Planetary reducer targeted temperature control method and system

Abstract

This application discloses a targeted temperature control method and system for planetary reducers, relating to the field of thermal management technology for mechanical transmission systems. The disclosed targeted temperature control method and system for planetary reducers accurately identify targeted control points by acquiring operating status and temperature distribution data, and execute targeted temperature control operations. This enables precise identification and location of high-temperature hot spots inside the planetary reducer, and implementation of targeted temperature control, thereby effectively suppressing local thermal deformation, protecting the lubricating oil film, and improving the transmission accuracy stability, operational reliability, and service life of the reducer under extreme operating conditions.

Description

Technical Field

[0001] This application relates to the field of thermal management technology for mechanical transmission systems, and in particular to a method and system for targeted temperature control of planetary reducers. Background Technology

[0002] Planetary reducers, as core transmission components in industrial robots and high-end equipment manufacturing, experience highly concentrated frictional heat in their critical friction pairs, particularly the contact area between the planetary gears and the pin shaft, under harsh conditions of high speed, heavy load, and continuous operation. This heat easily creates localized hot spots far exceeding the average temperature of the casing. These hot spots are typically located inside rotating components. Due to structural airtightness and dynamic operating characteristics, external sensors struggle to obtain accurate temperature distribution data in real time, resulting in ineffective monitoring of the true internal thermal state. Traditional thermal management solutions primarily rely on external casing cooling methods, such as air-cooled systems or water-cooled jackets. However, due to long heat transfer paths and delayed response, the cooling energy cannot directly act on the internal rotating heat sources, only providing uniform cooling to the entire casing. These passive cooling methods have the following drawbacks: firstly, they cannot accurately identify and locate internal high-temperature risk areas, creating blind spots in heat source monitoring; secondly, the cooling measures lack specificity and cannot actively intervene in specific hot spots, making it difficult to effectively suppress localized thermal deformation, accelerating the deterioration or even rupture of the lubricating oil film at high temperatures. The aforementioned problems severely restrict the transmission accuracy stability, operational reliability, and overall service life of the reducer under extreme operating conditions. In particular, under high dynamic load conditions, the risk of cascading failures caused by local overheating increases.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a targeted temperature control method and system for planetary reducers, which aims to improve the transmission accuracy, stability, and operational reliability of the reducer under extreme operating conditions.

[0005] To achieve the above objectives, this application proposes a targeted temperature control method for a planetary gear reducer, the method comprising: Acquire operating status data and internal temperature distribution data of the planetary reducer during operation; Based on the operating status data and internal temperature distribution data, at least one target control point requiring temperature control is identified, and a heat source location result containing the location information of the target control point is generated. Based on the heat source location results and the operating status data, the temperature control command corresponding to each target control point is determined; The temperature control command is sent to the thermoelectric actuator module located inside the planetary reducer and corresponding to the target control point, so as to drive the thermoelectric actuator module to perform a targeted temperature control operation on the target control point.

[0006] In one embodiment, the step of identifying at least one target control point requiring temperature control based on the operating status data and internal temperature distribution data, and generating a heat source location result containing the location information of the target control point, includes: Based on the aforementioned operating status data, the current operating condition of the planetary reducer is determined. Based on the internal temperature distribution data, calculate the relative temperature difference between multiple monitoring points; Call the preset temperature difference threshold corresponding to the operating condition, and determine whether the relative temperature difference data exceeds the corresponding preset temperature difference threshold. If so, the area with the largest temperature difference is identified as a high-temperature risk area. Within the high-temperature risk zone, the location with the largest temperature gradient in the internal temperature distribution data is analyzed, and this location is determined as the target control point. A heat source location result containing the location information of the target control point is then generated.

[0007] In one embodiment, the step of determining the temperature control command corresponding to each target control point based on the heat source location result and the operating status data includes: Based on the target control point location information in the heat source location results and the load information in the operating status data, the thermal risk level of each target control point is determined. Based on the thermal risk level, a target control mode is selected from multiple preset control modes; the preset control modes include a preventive micro-adjustment mode, a targeted active intervention mode, and a multi-point synergistic inhibition mode; Based on the thermal risk level, the target control mode, and the real-time temperature and temperature change rate of the target control point in the internal temperature distribution data, the target control parameters of the thermoelectric actuator corresponding to the target control point are calculated. The target control mode and the target control parameters are encapsulated into the temperature control command.

[0008] In one embodiment, when the target control mode is a multi-point cooperative suppression mode, the process of calculating the target control parameters includes: Based on the relative temperature difference data, the target control point with the largest temperature difference in the heat source location results is determined as the main control point, and the monitoring points adjacent to it are determined as auxiliary points. Based on the real-time temperature of the main control point in the internal temperature distribution data, the first control power of its thermoelectric execution module is calculated. Based on the difference between the real-time temperature of the auxiliary point and the real-time temperature of the main control point in the internal temperature distribution data, the second control power of the thermoelectric execution module of each auxiliary point is calculated, wherein the direction of action of the second control power is set to establish a reverse temperature gradient between the main control point and the auxiliary point. The first control power and the second control power are respectively used as the target control parameters of the main control point and each of the auxiliary points.

[0009] In one embodiment, the step of determining the temperature control command corresponding to each target control point based on the heat source location result and the operating status data further includes: Obtain the real-time meshing phase signal of the planetary gears; Based on the heat source location result and the real-time meshing phase signal, determine whether the temperature rise of the target control point is related to a specific meshing phase; If the temperature rise at the target control point is associated with a specific engagement phase, a phase synchronization marker is added to the temperature control command. The phase synchronization marker is used to instruct the thermoelectric actuator to pre-start before the specific engagement phase and enhance its control strength after the specific engagement phase.

[0010] In one embodiment, the step of determining the temperature control command corresponding to each target control point based on the heat source location result and the operating status data further includes: Based on the temperature of the target control point in the heat source location result, query the pre-trained lubricating oil performance relationship model to determine the theoretical oil film state at that temperature. Determine whether the determined theoretical oil film state is lower than the preset oil film safety threshold; If the theoretical oil film state is lower than the oil film safety threshold, the cooling operation on the target control point is strengthened by adjusting the target control parameter in the temperature control command. If the theoretical oil film state is still lower than the oil film safety threshold after the cooling operation reaches the preset intensity threshold, a command to trigger pulse lubrication near the target control point is added to the temperature control command.

[0011] In one embodiment, the method further includes: The internal temperature distribution data is continuously acquired, and the temperature data after temperature control at the target control point is extracted. Based on the temperature data after temperature control and the internal temperature distribution data before temperature control, the temperature control efficiency parameters of this targeted temperature control operation are calculated. The operating status data, the heat source location results, the temperature control commands, and the corresponding temperature control efficiency parameters are stored as historical cases in the thermal behavior pattern library. Based on multiple historical cases in the thermal behavior pattern library, dynamic optimization processing is performed. The dynamic optimization processing includes: dynamically optimizing and adjusting the preset temperature difference threshold corresponding to the operating condition, and dynamically optimizing and adjusting the processing logic for calculating the target control parameters.

[0012] In one embodiment, the dynamic optimization and adjustment of the processing logic for calculating the target control parameters includes: From the thermal behavior pattern library, historical cases similar to the current operating conditions and heat source location results are selected, and the historical control parameters and corresponding temperature control efficiency parameters recorded in the temperature control instructions of the historical cases are extracted. Based on the temperature control performance parameters, different adjustment weights are assigned to each selected historical case. Based on the weighted historical control parameters, the optimized control parameters applicable to the current operating conditions and heat source location are calculated. Based on the optimized control parameters, the processing logic for calculating the target control parameters is updated.

