CN116128453B - Online course inspection method, system, equipment and medium - Google Patents
Online course inspection method, system, equipment and medium Download PDFInfo
- Publication number
- CN116128453B CN116128453B CN202310143565.4A CN202310143565A CN116128453B CN 116128453 B CN116128453 B CN 116128453B CN 202310143565 A CN202310143565 A CN 202310143565A CN 116128453 B CN116128453 B CN 116128453B
- Authority
- CN
- China
- Prior art keywords
- inspection
- course
- online
- patrol
- conclusion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 450
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000013499 data model Methods 0.000 claims abstract description 49
- 230000002159 abnormal effect Effects 0.000 claims description 27
- 238000004458 analytical method Methods 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 13
- 238000012552 review Methods 0.000 claims description 9
- 230000006978 adaptation Effects 0.000 claims description 7
- 230000014759 maintenance of location Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 5
- 238000005516 engineering process Methods 0.000 description 23
- 230000006399 behavior Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000000007 visual effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Educational Technology (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses an online course inspection method, a system, equipment and a medium, wherein the online course inspection method comprises the following steps: s1, collecting data models of course objects of various online live broadcasting platforms, setting inspection checkpoints adapted to the online live broadcasting platforms one by one corresponding to the data models of the various online live broadcasting platforms, and accessing live broadcasting pages of the online live broadcasting platforms after the inspection checkpoints; s2, selecting a patrol mode associated with the course object; s3, determining a patrol target and a corresponding patrol conclusion template based on the patrol mode, executing system patrol based on the patrol conclusion template, and outputting a patrol conclusion; and S4, based on the inspection conclusion, counting online live courses in the system in a specified time period to obtain inspection statistical results in the specific time period. The application has the effect of being convenient for the teaching departments of schools to supervise the teaching conditions of online courses of various online live broadcast platforms.
Description
Technical Field
The application relates to the technical field of teaching management, in particular to an online course inspection method, an online course inspection system, online course inspection equipment and online course inspection media.
Background
With the continuous development of information technology, the way that teachers use online teaching systems or give lessons through online live broadcasting platforms is more and more common, and some schools even play as many as hundreds of live broadcasting lessons in one day;
In order to guarantee the quality of online teaching, the abnormal teaching condition is supervised, and the teaching department of the school needs to manage and supervise a large number of online live courses in time: such as live lesson management by supervising the live lessons by a patrolling member.
At present, the functions and technologies on various online live broadcast platforms have large differences, and a course inspection method capable of adapting to various online live broadcast platforms does not exist in the market; under the condition that different live broadcast platforms are adopted by each sub-school and sub-school within the school, the teaching department of the school is difficult to effectively manage and monitor each large net class live broadcast platform of each sub-school and sub-school, so that the quality problem of a large number of online live broadcast courses is easy to generate.
Therefore, the teaching department of the school is inconvenient to supervise the teaching situation of online courses of various online live broadcasting platforms.
Disclosure of Invention
In order to facilitate the teaching departments of schools to supervise the teaching situations of online courses of various online live broadcasting platforms, the application provides an online course inspection method, an online course inspection system, online course inspection equipment and a medium.
The first technical scheme adopted by the application is as follows:
an online course inspection method, comprising the steps of:
S1, collecting data models of course objects of various online live broadcasting platforms, setting inspection checkpoints adapted to the online live broadcasting platforms one by one corresponding to the data models of the various online live broadcasting platforms, and accessing live broadcasting pages of the online live broadcasting platforms after the inspection checkpoints;
s2, selecting a patrol mode associated with the course object;
S3, determining a patrol target and a corresponding patrol conclusion template based on the patrol mode, executing system patrol based on the patrol conclusion template, and outputting a patrol conclusion;
And S4, based on the inspection conclusion, counting online live courses in the system in a specified time period to obtain inspection statistical results in the specific time period.
By adopting the technical scheme, in the actual application process, the data model fields of different courses of different live broadcasting platforms may have differences, for example, a course object with authority control must provide course names and account information when logging in the online live broadcasting platform, and the public course information can only provide basic information such as online addresses, course names and the like; checkpoints based on a course platform means that the system can smoothly access a final playing page of an online course object after all inspection checkpoints pass through by automatically operating preset checkpoints (namely simulating keyboard operation and adopting a robot flow automation technology, PRA (simple PRA) technology); the automatic inspection template comprises any one or more of the following: black screen early warning, continuous same picture early warning, white screen early warning and unmanned image early warning, wherein the unmanned image early warning is suitable for online video teaching courses; the inspection conclusion is a course teaching condition judgment conclusion obtained after the system inspects according to the selected inspection mode, and the course teaching condition judgment conclusion comprises the following steps: normal, abnormal and unknown; the patrol personnel can obtain conclusions of the total teaching situation of courses and the teaching situation of appointed courses in a preset time period such as the previous day or the last month based on the patrol statistics automatically performed by the system; according to the technical scheme, by setting up inspection points on various live broadcast platforms, the robot flow automation technology is combined to simulate the role behaviors of the inspector, data of online live broadcast class in the course of the course are automatically collected on various live broadcast platforms, course conclusions are made after the course objects are analyzed according to the selected inspection conclusion templates, and the analysis conclusions of all courses can be counted within a specified time interval according to actual settings, so that the inspector is helped to automatically generate online live broadcast course inspection analysis reports, and teaching departments of schools can conveniently monitor the teaching conditions of online courses of various online live broadcast platforms.
