CN117634932B - Management system of platform for production test of intelligent watch - Google Patents
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Abstract
The invention discloses a management system of a platform for intelligent watch production test, and particularly relates to the technical field of intelligent watches, comprising a data acquisition module, a platform necessity evaluation module, an algorithm processing module and a platform usability evaluation module; and the data acquisition module is used for monitoring and acquiring the data of the intelligent watch and constructing related information required by the clustering algorithm model. According to the intelligent watch production test platform, the necessity of construction of the intelligent watch production test platform is evaluated based on after-sale information, cost consumption and target intelligent watch profit monitoring analysis, when the necessity is judged to be higher, a clustering algorithm model is constructed, an optimal intelligent watch test classification mode is determined, and the constructed intelligent watch production test platform is combined with platform improvement information and automatic coverage information to perform practical evaluation, so that real and reliable advice and data support are provided for the construction of the follow-up intelligent watch production test platform.
Description
Technical Field
The invention relates to the technical field of intelligent watches, in particular to a management system of a platform for intelligent watch production test.
Background
The functionality and characteristics of smartwatches vary from brand to brand, and model to model, and products from different vendors provide different experiences to users. The smart watch aims to provide the user with convenient, practical and personalized functions so that the user can manage time, monitor health, communicate and enjoy entertainment more conveniently in daily life.
In the production process of the intelligent watch, the intelligent watch production test platform is used for comprehensively and systematically testing the intelligent watch in the production process by establishing a special test platform so as to ensure the reliability, stability and user experience of products, therefore, the intelligent watch production test platform plays a vital role in the whole production process, ensures the quality, performance and reliability of the products, improves the user satisfaction and is beneficial to reducing the after-sale service cost.
However, the construction and operation of the platform for the production test of the smart watch need cost operation support, and meanwhile, the operation logic and specific function of the platform for the production test of the smart watch need to be established and evaluated.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides a management system of a smart watch production test platform, which is used for evaluating the necessity of building the smart watch production test platform based on after-sale information, cost consumption and target smart watch profit monitoring analysis in the smart watch production test platform, building a clustering algorithm model when judging that the necessity is higher, determining an optimal smart watch test classification mode, and performing practicability evaluation by combining the built smart watch production test platform with platform improvement information and automatic coverage information, so that real and reliable advice and data support are provided for the building of the subsequent smart watch production test platform.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A management system of a platform for smart watch production test comprises the following modules:
The data acquisition module is used for monitoring and acquiring after-sale application information, cost consumption information and intelligent watch price information of intelligent watch data, and constructing required sample data, and platform improvement information and automatic coverage information after the intelligent watch production test platform is realized by the clustering algorithm model;
The platform necessity evaluation module is used for constructing a platform necessity evaluation model and evaluating the necessity of the platform for the production test of the intelligent watch;
The algorithm processing module is used for determining an optimal intelligent watch test classification mode by constructing a clustering algorithm model after the intelligent watch is manufactured for the intelligent watch production test platform with high necessity;
The platform practicability evaluation module is used for evaluating the practicability of the constructed intelligent watch production test platform in combination with the platform improvement information and the automatic coverage information and giving reasonable suggestions according to the practicability evaluation of the intelligent watch production test platform.
Specifically, in the data acquisition module, sample data used in the intelligent watch production test platform are obtained by the joint operation of the sensor, the test script and the monitoring tool, so that the purpose of monitoring and analyzing relevant information of the intelligent watch production test platform is achieved. In actual use, due to factors such as equipment cost, complexity of real-time monitoring and the like, the intelligent watch is established under the conditions that the intelligent watch is supplied for a long time and the market environment is relatively stable, and the intelligent watch data is continuously monitored, so that the phenomenon that the necessity of a certain monitoring moment is changed can be caused.
