Method and system for controlling temperature of a device based on pid

Abstract

The application discloses a PID-based device temperature control method and system, and relates to the technical field of PID control.The method comprises the following steps: obtaining a device dynamic load, and obtaining a sampling parameter based on the device dynamic load; performing partition optimization on the sampling parameter based on a human body contact area, obtaining a partition sampling parameter, performing device temperature partition sampling, obtaining a plurality of partition temperature states, and judging a sensor state parameter in combination with the sampling effectiveness threshold; obtaining a comprehensive working condition based on the partition temperature state, the device dynamic load and the sensor state parameter, and performing PID parameter self-adaptive optimization based on the comprehensive working condition to obtain optimized PID parameters and control the device temperature.The application solves the technical problem that the PID temperature control method in the prior art cannot effectively adapt to the change of the device dynamic load, thereby resulting in poor temperature control effect.

Description

Technical Field

[0001] This invention relates to the field of PID control technology, and more specifically to a PID-based method and system for controlling equipment temperature. Background Technology

[0002] In industrial control and consumer electronics, temperature control is a crucial technology, widely used in various devices requiring precise thermal management. PID control, due to its simple structure and robustness, has become one of the mainstream solutions for temperature regulation. Classical PID control adjusts heating or cooling power based on the deviation between the target temperature and the actual temperature by setting fixed proportional, integral, and derivative parameters to achieve stable temperature control. However, current PID temperature control often fails to adaptively adjust to the overall operating conditions of the equipment. This leads to overshoot, oscillation, or slow response under sudden load changes or multi-regional coupled interference, affecting comfort and potentially causing localized overheating safety hazards. Summary of the Invention

[0003] This application provides a PID-based equipment temperature control method and system to address the technical problem that existing PID temperature control methods cannot effectively adapt to dynamic load changes in equipment, resulting in poor temperature control performance.

[0004] In view of the above problems, this application provides a device temperature control method and system based on PID.

[0005] In a first aspect, this application provides a PID-based device temperature control method, the method comprising:

[0006] The device dynamic load is obtained, and based on the device dynamic load, sampling parameters are obtained, wherein the sampling parameters include sampling frequency adjustment coefficients and sampling validity thresholds for multiple partitions;

[0007] Based on the human contact area, the sampling parameters are partitioned and optimized to obtain partitioned sampling parameters. Device temperature partition sampling is performed to obtain multiple partitioned temperature states. Combined with the sampling validity threshold, the sensor state parameters are determined and obtained.

[0008] Based on the zone temperature status, equipment dynamic load, and sensor status parameters, a comprehensive operating condition is obtained. Based on the comprehensive operating condition, PID parameters are adaptively optimized to obtain optimized PID parameters for equipment temperature control.

[0009] Secondly, this application provides a PID-based device temperature control system, including:

[0010] The sampling parameter acquisition module is used to acquire the dynamic load of the device and acquire sampling parameters based on the dynamic load of the device, wherein the sampling parameters include the sampling frequency adjustment coefficients and sampling validity thresholds for multiple partitions;

[0011] The partition optimization module is used to optimize the sampling parameters based on the human contact area, obtain partition sampling parameters, perform device temperature partition sampling, obtain multiple partition temperature states, and combine the sampling validity threshold to determine and obtain sensor state parameters.

[0012] The PID parameter optimization module is used to obtain comprehensive operating conditions based on the zone temperature status, equipment dynamic load, and sensor status parameters, and to perform adaptive optimization of PID parameters based on the comprehensive operating conditions to obtain optimized PID parameters for equipment temperature control.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0014] This application proposes a PID-based equipment temperature control method and system. By introducing dynamic load sensing, zonal sampling parameter optimization based on human contact areas, real-time sensor status diagnosis, and an adaptive optimization mechanism for PID parameters based on comprehensive operating conditions, the overall adaptability, accuracy, and reliability of the temperature control system are significantly improved. Compared with traditional methods, the technical solution provided in this application significantly overcomes the limitations of fixed-parameter PID control in the face of dynamic, multi-variable, and complex operating conditions, achieving improved adaptive control performance and overall robustness of the temperature control system when facing dynamic load changes, multi-regional differentiated heat demands, and complex operating conditions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of a PID-based device temperature control method provided in an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of a PID-based device temperature control system provided in an embodiment of this application.

[0018] The components represented by each number in the attached diagram are explained below:

[0019] The module includes a sampling parameter acquisition module 100, a partition optimization module 200, and a PID parameter optimization module 300. Detailed Implementation

[0020] This application provides a PID-based equipment temperature control method and system to address the technical problem that existing PID temperature control methods cannot effectively adapt to dynamic load changes in equipment, resulting in poor temperature control performance.

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0023] Example 1, as Figure 1 As shown, this application provides a PID-based device temperature control method, wherein the method includes:

[0024] S10: Obtain the dynamic load of the device, and based on the dynamic load of the device, obtain sampling parameters, wherein the sampling parameters include the sampling frequency adjustment coefficients of multiple partitions and the sampling validity threshold.

[0025] In traditional equipment temperature control methods, sampling parameters are typically set based on the equipment's rated operating conditions or empirical values, using a fixed sampling frequency and a uniform judgment threshold. However, the actual operating load of the equipment is dynamically changing, and the fixed sampling strategy has significant drawbacks when facing dynamic loads: when the actual load of the equipment is low, an excessively high fixed sampling frequency will cause unnecessary waste of computing and sensing resources, reducing system energy efficiency; while when the load suddenly increases or fluctuates drastically, an excessively low fixed sampling frequency cannot capture rapid temperature changes in time, resulting in a lag in the control system's perception.

