Vacuum brazing uniform heating and cooling system with multi-temperature-zone cooperative control

Abstract

The invention relates to the technical field of vacuum brazing furnace temperature control, and discloses a vacuum brazing uniform heating and cooling system with multi-temperature-zone cooperative control. According to the system, a temperature deviation trend spectrum is generated by matching a multi-zone actual temperature curve with a workpiece target temperature curve, multi-temperature-zone energy dynamic rebalance is started accordingly, and a preliminary power scheduling atlas and collaborative cooling trigger nodes are generated. And then heat flow disturbance compensation amount caused by heat radiation of the hearth is calculated and integrated with the atlas and the nodes to form a unified final process control atlas, and the atlas comprises heating correction and cooling scheduling instructions at the same time. Predictive regulation and control of the furnace temperature change trend and active compensation of temperature interval thermal disturbance can be achieved, the heating process and the cooling process are highly coordinated, and the temperature uniformity and the process consistency of a large or complex workpiece in the brazing process are improved.

Description

Technical Field

[0001] This invention relates to the field of vacuum brazing furnace temperature control technology, specifically a vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control. Background Technology

[0002] In the vacuum brazing process of large or complex structural components, the uniformity of the temperature profile across different parts of the workpiece is extremely important. The current mainstream technology employs multi-zone independent closed-loop control. Each zone adjusts the output power of its heating element via an independent PID controller based on the deviation between the temperature measured by its own thermocouple and the preset process curve. The cooling phase is typically triggered by a program-driven ventilation or spray system, whose start / stop logic is relatively independent of the power control of the heating zones.

[0003] This type of technical solution has shortcomings. Independent control of each temperature zone focuses only on its own setpoint, ignoring the strong temperature zone coupling effect caused by thermal radiation and convection within the furnace. Power adjustments in one temperature zone can act as thermal disturbances, affecting adjacent zones and causing overall temperature field fluctuations. PID control is essentially a lag correction, unable to predict or proactively compensate for non-uniform heating trends caused by differences in workpiece heat capacity or loading position. Heating and cooling are treated as two separate control stages; switching between them can easily generate new thermal stresses within the workpiece, affecting the final brazing quality. Existing methods lack a holistic analysis of the evolution trend of temperature deviations across multiple temperature zones and fail to quantify and incorporate the complex thermal flow disturbances within the furnace into the control model.

[0004] The core problem this invention aims to solve is how to overcome the lag and isolation inherent in independent control of multiple temperature zones, and achieve prediction and global coordinated regulation of the entire furnace temperature field trend. Simultaneously, it needs to address the issues of disconnect between heating and cooling process control, and the difficulty in compensating for thermal radiation disturbances within temperature zones, thereby obtaining truly uniform heating and controlled cooling. Summary of the Invention

[0005] The purpose of this invention is to provide a vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control, the system comprising: The data acquisition module obtains the actual temperature values ​​of multiple heating zones in the vacuum brazing furnace and the heating power time sequence record. The trend spectrum generation module matches the actual temperature value set with the preset target temperature reference curves for each part of the workpiece to generate a temperature deviation trend spectrum. The dynamic rebalancing module initiates a multi-temperature zone energy dynamic rebalancing process based on the temperature deviation trend spectrum. The multi-temperature zone energy dynamic rebalancing process generates a preliminary power scheduling spectrum and a collaborative cooling trigger node. The disturbance compensation module calculates the amount of heat flow disturbance compensation caused by heat radiation exchange inside the furnace based on the preliminary power scheduling map. The control map synthesis module integrates the coordinated cooling trigger node, the preliminary power scheduling map, and the heat flow disturbance compensation amount to form a final process control map that acts on all heating sections. The final process control map includes heating correction instructions and cooling scheduling instructions.

[0007] Preferably, the actual temperature value set is matched with a preset target temperature reference curve for each part of the workpiece to generate a temperature deviation trend spectrum, including: Traverse each heating zone and extract the power supply history of the current zone and the thermal radiation interference history of adjacent zones from the heating power time sequence record; The real-time measurement points in the actual temperature value set are mapped to the corresponding theoretical points of the target temperature reference curve of each part of the workpiece in the time dimension, and the real-time difference queue is calculated one by one. The real-time difference queue is subjected to time-series feature extraction to identify temperature deviation trends that are systematic, temperature disturbance components that are random fluctuations, and temperature delay components that are heat conduction hysteresis. By combining the temperature deviation trend, the temperature disturbance component, and the temperature delay component with the power supply history and the thermal radiation interference history, a multi-dimensional temperature deviation analysis model is constructed. The multi-dimensional temperature deviation analysis model is run to output a comprehensive data set that includes deviation direction, deviation change gradient, deviation duration and deviation spatial correlation. The comprehensive data set is defined as the temperature deviation trend spectrum.

[0008] Preferably, the initiation of the multi-temperature zone energy dynamic rebalancing process, which generates a preliminary power scheduling map and a collaborative cooling trigger node, includes: The deviation direction and deviation change gradient in the temperature deviation trend spectrum are input into an energy allocation evaluation network, which calculates the energy base value required to compensate for the temperature deviation trend, and the energy modulation amount required to smooth out the temperature disturbance component. Based on the spatial correlation of the deviation in the temperature deviation trend spectrum, the coupling strength of the thermal influence between multiple heating sections is analyzed, and a temperature zone interaction influence matrix is ​​established. Using the energy base value and the energy modulation amount as inputs, and the temperature zone interaction influence matrix as constraints, a multi-objective parallel optimization calculation is performed. The multi-objective parallel optimization calculation aims to solve a set of power adjustment amounts that make the overall temperature field closest to the target temperature reference curve of each part of the workpiece. From the solution results of the multi-objective parallel optimization calculation, a set of instructions for directly adjusting the output power of the heater is separated, and the set of instructions constitutes the preliminary power scheduling map; Simultaneously, based on the mapping relationship between the duration of deviation in the temperature deviation trend spectrum and the heating stage, the critical conditions for the process to enter the cooling stage are predicted, and the time point or temperature point that meets the critical conditions is marked as the collaborative cooling trigger node.

[0009] Preferably, the step of analyzing the coupling strength of thermal influences between multiple heating zones based on the spatial correlation of deviations in the temperature deviation trend spectrum, and establishing a temperature zone interaction influence matrix, includes: Spatial correlation data of deviations are extracted from the temperature deviation trend spectrum. The spatial correlation data of deviations includes the degree of spatial correlation of temperature deviations in different heating sections. The spatial correlation data of the deviation is processed by the correlation analysis method, and the temperature deviation correlation coefficient between each two heating sections is calculated. The temperature deviation correlation coefficient is used as the initial estimate of the thermal effect coupling strength. By incorporating the physical layout distance of the heating zone and historical heat transfer data, the initial estimate is weighted and corrected to obtain the final thermal influence coupling strength value. Using all heating sections as row and column indices, the final thermal influence coupling strength value between each pair of heating sections is filled into the corresponding positions in the matrix to form the temperature zone interaction influence matrix.

[0010] Preferably, based on the preliminary power scheduling diagram, the compensation amount for heat flow disturbance caused by heat radiation exchange inside the furnace is calculated, including: Based on the power adjustment amount of each heating section in the preliminary power scheduling map, the theoretical radiation intensity distribution on the surface of the heating body of each heating section after adjustment is simulated and calculated. Based on the geometric arrangement and surface characteristics of the heating element, heat shield, and workpiece in the vacuum brazing furnace, a radiation angle coefficient network model is established. The theoretical radiation intensity distribution is input into the radiation angle coefficient network model to calculate the net change in radiative heat flux received by the workpiece surface and key monitoring points inside the furnace. The net change in radiative heat flux is defined as a primary radiation disturbance. Further considering the furnace wall reflection and the workpiece's own re-radiation effect, the primary radiation disturbance is iteratively corrected and calculated to obtain the steady-state radiative heat flux offset after multiple reflections and absorptions. The steady-state radiative heat flux offset is converted into an equivalent heating power compensation value. This equivalent heating power compensation value is the heat flux disturbance compensation amount, which is used to offset the indirect radiative heat interference caused by active power adjustment.

