Performance detection method and system for automobile air conditioner fan controller

Abstract

The invention relates to the technical field of data processing, in particular to a performance detection method and system for an automobile air conditioner fan controller, and the method comprises the steps: obtaining a target environment parameter and an actual environment parameter of each adjustment operation of the automobile air conditioner fan controller; according to the fluctuation of the actual environmental parameters in the parameter response time period under each adjustment operation, a response effect evaluation value is obtained in combination with the response delay condition; obtaining a dynamic abnormal coefficient of the controller according to the change trend of the response effect evaluation value of each adjustment operation; obtaining a static abnormal coefficient of the controller according to the difference between the actual environment parameter and the target environment parameter at each moment in the parameter keeping time period under each adjustment operation and the difference duration; according to the dynamic abnormal coefficient and the static abnormal coefficient, the response abnormal condition of the controller is judged, and a performance detection result of the controller is obtained. According to the invention, the accuracy of fault identification is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a performance detection method and system for an automobile air-conditioning fan controller. Background Art

[0002] The automotive air conditioning fan controller is one of the core components of the automotive air conditioning system. It is mainly used to control the fan speed, start and stop, and operating mode to adjust the flow and temperature of the air inside the vehicle to meet the driver's and passengers' requirements for the temperature inside the vehicle. Therefore, the performance of the automotive air conditioning fan controller directly affects the regulation effect of the vehicle's interior environment, such as the air quality inside the vehicle, the temperature regulation efficiency, and the reliability of the regulation system. Existing automotive air conditioning fan controller performance testing methods usually analyze the changes in environmental parameters and focus on single-dimensional detection results. For example, they only focus on abnormal responses to dynamic adjustments or abnormalities in the static parameter maintenance stage. They lack consideration of coordinated abnormalities caused by multiple factors and full-cycle abnormalities, resulting in low accuracy in the controller's abnormality detection results. Summary of the Invention

[0003] In order to solve the technical problem that the existing performance testing methods for automobile air-conditioning fan controllers focus on a single dimension of test results, resulting in low accuracy in the detection results of controller abnormalities, the present invention aims to provide a performance testing method and system for automobile air-conditioning fan controllers. The technical solutions adopted are as follows: In a first aspect, the present invention provides a performance detection method for an automobile air-conditioning fan controller, comprising: Obtain the target environmental parameters for each adjustment operation of the automobile air-conditioning fan controller, as well as the actual environmental parameters at each moment in the parameter response time period and the parameter holding time period; The response effect evaluation value of each adjustment operation is obtained based on the fluctuation degree and frequency of the actual environmental parameters at each moment in the parameter response time period under each adjustment operation, combined with the response delay corresponding to the parameter response time period; According to the changing trend of the response effect evaluation value of each adjustment operation, the abnormal situation of the controller's adjustment operation is analyzed to obtain the dynamic abnormality coefficient of the controller; According to the difference between the actual environmental parameters and the target environmental parameters at each moment in the parameter holding period under each adjustment operation and the duration of the difference, the static abnormality coefficient of the controller is obtained; According to the dynamic abnormality coefficient and the static abnormality coefficient, the abnormal response of the controller is judged and the performance test result of the controller is obtained.

[0004] Preferably, the response effect evaluation value of each adjustment operation is obtained based on the fluctuation degree and frequency of the actual environmental parameters at each moment in the parameter response time period under each adjustment operation, combined with the response delay corresponding to the parameter response time period, specifically including: Obtaining a response delay factor for each adjustment operation according to the initial change time of the actual environmental parameter within the parameter response time period and the length of the parameter response time period for each adjustment operation; According to the fluctuation of the difference between the actual environmental parameters at adjacent moments within the parameter response time period under each adjustment operation, the parameter fluctuation factor under each adjustment operation is obtained; A negative correlation process is performed on the product of the response delay factor and the parameter fluctuation factor to obtain a response effect evaluation value of each adjustment operation.

[0005] Preferably, obtaining the response delay factor under each adjustment operation according to the initial change time of the actual environmental parameter within the parameter response time period under each adjustment operation and the time length of the parameter response time period specifically includes: For any adjustment operation, the earliest moment in the parameter response time period that is different from the actual environmental parameters of the first moment is recorded as the parameter change moment; the product of the time interval between the first moment in the parameter response time period and the parameter change moment and the time length of the parameter response time period is normalized to obtain the response delay factor under the any adjustment operation.

[0006] Preferably, obtaining the parameter fluctuation factor under each adjustment operation according to the fluctuation of the difference between the actual environmental parameters at adjacent moments within the parameter response time period under each adjustment operation specifically includes: For any adjustment operation, calculate the ratio of the difference between each two adjacent actual environmental parameters and the time interval within the parameter response time period to obtain the change rate of each parameter and obtain the extreme value point of the parameter change rate within the parameter response time period; The normalization coefficient of the product of the reciprocal of the mean of the time intervals between adjacent extreme points in the parameter response time period, the total number of extreme points, and the maximum absolute value of the parameter change rate is calculated to obtain the parameter fluctuation factor under any adjustment operation.

[0007] Preferably, the abnormality of the controller's adjustment operation is analyzed according to the change trend of the response effect evaluation value of each adjustment operation to obtain the dynamic abnormality coefficient of the controller, which specifically includes: The adjustment operation corresponding to the response effect evaluation value greater than or equal to the preset response threshold is recorded as a normal response operation, and the adjustment operation corresponding to the response effect evaluation value less than the preset response threshold is recorded as a suspected abnormal operation; Obtaining continuous characteristic values ​​based on the quantity distribution and continuous distribution of normal response operations and suspected abnormal operations; The ratio between the number of all suspected abnormal operations and the number of all adjustment operations is used as the first coefficient, and the negative correlation coefficient of the mean of the response effect evaluation values ​​of all suspected abnormal operations is used as the second coefficient; The dynamic abnormality coefficient of the controller is obtained according to the continuous characteristic value, the first coefficient and the second coefficient. The continuous characteristic value, the first coefficient and the second coefficient are all positively correlated with the dynamic abnormality coefficient.

