Data processing methods suitable for online training of power systems

Abstract

The embodiment of the present application proposes a data processing method suitable for online training of power systems, including obtaining a target scenario label related to an incorrect construction behavior at the current mobile device; controlling the mobile device to broadcast locally based on near-field communication technology to obtain the scenario labels of other mobile devices in the online learning state; obtaining the communication delay of the current mobile device, and reordering the order of test questions in the test question package corresponding to the target scenario label in combination with a test question replacement request; and starting online learning based on the test question package with the reordered suffix. By reordering the test questions obtained by the current mobile device, the difficulty of communicating with others and doing questions together is increased, and the possibility of cheating is reduced.

Description

Technical Field

[0001] The present application relates to the field of information processing, and in particular to a data processing method suitable for online training of power systems. Background Art

[0002] During power maintenance work in power systems, various errors often occur. To improve the success rate of operations and ensure the safety of construction personnel, periodic safety training is required.

[0003] Some companies use offline video viewing and centralized exams to train construction workers on safety. However, this approach can be superficial, and there's a risk of workers cheating by helping each other. It's impossible to determine whether workers have truly mastered safety knowledge, defeating the purpose of disseminating safety knowledge. Summary of the Invention

[0004] In order to address the shortcomings and deficiencies in the existing technology, this application proposes a data processing method suitable for online training of power systems. With the help of communication means, test questions with the same possibilities in the examination scenario are re-ordered and replaced, increasing the difficulty of mutual reference and improving the construction personnel's mastery of safe operation knowledge.

[0005] Specifically, this application proposes a data processing method suitable for online training of power systems, which is used for construction personnel to obtain a test package using a mobile device at a designated learning location and conduct online learning after incorrect construction behavior, including:

[0006] S1, parse the test question package on the current mobile device to obtain the target scene label related to the incorrect construction behavior;

[0007] S2, controls the mobile device to broadcast locally based on near-field communication technology to obtain scene labels of other mobile devices in online learning state within the maximum allowed distance;

[0008] S3: If the acquired scene label is the same as the target scene label, the current mobile device is instructed to generate a question replacement request;

[0009] S4, obtaining the communication delay of the current mobile device, and reordering the order of the test questions in the test question package corresponding to the target scenario label in combination with the test question replacement request, to generate a test question package with a reordered suffix;

[0010] S5, starting online learning according to the test question package with the reordered suffix, and modifying the device parameters of the mobile device entering the online learning state based on the target scenario label combined with the reordered suffix.

[0011] Optionally, the S1 includes:

[0012] S11, controlling the mobile device to receive test question packages corresponding to different incorrect construction behaviors at a designated learning location;

[0013] S12, parsing the header file of the test question package to obtain the file storage structure in the test question package;

[0014] S13, obtaining target scenario labels corresponding to incorrect construction behaviors of construction workers who log in to the mobile device and test questions in the corresponding target scenario labels based on the file storage structure.

[0015] Optionally, the S2 includes:

[0016] S21, controlling the current mobile device to send a broadcast message with a device parameter request at a designated learning location based on near field communication technology;

[0017] S22, receiving device parameters sent based on broadcast information from other mobile devices that are also in an online learning state and are within a maximum allowable distance covered by the near field communication technology;

[0018] S23, extracting scene labels from device parameters.

[0019] Optionally, the S23 includes:

[0020] S231, extracting a sample device name of the mobile device from the device parameters;

[0021] S232: Based on the naming rules of the device name, the standard device name field is removed from the sample device name, and the remaining characters are the scene label.

[0022] Optionally, the S3 includes:

[0023] S31, comparing the obtained scene label with the target scene label;

[0024] S32: If the comparison results are the same, the MAC address code of the current mobile device is combined with the target scenario tag to generate a test question replacement request.

