CN113887609A - Equipment screen aging detection model training method and equipment screen aging detection method - Google Patents
Equipment screen aging detection model training method and equipment screen aging detection method Download PDFInfo
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Abstract
The invention relates to a device screen aging detection model training method and a device screen aging detection method. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
Description
Technical Field
The invention relates to the technical field of electronic products, in particular to a training method of an equipment screen aging detection model and an equipment screen aging detection method.
Background
With the development of electronic product technology, various intelligent devices such as smart phones, notebook computers, tablet computers, and the like are developed. At present, along with the rapid development of economy and technology, the popularization and the updating speed of intelligent equipment are also faster and faster. Taking a smart phone as an example, the coming of the 5G era accelerates the generation change of the smart phone. In the iterative process of the intelligent equipment, effective recovery is one of effective utilization means of the residual value of the intelligent equipment, and the chemical pollution to the environment and the waste can be reduced.
In the recovery process of the intelligent equipment, the screen aging of the intelligent equipment has great influence on the recovery evaluation of the intelligent equipment. The traditional equipment screen aging detection method is to set the background color of the equipment screen, adjust light and shoot the equipment screen, and then carry out image detection on the equipment screen. However, the aging of the screen of the device can be detected only when the aging of the screen of the device is obvious. And when the screen of the equipment is shot when the light problem, the camera problem or the screen of the equipment becomes dark, the screen aging of the equipment cannot be accurately identified because the aging characteristic of the screen of the equipment cannot be acquired.
Therefore, the traditional device screen aging detection mode has the defects.
Disclosure of Invention
Therefore, it is necessary to provide a device screen aging detection model training method and a device screen aging detection method for overcoming the defects of the conventional device screen aging detection method.
A training method for an equipment screen aging detection model comprises the following steps:
acquiring equipment basic information of each intelligent device, and adding corresponding label data to the equipment basic information; the device basic information comprises attribute field information related to the use of a screen of the intelligent device; the label data is used for representing the aging or non-aging of the screen of the intelligent device;
converting the basic information of each device into information data with consistent standards;
dividing information data into a training set and a test set;
and carrying out modeling training according to the training set and the test set to obtain an equipment screen aging detection model for equipment screen aging detection.
According to the equipment screen aging detection model training method, after the equipment basic information of each intelligent equipment is obtained and the corresponding label data is added to the equipment basic information, the equipment basic information is converted into the information data with the same standard. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
In one embodiment, the process of converting the basic information of each device into information data with consistent standards includes the following steps:
improving basic information of the equipment;
performing characteristic conversion on the basic information of the equipment to remove part of the basic information of the equipment;
and carrying out data transformation processing on the basic information of the equipment and converting the basic information into information data.
In one embodiment, the process of refining the device grounding information includes the steps of:
and carrying out missing information processing and abnormal information processing on the basic information of the equipment.
In one embodiment, the process of performing feature transformation on the device basis information to remove part of the device basis information includes the steps of:
acquiring characteristic values of basic information of each device by a principal component analysis method;
and removing the basic information of the equipment with the characteristic value smaller than the characteristic threshold value.
In one embodiment, the process of performing data transformation processing on the device basic information and converting the device basic information into information data includes the steps of:
and carrying out discretization processing on the basic information of the equipment to obtain information data.
In one embodiment, the ratio of training set to test set is 8: 2.
in one embodiment, a process for modeling training based on a training set and a test set includes the steps of:
and based on the KNN algorithm, carrying out modeling training according to the training set and the test set.
In one embodiment, the method further comprises the following steps:
calculating the set number information data with the minimum distance between the test set and the training set;
and adjusting the process of modeling training according to the matching rate of the label data of the set number of information data and the detection result corresponding to the training set.
In one embodiment, the device basic information includes a device factory time, a device type, a device selling price, a device battery using time, a device screen breakage degree, a device size, a device holder gender or a device holder age.
A device screen aging detection model training device comprises the following steps:
the tag adding module is used for acquiring the equipment basic information of each intelligent equipment and adding corresponding tag data to the equipment basic information; the device basic information comprises attribute field information related to the use of a screen of the intelligent device; the label data is used for representing the aging or non-aging of the screen of the intelligent device;
the information conversion module is used for converting the basic information of each device into information data with consistent standards;
the data set dividing module is used for dividing the information data into a training set and a test set;
and the model training module is used for carrying out modeling training according to the training set and the test set to obtain an equipment screen aging detection model for equipment screen aging detection.
