A method, device and medium for testing and analyzing the junction temperature rise of a vehicle-mounted switch tube

Abstract

The present invention relates to the field of temperature rise test technology, and specifically to a node temperature rise test and analysis method, device and medium for a vehicle-mounted switch tube. The method determines the temperature change characteristic factor under different rated loads according to the degree of change of the node temperature data under different rated loads; determines the correlation factor according to the relationship between the environmental conditions and the temperature change characteristic factor; obtains the load change characteristic value in each dynamic simulation process; clusters the different dynamic model processes based on the correlation factor and the load change characteristic value to obtain a cluster cluster; compares the temperature rise difference between the temperature rise data and the rated temperature rise data at each time point in the dynamic model process, and performs data cleaning on the temperature rise data in combination with the temperature change characteristic factor to determine the temperature rise detection value. The present invention improves the accuracy of the test results by analyzing multiple dynamic simulation processes, performing data cleaning on the temperature rise data, and determining the temperature rise detection value.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature rise testing, and in particular to a method, device and medium for testing and analyzing the node temperature rise of a vehicle-mounted switch tube. Background Art

[0002] An on-board switch tube refers to an electronic switching device used in a vehicle's electronic system, whose function is to control the flow of current. A switch tube can be a semiconductor device, such as a metal-oxide-semiconductor field-effect transistor (MOSFET), an insulated gate bipolar transistor (IGBT), or other types of transistors. They play a key role in the vehicle's electrical system, as they are responsible for controlling the switching of current, and therefore generate heat when working. If this heat cannot be effectively dissipated, the temperature of the switch tube will rise, which may lead to performance degradation or even damage.

[0003] Currently, the common method for temperature rise testing of vehicle-mounted switch tubes is to collect the temperature data of the vehicle-mounted switch tubes, perform abnormal detection on the temperature difference between the temperature data and the temperature within the safety range, and then realize the node temperature rise analysis of the vehicle-mounted switch tube. However, since the vehicle-mounted switch tube uses the temperature difference between the constant value and the safety range to express the impact of the current load on the temperature of the switch tube too simply, the impact of continuous load changes during dynamic testing cannot be accurately tested for temperature rise, resulting in redundant analysis of the temperature rise test results. Summary of the invention

[0004] In order to solve the technical problem that the temperature rise test cannot be accurately performed due to the influence of continuous load changes during dynamic testing, the purpose of the present invention is to provide a node temperature rise test analysis method, device and medium for vehicle-mounted switch tubes. The technical solution adopted is as follows:

[0005] In a first aspect, an embodiment of the present invention provides a node temperature rise test and analysis method for an on-vehicle switch tube, the method comprising:

[0006] Obtain the junction temperature data of the vehicle switch tube under different rated loads and different environmental conditions;

[0007] According to the degree of change of the junction temperature data under different rated loads, the temperature change characteristic factors under different rated loads are determined;

[0008] Determine the correlation factor according to the relationship between the environmental conditions and the temperature change characteristic factors under different rated loads under the same environmental conditions; analyze the load change complexity in each dynamic simulation process to determine the load change characteristic value; cluster the different dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters;

[0009] The temperature rise data at each time point in the dynamic model process are compared with the temperature rise difference of the rated temperature rise data. Combined with the temperature change characteristic factor and the temperature rise difference, the temperature rise data at each time point in the cluster is cleaned to determine the cleaned temperature rise detection value.

[0010] Furthermore, determining the temperature variation characteristic factors under different rated loads according to the variation degree of the node temperature data under different rated loads includes:

[0011] Analyze the overall distribution of the node temperature data within the time after the node temperature data reaches a constant value, and determine the overall distribution value;

[0012] Analyze the data fluctuation of the node temperature data within a period of time after the node temperature data reaches a constant value, and determine the data fluctuation value;

[0013] Analyze the instantaneous change value and occurrence frequency of the node temperature data before the node temperature data reaches a constant value, and determine the degree of instantaneous change;

[0014] The temperature variation characteristic factors under different rated loads are determined by combining the overall distribution value, the data fluctuation value and the instantaneous variation degree.

[0015] Furthermore, the correlation factor is determined according to the relationship between the environmental conditions and the temperature change characteristic factors under different rated loads under the same environmental conditions, including:

[0016] The correlation coefficient between the environmental condition and the corresponding temperature change characteristic factor under the same environmental condition and the same rated load is taken as the single load correlation;

[0017] The overall distribution of the single load correlation between the environmental condition and the corresponding temperature change characteristic factor under different rated loads under the same environmental condition is analyzed to determine the correlation factor between the environmental condition and the temperature change characteristic factor.

[0018] Furthermore, the analysis of the complexity of load changes in each dynamic simulation process and determination of load change characteristic values ​​include:

[0019] The information entropy of the load during each dynamic simulation is obtained as the load change characteristic value.

[0020] Furthermore, clustering different sub-dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters includes:

[0021] By traversing the numerical differences of all environmental conditions, the correlation factor is used to weight the numerical differences of the corresponding environmental conditions, and the similarity discrimination value is obtained by combining the difference between the corresponding load change characteristic values. Based on the k-means clustering algorithm, the similarity discrimination value is used as the distance between different dynamic simulation processes, and the inflection point method is used to obtain the optimal k, and multiple clusters are obtained by clustering.

[0022] Furthermore, before comparing the temperature rise data at each time point in the dynamic model process with the temperature rise difference of the rated temperature rise data, the method further includes:

[0023] The difference between the current node temperature data of the on-board switch tube and the current ambient temperature is obtained as the temperature rise data.

