Control parameter judgment method, system and product

Abstract

The embodiment of the invention provides a control parameter judgment method and system and a product, and belongs to the technical field of computer systems.The method comprises the steps that current control parameters of a fan heat dissipation strategy based on the proportional-integral-derivative control process are obtained, and a pressurization test process is executed on any target component, acquiring a temperature change data set of any target part in the target time period; according to an evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, an evaluation score of the current control parameter is generated, the evaluation temperature value is a temperature balance point reached after the fan heat dissipation strategy is executed, and the temperature set value is the maximum temperature corresponding to any target component; and when the evaluation score meets a target condition, adding the current control parameter and the evaluation score into a control parameter data set. The embodiment of the invention aims to more accurately judge whether the PID control parameters are reasonable or not.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of computer systems, in particular, to a control parameter judgment method, system and product. BACKGROUND

[0002] PID control refers to a control process of proportion, integral and differential of error generated by comparing real-time data collected from a controlled object with a given value. Currently, PID control is the main technical means for fan heat dissipation regulation and control in computer systems such as servers.

[0003] The regulation and control performance of PID control is closely related to three parameters Kp, Ki and Kd. The most widely used in the heat dissipation strategy of computer systems is manual setting of PID parameters. Manual setting of PID parameters needs to rely on some experience data, determine a Kp, Ki and Kd selection range according to the experience data, then select a set of parameters in the selection range by manual and configure, then debug the whole machine based on the set of parameters, and based on the temperature change curve of the system obtained after the debugging, the test engineer subjectively judges the heat dissipation effect of the fan based on experience, and determines whether the PID parameter setting is reasonable and repeatedly adjusts.

[0004] However, this process of subjectively judging whether the PID parameter setting is reasonable based on the temperature change curve occupies a lot of human resources, and takes a long time, and the judgment result is affected by the experience of the test personnel, and is relatively subjective, the error of the judgment and evaluation result of the configured PID parameters is relatively large, and then it may not be possible to achieve the optimal PID parameter regulation and control effect. SUMMARY

[0005] Embodiments of the present application provide a control parameter judgment method, system and product, which aims to more accurately judge whether the PID control parameter is reasonable.

[0006] In a first aspect, the embodiments of the present application provide a control parameter judgment method, applied to a computer system, the method comprising: obtaining current control parameters of a fan heat dissipation strategy based on a proportional integral differential control process, performing a pressure test process on any target component, and collecting temperature change data sets of any target component in a target time period; generating an evaluation score of the current control parameters according to an evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, the evaluation temperature value being a temperature equilibrium point reached after the fan heat dissipation strategy is executed, and the temperature set value being a maximum temperature corresponding to any target component; When the evaluation score meets a target condition, the current control parameters and the evaluation score are added to a control parameter data set.

[0007] Optionally, the pressurization test procedure is performed on any target component, and a temperature change data set of any target component within a target time period is collected, including: The temperature data of any target component at multiple time points within the target time period is collected, starting from the moment when the pressurization test procedure is performed, and a temperature change data set of any target component within the target time period is constructed.

[0008] Optionally, after collecting the temperature change data set of any target component within the target time period, the method further includes: An evaluation temperature value satisfying an evaluation condition is determined in the temperature change data set. Wherein, the evaluation condition is that in a plurality of time periods in sequence, the maximum temperature in the first time period is greater than the maximum temperature in each of the remaining time periods, and the maximum temperature in the first time period is greater than or equal to a preset temperature threshold, and the maximum temperature in the first time period is taken as the evaluation temperature value.

[0009] Optionally, determining an evaluation temperature value satisfying an evaluation condition in the temperature change data set includes: A temperature sliding window is set in the temperature change data set, which includes a plurality of time periods with the same time length and in time sequence adjacent to each other. The temperature sliding window is slid from the first time point of the temperature change data set as the starting point, and the maximum temperature contained in each of the plurality of time periods in the temperature sliding window is obtained respectively. It is judged whether the maximum temperature of the first time period in the plurality of time periods satisfies the evaluation condition. When the evaluation condition is satisfied, the maximum temperature of the first time period is taken as the evaluation temperature value.

[0010] Optionally, after judging whether the maximum temperature of the first time period in the plurality of time periods satisfies the evaluation condition, the method further includes: When the maximum temperature of the first time period in the plurality of time periods does not satisfy the evaluation condition, the temperature sliding window is slid by a unit time length in the temperature change data set, and whether there is an evaluation temperature value satisfying the evaluation condition is continued to be judged according to the temperature data in the current temperature sliding window. Wherein, the time length of the unit time length is the same as the time length of any time period in the plurality of time periods.

[0011] Optionally, after the temperature sliding window in the temperature change data set is slid by a unit of time and it is determined whether there is an evaluation temperature value meeting the evaluation condition according to the temperature data in the current temperature sliding window, the method further comprises: When the temperature value at the last time in the temperature change data set is included in the current temperature sliding window and there is no evaluation temperature meeting the evaluation condition in the current temperature sliding window, the temperature value at the last time in the temperature change data set is taken as the evaluation temperature value.

[0012] Optionally, the evaluation score of the current control parameter is generated according to the evaluation temperature value determined in the temperature change data set and the temperature set value corresponding to the target component, comprising: When the evaluation temperature value is less than or equal to the temperature set value, the evaluation score is calculated according to the time corresponding to the evaluation temperature value, and the evaluation score is used to represent the speed of reaching the temperature balance point of the target component after the fan cooling strategy of the current control parameter is executed.

[0013] Optionally, the evaluation score of the current control parameter is generated according to the evaluation temperature value determined in the temperature change data set and the temperature set value corresponding to the target component, further comprising: When the evaluation temperature value is greater than the temperature set value, a fixed value is taken as the evaluation score of the current control parameter.

[0014] Optionally, the evaluation score of the current control parameter is generated according to the evaluation temperature value determined in the temperature change data set and the temperature set value corresponding to the target component, comprising: The evaluation temperature value and the temperature set value corresponding to the target component are input into an evaluation model. When the evaluation model determines that the evaluation temperature value is less than or equal to the temperature set value, the evaluation score is calculated and output according to the time corresponding to the evaluation temperature value and a heat dissipation risk and power consumption balance weight coefficient, and the heat dissipation risk and power consumption balance weight coefficient is used to limit the power consumption and rotating speed of the fan. When the evaluation model determines that the evaluation temperature value is greater than the temperature set value, a fixed value is output as the evaluation score of the current control parameter.