[0013] In one embodiment, the dynamic optimization and adjustment of the preset temperature difference threshold corresponding to the operating condition includes: From the thermal behavior pattern library, for a specific operating condition, the proportion of high-temperature risk areas identified in each instance that are ultimately confirmed as effective heat sources is statistically analyzed, which is taken as the risk area identification success rate under that operating condition. Obtain the power constraints of the system; If the success rate of risk zone identification continues to be higher than the preset first success rate threshold, then within the range allowed by the power constraint conditions, the preset temperature difference threshold corresponding to the operating condition is increased. If the success rate of risk zone identification continues to be lower than the preset second success rate threshold, then the preset temperature difference threshold corresponding to the operating condition is reduced.

[0014] Furthermore, to achieve the above objectives, this application also proposes a planetary gear reducer targeted temperature control system, which includes: a memory, a processor, and a planetary gear reducer targeted temperature control program stored in the memory and executable on the processor, wherein the planetary gear reducer targeted temperature control program is configured to implement the steps of the planetary gear reducer targeted temperature control method.

[0015] The planetary reducer targeted temperature control method and system proposed in this application accurately identify the targeted control point by acquiring operating status and temperature distribution data, and execute targeted temperature control operations. This enables precise identification and location of high-temperature hot spots inside the planetary reducer, and implementation of targeted temperature control, thereby effectively suppressing local thermal deformation, protecting the lubricating oil film, and improving the transmission accuracy stability, operational reliability and service life of the reducer under extreme operating conditions. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of an embodiment of the planetary reducer targeted temperature control method of this application; Figure 2 For this application Figure 1 A detailed flowchart of step S200; Figure 3 For this application Figure 1 Detailed flowchart of step S300; Figure 4 This is a schematic flowchart illustrating another embodiment of the planetary reducer targeted temperature control method of this application. Figure 5 This is a schematic diagram of a structure provided for an embodiment of the planetary reducer targeted temperature control system of this application.

[0019] Explanation of icon numbers: 10. Memory; 20. Processor.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] In existing technologies, planetary reducer thermal management solutions mainly rely on external casing cooling. However, this method has a long heat transfer path and a delayed response, making it impossible to directly target heat sources inside rotating components. This results in the difficulty of accurately detecting the actual high-temperature points inside the reducer in real time by external sensors. Furthermore, cooling measures cannot target and actively intervene in the internal rotating heat sources, and can only passively cool the entire system. Consequently, this severely restricts the reducer's transmission accuracy, reliability, and service life under extreme operating conditions.

[0024] Based on this, embodiments of this application provide a targeted temperature control method for a planetary reducer, referring to... Figure 1 The planetary reducer targeted temperature control method includes steps S100 to S400, wherein: Step S100: Obtain the operating status data and internal temperature distribution data of the planetary reducer during operation; Step S200: Based on the operating status data and internal temperature distribution data, identify at least one target control point that requires temperature control, and generate a heat source location result containing the location information of the target control point. Step S300: Based on the heat source location results and the operating status data, determine the temperature control command corresponding to each target control point; Step S400: The temperature control command is sent to the thermoelectric actuator module located inside the planetary reducer and corresponding to the target control point, so as to drive the thermoelectric actuator module to perform a targeted temperature control operation on the target control point.

[0025] In this embodiment, the planetary reducer is a transmission device widely used in industrial robots, high-end equipment, and other fields. It contains multiple gears, bearings, and other components to achieve speed reduction and torque increase. Operating status data refers to various real-time data generated by the planetary reducer during operation, such as load size, speed, torque, and vibration frequency. These data reflect the reducer's current operating characteristics. Internal temperature distribution data refers to real-time temperature measurements at different locations inside the planetary reducer or temperature field data obtained through simulation, reflecting the generation, transfer, and accumulation of heat within the reducer. Targeted control points refer to localized areas within the planetary reducer where abnormal temperature increases due to frictional heating or other reasons require precise temperature intervention. These points are typically critical friction pairs or components susceptible to heat.

[0026] In this embodiment, the heat source location result refers to the specific location information of the target control point determined by analyzing operating status data and internal temperature distribution data, such as its coordinates inside the reducer or the identifier of its component. The temperature control command is a set of control parameters calculated based on the heat source location result and operating status data, used to guide the thermoelectric actuator module to precisely adjust the temperature of the target control point, such as setting the target temperature, control power, or operating mode. The thermoelectric actuator module refers to a device installed inside the planetary reducer that can actively heat or cool a specific area according to the received temperature control command, such as a thermoelectric cooler based on the Peltier effect or a micro heater. Targeted temperature control operation refers to the localized and precise temperature adjustment activity performed by the thermoelectric actuator module on the identified target control point according to the temperature control command, aiming to maintain the temperature of that point within a preset safe range.

[0027] In this embodiment, the planetary reducer targeted temperature control method first acquires the operating status data and internal temperature distribution data of the planetary reducer during operation. The operating status data can be collected using sensors installed externally to the reducer, such as a speed sensor to obtain speed information and a load sensor to obtain load information. The internal temperature distribution data can be measured in real time by installing thermocouples or thermistors at multiple preset monitoring points inside the reducer, and these measurements are then aggregated. For example, temperature sensors can be placed in easily accessible locations such as the reducer housing or bearing housing.

[0028] In this embodiment, based on the acquired operating status data and internal temperature distribution data, at least one target control point requiring temperature control is identified, and a heat source location result containing the location information of the target control point is generated. Specifically, a global fixed temperature threshold can be set. When the temperature of any monitoring point exceeds this threshold, the area where that monitoring point is located is considered a potential high-temperature area. Then, the center location of this potential high-temperature area or the monitoring point with the highest temperature is determined as the target control point. The location information of the target control point can be a preset number or its approximate coordinates inside the reducer.

[0029] In this embodiment, based on the heat source location result and the operating status data, the temperature control command corresponding to each target control point is determined. For example, the corresponding cooling power value can be found from a preset fixed control parameter table based on the location of the target control point. Alternatively, a simple proportional control strategy can be adopted, that is, the required cooling power is calculated proportionally based on the difference between the real-time temperature of the target control point and a target temperature, and this is used as the temperature control command. This command can be a simple power setpoint.

[0030] In this embodiment, the temperature control command is finally sent to the thermoelectric actuator module located inside the planetary reducer and corresponding to the target control point, to drive the thermoelectric actuator module to perform targeted temperature control operation on the target control point. The temperature control command can be transmitted to the corresponding thermoelectric actuator module via wired or wireless communication. Upon receiving the command, the thermoelectric actuator module can, for example, activate its internal cooling or heating elements according to the power value set in the command, to locally adjust the temperature of the target control point. For example, when the command requires cooling, the thermoelectric actuator module will activate its cooling function to dissipate heat from the target control point area.

[0031] In this embodiment, the method enables precise identification and targeted intervention of local hot spots inside the planetary reducer, overcoming the limitations of traditional external cooling solutions in terms of sensing accuracy and control response. By actively controlling the temperature of specific hot spots, local thermal deformation and premature lubricant failure are effectively suppressed, thereby improving the reducer's transmission accuracy, operational reliability, and service life under harsh operating conditions.