[0011] Preferably, the step of simulating and calculating the theoretical radiation intensity distribution on the surface of the heating body in each heating section after adjustment, based on the power adjustment amount of each heating section in the preliminary power scheduling map, includes: Analyze the preliminary power scheduling map to obtain the planned power adjustment amount for each heating section; Based on the resistance characteristics and thermal capacity parameters of the heating element material, the power adjustment is converted into the expected temperature change value of the heating element surface; Based on the fundamental law of thermal radiation, the theoretical energy radiated per unit area of ​​the heating element per unit time is calculated based on the expected temperature change. By integrating the geometric shape factor and orientation characteristics of the heating element surface, the theoretical energy value per unit area is converted into a radiation intensity distribution map in three-dimensional space; Repeat the above process for each heating section to obtain the theoretical radiation intensity distribution on the surface of the heating body in all heating sections after adjustment.

[0012] Preferably, the step of establishing a radiation angle coefficient network model based on the geometric arrangement and surface characteristics of the heating element, heat shield, and workpiece in the vacuum brazing furnace includes: Collect three-dimensional geometric data of all key components in the vacuum brazing furnace, including the installation position of the heating element, the outline dimensions of the heat shield, and the specific shape of the workpiece; The radiation characteristics parameters of the surfaces of each key component, including emissivity and reflectivity, are obtained through surface testing devices or material databases. Using the principles of geometric optics, the radiation angle coefficient between any two surfaces is calculated. The radiation angle coefficient represents the proportion of radiation energy emitted from the first surface and directly reaching the second surface. Treating all surfaces inside the furnace as nodes and using the radiation angle coefficient as the edge weight, a fully connected weighted network model is constructed. The conservation of the weighted network model is verified to ensure that the sum of the radiation angle coefficients of all surfaces to other surfaces conforms to the law of conservation of energy, and the radiation angle coefficient network model is finally established.

[0013] Preferably, the coordinated cooling trigger node, the preliminary power scheduling map, and the heat flow disturbance compensation amount are integrated to form a final process control map acting on all heating sections, including: The heat flow disturbance compensation is superimposed on the power adjustment of the corresponding heating section in the preliminary power scheduling map to generate a comprehensive power scheduling scheme corrected for radiation effect. Analyze the power change trajectory of the integrated power scheduling scheme on the time axis to identify power abrupt change points that may cause uneven cooling rates or thermal stress; Near the power abrupt change point, a power ramp command with a smooth transition is inserted to form a smoothed integrated power scheduling scheme; Based on the aforementioned collaborative cooling trigger node, the startup logic for the cooling stage is formulated, which includes cooling startup conditions triggered by temperature and cooling startup conditions triggered by process time. For the cooling stage, based on the historical temperature uniformity of the workpiece, different initial flow rates and flow rate variation curves of the cooling medium are assigned to different heating zones. The smoothed integrated power scheduling scheme, the cooling stage startup scheme including startup logic, and the differentiated cooling medium control scheme are arranged and synchronized according to the process timeline, and encoded to generate the final process control map. The heating correction instruction comes from the smoothed integrated power scheduling scheme, and the cooling scheduling instruction comes from the cooling stage startup scheme and the differentiated cooling medium control scheme.

[0014] Preferably, after generating a comprehensive power scheduling scheme corrected for radiation effects, the method further includes: Monitor the real-time pressure inside the vacuum furnace and obtain the heat treatment gas composition data for the current heating stage; Based on the real-time pressure value inside the vacuum furnace, the influence factor of gas molecules on radiative heat transfer in the radiation angle coefficient network model is corrected. Based on the heat treatment gas composition data, calculate the estimated potential heat exchange capacity of gas convection; Using the corrected influence factor and the estimated potential heat exchange capacity, the integrated power dispatch scheme corrected for radiation effect is fine-tuned a second time to generate the final power dispatch command that takes into account the effects of pressure and gas composition.

[0015] Preferably, the allocation of differentiated initial flow rates and flow rate variation curves of cooling media to different heating zones includes: Based on the smoothed integrated power scheduling scheme, the residual thermal energy storage of each heating section at the cooling trigger moment is calculated in reverse. The heating sections are sorted according to the amount of residual heat energy stored, with higher initial flow rates of cooling medium allocated to heating sections with larger storage and lower initial flow rates of cooling medium allocated to heating sections with smaller storage. Set a target cooling temperature curve and calculate the theoretical heat transfer required for each heating section to cool from the current temperature to the target temperature. Based on the theoretical heat transfer and the initial flow rate allocated, the required flow rate of the cooling medium at each moment during the cooling process is dynamically calculated to form a flow rate reference curve that varies with time or temperature. A cooling uniformity feedback mechanism is introduced, which monitors the actual temperature drop rate of each heating section in real time during the cooling process and compares it with the average drop rate. Based on the comparison results, the flow reference curve is dynamically fine-tuned to ultimately form the differentiated initial flow rate and flow change curve of the cooling medium.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By matching the collected actual temperatures from multiple zones with the target temperature curve of the workpiece, a temperature deviation trend spectrum reflecting future deviations is generated. Based on this spectrum, a dynamic energy rebalancing process is initiated, enabling proactive scheduling of power output in each temperature zone and planning the triggering timing of coordinated cooling. This method changes the passive response mode to local temperature deviations, realizing proactive, pre-set power allocation based on the overall trend. It eliminates temperature overshoot or undershoot caused by control lag and isolated temperature zone actions, allowing the temperature of each part of the workpiece to more closely track the ideal process curve.

[0017] Based on the preliminary power scheduling diagram, the compensation amount for dynamic heat flow disturbances caused by heat radiation exchange within the furnace is specifically calculated. This compensation amount is integrated with the coordinated cooling trigger node and the preliminary scheduling diagram to form a unified final process control diagram. This diagram also includes refined heating correction commands and cooling scheduling commands. The active compensation mechanism suppresses mutual thermal interference between temperature ranges and stabilizes the thermal field within the furnace. The integrated diagram ensures a smooth and coordinated transition from heating to cooling. The triggering of cooling commands is precisely matched with the power attenuation in the heating zone, avoiding sudden temperature changes, reducing thermal stress on the workpiece during critical process stages, and improving the integrity and consistency of brazed joints. Attached Figure Description

[0018] Figure 1 This is a timing diagram of the vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control as described in this invention. Figure 2 A flowchart for generating a temperature deviation trend spectrum; Figure 3 A flowchart for establishing the temperature zone interaction influence matrix; Figure 4 A smoothed power scheduling diagram for multi-temperature zone collaborative control in vacuum brazing; Figure 5 This is a matrix diagram of the thermal influence coupling strength in the temperature zone of a vacuum brazing furnace. Detailed Implementation

[0019] 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 are only some embodiments of the present invention, and not all embodiments. 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.

