Intelligent teaching data processing method and related apparatus

Abstract

Embodiments of the present application provide a kind of intelligent teaching data processing method and related device, applied to intelligent teaching system, the intelligent teaching system includes first teaching equipment and second teaching equipment, the method includes: first teaching equipment receives the first application program code generated by target user;First teaching equipment sends first application program code to second teaching equipment;Second teaching equipment tests first application program code to obtain first test result;If first test result is abnormal test result, then second teaching equipment obtains the abnormal information in first test result;Second teaching equipment determines abnormal correction information according to abnormal information;Second teaching equipment sends abnormal correction information to first teaching equipment;First teaching equipment shows abnormal correction information, can when the test result of application program code is abnormal, corresponding abnormal correction information is determined and shows, improve the reliability when student carries out application program code programming.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to an intelligent teaching data processing method and related apparatus. Background Technology

[0002] Currently, application programming instruction is conducted in computer labs. These labs are equipped with simulation systems, allowing instructors to demonstrate on the mainframe while students operate offline on their own computers. Students have very few opportunities to actually handle the measuring machine. While this approach can train many students at once, they only learn offline programming and acquire application code, lacking practical experience. This makes it difficult to verify and correct the written application code later, reducing the reliability of the students' application code. Summary of the Invention

[0003] This application provides an intelligent teaching data processing method and related apparatus, which can determine and display the corresponding anomaly correction information when the test results of application code are abnormal, thereby improving the reliability of students writing application code.

[0004] A first aspect of this application provides an intelligent teaching data processing method applied to an intelligent teaching system, the intelligent teaching system including a first teaching device and a second teaching device, the method comprising:

[0005] The first teaching device receives the first application code generated by the target user;

[0006] The first teaching device sends the first application code to the second teaching device;

[0007] The second teaching device tests the first application code to obtain a first test result;

[0008] If the first test result is an abnormal test result, the second teaching device acquires the abnormal information in the first test result;

[0009] The second teaching device determines the anomaly correction information based on the anomaly information;

[0010] The second teaching device sends the anomaly correction information to the first teaching device;

[0011] The first teaching device displays the anomaly correction information.

[0012] In one possible implementation, the second teaching device determines anomaly correction information based on the anomaly information, including:

[0013] The second teaching device extracts k sub-anomaly information from the anomaly information, and the types of the k sub-anomaly information are different;

[0014] The second teaching device performs anomaly analysis on each of the k sub-anomaly information to obtain k anomaly factors;

[0015] The second teaching device acquires the key information of each of the k abnormal factors to obtain the key information of the k target abnormalities.

[0016] The second teaching device determines the sub-anomaly correction process information based on the process association information in each of the k target anomaly key information, so as to obtain k first sub-anomaly correction process information;

[0017] The second teaching device determines the first parameter corresponding to each first sub-anomaly correction process information based on the parameter association information in each of the k target anomaly key information, so as to obtain the first parameter set;

[0018] The second teaching device determines the anomaly correction information based on k pieces of the first sub-anomaly correction process information and the first parameter set.

[0019] In one possible implementation, the second teaching device determines sub-anomaly correction process information based on the process association information in each of the k target anomaly key information pieces, to obtain k first sub-anomaly correction process information pieces, including:

[0020] The second teaching device determines the process construction information based on the process association information in the key abnormal information of each target to obtain k first process construction information;

[0021] The second teaching device determines the process branch information based on the process association information in the key abnormal information of each target, so as to obtain k first process branch information;

[0022] The second teaching device determines k first sub-anomaly correction process information based on k first process construction information and k first process branch information.

[0023] In one possible implementation, the second teaching device determines the anomaly correction information based on k pieces of the first sub-anomaly correction process information and the first parameter set, including:

[0024] The second teaching device acquires the correlation information between k pieces of the first sub-anomaly correction process information;

[0025] The second teaching device performs process information fusion on the k first sub-anomaly correction process information according to the correlation information between the k first sub-anomaly correction process information to obtain reference anomaly correction information;

[0026] The second teaching device assigns values ​​to the reference anomaly correction information according to the first parameter set to obtain the anomaly correction information.