The device screen aging detection model training device obtains the device basic information of each intelligent device, adds corresponding label data to the device basic information, and converts the device basic information into information data with the same standard. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
A computer storage medium having computer instructions stored thereon, the computer instructions when executed by a processor implementing the device screen aging detection model training method of any of the above embodiments.
The computer storage medium obtains the device basic information of each intelligent device, adds corresponding label data to the device basic information, and converts the device basic information into information data with consistent standards. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
A computer device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to implement the device screen aging detection model training method of any one of the embodiments.
The computer equipment obtains the equipment basic information of each intelligent equipment, adds corresponding label data to the equipment basic information, and converts the equipment basic information into information data with consistent standards. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
A method for detecting the aging of a device screen comprises the following steps:
acquiring equipment basic information of the intelligent equipment to be tested;
and inputting the basic information of the equipment into the equipment screen aging detection model to obtain an equipment screen aging detection result for representing the intelligent equipment to be detected.
According to the equipment screen aging detection method, after the equipment basic information of the intelligent equipment to be detected is obtained, the equipment basic information is input into the equipment screen aging detection model, and an equipment screen aging detection result used for representing the intelligent equipment to be detected is obtained. Based on this, the ageing condition of smart machine screen is detected through the equipment screen ageing detection model after the training, acquires the label data that characterize the ageing condition of smart machine screen, prevents that environmental anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improves equipment screen ageing detection's rate of accuracy and efficiency.
An apparatus for detecting screen aging of a device, comprising the steps of:
the information acquisition module is used for acquiring the equipment basic information of the intelligent equipment to be tested;
and the aging identification module is used for inputting the basic information of the equipment into the equipment screen aging detection model to obtain an equipment screen aging detection result for representing the intelligent equipment to be detected.
After the device basic information of the intelligent device to be detected is obtained, the device basic information is input into the device screen aging detection model, and a device screen aging detection result for representing the intelligent device to be detected is obtained. Based on this, the ageing condition of smart machine screen is detected through the equipment screen ageing detection model after the training, acquires the label data that characterize the ageing condition of smart machine screen, prevents that environmental anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improves equipment screen ageing detection's rate of accuracy and efficiency.
A computer storage medium having stored thereon computer instructions which, when executed by a processor, implement the device screen degradation detection method of any of the above embodiments.
After the basic information of the intelligent device to be tested is obtained, the basic information of the intelligent device to be tested is input into the device screen aging detection model, and a device screen aging detection result used for representing the intelligent device to be tested is obtained. Based on this, the ageing condition of smart machine screen is detected through the equipment screen ageing detection model after the training, acquires the label data that characterize the ageing condition of smart machine screen, prevents that environmental anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improves equipment screen ageing detection's rate of accuracy and efficiency.
A computer device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the device screen aging detection method of any one of the embodiments.
After the basic information of the intelligent device to be tested is obtained, the basic information of the intelligent device to be tested is input into the device screen aging detection model, and a device screen aging detection result used for representing the intelligent device to be tested is obtained. Based on this, the ageing condition of smart machine screen is detected through the equipment screen ageing detection model after the training, acquires the label data that characterize the ageing condition of smart machine screen, prevents that environmental anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improves equipment screen ageing detection's rate of accuracy and efficiency.
Drawings
FIG. 1 is a flowchart of a device screen aging detection model training method according to an embodiment;
FIG. 2 is a flowchart of a training method of an equipment screen aging detection model according to another embodiment;
FIG. 3 is a flowchart of a training method of an equipment screen aging detection model according to yet another embodiment;
FIG. 4 is a block diagram of an apparatus screen aging detection model training apparatus according to an embodiment;
FIG. 5 is a flowchart of a method for detecting screen aging of a device according to an embodiment;
FIG. 6 is a block diagram of an apparatus for detecting screen degradation according to an embodiment;
fig. 7 is a schematic diagram of the internal structure of a computer according to another embodiment.