[0025] Optionally, the S4 includes:

[0026] S41, obtaining the communication delay between the current mobile device and the test question server;

[0027] S42, if the communication delay is higher than the threshold, performing localized test question replacement processing on the mobile device in combination with the test question replacement request;

[0028] S43: If the communication delay is lower than the threshold, the question replacement request is sent to the question server for remote question replacement processing.

[0029] Optionally, the S42 includes:

[0030] S421, if the communication delay is higher than the threshold, parsing the header file of the test question package in the current mobile device to obtain the file storage structure in the test question package;

[0031] S422, obtaining the test questions in the corresponding target scenario tag based on the file storage structure, and obtaining the hash value of the current test question;

[0032] S423, extracting test question serial numbers in sequence based on the blind box extraction mechanism, and reordering the test questions according to the extraction order;

[0033] S424, replacing the test questions with the reordered test questions;

[0034] S425, calculating the hash value of the replaced test question and comparing it with the hash value obtained in S422;

[0035] S426, if the comparison results are different, the reordered test questions are packaged to obtain a replacement test question package, and the hash value of the replaced test question is added as a reordered suffix to the replacement test question package to generate a test question package with the reordered suffix, thereby completing the localized test question replacement process;

[0036] S427: If the comparison results are the same, then S423 to S426 are repeated until the localized test question replacement process is completed.

[0037] Optionally, the S43 includes:

[0038] S431, if the communication delay is lower than the threshold, sending a question replacement request to the question server;

[0039] S432: Parse the question replacement request at the question server, obtain the target scenario tag and the MAC address of the mobile device that sent the question replacement request, obtain the original question order corresponding to the target scenario tag, and calculate the hash value of the question under the original question order;

[0040] S433, extracting test question serial numbers in sequence based on the blind box extraction mechanism, and reordering the test questions according to the extraction order;

[0041] S434, calculating the hash value of the reordered test questions and comparing it with the hash value obtained in S432;

[0042] S435: If the comparison results are different, the reordered test questions are packaged to obtain a replaced test question package, the hash value obtained in S434 is added as a reordered suffix to the replaced test question package to generate a test question package with the reordered suffix, and the test question package with the reordered suffix is ​​sent to the mobile device that sent the test question replacement request;

[0043] S436, adding a record of the number of times the test questions corresponding to the MAC address code are reordered;

[0044] S437: If the comparison results are the same, then S433 to S436 are repeatedly executed until the remote question replacement process is completed.

[0045] Optionally, the S5 includes:

[0046] S51, parsing the test question package with the reordered suffix at the current mobile device to obtain reordered test questions;

[0047] S52, displaying the reordered test questions on the current mobile device to enable the construction worker to start online learning;

[0048] S53, after starting online learning, the reordering suffix is ​​appended to the device name field of the current mobile device to complete the modification of the mobile device parameters.

[0049] Optionally, it also includes a processing step of implementing feedback adjustment on the test question package based on the answer data of the construction workers after online learning.

[0050] The beneficial effects of the technical solution provided by this application are:

[0051] Mobile devices used for online learning can broadcast within a limited distance using near-field communication devices, thereby receiving scene tags from other mobile devices involved in online learning. If the acquired scene tags match the target scene tags, the test questions received by the current mobile device are reordered, making it easier to communicate with others and work together, reducing the possibility of cheating. DETAILED DESCRIPTION

[0052] In order to make the structure and advantages of the present application clearer, the present application is further described below.

[0053] This embodiment proposes a data processing method suitable for online training of power systems, which is used by construction personnel to obtain a test package using a mobile device at a designated learning location and conduct online learning after incorrect construction behavior, including:

[0054] S1, parse the test question package on the current mobile device to obtain the target scene label related to the incorrect construction behavior;

[0055] S2, controls the mobile device to broadcast locally based on near-field communication technology to obtain scene labels of other mobile devices in online learning state within the maximum allowed distance;

[0056] S3: If the acquired scene label is the same as the target scene label, the current mobile device is instructed to generate a question replacement request;

[0057] S4, obtaining the communication delay of the current mobile device, and reordering the order of the test questions in the test question package corresponding to the target scenario label in combination with the test question replacement request, to generate a test question package with a reordered suffix;

[0058] S5, starting online learning according to the test question package with the reordered suffix, and modifying the device parameters of the mobile device entering the online learning state based on the target scenario label combined with the reordered suffix.