[0020] Please see Figure 1 This invention provides a multi-temperature zone coordinated control system for uniform heating and cooling in vacuum brazing. The system includes: a data acquisition module that acquires real-time temperature measurements of multiple preset heating zones within the vacuum brazing furnace, forming a set of these actual temperature values; and a module that also records the heating power output data of each heating zone during the process sequence. A trend spectrum generation module compares and matches the acquired set of actual temperature values ​​with preset target temperature reference curves required for different parts of the workpiece at various stages of the process, generating a temperature deviation trend spectrum reflecting the dynamic characteristics of temperature deviation based on this matching relationship. A dynamic rebalancing module initiates a multi-temperature zone energy dynamic rebalancing process based on the generated temperature deviation trend spectrum. This process generates a preliminary power scheduling map for adjusting the heating power of each heating zone through a series of calculations, and simultaneously determines the trigger nodes for coordinated cooling operations during the process. Based on the preliminary power scheduling map generated by the dynamic rebalancing module, the disturbance compensation module calculates the indirect heat flow disturbance caused by heat radiation exchange between components such as the heating element, workpiece, and heat shield inside the furnace, which changes with power adjustments, and quantitatively determines the compensation amount required to offset this disturbance. The control map synthesis module integrates the collaborative cooling trigger node from the dynamic rebalancing module, the preliminary power scheduling map, and the heat flow disturbance compensation amount from the disturbance compensation module. It fuses and processes this information to form a final process control map that can be applied to all heating sections. This final process control map contains heating correction instructions for precise heating control of each heating section, as well as cooling scheduling instructions for controlling the cooling rate of each temperature zone during the cooling stage.

[0021] Example 1: See Figure 2In this embodiment, the trend spectrum generation module performs the step of generating a temperature deviation trend spectrum. This module traverses each heating section of the vacuum brazing furnace. For the currently traversed section, it extracts the historical power supply data of that section within a historical time period from the heating power time-series records obtained by the data acquisition module. It also extracts the historical data of potential thermal radiation interference from adjacent sections in its physical location. The module maps each real-time measured temperature point in the actual temperature value set to the corresponding theoretical temperature point on the preset target temperature reference curve for each part of the workpiece, along the process time axis. By calculating the difference between each real-time measurement point and the corresponding theoretical point, a real-time difference queue arranged in chronological order is formed. Next, a time-series feature extraction analysis is performed on this real-time difference queue, identifying and separating three characteristic components from the queue data: a directional and persistent systematic temperature deviation trend, a random temperature disturbance component exhibiting high-frequency fluctuations, and a temperature delay component caused by the time required for heat transfer. This module correlates the identified temperature deviation trend, temperature disturbance component, and temperature delay component with previously extracted historical power supply and thermal radiation interference data to jointly construct a multi-dimensional temperature deviation analysis model. Running this multi-dimensional temperature deviation analysis model outputs a comprehensive dataset. This dataset systematically includes the directionality of the deviation, the gradient of the deviation over time, the duration of the deviation, and the spatial correlation characteristics of the deviation between different heating sections. This comprehensive dataset is defined as the temperature deviation trend spectrum used for subsequent control decisions.

[0022] In practical implementation, a vacuum brazing furnace is used to braze a large plate-fin heat exchanger with complex internal flow channels. The vacuum brazing furnace is vertically divided into three independent heating sections: upper, middle, and lower. The data acquisition module synchronously acquires the actual temperature values ​​of the three heating sections once per second, forming a set of actual temperature values ​​containing three temperature sequences. Simultaneously, the data acquisition module records the percentage output power of the heater in each heating section for each second over the past ten minutes, forming a heating power time-series record. The trend spectrum generation module begins the process of generating a temperature deviation trend spectrum. The trend spectrum generation module sequentially traverses the upper, middle, and lower heating sections. For the currently traversing upper heating section, the trend spectrum generation module extracts the power output percentage change sequence of the upper heating section over the past five minutes from the heating power time-series record as the power supply history. Simultaneously, the trend spectrum generation module extracts the heat flow fluctuation sequence caused by the adjacent middle heating section to the upper heating section over the past five minutes, estimated by a thermal radiation monitoring device, as the thermal radiation interference history. The trend spectrum generation module maps the Celsius temperature value measured at timestamp t for the upper heating section in the actual temperature value set to the theoretical Celsius temperature value at timestamp t for the target temperature reference curve of the upper part of the workpiece. It calculates the difference between the two and performs the same mapping and calculation operation on the middle heating section and the lower heating section, forming a real-time difference queue with a one-to-one correspondence between timestamps for each heating section.

[0023] In some embodiments, the trend spectrum generation module extracts time-series features from the real-time difference queue. The identification process is completed by analyzing the statistical characteristics and change patterns of the queue data. Systematic temperature deviation trends are manifested as the real-time difference being continuously positive or negative over multiple consecutive sampling periods, with the absolute value increasing or decreasing. Random temperature disturbance components are manifested as irregular oscillations of the real-time difference around a certain mean value at high frequency and low amplitude. Temperature delay components with heat conduction hysteresis are manifested as a measurable time delay in the response of the real-time difference after a step change in heating power. The trend spectrum generation module inputs the identified temperature deviation trends, temperature disturbance components, and temperature delay components, along with historical power supply data and historical thermal radiation interference data extracted from the heating power time-series records, into a multi-dimensional temperature deviation analysis model. The multi-dimensional temperature deviation analysis model is a set of algorithms pre-trained using historical process data to correlate input features with comprehensive output indicators.

[0024] It is understandable that running a multi-dimensional temperature deviation analysis model outputs a comprehensive dataset. This dataset includes the deviation direction, deviation gradient, deviation duration, and deviation spatial correlation. The deviation direction is indicated by "positive" or "negative" to signify the actual temperature offset from the target temperature baseline. The deviation gradient is obtained by calculating the rate of change of the deviation value per unit time. The deviation duration records the length of time during which the deviation occurs consecutively in a specific direction. The deviation spatial correlation is characterized by calculating the correlation coefficient matrix of the real-time difference sequences between different heating zones. In practical implementation, to quantify the temperature delay component of heat conduction hysteresis, an estimation formula for the delay time constant is introduced: ; in: This represents the estimated time delay constant. This represents the time increment sequence following a power change event. This represents the normalized sequence of heating power changes, and this formula provides a feature input when constructing a multi-dimensional temperature deviation analysis model. The comprehensive dataset is defined as a temperature deviation trend spectrum, which is passed to the dynamic rebalancing module in a structured data format.

[0025] Example 2: See Figure 3In this embodiment, the dynamic rebalancing module executes the step of initiating a multi-temperature zone energy dynamic rebalancing process. This module inputs the deviation direction and deviation change gradient data, represented in the temperature deviation trend spectrum, into a preset energy allocation evaluation network. This network calculates the base energy adjustment value required to compensate for the systematic temperature deviation trend (i.e., the energy base value), and simultaneously calculates the dynamic energy modulation amount required to smooth out random temperature disturbance components. Based on the deviation spatial correlation information recorded in the temperature deviation trend spectrum, the module analyzes the coupling strength of temperature interactions between multiple heating zones caused by heat conduction and heat radiation. This process is achieved by establishing a temperature zone interaction influence matrix. When establishing this matrix, the module first extracts deviation spatial correlation data from the temperature deviation trend spectrum, which reflects the synchronous or asynchronous degree of temperature deviation changes in different heating zones. Subsequently, correlation analysis is used to process this correlation data, calculating the temperature deviation correlation coefficient between each pair of heating zones. This correlation coefficient is used as the initial estimate of the thermal influence coupling strength between them. Subsequently, the physical layout spacing parameters of the heating zones and historical heat transfer efficiency data are introduced to weighted correct the initial estimates, thereby obtaining a final thermal influence coupling strength value that more closely reflects the actual physical process. Finally, using all heating zones as row and column indices, the calculated final thermal influence coupling strength values ​​between each pair of heating zones are filled into the corresponding positions in the matrix, forming a temperature zone interaction influence matrix describing the mutual influence relationships between temperature zones. Using the energy base value and energy modulation amount as optimization inputs, and the temperature zone interaction influence matrix as constraints describing the coupling relationships between temperature zones, this module performs multi-objective parallel optimization calculations. This calculation aims to solve for a set of power adjustment amounts for each heating zone, so that the temperature field inside the furnace, after adjustment, most closely approximates the preset target temperature reference curves for each part of the workpiece. From the solution results of the multi-objective parallel optimization calculations, a set of instructions for directly instructing the heaters in each heating zone to change their output power is extracted. This set of instructions constitutes a preliminary power scheduling map. Meanwhile, based on the mapping relationship between the duration of deviation recorded in the temperature deviation trend spectrum and the current heating stage of the process, the module predicts the critical conditions that the process needs to meet to enter the cooling stage, and marks the time point or temperature point that meets the critical conditions as the collaborative cooling trigger node.