The present application is in a preferred example: the step S1 comprises the following steps:
S11: acquiring characteristic description fields of data models of course objects of various online live broadcasting platforms to obtain course description information of the course objects;
S12: setting inspection checkpoints adapting to different online live broadcast platforms based on course description information of the course objects, wherein each online live broadcast platform is respectively provided with a plurality of inspection checkpoints;
S13: and locating and searching the inspection points by the data model of the course object, and sequentially passing through a plurality of inspection points one by one from the first inspection point, thereby accessing the course live page of the online live broadcast platform.
By adopting the technical scheme, the data model of the course object refers to a data structure tree formed by course description information such as the course date, the course time period, the course teachers, the online websites, the authorized accounts and the like; because the data model fields of different courses of various online live broadcast platforms may have differences, the inspection information and the corresponding inspection approaches of different online live broadcast platforms may have differences, and the subsequent automatic operation (simulated keyboard operation) on the preset inspection points can be facilitated by setting the inspection points adapted to various online live broadcast platforms, because the search login modes of different online live broadcast platforms are different, namely, the complexity of the login approaches is different: if the platform requiring account number and password login is used, checking points comprise each input box of a login page and a searching and positioning path of a submitting box; in a platform needing to search course names, the inspection points comprise a search box and a search positioning path of a search confirmation box, such as a mode that an xpath path sequentially searches all inspection points of unified course objects of the current online live broadcasting platform; the inspection system can obtain the capability of searching inspection points in the data structure tree of the online course page in a positioning and searching mode of the data model of the course object; the method comprises the steps that after different inspection points are configured in an inspection system engine, all inspection points of the same course object of the current online live platform are sequentially found, so that the inspection system can smoothly access online course pages, and the inspection system realizes the function of simulating the inspection work after the role of an inspector logs in the online live platform.
The present application is in a preferred example: the step S2 comprises the following steps:
S21: receiving inspection task parameters input by a foreground page of a system, wherein the inspection task parameters comprise inspection time and inspection frequency;
S22: selecting a tour inspection mode associated with the course object and reserving a file for storage; wherein the inspection mode includes: screen capturing, screen recording and recording.
By adopting the technical scheme, a user can input the inspection task parameters through a system foreground page, define the inspection starting time, the inspection frequency and the inspection ending time of the system for inspection, and then select one or more inspection modes of screen capturing, screen recording and recording for combination; if the inspection is performed at the course starting time of the course object or is performed after 5 minutes after the course starts, and/or the inspection is finished at the end of the course, the inspection can be performed at intervals of 10 minutes during the course, and the inspection result is saved, so that the variety of inspection modes is enriched, and the user experience is improved.
The present application is in a preferred example: the step S3 comprises the following steps:
s31: acquiring inspection conclusion template selection parameters input by a system foreground page, and determining an inspection target and a corresponding inspection conclusion template, wherein the inspection conclusion template comprises: an automatic inspection template and a manual inspection template;
S32: based on the determined inspection conclusion template, performing system inspection, and collecting course inspection information in the inspection process for file reservation and storage;
S33: and analyzing and comparing a plurality of course inspection information of the same course to obtain inspection analysis information, and outputting an abnormal early warning signal by the system if the inspection analysis information is larger than a preset threshold value.
By adopting the technical scheme, the inspection template comprises any one or more of the following: black screen early warning, continuous same picture early warning, white screen early warning and unmanned image early warning; for example: when a black screen early warning inspection template is set, the inspection system acquires screen capturing pictures at intervals of the whole online course time period, and the inspection system automatically judges whether N times of inspection are black screens (N can be defined in the process) by using an automatic analysis technology and an intelligent recognition analysis technology, and sends out a black screen early warning signal; the unmanned image early warning is suitable for an online video teaching mode, and when the unmanned image early warning template is set, the unmanned image early warning signal is automatically judged if no unmanned image appears on the N inspection pictures (N can be defined in the process). The manual inspection template does not make automatic judgment any more, and an inspector checks the inspection result and then manually marks whether the lesson condition of the online lesson is abnormal; by simulating the behavior of the character of the patrolling person, the application automatically collects the data in the class period on each large live broadcasting platform by utilizing the RPA technology and the automatic analysis technology, automatically analyzes the teaching data by combining with the visual and auditory intelligent recognition analysis technology, helps the patrolling person automatically generate the online live broadcasting class inspection analysis report, and realizes the supervision effect of online courses.
