Test box temperature intelligent regulation and control method

Abstract

The invention relates to the field of temperature regulation and control, in particular to an intelligent regulation and control method and system for the temperature of a test box, and the method comprises the steps: calculating an optimal disturbance range in a simulated annealing algorithm, and obtaining the temperature in the test box in a next time period through the simulated annealing algorithm; the calculation method of the optimal disturbance range comprises the steps that a data set is obtained, the data set comprises multiple pieces of temperature data in the current time period, the dispersion degree of the data in the data set is calculated, the disturbance range optimization factor in the current time period is calculated, and the disturbance range optimization factor is in positive correlation with the dispersion degree. And taking a product of the disturbance range optimization factor and a preset disturbance range as an optimal disturbance range. The method has the effect of improving the calculation efficiency and the control precision of the simulated annealing algorithm.

Description

Technical Field

[0001] The present invention relates to the field of temperature control, and in particular to a method for intelligently controlling the temperature of a test chamber. Background Art

[0002] A test chamber is a device used to test materials, products, equipment, etc. and simulate environmental conditions. The test chamber can simulate different environmental conditions, such as temperature, humidity, vibration, salt spray, etc., and is usually used to detect the performance, durability and stability of products in specific environments. Aging test chambers, as a type of test chamber, are used in electronics, automobiles, aviation, materials, home appliances and other industries to simulate the long-term use environment of products and evaluate the reliability and durability of products. Traditional temperature control systems usually rely on simple thermostats or timing controls, which have problems such as low accuracy, slow response, and high energy consumption.

[0003] The Chinese patent application document with the patent publication number CN118655932A discloses a temperature control monitoring system for the production process of fascia balls. The system includes a real-time temperature monitoring module, a temperature anomaly analysis module, a temperature control strategy module, a production scheduling optimization module, a temperature prediction model module, an equipment status diagnosis module, a parameter adjustment module, and a product quality inspection module. By introducing a long short-term memory network and a simulated annealing algorithm, the intelligent dynamic adjustment of the temperature control strategy is realized, allowing the system to intelligently judge the severity of the abnormal state based on the real-time data of temperature changes, and adjust the temperature control strategy accordingly. For high-frequency fluctuations, the system can quickly adopt short-term high-power regulation, and for low-frequency fluctuations, long-term low-power regulation is implemented.

[0004] The simulated annealing algorithm can be used to control the temperature of the aging test chamber. When the simulated annealing algorithm performs intelligent temperature control, the perturbation range selected for each simulated annealing process (i.e., each temperature control) is a fixed value. If the perturbation range is too large, the search process will be rough, the convergence speed will be slow, and it will not be able to converge quickly to the optimal solution. If the perturbation range is too small, the search process will become very detailed, requiring more iterations to gradually optimize, resulting in low computational efficiency. Summary of the invention

[0005] In order to solve the problem of low computational efficiency caused by a fixed disturbance range in the existing simulated annealing algorithm, the present invention provides a test chamber temperature intelligent control method and system.

[0006] The present invention provides a test chamber temperature intelligent control method, which adopts the following technical solutions: Calculate the optimal disturbance range in the simulated annealing algorithm, and use the simulated annealing algorithm to obtain the temperature in the test box in the next time period; Among them, the calculation method of the optimal disturbance range is: obtain a data set, the data set includes multiple temperature data in the current time period, calculate the discrete degree of the data in the data set, calculate the disturbance range optimization factor of the current time period, the disturbance range optimization factor is positively correlated with the discrete degree, and take the product of the disturbance range optimization factor and the preset disturbance range as the optimal disturbance range.

[0007] Based on the characteristics of the data set, the perturbation range optimization factor is obtained, and the optimal perturbation range is further obtained, which can avoid the search process being too violent due to excessive perturbation amplitude, and avoid the search being too slow due to too small perturbation amplitude, thereby accelerating the convergence of the algorithm while ensuring the search efficiency, making each temperature control process more accurate, avoiding interference caused by data noise, and improving the stability and accuracy of temperature control. The optimization of the dynamic perturbation range based on the characteristics of temperature data changes can better adapt to different temperature control scenarios, realize more refined intelligent control, and ultimately improve the computing efficiency and control accuracy of the entire system, reduce energy consumption, and improve the repeatability and reliability of the experiment.

[0008] Preferably, the expression of discrete degree is: ; In the formula, Indicates the discreteness of the data set referenced during the sth temperature control. Represents the number of data points in the data set. Indicates the Z-Score value of the rth data point in the data set referenced during the sth temperature control. Represents the information entropy value of the data set referenced during the s-th temperature control.

