Aging chamber temperature control method and system

Abstract

The present invention relates to the field of temperature control technology, and more specifically, to a method and system for controlling the temperature of an aging chamber, the method comprising: obtaining a temperature sequence in the aging chamber, dividing the temperature sequence into a plurality of data segments of equal length; calculating the chaos factor of any data segment, and using the normalized value of the difference between the chaos factor of the data segment and the average chaos factor of a plurality of data segments before the data segment as the oscillation index of the data segment; calculating the product of the temperature optimization index and the preset temperature to obtain the target temperature of the data segment, using the target temperature as the initial temperature, and obtaining the optimal control temperature of the data segment using a simulated annealing algorithm based on the initial temperature, so as to control the temperature of the aging chamber according to the optimal control temperature. The present invention can improve the accuracy and efficiency of the temperature control of the aging chamber.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and more specifically, to a temperature control method and system for an aging chamber. Background Art

[0002] With the development of industrialization, technological progress and product diversification, modern society has put forward higher and higher requirements on product quality, reliability and service life. In this context, aging tests for electrical, electronic and other industrial products have become particularly important. Aging tests can simulate the extreme environments that products may encounter in long-term use, so as to discover potential problems in advance. One of the core of aging tests is temperature control. Therefore, a precise temperature control system is the basis for ensuring the accuracy and reliability of aging tests.

[0003] In the related technology, for example, a patent application document with publication number CN116184807A discloses a furnace temperature control method and system for a steel rolling walking beam heating furnace, the method comprising: using RBF neural network online identification to obtain the first section, second section and equalizing section sub-models in the heating furnace, the first section, second section and equalizing section sub-models together constitute the furnace temperature model of the heating furnace; for any sub-model, the two-degree-of-freedom PID controller is connected to the sub-model in sequence, the PID parameters of the two-degree-of-freedom PID controller are adjusted using a hybrid optimization algorithm module to obtain the optimal solution, the sub-model is controlled by the optimal solution, and thus the furnace temperature model of the heating furnace is controlled to output the optimal furnace temperature of each section.

[0004] However, when the above scheme uses the simulated annealing algorithm to calculate the current solution, the initial temperature of the objective function is set according to a given value, but it does not take into account the influence of false data that may exist in the data, such as data transmission errors or outliers caused by noise. The setting of the initial temperature directly affects the number of iterations of the simulated annealing algorithm. An initial temperature that is too high or too low will affect the accuracy of the current solution determined by the algorithm, and may cause deviations in the determined optimal solution, thereby reducing the accuracy of temperature control. Summary of the invention

[0005] In order to solve the problem of poor temperature control accuracy, the present invention provides an aging chamber temperature control method and system.

[0006] According to a first aspect of the present invention, there is provided a method for controlling temperature of an aging chamber, comprising:

[0007] Obtain the temperature sequence in the aging chamber and divide the temperature sequence into several data segments of equal length;

[0008] Calculate the chaos factor of any data segment. The chaos factor is positively correlated with the ratio of the number of data categories in the data segment to the amount of data in the data segment, as well as the range of the data segment. The normalized value of the difference between the chaos factor of the data segment and the average chaos factor of multiple data segments before the data segment is used as the oscillation index of the data segment.

[0009] Calculate the temperature optimization index for this data segment: , For the The temperature optimization index of each data segment, For the The oscillation index of the data segment, For the The fitting curve of the data segment The area of ​​the triangle formed by the data and the adjacent data, is the summation function, is the normalization function, The natural constant An exponential function with base ;

[0010] The product of the temperature optimization index and the preset temperature is calculated to obtain the target temperature of the data segment. The target temperature is used as the initial temperature. Based on the initial temperature, the simulated annealing algorithm is used to obtain the optimal control temperature of the data segment, so as to control the temperature of the aging chamber according to the optimal control temperature.