The present application is in a preferred example: before step S4, the online course inspection method further includes:
s401: acquiring parameters of a system execution inspection plan, and executing the inspection plan at regular time;
s402: obtaining a patrol conclusion of a patrol plan based on the patrol conclusion template;
s403: if the manual review selection parameter input by the system foreground page is received, the step S402 is repeatedly executed, and the manually input review result information is acquired for file retention and storage.
By adopting the technical scheme, the inspection engine of the system periodically starts an inspection plan based on the set data model (class time period and the like) of the course object and an inspection point, and captures the class screen, screenshot picture and record of the corresponding course object; the system gives out the judgment of the teaching situation of the preliminary course based on the inspection conclusion template, and the judgment result comprises: normal, abnormal, unknown; after the program gives a judging conclusion of the teaching condition of the course, the patrol inspector can review the patrol inspection result; when the patrol inspector reviews the patrol result, a complete on-line course inspection flow is as follows: and (3) performing system inspection, making a conclusion by the system, manually checking, and obtaining manual checking result information by the system for reservation and storage.
The present application is in a preferred example: the acquisition adaptation module comprises:
step S22 further includes:
s221: analyzing the course live page of the online live platform to obtain a corresponding course teaching mode, wherein the course teaching mode comprises the following steps: on-line video teaching and on-line voice teaching;
s222: based on the course teaching mode, selecting a tour inspection mode associated with a course object and reserving files for storage, wherein the tour inspection mode comprises the following steps: screen capturing, screen recording and recording.
By adopting the technical scheme, the teaching mode of the course object can be generally divided into an online video teaching mode, namely, a teacher portrait teaching mode and an online voice teaching mode (namely, a mode that the teacher displays courseware and carries out voice explanation), or a teaching mode combining the two modes; when the teaching mode of the course object is online voice teaching, the inspection mode of screen capturing and recording is not applicable, and the inspection mode of recording can be applicable at the moment; when the teaching mode of the course object is online video teaching, the method is suitable for one or more teaching modes of screen capturing, screen recording and recording.
The second object of the application is realized by the following technical scheme:
An online course inspection system, comprising:
And an acquisition adaptation module: the online live broadcast platform comprises a course object data model, a course live broadcast page and a course live broadcast page, wherein the course object data model is used for acquiring course objects of various online live broadcast platforms, and inspection checkpoints which are adapted to the various online live broadcast platforms are correspondingly arranged one by one corresponding to the data models of the various online live broadcast platforms, and the course live broadcast page of the online live broadcast platform can be accessed after the inspection checkpoints are passed;
Selecting a patrol mode module: the method comprises the steps of selecting a patrol mode associated with a course object;
Executing a patrol module: the system inspection method comprises the steps of determining an inspection target and a corresponding inspection conclusion template based on the inspection mode, executing system inspection based on the inspection conclusion template, and outputting an inspection conclusion;
a specified time statistics module: and the system is used for counting online live courses in the system in a specified time period based on the inspection conclusion to obtain inspection statistical results in the specific time period.
By adopting the technical scheme, the acquisition adaptation module is used for acquiring the course description information of the course object and adapting the corresponding inspection point, so that the inspection system can smoothly access different types of online live broadcast platforms; the selected inspection mode module is used for selecting an inspection mode to be one or more of screen capturing, screen recording and recording; after the inspection module is executed to inspect according to the selected inspection mode, an inspection conclusion is made according to the determined inspection conclusion template, and the specified time statistics module can count inspection results of course objects in the system in a specified time period, so that teaching departments of schools can conveniently monitor teaching conditions of online courses of various online live broadcast platforms, and the time and the monitoring workload for the teaching departments of schools to monitor the authorization conditions of the online courses are reduced.
The third object of the application is realized by the following technical scheme:
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above-described online course inspection method when the computer program is executed.