[0015] Optionally, before the evaluation temperature value and the temperature set value corresponding to the target component are input into the evaluation model, the method further comprises: The time corresponding to the evaluation temperature value is normalized. The time corresponding to the evaluation temperature value after the normalization is input into the evaluation model.

[0016] Optionally, the evaluation model generates an evaluation score according to the following formula:

[0017] wherein, is the evaluation score; T1 is the evaluation temperature value; t is a time corresponding to the normalized evaluation temperature value; is the heat dissipation risk and power consumption balance weight coefficient, ∈[0, 1]; is the temperature setting value; is the fitting coefficient; is the fixed value, ≥2.

[0018] Optionally, after generating the evaluation score of the current control parameter according to the evaluation temperature value determined in the temperature change data set and the temperature setting value corresponding to the target component, the method further comprises: when the evaluation score does not satisfy a target condition, adding the current control parameter and the evaluation score in a parameter log data set.

[0019] Optionally, after generating the evaluation score of the current control parameter according to the evaluation temperature value determined in the temperature change data set and the temperature setting value corresponding to the target component, the method further comprises: judging whether the evaluation score of the current control parameter is less than or equal to a score threshold value; when the evaluation score is less than or equal to the score threshold value, the evaluation score satisfies the target condition; when the evaluation score is greater than the score threshold value, the evaluation score does not satisfy the target condition.

[0020] Optionally, the method further comprises: in response to a coefficient adjustment operation, obtaining an updated heat dissipation risk and power consumption balance weight coefficient; replacing the current heat dissipation risk and power consumption balance weight coefficient in the evaluation model with the updated heat dissipation risk and power consumption balance weight coefficient.

[0021] Optionally, obtaining the current control parameter of the fan heat dissipation strategy based on the proportional-integral-derivative control process comprises: listening to a change event of a control parameter of the fan heat dissipation strategy based on the proportional-integral-derivative control process; when the change event of the control parameter occurs, obtaining the current control parameter corresponding to the fan heat dissipation strategy based on the proportional-integral-derivative control process.

[0022] Optionally, the method further comprises: In response to the control parameter configuration operation, obtaining the control parameter to be configured; configuring the current control parameter in the fan heat dissipation strategy based on the proportional-integral-derivative control process as the control parameter to be configured, and triggering a change event of the control parameter.

[0023] In a second aspect, an embodiment of the present application provides a computer system, wherein a fan heat dissipation strategy based on a proportional-integral-derivative control process is applied, and the computer system is configured to execute the control parameter judgment method in the first aspect.

[0024] In a third aspect, an embodiment of the present application provides a computer device, comprising at least one processor and a memory, wherein the memory stores a computer program capable of running on the processor, and the processor executes the computer program to execute the control parameter judgment method in the first aspect.

[0025] In a fourth aspect, an embodiment of the present application provides a non-volatile readable storage medium, wherein the non-volatile readable storage medium stores a computer program, and the computer program is executed by a processor to execute the control parameter judgment method in the first aspect.

[0026] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the control parameter judgment method in the first aspect.

[0027] Advantages: obtaining a current control parameter of a fan heat dissipation strategy based on a proportional-integral-derivative control process, performing a pressure test process on any target component, collecting temperature change data set of any target component in a target time period, generating an evaluation score of the current control parameter according to an evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, and adding the current control parameter and the evaluation score in a control parameter data set when the evaluation score meets a target condition.

[0028] This method provides an automated process for evaluating the current control parameters in a fan cooling strategy based on proportional-integral-derivative (PID) control. It obtains a dataset of temperature changes of the target component within a target time period during the execution of the fan cooling strategy under the current control parameters, and selects the temperature equilibrium point reached after the strategy's execution as the evaluation temperature value. Based on the evaluation temperature value and the target component's temperature setpoint, an evaluation score corresponding to the current control parameter is obtained. Then, the current control parameter with the evaluation score that meets the target conditions is added to the control parameter dataset. Compared to subjective evaluation based on cooling results, this method not only more accurately determines the rationality of PID control parameters but also improves the efficiency of the control parameter evaluation process. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0030] Figure 1 A flowchart illustrating the steps of the method for determining control parameters provided in an embodiment of this application is shown. Figure 2 This application provides a flowchart illustrating the steps for obtaining an evaluation temperature value that meets the evaluation criteria, as illustrated in an embodiment of the present application. Figure 3 A flowchart illustrating the steps for evaluating scores provided in an embodiment of this application is shown; Figure 4 This diagram illustrates the rotational speeds of the target component and the fan under different heat dissipation risk and power consumption balance weighting coefficients provided in the embodiments of this application. Figure 5 A functional block diagram of the control parameter determination device provided in an embodiment of this application is shown; Figure 6 A schematic diagram of a computer device provided in an embodiment of this application is shown; Figure 7 A schematic diagram of a non-volatile readable storage medium provided in an embodiment of this application is shown; Figure 8 A schematic diagram of a computer program product provided in an embodiment of this application is shown. Detailed Implementation

[0031] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0032] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the various embodiments of the present application will be described in detail below with reference to the drawings. However, those skilled in the art can understand that, in the various embodiments of the present application, many technical details are presented in order to make the readers better understand the present application. However, the technical solutions claimed by the present application can be implemented even without these technical details and based on various changes and modifications of the following embodiments. The division of the following various embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the various embodiments can be combined and referenced with each other without contradiction.

[0033] PID: Proportional Integral Derivative, a control system that controls the proportion, integral and derivative of the error generated by comparing the real-time data collected by the controlled object with the given value; CPU: Central Processing Unit, central processor; BMC: Baseboard Management Controller, baseboard management controller.

[0034] PID control refers to the control process of the proportion, integral and derivative of the error generated by comparing the real-time data collected by the controlled object with the given value. For example, when the components such as CPU in the computer system are running, heat will be generated. In order to avoid the over-temperature of the components affecting the normal use, a fan needs to be set in the computer system to dissipate heat. Currently, PID control is the main technical means for the heat dissipation regulation of computer systems such as servers.

[0035] In actual application, the PID controller includes proportional (proportional), integral (integral) and derivative (derivative) three parts, and its control principle is relatively simple. In use, three parameters Kp, Ki and Kd are set, wherein the output of the proportional (P) controller is proportional to the input error signal, the output of the integral (I) controller is proportional to the integral of the input error signal, and the output of the derivative (D) controller is proportional to the derivative of the input error signal (i.e. the rate of change of the error). The three parameters assist each other to achieve a relatively ideal control effect.