[0059] During implementation, it's important to explain that scene labels are characteristic values ​​used to represent incorrect construction behaviors and have a direct correspondence with them. The same incorrect construction behaviors require the same knowledge points for online learning. Therefore, if the scene labels of other mobile devices match the target scene label of the current mobile device, it indicates that the online learning questions on the other mobile devices are likely to be the same as the questions that the current mobile device will use for online learning. This could create the opportunity for cheating by communicating with other construction workers taking the same test.

[0060] In order to solve the many defects in the online learning process of construction workers in the prior art, the embodiment of the present application proposes a data processing method, which uses the mobile device used for online learning to broadcast within a limited distance based on a near-field communication device, thereby receiving the scene labels of other mobile devices that are currently learning online. It is determined whether the obtained scene label is the same as the target scene label. If they are different, it indicates that there is no possibility of cheating, and online learning can be carried out directly based on the obtained test question package. If they are the same, in order to reduce the possibility of cheating, it is necessary to re-sort the test questions obtained by the current mobile device, thereby increasing the difficulty of communicating with others and doing questions together.

[0061] Specifically, step S1 of obtaining the target scene label includes:

[0062] S11, controlling the mobile device to receive test question packages corresponding to different incorrect construction behaviors at a designated learning location;

[0063] S12, parsing the header file of the test question package to obtain the file storage structure in the test question package;

[0064] S13, obtaining target scenario labels corresponding to incorrect construction behaviors of construction workers who log in to the mobile device and test questions in the corresponding target scenario labels based on the file storage structure.

[0065] In practice, the collection of test questions used for online learning, or the test package, is selected based on different incorrect construction behaviors. The package is also based on the scenario labels corresponding to these incorrect construction behaviors. Therefore, in addition to the test questions, the package also contains the scenario labels corresponding to the test questions. To distinguish it from the scenario labels subsequently acquired by other mobile devices, the scenario label acquired by the current mobile device is referred to as the target scenario label.

[0066] The target scene label can be obtained by directly parsing the test question package according to the preset file storage structure of the test question package, and directly obtaining the target scene label and the test question after parsing.

[0067] The operation for obtaining scene tags of other mobile devices, i.e., step S2, includes:

[0068] S21, controlling the current mobile device to send a broadcast message with a device parameter request at a designated learning location based on near field communication technology;

[0069] S22, receiving device parameters sent based on broadcast information from other mobile devices that are also in an online learning state and are within a maximum allowable distance covered by the near field communication technology;

[0070] S23, extracting scene labels from device parameters.

[0071] In implementation, unlike the existing technology that often manages mobile device information based on servers or unified management software, the technical solution of this application executes step S2 to obtain other device scene tags based on communication between mobile devices.

[0072] A typical, common near-field communication technology is iBeacon, a low-power wireless communication protocol based on Bluetooth. iBeacon works by using Bluetooth Low Energy (BLE) technology to transmit unique ID information to nearby devices. Applications or mobile devices that receive this information then perform specific actions based on the ID information. Due to its low power requirements, this type of near-field communication technology can only cover an area of ​​a certain size (e.g., approximately 10 meters), but this distance is sufficient for the anti-cheating scenarios described in this application.

[0073] Based on the above technical principles, the current mobile device can send a request to obtain parameters of other mobile devices at a designated learning location, so that mobile devices that are learning online within the network coverage of the near-field communication technology (i.e., the maximum allowed distance) can send their own device parameters to the current mobile device after receiving the broadcast information, thereby realizing the operation of extracting scene labels of other mobile devices from the device parameters.

