Intelligent lubrication collaborative control system for heat balance of servo press

Abstract

The application relates to the technical field of servo press control and thermal management, in particular to an intelligent lubrication collaborative control system for thermal balance of a servo press, wherein the system comprises: a temperature sensing network which collects temperature field data in real time and generates a two-dimensional temperature distribution cloud picture; a multi-channel temperature control lubrication module which comprises a plurality of independently controlled lubrication channels; a dynamic heat source tracking module which identifies the heat source center position and its diffusion trend through a heat source identification algorithm, and predicts the hot spot position at the next moment based on a heat network model; a channel activation and compensation module which dynamically activates the lubrication channels, calculates the heat interference compensation amount of adjacent associated channels, and obtains channel activation compensation information; and a collaborative control module which generates control instructions of each activated channel in real time through a multi-objective optimization control model, and adjusts the oil supply amount, oil supply pressure, oil supply temperature and injection timing of each channel. Thus, the problems of lack of dynamic response, extensive and lagging control strategy and thermal coupling interference in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of servo press control and thermal management technology, and in particular to an intelligent lubrication coordination control system for thermal balance of servo presses. Background Technology

[0002] While existing servo presses offer advantages such as high precision, programmability, and energy efficiency, under prolonged continuous operation, key components like the transmission system, crankshaft, and slide block experience significant temperature rises due to frictional heat generation and uneven cooling, leading to thermal deformation. This thermal deformation causes the bottom dead center position to drift, resulting in workpiece thickness deviations of up to 8%, severely impacting machining accuracy and consistency.

[0003] Existing technologies mainly employ two lubrication control methods: The first is timed and metered lubrication, which supplies oil to each lubrication point according to a preset time interval and oil supply quantity. This method is simple in structure and low in cost, but it cannot dynamically adjust the oil supply strategy according to changes in actual operating conditions. Under low-speed and light-load conditions, it is prone to excessive waste of lubricating oil, while under high-speed and heavy-load conditions, it is difficult to meet heat dissipation requirements, leading to uncontrolled temperature rise. The second is temperature feedback lubrication, which uses discrete temperature sensors at each lubrication point. When the detected temperature exceeds a set threshold, the PLC controller increases the oil supply flow of the corresponding lubrication branch. This method is an improvement over timed and metered lubrication, but it still suffers from insufficient sensing, coarse and lagging control strategies, thermal coupling interference problems in multi-channel parallel lubrication, and a lack of dynamic response and multi-variable collaborative control mechanisms, making it unable to effectively suppress thermal drift. Summary of the Invention

[0004] This application provides an intelligent lubrication coordination control system for thermal balance of servo presses, in order to solve problems such as lack of dynamic response, coarse and lagging control strategies, and thermal coupling interference in the prior art.

[0005] The first aspect of this application provides an intelligent lubrication collaborative control system for thermal balance of a servo press, comprising: a temperature sensing network, a multi-channel temperature-controlled lubrication module, a dynamic heat source tracking module, a channel activation and compensation module, and a collaborative control module. The temperature sensing network is used to collect temperature field data in real time and generate a two-dimensional temperature distribution cloud map by arranging a flexible thin-film thermistor array on the surface of key moving parts. The multi-channel temperature-controlled lubrication module includes multiple independently controlled lubrication channels, each corresponding to one or more key lubrication components of the servo press, and each lubrication channel is independently equipped with a flow regulation device, a pressure regulation device, a temperature regulation device, and a status monitoring device. The dynamic heat source... The tracking module, based on the two-dimensional temperature distribution cloud map, identifies the center location of the heat source and its diffusion trend in real time through a heat source identification algorithm, and predicts the hot spot location at the next moment based on a thermal network model. The channel activation and compensation module is used to dynamically activate one or more lubrication channels according to the center location of the heat source and the hot spot prediction results, and automatically calculates the thermal interference compensation amount of adjacent related channels according to the preset inter-channel thermal coupling matrix to obtain channel activation compensation information. The collaborative control module is used to generate control commands for each activated channel in real time through a multi-objective optimization control model, and adjust the oil supply, oil supply pressure, oil supply temperature and injection timing of each channel to realize dynamic tracking and zoned collaborative temperature control lubrication of the servo press heat source.

[0006] Optionally, the temperature sensing network includes: a sensor deployment unit, a temperature acquisition unit, and a cloud map generation unit. The sensor deployment unit uses flexible thin-film thermistors, deployed at non-uniform density in areas prone to deformation and friction hotspots within the servo press component, forming a flexible thin-film thermistor array. The temperature acquisition unit acquires temperature data from each sensing node in the flexible thin-film thermistor array in real time, and performs preprocessing on the temperature data, including filtering, noise reduction, and defect removal, to obtain discrete temperature field data. The cloud map generation unit reconstructs the global temperature distribution on the component surface using an interpolation fitting algorithm based on the temperature field data and the actual deployment coordinates of each sensing point, generating a two-dimensional temperature distribution cloud map.

[0007] Optionally, the multi-channel temperature-controlled lubrication module includes: a channel division unit, a three-dimensional control unit, a micro-injection execution unit, and a status monitoring unit. The channel division unit divides the lubrication area into multiple independently controlled lubrication channels based on the type, spatial location, and thermal characteristics of the key lubrication components of the servo press, assigning a unique channel identifier to each channel. Each channel corresponds to one or more adjacent key lubrication components. The three-dimensional control unit includes a flow regulating device, a pressure regulating device, and a temperature regulating device installed in each lubrication channel. The flow regulating device is a micro gear pump used for precise control of the oil supply. The pressure regulating device is a proportional relief valve used to regulate the channel's oil supply pressure. The temperature regulating device includes an integrated thin-film heater and a semiconductor cooling chip used to regulate the temperature of the lubricating oil at the channel outlet, making the lubricating oil itself a controllable heat exchange medium. The micro-injection execution unit includes an electromagnetic micro-injection valve installed at the end of each lubrication channel to control the timing, duration, and area of ​​the lubricating oil injection. The status monitoring unit collects the actual oil supply flow rate, oil supply pressure, and oil supply temperature of each channel in real time and feeds the collected data back to the collaborative control module in real time.

[0008] Optionally, the dynamic heat source tracking module includes: a heat source identification unit, a heat diffusion analysis unit, and a hotspot prediction unit. The heat source identification unit performs gradient analysis and local extremum search on the two-dimensional temperature distribution cloud map to identify the location of the heat source center and the temperature peak value of each heat source center in the temperature field, and marks the boundaries of the heat source region. The heat diffusion analysis unit traces the heat diffusion path along the decreasing temperature gradient direction, starting from the heat source center location, calculates the diffusion direction and diffusion rate of each heat source center, and generates a heat source diffusion trend field. The hotspot prediction unit, based on the heat source center location and the heat source diffusion trend field, calls a pre-set thermal network model for forward iterative calculation to predict the hotspot location in the next control cycle.

[0009] Optionally, the thermal network model includes: discretizing the predicted target area into several nodes based on the spatial location of key lubrication components, with the node locations corresponding to the spatial distribution of multiple lubrication channels; connecting the nodes via thermal resistance, defining and calibrating the thermal resistance parameters connecting any two adjacent nodes, and assigning each node a heat capacity parameter characterizing its local thermal inertia, the thermal resistance parameters being pre-calibrated based on the thermal conductivity, geometric dimensions, and contact thermal resistance of the component material; using each heat source center as a heat flux input, with its heat flux density dynamically calculated based on the temperature peak and the area of ​​the heat source region; and establishing a node temperature state equation based on the law of conservation of energy, the equation formula being:

[0010]

[0011] Where C is the heat capacity matrix, which is a diagonal matrix with heat capacity parameters as diagonal elements; The node temperature vector; The value represents the rate of change of temperature with time t; A is the thermal conductivity matrix, which is a symmetric matrix composed of the reciprocals of the thermal resistance parameters. Its off-diagonal elements are positive, representing the thermal conductivity between node i and node j, and its diagonal elements are negative to ensure energy conservation. The input vector is the heat flow source.

