control system

Abstract

The invention discloses an AI-based forming process control system, which comprises an AI forming control platform, the AI forming control platform comprises a process analysis module, a scene layout module, a forming control module, a processing control module and a data analysis module, and relates to the technical field of forming control. According to the AI-based forming process control system, monitoring equipment is positioned and installed in machining equipment according to the product machining technology, real-time and convenient collection of target parameters is guaranteed, automatic parameter correction is carried out on the machining equipment according to the comparison result of the target parameters and real-time monitoring data, the smooth proceeding of the product forming process is guaranteed, and the production efficiency is improved. A reliable analysis path is provided for rapid correction of relevant parameters of the machining equipment by drawing up a system control module and main influence machining equipment and combining comparison of the combined adjustment duration and the instruction adjustment duration, and then the purpose of rapid correction of the relevant parameters of the machining equipment is achieved.

Description

Technical Field

[0001] The present invention relates to the field of forming control technology, and in particular to an AI-based forming process control system. Background Art

[0002] AI, or artificial intelligence, is a technology that simulates, extends and expands human intelligence through computer systems. AI aims to enable computer systems to understand, learn, adapt and implement human intelligent behavior. It is not a single technology or field, but covers multiple disciplines, such as computer science, biology, psychology, linguistics, mathematics and engineering. Its core goal is to enable computers to have human-like intelligence capabilities, such as reasoning, knowledge acquisition, planning, learning, communication, perception, etc. through technical means. AI includes deep learning, machine learning, computer vision, natural language processing and other technologies and algorithms. These technologies gradually adjust the weights and biases of neural networks through continuous iterative training of large amounts of data to achieve more accurate and efficient recognition and decision-making.

[0003] During the forming process of the product, traditional processing equipment needs to adjust the parameters of the processing equipment according to the processing technology. During the processing, the relevant parameters will also change. Manual adjustment of parameters often cannot effectively and timely ensure the smooth progress of the product forming process. The traditional automatic control system cannot effectively match the processing technology of the product, and can often only perform regional control of the processing equipment. The control parameters are single and cannot be quickly corrected. For this reason, an AI-based forming process control system is proposed to realize automatic control of the processing equipment while achieving the purpose of rapid parameter correction, providing stable and reliable guarantee for the product forming process. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an AI-based forming process control system, which solves the problem that traditional automatic control systems cannot effectively adapt to the processing technology of products, and can often only perform regional control of processing equipment, and the control parameters are single and cannot achieve rapid parameter correction.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an AI-based forming process control system, including an AI forming control platform, the AI ​​forming control platform including a process analysis module, a scene layout module, a forming control module, a processing control module and a data analysis module, the process analysis module is respectively connected with the scene layout module and the forming control module, the scene layout module is connected with the forming control module, the forming control module is connected with the processing control module, the processing control module is connected with the data analysis module, and the data analysis module is connected with the processing control module;

[0006] The process analysis module is used to obtain the product processing technology, and determine the acquisition position of the process parameters corresponding to the processing equipment, which is recorded as the monitoring area, and the corresponding process parameters are used as the target parameters corresponding to the monitoring area.

[0007] The present invention is further configured as follows: the scene layout module includes a monitoring point planning unit and a data monitoring transmission unit, and the monitoring point planning unit is connected to the data monitoring transmission unit.

[0008] The present invention is further configured as follows: the monitoring point planning unit is used to determine the installation point of the corresponding target parameter monitoring equipment in the monitoring area;

[0009] The data monitoring transmission unit is used to upload the real-time monitoring data of the monitoring device to the molding control module after the monitoring device is installed at the monitoring device installation point and a unique number is added to the monitoring device.

[0010] The present invention is further configured as follows: the molding control module includes a molding control parameter integration unit, a data monitoring and comparison unit and a correction identification output unit; the molding control parameter integration unit is connected to the data monitoring and comparison unit; and the data monitoring and comparison unit is connected to the correction identification output unit.