[0011] The present invention adaptively adjusts the initial temperature when the simulated annealing algorithm determines the optimal control temperature of the corresponding data segment according to the temperature optimization index of each data segment, which can avoid the problem of poor optimization effect or low optimization efficiency caused by high or low initial temperature, thereby improving the accuracy and efficiency of temperature control in the aging chamber.

[0012] Preferably, the hash factor of any data segment satisfies the following relationship:

[0013] ;

[0014] In the formula, For the The clutter factor of each data segment; For the The number of data categories in a data segment; For the The amount of data in each data segment; For the The range of a data segment.

[0015] The present invention determines the chaos factor of a data segment by integrating data from multiple aspects, and can ensure the accuracy of the determined chaos factor.

[0016] Preferably, the method for obtaining the oscillation index includes:

[0017] Randomly select a preset number of data segments from the data segments before any data segment to obtain a reference data segment of any data segment;

[0018] The oscillation index of any data segment satisfies the following relationship:

[0019] ;

[0020] In the formula, For the Oscillation index of a data segment; For the The clutter factor of each data segment; For the The average clutter factor of the reference data segment of the data segment; is the normalization function.

[0021] The present invention can reduce the influence of the trend change of temperature, and thus can accurately measure the frequency of data changes in a data segment.

[0022] Preferably, before dividing the temperature sequence into a plurality of data segments of equal length, the method further comprises:

[0023] The temperature series is curve-fitted using the least square method to obtain a fitting curve of the temperature series. The fitting curve is divided into several curve segments with the same time period to obtain several data segments of equal length.

[0024] The present invention can ensure the accuracy of temperature control by performing a simulated annealing algorithm optimization on each data segment.

[0025] Preferably, the method for obtaining the optimal control temperature includes:

[0026] Obtain a pre-constructed objective function and a preset initial solution of any data segment, and sequentially reduce the initial temperature of any data segment when the simulated annealing algorithm performs temperature control according to a preset cooling coefficient;

[0027] Each time the temperature is lowered, the objective function value of the lowered temperature is compared with the objective function value of the initial solution. If the objective function value of the lowered temperature is greater than the objective function value of the initial solution, the lowered temperature is used as the current solution. If the objective function value of the lowered temperature is less than or equal to the objective function value of the initial solution, the initial solution is used as the current solution. This process is repeated and the current solution obtained when the preset termination condition is met is used as the optimal control temperature for any data segment.

[0028] Preferably, the method further comprises:

[0029] The optimal solution of the previous data segment is used as the preset initial solution of the current data segment.

[0030] The present invention can retain the advantages of known solutions, thereby accelerating the convergence speed of the algorithm.

[0031] Preferably, the method further comprises:

[0032] Set the perturbation range and obtain the integer solution interval of the initial solution within the perturbation range;

[0033] The data with the largest objective function value in the integer solution interval is taken as the initial optimal solution. Each time the temperature is lowered, the objective function value of the lowered temperature is compared with the objective function value of the initial optimal solution.

[0034] According to a second aspect of the present invention, an aging chamber temperature control system is provided. The system includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps of the first aspect of the present invention.

[0035] The present invention has the following effects:

[0036] 1. The present invention can adaptively adjust the simulated annealing algorithm to determine the initial temperature of the optimal control temperature in the corresponding data segment according to the characteristics of data changes in each data segment, thereby improving the accuracy and search efficiency of the optimal control temperature, thereby improving the accuracy and efficiency of the temperature control of the aging chamber.

[0037] 2. When determining the oscillation index of a data segment, the present invention can reduce the influence of the trend change of temperature, and when determining the temperature optimization index, it can eliminate the influence of untrue temperature data, thereby accurately measuring the data change characteristics within the data segment and ensuring the accuracy of the determined initial temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0039] Figure 1 The present invention is a schematic diagram of a temperature control method for an aging chamber according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0042] Reference Figure 1 , a temperature control method for an aging chamber, comprising steps S1 to S4, specifically as follows:

[0043] S1: Obtain the temperature sequence in the aging chamber and divide the temperature sequence into several data segments of equal length.