[0012] The forward Euler method is used to numerically solve the node temperature state equation and iteratively calculate the temperature value of each node at the next moment. Based on the calculated node temperature distribution, the temperature maxima are identified as predicted hotspot locations.

[0013] Optionally, the channel activation and compensation module includes: an activation decision unit, a thermal coupling compensation calculation unit, and a compensation integration unit. The activation decision unit is used to select one or more lubrication channels corresponding to the spatial location of the heat source region as channels to be activated based on the current heat source center location and the predicted hotspot location at the next moment, and to generate a channel activation command. The thermal coupling compensation calculation unit is used to query the thermal coupling coefficients of adjacent non-activated channels from a preset thermal coupling matrix based on the channel activation command, and calculate the thermal interference compensation amount for adjacent associated channels by combining the spatial adjacency relationship and heat conduction path of each channel. The compensation integration unit is used to integrate the channel activation commands of the activated lubrication channels and the thermal interference compensation amounts of adjacent associated channels to generate complete and standardized channel activation compensation information.

[0014] Optionally, the collaborative control module includes: a multi-objective optimization unit and an instruction synthesis unit. The multi-objective optimization unit acquires channel activation compensation information and the temperature at the center of the heat source. It sets constraints on the multi-objective optimization control model, with the optimization objectives being the minimization of the temperature deviation between the heat source region and the target temperature, the minimization of lubricating oil consumption, and the minimization of oil supply temperature fluctuations in each lubrication channel. The model is then solved under these constraints to obtain a Pareto optimal solution set. An optimal solution is selected from the Pareto optimal solution set as the optimal control parameter. The instruction synthesis unit converts the optimal control parameter into specific control instructions for each independent lubrication channel and issues the instructions to each independent lubrication channel for execution.

[0015] The second aspect of this application provides an intelligent lubrication collaborative control method for thermal balance of a servo press, comprising the following steps: deploying a flexible thin-film thermistor array on the surface of key moving parts to collect temperature field data in real time and generate a two-dimensional temperature distribution cloud map; constructing multiple independently controlled lubrication channels, each corresponding to one or more key lubrication components of the servo press, and each lubrication channel being independently equipped with a flow regulation device, a pressure regulation device, a temperature regulation device, and a status monitoring device; based on the two-dimensional temperature distribution cloud map, identifying the center position of the heat source and its diffusion trend in real time using a heat source identification algorithm, and predicting the hot spot position at the next moment based on a thermal network model; dynamically activating one or more lubrication channels according to the center position of the heat source and the hot spot prediction results, and automatically calculating the thermal interference compensation amount of adjacent associated channels according to a preset inter-channel thermal coupling matrix to obtain channel activation compensation information; generating control commands for each activated channel in real time through a multi-objective optimization control model to adjust the oil supply volume, oil supply pressure, oil supply temperature, and injection timing of each channel, thereby realizing dynamic tracking and zoned collaborative temperature-controlled lubrication of the servo press heat source.

[0016] A third aspect of this application provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform an intelligent lubrication coordination control method for thermal balance of a servo press as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer program product storing a computer program that, when executed by a processor, implements an intelligent lubrication coordination control method for thermal balance of a servo press as described in the above embodiments.

[0018] The beneficial effects of using the present invention are as follows:

[0019] This application employs a flexible thin-film thermistor array to acquire real-time two-dimensional temperature distribution cloud maps of key friction pair surfaces, enabling the system to comprehensively grasp the spatial gradient and uniformity of the temperature field. Through a dynamic heat source tracking module and a built-in thermal network model, it can not only identify current heat sources but also analyze their diffusion trends and predict hotspot locations at the next moment based on physical mechanisms. This allows lubrication and cooling actions to be deployed in advance before heat accumulation, providing preventative intervention and fundamentally reducing the magnitude of thermal deformation and response lag, effectively suppressing thermal drift at the bottom dead center. An inter-channel thermal coupling matrix is ​​introduced to quantify the degree of thermal conduction interference between lubrication channels. When the oil supply to a channel increases, the oil supply parameters of adjacent channels are simultaneously adjusted to counteract thermal interference, achieving decoupled and coordinated control of multiple channels. A multi-objective optimization control model is used to ensure cooling effect in the heat source area while minimizing inter-channel thermal interference, lubricant consumption, and oil supply temperature fluctuations, achieving a balance between cooling effect, operating cost, and thermal equilibrium. Therefore, it solves the problems of lack of dynamic response, coarse and lagging control strategies, and thermal coupling interference in existing technologies.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein...

[0022] Figure 1 This is a schematic diagram of the structure of an intelligent lubrication coordination control system for thermal balance of a servo press, according to an embodiment of this application.

[0023] Figure 2 This is a flowchart of an intelligent lubrication coordination control method for thermal balance of a servo press, according to an embodiment of this application.

[0024] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following description, with reference to the accompanying drawings, illustrates an intelligent lubrication and collaborative control system for thermal balance in a servo press, according to an embodiment of this application. Addressing the issues of lack of dynamic response, coarse and lagging control strategies, and thermal coupling interference mentioned in the background art, this application provides an intelligent lubrication and collaborative control system for thermal balance in a servo press. In this system, a flexible thin-film thermistor array is employed to acquire real-time two-dimensional temperature distribution cloud maps of key friction pair surfaces, enabling the system to comprehensively grasp the spatial gradient and uniformity of the temperature field. Through a dynamic heat source tracking module and a built-in thermal network model, not only can the current heat source be identified, but its diffusion trend can also be analyzed, and the hotspot location at the next moment can be predicted based on physical mechanisms, ensuring that the lubrication and cooling actions are timely and efficient. By proactively deploying preventative interventions before heat accumulation, the magnitude of thermal deformation and response lag are fundamentally reduced, effectively suppressing thermal drift at the bottom dead center. An inter-channel thermal coupling matrix is ​​introduced to quantify the degree of thermal conduction interference between lubrication channels. When the oil supply to a channel increases, the oil supply parameters of adjacent channels are simultaneously adjusted to counteract thermal interference, achieving decoupled and coordinated control of multiple channels. A multi-objective optimization control model is employed to ensure cooling effectiveness in the heat source area while minimizing inter-channel thermal interference, lubricant consumption, and oil supply temperature fluctuations, achieving a balance between cooling effect, operating costs, and thermal equilibrium. This solves the problems of lack of dynamic response, coarse and lagging control strategies, and thermal coupling interference in existing technologies.

[0027] Specifically, Figure 1 This is a schematic diagram of the structure of an intelligent lubrication coordination control system for thermal balance of a servo press, provided in an embodiment of this application.

[0028] like Figure 1 As shown, the intelligent lubrication collaborative control system 10 for thermal balance of a servo press includes: a temperature sensing network 100, a multi-channel temperature control lubrication module 200, a dynamic heat source tracking module 300, a channel activation and compensation module 400, and a collaborative control module 500.

[0029] The temperature sensing network 100 is used to deploy a flexible thin-film thermal array on the surface of key moving parts to collect temperature field data in real time and generate a two-dimensional temperature distribution cloud map. The multi-channel temperature control lubrication module 200 includes multiple independently controlled lubrication channels. Each lubrication channel corresponds to one or more key lubrication components of the servo press, and each lubrication channel is independently equipped with a flow regulation device, a pressure regulation device, a temperature regulation device, and a status monitoring device. The dynamic heat source tracking module 300 identifies the center position of the heat source and its diffusion trend in real time based on the two-dimensional temperature distribution cloud map and predicts the hot spot position at the next moment based on the thermal network model. The channel activation and compensation module 400 is used to dynamically activate one or more lubrication channels according to the center position of the heat source and the hot spot prediction results, and automatically calculates the thermal interference compensation amount of adjacent associated channels according to the preset inter-channel thermal coupling matrix to obtain channel activation compensation information. The collaborative control module 500 is used to generate control commands for each activated channel in real time through a multi-objective optimization control model, and adjust the oil supply, oil supply pressure, oil supply temperature and injection sequence of each channel to realize dynamic tracking and zoned collaborative temperature control lubrication of the servo press heat source.