[0024] Furthermore, the temperature rise data at each time point in the clustering cluster are cleaned by comparing the temperature rise difference between the temperature rise data at each time point and the rated temperature rise data in the dynamic model process, combining the temperature change characteristic factor and the temperature rise difference, and determining the cleaned temperature rise detection value, including:

[0025] When the temperature rise difference is greater than the preset difference threshold, the discreteness of the mean value of the temperature rise difference at each time point in different dynamic simulation processes is analyzed to determine the discrete value of the temperature rise;

[0026] When the temperature rise difference is greater than the preset difference threshold, the discrete situation of the time sequence position difference between the first and last time endpoints in different dynamic simulation processes is analyzed to determine the time sequence discrete value;

[0027] Determine the adjustment weight of the temperature rise difference by combining the temperature rise discrete value, the time series discrete value and the temperature change characteristic factor;

[0028] The temperature rise difference is weighted by the adjustment weight to obtain the temperature rise detection value after cleaning.

[0029] Furthermore, the combining of the temperature rise discrete value, the time series discrete value and the temperature change characteristic factor to determine the adjustment weight of the temperature rise difference includes:

[0030] Analyze the discrete values ​​of the first time endpoint and the last time endpoint to determine the rationality of the time sequence;

[0031] The adjustment weight of the temperature rise difference is determined according to the temperature change characteristic factor, time rationality and temperature rise discrete value; among them, the adjustment weight of the temperature rise difference is negatively correlated with the temperature change characteristic factor, and the adjustment weight of the temperature rise difference is positively correlated with the time rationality and temperature rise discrete value.

[0032] In a second aspect, a node temperature rise test and analysis device for a vehicle-mounted switch tube is provided, the device comprising the following modules:

[0033] An acquisition module is used to obtain the junction temperature data of the vehicle-mounted switch tube under different rated loads and different environmental conditions;

[0034] A change analysis module is used to determine the temperature change characteristic factors under different rated loads according to the degree of change of the node temperature data under different rated loads;

[0035] A clustering module is used to determine a correlation factor according to the relationship between environmental conditions and temperature change characteristic factors under different rated loads under the same environmental conditions; analyze the load change complexity in each dynamic simulation process to determine the load change characteristic value; cluster different sub-dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters;

[0036] The temperature rise detection module is used to compare the temperature rise data at each time point in the dynamic model process with the temperature rise difference of the rated temperature rise data, and to clean the temperature rise data at each time point in the cluster in combination with the temperature change characteristic factor and the temperature rise difference, and determine the cleaned temperature rise detection value.

[0037] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, various possible embodiments of the first aspect are implemented.

[0038] In a fourth aspect, an embodiment of the present invention provides a computer program product, which includes: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0039] In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute various possible implementations of the first aspect.

[0040] The embodiments of the present invention have at least the following beneficial effects:

[0041] By analyzing the influence of load changes on node temperature data during steady-state simulation test, a correlation factor that can reflect relevant characteristic values ​​is obtained. This operation avoids the situation that the influence of the current load on the temperature of the switch tube is too simple by only using the temperature difference between the constant value and the safe range. The influence of the continuous load change during the dynamic test cannot be accurately judged, resulting in redundant analysis of the temperature rise test results. The present invention analyzes the temperature data under different rated loads and different environmental conditions, thereby improving the detection efficiency. The present invention clusters the dynamic simulation process of temperature rise data with similar conditions by controlling environmental variables and distinguishing the expression of temperature change characteristic factors and load value changes under different environmental conditions. According to the location characteristics of the temperature rise data of the dynamic simulation process in the same cluster and the influence of load changes on the temperature rise data, all temperature rise data beyond the safe range are judged for data cleaning. This operation avoids the misjudgment of the rationality of the switch tube specifications caused by misjudging some abnormal temperature rise data as normal due to random changes in load and environment, thereby improving the accuracy of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0043] Figure 1 A method flow chart of a method for testing and analyzing a node temperature rise of a vehicle-mounted switch tube provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a full-size measurement record of the appearance of a sample diode provided by an embodiment of the present invention;

[0045] Figure 3 A schematic diagram of the steps of obtaining a temperature change characteristic factor provided by an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a step of obtaining clusters provided by an embodiment of the present invention;

[0047] Figure 5 A system block diagram of a node temperature rise test and analysis device for a vehicle-mounted switch tube provided by an embodiment of the present invention;

[0048] Figure 6 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in combination with the accompanying drawings and preferred embodiments, a node temperature rise test and analysis method, device and medium for a vehicle-mounted switch tube proposed in accordance with the present invention, its specific implementation method, structure, characteristics and effects are described in detail as follows.

[0050] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0051] Among them, in the description of the embodiments of the present invention, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" refers to two or more than two.

[0052] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0053] 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.

[0054] The embodiments of the present invention are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0055] The embodiment of the present invention provides a specific implementation method of a node temperature rise test and analysis method, device and medium for an on-board switch tube. The method is applicable to the node temperature rise test scenario of an on-board switch tube. The sample used for the test in this scenario is a diode with model number CTBAS21RQ. Figure 2 , Figure 2 Schematic diagram of the full-scale measurement record of the appearance of the sample diode.

[0056] The following is a detailed description of a specific solution of a node temperature rise test and analysis method, device and medium for an on-vehicle switch tube provided by the present invention in conjunction with the accompanying drawings.