The fourth object of the application is realized by the following technical scheme:
a computer readable storage medium storing a computer program which when executed by a processor performs the steps of the online course inspection method described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. By setting the inspection points matched with various different online live broadcast platforms, the automatic operation (simulated keyboard operation) of the preset inspection points can be facilitated, namely, after different inspection points are configured in an inspection system engine, the inspection system can smoothly access online course pages by combining a mode of locating data models of course objects to search all inspection points of the current online live broadcast platform in sequence, so that the inspection system realizes the function of simulating the role of an inspector to log in the online live broadcast platform and then carry out inspection;
2. The user can input the parameters of the inspection task through the front page of the system, define the inspection starting time, the inspection frequency and the inspection ending time of the inspection of the system, select one of the inspection modes of screen capturing, screen recording and recording, or combine the three inspection modes, and the like; if the inspection is performed at the course starting time of the course object or 5 minutes after the course starts, and/or the inspection is finished at the course end, the inspection can be further defined to be performed once every 10 minutes during the course, and the inspection result is saved by leaving files, so that the variety of inspection modes is enriched, and the use experience of a user is improved;
3. According to the application, by simulating the behavior of the character of the patrolling member, the RPA technology and the automatic analysis technology are utilized to automatically collect data in the classroom period on each large live broadcasting platform, and the visual and auditory intelligent recognition analysis technology is combined to automatically analyze the lesson data, so that the patrolling member is helped to automatically generate a lesson condition analysis report of the online live broadcasting lesson, and the supervision effect of the online lesson is realized;
4. the data models of course objects of different online live broadcasting platforms can be different, a plurality of inspection points can be conveniently adapted by collecting course description fields of a plurality of course objects, and then the capability of the inspection points is found in a data structure tree of online course pages through an xpath path, so that the inspection system can smoothly access the online course pages of a plurality of different online live broadcasting platforms.
Drawings
FIG. 1 is a flow chart of an online course inspection method according to an embodiment of the application;
FIG. 2 is a flowchart of step S1 in the on-line course inspection method according to an embodiment of the present application;
FIG. 3 is a flowchart of step S2 in the on-line course inspection method according to an embodiment of the present application;
FIG. 4 is a flowchart of step S22 in the on-line course inspection method according to an embodiment of the present application;
FIG. 5 is a flowchart of step S3 of the on-line course inspection method according to an embodiment of the present application;
FIG. 6 is a flowchart before step S4 of the on-line course inspection method according to an embodiment of the present application;
FIG. 7 is a schematic block diagram of an online course inspection system in accordance with an embodiment of the application;
Fig. 8 is a schematic diagram of an apparatus in an embodiment of the application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
In one embodiment, as shown in fig. 1, the application discloses an online course inspection method, which specifically comprises the following steps:
S1: the method comprises the steps of collecting data models of course objects of various online live broadcasting platforms, setting inspection checkpoints adapted to the online live broadcasting platforms one by one corresponding to the data models of the various online live broadcasting platforms, and accessing course live broadcasting pages of the online live broadcasting platforms after the inspection checkpoints.
In this embodiment, the data model of the course object is a data structure tree composed of a plurality of course description information such as a course name, a course address, a course teacher, and the like; the data models of the course objects of different online live platforms are different.
Specifically, the data model fields of different courses of different live broadcasting platforms may have differences, for example, a course object with authority control must provide course name and account information when logging in an online live broadcasting platform, and the public course information may only provide basic information such as online address and course name; therefore, when logging in the live course page before the course object is patrolled, the course object with authority control must provide account information, and key information of the public course information can only provide online addresses and basic course information, so that the patrol check point matched with the online live course platform is required to be set, and the live course page of the online live course platform can be smoothly accessed.
Further, the tour inspection system can smoothly access the online course live page of the online course after passing through a preset tour inspection point.
S2: and selecting a tour inspection mode associated with the course object.
In this embodiment, the inspection mode is screen capturing, screen recording and recording.
Specifically, in the lesson time period of the lesson object, after passing through the inspection point, the inspection mode of screen capturing can save the lesson picture at the current moment in the form of screen capturing, and the interval time of screen capturing inspection can be customized, for example, screen capturing is defined once every 10 minutes during lesson for inspection; the inspection mode of the screen recording can record the lesson pictures in the whole course in the lesson time period of the lesson object; the inspection mode of the recording records the sound of the lesson object in a certain time period, such as whether the whole course is silent in 10 minutes, whether the recording has long-time silence and excessive noise.
Further, the inspection mode can be one or more selected, that is, one inspection mode of screen capturing, screen recording and recording is selected singly, or recording can be performed while screen capturing or recording is performed while screen recording is performed, etc.
S3: based on the inspection mode, an inspection target and a corresponding inspection conclusion template are determined, system inspection is performed based on the inspection conclusion template, and an inspection conclusion is output.
In this embodiment, the inspection conclusion template includes an automatic inspection template and a manual inspection template, and the inspection conclusion template includes inspection information such as a black screen, continuous identical pictures, a white screen, an unmanned image, long-time silence, excessive noise, and the like; the inspection conclusion comprises: normal, abnormal and unknown.
Specifically, the inspection mode of the automatic inspection template outputs an inspection result after the system inspection is executed, and the inspection result can be automatically judged and an abnormal early warning signal is output; after the system inspection is executed, the inspection mode of the manual inspection template does not automatically judge any more, and the inspector checks the inspection result and then manually marks whether the class situation of the live-broadcast course on the line is abnormal or not.
Further, the inspection conclusion template may be pre-selected by the inspector through the system foreground page.
S4: based on the inspection conclusion, counting online live courses in the system in a specified time period to obtain inspection statistical results in the specific time period.