[0008] Preferably, obtaining the continuous characteristic value according to the quantity distribution and continuous distribution of normal response operations and suspected abnormal operations specifically includes: When all adjustment operations are suspected to be abnormal operations, the continuous characteristic value is set to a preset value; When there is a normal response operation among all the adjustment operations, all the adjustment operations are arranged in chronological order, all the consecutive suspected abnormal operations constitute a first feature sequence, and all the consecutive normal response operations constitute a second feature sequence; Based on the number of first feature sequences and the maximum number of suspected abnormal operations contained in all first feature sequences, and the maximum number of normal response operations contained in all second feature sequences, a continuous feature value is determined, and the preset value is greater than or equal to the continuous feature value at this time.

[0009] Preferably, the static abnormality coefficient of the controller is obtained according to the difference between the actual environmental parameter and the target environmental parameter at each moment in the parameter holding time period under each adjustment operation and the duration of the difference, which specifically includes: For any adjustment operation, based on the difference between the actual environmental parameters and the target environmental parameters at each moment during the parameter holding time period, the parameter difference amplitude at each moment is determined; the moment corresponding to the parameter difference amplitude being greater than or equal to the preset difference threshold is recorded as the oscillation moment, and based on the maximum value of the parameter difference amplitude of all oscillation moments during the parameter holding time period, the number of oscillation moments, and the inverse of the time interval between the first moment and the second oscillation moment, the static abnormality factor of the said any adjustment operation is obtained; The normalized value of the mean of the static anomaly factors of all regulation operations is used as the static anomaly coefficient of the controller.

[0010] Preferably, judging the abnormal response of the controller based on the dynamic abnormality coefficient and the static abnormality coefficient to obtain the performance test result of the controller specifically includes: The actual environmental parameters include actual temperature parameters and actual wind speed parameters; The product of the dynamic anomaly coefficient and the static anomaly coefficient corresponding to each actual environmental parameter is used as the full-cycle response anomaly index corresponding to each actual environmental parameter; The full-cycle response anomaly index corresponding to the actual temperature parameter and the full-cycle response anomaly index corresponding to the actual wind speed parameter are weighted and averaged to obtain the comprehensive anomaly characteristic value; The abnormal response of the controller is judged according to the comprehensive abnormal characteristic value, and the performance test result of the controller is obtained.

[0011] Preferably, the determining of the abnormal response of the controller based on the comprehensive abnormal characteristic value to obtain the performance test result of the controller specifically includes: When the comprehensive abnormal characteristic value is less than the preset abnormal threshold, there is no abnormality in the controller performance; when the comprehensive abnormal characteristic value is greater than or equal to the preset abnormal threshold, there is an abnormality in the controller performance.

[0012] In a second aspect, the present invention provides a performance detection system for an automobile air-conditioning fan controller, the system being used to implement the steps of a performance detection method for an automobile air-conditioning fan controller, the performance detection system for an automobile air-conditioning fan controller specifically comprising: A data acquisition module is used to obtain the target environmental parameters of each adjustment operation of the automobile air-conditioning fan controller, as well as the actual environmental parameters at each moment in the parameter response time period and the parameter holding time period; The response evaluation module is used to obtain the response effect evaluation value of each adjustment operation based on the fluctuation degree and frequency of the actual environmental parameters at each moment in the parameter response time period under each adjustment operation, combined with the response delay corresponding to the parameter response time period; The dynamic abnormality analysis module is used to analyze the abnormality of the controller's adjustment operation according to the change trend of the response effect evaluation value of each adjustment operation, and obtain the dynamic abnormality coefficient of the controller; The static anomaly analysis module is used to obtain the static anomaly coefficient of the controller based on the difference between the actual environmental parameters and the target environmental parameters at each moment in the parameter holding period under each adjustment operation and the duration of the difference; The performance detection module is used to judge the response abnormality of the controller based on the dynamic abnormality coefficient and the static abnormality coefficient, and obtain the performance detection result of the controller.

[0013] The embodiments of the present invention have at least the following beneficial effects: The present invention tests the performance of an automotive air conditioner fan controller by first collecting data monitoring results for each adjustment operation within the parameter response period and the parameter retention period. The parameter response period represents the response time for the environmental parameter to change from the initial value to the target value after the adjustment operation is completed, and the parameter retention period represents the stable period that the parameter adjustment response should remain after the adjustment response is completed. Then, based on the data change time and changes within the parameter response period until the actual environmental parameter reaches the target value, the response effect of each adjustment operation is analyzed, reflecting the controller's response delay to the adjustment instruction. Furthermore, first, during the dynamic phase of the parameter response, the response effects of multiple adjustment operations are compared to analyze the dynamic anomaly characteristics of the controller. Second, during the static phase when the parameters should remain stable, the difference and persistence between the actual environmental parameters and the target environmental parameters are analyzed to evaluate the static anomaly characteristics of the controller. Finally, by performing full-cycle dynamic and static analysis of the controller's key indicator parameters, the accuracy of fault identification is improved and intermittent faults that are often missed can be detected. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 paying any creative work.

[0015] Figure 1 This is a flow chart of the steps of a performance detection method for an automobile air-conditioning fan controller provided by the present invention; Figure 2 It is a flowchart of the steps of the method for obtaining the response effect evaluation value of each adjustment operation provided by the present invention; Figure 3 It is a flowchart of the steps of the method for obtaining the dynamic anomaly coefficient provided by the present invention; Figure 4 It is a flowchart of the steps of the method for obtaining the static abnormality factor of the adjustment operation provided by the present invention; Figure 5 is a flowchart of the steps of the method for obtaining the performance test results of the controller provided by the present invention; Figure 6 The present invention provides a structural block diagram of a performance detection system for an automobile air-conditioning fan controller. DETAILED DESCRIPTION

[0016] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, provides a detailed description of the performance testing method and system for an automotive air conditioner fan controller according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The following describes in detail a performance detection method and system for an automobile air-conditioning fan controller provided by the present invention with reference to the accompanying drawings.

[0019] See also Figure 1 , which shows a flowchart of a performance detection method for an automobile air-conditioning fan controller provided by one embodiment of the present invention, the method comprising the following steps: Step S100 , obtaining target environmental parameters for each adjustment operation of the automobile air-conditioning fan controller, as well as actual environmental parameters at each moment in a parameter response time period and a parameter holding time period.

[0020] First, within the vehicle space, the actual environmental parameters at each moment after the operation instruction corresponding to each adjustment operation is triggered are collected. The time period from the first moment to the first moment when the actual environmental parameters reach the target environmental parameters constitutes the parameter response time period of each adjustment operation, and the time period from the preset time interval after the parameter response time period constitutes the parameter holding time period of each adjustment operation.

