Component temperature control method, device, equipment and readable storage medium

Abstract

The present invention discloses a component temperature control method, device, equipment and readable storage medium, which relates to the field of component testing technology, and includes the following steps: step S10, obtaining the actual temperature value at the current sampling time of the component bench test and the time-domain temperature curve of the component during the road test; step S20, determining the target temperature value of the component bench test based on the time-domain temperature curve of the component during the road test and the current sampling time of the component bench test; step S30, correcting the PID control parameters of the PID controller based on the actual temperature value and the target temperature value; step S40, using the PID controller to adjust the speed of the component cooling fan based on the corrected PID control parameters so that the actual temperature value of the component during the bench test reaches the target temperature value. The present invention can accurately control the temperature of the component during the bench test and improve the reliability of the test results of the component bench test.

Description

Technical Field

[0001] The present invention relates to the technical field of component testing, and in particular to a component temperature control method, device, equipment and readable storage medium. Background Art

[0002] A car is a complex electromechanical hybrid system composed of tens of thousands of parts. To meet the high quality and safety requirements of automobile manufacturers, a series of tests are required on the vehicle and its parts. These tests on automobile parts include: environmental reliability testing, electrical performance testing, functional testing, EMC testing, material testing, and environmental protection testing.

[0003] In addition to the necessary test items mentioned above, some auto parts that are subjected to cyclic stress (shock absorbers, bushings, etc.) also need to undergo durability tests to determine whether their performance meets the requirements.

[0004] However, the existing technology does not consider the impact of temperature on the durability of automobile parts when conducting durability tests on these automobile parts, resulting in inaccurate test results. Summary of the Invention

[0005] Embodiments of the present invention provide a component temperature control method, device, equipment and readable storage medium, which can solve the technical problem that the temperature influencing factor is not considered when performing bench tests on existing components, resulting in insufficient reliability of test results.

[0006] In a first aspect, a component temperature control method is provided, comprising the following steps:

[0007] Obtain the actual temperature value at the current sampling moment during the component bench test and the time domain temperature curve during the component road test;

[0008] Determine the target temperature value of the component during the bench test based on the time domain temperature curve of the component during the road test and the current sampling time of the component during the bench test;

[0009] The PID control parameters of the PID controller are determined according to the actual temperature value and the target temperature value. The speed of the component cooling fan is regulated by the PID controller based on the determined PID control parameters so that the actual temperature value of the component during the bench test reaches the target temperature value.

[0010] In some embodiments, the step of determining a PID control parameter of a PID controller based on the actual temperature value and the target temperature value, and using the PID controller to control the speed of a component cooling fan based on the determined PID control parameter so that the actual temperature value of the component during the bench test reaches the target temperature value includes:

[0011] Calculate the temperature error and the temperature error change rate according to the actual temperature value and the target temperature value;

[0012] Calculate the correction amount of the PID control parameter of the PID controller according to the temperature error, the temperature error change rate and the preset neural network model;

[0013] The correction amount of the PID control parameter of the PID controller is added to the initial value of the PID control parameter of the PID controller to obtain the PID control parameter of the PID controller.

[0014] In some embodiments, the step of calculating the correction amount of the PID control parameter of the PID controller based on the temperature error, the temperature error change rate, and the preset neural network model includes:

[0015] Use the fuzzification layer of the preset neural network model to perform fuzzy processing on the temperature error and the temperature error change rate;

[0016] The fuzzy inference layer of the preset neural network model is used to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then the low-frequency feature processing of the gain temperature signal is performed;

[0017] The output result of the fuzzy reasoning layer is defuzzified using the defuzzification layer of the preset neural network model to obtain the correction amount of the PID control parameters of the PID controller.

[0018] In some embodiments, the step of using the fuzzy inference layer of a preset neural network model to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then performing low-frequency feature processing to increase the temperature signal, includes:

[0019] The double inverse tangent function is used to process the nonlinear characteristics of the supplementary temperature change process.