[0026] Step S10 in the method provided in this application embodiment includes:

[0027] The device dynamic load is obtained, wherein the device dynamic load includes the current operating power and the trend of load current change;

[0028] Based on the ratio of the device's dynamic load to its standard load, the device's dynamic load parameters are obtained.

[0029] Based on the device's dynamic load parameters, the standard sampling parameters are corrected to obtain the sampling parameters.

[0030] In this embodiment of the application, the dynamic load of the device is obtained, and based on the dynamic load of the device, sampling parameters are obtained, wherein the sampling parameters include the sampling frequency adjustment coefficients of multiple partitions and the sampling validity threshold.

[0031] Specifically, firstly, the dynamic load of the device is acquired, including the current operating power and the trend of load current changes. For example, when the device is a seat, a power sensor is used to acquire the dynamic load. For instance, a power sensor is used to monitor the current operating power of the seat heating pad in real time to obtain the current dynamic load. Similarly, the dynamic load of other devices can also be acquired in this way.

[0032] Furthermore, the dynamic load parameter of the device is obtained based on the ratio of the device's dynamic load to its standard load. Using the currently operating power obtained above and the device's preset standard load, such as the rated power of a seat heater, the dynamic load parameter is calculated. Dynamic load parameter = Dynamic load ÷ Standard load. For example, if the current operating power of a seat heater is 36W and the standard load during heating is 30W, then the dynamic load parameter = 36 / 30 = 1.2. If the current operating power is higher than the standard load, the ratio will be greater than 1, indicating a heavier load; conversely, it will be less than 1, indicating a lighter load. For other devices such as therapeutic mattresses, the standard load can be set according to its design rating.

[0033] Furthermore, based on the device's dynamic load parameters, the standard sampling parameters are corrected to obtain sampling parameters. For example, when the device's dynamic load parameter is 1.2, the standard sampling frequency adjustment coefficient of 1.0 is corrected to 1.2, and the standard sampling validity threshold of ±2℃ is corrected to ±1.7℃. The obtained sampling parameters, adapted to the dynamic load, ensure that the sampling strategy matches the load state.

[0034] Furthermore, based on the human contact area, the device is divided into zones, and the sampling parameters are adaptively adjusted according to these zones to obtain zoned sampling parameters. For example, when the device is a seat, the seat can be divided into multiple zones based on the human contact area, such as a backrest zone, a seat cushion zone, and an armrest zone. Further, the sampling parameters obtained in the previous step are adjusted according to the functional requirements and thermal characteristics of each zone. For example, because the seat cushion zone directly bears the weight of the human body and has poor ventilation, its temperature may change rapidly; therefore, its sampling frequency adjustment coefficient is increased from 1.2 to 1.5, and the sampling validity threshold is tightened from ±1.7℃ to ±1.5℃. The backrest zone prioritizes comfort and stability, so the sampling frequency adjustment coefficient remains at 1.2, and the sampling validity threshold is relaxed to ±2.0℃. The armrest zone has less contact, so the sampling frequency adjustment coefficient is reduced to 1.0. For other devices such as medical heating pads, similar adjustments can be made based on the contact area.

[0035] This application achieves intelligent matching between sampling strategies and actual equipment operating status by acquiring the dynamic load of the equipment in real time and dynamically determining the sampling parameters accordingly. This provides timely data input for control decisions, thereby significantly enhancing the responsiveness to changing operating conditions.

[0036] S20: Based on the human contact area, the sampling parameters are partitioned and optimized to obtain partitioned sampling parameters. Device temperature partition sampling is performed to obtain multiple partitioned temperature states. Combined with the sampling validity threshold, the sensor state parameters are determined and obtained.

[0037] Even after obtaining basic sampling parameters adapted to the load, if these parameters are uniformly applied to all parts of the equipment, the problem of coarse control still exists.

[0038] Step S20 in the method provided in this application embodiment includes:

[0039] The equipment is divided into zones based on the areas that human contact with the body.

[0040] Based on the functional requirements of the human contact area, the sampling parameters are adaptively adjusted by region to obtain the regional sampling parameters, wherein the functional requirements are obtained based on the contact parts of the device and the human body.

[0041] Using the aforementioned partition sampling parameters, the device temperature is sampled in partitions to obtain multiple partition temperature sequences;

[0042] Based on multiple partition temperature sequences, statistical features of partition temperature are extracted to obtain the temperature status of multiple partitions;

[0043] Based on the temperature status of the partition and the sampling validity threshold, the working status of the sensor is analyzed and judged to obtain the sensor status parameters.

[0044] In this embodiment, the sampling parameters are optimized by partitioning based on the human contact area to obtain partitioned sampling parameters. Device temperature partitioning is performed to obtain multiple partitioned temperature states. Combined with the sampling validity threshold, sensor state parameters are determined and obtained.

[0045] Specifically, firstly, the device is divided into zones based on the areas of human contact. For example, when the device is a seat, the seat can be divided into multiple zones according to the areas of human contact, such as a backrest area, a seat cushion area, and an armrest area.

