CN113050015A - Data processing method and electronic device - Google Patents
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Abstract
The embodiment of the application provides a data processing method and an electronic device, wherein the method comprises the following steps: determining the testing accuracy of a testing system, wherein the testing system comprises a plurality of testing machine tables; and under the condition that the test accuracy meets the trigger calibration condition, determining a target test machine table based on the test accuracy in an auxiliary mode, wherein the target test machine table has an association relation with the trigger calibration condition which causes the test accuracy to meet. The data processing method can timely determine the target test machine table influencing the test effect according to the actual test condition, and assist a test system or a worker in carrying out targeted machine table calibration.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a data processing method and an electronic device.
Background
In an electronic product factory, the assembled electronic product is transported to a testing workshop for quality testing, so as to ensure the completeness of quality and function. Typically, a test shop will have tens of test stations, each of which will test one or more test items. Such as handset antenna radio performance, bluetooth performance, speakers, etc.
The calibration scheme of the test machine in the existing factory basically sets a calibration time period, and periodically prompts all machines to be calibrated. However, the method has the disadvantage that the test machine with inaccurate test cannot be calibrated in time according to the actual test condition.
Disclosure of Invention
The application provides a data processing method of a target test machine capable of determining influence on a test effect in time according to an actual test condition and an electronic device applying the method.
In order to solve the above technical problem, an embodiment of the present application provides a data processing method, including:
determining the testing accuracy of a testing system, wherein the testing system comprises a plurality of testing machine tables;
and under the condition that the test accuracy meets the trigger calibration condition, determining a target test machine table based on the test accuracy in an auxiliary mode, wherein the target test machine table has an association relation with the trigger calibration condition which causes the test accuracy to meet.
Optionally, the determining the test accuracy of the test system includes:
determining the number of products tested by the test system and a test result;
determining the test accuracy based on the product quantity and test results.
Optionally, the determining the test accuracy based on the product quantity and the test result comprises:
determining the misjudgment quantity representing the test error in the test result;
and determining the misjudgment rate in the test based on the product quantity and the misjudgment quantity, wherein the misjudgment rate is negatively correlated with the test accuracy.
Optionally, the determining the test accuracy based on the product quantity and the test result further includes:
determining the number of passes that characterize the test as correct in the test results;
determining a pass rate in a test based on the product quantity and the pass quantity, the pass rate being positively correlated with the test accuracy.
Optionally, the test accuracy satisfies a trigger calibration condition, including:
and determining that the test accuracy meets the trigger calibration condition under the condition that the misjudgment rate and/or the passing rate meet the corresponding threshold value.
Optionally, the assisting in determining the target test machine based on the test accuracy includes:
obtaining cause data that will induce the test accuracy to meet a trigger calibration condition;
calculating the information entropy corresponding to each piece of reason data based on the reason data and the misjudgment rate;
and determining the target test machine platform based on the information entropy.
Optionally, the reason data at least includes one or more of a test item of the test machine, test data for correspondingly indicating different test machines, machine state data of the test machine, environmental data of an environment where the test machine is located, and operation flow data corresponding to each of the test machines.
Optionally, determining the target test machine based on the information entropy includes:
determining a maximum value of the information entropies respectively corresponding to different reason data;
and determining the target testing machine platform based on the information indicated by the maximum value.
Optionally, the determining the target test equipment based on the information indicated by the maximum value includes:
determining the cause data corresponding to the maximum value;
and determining the test machine station used for executing the test item as the target test machine station based on the test item indicated by the reason data.
The invention also provides an electronic device, comprising:
the system comprises a first processing module, a second processing module and a control module, wherein the first processing module is used for determining the test accuracy of a test system, and the test system comprises a plurality of test machines;
and the second processing module is used for assisting in determining a target test machine table based on the test accuracy under the condition that the test accuracy meets the trigger calibration condition, wherein the target test machine table has an association relation with the trigger calibration condition which causes the test accuracy to meet.
Based on the disclosure of the embodiment, the beneficial effects of the embodiment of the application include that the target test machine with the problem in the test can be determined in time according to the actual test condition, so that the auxiliary worker can calibrate the test machine in time, and the test accuracy of subsequent products is prevented from being influenced.
Drawings
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention.
FIG. 2 is a flow chart of a data processing method according to another embodiment of the present invention.
FIG. 3 is a process flow diagram of a test system in an embodiment of the invention.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Specific embodiments of the present application will be described in detail below with reference to the accompanying drawings, but the present application is not limited thereto.
