CN117112385B - Mobile terminal performance test system based on data analysis - Google Patents
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Abstract
The invention discloses a mobile terminal performance test system based on data analysis, which belongs to the technical field of mobile terminal performance test, wherein the mobile terminal is placed in different temperature environments to carry out calculation full-load operation, so that temperature values of different operation times under each temperature environment are collected, the temperature values are built into a temperature value matrix, row elements of the temperature value matrix represent temperature values which change along with the operation time, and row elements of the temperature value matrix represent temperature values which change along with the different environments, therefore, row characteristics are extracted for row elements, column characteristics are extracted for column elements, and the heat dissipation performance value of the mobile terminal is estimated through two dimensions of the row characteristics and the column characteristics, so that the heat dissipation condition of the mobile terminal is estimated.
Description
Technical Field
The invention relates to the technical field of mobile terminal performance test, in particular to a mobile terminal performance test system based on data analysis.
Background
With the rapid development of the mobile internet, mobile terminal devices play an increasingly important role in the life of people. In the use process of the mobile terminal, due to different temperatures of the use environments and different running time, the mobile terminal generates heat to different degrees, the service life of the battery can be reduced due to excessive heat generation, and if severe, solder can be melted to cause cold solder leakage, so that short circuit and open circuit are caused. According to the existing mobile terminal performance test scheme, the mobile terminal is only subjected to long-time calculation full-load operation, the temperature of the mobile terminal in the operation process is measured, and when the temperature is higher than a temperature threshold value, the heat dissipation of the mobile terminal does not reach the standard. The existing mobile terminal performance test scheme can only test whether the heat dissipation of the mobile terminal meets the minimum requirement, and cannot evaluate the heat dissipation performance of the mobile terminal, so that the heat resistance of the mobile terminal in different environments cannot be evaluated.
Disclosure of Invention
Aiming at the defects in the prior art, the mobile terminal performance test system based on data analysis solves the problem that a method for evaluating the heat radiation performance of a mobile terminal is lacking in the prior art.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a mobile terminal performance testing system based on data analysis, comprising: the system comprises a mobile terminal, a temperature monitoring unit, a transverse characteristic extraction unit, a longitudinal characteristic extraction unit and a performance value calculation unit;
the mobile terminal performs full load operation under different temperature environments;
the temperature monitoring unit is used for collecting a plurality of temperature values of the mobile terminal in each temperature environment and constructing a temperature value matrix;
the transverse feature extraction unit is used for extracting row features from row elements of the temperature value matrix;
the longitudinal feature extraction unit is used for extracting column features from column elements of the temperature value matrix;
the performance value calculating unit is used for calculating the heat dissipation performance value of the mobile terminal based on the performance value estimation model according to the row characteristics and the column characteristics.
The beneficial effects of the invention are as follows: according to the invention, the mobile terminal is placed in different temperature environments to perform calculation full-load operation, so that temperature values of different operation times in each temperature environment are collected, a temperature value matrix is constructed, row elements of the temperature value matrix represent temperature values which change along with the operation time, row elements of the temperature value matrix represent temperature values which change along with the different environments, therefore, row characteristics are extracted for row elements, column characteristics are extracted for column elements, and the heat dissipation performance value of the mobile terminal is estimated through two dimensions of the row characteristics and the column characteristics, so that the heat dissipation performance of the mobile terminal is estimated, and the better the heat dissipation performance of the mobile terminal is, the mobile terminal is more heat-resistant in the environment with high temperature and can bear long-time operation.
Further, the temperature monitoring unit includes: the system comprises a temperature acquisition subunit, a temperature smoothing subunit, a temperature value calculation subunit and a matrix construction subunit;
the temperature acquisition subunit is used for acquiring temperature sensing data of the mobile terminal;
the temperature smoothing processing subunit is used for carrying out smoothing processing on the temperature sensing data to obtain smoothed sensing data;
the temperature value calculating subunit is used for calculating the temperature value of each operation time under different temperature environments according to the smooth sensing data in the operation time under the different temperature environments;
the matrix construction subunit is used for sampling the temperature values in different temperature environments to obtain a plurality of sampling temperature values in each temperature environment, and constructing the sampling temperature values into a temperature value matrix.