[0046] Furthermore, based on the functional requirements of the human contact areas, the sampling parameters are adaptively adjusted by region to obtain region-specific sampling parameters. The functional requirements are obtained based on the contact points between the device and the human body. For example, the sampling parameters obtained in the previous step are further adjusted according to the functional requirements and thermal characteristics of each region. For instance, the seat cushion area, because it directly bears the weight of the human body and has poor ventilation, may experience rapid temperature changes; therefore, its sampling frequency adjustment coefficient is increased from 1.2 to 1.5, and the sampling validity threshold is tightened from ±1.7℃ to ±1.5℃. The backrest area prioritizes comfort and stability, so the sampling frequency adjustment coefficient remains at 1.2, and the sampling validity threshold is relaxed to ±2.0℃. The armrest area has less contact, so the sampling frequency adjustment coefficient is reduced to 1.0. For other devices such as medical heating pads, similar adjustments can be made based on the contact points.

[0047] Furthermore, the device temperature is sampled in zones using the aforementioned sampling parameters to obtain multiple zone temperature sequences. Within each sampling period, temperature values ​​are read from the temperature sensors installed in the corresponding zones according to their own sampling frequencies and recorded chronologically. For example, in the most recent 10-second sampling window, 20 temperature values ​​are collected at a higher frequency (e.g., twice per second) for the seat cushion zone, and 10 temperature values ​​are collected at a lower frequency (e.g., once per second) for the backrest zone. These time-arranged temperature data sets constitute the zone temperature sequences for multiple zones.

[0048] Furthermore, based on multiple partition temperature sequences, statistical features of the partition temperatures are extracted to obtain the temperature states of multiple partitions. For example, for 10 temperature sampling values ​​of a partition, the average value is calculated to be 36.5℃, the standard deviation is 0.3℃, and the temperature rise rate in the last 5 seconds is 0.4℃ / second.

[0049] Further, based on the partition temperature status and the sampling validity threshold, the sensor's operating status is analyzed and judged to obtain sensor status parameters. Specifically, the temperature status calculated for each partition, specifically the average temperature value and temperature standard deviation of that partition, is compared and calculated with the partition's own preset sampling validity threshold to obtain a continuously quantified sensor status parameter. This parameter ranges from 0 to 1, with a higher value indicating more reliable sensor data. 0 represents highly questionable data (very likely to be invalid), and 1 represents completely unquestionable data (highly reliable). First, it is determined whether the average temperature of the partition exceeds the validity threshold range centered on the target temperature. For example, if the target temperature is 38℃ and the threshold range is ±2℃, then the validity threshold range is 36℃ to 40℃. If the average value exceeds this range, the reliability score is reduced according to the proportion of the excess. For example, if the partition temperature average is 35℃, exceeding the lower limit by 1℃ and the maximum allowable excess by 2℃, the proportionality coefficient is 0.5. When exceeding the maximum allowable excess value, the proportionality coefficient is 1. Further, it is checked whether the temperature standard deviation exceeds the preset maximum allowable fluctuation value, such as 0.5℃. If the value exceeds the limit, the score is reduced according to the same method based on the excess percentage. When the maximum allowable excess value is exceeded, the percentage coefficient is 1. The final reliability score starts at 1 and is reduced based on the results of the two checks mentioned above. The reduction coefficient is (1 - the average percentage coefficient). For example, if the average temperature of a certain zone is 36.8℃ (not exceeded), the temperature standard deviation is 0.7℃, and the maximum allowable deviation is 0.5℃, the excess percentage = (0.7 - 0.5) / 0.5 = 0.4. Then the sensor status parameter is 1 - (0.4 + 0) / 2 = 0.8.

[0050] By optimizing sampling parameters based on the human contact area and performing zoned temperature sampling, it is possible to perform refined zoned sensing while monitoring the sensor status. For example, more intensive sampling can be implemented in areas requiring rapid response or high temperature stability, while a more lenient sampling strategy can be used in areas with slow temperature changes or less important conditions to conserve sensing resources.

[0051] S30: Based on the zone temperature status, equipment dynamic load, and sensor status parameters, obtain the comprehensive operating condition, and based on the comprehensive operating condition, perform adaptive optimization of PID parameters to obtain optimized PID parameters for equipment temperature control.

[0052] After achieving dynamic, zoned, and refined temperature sensing and sensor status monitoring, the urgent technical problem to be solved is how to effectively integrate this multi-source and heterogeneous information and make precise adjustments to the PID controller accordingly.

[0053] Step S30 in the method provided in this application embodiment includes:

[0054] Based on the partition temperature status, device dynamic load and sensor status parameters, the partition operating condition is obtained, wherein the partition operating condition includes partition operating condition reliability, partition load and partition temperature target.

[0055] The comprehensive operating conditions are obtained by weighted summation of the partitioned operating conditions and the functional requirements of the human contact area.

[0056] Get the current PID parameters;

[0057] Based on the partitioned operating conditions, the partitioned PID optimization step size is obtained, and combined with the comprehensive operating conditions, the PID optimization round is obtained;

[0058] The step size for partition PID optimization, based on the partitioned operating conditions, includes:

[0059] Based on the partitioned operating conditions, the minimum operating condition confidence level, the maximum partitioned load, and the maximum partitioned temperature target difference are obtained.

[0060] Based on the minimum operating condition confidence level, the maximum partition load, and the maximum partition temperature target difference, the partition priority is obtained;

[0061] The optimization step size is obtained based on the operating conditions of each partition, and a weighted sum is performed in combination with the partition priority and the functional requirements of the human contact area to obtain the partition PID optimization step size.