[0032] In one feasible implementation, refer to Figure 2 Step S200 includes steps S210 to S240, wherein: Step S210: Based on the operating status data, determine the current operating condition of the planetary reducer; Step S220: Based on the internal temperature distribution data, calculate the relative temperature difference data between multiple monitoring points; Step S230: Call the preset temperature difference threshold corresponding to the operating condition, and determine whether the relative temperature difference data exceeds the corresponding preset temperature difference threshold. If so, the area with the largest temperature difference is identified as a high temperature risk area. Step S240: Within the high-temperature risk zone, analyze the location with the largest temperature gradient in the internal temperature distribution data, determine this location as the target control point, and generate a heat source location result containing the location information of the target control point.

[0033] In this embodiment, when determining the current operating condition of the planetary reducer, the system continuously monitors operating parameters such as the input speed, output torque, load, lubricating oil flow, and ambient temperature. These parameters are collected in real time by sensors and processed by the control unit. The operating condition can be determined through a preset rule set, lookup table, or classification based on a machine learning model. For example, when the input speed is below a certain threshold and the output torque is at a low level, it can be determined as a "light load, low speed" condition; when both the input speed and output torque are at a high level, it is determined as a "heavy load, high speed" condition. This identification of the operating condition provides important contextual information for subsequent temperature risk assessment.

[0034] Based on this, to more precisely identify potential hotspots, the system calculates the relative temperature difference data between multiple monitoring points based on the internal temperature distribution data. This internal temperature distribution data is typically collected in real-time by multiple temperature sensors (e.g., thermocouples, thermistors, or infrared sensor arrays) located in key areas inside the planetary gear reducer (such as gear meshing areas, bearings, lubrication channels, etc.). The relative temperature difference data can be obtained by comparing the temperature differences between adjacent monitoring points, or by comparing the difference between the temperature of a local monitoring point and the average temperature of the area. For example, the temperature difference ΔT_ij = T_i - T_j between any two adjacent monitoring points T_i and T_j can be calculated. This calculation of relative temperature differences helps to highlight areas of concentrated local heat, effectively identifying locations of abnormal temperature rises even when the overall temperature is high.

[0035] In this embodiment, the system calls a preset temperature difference threshold corresponding to the current operating condition and determines whether the relative temperature difference data exceeds the corresponding preset temperature difference threshold. These preset temperature difference thresholds are established based on the normal thermal behavior characteristics of the planetary reducer under different operating conditions, through experimental testing, simulation analysis, or historical data statistics. For example, under "light load, low speed" conditions, there may be stricter threshold requirements for relative temperature difference; while under "heavy load, high speed" conditions, due to the increase in overall heat generation, the corresponding temperature difference threshold may be appropriately relaxed. If any relative temperature difference data is found to exceed the preset threshold for the current operating condition, the area with the largest temperature difference is identified as a high-temperature risk zone. This step ensures the dynamism and accuracy of thermal risk assessment and avoids false alarms or missed alarms that may be caused by using a single fixed threshold.

[0036] Furthermore, within the high-temperature risk zone, the system analyzes the location with the largest temperature gradient in the internal temperature distribution data and identifies this location as the target control point, generating a heat source location result containing the location information of the target control point. Once the high-temperature risk zone is identified, the system performs a more detailed analysis of the temperature data within that area. The temperature gradient can be obtained by interpolating the temperature data from multiple monitoring points within the risk zone to form a more refined temperature field, and then calculating the gradient value at each point in the temperature field. The location with the largest temperature gradient typically indicates the area where heat generation is most concentrated or the heat flux density is highest, i.e., the actual core of the heat source. Accurately identifying this location as the target control point and encapsulating its location information (e.g., three-dimensional coordinates or specific component identification) into the heat source location result provides clear instructions for the subsequent thermoelectric actuator to perform precise targeted temperature control.

[0037] In this embodiment, through the above technical solution, the system can dynamically adjust the thermal risk assessment criteria based on the current operating conditions of the planetary reducer, thereby more accurately identifying potential high-temperature risk zones. By calculating relative temperature difference data, local hot spots can be effectively captured, rather than relying solely on absolute temperature, which is particularly important for locating heat sources in complex internal structures. Within the identified high-temperature risk zone, the target control point is determined by analyzing the location of the largest temperature gradient, ensuring that control commands can accurately act on the core area of ​​the heat source, thereby improving the efficiency and response speed of targeted temperature control. This layered and refined identification strategy enables the planetary reducer to detect potential thermal imbalance problems earlier and more accurately, providing a solid foundation for subsequent targeted temperature control operations, effectively avoiding component overheating and performance degradation caused by heat accumulation, and extending equipment life.

[0038] In one feasible implementation, refer to Figure 3 Step S300 includes steps S310 to S340, wherein: Step S310: Based on the target control point location information in the heat source location results and the load information in the operating status data, determine the thermal risk level of each target control point; Step S320: Select a target control mode from multiple preset control modes according to the thermal risk level; the preset control modes include preventive micro-adjustment mode, targeted active intervention mode and multi-point synergistic inhibition mode; Step S330: Based on the thermal risk level, the target control mode, and the real-time temperature and temperature change rate of the target control point in the internal temperature distribution data, calculate the target control parameters of the thermoelectric actuator corresponding to the target control point. Step S340: Encapsulate the target control mode and the target control parameters into the temperature control command.

[0039] In this embodiment, the determination of the thermal risk level considers not only the current temperature value, but also the specific location of the target control point and its criticality in the overall operation of the planetary reducer, as well as the load information in the operating status data. For example, a target control point located in a high-load area or near a critical component that has a significant impact on performance may be assigned a higher thermal risk level even if the current temperature has not yet reached the critical value, in order to prevent potential overheating risks. Specifically, the system can perform correlation analysis between the location information of the target control point (e.g., whether it is close to a bearing, gear meshing area, etc.) and the load information (e.g., torque, speed, power, etc.) in the operating status data, based on a preset rule base, expert experience system, or through machine learning model, thereby outputting discrete or continuous thermal risk levels such as "low risk," "medium risk," "high risk," or "emergency risk."

[0040] In this embodiment, after determining the thermal risk level of each target control point, the system intelligently selects the most suitable control mode based on that level. This mode selection mechanism ensures the flexibility and specificity of the temperature control strategy. When the thermal risk level is low, the system may select a preventative micro-adjustment mode. This mode aims to maintain the stability of the target control point's temperature by applying small, continuous power adjustments to the thermoelectric actuator module, preventing it from developing into a dangerous area, rather than intervening only after the temperature has significantly increased. For example, this mode can be activated for minor cooling when the temperature shows a slight upward trend but is still within a safe range. When the thermal risk level is high and more direct intervention is required, the system will select a targeted active intervention mode. In this mode, the thermoelectric actuator module will cool with stronger power, aiming to quickly reduce the temperature of the target control point and bring it back to a safe range. This is typically applicable when the temperature is close to or exceeds a preset threshold. When the thermal risk level is extremely high, or when there are multiple mutually influencing target control points, the system may select a multi-point cooperative suppression mode. This mode involves coordinating the work of multiple thermoelectric actuators to achieve broader or more complex temperature field control, such as suppressing heat diffusion or establishing local temperature gradients by simultaneously cooling the main heat source and its surrounding area.

[0041] In this embodiment, the target control parameters of the thermoelectric actuator corresponding to the target control point are calculated based on the thermal risk level, the target control mode, and the real-time temperature and temperature change rate of the target control point in the internal temperature distribution data. This step precisely quantifies the specific actions that the thermoelectric actuator should perform based on the selected target control mode and the current thermal conditions. The calculation process comprehensively considers the thermal risk level, the target control mode, the real-time temperature of the target control point, and the temperature change rate. The real-time temperature provides information on the current thermal state, while the temperature change rate reflects the dynamic trend of heat accumulation or dissipation, which is crucial for predicting future temperature trends. For example, in the targeted active intervention mode, if the real-time temperature is high and the temperature change rate is also high, the calculated target control parameters (such as cooling power) will be larger to achieve rapid cooling. This calculation can employ PID control algorithms, fuzzy control, model predictive control, or other algorithms based on physical models or data-driven approaches to generate specific power values, current values, or target temperature setpoints required by the thermoelectric actuator.