[0021] The adjustment operations include wind speed adjustment and temperature adjustment. Correspondingly, the actual environmental parameter for wind speed adjustment is the actual wind speed parameter, and the actual environmental parameter for temperature adjustment is the actual temperature parameter. The target environmental parameter refers to the parameter index that the corresponding adjustment operation intends to adjust. For example, the target environmental parameter for wind speed adjustment refers to the wind speed level that the adjustment operation intends to achieve, and the target environmental parameter for temperature adjustment refers to the temperature that the adjustment operation intends to achieve.

[0022] As a specific example, an anemometer is installed at the center of the fan outlet to collect wind speed data; determine the temperature monitoring point in the car, such as near the instrument panel or the center of the steering wheel, and arrange a high-precision temperature sensor at the temperature monitoring point to collect temperature data; connect an oscilloscope to monitor the wind speed control and temperature signals output by the controller, and use a data recorder to synchronously record the wind speed and temperature. Adjust different wind speed gears or different temperatures by operating the controller panel, and use a data recorder to collect data at a frequency of 0.2 seconds per time. In this embodiment, the preset time interval is 10 minutes, which indicates the length of time to maintain the set wind speed or temperature. In other embodiments, the implementer can set it according to the specific implementation scenario.

[0023] It should be noted that in order to accurately test the performance of the air conditioner fan controller, all adjustment operations should be in the same operating mode, such as the same cooling mode or the same heating mode. The wind speed should be kept constant when adjusting the temperature, and the temperature should be kept constant when adjusting the wind speed. The adjustment amplitude should be the same each time. For example, in the wind speed adjustment operation, only one wind speed level is adjusted each time; in the temperature adjustment operation, the temperature amplitude should be the same each time, for example, only 1°C or 2°C is adjusted each time. There is no restriction here.

[0024] It should be understood that the main purpose of this embodiment is to collect relevant parameters corresponding to each adjustment operation in historical data when the performance of the automobile air-conditioning fan controller is currently being tested. Each adjustment operation can be wind speed adjustment or temperature adjustment, and the parameter response time period and parameter holding time period of each adjustment operation are continuous in time sequence.

[0025] The core function of a car air conditioner is to adjust the air conditioning temperature or air conditioning speed to create a comfortable and suitable driving environment for users. The air conditioning fan controller is a key component to achieve this function. It is responsible for receiving temperature commands, regulating the fan speed, and coordinating the operation of the heat exchange components. When it has performance problems, it will directly interfere with the accuracy and timeliness of temperature regulation, causing temperature regulation deviations, response delays and other problems. Therefore, in order to accurately test the performance of the car air conditioning fan controller in this embodiment, it is necessary to first analyze the parameter adjustment effect under different adjustment commands, and then evaluate the abnormal performance under different adjustment commands. The specific analysis process is shown in steps S200 to S400.

[0026] Step S200 , obtaining a response effect evaluation value of each adjustment operation based on the fluctuation degree and frequency of the actual environmental parameters at each moment within the parameter response time period under each adjustment operation and the response delay corresponding to the parameter response time period.

[0027] When an automotive air conditioning fan controller performs well, it responds quickly to adjustment commands. For example, the interior temperature changes at a steady rate until it reaches the target value, and the temperature curve is smooth with no sudden changes. However, if the controller's performance is abnormal, internal signal processing delays, aging of the drive circuit, and other factors can cause output lags, resulting in significant delays in actual environmental parameters. This can include large fluctuations in the rate of change of the interior temperature or a long delay in reaching the target temperature.

[0028] Based on this feature, by analyzing the fluctuation characteristics, frequency characteristics of fluctuations, and response delay characteristics of the actual environmental parameters within the parameter response time period under each adjustment operation, and the characteristic performance under multiple feature dimensions, the response effect of the controller under the corresponding adjustment operation is comprehensively evaluated.

[0029] As a specific example, Figure 2 As shown, the method for obtaining the response effect evaluation value of each adjustment operation can be implemented by steps S201 to S203.

[0030] Step S201 : obtaining a response delay factor for each adjustment operation according to the initial change time of the actual environmental parameter within the parameter response time period and the time length of the parameter response time period for each adjustment operation.

[0031] For the convenience of analysis, this embodiment is described by taking any adjustment operation as an example.

[0032] In the first step, for any adjustment operation, the earliest moment in the parameter response time period that is different from the actual environmental parameters at the first moment is recorded as the parameter change moment.

[0033] For example, under this adjustment operation, the first moment in the parameter response time period The actual environmental parameters are , filter the actual environment parameters not equal to within the parameter response time period The time corresponding to the time, and then the time when these moments first appear is the parameter change time. That is, when the actual environmental parameter values ​​are different, the environmental parameters change, and then the parameter change time represents the starting point of the change of the environmental parameters.

[0034] In the second step, the product of the time interval between the first moment in the parameter response time period and the parameter change moment and the time length of the parameter response time period is normalized to obtain the response delay factor under any adjustment operation.

[0035] The parameter response time period under each adjustment operation represents the response length of the environmental parameter from the initial value to the target value after the current adjustment operation is completed. The larger the value, the greater the time delay under this adjustment operation, and the more likely the controller has performance problems that cause response time delay.

[0036] The time interval between the first moment of the parameter response time period and the parameter change moment represents the length of time it takes for the environmental parameter to change from the initial value after the current adjustment operation is completed. The larger the value, the more serious the delay in the starting point of the actual environmental parameter change.

[0037] In this embodiment, the maximum-minimum normalization method is used for processing. The normalization method is a well-known technology and will not be introduced in detail here. In other embodiments, the implementer can choose according to the specific implementation scenario.

[0038] Step S202 : obtaining a parameter fluctuation factor for each adjustment operation according to the fluctuation of the difference between actual environmental parameters at adjacent moments within the parameter response time period for each adjustment operation.

[0039] In the first step, for any adjustment operation, the ratio of the difference between each two adjacent actual environmental parameters and the time interval within the parameter response time period is calculated to obtain the change rate of each parameter.

[0040] There is a parameter change rate between each moment and the adjacent previous moment in the parameter response time period of the adjustment operation, which reflects the speed of change of the actual environmental parameters in the parameter response time period.