[0011] The present invention is further configured as follows: the molding control parameter integration unit is used to establish a reference table according to the mapping relationship between the monitoring area and the target parameter, divide the target parameter into thresholds, obtain two alarm threshold ranges and an adjustment threshold range between the two alarm threshold ranges, and after receiving the real-time monitoring data, record the real-time monitoring data at the corresponding monitoring area in the reference table, and record the data type of the real-time monitoring data, wherein the data type includes a conveying type, a cooling type, and a heating type;

[0012] The data monitoring comparison unit is used to compare the real-time monitoring data with the adjustment threshold range to determine whether the real-time monitoring data is within the adjustment reference threshold range:

[0013] If yes, it means the processing equipment is operating normally;

[0014] If no, continue to determine whether the real-time monitoring data is within the alarm threshold range; if yes, issue a control adjustment instruction with the data type; if no, issue an abnormal alarm for the processing equipment;

[0015] The correction identification output unit is used to receive control adjustment instructions and determine whether the real-time monitoring data exceeds the adjustment threshold range. If yes, a slowdown instruction with the data type is issued; if no, a speed-up instruction with the data type is issued.

[0016] The present invention is further configured as follows: the processing control module includes a type screening unit, an output control unit, a cooling control unit and a heating control unit, and the type screening unit is connected to the output control unit, the cooling control unit and the heating control unit respectively.

[0017] The present invention is further configured as follows: the type screening unit is used to receive a slowdown instruction with a data type and a boost instruction with a data type, screen the processing equipment in the corresponding monitoring area as the equipment to be adjusted according to the data type, and issue a slowdown instruction or a boost instruction to the equipment to be adjusted;

[0018] The output control unit is used to control the conveying processing equipment to decelerate when receiving a slowdown instruction, and to control the conveying processing equipment to accelerate when receiving a speed-up instruction;

[0019] The cooling control unit is used to control the cooling processing equipment to slow down when receiving a slowdown instruction, and to control the conveying processing equipment to speed up when receiving a speed-up instruction;

[0020] The heating control unit controls the heating processing equipment to cool down when receiving a slow-down instruction, and controls the heating processing equipment to heat up when receiving a heating-up instruction.

[0021] The present invention is further configured as follows: the data analysis module includes a rating association unit, a data sorting unit and a control parameter growth unit, the rating association unit is connected to the data sorting unit, and the data sorting unit is connected to the control parameter growth unit.

[0022] The present invention is further configured as follows: the rating association unit is used for the technical expert to determine whether there is a correlation between different process parameters and processing equipment, and to construct a collaborative control module of all processing equipment with correlation corresponding to the process parameters according to the determination result, and to determine the processing equipment directly associated with the process parameters as the main influencing processing equipment corresponding to the collaborative control module;

[0023] The data sorting unit is used to sort the real-time monitoring data of the same monitoring device according to the time series, and select a number of continuous real-time monitoring data that exceeds the adjustment threshold range but does not exceed the alarm threshold range as the data to be analyzed, obtain the duration of the data to be analyzed and the instructions that affect the change of the real-time monitoring data during the period, and obtain the instruction adjustment duration;

[0024] The control parameter growth unit is used to obtain the instruction adjustment time of the main influencing processing equipment, record the instruction adjustment time as the reference time, select different processing equipment and the main influencing processing equipment from the collaborative control module and combine them in sequence, and obtain the instruction adjustment time under the combination as the combined adjustment time of the corresponding combination, and judge whether the combined adjustment time is less than the instruction adjustment time. If so, use the combination as a common optimization adjustment response device for the corresponding process parameters, and make corresponding adjustments according to the slowdown instructions with data types and the increase instructions with data types.

[0025] The present invention provides a forming process control system based on AI. It has the following beneficial effects:

[0026] (1) The present invention ensures the real-time and convenient collection of target parameters by positioning and installing monitoring equipment in the processing equipment according to the product processing technology. After dividing the target parameters into two alarm threshold ranges and an adjustment threshold range, the corresponding real-time monitoring data is compared with the real-time monitoring data to determine whether it meets the processing technology requirements. Based on the comparison results, the processing equipment is automatically corrected for parameters to ensure the smooth progress of the product molding process.