Detailed Description
For better understanding of the objects, technical solutions and effects of the present invention, the present invention will be further explained with reference to the accompanying drawings and examples. Meanwhile, the following described examples are only for explaining the present invention, and are not intended to limit the present invention.
The embodiment of the invention provides a training method of an equipment screen aging detection model.
Fig. 1 is a flowchart of a training method of an apparatus screen aging detection model according to an embodiment, and as shown in fig. 1, the training method of the apparatus screen aging detection model according to an embodiment includes steps S100 to S103:
s100, acquiring the basic information of each intelligent device, and adding corresponding label data to the basic information of the intelligent device; the device basic information comprises attribute field information related to the use of a screen of the intelligent device; the label data is used for representing the aging or non-aging of the screen of the intelligent device;
s101, converting basic information of each device into information data with consistent standards;
s102, dividing information data into a training set and a test set;
s103, carrying out modeling training according to the training set and the test set to obtain an equipment screen aging detection model for equipment screen aging detection.
Wherein, in the recovery process of smart machine, accessible self service terminal or recovery machine retrieve the detection to smart machine. The self-service terminal or the recovery machine can establish data connection with the intelligent equipment in a wired connection or wireless connection mode, and acquire corresponding data of the intelligent equipment or transmit the corresponding data to the intelligent equipment. The self-service terminal or the recovery machine serves as an execution main body to complete the execution of the equipment screen aging detection model training method of one embodiment, or the cloud server transmits the camera data to the cloud server and serves as the execution main body to complete the execution of the equipment screen aging detection model training method of one embodiment.
And acquiring the equipment basic information of each intelligent equipment in the data acquisition process of each intelligent equipment. The device infrastructure information includes attribute field information related to screen usage of the smart device. In one embodiment, the device basic information includes a device factory time, a device type, a device selling price, a device battery using time, a device screen breakage degree, a device size, a device holder gender or a device holder age. Namely, each piece of equipment basic information has usage correlation such as positive correlation or negative correlation with the usage of the screen of the intelligent equipment.
The device basis information is attribute field information, for example, the service life of a mobile phone battery is 1058 hours, and the corresponding attribute field information is 1058. The format or form of the attribute field information may be determined according to the acquisition process of the attribute field information.
Meanwhile, because the attribute field information has format difference, the basic information of the device needs to be converted into information data with consistent standard so as to unify the data standard of the attribute field information.
In one embodiment, fig. 2 is a flowchart of a training method of an equipment screen aging detection model according to another embodiment, and as shown in fig. 2, a process of converting basic information of each piece of equipment into information data with a consistent standard in step S101 includes steps S200 to S202:
s200, perfecting basic information of the equipment;
s201, performing feature conversion on the basic information of the equipment to remove part of the basic information of the equipment;
and S202, performing data transformation processing on the basic equipment information, and converting the basic equipment information into information data.
The difference exists between the intelligent devices, so that the basic information of the devices is difficult to unify and consistent, the basic information of the devices needs to be perfectly processed, and the subsequent processing of the basic information of the devices is facilitated.
In one embodiment, fig. 3 is a flowchart of a training method of an equipment screen aging detection model according to yet another embodiment, and as shown in fig. 3, the process of refining the basic information of the equipment in step S200 includes step S300:
and S300, performing missing information processing and abnormal information processing on the basic equipment information.
The missing information processing is used for processing missing information in the device basic information. In one embodiment, for the common attribute field information in the device basic information, the mean value of the corresponding values of the attribute field information is calculated, and the missing values in the attribute field information are filled by the mean value. For example, attribute field information associated with other device infrastructure information, such as the selling price of the smart device, may be averaged to fill in the gap by grouping smart device types.
The exception information processing is used for removing exception information in the device basic information. In one embodiment, for the type of the intelligent device which is already stopped, the device basic information of the part of the intelligent device can be removed; and removing the repeated device basic information for the repeated device basic information.