[0027] In one possible implementation, after the second teaching device assigns values ​​to the reference anomaly correction information according to the first parameter set to obtain the anomaly correction information, the method further includes:

[0028] The second teaching device acquires the first user characteristic information of the target user when the application code is generated;

[0029] The second teaching device determines the correction and adjustment information based on the first user characteristic information;

[0030] The second teaching device adjusts the abnormal correction information according to the correction adjustment information to obtain the adjusted abnormal correction information.

[0031] A second aspect of this application provides an intelligent teaching data processing device, the device comprising:

[0032] The receiving unit is used to receive the first application code sent by the first teaching device;

[0033] A testing unit is used to test the first application code to obtain a first test result;

[0034] The acquisition unit is used to acquire abnormal information in the first test result if the first test result is an abnormal test result;

[0035] The determining unit is used to determine anomaly correction information based on the anomaly information;

[0036] A sending unit is configured to send the anomaly correction information to the first teaching device to instruct the first teaching device to display the anomaly correction information;

[0037] In one possible implementation, the determining unit is used for:

[0038] Extract k sub-anomaly information from the anomaly information, where the types of the k sub-anomaly information are different;

[0039] Anomaly analysis is performed on each of the k sub-anomaly information to obtain k anomaly factors;

[0040] Obtain the key information of each of the k anomalous factors to obtain the key information of the k target anomalous factors;

[0041] Based on the process association information in each of the k target anomaly key information, determine the sub-anomaly correction process information to obtain k first sub-anomaly correction process information;

[0042] Based on the parameter association information in each of the k target anomaly key information, determine the first parameter corresponding to each first sub-anomaly correction process information to obtain the first parameter set;

[0043] The anomaly correction information is determined based on k pieces of the first sub-anomaly correction process information and the first parameter set.

[0044] In one possible implementation, in determining sub-anomaly correction process information based on process association information in each of the k target anomaly key information to obtain k first sub-anomaly correction process information, the determining unit is configured to:

[0045] The process construction information is determined based on the process association information in the key information of each target anomaly, so as to obtain k first process construction information;

[0046] Based on the process association information in the key information of each target anomaly, determine the process branch information to obtain k first process branch information;

[0047] Based on the k first process construction information and the k first process branch information, determine the k first sub-anomaly correction process information.

[0048] In one possible implementation, regarding the determination of the anomaly correction information based on k pieces of the first sub-anomaly correction process information and the first parameter set, the determining unit is configured to:

[0049] Obtain the correlation information among the k first sub-anomaly correction process information;

[0050] Based on the correlation information between the k first sub-anomaly correction process information, process information fusion is performed on the k first sub-anomaly correction process information to obtain reference anomaly correction information;

[0051] The reference anomaly correction information is assigned values ​​based on the first parameter set to obtain the anomaly correction information.

[0052] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0053] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0054] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0055] Implementing the embodiments of this application has at least the following beneficial effects:

[0056] The first teaching device receives first application code generated by the target user, sends the first application code to a second teaching device, and tests the first application code to obtain a first test result. If the first test result is an abnormal test result, the second teaching device obtains the abnormal information in the first test result, determines abnormal correction information based on the abnormal information, sends the abnormal correction information to the first teaching device, and displays the abnormal correction information. Therefore, when the test result of the first application code is abnormal, the corresponding abnormal correction information can be determined and displayed, improving the reliability of students writing application code. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This application provides a flowchart illustrating an intelligent teaching data processing method.

[0059] Figure 2 A schematic diagram of another intelligent teaching system is provided for the embodiments of this application;

[0060] Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0061] Figure 4 This application provides a schematic diagram of the structure of an intelligent teaching data processing device. Detailed Implementation

[0062] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0064] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0065] Please see Figure 1 , Figure 1 This document provides a flowchart illustrating an intelligent teaching data processing method as described in an embodiment of this application. Figure 1 As shown, the intelligent teaching data processing method is applied to an intelligent teaching system, which includes a first teaching device and a second teaching device. The intelligent teaching data processing method includes:

[0066] 101. The first teaching device receives the first application code generated by the target user.

[0067] The first teaching device can be a student computer, specifically a device used by students to encode applications and obtain application code. This device typically does not have application execution capabilities; students can usually only execute the first application code offline and cannot connect it to the corresponding physical device. The target user can be a student.

