Test run rack performance test method, system and equipment and storage medium

Abstract

The invention discloses a test run rack performance test method, system and device and a storage medium, and the method comprises the steps: obtaining the performance data of all test run racks, and the performance data comprises a plurality of attribute measurement values; vectorization processing is carried out on all performance data of each test bench to obtain a performance vector of each test bench, and the performance vector comprises all attribute measurement values of the test bench; performing weighting processing on each performance vector to obtain a weighted performance vector; calculating a correlation coefficient for the weighted performance vectors of any two test run racks; and obtaining test results of all test run racks according to all correlation coefficients. Attribute measurement values of all qualified engines on each test bench in a period of time are selected, quantitative analysis and calculation are carried out, and the obtained result can be used as a basis for judging the consistency of the test benches, so that the test benches with poor consistency are screened out.

Description

Technical Field

[0001] The present invention relates to the field of testing, and in particular to a test bench performance testing method, system, equipment and storage medium. Background Art

[0002] In order to ensure the consistency of engine performance and improve the pass rate of bench tests, it is necessary to monitor the consistency of the test bench. This involves calculating the standard values ​​of the standard engine to make up for the objective differences of each bench.

[0003] There are many methods for monitoring the consistency of test benches at present. The more common method is to evaluate by the percentage of absolute deviation value to the average value. An engine with stable performance and moderate power is selected as the standard engine, and a running-in test is carried out on the engine to ensure that the engine has no quality problems and meets the technical requirements for leaving the factory. The engine is tested N times on all test benches, and M specific attributes (such as speed, torque, power, etc.) are selected, and the mean of these attributes is calculated respectively. The deviation value of the measured value of each attribute on each bench relative to the mean value of the attribute is calculated separately, and then this deviation is divided by the mean value as the evaluation value of the attribute. If the evaluation value of a certain attribute of a test bench is not within the qualified range of the engine, it is considered that the consistency of the test bench is poor.

[0004] The data sample of this method is very small, and only M (number of attributes) * N (number of experiments) samples can be obtained. In addition, the same engine is running continuously, so there is a high possibility of abnormal values. The labor cost is high, and a dedicated person is required to conduct the test under the condition of stopping production, which is time-consuming. Only each attribute is evaluated independently, ignoring the relationship between the attributes and the degree of influence of each attribute on the performance of the test bench. The performance of the test bench is a combination of many attributes, and the independent evaluation is not comprehensive enough. Summary of the invention

[0005] The main purpose of the present invention is to provide a test bench performance testing method, system, equipment and storage medium. Compared with the traditional method of judging the consistency of the test bench through manual experiments, by selecting the attribute measurement values ​​of all qualified engines on each test bench within a period of time, quantitative analysis and calculation are performed, and the results obtained can be used as the basis for judging the consistency of the test bench, thereby screening out test benches with poor consistency.

[0006] In order to achieve the above objectives, the embodiments of the present application provide the following technical solutions:

[0007] According to a first aspect of an embodiment of the present application, a test bench performance testing method is provided, the method comprising:

[0008] Acquire performance data of each of all test benches, wherein the performance data includes a plurality of property measurement values;

[0009] Vectorize all performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench;

[0010] Perform weighted processing on each performance vector to obtain a weighted performance vector;

[0011] Calculate the correlation coefficient for the weighted performance vectors of any two test benches;

[0012] The test results of all test benches are obtained based on all correlation coefficients.

[0013] Optionally, the weighted performance vectors of any two test benches are used to calculate the correlation coefficient, which is calculated according to the following formula:

[0014]

[0015] Among them, A and B represent the weighted performance vectors of any two test benches, A i represents the i-th attribute measurement value in A, A avg represents the mean of A, B i represents the i-th attribute measurement value in B, B avg represents the mean of B.

[0016] Optionally, obtaining the test results of all test benches according to all correlation coefficients includes:

[0017] Get all the correlation coefficients associated with each test bench;

[0018] If all correlation coefficients of the set proportions associated with the test bench are lower than the set consistency threshold, the consistency of the test bench is determined to be inconsistent with the target performance standard and is marked as a test bench to be repaired.