In this embodiment, the inspection statistics is to count inspection results of course objects of multiple online live broadcast platforms within the same time period.
Specifically, the starting time and the ending time of the patrol statistics can be set by a patrol inspector, the patrol inspector can select and count the number of courses which are judged to be in class abnormality in the previous day, and an intuitive list and a detail viewing page are provided; or statistically checking the number of courses for anomalies for one month. Based on the statistics, the inspector obtains the inspection conclusion of the teaching situation of the course population and the teaching situation of the specific course in the specific time.
Further, a selection strategy for controlling the inspector, such as the permission of the inspector to view and inspect different platform courses for different school, can be selected.
In an embodiment, as shown in fig. 2, in step S1, data models of course objects of multiple different online live broadcast platforms are collected, inspection checkpoints adapted to different online live broadcast platforms are set in one-to-one correspondence with the data models of multiple different course objects, and after passing through the inspection checkpoints, a live course page of the online live broadcast platform can be accessed, which specifically includes:
S11: and acquiring characteristic description fields of the data models of the course objects of the various online live broadcasting platforms to obtain course description information of the course objects.
In this embodiment, course description information refers to feature description fields such as a lesson date, a lesson time period, an online address, an authorized account, a lesson teacher, etc.; because the same course of the same teacher can give repeated lessons to different classes at different times and/or the different teachers give complicated lessons to different classes at the same time and the same course, when describing the course, the plurality of course description information needs to be collected, namely, a single course object is split into a plurality of course description information, so that the plurality of course description information corresponds to a certain appointed course object, thereby being convenient for managing and inspecting a huge number of course objects and improving the system inspection accuracy.
Furthermore, the inspection method also provides an online course data batch importing function based on the form, and after the inspection personnel imports the form into the inspection system in batch, the inspection system automatically collects course description information of course objects, such as the type of online live broadcasting platform, course time, course website, teaching teacher and other course description information.
S12: based on course description information of course objects, setting inspection checkpoints adapting to data models of different course objects, wherein the data model of each course object is respectively provided with a plurality of inspection checkpoints.
In this embodiment, after the inspection point is set, the inspection system may perform automatic operation, that is, simulated keyboard operation, in combination with a robot flow automation technology (PRA technology), on the prefabricated inspection point, so that the inspection system may simulate the role of an inspector to log into a live broadcast page of a course object; because course description information of different courses and different live broadcasting platforms are different, the mode of matching the course description information with the inspection points one by one is beneficial to improving the matching rate of the inspection points and the course objects, and therefore, a plurality of inspection points are correspondingly adapted to a single course object, for example, in a platform requiring account password login, the inspection points comprise a login page, an account number, a password, a course name and other course information input frame and a submission frame.
S13: by means of locating and searching the inspection points on the data model of the course object, the first inspection point sequentially passes through a plurality of inspection points one by one, and therefore course live broadcast pages of the online live broadcast platform are accessed.
In this embodiment, the positioning and searching method for the data model of the course object is a path positioning method for positioning and searching elements of a data structure tree formed by the data model of the course object, such as an xpath path, UIAutomator positioning and name identification positioning method; in this embodiment, an xpath path is used to find inspection points, where the xpath path refers to a manner that an xpath language uses a path expression to select nodes, and the nodes sequentially pass through a plurality of inspection points one by one, and are selected along the path; the xpath path is suitable for positioning more page elements, can adopt any attribute to position the elements, and has strong universality and uniqueness.
Specifically, each course object corresponds to a data structure tree composed of inspection points corresponding to a plurality of course description information, so that the inspection system obtains the capability of searching the inspection points in the data structure tree of the online course page based on the xpath path; for example, on an online live broadcast platform requiring account number and password login, the routing inspection mode comprises an xpath path of each input box and a submitting box of a login page; and on-line live broadcasting platform needing to search course names, wherein the routing inspection mode comprises a search box and an xpath path of a search confirmation box. After different inspection points are configured in the inspection engine, the engine can smoothly access to online course pages and adapt to online course inspection of different online live broadcast platforms.
In one embodiment, as shown in fig. 3, in step S2, a patrol method associated with the course object is selected, which specifically includes:
S21: receiving inspection task parameters input by a foreground page of a system, wherein the inspection task parameters comprise inspection time and inspection frequency;
in this embodiment, the patrol task parameters include patrol start time and patrol end time, where the patrol frequency includes patrol times of the same course object and duration of single patrol, such as performing screen capturing every 5 minutes, recording every 5 minutes for 5 minutes, and the like in the course time.
Specifically, the patrol inspector can input patrol task parameters in a system foreground page in advance before the start of patrol, and the patrol inspection system obtains the patrol task parameters of the patrol inspection system after smoothly accessing a live course page of a course object, so that the patrol inspection system performs patrol inspection according to the parameters set by the patrol inspector, and the patrol inspection mode is richer.