The beneficial effects of the above further scheme are: after the temperature sensing data of the mobile terminal are acquired, the temperature sensing data are subjected to smoothing processing, abnormal values are filtered, the accuracy of the temperature values is guaranteed, and then the smoothed sensing data are sampled to obtain sampling temperature values of different running times under each environmental temperature.
Further, the expression of the temperature smoothing subunit is:
，
wherein,for the ith ambient temperature T run time smooth sensed data, T i,t Is the temperature sensing data of the ith running time at the ith ambient temperature, T i,t-1 Is the temperature sensing data of the (T-1) th running time at the ith ambient temperature, T i,t+1 Is the temperature sensing data of the (t+1) th running time at the ith ambient temperature, r i,t For the smoothing factor, N is the number of temperature sensing data in a period of operation time, i is the number of ambient temperature, and t is the number of operation time.
The beneficial effects of the above further scheme are: the invention takes the average value of three temperature sensing dataAnd the mean value of the temperature-sensitive data over a period of operation time +.>As a reference value for the smoothing process, timeliness and accuracy of the smoothing process are ensured.
Further, the smoothing factor r i,t The expression of (2) is:
，
where exp is an exponential function with a natural constant as a base, and u is a fixed constant.
The beneficial effects of the above further scheme are: smoothing factor r in the present invention i,t Considering the relation between adjacent three temperature sensing data, when the temperature sensing data are acquired through the sensor, the values of the adjacent temperature sensing data are similar, if the difference between the adjacent three temperature sensing data is larger, at least one abnormal value exists in the three temperature sensing data, and if the difference between the adjacent three temperature sensing data is larger, the smoothing factor r is greater i,t The larger the smoothing process, the stronger the smoothing process.
Further, the expression of the temperature value calculating subunit is:
，
wherein,a is the temperature value of the ith operating time at the ith ambient temperature, a 1 A is a first temperature coefficient, a 2 A is a second temperature coefficient, a 3 For the third temperature coefficient, +.>Smooth sensing data for the ith run time at the ith ambient temperature;
the temperature value matrix is as follows:
，
，
，
，
wherein A is a temperature value matrix,for the 1 st ambient temperature vector, +.>Is the 1 st sampling temperature value at the 1 st ambient temperature,>for the jth sample temperature value at ambient temperature 1 +.>The M-th sampling temperature value is the 1 st environmental temperature, j is the number of the sampling temperature value, the value range of j is 1-M, M is the number of the sampling temperature values at each environmental temperature, and the number of the sampling temperature values at each environmental temperature is->I is the number of the ambient temperature and is the value range of 1~I, < + > for the i-th ambient temperature vector>The I-th ambient temperature vector, I is the number of the ambient temperature vectors,/the number of the ambient temperature vectors>For the 1 st sample temperature value at the i-th ambient temperature,/th>For the jth sample temperature value at the ith ambient temperature,/">For the mth sample temperature value at the ith ambient temperature, +.>Is the 1 st sampling temperature value at the I-th ambient temperature,>for the jth sample temperature value at the ith ambient temperature,/">The M-th sampling temperature value is the I-th environmental temperature, and T is the transposition operation.
Further, the row feature includes: line average value, line average growth amplitude and line peak value;
the row average value is the average value of each row element in the temperature value matrix A, and the expression of the row average value is:
，
wherein,is the ith row mean;
the expression of the row average growth amplitude is:
，
wherein,for the i-th row average increment amplitude, +.>The j-1 th sampling temperature value is the i-th environmental temperature;
the row peak value is the maximum value of each row element in the temperature value matrix A.
Further, the column feature includes: column mean, column average increase amplitude, and column peak;
the column average value is the average value of each column of elements in the temperature value matrix A, and the expression of the column average value is:
，
wherein,is the jth column mean;
the expression of the column average growth amplitude is:
，
wherein,for the j-th column average increment magnitude, +.>The j-th sampling temperature value is the i-1-th environmental temperature;
and the column peak value is the maximum value of each column element in the temperature value matrix A.