[0068] The method by which the first teaching device receives the first application code generated by the target user can be that the target user encodes the application on the first teaching device, inputting the corresponding application code, thereby allowing the first teaching device to receive the first application code. The first application code has a specific physical device for its use, and in actual operation, it needs to be copied to that physical device and run.

[0069] like Figure 2 As shown, Figure 2 A schematic diagram of an intelligent teaching system is shown. The intelligent teaching system includes a first teaching device, a second teaching device, and physical devices.

[0070] 102. The first teaching device sends the first application code to the second teaching device.

[0071] The first teaching device and the second teaching device are connected by a network, such as a wired network. Specifically, for example, the first teaching device can send the first application code to the second teaching device via the wired network.

[0072] The second teaching device can be understood as a teacher's computer. It can connect to the physical device associated with the first application code and control that physical device to execute the first application code. The second teaching device can connect to multiple first teaching devices and send teaching tasks to them. Upon receiving the teaching tasks, the first teaching devices display them, and students can then write the first application code on the first teaching device after viewing the displayed tasks.

[0073] 103. The second teaching device tests the first application code to obtain the first test result.

[0074] The second teaching device can transmit the first application code to the corresponding physical device and test the first application code through the physical device to obtain the first test result. Specifically, a general testing method can be used. For example, the first application code is burned into the physical device, and after burning, the physical device is started to run it and obtain the running result, which is then determined as the first test result. If the first application code is error-free, the first test result is usually error-free; if the first application code is error-prone, the first test result is usually error-prone. Error-prone code can be understood as the first application code failing to run according to the preset running mode and obtain the corresponding running result. The preset running mode is set based on empirical values ​​or historical data.

[0075] In existing solutions, after obtaining the first test result, it is usually fed back to the second teaching device. The first test result is then manually determined to be abnormal. If the first test result is abnormal, a person checks the cause of the abnormality and feeds it back to the first teaching device for display. Since the second teaching device is connected to multiple first teaching devices, the abnormal result determination process is time-consuming, resulting in low efficiency. Therefore, in this embodiment, the second teaching device can automatically determine whether the first test result is abnormal. If the first test result is abnormal, abnormal correction information is generated and displayed through the first teaching device, thereby improving the efficiency of processing abnormal test results.

[0076] 104. If the first test result is an abnormal test result, the second teaching device acquires the abnormal information in the first test result.

[0077] The first test result can be compared with a preset test result to obtain sub-test results that differ from the preset test results. These sub-test results are then identified as exception information. Alternatively, the first test result can be split into multiple sub-test results. Exception information is extracted from each sub-test result to obtain the exception information within the first test result. Specifically, for example, the first test result can be split according to the implementation logic of the first application code to obtain multiple sub-test results. The implementation logic of the first application code can be understood as the execution logic of multiple subtasks included in the first application code. Different subtasks have corresponding test results after testing, allowing multiple sub-test results to be determined based on their execution logic. Each sub-test result has a corresponding standard test result. If a sub-test result differs from its corresponding standard test result, the portion of the sub-test result that differs from the standard test result is identified as exception information.

[0078] 105. The second teaching equipment determines the abnormality correction information based on the abnormality information.

[0079] The second teaching device can extract k sub-abnormal information from abnormal information, analyze and process the k sub-abnormal information to obtain the corresponding key abnormal information, and determine the abnormal correction information based on the key abnormal information, thereby accurately determining the abnormal correction information.

[0080] 106. The second teaching device sends the anomaly correction information to the first teaching device.

[0081] The second teaching device can send anomaly correction information to the first teaching device via a wired connection.

[0082] 107. The first teaching device displays the anomaly correction information.

[0083] After receiving the anomaly correction information, the first teaching device can display it. Specifically, for example, the anomaly correction information can be displayed on the display screen of the first teaching device. After receiving the anomaly correction information, the target user can adjust the corresponding anomaly code in the first application code according to the anomaly correction information, thereby improving the efficiency of the target user when adjusting the first application code.