[0062] Using the partitioned PID optimization step size and the PID optimization round, the current PID parameters are adaptively optimized to obtain optimized PID parameters for equipment temperature control.

[0063] Specifically, the current PID parameters are adaptively optimized using the partitioned PID optimization step size and the PID optimization round to obtain optimized PID parameters for equipment temperature control, including:

[0064] Obtain adaptation evaluation metrics, wherein the adaptation evaluation metrics are obtained based on the functional requirements of the partition;

[0065] Based on the current PID parameters, multiple candidate PID parameters are randomly generated in combination with the partitioned PID optimization step size, and the adaptation evaluation index of each candidate PID parameter is obtained.

[0066] With the goal of maximizing the adaptation evaluation index, multiple candidate PID parameters are jointly optimized until the PID optimization round is reached or the adaptation evaluation index of a PID parameter is greater than the adaptation threshold. The PID parameter with the largest adaptation evaluation index in the current optimization is then used as the optimized PID parameter for equipment temperature control.

[0067] The optimization of multiple candidate PID parameters, with the goal of maximizing the adaptation evaluation index, includes:

[0068] Record the optimal fit evaluation index and corresponding PID parameter for each candidate PID parameter during its own iteration process, as the iteration dataset;

[0069] The maximum value of the adaptive evaluation index in the iterative dataset is selected as the global optimum, and the corresponding PID parameter is selected as the global optimum parameter.

[0070] For each candidate PID parameter, the update direction and magnitude are dynamically adjusted based on the difference between its own optimal parameter and the global optimal parameter, as well as the gap between its own adaptation evaluation index and the global optimal value.

[0071] Calculate the parameter similarity between each candidate PID parameter, and merge candidate PID parameters with similarity higher than a preset threshold;

[0072] If the change in the global optimal value within the preset number of iterations is less than the stagnation threshold, new candidate PID parameters are added based on the stagnation magnitude, and the system continues to participate in collaborative optimization until the number of PID optimization rounds is reached or the adaptation evaluation index of the candidate PID parameters is greater than the adaptation threshold. The stagnation threshold is obtained based on comprehensive operating conditions.

[0073] In this embodiment, a comprehensive operating condition is obtained based on the zone temperature status, equipment dynamic load, and sensor status parameters. Based on the comprehensive operating condition, PID parameters are adaptively optimized to obtain optimized PID parameters for equipment temperature control.

[0074] Specifically, firstly, based on the zone temperature status, device dynamic load, and sensor status parameters, the zone operating condition is obtained. This zone operating condition includes zone operating condition reliability, zone load, and zone temperature target. For example, the zone operating condition reliability is directly obtained from the sensor status parameter value of that zone; the closer this value is to 1, the more reliable the data. The zone load is obtained from the device dynamic load parameter value, which reflects the overall load strength of the device. The zone temperature target difference is calculated by subtracting the currently measured average temperature from the preset target temperature of that zone. For instance, if the sensor status parameter of the seat area is 0.8, the device dynamic load parameter is 1.2, the target temperature is 38℃, and the current average temperature is 36℃, then the zone operating condition is obtained as follows: reliability 0.8, load 1.2, and temperature target difference 2℃.

[0075] Furthermore, the functional requirements of the human contact areas are combined with a weighted summation of the zoned operating conditions to obtain the comprehensive operating condition. For example, firstly, weights are assigned to each zone based on its functional importance. For instance, the seat cushion area, requiring rapid response and bearing weight, is assigned a higher weight (e.g., 0.5); the backrest area, requiring stability and comfort, is assigned a medium weight (e.g., 0.3); and the armrest area, with less important functions, is assigned a lower weight (e.g., 0.2). Then, the three components of the zoned operating conditions are weighted and summed: the comprehensive reliability equals the sum of the reliability of each zone multiplied by its corresponding weight; the comprehensive load equals the sum of the load of each zone multiplied by its corresponding weight; and the comprehensive temperature target difference equals the sum of the temperature target difference of each zone multiplied by its corresponding weight. For example, based on the operating conditions and weights of the seat cushion, backrest, and armrest areas, the comprehensive reliability, comprehensive load, and comprehensive temperature target difference are calculated, together constituting the comprehensive operating condition.

[0076] Furthermore, obtain the current PID parameters, such as reading the parameters of the currently used PID controller directly from the temperature control system, including the proportional coefficient, integral coefficient, and derivative coefficient.

[0077] Furthermore, based on the partitioned operating conditions, the partitioned PID optimization step size is obtained, and combined with the comprehensive operating conditions, the PID optimization round is obtained.

[0078] Specifically, firstly, based on the partitioned operating conditions, the minimum operating condition confidence level, the maximum partition load, and the maximum partition temperature target difference are obtained. For example, the minimum confidence level is 0.8, the maximum load is 1.2, and the maximum temperature target difference is 2℃.

[0079] Further, based on the minimum operating condition reliability, the maximum partition load, and the maximum partition temperature target difference, the partition priority is obtained. For example, for each partition, three contribution factors are calculated: reliability contribution = (partition reliability - minimum reliability) / (1 - minimum reliability), used to measure the relative reliability of the partition data; load contribution = partition load divided by maximum load; temperature difference contribution = partition temperature target difference / maximum temperature target difference. Partition priority = reliability contribution × weight A + load contribution × weight B + temperature difference contribution × weight C. The weights A, B, and C can be set empirically, with A + B + C = 1, for example, set to 0.3, 0.4, and 0.3 respectively, to balance different factors.