[0027] (2) The present invention divides the target parameters into two alarm threshold ranges and an adjustment threshold range, wherein both the alarm threshold ranges and the adjustment threshold range are within the safe working range. When the real-time monitoring data exceeds the adjustment threshold range but does not exceed the alarm threshold range, automatic parameter correction is performed, which can effectively ensure the quality of product forming processing and provide more precise judgment conditions for high-quality product forming processing.

[0028] (3) The present invention provides a reliable analysis path for the rapid correction of the relevant parameters of the processing equipment through the formulation of the system control module and the main influencing processing equipment, combined with the comparison of the combined adjustment time and the instruction adjustment time, thereby achieving the purpose of rapid correction of the relevant parameters of the processing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a system principle block diagram of the present invention;

[0030] Figure 2 This is a system principle block diagram of the scenario layout module of the present invention;

[0031] Figure 3 It is a system principle block diagram of the molding control module of the present invention;

[0032] Figure 4 It is a system principle block diagram of the processing control module of the present invention;

[0033] Figure 5 This is a system principle block diagram of the data analysis module of the present invention.

[0034] In the figure:

[0035] 1. AI molding control platform;

[0036] 2. Process analysis module;

[0037] 3. Scene layout module; 301. Monitoring point planning unit; 302. Data monitoring transmission unit;

[0038] 4. Molding control module; 401. Molding control parameter integration unit; 402. Data monitoring and comparison unit; 403. Correction identification and output unit;

[0039] 5. Processing control module; 501. Type screening unit; 502. Output control unit; 503. Cooling control unit; 504. Heating control unit;

[0040] 6. Data analysis module; 601. Rating association unit; 602. Data sorting unit; 603. Control parameter growth unit. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0042] See also Figure 1-5 , the embodiment of the present invention provides the following technical solutions:

[0043] Embodiment 1: A forming process control system based on AI comprises an AI forming control platform 1 consisting of a process analysis module 2, a scene layout module 3, a forming control module 4 and a processing control module 5, wherein the process analysis module 2 is used to obtain the product processing technology and determine the acquisition position corresponding to the process parameters in the processing equipment, recorded as a monitoring area, and the corresponding process parameters are used as the target parameters corresponding to the monitoring area.

[0044] As a preferred solution, the process analysis module 2 is connected to the scene layout module 3, and the scene layout module 3 includes a monitoring point planning unit 301 and a data monitoring transmission unit 302. The monitoring point planning unit 301 is used to determine the installation point of the corresponding target parameter monitoring equipment in the monitoring area. The monitoring point planning unit 301 is connected to the data monitoring transmission unit 302. The data monitoring transmission unit 302 is used to complete the installation of the monitoring equipment at the monitoring equipment installation point, add a unique number to the monitoring equipment, and upload the real-time monitoring data of the monitoring equipment to the molding control module 4.

[0045] As a preferred solution, the process analysis module 2 and the scene layout module 3 are both connected to the molding control module 4, and the molding control module 4 includes a molding control parameter integration unit 401, a data monitoring and comparison unit 402, and a correction identification output unit 403. The molding control parameter integration unit 401 is used to establish a reference table according to the mapping relationship between the monitoring area and the target parameter, divide the target parameter into thresholds, and obtain two alarm threshold ranges and an adjustment threshold range between the two alarm threshold ranges, wherein the two alarm threshold ranges and the adjustment threshold range together constitute the inclusion range of the entire target parameter, which is used to achieve a refined division of the target parameter. After receiving the real-time monitoring data, the real-time monitoring data is recorded in the corresponding monitoring area in the reference table, and the data type of the real-time monitoring data is recorded, wherein the data type includes conveying, cooling and heating. Taking the use of a screw extruder as a processing equipment for plastic forming processing as an example, the conveying type is but not limited to the drive structure for extruding materials in the barrel, the cooling type includes but is not limited to the barrel cooling sleeve drive structure, and the heating type includes but is not limited to the heating structure for melting the masterbatch at the barrel.