[0009] The above formula can be used to calculate the discrete degree of data in a data set, thereby improving the accuracy of the discrete degree calculation results.

[0010] Preferably, the expression of the disturbance range optimization factor is: ; In the formula, represents the disturbance range optimization factor corresponding to the sth temperature control, Indicates the discreteness of the data set referenced during the sth temperature control. Indicates the maximum number of monotonically increasing or monotonically decreasing data points in the data set referenced during the sth temperature control. Represents the number of data points in the data set. It represents the preset correlation coefficient of the data set referenced during the s-th temperature control, exp represents the exponential function with e as the base, and norm represents the normalization function.

[0011] By analyzing the characteristics of temperature data, noise data can be identified, which makes it easier to adjust the optimization strategy of the disturbance range on this basis, reducing the interference of noise data on the disturbance range optimization factor, making it more in line with the actual needs of the current temperature state.

[0012] Preferably, the expression of the disturbance range optimization factor is: ; In the formula, represents the disturbance range optimization factor corresponding to the sth temperature control, Indicates the discreteness of the data set referenced during the sth temperature control. Indicates the maximum number of monotonically increasing or monotonically decreasing data points in the data set referenced during the sth temperature control. Represents the number of data points in the data set. Indicates the correlation coefficient of the preset data set referenced during the sth temperature control. represents the logarithmic function with base e, Represents a preset constant, and norm represents a normalization function.

[0013] Preferably, the correlation coefficient is calculated as follows: Calculate the first difference between each data point and its adjacent data point on the left, and calculate the second difference between the corresponding data point and its adjacent data point on the right; if the product of the first difference and the second difference is less than 0, take the corresponding data point as the reference data point, and take the number of reference data points as the correlation coefficient of the data set.

[0014] By calculating the correlation coefficient, we can understand the credibility of the data fluctuation range in the data set. The larger the value, the greater the possibility that the data fluctuation range in the data set is larger.

[0015] Preferably, the method of obtaining the temperature in the test box in the next time period by using the simulated annealing algorithm includes: Randomly generate new solutions from the best perturbation range of the current solution; Construct an objective function and use it to calculate the objective function values ​​of the current solution and the new solution; The difference between the objective function values ​​of the current solution and the new solution is taken as the energy difference; In response to the energy difference being less than or equal to 0, the new solution is accepted as the current solution, otherwise the new solution is accepted with a certain probability.

[0016] Preferably, the expression of the objective function is: ; In the formula, E represents the objective function value, τ represents the preset first weight, represents the preset second weight, q represents the partial discharge amplitude of the test product in the test chamber at the corresponding temperature during the simulated annealing process, v represents the partial discharge frequency of the test product in the test chamber at the corresponding temperature during the simulated annealing process, and N represents the energy consumption of the test chamber at the corresponding temperature during the simulated annealing process.

[0017] By constructing the objective function value, it is easy to understand the energy value of each new solution, so as to calculate the energy difference between the current solution and the new solution in order to determine whether to directly accept the new solution.

[0018] Preferably, the expression of the objective function is: ; In the formula, E represents the objective function value, τ represents the preset first weight, represents the preset second weight, R represents the insulation resistance value of the test product in the test chamber at the corresponding temperature during the simulated annealing process, and N represents the energy consumption of the test chamber at the corresponding temperature during the simulated annealing process.

[0019] The present invention has the following technical effects: 1. Based on the characteristics of the data set, the perturbation range optimization factor is obtained, and the optimal perturbation range is further obtained, which can avoid the search process being too violent due to excessive perturbation amplitude, and avoid the search being too slow due to too small perturbation amplitude, thereby accelerating the convergence of the algorithm while ensuring the search efficiency, making each temperature control process more accurate, avoiding interference caused by data noise, and improving the stability and accuracy of temperature control. The optimization of the dynamic perturbation range based on the characteristics of temperature data changes can better adapt to different temperature control scenarios, realize more refined intelligent control, and ultimately improve the computational efficiency and control accuracy of the simulated annealing algorithm, reduce the energy consumption of the aging test chamber, and improve the reliability of the experiment.

[0020] 2. By analyzing the changing characteristics of the temperature data in the test chamber and obtaining the disturbance range optimization factor for each temperature control, the accuracy and efficiency of the simulated annealing algorithm in temperature intelligent control can be significantly improved. By deeply analyzing the characteristics of the temperature data, noise data can be identified, and on this basis, the optimization strategy of the disturbance range can be adjusted to make it more in line with the actual needs of the current temperature state. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, 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-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 The present invention is a flow chart of a method for intelligently controlling the temperature of a test chamber. DETAILED DESCRIPTION

[0023] 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.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The embodiment of the present invention discloses a test chamber temperature intelligent control method, referring to Figure 1 , including steps S1-S2, which are as follows: S1: Calculate the optimal perturbation range in the simulated annealing algorithm.