[0080] Further, an optimized step size is obtained based on the operating conditions of each partition, and a weighted sum is performed in conjunction with the partition priority and the functional requirements of the human contact area to obtain the partition PID optimized step size. An optimized step size base value is obtained, exemplarily set to a proportional coefficient of 0.01, an integral coefficient of 0.01, and a derivative coefficient of 0.01. Based on the partition operating conditions, the optimized step size base value is adjusted slightly. For example, when the operating condition confidence of a partition is low, such as below 0.9, the step size base value is reduced proportionally, for example, by multiplying it by a confidence value of 0.8 for cautious adjustment; when the temperature target difference of a partition is large, such as greater than 1.5℃, the step size base value is increased proportionally, such as by multiplying it by 1.2 to accelerate the response. Further, the optimized step size of each partition is multiplied by its calculated partition priority, then multiplied by the functional requirement weight of that partition, and these products of all partitions are summed to obtain the final partition PID optimized step size used for global adjustment. For example, the step size is calculated separately for the seat cushion area, backrest area, and armrest area, and then summed to obtain a total step size value. PID optimization step size = ∑(optimization step size × partition priority × functional requirement weight).

[0081] Furthermore, the PID optimization rounds are obtained by combining the comprehensive operating conditions. For example, the PID optimization rounds are obtained by multiplying the deviation between the comprehensive operating conditions and the standard comprehensive operating conditions by the basic optimization rounds. For instance, if the basic optimization rounds are 100 and the deviation between the comprehensive operating conditions and the standard comprehensive operating conditions is 20%, then the PID optimization rounds = 100 × (1 + 20%) = 120.

[0082] Furthermore, the current PID parameters are adaptively optimized using the partitioned PID optimization step size and the PID optimization round to obtain optimized PID parameters for equipment temperature control.

[0083] Specifically, firstly, adaptation evaluation metrics are obtained, which are based on the functional requirements of each partition. For example, based on the functional requirements of each partition, a set of performance metrics for evaluating the effectiveness of PID parameter control is defined. For instance, for a seat, the functional requirement of the seat cushion area is rapid heating to provide immediate warmth; therefore, response speed is defined as the primary metric, and the time required to reach the target temperature from the current temperature is calculated. The functional requirement of the backrest area is to maintain a stable and comfortable temperature; therefore, the magnitude of fluctuations around the target temperature, i.e., the temperature standard deviation, is calculated. The functional requirement of the armrest area is lower; therefore, the difference between the average temperature and the target temperature is calculated. Each metric is normalized to a baseline score, such as 0 to 10. Further, weights are assigned to the metrics of each partition according to the importance of each partition in the overall function. For example, the weight of the seat cushion area is 0.4, the weight of the backrest area is 0.4, and the weight of the armrest area is 0.2. For a given candidate PID parameter, this set of parameters is used to perform independent simulation control on each partition, and the scores of the main performance metrics of interest for each partition under that parameter are calculated. Finally, the scores of each zone are multiplied by their corresponding weights and summed to obtain the adaptation evaluation index. For example, a candidate parameter might simulate a fast response in the seat area (8 points), good stability in the backrest area (9 points), and a small steady-state error in the armrest area (7 points). Its adaptation evaluation index would then be 8 × 0.4 + 9 × 0.4 + 7 × 0.2 = 8.2. This index quantitatively reflects the degree to which the set of PID parameters meets the overall control effect based on the zone-based functional requirements.

[0084] Furthermore, based on the current PID parameters and the partitioned PID optimization step size, multiple candidate PID parameters are randomly generated, and an adaptation evaluation index for each candidate PID parameter is obtained. For example, if the current proportional coefficient is 0.01 and the step size is 0.005, a new proportional coefficient value is randomly generated within the range of 0.015 to 0.095, and the integral and derivative coefficients are generated using the same method to obtain multiple candidate PID parameters.

[0085] Furthermore, with the goal of maximizing the adaptation evaluation index, multiple candidate PID parameters are jointly optimized until the PID optimization round is reached or the adaptation evaluation index corresponding to a PID parameter is greater than the adaptation threshold. The PID parameter with the largest adaptation evaluation index in the current optimization is then used as the optimized PID parameter for equipment temperature control.

[0086] Specifically, the optimal fit evaluation index and corresponding PID parameter for each candidate PID parameter during its iteration process are recorded as the iterative dataset. During optimization, the highest fit evaluation index achieved by each candidate parameter and its corresponding parameter value are recorded as its individual historical optimum. The individual historical optimum and its corresponding PID parameter are then added to the iterative dataset.

[0087] Furthermore, the maximum value of the adaptive evaluation index in the iterative dataset is selected as the global optimum, and the corresponding PID parameter is selected as the global optimum parameter.

[0088] Furthermore, for each candidate PID parameter, the update direction and magnitude are dynamically adjusted based on the difference between its own optimal parameter and the global optimal parameter, and the gap between its own adaptation evaluation index and the global optimal value. For example, a weighted direction vector is calculated using the formula: Update direction = Random coefficient A × (Individual historical optimal parameter - Current parameter) + Random coefficient B × (Global optimal parameter - Current parameter), where random coefficients A and B are random numbers within a preset range, such as between 0 and 1.5, used to introduce randomness to explore optimization possibilities. The update magnitude is dynamically determined by the gap between the candidate parameter's current adaptation evaluation index and the global optimal value. A larger gap indicates worse performance of the current parameter, thus a relatively larger base update magnitude is given; a smaller gap uses a smaller base update magnitude for finer searching. For example, Update magnitude = Base magnitude × Step scaling factor × Partition PID optimization step size. The obtained update direction and update magnitude are then used for further update and optimization.