[0030] It is understood that the embodiments of this application, by employing a flexible thin-film thermistor array, can acquire a two-dimensional temperature distribution cloud map of the key friction pair surface in real time, enabling the system to fully grasp the spatial gradient and uniformity of the temperature field. Through the dynamic heat source tracking module and the built-in thermal network model, it can not only identify the current heat source, but also analyze its diffusion trend and predict the hot spot location at the next moment based on the physical mechanism. This allows the lubrication and cooling actions to be deployed in advance before heat accumulation, providing preventative intervention and fundamentally reducing the magnitude of thermal deformation and response lag, effectively suppressing thermal drift at the bottom dead center. The introduction of an inter-channel thermal coupling matrix quantifies the degree of thermal conduction interference between each lubrication channel. When the oil supply of a certain channel increases, the oil supply parameters of adjacent channels are adjusted synchronously to offset the thermal interference, realizing decoupled and coordinated control of multiple channels. The adoption of a multi-objective optimization control model ensures the cooling effect in the heat source area while minimizing inter-channel thermal interference, lubricating oil consumption, and oil supply temperature fluctuations, achieving a balance between cooling effect, operating cost, and thermal balance.

[0031] In this embodiment, the temperature sensing network includes: a sensor deployment unit, a temperature acquisition unit, and a cloud map generation unit.

[0032] The sensor deployment unit employs flexible thin-film thermistors to form a flexible thin-film thermistor array by non-uniformly distributing them to areas with high friction hotspots and deformation trajectories of the servo press components. The temperature acquisition unit collects temperature data from each sensing node in the flexible thin-film thermistor array in real time and performs preprocessing such as filtering, noise reduction, and defect removal on the temperature data to obtain discrete temperature field data. The cloud map generation unit reconstructs the global temperature distribution on the component surface using an interpolation fitting algorithm based on the temperature field data and the actual deployment coordinates of each sensing point, generating a two-dimensional temperature distribution cloud map.

[0033] Specifically, the flexible thin-film thermistor uses polyimide or PET as the substrate material. Through micro-nano fabrication processes, the thermistor is integrated onto a flexible thin film, giving it excellent flexibility and adhesion, allowing it to fit tightly onto the surfaces of complex curved components such as guide rails, sliders, and bearing housings. A non-uniform density deployment principle is adopted during deployment. For high-heat areas such as stress concentration points, friction pair meshing areas, and reciprocating friction sections, the spacing between sensor nodes is reduced and the number of sensors is increased to ensure accurate detection of even minor local temperature rises. For non-loaded, low-friction conventional areas, the node spacing is appropriately increased to reduce redundant sensor nodes while ensuring full temperature measurement coverage and lowering data processing pressure.

[0034] A high-precision clock synchronization signal ensures that the temperature data of all sensing nodes in the array are sampled at the same time. The sampled temperature data is filtered by moving average to suppress electrical noise and high-frequency interference from the environment. For bad points that are outside the reasonable range or seriously conflict with the data of surrounding nodes, they are automatically marked and removed. Based on the data of adjacent valid nodes, the data of the point is temporarily repaired by spatial neighborhood interpolation.

[0035] A two-dimensional coordinate system is established on the surface of the component, mapping the actual placement of each sensor node to coordinate points. The pre-processed temperature value is used as the attribute value of each point. Inverse distance-weighted interpolation based on distance weighting is employed to extend the discrete point temperature into a continuous temperature field. This continuous temperature field is then mapped as a color cloud map, with different colors representing different temperature ranges, generating a two-dimensional temperature distribution cloud map.

[0036] It is understandable that the embodiments of this application employ a non-uniform density deployment strategy, investing more sensor resources in high-hotspot areas to ensure monitoring accuracy, while reducing the number of sensors in non-hotspot areas to control costs, thus achieving a balance between sensing accuracy and cost. A preprocessing mechanism ensures that the temperature data input to subsequent modules has high reliability and high fidelity. Based on discrete measurement points, a continuous temperature field is generated using a spatial interpolation algorithm, overcoming the information limitations of discrete point temperature measurement. This enables the system to perceive the spatial distribution of heat sources, the direction of heat diffusion, and the boundaries of the heat-affected area, providing data support for advanced functions such as heat source identification and hotspot prediction.

[0037] In this embodiment, the multi-channel temperature-controlled lubrication module includes: a channel division unit, a three-dimensional control unit, a micro-injection execution unit, and a status monitoring unit.

[0038] The system includes a channel division unit that divides the lubrication area into multiple independently controlled lubrication channels based on the type, spatial location, and thermal characteristics of the key lubrication components of the servo press. Each channel is assigned a unique channel identifier, and each channel corresponds to one or more adjacent key lubrication components. The three-dimensional control unit includes a flow regulating device, a pressure regulating device, and a temperature regulating device installed in each lubrication channel. The flow regulating device is a micro gear pump used to achieve precise control of the oil supply. The pressure regulating device is a proportional relief valve used to regulate the oil supply pressure of the channel. The temperature regulating device includes an integrated thin-film heater and a semiconductor cooling chip used to regulate the temperature of the lubricating oil at the channel outlet, making the lubricating oil itself a controllable heat exchange medium. The micro-injection execution unit includes an electromagnetic micro-injection valve installed at the end of each lubrication channel to control the timing, duration, and area of ​​the lubricating oil injection. The status monitoring unit is used to collect the actual oil supply flow, pressure, and temperature of each channel in real time and feed the collected data back to the collaborative control module in real time.

[0039] Specifically, the micro gear pump is driven by a servo motor, and precise control of the oil supply flow is achieved by adjusting the motor speed. When the coordinated control module issues a flow adjustment command, the micro gear pump driver calculates the required speed based on the target flow value and controls the motor rotation accordingly, so that the actual oil supply flow quickly reaches the set value. A proportional relief valve is installed between the micro gear pump outlet and the lubrication pipeline. The oil supply pressure of the channel is controlled by adjusting the valve opening. The valve opening is proportional to the signal voltage, enabling stepless adjustment of the oil supply pressure. Integrated thin-film heaters and semiconductor cooling chips are installed near the lubricating oil outlet of each channel to independently adjust the temperature of the lubricating oil at the channel outlet. When it is necessary to increase the lubricating oil temperature, the thin-film heater is energized and heats up the flowing lubricating oil to the target temperature; when the component temperature is too high, the semiconductor cooling chip operates to lower the lubricating oil temperature, using the low-temperature lubricating oil to quickly absorb the heat from the component.

[0040] For example, for a 1600-ton servo press, the channel partitioning unit divides its lubrication system into 12 independent control channels. CH01-CH03 represent the upper, middle, and lower sections on the left side of the main slide guide, respectively; CH04-CH06 represent the upper, middle, and lower sections on the right side of the main slide guide, respectively; CH07 represents the front crankshaft main bearing; CH08 represents the rear crankshaft main bearing; CH09 represents the left side of the connecting rod big end bearing; CH10 represents the right side of the connecting rod big end bearing; CH11 represents the position of the balance cylinder guide; and CH12 represents the position of the flywheel bearing. The corresponding identifier for each channel is stored in the channel configuration table, containing information such as the channel identifier, corresponding component name, spatial coordinate range, and thermal characteristic parameters.