[0019] Optionally, all performance data of each test bench are vectorized according to the following formula to obtain the performance vector X of each test bench: n :

[0020]

[0021] Among them, X avg is the mean of the measured values ​​of a property, X std is the standard deviation of the attribute, 1<=n<=M, M is the number of attribute measurements.

[0022] Optionally, before vectorizing all performance data of each test bench, the method further includes:

[0023] For each attribute measurement value X, normalization is performed according to the following formula:

[0024]

[0025] Among them, X max is the maximum value of the attribute measurement, X min It is the minimum value among the measured values ​​of this attribute, and the value of X' is between 0 and 1.

[0026] Optionally, after obtaining the performance data of all the test benches, the method further includes:

[0027] Filter out negative data, empty data, and data exceeding the process threshold in the performance data of all test benches.

[0028] Optionally, after obtaining the test results of all test benches according to all correlation coefficients, the method further includes:

[0029] All test benches, their respective correlation coefficients and test results are combined into a two-dimensional matrix; the rows of the matrix represent different test benches and the columns represent different attributes;

[0030] Draw a heat map based on the two-dimensional matrix, add test bench, attribute labels and titles to the heat map, and the depth of color represents the size of the correlation coefficient.

[0031] According to a second aspect of an embodiment of the present application, a test bench performance testing system is provided, the system comprising:

[0032] A data acquisition module, used to acquire the performance data of all test benches, wherein the performance data includes a plurality of property measurement values;

[0033] A vector processing module is used to vectorize all performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench;

[0034] A weighting module, used for performing weighted processing on each performance vector to obtain a weighted performance vector;

[0035] A correlation coefficient module is used to calculate the correlation coefficient of the weighted performance vectors of any two test benches;

[0036] The test result module is used to obtain the test results of all test benches according to all correlation coefficients.

[0037] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0038] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. The computer-readable instructions can be executed by a processor to implement the method described in the first aspect above.

[0039] In summary, the embodiments of the present application provide a test bench performance testing method, system, device and storage medium, which obtains the performance data of all test benches, wherein the performance data includes several attribute measurement values; vectorizes all the performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench; performs weighted processing on each performance vector to obtain a weighted performance vector; calculates the correlation coefficient for the weighted performance vectors of any two test benches; and obtains the test results of all test benches based on all correlation coefficients. By selecting the attribute measurement values ​​of all qualified engines on each test bench within a period of time, and performing quantitative analysis and calculation, the obtained results can be used as a basis for judging the consistency of the test benches, thereby screening out test benches with poor consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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 the structures shown in these drawings without paying creative work.

[0041] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0042] Figure 1 A test bench performance testing method provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of a data processing flow provided for an embodiment of the present application;

[0044] Figure 3 A block diagram of a test bench performance test system provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown;

[0046] Figure 5 A schematic diagram of a computer-readable storage medium provided in an embodiment of the present application is shown.

[0047] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0050] In addition, in the present invention, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0051] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0053] The technical terms involved in the embodiments of the present application are explained in detail below.

[0054] Engine: A machine that converts other forms of energy (such as diesel) into mechanical energy.

[0055] Test bench: Equipment specially used to test engine properties such as performance, reliability and durability.

[0056] Attributes: The properties and relationships of things. In this article, attributes refer to the engine's torque, speed, power, etc.

[0057] Consistency: The same or similar properties between different things.

[0058] Normalization: A data processing method that can limit data of different dimensions to the same dimension (such as between 0 and 1) after processing.

[0059] Vectorization: A computer science technique that refers to the process of converting scalar code in a program into vector code.

[0060] Weight: The importance of a certain attribute of an object relative to the object.

[0061] Pearson correlation coefficient: It is a linear correlation coefficient used to reflect the degree of linear correlation between variables X and Y. The value is between -1 and 1. The larger the absolute value, the stronger the correlation.

[0062] Coefficient of dispersion: Also known as the coefficient of variation, it is the ratio of the standard deviation of a set of data to its corresponding mean. This coefficient is used to solve problems with unequal means or different units.

[0063] Under the same working conditions, the running conditions of qualified engines on different test benches should be similar. The measured values ​​of various engine properties obtained on the test bench can be regarded as the conditions of the test bench itself. Generally speaking, the more engine property data obtained, the more complete the test bench conditions are. If a problem occurs on a test bench, the data of the test bench will deviate from that of most other test benches. By calculating the correlation between each test bench, it can be determined which test benches have deviated in consistency. The criterion for judgment is that if the correlation coefficients of a test bench with other test benches are all lower than a certain threshold, the consistency of the test bench is poor.