[0089] Furthermore, the parameter similarity between each candidate PID parameter is calculated, and candidate PID parameters with similarity higher than a preset threshold are merged. The Euclidean distance between all candidate PID parameter combinations is calculated as the parameter similarity. If the similarity between any two parameter combinations is higher than the preset merging threshold, the two candidate parameters are merged, and only the one with the higher adaptation evaluation index is retained, in order to save computing power and speed up the optimization process.

[0090] If the change in the global optimum within a preset number of iterations is less than the stagnation threshold, new candidate PID parameters are added based on the stagnation magnitude, and they continue to participate in collaborative optimization until the number of PID optimization rounds is reached or the fit evaluation index of the candidate PID parameters exceeds the fit threshold. The stagnation threshold is obtained based on comprehensive operating conditions. For example, a very small base stagnation coefficient, such as 0.005, is set. Stagnation threshold = base stagnation coefficient / comprehensive reliability. When the overall data reliability of the system is low, the threshold will increase accordingly, meaning that the judgment standard for stagnation in the optimization process will be relaxed, as data noise may cause inherent fluctuations in the evaluation index. Furthermore, based on the degree of stagnation, new candidate parameters proportional to the number of stagnant candidate parameters are randomly generated and added to the optimization group. For example, if there are three consecutive stagnation rounds, the increase in the number of new candidate parameters = number of stagnation rounds × number of stagnant candidate parameters × proportional adjustment coefficient. The proportional adjustment coefficient can be set according to the actual scenario, for example, to 0.1, to increase population diversity. The joint optimization process is repeated until the preset total number of PID optimization rounds is reached, or the adaptation evaluation index of a certain candidate parameter exceeds the preset adaptation threshold, such as setting the adaptation threshold to 9.

[0091] Furthermore, the PID parameter with the highest fit evaluation index in the current optimization is used as the optimized PID parameter for equipment temperature control.

[0092] By integrating zone temperature status, equipment dynamic load, and sensor status parameters to construct a comprehensive operating condition, and then performing adaptive optimization of PID parameters based on this comprehensive operating condition, global intelligence and deep adaptation of the control strategy are achieved, resulting in improved overall system control performance and robustness. During the PID parameter adaptive optimization process, when the comprehensive operating condition indicates high system load and the need for rapid response in critical areas, the optimization algorithm tends to seek parameter combinations that accelerate the response speed. Conversely, when the operating condition indicates low reliability of sensor data for a particular zone, the optimization process reduces the dependence weight of that zone's data or adjusts parameters to avoid control oscillations caused by questionable data.

[0093] Example 2, as Figure 2 As shown, based on the same inventive concept as the PID-based equipment temperature control method provided in Embodiment 1, this embodiment of the invention also provides a PID-based equipment temperature control system, including:

[0094] The sampling parameter acquisition module 100 is used to acquire the dynamic load of the device and acquire sampling parameters based on the dynamic load of the device, wherein the sampling parameters include the sampling frequency adjustment coefficients and sampling validity thresholds of multiple partitions.

[0095] The partition optimization module 200 is used to optimize the sampling parameters based on the human contact area, obtain partition sampling parameters, perform device temperature partition sampling, obtain multiple partition temperature states, and determine and obtain sensor state parameters by combining the sampling validity threshold.

[0096] The PID parameter optimization module 300 is used to obtain comprehensive operating conditions based on the zone temperature status, equipment dynamic load and sensor status parameters, and to perform adaptive optimization of PID parameters based on the comprehensive operating conditions to obtain optimized PID parameters for equipment temperature control.

[0097] In one embodiment, the sampling parameter acquisition module 100 is further configured to: acquire the dynamic load of the device, wherein the dynamic load of the device includes the current operating power and the trend of load current change;

[0098] Based on the ratio of the device's dynamic load to its standard load, the device's dynamic load parameters are obtained.

[0099] Based on the device's dynamic load parameters, the standard sampling parameters are corrected to obtain the sampling parameters.

[0100] In one embodiment, the partition optimization module 200 is further configured to:

[0101] The equipment is divided into zones based on the areas that human contact with the body.

[0102] Based on the functional requirements of the human contact area, the sampling parameters are adaptively adjusted by region to obtain the regional sampling parameters, wherein the functional requirements are obtained based on the contact parts of the device and the human body.

[0103] Using the aforementioned partition sampling parameters, the device temperature is sampled in partitions to obtain multiple partition temperature sequences;

[0104] Based on multiple partition temperature sequences, statistical features of partition temperature are extracted to obtain the temperature status of multiple partitions;

[0105] Based on the temperature status of the partition and the sampling validity threshold, the working status of the sensor is analyzed and judged to obtain the sensor status parameters.

[0106] In one embodiment, the PID parameter optimization module 300 is further configured to:

[0107] Based on the partition temperature status, device dynamic load and sensor status parameters, the partition operating condition is obtained, wherein the partition operating condition includes partition operating condition reliability, partition load and partition temperature target.

[0108] The comprehensive operating conditions are obtained by weighted summation of the partitioned operating conditions and the functional requirements of the human contact area.