[0057] See also Figure 1, which shows a flowchart of a node temperature rise test and analysis method for a vehicle-mounted switch tube provided by an embodiment of the present invention, the method comprising the following steps:

[0058] Step S100, obtaining the junction temperature data of the vehicle-mounted switch tube under different rated loads and different environmental conditions.

[0059] First select a suitable test environment, ensure the ambient temperature, and maintain the temperature.

[0060] Further, prepare the test equipment, including temperature sensors, data loggers, power supplies, and loads. Ensure that the onboard switch tube and heat sink are installed correctly and in good contact. Fix the temperature sensor on the key nodes of the switch tube, such as the chip surface or near the solder joint, to accurately measure the temperature. Among them, the temperature sensor is usually a thermocouple.

[0061] Then, a steady-state test is performed. When the on-board switch tube works under rated load, the change of its junction temperature over time is recorded until the temperature stabilizes at a constant value. It should be noted that the constant value is a relatively stable value, which will still fluctuate to a certain extent. The size of the constant value is set in advance by the implementer according to the actual situation. Record the ambient temperature and junction temperature data, and obtain the temperature rise data. More specifically, the temperature rise data is the difference between the junction temperature data and the ambient temperature.

[0062] Furthermore, a dynamic simulation test is conducted to simulate the dynamic load changes during vehicle operation by adjusting the current power and record the changes in the node temperature data of the on-board switch tube. It is analyzed whether the temperature change is within the safe range and whether the switch tube can respond quickly to the load change.

[0063] Finally, analyze the test results and compare the temperature rise data obtained from the test with the rated temperature rise data of the switch tube to ensure that the temperature rise does not exceed the maximum allowable value specified by the manufacturer. If the temperature rise is too high, it is necessary to analyze the cause and it may be necessary to improve the heat dissipation design or select a higher specification switch tube.

[0064] Therefore, the data curve of temperature rise data and time corresponding to each node is obtained under different rated loads during the above operation process, and the curve shows the relevant data characteristics during the steady-state test. It should be noted that the data curve of temperature rise data and time, that is, the curve with the horizontal axis being time and the vertical axis being the temperature rise data, is fitted by the temperature rise data at different times.

[0065] Then, during the steady-state test, first set the variables of various environmental factors faced by common automobile operating conditions, such as temperature, humidity and other multi-dimensional data. While ensuring the data changes of single-dimensional environmental conditions and other data remain unchanged, obtain the actual load changes and corresponding temperature changes of each node under different historical states one by one, thereby obtaining the temperature curve of each node with respect to time under a certain dimensional feature; then, in the currently set dynamic test process, obtain the numerical value of the environmental factor, and obtain the load change curve and temperature change curve in the dynamic test process; and it should be noted that during the dynamic test process, the actual load changes should be simulated to meet the relevant conditions of the actual vehicle when driving under different conditions, so as to achieve the best simulation environment control.

[0066] Step S200, determining temperature variation characteristic factors under different rated loads according to the variation degree of the node temperature data under different rated loads.

[0067] According to prior knowledge, during the steady-state test of a switch tube at a certain node position, its temperature rise rate under different rated loads is different. Since the temperature change of the switch tube is nonlinear, if you want to show the impact of any load on the temperature of the switch tube, it is too simple to only use the temperature difference between the constant value and the safe range to show the impact of the current load on the temperature of the switch tube. It is impossible to accurately judge the impact of continuous load changes during the dynamic test, resulting in redundant analysis of the temperature rise test results and reduced efficiency.

[0068] First, for the relevant temperature change data graph during the steady-state test obtained above, obtain its temperature curve at a constant value. According to prior knowledge, for any rated load, the temperature will still fluctuate to a certain extent when it is at a constant value. For the switching tube, the greater the temperature fluctuation when it is at a constant value under this load, the greater the instability relative to the constant value under other loads, and the greater the impact of different loads on the changing trend of temperature changes during dynamic simulation tests.

[0069] Therefore, when the temperature data reaches a constant value, the overall distribution of the temperature in all subsequent time and the temperature data fluctuation are obtained, and then the data fluctuation is used to reflect the constant temperature stability under the current rated load. The larger the value corresponding to the data fluctuation, the lower the stability.

[0070] Furthermore, due to the nonlinear change when the temperature rises, and according to the analysis, the faster the temperature rises in the switch tube, the smaller the difference between the constant temperature average and the maximum value of the safety range, the greater the impact on the switch tube under this load, and the greater the impact on the stability of the vehicle.

[0071] Therefore, the node temperature data before reaching a constant value is analyzed to obtain the instantaneous slope of the temperature value corresponding to each time point, and the frequency of occurrence of each instantaneous slope. At the same time, the ratio of the overall distribution value corresponding to the overall distribution of the temperature data at a constant value to the maximum value of the safety range is compared, and combined with the data fluctuation value corresponding to the data fluctuation of the node temperature data obtained above, the temperature change characteristic factor at the current rated load is obtained.

[0072] In some embodiments, the temperature variation characteristic factors under different rated loads are determined by analyzing the variation degree of the node temperature data under different rated loads. That is, the above step S200 can be performed by Figure 3 The following steps S210 to S240 are implemented as shown:

[0073] Step S210, analyzing the overall distribution of the node temperature data within the time after the node temperature data reaches a constant value, and determining the overall distribution value.

[0074] In one embodiment of the present invention, the average value of the junction temperature data within the time after the junction temperature data reaches the constant value may be used as the temperature average value.

[0075] In other embodiments of the present invention, the median or mode of the node temperature data within the time after the node temperature data reaches a constant value may also be used as the temperature mean.