[0026] In practical implementation, the dynamic rebalancing module receives a temperature deviation trend spectrum from the trend spectrum generation module. This spectrum contains a comprehensive data set for the upper, middle, and lower heating zones. The dynamic rebalancing module initiates a multi-temperature zone energy dynamic rebalancing process. It inputs the deviation direction and gradient data recorded in the temperature deviation trend spectrum into a pre-constructed energy allocation evaluation network. This network is a computational model containing multiple layers of neurons. Based on the input deviation direction and gradient data, the network calculates in parallel the steady-state energy adjustment base required to compensate for the systematic temperature deviation trend, i.e., the energy base value. Simultaneously, the network calculates the dynamic energy adjustment amplitude required to smooth out random temperature fluctuations, i.e., the energy modulation amount.

[0027] In some embodiments, the dynamic rebalancing module analyzes the coupling strength of thermal effects between multiple heating sections and establishes a temperature zone interaction influence matrix based on the deviation spatial correlation information recorded in the temperature deviation trend spectrum. The dynamic rebalancing module extracts deviation spatial correlation data from the temperature deviation trend spectrum. This data exists in the form of a correlation coefficient matrix, reflecting the degree of correlation between temperature deviation changes between each pair of the upper, middle, and lower heating sections. The dynamic rebalancing module uses correlation analysis to process the deviation spatial correlation data, calculating the temperature deviation correlation coefficient between each pair of heating sections, and directly using this correlation coefficient as the initial estimate of the thermal effect coupling strength. The dynamic rebalancing module introduces the physical layout distance parameters of the heating sections stored in the system database. These distance parameters are in meters. Simultaneously, the dynamic rebalancing module calls the heat transfer efficiency correction coefficient recorded in the historical process database, using the layout distance parameters and the heat transfer efficiency correction coefficient to perform a weighted correction on the initial estimate of the thermal effect coupling strength. The weighted correction operation produces the final thermal effect coupling strength value. The dynamic rebalancing module uses the upper heating section, middle heating section, and lower heating section as the row and column indices of the matrix. It fills the corresponding row and column positions of the matrix with the calculated final thermal influence coupling strength values ​​between each pair of heating sections, forming a third-order square matrix. This third-order square matrix is ​​defined as the temperature zone interaction influence matrix.

[0028] It can be understood that the dynamic rebalancing module uses the energy baseline and energy modulation output from the energy allocation evaluation network as input variables for optimization calculations, and the temperature zone interaction influence matrix as a condition describing the energy transfer constraints between temperature zones, to perform a multi-objective parallel optimization calculation. The multi-objective parallel optimization calculation aims to solve for a set of power adjustment amounts for the upper, middle, and lower heating zones, minimizing the overall deviation between the entire furnace temperature field and the target temperature reference curves of various parts of the workpiece after applying the power adjustment amounts under the constraints of the temperature zone interaction influence matrix. From the solution results of the multi-objective parallel optimization calculation, the dynamic rebalancing module extracts a set of instructions for directly controlling the output power of the heaters in the upper, middle, and lower heating zones. This set of instructions includes the magnitude and direction of the power adjustment, and constitutes a preliminary power scheduling map. Simultaneously, based on the mapping relationship between the deviation duration data in the temperature deviation trend spectrum and the current process stage information, the dynamic rebalancing module predicts the critical conditions that the process needs to meet to enter the cooling stage. The critical conditions include temperature threshold conditions and time threshold conditions. The dynamic rebalancing module records the process time points that meet the critical conditions as collaborative cooling trigger nodes. In practical implementation, a coupling strength correction formula is introduced to quantify the weighted correction process: ; in: This represents the final thermal influence coupling strength value of heating section i on heating section j. This represents the correlation coefficient between the temperature deviations of heating section i and heating section j, i.e., the initial estimate. This represents the physical layout distance between heating segment i and heating segment j. This represents the correction factor for the heat transfer efficiency of heating section i relative to heating section j, obtained from historical data. and The dynamic rebalancing module uses the preset weighting coefficients to correct the initial estimates based on this formula. The preliminary power scheduling map and the coordinated cooling trigger node are output to the disturbance compensation module and the control map synthesis module.

[0029] Example 3: In this example, the disturbance compensation module performs the step of calculating the thermal flux disturbance compensation amount. This module first analyzes the preliminary power scheduling map from the dynamic rebalancing module to obtain the power adjustment amount planned for each heating segment in the map. Based on the power adjustment amount of each heating segment, it simulates and calculates the theoretical radiation intensity distribution of the heating body surface of each heating segment after adjustment. This simulation process includes analyzing the preliminary power scheduling map to obtain the planned power adjustment amount applied to each segment, and converting the electrical power adjustment amount into the expected temperature change value of the heating body surface based on the resistivity and heat capacity parameters of the heating body material. According to the fundamental law of thermal radiation, the theoretical energy value radiated per unit area per unit time of the heating body surface is calculated based on this expected temperature change value. Integrating the geometric shape factor and orientation characteristics of the heating body surface, the theoretical energy value per unit area is converted into a radiation intensity distribution map in three-dimensional space. This process is repeated for each heating segment, finally obtaining the theoretical radiation intensity distribution of the heating body surface of all heating segments after adjustment. This module establishes a radiation angle coefficient network model based on the actual geometric arrangement and surface characteristics of the heating body, heat insulation screen, and workpiece inside the vacuum brazing furnace. When establishing this model, three-dimensional geometric data of all key components inside the furnace were collected, including the installation position of the heating element, the outline dimensions of the heat shield, and the specific shape of the workpiece. Radiation characteristic parameters of each key component surface, including emissivity and reflectivity, were obtained by querying a surface detection device or from a material database. Using the principles of geometric optics, the radiation angle coefficient between any two surfaces was calculated. This coefficient characterizes the proportion of radiant energy emitted from one surface and directly reaching another. All surfaces inside the furnace were considered as network nodes, and the calculated radiation angle coefficients were used as the weights of the edges connecting these nodes to construct a fully connected weighted network model. The energy conservation of this weighted network model was verified, ensuring that the sum of the radiation angle coefficients of all surfaces with respect to all other surfaces conforms to the laws of physics. This model was ultimately established as the radiation angle coefficient network model for radiative heat transfer analysis. The simulated theoretical radiation intensity distribution was input into this radiation angle coefficient network model, and the net change in radiative heat flux received by the workpiece surface and the preset key monitoring points inside the furnace was calculated using the model. This change was defined as a first-order radiative disturbance. Further considering the furnace wall reflection effect and the re-radiation effect of the workpiece itself due to temperature changes, an iterative correction calculation is performed on the primary radiation disturbance to obtain the radiative heat flux shift when reaching a relatively steady state after multiple reflections and absorptions in the furnace environment. Finally, this steady-state radiative heat flux shift is converted into an equivalent heating power compensation value through a heat-to-electricity conversion relationship. This equivalent heating power compensation value is the heat flux disturbance compensation amount, which is used to offset the indirect thermal interference caused by actively adjusting the heater power through the radiation path.

