System and method for intelligently regulating and controlling cooling rate of solution treatment of aluminum alloy

Abstract

The invention relates to the technical field of intelligent regulation and control, and discloses an aluminum alloy solution treatment cooling rate intelligent regulation and control system and method, and the system comprises a temperature monitoring module, a thermal imaging reconstruction module, a mode recognition module, a cooling medium deployment module, a dynamic deviation evaluation module and a real-time regulation module. Carrying out distributed spatial synchronous temperature measurement on the aluminum alloy workpiece to obtain real-time temperature field distribution data; performing thermal imaging reconstruction on the real-time temperature field distribution data to obtain a surface temperature field distribution diagram; performing pattern recognition mapping on the surface temperature field distribution diagram to obtain cooling rate distribution data; cooling medium deployment is conducted on the aluminum alloy workpiece according to needs, and a space gradient cooling field is obtained; carrying out dynamic association analysis on the distribution data of the space gradient cooling field and the real-time temperature field to obtain cooling rate gradient information; correspondingly adjusting the flow velocity of the cooling medium in the cooling process to obtain a heat-treated workpiece; the regulation and control efficiency of the aluminum alloy solution treatment cooling rate can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent regulation, and in particular to an aluminum alloy solid solution treatment cooling rate intelligent regulation system and method. BACKGROUND

[0002] In the aluminum alloy solid solution treatment process, the precise regulation of the cooling rate directly affects the final mechanical properties and microstructure quality of the workpiece, but the existing technology has obvious defects in the synergy of temperature monitoring and cooling control. The existing cooling regulation scheme mostly uses overall temperature measurement and unified cooling strategy, which cannot realize distributed space synchronous temperature measurement of key parts of the aluminum alloy workpiece, cannot obtain comprehensive and real-time temperature field distribution data, cannot accurately identify the cooling demand differences of different regions of the workpiece, and thus causes the lack of pertinence of the cooling medium deployment, which easily causes local cooling to be too fast or too slow, causing stress concentration, deformation and even cracking inside the workpiece, and seriously affecting the performance stability of the workpiece after heat treatment.

[0003] At the same time, the existing technology lacks an intelligent dynamic adjustment mechanism based on process data, and it is difficult to cope with dynamic changes in the cooling process. The cooling parameters mostly depend on preset fixed values, and no association with the aluminum alloy process database is established, the cooling rate characteristics corresponding to the surface temperature field distribution cannot be analyzed through pattern recognition, and the deviation between the cooling field and the real-time temperature field cannot be dynamically evaluated. This makes the rate gradient anomaly in the cooling process cannot be perceived and corrected in time, and the cooling medium flow rate cannot be adjusted in real time according to the actual cooling state of the workpiece, which not only leads to poor consistency of the heat treatment quality of different batches of workpieces, but also reduces the overall processing efficiency, and is difficult to adapt to the diversified processing needs of aluminum alloy workpieces of different material compositions and different geometric sizes. Therefore, how to improve the regulation efficiency of the cooling rate of the aluminum alloy solid solution treatment has become a problem to be solved. SUMMARY

[0004] The present application provides an aluminum alloy solid solution treatment cooling rate intelligent regulation system and method to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides an aluminum alloy solid solution treatment cooling rate intelligent regulation system, characterized in that the system comprises a temperature monitoring module, a thermal imaging reconstruction module, a pattern recognition module, a cooling medium deployment module, a dynamic deviation evaluation module and a real-time adjustment module, wherein:

[0006] The thermal imaging reconstruction module is configured to perform thermal imaging reconstruction on the real-time temperature field distribution data to obtain a surface temperature field distribution map of the aluminum alloy workpiece.

[0007] The mode recognition module is configured to perform mode recognition mapping on the surface temperature field distribution map based on a preset aluminum alloy process database, so as to obtain cooling rate distribution data of the aluminum alloy workpiece.

[0008] The cooling medium deployment module is configured to perform on-demand cooling medium deployment on the aluminum alloy workpiece based on the cooling rate distribution data, so as to obtain a spatial gradient cooling field of the aluminum alloy workpiece.

[0009] The dynamic deviation evaluation module is configured to perform dynamic correlation analysis on the spatial gradient cooling field and the real-time temperature field distribution data, so as to obtain cooling rate gradient information of the aluminum alloy workpiece.

[0010] The real-time adjustment module is configured to perform corresponding adjustment on a cooling medium flow rate in a cooling process of the aluminum alloy workpiece according to the cooling rate gradient information, so as to obtain a heat-treated workpiece of the aluminum alloy workpiece.

[0011] In one preferred embodiment, when the temperature monitoring module performs distributed spatial synchronous temperature measurement on key part temperatures of the aluminum alloy workpiece in the solution treatment, so as to obtain real-time temperature field distribution data of the aluminum alloy workpiece, the temperature monitoring module is specifically configured to:

[0012] Fuse material composition parameters and geometric size parameters of the aluminum alloy workpiece to obtain workpiece characteristic information of the aluminum alloy workpiece;

[0013] Perform synchronous temperature measurement collection on surface key parts of the aluminum alloy workpiece based on the workpiece characteristic information, so as to obtain temperature data of the aluminum alloy workpiece;

[0014] Align the temperature data in space, so as to obtain real-time temperature field distribution data of the aluminum alloy workpiece.

[0015] In one preferred embodiment, when the thermal imaging reconstruction module performs thermal imaging reconstruction on the real-time temperature field distribution data, so as to obtain a surface temperature field distribution map of the aluminum alloy workpiece, the thermal imaging reconstruction module is specifically configured to:

[0016] Perform geometric space registration on the real-time temperature field distribution data, so as to obtain a temperature distribution matrix of the aluminum alloy workpiece;

[0017] Perform pseudo-color coding processing on the temperature distribution matrix, so as to obtain a pseudo-color temperature cloud picture of the aluminum alloy workpiece;

[0018] Perform edge contour fusion on the pseudo-color temperature cloud picture, so as to obtain the surface temperature field distribution map of the aluminum alloy workpiece.

[0019] In a preferred embodiment, when the mode recognition module performs the mode recognition mapping on the surface temperature field distribution based on the preset aluminum alloy process database to obtain the cooling rate distribution data of the aluminum alloy workpiece, it is specifically used for:

[0020] extracting and analyzing the temperature gradient distribution characteristics and isotherm evolution characteristics in the surface temperature field distribution to obtain the thermal process feature vector of the surface temperature field distribution;

[0021] based on the preset aluminum alloy process database, the thermal process feature vector is matched to obtain the dynamic cooling mode and associated process parameters of the aluminum alloy workpiece;

[0022] fuse the associated process parameters and the thermal process feature vector in multiple dimensions to obtain the process characteristics of the aluminum alloy workpiece;

[0023] co-render the process characteristics to obtain the process characteristic map of the aluminum alloy workpiece;

[0024] based on the dynamic cooling mode, the process characteristic map is segmented and quantized to obtain the cooling rate distribution data of the aluminum alloy workpiece.

[0025] In a preferred embodiment, when the mode recognition module performs the mode recognition mapping on the surface temperature field distribution based on the preset aluminum alloy process database to obtain the cooling rate distribution data of the aluminum alloy workpiece, it is specifically used for:

[0026] determine the cooling feature region boundary of the process characteristic map according to the cooling intensity distribution law in the dynamic cooling mode;

[0027] region fusion verification is performed on the cooling feature region boundary to obtain an optimized partition map of the cooling feature region boundary;

[0028] perform thermal conduction characteristic analysis on the optimized partition map to obtain the regional thermal feature parameters of the aluminum alloy workpiece;

[0029] fuse the regional thermal feature parameters to obtain the cooling rate distribution data of the aluminum alloy workpiece, wherein the calculation formula of the regional cooling rate is as follows:

[0030] ;

[0031] wherein, is the regional cooling rate of the optimized partition map, is the temperature gradient change rate of the regional thermal feature parameter, is the regional temperature difference, is the characteristic cooling time, an isotherm evolution rate in the area thermal characteristic parameter, a material thermal response coefficient of the aluminum alloy workpiece, a preset phase change suppression factor, a preset distribution uniformity correction factor.

[0032] In a preferred embodiment, when the cooling medium deployment module performs on-demand cooling medium deployment on the aluminum alloy workpiece based on the cooling rate distribution data to obtain a spatial gradient cooling field of the aluminum alloy workpiece, it is specifically used for:

[0033] based on the cooling rate distribution data, identifying surface high cooling demand areas and low cooling demand areas of the aluminum alloy workpiece to obtain a cooling demand partition of the aluminum alloy workpiece;

[0034] differential strategy making for the cooling demand partition to obtain a partition medium allocation scheme for the cooling demand partition;

[0035] based on the partition medium allocation scheme, synchronously adjusting operation parameters of cooling elements in the cooling demand partition to obtain a spatially coordinated cooling instruction;

[0036] executing the spatially coordinated cooling instruction to obtain a spatial gradient cooling field of the aluminum alloy workpiece.