[0046] The molding control parameter integration unit 401 is connected to the data monitoring and comparison unit 402, and the data monitoring and comparison unit 402 is used to compare the real-time monitoring data with the adjustment threshold range to determine whether the real-time monitoring data is within the adjustment reference threshold range:

[0047] If yes, it means the processing equipment is operating normally;

[0048] If no, continue to determine whether the real-time monitoring data is within the alarm threshold range; if yes, issue a control adjustment instruction with the data type; if no, issue an abnormal alarm for the processing equipment.

[0049] The data monitoring and comparison unit 402 is connected to the correction and identification output unit 403, which is used to receive control adjustment instructions and determine whether the real-time monitoring data exceeds the adjustment threshold range. If yes, a slowdown instruction with the data type is issued; if no, an increase instruction with the data type is issued.

[0050] As a preferred solution, the molding control module 4 is connected to the processing control module 5, and the processing control module 5 includes a type screening unit 501, an output control unit 502, a cooling control unit 503 and a heating control unit 504. The type screening unit 501 is connected to the output control unit 502, the cooling control unit 503 and the heating control unit 504 respectively. The type screening unit 501 is used to receive slowdown instructions with data types and increase instructions with data types, and screen the processing equipment in the corresponding monitoring area according to the data type as the equipment to be adjusted, and issue a slowdown instruction or an increase instruction to the equipment to be adjusted, wherein when the type screening unit 501 receives a slowdown instruction with a data type, it issues a slowdown instruction to the equipment to be adjusted, and when the type screening unit 501 receives an increase instruction with a data type, it issues an increase instruction to the equipment to be adjusted.

[0051] The type screening unit 501 is connected to the output control unit 502. When the type screening unit 501 determines that the data type is a conveying type, the output control unit 502 is used to control the conveying type processing equipment to slow down when receiving a slow-down instruction, and control the conveying type processing equipment to speed up when receiving a speed-up instruction;

[0052] The type screening unit 501 is connected to the cooling control unit 503. When the type screening unit 501 determines that the data type is cooling type, the cooling control unit 503 is used to control the cooling type processing equipment to slow down when receiving a slow-down instruction, and control the conveying type processing equipment to speed up when receiving a speed-up instruction;

[0053] The type screening unit 501 is connected to the heating control unit 504. When the type screening unit 501 determines that the data type is heating type, the heating control unit 504 controls the heating type processing equipment to cool down when receiving a slowdown instruction, and controls the heating type processing equipment to heat up when receiving a heating instruction.

[0054] In this embodiment, by finely dividing the target parameters, more accurate parameter correction of the processing equipment is achieved, thereby achieving the purpose of ensuring a highly stable product forming process.

[0055] Embodiment 2. This embodiment is an improvement of the previous embodiment. The AI-based forming process control system also includes a data analysis module 6. The processing control module 5 is connected to the data analysis module 6. The data analysis module 6 includes a rating association unit 601, a data sorting unit 602 and a control parameter growth unit 603. The rating association unit 601 is connected to the data sorting unit 602, and the data sorting unit 602 is connected to the control parameter growth unit 603. The rating association unit 601 is used for technical experts to determine whether there is a correlation between different process parameters and processing equipment. According to the judgment result, a collaborative control module of all processing equipment with correlation corresponding to the process parameters is constructed, and the processing equipment directly associated with the process parameters is determined as the main influencing processing equipment corresponding to the collaborative control module.

[0056] The data sorting unit 602 is used to sort the real-time monitoring data of the same monitoring device according to the time series, and screen a number of continuous real-time monitoring data that exceed the adjustment threshold range but do not exceed the alarm threshold range as the data to be analyzed, obtain the duration of the data to be analyzed and the instructions that affect the changes in the real-time monitoring data during the period, and obtain the instruction adjustment duration.

[0057] The control parameter growth unit 603 is used to obtain the instruction adjustment time of the main influencing processing equipment, record the instruction adjustment time as the reference time, select different processing equipment and the main influencing processing equipment from the collaborative control module and combine them in sequence, and obtain the instruction adjustment time under the combination as the combined adjustment time of the corresponding combination, and judge whether the combined adjustment time is less than the instruction adjustment time. If so, use the combination as a common optimization adjustment response device for the corresponding process parameters, and make corresponding adjustments according to the slowdown instructions with data types and the increase instructions with data types.