After the basic information of the equipment is perfected, a characteristic value corresponding to the basic information of the equipment is obtained through characteristic conversion, and part of the basic information of the equipment is removed according to the characteristic value, so that the reference value of the basic information of the subsequent equipment is improved, and the calculated amount is reduced. Wherein, part of the basic information of the equipment can be removed according to the maximum value and the minimum value or removed by sampling,
in one embodiment, as shown in fig. 3, the process of performing feature transformation on the device basis information in step S201 to remove part of the device basis information includes the steps of:
s301, obtaining characteristic values of basic information of each device through a principal component analysis method;
and S302, removing the basic information of the equipment with the characteristic value smaller than the characteristic threshold value.
The characteristic value of the basic information of the equipment can be obtained by a principal component analysis method in SPSS software. As a preferred embodiment, the formula for obtaining the characteristic value of the attribute field information in the device basis information is as follows:
assuming that p attribute field information is provided, that is, a data X in the data set D is { X1, X2, x3.., xp }, and an eigenvalue decomposition method is used to solve an eigenvalue and an eigenvector of the covariance matrix 1/p XX ^ (T), so as to obtain an eigenvalue corresponding to each attribute field information.
Wherein the characteristic threshold is determined according to the eliminated data latitude. In one embodiment, the feature threshold is 1, i.e. the device basis information with a feature value less than 1 is rejected to reduce the data dimension.
After the data dimensionality is reduced by removing part of the basic information of the equipment, the basic information of the equipment is subjected to data transformation processing and converted into information data, so that the data set is divided and the subsequent model training is carried out.
In one embodiment, the data transformation processing on the device basic information includes discretization processing or normalization processing. As a preferred embodiment, the process of performing data transformation processing on the device basic information in step S202 to convert the device basic information into information data includes step S303:
s303, discretizing the basic information of the equipment to obtain information data.
Taking the example that the equipment delivery time of the intelligent equipment is discretized according to the years, the time of N month in 2020 is converted into 2020, the time of N month in 2010 is converted into 2010, and so on;
the selling price of the intelligent equipment is gradually discretized according to a three-thousand yuan interval, namely 0 yuan to 3000 yuan is converted into 1, 3000 yuan to 6000 yuan is converted into 2, 6000 yuan to 9000 yuan is converted into 3, and the like;
taking data of fields with combinations of damage degrees of equipment screens of intelligent equipment as examples, information fields such as scratches, implosions and liquid leakage are classified according to the severity grades of the contained damages, namely, only the scratches are 1, the most severe damage in the combinations is the implosion, namely, the field data is converted into 2, the most severe damage in the combinations is the small aging damage such as bright spots, dead spots, crushing wounds and the like, namely, the field data is converted into 3, the most severe damage in the combinations is the large aging damage such as liquid leakage, a flower screen, a wire screen and the like, the most severe damage in the combinations is the transparent graph and the character is 5, and the conversion is carried out in the same way.
After the converted information data is obtained, the information data is divided into a training set and a test set. In one embodiment, the ratio of training set to test set is 8: 2.
and carrying out modeling training according to the training set and the test set to obtain an equipment screen aging detection model for equipment screen aging detection. In one embodiment, a linear regression algorithm is used for modeling prediction, and the formula is as follows: y _ i ═ β _0+ β _ 1X _ i1+ β _ 2X _ i2+ … + β _ p X _ ip + epsilon _ i, i ═ 1, …, n;
where p denotes that there are p pieces of attribute field information, and β is a coefficient, i.e., a characteristic value, of each piece of attribute field information. Epsilon is an error term, so that the phenomenon that the test accuracy is low due to overfitting of data is avoided. i denotes a data record, i.e. an intelligent device.
In one embodiment, as shown in fig. 2, the process of performing modeling training according to the training set and the test set in step S103 includes step S203:
s203, based on a KNN (k-nearest neighbor class) algorithm, modeling training is carried out according to the training set and the test set.
In one embodiment, as shown in fig. 2, the device screen aging detection model training method of another embodiment further includes step S204 and step S205:
s204, calculating the set number information data with the minimum distance between the test set and the training set;
and S205, adjusting the modeling training process according to the matching rate of the label data of the set number of information data and the detection result corresponding to the training set.
It is assumed whether X _ a in the prediction test set is aged, i.e. belongs to which aging class. Only K training data with the closest distance from X _ a to the training set need to be calculated to obtain aging categories of the K training data, and then the count of two categories of 0 and 1 is calculated, namely, the number of aging categories is 0 and the number of aging categories is 1. And taking the aging category with the largest count as the aging category of the test data. Wherein the aging category is characterized by the tag data.