[0037] In a preferred embodiment, when the dynamic deviation evaluation module performs dynamic correlation analysis on the spatial gradient cooling field and the real-time temperature field distribution data to obtain cooling rate gradient information of the aluminum alloy workpiece, it is specifically used for:

[0038] reconstructing and comparing the distribution characteristics of the spatial gradient cooling field with the real-time temperature field distribution data to obtain a cooling efficiency evaluation matrix of the aluminum alloy workpiece;

[0039] spatially interpolating the cooling efficiency evaluation matrix to obtain a regional cooling efficiency distribution map of the aluminum alloy workpiece;

[0040] performing similarity matching analysis on the regional cooling efficiency distribution map and process characteristics in the aluminum alloy process database to obtain a characteristic evolution trend in the cooling process of the aluminum alloy workpiece;

[0041] determining a dynamic process parameter set of the aluminum alloy workpiece in the cooling rate space variation according to the characteristic evolution trend;

[0042] performing regional characteristic analysis on the dynamic process parameter set to obtain cooling rate gradient information of the aluminum alloy workpiece.

[0043] In a preferred embodiment, when the dynamic deviation evaluation module performs similarity matching analysis between the regional cooling efficiency distribution map and the process features in the aluminum alloy process database to obtain the feature evolution trend during the cooling process of the aluminum alloy workpiece, it is specifically used for:

[0044] The cooling efficiency characteristic data of historical process cases in the aluminum alloy process database are extracted and statistically analyzed to obtain the reference feature set of the aluminum alloy process.

[0045] The regional cooling efficiency distribution map is compared with the reference feature set in multiple dimensions to obtain a similarity index set of regional cooling efficiency. The similarity calculation formula is as follows:

[0046] ;

[0047] In the formula, For the current feature vector and the first Similarity scores of each reference feature vector. The th feature vector of the current feature vector 1 eigenvalue, It is a natural exponential function. For the first in the reference feature set The first reference feature vector 1 eigenvalue, For the first Preset weighting factors for each feature dimension, Scaling parameters for similarity calculation The total dimension of the feature vectors;

[0048] Based on the similarity index set, the distribution consistency of typical change patterns in the region of the regional cooling efficiency distribution map is evaluated to obtain the characteristic change pattern set of the regional cooling efficiency distribution map;

[0049] Based on historical process cases of the aluminum alloy workpiece, the potential development trend of the feature change pattern set is deduced to obtain the feature evolution trend of the aluminum alloy workpiece.

[0050] In a preferred embodiment, when the real-time adjustment module adjusts the cooling medium flow rate of the aluminum alloy workpiece according to the cooling rate gradient information to obtain the heat-treated workpiece, it is specifically used for:

[0051] The cooling rate gradient information is deconstructed to obtain the partitioned control strategy of the cooling rate gradient information;

[0052] The execution of the partition control strategy is monitored to obtain the dynamic strategy response effect of the partition control strategy;

[0053] Based on the response effect of the dynamic strategy, the partition control strategy is adjusted synchronously to obtain an optimized strategy for the partition control strategy.

[0054] Based on the optimization strategy, the parameters of the cooling elements in the cooling demand zone are controlled in real time to obtain the heat-treated workpiece of the aluminum alloy workpiece.

[0055] To address the above problems, the present invention also provides a method for intelligently controlling the cooling rate of aluminum alloy solution treatment, the method comprising:

[0056] S1. Distributed spatial synchronous temperature measurement is performed on the temperature of key parts of the aluminum alloy workpiece during solution treatment to obtain real-time temperature field distribution data of the aluminum alloy workpiece.

[0057] S2. Perform thermal imaging reconstruction on the real-time temperature field distribution data to obtain the surface temperature field distribution map of the aluminum alloy workpiece.

[0058] S3. Based on a preset aluminum alloy process database, perform pattern recognition mapping on the surface temperature field distribution map to obtain the cooling rate distribution data of the aluminum alloy workpiece.

[0059] S4. Based on the cooling rate distribution data, the aluminum alloy workpiece is deployed with cooling medium as needed to obtain the spatial gradient cooling field of the aluminum alloy workpiece.

[0060] S5. Perform dynamic correlation analysis on the spatial gradient cooling field and the real-time temperature field distribution data to obtain the cooling rate gradient information of the aluminum alloy workpiece;

[0061] S6. Based on the cooling rate gradient information, adjust the flow rate of the cooling medium during the cooling process of the aluminum alloy workpiece accordingly to obtain the heat-treated workpiece of the aluminum alloy workpiece.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. This invention achieves distributed, synchronous spatial temperature measurement of key parts of aluminum alloy workpieces through a temperature monitoring module. Combined with a thermal imaging reconstruction module, it reconstructs real-time temperature field data to generate a surface temperature field distribution map. Then, a pattern recognition module maps temperature field characteristics to cooling rates based on a preset aluminum alloy process database, enabling accurate acquisition of workpiece cooling rate distribution data and providing precise data support for subsequent cooling control. Simultaneously, a cooling medium deployment module deploys cooling medium on demand based on the cooling rate distribution data, constructing a spatial gradient cooling field. This ensures that different areas of the workpiece receive appropriate cooling conditions, effectively protecting the mechanical properties and microstructure quality of the workpiece after heat treatment and improving workpiece performance stability.

[0064] 2. The dynamic deviation assessment module of this invention can dynamically correlate and analyze the spatial gradient cooling field and real-time temperature field distribution data, accurately capture the rate gradient information in the cooling process, and provide real-time deviation basis for cooling adjustment; the real-time adjustment module realizes the corresponding adjustment of the cooling medium flow rate based on the rate gradient information, and ensures that the cooling process always matches the actual cooling requirements of the workpiece through strategy monitoring and synchronous optimization. It not only realizes intelligent dynamic control of the cooling process, but also adapts to the processing requirements of aluminum alloy workpieces with different material compositions and geometric dimensions, improving the overall efficiency and processing consistency of aluminum alloy solution treatment. Attached Figure Description

[0065] Figure 1 This is a system architecture diagram of an intelligent control system for cooling rate during aluminum alloy solution treatment, provided in an embodiment of the present invention.

[0066] Figure 2 This is a flowchart illustrating an intelligent control method for cooling rate during aluminum alloy solution treatment, provided in one embodiment of the present invention.

[0067] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0070] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0071] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0072] In practice, the server-side equipment deployed in an intelligent control system for the cooling rate of aluminum alloy solution treatment may consist of one or more devices. This intelligent control system for the cooling rate of aluminum alloy solution treatment can be implemented as: a business instance, a virtual machine, or hardware devices. For example, this intelligent control system for the cooling rate of aluminum alloy solution treatment can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this intelligent control system for the cooling rate of aluminum alloy solution treatment can be understood as software deployed on a cloud node, used to provide each user terminal with an intelligent control system for the cooling rate of aluminum alloy solution treatment. Alternatively, this intelligent control system for the cooling rate of aluminum alloy solution treatment can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing each user terminal. Alternatively, this intelligent control system for the cooling rate of aluminum alloy solution treatment can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide each user terminal with an intelligent control system for the cooling rate of aluminum alloy solution treatment.

[0073] In terms of implementation, the intelligent control system for cooling rate of aluminum alloy solution treatment and the user terminal are mutually compatible. That is, if the intelligent control system for cooling rate of aluminum alloy solution treatment is implemented as an application installed on a cloud service platform, the user terminal is implemented as a client that establishes a communication connection with the application; or if the intelligent control system for cooling rate of aluminum alloy solution treatment is implemented as a website, the user terminal is implemented as a webpage; or if the intelligent control system for cooling rate of aluminum alloy solution treatment is implemented as a cloud service platform, the user terminal is implemented as a mini-program in an instant messaging application.

[0074] like Figure 1The figure shown is a system architecture diagram of an intelligent control system for cooling rate of aluminum alloy solution treatment provided in an embodiment of the present invention.

[0075] The intelligent cooling rate control system 100 for aluminum alloy solution treatment described in this invention can be installed on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the intelligent cooling rate control system 100 for aluminum alloy solution treatment may include a temperature monitoring module 101, a thermal imaging reconstruction module 102, a pattern recognition module 103, a cooling medium deployment module 104, a dynamic deviation evaluation module 105, and a real-time adjustment module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0076] In this embodiment of the invention, in an intelligent control system for the cooling rate of aluminum alloy solution treatment, each of the above-mentioned modules can be implemented independently and can be called upon with other modules. Here, "calling upon" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the intelligent control system for the cooling rate of aluminum alloy solution treatment provided by this embodiment of the invention, without modifying the program code, the applicable scope of the intelligent control system architecture for aluminum alloy solution treatment cooling rate can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion to quickly and flexibly expand the intelligent control system for the cooling rate of aluminum alloy solution treatment. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0077] The following describes, with reference to specific embodiments, each component and its specific workflow of an intelligent control system for cooling rate during aluminum alloy solution treatment:

[0078] The temperature monitoring module 101 is used to perform distributed spatial synchronous temperature measurement on key parts of the aluminum alloy workpiece during solution treatment, and obtain real-time temperature field distribution data of the aluminum alloy workpiece.

[0079] In this embodiment of the invention, when the temperature monitoring module performs distributed spatial synchronous temperature measurement of key parts of the aluminum alloy workpiece during solution treatment to obtain real-time temperature field distribution data of the aluminum alloy workpiece, it is specifically used for:

[0080] The material composition parameters and geometric dimension parameters of the aluminum alloy workpiece are fused to obtain the workpiece feature information.

[0081] Based on the workpiece feature information, synchronous temperature measurement and acquisition are performed on key parts of the surface of the aluminum alloy workpiece to obtain the temperature data of the aluminum alloy workpiece.

[0082] Spatially align the temperature data to obtain the real-time temperature field distribution data of the aluminum alloy workpiece.