[0084] In this example, a first teaching device receives first application code generated by a target user. The first teaching device then sends the first application code to a second teaching device. The second teaching device tests the first application code to obtain a first test result. If the first test result is an abnormal test result, the second teaching device obtains the abnormal information in the first test result. The second teaching device determines abnormal correction information based on the abnormal information and sends the abnormal correction information to the first teaching device. The first teaching device then displays the abnormal correction information. Therefore, when the test result of the first application code is abnormal, the corresponding abnormal correction information can be determined and displayed, improving the reliability of students writing application code.

[0085] In one possible implementation, a method for a second teaching device to determine anomaly correction information based on the anomaly information includes:

[0086] A1. The second teaching device extracts k sub-abnormal information from the abnormal information, and the types of the k sub-abnormal information are different;

[0087] A2. The second teaching equipment performs anomaly analysis on each of the k sub-anomaly information to obtain k anomaly factors;

[0088] A3. The second teaching device acquires the key information of each of the k abnormal factors to obtain the key information of the k target abnormalities.

[0089] A4. The second teaching equipment determines the sub-anomaly correction process information based on the process association information in each of the k target anomaly key information, so as to obtain k first sub-anomaly correction process information.

[0090] A5. The second teaching equipment determines the first parameter corresponding to each first sub-anomaly correction process information based on the parameter association information in each of the k target anomaly key information, so as to obtain the first parameter set;

[0091] A6. The second teaching device determines the anomaly correction information based on k pieces of the first sub-anomaly correction process information and the first parameter set.

[0092] The method for the second teaching device to extract k sub-anomaly information from the anomaly information can be as follows: Extract k sub-anomaly information from the anomaly information based on the implementation logic of the first application code. Specifically, for example, the first test result can be split according to the implementation logic of the first application code to obtain multiple sub-test results. The implementation logic of the first application code can be understood as the execution logic of multiple subtasks included in the first application code. Different subtasks, after testing, have corresponding test results, thus allowing the determination of multiple sub-test results based on the execution logic of each subtask. Each sub-test result has its corresponding standard test result. If a sub-test result differs from its corresponding standard test result, the difference between the sub-test result and the standard test result is identified as anomaly information.

[0093] Different anomalies have their own anomaly factors. For example, if the anomaly is caused by data assignment exceeding the normal assignment range, the corresponding anomaly factor is "assignment out of range." Similarly, if the anomaly is due to illogical execution, the corresponding anomaly factor is "error in execution logic," such as an incorrect execution order of the first and second steps. Therefore, the anomaly factor corresponding to the anomaly can be determined based on this mapping relationship.

[0094] Anomaly factors can characterize the causes of anomalies. Therefore, key anomaly information can be determined based on the causes represented by these factors. Specifically, the cause of the anomaly can be identified as the key anomaly information. For example, if the anomaly factor is a logical error, then the key anomaly information would be the incorrect execution order of the first and second steps. Thus, anomaly analysis can be performed on k anomaly information to obtain their corresponding target key anomaly information. Target key anomaly information can include process-related information and parameter-related information. Process-related information can be understood as information related to the execution of process steps, while parameter-related information can be understood as information related to execution parameters. Specifically, information related to the execution of process steps can be understood as whether there are errors in process execution, and anomaly information during process execution (e.g., incorrect execution order of the first and second steps).

[0095] K sub-anomaly correction process information can be determined based on process association information, and a first parameter corresponding to each sub-anomaly correction process information can be determined based on parameter association information to obtain a first parameter set. Anomaly correction information is then determined based on the k sub-anomaly correction process information and the first parameter set. Specifically, the corresponding first sub-anomaly correction process information can be determined based on the process construction information and corresponding process branch information determined by process association information. The method for determining the first parameter based on parameter association information is as follows: parameter association information can characterize the associated parameters in the corresponding first sub-anomaly correction process information, such as the range of anomaly values ​​that need to be adjusted. The first parameter can then be determined based on this adjustment range. If there is no anomaly value adjustment range, the first parameter can be empty.

[0096] In this example, abnormal factors are obtained by performing anomaly analysis on abnormal information, and key abnormal information is extracted from the abnormal factors to obtain k key abnormal information of targets. Based on these k key abnormal information of targets, abnormal correction information is determined, which improves the accuracy of obtaining abnormal correction information.