[0041] It is understood that the embodiments of this application break through the limitations of the traditional centralized oil supply mode, which has uniform parameters and cannot be adapted to different areas, by independently dividing multiple channels and coordinating three dimensions. Each channel can independently perform fine adjustment of flow rate, pressure and temperature, transforming the lubricating oil into a controllable heat exchange medium, taking into account both lubrication and friction reduction and active heat exchange functions. Combined with electromagnetic micro-injection valve, it realizes time-sequential precise injection, which greatly improves the targeting of lubrication and temperature control.

[0042] In this embodiment, the dynamic heat source tracking module includes: a heat source identification unit, a heat diffusion analysis unit, and a hotspot prediction unit.

[0043] The heat source identification unit is used to perform gradient analysis and local extremum search on the two-dimensional temperature distribution cloud map, identify the location of the heat source center and the temperature peak of each heat source center in the temperature field, and mark the boundary of the heat source region; the heat diffusion analysis unit is used to trace the heat diffusion path along the temperature gradient descent direction starting from the location of the heat source center, calculate the diffusion direction and diffusion rate of each heat source center, and generate a heat source diffusion trend field; the hot spot prediction unit is used to call a preset thermal network model to perform forward iterative calculation based on the location of the heat source center and the heat source diffusion trend field, and predict the location of the hot spot in the next control cycle.

[0044] Specifically, after applying Gaussian filtering to the two-dimensional temperature distribution cloud map, the spatial gradient of the temperature field is calculated to obtain the rate of temperature change in the X and Y directions for each pixel. The magnitude of the gradient reflects the drastic temperature change, and the direction of the gradient points in the direction of the fastest temperature increase. Local maxima are searched in the temperature field, i.e., points whose temperature is higher than the temperatures of all points in their neighborhood; these local maxima are potential heat source centers. To avoid noise interference, a temperature threshold is set; only when the temperature of a local maximum exceeds the set temperature threshold is it identified as a valid heat source center. Starting from the heat source center, the search proceeds outward along the decreasing direction of the temperature gradient until the temperature drops to a preset boundary threshold. Grid points that meet the conditions are marked as the boundary of the heat source region. When multiple heat sources exist in the temperature field, a connected component analysis algorithm is used to separately mark different heat sources to avoid overlapping or confusion of heat source boundaries. Each heat source is assigned a unique heat source ID, recording its center coordinates, peak temperature, region area, boundary coordinates, and other information.

[0045] Starting from the center of the heat source, the heat diffusion path is traced along the direction of the fastest temperature gradient decrease. A streamline tracing algorithm is used, starting from the center of the heat source with a step size of one grid cell. Each step moves along the negative gradient direction of the current point until the heat source boundary is reached or the temperature gradient approaches zero. The coordinates and temperature values ​​of the grid points traversed are recorded for each path. Statistical analysis is performed on all traced diffusion paths. A vector synthesis method is used to treat the final direction of each path as a direction vector. All direction vectors are synthesized, and the direction of the synthesized vector is the main diffusion direction. Based on heat source data from multiple consecutive time frames, the movement rate of the heat source boundary is calculated. Let P(t) be the coordinates of a point on the heat source boundary at time t, and t+... At time t+, the point moves to P(t+). If the diffusion rate in that direction is..., then the diffusion rate in that direction is... The average diffusion rate of the heat source is obtained by calculating the diffusion rate at all boundary points and then averaging it. Information such as the heat source center, main diffusion direction, diffusion rate, and diffusion path are then fused to generate a heat source diffusion trend field. This diffusion trend field is represented as a vector field, where the vector at each grid point represents the direction and intensity of heat diffusion at that point, pointing towards the direction of the fastest temperature decrease, and the vector magnitude represents the temperature gradient magnitude or diffusion rate.

[0046] For example, in a two-dimensional temperature cloud map at a certain moment, the temperature matrix shows two high-temperature zones in the slider guide area. The heat source identification unit, through gradient calculation, finds that the temperature gradient in the left region points to the center point (125, 80), where the temperature of 78.3℃ is a local maximum, exceeding the set threshold of 60℃, and is confirmed as the heat source center HS1; the center point (420, 85) in the right region has a temperature of 65.8℃, and is confirmed as the heat source center HS2. Subsequently, searching along the gradient descent direction with HS1 as the center, when the temperature drops to 78.3 × 60% ≈ 47℃, this boundary is marked, resulting in an influence area of ​​approximately 120mm × 80mm for HS1; similarly, the influence area of ​​approximately 90mm × 60mm for HS2 is marked. The two heat source boundaries do not overlap and are marked as independent heat sources. For heat source HS1, the thermal diffusion analysis unit traced eight main diffusion paths originating from the center. The longest path was found to be along the length of the guide rail (positive Y-axis), reaching a temperature of 47°C at the boundary, approximately 60mm from the center. The shortest path was along the width of the guide rail (positive X-axis), approximately 40mm from the center. Vector synthesis results showed the main diffusion direction was 15° off the positive X-axis, indicating that heat primarily diffused downstream along the length of the guide rail. Analysis of five consecutive frames of data showed that the boundary of HS1 moved at a speed of 0.8mm / s in the positive Y-axis and 0.3mm / s in the positive X-axis, resulting in a composite diffusion rate of approximately 0.85mm / s and a direction angle of approximately 20°. After generating the diffusion trend field, each grid point was assigned a direction vector and diffusion rate information.

[0047] In this embodiment of the application, the thermal network model includes:

[0048] The predicted target area is discretized into several nodes based on the spatial location of key lubrication components, and the node locations correspond to the spatial distribution of multiple lubrication channels.

[0049] The nodes are connected by thermal resistance. The thermal resistance parameters connecting any two adjacent nodes are defined and calibrated. At the same time, each node is assigned a thermal capacity parameter that characterizes its local thermal inertia. The thermal resistance parameters are pre-calibrated based on the thermal conductivity, geometric dimensions and contact thermal resistance of the component material.

[0050] Each heat source center is used as a heat flux input, and its heat flux density is dynamically calculated based on the temperature peak and the area of ​​the heat source region.

[0051] Based on the law of conservation of energy, the nodal temperature state equation is established, and the equation formula is as follows:

[0052]

[0053] Where C is the heat capacity matrix, which is a diagonal matrix with heat capacity parameters as diagonal elements; The node temperature vector; The value represents the rate of change of temperature with time t; A is the thermal conductivity matrix, which is a symmetric matrix composed of the reciprocals of the thermal resistance parameters. Its off-diagonal elements are positive, representing the thermal conductivity between node i and node j, and its diagonal elements are negative to ensure energy conservation. The input vector is the heat flow source.

[0054] The forward Euler method is used to numerically solve the nodal temperature state equation and iteratively calculate the temperature value of each node at the next time step.

[0055] Based on the calculated node temperature distribution, the points with maximum temperature are identified as predicted hotspot locations.

[0056] Specifically, the values ​​of the thermal resistance parameters are pre-calibrated based on the thermal conductivity, geometric dimensions, and contact thermal resistance of the component material, and are dynamically corrected in conjunction with the changes in diffusion rate recorded in the heat source diffusion trend field. Among them, a smaller thermal resistance is set along the main diffusion direction of the heat source to allow rapid heat transfer; a larger thermal resistance is set perpendicular to the main diffusion direction to suppress lateral heat diffusion.

[0057] The heat source diffusion trend field provides prediction information on the movement trajectory of each heat source center, serving as a reference for the movement of heat flow source positions over time and guiding the thermal network model to perform forward iterative solutions.

[0058] The forward Euler method is used to numerically solve the nodal temperature state equations, and the temperature values ​​of each node at the next time step are calculated. The calculation formula is as follows:

[0059]

[0060] in, The temperature value at the next moment of the node. For the current time, The time increment; It is the inverse of the heat capacity matrix.