[0083] Specifically, for the material composition parameters of aluminum alloy workpieces, a spectrometer is used to perform material detection on each area of ​​the workpiece to obtain the types and contents of alloy elements in each area. At the same time, a laser scanner is used to scan the overall shape of the workpiece to obtain the geometric dimensions such as length, width, and height of each part of the workpiece. Then, the obtained material composition parameters and geometric dimensions are correlated according to the different structural areas of the workpiece. For example, the alloy element content of the main body area of ​​the workpiece is correlated with the length and width parameters of that area, and the alloy element content of the corner areas of the workpiece is correlated with the geometric dimensions of that area. Through this regional correlation method, feature data fusion is completed, and finally, workpiece feature information containing the material composition and geometric dimensions of each area of ​​the aluminum alloy workpiece is formed.

[0084] Furthermore, based on the obtained workpiece feature information, the heating conditions of each region of the workpiece during the solution treatment process are analyzed to identify key areas on the workpiece surface that are prone to temperature deviations and have a significant impact on the quality of the solution treatment, such as thick-walled areas, thin-walled areas, and transition areas with different wall thicknesses. Then, thermocouple temperature measuring elements are installed at these key areas to ensure that the thermocouples at each identified key area can fit tightly against the workpiece surface. Subsequently, a data acquisition device capable of simultaneously receiving multiple temperature measurement signals is connected to all thermocouples, and the synchronous triggering function of the data acquisition device is set. When the solution treatment reaches the point where temperature measurement is required, the data acquisition device simultaneously triggers all thermocouples to start collecting temperature. Each thermocouple transmits the temperature value of the corresponding key area to the data acquisition device in real time. The data acquisition device records these temperature values ​​in real time, ultimately obtaining temperature data containing the temperatures of key areas on each surface of the aluminum alloy workpiece.

[0085] Furthermore, a fixed, easily identifiable point on the aluminum alloy workpiece is first used as the origin of the spatial coordinate system, such as a corner point at the bottom of the workpiece. A three-dimensional spatial coordinate system is then established. Based on the geometric dimension parameters of each key part in the previously acquired workpiece feature information, the specific coordinate position of each key part in the three-dimensional spatial coordinate system is determined. For example, the specific coordinates are determined by parameters such as the distance from the key part to the origin and the offset in the three coordinate axes. Then, the temperature value of each key part in the temperature data is matched one-to-one with the coordinate position of the key part in the three-dimensional spatial coordinate system. For example, the temperature value corresponding to the key part at a certain coordinate position is associated, and the temperature value corresponding to the key part at another coordinate position is associated. Finally, according to the spatial distribution order of the three-dimensional spatial coordinate system, all the associated coordinate positions and temperature values ​​are organized and arranged to form real-time temperature field distribution data that can reflect the spatial distribution of temperature in various parts of the aluminum alloy workpiece.

[0086] In general, the process involves fusing the material composition parameters and geometric dimension parameters of aluminum alloy workpieces with feature data. By integrating the two types of parameters according to the actual structural regions of the workpiece, the final result is workpiece feature information containing the material composition and geometric dimensions of each region of the workpiece.

[0087] In general, based on the acquired workpiece feature information, the key parts of the aluminum alloy workpiece surface that have a significant impact on the solid solution treatment quality are first analyzed and identified. Then, synchronous temperature measurement and acquisition operations are carried out on these key parts to obtain temperature data including the temperature of each key part.

[0088] In general, the process involves spatial alignment of the collected temperature data, associating and matching the temperature value of each key part with its corresponding spatial location, and finally obtaining real-time temperature field distribution data that reflects the spatial distribution of temperature in various parts of the workpiece.

[0089] The thermal imaging reconstruction module 102 is used to perform thermal imaging reconstruction on the real-time temperature field distribution data to obtain the surface temperature field distribution map of the aluminum alloy workpiece.

[0090] In this embodiment of the invention, when the temperature monitoring module performs distributed spatial synchronous temperature measurement of key parts of the aluminum alloy workpiece during solution treatment to obtain real-time temperature field distribution data of the aluminum alloy workpiece, it is specifically used for:

[0091] The material composition parameters and geometric dimension parameters of the aluminum alloy workpiece are fused to obtain the workpiece feature information.

[0092] Based on the workpiece feature information, synchronous temperature measurement and acquisition are performed on key parts of the surface of the aluminum alloy workpiece to obtain the temperature data of the aluminum alloy workpiece.

[0093] Spatially align the temperature data to obtain the real-time temperature field distribution data of the aluminum alloy workpiece.

[0094] Specifically, for the material composition parameters of the aluminum alloy workpiece, a spectrometer is used to scan and detect different structural regions of the workpiece. During the scanning of each region, the detection probe of the spectrometer is kept at a fixed distance from the workpiece surface and perpendicular to the detection surface. The probe receives the spectral signals reflected from the workpiece surface, and the types of alloying elements present in that region are identified based on the different wavelengths of the spectral signals. Simultaneously, the content of each alloying element is calculated based on the intensity of the spectral signals, and the material composition information of each region is recorded. For the geometric dimensional parameters of the aluminum alloy workpiece, a laser scanner is used to scan the entire workpiece. During the scanning process, the scanner is slowly moved around the workpiece to ensure that the laser beam covers all outer surfaces of the workpiece. After the laser beam contacts the workpiece surface, it reflects back to the scanner. The scanner calculates the distance from the scanning point to the scanner based on the time difference between laser emission and reception. Combined with the scanner's movement trajectory, it generates geometric dimension data such as the length, width, and height of each part of the workpiece, and records them according to different structural regions. Then, the material composition information of the same structural region is integrated with the geometric dimension data. For example, the types and contents of alloy elements in the main region are associated with the length and width data of that region, and the material composition information of the corner regions is associated with the geometric dimension data of that region. Through this region-based integration method, feature data fusion is completed, and finally, workpiece feature information containing the material composition and geometric dimensions of each structural region of the aluminum alloy workpiece is obtained.

[0095] Furthermore, based on the acquired workpiece feature information, the characteristics of each structural region are first analyzed to determine their impact on the heating during solution treatment. For example, thick-walled regions tend to have lower temperatures due to slower heat conduction, while thin-walled regions tend to have higher temperatures due to faster heat conduction. Temperature gradient differences are also common in transition areas with different wall thicknesses. Based on this, key surface areas that directly affect the quality of solution treatment and are prone to temperature anomalies are identified. Subsequently, thermocouple temperature sensing elements of the same number as those for the key areas are prepared. Oil and oxide layers are first cleaned from the surface of each key area to ensure a smooth surface. Then, the temperature sensing ends of the thermocouples are tightly attached to the surface of the key areas, and a high-temperature adhesive is used to secure the thermocouples. Secure the thermocouples to ensure there is no gap between the measuring end and the workpiece surface. Then, connect the signal output terminals of all thermocouples to the multi-channel data acquisition unit, turn on the data acquisition unit, and set the synchronous trigger mode so that all thermocouples can start measuring the temperature at the same time. When the aluminum alloy workpiece enters the solution treatment equipment and reaches the stage where temperature measurement is required, activate the synchronous trigger function of the data acquisition unit. All thermocouples simultaneously collect the temperature of the corresponding key parts. The thermocouples convert the temperature signal into an electrical signal and transmit it to the data acquisition unit in real time. The data acquisition unit records the temperature value corresponding to each electrical signal in real time, and finally obtains temperature data containing the temperature of key parts of each surface of the aluminum alloy workpiece.

[0096] Furthermore, first, a fixed and easily locatable point on the aluminum alloy workpiece is selected as the origin of the spatial coordinate system, such as the midpoint of the bottom edge of the workpiece. Using this origin as a reference, a coordinate axis is set along the length of the workpiece, a second coordinate axis along the width of the workpiece, and a third coordinate axis along the height of the workpiece, thus establishing a three-dimensional spatial coordinate system. Then, the geometric dimensions of each key part in the workpiece's feature information are consulted, such as the distance from a key part to the origin in the length, width, and height directions. Based on these distances, the specific coordinate position of the key part is determined in the three-dimensional spatial coordinate system, and so on. The method involves sequentially determining the coordinates of all key components in the coordinate system; then extracting the temperature value corresponding to each key component from the temperature data, and mapping each key component's temperature value to its coordinates in the three-dimensional spatial coordinate system. For example, a key component with coordinates at a specific location corresponds to the temperature value recorded for that location in the temperature data. Finally, according to the distribution order of the coordinates in the three-dimensional spatial coordinate system, all the data with associated coordinates and temperature values ​​are organized and arranged in a way that expands outward from the origin or is arranged sequentially along the coordinate axis direction, forming real-time temperature field distribution data that clearly reflects the spatial distribution of temperature in various parts of the aluminum alloy workpiece.

[0097] In general, the process involves fusing the material composition parameters and geometric dimensions of aluminum alloy workpieces. By integrating these two types of parameters according to different structural regions of the workpiece, the final result is workpiece feature information containing the material composition and geometric dimensions of each region of the aluminum alloy workpiece.

[0098] In general, based on the acquired workpiece feature information, the key parts of the aluminum alloy workpiece surface that have an important impact on the solid solution treatment quality are first identified, and then synchronous temperature measurement and acquisition operations are carried out on these key parts to obtain temperature data containing the temperature of each key part of the aluminum alloy workpiece surface.

[0099] In general, the process involves spatial alignment of the collected temperature data, associating and matching the temperature value of each key component with its specific location in the set spatial coordinate system, and finally obtaining real-time temperature field distribution data that reflects the spatial distribution of temperature in various parts of the aluminum alloy workpiece.