The beneficial effects of the above further scheme are: in the invention, the row average value and the column average value represent the overall temperature level, the row peak value and the column peak value represent the maximum temperature level, the row average increasing amplitude value and the column average increasing amplitude value represent the variation condition of the overall temperature, and the heating condition of the mobile terminal is reflected through the average value, the peak value and the average increasing amplitude value.
Further, the performance value estimation model includes: the running time length influencing submodel, the environment influencing submodel and the heat radiation performance value outputting submodel;
the operation time length influence sub-model is used for calculating an operation time length influence value according to the line average value, the line average increasing amplitude value and the line peak value;
the environmental impact sub-model is used for calculating an environmental impact value according to the column average value, the column average increasing amplitude value and the column peak value;
and the heat radiation performance value output sub-model is used for calculating the heat radiation performance value of the mobile terminal according to the operation duration influence value and the environment influence value.
Further, the expression of the operation duration influence submodel is:
，
wherein f 1 For the running duration impact value, σ is a sigmoid function, w g,1,i For the ith row meanWeights, w g,2,i Average growth amplitude for the ith row +.>Weight of->For the ith row peak, w g,3,i Peak for ith row->Weights of b g Offset for the rows;
the expression of the environmental impact submodel is:
，
wherein f 2 Is the environmental impact value, w a,1,j For the jth column meanWeights, w a,2,j Mean growth amplitude for the j-th column +.>Weight of->For the j-th column peak, w a,3,j For the j-th column peak->Weights of b a Offset for the columns.
Further, the expression of the heat dissipation performance value output submodel is:
，
wherein y is a heat dissipation performance value, w 1 For the duration of operation influence value f 1 Weights, w 2 Is the environmental impact value f 2 Tan h is the hyperbolic tangent function.
The beneficial effects of the above further scheme are: according to the method, the influence of the temperature characteristics corresponding to different operation time lengths on the heat radiation performance is estimated through the operation time length influence sub-model, the influence of the temperature characteristics corresponding to different temperature environments on the heat radiation performance is estimated through the environment influence sub-model, and the heat radiation performance of the mobile terminal is estimated through the comprehensive operation time length and the temperature environments.
Drawings
Fig. 1 is a system block diagram of a mobile terminal performance test system based on data analysis.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a mobile terminal performance test system based on data analysis includes: the system comprises a mobile terminal, a temperature monitoring unit, a transverse characteristic extraction unit, a longitudinal characteristic extraction unit and a performance value calculation unit;
the mobile terminal performs full load operation under different temperature environments;
the temperature monitoring unit is used for collecting a plurality of temperature values of the mobile terminal in each temperature environment and constructing a temperature value matrix;
the transverse feature extraction unit is used for extracting row features from row elements of the temperature value matrix;
the longitudinal feature extraction unit is used for extracting column features from column elements of the temperature value matrix;
the performance value calculating unit is used for calculating the heat dissipation performance value of the mobile terminal based on the performance value estimation model according to the row characteristics and the column characteristics.
The temperature monitoring unit includes: the system comprises a temperature acquisition subunit, a temperature smoothing subunit, a temperature value calculation subunit and a matrix construction subunit;
the temperature acquisition subunit is used for acquiring temperature sensing data of the mobile terminal;
the temperature smoothing processing subunit is used for carrying out smoothing processing on the temperature sensing data to obtain smoothed sensing data;
the temperature value calculating subunit is used for calculating the temperature value of each operation time under different temperature environments according to the smooth sensing data in the operation time under the different temperature environments;
the matrix construction subunit is used for sampling the temperature values in different temperature environments to obtain a plurality of sampling temperature values in each temperature environment, and constructing the sampling temperature values into a temperature value matrix.
After the temperature sensing data of the mobile terminal are acquired, the temperature sensing data are subjected to smoothing processing, abnormal values are filtered, the accuracy of the temperature values is guaranteed, and then the smoothed sensing data are sampled to obtain sampling temperature values of different running times under each environmental temperature.