It will be understood that various modifications may be made to the embodiments disclosed herein. The following description is, therefore, not to be taken in a limiting sense, but is made merely as an exemplification of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present application has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of application, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
Hereinafter, embodiments of the present application will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention discloses a data processing method, including:
determining the test accuracy of a test system, wherein the test system comprises a plurality of test machines;
and under the condition that the test accuracy meets the trigger calibration condition, determining a target test machine table based on the test accuracy in an auxiliary manner, wherein the target test machine table has an incidence relation with the trigger calibration condition which causes the test accuracy to meet.
For example, the worker may manually determine according to the test result output by the test system, or compare the test result with past historical data, or calculate the test accuracy of the test system according to the input test data according to a target program/algorithm set in the test system, so as to determine the accuracy of representing the whole of the plurality of test machines in the test system. Then, the test system may determine whether a calibration condition is triggered according to the test accuracy, that is, determine whether the test accuracy does not satisfy a requirement based on the calibration condition, and whether the test machine needs to be calibrated, and when the test system determines that the test accuracy satisfies the trigger calibration condition, determine a target test machine based on the test accuracy, where the target test machine is a test machine that affects the condition that the test accuracy does not satisfy the condition, and the test accuracy of the whole test system is affected because the test machine has low accuracy of a test result of a product. After the target test machine is determined, a worker can calibrate the target test machine without calibrating all machines, so that the workload of the worker is greatly reduced, the test work is not greatly influenced, and the overall calibration efficiency is improved.
Therefore, the embodiment has the beneficial effects that the data processing method based on the embodiment can enable the test system to timely determine the target test machine with the problem in the test according to the actual test condition, so that the auxiliary staff can timely and pertinently calibrate the test machine, the test accuracy of subsequent products is prevented from being influenced, and the misjudgment rate is reduced.
Specifically, in practical application, an algorithm system may be prepared by the method in this embodiment to form an algorithm system such as an AI algorithm system, and then the algorithm system is accessed in the test system, so that the algorithm system can determine as soon as possible whether to remind the worker to calibrate the test machine according to data information such as a real-time streaming test result, and at the same time, inform the worker of a target test machine to be calibrated. Of course, an automatic calibration system may also be added in the test system, so that the test system directly calibrates the target test machine after determining the target test machine, and records the information of this calibration for subsequent staff to look up.
Further, in this embodiment, the determining the test accuracy of the test system includes:
determining the number of products tested by the test system and a test result;
and determining the test accuracy based on the number of the products and the test result.
For example, the probability of the test inaccuracy and the probability of the test accuracy can be determined according to different test results, such as quoting the total number of the tested products according to the test inaccuracy and the test accuracy.
Specifically, in this embodiment, determining the test accuracy based on the number of products and the test result includes:
determining the misjudgment quantity representing the test error in the test result;
and determining the misjudgment rate in the test based on the product quantity and the misjudgment quantity, wherein the misjudgment rate is negatively correlated with the test accuracy.
For example, the worker may determine, through auditing, that a test error occurs in a product that is subjected to a test, which results in a number of misjudgments, that is, a misjudgments number, and then determine, based on the product number and the misjudgments number, a misjudgments rate in the test, that is, a quotient of the misjudgments number and the product number, so as to measure the test accuracy based on the misjudgments rate, where the misjudgments rate and the test accuracy are in a negative correlation relationship. If the misjudgment rate is high, the testing accuracy is low, and if the misjudgment rate is low, the testing accuracy is high.
Further, in this embodiment, determining the test accuracy based on the number of products and the test result further includes:
determining the passing number which represents the correct test in the test result;
and determining the passing rate in the test based on the product quantity and the passing quantity, wherein the passing rate is positively correlated with the test accuracy.
For example, a worker can manually or by using a machine to identify whether the test result is correct, then determine the passing number which characterizes the correct test in the test result, then can carry out quotient calculation according to the passing number and the product number, determine the passing rate, and further reflect the test accuracy problem by using the passing rate. The passing rate is positively correlated with the test accuracy, and the higher the passing rate, the higher the test accuracy.
Further, the test accuracy meeting the trigger calibration condition in this embodiment includes:
and determining that the test accuracy meets the trigger calibration condition under the condition that the misjudgment rate and/or the passing rate meet the corresponding threshold.
That is, whether the trigger calibration condition is met or not can be determined through the relationship between the false positive rate or the pass rate and the corresponding threshold, or whether the trigger calibration condition is met or not can be determined through the relationship between the false positive rate and the corresponding threshold, that is, both the false positive rate and the pass rate meet the condition for triggering calibration, and then it can be determined that the current state of the test system needs to be calibrated.
Further, as shown in fig. 2 and fig. 3, in the present embodiment, the determining the target test machine based on the test accuracy includes:
obtaining reason data which can induce the test accuracy to meet the trigger calibration condition;
calculating the information entropy corresponding to each piece of reason data based on the reason data and the misjudgment rate;
and determining a target test machine platform based on the information entropy.