[0076] The ratio of the temperature mean and the maximum value of the safety range of the node temperature data is taken as the overall distribution value.

[0077] Step S220, analyzing the data fluctuation of the node temperature data within the time after the node temperature data reaches a constant value, and determining the data fluctuation value.

[0078] In one embodiment of the present invention, the standard deviation of the junction temperature data within a period of time after the junction temperature data reaches a constant value may be used as the data fluctuation value.

[0079] In other embodiments of the present invention, the variance or coefficient of variation of the node temperature data within a period of time after the node temperature data reaches a constant value may be used as the data fluctuation value.

[0080] Step S230, analyzing the instantaneous change value and the occurrence frequency of the node temperature data before the node temperature data reaches a constant value, and determining the instantaneous change degree.

[0081] Obtain the instantaneous slope of the node temperature data before the node temperature data reaches a constant value, and the occurrence frequency corresponding to the slope;

[0082] The average of the product of the instantaneous slope of the node temperature data and the corresponding occurrence frequency in all the time before the node temperature data reaches a constant value is taken as the instantaneous change degree. The calculation formula for the instantaneous change degree is: Among them, E3 i is the instantaneous change degree under the i-th rated load; N i is the number of time points before reaching a constant value under the i-th rated load; k t P is the instantaneous slope of the junction temperature data at the tth time point before reaching a constant value under the i-th rated load; kt is the instantaneous slope k of the junction temperature data before the tth time point before reaching a constant value under the i-th rated load t The frequency of occurrence.

[0083] Step S240, combining the overall distribution value, the data fluctuation value and the instantaneous change degree to determine the temperature change characteristic factor under different rated loads.

[0084] The overall distribution value, the data fluctuation value and the instantaneous change degree are all positively correlated with the temperature change characteristic factor. In one embodiment of the present invention, the product of the overall distribution value, the data fluctuation value and the instantaneous change degree can be directly used as the temperature change characteristic factor under the corresponding rated load.

[0085] In other embodiments of the present invention, the sum of the overall distribution value, the data fluctuation value and the instantaneous change degree may also be directly used as a temperature change characteristic factor corresponding to the rated load.

[0086] Instantaneous change That is, it means the weighted mean of the temperature slope in the temperature rising stage before the constant value of the temperature. The larger the value of the instantaneous change degree, the faster the temperature change rate under the current i-th rated load, and the greater the impact of the rated load on the temperature change in the dynamic monitoring process. At the same time, for the time period after the temperature is constant, the closer the overall distribution value composed of the ratio of the temperature mean to the maximum value of the safety range is to 1, the closer the temperature mean is to the maximum value of the safety range. At the same time, the larger the overall distribution value is, the higher the constant value is. The larger the data fluctuation value of the temperature in this time period, the lower the stability, and the greater the impact of the current load on the temperature change in the dynamic simulation process. Therefore, the above three characteristics are combined to obtain the temperature change characteristic factor E. i The larger the value is, the greater the impact of the current load on the temperature change of the switch tube is.

[0087] The calculation formula of the temperature change characteristic factor is: Among them, Ei is the temperature variation characteristic factor under the i-th rated load; σ i is the standard deviation of the node temperature data under the i-th rated load within the time after the node temperature data reaches a constant value, that is, the data fluctuation value; is the overall distribution value of the node temperature data under the i-th rated load within the time after the node temperature data reaches a constant value; is the temperature average of the junction temperature data under the i-th rated load within the time after the junction temperature data reaches a constant value; T 0 The maximum value of the safe range of the junction temperature data; E3 i is the instantaneous change degree under the i-th rated load.

[0088] Step S300, determining a correlation factor according to the relationship between environmental conditions and temperature change characteristic factors under different rated loads under the same environmental conditions; analyzing the complexity of load changes in each dynamic simulation process to determine the load change characteristic value; clustering different sub-dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters.

[0089] Under different environmental conditions and different rated loads, the correlation between environmental conditions and temperature change characteristic factors can be determined, because the temperature change characteristic factors reflect the junction temperature rise of the on-board switch tube. Furthermore, the load transformation in the dynamic simulation process under changing environmental conditions and rated loads is analyzed, and the load change characteristic value is determined, because the load will also undergo certain changes under the simulated vehicle operation process. When the complexity of the load change can also reflect the simulation conditions of the dynamic simulation environment to a certain extent. Finally, the dynamic simulation process is clustered by combining the correlation factor and the load change characteristic value to obtain clusters, where the simulation conditions of the dynamic simulation process belonging to the same cluster are similar.

[0090] In some embodiments, by analyzing the temperature characteristics under different environmental conditions and different rated loads, different sub-dynamic model processes are clustered to obtain cluster clusters, that is, the above step S300 can be achieved by Figure 4 The steps shown achieve:

[0091] Step S310 , determining a correlation factor according to the relationship between the environmental conditions and the temperature change characteristic factors under different rated loads under the same environmental conditions.

[0092] According to the environmental condition factor variables of various dimensions obtained in step S100, such as multi-dimensional data variables such as environmental humidity and environmental temperature, the load change and temperature change during the operation of the vehicle are simulated. At this time, according to the temperature change characteristic factor obtained in step S200, the temperature change characteristic factor changes with respect to the change of the rated load. Therefore, in the process of obtaining the linear change of the environmental conditions under the same dimension, for the correlation of the temperature change under a certain rated load, that is, for the data change process of any dimension, the correlation factor is determined according to the correlation of the temperature change under any rated load.