[0041] The second step is to obtain the extreme value points of the parameter change rate within the parameter response time period.

[0042] It should be noted that the method for obtaining extreme points is a well-known technology and is only briefly introduced here. Within the parameter response time period of the adjustment operation, in chronological order, when the target parameter change rate is greater than the adjacent parameter change rate before and after it, or the target parameter change rate is less than the adjacent parameter change rate before and after it, the target parameter change rate is the extreme point, where the target parameter change rate is any parameter change rate.

[0043] It should be understood that the extreme point reflects that there are local large fluctuations in the changing trend of the actual environmental parameters within the parameter response time period of the adjustment operation.

[0044] The third step is to calculate the normalization coefficient of the product of the inverse of the mean of the time intervals between adjacent extreme points in the parameter response time period, the total number of extreme points, and the maximum absolute value of the parameter change rate to obtain the parameter fluctuation factor under any adjustment operation.

[0045] The reciprocal of the mean of the time intervals between two adjacent extreme value points within the parameter response time period of the adjustment operation represents the frequency of environmental parameter fluctuations within the same parameter time period. A larger value indicates a smaller interval between extreme value points, which in turn indicates more frequent and severe fluctuations in the actual environmental parameters during this adjustment operation. It should be noted that within the parameter response time period, each extreme value point corresponds to two moments. In this embodiment, the maximum time interval between two adjacent extreme value points is used for calculation.

[0046] The greater the total number of extreme points within the parameter response time period of an adjustment operation, the more fluctuations in the actual environmental parameters during that adjustment operation. The larger the corresponding parameter fluctuation factor, the more severe the parameter fluctuations. The absolute values ​​of all parameter change rates within the parameter response time period are taken. The larger the maximum absolute value, the greater the parameter fluctuation amplitude during that adjustment operation, and the more pronounced the parameter fluctuation characteristics. The parameter fluctuation factor represents the severity of the fluctuations in the actual environmental parameters after the corresponding adjustment operation is completed.

[0047] In this embodiment, the product may be normalized using a maximum-minimum normalization method. In other embodiments, implementers may select other appropriate normalization methods according to specific implementation scenarios.

[0048] Step S203 : performing negative correlation processing on the product of the response delay factor and the parameter fluctuation factor to obtain a response effect evaluation value of each adjustment operation.

[0049] The response delay factor reflects the degree of delay characteristics of the adjustment operation from the perspective of the time delay of the actual environmental parameters after the corresponding adjustment operation is completed. The parameter fluctuation factor reflects the degree of parameter fluctuation characteristics of the adjustment operation from the perspective of the fluctuation of the rate of change of the actual environmental parameters after the corresponding adjustment operation is completed.

[0050] When the value of the response delay factor is larger, the actual environmental parameters of the corresponding adjustment operation have a higher time delay phenomenon. When the value of the parameter fluctuation factor is larger, the fluctuation characteristics of the actual environmental parameters of the corresponding adjustment operation are more obvious, which means that the response effect of the adjustment operation is worse and the response effect evaluation value is smaller.

[0051] In this embodiment, the The product of the response delay factor and the parameter fluctuation factor is negatively correlated in the form of is the normalization function, Indicates the parameter to be negatively correlated.

[0052] From a full-cycle perspective, the parameter monitoring process for an adjustment operation consists of two key phases: a dynamic phase and a static phase. The parameter response period of the adjustment operation represents the dynamic phase, while the parameter retention period represents the static phase. Step S300 illustrates the analysis process for the possibility of controller performance anomalies during the dynamic phase, while step S400 illustrates the analysis process for the possibility of controller performance anomalies during the static phase.

[0053] Step S300 : analyzing abnormal conditions of the controller's adjustment operation according to the change trend of the response effect evaluation value of each adjustment operation to obtain a dynamic abnormality coefficient of the controller.

[0054] The response effects at different times are not exactly the same. The different patterns exhibited by all response effects reflect different degrees of performance abnormalities in the controller. When the response effects of multiple adjustments are consistently poor, it often means that the controller has systematic performance defects, such as aging or damage to internal key hardware or accuracy offset of the sensor, which makes it unable to stably output accurate control signals, or it is always unable to make correct control decisions when faced with adjustment instructions; and when the response effects of multiple adjustments fluctuate, showing a phenomenon of sometimes good and sometimes bad, it means that the controller has intermittent or random performance failures, such as loose line connectors, oxidation of relay contacts, etc., or occasional program errors.

[0055] For example, in the dynamic phase of temperature regulation, the controller must quickly respond to control commands, driving the temperature toward the target value at a steady rate. Abnormalities are primarily manifested by delayed response, frequent temperature fluctuations, and persistent poor response. Based on these characteristics, the instability of the dynamic response to each adjustment operation is analyzed to assess the presence of abnormalities in the dynamic phase corresponding to the parameter response time period.

[0056] As a specific example, Figure 3 As shown, the method for obtaining the dynamic anomaly coefficient can be implemented by steps S301 to S303.

[0057] In step S301 , an adjustment operation corresponding to a response effect evaluation value greater than or equal to a preset response threshold is recorded as a normal response operation, and an adjustment operation corresponding to a response effect evaluation value less than the preset response threshold is recorded as a suspected abnormal operation.

[0058] In this embodiment, the response threshold is set to 0.5. When the response effect evaluation value of the adjustment operation is greater than or equal to the response threshold, it indicates that the parameter characteristics of the adjustment operation are good and the corresponding adjustment operation response effect is good, so it is recorded as a normal response operation. When the response effect evaluation value of the adjustment operation is less than the response threshold, it indicates that the parameter characteristics of the adjustment operation show a significant abnormality and the corresponding adjustment operation response effect is poor, so it is recorded as a suspected abnormal operation, indicating a poor response phenomenon.

[0059] Step S302 : obtaining a continuous characteristic value according to the quantity distribution and continuous distribution of normal response operations and suspected abnormal operations.

[0060] In the first step, when there is a normal response operation among all the adjustment operations, all the adjustment operations are arranged in chronological order, all consecutive suspected abnormal operations constitute a first feature sequence, and all consecutive normal response operations constitute a second feature sequence.