[0036] However, the regulation performance of PID control is closely related to three parameters Kp, Ki and Kd. If the three PID control parameters do not match the input and output of the computer system, that is, the PID control parameters are not reasonably set, various problems may occur, such as slow system convergence, large system adjustment process fluctuation, fast and slow fan speed, poor system man-machine efficiency, and even component over-temperature due to weak heat dissipation effect, causing serious consequences.

[0037] At present, the most widely used heat dissipation strategy for computer systems is manual PID parameter setting. Manual PID parameter setting needs to rely on some experience data to determine a Kp, Ki and Kd selection range, and then a set of parameters are selected in the selection range by manual configuration, and then the system is debugged based on the set of parameters. Based on the temperature change curve obtained after debugging, the test engineer subjectively judges the fan heat dissipation effect based on experience, and determines whether the PID parameter setting is reasonable and repeatedly adjusts.

[0038] However, this process of subjectively evaluating whether the PID parameter setting is reasonable based on the temperature change curve occupies a lot of human resources, takes a long time, and the judgment result is influenced by the experience of the test personnel, is relatively subjective, and the error of the judgment result of the configured PID parameters is large, which may further lead to the failure to achieve the optimal PID parameter regulation effect.

[0039] Therefore, in order to more accurately determine whether the PID control parameters are reasonable, the embodiment of the present application provides a control parameter judgment method.

[0040] Referring to Figure 1 , a step flowchart of a control parameter judgment method provided by the embodiment of the present application is shown, the method is applied to a computer system, a fan heat dissipation strategy based on a proportional integral differential control process is applied in the computer system, for example, a fan heat dissipation strategy is set in the BMC of the computer system to dissipate heat for multiple components of the computer system, and the speed of the fan is controlled by PID in the fan heat dissipation strategy; the method can specifically include the following steps: S101: Obtain the current control parameters of the fan heat dissipation strategy based on the proportional integral differential control process, perform a pressure test process on any target component, and collect the temperature change data set of any target component within a target time period.

[0041] In a computer system such as a server, various components such as CPUs and the like generate heat during operation, and a fan cooling strategy is used to cool the components in the server to avoid the operating temperature of each component being too high to cause failure. The fan cooling strategy is based on a proportional-integral-derivative control process, i.e., a PID control. In the PID control process, three parameters are included: a proportional coefficient parameter Kp, an integral coefficient parameter Ki, and a derivative coefficient parameter Kd. The numerical values of the three parameters need to be configured, evaluated, and debugged.

[0042] A user can customize the settings of the control parameters such as the proportional coefficient parameter Kp, the integral coefficient parameter Ki, and the derivative coefficient parameter Kd according to the actual application requirements.

[0043] In a feasible implementation, in the process of obtaining the current control parameters of the fan cooling strategy based on the proportional-integral-derivative control process, a change event of the control parameters of the fan cooling strategy based on the proportional-integral-derivative control process is monitored in real time. In response to a control parameter configuration operation of a user, a control parameter to be configured is obtained, the current control parameter in the fan cooling strategy based on the proportional-integral-derivative control process is configured as the control parameter to be configured, a change event of the control parameter is triggered, and the current control parameters of the fan cooling strategy based on the proportional-integral-derivative control process can be obtained at this time.

[0044] When the current control parameters of the fan cooling strategy change, a stress test process is performed on any target component to generate a temperature control result of the fan cooling strategy under the current control parameters. For example, a stress instruction can be sent to the target component. For example, when the target component is a CPU, a stress instruction can be sent to the CPU to make the CPU run at full capacity. After the CPU responds to the stress instruction and runs at full capacity, the temperature of the CPU rises, and the fan cooling strategy of the computer system is triggered. Based on the PID control, the fan is started and the speed of the fan is controlled to cool the CPU.

[0045] In this embodiment, the temperature control result of any target component is collected to obtain a temperature change data set of any target component in a target time period. Specifically, the temperature data of any target component at multiple time points in the target time period is collected from the starting time point of the stress test process to construct the temperature change data set of any target component in the target time period.

[0046] For example, the target time period can be set to 600s, and the temperature data of multiple time points within 600s is collected to obtain the temperature change of the target component. In actual implementation, in order to ensure the richness of the collected data and avoid missing the temperature balance point reached after the fan cooling strategy is executed, the maximum collection interval during the temperature data collection process is not greater than 2 seconds / time, and the average collection time interval is not greater than 1.2 seconds / time. If the target time period of the entire data collection process is 600s, a maximum of 600 pieces of temperature change data can be recorded, and a minimum of 500 pieces of temperature change data cannot be less than 500 pieces of temperature change data.

[0047] Further, the temperature data in the temperature change data set can also be processed. Specifically, any temperature data in the temperature change data set is rounded, for example, any temperature data is rounded according to the rounding rule.

[0048] Any target component is provided with a temperature set value according to its actual application. The temperature set value refers to the maximum temperature or the highest temperature that the target component allows to reach in the actual application process. If the temperature exceeds this temperature, the target component may be damaged or even destroyed. The temperature set value can be a set value set when the target component is shipped, or a temperature value with a margin set for the safety of the component in the actual application process. For example, assuming that the highest temperature of a component is 100 degrees, the temperature set value can be set to 80 degrees.

[0049] S102: generating an evaluation score of the current control parameter according to the evaluation temperature value determined in the temperature change data set and the temperature set value corresponding to the target component, the evaluation temperature value being the temperature balance point reached after the fan cooling strategy is executed, and the temperature set value being the maximum temperature corresponding to any target component.

[0050] After the temperature change data set of any target component is collected, the evaluation temperature value satisfying the evaluation condition in the temperature change data set needs to be determined first. The evaluation condition is that in the plurality of time periods in sequence, the maximum temperature in the first time period is greater than the maximum temperature in each of the remaining time periods, and the maximum temperature in the first time period is greater than or equal to the preset temperature threshold. When the maximum temperature in the first time period is greater than or equal to the preset temperature threshold, the maximum temperature in the first time period is taken as the evaluation temperature value. The temperature threshold can be set according to the actual application requirement. For example, the temperature threshold can be set to the temperature set value-1.