[0093] That is, the correlation coefficient between the environmental condition and the corresponding temperature change characteristic factor under the same environmental condition and the same rated load is used as the single load correlation. For different environmental conditions, the correlation coefficient is used to calculate the relationship between different environmental conditions and the temperature change characteristic factor under the same rated load and different environmental conditions. It should be noted that the correlation coefficient in the embodiment of the present invention is the Pearson correlation coefficient.

[0094] Furthermore, the overall distribution of the single load correlation between the environmental condition and the corresponding temperature change characteristic factor under different rated loads under the same environmental condition is analyzed to determine the correlation factor between the environmental condition and the temperature change characteristic factor.

[0095] In one embodiment of the present invention, the overall distribution of the single load correlations may be reflected by the mean of the absolute values ​​of the single load correlations.

[0096] In another embodiment of the present invention, the overall distribution of the single load correlation may also be reflected by the absolute value of the mode or median of the single load correlation.

[0097] In an optional embodiment of the present invention, the correlation coefficient μ between the environmental conditions and the corresponding temperature characteristic factor changes is j,p , where j represents an independent variable dimension data, that is, an environmental condition, and p represents the corresponding rated load; by traversing all loads and all current conditions.

[0098] The calculation formula of the correlation factor is: Among them, G j,p is the correlation factor under the pth rated load and the jth environmental condition; M is the total number of rated loads set in the steady-state test; μ j,p is the correlation coefficient between the environmental condition and the temperature change characteristic factor under the pth rated load under the jth environmental condition.

[0099] The correlation factor indicates the correlation between the current environmental conditions and the temperature change of the switch tube. The closer the correlation factor is to 1, the higher the correlation is, indicating that the current environmental conditions have a greater impact on the temperature change. The characteristic factors for the above calculation can be obtained by traversing all external environmental conditions.

[0100] Step S320, analyzing the complexity of load changes in each dynamic simulation process to determine the load change characteristic value.

[0101] Since several environmental variables of different dimensions are set to simulate the actual vehicle driving process in the current dynamic simulation process, that is, several different environmental conditions are set to simulate the actual vehicle driving process. According to the analysis, under different environmental factors and load changes, the temperature rise performance of the switch tube node may still have certain similarities.

[0102] The dynamic simulation process is a process of continuously simulating the environmental conditions and rated loads of the actual vehicle during driving.

[0103] At the same time, for each dynamic simulation process, the load will also undergo certain changes when simulating the actual vehicle operating conditions. Therefore, firstly, according to the existing information entropy calculation method, the load change complexity in each dynamic simulation process is obtained, and it is used as the load change characteristic value, that is, the information entropy of the load in each dynamic simulation process is obtained as the load change characteristic value. The load change characteristic value in any dynamic simulation process is obtained. For example, for the qth dynamic simulation process, the corresponding load change characteristic value is recorded as U q .

[0104] Step S330: clustering different sub-dynamic model processes based on the correlation factor and the load change characteristic value to obtain clusters.

[0105] Then, the dynamic simulation processes under multiple different conditions are clustered. The clustering method is calculated using the existing Euclidean norm. During the calculation process, for any two dynamic simulations, the numerical differences of all environmental conditions are traversed, and the numerical differences of the corresponding environmental conditions are weighted using the correlation factor G. The similarity discrimination value is obtained by combining the difference between the corresponding load change characteristic values. Based on the k-means clustering algorithm, the similarity discrimination value is used as the distance between different dynamic simulation processes. Finally, the optimal k is obtained through the inflection point method, and multiple clusters are obtained by clustering.

[0106] The calculation formula of the similarity discriminant value is:

[0107] Among them, F a,bis the similarity discrimination value between dynamic simulation process a and dynamic simulation process b; N is the total number of different environmental conditions; G j is the correlation factor under the jth environmental condition; T j,a is the actual value of the jth environmental condition in the dynamic simulation process a; T j,b is the actual value of the jth environmental condition in the dynamic simulation process b; T j,a -T j,b is the difference between the actual values ​​of the jth environmental condition in the dynamic simulation process a and the dynamic simulation process b; U a is the load change characteristic value in the dynamic simulation process a; U b is the load change characteristic value in the dynamic simulation process b.

[0108] Step S400, comparing the temperature rise data at each time point in the dynamic model process with the temperature rise difference of the rated temperature rise data, combining the temperature change characteristic factor and the temperature rise difference, performing data cleaning on the temperature rise data at each time point in the cluster, and determining the cleaned temperature rise detection value.

[0109] Based on the temperature rise data obtained in step S100, the credibility of the temperature rise difference exceeding the safety range is determined, the temperature rise data is cleaned, and the rationality of the specifications of the switch tube is analyzed.

[0110] At this time, for any dynamic simulation process in any cluster, the temperature rise difference ΔT between the temperature rise value corresponding to each time point in the dynamic simulation process and the rated temperature rise data can be obtained. 0,r , wherein r represents any time point. In the embodiment of the present invention, the rated temperature rise data is the maximum temperature rise value required within the safety range.

[0111] Compare the temperature rise data at each time point in the dynamic model process and the temperature rise difference with the rated temperature rise data. More specifically: Statistical calculation of ΔT during each dynamic simulation process 0,r >0 and the corresponding timing position and the size of the temperature rise difference.