[0074] Specifically, the operation of extracting the scene label from the device parameters, i.e., S23, includes:

[0075] S231, extracting a sample device name of the mobile device from the device parameters;

[0076] S232: Based on the naming rules of the device name, the standard device name field is removed from the sample device name, and the remaining characters are the scene label.

[0077] In practice, to facilitate identification by other devices, each mobile device has a unique device name, such as a device name represented by a model number, a device serial number represented by a specific alphanumeric combination, an IMEI code, etc. Regardless of the specific name used, there are specific naming rules.

[0078] According to the subsequent step S5, all mobile devices conducting online learning at the designated location will modify their own scene tags in combination with the scene tags. Therefore, executing the operation of step S232 can obtain the required scene tags from the mobile devices in the online learning state.

[0079] In order to implement question replacement, it is necessary to generate a question replacement request containing a lot of information, that is, step S3 includes:

[0080] S31, comparing the obtained scene label with the target scene label;

[0081] S32: If the comparison results are the same, the MAC address code of the current mobile device is combined with the target scenario tag to generate a test question replacement request.

[0082] In implementation, the purpose of the question replacement request is to complete the question replacement operation when the scene label is the same as the target scene label, indicating that there is a possibility of cheating. If the scene label is different from the target scene label, it indicates that the possibility of cheating is very low and no question replacement is required.

[0083] In order to leave a modification record of the question replacement operation, it is necessary to add the MAC address code of the current mobile device that makes the question replacement request in the question replacement request to facilitate subsequent statistical management.

[0084] The operation for completing the test question replacement according to the test question replacement request, that is, step S4 includes:

[0085] S41, obtaining the communication delay between the current mobile device and the test question server;

[0086] S42, if the communication delay is higher than the threshold, performing localized test question replacement processing on the mobile device in combination with the test question replacement request;

[0087] S43: If the communication delay is lower than the threshold, the question replacement request is sent to the question server for remote question replacement processing.

[0088] In practice, exam questions are typically stored on a remote exam question server, so regular exam question replacement operations require access to the server. However, given the uncertainty of mobile network coverage at a given learning location, we propose two approaches for exam question replacement: local and remote. This approach uses the communication latency parameter—a key communication parameter between mobile devices and the exam question server—to achieve this goal. A latency above a threshold indicates poor communication quality between the mobile device and the exam question server, necessitating localized exam question replacement. Conversely, this indicates high communication quality, necessitating remote exam question replacement.

[0089] The localized question replacement operation, i.e., step S42, includes:

[0090] S421, if the communication delay is higher than the threshold, parsing the header file of the test question package in the current mobile device to obtain the file storage structure in the test question package;

[0091] S422, obtaining the test questions in the corresponding target scenario tag based on the file storage structure, and obtaining the hash value of the current test question;

[0092] S423, extracting test question serial numbers in sequence based on the blind box extraction mechanism, and reordering the test questions according to the extraction order;

[0093] S424, replacing the test questions with the reordered test questions;

[0094] S425, calculating the hash value of the replaced test question and comparing it with the hash value obtained in S422;

[0095] S426, if the comparison results are different, the reordered test questions are packaged to obtain a replacement test question package, and the hash value of the replaced test question is added as a reordered suffix to the replacement test question package to generate a test question package with the reordered suffix, thereby completing the localized test question replacement process;

[0096] S427: If the comparison results are the same, then S423 to S426 are repeated until the localized test question replacement process is completed.