[0020] In some embodiments, the step of using the fuzzy inference layer of a preset neural network model to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then performing low-frequency feature processing to increase the temperature signal, includes:

[0021] A linear function is used to process the low-frequency characteristics of the gain temperature signal.

[0022] In some embodiments, the step of using the defuzzification layer of the preset neural network model to defuzzify the output result of the fuzzy inference layer to obtain the correction value of the PID control parameter of the PID controller includes:

[0023] The output of the fuzzy inference layer is defuzzified using a cubic function.

[0024] In some embodiments, the step of obtaining the actual temperature value at the current sampling moment during the component bench test includes:

[0025] The actual temperature value at the current sampling moment during the component bench test is obtained in real time through thermocouple temperature sensors.

[0026] In a second aspect, a component temperature control device is provided, comprising:

[0027] An acquisition unit, configured to acquire an actual temperature value at a current sampling moment during a component bench test and a time-domain temperature curve during a component road test;

[0028] a determination unit, the determination unit being configured to determine a target temperature value during a component bench test based on a time-domain temperature curve during a component road test and a current sampling time during a component bench test;

[0029] The control unit is used to determine the PID control parameters of the PID controller according to the actual temperature value and the target temperature value, and use the PID controller to control the speed of the component cooling fan based on the determined PID control parameters so that the actual temperature value of the component during the bench test reaches the target temperature value.

[0030] In a third aspect, a computer device is provided, comprising: a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the aforementioned component temperature control method.

[0031] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed by a computer, the computer executes the aforementioned component temperature control method.

[0032] The beneficial effects brought about by the technical solution provided by the present invention include:

[0033] Embodiments of the present invention provide a component temperature control method, apparatus, device, and readable storage medium. The method first obtains the actual temperature value at the current sampling time during a component bench test and the time-domain temperature curve during a component road test. The method then determines the target temperature value during the component bench test based on the time-domain temperature curve during the component road test and the current sampling time during the component bench test. Finally, the PID control parameters of a PID controller are determined based on the actual temperature value and the target temperature value. The PID controller is used to regulate the speed of the component cooling fan so that the actual temperature value during the component bench test reaches the target temperature value. Specifically, the method uses a PID controller to regulate the speed of the component cooling fan, thereby accurately controlling the temperature of the component during bench testing and improving the reliability of the test results of the component bench test. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A schematic flow chart of a component temperature control method provided by an embodiment of the present invention;

[0036] Figure 2 A time-domain temperature curve showing the temperature change over time of a component provided by an embodiment of the present invention during a road test on a vehicle;

[0037] Figure 3 Implementation provided by the embodiment of the present invention Figure 1 A flow chart of step S30;

[0038] Figure 4 Implementation provided by the embodiment of the present invention Figure 3 A flow chart of step S302;

[0039] Figure 5 Implementation provided by the embodiment of the present invention Figure 3 A structural diagram of step S302;

[0040] Figure 6 A schematic structural diagram of a component temperature control device provided by an embodiment of the present invention;

[0041] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0043] An embodiment of the present invention provides a component temperature control method, which can solve the technical problem that the temperature influence factor is not considered when performing bench tests on existing components, resulting in inaccurate test results.

[0044] See also Figure 1As shown, an embodiment of the present invention provides a component temperature control method, comprising the following steps:

[0045] Step S10 , obtaining the actual temperature value at the current sampling moment during the component bench test and the time domain temperature curve during the component road test.

[0046] Specifically, the step of obtaining the actual temperature value at the current sampling moment during the component bench test includes:

[0047] Thermocouple temperature sensors are used to measure the actual temperature of components at the current sampling time during bench testing. These sensors can be attached to components and convert the component's temperature during the bench test into a voltage value in real time. The voltage value is then used to reverse-calculate the actual temperature of the component at the current sampling time during the bench test.