[0109] Get the current PID parameters;

[0110] Based on the partitioned operating conditions, the partitioned PID optimization step size is obtained, and combined with the comprehensive operating conditions, the PID optimization round is obtained;

[0111] The step size for partition PID optimization, based on the partitioned operating conditions, includes:

[0112] Based on the partitioned operating conditions, the minimum operating condition confidence level, the maximum partitioned load, and the maximum partitioned temperature target difference are obtained.

[0113] Based on the minimum operating condition confidence level, the maximum partition load, and the maximum partition temperature target difference, the partition priority is obtained;

[0114] The optimization step size is obtained based on the operating conditions of each partition, and a weighted sum is performed in combination with the partition priority and the functional requirements of the human contact area to obtain the partition PID optimization step size.

[0115] Using the partitioned PID optimization step size and the PID optimization round, the current PID parameters are adaptively optimized to obtain optimized PID parameters for equipment temperature control.

[0116] Specifically, the current PID parameters are adaptively optimized using the partitioned PID optimization step size and the PID optimization round to obtain optimized PID parameters for equipment temperature control, including:

[0117] Obtain adaptation evaluation metrics, wherein the adaptation evaluation metrics are obtained based on the functional requirements of the partition;

[0118] Based on the current PID parameters, multiple candidate PID parameters are randomly generated in combination with the partitioned PID optimization step size, and the adaptation evaluation index of each candidate PID parameter is obtained.

[0119] With the goal of maximizing the adaptation evaluation index, multiple candidate PID parameters are jointly optimized until the PID optimization round is reached or the adaptation evaluation index of a PID parameter is greater than the adaptation threshold. The PID parameter with the largest adaptation evaluation index in the current optimization is then used as the optimized PID parameter for equipment temperature control.

[0120] The optimization of multiple candidate PID parameters, with the goal of maximizing the adaptation evaluation index, includes:

[0121] Record the optimal fit evaluation index and corresponding PID parameter for each candidate PID parameter during its own iteration process, as the iteration dataset;

[0122] The maximum value of the adaptive evaluation index in the iterative dataset is selected as the global optimum, and the corresponding PID parameter is selected as the global optimum parameter.

[0123] For each candidate PID parameter, the update direction and magnitude are dynamically adjusted based on the difference between its own optimal parameter and the global optimal parameter, as well as the gap between its own adaptation evaluation index and the global optimal value.

[0124] Calculate the parameter similarity between each candidate PID parameter, and merge candidate PID parameters with similarity higher than a preset threshold;

[0125] If the change in the global optimal value within the preset number of iterations is less than the stagnation threshold, new candidate PID parameters are added based on the stagnation magnitude, and the system continues to participate in collaborative optimization until the number of PID optimization rounds is reached or the adaptation evaluation index of the candidate PID parameters is greater than the adaptation threshold. The stagnation threshold is obtained based on comprehensive operating conditions.

[0126] In summary, the embodiments of this application have at least the following technical effects:

[0127] This application proposes a PID-based equipment temperature control method and system. By introducing dynamic load sensing, zonal sampling parameter optimization based on human contact areas, real-time sensor status diagnosis, and an adaptive optimization mechanism for PID parameters based on comprehensive operating conditions, the overall adaptability, accuracy, and reliability of the temperature control system are significantly improved. Specifically, by acquiring and analyzing the dynamic load of the equipment in real time, the sampling strategy can be flexibly adjusted according to the load intensity, avoiding control lag due to insufficient sampling or resource waste caused by oversampling during sudden load changes, thus ensuring the timeliness of control response and energy efficiency. Furthermore, by zonal optimization of sampling parameters based on human contact areas and performing zonal temperature sampling, the system acknowledges and responds to the differentiated thermal management needs of different parts of the equipment due to different contact pressures, heat dissipation conditions, and functional requirements, achieving precise temperature sensing "on demand" and providing a data foundation for subsequent precise control. At the same time, by combining sampling validity thresholds to judge sensor status, the robustness of the system when some sensors are abnormal is enhanced, avoiding the risk of overall control failure caused by single-point data failure. Based on this, the system integrates zone temperature status, equipment dynamic load, and sensor status parameters to comprehensively determine real-time operating conditions. This operating condition then guides the adaptive optimization process of PID parameters, ensuring that the controller's proportional, integral, and derivative parameters are no longer preset fixed values ​​but dynamically adjusted variables based on the system's actual operating state and task requirements. This allows PID parameter adjustment to automatically match the optimal or near-optimal control strategy during start-up, steady-state maintenance, or response to sudden disturbances, effectively suppressing overshoot, reducing steady-state error, and accelerating response speed. Compared to traditional methods, the technical solution provided in this application significantly overcomes the limitations of fixed-parameter PID control in dynamic, multi-variable, and complex operating conditions, achieving improved adaptive control performance and overall robustness of the temperature control system when facing dynamic load changes, multi-regional differentiated heat demands, and complex operating conditions.