[0061] When normal response operations exist in all adjustment operations, it means that when the controller performance test is currently being performed, there are at least two discontinuous data segments of suspected abnormal operations with poor responses. It should be understood that the first characteristic sequence contains all suspected abnormal operations and is continuous in time sequence, and the second characteristic sequence contains all normal response operations and is continuous in time sequence.

[0062] Furthermore, a continuous feature value is determined based on the number of first feature sequences, the maximum number of suspected abnormal operations included in all first feature sequences, and the maximum number of normal response operations included in all second feature sequences.

[0063] As a specific example, the method for obtaining the continuous eigenvalue can be expressed as follows: ;in, is a continuous eigenvalue, represents the number of first feature sequences in all adjustment operations, Indicates the maximum total number of suspected abnormal operations contained in all first feature sequences, Represents the total number of all adjustment operations, Indicates the maximum total number of normal response operations contained in all second feature sequences, It is a normalization function and is not restricted here.

[0064] It reflects the proportion of the maximum value of suspected abnormal operations that exist continuously. It indicates the continuity feature of the adjustment operation with poor response among all adjustment operations. When there are more data segments with continuous suspected abnormal operations and more data segments contain a larger number of abnormal operations, the continuity feature of the poor response operation is more obvious.

[0065] Because the existence of the second characteristic sequence makes the first characteristic sequence discontinuous, It can represent the maximum interval of discontinuity of poor response operation. The smaller its value is, the shorter the maximum interval is. It further indicates that when the poor response operation is discontinuous, there is also a relatively tight distribution feature, which reflects that the poor response of the overall regulation operation is more persistent.

[0066] Based on this, the persistence eigenvalue characterizes the degree of persistence characteristics of the adjustment operation with poor response during the current controller performance test.

[0067] In the second step, when all adjustment operations are suspected to be abnormal operations, the continuous characteristic value is set to a preset value.

[0068] When there is no normal response operation among all adjustment operations, all adjustment operations are suspected abnormal operations. At this time, the continuity characteristics of the adjustment operations with poor response are shown to the maximum extent. At this extreme case, the continuity characteristic value is set to a preset value. The preset value is the maximum value that the continuity characteristic value can achieve. In this embodiment, the preset value is 1.

[0069] In step S303, the dynamic abnormality coefficient of the controller is obtained according to the proportion of all suspected abnormal operations and the response effect evaluation value corresponding to the suspected abnormal operation, combined with the continuous characteristic value.

[0070] The persistence characteristic value characterizes the persistence distribution characteristics of the operations with poor responses among all adjustment operations. It is also necessary to combine the number distribution of suspected abnormal operations and the distribution of response effects to further analyze the severity of the poor responses presented by the operations with poor responses among all adjustment operations in order to evaluate the comprehensive abnormal situation in the dynamic stage.

[0071] Specifically, the ratio between the number of all suspected abnormal operations and the number of all adjustment operations is used as the first coefficient, and the negative correlation coefficient of the mean of the response effect evaluation values ​​of all suspected abnormal operations is used as the second coefficient; the dynamic abnormality coefficient of the controller is obtained according to the continuous characteristic value, the first coefficient and the second coefficient, and the continuous characteristic value, the first coefficient and the second coefficient are all positively correlated with the dynamic abnormality coefficient.

[0072] As a specific example, the method for obtaining the dynamic anomaly coefficient can be expressed by the formula: ;in, is the dynamic abnormal coefficient of the controller, is a continuous eigenvalue, Indicates the number of all suspected abnormal operations. represents the number of all adjustment operations, Represents the mean of the response effect evaluation values ​​of all suspected abnormal operations.

[0073] First coefficient It reflects the proportion of poor response operations in all adjustment operations. The larger the value, the greater the frequency of poor response, indicating that the poor response phenomenon is more frequent. It reflects the severity of the poor response operation among all adjustment operations. The larger the value, the more serious the response abnormality of the single poor response operation. The denominator is added with 1 to prevent the value from being 0, which will affect the calculation results.

[0074] The product of the first and second coefficients reflects the cumulative impact of poor responses across all control operations. Even if poor responses are scattered, their cumulative impact can still be significant if they are numerous and their individual effects are extremely poor. Combined with the persistence eigenvalue, this indicates that when poor responses occur frequently and severely across all control operations, the more severe the controller's systemic dynamic anomaly, and therefore the larger the value of the controller's dynamic anomaly coefficient, the greater the severity of the controller's systemic dynamic anomaly.

[0075] Step S400 , obtaining a static abnormality coefficient of the controller according to the difference between the actual environmental parameter and the target environmental parameter at each moment in the parameter holding time period under each adjustment operation and the duration of the difference.

[0076] The impact of abnormal performance of automobile air-conditioning fan controllers on environmental parameter adjustment runs through the entire cycle of environmental parameter adjustment, and is not only reflected in the regulation process from the initial actual environmental parameters to the target environmental parameters, but also in the maintenance of the target environmental parameters after reaching them. For example, for temperature adjustment operations, after the temperature reaches the target value, the system enters the constant temperature maintenance stage. At this time, the controller needs to fine-tune the fan speed based on slight temperature fluctuations. A controller with abnormal performance may cause frequent fluctuations in the fan speed due to unstable signal output, failure of the feedback mechanism, etc., resulting in large fluctuations in the temperature near the target value and failure to maintain it within the ideal fluctuation range.

[0077] Furthermore, for temperature regulation, the core task of the controller during the static phase is to maintain a stable temperature near the target value. Abnormalities are primarily manifested as large fluctuations in temperature after stabilization, such as large amplitude, high frequency, and early onset. Based on this characteristic, by analyzing the data fluctuations of the actual environmental parameters during the parameter holding period, we can assess the manifestation of abnormalities during the static phase corresponding to the parameter holding period.

[0078] The first step is to take any adjustment operation as an example, such as Figure 4 As shown, the method for obtaining the static abnormality factor of the adjustment operation can be implemented by steps S401 to S403.

[0079] Step S401 : for any adjustment operation, based on the difference between the actual environmental parameter and the target environmental parameter at each moment within the parameter holding time period, determine the parameter difference amplitude at each moment.

[0080] During the parameter holding period of any adjustment operation, the absolute value of the difference between the actual environmental parameter and the target environmental parameter at each moment is calculated, and normalized to obtain the parameter difference amplitude at each moment.