[0097] Correspondingly, the remote question replacement operation, i.e., step S43, includes:

[0098] S431, if the communication delay is lower than the threshold, sending a question replacement request to the question server;

[0099] S432: Parse the question replacement request at the question server, obtain the target scenario tag and the MAC address of the mobile device that sent the question replacement request, obtain the original question order corresponding to the target scenario tag, and calculate the hash value of the question under the original question order;

[0100] S433, extracting test question serial numbers in sequence based on the blind box extraction mechanism, and reordering the test questions according to the extraction order;

[0101] S434, calculating the hash value of the reordered test questions and comparing it with the hash value obtained in S432;

[0102] S435: If the comparison results are different, the reordered test questions are packaged to obtain a replaced test question package, the hash value obtained in S434 is added as a reordered suffix to the replaced test question package to generate a test question package with the reordered suffix, and the test question package with the reordered suffix is ​​sent to the mobile device that sent the test question replacement request;

[0103] S436, adding a record of the number of times the test questions corresponding to the MAC address code are reordered;

[0104] S437: If the comparison results are the same, then S433 to S436 are repeatedly executed until the remote question replacement process is completed.

[0105] In implementation, according to the above two sets of implementation methods, in order to ensure that the order of questions will not be the same before and after reordering, localized question replacement and remote question replacement both adopt the method of calculating the hash values ​​of the questions before and after reordering and judging the validity of the reordering based on the comparison of hash values; at the same time, in order to indicate that the obtained questions have been replaced, the hash value of the replaced questions is added to the question package as a reordering suffix, so that the current mobile device can modify its own device parameters based on the target scenario label and the reordering suffix after entering the online learning state, so as to facilitate the identification of mobile devices that start online learning later.

[0106] It's worth noting that both question replacement operations involve reordering the questions. This is based on the assumption that, given a sufficiently large number of questions, adjusting the question order is the optimal solution based on both mobile device performance and anti-cheating reliability. Furthermore, when performing remote question replacement, the additional processing step S436 is included to leave a record of the question replacement operation on the question server, facilitating subsequent management and accountability.

[0107] Step S5 of starting online learning according to the test question package with the reordered suffix and modifying the device parameters of the current mobile device includes:

[0108] S51, parsing the test question package with the reordered suffix at the current mobile device to obtain reordered test questions;

[0109] S52, displaying the reordered test questions on the current mobile device to enable the construction worker to start online learning;

[0110] S53, after starting online learning, the reordering suffix is ​​appended to the device name field of the current mobile device to complete the modification of the device parameters of the mobile device.

[0111] In practice, after executing the aforementioned steps to obtain a test question with sufficient anti-cheating capabilities, the replaced test question can be displayed on the current mobile device for online learning. At the same time, in order to enable subsequent mobile devices for online learning to perform the same scene label comparison and test question replacement operations, it is also necessary to control the current mobile device to append the reordered suffix to the device name field of the current mobile device to complete the modification of the mobile device's device parameters.

[0112] The serial numbers in the above embodiments are for description only and do not represent the order of assembly or use of the components.

[0113] The above description is merely an embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A data processing method for online training of power systems, which is used by practitioners to obtain a test package using a mobile device at a designated learning location for online learning after an incorrect construction behavior, characterized in that: The data processing method includes: S1, parse the test question package on the current mobile device to obtain the target scene label related to the incorrect construction behavior; S2, controls the mobile device to broadcast locally based on near-field communication technology to obtain scene labels of other mobile devices in online learning state within the maximum allowed distance; S3: If the acquired scene label is the same as the target scene label, the current mobile device is instructed to generate a question replacement request; S4, obtaining the communication delay of the current mobile device, and reordering the order of the test questions in the test question package corresponding to the target scenario label in combination with the test question replacement request, to generate a test question package with a reordered suffix; S5, starting online learning according to the test question package with the reordered suffix, and modifying the device parameters of the mobile device entering the online learning state based on the target scenario label and the reordered suffix; Said S1 comprises: S11, controlling the mobile device to receive test question packages corresponding to different incorrect construction behaviors at a designated learning location; S12, parsing the header file of the test question package to obtain the file storage structure in the test question package; S13, obtaining, based on the file storage structure, a target scenario label corresponding to the incorrect construction behavior of the construction worker who logged into the mobile device, and a test question corresponding to the target scenario label; The S4 includes: S41, obtaining the communication delay between the current mobile device and the test question server; S42, if the communication delay is higher than the threshold, performing localized test question replacement processing on the mobile device in combination with the test question replacement request; S43: If the communication delay is lower than the threshold, the question replacement request is sent to the question server for remote question replacement processing.