The Euclidean distance and the Manhattan distance are combined to calculate the distance from the training set, and the formula is as follows:
based on the method, the predicted equipment screen aging detection model outputs the aging category represented by the label data when the intelligent equipment is detected.
In one embodiment, the accuracy is derived based on checking whether the derived aging category matches the real aging category (tag data characterization). If the test accuracy is lower than the preset accuracy, returning to modify the data again or adding the data or modifying parameters such as a K value and the like for training. And finally, taking the obtained optimal model as an equipment screen aging detection model.
In one embodiment, a decision tree algorithm or a random forest algorithm may also be used to perform modeling training based on the training set and the test set.
In the method for training the equipment screen aging detection model according to any embodiment, after the equipment basic information of each intelligent equipment is acquired and the corresponding label data is added to the equipment basic information, the equipment basic information is converted into the information data with the same standard. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
The embodiment of the invention also provides a device screen aging detection model training device.
Fig. 4 is a block diagram of an apparatus screen degradation detection model training apparatus according to an embodiment, and as shown in fig. 4, the apparatus screen degradation detection model training apparatus according to an embodiment includes a module 100, a module 101, a module 102, and a module 103:
the tag adding module 100 is configured to obtain device basic information of each intelligent device, and add corresponding tag data to the device basic information; the device basic information comprises attribute field information related to the use of a screen of the intelligent device; the label data is used for representing the aging or non-aging of the screen of the intelligent device;
the information conversion module 101 is used for converting the basic information of each device into information data with consistent standards;
a data set dividing module 102, configured to divide information data into a training set and a test set;
and the model training module 103 is used for performing modeling training according to the training set and the test set to obtain an equipment screen aging detection model for equipment screen aging detection.
The device screen aging detection model training device obtains the device basic information of each intelligent device, adds corresponding label data to the device basic information, and converts the device basic information into information data with the same standard. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
The embodiment of the invention also provides a method for detecting the screen aging of the equipment.
Fig. 5 is a flowchart illustrating a method for detecting screen degradation of a device according to an embodiment, where as shown in fig. 5, the method for detecting screen degradation of a device according to an embodiment includes steps S400 and S401:
s400, acquiring basic equipment information of the intelligent equipment to be tested;
s401, inputting the basic information of the equipment into the screen aging detection model of the equipment, and obtaining an equipment screen aging detection result for representing the intelligent equipment to be detected.
In one embodiment, before the device basic information is input into the device screen aging detection model, the data processing of the steps S200 to S202 or the steps S300 to S303 is performed on the device basic information of the smart device to be tested.
In the method for detecting aging of an equipment screen according to any embodiment, after the basic information of the intelligent equipment to be detected is obtained, the basic information of the equipment is input into the equipment screen aging detection model, and an equipment screen aging detection result for representing the intelligent equipment to be detected is obtained. Based on this, the ageing condition of smart machine screen is detected through the equipment screen ageing detection model after the training, acquires the label data that characterize the ageing condition of smart machine screen, prevents that environmental anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improves equipment screen ageing detection's rate of accuracy and efficiency.
The embodiment of the invention also provides a device for detecting the aging of the equipment screen.
Fig. 6 is a block diagram of an apparatus screen degradation detection device according to an embodiment, and as shown in fig. 6, the apparatus screen degradation detection device according to an embodiment includes a block 200 and a block 201:
the information acquisition module 200 is used for acquiring the equipment basic information of the intelligent equipment to be tested;
and the aging identification module 201 is used for inputting the basic information of the equipment into the equipment screen aging detection model to obtain an equipment screen aging detection result for representing the intelligent equipment to be detected.
After the device basic information of the intelligent device to be detected is obtained, the device basic information is input into the device screen aging detection model, and a device screen aging detection result for representing the intelligent device to be detected is obtained. Based on this, the ageing condition of smart machine screen is detected through the equipment screen ageing detection model after the training, acquires the label data that characterize the ageing condition of smart machine screen, prevents that environmental anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improves equipment screen ageing detection's rate of accuracy and efficiency.