[0030] In practical implementation, the disturbance compensation module receives a preliminary power scheduling map from the dynamic rebalancing module. This map contains power adjustment instructions for the upper, middle, and lower heating sections within the vacuum brazing furnace, expressed as a percentage change relative to the current power setting. Based on the preliminary power scheduling map, the disturbance compensation module calculates the compensation amount for heat flow disturbances caused by thermal radiation exchange within the furnace. It first analyzes the preliminary power scheduling map and obtains the planned power adjustment for each heating section. Then, based on the heating element's resistance characteristics and thermal capacity parameters stored in the system parameter library, the module converts the power adjustment into the expected temperature change value on the heating element's surface. For example, based on the heating element's resistivity and thermal capacity model, it calculates an expected temperature rise of 15 degrees Celsius on the upper heating section, a expected temperature drop of 6 degrees Celsius on the middle heating section, and an expected temperature rise of 9 degrees Celsius on the lower heating section. Based on the fundamental law of thermal radiation, the theoretical energy radiated per unit area per unit time on the surface of the heating body is calculated based on the expected temperature change. The geometric shape factor and directional characteristics of the surface of the heating body are integrated, and the theoretical energy value per unit area is converted into a radiation intensity distribution map in three-dimensional space. The above process is repeated for the upper heating section, the middle heating section, and the lower heating section to obtain the adjusted theoretical radiation intensity distribution of the surface of the heating body in all heating sections.

[0031] In some embodiments, the disturbance compensation module establishes a radiation angle coefficient network model based on the geometric arrangement and surface characteristics of the heating element, heat shield, and workpiece within the vacuum brazing furnace. The module collects three-dimensional geometric data of all key components within the vacuum brazing furnace by accessing a 3D model library. This data includes the coordinates of the tubular spiral installation position of the heating element, the cylindrical contour dimensions of the heat shield, and the specific shape data of the workpiece's plate-fin structure. The module obtains radiation characteristic parameters of each key component's surface by connecting to a surface detection device or querying a material database. These parameters include emissivity and reflectivity expressed numerically. Utilizing geometric optics principles, the module calculates the radiation angle coefficient between any two surfaces. The radiation angle coefficient is a dimensionless value between zero and one, representing the proportion of radiant energy emitted from the first surface and directly reaching the second surface. The module treats all surfaces within the furnace as network nodes and uses the calculated radiation angle coefficient as the edge weights connecting these nodes to construct a fully connected weighted network model.

[0032] It is understandable that the disturbance compensation module inputs the simulated theoretical radiation intensity distribution into the radiation angle coefficient network model, and calculates the net radiative heat flux change received by the workpiece surface and preset key monitoring points inside the furnace through matrix operations. The net radiative heat flux change is defined as a primary radiation disturbance, with the unit of primary radiation disturbance being watts per square meter. The disturbance compensation module further considers the furnace wall reflection effect and the re-radiation effect caused by the workpiece itself due to temperature changes, and performs iterative correction calculations on the primary radiation disturbance. The iterative correction calculation simulates the process of multiple reflections and absorptions of radiant energy inside the furnace until the difference between two adjacent iterations is less than a preset threshold, thus obtaining the steady-state radiative heat flux offset after multiple reflections and absorptions. The disturbance compensation module converts the steady-state radiative heat flux offset into an equivalent heating power compensation value. The conversion process is based on the radiant area of ​​the heating element and the electrothermal conversion efficiency. For example, the steady-state radiative heat flux offset is multiplied by the effective heated area of ​​the workpiece and then divided by the electrothermal efficiency coefficient of the heater to obtain the heat flux disturbance compensation amount in kilowatts. In practical implementation, to calculate the theoretical energy value per unit area of ​​the heated body surface, the expression of the Stefan-Boltzmann law is introduced: ; in: This represents the theoretical energy radiated per unit area of ​​the heating element's surface per unit time. This indicates the emissivity of the heating element surface. This represents the Stefan-Boltzmann constant, whose value is approximately ; This represents the absolute temperature value of the heating element surface. The heat flux disturbance compensation is output to the control graph synthesis module.

[0033] Example 4: In this example, the control map synthesis module performs the step of forming the final process control map. This module superimposes the heat flow disturbance compensation amount calculated by the disturbance compensation module onto the power adjustment amount of the corresponding heating section in the preliminary power scheduling map generated by the dynamic rebalancing module, generating a comprehensive power scheduling scheme that has been corrected for radiation effects. The module analyzes the power change trajectory of this comprehensive power scheduling scheme on the process time axis, identifying power abrupt change points that may cause uneven workpiece cooling rates or generate large thermal stress. Near these identified power abrupt change points, preset smooth transition power ramp commands are inserted, thereby forming a smoothed comprehensive power scheduling scheme. Based on the collaborative cooling trigger node provided by the dynamic rebalancing module, the startup logic for the cooling stage is formulated. This startup logic includes cooling startup conditions triggered by temperature sensor readings and cooling startup conditions triggered by the process timer. For control after entering the cooling stage, the module combines the historical temperature uniformity data of the workpiece during the heating stage to allocate differentiated initial flow rates of the cooling medium and preset flow rate change curves to different heating sections. During allocation, the residual heat energy storage of each heating section at the cooling trigger moment is calculated back-calculated based on the smoothed integrated power scheduling scheme. Heating sections are sorted according to the size of their residual heat energy storage, with higher initial cooling medium flow rates allocated to sections with larger storage and lower initial flow rates to sections with smaller storage. A target cooling temperature curve is set according to process requirements, and the total heat removed from each heating section from the temperature at the trigger cooling point to the target temperature is calculated, i.e., the theoretical heat removal. Based on the theoretical heat removal and the initial flow rate allocated to each section, the required cooling medium flow rate at each moment during the cooling process is dynamically calculated, forming a flow rate baseline curve that varies with time or temperature. A cooling uniformity feedback mechanism is introduced. After the cooling process starts, this mechanism monitors the actual temperature drop rate of each heating section in real time and compares it with the preset average drop rate. Based on the comparison results, the previously generated flow rate baseline curve is dynamically fine-tuned, ultimately forming a set of differentiated initial cooling medium flow rate and flow rate variation curves. Finally, the control graph synthesis module arranges and synchronizes the smoothed integrated power scheduling scheme, the cooling stage start-up scheme including start-up logic, and the differentiated cooling medium control scheme according to the timeline of the entire brazing process, and encodes a complete final process control graph that can be executed by the control system. The heating correction instructions come from the smoothed integrated power scheduling scheme, while the cooling scheduling instructions come from the cooling stage start-up scheme and the differentiated cooling medium control scheme.