[0112] According to the analysis, for each dynamic simulation process in the same cluster under the same dynamic simulation conditions, that is, the change over time, the larger the cumulative mean of the temperature change characteristic factor E corresponding to the rated load at any time point and other time points before it, the higher the impact of the rated load change before it on the temperature rise data. At this time, for the time point r, if the temperature rise difference is greater than 0, the higher the credibility of the temperature rise difference exceeding the safety range.

[0113] At the same time, for different dynamic simulation processes within the cluster, if the current specifications of the switch tube do not meet the actual needs, the time points when the temperature rise difference is greater than 0 will be concentrated in the time period with smaller difference.

[0114] Therefore, when the temperature rise difference is greater than the preset difference threshold, the discrete situation of the mean value of the temperature rise difference at each time point in different dynamic simulation processes is analyzed to determine the temperature rise discrete value. In the embodiment of the present invention, the preset difference threshold is 0, and in other embodiments, the implementer can adjust the value according to the actual situation.

[0115] As an embodiment of the present invention, when the temperature rise difference is greater than the preset difference threshold, the standard deviation of the mean of the temperature rise difference at each time point in different dynamic simulation processes can also be obtained, which is recorded as the temperature rise discrete value. That is, by obtaining the temperature rise difference in different simulation processes, ΔT 0,r >0 The standard deviation of the mean temperature rise difference at the corresponding time point

[0116] As another embodiment of the present invention, when the temperature rise difference is greater than a preset difference threshold, the variance or coefficient of variation of the mean of the temperature rise difference at each time point in different dynamic simulation processes may be obtained and recorded as the temperature rise discrete value.

[0117] When the temperature rise difference is greater than a preset difference threshold, the discrete situation of the timing position difference of the first and last time endpoints in different dynamic simulation processes is analyzed to determine the timing discrete value.

[0118] As an embodiment of the present invention, when the temperature rise difference is greater than the preset difference threshold, the standard deviation of the time sequence position difference of the time endpoints in different dynamic simulation processes can also be obtained, which is recorded as the time sequence discrete value. That is, in different simulation processes, when ΔT 0,r >0, calculate the standard deviation of the time series position difference between the first and last time points That is, when ΔT 0,r >0, calculate the standard deviation of the temporal position difference between the corresponding first time points Recorded as a discrete time value, and when ΔT 0,r >0 The standard deviation of the temporal position difference between the last time points Recorded as another discrete value of the time series, the inertia of abnormal data in the same cluster is obtained. It should be noted that the standard deviation of the time series position difference between the first time points is the standard deviation of the time series difference between the adjacent first time points. Similarly, the standard deviation of the time series position difference between the last time points is the standard deviation of the time series difference between the adjacent last time points.

[0119] As another embodiment of the present invention, when the temperature rise difference is greater than a preset difference threshold, the variance or coefficient of variation of the time series position difference of the time endpoints in different dynamic simulation processes may be obtained and recorded as a time series discrete value.

[0120] Combining the above logic, for each ΔT in the current cluster 0,r The temperature rise data corresponding to the time point corresponding to time >0 is cleaned, thereby obtaining a certain detection value after cleaning, that is, obtaining the temperature rise detection value after cleaning.

[0121] The adjustment weight of the temperature rise difference is determined by combining the temperature rise discrete value, the time series discrete value and the temperature change characteristic factor.

[0122] Analyze the timing discrete values ​​of the first time endpoint and the last time endpoint to determine the timing rationality. In an embodiment of the present invention, the average of the timing discrete values ​​of the first time endpoint and the last time endpoint can be recorded as the timing rationality. The timing discrete values ​​of the first time endpoint and the last time endpoint can also be weighted and summed according to different weight ratios, and the result of the weighted summation is used as the timing rationality. It should be noted that the timing discrete value of the first time endpoint is also the timing discrete value corresponding to the first time point, and the timing discrete value of the last time endpoint is also the timing discrete value corresponding to the last time point.

[0123] According to the temperature change characteristic factor, time rationality and temperature rise discrete value, the adjustment weight of the temperature rise difference is determined; wherein the adjustment weight of the temperature rise difference is negatively correlated with the temperature change characteristic factor, and the adjustment weight of the temperature rise difference is positively correlated with the time rationality and the temperature rise discrete value. In the embodiment of the present invention, the positive correlation can be in the form of addition, multiplication, or the power of an exponential function, and the negative correlation can be in the form of subtraction, division, or the negative power of an exponential function.

[0124] In the embodiment of the present invention, the normalized value of the product of the mean value of the negative correlation mapping value of the temperature change characteristic factor, the time rationality and the temperature rise discrete value can be used as the adjustment weight of the temperature rise difference. The negative correlation mapping value of the temperature change characteristic factor can be a reciprocal or a negative number.

[0125] Then, the temperature rise difference is weighted by the adjustment weight to obtain the temperature rise detection value after cleaning.

[0126] As an optional embodiment of the present invention, the calculation formula of the temperature rise detection value is:

[0127]

[0128] Where, ΔT 0 ' ,r is the temperature rise detection value corresponding to time point r; ΔT 0,r is the temperature rise difference corresponding to time point r; th[] is the hyperbolic tangent function; H r is the number of time points; E′ ris the mean value of the temperature variation characteristic factor of the rated load corresponding to the time point r; is the mean of the negative correlation mapping values ​​of the temperature change characteristic factors of different rated loads corresponding to all time points before the time point r; is the standard deviation of temperature rise; is the time series standard deviation corresponding to a time endpoint; is the timing standard deviation corresponding to another time endpoint.