[0042] In this embodiment, the calculated target control parameters and the selected target control mode are finally encapsulated to form a complete temperature control command. This command is a structured data packet containing all the information required for the thermoelectric actuator to perform the temperature control operation, such as the module ID, control mode identifier, specific power setpoint, duration, etc. The encapsulated command is then sent to the corresponding thermoelectric actuator to drive it to perform the corresponding targeted temperature control operation.

[0043] In this embodiment, through the above technical solution, this application can intelligently select the most suitable control mode based on the specific thermal risk level of each target control point inside the planetary reducer, thereby avoiding the limitations of a single control strategy. This risk-level-based adaptive control, combined with the real-time temperature and temperature change rate of the target control point, can accurately calculate the control parameters required by the thermoelectric actuator, ensuring the pertinence and effectiveness of temperature control commands. This enables the planetary reducer to accurately and efficiently intervene in potential or existing thermal problems under different operating conditions, effectively suppressing local overheating, improving the precision of temperature control and the overall system's thermal management efficiency, thereby ensuring the stable operation of the planetary reducer and extending its service life.

[0044] In one feasible implementation, when the target control mode is a multi-point collaborative suppression mode, the process of calculating the target control parameters includes: based on the relative temperature difference data, determining the target control point with the largest temperature difference in the heat source location results as the main control point, and determining its adjacent monitoring points as auxiliary points; based on the real-time temperature of the main control point in the internal temperature distribution data, calculating the first control power of its thermoelectric execution module; based on the difference between the real-time temperature of the auxiliary points and the real-time temperature of the main control point in the internal temperature distribution data, calculating the second control power of the thermoelectric execution module of each auxiliary point, wherein the direction of action of the second control power is set to establish a reverse temperature gradient between the main control point and the auxiliary points; and using the first control power and the second control power as the target control parameters of the main control point and each of the auxiliary points, respectively.

[0045] In this embodiment, relative temperature difference data refers to the temperature difference data between multiple monitoring points inside the planetary reducer, reflecting the uneven distribution of local heat. This data can be calculated from internal temperature distribution data, for example, by comparing the highest and lowest temperatures of adjacent monitoring points or a specific area. The heat source location result is the location information of at least one target control point requiring temperature control, identified based on operating status data and internal temperature distribution data. The master control point refers to the target control point with the largest temperature difference determined from the relative temperature difference data in the heat source location result. This point usually represents the area with the most concentrated current heat load and the most significant temperature anomaly, and is the core target of temperature control in the multi-point collaborative suppression mode. Auxiliary points refer to monitoring points that are spatially adjacent to the master control point. These auxiliary points may be directly affected by the heat of the master control point, or have a thermal coupling relationship with the master control point through heat conduction. Determining auxiliary points helps to form a collaborative control area around the master control point to more effectively manage heat diffusion. The determination of adjacent monitoring points can be based on preset distance thresholds, heat conduction path analysis, or topological relationships.

[0046] In this embodiment, the internal temperature distribution data refers to the temperature measurement values ​​of each monitoring point inside the planetary reducer at different times. Real-time temperature refers to the temperature data acquired at the current moment without any delay processing. The thermoelectric actuator module of the master control point is a temperature regulation device, such as a thermoelectric cooler (TEC) or a micro heater, located inside the planetary reducer and corresponding to the master control point. This module can actively heat or cool the master control point according to received instructions. The first control power is the output power set for the thermoelectric actuator module of the master control point. Its calculation can be based on the classic PID (proportional-integral-derivative) control algorithm, dynamically adjusted according to the deviation between the real-time temperature of the master control point and the preset target temperature, the rate of change of the deviation, and the cumulative amount of the deviation. Alternatively, more complex algorithms such as model predictive control (MPC) or fuzzy control can be used, combined with the thermal model and operating conditions of the planetary reducer for optimization calculations to ensure that the temperature of the master control point can quickly and stably reach the target value.

[0047] In this embodiment, the difference between the real-time temperature of the auxiliary point and the real-time temperature of the main control point reflects the trend and intensity of heat transfer between the main control point and adjacent areas. The second control power is the output power set for the thermoelectric execution module of each auxiliary point. Its calculation aims to influence the thermal field around the main control point by actively adjusting the temperature of the auxiliary points. For example, if the temperature of the main control point is much higher than that of the auxiliary points, the second control power can be set to cool the auxiliary points, thereby creating a temperature gradient between the main control point and the auxiliary points that is conducive to the diffusion of heat from the main control point to the auxiliary points. Conversely, if the temperature of the auxiliary points is too high, cooling the auxiliary points can prevent heat transfer to the main control point. Establishing a reverse temperature gradient means that by actively controlling the auxiliary points, heat can flow in a desired direction, such as guiding excess heat from the main control point to the auxiliary point area, or preventing heat from the auxiliary point area from concentrating on the main control point, thereby achieving synergistic suppression of the main control point temperature. This can be achieved by setting the second control power as a function proportional to the temperature difference and determining the direction and intensity of cooling or heating based on the sign of the temperature difference. The target control parameters are the specific values ​​in the temperature control command, used to guide the operation of the thermoelectric actuators. The calculated first and second control powers are assigned to the master control point and each auxiliary point, respectively, ensuring that each thermoelectric actuator receives precise commands consistent with its role and the cooperative control objective. These parameters are then encapsulated into the temperature control command and sent to the corresponding thermoelectric actuator.

[0048] In this embodiment, by clearly distinguishing between the main control point and the auxiliary point, and calculating their first and second control powers respectively, this method can focus on controlling the area with the most concentrated heat load, while actively adjusting the surrounding thermal environment using the auxiliary point. In particular, by setting the direction of the second control power to establish a reverse temperature gradient between the main control point and the auxiliary point, heat flow can be effectively guided, preventing excessive heat accumulation in critical areas, or guiding excess heat from the main control point area to other areas, thereby achieving coordinated heat suppression and uniform distribution. This coordinated control strategy avoids localized heat transfer or control blind spots that may result from single-hotspot control, improves the accuracy and efficiency of temperature control, helps maintain the thermal stability of the planetary reducer under complex operating conditions, extends its service life, and ensures its operational reliability.

[0049] In one feasible implementation, the step of determining the temperature control command corresponding to each target control point based on the heat source positioning result and the operating status data further includes: acquiring the real-time meshing phase signal of the planetary gear; determining whether the temperature rise of the target control point is associated with a specific meshing phase based on the heat source positioning result and the real-time meshing phase signal; if the temperature rise of the target control point is associated with a specific meshing phase, then adding a phase synchronization mark to the temperature control command, the phase synchronization mark being used to instruct the thermoelectric actuator to start in advance before the specific meshing phase and to enhance its control strength after the specific meshing phase.