[0064] Figure 1A test bench performance testing method provided in an embodiment of the present application is shown, the method comprising:

[0065] Step 101: Acquire performance data of all test benches, wherein the performance data includes a plurality of property measurement values;

[0066] Step 102: vectorize all performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench;

[0067] Step 103: performing weighted processing on each performance vector to obtain a weighted performance vector;

[0068] Step 104: Calculate the correlation coefficient for the weighted performance vectors of any two test benches;

[0069] Step 105: Obtain the test results of all test benches according to all correlation coefficients.

[0070] In a possible implementation manner, after obtaining the performance data of all test benches in step 101, the method further includes:

[0071] Filter out negative data, empty data, and data exceeding the process threshold in the performance data of all test benches.

[0072] Relying on a large amount of historical data, it can provide in-depth data-based analysis and decision support, thereby improving the accuracy and reliability of decision-making. By selecting a data set with a longer time span, it reduces the data skew problem caused by excessive data on a specific model of engine, making the analysis results more balanced and comprehensive.

[0073] In a possible implementation manner, before vectorizing all performance data of each test bench, the method further includes: for each attribute measurement value X, performing normalization processing according to the following formula:

[0074]

[0075] Among them, X max is the maximum value of the attribute measurement, X min It is the minimum value among the measured values ​​of this attribute, and the value of X' is between 0 and 1.

[0076] Through the normalization step, the dimensional differences of the same attribute data of different engine models are eliminated, making different data directly comparable and enhancing the fairness and accuracy of the analysis.

[0077] In a possible implementation, in step 103, all performance data of each test bench are vectorized according to the following formula to obtain a performance vector X of each test bench: n :

[0078]

[0079] Among them, X avg is the mean of the measured values ​​of a property, X std is the standard deviation of the attribute, 1<=n<=M, M is the number of attribute measurements.

[0080] Converting complex data sets into a limited number of variable values ​​simplifies the data processing process while retaining the effective value of the data for subsequent statistical analysis.

[0081] In a possible implementation, in step 104, the correlation coefficient of the weighted performance vectors of any two test benches is calculated according to the following formula:

[0082]

[0083] Among them, A and B represent the weighted performance vectors of any two test benches, A i represents the i-th attribute measurement value in A, A avg represents the mean of A, B i represents the i-th attribute measurement value in B, B avg represents the mean of B.

[0084] Using the Pearson correlation coefficient as the evaluation criterion provides a quantitative method to measure the consistency between test benches, making the results more objective and easier to understand.

[0085] In a possible implementation manner, in step 105, obtaining the test results of all test benches according to all correlation coefficients includes:

[0086] All correlation coefficients associated with each test bench are obtained; if all correlation coefficients of a set proportion associated with the test bench are lower than a set consistency threshold, it is determined that the consistency of the test bench does not meet the target performance standard and is marked as a test bench to be repaired.

[0087] By setting a threshold for the correlation coefficient, test benches with poor consistency can be quickly identified, providing a clear direction for further diagnosis and calibration.

[0088] In a possible implementation manner, after obtaining the test results of all test benches according to all correlation coefficients in step 105, the method further includes:

[0089] All test benches, their respective correlation coefficients and test results are combined into a two-dimensional matrix; the rows of the matrix represent different test benches and the columns represent different attributes; a heat map is drawn based on the two-dimensional matrix, and labels and titles of the test benches and attributes are added to the heat map, where the depth of the color represents the size of the correlation coefficient.

[0090] The following is combined with Figure 2 The test bench performance testing method provided in the embodiment of the present application is described in detail.

[0091] The source data used in the embodiments of the present application are derived from the measured values ​​of various properties of qualified engines on various test benches, including but not limited to the following 20 common properties: piston leakage, torque, power, fuel pressure, nitric oxide exhaust volume, intake temperature after intercooler, intake pressure after intercooler, carbon dioxide exhaust volume, smoke density, speed, atmospheric temperature, atmospheric humidity, oil pressure, air temperature before intercooler, oxygen emissions, fuel consumption, atmospheric pressure, main oil gallery oil temperature, exhaust temperature and water outlet temperature.