[0100] The pattern recognition module 103 is used to perform pattern recognition mapping on the surface temperature field distribution map based on a preset aluminum alloy process database to obtain the cooling rate distribution data of the aluminum alloy workpiece.

[0101] In this embodiment of the invention, when the pattern recognition module performs pattern recognition mapping on the surface temperature field distribution map based on a preset aluminum alloy process database to obtain the cooling rate distribution data of the aluminum alloy workpiece, it is specifically used for:

[0102] The thermal process feature vector of the surface temperature field distribution map is obtained by extracting and analyzing the temperature gradient distribution characteristics and isotherm evolution characteristics of the surface temperature field distribution map.

[0103] Based on a pre-set aluminum alloy process database, feature module matching is performed on the thermal process feature vector to obtain the dynamic cooling mode and associated process parameters of the aluminum alloy workpiece.

[0104] The process characteristics of the aluminum alloy workpiece are obtained by multi-dimensional data fusion of the associated process parameters and the thermal process feature vector.

[0105] The process features are collaboratively rendered to obtain the process feature map of the aluminum alloy workpiece;

[0106] Based on the dynamic cooling mode, the process feature map is segmented and quantized to obtain the cooling rate distribution data of the aluminum alloy workpiece.

[0107] When the pattern recognition module performs feature region segmentation and quantization on the process feature map based on the dynamic cooling mode to obtain the cooling rate distribution data of the aluminum alloy workpiece, it is specifically used for:

[0108] Based on the distribution law of cooling intensity in the dynamic cooling mode, the boundary of the cooling characteristic region of the process characteristic map is determined;

[0109] The boundaries of the cooling feature regions are fused and verified to obtain an optimized partition map of the cooling feature region boundaries.

[0110] The optimized zoning map is analyzed for thermal conductivity characteristics to obtain the regional thermal characteristic parameters of the aluminum alloy workpiece.

[0111] The cooling rate distribution data of the aluminum alloy workpiece is obtained by transient thermal fusion of the regional thermal characteristic parameters. The formula for calculating the regional cooling rate is as follows:

[0112] ;

[0113] In the formula, The regional cooling rate of the optimized partition map. The temperature gradient change rate of the thermal characteristic parameters of the region. For regional temperature difference, Characteristic cooling time, The isotherm evolution rate is one of the thermal characteristic parameters of the region. The thermal response coefficient of the aluminum alloy workpiece is given. The preset phase transition suppression factor, This is a preset distribution uniformity correction factor.

[0114] Specifically, for the surface temperature field distribution map, firstly, the direction of temperature change from high temperature region to low temperature region is identified, and the difference in temperature value between different adjacent regions is observed to determine the temperature gradient distribution characteristics. Then, the shape of the isotherms, the density between isotherms, and the direction of extension of isotherms in the map are identified to determine the isotherm evolution characteristics. Subsequently, the extracted temperature gradient distribution characteristics and isotherm evolution characteristics are integrated according to the regional division order of the surface temperature field distribution map, and vector data containing temperature gradient and isotherm characteristics are formed in a fixed arrangement to obtain the thermal process characteristic vector of the surface temperature field distribution map.

[0115] Furthermore, the pre-established aluminum alloy process database stores feature modules classified according to different dynamic cooling modes. Each feature module contains typical thermal process feature information under the corresponding dynamic cooling mode and the process parameters associated with that mode. The obtained thermal process feature vector is compared one by one with the typical thermal process feature information of each feature module in the database. During the comparison, the consistency between the temperature gradient distribution feature in the vector and the typical temperature gradient feature in the module is matched first, and then the consistency between the isotherm evolution feature in the vector and the typical isotherm feature in the module is matched. The feature module that completely matches the thermal process feature vector is found. The dynamic cooling mode corresponding to the feature module is the dynamic cooling mode of the aluminum alloy workpiece, and the process parameters attached to the feature module are the associated process parameters of the aluminum alloy workpiece.

[0116] Furthermore, two core dimensions for data fusion are first determined: the thermal process feature dimension and the process parameter dimension. Then, each specific parameter in the associated process parameter is associated and bound to the feature information of the corresponding region in the thermal process feature vector. For example, the temperature gradient feature of a certain region is bound to the medium parameter used when cooling that region, and the isotherm feature of another region is bound to the cooling time parameter of that region. Subsequently, all the associated and bound information is integrated into a unified data set containing thermal process features and process parameters to obtain the process features of the aluminum alloy workpiece.

[0117] Furthermore, collaborative rendering rules are first set, including: using different color schemes to distinguish the temperature gradient levels of different regions in the process features, and using different line styles to distinguish the different types of associated process parameters in the process features. Then, according to these rules, on a blank map template, the region boundaries of the map are first determined based on the region division in the process features, and then the temperature gradient features of each region are filled with the corresponding color scheme. At the same time, the associated process parameters of the region are marked in the region with the corresponding line styles to ensure that the color and line style of each region accurately match the information in the process features, and finally the process feature map of the aluminum alloy workpiece is generated.

[0118] Furthermore, based on the obtained dynamic cooling mode, the criteria for segmenting the feature regions are determined. For example, under a certain dynamic cooling mode, the regions in the process feature map with a temperature gradient greater than a specific range and associated process parameter of water cooling are classified as rapid cooling zones; the regions with a temperature gradient in the medium range and associated process parameter of air cooling are classified as medium-speed cooling zones; and the regions with a temperature gradient less than a specific range and associated process parameter of natural cooling are classified as slow cooling zones. According to this criterion, feature regions such as rapid cooling zones, medium-speed cooling zones, and slow cooling zones are divided on the process feature map with clear boundary lines. Then, for each segmented feature region, combined with the cooling pattern of this type of region under the dynamic cooling mode, the temperature change information in the process features within the region and the time information of the associated process parameters are statistically analyzed to calculate the temperature drop rate per unit time for each region. The temperature drop rate per unit time for all regions is organized into a data table according to the region location to obtain the cooling rate distribution data of the aluminum alloy workpiece.

[0119] Specifically, based on the cooling intensity distribution pattern in the dynamic cooling mode, on the process feature map, according to the boundary line of color and line style, a map boundary drawing tool is used to draw a closed boundary line along the boundary line. Each boundary line accurately surrounds a complete cooling intensity level area, ensuring that the boundary lines are unbroken and unoverlapping, and finally determining the cooling feature area boundary of the process feature map.

[0120] Furthermore, the initially determined boundaries of the cooling feature regions are imported into the region verification template, and the boundaries of adjacent cooling feature regions are checked one by one: if there is an overlap, the overlapping regions are compared with the corresponding cooling intensity levels in the dynamic cooling mode, the boundary segments that match the level are retained, and the redundant overlapping parts are deleted; if there is a gap, the region to which the gap belongs is determined according to the cooling intensity levels of the regions on both sides of the gap, and the corresponding region boundary line is extended to cover the gap to achieve seamless boundary connection. At the same time, it is verified whether the boundary of each region completely surrounds the map region of the corresponding cooling intensity level, without omissions or breaks. After the adjustment is completed, the boundary lines are redrawn to form a partition map with a complete structure and clear boundaries, and the optimized partition map of the cooling feature region boundaries is obtained.

[0121] Furthermore, standard thermal conductivity data of aluminum alloy materials are collected. Each partition of the optimized partition map is treated as an independent unit. The temperature change of each partition over a continuous time period is recorded using thermal characteristic observation equipment. The rate of temperature transfer from the edge to the center and the temperature stabilization time at each point are observed. Combined with standard thermal conductivity data, the heat transfer efficiency and temperature stability of each partition are determined. These information reflecting the heat conduction are organized into a parameter set in a unified format to obtain the regional thermal characteristic parameters of the aluminum alloy workpiece.

[0122] Furthermore, the time range of transient thermal fusion is determined, and this time period is divided into multiple time nodes at fixed intervals. At each node, the regional thermal characteristic parameters of each partition are extracted. The parameters of different nodes in the same partition are arranged in chronological order to form a transient thermal characteristic sequence. The temperature change difference between adjacent nodes in that partition is calculated. The difference is divided by the node interval to obtain the temperature change value per unit time. Finally, the temperature change values ​​per unit time of all partitions are arranged according to their corresponding positions in the optimized partition map to form a complete data set containing the cooling rate information of each partition, thus obtaining the cooling rate distribution data of the aluminum alloy workpiece.

[0123] In general, the cooling intensity distribution pattern contained in the dynamic cooling mode is combined with the manifestation of different cooling intensities on the process feature map. The area range is clearly defined by drawing boundary lines, and finally the cooling feature area boundary of the process feature map is determined.

[0124] In summary, the process involves checking and adjusting the boundaries of the initially determined cooling feature regions, addressing issues such as boundary overlaps and gaps, ensuring that the region boundaries are complete and smoothly connected, and after verification, forming a clearly structured partition map, thus obtaining an optimized partition map of the cooling feature region boundaries.

[0125] In general, each partition of the optimized partition map is treated as an independent unit. Combined with the standard thermal conductivity characteristics of aluminum alloy materials, the heat transfer efficiency, temperature stability and other thermal conductivity-related factors of each partition are observed and analyzed. After processing, the regional thermal characteristic parameters of the aluminum alloy workpiece are obtained.