[0097] In one possible implementation, a method for a second teaching device to determine sub-anomaly correction process information based on process association information in each of k target anomaly key information to obtain k first sub-anomaly correction process information includes:

[0098] B1. The second teaching equipment determines the process construction information based on the process association information in the key information of each target anomaly, so as to obtain k first process construction information;

[0099] B2. The second teaching equipment determines the process branch information based on the process association information in the key abnormal information of each target to obtain k first process branch information;

[0100] B3. The second teaching equipment determines k first sub-anomaly correction process information based on k first process construction information and k first process branch information.

[0101] Different process association information has corresponding process construction information. For example, if the process association information is an error in the execution order of the first and second steps, then the corresponding process construction information could be to swap the execution order of the first and second steps. Process branch information can be the process branch information corresponding to the implementation logic of the first application code corresponding to the first and second steps. This only illustrates the case of one process construction information and one process branch information; of course, it can also include cases with multiple process construction information and process branch information. This is only an example and not a specific limitation.

[0102] Of course, process association information can also be code writing process information that generates anomalies. For example, the implementation logic of the first application code corresponding to the anomaly factor is to obtain the current ambient temperature, but the anomaly information is that the current ambient humidity is obtained. Therefore, the code writing process information that generates anomalies could be sensor call anomalies or sensor parameter usage anomalies when the target user is writing code. Thus, multiple first process construction information can be determined based on the code writing process information that generates anomalies. This first process architecture information could be, for example, the process construction information corresponding to sensor calls, the process construction information for sensor parameter settings, etc. Process association information can also determine process branch information, specifically, process branch information could be process branch information for obtaining environmental parameters, etc. Finally, k first process construction information and their corresponding first process branch information can be fused to obtain k first sub-anomaly correction process information. Specifically, the first process construction information and their corresponding first process branch information can be fused by text concatenation to obtain the corresponding first sub-anomaly correction process information. The method for text concatenation fusion can be performed using a general method, which will not be elaborated here.

[0103] In one possible implementation, a method for a second teaching device to determine the anomaly correction information based on k pieces of the first sub-anomaly correction process information and the first parameter set includes:

[0104] C1. The second teaching device acquires the correlation information between k pieces of the first sub-anomaly correction process information;

[0105] C2. The second teaching device performs process information fusion on the k first sub-anomaly correction process information according to the correlation information between the k first sub-anomaly correction process information to obtain reference anomaly correction information.

[0106] C3. The second teaching device assigns values ​​to the reference anomaly correction information according to the first parameter set to obtain the anomaly correction information.

[0107] Since there may be inter-call relationships between process branches corresponding to different first sub-anomaly correction process information, the inter-call relationship information between process branches corresponding to different first sub-anomaly correction process information can be obtained and the inter-call relationship information can be determined as the relationship information between the first sub-anomaly correction process information.

[0108] The associated first sub-anomaly correction process information can be fused based on the associated call information to obtain reference anomaly correction information. The first parameter in the first parameter set can be used to assign values ​​to the corresponding first sub-anomaly correction process information to obtain anomaly correction information. If multiple first sub-anomaly correction process information have already been fused, the first parameter in the first parameter set is used to assign values ​​to the fused portion.

[0109] In this example, when there is a correlation between the information in the first sub-anomaly correction process, performing correlation fusion processing can better characterize the upstream and downstream correlations. After fusion, the information is assigned a value to obtain anomaly correction information, thereby improving the accuracy of anomaly correction information determination.

[0110] In one possible implementation, after the second teaching device assigns values ​​to the reference anomaly correction information according to the first parameter set to obtain the anomaly correction information, the method further includes:

[0111] D1. The second teaching device acquires the first user characteristic information of the target user when generating application code;

[0112] D2. The second teaching device determines the correction and adjustment information based on the first user characteristic information;

[0113] D3. The second teaching device adjusts the abnormal correction information according to the correction adjustment information to obtain the adjusted abnormal correction information.

[0114] Among them, the first user characteristic information of the target user when generating application code can be understood as: the application writing profile of the target user when writing the application, which can be specifically understood as the application writing mindset, the state of the application writing, and the software usage habits when writing the application.