[0048] The step of obtaining the time-domain temperature curve of a component during a road test includes:

[0049] The components are installed on the vehicle for road testing. The temperature of the components is measured in real time by thermocouple temperature sensors to obtain a time-domain temperature curve showing the temperature change of the components over time.

[0050] Step S20 , determining a target temperature value during the component bench test based on the time-domain temperature curve of the component road test and the current sampling time during the component bench test.

[0051] Based on the time domain temperature curve, when the current sampling time of the component bench test corresponds to a certain time t in the time domain temperature curve, the temperature value corresponding to the time t in the time domain temperature curve is the target temperature value. Figure 2 As shown, Figure 2 The dotted line in the figure is the time domain temperature curve of the component road test.

[0052] Step S30, determining the PID control parameters of the PID controller according to the actual temperature value and the target temperature value, and using the PID controller based on the determined PID control parameters to regulate the speed of the component cooling fan so that the actual temperature value of the component during the bench test reaches the target temperature value.

[0053] Specifically, a component cooling fan can be set up around the component bench test, and the PID controller can be electrically connected to the component cooling fan. The PID control parameters of the PID controller can be determined according to the actual temperature value and the target temperature value, and the speed of the component cooling fan can be accurately controlled to make the actual temperature value during the component bench test approach the target temperature value; among them, the PID control parameter is the proportional coefficient K P , integral coefficient K I and differential coefficient K DAlternatively, a PID controller may be provided by a PLC or a single-chip microcomputer, which accurately controls the speed of the component cooling fan by outputting a PWM (pulse width modulation) signal based on the PID control parameters. Figure 2 As shown, Figure 2 The solid line in the figure is the time-domain temperature curve of the component during bench test, which is approximately consistent with the time-domain temperature curve of the component during road test.

[0054] The component temperature control method in an embodiment of the present invention first obtains the actual temperature value at the current sampling time during the component bench test and the time-domain temperature curve during the component road test; then, based on the time-domain temperature curve during the component road test and the current sampling time during the component bench test, determines the target temperature value during the component bench test; finally, based on the actual temperature value and the target temperature value, determines the PID control parameters of the PID controller, and uses the PID controller to adjust the speed of the component cooling fan so that the actual temperature value during the component bench test reaches the target temperature value. In other words, the present invention uses a PID controller to adjust the speed of the component cooling fan, thereby accurately controlling the temperature of the component during the bench test and improving the reliability of the test results of the component bench test.

[0055] As an optional implementation, in one embodiment of the invention, see Figure 3 As shown, the steps of determining the PID control parameters of the PID controller according to the actual temperature value and the target temperature value, and using the PID controller to control the speed of the component cooling fan based on the determined PID control parameters so that the actual temperature value of the component during the bench test reaches the target temperature value include:

[0056] Step S301 : Calculate the temperature error and the temperature error change rate according to the actual temperature value and the target temperature value.

[0057] The calculation formulas for temperature error ΔT and temperature error change rate ΔT' are as follows:

[0058] ΔT=T d (t)-T r (t);

[0059] ΔT'=(ΔT(t)-ΔT(tD)) / D;

[0060] Where: T d (t) is the target temperature value; T r (t) is the actual temperature value; t is the current sampling time; D is the sampling period.

[0061] Step S302 , calculating a correction amount of a PID control parameter of the PID controller according to the temperature error, the temperature error change rate, and a preset neural network model.

[0062] Specifically, see Figure 4 and Figure 5 As shown, the step of calculating the correction amount of the PID control parameter of the PID controller according to the temperature error, the temperature error change rate and the preset neural network model includes:

[0063] Step S3021: Use the fuzzification layer of the preset neural network model to perform fuzzy processing on the temperature error and the temperature error change rate.

[0064] The fuzzy logic of the two input variables, temperature error and temperature error change rate, is shown as follows:

[0065]

[0066] Where: I k (t) is the fuzzy output value; k is the number of bits of the input variable. When the input is ΔT, k = 1; when the input is ΔT', k = 2; x is the intermediate input. When k = 1, When k=2, a is the upper threshold value, when k=1, a is 10%, when k=2, a is 5%; b is the lower threshold value, when k=1, b is 5%, when k=2, b is 2%.