[0034] In practical implementation, the control map synthesis module receives the coordinated cooling trigger node and preliminary power scheduling map from the dynamic rebalancing module, and simultaneously receives the heat flow disturbance compensation amount from the disturbance compensation module. The control map synthesis module integrates the coordinated cooling trigger node, preliminary power scheduling map, and heat flow disturbance compensation amount to form the final process control map acting on all heating sections. The control map synthesis module superimposes the heat flow disturbance compensation amount onto the power adjustment amount of the corresponding heating section in the preliminary power scheduling map. For example, if the preliminary power scheduling map indicates a 5% increase in power for the upper heating section, the corresponding heat flow disturbance compensation amount is -0.8%. After superposition, the comprehensive power adjustment amount for the upper heating section is +4.2%. After performing this superposition operation on all heating sections, a comprehensive power scheduling scheme corrected for radiation effects is generated. The control graph synthesis module analyzes the power change trajectory of the integrated power scheduling scheme after radiation effect correction on the process time axis, and identifies power abrupt change points that may cause uneven cooling rate or thermal stress. The determination of power abrupt change points is that the absolute value of the power adjustment per unit time exceeds the preset threshold. For example, within one second from time point t1 to t2, the power setting value of the middle heating section jumps from 65% to 80%, and this change is marked as a power abrupt change point.

[0035] In some embodiments, the control map synthesis module inserts a smooth power ramp command near the identified power abrupt change point. The power ramp command extends for a preset time before and after the abrupt change point in the form of a linear or exponential function, converting the step change in power into a ramp change. After this processing, a smoothed comprehensive power scheduling scheme is formed. The control map synthesis module formulates the start-up logic of the cooling stage based on the collaborative cooling trigger node. The start-up logic includes cooling start-up conditions triggered by temperature and cooling start-up conditions triggered by process time. For example, the temperature trigger condition is that the actual temperature of any heating section reaches 750°C, and the time trigger condition is that the total process time reaches 3600 seconds. For the cooling stage, the control map synthesis module combines the historical temperature uniformity performance data of the workpiece during the heating stage. The historical temperature uniformity performance data records the maximum deviation value between the temperature of each heating section and the target temperature reference curve during the heating process, and assigns differentiated initial flow rate and flow rate change curve of cooling medium to different heating sections. During the allocation process, the control graph synthesis module calculates the residual thermal energy storage of each heating section at the cooling trigger moment based on the smoothed integrated power scheduling scheme. The residual thermal energy storage is estimated by integrating the heater power curve and considering the heat loss model.

[0036] It is understandable that the control spectrum synthesis module sorts the heating sections according to the size of the residual thermal energy storage, allocating a higher initial flow rate of cooling medium to the heating sections with a large storage capacity and a lower initial flow rate of cooling medium to the heating sections with a small storage capacity. See Table 1 for the specific allocation relationship.

[0037] Table 1: Residual Thermal Energy Storage and Initial Flow Distribution in Each Heating Zone

[0038] The control chart synthesis module sets a target cooling temperature curve, which defines the temperature trajectory over time from the start of cooling until the workpiece temperature drops to 150°C. The module calculates the theoretical heat transfer required for each heating zone to cool from its current temperature to the target temperature. This theoretical heat transfer is calculated based on the workpiece's mass, specific heat capacity, and temperature range. Based on the theoretical heat transfer and the initial flow rate allocated to each zone, the module dynamically calculates the required cooling medium flow rate at each moment during the cooling process, forming a flow rate baseline curve that varies with time or temperature. The dynamic calculation process incorporates a flow rate fine-tuning formula to respond to temperature changes. ; in: This indicates the required cooling medium flow rate at time t, expressed in liters per minute. Indicates the initial flow rate of the allocated cooling medium; This represents the system's preset flow regulation coefficient, which is a dimensionless constant. This represents the actual temperature of the heating section at time t; This indicates the target temperature for the current cooling phase; This indicates the temperature at the start of the cooling phase. A cooling evenness feedback mechanism is introduced, which monitors the actual temperature drop rate of each heating section in real time during the cooling process and compares the actual temperature drop rate with the overall average drop rate. Based on the comparison result, the flow rate baseline curve is dynamically fine-tuned. The control graph synthesis module arranges and synchronizes the smoothed integrated power scheduling scheme, the cooling phase start-up scheme including start-up logic, and the differentiated cooling medium control scheme according to the process timeline, and encodes them to generate the final process control graph. The heating correction instructions come from the smoothed integrated power scheduling scheme, and the cooling scheduling instructions come from the cooling phase start-up scheme and the differentiated cooling medium control scheme.

[0039] See Figure 4In the final process control diagram of multi-temperature zone collaborative control in vacuum brazing, the implementation of the smoothed power scheduling scheme reflects the synergistic effect of dynamic energy rebalancing and thermal flux disturbance compensation in multiple temperature zones. In specific operation, the diagram uses the total process time (seconds) as the horizontal axis and the power adjustment amount (%) as the vertical axis, presenting the power timing trajectory of the upper heating zone (cyan curve), the middle heating zone (purple curve), and the lower heating zone (orange curve), and marks the cooling trigger time (3600 seconds, red dashed line). In actual scheduling, the power adjustment of each heating section is generated by smoothing the initial power scheduling map after superimposing the heat flow disturbance compensation. In the initial stage, the power of the heating section is preferentially increased to quickly approach the target temperature. Subsequently, through the constraint of the temperature zone interaction influence matrix, the power of the upper and lower heating sections is gradually adjusted to balance the thermal radiation coupling effect. In the middle of the process (about 2000 seconds), the power of the upper heating section drops to the lowest level (about 2.4%), corresponding to the correction effect of its heat flow disturbance compensation on the initial power. When approaching the cooling trigger node, the power of each temperature zone is synchronously increased to compensate for heat loss. At the same time, the power ramp command is used to eliminate abrupt changes and ensure the uniformity of the temperature field. In terms of parameter characteristics, the fluctuation range of the power adjustment is controlled within the range of 4%-6.2%. The time synchronization between the cooling trigger node (3600 seconds) and the peak power of each temperature zone reflects the dual constraint of "time trigger + temperature trigger" in the collaborative cooling start-up logic.

[0040] Example 5: In this example, after generating the radiation-corrected integrated power scheduling scheme, the control map synthesis module performs further correction steps. This module continuously monitors the real-time pressure inside the vacuum furnace and acquires the composition data of the heat treatment gas used in the current heating stage. Based on the monitored real-time pressure inside the vacuum furnace, it corrects the influence factor in the radiation angle coefficient network model used to characterize the effect of gas molecules on radiative heat transfer under rarefied gas or specific pressure. Based on the acquired heat treatment gas composition data, it calculates the estimated potential heat exchange capacity generated by gas convection under the current furnace structure and flow field. Using the corrected radiation influence factor and the calculated estimated potential heat exchange capacity of gas convection, it performs a second fine-tuning of the previously generated radiation-corrected integrated power scheduling scheme, generating a final power scheduling instruction that simultaneously considers the effects of pressure environment and gas composition. When allocating differentiated initial flow rates and flow rate change curves of the cooling medium, this module, based on the smoothed integrated power scheduling scheme, calculates the residual heat energy storage of each heating section at the time corresponding to the collaborative cooling trigger node. Heating zones are sorted according to their residual heat energy storage capacity, with higher initial cooling medium flow rates allocated to zones with larger storage capacity and lower initial cooling medium flow rates to zones with smaller storage capacity. A target cooling temperature curve is set according to process requirements, and the theoretical heat transfer required for each heating zone to cool from its current temperature to the target temperature is calculated. Based on the theoretical heat transfer and the allocated initial flow rate, the required cooling medium flow rate at each moment during the cooling process is dynamically calculated, forming a flow rate reference curve that varies with time or temperature. A cooling uniformity feedback mechanism is introduced. During the actual cooling process, this mechanism monitors the actual temperature drop rate of each heating zone in real time and compares this rate with the overall average drop rate. Based on the comparison results, the previously calculated flow rate reference curve is dynamically fine-tuned, ultimately forming differentiated initial cooling medium flow rate and flow rate variation curves for precise control of cooling uniformity.