[0051] The evaluation temperature value is a temperature balance point reached after the fan cooling strategy is executed. In actual implementation, when the temperature of the target component exceeds the temperature at which the fan cooling strategy is triggered, the fan is started. During the operation of the fan based on PID control, the temperature of the target component does not continue to rise. Instead, at the initial stage of the start of the fan, both the temperature of the target component and the rotating speed of the fan increase, but the temperature of the target component does not continue to rise. Instead, after the fan operates for a period of time, a temperature balance point is reached.

[0052] In an available implementation, in the process of determining the evaluation temperature value that meets the evaluation condition in the temperature change data set, a temperature sliding window can be set in the temperature change data set, the temperature sliding window including a plurality of time periods that are adjacent in time sequence and have the same time length; the temperature sliding window is slid from the first time in the temperature change data set as a starting point, and the maximum temperature in each of the plurality of time periods included in the temperature sliding window is obtained; and it is determined whether the maximum temperature in the first time period meets the evaluation condition.

[0053] When the maximum temperature in the first time period meets the evaluation condition, the maximum temperature in the first time period is taken as the evaluation temperature value.

[0054] When the maximum temperature in the first time period does not meet the evaluation condition, the temperature sliding window is slid by a unit time length in the temperature change data set, and it is determined whether there is an evaluation temperature value that meets the evaluation condition based on the temperature data in the current temperature sliding window; the unit time length of the temperature sliding window each time is the same as the time length of any time period in the plurality of time periods.

[0055] When the temperature value at the last time in the temperature change data set is included in the current temperature sliding window and there is no evaluation temperature value that meets the evaluation condition in the current temperature sliding window during the process of continuously sliding the temperature sliding window, the temperature value at the last time in the temperature change data set is taken as the evaluation temperature value.

[0056] In actual implementation, the number of time periods in the temperature sliding window can be customized according to actual application requirements. For example, three or more time periods that are continuous and have the same time length can be set. The time length of the time period can also be customized according to actual application requirements.

[0057] Reference Figure 2, a step flow chart for obtaining an evaluation temperature value satisfying an evaluation condition is shown, in a feasible implementation, taking three time periods set by a temperature sliding window as an example, the process of determining the evaluation temperature value satisfying the evaluation condition in the temperature change data set can include the following steps: A1: setting a temperature sliding window in the temperature change data set, the temperature sliding window including a first time period, a second time period and a third time period with the same time length and sequentially adjacent in time order.

[0058] For example, the total duration of the temperature sliding window can be set to 90 seconds, and the time length of the first time period, the second time period and the third time period is 30 seconds, i.e. the first time period is N seconds-N+30 seconds, the second time period is N+30 seconds-N+60 seconds, and the third time period is N+60 seconds-N+90 seconds, and N is 0, 1, 2, 3, 4, ….

[0059] A2: obtaining a first temperature maximum value in the first time period, a second temperature maximum value in the second time period and a third temperature maximum value in the third time period.

[0060] The temperature maximum value is obtained from the temperature data corresponding to each time point in the respective 30 seconds of the first time period, the second time period and the third time period.

[0061] A3: determining whether the first temperature maximum value, the second temperature maximum value and the third temperature maximum value satisfy the evaluation condition.

[0062] The evaluation condition includes that the first temperature maximum value is greater than the second temperature maximum value and the third temperature maximum value, and the first temperature maximum value is greater than or equal to a temperature threshold value, which can be a numerical value of a temperature set value, and in this embodiment, the temperature threshold value is -1 of the temperature set value.

[0063] For example, if the first temperature maximum value in the first time period is denoted as T1, the second temperature maximum value in the second time period is denoted as T2, the third temperature maximum value in the third time period is denoted as T3, and the temperature set value is denoted as Setpoint, the temperature threshold value is Setpoint-1, and the evaluation condition is: T1≥T2 and T1≥T3 and T1≥setpoint-1.

[0064] A4: when the evaluation condition is satisfied, the first temperature maximum value is taken as the evaluation temperature value.

[0065] If the first temperature maximum value is greater than the second temperature maximum value and the third temperature maximum value, and the first temperature maximum value is greater than or equal to the temperature threshold value, the first temperature maximum value T1 is taken as the evaluation temperature value, which represents the temperature balance point reached during the fan cooling and the target component running temperature rise process.

[0066] A5: When the evaluation condition is not met, the temperature sliding window is slid by a unit time length in the temperature change data set, and according to the temperature data in the current temperature sliding window, it is continuously judged whether there is an evaluation temperature value that meets the evaluation condition.

[0067] The time length of the unit time length of each sliding of the temperature sliding window is the same as the time length of the first time period, the second time period and the third time period. For example, when the temperature sliding window is set to 90 seconds, the unit time length is 30 seconds.

[0068] If the first maximum temperature value of the first time period in the temperature sliding window does not meet the evaluation condition, the first time period is N seconds-N+30 seconds, the second time period is N+30 seconds-N+60 seconds, and the third time period is N+60 seconds-N+90 seconds, N+1, and the first maximum temperature value of the first time period in the current temperature sliding window is continuously judged whether it meets the evaluation condition, until the first maximum temperature value of the first time period in any temperature sliding window meets the evaluation condition.

[0069] A6: The temperature value of the last time in the temperature change data set is included in the current temperature sliding window, and when there is no evaluation temperature value that meets the evaluation condition in the current temperature sliding window, the temperature value of the last time in the temperature change data set is taken as the evaluation temperature value.

[0070] If the temperature sliding window slides to have included the temperature value of the last time in the temperature change data set, and still does not meet the evaluation condition, the temperature value of the last time is taken as the evaluation temperature value. For example, if the temperature change data set includes temperature data of 600 seconds respectively, when it is extracted to 600 seconds, it still does not meet the evaluation condition, and the temperature value of the 600th second is taken as the evaluation temperature value T1.

[0071] After determining the evaluation temperature value, the process of generating the evaluation score of the current control parameter according to the evaluation temperature value and the temperature set value corresponding to the target component can include: When the evaluation temperature value is less than or equal to the temperature set value, the evaluation score is calculated according to the time corresponding to the evaluation temperature value, and the evaluation score is used to represent the speed of the temperature balance point reached by the target component after the fan cooling strategy of the current control parameter is executed.

[0072] When the evaluation temperature value is greater than the temperature setting value, a fixed value is taken as the evaluation score of the current control parameter.