In the platform necessity evaluation module, a platform necessity evaluation model is constructed, and the specific process is to collect after-sales application information, cost consumption information and intelligent watch price information of intelligent watch data, wherein the after-sales application information comprises after-sales application coefficients and is calibrated asThe cost spending information includes a cost spending coefficient, calibrated as/>The smart watch price information comprises a smart watch price coefficient, and is calibrated as/>；
After-sales application coefficientThe number of after-sales orders received in the past month of the target type intelligent watch is collected and calibrated to be/>Counting the sales number of the selected intelligent watch in the warranty period, and calibrating as/>After-sales application coefficient；
Cost consumption coefficientThe estimated cost amount of the platform for smart watch production test is established through collection and calibrated as/>Counting the number of smart watches to be produced, marked as/>Cost-effectiveness factor/>；
Price coefficient of intelligent watchBy collecting the average selling price of the smart watch of the target product in the market when the smart watch is marketed, the average selling price is marked as/>Counting estimated pricing of smart watches to be produced, and calibrating as/>Then the price coefficient of the intelligent watch;
The platform necessity evaluation model is respectively formed by weighting and constructing three aspects of after-sale application information, cost consumption information and price information of the intelligent watch data to generate a platform necessity evaluation indexThe corresponding coefficients are the after-sales application coefficients/>, respectivelyCost consumption coefficient/>Price coefficient of smartwatch/>The formula is;
At the same time, the method comprises the steps of,All are larger than 0, and are set according to actual conditions, for example, an expert weighting method is adopted, namely, experts in related fields are invited to determine the weight of each index through professional opinion investigation and comprehensive evaluation, so that the weight coefficient can accurately reflect the importance of each index in the platform necessity evaluation. In addition, a plurality of methods such as an analytic hierarchy process, a fuzzy comprehensive evaluation method and the like can be considered to determine the weight coefficient so as to ensure the objectivity and scientificity of the weight coefficient. And will not be described in detail herein.
In the platform necessity evaluation module, the platform necessity evaluation index obtained from the platform necessity evaluation model is used for reflecting the necessity of constructing a platform for production test in the production process of the smart watch, and the larger the value of the platform necessity evaluation index is, the larger the value of the platform is the more after-sales application pressure facing the smart watch is, the lower the cost consumption of the platform is spread on each smart watch, and the higher the profit generated by sales is, the larger the necessity of constructing the platform for production test is.
In the platform necessity assessment module, when the platform necessity assessment indexWhen the data signal is larger than or equal to the set necessity threshold, the process that the production test platform is necessary to be constructed in the production process of the intelligent watch is described, necessary data signals are sent out, and subsequent evaluation is carried out on the production test platform of the intelligent watch;
when the platform necessity assessment index When the requirement threshold value is smaller than the preset requirement threshold value, the fact that a production test platform is not needed to be built temporarily in the production process of the intelligent watch is indicated, no follow-up steps are carried out, and the evaluation result is directly output.
In the invention, the performance characteristics, hardware characteristics, environmental characteristics and stability characteristics of the intelligent watch are selected as test case grouping characteristics;
further, the method for constructing the clustering algorithm model comprises the following steps:
Step 3.1, collecting sample data, cleaning and preprocessing the data, processing missing values, abnormal values and the like, performing feature engineering, and extracting or constructing features significant to clustering tasks, wherein in the invention, performance features, hardware features, environmental features and stability features of the intelligent watch are used as test case grouping features;
step 3.2, normalizing the features to ensure that they have the same dimensions, using a Min-Max normalization, expressed as In the above, the ratio of/>As a feature of the sample,The value is normalized, and max and min are the maximum value and the minimum value of all samples respectively;
Step 3.3, determining the number K of clusters by using Elbow Method (elbow method), specifically, setting the K value to be 1 to n, wherein n is the total number of samples, running a K mean value clustering algorithm for each K value, calculating the internal square sum WCSS of the clusters, wherein WCSS is the square sum of the distances from each point to the center (average value) in the clusters, drawing WCSS under each K value into a chart, usually an elbow-shaped curve, observing the WCSS curve, finding an elbow point, namely, the place where the curve starts to bend, and the K value corresponding to the elbow point is the optimal clustering number;
And 3.4, initializing a cluster center, namely randomly selecting K samples as an initial cluster center, calculating the distance between each sample data and each cluster center by using methods such as Euclidean distance, manhattan distance, cosine similarity and the like, distributing the sample data to the cluster center with the smallest distance, updating the members of the cluster to all samples distributed to the cluster for each cluster constructed based on the cluster center, calculating a new cluster center which is generally the average value of all test cases in the cluster for each cluster, and repeating the steps until a convergence condition is met, wherein the convergence condition is a set convergence threshold or maximum iteration number.
