CN116992388B - Membrane switch test data processing method based on data analysis - Google Patents
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Abstract
The invention relates to the technical field of data processing, and provides a membrane switch test data processing method based on data analysis, which comprises the following steps: acquiring test data of a membrane switch; calculating a neighboring feature data set according to the membrane switch test data, and calculating response compactness of each membrane switch test data according to the neighboring feature data set; calculating a switching response fluctuation coefficient of each membrane switch test data according to the response compactness, calculating a steady-state variation coefficient of each membrane switch test data according to the switching response fluctuation coefficient, calculating a shrinkage factor coefficient of each membrane switch test data according to the steady-state variation coefficient, and acquiring each membrane switch abnormal state data set according to the shrinkage factor coefficient; and acquiring a membrane switch test sample matrix according to the abnormal state data set, and acquiring an abnormal reference value of the membrane switch test data according to the membrane switch test sample matrix. The method effectively improves the accuracy of detecting the abnormal test data of the membrane switch.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a membrane switch test data processing method based on data analysis.
Background
The membrane switch is an operation system integrating key functions, indication elements and instrument panels, has the characteristics of strict structure, attractive appearance, good sealing performance, long service life and the like, and has more kinds of production. The good performance and variety of the membrane switch also make the membrane switch widely applied to the fields of electronic communication, electronic measurement instruments, industrial control, medical equipment, household appliances and the like.
The quality detection of the membrane switch is an important task, and the data analysis of the membrane switch test data can help us to know the performance characteristics of the membrane switch, so that the quality of a product can be evaluated, and a reference basis can be provided for the optimization of the produced membrane product. The method can analyze the membrane switch test data by adopting a clustering analysis method, and the traditional clustering algorithm is inaccurate in analysis of the characteristics of the membrane switch test data of different samples, so that the clustering of the membrane switch test data of different samples is inaccurate, the analysis of the membrane switch performance among different samples is inaccurate, and the error for evaluating the membrane switch quality is larger.
Disclosure of Invention
The invention provides a membrane switch test data processing method based on data analysis, which aims to solve the problems, and adopts the following technical scheme:
the invention relates to a membrane switch test data processing method based on data analysis, which comprises the following steps:
acquiring test data of a membrane switch;
calculating the adjacent characteristic data set of each membrane switch according to the membrane switch test data, and respectively calculating the response compactness of each membrane switch test data according to the adjacent characteristic data set of each membrane switch;
calculating a switching response fluctuation coefficient of each membrane switch test data according to the response compactness of each membrane switch test data, calculating a steady-state variation coefficient of each membrane switch test data according to the switching response fluctuation coefficient of each membrane switch test data, calculating a shrinkage factor coefficient of each membrane switch test data according to the steady-state variation coefficient of each membrane switch test data, and acquiring an abnormal state data set of each membrane switch according to the shrinkage factor coefficient of each membrane switch test data;
and acquiring a membrane switch test sample matrix according to each membrane switch abnormal state data set, and acquiring a membrane switch test data abnormal reference value according to the membrane switch test sample matrix.
Preferably, the membrane switch test data includes response time, switching voltage, switching current and contact resistance data for each membrane switch.
Preferably, the method for acquiring the adjacent characteristic data set of each membrane switch comprises the following steps:
and taking the test data of the membrane switch as input, respectively using an adjacent dividing algorithm to obtain adjacent characteristic data sets in each test data of the membrane switch, and respectively marking the adjacent characteristic data sets of each response time, switching voltage, switching current and contact resistance data of the divided membrane switch as a first adjacent characteristic set, a second adjacent characteristic set, a third adjacent characteristic set and a fourth adjacent characteristic set.
Preferably, the method for calculating the response compactness of the membrane switch test data comprises the following steps:
and (3) marking the sum of the squares of the average value differences of each datum in the first adjacent feature set and the datum in the first adjacent feature set as a first sum, marking the difference between the maximum numerical value in the first adjacent feature set and the minimum numerical value in the first adjacent feature set as a first difference value, and marking the product of the average value of the first sum and the first difference value as the response compactness of the membrane switch test data.