[0115] Since different users may have their own unique coding habits when coding applications, correction and adjustment information can be determined based on the target user's application profiling. For example, the text style and language descriptions in the anomaly correction information can be determined based on the first user's characteristic information. Specifically, the language description word order in certain code fields can be determined based on the application profiling. The corresponding language description word order in the anomaly correction information can then be adjusted to match the user's preferred word order. This makes the adjusted anomaly correction information more suitable for the target user and improves the efficiency of subsequent anomaly correction for other users.

[0116] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0117] Receive the first application code sent by the first teaching device;

[0118] The first application code is tested to obtain a first test result;

[0119] If the first test result is an abnormal test result, then obtain the abnormal information in the first test result;

[0120] Based on the anomaly information, determine the anomaly correction information;

[0121] The anomaly correction information is sent to the first teaching device to instruct the first teaching device to display the anomaly correction information.

[0122] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0124] For those consistent with the above, please refer to Figure 4 , Figure 4This application provides a schematic diagram of the structure of an intelligent teaching data processing device according to an embodiment. Figure 4 As shown, the device is applied to a second teaching device, and the device includes:

[0125] The receiving unit 401 is used to receive the first application code sent by the first teaching device;

[0126] Test unit 402 is used to test the first application code to obtain a first test result;

[0127] The acquisition unit 403 is used to acquire the abnormal information in the first test result if the first test result is an abnormal test result;

[0128] Determining unit 404 is used to determine anomaly correction information based on the anomaly information;

[0129] The sending unit 405 is used to send the anomaly correction information to the first teaching device to instruct the first teaching device to display the anomaly correction information.

[0130] In one possible implementation, the determining unit 404 is used to:

[0131] Extract k sub-anomaly information from the anomaly information, where the types of the k sub-anomaly information are different;

[0132] Anomaly analysis is performed on each of the k sub-anomaly information to obtain k anomaly factors;

[0133] Obtain the key information of each of the k anomalous factors to obtain the key information of the k target anomalous factors;

[0134] Based on the process association information in each of the k target anomaly key information, determine the sub-anomaly correction process information to obtain k first sub-anomaly correction process information;

[0135] Based on the parameter association information in each of the k target anomaly key information, determine the first parameter corresponding to each first sub-anomaly correction process information to obtain the first parameter set;

[0136] The anomaly correction information is determined based on k pieces of the first sub-anomaly correction process information and the first parameter set.

[0137] In one possible implementation, in determining sub-anomaly correction process information based on process association information in each of the k target anomaly key information to obtain k first sub-anomaly correction process information, the determining unit 404 is configured to:

[0138] The process construction information is determined based on the process association information in the key information of each target anomaly, so as to obtain k first process construction information;

[0139] Based on the process association information in the key information of each target anomaly, determine the process branch information to obtain k first process branch information;

[0140] Based on the k first process construction information and the k first process branch information, determine the k first sub-anomaly correction process information.

[0141] In one possible implementation, regarding the determination of the anomaly correction information based on k pieces of the first sub-anomaly correction process information and the first parameter set, the determining unit 404 is configured to:

[0142] Obtain the correlation information among the k first sub-anomaly correction process information;

[0143] Based on the correlation information between the k first sub-anomaly correction process information, process information fusion is performed on the k first sub-anomaly correction process information to obtain reference anomaly correction information;

[0144] The reference anomaly correction information is assigned values ​​based on the first parameter set to obtain the anomaly correction information.

[0145] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the intelligent teaching data processing methods described in the above method embodiments.

[0146] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the intelligent teaching data processing methods described in the above method embodiments.