In the platform practicability evaluation module, after the platform for the intelligent watch production test is realized by constructing a clustering algorithm model, a platform practicability evaluation model is constructed, the practicability of the platform is evaluated, and an evaluation result is recorded and is used as a necessity threshold reference value when the next intelligent watch production test platform is constructed.
The method comprises the steps of carrying out practical evaluation on a constructed platform for the production test of the intelligent watch, wherein the specific process is to collect platform improvement information and automatic coverage information of the platform for the production test of the intelligent watch, and the platform improvement information of the platform for the production test of the intelligent watch comprises a test comprehensiveness coefficient and an improvement result coefficient which are respectively calibrated as followsAnd/>The automated overlay information includes automated overlay coefficients, calibrated as/>；
Testing of the coefficient of comprehensivenessThe total number of types of test contents of the platform for smart watch production test is collected and calibrated as/>And the number of kinds of problems occurring in after-sales orders of the intelligent watch is counted and calibrated as/>Then test the comprehensiveness coefficient/>；
Improved coefficient of performanceThe sales number of the target product sold in the market within one year is collected and is marked as/>Counting sales number of the target intelligent watch in one year after sales, and calibrating as/>Then improve the outcome coefficient/>；
Automated coverage factorThe total expenditure amount of the platform for intelligent watch production test is collected and calibrated as/>Counting the manual expenditure cost in the platform for the production and test of the intelligent watch, and calibrating as/>Then the coverage coefficient is automated;
The platform usability evaluation model is respectively formed by weighting and constructing two aspects of platform improvement information and automatic coverage information, and generates a platform usability evaluation indexThe corresponding coefficients are test comprehensiveness coefficients/>, respectivelyCoefficient of improvementAutomated coverage factor/>The formula is/>，/>The weight coefficients of the corresponding indexes are all larger than 0.
In the platform practicability evaluation module, when the platform practicability evaluation index is calculatedWhen the practical threshold value is smaller than or equal to the practical threshold value, the test direction of the constructed platform for the production test of the intelligent watch can solve the problems facing after sales to a large extent, the sales quantity is improved, the automation degree is high, the practicability is extremely high, and the scope of the necessity threshold value is suggested to be enlarged when the next platform for the production test of the intelligent watch is constructed;
When the platform is used, the index is evaluated When the practical threshold value is larger than the practical threshold value, the built platform test direction for the production test of the intelligent watch is described as long as the problem faced after sales can be solved to a small extent, meanwhile, the promotion of the sales quantity is limited, the automation degree is low, the practical threshold value is low, and the scope of the necessity threshold value is suggested to be reduced when the next platform for the production test of the intelligent watch is built.
The invention has the technical effects and advantages that:
According to the intelligent watch production test platform, the necessity of construction of the intelligent watch production test platform is evaluated based on after-sale information, cost consumption and target intelligent watch profit monitoring analysis, when the necessity is judged to be higher, a clustering algorithm model is constructed, an optimal intelligent watch test classification mode is determined, and the constructed intelligent watch production test platform is combined with platform improvement information and automatic coverage information to perform practical evaluation, so that real and reliable advice and data support are provided for the construction of the follow-up intelligent watch production test platform.
Drawings
Fig. 1 is a flowchart of a management system of a platform for smart watch production test according to the present invention, and fig. 2 is a flowchart of a method of a platform for smart watch production test according to the present 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.
The intelligent watch production test platform based on the after-sales information, the cost consumption and the target intelligent watch profit monitoring analysis in the intelligent watch production test platform is used for evaluating the necessity of building the intelligent watch production test platform, when the necessity is judged to be higher, a clustering algorithm model is built, the optimal intelligent watch test classification mode is determined, and the built intelligent watch production test platform is combined with the platform improvement information and the automatic coverage information to perform the practicability evaluation, so that real and reliable advice and data support are provided for the subsequent building of the intelligent watch production test platform.