[0126] In general, the process involves extracting regional thermal characteristic parameters at fixed intervals from different time points within each partition during the complete cooling period, calculating the temperature change per unit time, and then arranging these values ​​according to the partition location to ultimately obtain the cooling rate distribution data of the aluminum alloy workpiece.

[0127] In summary, the process involves extracting and analyzing the temperature gradient distribution characteristics and isotherm evolution characteristics from the surface temperature field distribution map, integrating the two types of feature information into vector data in a corresponding manner, and finally obtaining the thermal process feature vector of the surface temperature field distribution map.

[0128] In general, it relies on a pre-set aluminum alloy process database, compares and matches the obtained thermal process feature vectors with the feature modules corresponding to different dynamic cooling modes in the database one by one, and after finding the matching feature modules, the dynamic cooling mode and associated process parameters of the aluminum alloy workpiece are obtained.

[0129] In general, the process involves associating and binding the associated process parameters with the thermal process feature vector from two dimensions: thermal process features and process parameters. All the bound information is then integrated into a unified data set to ultimately obtain the process features of the aluminum alloy workpiece.

[0130] In general, the process involves dividing the region of the process features, temperature gradient features, and related process parameters on the map template according to the set collaborative rendering rules, completing color filling and line annotation, and finally obtaining the process feature map of the aluminum alloy workpiece.

[0131] In general, the characteristic region segmentation criteria are determined based on the dynamic cooling mode. Different characteristic regions are divided on the process characteristic map. Then, the temperature change and process parameter time information of each region are statistically analyzed and the temperature drop rate per unit time is calculated. After processing, the cooling rate distribution data of aluminum alloy workpieces is obtained.

[0132] The cooling medium deployment module 104 is used to deploy cooling medium on demand for the aluminum alloy workpiece based on the cooling rate distribution data, thereby obtaining a spatial gradient cooling field for the aluminum alloy workpiece. In this embodiment of the invention, when the cooling medium deployment module performs on-demand cooling medium deployment for the aluminum alloy workpiece based on the cooling rate distribution data to obtain a spatial gradient cooling field for the aluminum alloy workpiece, it is specifically used for:

[0133] Based on the cooling rate distribution data, the high cooling demand area and low cooling demand area on the surface of the aluminum alloy workpiece are identified to obtain the cooling demand partition of the aluminum alloy workpiece.

[0134] Differentiated strategies are formulated for the cooling demand zones to obtain the partitioned media allocation scheme for the cooling demand zones;

[0135] Based on the partitioned medium allocation scheme, the operating parameters of the cooling elements in the cooling demand partition are adjusted synchronously to obtain a spatially coordinated cooling command.

[0136] Executing the spatial coordinated cooling command yields a spatial gradient cooling field for the aluminum alloy workpiece.

[0137] Specifically, based on the cooling rate distribution data, the criteria for judging cooling demand are first defined: areas with a small temperature drop per unit time in the cooling rate distribution data indicate insufficient cooling effect and belong to high cooling demand areas; areas with a large temperature drop per unit time indicate that the current cooling effect meets basic requirements and belong to low cooling demand areas. Then, the cooling rate distribution data is mapped one-to-one with the surface spatial positions of the aluminum alloy workpiece. On the workpiece surface schematic diagram, all areas with a small temperature drop per unit time are marked with a specific marker, and all areas with a large temperature drop per unit time are marked with another specific marker. Clear lines are used to delineate the boundaries of the different marked areas to ensure that the scope of each area is clear, non-overlapping, and without gaps, ultimately obtaining the cooling demand zones of the aluminum alloy workpiece.

[0138] Furthermore, for the obtained cooling demand zones, cooling medium-related strategies are formulated for high-cooling-demand areas and low-cooling-demand areas respectively: For high-cooling-demand areas, a cooling medium with higher cooling intensity is selected, and the medium supply method is determined to be close-range directional spraying to ensure that the medium can directly and quickly act on the area to improve cooling efficiency; for low-cooling-demand areas, a cooling medium with lower cooling intensity is selected, and the medium supply method is determined to be long-range diffusion spraying to avoid over-cooling affecting workpiece performance. At the same time, the supply duration and spraying frequency of the cooling medium in each zone are defined, with longer supply duration and higher spraying frequency in high-cooling-demand areas, and shorter supply duration and lower spraying frequency in low-cooling-demand areas. These medium types, supply methods, supply durations, and spraying frequencies for different zones are organized into a structured document to obtain the zoned medium allocation scheme for the cooling demand zones.

[0139] Furthermore, the requirements in the zoned media distribution scheme are first converted into specific operating parameters for the cooling elements: For cooling elements corresponding to areas with high cooling demand, the nozzle spray angle is adjusted to a position close to the workpiece surface according to the media supply method; the nozzle flow rate is adjusted to the maximum level according to the media supply flow rate requirements; and the nozzle spray interval time is set according to the spray frequency requirements. For cooling elements corresponding to areas with low cooling demand, the nozzle spray angle is adjusted to a certain angle with the workpiece surface; the flow rate is adjusted to a medium-low level; and a longer spray interval time is set. Subsequently, the operating parameters of each cooling element are input into the control interface one by one using the centralized control device of the cooling system. After input, parameter co-verification is initiated to check whether there are conflicts between the parameters of cooling elements in different areas. If conflicts exist, the parameters are fine-tuned to eliminate the conflicts, ensuring that the operating parameters of all cooling elements are coordinated. Finally, all the verified operating parameters of the cooling elements are integrated into a unified control command to obtain a spatially coordinated cooling command.

[0140] Furthermore, the obtained spatially coordinated cooling command is transmitted to the control unit of the cooling medium supply system. Based on the parameters in the command, the control unit controls the corresponding cooling elements to start operation one by one: cooling elements in areas with high cooling demand spray water-cooling medium at a set angle, flow rate, and frequency; cooling elements in areas with low cooling demand spray air-cooling medium at a set angle, flow rate, and frequency. During the operation of the cooling elements, temperature sensors monitor the temperature changes in various areas of the aluminum alloy workpiece in real time, confirming that the actual operating state of the cooling elements is consistent with the requirements of the spatially coordinated cooling command, with no parameter deviations or element malfunctions. As the cooling elements continue to operate according to the command, different areas on the surface of the aluminum alloy workpiece develop cooling intensity gradients, ultimately forming a stable spatial distribution of cooling intensity that meets the requirements on the workpiece surface, thus obtaining the spatial gradient cooling field of the aluminum alloy workpiece.

[0141] In general, based on the cooling rate distribution data, the high cooling demand area and the low cooling demand area are distinguished by judging the temperature drop rate of the area per unit time. The partition boundaries are marked on the schematic diagram of the workpiece surface, and finally the cooling demand partition of the aluminum alloy workpiece is obtained.

[0142] In general, the approach involves developing differentiated strategies for different cooling demand zones. For areas with high cooling demand, a medium with high cooling intensity and corresponding supply methods, durations, and frequencies are selected. For areas with low cooling demand, a medium with low cooling intensity and matching parameters are selected. After processing, a zoned medium allocation scheme for cooling demand zones is obtained.

[0143] In general, the partitioned medium allocation scheme is transformed into specific operating parameters for cooling elements. These parameters are then input into a centralized control device and undergo collaborative verification to eliminate conflicts. The resulting integrated spatially coordinated cooling command is then obtained.

[0144] In general, the spatial coordinated cooling command is transmitted to the cooling system control unit, which controls the corresponding cooling elements to operate according to the command, monitors in real time to ensure that the operation status meets the requirements, so that the cooling intensity gradient difference is formed on the surface of the workpiece, and finally obtains the spatial gradient cooling field of the aluminum alloy workpiece.

[0145] The dynamic deviation evaluation module 105 is used to dynamically correlate and analyze the spatial gradient cooling field and the real-time temperature field distribution data to obtain the cooling rate gradient information of the aluminum alloy workpiece.

[0146] In this embodiment of the invention, when the dynamic deviation evaluation module performs dynamic correlation analysis on the spatial gradient cooling field and the real-time temperature field distribution data to obtain the cooling rate gradient information of the aluminum alloy workpiece, it is specifically used for:

[0147] The distribution characteristics of the spatial gradient cooling field are compared with the real-time temperature field distribution data to reconstruct the heat flux density field, thereby obtaining the cooling efficiency evaluation matrix of the aluminum alloy workpiece.

[0148] Spatial interpolation is performed on the cooling efficiency evaluation matrix to obtain a regional cooling efficiency distribution map of the aluminum alloy workpiece;

[0149] By performing similarity matching analysis between the regional cooling efficiency distribution map and the process features in the aluminum alloy process database, the characteristic evolution trend of the aluminum alloy workpiece during the cooling process can be obtained.

[0150] Based on the aforementioned characteristic evolution trend, a dynamic set of process parameters for the aluminum alloy workpiece is determined as the cooling rate varies in space.

[0151] Regional feature analysis is performed on the dynamic process parameter set to obtain the cooling rate gradient information of the aluminum alloy workpiece.

[0152] The dynamic deviation evaluation module, when performing similarity matching analysis between the regional cooling efficiency distribution map and the process features in the aluminum alloy process database to obtain the feature evolution trend during the cooling process of the aluminum alloy workpiece, is specifically used for:

[0153] The cooling efficiency characteristic data of historical process cases in the aluminum alloy process database are extracted and statistically analyzed to obtain the reference feature set of the aluminum alloy process.