[0073] The method determines the evaluation score of the current control parameter according to the temperature value and the time in the heat dissipation process, and can further automatically and quantitatively determine whether the current control parameter setting is reasonable.

[0074] If the evaluation temperature value reaching the temperature balance point after the fan heat dissipation strategy with the current control parameter is executed has exceeded the temperature setting value of the target component, it is indicated that the current control parameter cannot meet the heat dissipation target of the target component, and at this time, it is not necessary to further determine the speed of the target component reaching the temperature balance point, and the evaluation score can be directly set to a fixed value to indicate that the current control parameter is not recommended to be used and can be discarded.

[0075] If the evaluation temperature value reaching the temperature balance point after the fan heat dissipation strategy with the current control parameter is executed has not exceeded the temperature setting value of the target component, it is indicated that the current control parameter at least meets the basic condition that the target component does not have an overshoot phenomenon, and at this time, it can be further determined that the speed of the target component reaching the temperature balance point under the current control parameter, if the evaluation score indicates that the faster the target component reaches the temperature balance point after the fan heat dissipation strategy with the current control parameter is executed, the better the heat dissipation effect of the current control parameter configuration; if the evaluation score indicates that the slower the target component reaches the temperature balance point after the fan heat dissipation strategy with the current control parameter is executed, the weaker the effect of the current control parameter configuration.

[0076] However, in actual application, the faster the speed of reaching the temperature balance point is not necessarily better, because the speed of reaching the temperature balance point is also related to the power consumption of the fan, and if the speed of reaching the temperature balance point is faster, the corresponding rotation speed of the fan is higher, the power consumption is greater, and the noise is greater.

[0077] Therefore, in order to make the judgment result more suitable for the needs of actual application, in the process of generating the evaluation score, in addition to the size relationship between the temperature setting value and the evaluation temperature value and the time corresponding to the evaluation temperature value, a heat dissipation risk and power consumption balance weight coefficient can also be introduced to limit the reasonable power consumption and rotation speed of the fan, so as to obtain a more accurate evaluation score.

[0078] In a feasible implementation manner, an evaluation model can be established to generate the evaluation score of the current control parameter, and the evaluation model is mainly used to determine the speed of reaching the temperature balance point under the current control parameter of the PID control.

[0079] Reference Figure 3, a step flow chart of generating an evaluation score provided by the embodiment of the application is shown, in a feasible implementation, the process of generating the evaluation score corresponding to the current control parameter by the evaluation model can include the following steps: B1: input the evaluation temperature value and the temperature set value corresponding to the target component into the evaluation model.

[0080] Specifically, first, the time corresponding to the evaluation temperature value is normalized, and the time corresponding to the normalized evaluation temperature value is input into the evaluation model.

[0081] Specifically, first, the time ts corresponding to the evaluation temperature value is normalized, so that t∈[0, 1]; for example, when the target time period is 600s, the normalization is as follows:

[0082] Then, the time t corresponding to the normalized evaluation temperature value is input into the evaluation model, and the evaluation model can calculate the evaluation score corresponding to the current control parameter according to the time corresponding to the evaluation temperature value, the temperature set value and the heat dissipation risk and power consumption balance weight coefficient.

[0083] B2: when the evaluation model determines that the evaluation temperature value is less than or equal to the temperature set value, the evaluation score is calculated and output according to the time corresponding to the evaluation temperature value and the heat dissipation risk and power consumption balance weight coefficient.

[0084] Specifically, the formula for the evaluation model to generate the evaluation score can be:

[0085] wherein, is the evaluation score; T1 is the evaluation temperature value; t is the time corresponding to the normalized evaluation temperature value; is the heat dissipation risk and power consumption balance weight coefficient, ∈[0, 1]; is the temperature set value; is the fitting coefficient; is the fixed value, ≥2.

[0086] When the evaluation model determines that the evaluation temperature value is less than or equal to the temperature set value, the evaluation score of the current control parameter can be obtained by a quadratic function with time as the independent variable.

[0087] For example, As the fitting coefficient, it can be obtained by multiple data simulations of the actual application computer system in the actual implementation process, in the embodiment, may take a value of 1.8, i.e. when , .

[0088] Thermal risk and power consumption balance weight coefficient The greater the value, the more aggressive the fan response in the PID control fan cooling strategy of the computer system, the greater the maximum fan speed, and thus the greater the fan power consumption and noise, but at the same time, the probability of temperature overshoot of the target component is smaller, and the risk of temperature overshoot of the target component is lower. The smaller the value, the more gentle the fan response in the PID control fan cooling strategy of the computer system, the smaller the fan power consumption and noise, but the probability of temperature overshoot of the target component increases; The value of the thermal risk and power consumption balance weight coefficient can be customized according to the actual application requirements, and the thermal risk and power consumption balance weight coefficient can also be adjusted in response to coefficient adjustment operations.

[0089] Referring to Figure 4 , a schematic diagram of the speed of the target component and the fan under different thermal risk and power consumption balance weight coefficients provided by the embodiments of the present application is shown, the abscissa represents the time, the ordinate on the left represents the temperature of the target component, and the ordinate on the right represents the speed of the fan. For example, when = 0, the fan response is slow after the initial time, and the response is more gentle; when = 1, the fan response is more aggressive after the initial time, and the maximum speed is also greater than the fan speed when = 0; the temperature of the target component when = 0 is generally higher than the temperature when = 1, and The speed at which the target component reaches high temperature when = 0 is obviously faster than when

[0090] In the present embodiment, when , the temperature of the target component does not exceed the preset temperature setting value, indicating that the target component does not appear overshoot phenomenon, then the time value t corresponding to the evaluation temperature value T1 can be taken as the independent variable of the evaluation model, and the thermal risk and power consumption balance weight coefficient is calculated to obtain the evaluation score value. Since the values of the normalized time t and the thermal risk and power consumption balance weight coefficient are both in the range of [0, 1], when , The value is between 0.19 and 2.

[0091] In a feasible implementation, the weight coefficient of the balance between heat dissipation risk and power consumption is also allowed to be modified according to the requirements of actual applications , and specifically, in response to a coefficient adjustment operation, an updated weight coefficient of the balance between heat dissipation risk and power consumption is obtained; and the current weight coefficient of the balance between heat dissipation risk and power consumption in the evaluation model is replaced by the updated weight coefficient of the balance between heat dissipation risk and power consumption.

[0092] B3: When the evaluation model determines that the evaluation temperature value is greater than the temperature setting value, a fixed value is output as the evaluation score of the current control parameter.