Example 1
As shown in fig. 1, a management system of a platform for smart watch production test comprises the following steps:
The data acquisition module is used for monitoring and acquiring after-sale application information, cost consumption information and intelligent watch price information of intelligent watch data, and constructing required sample data, and platform improvement information and automatic coverage information after the intelligent watch production test platform is realized by the clustering algorithm model;
The platform necessity evaluation module is used for constructing a platform necessity evaluation model and evaluating the necessity of the platform for the production test of the intelligent watch;
The algorithm processing module is used for determining an optimal intelligent watch test classification mode by constructing a clustering algorithm model after the intelligent watch is manufactured for the intelligent watch production test platform with high necessity;
The platform practicability evaluation module is used for evaluating the practicability of the constructed intelligent watch production test platform in combination with the platform improvement information and the automatic coverage information and giving reasonable suggestions according to the practicability evaluation of the intelligent watch production test platform.
Specifically, in the data acquisition module, sample data used in the intelligent watch production test platform are obtained by the joint operation of the sensor, the test script and the monitoring tool, so that the purpose of monitoring and analyzing relevant information of the intelligent watch production test platform is achieved. In actual use, due to factors such as equipment cost, complexity of real-time monitoring and the like, the intelligent watch is established under the conditions that the intelligent watch is supplied for a long time and the market environment is relatively stable, and the intelligent watch data is continuously monitored, so that the phenomenon that the necessity of a certain monitoring moment is changed can be caused.
In the platform necessity evaluation module, a platform necessity evaluation model is constructed, and the specific process is to collect after-sales application information, cost consumption information and intelligent watch price information of intelligent watch data, wherein the after-sales application information comprises after-sales application coefficients and is calibrated asThe cost spending information includes a cost spending coefficient, calibrated as/>The smart watch price information comprises a smart watch price coefficient, and is calibrated as/>；
After-sales application coefficientThe number of after-sales orders received in the past month is calibrated as/>, by collecting the number of after-sales orders received in the target type of intelligent watch or intelligent watch of the same technologyCounting the sales number of the selected intelligent watch in the warranty period, and calibrating asAfter-sales application coefficient/>；
Cost consumption coefficientThe estimated cost amount of the platform for smart watch production test is established through collection and calibrated as/>Counting the number of smart watches to be produced, marked as/>Cost-effectiveness factor/>；
Price coefficient of intelligent watchBy collecting the average selling price of the smart watch of the target product in the market when the smart watch is marketed, the average selling price is marked as/>Counting estimated pricing of smart watches to be produced, and calibrating as/>Then the price coefficient of the intelligent watch;
The platform necessity evaluation model is respectively formed by weighting and constructing three aspects of after-sale application information, cost consumption information and price information of the intelligent watch data to generate a platform necessity evaluation indexThe corresponding coefficients are the after-sales application coefficients/>, respectivelyCost consumption coefficient/>Price coefficient of smartwatch/>The formula is;
At the same time, the method comprises the steps of,All are larger than 0, and are set according to actual conditions, for example, an expert weighting method is adopted, namely, experts in related fields are invited to determine the weight of each index through professional opinion investigation and comprehensive evaluation, so that the weight coefficient can accurately reflect the importance of each index in the platform necessity evaluation. In addition, a plurality of methods such as an analytic hierarchy process, a fuzzy comprehensive evaluation method and the like can be considered to determine the weight coefficient so as to ensure the objectivity and scientificity of the weight coefficient. And will not be described in detail herein.
In the platform necessity evaluation module, the platform necessity evaluation index obtained from the platform necessity evaluation model is used for reflecting the necessity of constructing a platform for production test in the production process of the smart watch, and the larger the value of the platform necessity evaluation index is, the larger the value of the platform is the more after-sales application pressure facing the smart watch is, the lower the cost consumption of the platform is spread on each smart watch, and the higher the profit generated by sales is, the larger the necessity of constructing the platform for production test is.
In the platform necessity assessment module, when the platform necessity assessment indexWhen the data signal is larger than or equal to the set necessity threshold, the process that the production test platform is necessary to be constructed in the production process of the intelligent watch is described, necessary data signals are sent out, and subsequent evaluation is carried out on the production test platform of the intelligent watch;
when the platform necessity assessment index When the requirement threshold value is smaller than the preset requirement threshold value, the fact that a production test platform is not needed to be built temporarily in the production process of the intelligent watch is indicated, no follow-up steps are carried out, and the evaluation result is directly output.