The expression of the temperature smoothing subunit is:
，
wherein,for the ith ambient temperature T run time smooth sensed data, T i,t Is the temperature sensing data of the ith running time at the ith ambient temperature, T i,t-1 Is the temperature sensing data of the (T-1) th running time at the ith ambient temperature, T i,t+1 Is the temperature sensing data of the (t+1) th running time at the ith ambient temperature, r i,t For the smoothing factor, N is the number of temperature sensing data in a period of operation time, i is the number of ambient temperature, and t is the number of operation time.
The invention takes the average value of three temperature sensing dataAnd the mean value of the temperature-sensitive data over a period of operation time +.>As a reference value for the smoothing process, timeliness and accuracy of the smoothing process are ensured.
The smoothing factor r i,t The expression of (2) is:
，
where exp is an exponential function with a natural constant as a base, and u is a fixed constant.
Smoothing factor r in the present invention i,t Considering the relation between adjacent three temperature sensing data, when the temperature sensing data are acquired through the sensor, the values of the adjacent temperature sensing data are similar, if the difference between the adjacent three temperature sensing data is larger, at least one abnormal value exists in the three temperature sensing data, and if the difference between the adjacent three temperature sensing data is larger, the smoothing factor r is greater i,t The larger the smoothing process, the stronger the smoothing process.
The expression of the temperature value calculating subunit is as follows:
，
wherein,a is the temperature value of the ith operating time at the ith ambient temperature, a 1 A is a first temperature coefficient, a 2 A is a second temperature coefficient, a 3 For the third temperature coefficient, +.>Smooth sensing data for the ith run time at the ith ambient temperature;
the temperature value matrix is as follows:
，
，
，
，
wherein A is a temperature value matrix,for the 1 st ambient temperature vector, +.>Is the 1 st sampling temperature value at the 1 st ambient temperature,>for the jth sample temperature value at ambient temperature 1 +.>Is the 1 st environmentThe Mth sampling temperature value under temperature, j is the number of the sampling temperature value, the value range of j is 1-M, M is the number of the sampling temperature values under each environmental temperature,/for each environmental temperature>I is the number of the ambient temperature and is the value range of 1~I, < + > for the i-th ambient temperature vector>The I-th ambient temperature vector, I is the number of the ambient temperature vectors,/the number of the ambient temperature vectors>For the 1 st sample temperature value at the i-th ambient temperature,/th>For the jth sample temperature value at the ith ambient temperature,/">For the mth sample temperature value at the ith ambient temperature, +.>Is the 1 st sampling temperature value at the I-th ambient temperature,>for the jth sample temperature value at the ith ambient temperature,/">The M-th sampling temperature value is the I-th environmental temperature, and T is the transposition operation.
In the invention, the transverse change factor of the temperature value matrix A is running time, different running times correspond to different sampling temperature values, namely 1 sampling temperature value corresponds to one sampling time, and the longitudinal change factor of the temperature value matrix A is different environmental temperatures.
The row feature includes: line average value, line average growth amplitude and line peak value;
the row average value is the average value of each row element in the temperature value matrix A, and the expression of the row average value is:
，
wherein,is the ith row mean;
the expression of the row average growth amplitude is:
，
wherein,for the i-th row average increment amplitude, +.>The j-1 th sampling temperature value is the i-th environmental temperature;
the row peak value is the maximum value of each row element in the temperature value matrix A.
The column feature includes: column mean, column average increase amplitude, and column peak;
the column average value is the average value of each column of elements in the temperature value matrix A, and the expression of the column average value is:
，
wherein,is the jth column mean;
the expression of the column average growth amplitude is:
，
wherein,for the j-th column average increment magnitude, +.>The j-th sampling temperature value is the i-1-th environmental temperature;
and the column peak value is the maximum value of each column element in the temperature value matrix A.
In the invention, the row average value and the column average value represent the overall temperature level, the row peak value and the column peak value represent the maximum temperature level, the row average increasing amplitude value and the column average increasing amplitude value represent the variation condition of the overall temperature, and the heating condition of the mobile terminal is reflected through the average value, the peak value and the average increasing amplitude value.
In the invention, the heat dissipation capacity of the mobile terminal is embodied by the heating condition of the mobile terminal.