[0044] Specifically, the readings of the temperature sensor disposed in the aging chamber can be collected within a sampling period, such as 2 hours, at a certain sampling frequency, such as 1 time / second, to obtain the temperature sequence in the aging chamber. The present invention does not specifically limit the sampling period and the sampling frequency.

[0045] In an exemplary embodiment of the present invention, before dividing the temperature sequence into a number of data segments of equal length, the method further includes:

[0046] The temperature series is curve-fitted using the least square method to obtain a fitting curve of the temperature series. The fitting curve is divided into several curve segments with the same time period to obtain several data segments of equal length.

[0047] Optionally, a polynomial fitting method may be used to perform curve fitting on the temperature series. This embodiment does not specifically limit the fitting method used.

[0048] Furthermore, after the fitting curve is determined, the curve can be divided into multiple curve segments with a fixed duration, such as 5 minutes, to obtain several data segments with the same time period. It should be noted that for each time period corresponding to each data segment, the aging chamber needs to be temperature-controlled once.

[0049] S2: Calculate the clutter factor of any data segment. The clutter factor is positively correlated with the ratio of the number of data categories in the data segment and the amount of data in the data segment, as well as the range of the data segment. The normalized value of the difference between the clutter factor of the data segment and the average clutter factor of multiple data segments before the data segment is used as the oscillation index of the data segment.

[0050] In an exemplary embodiment of the present invention, the chaos factor refers to a parameter used to measure the distribution of data in a data segment. If there are more types of data in any data segment and the range of data values ​​is wider, it means that the data in the data segment changes more frequently, and the chaos factor of the data segment is larger.

[0051] It should be noted that the trend change of temperature will affect the chaos factor of the data segment, thereby causing the chaos factor of the determined data segment to deviate. Therefore, the present invention combines the difference between the chaos factors of any data segment and the previous multiple data segments to calculate the oscillation index of the data segment, which can more accurately measure the oscillation of the data segment.

[0052] Specifically, the chaos factor of any data segment satisfies the following relationship:

[0053] ;

[0054] In the formula, For the The clutter factor of each data segment; For the The number of categories of data in a data segment, where the number of categories refers to the number of types of data values ​​in the data segment. For example, when any data segment is {1,2,4,3,2,4,5}, the number of categories of the data segment is 5, that is, {1,2,4,3,5}; For the The amount of data in each data segment; For the The range of the data segment, ,in, , Respectively The maximum and minimum values ​​of the data in a data segment.

[0055] It reflects the degree of data confusion in the data segment. The larger the value, the more frequent the changes in the data in the data segment. At the same time, if the distribution range of the data in the data segment is wide, that is, When it is larger, it means that the credibility of the degree of data chaos in the data segment is higher, and the credibility of the frequent changes in the corresponding data in the data segment is higher, then the chaos factor of the data segment is relatively large.

[0056] In another embodiment, the hash factor of the data segment may also be determined by other calculation formulas, such as by a summation formula instead of a product formula.

[0057] Furthermore, the calculation formula of the chaos factor can be used to calculate the chaos factor of each data segment.

[0058] In an exemplary embodiment of the present invention, the determination of the oscillation index of the data segment may be achieved by the following steps:

[0059] A preset number of data segments are randomly selected from the data segments before any data segment to obtain a reference data segment of any data segment; the oscillation index of any data segment satisfies the following relationship:

[0060] ;

[0061] In the formula, For the Oscillation index of a data segment; For the The clutter factor of each data segment; For the The average clutter factor of the reference data segment of the data segment; is the normalization function.

[0062] in, Reflects the The difference between the average clutter factor of a data segment and the reference data segment of the data segment. The larger the value, the more reliable it is that the data in the data segment changes frequently, and the corresponding oscillation index of the data segment is relatively large.

[0063] Optionally, the preset number can be set to 5, then If the oscillation index of a data segment is -5 data segments to -1 data segment in the data segment, as the first The present embodiment does not specifically limit the number of reference data segments selected.