Preferably, the specific calculation method of the switching response fluctuation coefficient of each membrane switch test data is as follows:
in the method, in the process of the invention,indicating the first part of the membrane switch>Compactness of individual test data response, +.>Represents an exponential function based on natural constants, < ->Indicating the first part of the membrane switch>Information entropy of the second neighboring feature set of the test data,/->Indicating the first part of the membrane switch>Information entropy of third neighboring feature set of the test data,/->Indicating the first part of the membrane switch>Information entropy of the fourth neighboring feature set of the test data,/->Represents the membrane switch response adjustment constant, +.>Indicating the first part of the membrane switch>The switching response of the individual test data fluctuates by a factor.
Preferably, the calculating method of the steady-state variation coefficient of each membrane switch test data comprises the following steps:
calculating the ratio of the mean square error to the mean value of the data in the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch, respectively marking the ratio as a first ratio, a second ratio, a third ratio and a fourth ratio, and marking the sum of the first ratio, the second ratio, the third ratio and the fourth ratio as the steady-state variation coefficient of the membrane switch.
Preferably, the method for calculating the shrinkage factor of each membrane switch test data according to the steady-state variation coefficient of each membrane switch test data comprises the following steps:
and arranging the switching response fluctuation coefficients of the membrane switch test data according to the order from the small value to the large value to form a first sequence, calculating the difference value between the median and the mean square error of the first sequence, marking the difference value as a second difference value, and marking the normalization result of the product of the second difference value and the steady-state variation coefficient of the membrane switch as the contraction factor coefficient of the membrane switch.
Preferably, the method for obtaining the abnormal state data set of each membrane switch according to the shrinkage factor coefficient of the test data of each membrane switch comprises the following steps:
and taking the shrinkage factor coefficient of the membrane switch as the input of a data anomaly clustering algorithm, acquiring anomaly data in a response time sequence, a switching voltage sequence, a switching current sequence and a contact resistance sequence of the membrane switch, and forming a first anomaly set, a second anomaly set, a third anomaly set and a fourth anomaly set according to the sequence.
Preferably, the method for obtaining the membrane switch test sample matrix according to each membrane switch abnormal state data set comprises the following steps:
and (3) keeping the same values in the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch as those in the first abnormal set, the second abnormal set, the third abnormal set and the fourth abnormal set, adjusting different values in the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch as preset values from the values in the first abnormal set, the second abnormal set, the third abnormal set and the fourth abnormal set, and forming a membrane switch test sample matrix according to the sequence of the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch after adjustment.
Preferably, the method for obtaining the abnormal reference value of the membrane switch test data according to the membrane switch test sample matrix comprises the following steps:
encoding all preset values in the membrane switch test sample matrix into digital valuesCoding all non-preset values in the membrane switch test sample matrix into numbers +.>And forming a first test matrix by arranging each column of the coded matrix from small to large, calculating the decimal value mean value of each column of binary codes of the first test matrix, and marking the decimal value mean value as an abnormal reference value of the test data of the membrane switch.
The beneficial effects of the invention are as follows: and (3) switching response fluctuation coefficients of the test data of the membrane switch are constructed, and shrinkage factor reference coefficients are calculated according to the switching response fluctuation coefficients and the variation coefficients of the switching current. The method has the beneficial effects that the shrinkage factor reference coefficient considers the distribution characteristics of the test data of the membrane switch, so that inaccurate clustering results caused by smaller setting of the selected shrinkage factor parameters when the data distribution is discrete are avoided, the accuracy of clustering the test data of the membrane switch is improved, and the accuracy of evaluating the quality of the membrane switch is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a flow chart of a method for processing data of a membrane switch test based on data analysis according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flow chart of a method for processing test data of a membrane switch based on data analysis according to an embodiment of the invention is shown, the method includes the following steps:
and S001, obtaining the test data of the membrane switch.
And switching voltage, switching current and contact resistance data in the performance test process of the membrane switch, which are obtained through the current sensor, the voltage sensor and the resistance sensor, and recording the response time of the sensor as response time data in the test process of the membrane switch. Meanwhile, the acquired data respectively form a response time sequence, a switching voltage sequence, a switching current sequence and a contact resistance sequence according to time sequence.
Step S002, calculating the adjacent characteristic data set of each membrane switch according to the membrane switch test data, and calculating the response compactness of each membrane switch test data according to the adjacent characteristic data set of each membrane switch.