[0050] In this embodiment, to achieve precise control of the periodic heat source, it is first necessary to acquire the real-time meshing phase signal of the planetary gears. This signal refers to the instantaneous angle or time information of the tooth surface contact point relative to a certain reference position during the operation of the gear pair inside the planetary reducer, reflecting the periodic characteristics of gear meshing. This signal can be acquired by installing rotary encoders, Hall sensors, or photoelectric sensors inside or outside the planetary reducer. These sensors can monitor the rotational speed and angular position of the planet carrier, sun gear, or ring gear. Combining the gear geometry and transmission ratio, the system can accurately calculate the real-time meshing phase of a specific gear pair. Alternatively, the meshing phase information can also be indirectly inferred by analyzing vibration or acoustic signals and combining them with signal processing techniques.

[0051] Based on this, the system will determine whether the temperature rise at the target control point is related to a specific meshing phase, based on the heat source location results and the real-time meshing phase signal. This determination aims to identify the dynamic characteristics of the heat source, particularly whether it has a causal relationship with the periodic meshing behavior of the gears. Specifically, the acquired real-time meshing phase signal can be correlated with the real-time temperature data of the target control point. This can be achieved through methods such as time series analysis, correlation analysis, or spectral analysis. For example, a Fourier transform can be performed on the temperature fluctuation data of the target control point to analyze whether its frequency components match the gear meshing frequency or its harmonics. If a significant correlation exists, and the temperature rise trend occurs periodically with a specific meshing phase, then it can be determined that the temperature rise is related to the specific meshing phase. The system can preset a correlation threshold; when the correlation coefficient exceeds this threshold, a correlation is considered to exist.

[0052] In this embodiment, if it is determined that the temperature rise at the target control point is associated with a specific meshing phase, a phase synchronization marker is added to the temperature control command. The phase synchronization marker instructs the thermoelectric actuator to pre-start before the specific meshing phase and enhance its control strength after the specific meshing phase. When it is determined that the temperature rise is associated with a specific meshing phase, the system embeds a special "phase synchronization marker" into the generated temperature control command. This marker contains information such as the pre-start time, the duration of enhanced control, and the enhancement magnitude. After receiving the command with this marker, the thermoelectric actuator no longer passively responds to the real-time temperature but pre-starts or increases power before the specific meshing phase arrives (e.g., before the gear is about to enter the high-load meshing region) to pre-absorb or dissipate heat. After the specific meshing phase (e.g., after the gear has just completed high-load meshing and the heat accumulation reaches its peak), the control strength is further enhanced to quickly suppress heat diffusion and prevent excessive temperature. This pre-start and enhanced control strategy can be dynamically adjusted and optimized based on actual operating data and historical experience.

[0053] In this embodiment, through the above technical solution, this application can acquire the real-time meshing phase signal of the planetary gear and determine whether the temperature rise at the target control point is related to a specific meshing phase. This correlation analysis enables the system to identify transient heat sources caused by periodic meshing behavior. When such a correlation is confirmed, a phase synchronization marker is added to the temperature control command, instructing the thermoelectric actuator to pre-start before the specific meshing phase and enhance its control strength after the specific meshing phase. This forward-looking and dynamically adjusted control strategy enables the thermoelectric actuator to respond more accurately and promptly to transient heat accumulation, effectively avoiding temperature overshoot and local overheating, thereby improving the efficiency and accuracy of targeted temperature control for periodic and transient heat sources, reducing the wear and failure risk of the planetary reducer due to local thermal stress concentration, and extending the service life of the equipment.

[0054] In one feasible implementation, the step of determining the temperature control command corresponding to each target control point based on the heat source location result and the operating status data further includes: querying a pre-trained lubricating oil performance relationship model based on the temperature of the target control point in the heat source location result to determine the theoretical oil film state at that temperature; determining whether the determined theoretical oil film state is lower than a preset oil film safety threshold; if the theoretical oil film state is lower than the oil film safety threshold, then prioritizing the adjustment of the target control parameters in the temperature control command to enhance the cooling operation of the target control point; if the theoretical oil film state is still lower than the oil film safety threshold after the cooling operation reaches a preset intensity threshold, then adding a command to the temperature control command to trigger pulse lubrication near the target control point.

[0055] In this embodiment, based on the temperature of the target control point in the heat source location result, a pre-trained lubricating oil performance relationship model is queried to determine the theoretical oil film state at that temperature. This means that after the system obtains the real-time temperature data of the identified target control point, it uses this data as input into a pre-established lubricating oil performance relationship model. This model can be constructed based on a large amount of experimental data, simulation results, or empirical formulas, and is used to describe key performance parameters of lubricating oil at different temperatures, such as viscosity, load-bearing capacity, and oil film thickness. By querying or calculating, the theoretical oil film state of the lubricating oil at the target control point at that specific temperature can be obtained. For example, it can be quantified as minimum oil film thickness, viscosity index, or a comprehensive oil film health score. The "pre-trained lubricating oil performance relationship model" can be a multi-dimensional lookup table storing lubricating oil performance data under different temperatures, pressures, shear rates, etc.; it can also be a predictive model trained based on machine learning algorithms (such as neural networks or support vector machines), which can accurately predict the oil film state based on the input temperature; or it can be a physical model based on the Reynolds equation or elastohydrodynamic lubrication theory, which predicts the oil film thickness through numerical solutions.

[0056] In this embodiment, determining whether the determined theoretical oil film state is lower than a preset oil film safety threshold means comparing the theoretical oil film state obtained in the above steps with a pre-set safety threshold. This "oil film safety threshold" is determined comprehensively based on factors such as the planetary reducer's design requirements, material properties, expected lifespan, and operational reliability, and represents the minimum oil film state required to maintain normal lubrication and avoid wear. For example, if the theoretical oil film thickness is lower than a certain critical value, or the oil film health score is lower than a certain safety score, the oil film state is considered unsafe.

[0057] In this embodiment, if the theoretical oil film condition is lower than the oil film safety threshold, the cooling operation at the target control point is enhanced by adjusting the target control parameters in the temperature control command. This means that once a risk to the oil film condition is detected, the system will take immediate action. First, it will prioritize improving the oil film condition by enhancing the cooling capability of the thermoelectric actuator module. This can be achieved by modifying the target control parameters in the temperature control command, for example, by increasing the cooling power of the thermoelectric actuator module, extending the cooling time, or adjusting the cooling strategy, in order to rapidly reduce the temperature at the target control point, thereby increasing the viscosity of the lubricating oil and restoring the integrity of the oil film.

[0058] In this embodiment, if the theoretical oil film state is still below the oil film safety threshold after the cooling operation reaches a preset intensity threshold, an instruction to trigger pulse lubrication near the target control point is added to the temperature control instruction. This means that if an intensified cooling operation has been performed, and the cooling operation has reached its maximum tolerable "preset intensity threshold" (e.g., the thermoelectric actuator has reached its maximum cooling power or the continuous cooling time has reached its upper limit), but the theoretical oil film state still fails to recover to above the safety threshold, it indicates that simple temperature control is insufficient to solve the problem. In this case, the system will further add a "pulse lubrication instruction" to the temperature control instruction. This instruction is used to trigger a local lubrication device (e.g., a micro-injector or micro-pump) located near the target control point to precisely spray or inject a small amount of fresh lubricating oil into the area within a short time to quickly replenish or rebuild the oil film, thereby directly solving the oil film failure problem.

[0059] In this embodiment, through the above technical solution, this application can more comprehensively evaluate the operating status of key points inside the planetary reducer, focusing not only on temperature itself but also on the direct impact of temperature on the lubricating oil film state. When a risk of lubricating oil film failure is detected, the system can intelligently prioritize enhanced cooling measures to restore oil film performance by improving temperature. If the cooling measures reach their limit and still cannot solve the problem, precise pulse lubrication can be initiated in a timely manner to directly replenish lubricating oil, effectively avoiding component wear and failure caused by oil film rupture. This strategy, combining temperature control and oil film state monitoring, improves the reliability and durability of the planetary reducer under complex operating conditions, achieving a leap from passive temperature management to active oil film protection, thereby extending equipment life and reducing maintenance costs.