Further, inspection parameters of the inspection task can be customized by an inspector, such as one inspection per 5 minutes or 10 minutes during a lesson, and duration of a single inspection can be customized, so that user experience is improved.
S22: selecting a tour inspection mode associated with the course object and reserving a file for storage; the inspection mode comprises the following steps: screen capturing, screen recording and recording.
In this embodiment, screen capturing refers to saving a lesson picture at the current moment in a screenshot form; the screen recording means that the lesson-taking time period of the lesson-taking date of the lesson object is recorded and the lesson-taking picture is stored; the recording means that the sound signal of the lesson time period of the lesson date of the lesson object is stored.
Specifically, the inspection mode of the same course object can be one or more of screen capturing, screen recording and recording, such as inspection at the start time of the course object or 5 minutes after the start of the course, and/or inspection ending at the end of the course, etc., and the inspection mode can also be defined that inspection is performed once every 10 minutes during the course and the inspection result is saved in a file, which is beneficial to the system to find whether abnormal conditions such as soundless, black and white screen, etc. exist in the inspection process. The inspection system starts inspection at regular time in the class time period of the class date of the class object, smoothly accesses the online live page of the class object after finishing inspection, and can save the class picture at the current moment in the forms of screenshot, screen recording and the like.
In one embodiment, as shown in fig. 4, step S22, namely selecting the inspection mode associated with the course object, specifically includes:
s221: analyzing the live course page of the live course platform to obtain a corresponding course teaching mode, wherein the course teaching mode comprises the following steps: online video lectures and online voice lectures.
In this embodiment, the course teaching mode includes online video teaching, online voice teaching, or a combination of two teaching modes.
Specifically, the teaching modes of the lesson objects can be generally classified into online video teaching, i.e., image teaching of a teacher, and online voice teaching (i.e., a mode that the teacher displays courseware and performs voice explanation), or a combined teaching mode of the two.
S222: based on course teaching mode, selecting a tour inspection mode associated with a course object and reserving files for storage, wherein the tour inspection mode comprises the following steps: screen capturing, screen recording and recording.
In this embodiment, when the teaching mode of the course object is online voice teaching, the inspection mode of screen capturing and recording is not applicable, and at this time, the inspection mode of recording is applicable; when the teaching mode of the course object is online video teaching, the method is suitable for one or more teaching modes of screen capturing, screen recording and recording; the corresponding inspection mode is matched through the course teaching mode, so that the data retention and the analysis calculated amount of the inspection system can be reduced.
In one embodiment, as shown in fig. 5, in step S3, that is, based on the inspection mode, an inspection conclusion template corresponding to the inspection target inspection is determined, and system inspection is performed, and the system makes an inspection conclusion, which specifically includes:
S31: obtaining inspection conclusion template selection parameters input by a foreground page of a system, and determining an inspection conclusion template corresponding to inspection target inspection, wherein the inspection conclusion template comprises: automatic inspection templates and manual inspection templates.
In this embodiment, the inspection template includes any one or more of the following: black screen early warning, continuous same picture early warning, white screen early warning, unmanned image early warning, excessive noise early warning, long-time silent early warning and the like; the automatic inspection template automatically judges inspection information and then sends out an early warning signal, and if the screen capturing course object images continuously appear in black screen, the black screen early warning is output; and the manual inspection template does not make automatic judgment, the inspection result is directly output, and the inspector checks the inspection result and then manually marks whether the class situation of the online course is abnormal.
Specifically, a patrol inspector inputs patrol conclusion template selection parameters through a system foreground page to determine a patrol conclusion template of a patrol inspection system, and can select a plurality of patrol inspection modes such as a patrol inspection black screen, continuous identical pictures, a white screen and the like to carry out patrol inspection on the same course object at the same time; if the inspection conclusion template selection parameters input by the foreground page of the system are not obtained, the system automatically selects an automatic inspection template after reaching the preset inspection time.
S32: based on the determined inspection conclusion template, performing system inspection, and collecting course inspection information in the inspection process for reservation and storage.
Specifically, after determining the patrol conclusion template, the system automatically collects data during the class on each large live broadcast platform by simulating the behavior of the character of the patrol class man and utilizing the RPA technology and the automatic analysis technology, and performs system patrol on the course object by combining with the visual and auditory intelligent recognition analysis technology.
S33: and analyzing and comparing the inspection information of a plurality of courses of the same course to obtain inspection analysis information, and outputting an abnormal early warning signal by the system if the inspection analysis information is larger than a preset threshold value.
In the embodiment, an automatic inspection template system automatically judges the abnormal condition of the course object and makes an inspection conclusion; the manual inspection template system directly outputs inspection results; the inspection result is inspection information stored in the modes of screen capturing, screen recording and recording in the inspection process, and the inspection conclusion comprises: normal, abnormal and unknown.