As the performance test of the membrane switch relates to more aspects, the production quality of the membrane switch can be comprehensively evaluated by analyzing the performance test data of the membrane switch in different aspects. The response time in the performance test data of the membrane switch is the time required from signal input to switch action, reflects the working efficiency of the membrane switch, and the lower the response time is, the higher the working efficiency of the membrane switch is; the switching voltage is the voltage required by the action of the membrane switch, and the lower the value is, the switching state of the membrane switch can be switched under the lower voltage; the contact resistance is a resistance value in a switch-on state, and the smaller the resistance value is, the better the membrane switch performance is. However, the switching current represents a current value that the membrane switch can withstand and conduct when the switch state is switched, and the larger the value is, the larger the withstand and conduction capability of the membrane switch is, and the better the performance of the membrane switch is.
The increase of the switching voltage and the contact resistance can lead the membrane switch to need more response time to finish the switching state conversion, and meanwhile, the switching current is larger, which indicates that the working current is larger in the working state of the membrane switch, on one hand, the performance of the membrane switch capable of bearing larger current is reflected, on the other hand, the switching voltage and the contact resistance are larger in the working process of the membrane switch, and the response time is longer. Therefore, the analysis of the numerical change switching current is opposite to the other three performance indexes in terms of performance, but the abnormal changes of the contact resistance, the switching voltage and the response time of the switching current are the same in terms of numerical influence.
Response to thin film switches using K nearest neighbor algorithmProcessing in which parameters are inputThe value is 10, and K neighbor data of response time data of each membrane switch are obtained, because the collection of performance test data of each membrane switch sample is the data of the four aspects, namely, one production membrane switch sample corresponds to one group of data. Processing responsive to the four data of response time, switching voltage, switching current and contact resistance respectively to obtain adjacent characteristic data sets respectively recorded as，/>，/>，/>。
In the above, the first step of,representing the number of different data in the response time proximity feature data set, +.>Representing the +.f in the response time neighborhood characteristic data set>Data of->Indicating the first part of the membrane switch>Adjacent set of individual test data->Mean value of all data in>And->Respectively show the thin film switch +.>Adjacent set of individual test data->Medium maximum and minimum.
The first thin film switch can be calculated by the formulaResponse time compactness of individual test data +.>When the response time of the produced membrane switch samples is smaller, namely the performance of the membrane switch is better, the calculated +.>The extremely small adjacent characteristic data sets of the individual response time data represent small abnormal data density in the data; calculated set of neighboring feature data +.>If the fluctuation degree of the medium value is small, the data distribution is dense, and the response compactness is calculated>The values of (2) are smaller, the distribution of the response time data of the membrane switch is dense, and the values are close; otherwise, the data distribution is discrete, and the numerical variation difference is large.
Step S003, calculating a switching response fluctuation coefficient of each membrane switch test data according to the response compactness, calculating a steady-state variation coefficient according to the switching response fluctuation coefficient, calculating a shrinkage factor coefficient of each membrane switch test data according to the steady-state variation coefficient, and acquiring each membrane switch abnormal state data set according to the shrinkage factor coefficient.
The response compactness of the membrane switch test data can be obtained through the calculation and analysis of the steps, and a theoretical basis is provided for the subsequent calculation of the abnormal state set of the membrane switch test data.
In the above-mentioned method, the step of,，/>，/>respectively represent +.>The individual test data are adjacent to the characteristic data set +.>，/>，，/>Information entropy size of numerical value,/-, and>the membrane switch response adjustment constant is taken to be 1.
The first thin film switch can be calculated by the formulaSwitching response fluctuation coefficient of individual test data +.>When the performance of the membrane switch is better in all aspects, namely the change of the values of the switching voltage and the contact resistance is stable, the value of the membrane switch is calculated>The smaller the value of (2) is, the film switch is>Different adjacent characteristic data sets in the test data，/>，/>The closer the information entropy of (c) is, while the response compactness of the response time is +.>Smaller, the calculated firstSwitching response fluctuation coefficient of individual test data +.>The smaller the value.
The switching response fluctuation coefficient can be calculated for all data of the membrane switch, thereby reducing the different switching response fluctuation coefficients from small to small in valueLarge order construction of switching fluctuation coefficient sequences，/>For taking the sequence->The median of the median data; />The thin film switch test data sequence is shown, i.e. when +.>When (I)>A membrane switch response time data sequence is shown. />Representing the mean square error of the data in the calculation sequence, +.>Representing the mean of the data in the computed sequence.