[0128] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0129] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0130] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A device temperature control method based on PID control, characterized in that, include: The device dynamic load is obtained, and based on the device dynamic load, sampling parameters are obtained, wherein the sampling parameters include sampling frequency adjustment coefficients and sampling validity thresholds for multiple partitions; Based on the human contact area, the sampling parameters are partitioned and optimized to obtain partitioned sampling parameters. Device temperature partition sampling is performed to obtain multiple partitioned temperature states. Combined with the sampling validity threshold, the sensor state parameters are determined and obtained. Based on the zone temperature status, equipment dynamic load, and sensor status parameters, a comprehensive operating condition is obtained. Based on this comprehensive operating condition, adaptive optimization of PID parameters is performed to obtain optimized PID parameters for equipment temperature control, including: Based on the partition temperature status, device dynamic load and sensor status parameters, the partition operating condition is obtained, wherein the partition operating condition includes partition operating condition reliability, partition load and partition temperature target. The comprehensive operating conditions are obtained by weighted summation of the partitioned operating conditions and the functional requirements of the human contact area. Get the current PID parameters; Based on the partitioned operating conditions, the partitioned PID optimization step size is obtained, and combined with the comprehensive operating conditions, the PID optimization round is obtained; Using the partitioned PID optimization step size and the PID optimization round, the current PID parameters are adaptively optimized to obtain optimized PID parameters for equipment temperature control. Based on the human contact area, the sampling parameters are partitioned and optimized to obtain partitioned sampling parameters, including: The equipment is divided into zones based on the areas that human contact with the body. Based on the functional requirements of the human contact area, the sampling parameters are adaptively adjusted by region to obtain the regional sampling parameters, wherein the functional requirements are obtained based on the contact parts of the device and the human body. Perform temperature zone sampling on the equipment to obtain the temperature status of multiple zones, and combine the sampling validity threshold to determine the obtained sensor status parameters, including: Using the aforementioned partition sampling parameters, the device temperature is sampled in partitions to obtain multiple partition temperature sequences; Based on multiple partition temperature sequences, statistical features of partition temperature are extracted to obtain the temperature status of multiple partitions; Based on the zone temperature status and the sampling validity threshold, the sensor working status is analyzed and judged to obtain sensor status parameters. Based on the aforementioned partition conditions, the partition PID optimization step size is obtained, including: Based on the partitioned operating conditions, the minimum operating condition confidence level, the maximum partitioned load, and the maximum partitioned temperature target difference are obtained. Based on the minimum operating condition confidence level, the maximum partition load, and the maximum partition temperature target difference, the partition priority is obtained; The optimization step size is obtained based on the operating conditions of each partition, and a weighted sum is performed in combination with the partition priority and the functional requirements of the human contact area to obtain the partition PID optimization step size. Using the partitioned PID optimization step size and the PID optimization round, the current PID parameters are adaptively optimized to obtain optimized PID parameters for equipment temperature control, including: Obtain adaptation evaluation metrics, wherein the adaptation evaluation metrics are obtained based on the functional requirements of the partition; Based on the current PID parameters, multiple candidate PID parameters are randomly generated in combination with the partitioned PID optimization step size, and the adaptation evaluation index of each candidate PID parameter is obtained. With the goal of maximizing the adaptation evaluation index, multiple candidate PID parameters are jointly optimized until the PID optimization round is reached or the adaptation evaluation index of a PID parameter is greater than the adaptation threshold. The PID parameter with the largest adaptation evaluation index in the current optimization is then used as the optimized PID parameter for equipment temperature control.

2. The PID-based equipment temperature control method according to claim 1, characterized in that, The device dynamic load is obtained, and based on the device dynamic load, sampling parameters are obtained, wherein the sampling parameters include a sampling frequency adjustment coefficient and a sampling validity threshold, including: The device dynamic load is obtained, wherein the device dynamic load includes the current operating power and the trend of load current change; Based on the ratio of the device's dynamic load to its standard load, the device's dynamic load parameters are obtained. Based on the device's dynamic load parameters, the standard sampling parameters are corrected to obtain the sampling parameters.

3. The PID-based equipment temperature control method according to claim 1, characterized in that, With the goal of maximizing the adaptation evaluation index, the multiple candidate PID parameters are jointly optimized, including: Record the optimal fit evaluation index and corresponding PID parameter for each candidate PID parameter during its own iteration process, as the iteration dataset; The maximum value of the adaptive evaluation index in the iterative dataset is selected as the global optimum, and the corresponding PID parameter is selected as the global optimum parameter. For each candidate PID parameter, the update direction and magnitude are dynamically adjusted based on the difference between its own optimal parameter and the global optimal parameter, as well as the gap between its own adaptation evaluation index and the global optimal value. Calculate the parameter similarity between each candidate PID parameter, and merge candidate PID parameters with similarity higher than a preset threshold; If the change in the global optimal value within the preset number of iterations is less than the stagnation threshold, new candidate PID parameters are added based on the stagnation magnitude, and the system continues to participate in collaborative optimization until the number of PID optimization rounds is reached or the adaptation evaluation index of the candidate PID parameters is greater than the adaptation threshold. The stagnation threshold is obtained based on comprehensive operating conditions.

4. A PID-based equipment temperature control system, characterized in that, For implementing the PID-based device temperature control method according to any one of claims 1-3, the system comprises: The sampling parameter acquisition module is used to acquire the dynamic load of the device and acquire sampling parameters based on the dynamic load of the device, wherein the sampling parameters include the sampling frequency adjustment coefficients and sampling validity thresholds for multiple partitions; The partition optimization module is used to optimize the sampling parameters based on the human contact area, obtain partition sampling parameters, perform device temperature partition sampling, obtain multiple partition temperature states, and combine the sampling validity threshold to determine and obtain sensor state parameters. The PID parameter optimization module is used to obtain comprehensive operating conditions based on the zone temperature status, equipment dynamic load, and sensor status parameters, and to perform adaptive optimization of PID parameters based on the comprehensive operating conditions to obtain optimized PID parameters for equipment temperature control.

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