[0067] Step S3022: Use the fuzzy inference layer of the preset neural network model to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then perform low-frequency feature processing on the gain temperature signal.

[0068] The double inverse tangent function is used to process the nonlinear characteristics of the temperature change process, and the linear function is used to process the low-frequency characteristics of the gain temperature signal. The specific formula is as follows:

[0069]

[0070] Where: for Figure 5 The output value of the jth node (j=1-7) in the first layer of the middle fuzzy inference layer;

[0071] for Figure 5 The output value of the mth node (m=1-7) in the second layer of the middle fuzzy inference layer;

[0072] for Figure 5 The hidden layer weight coefficient of the connection between the k-th input value of the first layer of the fuzzy inference layer and the j-th node;

[0073] for Figure 5 The hidden layer weight coefficient of the connection between the j-th input value of the second layer of the fuzzy inference layer and the m-th node;

[0074] f 1 is the double inverse tangent function. For any input z, the calculation formula is:

[0075] f 2 It is a linear function. For any input z, the calculation formula is: 2 (z) = 2.5z;

[0076] y 0j 、y 1j They are Figure 5 The threshold of the j-th node in the first and second layers of the fuzzy inference layer.

[0077] Step S3023: Defuzzify the output result of the fuzzy reasoning layer using the defuzzification layer of the preset neural network model to obtain the correction amount of the PID control parameter of the PID controller.

[0078] The linear function is used to defuzzify the output of the fuzzy inference layer. The specific formula is as follows:

[0079]

[0080] Where: for Figure 5 The output value of the nth node (n=1-3) in the non-fuzzified layer;

[0081] f 3 It is a cubic function. For any input z, the calculation formula is:

[0082] f 3 (z)=5.6z 3 +4.8z 2 +8.5;

[0083] for Figure 5 The hidden layer weight coefficient connecting the mth input value of the non-fuzzified layer and the nth node;

[0084] y 3n for Figure 5 The threshold for the nth node in the unfuzzified layer.

[0085] Step S303: The correction value (ΔK P , ΔK I , ΔK D ) is added to the initial value of the PID control parameter of the PID controller to obtain the PID control parameter of the PID controller.

[0086] The embodiment of the present invention is equivalent to designing a neural network-based fuzzy PID controller, whose inputs are the temperature error and the temperature error change rate, and whose output is the speed control signal for the component cooling fan. Certain rules are set in the fuzzy PID controller to specify the regulatory relationship between the temperature error and the temperature error change rate and the fan speed. For example, if the temperature error is large and changes in a positive direction (the temperature error change rate), the fan speed is increased; if the temperature error is small and changes in a negative direction, the fan speed is reduced. The speed adjustment amount for the component cooling fan is obtained through fuzzy reasoning, and the fan speed is finally controlled according to the control signal output by the fuzzy PID controller. In other words, the embodiment of the present invention proposes a PID control strategy that integrates a neural network. This control strategy has the characteristics of fast temperature regulation response and good universality. It can adapt to the problem of sudden temperature changes in components and improve the accuracy of temperature control.

[0087] See also Figure 6 As shown, an embodiment of the present invention further provides a component temperature control device, including: an acquisition unit, a determination unit and a regulation unit.

[0088] The acquisition unit is used to acquire the actual temperature value at the current sampling moment during the component bench test and the time domain temperature curve during the component road test.

[0089] The determining unit is used to determine a target temperature value during a component bench test according to a time domain temperature curve during a component road test and a current sampling moment during a component bench test.

[0090] The control unit is used to determine the PID control parameters of the PID controller according to the actual temperature value and the target temperature value, and use the PID controller based on the determined PID control parameters to control the speed of the component cooling fan so that the actual temperature value of the component during the bench test reaches the target temperature value.