The steady-state variation coefficient of the membrane switch can be calculated by the formulaIs a numerical value of (1) and a reference coefficient for the contraction factor +.>Is a numerical value of (a). When the switching response fluctuation coefficient calculated in the collected test data of the membrane switch has abnormal change, the distribution of the test data of the membrane switch produced in the batch is discrete, and the distribution range of the abnormal data is disordered, namely +.>The value of (2) is larger; the more violent the change of the membrane switch test data, namely the calculated membrane switch state becomesDifferent coefficient->Larger, the obtained reference coefficient of the contraction factor +.>The value of (2) is larger, which means that the parameter based on CURE anomaly detection algorithm is adopted for the collected membrane switch test data>The value of (2) is larger.
By reference to calculated contraction factorNormalized to the value of (2) to obtain the parameter contraction factor +.>Is of a size of (a) and (b). Shrink factor according to input parameters>Parameter shrinkage factor +.>Is related to the clustering result of the data, and further judges the parameter shrinkage factor +_ according to the distribution characteristics of the membrane switch performance test data and the correlation between the changes of different performances>Is the size of the representative point +.>The number of cluster classes is +.>Clustering analysis is carried out on test data of the membrane switchAnd obtaining a clustering result of the test data of each membrane switch.
The clustering result of the membrane switch test data can be obtained by the steps, the distance average value from the data in the cluster to the representative point of each membrane switch test data is calculated, the data in the cluster are ordered according to the calculated distance from big to small, the data with larger distance is selected to be regarded as abnormal data, and the number of the abnormal points selected in each cluster is as followsThus, an abnormal state data set of the membrane switch test data can be obtained>、/>、/>、/>And respectively corresponding to the response time, the switching voltage, the switching current and the abnormal state data set of the contact resistance membrane switch.
Step S004, a membrane switch test sample matrix is obtained according to each membrane switch abnormal state data set, a membrane switch test data abnormal reference value is obtained according to the membrane switch test sample matrix, and the abnormal state of the membrane switch test data is detected.
Because each sample corresponds to one group of performance index data, a test sample matrix can be formed according to the data corresponding to the samples. Preserving the set of the membrane switch response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence>、/>、/>、/>The other data is set to 0, and the matrix is obtained by reserving non-full 0 columns>. Setting each column of elements of the matrix to be binary coded, setting non-0 elements to be 1, and arranging each column in order from 0 to 1 to obtain binary coding of each column of elements.
In the aboveRepresenting the matrix +.>Middle->Binary coding of column data into decimal values, matrix +.>Common->Column data, calculated +.>And representing abnormal reference values of the test data of the batch production membrane switch.
The interference condition of the abnormal test data in the performance test process of the membrane switch can be obtained through the abnormal reference value of the test data of the membrane switch, so that the performance state of the current membrane switch can be judged. When the calculated abnormal reference value of the membrane switch test data is larger than or equal to a first preset value, the membrane switch test performance is considered to be abnormal, the quality of the current membrane switch component is required to be further checked, and the number of defective products of the membrane switch test performance is reduced.