[0060] In one feasible implementation, refer to Figure 4 The method further includes steps S510 to S540, wherein: Step S510: Continuously acquire the internal temperature distribution data and extract the temperature data after temperature control of the target control point; Step S520: Based on the temperature data after temperature control and the internal temperature distribution data before temperature control, calculate the temperature control performance parameters of this targeted temperature control operation. Step S530: Store the operating status data, the heat source location result, the temperature control command and the corresponding temperature control efficiency parameters as historical cases in the thermal behavior pattern library; Step S540: Based on multiple historical cases in the thermal behavior pattern library, perform dynamic optimization processing. The dynamic optimization processing includes: dynamically optimizing and adjusting the preset temperature difference threshold corresponding to the operating condition, and dynamically optimizing and adjusting the processing logic for calculating the target control parameters.

[0061] In this embodiment, to evaluate the actual effect of each targeted temperature control operation, this application proposes that after performing a temperature control operation, the system continuously acquires temperature distribution data inside the planetary reducer and extracts the temperature data of a specific targeted control point after the temperature control operation. This internal temperature distribution data is typically acquired in real time through a distributed temperature sensor network (e.g., thermocouples, thermistors, or infrared sensors) and transmitted to the processor for processing. The processor accurately filters and records the temperature values ​​after temperature control based on preset targeted control point location information.

[0062] In this embodiment, based on the acquired temperature data after temperature control and the internal temperature distribution data before temperature control, the system calculates the temperature control efficiency parameters for this targeted temperature control operation. Temperature control efficiency parameters are quantitative indicators that measure the effectiveness of a single targeted temperature control operation, and may include, but are not limited to: the temperature drop rate (temperature before temperature control minus the temperature after temperature control), the time required to reach the target temperature, the temperature fluctuation range, and the energy consumed during the control process. For example, (temperature before temperature control - temperature after temperature control) / (control time) can be calculated as the cooling rate, or (temperature before temperature control - target temperature) / (temperature after temperature control - target temperature) can be calculated as the temperature convergence ratio. These parameters comprehensively reflect the efficiency and effectiveness of the control.

[0063] In this embodiment, to accumulate and manage thermal behavior data and control experience of the planetary reducer under different operating conditions, this application stores operating status data, heat source location results, temperature control commands, and corresponding temperature control performance parameters as historical cases in a thermal behavior pattern library. The thermal behavior pattern library can be a database or storage module. Each historical case is a data record containing fields such as: operating conditions (load, speed, ambient temperature, etc.), target control point location coordinates, identified thermal risk level, sent temperature control commands (including target control mode and target control parameters), and calculated temperature control performance parameters. Cases are managed using timestamps or other unique identifiers.

[0064] Based on this, this application performs dynamic optimization processing using multiple historical cases from the thermal behavior pattern library. Dynamic optimization processing refers to the system continuously learning and adjusting its control strategies and parameters using historical experience data to adapt to changes in the planetary reducer's operating environment and thermal behavior patterns, thereby improving the accuracy and efficiency of future temperature control. This dynamic optimization processing includes two main aspects: On the one hand, the preset temperature difference threshold corresponding to the operating conditions is dynamically optimized and adjusted. The preset temperature difference threshold is an important parameter for identifying high-temperature risk areas. Under different operating conditions, the normal temperature difference distribution inside the planetary reducer may be different. Therefore, it is necessary to dynamically adjust this threshold to ensure accurate identification of heat sources and avoid false alarms or missed alarms. Optimization and adjustment can be based on the success rate of identifying high-temperature risk areas in historical cases. For example, if the system frequently misclassifies non-heat source areas as high-temperature risk areas under a certain operating condition (low success rate), it may be necessary to increase the temperature difference threshold; conversely, if the system frequently misses real heat sources (low success rate), it may be necessary to decrease the temperature difference threshold.

[0065] On the other hand, the processing logic for calculating the target control parameters is dynamically optimized and adjusted. Target control parameters (such as the power and duration of the thermoelectric actuator) are crucial to the temperature control effect. Over time, the heat conduction characteristics inside the planetary reducer and the performance of the thermoelectric actuator may change. Therefore, the logic for calculating these parameters needs to be dynamically optimized to ensure optimal performance for each control operation. Optimization can be based on the temperature control performance of different combinations of control parameters in historical cases. For example, for a specific target control point and thermal risk level, the system can analyze historical data to identify which combinations of control parameters achieved the best temperature control performance under similar conditions. Then, the system can update its internal control parameter calculation model (such as a lookup table, regression model, or neural network model) to predict better target control parameters based on current operating conditions and heat source conditions.

[0066] In this embodiment, through the above technical solution, this application establishes a self-learning, adaptive temperature control closed loop. The system can continuously acquire temperature data after temperature control and calculate temperature control efficiency parameters to quantitatively evaluate the effect of each targeted temperature control operation. These evaluation results, along with operating status, heat source location, and control commands, are stored as historical cases in a thermal behavior pattern library, thereby constructing a rich experience knowledge base. Based on this pattern library, the system can perform dynamic optimization processing, continuously learning and improving its control strategy. Specifically, by dynamically adjusting the preset temperature difference threshold corresponding to the operating conditions, the system can more accurately and robustly identify high-temperature risk zones, effectively avoiding misjudgments or omissions caused by fixed thresholds. At the same time, through dynamically optimizing the processing logic for calculating target control parameters, the system can generate more accurate and effective temperature control commands based on actual operating conditions and historical experience, thereby improving the adaptability, accuracy, and long-term stability of targeted temperature control. This adaptive optimization mechanism enables the planetary reducer to maintain optimal thermal management under various complex operating conditions, effectively extending equipment life and improving operational reliability.

[0067] In one feasible implementation, the dynamic optimization and adjustment of the processing logic for calculating the target control parameters includes: selecting historical cases similar to the current operating conditions and heat source location results from the thermal behavior pattern library; extracting historical control parameters and corresponding temperature control performance parameters recorded in the temperature control instructions of the historical cases; assigning different adjustment weights to each selected historical case according to the level of the temperature control performance parameters; calculating optimized control parameters suitable for the current operating conditions and heat source location based on the weighted historical control parameters; and updating the processing logic for calculating the target control parameters based on the optimized control parameters.

[0068] In this embodiment, when filtering historical cases similar to the current operating conditions and heat source location results from the thermal behavior pattern library, this step aims to identify the data most relevant to the current actual operating conditions of the planetary reducer from a large historical dataset. Operating conditions may include parameters such as speed, torque, load, and ambient temperature, while the heat source location results clearly define the area that needs to be controlled. Similarity filtering can be implemented using various algorithms, such as distance metrics based on Euclidean distance or cosine similarity, or by using clustering or classification algorithms in machine learning to pre-classify historical cases, and then matching them based on the current operating conditions and heat source characteristics. This approach ensures that subsequent optimization processes are based on empirical data highly relevant to the current scenario, avoiding interference from irrelevant data in the optimization results.