[0092] Phase 1: Data preprocessing. Before conducting the test bench consistency analysis, it is necessary to select the time period and clean the source data.

[0093] In the selection of source data, the longer the time span, the better, which helps to reduce the potential impact of excessive data of a specific engine model (i.e. data skew) on the analysis results. During the selected time period, the test bench did not undergo special directional adjustments to avoid these adjustments causing bias in the data analysis results.

[0094] The following data are mainly cleaned up: first, data with negative measurement values, which should be incorrectly entered and should be deleted directly; second, data with empty values, which should be data with attributes that do not exist in certain engine models and can be filled with zeros; third, data with values ​​outside the upper and lower limits of the process standards are unqualified data and should be deleted directly.

[0095] Delete negative data: Since physical measurements cannot be negative, negative data usually represent erroneous entries and should be deleted.

[0096] Handling missing data: For missing data, if it is because some engine models do not contain this attribute, you can fill it with zeros.

[0097] Delete unqualified data: Any value outside the upper and lower limits of the process standard is considered unqualified and should be deleted.

[0098] The second stage: data normalization processing.

[0099] Since there are many types of engines, and the performance of the same attribute of different engines is inconsistent, or even very different, the dimensions of the same attribute of different engines are inconsistent. This step is to unify the dimensions of all attribute values. For each attribute value X of each type of engine, it is processed according to the following formula (the value of X' is between 0 and 1):

[0100]

[0101] Among them, X max is the maximum value of the attribute measurement value of this type of engine, X min is the minimum value of this attribute measurement value of this type of engine, X max and X min When they are the same, it means that there is only one record for this type of engine, and X′ is recorded as 1.

[0102] The third stage: data vectorization.

[0103] Step 1: Vectorization. All data on each test bench (N in total) are integrated into a vector, each vector contains M attribute values ​​of the engine (X1, X2, ..., X M ).

[0104] For each attribute value (X1, X2, ..., X M ) is calculated according to the dispersion coefficient formula:

[0105]

[0106] Among them, X avg is the mean of an attribute, X std is the standard deviation of the attribute, 1<=n<=M.

[0107] After the above calculations, a total of N vectors are generated, each vector includes M elements.

[0108] Step 2: Perform weighted processing on each element in the vector.

[0109] There are two ways to weight: one is average weighting, that is, the importance of each element to the whole is the same; the other is non-even weighting, which requires setting the weight ratio of each element according to the rules.

[0110] (X1, X2, ..., X M )—>(a1X1, a2X2, …, a M X M )

[0111] Among them, a1, a2, …, a M is the weight value. If the weight value is 0, it means that the attribute has no effect or little effect on the overall performance.

[0112] The present invention preferably adopts an average weighting method, that is, the weight value of each element is the same.

[0113] Weight management allows the importance of different attributes to be adjusted, which helps optimize resource allocation and focus on the most critical attributes. Through weight management, users are allowed to adjust the importance of each attribute according to different needs, which increases the flexibility and applicability of the method.

[0114] In the fourth stage, Pearson correlation coefficient was calculated.

[0115] For any two test benches, whose weighted vectors are A and B, the Pearson correlation coefficient between them is calculated. The Pearson correlation coefficient is calculated between each pair, and the calculation formula is as follows:

[0116]

[0117] Among them, A i Represents the value of the i-th element in A, A avg represents the mean of A, B i Represents the value of the i-th element in B, B avg represents the mean of B.

[0118] Convert raw data into a format that can be used for analysis and comparison, and explore relationships between different data sets through correlation analysis.

[0119] For other correlation coefficients, the calculation methods are also different. The present application embodiment provides some other calculation methods of correlation coefficients:

[0120] 1. The calculation formula of Spearman's rank correlation coefficient is as follows:

[0121]

[0122] Among them, d i is the absolute value of the difference in rank between the two variables, and n is the total number of data points.

[0123] 2. Point-by-point correlation coefficient: used for the correlation between a binary variable and a continuous variable. The calculation method is similar to the Pearson correlation coefficient.