Wherein the first preset value has an empirical value of 0.24, and the practitioner can set the first preset value according to the needs.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. The membrane switch test data processing method based on data analysis is characterized by comprising the following steps of:
acquiring test data of a membrane switch;
calculating the adjacent characteristic data set of each membrane switch according to the membrane switch test data, and respectively calculating the response compactness of each membrane switch test data according to the adjacent characteristic data set of each membrane switch;
calculating a switching response fluctuation coefficient of each membrane switch test data according to the response compactness of each membrane switch test data, calculating a steady-state variation coefficient of each membrane switch test data according to the switching response fluctuation coefficient of each membrane switch test data, calculating a shrinkage factor coefficient of each membrane switch test data according to the steady-state variation coefficient of each membrane switch test data, and acquiring an abnormal state data set of each membrane switch according to the shrinkage factor coefficient of each membrane switch test data;
acquiring a membrane switch test sample matrix according to each membrane switch abnormal state data set, and acquiring a membrane switch test data abnormal reference value according to the membrane switch test sample matrix;
the method for calculating the response compactness of the membrane switch test data comprises the following steps:
the method comprises the steps of recording a sum of squares of average differences between each datum in a first adjacent feature set and the datum in the first adjacent feature set as a first sum, recording a difference between a maximum numerical value in the first adjacent feature set and a minimum numerical value in the first adjacent feature set as a first difference, and recording a product of the average value of the first sum and the first difference as response compactness of the membrane switch test data;
the specific calculation method of the switching response fluctuation coefficient of the test data of each membrane switch comprises the following steps:
in the method, in the process of the invention,indicating the first part of the membrane switch>Compactness of individual test data response, +.>Represents an exponential function based on natural constants, < ->Indicating the first part of the membrane switch>Information entropy of the second neighboring feature set of the test data,/->Indicating the first part of the membrane switch>Information entropy of third neighboring feature set of the test data,/->Indicating the first part of the membrane switch>Individual test dataInformation entropy of the fourth neighboring feature set, < ->Represents the membrane switch response adjustment constant, +.>Indicating the first part of the membrane switch>Switching response fluctuation coefficients of the test data;
the calculating method of the steady-state variation coefficient of the test data of each membrane switch comprises the following steps:
calculating the ratio of the mean square error to the mean value of the data in the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch, respectively marking the ratio as a first ratio, a second ratio, a third ratio and a fourth ratio, and marking the sum of the first ratio, the second ratio, the third ratio and the fourth ratio as a steady-state variation coefficient of the membrane switch;
the method for calculating the shrinkage factor of each membrane switch test data according to the steady-state variation coefficient of each membrane switch test data comprises the following steps:
and arranging the switching response fluctuation coefficients of the membrane switch test data according to the order from the small value to the large value to form a first sequence, calculating the difference value between the median and the mean square error of the first sequence, marking the difference value as a second difference value, and marking the normalization result of the product of the second difference value and the steady-state variation coefficient of the membrane switch as the contraction factor coefficient of the membrane switch.
2. The data analysis-based membrane switch test data processing method according to claim 1, wherein: the membrane switch test data includes response time, switching voltage, switching current, and contact resistance data for each membrane switch.
3. The method for processing the data analysis-based membrane switch test data according to claim 2, wherein the method for calculating the proximity characteristic data set of each membrane switch is as follows:
and taking the test data of the membrane switch as input, respectively using a proximity division algorithm to obtain a proximity characteristic data set in each test data of the membrane switch, and respectively marking the proximity characteristic data sets of each response time, switching voltage, switching current and contact resistance data of the membrane switch after division as a first proximity characteristic set, a second proximity characteristic set, a third proximity characteristic set and a fourth proximity characteristic set.
4. The method for processing the data analysis-based membrane switch test data according to claim 1, wherein the method for acquiring each abnormal state data set of the membrane switch according to the shrinkage factor coefficient of each membrane switch test data comprises the following steps:
and taking the shrinkage factor coefficient of the membrane switch as the input of a data anomaly clustering algorithm, acquiring anomaly data in a response time sequence, a switching voltage sequence, a switching current sequence and a contact resistance sequence of the membrane switch, and forming a first anomaly set, a second anomaly set, a third anomaly set and a fourth anomaly set according to the sequence.
5. The method for processing the data of the membrane switch test based on the data analysis according to claim 4, wherein the method for obtaining the membrane switch test sample matrix according to each abnormal state data set of the membrane switch is as follows:
and (3) keeping the same values in the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch as those in the first abnormal set, the second abnormal set, the third abnormal set and the fourth abnormal set, adjusting different values in the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch as preset values from the values in the first abnormal set, the second abnormal set, the third abnormal set and the fourth abnormal set, and forming a membrane switch test sample matrix according to the sequence of the response time sequence, the switching voltage sequence, the switching current sequence and the contact resistance sequence of the membrane switch after adjustment.
6. The method for processing the membrane switch test data based on the data analysis according to claim 1, wherein the method for acquiring the abnormal reference value of the membrane switch test data according to the membrane switch test sample matrix is as follows:
encoding all preset values in the membrane switch test sample matrix into digital valuesCoding all non-preset values in the membrane switch test sample matrix into numbers +.>And forming a first test matrix by arranging each column of the coded matrix from small to large, calculating the decimal value mean value of each column of binary codes of the first test matrix, and marking the decimal value mean value as an abnormal reference value of the test data of the membrane switch.
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