[0069] In this embodiment, once similar historical cases are identified, key information needs to be extracted from them. This involves extracting the historical control parameters and corresponding temperature control performance parameters recorded in the temperature control commands of the historical cases. Historical control parameters refer to the specific command parameters issued by the system under similar past operating conditions to achieve targeted temperature control, such as the power, current, and voltage of the thermoelectric actuator module. Temperature control performance parameters are a quantitative evaluation of the effectiveness of these historical control operations, such as the temperature drop, the time required to reach the target temperature, and energy consumption. These parameters reflect the success or efficiency of the historical control strategy. The extraction of this data forms the basis for subsequent weighted and optimized calculations.

[0070] Based on this, different adjustment weights are assigned to each selected historical case according to the level of the temperature control efficiency parameter. This step is the core of intelligent optimization. Not all similar historical cases have equal reference value. Historical cases with high temperature control efficiency parameters indicate that their corresponding control strategies achieved better results at the time, and therefore should be given higher weights to exert a greater influence in subsequent optimization calculations. Conversely, cases with low temperature control efficiency parameters should be given lower weights. Weight assignment can employ various function models, such as linear weighting, exponential weighting, or a weight allocation mechanism based on fuzzy logic, to accurately reflect the reference value of historical cases.

[0071] In this embodiment, optimized control parameters suitable for the current operating conditions and heat source location are calculated based on weighted historical control parameters. After assigning weights to historical cases, these weighted historical control parameters need to be combined to generate optimized control parameters for the current operating conditions and heat source location. This can be achieved through weighted averaging, weighted regression, or machine learning models. For example, a model can be built that takes the current operating conditions and heat source location results as input, combines them with weighted historical control parameters, and outputs a predictive optimized control parameter. This optimized control parameter is an intelligent aggregation of historical experience, aiming to provide the best control strategy for the current scenario.

[0072] In this embodiment, the processing logic for calculating the target control parameters is finally updated based on the optimized control parameters. The calculated optimized control parameters are used to update or adjust the system's original processing logic for calculating the target control parameters. This means that the system no longer relies solely on preset rules or models to calculate control parameters, but can adaptively adjust based on historical experience and current conditions. The update processing logic can manifest as adjusting coefficients in the model, modifying the rules of the decision tree, or directly using the optimized control parameters as new baseline values. This dynamic update mechanism enables the planetary reducer targeted temperature control system to have the ability to learn and evolve, continuously improving its control accuracy and efficiency as operating time increases and historical data accumulates.

[0073] In this embodiment, by meticulously screening historical cases similar to the current operating conditions and heat source location results, and assigning different adjustment weights based on their temperature control performance parameters, the system can more accurately identify past successful control experiences. Based on these weighted historical control parameters, the system can calculate optimized control parameters highly suitable for the current scenario, thereby avoiding control deviations that may be caused by simple rules or fixed models. This mechanism of dynamically updating the target control parameter processing logic enables the planetary reducer targeted temperature control system to possess self-learning and adaptive capabilities. It can continuously optimize its control strategy as operating data accumulates, improving the accuracy, response speed, and overall performance of targeted temperature control. This ensures that the temperature of key components inside the planetary reducer can be maintained within a safe range under various operating conditions, thereby extending equipment life and improving operational reliability.

[0074] In one feasible implementation, the dynamic optimization and adjustment of the preset temperature difference threshold corresponding to the operating condition includes: from the thermal behavior pattern library, for a specific operating condition, calculating the proportion of high-temperature risk zones identified in each instance that are ultimately confirmed as effective heat sources, as the risk zone identification success rate under that operating condition; obtaining the system's power constraints; if the risk zone identification success rate is consistently higher than a preset first success rate threshold, then within the range allowed by the power constraints, increasing the preset temperature difference threshold corresponding to the operating condition; if the risk zone identification success rate is consistently lower than a preset second success rate threshold, then decreasing the preset temperature difference threshold corresponding to the operating condition.

[0075] In this embodiment, the proportion of high-temperature risk areas identified in each instance of a thermal behavior pattern library and ultimately confirmed as effective heat sources under specific operating conditions is statistically analyzed. This proportion serves as the risk area identification success rate for that operating condition. The risk area identification success rate is a key indicator for measuring the accuracy of high-temperature risk area identification. It assesses, through analysis of historical data, what percentage of the identified high-temperature risk areas under specific operating conditions ultimately require temperature control, i.e., are confirmed as effective heat sources. For example, the system can extract historical records from the thermal behavior pattern library. For each identified high-temperature risk area, it records whether it triggered an actual temperature control operation and whether the operation successfully kept the temperature within a safe range. By statistically analyzing the ratio of successful identifications under a specific operating condition to the total number of identifications, the corresponding risk area identification success rate can be obtained.

[0076] In this embodiment, the system acquires the system's power constraints. These power constraints refer to the maximum power limit that the thermoelectric actuator inside the planetary reducer can use during temperature control operations. This ensures that the temperature control operation does not exceed the system hardware's capacity, preventing overload or damage. These power constraints can be pre-stored in the system's configuration parameters or dynamically acquired by monitoring the power module's output capability in real time.

[0077] Based on this, if the success rate of risk zone identification consistently exceeds a preset first success rate threshold, the preset temperature difference threshold corresponding to the operating condition will be increased within the power constraint limits. A high success rate indicates that the current preset temperature difference threshold may be overly sensitive, leading to the identification of some non-urgent "high-temperature risk zones," thus increasing unnecessary control burden. Increasing the threshold can reduce false alarms and allow the system to focus more on genuine hotspots. For example, the system can set a first success rate threshold (e.g., 90%). If the success rate of risk zone identification exceeds this threshold multiple times under a certain operating condition, the system will attempt to increase the preset temperature difference threshold corresponding to that condition by a preset step size. Before increasing the threshold, the system will simulate and evaluate the total power required for control operations that may be triggered under the new threshold and compare it with the power constraints. The threshold will only be updated if the simulation results show that the total power is still within the constraints.

[0078] In this embodiment, if the success rate of risk area identification remains below a preset second success rate threshold, the preset temperature difference threshold corresponding to the operating condition is lowered. A low success rate indicates that the current preset temperature difference threshold may not be sensitive enough, potentially leading to missed detections of genuine "high-temperature risk areas." Lowering the threshold improves the system's sensitivity and ensures timely detection of potential hotspots. For example, the system can set a second success rate threshold (e.g., 70%). If the success rate of risk area identification is consistently below this threshold under a certain operating condition, the system will lower the preset temperature difference threshold corresponding to that condition by a preset step size.

[0079] In this embodiment, through the above technical solution, the system can dynamically adjust the identification sensitivity of high-temperature risk zones based on historical operating data and actual control effects. When the identification accuracy is high, moderately increasing the threshold can avoid excessive intervention, reduce unnecessary energy consumption and wear on the thermoelectric actuator module; when the identification accuracy is low, lowering the threshold can enhance the system's ability to perceive potential hotspots, ensuring that the temperature of critical areas is controlled in a timely and effective manner, thereby preventing thermal damage. This adaptive threshold optimization mechanism, combined with power constraints, makes the target temperature control system of the planetary reducer more intelligent, efficient, and reliable, maximizing energy utilization and extending equipment life while ensuring operational safety.

[0080] In the embodiments of this application, the planetary reducer targeted temperature control method accurately identifies the target control point by acquiring operating status and temperature distribution data, and executes targeted temperature control operations. This method can accurately identify and locate high-temperature hot spots inside the planetary reducer, implement targeted temperature control, thereby effectively suppressing local thermal deformation, protecting the lubricating oil film, and improving the transmission accuracy stability, operational reliability, and service life of the reducer under extreme operating conditions.