[0093] When the evaluation temperature value is greater than the temperature setting value, that is, when , the evaluation model outputs , for example, is a fixed value greater than or equal to 2, The size of the fixed value can be customized according to the requirements of actual applications.

[0094] Because when the evaluation temperature value is greater than the temperature setting value, the temperature of the target component has exceeded the temperature setting value, that is, the temperature of the target component has appeared overshoot phenomenon, the fixed value can be directly defined as , which indicates that the configuration of the current control parameter cannot completely meet the target requirements of PID control heat dissipation, and the three parameters Kp, Ki and Kd of the corresponding PID control can be completely discarded, and is not recommended to be used.

[0095] The evaluation score calculated by the evaluation model can be regarded as an evaluation of the speed of the effect of fan heat dissipation, and can further represent the pros and cons of the current control parameter setting, for example, when the weight coefficient of the balance between heat dissipation risk and power consumption is unchanged, if the speed of reaching the temperature balance point after the fan heat dissipation strategy is triggered is faster, the current control parameter setting makes the cooling effect better, at this time, the time t of the evaluation temperature value is smaller, and the evaluation score obtained by the evaluation model is smaller; if the speed of reaching the temperature balance point after the fan heat dissipation strategy is triggered is slower, the current control parameter setting makes the cooling effect weaker, at this time, the time t of the evaluation temperature value is larger, and the evaluation score obtained by the evaluation model is larger.

[0096] S103: When the evaluation score meets the target condition, the current control parameter and the evaluation score are added to the control parameter data set.

[0097] Under the configuration of the current control parameter, after the evaluation score is calculated according to the size relationship between the evaluation temperature value and the temperature set value, the time of the evaluation temperature value, and the heat dissipation risk and power consumption balance weight coefficient, it can be further judged whether the evaluation score meets the target condition.

[0098] Specifically, it can be judged whether the evaluation score of the current control parameter is less than or equal to the score threshold value; When the evaluation score is less than or equal to the score threshold value, the evaluation score meets the target condition; When the evaluation score is greater than the score threshold value, the evaluation score does not meet the target condition.

[0099] The size of the score threshold value can be customized according to the actual application requirements. For example, if The value is 0.19-2, the score threshold value can be selected as 1.5.

[0100] For the evaluation score less than or equal to the score threshold value, the current control parameter and the evaluation score can be added to the control parameter data set. The current control parameter in the control parameter data set is a parameter configuration that meets the heat dissipation demand of the target component and has better heat dissipation effect. It can facilitate users to select any current control parameter in the control parameter data set for actual application in the fan heat dissipation strategy.

[0101] For example, the control parameter data set can also sort the plurality of control parameters according to the order from small to large evaluation score, so as to facilitate users to intuitively select the control parameter with more reasonable heat dissipation effect.

[0102] For the evaluation score greater than the score threshold value, the current control parameter and the evaluation score can be added to the parameter log data set. By recording the control parameter with weaker heat dissipation effect in the parameter log data set, the user can be prompted in subsequent parameter configuration, prompting the user to avoid applying this part of the control parameter.

[0103] The parameter evaluation method provided in the embodiment has at least the following beneficial effects: 1. A complete evaluation process and evaluation model for the current control parameter of PID control in the scene of PID control heat dissipation are designed. By collecting and recording the temperature change of the target component in the PID control heat dissipation process, the evaluation score is calculated according to the mathematical model of the evaluation model. Compared with the process of subjectively judging the PID control parameter according to the temperature change result, a more objective and accurate evaluation and judgment result can be generated; 2. The judgment result of the control parameter in the PID control heat dissipation process can be automatically generated. Compared with manual judgment, the evaluation efficiency can be improved, and the parameter regulation efficiency in the PID control heat dissipation process can be improved; 3. By setting the heat dissipation risk and power consumption balance weight coefficient , different application scenarios can be coped with for noise and power consumption balance demand; for the use scene with higher heat dissipation safety requirement, higher heat dissipation risk and power consumption balance weight coefficient can be selected; for the scene with more strict noise or power consumption requirement, lower heat dissipation risk and power consumption balance weight coefficient .

[0104] Referring to Figure 5 , a functional module diagram of a control parameter judgment device provided by the embodiment of the application is shown, which is applied to a computer system, and the device comprises: An acquisition module 100 is configured to acquire a current control parameter of a fan heat dissipation strategy based on a proportional integral differential control process, perform a pressure test process on any target component, and collect a temperature change data set of any target component within a target time period; A score generation module 200 is configured to generate an evaluation score of the current control parameter according to an evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, the evaluation temperature value being a temperature equilibrium point reached after the fan heat dissipation strategy is executed, and the temperature set value being a maximum temperature corresponding to any target component; A judgment module 300 is configured to add the current control parameter and the evaluation score in a control parameter data set when the evaluation score meets a target condition.

[0105] Optionally, the acquisition module comprises: A collection unit is configured to collect temperature data of any target component at multiple time points within a target time period, taking a time point when the pressure test process is performed as a starting time point, and construct a temperature change data set of any target component within a target time period.

[0106] Optionally, the acquisition module further comprises a temperature determination unit configured to: determine an evaluation temperature value meeting an evaluation condition in the temperature change data set; wherein the evaluation condition is that, in a plurality of time periods in time sequence, a maximum temperature in a first time period is greater than a maximum temperature in each of the remaining time periods in the plurality of time periods, and the maximum temperature in the first time period is greater than or equal to a preset temperature threshold, and the maximum temperature in the first time period is taken as the evaluation temperature value.

[0107] Optionally, the temperature determination unit further comprises: A window setting subunit is configured to set a temperature sliding window in the temperature change data set, the temperature sliding window comprising a plurality of time periods with the same time length and in time sequence adjacent to each other. a temperature acquisition subunit, which acquires a maximum temperature in each of the plurality of time periods from a first time point of the temperature change data set as a starting point of sliding the temperature sliding window; a first judgment subunit, which is configured to determine whether the maximum temperature in a first time period of the plurality of time periods meets the evaluation condition, and when the evaluation condition is met, take the maximum temperature in the first time period as the evaluation temperature value.

[0108] Optionally, the device further comprises: a sliding judgment unit, which is configured to, when the maximum temperature in the first time period of the plurality of time periods does not meet the evaluation condition, slide the temperature sliding window by a unit time length in the temperature change data set, and continue to determine whether there is an evaluation temperature value meeting the evaluation condition according to temperature data in the current temperature sliding window, wherein the unit time length is the same as a time length of any time period of the plurality of time periods.