The performance value estimation model includes: the running time length influencing submodel, the environment influencing submodel and the heat radiation performance value outputting submodel;
the operation time length influence sub-model is used for calculating an operation time length influence value according to the line average value, the line average increasing amplitude value and the line peak value;
the environmental impact sub-model is used for calculating an environmental impact value according to the column average value, the column average increasing amplitude value and the column peak value;
and the heat radiation performance value output sub-model is used for calculating the heat radiation performance value of the mobile terminal according to the operation duration influence value and the environment influence value.
The expression of the operation duration influence submodel is as follows:
，
wherein f 1 For the running duration impact value, σ is a sigmoid function, w g,1,i For the ith row meanWeights, w g,2,i Average growth amplitude for the ith row +.>Weight of->For the ith row peak, w g,3,i Peak for ith row->Weights of b g Offset for the rows;
the expression of the environmental impact submodel is:
，
wherein f 2 Is the environmental impact value, w a,1,j For the jth column meanWeights, w a,2,j Mean growth amplitude for the j-th column +.>Weight of->For the j-th column peak, w a,3,j For the j-th column peak->Weights of b a Offset for the columns.
The expression of the heat dissipation performance value output submodel is as follows:
，
wherein y is a heat dissipation performance value, w 1 For the duration of operation influence value f 1 Weights, w 2 Is the environmental impact value f 2 Tan h is the hyperbolic tangent function.
According to the method, the influence of the temperature characteristics corresponding to different operation time lengths on the heat radiation performance is estimated through the operation time length influence sub-model, the influence of the temperature characteristics corresponding to different temperature environments on the heat radiation performance is estimated through the environment influence sub-model, and the heat radiation performance of the mobile terminal is estimated through the comprehensive operation time length and the temperature environments.
The weight and bias in the performance value estimation model can be trained by adopting a GA genetic algorithm or by adopting a gradient descent method, and the scheme for training the weight and bias in the performance value estimation model is the prior art.
According to the invention, the mobile terminal is placed in different temperature environments to perform calculation full-load operation, so that temperature values of different operation times in each temperature environment are collected, a temperature value matrix is constructed, row elements of the temperature value matrix represent temperature values which change along with the operation time, row elements of the temperature value matrix represent temperature values which change along with the different environments, therefore, row characteristics are extracted for row elements, column characteristics are extracted for column elements, and the heat dissipation performance value of the mobile terminal is estimated through two dimensions of the row characteristics and the column characteristics, so that the heat dissipation performance of the mobile terminal is estimated, and the better the heat dissipation performance of the mobile terminal is, the mobile terminal is more heat-resistant in the environment with high temperature and can bear long-time operation.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A mobile terminal performance testing system based on data analysis, comprising: the system comprises a mobile terminal, a temperature monitoring unit, a transverse characteristic extraction unit, a longitudinal characteristic extraction unit and a performance value calculation unit;
the mobile terminal performs full load operation under different temperature environments;
the temperature monitoring unit is used for collecting a plurality of temperature values of the mobile terminal in each temperature environment and constructing a temperature value matrix;
the transverse feature extraction unit is used for extracting row features from row elements of the temperature value matrix;
the longitudinal feature extraction unit is used for extracting column features from column elements of the temperature value matrix;
the performance value calculating unit is used for calculating the heat dissipation performance value of the mobile terminal based on the performance value estimation model according to the row characteristics and the column characteristics;
the temperature monitoring unit includes: the system comprises a temperature acquisition subunit, a temperature smoothing subunit, a temperature value calculation subunit and a matrix construction subunit;
the temperature acquisition subunit is used for acquiring temperature sensing data of the mobile terminal;
the temperature smoothing processing subunit is used for carrying out smoothing processing on the temperature sensing data to obtain smoothed sensing data;
the temperature value calculating subunit is used for calculating the temperature value of each operation time under different temperature environments according to the smooth sensing data in the operation time under the different temperature environments;