[0058] The advantage of Example 2 over Example 1 is that by judging the main influencing processing equipment and combining it with the analysis of the instruction adjustment time, it provides an accurate judgment basis for whether the combined adjustment time that can be achieved by different combinations in the collaborative control module can achieve the purpose of reducing the adjustment time, thereby providing support for obtaining a shorter effective combination.

[0059] When using, taking the plastic forming process using a screw extruder as a processing equipment as an example, the specific steps include:

[0060] S1, process analysis module 2 is used for plastic product processing technology, and determines the acquisition position of process parameters corresponding to the screw extruder, which is recorded as the monitoring area, and the corresponding process parameters are used as the target parameters corresponding to the monitoring area. Specifically, when the process parameter is the melting temperature AD degrees Celsius, it is judged that its acquisition position is inside the barrel, and it is a heating data type. The technical expert judges that the process parameter is associated with the barrel cooling jacket drive structure and the heating structure for masterbatch melting at the barrel. The rating association unit 601 constructs a collaborative control module for all associated processing equipment corresponding to the process parameter according to the judgment result. At this time, the collaborative control module includes the barrel cooling jacket drive structure and the heating structure for masterbatch melting at the barrel, and determines that the heating structure for masterbatch melting at the barrel is directly associated with the processing equipment of the process parameter, that is, the heating structure for masterbatch melting at the barrel is the main influencing processing equipment corresponding to the collaborative control module;

[0061] After the monitoring device is installed at the monitoring device installation point, the data monitoring transmission unit 302 adds a unique number to the monitoring device and uploads the real-time monitoring data of the monitoring device to S2;

[0062] S2. The molding control parameter integration unit 401 establishes a reference table according to the mapping relationship between the monitoring area and the target parameter, divides the target parameter into threshold values, obtains two alarm threshold ranges and an adjustment threshold range between the two alarm threshold ranges, and takes the melting temperature AD degrees Celsius as an example. AB degrees Celsius and CD degrees Celsius are two alarm threshold ranges, and BC degrees Celsius is an adjustment threshold range. After receiving the real-time monitoring data, the real-time monitoring data is recorded in the corresponding monitoring area in the reference table, and the data type of the real-time monitoring data is recorded;

[0063] S3, the data monitoring comparison unit 402 compares the real-time monitoring data with the adjustment threshold range to determine whether the real-time monitoring data is within the adjustment reference threshold range. If yes, it indicates that the processing equipment is operating normally; if no, continue to determine whether the real-time monitoring data is within the alarm threshold range. If yes, issue a control adjustment instruction with the data type and go to S4. If no, issue an abnormal alarm of the processing equipment.

[0064] S4, the correction identification output unit 403 receives the control adjustment instruction, determines whether the real-time monitoring data exceeds the adjustment threshold range, and if so, issues a slowdown instruction with the data type, and if not, issues an increase instruction with the data type;

[0065] S5. The type screening unit 501 receives a slowdown instruction with a data type and a boost instruction with a data type, and screens the processing equipment in the corresponding monitoring area according to the data type as the equipment to be adjusted. Specifically, if the data type is heating, and the monitoring area corresponding to the data type is a process parameter, and the process parameter is a melting temperature AD degrees Celsius, the equipment to be adjusted is a heating structure for masterbatch melting at the barrel. When the real-time monitoring data is at AB degrees Celsius, a boost instruction is issued to the equipment to be adjusted. When the real-time monitoring data is at CD degrees Celsius, a slowdown instruction is issued to the equipment to be adjusted. When receiving the slowdown instruction, the heating control unit 504 controls the heating structure for masterbatch melting at the barrel to cool down, and when receiving the boost instruction, controls the heating structure for masterbatch melting at the barrel to heat up.