[0064] Furthermore, the oscillation index of all data segments can be calculated using the calculation formula of the oscillation index.

[0065] S3: Calculate the temperature optimization index of the data segment.

[0066] The temperature optimization index refers to a parameter used to adjust a preset temperature of a data segment when temperature control is performed on the data segment.

[0067] It should be noted that, during the temperature data collection process, network instability may cause temperature data transmission errors and unreal temperature data, and the presence of noise may also cause unreal temperature data. Since the determination of the oscillation index depends only on the change characteristics of the data in the data segment, if the temperature optimization parameters of the data segment are determined only based on the oscillation index of the data segment, the interference of unreal temperature data may not be eliminated. However, since the temperature control system in the aging room usually adopts a progressive control method for temperature regulation, the conduction and convection of heat require a certain amount of time to reach a steady state, that is, unreal temperature data has a certain degree of mutation relative to the real temperature data. Therefore, the present invention combines this feature and the oscillation index of the data segment to calculate the temperature optimization index of the data segment, thereby ensuring the accuracy of the temperature optimization index.

[0068] Specifically, the temperature optimization index of any data segment satisfies the following relationship:

[0069] ;

[0070] In the formula, For the Temperature optimization index for each data segment; For the Oscillation index of a data segment; For the The first The area of ​​the triangle formed by the data and the adjacent data; is the sum function; is the normalization function; The natural constant An exponential function with base .

[0071] in, The larger the value is, the more frequent the changes in the data in the data segment are, and the more complex the change characteristics in the data segment are. In this case, a larger temperature optimization index should be set to adjust the preset temperature to a greater extent.

[0072] The severity of data changes in the data segment is quantified. The smaller the value is, the smaller the difference between the data in the data segment and the adjacent data is, which means that the data changes in the data segment are relatively stable. In this case, the data in the data segment is more likely to be real temperature data, and the corresponding oscillation index of the data segment is more authentic, which can ensure the accuracy of the determined temperature optimization index.

[0073] Furthermore, the temperature optimization index of each data segment can be calculated using a calculation formula of the temperature optimization index.

[0074] S4: Calculate the product of the temperature optimization index and the preset temperature to obtain the target temperature of the data segment, take the target temperature as the initial temperature, and use the simulated annealing algorithm based on the initial temperature to obtain the optimal control temperature of the data segment, so as to control the temperature of the aging chamber according to the optimal control temperature.

[0075] It should be noted that the simulated annealing algorithm is a randomized local search algorithm suitable for large-scale and complex combinatorial optimization problems. The basic principle of the algorithm is to search near the current solution through random perturbations in the search space, and accept a worse solution according to a certain probability, thereby jumping out of the local optimal solution and eventually tending to the global optimal solution. However, the traditional simulated annealing algorithm often uses the same initial temperature when searching for the optimal control temperature of each data segment, but the size of the initial temperature affects the number of iterations of the simulated annealing algorithm, and the initial temperature that is too low or too high will cause the optimal control temperature output by the algorithm to be inaccurate or not efficient enough. Therefore, the present invention improves the traditional simulated annealing algorithm, and uses the target temperature of each data segment as the initial temperature when the simulated annealing algorithm searches for the optimal control temperature in the corresponding data segment. By adaptively adjusting the initial temperature, the exploration ability and convergence speed of the simulated annealing algorithm can be better balanced, thereby improving the effect of its search for the optimal solution.

[0076] Specifically, the target temperature of any data segment satisfies the following relationship:

[0077] ;

[0078] In the formula, For the The target temperature for each data segment; is a preset temperature. In the present invention, the preset temperature =120℃; For the The temperature optimization index for each data segment.

[0079] Optionally, when the temperature optimization index of any data segment is high, it means that the data in the data segment changes frequently and the data change characteristics in the data segment are complex. Setting a higher initial temperature can increase the number of iterations when the simulated annealing algorithm determines the optimal control temperature of the data segment, thereby ensuring the accuracy of the determined optimal control temperature. On the contrary, when the temperature optimization index of the data segment is low, setting a lower initial temperature can improve the optimization efficiency of the algorithm.