[0091] In the component temperature control device of an embodiment of the present invention, the acquisition unit first acquires the actual temperature value at the current sampling time during the component bench test and the time-domain temperature curve during the component road test; the determination unit then determines the target temperature value during the component bench test based on the time-domain temperature curve during the component road test and the current sampling time during the component bench test; and the control unit finally determines the PID control parameters of the PID controller based on the actual temperature value and the target temperature value, and uses the PID controller to control the speed of the component cooling fan so that the actual temperature value during the component bench test reaches the target temperature value. That is, the present invention uses the PID controller to control the speed of the component cooling fan, which can accurately control the temperature of the component during the bench test and improve the reliability of the test results of the component bench test.

[0092] An embodiment of the present invention also provides a computer device, comprising: a memory, a processor, and a network interface connected via a system bus, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement all or part of the steps of the aforementioned component temperature control method.

[0093] Among them, the network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 7 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0094] The processor may be a CPU, other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor, or any conventional processor. The processor is the control center of a computer device, connecting various parts of the entire computer device using various interfaces and lines.

[0095] The memory can be used to store computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application required for a function (such as a video playback function, an image playback function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as video data, image data, etc.). In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0096] In one embodiment of the invention, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0097] Step S10, obtaining the actual temperature value at the current sampling moment during the component bench test and the time domain temperature curve during the component road test;

[0098] Step S20, determining a target temperature value for the component during the bench test based on the time-domain temperature curve of the component during the road test and the current sampling time of the component during the bench test;

[0099] Step S30, determining the PID control parameters of the PID controller according to the actual temperature value and the target temperature value, and using the PID controller based on the determined PID control parameters to regulate the speed of the component cooling fan so that the actual temperature value of the component during the bench test reaches the target temperature value.

[0100] As an optional implementation, in one embodiment of the invention, the step of determining a PID control parameter of a PID controller according to the actual temperature value and the target temperature value, and using the PID controller to control the speed of a component cooling fan based on the determined PID control parameter so that the actual temperature value of the component during the bench test reaches the target temperature value includes:

[0101] Step S301, calculating the temperature error and the temperature error change rate according to the actual temperature value and the target temperature value;

[0102] Step S302, calculating a correction amount of a PID control parameter of a PID controller according to the temperature error, the temperature error change rate, and a preset neural network model;

[0103] Step S303 : adding the correction amount of the PID control parameter of the PID controller to the initial value of the PID control parameter of the PID controller to obtain the PID control parameter of the PID controller.

[0104] As an optional implementation manner, in one embodiment of the invention, the step of calculating the correction amount of the PID control parameter of the PID controller based on the temperature error, the temperature error change rate, and the preset neural network model includes:

[0105] Step S3021, using the fuzzification layer of the preset neural network model to perform fuzzy processing on the temperature error and the temperature error change rate;

[0106] Step S3022: Using the fuzzy inference layer of the preset neural network model, the output result of the fuzzification layer is processed to supplement the nonlinear characteristics of the temperature change process, and then the low-frequency characteristics of the gain temperature signal are processed;

[0107] Step S3023: Defuzzify the output result of the fuzzy reasoning layer using the defuzzification layer of the preset neural network model to obtain the correction amount of the PID control parameter of the PID controller.

[0108] As an optional implementation manner, in one embodiment of the invention, the step of using the fuzzy inference layer of a preset neural network model to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then performing low-frequency feature processing on the gain temperature signal includes:

[0109] The double inverse tangent function is used to process the nonlinear characteristics of the supplementary temperature change process.

[0110] As an optional implementation manner, in one embodiment of the invention, the step of using the fuzzy inference layer of a preset neural network model to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then performing low-frequency feature processing on the gain temperature signal includes:

[0111] A linear function is used to process the low-frequency characteristics of the gain temperature signal.