In the invention, the performance characteristics, hardware characteristics, environmental characteristics and stability characteristics of the intelligent watch are selected as test case grouping characteristics;
further, the method for constructing the clustering algorithm model comprises the following steps:
Step 3.1, collecting sample data, cleaning and preprocessing the data, processing missing values, abnormal values and the like, performing feature engineering, and extracting or constructing features significant to clustering tasks, wherein in the invention, performance features, hardware features, environmental features and stability features of the intelligent watch are used as test case grouping features;
step 3.2, normalizing the features to ensure that they have the same dimensions, using a Min-Max normalization, expressed as In the above, the ratio of/>As a feature of the sample,The value is normalized, and max and min are the maximum value and the minimum value of all samples respectively;
Step 3.3, determining the number K of clusters by using Elbow Method (elbow method), specifically, setting the K value to be 1 to n, wherein n is the total number of samples, running a K mean value clustering algorithm for each K value, calculating the internal square sum WCSS of the clusters, wherein WCSS is the square sum of the distances from each point to the center (average value) in the clusters, drawing WCSS under each K value into a chart, usually an elbow-shaped curve, observing the WCSS curve, finding an elbow point, namely, the place where the curve starts to bend, and the K value corresponding to the elbow point is the optimal clustering number;
And 3.4, initializing a cluster center, namely randomly selecting K samples as an initial cluster center, calculating the distance between each sample data and each cluster center by using methods such as Euclidean distance, manhattan distance, cosine similarity and the like, distributing the sample data to the cluster center with the smallest distance, updating the members of the cluster to all samples distributed to the cluster for each cluster constructed based on the cluster center, calculating a new cluster center which is generally the average value of all test cases in the cluster for each cluster, and repeating the steps until a convergence condition is met, wherein the convergence condition is a set convergence threshold or maximum iteration number.
In the platform practicability evaluation module, after the platform for the intelligent watch production test is realized by constructing a clustering algorithm model, a platform practicability evaluation model is constructed, the practicability of the platform is evaluated, and an evaluation result is recorded and is used as a necessity threshold reference value when the next intelligent watch production test platform is constructed.
The method comprises the steps of carrying out practical evaluation on a constructed platform for the production test of the intelligent watch, wherein the specific process is to collect platform improvement information and automatic coverage information of the platform for the production test of the intelligent watch, and the platform improvement information of the platform for the production test of the intelligent watch comprises a test comprehensiveness coefficient and an improvement result coefficient which are respectively calibrated as followsAnd/>The automated overlay information includes automated overlay coefficients, calibrated as/>；
Testing of the coefficient of comprehensivenessThe total number of types of test contents of the platform for smart watch production test is collected and calibrated as/>And the number of kinds of problems occurring in after-sales orders of the intelligent watch is counted and calibrated as/>Then test the comprehensiveness coefficient/>；
Improved coefficient of performanceThe sales number of the target product sold in the market within one year is collected and is marked as/>Counting sales number of the target intelligent watch in one year after sales, and calibrating as/>Then improve the outcome coefficient/>；
Automated coverage factorThe total expenditure amount of the platform for intelligent watch production test is collected and calibrated as/>Counting the manual expenditure cost in the platform for the production and test of the intelligent watch, and calibrating as/>Then the coverage coefficient is automated;
The platform usability evaluation model is respectively formed by weighting and constructing two aspects of platform improvement information and automatic coverage information, and generates a platform usability evaluation indexThe corresponding coefficients are test comprehensiveness coefficients/>, respectivelyCoefficient of improvementAutomated coverage factor/>The formula is/>，/>The weight coefficients of the corresponding indexes are all larger than 0.
In the platform practicability evaluation module, when the platform practicability evaluation index is calculatedWhen the practical threshold value is smaller than or equal to the practical threshold value, the test direction of the constructed platform for the production test of the intelligent watch can solve the problems facing after sales to a large extent, the sales quantity is improved, the automation degree is high, the practicability is extremely high, and the scope of the necessity threshold value is suggested to be enlarged when the next platform for the production test of the intelligent watch is constructed;
When the platform is used, the index is evaluated When the practical threshold value is larger than the practical threshold value, the built platform test direction for the production test of the intelligent watch is described as long as the problem faced after sales can be solved to a small extent, meanwhile, the promotion of the sales quantity is limited, the automation degree is low, the practical threshold value is low, and the scope of the necessity threshold value is suggested to be reduced when the next platform for the production test of the intelligent watch is built.