[0081] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the planetary reducer targeted temperature control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0082] This application also provides a planetary gear reducer targeted temperature control system, referenced... Figure 5 The planetary reducer targeted temperature control system includes: a memory 10, a processor 20, and a planetary reducer targeted temperature control program stored in the memory 10 and executable on the processor 20. The planetary reducer targeted temperature control program is configured to implement the steps of the planetary reducer targeted temperature control method.

[0083] The planetary reducer targeted temperature control system provided in this application, employing the planetary reducer targeted temperature control method in the above embodiments, can improve the transmission accuracy stability and operational reliability of the reducer under extreme operating conditions. Compared with the prior art, the beneficial effects of the planetary reducer targeted temperature control system provided in this application are the same as those of the planetary reducer targeted temperature control method provided in the above embodiments, and other technical features in the planetary reducer targeted temperature control system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0084] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0085] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A method for targeted temperature control of a planetary reducer, characterized in that, The method includes: Acquire operating status data and internal temperature distribution data of the planetary reducer during operation; Based on the operating status data and internal temperature distribution data, at least one target control point requiring temperature control is identified, and a heat source location result containing the location information of the target control point is generated. Based on the heat source location results and the operating status data, the temperature control command corresponding to each target control point is determined; The temperature control command is sent to the thermoelectric actuator module located inside the planetary reducer and corresponding to the target control point, so as to drive the thermoelectric actuator module to perform a targeted temperature control operation on the target control point.

2. The targeted temperature control method for a planetary reducer as described in claim 1, characterized in that, Based on the operating status data and internal temperature distribution data, the steps of identifying at least one target control point requiring temperature control and generating a heat source location result containing the location information of the target control point include: Based on the aforementioned operating status data, the current operating condition of the planetary reducer is determined. Based on the internal temperature distribution data, calculate the relative temperature difference between multiple monitoring points; Call the preset temperature difference threshold corresponding to the operating condition, and determine whether the relative temperature difference data exceeds the corresponding preset temperature difference threshold. If so, the area with the largest temperature difference is identified as a high-temperature risk area. Within the high-temperature risk zone, the location with the largest temperature gradient in the internal temperature distribution data is analyzed, and this location is determined as the target control point. A heat source location result containing the location information of the target control point is then generated.

3. The planetary reducer targeted temperature control method as described in claim 2, characterized in that, The steps for determining the temperature control command corresponding to each target control point based on the heat source location results and the operating status data include: Based on the target control point location information in the heat source location results and the load information in the operating status data, the thermal risk level of each target control point is determined. Based on the thermal risk level, a target control mode is selected from multiple preset control modes; the preset control modes include a preventive micro-adjustment mode, a targeted active intervention mode, and a multi-point synergistic inhibition mode; Based on the thermal risk level, the target control mode, and the real-time temperature and temperature change rate of the target control point in the internal temperature distribution data, the target control parameters of the thermoelectric actuator corresponding to the target control point are calculated. The target control mode and the target control parameters are encapsulated into the temperature control command.

4. The targeted temperature control method for a planetary reducer as described in claim 3, characterized in that, When the target control mode is a multi-point cooperative suppression mode, the process of calculating the target control parameters includes: Based on the relative temperature difference data, the target control point with the largest temperature difference in the heat source location results is determined as the main control point, and the monitoring points adjacent to it are determined as auxiliary points. Based on the real-time temperature of the main control point in the internal temperature distribution data, the first control power of its thermoelectric execution module is calculated. Based on the difference between the real-time temperature of the auxiliary point and the real-time temperature of the main control point in the internal temperature distribution data, the second control power of the thermoelectric execution module of each auxiliary point is calculated, wherein the direction of action of the second control power is set to establish a reverse temperature gradient between the main control point and the auxiliary point. The first control power and the second control power are respectively used as the target control parameters of the main control point and each of the auxiliary points.

5. The targeted temperature control method for a planetary reducer as described in claim 4, characterized in that, The step of determining the temperature control command corresponding to each target control point based on the heat source location result and the operating status data further includes: Obtain the real-time meshing phase signal of the planetary gears; Based on the heat source location result and the real-time meshing phase signal, determine whether the temperature rise of the target control point is related to a specific meshing phase; If the temperature rise at the target control point is associated with a specific engagement phase, a phase synchronization marker is added to the temperature control command. The phase synchronization marker is used to instruct the thermoelectric actuator to pre-start before the specific engagement phase and enhance its control strength after the specific engagement phase.

6. The targeted temperature control method for a planetary reducer as described in claim 4, characterized in that, The step of determining the temperature control command corresponding to each target control point based on the heat source location result and the operating status data further includes: Based on the temperature of the target control point in the heat source location result, query the pre-trained lubricating oil performance relationship model to determine the theoretical oil film state at that temperature. Determine whether the determined theoretical oil film state is lower than the preset oil film safety threshold; If the theoretical oil film state is lower than the oil film safety threshold, the cooling operation on the target control point is strengthened by adjusting the target control parameter in the temperature control command. If the theoretical oil film state is still lower than the oil film safety threshold after the cooling operation reaches the preset intensity threshold, a command to trigger pulse lubrication near the target control point is added to the temperature control command.

7. The planetary reducer targeted temperature control method as described in claim 4, characterized in that, The method further includes: The internal temperature distribution data is continuously acquired, and the temperature data after temperature control at the target control point is extracted. Based on the temperature data after temperature control and the internal temperature distribution data before temperature control, the temperature control efficiency parameters of this targeted temperature control operation are calculated. The operating status data, the heat source location results, the temperature control commands, and the corresponding temperature control efficiency parameters are stored as historical cases in the thermal behavior pattern library. Based on multiple historical cases in the thermal behavior pattern library, dynamic optimization processing is performed. The dynamic optimization processing includes: dynamically optimizing and adjusting the preset temperature difference threshold corresponding to the operating condition, and dynamically optimizing and adjusting the processing logic for calculating the target control parameters.

8. The targeted temperature control method for a planetary reducer as described in claim 7, characterized in that, The dynamic optimization and adjustment of the processing logic for calculating the target control parameters includes: From the thermal behavior pattern library, historical cases similar to the current operating conditions and heat source location results are selected, and the historical control parameters and corresponding temperature control efficiency parameters recorded in the temperature control instructions of the historical cases are extracted. Based on the temperature control performance parameters, different adjustment weights are assigned to each selected historical case. Based on the weighted historical control parameters, the optimized control parameters applicable to the current operating conditions and heat source location are calculated. Based on the optimized control parameters, the processing logic for calculating the target control parameters is updated.

9. The targeted temperature control method for a planetary reducer as described in claim 7, characterized in that, The dynamic optimization and adjustment of the preset temperature difference threshold corresponding to the operating condition includes: From the thermal behavior pattern library, for a specific operating condition, the proportion of high-temperature risk areas identified in each instance that are ultimately confirmed as effective heat sources is statistically analyzed, which is taken as the risk area identification success rate under that operating condition. Obtain the power constraints of the system; If the success rate of risk zone identification continues to be higher than the preset first success rate threshold, then within the range allowed by the power constraint conditions, the preset temperature difference threshold corresponding to the operating condition is increased. If the success rate of risk zone identification continues to be lower than the preset second success rate threshold, then the preset temperature difference threshold corresponding to the operating condition is reduced.

10. A targeted temperature control system for a planetary reducer, characterized in that, The planetary reducer targeted temperature control system includes: a memory, a processor, and a planetary reducer targeted temperature control program stored in the memory and executable on the processor, the planetary reducer targeted temperature control program being configured to implement the steps of the planetary reducer targeted temperature control method as described in any one of claims 1 to 9.

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