[0080] Furthermore, the target temperature of each data segment can be calculated using a calculation formula for the target temperature.

[0081] Furthermore, after the target temperature of each data segment is determined, the target temperature of each data segment can be used as the initial temperature of the corresponding data segment, so as to obtain the optimal control temperature of each data segment using the simulated annealing algorithm.

[0082] In an exemplary embodiment of the present invention, the determination of the optimal control temperature of any data segment can be achieved by the following steps:

[0083] Step 1: Obtain a pre-constructed objective function and a preset initial solution of any data segment, and sequentially reduce the initial temperature of any data segment when the simulated annealing algorithm performs temperature control according to the preset cooling coefficient;

[0084] Optionally, the objective function constructed in this embodiment is: , where is the objective function value; , is a weight value used to adjust the weights of the two influencing factors in the objective function. In this embodiment =0.3, =0.7; The energy consumption of the aging chamber during the aging process at the temperature corresponding to any data segment during the corresponding period of time of the data segment; , They are respectively the insulation resistance values ​​of the device at the start time and the end time of the time period corresponding to any data segment when a device having the same insulation resistance value as that corresponding to the start time of the previous data segment is subjected to the temperature aging treatment corresponding to the any data segment.

[0085] in, It reflects the aging effect of the equipment in the corresponding period of any data segment. , as well as All of them can be obtained from the historical data of aging tests.

[0086] Optionally, when using the simulated annealing algorithm to determine the optimal control temperature in the first data segment, an initial solution may be set first, so as to find the optimal control temperature in the first data segment based on the initial solution. In this embodiment, the initial solution set is 80°C.

[0087] In an exemplary embodiment of the present invention, the setting of the initial solution of the data segment further includes:

[0088] The optimal solution of the previous data segment is used as the preset initial solution of the current data segment.

[0089] Specifically, starting from the second data segment, the initial solution of any data segment is the optimal solution of the previous data segment, thereby retaining the advantages of the known solutions and accelerating the convergence speed of the simulated annealing algorithm.

[0090] Optionally, the cooling coefficient may be set to 0.95. This embodiment does not specifically limit the setting of the cooling coefficient.

[0091] Step 2: Each time the temperature is lowered, compare the objective function value of the lowered temperature with the objective function value of the initial solution. If the objective function value of the lowered temperature is greater than the objective function value of the initial solution, the lowered temperature is used as the current solution. If the objective function value of the lowered temperature is less than or equal to the objective function value of the initial solution, the initial solution is used as the current solution. This process is repeated and the current solution obtained when the preset termination condition is met is used as the optimal control temperature for any data segment.

[0092] In an exemplary embodiment of the present invention, the objective function value of the reduced temperature and the objective function value of the initial solution may be compared by the following steps:

[0093] Set the perturbation range and obtain the integer solution interval of the initial solution within the perturbation range; take the data with the largest objective function value in the integer solution interval as the initial optimal solution, and compare the objective function value of the lowered temperature with the objective function value of the initial optimal solution each time the temperature is lowered.

[0094] Optionally, the perturbation range can be set to 3° C., when the initial solution is 80° C., the integer solution interval of the initial solution within the disturbance range is an interval consisting of integers between 77° C. and 83° C. This embodiment does not specifically limit the setting of the disturbance range.

[0095] Optionally, for any data segment, when the lowered temperature is less than a preset temperature threshold such as 50°C, the iterative process of the simulated annealing algorithm is determined to meet a preset termination condition, so that the most recent current solution can be used as the optimal solution output by the simulated annealing algorithm, that is, the optimal control temperature of any data segment.

[0096] Furthermore, the optimal control temperature determination method can be used to obtain the optimal control temperature of each data segment, and then at the control time of the corresponding time period of each data segment, the temperature of the aging chamber is adjusted to the optimal control temperature of the corresponding data segment, thereby realizing the control of the temperature of the aging chamber.