[0026] S11: Acquire a data set, where the data set includes a plurality of temperature data within a current time period.

[0027] A temperature sensor is used to collect temperature data in the test chamber in real time. The collection time is the entire aging test process of the aging test chamber, and the collection frequency is once per second. The data set contains multiple data in the current time period, and the multiple data are arranged in chronological order. For example, the data set is the temperature data in the current 3-minute time period.

[0028] S12: Calculate the degree of dispersion of the data in the data set.

[0029] Based on the data variation characteristics of the temperature data in the data set, the optimal amplitude of the disturbance range during each temperature control is calculated. First, the discrete degree of the data in the data set is calculated.

[0030] The expression of the degree of dispersion is: ; In the formula, Indicates the discreteness of the data set referenced during the sth temperature control. Represents the number of data points in the data set. It represents the Z-Score value of the rth data point in the data set referenced during the sth temperature control, which is calculated by the Z-Score model. The Z-Score model is a prior art, and the specific calculation steps are not repeated here. The information entropy value of the data set referenced during the s-th temperature control is the temperature data within the time period of the s-th temperature control.

[0031] in the formula It represents the distance between the rth data point in the data set referenced during the sth temperature control and the mean value of the data in the data set. It represents the sum of the distances of all data points in the data set referenced during the s-th temperature control relative to the mean value of the data in the data set. The larger the value, the more discrete the data distribution of the data set referenced during the s-th temperature control, and the greater the corresponding degree of discreteness. The larger it is, the more times different temperatures appear in the data set referenced during the sth temperature control, which means that the data distribution in the data set referenced during the sth temperature control is more chaotic and the greater the degree of discreteness of the data set.

[0032] S13: Calculate the disturbance range optimization factor of the current time period, where the disturbance range optimization factor is positively correlated with the degree of dispersion.

[0033] There is noise data in the data set, which interferes with the optimization range of the disturbance range during each temperature control. The difference between noise data and normal data is that normal temperature data has a stable change trend, while noise data shows irregular and random fluctuations, which usually deviate from the normal temperature change trend. Therefore, the numerical changes in the data set referenced for each temperature control are analyzed to obtain the corresponding disturbance range optimization factor.

[0034] In the data set, the first difference between each data point and its adjacent data point on the left is calculated, and the second difference between the corresponding data point and its adjacent data point on the right is calculated; if the product of the first difference and the second difference is less than 0, the corresponding data point is used as the reference data point, and the number of reference data points is used as the correlation coefficient of the data set.

[0035] In one embodiment, the expression of the disturbance range optimization factor is: ; In the formula, represents the disturbance range optimization factor corresponding to the sth temperature control, Indicates the discreteness of the data set referenced during the sth temperature control. Indicates the maximum number of monotonically increasing or monotonically decreasing data points in the data set referenced during the sth temperature control. Represents the number of data points in the data set. It represents the preset correlation coefficient of the data set referenced during the s-th temperature control, exp represents the exponential function with e as the base, and norm represents the normalization function.

[0036] In the formula The larger it is, the more discrete the data distribution in the data set corresponding to the sth temperature control is, and the larger the disturbance range required for temperature control is, to ensure that the search flexibility can be maintained in large temperature fluctuations, making it easier to find the global optimal solution or avoid the system from falling into a local extreme value.

[0037] in the formula The fluctuation range of the data in the reference data set during the s-th temperature control is quantified. The larger the value is, the more noise data there may be in the data segment, and the more likely the discreteness of the data set is to be affected by the noise data. Then the corresponding disturbance range during temperature control should be smaller, and the corresponding disturbance range optimization factor should also be smaller. Quantifies the positive and negative attributes of the numerical changes between data points in the data set. The smaller the value, the fewer the number of data points in the data set that are continuously monotonically increasing or monotonically decreasing, which means that the fluctuation range of the data in the data set is larger, and the corresponding disturbance range optimization factor should also be smaller. The larger the value is, the greater the credibility of the data fluctuation in the data set is, which means the corresponding disturbance range optimization factor is smaller.