[0061] The predicted future temperature distribution Then, a local extremum search is performed again to identify local maxima points in the temperature field as predicted hotspot locations.

[0062] It is understood that the embodiments of this application achieve dynamic tracking from current heat source location to future trend prediction through a three-level linkage of heat source identification, heat diffusion analysis, and hotspot prediction, no longer limited to passive monitoring of the existing temperature field. The node division of the thermal network model corresponds to the spatial distribution of lubrication channels, enabling the prediction results to be directly used for channel activation decisions, achieving seamless integration of prediction and control.

[0063] In this embodiment, the channel activation and compensation module includes: an activation decision unit, a thermal coupling compensation calculation unit, and a compensation integration unit.

[0064] The activation decision unit is used to select one or more lubrication channels corresponding to the spatial location of the heat source area as channels to be activated based on the current location of the heat source center and the predicted hot spot location at the next moment, and generate channel activation instructions; the thermal coupling compensation calculation unit is used to query the thermal coupling coefficient of adjacent non-activated channels from the preset thermal coupling matrix according to the channel activation instructions, and calculate the thermal interference compensation amount for adjacent associated channels by combining the spatial adjacency relationship and heat conduction path of each channel; the compensation integration unit is used to integrate the channel activation instructions of the activated lubrication channels and the thermal interference compensation amount of adjacent associated channels to generate complete and standardized channel activation compensation information.

[0065] Specifically, the thermal coupling matrix is ​​an N×N matrix, where N is the total number of lubrication channels, and the matrix elements are... This represents the thermal coupling coefficient between the i-th channel and the j-th channel, with a value ranging from 0 to 1. The larger the value, the stronger the thermal interference of the i-th channel on the j-th channel when it is working.

[0066] For each channel to be activated, the thermal coupling coefficient of that channel to all other channels is extracted from the thermal coupling matrix, and the adjacent channels that need to be compensated are selected based on spatial adjacency. An adjacent channel is defined as a channel that is directly adjacent to the activated channel in space or indirectly adjacent to it through a thermal conduction path (with an interval of no more than one channel).

[0067] Based on the current actual oil supply flow rate, oil supply pressure, and oil supply temperature parameters of the channel to be activated, and combined with the thermal coupling coefficient, the thermal interference compensation for adjacent inactive channels is calculated. The thermal interference compensation includes temperature compensation, flow rate compensation, and pressure compensation. The formula for calculating the temperature compensation is as follows:

[0068]

[0069] in, A represents the temperature compensation amount of adjacent inactive channel j; A represents the set of active channels. This represents the thermal coupling coefficient between the i-th channel and the j-th channel; This indicates the oil supply temperature for activating channel i; Indicates the reference temperature; This represents the temperature compensation coefficient, with a value ranging from 0.3 to 0.8, calibrated based on the heat transfer efficiency.

[0070] The formula for calculating the flow compensation amount is:

[0071]

[0072] in, This represents the flow compensation amount for the adjacent inactive channel j; This indicates the oil supply flow rate of activated channel i; This represents the basic coefficient for flow compensation, with a value ranging from 0.2 to 0.5, reflecting the degree to which flow contributes to heat transfer. This represents the temperature-flow coupling coefficient, with a value ranging from 0.1 to 0.3, reflecting the correction of flow compensation by the fuel supply temperature. Indicates the system's highest oil supply temperature; This indicates the system's lowest oil supply temperature.

[0073] The formula for calculating the pressure compensation is:

[0074]

[0075] in, This represents the pressure compensation amount for the adjacent inactive channel j; This indicates the oil supply pressure for activating channel i; Indicates the reference pressure; This represents the pressure compensation coefficient, ranging from 0.1 to 0.4, reflecting the degree to which pressure contributes to heat conduction. This represents the flow-pressure decoupling coefficient, with a value ranging from 0.1 to 0.2, reflecting the degree of attenuation of pressure compensation when the flow rate changes; This represents the system's average oil supply flow rate; This indicates the system's maximum oil supply flow rate; This indicates the minimum oil supply flow rate of the system.

[0076] When an adjacent inactive channel is simultaneously subjected to thermal interference from multiple active channels, the compensation amounts generated by each active channel are superimposed.

[0077] It is understood that the embodiments of this application screen the channels to be activated based on the center location of the heat source and the predicted hot spot location, thereby achieving precise activation driven by the heat source; through the pre-calibrated thermal coupling matrix, the thermal conduction interference between adjacent channels is quantified into specific values, and the compensation amount is calculated according to the control parameters of the activated channel, thereby achieving decoupling control between multiple channels; based on the hot spot prediction results, the area that is about to become a heat source is pre-activated, thereby achieving a leap from post-event response to pre-event intervention.

[0078] In this embodiment, the collaborative control module includes: a multi-objective optimization unit and an instruction synthesis unit.

[0079] The multi-objective optimization unit is used to acquire channel activation compensation information and the temperature of the heat source center. The optimization objectives are to minimize the temperature deviation between the heat source area and the target temperature, minimize the lubricating oil consumption, and minimize the oil supply temperature fluctuation of each lubrication channel. The multi-objective optimization control model is set with constraints, and the solution is obtained under the constraints to obtain a Pareto optimal solution set. An optimal solution is selected from the Pareto optimal solution set as the optimal control parameter. The instruction synthesis unit is used to convert the optimal control parameter into specific control instructions for each independent lubrication channel and issue the instructions to each independent lubrication channel for execution.

[0080] Specifically, minimizing the temperature deviation between the heat source area and the target temperature aims to quickly bring back local overheating temperatures, ensure stable thermal balance of components, and prevent thermal deformation. Minimizing lubricant consumption aims to prevent excessive oil supply while meeting lubrication and cooling requirements, thereby reducing consumable costs and energy consumption. Minimizing oil supply temperature fluctuations in each lubrication channel aims to avoid sudden rises and falls in oil temperature affecting the performance of the lubricating medium and to ensure stable lubrication and heat exchange effects.

[0081] The constraints of the multi-objective optimization control model include fuel supply quantity constraints, fuel supply pressure constraints, fuel supply temperature constraints, and heat source center temperature constraints. In addition, channel activation compensation information is also used as a boundary constraint.

[0082] The fuzzy membership function method is used to normalize the three objective function values ​​of each solution in the Pareto optimal solution set, calculate the comprehensive satisfaction of each solution, and select the solution with the highest comprehensive satisfaction as the optimal control parameter.

[0083] It is understandable that the embodiments of this application achieve the best balance between cooling effect and operating cost by simultaneously optimizing three objectives: temperature control, energy saving and consumption reduction, and temperature stability. This avoids the problem of blindly increasing the oil supply and causing waste in pursuit of cooling effect, as seen in traditional solutions. A multi-objective evolutionary algorithm is used to obtain the Pareto optimal solution set, retaining multiple non-dominated solutions for selection. The solution with the highest overall satisfaction is selected from the solution set using the fuzzy membership function method, ensuring both optimality and consideration of practical engineering needs.

[0084] The following is a detailed description of an intelligent lubrication coordination control system for thermal balance in a servo press, using a specific embodiment:

[0085] In the large stamping workshop of a car manufacturing plant, a 1600-ton servo press is used to produce the outer side panels of automobiles. The main slide rail of the press is 2.4 meters long and is equipped with key moving components such as crankshaft main bearings, connecting rod big end bearings, balance cylinder guide rails, and flywheel bearings.