[0109] Optionally, the sliding judgment unit further comprises: a final judgment subunit, which is configured to, when the temperature value at the last time point in the temperature change data set is included in the current temperature sliding window and there is no evaluation temperature meeting the evaluation condition in the current temperature sliding window, take the temperature value at the last time point in the temperature change data set as the evaluation temperature value.

[0110] Optionally, the score generation module comprises: a first score generation unit, which is configured to, when the evaluation temperature value is less than or equal to the temperature set value, calculate an evaluation score according to a time point corresponding to the evaluation temperature value, and the evaluation score is used to represent a speed of reaching a temperature balance point of the target component after a fan cooling strategy configured with the current control parameter is executed.

[0111] Optionally, the score generation module comprises: a second score generation unit, which is configured to, when the evaluation temperature value is greater than the temperature set value, take a fixed value as the evaluation score of the current control parameter.

[0112] Optionally, the score generation module further comprises: an input unit, which is configured to input the evaluation temperature value and a temperature set value corresponding to the target component into an evaluation model; a first generation unit, which is configured to, when the evaluation model determines that the evaluation temperature value is less than or equal to the temperature set value, calculate and output an evaluation score according to a time point corresponding to the evaluation temperature value and a heat dissipation risk and power consumption balance weight coefficient, and the heat dissipation risk and power consumption balance weight coefficient is used to limit power consumption and rotation speed of the fan. The second generation unit is configured to output a fixed value as the evaluation score of the current control parameter when the evaluation model determines that the evaluation temperature value is greater than the temperature setting value.

[0113] Optionally, the score generation module further comprises: A normalization module configured to normalize the time corresponding to the evaluation temperature value. The time corresponding to the normalized evaluation temperature value is input into the evaluation model.

[0114] Optionally, the determination module is further configured to: When the evaluation score does not satisfy the target condition, the current control parameter and the evaluation score are added to the parameter log data set.

[0115] Optionally, the apparatus further comprises: A target determination module configured to determine whether the evaluation score of the current control parameter is less than or equal to a score threshold value; when the evaluation score is less than or equal to the score threshold value, the evaluation score satisfies the target condition; and when the evaluation score is greater than the score threshold value, the evaluation score does not satisfy the target condition.

[0116] Optionally, the apparatus further comprises: A system adjustment module configured to, in response to a coefficient adjustment operation, obtain an updated heat dissipation risk and power consumption balance weight coefficient; and replace the current heat dissipation risk and power consumption balance weight coefficient in the evaluation model with the updated heat dissipation risk and power consumption balance weight coefficient.

[0117] Optionally, the obtaining module is further configured to: Listen to a change event of a control parameter of the fan heat dissipation strategy based on the proportional-integral-derivative control process; and when the change event of the control parameter occurs, obtain the current control parameter corresponding to the fan heat dissipation strategy based on the proportional-integral-derivative control process.

[0118] Optionally, the apparatus further comprises a parameter configuration module configured to: In response to a control parameter configuration operation, obtain a control parameter to be configured; Configure the current control parameter in the fan heat dissipation strategy based on the proportional-integral-derivative control process as the control parameter to be configured, and trigger a change event of the control parameter.

[0119] Embodiments of the present application also provide a computer system in which a fan heat dissipation strategy based on a proportional-integral-derivative control process is applied, and the computer system is configured to execute the parameter evaluation method described in the embodiments.

[0120] ReferenceFigure 6 FIG. 1 shows a schematic diagram of a computer device according to an embodiment of the present application. The computer device comprises at least one processor 601 and a memory 602 storing a computer program executable on the processor 601. When the processor 601 executes the computer program, the processor 601 performs the method for determining a control parameter according to an embodiment of the present application.

[0121] With reference to Figure 7 FIG. 7 shows a schematic diagram of a non-volatile readable storage medium according to an embodiment of the present application. The non-volatile readable storage medium 700 stores a computer program 701. When the computer program 701 is executed by a processor, the computer program 701 performs the method for determining a control parameter according to an embodiment of the present application.

[0122] With reference to Figure 8 FIG. 8 shows a schematic diagram of a computer program product according to an embodiment of the present application. The computer program product comprises computer programs / instructions 801. When the computer programs / instructions 801 are executed by a processor, the computer programs / instructions 801 implement the method for determining a control parameter according to an embodiment of the present application.

[0123] Each of the embodiments in the present specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other.

[0124] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, device, or computer program product. Therefore, the embodiments of the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0125] The embodiments of the present application are described with reference to flowcharts and / or block diagrams according to the method, terminal device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows 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 general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the computer or other programmable data processing terminal device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0126] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flow diagrams and / or blocks Figure 1 one or more flow diagrams and / or blocks

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flow diagrams and / or blocks Figure 1 one or more flow diagrams and / or blocks

[0128] Although preferred embodiments of the application have been described, those skilled in the art will recognize that additional modifications and changes can be made thereto without departing from the scope of the present application. Accordingly, the appended claims are intended to cover all such modifications and changes as fall within the scope of the application.

[0129] Finally, it should be noted that the terms "first", "second", and the like, herein do not denote any order, quantity, combination, or importance, but rather are used to distinguish one element from another, and do not imply singular or plural. Moreover, terms such as "include", "have", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0130] The principles and implementations of the present application have been described above with the specific examples. The above descriptions of the embodiments are only intended to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation and application range can be changed, and the above descriptions of the embodiments should not be understood as limiting the present application.

Claims

1. A method of determining a control parameter, characterized by, The method is applied to a computer system and comprises the following steps: obtaining a current control parameter of a fan cooling strategy based on a proportional-integral-derivative control process, performing a pressure test procedure on any target component, and collecting a temperature change data set of any target component within a target time period; generating an evaluation score of the current control parameter according to an evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, the evaluation temperature value being a temperature equilibrium point reached after the fan cooling strategy is executed, and the temperature set value being a maximum temperature corresponding to any target component; when the evaluation score meets a target condition, adding the current control parameter and the evaluation score in a control parameter data set.

2. The method of claim 1, wherein, performing a pressure test procedure on any target component, and collecting a temperature change data set of any target component within a target time period, comprises the following steps: collecting temperature data of any target component at multiple time points within the target time period, and constructing a temperature change data set of any target component within the target time period.