[0041] In practical implementation, after generating the comprehensive power scheduling scheme corrected for radiation effects, the control graph synthesis module performs further corrections and optimizations. The module continuously monitors the real-time pressure inside the vacuum brazing furnace using a pressure sensor connected to it. The real-time values ​​are displayed and recorded in Pascals. Simultaneously, the module retrieves the composition data of the heat treatment gases introduced during the current heating stage from the process formula database. This data includes the volume percentage concentrations of nitrogen, hydrogen, and argon. Based on the real-time pressure values ​​inside the vacuum furnace, the module corrects the influence factor of gas molecules on radiative heat transfer in the radiation angle coefficient network model. This influence factor is a correction coefficient related to gas pressure and gas type, and its value increases with increasing pressure. The correction process is completed by querying a preset pressure-influence factor relationship table. Finally, based on the heat treatment gas composition data, the module calculates an estimate of the potential heat exchange capacity of the gas convection. This calculation is based on a comprehensive estimation of the thermal conductivity, specific heat capacity, and rated flow parameters of the forced convection circulating fan inside the furnace.

[0042] In some embodiments, the control map synthesis module uses the corrected influence factors and the calculated estimated potential heat exchange capacity of the gas convection to perform a secondary fine-tuning of the comprehensive power scheduling scheme corrected for radiation effects. This secondary fine-tuning is achieved through a weighted compensation algorithm, which superimposes the radiation heat transfer correction term and the convection heat transfer correction term according to the current pressure and gas flow field state, generating a final power scheduling instruction that takes into account the effects of pressure and gas composition. The final power scheduling instruction provides precise setpoints for the upper heating section, middle heating section, and lower heating section in the form of power percentage values. For example, in an atmosphere with a pressure of 15 Pa and a gas mixture of 75% nitrogen and 25% hydrogen, the final power scheduling instruction might fine-tune the power setpoint for the upper heating section in the original scheme from 62.5% to 63.1%. The control graph synthesis module assigns differentiated initial flow rate and flow rate change curves of cooling medium to different heating sections. The allocation process is based on the smoothed comprehensive power scheduling scheme. The control graph synthesis module back-calculates the residual heat energy storage at the cooling trigger moment of each heating section based on the smoothed comprehensive power scheduling scheme. The calculation of residual heat energy storage is completed by integrating the heater power history curve and multiplying it by an empirical heat accumulation coefficient.

[0043] It is understandable that the control chart synthesis module sorts heating sections according to the amount of residual thermal energy stored, allocating higher initial cooling medium flow rates to heating sections with larger storage and lower initial cooling medium flow rates to heating sections with smaller storage. For example, three sections with residual thermal energy storage of 12.5 MJ, 10.2 MJ, and 14.1 MJ respectively might receive initial flow rates of 22.0 L / min, 18.5 L / min, and 25.0 L / min respectively. The control chart synthesis module sets a target cooling temperature curve, which defines the temperature-time path from the start to the end of cooling. The module calculates the theoretical heat transfer required for each heating section to cool from the current temperature to the target temperature. The theoretical heat transfer is calculated based on the estimated heat capacity and temperature difference of the corresponding furnace area for each section. Based on the theoretical heat transfer and the allocated initial flow rate, the module dynamically calculates the required cooling medium flow rate at each moment during the cooling process, forming a flow rate baseline curve that varies with time or temperature. A cooling uniformity feedback mechanism is introduced. During the cooling process, the actual temperature drop rate of each heating section is monitored in real time and compared with the overall average drop rate. Based on the comparison results, the flow reference curve is dynamically fine-tuned, and finally a set of differentiated initial flow rate and flow change curves of the cooling medium are formed.

[0044] See Figure 5 In the construction of the temperature zone interaction influence matrix for the multi-temperature zone energy dynamic rebalancing process, the thermal influence coupling strength between the upper, middle, and lower heating zones of the vacuum brazing furnace is intuitively presented. The matrix uses each heating zone as its row and column index. The coupling strength at the diagonal position (within the same zone) is 1.00, representing the complete coupling of the thermal influence within that zone. The off-diagonal positions correspond to the coupling strength between different zones: the coupling strength between the upper and middle heating zones is 0.78, between the upper and lower heating zones is 0.82, and between the middle and lower heating zones is 0.85. These values ​​are obtained from the deviation spatial correlation data after correlation analysis, weighted correction based on physical layout distance and heat transfer history. They reflect the degree of correlation of thermal radiation interference between different temperature zones and can be used as constraints for multi-objective parallel optimization calculations to solve for the power adjustment amount that makes the overall temperature field approach the target reference curve.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control, characterized in that, The method executed by the system includes: The data acquisition module obtains the actual temperature values ​​of multiple heating zones in the vacuum brazing furnace and the heating power time sequence record. The trend spectrum generation module matches the actual temperature value set with the preset target temperature reference curves for each part of the workpiece to generate a temperature deviation trend spectrum. The dynamic rebalancing module initiates a multi-temperature zone energy dynamic rebalancing process based on the temperature deviation trend spectrum. The multi-temperature zone energy dynamic rebalancing process generates a preliminary power scheduling spectrum and a collaborative cooling trigger node. The disturbance compensation module calculates the amount of heat flow disturbance compensation caused by heat radiation exchange inside the furnace based on the preliminary power scheduling map. The control map synthesis module integrates the coordinated cooling trigger node, the preliminary power scheduling map, and the heat flow disturbance compensation amount to form a final process control map that acts on all heating sections. The final process control map includes heating correction instructions and cooling scheduling instructions.

2. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 1, characterized in that, The actual temperature value set is matched with the preset target temperature reference curves for various parts of the workpiece to generate a temperature deviation trend spectrum, including: Traverse each heating zone and extract the power supply history of the current zone and the thermal radiation interference history of adjacent zones from the heating power time sequence record; The real-time measurement points in the actual temperature value set are mapped to the corresponding theoretical points of the target temperature reference curve of each part of the workpiece in the time dimension, and the real-time difference queue is calculated one by one. The real-time difference queue is subjected to time-series feature extraction to identify temperature deviation trends that are systematic, temperature disturbance components that are random fluctuations, and temperature delay components that are heat conduction hysteresis. By combining the temperature deviation trend, the temperature disturbance component, and the temperature delay component with the power supply history and the thermal radiation interference history, a multi-dimensional temperature deviation analysis model is constructed. The multi-dimensional temperature deviation analysis model is run to output a comprehensive data set that includes deviation direction, deviation change gradient, deviation duration and deviation spatial correlation. The comprehensive data set is defined as the temperature deviation trend spectrum.

3. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 2, characterized in that, The initiation of the multi-temperature zone energy dynamic rebalancing process, which generates a preliminary power scheduling map and a collaborative cooling trigger node, includes: The deviation direction and deviation change gradient in the temperature deviation trend spectrum are input into an energy allocation evaluation network, which calculates the energy base value required to compensate for the temperature deviation trend, and the energy modulation amount required to smooth out the temperature disturbance component. Based on the spatial correlation of the deviation in the temperature deviation trend spectrum, the coupling strength of the thermal influence between multiple heating sections is analyzed, and a temperature zone interaction influence matrix is ​​established. Using the energy base value and the energy modulation amount as inputs, and the temperature zone interaction influence matrix as constraints, a multi-objective parallel optimization calculation is performed. The multi-objective parallel optimization calculation aims to solve a set of power adjustment amounts that make the overall temperature field closest to the target temperature reference curve of each part of the workpiece. From the solution results of the multi-objective parallel optimization calculation, a set of instructions for directly adjusting the output power of the heater is separated, and the set of instructions constitutes the preliminary power scheduling map; Simultaneously, based on the mapping relationship between the duration of deviation in the temperature deviation trend spectrum and the heating stage, the critical conditions for the process to enter the cooling stage are predicted, and the time point or temperature point that meets the critical conditions is marked as the collaborative cooling trigger node.

4. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 3, characterized in that, The step of analyzing the coupling strength of thermal influence between multiple heating sections based on the spatial correlation of deviations in the temperature deviation trend spectrum, and establishing a temperature zone interaction influence matrix includes: Spatial correlation data of deviations are extracted from the temperature deviation trend spectrum. The spatial correlation data of deviations includes the degree of spatial correlation of temperature deviations in different heating sections. The spatial correlation data of the deviation is processed by the correlation analysis method, and the temperature deviation correlation coefficient between each two heating sections is calculated. The temperature deviation correlation coefficient is used as the initial estimate of the thermal effect coupling strength. By incorporating the physical layout distance of the heating zone and historical heat transfer data, the initial estimate is weighted and corrected to obtain the final thermal influence coupling strength value. Using all heating sections as row and column indices, the final thermal influence coupling strength value between each pair of heating sections is filled into the corresponding positions in the matrix to form the temperature zone interaction influence matrix.

5. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 3, characterized in that, Based on the preliminary power scheduling diagram, the compensation amount for heat flow disturbance caused by heat radiation exchange inside the furnace is calculated, including: Based on the power adjustment amount of each heating section in the preliminary power scheduling map, the theoretical radiation intensity distribution on the surface of the heating body of each heating section after adjustment is simulated and calculated. Based on the geometric arrangement and surface characteristics of the heating element, heat shield, and workpiece in the vacuum brazing furnace, a radiation angle coefficient network model is established. The theoretical radiation intensity distribution is input into the radiation angle coefficient network model to calculate the net change in radiative heat flux received by the workpiece surface and key monitoring points inside the furnace. The net change in radiative heat flux is defined as a primary radiation disturbance. Further considering the furnace wall reflection and the workpiece's own re-radiation effect, the primary radiation disturbance is iteratively corrected and calculated to obtain the steady-state radiative heat flux offset after multiple reflections and absorptions. The steady-state radiative heat flux offset is converted into an equivalent heating power compensation value. This equivalent heating power compensation value is the heat flux disturbance compensation amount, which is used to offset the indirect radiative heat interference caused by active power adjustment.

6. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 5, characterized in that, The step of simulating and calculating the theoretical radiation intensity distribution on the surface of the heating body in each heating section after adjustment, based on the power adjustment amount of each heating section in the preliminary power scheduling map, includes: Analyze the preliminary power scheduling map to obtain the planned power adjustment amount for each heating section; Based on the resistance characteristics and thermal capacity parameters of the heating element material, the power adjustment is converted into the expected temperature change value of the heating element surface; Based on the fundamental law of thermal radiation, the theoretical energy radiated per unit area of ​​the heating element per unit time is calculated based on the expected temperature change. By integrating the geometric shape factor and orientation characteristics of the heating element surface, the theoretical energy value per unit area is converted into a radiation intensity distribution map in three-dimensional space; Repeat the above process for each heating section to obtain the theoretical radiation intensity distribution on the surface of the heating body in all heating sections after adjustment.

7. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 5, characterized in that, Based on the geometric arrangement and surface characteristics of the heating element, heat shield, and workpiece inside the vacuum brazing furnace, a radiation angle coefficient network model is established, including: Collect three-dimensional geometric data of all key components in the vacuum brazing furnace, including the installation position of the heating element, the outline dimensions of the heat shield, and the specific shape of the workpiece; The radiation characteristics parameters of the surfaces of each key component, including emissivity and reflectivity, are obtained through surface testing devices or material databases. Using the principles of geometric optics, the radiation angle coefficient between any two surfaces is calculated. The radiation angle coefficient represents the proportion of radiation energy emitted from the first surface and directly reaching the second surface. Treating all surfaces inside the furnace as nodes and using the radiation angle coefficient as the edge weight, a fully connected weighted network model is constructed. The conservation of the weighted network model is verified to ensure that the sum of the radiation angle coefficients of all surfaces to other surfaces conforms to the law of conservation of energy, and the radiation angle coefficient network model is finally established.

8. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 5, characterized in that, Integrating the coordinated cooling trigger node, the preliminary power scheduling map, and the heat flow disturbance compensation amount, a final process control map is formed that applies to all heating sections, including: The heat flow disturbance compensation is superimposed on the power adjustment of the corresponding heating section in the preliminary power scheduling map to generate a comprehensive power scheduling scheme corrected for radiation effect. Analyze the power change trajectory of the integrated power scheduling scheme on the time axis to identify power abrupt change points that may cause uneven cooling rates or thermal stress; Near the power abrupt change point, a power ramp command with a smooth transition is inserted to form a smoothed integrated power scheduling scheme; Based on the aforementioned collaborative cooling trigger node, the startup logic for the cooling stage is formulated, which includes cooling startup conditions triggered by temperature and cooling startup conditions triggered by process time. For the cooling stage, based on the historical temperature uniformity of the workpiece, different initial flow rates and flow rate variation curves of the cooling medium are assigned to different heating zones. The smoothed integrated power scheduling scheme, the cooling stage startup scheme including startup logic, and the differentiated cooling medium control scheme are arranged and synchronized according to the process timeline, and encoded to generate the final process control map. The heating correction instruction comes from the smoothed integrated power scheduling scheme, and the cooling scheduling instruction comes from the cooling stage startup scheme and the differentiated cooling medium control scheme.

9. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 8, characterized in that, After generating a comprehensive power scheduling scheme corrected for radiation effects, the method further includes: Monitor the real-time pressure inside the vacuum furnace and obtain the heat treatment gas composition data for the current heating stage; Based on the real-time pressure value inside the vacuum furnace, the influence factor of gas molecules on radiative heat transfer in the radiation angle coefficient network model is corrected. Based on the heat treatment gas composition data, calculate the estimated potential heat exchange capacity of gas convection; Using the corrected influence factor and the estimated potential heat exchange capacity, the integrated power dispatch scheme corrected for radiation effect is fine-tuned a second time to generate the final power dispatch command that takes into account the effects of pressure and gas composition.

10. The vacuum brazing uniform heating and cooling system with multi-temperature zone coordinated control according to claim 8, characterized in that, The method of allocating differentiated initial flow rates and flow rate variation curves of cooling media to different heating zones includes: Based on the smoothed integrated power scheduling scheme, the residual thermal energy storage of each heating section at the cooling trigger moment is calculated in reverse. The heating sections are sorted according to the amount of residual heat energy stored, with higher initial flow rates of cooling medium allocated to heating sections with larger storage and lower initial flow rates of cooling medium allocated to heating sections with smaller storage. Set a target cooling temperature curve and calculate the theoretical heat transfer required for each heating section to cool from the current temperature to the target temperature. Based on the theoretical heat transfer and the initial flow rate allocated, the required flow rate of the cooling medium at each moment during the cooling process is dynamically calculated to form a flow rate reference curve that varies with time or temperature. A cooling uniformity feedback mechanism is introduced, which monitors the actual temperature drop rate of each heating section in real time during the cooling process and compares it with the average drop rate. Based on the comparison results, the flow reference curve is dynamically fine-tuned to ultimately form the differentiated initial flow rate and flow change curve of the cooling medium.

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