[0086] For this servo press, the channel division unit divides its lubrication system into 12 independent control channels. CH01-CH03 represent the upper, middle, and lower sections on the left side of the main slide guide, respectively; CH04-CH06 represent the upper, middle, and lower sections on the right side of the main slide guide, respectively; CH07 represents the front crankshaft main bearing; CH08 represents the rear crankshaft main bearing; CH09 represents the left side of the connecting rod big end bearing; CH10 represents the right side of the connecting rod big end bearing; CH11 represents the position of the balance cylinder guide; and CH12 represents the position of the flywheel bearing. Each channel is equipped with a micro gear pump (flow rate range 0.2-1.2 L / min), a proportional relief valve (pressure range 1.5-4.0 MPa), a thin-film heater and a semiconductor cooling chip (temperature adjustment range 10-50℃), and an electromagnetic micro-injection valve at the end. The inter-channel thermal coupling matrix was obtained through a calibration experiment during system startup: each channel was activated individually for oil supply, and the temperature response of other channels was recorded. After system identification, a 12×12 thermal coupling coefficient matrix was obtained. For example, when CH02 was activated, the thermal coupling coefficient to CH01 was 0.25, to CH03 was 0.30, while to the far-end CH11 was only 0.02.

[0087] After the press operated continuously for 30 minutes at a stroke rate of 120 times per minute, the flexible thin-film thermal array collected the temperature distribution data on the surface of the main slider guide rail. The array employed a non-uniform density layout: in the reversing impact zone at both ends of the guide rail (400mm each), the sensor spacing was 8mm, with a total of 100 sensor nodes; in the stable sliding zone in the middle (1600mm), the sensor spacing was 25mm, with a total of 64 sensor nodes. The cloud map generation unit generated a two-dimensional temperature distribution cloud map using inverse distance weighted interpolation. The heat source identification unit of the dynamic heat source tracking module performed gradient analysis on this cloud map, identifying the heat source center HS1 located at coordinates (125, 80), with a peak temperature of 78.3℃, and marking the boundary of the heat source region. The thermal diffusion analysis unit traced the thermal diffusion path starting from HS1 and found that the longest diffusion path was along the length of the guide rail (positive Y-axis). The temperature dropped to 47℃ at 60mm from the center. The main diffusion direction was 15° off the positive Y-axis and the positive X-axis, with a diffusion rate of 0.85mm / s. The hot spot prediction unit called the thermal network model and discretized the guide rail surface into 120 nodes corresponding to the lubrication channel space. Each node was assigned heat capacity and thermal resistance parameters calculated based on the material's thermal properties. Using HS1 as the heat source input, the forward Euler method was used for iterative solution. It predicted that after 100ms, the center of the heat source would move to (128,92), and a new hot spot HS3 with a temperature of about 52℃ would appear in the (280,90) region due to heat superposition.

[0088] Based on the current location of the heat source center HS1 at (125, 80) and the predicted hot spot at (128, 92), it is determined that it is located in channel CH02, and CH02 is marked as the active channel. The thermal coupling compensation calculation unit queries the thermal coupling matrix to calculate the thermal interference compensation amount of the active channel CH02 to the adjacent channels: the current control parameters of CH02 are oil supply 0.8L / min, pressure 2.5MPa, temperature 28℃, reference temperature 25℃, and reference pressure 2.0MPa. The temperature compensation amount for CH01 is 0.375℃, the flow rate compensation amount is 0.0607L / min, and the pressure compensation amount is 0.024MPa; the compensation amounts for CH03 are 0.45℃, 0.0729L / min, and 0.0288MPa, respectively. The compensation integration unit integrates the above information into channel activation compensation information.

[0089] Acquire channel activation compensation information and heat source center temperature, and obtain optimal control parameters through a multi-objective optimization control model. Its objective function is... This represents the weighted squared deviation between the temperature in the heat source region and the target temperature of 45℃. The sum of the total oil supply flow. The sum of squares of the rate of change of oil supply temperature for each channel is defined. Constraints include flow rate of 0.2-1.2 L / min, pressure of 1.5-4.0 MPa, temperature of 15-50℃, and limits on the rate of change between adjacent cycles. The NSGA-II algorithm is used to solve the problem. With a population size of 100, a Pareto optimal solution set is obtained after 200 iterations. The solution with the highest overall satisfaction is selected using the fuzzy membership function method: CH02 oil supply rate 0.85 L / min, pressure 2.6 MPa, temperature 27℃; CH01 oil supply rate 0.32 L / min, pressure 2.1 MPa, temperature 25.5℃; CH03 oil supply rate 0.35 L / min, pressure 2.1 MPa, temperature 25.8℃. The instruction synthesis unit converts these parameters into execution instructions: the micro gear pump speed of channel CH02 is 510 rpm, the proportional relief valve voltage is 4.8V, and the semiconductor cooling chip power is 45W; the micro gear pump speed of channel CH01 is 192 rpm, and the proportional relief valve voltage is 3.6V; the micro gear pump speed of channel CH03 is 210 rpm, and the proportional relief valve voltage is 3.7V. The electromagnetic micro-injection valve executes injection according to the set timing sequence: CH02 injects immediately for 20ms, CH01 injects 10ms in advance for 15ms, and CH03 injects 50ms delayed for 18ms.

[0090] After running for 15 minutes, the temperature of the heat source center HS1 dropped to 48℃ and stabilized within the preset target temperature range. There was no lateral heat diffusion that caused continuous overheating. The oil supply temperature fluctuation of each channel was controlled within ±1.5℃. Compared with the traditional centralized oil supply mode, the lubricating oil consumption was reduced by 22%, avoiding the problem of component thermal deformation.

[0091] In summary, the embodiments of this application, through full-domain flexible temperature measurement, dynamic heat source prediction, multi-channel independent control, thermal coupling compensation, and multi-objective optimized collaborative control, achieve rapid suppression of local overheating and stable thermal field of the entire machine in the continuous stamping operation of the servo press, effectively avoiding thermal deformation of components. At the same time, while ensuring lubrication and cooling effects, it reduces lubricating oil consumption, reduces oil supply temperature fluctuations, and significantly improves the operating accuracy, reliability, and service life of the equipment. It solves the core pain points of traditional lubrication systems, such as lag response, coarse control, and thermal coupling interference.

[0092] Next, referring to the accompanying drawings, an intelligent lubrication coordination control method for thermal balance of a servo press is described according to an embodiment of this application.

[0093] Specifically, Figure 2 This is a flowchart illustrating an intelligent lubrication coordination control method for thermal balance of a servo press, provided as an embodiment of this application.

[0094] like Figure 2 As shown, the intelligent lubrication coordination control method for thermal balance of a servo press includes the following steps:

[0095] In step S101, a flexible thin-film thermal array is arranged on the surface of the key moving parts to collect temperature field data in real time and generate a two-dimensional temperature distribution cloud map.

[0096] Specifically, flexible thin-film thermistors are used, and non-uniform density arrays are deployed in areas with high friction hotspots and deformation trajectories of the servo press components to form a flexible thin-film thermistor array. Temperature data from each sensing node in the array is acquired in real time, and the data undergoes preprocessing including filtering, noise reduction, and defect removal to obtain discrete temperature field data. Based on the temperature field data and the actual coordinates of each sensing point, an interpolation fitting algorithm is used to reconstruct the global temperature distribution on the component surface, generating a two-dimensional temperature distribution cloud map.

[0097] It is understood that the embodiments of this application solve the problems of incomplete coverage and many blind spots in traditional single-point temperature measurement by non-uniform array deployment. The preprocessing process ensures the authenticity and reliability of temperature data. The visualized cloud map provides an intuitive and solid data foundation for subsequent accurate identification of heat sources. It is suitable for the temperature measurement needs of various curved and irregular moving parts, and the temperature measurement response is faster and the accuracy is higher.

[0098] In step S102, multiple independently controlled lubrication channels are constructed. Each lubrication channel corresponds to one or more key lubrication components of the servo press, and each lubrication channel is independently equipped with a flow regulating device, a pressure regulating device, a temperature regulating device, and a status monitoring device.