The embodiment of the invention also provides a computer storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the method for training the equipment screen aging detection model or the method for detecting the equipment screen aging of any one of the above embodiments is realized.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a RAM, a ROM, a magnetic or optical disk, or various other media that can store program code.
Corresponding to the computer storage medium, in an embodiment, there is also provided a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the device screen aging detection model training method and the device screen aging detection method in the embodiments.
The computer device may be a terminal, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a device screen aging detection model training method or a device screen aging detection method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
The computer equipment obtains the equipment basic information of each intelligent equipment, adds corresponding label data to the equipment basic information, and converts the equipment basic information into information data with consistent standards. Further, the information data are divided into a training set and a testing set, modeling training is carried out according to the training set and the testing set, and an equipment screen aging detection model for equipment screen aging detection is obtained. And detecting the aging condition of the screen of the intelligent equipment through the trained equipment screen aging detection model, and acquiring label data representing the aging condition of the screen of the intelligent equipment. Based on this, prevent that environment anomaly such as light problem, camera problem or equipment screen are dark from to the influence of equipment screen ageing discernment, improve equipment screen ageing detection's rate of accuracy and efficiency.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A training method for an equipment screen aging detection model is characterized by comprising the following steps:
acquiring equipment basic information of each intelligent equipment, and adding corresponding label data to the equipment basic information; wherein the device grounding information comprises attribute field information related to the screen usage of the smart device; the label data is used for representing the aging or non-aging of the screen of the intelligent device;
converting the basic information of each device into information data with consistent standards;
dividing the information data into a training set and a test set;
and carrying out modeling training according to the training set and the test set to obtain an equipment screen aging detection model for equipment screen aging detection.
2. The device screen aging detection model training method according to claim 1, wherein the process of converting each piece of device basic information into information data with consistent standards comprises the steps of:
perfecting the basic information of the equipment;
performing feature conversion on the basic information of the equipment to remove part of the basic information of the equipment;
and performing data transformation processing on the equipment basic information to convert the equipment basic information into the information data.
3. The device screen aging detection model training method according to claim 2, wherein the process of refining the device basic information includes the steps of:
and processing missing information and abnormal information of the basic information of the equipment.
4. The method for training the device screen aging detection model according to claim 2, wherein the process of performing the feature transformation on the device basis information to reject part of the device basis information comprises the steps of:
obtaining characteristic values of the basic information of each device through a principal component analysis method;
and removing the basic information of the equipment with the characteristic value smaller than the characteristic threshold value.
5. The device screen aging detection model training method according to claim 2, wherein the process of performing data transformation processing on the device basic information to convert the device basic information into the information data comprises the steps of:
and carrying out discretization processing on the basic information of the equipment to obtain the information data.
6. The device screen aging detection model training method according to claim 1, wherein the ratio of the training set to the test set is 8: 2.
7. the device screen aging detection model training method according to claim 1, wherein the process of performing modeling training according to the training set and the test set comprises the steps of:
and based on the KNN algorithm, carrying out modeling training according to the training set and the test set.
8. The device screen aging detection model training method according to claim 1, further comprising the steps of:
calculating the set number information data with the minimum distance between the test set and the training set;
and adjusting the modeling training process according to the matching rate of the label data of the set number of information data and the detection result corresponding to the training set.
9. The training method of the device screen aging detection model according to any one of claims 1 to 8, wherein the device basic information includes device factory time, device type, device selling price, device battery using time, device screen breakage degree, device size, device holder gender or device holder age.
10. A method for detecting the aging of a device screen is characterized by comprising the following steps:
acquiring equipment basic information of the intelligent equipment to be tested;
and inputting the basic information of the equipment into the equipment screen aging detection model to obtain an equipment screen aging detection result for representing the intelligent equipment to be detected.
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| US11989701B2 (en) | 2014-10-03 | 2024-05-21 | Ecoatm, Llc | System for electrically testing mobile devices at a consumer-operated kiosk, and associated devices and methods |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US11989701B2 (en) | 2014-10-03 | 2024-05-21 | Ecoatm, Llc | System for electrically testing mobile devices at a consumer-operated kiosk, and associated devices and methods |
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