Specifically, when the inspection system is provided with a black screen early warning inspection template, after 5-minute interval screen capturing pictures of the whole online course time period are obtained, automatically judging that if N times of inspection are black screens (N can be defined in the process), if more than 2 times of screen capturing of course objects are black screens, sending a black screen early warning signal, and if the automatic inspection conclusion template outputs an inspection conclusion as abnormal, otherwise, the inspection conclusion is normal; or the number of times that the manual inspection conclusion template outputs the black screen early warning signal does not make inspection conclusion, and the inspector manually makes normal or abnormal inspection conclusion; if no portrait appears on the inspection picture for N times (N can be defined in the self-defining way), if no portrait exists in the continuous multi-frame screen capturing or no portrait exists in the 5-minute video recording, a portrait-free warning signal is sent out; when the inspector only inspects one or a few of course objects, the inspection conclusion of the selected piece mode is abnormal or normal, and the inspection conclusion of other unselected inspection modes is unknown; if only the course object is subjected to black screen and white screen inspection, the inspection conclusion display of unmanned image inspection and excessive noise inspection is unknown.
When the manual inspection conclusion template is selected, the option conclusion of the online course is marked as a waiting mark before the inspection conclusion is marked manually.
Further, the value of N can be defined by the inspector, namely, a preset threshold signal can be set by the inspector, so that the requirements of different users on different definition standards of on-line course quality are met; therefore, the application automatically collects the data in the class period on each big live broadcasting platform by simulating the behavior of the character of the patrolling member and utilizing the RPA technology and the automatic analysis technology, and automatically analyzes the teaching data by combining with the visual and auditory intelligent recognition analysis technology, thereby helping the patrolling member automatically generate the teaching condition analysis report of the live broadcasting class on line and being convenient for realizing the supervision effect of the on-line class.
In one embodiment, after step S33, the online course inspection method further includes:
S34: acquiring face information and sound information of a teacher corresponding to a course object, and acquiring and comparing the face information and/or the sound information of the teacher end of the course object in real time in the process of executing system inspection; if the face information and/or the sound information are not matched, the system outputs an abnormal early warning signal of abnormal teaching.
In this embodiment, when there is a phenomenon that the teacher end plays video of giving lessons and audio of giving lessons recorded by others in the whole course or for a long time to give lessons, the lesson object is still giving lessons but there is a mismatch between the lesson objects, so that the system is difficult to judge and judge the abnormal lesson behavior; at the moment, the face information of the teacher end is compared in real time by adopting a face recognition technology, and/or the voice information of the teacher is compared by adopting a voiceprint recognition matching technology, so that whether the teacher gives lessons normally or not is judged; when the face information and/or the sound information are not matched, the teaching of the current course object is abnormal, and the system outputs an unmanned abnormal early warning signal.
Specifically, the abnormal early warning signal comprises an early warning of mismatching of the teaching object; the tour inspection system collects face information and sound information of a teacher corresponding to the course object in advance and keeps a file; when the teacher end plays video teaching videos and/or audio recording teaching audios of other people in the whole course or for a long time to give lessons, the patrol system collects face information of the teacher end in real time and performs face recognition comparison, when the face recognition information is not matched, if the face information collected in advance is continuously present within 10 minutes and the face information of the teacher end collected in real time is not matched, an early warning prompt that the teaching object is not matched is triggered; and/or the patrol system collects the voice information of the teacher end in real time and compares the voice print information, when the voice information is not matched, if the voice information collected in advance continuously exists within 10 minutes and the voice information of the teacher end collected in real time is not matched, an early warning prompt of mismatch of the teaching object is triggered; therefore, the practicability of abnormal early warning of the inspection system is improved.
S35: if the teaching mode of the course object is online voice teaching, closing the inspection mode of screen capturing and recording.
In this embodiment, when the teaching mode of the lesson object is online voice teaching, no portrait exists in the teaching process, so that the inspection analysis information of the portrait-free early warning easily triggers the abnormal early warning signal of the portrait-free, and the portrait-free false early warning rate is high.
Specifically, because no portrait is generated in the online voice teaching process, the teaching mode is not suitable for the inspection mode of screen capturing and recording, and the alarm prompt of the unmanned abnormal signal is closed at the moment, so that the false early warning rate of unmanned early warning information is reduced, and the abnormal early warning accuracy of the inspection system is improved.
In one embodiment, as shown in fig. 6, before step S4, the online course inspection method further includes:
S401: the acquisition system executes the inspection plan parameters and periodically executes the inspection plan.
Specifically, the inspector can customize the inspection plan parameters of the system inspection by executing the inspection plan parameters input by the system foreground page, periodically start the inspection plan, and grasp the screen of the lesson, the screenshot picture and the sound recording of the corresponding lesson object.
S402: and obtaining the inspection conclusion of the inspection plan based on the inspection conclusion template.