[0112] As an optional implementation manner, in one embodiment of the invention, the step of using the defuzzification layer of the preset neural network model to defuzzify the output result of the fuzzy inference layer to obtain the correction amount of the PID control parameter of the PID controller includes:

[0113] The output of the fuzzy inference layer is defuzzified using a cubic function.

[0114] As an optional implementation manner, in one embodiment of the invention, the step of obtaining the actual temperature value at the current sampling moment during the component bench test includes:

[0115] The actual temperature value at the current sampling moment during the component bench test is obtained in real time through thermocouple temperature sensors.

[0116] The embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, all or part of the steps of the aforementioned component temperature control method are implemented.

[0117] The embodiments of the present invention implement all or part of the aforementioned processes, and may also be accomplished by instructing related hardware through a computer program. The computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the aforementioned methods may be implemented. The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0118] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, servers, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0119] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0120] The serial numbers in the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0121] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A component temperature control method, characterized in that: The following steps are involved: Obtain the actual temperature value at the current sampling moment during the component bench test and the time domain temperature curve during the component road test; Determine the target temperature value of the component during the bench test based on the time domain temperature curve of the component during the road test and the current sampling time of the component during the bench test; Calculate the temperature error and the temperature error change rate according to the actual temperature value and the target temperature value; Calculate the correction amount of the PID control parameter of the PID controller according to the temperature error, the temperature error change rate and the preset neural network model; The correction amount of the PID control parameter of the PID controller is added to the initial value of the PID control parameter of the PID controller to obtain the PID control parameter of the PID controller; The step of calculating the correction amount of the PID control parameter of the PID controller according to the temperature error, the temperature error change rate and the preset neural network model includes: Use the fuzzification layer of the preset neural network model to perform fuzzy processing on the temperature error and the temperature error change rate; The fuzzy inference layer of the preset neural network model is used to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then the low-frequency feature processing of the gain temperature signal is performed; The output result of the fuzzy reasoning layer is defuzzified using the defuzzification layer of the preset neural network model to obtain the correction amount of the PID control parameters of the PID controller.

2. The component temperature control method according to claim 1, characterized in that: The step of using the fuzzy inference layer of the preset neural network model to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then performing low-frequency feature processing on the gain temperature signal includes: The double inverse tangent function is used to process the nonlinear characteristics of the supplementary temperature change process.

3. The component temperature control method according to claim 1, characterized in that: The step of using the fuzzy inference layer of the preset neural network model to perform nonlinear feature processing on the output result of the fuzzification layer during the temperature change process, and then performing low-frequency feature processing on the gain temperature signal includes: A linear function is used to process the low-frequency characteristics of the gain temperature signal.

4. The component temperature control method according to claim 1, characterized in that: The step of using the defuzzification layer of the preset neural network model to defuzzify the output result of the fuzzy inference layer to obtain the correction amount of the PID control parameter of the PID controller includes: The output of the fuzzy inference layer is defuzzified using a cubic function.

5. The component temperature control method according to claim 1, characterized in that: The step of obtaining the actual temperature value at the current sampling moment during the component bench test includes: The actual temperature value at the current sampling moment during the component bench test is obtained in real time through thermocouple temperature sensors.

6. A component temperature control device, used to execute the component temperature control method according to claim 1, characterized in that: include: An acquisition unit, configured to acquire an actual temperature value at a current sampling moment during a component bench test and a time-domain temperature curve during a component road test; a determination unit, the determination unit being configured to determine a target temperature value during a component bench test based on a time-domain temperature curve during a component road test and a current sampling time during a component bench test; The control unit is used to determine the PID control parameters of the PID controller according to the actual temperature value and the target temperature value, and use the PID controller to control the speed of the component cooling fan based on the determined PID control parameters so that the actual temperature value of the component during the bench test reaches the target temperature value.

7. A computer device, characterized in that: include: A memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the component temperature control method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a computer, the computer is enabled to execute the component temperature control method according to any one of claims 1 to 5.

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