Specifically, the worker may manually collect cause data that may induce the test accuracy to satisfy the trigger calibration condition, and then input the cause data into the test system, or the test system may analyze the cause data based on the recorded information such as the test data, and determine the cause data. After the test system determines the reason data, the information entropy corresponding to each reason data is calculated based on the different reason data and the misjudgment rate, and the following formula can be specifically adopted:
I＝(ΣH_y–ΣH_xy)/ΣH_y
Where H_x＝Σ(P_x*log(P_x)),H_xy＝Σ(P_xy*log(P_y))
wherein x is a variable and is different reason data, y is the overall misjudgment rate, and I is the finally calculated information entropy. After the information entropies corresponding to different reason data are calculated based on the formula, a final target test machine can be determined based on the information entropies.
Further, the reason data in this embodiment at least includes one or more of test items of the test equipment, test data for correspondingly indicating different test equipments, equipment state data of the test equipment, environmental data of the environment, and operation flow data corresponding to each test equipment.
For example, the test items include test bluetooth, a wireless network, a loudspeaker, a radiator and the like, and the test data includes data of each functional device based on different power consumption states, running under different use environment conditions, power consumption data of the test machine, environment humidity, the number of people involved in the test of each test machine and the like, which can be used as reason data to calculate the information entropy. In practical application, a worker can input various different reason data into the test system, so that the test system calculates the information entropy based on the various different reason data, and the target test machine is comprehensively judged.
Specifically, in this embodiment, determining the target test machine based on the information entropy includes:
determining the maximum value of a plurality of information entropies respectively corresponding to different reason data;
and determining the target test machine platform based on the information indicated by the maximum value.
That is, the maximum value is determined from a plurality of information entropies, or an information entropy satisfying a threshold value may be determined, and then the target test machine is determined based on the determined information entropy.
For example, the reason data is the test items of the test equipment, and if the test items of a plurality of different test equipments are different, it is also partially the same, and the embodiment is described by only using the test items of the test equipments to be different. When the test system uses the test items of a plurality of test machines as the reason data,
determining a target test machine based on the information indicated by the maximum value, comprising:
determining cause data corresponding to the maximum value;
and determining the test machine used for executing the test item as a target test machine based on the test item indicated by the reason data.
Specifically, after a plurality of information entropies are obtained, the maximum value of the information entropy is determined, and then the reason data corresponding to the maximum value of the information entropy is determined based on the maximum value of the information entropy, if the corresponding reason data is a bluetooth test item, then the test system or the worker can correspondingly determine a test machine for performing bluetooth test based on the determined reason data, and determine that the test machine is the target test machine. That is, based on the maximum value of the entropy, the reason data corresponding to the maximum value of the entropy may be determined, and the reason data is the reason data that most affects the test result, so that the misjudgment may be caused.
In addition, the method process in this embodiment may be performed in real time, or may be performed periodically, for example, manually set, or a time period is determined based on historical data, and the method steps in this embodiment are started based on the time period to determine whether there is a test machine that needs to be calibrated.
As shown in fig. 4, another embodiment of the present invention further provides an electronic device, including:
the first processing module is used for determining the testing accuracy of a testing system, and the testing system comprises a plurality of testing machine tables;
and the second processing module is used for determining the target test machine table based on the test accuracy in an auxiliary manner under the condition that the test accuracy meets the trigger calibration condition, and the target test machine table has an incidence relation with the trigger calibration condition which causes the test accuracy to meet.
Therefore, the embodiment has the advantages that the electronic device based on the embodiment can enable the test system to timely determine the target test machine with the problem in the test according to the actual test condition, so that the auxiliary worker can timely and pertinently calibrate the test machine, the test accuracy of subsequent products is prevented from being influenced, and the misjudgment rate is reduced.
Optionally, the first processing module determines the test accuracy of the test system, including:
determining the number of products tested by the test system and a test result;
determining the test accuracy based on the product quantity and test results.
Optionally, the first processing module determines the test accuracy based on the product quantity and the test result, and includes:
determining the misjudgment quantity representing the test error in the test result;
and determining the misjudgment rate in the test based on the product quantity and the misjudgment quantity, wherein the misjudgment rate is negatively correlated with the test accuracy.
Optionally, the first processing module determines the test accuracy based on the product quantity and the test result, and further includes:
determining the number of passes that characterize the test as correct in the test results;
determining a pass rate in a test based on the product quantity and the pass quantity, the pass rate being positively correlated with the test accuracy.
Optionally, the second processing module tests that the accuracy meets the trigger calibration condition, including:
and determining that the test accuracy meets the trigger calibration condition under the condition that the misjudgment rate and/or the passing rate meet the corresponding threshold value.