[0097] The present invention also provides an aging chamber temperature control system, the system includes a memory and a processor, and a computer program is stored in the memory, the computer program integrates the function of an aging chamber temperature control method, when the computer program is executed, the aging chamber temperature control method can improve the accuracy and efficiency of the aging chamber temperature control.

[0098] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0099] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for controlling the temperature of an aging chamber, characterized in that: include: Acquire the temperature sequence in the aging chamber, and divide the temperature sequence into a number of data segments of equal length; Calculate the chaos factor of any data segment, the chaos factor is positively correlated with the ratio of the number of categories of data in the data segment to the amount of data in the data segment, and the range of the data segment; take the normalized value of the difference between the chaos factor of the data segment and the average chaos factor of multiple data segments before the data segment as the oscillation index of the data segment; Calculate the temperature optimization index for this data segment: , For the The temperature optimization index of each data segment, For the The oscillation index of the data segment, For the The fitting curve of the data segment The area of ​​the triangle formed by the data and the adjacent data, is the summation function, is the normalization function, The natural constant An exponential function with base ; The product of the temperature optimization index and the preset temperature is calculated to obtain the target temperature of the data segment. The target temperature is used as the initial temperature. Based on the initial temperature, the optimal control temperature of the data segment is obtained using a simulated annealing algorithm to control the temperature of the aging chamber according to the optimal control temperature.

2. The temperature control method of an aging chamber according to claim 1, characterized in that: The chaos factor of any data segment satisfies the following relationship: ； In the formula, For the The clutter factor of each data segment; For the The number of data categories in a data segment; For the The amount of data in each data segment; For the The range of a data segment.

3. A temperature control method for an aging chamber according to claim 2, characterized in that: The method for obtaining the oscillation index comprises: Randomly selecting a preset number of data segments from the data segments before any data segment to obtain a reference data segment of the any data segment; The oscillation index of any data segment satisfies the following relationship: ； In the formula, For the Oscillation index of a data segment; For the The clutter factor of each data segment; For the The average clutter factor of the reference data segment of the data segment; is the normalization function.

4. The temperature control method of an aging chamber according to claim 1, characterized in that: Before dividing the temperature sequence into a plurality of data segments of equal length, the method further comprises: The temperature sequence is curve-fitted by using the least square method to obtain a fitting curve of the temperature sequence, and the fitting curve is divided into a number of curve segments with the same time period to obtain the number of data segments with equal lengths.

5. The temperature control method of an aging chamber according to claim 1, characterized in that: The method for obtaining the optimal control temperature comprises: Obtaining a pre-constructed objective function and a preset initial solution of any data segment, and sequentially reducing the initial temperature of any data segment when the simulated annealing algorithm performs temperature control on the simulated annealing algorithm according to a preset cooling coefficient; Each time the temperature is lowered, the objective function value of the lowered temperature is compared with the objective function value of the initial solution. If the objective function value of the lowered temperature is greater than the objective function value of the initial solution, the lowered temperature is used as the current solution. If the objective function value of the lowered temperature is less than or equal to the objective function value of the initial solution, the initial solution is used as the current solution, and so on. The current solution obtained when the preset termination condition is met is used as the optimal control temperature of any data segment.

6. A temperature control method for an aging chamber according to claim 5, characterized in that: The method further comprises: The optimal solution of the previous data segment is used as the preset initial solution of the current data segment.

7. The temperature control method of an aging chamber according to claim 5, characterized in that: The method further comprises: Setting a disturbance range, and obtaining an integer solution interval of the initial solution within the disturbance range; The data with the largest objective function value within the integer solution interval is taken as the initial optimal solution. Each time the temperature is lowered, the objective function value of the lowered temperature is compared with the objective function value of the initial optimal solution.

8. An aging chamber temperature control system, characterized in that: The aging chamber temperature control system comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

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