[0099] Specifically, based on the types, spatial locations, and thermal characteristics of the key lubrication components of the servo press, the lubrication area is divided into multiple independently controlled lubrication channels, each assigned a unique channel identifier. Each channel corresponds to one or more adjacent key lubrication components. Each lubrication channel is equipped with a flow regulation device, a pressure regulation device, and a temperature regulation device. The flow regulation device is a micro gear pump for precise control of the oil supply; the pressure regulation device is a proportional relief valve for regulating the channel's oil supply pressure; the temperature regulation device includes an integrated thin-film heater and a semiconductor cooling chip to regulate the temperature of the lubricating oil at the channel outlet, making the lubricating oil itself a controllable heat exchange medium. An electromagnetic micro-injection valve located at the end of each lubrication channel controls the timing, duration, and area of ​​lubricating oil injection. A status monitoring device collects the actual oil supply flow rate, oil supply pressure, and oil supply temperature of each channel in real time and provides real-time feedback of the collected data.

[0100] It is understood that the embodiments of this application, through the construction of multi-channel independent partitions, break the drawback of the traditional centralized oil supply mode that cannot be differentiated and controlled. The four independent devices in a single channel work together to achieve all-round control of flow rate, pressure, temperature and injection timing, taking into account both lubrication and friction reduction and active temperature control functions, and improving the targeting of lubrication and cooling and the overall adaptability of the system.

[0101] In step S103, based on the two-dimensional temperature distribution cloud map, the heat source center location and its diffusion trend are identified in real time through the heat source identification algorithm, and the hot spot location at the next moment is predicted based on the heat network model.

[0102] Specifically, gradient analysis and local extremum search are performed on the two-dimensional temperature distribution cloud map to identify the location of heat source centers and the temperature peak values ​​at each heat source center, and to mark the boundaries of the heat source regions. Starting from the location of the heat source center, the heat diffusion path is traced along the direction of temperature gradient descent, and the diffusion direction and diffusion rate of each heat source center are calculated to generate a heat source diffusion trend field. Based on the location of the heat source center and the heat source diffusion trend field, a pre-set thermal network model is invoked for forward iterative calculations to predict the hotspot locations in the next control cycle.

[0103] It is understood that the embodiments of this application realize the dynamic tracking of the entire link from real-time heat source location to future hot spot prediction, changing the passive and lagging response to the active and advanced prediction, and greatly improving the prediction accuracy by relying on the thermal network model. It can adapt to the complex thermal change law of servo press under changing working conditions and loads, and provide core basis for advanced regulation.

[0104] In step S104, one or more lubrication channels are dynamically activated based on the location of the heat source center and the hot spot prediction results. The thermal interference compensation amount of adjacent associated channels is automatically calculated based on the preset inter-channel thermal coupling matrix to obtain channel activation compensation information.

[0105] Specifically, based on the current location of the heat source center and the predicted hotspot location at the next moment, one or more lubrication channels corresponding to the spatial location of the heat source area are selected as channels to be activated, and channel activation commands are generated. According to the channel activation commands, the thermal coupling coefficients of adjacent inactive channels are queried from a preset thermal coupling matrix. Combining the spatial adjacency relationships and heat conduction paths of each channel, the thermal interference compensation amount for adjacent associated channels is calculated. The channel activation commands of the activated lubrication channels and the thermal interference compensation amounts of adjacent associated channels are integrated to generate complete and standardized channel activation compensation information.

[0106] It is understood that the embodiments of this application realize the precise dynamic activation of the heat source corresponding channel, and also take into account the thermal coupling interference between channels. The preset matrix completes the advance compensation, effectively suppresses the lateral diffusion of heat, avoids the overall thermal imbalance caused by single-area control, and makes the temperature control and lubrication management more holistic and forward-looking.

[0107] In step S105, control commands for each activated channel are generated in real time through a multi-objective optimization control model, adjusting the oil supply volume, oil supply pressure, oil supply temperature and injection timing of each channel to achieve dynamic tracking of the heat source of the servo press and zoned collaborative temperature control lubrication.

[0108] Specifically, the activation compensation information of the channels and the temperature of the heat source center are obtained. The optimization objectives are to minimize the temperature deviation between the heat source area and the target temperature, minimize lubricating oil consumption, and minimize the temperature fluctuation of the oil supply to each lubrication channel. Constraints are set for a multi-objective optimization control model, and the model is solved under these constraints to obtain a Pareto optimal solution set. An optimal solution is selected from this set as the optimal control parameter. The optimal control parameter is then converted into specific control commands for each independent lubrication channel, and these commands are issued to each channel for execution.

[0109] It is understood that the embodiments of this application balance the three core requirements of temperature control accuracy, energy consumption cost and operational stability through multi-objective collaborative optimization, avoid the drawbacks of single-objective control, realize intelligent and refined collaborative control of the whole process, effectively maintain the thermal balance of the servo press, extend the service life of components, and improve the operational stability and processing accuracy of the equipment.

[0110] According to the embodiments of this application, an intelligent lubrication collaborative control method for thermal balance of a servo press is proposed. By employing a flexible thin-film thermistor array, it can acquire the two-dimensional temperature distribution cloud map of the key friction pair surface in real time, enabling the system to fully grasp the spatial gradient and uniformity of the temperature field. Through a dynamic heat source tracking module and a built-in thermal network model, it can not only identify the current heat source, but also analyze its diffusion trend and predict the hot spot location at the next moment based on physical mechanisms. This allows lubrication cooling actions to be deployed in advance before heat accumulation, providing preventative intervention and fundamentally reducing the magnitude of thermal deformation and response lag, effectively suppressing thermal drift at the bottom dead center. By introducing a thermal coupling matrix between channels, the degree of thermal conduction interference between each lubrication channel is quantified. When the oil supply of a certain channel increases, the oil supply parameters of adjacent channels are adjusted synchronously to offset the thermal interference, realizing decoupled collaborative control of multiple channels. By adopting a multi-objective optimization control model, while ensuring the cooling effect in the heat source area, it also takes into account the minimization of inter-channel thermal interference, lubricating oil consumption, and oil supply temperature fluctuation, achieving a balance between cooling effect, operating cost, and thermal balance. This solves the problems of lack of dynamic response, crude and lagging control strategies, and thermal coupling interference in existing technologies.

[0111] Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0112] The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0113] When the processor 302 executes the program, it implements the intelligent lubrication coordination control method for thermal balance of a servo press provided in the above embodiments.

[0114] Furthermore, electronic devices also include:

[0115] Communication interface 303 is used for communication between memory 301 and processor 302.

[0116] The memory 301 is used to store computer programs that can run on the processor 302.

[0117] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0118] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0119] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0120] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0121] This application also provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described intelligent lubrication coordination control method for thermal balance of a servo press.

[0122] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0123] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0124] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0125] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0126] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. An intelligent lubrication coordination control system for thermal balance of a servo press, characterized in that, include: The system includes a temperature sensing network, a multi-channel temperature control and lubrication module, a dynamic heat source tracking module, a channel activation and compensation module, and a collaborative control module. The temperature sensing network is used to deploy a flexible thin-film thermistor array on the surface of key moving parts to collect temperature field data in real time and generate a two-dimensional temperature distribution cloud map. The multi-channel temperature-controlled lubrication module includes multiple independently controlled lubrication channels. Each lubrication channel corresponds to one or more key lubrication components of the servo press, and each lubrication channel is independently equipped with a flow regulating device, a pressure regulating device, a temperature regulating device, and a status monitoring device. The dynamic heat source tracking module, based on the two-dimensional temperature distribution cloud map, identifies the center location of the heat source and its diffusion trend in real time through a heat source identification algorithm, and predicts the hot spot location at the next moment based on a heat network model. The channel activation and compensation module is used to dynamically activate one or more lubrication channels based on the center location of the heat source and the hot spot prediction results, and automatically calculate the thermal interference compensation amount of adjacent associated channels according to the preset inter-channel thermal coupling matrix to obtain channel activation compensation information. The collaborative control module is used to generate control commands for each activated channel in real time through a multi-objective optimization control model, and adjust the oil supply, oil supply pressure, oil supply temperature and injection timing of each channel to achieve dynamic tracking of the heat source of the servo press and zoned collaborative temperature control lubrication.