[0081] It should be noted that when the adjustment operation is temperature adjustment, the target environmental parameter is the target temperature parameter, which refers to the target value of the temperature adjustment; when the adjustment operation is wind speed adjustment, the target environmental parameter is the target wind speed parameter, which refers to the target value of the wind speed adjustment. At the same time, the normalization method can adopt, for example, the maximum-minimum normalization method, which is not limited here. The parameter difference amplitude at each moment reflects the magnitude of the oscillation of the environmental parameter at each moment during the parameter maintenance period.

[0082] Step S402 : Record the moment when the parameter difference amplitude is greater than or equal to a preset difference threshold as the oscillation moment.

[0083] In this embodiment, the difference threshold is set to 0.5, and the implementer can set it according to the specific implementation scenario. During the parameter holding period of the adjustment operation, when the parameter difference amplitude at a certain moment is greater than or equal to the difference threshold, it indicates that the parameter change amplitude is large at this time, and the possibility of a large fluctuation is greater, which is marked as the fluctuation moment.

[0084] When the parameter difference amplitude at a certain moment is less than the difference threshold, it means that the parameter change amplitude is small at this time, and the possibility of oscillation is smaller. This embodiment subsequently mainly analyzes the data oscillation performance based on the data change at the oscillation moment, so this moment is not marked.

[0085] Step S403 , obtaining a static abnormality factor of any adjustment operation based on the maximum value of the parameter difference amplitude of all oscillation moments within the parameter holding period, the number of oscillation moments, and the inverse of the time interval between the first moment and the second oscillation moment.

[0086] As a specific example, for any adjustment operation, the method for obtaining the static anomaly factor can be expressed as follows: ,in To adjust the static abnormality factor of the operation, Indicates the maximum value of the parameter difference amplitude at all oscillation moments within the parameter holding period of the adjustment operation. Indicates the number of oscillation moments during the parameter holding period of the adjustment operation, Indicates the time interval between the first moment of the parameter holding period of the adjustment operation and the first moment of oscillation.

[0087] It reflects the maximum amplitude of data oscillation in the static stage when the parameters are maintained under this adjustment operation. It reflects the number of data oscillations during the parameter holding phase of this adjustment operation. The larger the value, the more frequent the parameter oscillation phenomenon, and the larger the value of the static anomaly factor. It reflects the duration of the earliest large-scale oscillation phenomenon. The smaller its value is, the larger the oscillation occurs as soon as the actual environmental parameters stabilize, which further indicates that the controller has weak ability to maintain stability, the anomaly occurs early and the impact lasts for a long time. At this time, the larger the value of the static anomaly factor is; the larger its value is, the later the parameter oscillation phenomenon occurs, and the impact on the overall stability is relatively small. At this time, the smaller the value of the static anomaly factor is.

[0088] In the second step, the normalized value of the mean of the static anomaly factors of all regulation operations is used as the static anomaly coefficient of the controller.

[0089] A larger value for the static anomaly coefficient indicates that actual environmental parameters fluctuate more rapidly, frequently, and early during the static phase, leading to more pronounced performance deficiencies in the controller's constant temperature maintenance phase. The mean represents the equilibrium of static anomaly performance across all control operations. The normalization method can be a maximum-minimum normalization method, which is not a limitation here.

[0090] Step S500: judging the abnormal response of the controller according to the dynamic abnormal coefficient and the static abnormal coefficient, and obtaining the performance test result of the controller.

[0091] When adjusting the temperature inside the car, it is mainly adjusted according to the temperature and wind speed on the controller, and the adjustment function of the temperature inside the car is achieved through the coordinated control of the two. In this embodiment, the adjustment operation includes temperature adjustment and wind speed adjustment, and the actual environmental parameters include actual temperature parameters and actual wind speed parameters.

[0092] According to the method of step S200 to step S400, the dynamic abnormal conditions and static abnormal conditions exhibited by the controller under two different actual environmental parameters can be obtained, and then the abnormal analysis results under different dimensions corresponding to the two environmental parameters can be combined to obtain high-precision detection results of the performance of the automobile air-conditioning fan controller.

[0093] It should be understood that in the process of dynamic and static anomaly analysis of a single type of actual environmental parameters, all adjustment operations involved in the feature analysis should be consistent with the actual environmental parameters. For example, in the feature analysis process of obtaining the abnormal performance of temperature parameters, the actual temperature parameters under the temperature adjustment operation are all involved in the calculation.

[0094] As a specific example, Figure 5 As shown, the method for obtaining the performance detection result of the controller can be implemented by steps S501 to S503.

[0095] Step S501 : The product of the dynamic abnormality coefficient and the static abnormality coefficient corresponding to each actual environmental parameter is used as the full-cycle response abnormality index corresponding to each actual environmental parameter.

[0096] For any actual environmental parameter, the dynamic anomaly coefficient reflects any anomalies from the start of regulation to reaching the target value, such as response delays and significant parameter fluctuations. The static anomaly coefficient reflects any anomalies during the theoretical stability phase after the actual environmental parameter reaches the target value, such as frequent oscillations and large data deviations. Full-cycle anomalies of the controller often manifest themselves in both of these distinct phases, and together they determine the overall quality of the regulation operation.

[0097] Based on this, the abnormal characteristic analysis results of two different stages are integrated through product calculation to obtain the full-cycle response abnormality index of each actual environmental parameter, which characterizes the full-cycle response abnormality characteristics under temperature regulation or wind speed regulation.

[0098] Step S502 : performing weighted averaging on the full-cycle response anomaly index corresponding to the actual temperature parameter and the full-cycle response anomaly index corresponding to the actual wind speed parameter to obtain a comprehensive anomaly characteristic value.

[0099] The weight for temperature adjustment is calculated by taking the ratio of the number of temperature adjustment operations to the total number of adjustment operations among all adjustment operations involved in the current automotive air conditioning fan controller performance test. This weight represents the usage percentage of temperature adjustment operations in the total number of adjustment operations. The weight for wind speed adjustment is calculated by taking the ratio of the number of wind speed adjustment operations to the total number of adjustment operations as the weight representing the usage percentage of wind speed adjustment operations in the total number of adjustment operations.

[0100] By using the weight values ​​corresponding to temperature adjustment and wind speed adjustment, the full-cycle response anomaly index corresponding to the actual temperature parameter and the full-cycle response anomaly index corresponding to the actual wind speed parameter are weighted and averaged to obtain the comprehensive anomaly characteristic value of the controller.