[0124] 3. Cosine similarity: It measures the similarity in the direction of the vector. The calculation formula is as follows:

[0125]

[0126] Here, xi and yi are corresponding components of two vectors.

[0127] 4. Weighted least squares regression is a linear regression analysis method that handles heteroscedasticity in data by assigning weights to each observation. In ordinary least squares regression, all observations are assumed to have the same variance, but in practical applications, this assumption is often not true. Allowing each observation to have a different variance, and adjusting the importance of each observation in the regression analysis through weights.

[0128] In the fifth stage, the calculation results are displayed graphically. This application prefers to use heat maps to display data.

[0129] A heat map is a data visualization technique that uses the depth of color to represent the size or density of data, so that the observer can intuitively identify patterns, trends or outliers in the data set. The steps of constructing a heat map provided in the embodiment of the present application may include the following steps:

[0130] Step 1: Prepare data: First, the correlation coefficient calculation results of N test benches and M attributes need to be organized into a two-dimensional matrix. The rows of the matrix represent different test benches, and the columns represent different attributes.

[0131] Step 2: Choose software or programming library for drawing heat maps, such as Matplotlib, Seaborn library in Python, or Excel.

[0132] Step 3: Draw a heat map: Use the selected tool to draw a heat map based on the correlation coefficient matrix. The depth of the color represents the size of the correlation coefficient, and the darker the color, the stronger the correlation.

[0133] Step 4: Adjust the color gradient: In order to make the hierarchy clear, the color gradient needs to be adjusted to ensure that the color change from low correlation to high correlation is smooth and easy to distinguish.

[0134] Step 5: Add labels and titles: Add labels of the test bench and attributes as well as titles on the heat map to help users understand the content of the chart.

[0135] In the sixth stage, result evaluation, the consistency between different test benches is judged based on the Pearson correlation coefficient.

[0136] The correlation coefficient and correlation degree are generally divided into: 0.8-1.0 extremely strong correlation; 0.6-0.8 strong correlation; 0.4-0.6 moderate correlation; 0.2-0.4 weak correlation; 0.0-0.2 extremely weak correlation or no correlation.

[0137] Set a threshold to evaluate the consistency. In this example, the threshold is set to 0.4, meaning that any correlation coefficient below this value indicates poor consistency between the racks. If 0.4 is used as the threshold, it can be found that rack 8 has poor consistency and the correlation coefficients with all other racks are below 0.4.

[0138] Based on this assessment, actions can be taken to improve the performance of test bench 8, such as calibration, repair or further analysis to ensure consistency and accuracy across all test benches.

[0139] In summary, a systematic, data-driven approach is provided to monitor and evaluate the consistency of the test bench, which helps to improve the accuracy and reliability of engine testing. It is not dependent on a specific data set or test environment, so it can adapt to different test requirements and conditions and has good versatility.

[0140] In summary, the embodiment of the present application provides a test bench performance test method, which obtains the performance data of all test benches, wherein the performance data includes several attribute measurement values; vectorizes all the performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench; performs weighted processing on each performance vector to obtain a weighted performance vector; calculates the correlation coefficient for the weighted performance vectors of any two test benches; and obtains the test results of all test benches based on all correlation coefficients. By selecting the attribute measurement values ​​of all qualified engines on each test bench within a period of time, and performing quantitative analysis and calculation, the obtained results can be used as a basis for judging the consistency of the test benches, thereby screening out test benches with poor consistency.

[0141] Based on the same technical concept, the present application also provides a test bench performance test system, such as Figure 3 As shown, the system comprises:

[0142] A data acquisition module 301 is used to acquire the performance data of all test benches, wherein the performance data includes a plurality of property measurement values;

[0143] A vector processing module 302 is used to vectorize all performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench;

[0144] A weighting module 303, used to perform weighted processing on each performance vector to obtain a weighted performance vector;

[0145] A correlation coefficient module 304 is used to calculate a correlation coefficient for the weighted performance vectors of any two test benches;

[0146] The test result module 305 is used to obtain the test results of all test benches according to all correlation coefficients.

[0147] The present application also provides an electronic device corresponding to the method provided in the above embodiment. Figure 4 , which shows a schematic diagram of an electronic device provided by some embodiments of the present application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected via the bus 202; the memory 201 stores a computer program that can be run on the processor 200, and the processor 200 executes the method provided by any of the aforementioned embodiments of the present application when running the computer program.