The above formulas are all formulas for removing dimensions and taking numerical calculation, and specific dimensions can be removed by adopting various means such as standardization, and the like, which are not described in detail herein, wherein the formulas are formulas for acquiring a large amount of data and performing software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, ATA hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state ATA hard disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a mobile ATA hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The management system of the platform for the production test of the intelligent watch is characterized by comprising the following contents:
The data acquisition module is used for monitoring and acquiring after-sale application information, cost consumption information and intelligent watch price information of intelligent watch data, and constructing required sample data, and platform improvement information and automatic coverage information after the intelligent watch production test platform is realized by the clustering algorithm model;
The platform necessity evaluation module is used for constructing a platform necessity evaluation model and evaluating the necessity of the platform for the production test of the intelligent watch;
The algorithm processing module is used for determining an optimal intelligent watch test classification mode by constructing a clustering algorithm model after the intelligent watch is manufactured for the intelligent watch production test platform with high necessity;
The platform practicability evaluation module is used for evaluating the practicability of the constructed intelligent watch production test platform in combination with the platform improvement information and the automatic coverage information and giving reasonable suggestions according to the practicability evaluation of the intelligent watch production test platform;
In the platform necessity evaluation module, a platform necessity evaluation model is constructed, and the specific process is to collect after-sales application information, cost consumption information and intelligent watch price information of intelligent watch data, wherein the after-sales application information comprises after-sales application coefficients and is calibrated as The cost spending information includes a cost spending coefficient, calibrated as/>The smart watch price information comprises a smart watch price coefficient, and is calibrated as/>；
After-sales application coefficientAfter-sales orders are received in the past month by collecting the number of the target type intelligent watch or the intelligent watch of the same technology, and the number is calibrated as/>Counting the sales number of the selected intelligent watch in the warranty period, and calibrating as/>After-sales application coefficient/>；
Cost consumption coefficientThe estimated cost amount of the platform for smart watch production test is established through collection and calibrated as/>Counting the number of smart watches to be produced, marked as/>Cost-effectiveness factor/>；
Price coefficient of intelligent watchThe average selling price of the smart watch of the target product in the market is collected and calibrated asCounting estimated pricing of smart watches to be produced, and calibrating as/>Then the price coefficient of the intelligent watch；
The platform necessity evaluation model is respectively formed by weighting and constructing three aspects of after-sale application information, cost consumption information and price information of the intelligent watch data to generate a platform necessity evaluation indexThe corresponding coefficients are the after-sales application coefficients/>, respectivelyCost consumption coefficient/>Price coefficient of smartwatch/>The formula is，/>The weight coefficients of the corresponding indexes are all larger than 0;
The method comprises the steps of carrying out practical evaluation on a constructed platform for the production test of the intelligent watch, wherein the specific process is to collect platform improvement information and automatic coverage information of the platform for the production test of the intelligent watch, and the platform improvement information of the platform for the production test of the intelligent watch comprises a test comprehensiveness coefficient and an improvement result coefficient which are respectively calibrated as follows And/>The automated overlay information includes automated overlay coefficients, calibrated as/>；
Testing of the coefficient of comprehensivenessThe total number of types of test contents of the platform for smart watch production test is collected and calibrated asAnd the number of kinds of problems occurring in after-sales orders of the intelligent watch is counted and calibrated as/>Then test the comprehensiveness coefficient/>；
Improved coefficient of performanceThe sales number of the target product sold in the market within one year is collected and is marked as/>Counting sales number of the target intelligent watch in one year after sales, and calibrating as/>Then improve the outcome coefficient/>；
Automated coverage factorThe total expenditure amount of the platform for intelligent watch production test is collected and calibrated as/>Counting the manual expenditure cost in the platform for the production and test of the intelligent watch, and calibrating as/>Then the coverage coefficient is automated；
The platform usability evaluation model is respectively formed by weighting and constructing two aspects of platform improvement information and automatic coverage information, and generates a platform usability evaluation indexThe corresponding coefficients are test comprehensiveness coefficients/>, respectivelyCoefficient of improvement effort/>Automated coverage factor/>The formula is/>，/>The weight coefficients of the corresponding indexes are all larger than 0.