[0147] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0152] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0153] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0154] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An intelligent teaching data processing method, characterized in that, The method is applied to an intelligent teaching system, the intelligent teaching system comprising a first teaching device and a second teaching device, and the method comprises: The first teaching device receives first application program code generated by a target user; The first teaching device sends the first application program code to the second teaching device; The second teaching device tests the first application program code to obtain a first test result; If the first test result is an abnormal test result, the second teaching device obtains abnormal information in the first test result; The second teaching device determines abnormal correction information according to the abnormal information; The second teaching device sends the abnormal correction information to the first teaching device; The first teaching device displays the abnormal correction information; The second teaching device determines abnormal correction information according to the abnormal information, comprising: The second teaching device extracts k pieces of sub-abnormal information from the abnormal information, the types of the k pieces of sub-abnormal information being different; The second teaching device respectively performs abnormal analysis on the k pieces of sub-abnormal information to obtain k abnormal factors; The second teaching device obtains abnormal key information corresponding to each abnormal factor in the k abnormal factors to obtain k target abnormal key information; The second teaching device determines sub-abnormal correction process information according to process association information in each target abnormal key information in the k target abnormal key information to obtain k first sub-abnormal correction process information; The second teaching device determines a first parameter corresponding to each first sub-abnormal correction process information according to parameter association information in each target abnormal key information in the k target abnormal key information to obtain a first parameter set; The second teaching device determines the abnormal correction information according to the k first sub-abnormal correction process information and the first parameter set. 2.The intelligent teaching data processing method of claim 1, wherein, The second teaching device determines sub-abnormal correction process information according to process association information in each target abnormal key information in the k target abnormal key information to obtain k first sub-abnormal correction process information, comprising: The second teaching device determines process construction information according to the process association information in each target abnormal key information to obtain k first process construction information; The second teaching device determines process branch information according to the process association information in each target abnormal key information to obtain k first process branch information; The second teaching device determines k first sub-abnormal correction process information according to the k first process construction information and the k first process branch information. 3.The intelligent teaching data processing method of claim 2, wherein, The second teaching device determines the abnormal correction information according to the k first sub-abnormal correction process information and the first parameter set, comprising: The second teaching device obtains association information between the k first sub-abnormal correction process information; The second teaching device performs process information fusion on the k first sub-abnormal correction process information according to the association information between the k first sub-abnormal correction process information to obtain reference abnormal correction information; The second teaching device performs value assignment processing on the reference exception correction information according to the first parameter set to obtain the exception correction information.

4. The intelligent teaching data processing method of claim 3, wherein, After the second teaching device performs value assignment processing on the reference exception correction information according to the first parameter set to obtain the exception correction information, the method further includes: The second teaching device obtains first user feature information of the target user when generating the application program code; The second teaching device determines correction adjustment information according to the first user feature information; The second teaching device adjusts the exception correction information according to the correction adjustment information to obtain adjusted exception correction information.

5. An intelligent teaching data processing apparatus characterized by comprising: The device is applied to a second teaching device and includes: A receiving unit configured to receive first application program code sent by a first teaching device; A testing unit configured to test the first application program code to obtain a first test result; An obtaining unit configured to, if the first test result is an abnormal test result, obtain exception information in the first test result; A determining unit configured to determine exception correction information according to the exception information; A sending unit configured to send the exception correction information to the first teaching device to instruct the first teaching device to display the exception correction information; The determining unit is configured to: extract k pieces of sub-exception information from the exception information, the types of the k pieces of sub-exception information being different; perform exception analysis on the k pieces of sub-exception information respectively to obtain k exception factors; obtain exception key information corresponding to each of the k exception factors to obtain k target exception key information; determine sub-exception correction process information according to flow association information in each of the k target exception key information to obtain k first sub-exception correction process information; determine a first parameter corresponding to each first sub-exception correction process information according to parameter association information in each of the k target exception key information to obtain a first parameter set; determine the exception correction information according to the k first sub-exception correction process information and the first parameter set. 6.The intelligent teaching data processing apparatus according to claim 5, wherein, In the aspect of determining sub-exception correction process information according to flow association information in each of the k target exception key information to obtain k first sub-exception correction process information, the determining unit is configured to: determine flow construction information according to the flow association information in each target exception key information to obtain k first flow construction information; determine flow branch information according to the flow association information in each target exception key information to obtain k first flow branch information; determine the k first sub-exception correction process information according to the k first flow construction information and the k first flow branch information. 7.The intelligent teaching data processing apparatus according to claim 6, wherein, In the aspect of determining the exception correction information according to the k first sub-exception correction process information and the first parameter set, the determining unit is configured to: obtain association information between the k first sub-exception correction process information; According to the association information between the k first sub-abnormal correction process information, the k first sub-abnormal correction process information is fused to obtain reference abnormal correction information; According to the first parameter set, the reference abnormal correction information is assigned to obtain the abnormal correction information.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program includes program instructions, the program instructions are executed by the processor to make the processor execute the method in any one of claims 1-4.

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