[0101] In the adjustment operation instructions of historical data, the more operations account for, the more frequently the corresponding adjustment function is used, and the larger the corresponding weight value. Then, by adopting the weighted average method and integrating the feature analysis results of multiple dimensions, the performance abnormalities of the currently tested controller can be more comprehensively characterized.

[0102] As a specific example, the controller's comprehensive abnormal characteristic value The calculation formula can be expressed as: ; Among them, m represents the weight value corresponding to temperature regulation; R represents the full-cycle response abnormality index of the controller under temperature regulation; n represents the weight value corresponding to wind speed regulation; G represents the full-cycle response abnormality index of the controller under wind speed regulation; the larger the weight value and the larger the corresponding response abnormality feature, the more obvious the controller performance abnormality reflected by it, and the larger the comprehensive abnormality feature value Y of the corresponding controller response.

[0103] Step S503: determine the abnormal response of the controller based on the comprehensive abnormal characteristic value, and obtain the performance test result of the controller.

[0104] Specifically, when the comprehensive abnormal characteristic value is less than the preset abnormal threshold, there is no abnormality in the controller performance; when the comprehensive abnormal characteristic value is greater than or equal to the preset abnormal threshold, there is an abnormality in the controller performance.

[0105] In this embodiment, the abnormality threshold is set to 0.7. In other embodiments, implementers may set this threshold based on specific implementation scenarios. When the combined abnormality characteristic value is less than the abnormality threshold, the controller is performing well and can continue to be used. At this point, there are no abnormalities in the controller performance. When the combined abnormality characteristic value is greater than or equal to the abnormality threshold, the automotive air conditioning fan controller has performance issues that affect functions such as temperature and wind speed, requiring repair or replacement. Early warnings or other means can be used to alert the user to perform further repairs on the controller.

[0106] In summary, the present invention monitors the actual environmental parameters for each adjustment operation during wind speed and temperature control tests. The response time and variation of the actual environmental parameters to reach the target values ​​are used to analyze the response effect of each adjustment operation, reflecting the controller's response delay to the adjustment instructions. The response effects of multiple adjustment operations are compared to determine the controller's dynamic anomaly coefficient. The static anomaly coefficient is then determined based on the stability of the actual environmental parameters after reaching the target values. Based on the dynamic and static anomaly characteristic analysis processes, the controller's response anomaly characteristics for the full wind speed adjustment cycle and the full temperature adjustment cycle are similarly determined. The corresponding weights are then determined based on the historical usage frequency of temperature and wind speed control. The weights are then combined with the corresponding response anomaly characteristics to obtain the controller's comprehensive anomaly characteristic value. This analysis process improves fault identification accuracy by performing dynamic and static full-cycle analysis of the controller's key indicator parameters and can detect intermittent faults that are often missed. By combining the usage frequency of different adjustment instructions, the detection results are more consistent with the user's actual usage scenarios.

[0107] like Figure 6 As shown, an embodiment of the present invention further provides a performance detection system for an automobile air-conditioning fan controller, which is used to implement the steps of a performance detection method for an automobile air-conditioning fan controller. The performance detection system for an automobile air-conditioning fan controller specifically includes: A data acquisition module is used to obtain the target environmental parameters of each adjustment operation of the automobile air-conditioning fan controller, as well as the actual environmental parameters at each moment in the parameter response time period and the parameter holding time period; The response evaluation module is used to obtain the response effect evaluation value of each adjustment operation based on the fluctuation degree and frequency of the actual environmental parameters at each moment in the parameter response time period under each adjustment operation, combined with the response delay corresponding to the parameter response time period; The dynamic abnormality analysis module is used to analyze the abnormality of the controller's adjustment operation according to the change trend of the response effect evaluation value of each adjustment operation, and obtain the dynamic abnormality coefficient of the controller; The static anomaly analysis module is used to obtain the static anomaly coefficient of the controller based on the difference between the actual environmental parameters and the target environmental parameters at each moment in the parameter holding period under each adjustment operation and the duration of the difference; The performance detection module is used to judge the response abnormality of the controller based on the dynamic abnormality coefficient and the static abnormality coefficient, and obtain the performance detection result of the controller.

[0108] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A performance detection method for an automobile air-conditioning fan controller, characterized in that: The method comprises the following steps: Obtain the target environmental parameters for each adjustment operation of the automobile air-conditioning fan controller, as well as the actual environmental parameters at each moment in the parameter response time period and the parameter holding time period; The response effect evaluation value of each adjustment operation is obtained based on the fluctuation degree and frequency of the actual environmental parameters at each moment in the parameter response time period under each adjustment operation, combined with the response delay corresponding to the parameter response time period; According to the changing trend of the response effect evaluation value of each adjustment operation, the abnormal situation of the controller's adjustment operation is analyzed to obtain the dynamic abnormality coefficient of the controller; According to the difference between the actual environmental parameters and the target environmental parameters at each moment in the parameter holding period under each adjustment operation and the duration of the difference, the static abnormality coefficient of the controller is obtained; According to the dynamic abnormality coefficient and the static abnormality coefficient, the abnormal response of the controller is judged and the performance test result of the controller is obtained.

2. The performance detection method of a vehicle air-conditioning fan controller according to claim 1, characterized in that: The response effect evaluation value of each adjustment operation is obtained based on the fluctuation degree and frequency of the actual environmental parameters at each moment in the parameter response time period under each adjustment operation, combined with the response delay corresponding to the parameter response time period, specifically including: Obtaining a response delay factor for each adjustment operation according to the initial change time of the actual environmental parameter within the parameter response time period and the length of the parameter response time period for each adjustment operation; According to the fluctuation of the difference between the actual environmental parameters at adjacent moments within the parameter response time period under each adjustment operation, the parameter fluctuation factor under each adjustment operation is obtained; A negative correlation process is performed on the product of the response delay factor and the parameter fluctuation factor to obtain a response effect evaluation value of each adjustment operation.

3. The performance detection method of a vehicle air-conditioning fan controller according to claim 2, characterized in that: Obtaining the response delay factor for each adjustment operation according to the initial change time of the actual environmental parameter within the parameter response time period and the length of the parameter response time period for each adjustment operation specifically includes: For any adjustment operation, the earliest moment in the parameter response time period that is different from the actual environmental parameters of the first moment is recorded as the parameter change moment; the product of the time interval between the first moment in the parameter response time period and the parameter change moment and the time length of the parameter response time period is normalized to obtain the response delay factor under the any adjustment operation.