2. The intelligent lubrication coordination control system for thermal balance of a servo press according to claim 1, characterized in that, The temperature sensing network includes: a sensor deployment unit, a temperature acquisition unit, and a cloud map generation unit, wherein... The sensor deployment unit is used to employ flexible thin-film thermal sensors to form a flexible thin-film thermal array by non-uniform density deployment of the force deformation trajectory and friction hotspot areas of the servo press component. The temperature acquisition unit is used to acquire temperature data of each sensing node in the flexible thin film thermistor array in real time, and to perform preprocessing such as filtering, noise reduction and defect removal on the temperature data to obtain discrete temperature field data. The cloud map generation unit is used to reconstruct the global temperature distribution of the component surface based on the temperature field data and the actual layout coordinates of each sensing point, and generate a two-dimensional temperature distribution cloud map by interpolation fitting algorithm.

3. The intelligent lubrication coordination control system for thermal balance of a servo press according to claim 1, characterized in that, The multi-channel temperature-controlled lubrication module includes: a channel division unit, a three-dimensional control unit, a micro-injection execution unit, and a status monitoring unit. The channel division unit is used to divide the lubrication area into multiple independently controlled lubrication channels according to the type, spatial location and thermal characteristics of the key lubrication components of the servo press, and assign a unique channel identifier to each lubrication channel. Each channel corresponds to one or more adjacent key lubrication components. The three-dimensional control unit includes a flow regulating device, a pressure regulating device, and a temperature regulating device installed in each lubrication channel. The flow regulating device is a micro gear pump used to achieve precise control of the oil supply. The pressure regulating device is a proportional relief valve used to regulate the oil supply pressure in the channel. The temperature regulating device includes an integrated thin-film heater and a semiconductor cooling chip used to regulate the temperature of the lubricating oil at the channel outlet, making the lubricating oil itself a controllable heat exchange medium. The micro-injection execution unit includes an electromagnetic micro-injection valve located at the end of each lubrication channel to control the timing, duration, and area of ​​lubricating oil injection. The status monitoring unit is used to collect the actual oil supply flow, oil supply pressure and oil supply temperature of each channel in real time, and feed the collected data back to the collaborative control module in real time.

4. The intelligent lubrication coordination control system for thermal balance of a servo press according to claim 1, characterized in that, The dynamic heat source tracking module includes: a heat source identification unit, a heat diffusion analysis unit, and a hotspot prediction unit, wherein... The heat source identification unit is used to perform gradient analysis and local extremum search on the two-dimensional temperature distribution cloud map, identify the location of the heat source center in the temperature field and the temperature peak value of each heat source center, and mark the boundary of the heat source region. The heat diffusion analysis unit is used to trace the heat diffusion path along the decreasing temperature gradient starting from the center of the heat source, calculate the diffusion direction and diffusion rate of each heat source center, and generate a heat source diffusion trend field. The hotspot prediction unit is used to predict the hotspot location in the next control cycle by calling a preset thermal network model to perform forward iterative calculations based on the location of the heat source center and the heat source diffusion trend field.

5. The intelligent lubrication coordination control system for thermal balance of a servo press according to claim 4, characterized in that, The heat network model includes: The predicted target area is discretized into several nodes based on the spatial location of key lubrication components, and the node locations correspond to the spatial distribution of multiple lubrication channels. The nodes are connected by thermal resistance. The thermal resistance parameters connecting any two adjacent nodes are defined and calibrated. At the same time, each node is assigned a thermal capacity parameter that characterizes its local thermal inertia. The thermal resistance parameters are pre-calibrated based on the thermal conductivity, geometric dimensions and contact thermal resistance of the component material. Each heat source center is taken as the heat flux input, and its heat flux density is dynamically calculated based on the peak temperature and the area of ​​the heat source region. Based on the law of conservation of energy, the nodal temperature state equation is established, and the equation formula is as follows: ； Where C is the heat capacity matrix, which is a diagonal matrix with heat capacity parameters as diagonal elements; The node temperature vector; The value represents the rate of change of temperature with time t; A is the thermal conductivity matrix, which is a symmetric matrix composed of the reciprocals of the thermal resistance parameters. Its off-diagonal elements are positive, representing the thermal conductivity between node i and node j, and its diagonal elements are negative to ensure energy conservation. The input vector is the heat flow source. The forward Euler method is used to numerically solve the nodal temperature state equation, and the temperature value of each node at the next moment is calculated iteratively. Based on the calculated node temperature distribution, the points with maximum temperature are identified as predicted hotspot locations.

6. The intelligent lubrication coordination control system for thermal balance of a servo press according to claim 1, characterized in that, The channel activation and compensation module includes: an activation decision unit, a thermally coupled compensation calculation unit, and a compensation integration unit, wherein... The activation decision unit is used to select one or more lubrication channels corresponding to the spatial location of the heat source area as channels to be activated based on the current location of the heat source center and the predicted location of the hot spot at the next moment, and to generate a channel activation command. The thermal coupling compensation calculation unit is used to query the thermal coupling coefficient of adjacent non-activated channels from the preset thermal coupling matrix according to the channel activation command, and calculate the thermal interference compensation amount for adjacent associated channels by combining the spatial adjacency relationship and heat conduction path of each channel. The compensation integration unit is used to integrate the channel activation command of the activated lubrication channel and the thermal interference compensation amount of adjacent associated channels to generate complete and standardized channel activation compensation information.

7. The intelligent lubrication coordination control system for thermal balance of a servo press according to claim 1, characterized in that, The collaborative control module includes: a multi-objective optimization unit and an instruction synthesis unit, wherein... The multi-objective optimization unit is used to acquire channel activation compensation information and heat source center temperature. The optimization objectives are to minimize the temperature deviation between the heat source area and the target temperature, minimize the lubricating oil consumption, and minimize the oil supply temperature fluctuation of each lubrication channel. The constraints of the multi-objective optimization control model are set, and the solution is obtained under the constraints to obtain a Pareto optimal solution set. An optimal solution is selected from the Pareto optimal solution set as the optimal control parameter. The instruction synthesis unit is used to convert the optimal control parameters into specific control instructions for each independent lubrication channel, and then issue the instructions to each independent lubrication channel for execution.

8. A smart lubrication coordination control method for thermal balance of a servo press, characterized in that, Includes the following steps: A flexible thin-film thermal array is deployed on the surface of key moving parts to collect temperature field data in real time and generate a two-dimensional temperature distribution cloud map. Multiple independently controlled lubrication channels are constructed, each corresponding to one or more key lubrication components of the servo press, and each lubrication channel is independently equipped with a flow regulation device, a pressure regulation device, a temperature regulation device, and a status monitoring device. Based on the two-dimensional temperature distribution cloud map, the location of the heat source center and its diffusion trend are identified in real time through the heat source identification algorithm, and the hot spot location at the next moment is predicted based on the heat network model. Based on the location of the heat source center and the hot spot prediction results, one or more lubrication channels are dynamically activated, and the thermal interference compensation amount of adjacent related channels is automatically calculated according to the preset inter-channel thermal coupling matrix to obtain channel activation compensation information. The multi-objective optimization control model generates control commands for each activated channel in real time, adjusting the oil supply, oil supply pressure, oil supply temperature and injection timing of each channel, thereby realizing dynamic tracking of the heat source of the servo press and zoned collaborative temperature control lubrication.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the intelligent lubrication coordination control method for thermal balance of a servo press as described in claim 8.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the intelligent lubrication coordination control method for thermal balance of a servo press as described in claim 8.

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