[0148] The memory 201 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one physical port 203 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0149] The bus 202 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs, and the processor 200 executes the programs after receiving execution instructions. The method disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the processor 200, or implemented by the processor 200.

[0150] The processor 200 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 200. The above processor 200 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the above method in combination with its hardware.

[0151] The electronic device provided in the embodiment of the present application and the method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by them.

[0152] The present application also provides a computer-readable storage medium corresponding to the method provided in the above embodiment. Figure 5 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the method provided in any of the aforementioned embodiments is executed.

[0153] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0154] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0155] It should be noted that:

[0156] The algorithm and display provided herein are not inherently related to any particular computer, virtual device or other equipment. Various general purpose devices can also be used together with the teachings based on this. According to the above description, it is obvious to construct the structure required for this type of device. In addition, the application is not directed to any specific programming language. It should be understood that the content of the application described herein can be realized using various programming languages, and the description of the specific language above is to disclose the best mode of implementation of the application.

[0157] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0158] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the claims below, the inventive aspects are less than all the features of the single embodiment disclosed above. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0159] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0160] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0161] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components in the creation device of the virtual machine according to the embodiment of the present application. The application can also be implemented as a device or device program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0162] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0163] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

[0164] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A test bench performance testing method, characterized in that: The method comprises: Acquire performance data of each of all test benches, wherein the performance data includes a plurality of property measurement values; Vectorize all performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench; Perform weighted processing on each performance vector to obtain a weighted performance vector; Calculate the correlation coefficient for the weighted performance vectors of any two test benches; The test results of all test benches are obtained based on all correlation coefficients.

2. The method according to claim 1, characterized in that The correlation coefficient of the weighted performance vectors of any two test benches is calculated according to the following formula: Among them, A and B represent the weighted performance vectors of any two test benches, A i represents the i-th attribute measurement value in A, A avg represents the mean of A, B i represents the i-th attribute measurement value in B, B avg represents the mean of B.

3. The method according to claim 1, characterized in that The test results of all test benches are obtained according to all correlation coefficients, including: Get all the correlation coefficients associated with each test bench; If all correlation coefficients of the set proportions associated with the test bench are lower than the set consistency threshold, the consistency of the test bench is determined to be inconsistent with the target performance standard and is marked as a test bench to be repaired.

4. The method according to claim 1, characterized in that According to the following formula, all performance data of each test bench are vectorized to obtain the performance vector X of each test bench. n : Among them, X avg is the mean of the measured values ​​of a property, X std is the standard deviation of the attribute, 1<=n<=M, M is the number of attribute measurements.

5. The method according to claim 1, characterized in that Before vectorizing all performance data of each test bench, the method further includes: For each attribute measurement value X, normalization is performed according to the following formula: Among them, X max is the maximum value of the attribute measurement, X min It is the minimum value among the measured values ​​of this attribute, and the value of X' is between 0 and 1.

6. The method according to claim 1, characterized in that After obtaining the performance data of all the test benches, the method further includes: Filter out negative data, empty data, and data exceeding the process threshold in the performance data of all test benches.

7. The method according to claim 1, characterized in that After obtaining the test results of all test benches according to all correlation coefficients, the method further includes: All test benches, their respective correlation coefficients and test results are combined into a two-dimensional matrix; the rows of the matrix represent different test benches and the columns represent different attributes; Draw a heat map based on the two-dimensional matrix, add test bench, attribute labels and titles to the heat map, and the depth of color represents the size of the correlation coefficient.

8. A test bench performance test system, characterized in that: The system comprises: A data acquisition module, used to acquire the performance data of all test benches, wherein the performance data includes a plurality of property measurement values; A vector processing module is used to vectorize all performance data of each test bench to obtain a performance vector of each test bench, wherein the performance vector includes all attribute measurement values ​​of the test bench; A weighting module, used for performing weighted processing on each performance vector to obtain a weighted performance vector; A correlation coefficient module is used to calculate the correlation coefficient of the weighted performance vectors of any two test benches; The test result module is used to obtain the test results of all test benches according to all correlation coefficients.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1-7.

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