4. The performance testing method of a vehicle air-conditioning fan controller according to claim 2, characterized in that: The parameter fluctuation factor under each adjustment operation is obtained according to the fluctuation of the difference between the actual environmental parameters at adjacent moments within the parameter response time period under each adjustment operation, specifically including: For any adjustment operation, calculate the ratio of the difference between each two adjacent actual environmental parameters and the time interval within the parameter response time period to obtain the change rate of each parameter and obtain the extreme value point of the parameter change rate within the parameter response time period; The normalization coefficient of the product of the reciprocal of the mean of the time intervals between adjacent extreme points in the parameter response time period, the total number of extreme points, and the maximum absolute value of the parameter change rate is calculated to obtain the parameter fluctuation factor under any adjustment operation.

5. The performance testing method of a vehicle air-conditioning fan controller according to claim 1, characterized in that: The abnormality of the controller's adjustment operation is analyzed according to the change trend of the response effect evaluation value of each adjustment operation to obtain the dynamic abnormality coefficient of the controller, which specifically includes: The adjustment operation corresponding to the response effect evaluation value greater than or equal to the preset response threshold is recorded as a normal response operation, and the adjustment operation corresponding to the response effect evaluation value less than the preset response threshold is recorded as a suspected abnormal operation; Obtaining continuous characteristic values ​​based on the quantity distribution and continuous distribution of normal response operations and suspected abnormal operations; The ratio between the number of all suspected abnormal operations and the number of all adjustment operations is used as the first coefficient, and the negative correlation coefficient of the mean of the response effect evaluation values ​​of all suspected abnormal operations is used as the second coefficient; The dynamic abnormality coefficient of the controller is obtained according to the continuous characteristic value, the first coefficient and the second coefficient. The continuous characteristic value, the first coefficient and the second coefficient are all positively correlated with the dynamic abnormality coefficient.

6. The performance testing method of a vehicle air-conditioning fan controller according to claim 5, characterized in that: The continuous characteristic value is obtained according to the quantity distribution and continuous distribution of normal response operations and suspected abnormal operations, specifically including: When all adjustment operations are suspected to be abnormal operations, the continuous characteristic value is set to a preset value; When there is a normal response operation among all the adjustment operations, all the adjustment operations are arranged in chronological order, all the consecutive suspected abnormal operations constitute a first feature sequence, and all the consecutive normal response operations constitute a second feature sequence; Based on the number of first feature sequences and the maximum number of suspected abnormal operations contained in all first feature sequences, and the maximum number of normal response operations contained in all second feature sequences, a continuous feature value is determined, and the preset value is greater than or equal to the continuous feature value at this time.

7. The performance testing method of a vehicle air-conditioning fan controller according to claim 1, characterized in that: The static abnormality coefficient of the controller is obtained according to the difference between the actual environmental parameters and the target environmental parameters at each moment in the parameter holding time period under each adjustment operation and the duration of the difference, specifically including: For any adjustment operation, based on the difference between the actual environmental parameters and the target environmental parameters at each moment during the parameter holding time period, the parameter difference amplitude at each moment is determined; the moment corresponding to the parameter difference amplitude being greater than or equal to the preset difference threshold is recorded as the oscillation moment, and based on the maximum value of the parameter difference amplitude of all oscillation moments during the parameter holding time period, the number of oscillation moments, and the inverse of the time interval between the first moment and the second oscillation moment, the static abnormality factor of the said any adjustment operation is obtained; The normalized value of the mean of the static anomaly factors of all regulation operations is used as the static anomaly coefficient of the controller.

8. The performance testing method of a vehicle air-conditioning fan controller according to claim 1, characterized in that: The method of determining the abnormal response of the controller based on the dynamic abnormality coefficient and the static abnormality coefficient to obtain the performance test result of the controller specifically includes: The actual environmental parameters include actual temperature parameters and actual wind speed parameters; The product of the dynamic anomaly coefficient and the static anomaly coefficient corresponding to each actual environmental parameter is used as the full-cycle response anomaly index corresponding to each actual environmental parameter; The full-cycle response anomaly index corresponding to the actual temperature parameter and the full-cycle response anomaly index corresponding to the actual wind speed parameter are weighted and averaged to obtain the comprehensive anomaly characteristic value; The abnormal response of the controller is judged according to the comprehensive abnormal characteristic value, and the performance test result of the controller is obtained.

9. The performance testing method of a vehicle air-conditioning fan controller according to claim 8, characterized in that: The step of determining the abnormal response of the controller based on the comprehensive abnormal characteristic value and obtaining the performance test result of the controller specifically includes: When the comprehensive abnormal characteristic value is less than the preset abnormal threshold, there is no abnormality in the controller performance; when the comprehensive abnormal characteristic value is greater than or equal to the preset abnormal threshold, there is an abnormality in the controller performance.

10. A performance detection system for an automobile air-conditioning fan controller, characterized in that: The system is used to implement the steps of a performance detection method for an automobile air-conditioning fan controller according to any one of claims 1 to 9, wherein the performance detection system for the automobile air-conditioning fan controller specifically comprises: A data acquisition module is used to obtain the target environmental parameters of each adjustment operation of the automobile air-conditioning fan controller, as well as the actual environmental parameters at each moment in the parameter response time period and the parameter holding time period; The response evaluation module is used to obtain the response effect evaluation value of each adjustment operation based on the fluctuation degree and frequency of the actual environmental parameters at each moment in the parameter response time period under each adjustment operation, combined with the response delay corresponding to the parameter response time period; The dynamic abnormality analysis module is used to analyze the abnormality of the controller's adjustment operation according to the change trend of the response effect evaluation value of each adjustment operation, and obtain the dynamic abnormality coefficient of the controller; The static anomaly analysis module is used to obtain the static anomaly coefficient of the controller based on the difference between the actual environmental parameters and the target environmental parameters at each moment in the parameter holding period under each adjustment operation and the duration of the difference; The performance detection module is used to judge the response abnormality of the controller based on the dynamic abnormality coefficient and the static abnormality coefficient, and obtain the performance detection result of the controller.

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