[0154] The regional cooling efficiency distribution map is compared with the reference feature set in multiple dimensions to obtain a similarity index set of regional cooling efficiency. The similarity calculation formula is as follows:

[0155] ;

[0156] In the formula, For the current feature vector and the first Similarity scores of each reference feature vector. The th feature vector of the current feature vector 1 eigenvalue, It is a natural exponential function. For the first in the reference feature set The first reference feature vector 1 eigenvalue, For the first Preset weighting factors for each feature dimension, Scaling parameters for similarity calculation The total dimension of the feature vectors;

[0157] Based on the similarity index set, the distribution consistency of typical change patterns in the region of the regional cooling efficiency distribution map is evaluated to obtain the characteristic change pattern set of the regional cooling efficiency distribution map;

[0158] Based on historical process cases of the aluminum alloy workpiece, the potential development trend of the feature change pattern set is deduced to obtain the feature evolution trend of the aluminum alloy workpiece.

[0159] Specifically, the distribution characteristics of the spatial gradient cooling field are first defined as the spatial distribution of cooling intensity in each region, and the real-time temperature field distribution data are the real-time temperature values ​​of each region of the aluminum alloy workpiece. Then, based on the spatial coordinates of the workpiece surface, the cooling intensity of each region in the spatial gradient cooling field is correlated with the temperature value of the corresponding coordinate region in the real-time temperature field distribution data to construct a heat flux density field model. The differences in heat flux density in different regions of the model are then compared, and these differences are organized into a table according to the workpiece surface coordinates. Each row in the table corresponds to a coordinate region, and each column corresponds to the heat flux density-related evaluation index, thus obtaining the cooling efficiency evaluation matrix of the aluminum alloy workpiece.

[0160] Furthermore, the existing coordinate region evaluation data in the cooling efficiency evaluation matrix is ​​extracted, and the blank area between two adjacent existing evaluation points is determined. The distance-weighted interpolation method is used: the distance from the blank area to two adjacent evaluation points is used as the weight to calculate the cooling efficiency value of the blank area. The cooling efficiency value of all blank areas is calculated in turn. Then, on the schematic diagram of the aluminum alloy workpiece surface, all existing evaluation points and the calculated cooling efficiency values ​​of the blank areas are marked with different shades of color, and the marked points of adjacent areas are connected to form a continuous color transition area to ensure that the entire workpiece surface has corresponding cooling efficiency markings, thus obtaining the regional cooling efficiency distribution map of the aluminum alloy workpiece.

[0161] Furthermore, standard process features stored in the preset aluminum alloy process database are retrieved, and the obtained regional cooling efficiency distribution map is compared with the standard process features in the database one by one: first, the overlap between the positions of high, medium, and low cooling efficiency regions in the distribution map and the corresponding regions in the standard features is compared; then, the consistency between the changing trends of cooling efficiency in each region in the distribution map and the changing trends of efficiency in the standard features is compared, and the standard process feature with the highest similarity to the current distribution map is found. Subsequently, based on the time evolution law of the standard process feature in the cooling process, combined with the cooling stage of the current distribution map, the changing direction and range of regional cooling efficiency distribution in the subsequent cooling process are inferred, and the characteristic evolution trend in the cooling process of the aluminum alloy workpiece is obtained.

[0162] Furthermore, based on the clearly defined direction of cooling rate change in the characteristic evolution trend, the corresponding dynamic process parameters are determined: for regions where the cooling rate will accelerate, cooling medium flow rate parameters and cooling element injection angle parameters that can maintain this rate are matched; for regions where the cooling rate will slow down, cooling medium supply frequency parameters and cooling element spacing parameters that can increase the rate are matched. At the same time, these parameters are divided according to cooling time stages to ensure that the parameters of each stage can adapt to the characteristic evolution trend. The parameters of all stages are organized into a structured set that includes parameter type, applicable region, and applicable time, thus obtaining the dynamic process parameter set of the aluminum alloy workpiece in the space of cooling rate change.

[0163] Furthermore, the dynamic process parameter set is divided according to the surface area of ​​the aluminum alloy workpiece, with each area corresponding to a set of process parameters. The changes in cooling rate reflected by each set of parameters are analyzed: for example, if the flow rate of the cooling medium in a certain area increases, the cooling rate in that area will increase, and the magnitude of the increase in flow rate corresponds to the magnitude of the rate increase; if the spray angle of the cooling element in a certain area is adjusted, the uniformity of the cooling rate in that area will change, and the magnitude of the angle adjustment corresponds to the degree of uniformity change in the rate. Then, the information such as the magnitude of the cooling rate change and the uniformity of the change in each area is associated with the spatial location of that area. This information is organized according to the spatial distribution order of the workpiece surface to clarify the degree of difference in cooling rate between different areas and obtain the cooling rate gradient information of the aluminum alloy workpiece.

[0164] Specifically, historical process cases stored in the aluminum alloy process database are screened one by one, and cases that are consistent with the current aluminum alloy workpiece material and structure type are selected. Cooling efficiency-related feature data are extracted from each selected case, including the location of high cooling efficiency areas, the range of medium cooling efficiency areas, the distribution of low cooling efficiency areas in different cooling stages of the case, and the direction of change of cooling efficiency in each area over time. These extracted feature data are classified and organized according to cooling stages, and feature data of the same cooling stage are grouped together. The regional characteristics and change trends are clearly marked in each group, and finally a set containing typical cooling efficiency features of different cooling stages is formed, which is the reference feature set of the aluminum alloy process.

[0165] Furthermore, three core dimensions for multi-dimensional similarity comparison were identified: regional location dimension, range dimension, and trend dimension. First, in the regional location dimension, the high, medium, and low cooling efficiency regions in the regional cooling efficiency distribution map were compared with the corresponding efficiency regions in the reference feature set for each cooling stage, observing the degree of overlap between their regional boundaries. In the range dimension, the area size of each efficiency region in the distribution map was compared with the area of ​​the corresponding region in the reference feature set. In the trend dimension, the consistency between the expansion or contraction direction of each efficiency region in the distribution map and the change direction of the corresponding region in the reference feature set was examined. The comparison results for each dimension were labeled with three levels: "highly similar," "basically similar," and "significantly different." The labeling results of the three dimensions were integrated into a set of indicators. This comparison and indicator integration was performed once for the feature data of each cooling stage in the reference feature set, ultimately forming a set containing multiple sets of comparison indicators, thus obtaining the similarity index set of the regional cooling efficiency.

[0166] Furthermore, we first analyze the overall situation of each group of indicators in the similarity index set. If the three dimensions of regional location, range, and change trend in a certain group of indicators are all "highly consistent", then we determine that the cooling efficiency change pattern in the reference feature set corresponding to that group is consistent with the distribution of the regional cooling efficiency distribution map, and directly extract the change pattern in the reference feature set. If there are dimensions that are "basically consistent" in a certain group of indicators, we only need to fine-tune the features corresponding to that dimension. For example, if the high cooling efficiency area in the reference feature set is on the left and the high cooling efficiency area in the distribution map is in the middle left, we fine-tune it to "the high cooling efficiency area slowly shrinks in the middle left", retaining the consistent features of other dimensions to form a change pattern that adapts to the distribution map. We then arrange all the change patterns that have passed the consistency evaluation and are adapted to the current distribution map in the order of cooling stages to obtain the feature change pattern set of the regional cooling efficiency distribution map.

[0167] Furthermore, from historical process cases of aluminum alloy workpieces, cases that are completely consistent with the first half of the current feature change pattern set are selected. The actual development of the second half of the feature change pattern set in these selected cases is examined. For example, in historical cases, the first half is "the high cooling efficiency region slowly shrinks to the left and center, and the medium cooling efficiency region steadily expands," while the second half develops into "the high cooling efficiency region completely disappears and the medium cooling efficiency region extends to the right." At the same time, the time interval and cooling environment conditions from the current cooling stage to the subsequent stage in these cases are recorded. Combined with the actual cooling progress of the current aluminum alloy workpiece, the duration of the subsequent cooling stage of the current workpiece is inferred by referring to the time interval of historical cases. Combined with the environmental conditions of historical cases, it is confirmed that there are no special interferences in the current environment. Thus, it is deduced that the current workpiece will subsequently show the change direction of "the high cooling efficiency region gradually disappears and the medium cooling efficiency region extends to the right." This change direction is combined with the stage duration to form a complete trend description, thus obtaining the feature evolution trend of the aluminum alloy workpiece.

[0168] In summary, the distribution characteristics of the spatial gradient cooling field are compared with the real-time temperature field distribution data to reconstruct the heat flux density field. The information of the two is associated with the workpiece surface coordinates as a reference and the differences are compared. The results are compiled into a table containing evaluation indicators, and finally the cooling efficiency evaluation matrix of the aluminum alloy workpiece is obtained.

[0169] In summary, the cooling efficiency evaluation matrix is ​​calculated using a distance-weighted interpolation method to calculate the cooling efficiency value of the blank area between adjacent evaluation points. Then, the efficiency values ​​of all areas are marked with color on the schematic diagram of the workpiece surface to form a continuous transition, and finally, a regional cooling efficiency distribution map of the aluminum alloy workpiece is obtained.

[0170] In general, the method involves comparing the regional cooling efficiency distribution map with the standard process features in the aluminum alloy process database one by one, finding the standard feature with the highest similarity, and inferring subsequent changes based on its time evolution pattern, ultimately obtaining the feature evolution trend in the cooling process of aluminum alloy workpieces.