[0066] S6. The data sorting unit 602 sorts the real-time monitoring data of the same monitoring device according to the time series, and selects a number of continuous real-time monitoring data that exceeds the adjustment threshold range but does not exceed the alarm threshold range as the data to be analyzed, obtains the duration of the data to be analyzed and the instructions that affect the change of the real-time monitoring data during the period, and obtains the instruction adjustment duration;

[0067] S7. After obtaining the instruction adjustment duration of the main influencing processing equipment, the control parameter growth unit 603 records the instruction adjustment duration as the reference duration, selects the barrel cooling jacket drive structure and the main influencing processing equipment from the collaborative control module for combination, and the type screening unit 501 receives the slowdown instruction with the data type and the increase instruction with the data type, and screens the processing equipment in the corresponding monitoring area according to the data type as the equipment to be adjusted. Specifically, if the data type is heating type, and the monitoring area corresponding to the data type is the process parameter, and the process parameter is the melting temperature AD degrees Celsius, then the equipment to be adjusted is the barrel cooling jacket drive structure and the heating structure for masterbatch melting at the barrel. When the real-time monitoring data is at AB degrees Celsius, a increase instruction is issued to the equipment to be adjusted. When the monitoring data is at CD degrees Celsius, a slowdown instruction is issued to the equipment to be adjusted. When receiving the slowdown instruction, the heating control unit 504 and the cooling control unit 503 control the heating structure of the masterbatch melt at the barrel to cool down, and control the barrel cooling jacket drive structure to slow down, reduce the cooling water flow rate, and when receiving the increase instruction, control the heating structure of the masterbatch melt at the barrel to heat up, and control the barrel cooling jacket drive structure to accelerate, accelerate the cooling water flow rate, and obtain the instruction adjustment time under the combination through the data sorting unit 602 as the combined adjustment time of the corresponding combination, and judge whether the combined adjustment time is less than the instruction adjustment time. If yes, use the combination as the common optimization adjustment response device for the corresponding process parameters, and use the combined adjustment time as the effective time for parameter correction.

[0068] Similarly, when the target parameter is the output pressure, the corresponding collaborative control module includes a heating structure for melting the masterbatch at the barrel and a driving structure for extruding the material in the barrel, wherein the main influencing processing equipment is the driving structure for extruding the material in the barrel. Specifically, when receiving a slowdown instruction, the output control unit 502 is used to control the driving structure for extruding the material in the barrel to slow down, thereby reducing the extrusion speed of the molten material, that is, reducing the extrusion pressure. When receiving a boost instruction, the output control unit 502 is used to control the driving structure for extruding the material in the barrel to accelerate, thereby accelerating the extrusion speed of the molten material and increasing the extrusion pressure.

[0069] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI-based forming process control system, comprising an AI forming control platform (1), characterized in that: The AI ​​molding control platform (1) comprises a process analysis module (2), a scene layout module (3), a molding control module (4), a processing control module (5) and a data analysis module (6); the process analysis module (2) is connected to the scene layout module (3) and the molding control module (4) respectively; the scene layout module (3) is connected to the molding control module (4); the molding control module (4) is connected to the processing control module (5); the processing control module (5) is connected to the data analysis module (6); and the data analysis module (6) is connected to the processing control module (5); The process analysis module (2) is used to obtain the product processing technology, and determine the acquisition position corresponding to the process parameters in the processing equipment, record it as the monitoring area, and use the corresponding process parameters as the target parameters corresponding to the monitoring area.

2. The AI-based forming process control system according to claim 1, characterized in that: The scene layout module (3) comprises a monitoring point planning unit (301) and a data monitoring transmission unit (302), and the monitoring point planning unit (301) is connected to the data monitoring transmission unit (302).

3. The AI-based forming process control system according to claim 2, characterized in that: The monitoring point planning unit (301) is used to determine the installation points of the corresponding target parameter monitoring equipment in the monitoring area; The data monitoring transmission unit (302) is used to upload the real-time monitoring data of the monitoring device to the molding control module (4) after the monitoring device is installed at the monitoring device installation point and a unique number is added to the monitoring device.