[0129] in, It represents the mean of the temperature change characteristic factors of the rated load corresponding to all time points before the time point r. The larger the value is, the smaller the temperature accumulation caused by the load change is before the current time point r, and the smaller the rationality of the data higher than the maximum value of the safety range at the current moment is, that is, the smaller the rationality of the data higher than the rated temperature rise at the current moment is, the larger the cleaning amplitude of this value should be, so it is expressed by a fraction. At the same time, the larger the standard deviation of the mean of the temperature rise difference of all data exceeding the rated temperature rise between different simulation processes in the current cluster, and the standard deviation of the time series position of the initial and final data exceeding the rated temperature rise is, the smaller the rationality of the data higher than the rated temperature rise generated at the previous moment is, and the larger the cleaning amplitude of this value should be; multiply the above polynomials and normalize them using the hyperbolic tangent function, thus obtaining The larger the value, the greater the cleaning range, which will affect the temperature rise difference ΔT at the current moment. 0,r Cleaning is performed to obtain the temperature rise detection value ΔT after cleaning 0,r ′.

[0130] Traverse all different clusters and clean the data within the clusters.

[0131] As a preferred embodiment of the present invention, the temperature rise detection value of the dynamic simulation process after cleaning is obtained according to the above method; at this time, the actual working safety of the switching tube is evaluated according to the frequency of occurrence and the size of the values ​​exceeding the safety range. If the temperature rise detection value after cleaning is too high, it is necessary to analyze the cause, and it may be necessary to improve the heat dissipation design or select a higher specification switching tube.

[0132] See also Figure 5 , Figure 5 The present invention is a system block diagram of a node temperature rise test and analysis device for an on-vehicle switch tube.

[0133] The embodiment of the present invention provides a node temperature rise test and analysis device for a vehicle-mounted switch tube, the device comprising the following modules:

[0134] An acquisition module is used to obtain the junction temperature data of the vehicle-mounted switch tube under different rated loads and different environmental conditions;

[0135] A change analysis module is used to determine the temperature change characteristic factors under different rated loads according to the degree of change of the node temperature data under different rated loads;

[0136] A clustering module is used to determine a correlation factor according to the relationship between environmental conditions and temperature change characteristic factors under different rated loads under the same environmental conditions; analyze the load change complexity in each dynamic simulation process to determine the load change characteristic value; cluster different sub-dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters;

[0137] The temperature rise detection module is used to compare the temperature rise data at each time point in the dynamic model process with the temperature rise difference of the rated temperature rise data, and to clean the temperature rise data at each time point in the cluster in combination with the temperature change characteristic factor and the temperature rise difference, and determine the cleaned temperature rise detection value.

[0138] Optionally, the transmission medium can be a wired link, such as but not limited to coaxial cable, optical fiber and digital subscriber line, or a wireless link, such as but not limited to Wireless Fidelity (WIFI), Bluetooth and mobile device network.

[0139] It should be noted that: the device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the shopping guide device provided in the above embodiment and the image extension method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0140] Figure 6 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 6 As shown, the computer device 600 includes: a memory 610, a processor 620, and a computer program 630 stored in the memory 610 and running on the processor 620, wherein when the processor 620 executes the computer program 630, the computer device can execute any of the node temperature rise test and analysis methods for vehicle-mounted switch tubes introduced above.

[0141] In addition, an embodiment of the present invention also protects a readable storage medium, on which a program code is stored. When the program code is executed by a processor, a node temperature rise test and analysis method for a vehicle-mounted switch tube provided by an embodiment of the present invention is implemented.

[0142] The embodiment of the present invention can divide the functional modules of the device according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0143] In the case of dividing each module according to each function, the device may also include a signal uploading module, a determining module, an adjusting module, etc. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0144] It should be understood that the device provided in the embodiment of the present invention is used to execute the above-mentioned node temperature rise test and analysis method of an on-vehicle switch tube, and thus can achieve the same effect as the above-mentioned implementation method.

[0145] In the case of an integrated unit, the device may include a processing module and a storage module. When the device is applied to a device, the processing module may be used to control and manage the actions of the device. The storage module may be used to support the device to execute mutual program codes, etc. The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. The processor may also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0146] In addition, the device provided in an embodiment of the present invention may specifically be a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a node temperature rise test and analysis method for a vehicle-mounted switch tube provided in the above embodiment.

[0147] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement a node temperature rise test and analysis method for a vehicle-mounted switch tube provided in the above embodiment.

[0148] An embodiment of the present invention also provides a computer program product. When the computer program product runs on a computer, the computer executes the above-mentioned related steps to implement a node temperature rise test and analysis method for a vehicle-mounted switch tube provided in the above embodiment.

[0149] Among them, the device, computer-readable storage medium, computer program product or chip provided in the embodiments of the present invention are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here. Through the description of the above implementation methods, technicians in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways.

[0150] The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. There may be other divisions in actual implementation. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0151] It should also be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0152] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0153] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0154] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A node temperature rise test and analysis method for a vehicle-mounted switch tube, characterized in that: The method comprises the following steps: Obtain the junction temperature data of the vehicle switch tube under different rated loads and different environmental conditions; According to the degree of change of the junction temperature data under different rated loads, the temperature change characteristic factors under different rated loads are determined; Determine the correlation factor according to the relationship between the environmental conditions and the temperature change characteristic factors under different rated loads under the same environmental conditions; analyze the load change complexity in each dynamic simulation process to determine the load change characteristic value; cluster the different dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters; Compare the temperature rise data at each time point in the dynamic model process with the temperature rise difference of the rated temperature rise data, and perform data cleaning on the temperature rise data at each time point in the cluster in combination with the temperature change characteristic factor and the temperature rise difference, and determine the cleaned temperature rise detection value; Wherein, determining the temperature change characteristic factors under different rated loads according to the degree of change of the node temperature data under different rated loads includes: Analyze the overall distribution of the node temperature data within the time after the node temperature data reaches a constant value, and determine the overall distribution value; Analyze the data fluctuation of the node temperature data within a period of time after the node temperature data reaches a constant value, and determine the data fluctuation value; Analyze the instantaneous change value and occurrence frequency of the node temperature data before the node temperature data reaches a constant value, and determine the degree of instantaneous change; The temperature variation characteristic factors under different rated loads are determined by combining the overall distribution value, the data fluctuation value and the instantaneous variation degree.