2. The system for managing a platform for smart watch manufacturing and testing of claim 1, wherein:
In the platform necessity assessment module, when the platform necessity assessment index When the data is larger than or equal to the set necessity threshold, sending out necessary data signals, and carrying out subsequent evaluation on a production test platform of the intelligent watch;
when the platform necessity assessment index And when the value is smaller than the set necessity threshold, directly outputting the evaluation result without carrying out subsequent steps.
3. The system for managing a platform for smart watch manufacturing and testing of claim 1, wherein:
In the algorithm processing module, the more common problems in the after-sale of the intelligent watch are collected as characteristics, and a clustering algorithm model is constructed to realize the processing mode of the intelligent watch production test platform on the intelligent watch;
further, the method for constructing the clustering algorithm model comprises the following steps:
Step 3.1, collecting sample data, cleaning and preprocessing the data, processing missing values and abnormal values, performing feature engineering, extracting and constructing features which are significant for clustering tasks;
Step 3.2, standardizing the features to ensure that they have the same dimensions;
Step 3.3, determining the number K of clusters by using an elbow method, and finding the K value at the starting bending place of the curve as the optimal cluster number by observing the constructed elbow method curve;
and 3.4, initializing a cluster center, distributing sample data to the cluster center with the smallest distance, calculating a new cluster center for each constructed cluster after distribution, and repeating the steps until convergence conditions are met.
4. The system for managing a platform for smart watch manufacturing and testing of claim 1, wherein:
In the platform practicability evaluation module, when the platform practicability evaluation index is calculated When the practical threshold value is smaller than or equal to the practical threshold value, the range of the necessity threshold value is recommended to be enlarged when the next intelligent watch production test platform is constructed;
When the platform is used, the index is evaluated If the threshold is larger than the practical threshold, the range of the necessity threshold is suggested to be narrowed when the platform for the next smart watch production test is constructed.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8311863B1 (en) * | 2009-02-24 | 2012-11-13 | Accenture Global Services Limited | Utility high performance capability assessment |
| CN106093613A (en) * | 2016-05-25 | 2016-11-09 | 广东蓄能发电有限公司 | Pump-storage generator equipment dependability analysis platform and method thereof |
| CN109242363A (en) * | 2018-11-01 | 2019-01-18 | 大连飞创信息技术有限公司 | Full life cycle test management platform based on multiple quality control models |
| CN116932232A (en) * | 2023-09-18 | 2023-10-24 | 湖南远跃科技发展有限公司 | Data processing method of development platform based on BS architecture |
| CN117132153A (en) * | 2023-08-08 | 2023-11-28 | 深圳白马互娱科技有限公司 | Supervision method and system applied to wholesale of electronic products |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102014006322A1 (en) * | 2014-04-30 | 2015-11-05 | Avl List Gmbh | System and method for analyzing the energy efficiency of a vehicle |
| CN111316312A (en) * | 2017-11-08 | 2020-06-19 | 株式会社东芝 | Skill base system, skill modeling device, and skill distribution method |
| US11698849B2 (en) * | 2021-03-15 | 2023-07-11 | Micro Focus Llc | Automated application testing of mutable interfaces |
-
2024
- 2024-01-25 CN CN202410104220.2A patent/CN117634932B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8311863B1 (en) * | 2009-02-24 | 2012-11-13 | Accenture Global Services Limited | Utility high performance capability assessment |
| CN106093613A (en) * | 2016-05-25 | 2016-11-09 | 广东蓄能发电有限公司 | Pump-storage generator equipment dependability analysis platform and method thereof |
| CN109242363A (en) * | 2018-11-01 | 2019-01-18 | 大连飞创信息技术有限公司 | Full life cycle test management platform based on multiple quality control models |
| CN117132153A (en) * | 2023-08-08 | 2023-11-28 | 深圳白马互娱科技有限公司 | Supervision method and system applied to wholesale of electronic products |
| CN116932232A (en) * | 2023-09-18 | 2023-10-24 | 湖南远跃科技发展有限公司 | Data processing method of development platform based on BS architecture |
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