4. The AI-based forming process control system according to claim 3 is characterized in that: The molding control module (4) comprises a molding control parameter integration unit (401), a data monitoring and comparison unit (402) and a correction and identification output unit (403); the molding control parameter integration unit (401) is connected to the data monitoring and comparison unit (402), and the data monitoring and comparison unit (402) is connected to the correction and identification output unit (403).

5. The AI-based forming process control system according to claim 4, characterized in that: The molding control parameter integration unit (401) is used to establish a reference table according to the mapping relationship between the monitoring area and the target parameter, divide the target parameter into threshold values, obtain two alarm threshold ranges and an adjustment threshold range between the two alarm threshold ranges, and after receiving the real-time monitoring data, record the real-time monitoring data at the corresponding monitoring area in the reference table, and record the data type of the real-time monitoring data, wherein the data type includes a conveying type, a cooling type, and a heating type; The data monitoring comparison unit (402) is used to compare the real-time monitoring data with the adjustment threshold range to determine whether the real-time monitoring data is within the adjustment reference threshold range: If yes, it means the processing equipment is operating normally; If no, continue to determine whether the real-time monitoring data is within the alarm threshold range; if yes, issue a control adjustment instruction with the data type; if no, issue an abnormal alarm for the processing equipment; The correction identification output unit (403) is used to receive a control adjustment instruction, determine whether the real-time monitoring data exceeds the adjustment threshold range, and if so, issue a slowdown instruction with the data type; if not, issue a speed-up instruction with the data type.

6. The AI-based forming process control system according to claim 1, characterized in that: The processing control module (5) comprises a type screening unit (501), an output control unit (502), a cooling control unit (503) and a heating control unit (504), and the type screening unit (501) is respectively connected to the output control unit (502), the cooling control unit (503) and the heating control unit (504).

7. The AI-based forming process control system according to claim 6, characterized in that: The type screening unit (501) is used to receive a slowdown instruction with a data type and a boost instruction with a data type, screen processing equipment in a corresponding monitoring area according to the data type as equipment to be adjusted, and issue a slowdown instruction or a boost instruction to the equipment to be adjusted; The output control unit (502) is used to control the conveying type processing equipment to slow down when receiving a slow-down instruction, and to control the conveying type processing equipment to speed up when receiving a speed-up instruction; The cooling control unit (503) is used to control the cooling processing equipment to slow down when receiving a slowdown instruction, and to control the conveying processing equipment to speed up when receiving a speed-up instruction; The heating control unit (504) is used to control the heating processing equipment to cool down when receiving a slowdown instruction, and to control the heating processing equipment to heat up when receiving a speed-up instruction.

8. The AI-based forming process control system according to claim 7, characterized in that: The data analysis module (6) includes a rating association unit (601), a data sorting unit (602) and a control parameter growth unit (603). The rating association unit (601) is connected to the data sorting unit (602), and the data sorting unit (602) is connected to the control parameter growth unit (603).

9. The AI-based forming process control system according to claim 8, characterized in that: The rating association unit (601) is used for the technical expert to judge whether there is a correlation between different process parameters and processing equipment, and to construct a collaborative control module of all the processing equipment with correlation corresponding to the process parameters according to the judgment result, and to determine the processing equipment directly associated with the process parameters as the main influencing processing equipment corresponding to the collaborative control module; The data sorting unit (602) is used to sort the real-time monitoring data of the same monitoring device according to the time series, and select a number of continuous real-time monitoring data that exceeds the adjustment threshold range but does not exceed the alarm threshold range as the data to be analyzed, obtain the duration of the data to be analyzed and the instructions that affect the change of the real-time monitoring data during the period, and obtain the instruction adjustment duration; The control parameter growth unit (603) is used to record the instruction adjustment time as a reference time after obtaining the instruction adjustment time of the main influencing processing equipment, select different processing equipment and the main influencing processing equipment from the collaborative control module for sequential combination, and obtain the instruction adjustment time under the combination as the combined adjustment time of the corresponding combination, and judge whether the combined adjustment time is less than the instruction adjustment time. If so, the combination is used as a common optimization adjustment response device for the corresponding process parameters, and corresponding adjustments are made according to the slowdown instruction with the data type and the increase instruction with the data type.

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