3. The method of claim 1, wherein, after collecting the temperature change data set of any target component within the target time period, the method further comprises the following steps: determining an evaluation temperature value meeting an evaluation condition in the temperature change data set; wherein the evaluation condition is that, in a plurality of time periods in time sequence, a maximum temperature in a first time period is greater than maximum temperatures in the remaining time periods in the plurality of time periods, and the maximum temperature in the first time period is greater than or equal to a preset temperature threshold, and the maximum temperature in the first time period is taken as the evaluation temperature value.

4. The method of claim 3, wherein, determining an evaluation temperature value meeting an evaluation condition in the temperature change data set, comprises the following steps: setting a temperature sliding window in the temperature change data set, the temperature sliding window including a plurality of time periods with the same time length and in time sequence adjacent to each other; sliding the temperature sliding window from a first time point of the temperature change data set as a starting point, and respectively obtaining maximum temperatures contained in the plurality of time periods in the temperature sliding window; judging whether a maximum temperature in a first time period in the plurality of time periods meets the evaluation condition; when the evaluation condition is met, taking the maximum temperature in the first time period as the evaluation temperature value.

5. The method of claim 4, wherein, after judging whether a maximum temperature in a first time period in the plurality of time periods meets the evaluation condition, the method further comprises the following steps: when the maximum temperature in the first time period in the plurality of time periods does not meet the evaluation condition, sliding the temperature sliding window by a unit time length in the temperature change data set, and continuing to judge whether there is an evaluation temperature value meeting the evaluation condition according to temperature data in the current temperature sliding window; wherein the unit time length is the same as a time length of any time period in the plurality of time periods.

6. The method of claim 5, wherein, after sliding the temperature sliding window by a unit time length in the temperature change data set and continuing to judge whether there is an evaluation temperature value meeting the evaluation condition according to temperature data in the current temperature sliding window, the method further comprises the following steps: When the current temperature sliding window includes a temperature value of a last time in the temperature change data set and the current temperature sliding window does not have an evaluation temperature satisfying an evaluation condition, the temperature value of the last time in the temperature change data set is taken as the evaluation temperature value.

7. The method of claim 1, wherein, According to the evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, an evaluation score of the current control parameter is generated, including: When the evaluation temperature value is less than or equal to the temperature set value, an evaluation score is calculated according to a time corresponding to the evaluation temperature value, and the evaluation score is used to represent a speed of a temperature balance point of the target component after a fan heat dissipation strategy configured by the current control parameter is executed.

8. The method of claim 7, wherein, According to the evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, an evaluation score of the current control parameter is generated, and the method further includes: When the evaluation temperature value is greater than the temperature set value, a fixed value is taken as the evaluation score of the current control parameter.

9. The method of claim 1, wherein, According to the evaluation temperature value determined in the temperature change data set and a temperature set value corresponding to the target component, an evaluation score of the current control parameter is generated, including: The evaluation temperature value and the temperature set value corresponding to the target component are input into an evaluation model. When the evaluation model determines that the evaluation temperature value is less than or equal to the temperature set value, an evaluation score is calculated and output according to a time corresponding to the evaluation temperature value and a heat dissipation risk and power consumption balance weight coefficient, and the heat dissipation risk and power consumption balance weight coefficient is used to limit power consumption and rotation speed of the fan. When the evaluation model determines that the evaluation temperature value is greater than the temperature set value, a fixed value is output as the evaluation score of the current control parameter.

10. The method of claim 9, wherein, Before the evaluation temperature value and the temperature set value corresponding to the target component are input into the evaluation model, the method further includes: The time corresponding to the evaluation temperature value is normalized. The time corresponding to the normalized evaluation temperature value is input into the evaluation model.

11. The method of claim 10, wherein, A formula for generating the evaluation score by the evaluation model is: wherein, is the evaluation score; T1 is the evaluation temperature value; t is the time corresponding to the normalized evaluation temperature value; is the heat dissipation risk and power consumption balance weight coefficient, ∈[0, 1]; is the temperature set value; is the fitting coefficient; is the fixed value, ≥2.

12. The method of claim 1, wherein, After the evaluation score of the current control parameter is generated according to the evaluation temperature value determined in the temperature change data set and the temperature set value corresponding to the target component, the method further includes: When the evaluation score does not satisfy a target condition, the current control parameter and the evaluation score are added in a parameter log data set.

13. The method of claim 12, wherein, After the evaluation score of the current control parameter is generated according to the evaluation temperature value determined in the temperature change data set and the temperature set value corresponding to the target component, the method further includes: It is judged whether the evaluation score of the current control parameter is less than or equal to a score threshold value. When the evaluation score is less than or equal to the score threshold value, the evaluation score satisfies the target condition. When the evaluation score is greater than the score threshold value, the evaluation score does not satisfy the target condition.

14. The method of claim 9, wherein, The method further includes: In response to a coefficient adjustment operation, an updated heat dissipation risk and power consumption balance weight coefficient is obtained. The current heat dissipation risk in the evaluation model is replaced by the updated heat dissipation risk and power consumption balance weight coefficient.

15. The method of claim 1, wherein, Obtaining the current control parameter of the fan heat dissipation strategy based on the proportional integral derivative control process, including: Monitoring the change event of the control parameter of the fan heat dissipation strategy based on the proportional integral derivative control process; When the change event of the control parameter occurs, the current control parameter corresponding to the fan heat dissipation strategy based on the proportional integral derivative control process is obtained.

16. The method of claim 15, wherein, The method further comprises: In response to the control parameter configuration operation, obtaining the to-be-configured control parameter; The current control parameter in the fan heat dissipation strategy based on the proportional integral derivative control process is configured as the to-be-configured control parameter, triggering the change event of the control parameter.

17. A computer system, characterized in that The computer system applies the fan heat dissipation strategy based on the proportional integral derivative control process, and the computer system is used to execute the control parameter judgment method in any one of claims 1-16.

18. A computer device, comprising: Including: At least one processor and a memory, the memory stores a computer program executable on the processor, wherein the processor executes the computer program to execute the control parameter judgment method in any one of claims 1-16.

19. A non-volatile readable storage medium, characterized by The non-volatile readable storage medium stores a computer program, wherein the computer program is executed by the processor to execute the control parameter judgment method in any one of claims 1-16.

20. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to realize the control parameter judgment method in any one of claims 1-16.

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