[0171] In general, the dynamic process parameter set of aluminum alloy workpieces with varying cooling rates in the space of cooling rate variation is obtained by matching the cooling medium parameters and cooling element parameters of the corresponding regions according to the direction of cooling rate change in the characteristic evolution trend, and then dividing and organizing them according to the cooling stage.

[0172] In general, the dynamic process parameter set is divided according to the surface area of ​​the workpiece, the cooling rate change reflected by each set of parameters is analyzed, the spatial position of the associated regions is sorted out to determine the degree of difference, and finally the cooling rate gradient information of the aluminum alloy workpiece is obtained.

[0173] In summary, the process involves selecting historical process cases from the aluminum alloy process database that are consistent with the current aluminum alloy workpiece material and structure, extracting the cooling efficiency characteristic data of different cooling stages in these cases, classifying and organizing them according to stage, and finally obtaining a reference feature set of aluminum alloy processes.

[0174] In summary, the method compares the regional cooling efficiency distribution map with the feature data of each cooling stage in the reference feature set from three dimensions: regional location, range, and trend. The comparison results of each dimension are labeled with a level and integrated into an index, ultimately resulting in a similarity index set of regional cooling efficiency.

[0175] In general, the consistency between the change patterns in the reference feature set and the regional cooling efficiency distribution map is judged based on the similarity index set. Patterns that meet the conditions are directly extracted or finely adjusted for adaptation, and then arranged according to the cooling stage to finally obtain the feature change pattern set of the regional cooling efficiency distribution map.

[0176] In summary, the process involves selecting historical process cases of aluminum alloy workpieces that are consistent with the first half of the feature change pattern set, referring to the actual development of the second half of the pattern in the case, and combining the current workpiece cooling progress and environmental conditions to deduce potential trends, ultimately obtaining the feature evolution trend of aluminum alloy workpieces.

[0177] The real-time adjustment module 106 is used to adjust the flow rate of the cooling medium during the cooling process of the aluminum alloy workpiece according to the cooling rate gradient information, so as to obtain the heat-treated workpiece of the aluminum alloy workpiece.

[0178] In this embodiment of the invention, when the real-time adjustment module adjusts the flow rate of the cooling medium during the cooling process of the aluminum alloy workpiece according to the cooling rate gradient information to obtain the heat-treated workpiece, it is specifically used for:

[0179] The cooling rate gradient information is deconstructed to obtain the partitioned control strategy of the cooling rate gradient information;

[0180] The execution of the partition control strategy is monitored to obtain the dynamic strategy response effect of the partition control strategy;

[0181] Based on the response effect of the dynamic strategy, the partition control strategy is adjusted synchronously to obtain an optimized strategy for the partition control strategy.

[0182] Based on the optimization strategy, the parameters of the cooling elements in the cooling demand zone are controlled in real time to obtain the heat-treated workpiece of the aluminum alloy workpiece.

[0183] Specifically, the cooling rate gradient information is analyzed to first clarify the differences in cooling rates in each region, such as which regions have cooling rates lower than expected, which regions have cooling rates in line with expectations, and which regions have cooling rates higher than expected. Then, based on these differences, the direction of cooling medium flow rate adjustment for each region is determined: for regions with cooling rates lower than expected, the cooling medium flow rate of the corresponding cooling element needs to be increased; for regions with cooling rates in line with expectations, the cooling medium flow rate of the corresponding cooling element remains unchanged; for regions with cooling rates higher than expected, the cooling medium flow rate of the corresponding cooling element is reduced. Subsequently, the adjustment direction of each region, the corresponding cooling element identification, and the specific direction of flow rate adjustment are organized by region to form a specific flow rate control scheme for different cooling demand zones, thus obtaining the zoned control strategy for the cooling rate gradient information.

[0184] Furthermore, during the execution of the partition control strategy, a flow rate sensor is used to collect the actual cooling medium flow rate of each cooling element in real time, and a temperature sensor is used to collect the temperature change data of each cooling demand partition in real time. The actual cooling rate of each partition is calculated based on the temperature change data. The collected actual flow rate is compared with the target flow rate set in the partition control strategy to determine whether the actual flow rate meets the strategy requirements. At the same time, the calculated actual cooling rate is compared with the expected cooling rate to determine whether the cooling effect after the strategy is executed meets the expectations. The flow rate comparison results, cooling rate comparison results, and any deviations of each partition are recorded in real time and compiled into a document containing the strategy execution status and effect feedback of each partition, thus obtaining the dynamic strategy response effect of the partition control strategy.

[0185] Furthermore, the deviations in the dynamic strategy response are analyzed. If the actual flow rate of a cooling demand zone is lower than the target flow rate and the actual cooling rate does not meet expectations, it is determined that the cooling medium flow rate of the corresponding cooling element in that zone needs to be further increased. If the actual flow rate of a zone is higher than the target flow rate and the actual cooling rate exceeds expectations, it is determined that the cooling medium flow rate of the corresponding cooling element in that zone needs to be further reduced. If the actual flow rate of a zone is the same as the target flow rate, but the actual cooling rate still does not meet the standard, it is investigated whether the medium flow is obstructed. In this case, the spray angle of the cooling element needs to be fine-tuned to optimize the flow rate utilization efficiency. Specific adjustment plans are formulated for each zone with deviations, and the corresponding flow rate parameters or element auxiliary parameters in the zone control strategy are modified. At the same time, it is ensured that the adjusted parameters will not interfere with the cooling effect of adjacent zones. All modified zone control contents are integrated to obtain the optimized strategy of the zone control strategy.

[0186] Furthermore, the cooling medium flow rate parameters and injection angle parameters corresponding to each cooling demand zone in the optimization strategy are converted into specific control commands for the cooling elements. These commands are sent one by one to the corresponding cooling elements through the centralized control unit of the cooling system. The control unit receives the operating status data fed back by each cooling element in real time, and at the same time, the real-time temperature and cooling rate of each zone of the aluminum alloy workpiece are continuously monitored by temperature sensors to ensure that the cooling rate of each zone is always maintained within the expected range. When the workpiece temperature drops to the target temperature required for solution treatment and the cooling rate of each zone stabilizes within a reasonable range, the control unit sends a stop command to stop the supply of cooling medium, thereby completing the cooling heat treatment process of the aluminum alloy workpiece and obtaining the heat-treated aluminum alloy workpiece.

[0187] In summary, the analysis involves identifying the differences in cooling rates across different regions within the cooling rate gradient information, determining the direction of flow rate regulation for the cooling medium in different regions, organizing these into specific flow rate regulation schemes for each region, and ultimately obtaining a zoned regulation strategy for the cooling rate gradient information.

[0188] In summary, when the zone control strategy is executed, sensors are used to collect the actual flow rate of the cooling element and the temperature of the corresponding area in real time, calculate the actual cooling rate and compare it with the expectation, record the deviation of the flow rate and cooling rate, and finally obtain the dynamic strategy response effect of the zone control strategy.

[0189] In summary, the process involves analyzing deviations in the dynamic strategy response, developing adjustment plans to address issues such as insufficient or excessive flow rate or parameter mismatch, modifying the flow rate or auxiliary parameters of components in the zonal control strategy to ensure no interference with adjacent areas, and ultimately obtaining an optimized zonal control strategy.

[0190] In general, the optimization strategy is translated into control commands for the cooling elements and sent out. The operating status of the elements, workpiece temperature, and cooling rate are monitored in real time. Cooling is stopped once the workpiece temperature reaches the standard and the cooling rate stabilizes, and finally the heat-treated aluminum alloy workpiece is obtained.

[0191] Reference Figure 2 The diagram shown is a flowchart illustrating an intelligent control method for the cooling rate of aluminum alloy solution treatment according to an embodiment of the present invention. In this embodiment, the intelligent control method for the cooling rate of aluminum alloy solution treatment includes:

[0192] S1. Distributed spatial synchronous temperature measurement is performed on the temperature of key parts of the aluminum alloy workpiece during solution treatment to obtain real-time temperature field distribution data of the aluminum alloy workpiece.

[0193] S2. Perform thermal imaging reconstruction on the real-time temperature field distribution data to obtain the surface temperature field distribution map of the aluminum alloy workpiece.

[0194] S3. Based on a preset aluminum alloy process database, perform pattern recognition mapping on the surface temperature field distribution map to obtain the cooling rate distribution data of the aluminum alloy workpiece.

[0195] S4. Based on the cooling rate distribution data, the aluminum alloy workpiece is deployed with cooling medium as needed to obtain the spatial gradient cooling field of the aluminum alloy workpiece.

[0196] S5. Perform dynamic correlation analysis on the spatial gradient cooling field and the real-time temperature field distribution data to obtain the cooling rate gradient information of the aluminum alloy workpiece;

[0197] S6. Based on the cooling rate gradient information, adjust the flow rate of the cooling medium during the cooling process of the aluminum alloy workpiece accordingly to obtain the heat-treated workpiece of the aluminum alloy workpiece.