[0038] In one embodiment, the expression of the disturbance range optimization factor is: ; In the formula, represents the disturbance range optimization factor corresponding to the sth temperature control, Indicates the discreteness of the data set referenced during the sth temperature control. Indicates the maximum number of monotonically increasing or monotonically decreasing data points in the data set referenced during the sth temperature control. Represents the number of data points in the data set. Indicates the correlation coefficient of the preset data set referenced during the sth temperature control. represents the logarithmic function with base e, represents a preset constant, exemplarily, the value of M is 10, and norm represents a normalization function.

[0039] S14: taking the product of the disturbance range optimization factor and the preset disturbance range as the optimal disturbance range.

[0040] The expression for the optimal perturbation range is: ; In the formula, represents the optimal disturbance range during the sth temperature control, It indicates the disturbance range set by the simulated annealing algorithm during temperature intelligent control. Represents the disturbance range optimization factor corresponding to the sth temperature control.

[0041] S2: Use the simulated annealing algorithm to obtain the temperature in the test chamber in the next time period.

[0042] S21: Construct objective function.

[0043] In one embodiment, the objective function is expressed as: ; In the formula, E represents the objective function value, τ represents the preset first weight, represents the preset second weight, q represents the partial discharge amplitude of the test product in the test chamber at the corresponding temperature during the simulated annealing process, v represents the partial discharge frequency of the test product in the test chamber at the corresponding temperature during the simulated annealing process, and N represents the energy consumption of the test chamber at the corresponding temperature during the simulated annealing process, wherein q and v are obtained from the corresponding test product in the historical test process.

[0044] In the objective function, the aging effect of a temperature value within an aging interval is quantified by (q×v). The smaller the value, the worse the aging effect. The larger the objective function value, the worse the priority of the corresponding temperature value in the process of selecting the optimal temperature solution. The larger N in the objective function, the greater the energy consumption required by the corresponding temperature value. The larger the objective function value, the worse the priority of the corresponding temperature value in the process of selecting the optimal temperature solution.

[0045] In one embodiment, the objective function is expressed as: ; In the formula, E represents the objective function value, τ represents the preset first weight, represents the preset second weight, R represents the insulation resistance value of the test product in the test chamber at the corresponding temperature during the simulated annealing process, and N represents the energy consumption of the test chamber at the corresponding temperature during the simulated annealing process.

[0046] In the objective function, the aging effect of a temperature value within an aging range is quantified by the insulation resistance value R. The smaller the value, the worse the aging effect. The larger the objective function value, the worse the priority of the corresponding temperature value in the process of selecting the optimal temperature solution. The larger N in the objective function, the greater the energy consumption required for the corresponding temperature value. The larger the objective function value, the worse the priority of the corresponding temperature value in the process of selecting the optimal temperature solution.

[0047] S22: Calculate the temperature in the test chamber in the next time period using a simulated annealing algorithm.

[0048] Set the initialization parameters in the simulated annealing algorithm, including the initial solution, initial temperature, termination temperature, and cooling rate; start from the initial solution and initial temperature, and enter the iterative loop until the temperature drops to the termination temperature. For example, the initial solution is set to the empirical value of 80°C, the initial temperature is set to 100°C, the termination temperature is preset to 50°C, and the cooling rate is set to 0.96.

[0049] In the iterative cycle, the initial solution is first taken as the current solution, and a new solution is randomly selected within the optimal perturbation range of the current solution. The objective function value of the new solution is taken as the first function value, the objective function value of the current solution is taken as the second function value, and the difference between the first function value and the second function value is taken as the energy difference. In response to the energy difference being less than or equal to 0, the new solution is accepted as the current solution. Otherwise, the new solution is accepted with a certain probability to avoid falling into the local optimal solution too early. Then the temperature is updated according to the set cooling rate, and the search for new solutions continues. When the temperature drops to the termination temperature or the maximum number of iterations is reached, the algorithm ends and outputs the current solution as the optimal solution. The optimal solution is the optimal temperature in the test chamber in the next time period.

[0050] Exemplarily, every 3 minutes is a temperature control cycle. At the end of the 3rd minute, an optimal disturbance range is calculated based on the temperature data within 3 minutes obtained in the data set. The simulated annealing algorithm is used to search for the optimal temperature in the test chamber in the next time period, that is, the optimal temperature in the test chamber in the next temperature control cycle. Then, the temperature in the test chamber is adjusted to the optimal temperature to carry out aging tests on the test products.