Optionally, the determining, by the second processing module, a target test machine based on the test accuracy includes:
obtaining cause data that will induce the test accuracy to meet a trigger calibration condition;
calculating the information entropy corresponding to each piece of reason data based on the reason data and the misjudgment rate;
and determining the target test machine platform based on the information entropy.
Optionally, the reason data at least includes one or more of a test item of the test machine, test data for correspondingly indicating different test machines, machine state data of the test machine, environmental data of an environment where the test machine is located, and operation flow data corresponding to each of the test machines.
Optionally, determining the target test machine based on the information entropy includes:
determining a maximum value of the information entropies respectively corresponding to different reason data;
and determining the target testing machine platform based on the information indicated by the maximum value.
Optionally, the reason data is a test item of the test machine, and the determining, by the second processing module, the target test machine based on the information indicated by the maximum value includes:
determining the cause data corresponding to the maximum value;
and determining the test machine station used for executing the test item as the target test machine station based on the test item indicated by the reason data.
An embodiment of the present application also provides a storage medium, on which a computer program is stored, which when executed by a processor implements the data processing method as described above. It should be understood that each solution in this embodiment has a corresponding technical effect in the foregoing method embodiments, and details are not described here.
Embodiments of the present application also provide a computer program product, tangibly stored on a computer-readable medium and comprising computer-executable instructions that, when executed, cause at least one processor to perform a data processing method, such as the embodiments described above. It should be understood that each solution in this embodiment has a corresponding technical effect in the foregoing method embodiments, and details are not described here.
It should be noted that the computer storage media of the present application can be computer readable signal media or computer readable storage media or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access storage media (RAM), a read-only storage media (ROM), an erasable programmable read-only storage media (EPROM or flash memory), an optical fiber, a portable compact disc read-only storage media (CD-ROM), an optical storage media piece, a magnetic storage media piece, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, antenna, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
It should be understood that although the present application has been described in terms of various embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and those skilled in the art will recognize that the embodiments described herein may be combined as suitable to form other embodiments, as will be appreciated by those skilled in the art.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. A method of data processing, comprising:
determining the testing accuracy of a testing system, wherein the testing system comprises a plurality of testing machine tables;
and under the condition that the test accuracy meets the trigger calibration condition, determining a target test machine table based on the test accuracy in an auxiliary mode, wherein the target test machine table has an association relation with the trigger calibration condition which causes the test accuracy to meet.
2. The method of claim 1, wherein the determining the test accuracy of the test system comprises:
determining the number of products tested by the test system and a test result;
determining the test accuracy based on the product quantity and test results.
3. The method of claim 2, wherein said determining said test accuracy based on said product quantity and test results comprises:
determining the misjudgment quantity representing the test error in the test result;
and determining the misjudgment rate in the test based on the product quantity and the misjudgment quantity, wherein the misjudgment rate is negatively correlated with the test accuracy.
4. The method of claim 3, wherein said determining said test accuracy based on said product quantity and test results further comprises:
determining the number of passes that characterize the test as correct in the test results;
determining a pass rate in a test based on the product quantity and the pass quantity, the pass rate being positively correlated with the test accuracy.
5. The method of claim 4, wherein the test accuracy satisfies a trigger calibration condition, comprising:
and determining that the test accuracy meets the trigger calibration condition under the condition that the misjudgment rate and/or the passing rate meet the corresponding threshold value.
6. The method of claim 3, wherein the assisting in determining a target test machine based on the test accuracy comprises:
obtaining cause data that will induce the test accuracy to meet a trigger calibration condition;
calculating the information entropy corresponding to each piece of reason data based on the reason data and the misjudgment rate;
and determining the target test machine platform based on the information entropy.
7. The method of claim 6, wherein the reason data at least comprises one or more of test items of the testing machines, test data corresponding to different testing machines, machine state data of the testing machines, environmental data of the environment, and operation flow data corresponding to each testing machine.
8. The method of claim 6, wherein determining the target test machine based on the entropy of the information comprises:
determining a maximum value of the information entropies respectively corresponding to different reason data;
and determining the target testing machine platform based on the information indicated by the maximum value.
9. The method of claim 8, wherein the reason data is a test item of the testing machine, and the determining the target testing machine based on the information indicated by the maximum value comprises:
determining the cause data corresponding to the maximum value;
and determining the test machine station used for executing the test item as the target test machine station based on the test item indicated by the reason data.
10. An electronic device, comprising:
the system comprises a first processing module, a second processing module and a control module, wherein the first processing module is used for determining the test accuracy of a test system, and the test system comprises a plurality of test machines;
and the second processing module is used for assisting in determining a target test machine table based on the test accuracy under the condition that the test accuracy meets the trigger calibration condition, wherein the target test machine table has an association relation with the trigger calibration condition which causes the test accuracy to meet.
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