[0198] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0199] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent control system for cooling rate during solution treatment of aluminum alloys, characterized in that, The system includes a temperature monitoring module, a thermal imaging reconstruction module, a pattern recognition module, a cooling medium deployment module, a dynamic deviation assessment module, and a real-time adjustment module, wherein: The temperature monitoring module is used to perform distributed spatial synchronous temperature measurement of key parts of the aluminum alloy workpiece during solution treatment, and obtain real-time temperature field distribution data of the aluminum alloy workpiece. The thermal imaging reconstruction module is used to perform thermal imaging reconstruction on the real-time temperature field distribution data to obtain a surface temperature field distribution map of the aluminum alloy workpiece. The pattern recognition module is used to perform pattern recognition mapping on the surface temperature field distribution map based on a preset aluminum alloy process database to obtain the cooling rate distribution data of the aluminum alloy workpiece. The cooling medium deployment module is used to deploy the cooling medium on demand for the aluminum alloy workpiece based on the cooling rate distribution data, thereby obtaining a spatial gradient cooling field for the aluminum alloy workpiece. The dynamic deviation evaluation module is used to dynamically correlate and analyze the spatial gradient cooling field and the real-time temperature field distribution data to obtain the cooling rate gradient information of the aluminum alloy workpiece. The real-time adjustment module is used to adjust the flow rate of the cooling medium during the cooling process of the aluminum alloy workpiece according to the cooling rate gradient information, so as to obtain the heat-treated workpiece of the aluminum alloy workpiece.

2. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 1, characterized in that, When the temperature monitoring module performs distributed spatial synchronous temperature measurement of key parts of the aluminum alloy workpiece during solution treatment to obtain real-time temperature field distribution data of the aluminum alloy workpiece, it is specifically used for: The material composition parameters and geometric dimension parameters of the aluminum alloy workpiece are fused to obtain the workpiece feature information. Based on the workpiece feature information, synchronous temperature measurement and acquisition are performed on key parts of the surface of the aluminum alloy workpiece to obtain the temperature data of the aluminum alloy workpiece. Spatially align the temperature data to obtain the real-time temperature field distribution data of the aluminum alloy workpiece.

3. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 1, characterized in that, When the thermal imaging reconstruction module performs thermal imaging reconstruction on the real-time temperature field distribution data to obtain the surface temperature field distribution map of the aluminum alloy workpiece, it is specifically used for: Geometric spatial registration is performed on the real-time temperature field distribution data to obtain the temperature distribution matrix of the aluminum alloy workpiece. The temperature distribution matrix is ​​processed by pseudo-color encoding to obtain a pseudo-color temperature cloud map of the aluminum alloy workpiece. Edge contour fusion is performed on the pseudo-color temperature cloud map to obtain the surface temperature field distribution map of the aluminum alloy workpiece.

4. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 1, characterized in that, When the pattern recognition module performs pattern recognition mapping on the surface temperature field distribution map based on a preset aluminum alloy process database to obtain the cooling rate distribution data of the aluminum alloy workpiece, it is specifically used for: The thermal process feature vector of the surface temperature field distribution map is obtained by extracting and analyzing the temperature gradient distribution characteristics and isotherm evolution characteristics of the surface temperature field distribution map. Based on a pre-set aluminum alloy process database, feature module matching is performed on the thermal process feature vector to obtain the dynamic cooling mode and associated process parameters of the aluminum alloy workpiece. The process characteristics of the aluminum alloy workpiece are obtained by multi-dimensional data fusion of the associated process parameters and the thermal process feature vector. The process features are collaboratively rendered to obtain the process feature map of the aluminum alloy workpiece; Based on the dynamic cooling mode, the process feature map is segmented and quantized to obtain the cooling rate distribution data of the aluminum alloy workpiece.

5. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 4, characterized in that, When the pattern recognition module performs feature region segmentation and quantization on the process feature map based on the dynamic cooling mode to obtain the cooling rate distribution data of the aluminum alloy workpiece, it is specifically used for: Based on the distribution law of cooling intensity in the dynamic cooling mode, the boundary of the cooling characteristic region of the process characteristic map is determined; The boundaries of the cooling feature regions are fused and verified to obtain an optimized partition map of the cooling feature region boundaries. The optimized zoning map is analyzed for thermal conductivity characteristics to obtain the regional thermal characteristic parameters of the aluminum alloy workpiece. The cooling rate distribution data of the aluminum alloy workpiece is obtained by transient thermal fusion of the regional thermal characteristic parameters. The formula for calculating the regional cooling rate is as follows: ； In the formula, The regional cooling rate of the optimized partition map. The temperature gradient change rate of the thermal characteristic parameters of the region. For regional temperature difference, Characteristic cooling time, The isotherm evolution rate is one of the thermal characteristic parameters of the region. The thermal response coefficient of the aluminum alloy workpiece is given. The preset phase transition suppression factor, This is a preset distribution uniformity correction factor.

6. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 1, characterized in that, When the cooling medium deployment module performs on-demand cooling medium deployment on the aluminum alloy workpiece based on the cooling rate distribution data to obtain the spatial gradient cooling field of the aluminum alloy workpiece, it is specifically used for: Based on the cooling rate distribution data, the high cooling demand area and low cooling demand area on the surface of the aluminum alloy workpiece are identified to obtain the cooling demand partition of the aluminum alloy workpiece. Differentiated strategies are formulated for the cooling demand zones to obtain the partitioned media allocation scheme for the cooling demand zones; Based on the partitioned medium allocation scheme, the operating parameters of the cooling elements in the cooling demand partition are adjusted synchronously to obtain a spatially coordinated cooling command. Executing the spatial coordinated cooling command yields a spatial gradient cooling field for the aluminum alloy workpiece.

7. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 1, characterized in that, When the dynamic deviation evaluation module performs dynamic correlation analysis on the spatial gradient cooling field and the real-time temperature field distribution data to obtain the cooling rate gradient information of the aluminum alloy workpiece, it is specifically used for: The distribution characteristics of the spatial gradient cooling field are compared with the real-time temperature field distribution data to reconstruct the heat flux density field, thereby obtaining the cooling efficiency evaluation matrix of the aluminum alloy workpiece. Spatial interpolation is performed on the cooling efficiency evaluation matrix to obtain a regional cooling efficiency distribution map of the aluminum alloy workpiece; By performing similarity matching analysis between the regional cooling efficiency distribution map and the process features in the aluminum alloy process database, the characteristic evolution trend of the aluminum alloy workpiece during the cooling process can be obtained. Based on the aforementioned characteristic evolution trend, a dynamic set of process parameters for the aluminum alloy workpiece is determined as the cooling rate varies in space. Regional feature analysis is performed on the dynamic process parameter set to obtain the cooling rate gradient information of the aluminum alloy workpiece.

8. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 7, characterized in that, The dynamic deviation evaluation module, when performing similarity matching analysis between the regional cooling efficiency distribution map and the process features in the aluminum alloy process database to obtain the feature evolution trend during the cooling process of the aluminum alloy workpiece, is specifically used for: The cooling efficiency characteristic data of historical process cases in the aluminum alloy process database are extracted and statistically analyzed to obtain the reference feature set of the aluminum alloy process. The regional cooling efficiency distribution map is compared with the reference feature set in multiple dimensions to obtain a similarity index set of regional cooling efficiency. The similarity calculation formula is as follows: ； In the formula, For the current feature vector and the first Similarity scores of each reference feature vector. The th feature vector of the current feature vector 1 eigenvalue, It is a natural exponential function. For the first in the reference feature set The first reference feature vector 1 eigenvalue, For the first Preset weighting factors for each feature dimension, Scaling parameters for similarity calculation The total dimension of the feature vectors; Based on the similarity index set, the distribution consistency of typical change patterns in the region of the regional cooling efficiency distribution map is evaluated to obtain the characteristic change pattern set of the regional cooling efficiency distribution map; Based on historical process cases of the aluminum alloy workpiece, the potential development trend of the feature change pattern set is deduced to obtain the feature evolution trend of the aluminum alloy workpiece.

9. The intelligent control system for cooling rate of aluminum alloy solution treatment as described in claim 1, characterized in that, When the real-time adjustment module adjusts the cooling medium flow rate of the aluminum alloy workpiece according to the cooling rate gradient information to obtain the heat-treated workpiece, it is specifically used for: The cooling rate gradient information is deconstructed to obtain the partitioned control strategy of the cooling rate gradient information; The execution of the partition control strategy is monitored to obtain the dynamic strategy response effect of the partition control strategy; Based on the response effect of the dynamic strategy, the partition control strategy is adjusted synchronously to obtain an optimized strategy for the partition control strategy. Based on the optimization strategy, the parameters of the cooling elements in the cooling demand zone are controlled in real time to obtain the heat-treated workpiece of the aluminum alloy workpiece.

10. A method for intelligently controlling the cooling rate during solution treatment of aluminum alloys, characterized in that, The method includes: S1. Distributed spatial synchronous temperature measurement is performed on the temperature of key parts of the aluminum alloy workpiece during solution treatment to obtain real-time temperature field distribution data of the aluminum alloy workpiece. S2. Perform thermal imaging reconstruction on the real-time temperature field distribution data to obtain the surface temperature field distribution map of the aluminum alloy workpiece. S3. Based on a preset aluminum alloy process database, perform pattern recognition mapping on the surface temperature field distribution map to obtain the cooling rate distribution data of the aluminum alloy workpiece. S4. Based on the cooling rate distribution data, the aluminum alloy workpiece is deployed with cooling medium as needed to obtain the spatial gradient cooling field of the aluminum alloy workpiece. S5. Perform dynamic correlation analysis on the spatial gradient cooling field and the real-time temperature field distribution data to obtain the cooling rate gradient information of the aluminum alloy workpiece; S6. Based on the cooling rate gradient information, adjust the flow rate of the cooling medium during the cooling process of the aluminum alloy workpiece accordingly to obtain the heat-treated workpiece of the aluminum alloy workpiece.

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