[0051] This solution can significantly improve the accuracy and efficiency of the simulated annealing algorithm in intelligent temperature control by analyzing the changing characteristics of the temperature data in the test chamber and obtaining the optimization factor of the perturbation range during each temperature control. Optimizing the perturbation range based on data characteristics avoids excessive perturbation amplitudes that lead to an overly violent search process, and also avoids too small perturbation amplitudes that lead to slow searches. This accelerates the convergence of the algorithm while ensuring search efficiency, making each temperature control process more accurate, avoiding interference caused by data noise, and improving the stability and accuracy of temperature control. In addition, the optimization of the dynamic perturbation range based on the changing characteristics of temperature data can better adapt to different temperature control scenarios, achieve more refined intelligent control, and ultimately improve the computing efficiency and control accuracy of the entire system, reduce energy consumption, and improve the repeatability and reliability of the experiment.

[0052] 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.

[0053] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A test chamber temperature intelligent control method, characterized in that: Includes steps: Calculate the optimal disturbance range in the simulated annealing algorithm, and use the simulated annealing algorithm to obtain the temperature in the test box in the next time period; Among them, the calculation method of the optimal disturbance range is: obtain a data set, the data set includes multiple temperature data in the current time period, calculate the discrete degree of the data in the data set, calculate the disturbance range optimization factor of the current time period, the disturbance range optimization factor is positively correlated with the discrete degree, and take the product of the disturbance range optimization factor and the preset disturbance range as the optimal disturbance range.

2. The method for intelligently controlling the temperature of a test chamber according to claim 1, characterized in that: The expression of the degree of dispersion is: ； In the formula, Indicates the discreteness of the data set referenced during the sth temperature control. Represents the number of data points in the data set. Indicates the Z-Score value of the rth data point in the data set referenced during the sth temperature control. Represents the information entropy value of the data set referenced during the s-th temperature control.

3. The method for intelligently controlling the temperature of a test chamber according to claim 1, characterized in that: The expression of the disturbance range optimization factor is: ； In the formula, represents the disturbance range optimization factor corresponding to the sth temperature control, Indicates the discreteness of the data set referenced during the sth temperature control. Indicates the maximum number of monotonically increasing or monotonically decreasing data points in the data set referenced during the sth temperature control. Represents the number of data points in the data set. It represents the preset correlation coefficient of the data set referenced during the s-th temperature control, exp represents the exponential function with e as the base, and norm represents the normalization function.

4. The method for intelligently controlling the temperature of a test chamber according to claim 1, characterized in that: The expression of the disturbance range optimization factor is: ； In the formula, represents the disturbance range optimization factor corresponding to the sth temperature control, Indicates the discreteness of the data set referenced during the sth temperature control. Indicates the maximum number of monotonically increasing or monotonically decreasing data points in the data set referenced during the sth temperature control. Represents the number of data points in the data set. Indicates the correlation coefficient of the preset data set referenced during the sth temperature control. represents the logarithmic function with base e, Represents a preset constant, and norm represents a normalization function.

5. A test chamber temperature intelligent control method according to claim 3 or 4, characterized in that: The calculation method of correlation coefficient is: Calculate the first difference between each data point and its adjacent data point on the left, and calculate the second difference between the corresponding data point and its adjacent data point on the right; If the product of the first difference and the second difference is less than 0, the corresponding data point is used as the reference data point, and the number of reference data points is used as the correlation coefficient of the data set.

6. The method for intelligently controlling the temperature of a test chamber according to claim 1, characterized in that: Methods for obtaining the temperature in the test box in the next time period using the simulated annealing algorithm include: Randomly generate new solutions from the best perturbation range of the current solution; Construct an objective function and use it to calculate the objective function values ​​of the current solution and the new solution; The difference between the objective function values ​​of the current solution and the new solution is taken as the energy difference; In response to the energy difference being less than or equal to 0, the new solution is accepted as the current solution, otherwise the new solution is accepted with a certain probability.

7. The method for intelligently controlling the temperature of a test chamber according to claim 6, characterized in that: The expression of the objective function is: ; In the formula, E represents the objective function value, τ represents the preset first weight, represents the preset second weight, q represents the partial discharge amplitude of the test product in the test chamber at the corresponding temperature during the simulated annealing process, v represents the partial discharge frequency of the test product in the test chamber at the corresponding temperature during the simulated annealing process, and N represents the energy consumption of the test chamber at the corresponding temperature during the simulated annealing process.

8. The method for intelligently controlling the temperature of a test chamber according to claim 6, characterized in that: The expression of the objective function is: ; In the formula, E represents the objective function value, τ represents the preset first weight, represents the preset second weight, R represents the insulation resistance value of the test product in the test chamber at the corresponding temperature during the simulated annealing process, and N represents the energy consumption of the test chamber at the corresponding temperature during the simulated annealing process.

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