2. The data processing method suitable for online training of power system according to claim 1, characterized in that: The S2 includes: S21, controlling the current mobile device to send a broadcast message with a device parameter request at a designated learning location based on near field communication technology; S22, receiving device parameters sent based on broadcast information from other mobile devices that are also in an online learning state and are within a maximum allowable distance covered by the near field communication technology; S23, extracting scene labels from device parameters.

3. The data processing method suitable for online training of power system according to claim 2, characterized in that: The S23 includes: S231, extracting a sample device name of the mobile device from the device parameters; S232: Based on the naming rules of the device name, the standard device name field is removed from the sample device name, and the remaining characters are the scene label.

4. The data processing method suitable for online training of power system according to claim 1, characterized in that: The S3 includes: S31, comparing the obtained scene label with the target scene label; S32: If the comparison results are the same, the MAC address code of the current mobile device is combined with the target scenario tag to generate a test question replacement request.

5. The data processing method suitable for online training of power system according to claim 1, characterized in that: The S42 includes: S421, if the communication delay is higher than the threshold, parsing the header file of the test question package in the current mobile device to obtain the file storage structure in the test question package; S422, obtaining the test questions in the corresponding target scenario tag based on the file storage structure, and obtaining the hash value of the current test question; S423, extracting test question serial numbers in sequence based on the blind box extraction mechanism, and reordering the test questions according to the extraction order; S424, replacing the test questions with the reordered test questions; S425, calculating the hash value of the replaced test question and comparing it with the hash value obtained in S422; S426, if the comparison results are different, the reordered test questions are packaged to obtain a replacement test question package, and the hash value of the replaced test question is added as a reordered suffix to the replacement test question package to generate a test question package with the reordered suffix, thereby completing the localized test question replacement process; S427: If the comparison results are the same, then S423 to S426 are repeated until the localized test question replacement process is completed.

6. The data processing method suitable for online training of power system according to claim 1, characterized in that: The S43 includes: S431, if the communication delay is lower than the threshold, sending a question replacement request to the question server; S432: Parse the question replacement request at the question server, obtain the target scenario tag and the MAC address of the mobile device that sent the question replacement request, obtain the original question order corresponding to the target scenario tag, and calculate the hash value of the question under the original question order; S433, extracting test question serial numbers in sequence based on the blind box extraction mechanism, and reordering the test questions according to the extraction order; S434, calculating the hash value of the reordered test questions and comparing it with the hash value obtained in S432; S435: If the comparison results are different, the reordered test questions are packaged to obtain a replaced test question package, the hash value obtained in S434 is added as a reordered suffix to the replaced test question package to generate a test question package with the reordered suffix, and the test question package with the reordered suffix is ​​sent to the mobile device that sent the test question replacement request; S436, adding a record of the number of times the test questions corresponding to the MAC address code are reordered; S437: If the comparison results are the same, then S433 to S436 are repeatedly executed until the remote question replacement process is completed.

7. The data processing method suitable for online training of power system according to claim 1, characterized in that: The S5 includes: S51, parsing the test question package with the reordered suffix at the current mobile device to obtain reordered test questions; S52, displaying the reordered test questions on the current mobile device to enable the construction worker to start online learning; S53, after starting online learning, the reordered suffix is ​​appended to the device name field of the current mobile device, thereby completing the modification of the device parameters of the mobile device.

8. The data processing method suitable for online training of power system according to claim 1, characterized in that: It also includes processing steps for implementing feedback adjustment on the test question package based on the answer data of the construction personnel after online learning.