2. The node temperature rise test and analysis method of the vehicle-mounted switch tube according to claim 1 is characterized in that: Determining the correlation factor according to the relationship between the environmental conditions and the temperature change characteristic factors under different rated loads under the same environmental conditions includes: The correlation coefficient between the environmental condition and the corresponding temperature change characteristic factor under the same environmental condition and the same rated load is taken as the single load correlation; The overall distribution of the single load correlation between the environmental condition and the corresponding temperature change characteristic factor under different rated loads under the same environmental condition is analyzed to determine the correlation factor between the environmental condition and the temperature change characteristic factor.

3. The node temperature rise test and analysis method of the vehicle-mounted switch tube according to claim 1 is characterized in that: The analysis of the load variation complexity during each dynamic simulation process and determination of the load variation characteristic value include: The information entropy of the load during each dynamic simulation is obtained as the load change characteristic value.

4. The node temperature rise test and analysis method of the vehicle-mounted switch tube according to claim 1 is characterized in that: The clustering of different sub-dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters includes: By traversing the numerical differences of all environmental conditions, the correlation factor is used to weight the numerical differences of the corresponding environmental conditions, and the similarity discrimination value is obtained by combining the difference between the corresponding load change characteristic values. Based on the k-means clustering algorithm, the similarity discrimination value is used as the distance between different dynamic simulation processes, and the inflection point method is used to obtain the optimal k, and multiple clusters are obtained by clustering.

5. The node temperature rise test and analysis method of the vehicle-mounted switch tube according to claim 1 is characterized in that: Before comparing the temperature rise data at each time point in the dynamic model process with the temperature rise difference of the rated temperature rise data, the method further includes: The difference between the current node temperature data of the on-board switch tube and the current ambient temperature is obtained as the temperature rise data.

6. The node temperature rise test and analysis method of the vehicle-mounted switch tube according to claim 1 is characterized in that: The temperature rise data at each time point in the clustering cluster are cleaned by comparing the temperature rise difference between the temperature rise data at each time point and the rated temperature rise data in the dynamic model process, combining the temperature change characteristic factor and the temperature rise difference, and determining the cleaned temperature rise detection value, including: When the temperature rise difference is greater than the preset difference threshold, the discreteness of the mean value of the temperature rise difference at each time point in different dynamic simulation processes is analyzed to determine the discrete value of the temperature rise; When the temperature rise difference is greater than the preset difference threshold, the discrete situation of the time sequence position difference between the first and last time endpoints in different dynamic simulation processes is analyzed to determine the time sequence discrete value; Determine the adjustment weight of the temperature rise difference by combining the temperature rise discrete value, the time series discrete value and the temperature change characteristic factor; The temperature rise difference is weighted by the adjustment weight to obtain the temperature rise detection value after cleaning.

7. The node temperature rise test and analysis method of the vehicle-mounted switch tube according to claim 6 is characterized in that: The step of combining the temperature rise discrete value, the time series discrete value and the temperature change characteristic factor to determine the adjustment weight of the temperature rise difference includes: Analyze the discrete values ​​of the first time endpoint and the last time endpoint to determine the rationality of time; The adjustment weight of the temperature rise difference is determined according to the temperature change characteristic factor, time rationality and temperature rise discrete value; among them, the adjustment weight of the temperature rise difference is negatively correlated with the temperature change characteristic factor, and the adjustment weight of the temperature rise difference is positively correlated with the time rationality and temperature rise discrete value.

8. A node temperature rise test and analysis device for a vehicle-mounted switch tube, characterized in that: The device is used to implement a node temperature rise test and analysis method for a vehicle-mounted switch tube as described in any one of claims 1 to 7, and the device includes the following modules: An acquisition module is used to obtain the junction temperature data of the vehicle-mounted switch tube under different rated loads and different environmental conditions; A change analysis module is used to determine the temperature change characteristic factors under different rated loads according to the degree of change of the node temperature data under different rated loads; A clustering module is used to determine a correlation factor according to the relationship between environmental conditions and temperature change characteristic factors under different rated loads under the same environmental conditions; analyze the load change complexity in each dynamic simulation process to determine the load change characteristic value; cluster different sub-dynamic model processes based on the correlation factor and the load change characteristic value to obtain cluster clusters; The temperature rise detection module is used to compare the temperature rise data at each time point in the dynamic model process with the temperature rise difference of the rated temperature rise data, and to clean the temperature rise data at each time point in the cluster in combination with the temperature change characteristic factor and the temperature rise difference, and determine the cleaned temperature rise detection value.

9. A readable storage medium, characterized in that: The readable storage medium stores program codes, and when the program codes are executed by a processor, the steps of the node temperature rise test and analysis method for an on-vehicle switch tube are implemented as described in any one of claims 1 to 7.

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