the matrix construction subunit is used for sampling temperature values in different temperature environments to obtain a plurality of sampling temperature values in each temperature environment, and constructing the sampling temperature values into a temperature value matrix;
the expression of the temperature smoothing subunit is:
，
wherein,for the ith ambient temperature T run time smooth sensed data, T i,t Is the temperature sensing data of the ith running time at the ith ambient temperature, T i,t-1 Is the temperature sensing data of the (T-1) th running time at the ith ambient temperature, T i,t+1 Is the temperature sensing data of the (t+1) th running time at the ith ambient temperature, r i,t As a smoothing factor, N is the number of temperature sensing data in a period of operation time, i is the number of the ambient temperature, and t is the number of the operation time;
the flatSlip factor r i,t The expression of (2) is:
，
wherein exp is an exponential function with a natural constant as a base, and u is a fixed constant;
the expression of the temperature value calculating subunit is as follows:
，
wherein,a is the temperature value of the ith operating time at the ith ambient temperature, a 1 A is a first temperature coefficient, a 2 A is a second temperature coefficient, a 3 For the third temperature coefficient, +.>Smooth sensing data for the ith run time at the ith ambient temperature;
the temperature value matrix is as follows:，/>，，/>wherein A is a temperature value matrix, < >>For the 1 st ambient temperature vector, +.>Is the 1 st sampling temperature value at the 1 st ambient temperature,>for the jth sample temperature value at ambient temperature 1 +.>The M-th sampling temperature value is the 1 st environmental temperature, j is the number of the sampling temperature value, the value range of j is 1-M, M is the number of the sampling temperature values at each environmental temperature, and the number of the sampling temperature values at each environmental temperature is->I is the number of the ambient temperature and is the value range of 1~I, < + > for the i-th ambient temperature vector>The I-th ambient temperature vector, I is the number of the ambient temperature vectors,/the number of the ambient temperature vectors>For the 1 st sample temperature value at the i-th ambient temperature,/th>For the jth sample temperature value at the ith ambient temperature,/">For the mth sample temperature value at the ith ambient temperature, +.>Is the 1 st sampling temperature value at the I-th ambient temperature,>for the jth sample temperature value at the ith ambient temperature,/">Is the first ambient temperatureM sampling temperature values, T is transposition operation;
the row feature includes: line average value, line average growth amplitude and line peak value;
the row average value is the average value of each row element in the temperature value matrix A, and the expression of the row average value is:wherein, the method comprises the steps of, wherein,is the ith row mean;
the expression of the row average growth amplitude is:wherein->For the i-th row average increment amplitude, +.>The j-1 th sampling temperature value is the i-th environmental temperature;
the row peak value is the maximum value of each row element in the temperature value matrix A;
the column feature includes: column mean, column average increase amplitude, and column peak;
the column average value is the average value of each column of elements in the temperature value matrix A, and the expression of the column average value is:wherein->Is the jth column mean;
the expression of the column average growth amplitude is:wherein->For the j-th column average increment magnitude, +.>The j-th sampling temperature value is the i-1-th environmental temperature;
the column peak value is the maximum value of each column element in the temperature value matrix A;
the performance value estimation model includes: the running time length influencing submodel, the environment influencing submodel and the heat radiation performance value outputting submodel;
the operation time length influence sub-model is used for calculating an operation time length influence value according to the line average value, the line average increasing amplitude value and the line peak value;
the environmental impact sub-model is used for calculating an environmental impact value according to the column average value, the column average increasing amplitude value and the column peak value;
the heat radiation performance value output sub-model is used for calculating the heat radiation performance value of the mobile terminal according to the operation duration influence value and the environment influence value;
the expression of the operation duration influence submodel is as follows:
，
wherein f 1 For the running duration impact value, σ is a sigmoid function, w g,1,i For the ith row meanWeights, w g,2,i Average growth amplitude for the ith row +.>Weight of->For the ith row peak, w g,3,i Peak for ith row->Weights of b g Offset for the rows;
the expression of the environmental impact submodel is:
，
wherein f 2 Is the environmental impact value, w a,1,j For the jth column meanWeights, w a,2,j Average growth amplitude for jth columnWeight of->For the j-th column peak, w a,3,j For the j-th column peak->Weights of b a Offset for the columns;
the expression of the heat dissipation performance value output submodel is as follows:
，
wherein y is a heat dissipation performance value, w 1 For the duration of operation influence value f 1 Weights, w 2 Is the environmental impact value f 2 Tan h is the hyperbolic tangent function.
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