Specifically, the system gives a preliminary course teaching condition judgment based on the inspection plan and the inspection conclusion template.
S403: if the manual review selection parameter input by the system foreground page is received, the step S402 is repeatedly executed, and the manually input review result information is acquired for file retention and storage.
Specifically, after the program gives a course inspection judgment conclusion, inspection personnel can also review the result; a complete on-line course inspection flow is as follows: executing system inspection, system conclusion-manual rechecking; when the inspection conclusion is required to be checked manually, the manual check result is kept in a file.
It should be understood that the sequence number of each step in the above embodiment does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not be construed as limiting the implementation process of the embodiment of the present application.
In one embodiment, an online course inspection system is provided, which corresponds to the online course inspection method in the above embodiment.
As shown in fig. 7, an online course inspection system includes an acquisition adaptation module 10, a selected inspection mode module 20, an execution inspection module 30, and a specified time statistics module 40. The detailed description of each functional module is as follows:
The acquisition adaptation module 10 is used for acquiring data models of course objects of various online live broadcast platforms, setting inspection checkpoints adapted to the various online live broadcast platforms in a one-to-one correspondence manner corresponding to the various online live broadcast platforms, and accessing course live broadcast pages of the online live broadcast platforms after passing the inspection checkpoints;
Selecting a patrol mode module 20: the method comprises the steps of selecting a patrol mode associated with a course object;
executing the inspection module 30: the system comprises a system inspection module, a system inspection module and a system inspection module, wherein the system inspection module is used for determining an inspection conclusion template corresponding to inspection of an inspection target based on an inspection mode and outputting an inspection conclusion;
The specified time statistics module 40 is configured to, based on the inspection conclusion, perform statistics on online live courses in the system in a specified time period, so as to obtain inspection statistics in a specific time period.
For specific limitations on the online course inspection system, reference may be made to the above limitation on the online course inspection method, and no further description is given here; all or part of the modules in the online course inspection system can be realized by software, hardware and a combination thereof; the above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing course patrol information of the course object. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements an online course patrol method.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
S1, collecting data models of course objects of various online live broadcasting platforms, setting inspection checkpoints adapted to the various online live broadcasting platforms in a one-to-one correspondence manner corresponding to the data models of the various online live broadcasting platforms, and accessing course live broadcasting pages of the online live broadcasting platforms after the inspection checkpoints;
s2, selecting a patrol mode associated with the course object;
S3, based on the inspection mode, determining an inspection target and a corresponding inspection conclusion template, performing system inspection based on the inspection conclusion template, and outputting an inspection conclusion;
And S4, based on the inspection conclusion, counting online live courses in the system in a specified time period to obtain inspection statistical results in the specific time period.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
S1, collecting data models of course objects of various online live broadcasting platforms, setting inspection checkpoints adapted to the various online live broadcasting platforms in a one-to-one correspondence manner corresponding to the data models of the various online live broadcasting platforms, and accessing course live broadcasting pages of the online live broadcasting platforms after the inspection checkpoints;
s2, selecting a patrol mode associated with the course object;
S3, based on the inspection mode, determining an inspection target and a corresponding inspection conclusion template, performing system inspection based on the inspection conclusion template, and outputting an inspection conclusion;
And S4, based on the inspection conclusion, counting online live courses in the system in a specified time period to obtain inspection statistical results in the specific time period.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme described in the foregoing embodiments can be modified or some of the features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (7)
1. An online course inspection method is characterized in that: the online course inspection method comprises the following steps:
s1, collecting data models of course objects of various online live broadcasting platforms, setting inspection checkpoints adapted to the online live broadcasting platforms one by one corresponding to the data models of the various online live broadcasting platforms, and accessing live broadcasting pages of the online live broadcasting platforms after the inspection checkpoints; the data model of the course object is a data structure tree composed of a plurality of course information; the inspection checkpoint includes a plurality of different course information;
s2, selecting a patrol mode associated with the course object;
S3, determining a patrol target and a corresponding patrol conclusion template based on the patrol mode, executing system patrol based on the patrol conclusion template, and outputting a patrol conclusion; the inspection conclusion template comprises an automatic inspection template and a manual inspection template;
s4, based on the inspection conclusion, counting online live courses in the system in a specified time period to obtain inspection statistical results in the specified time period;
Wherein, step S1 includes:
S11: acquiring characteristic description fields of data models of course objects of various online live broadcasting platforms to obtain course description information of the course objects;
S12: setting inspection checkpoints adapting to data models of different course objects based on course description information of the course objects, wherein each data model of the course objects is respectively provided with a plurality of inspection checkpoints;
s13: the method comprises the steps of locating and searching the inspection points by means of a data model of a course object, sequentially passing through a plurality of inspection points one by one from the first inspection point, and accessing a course live page of an online live broadcast platform;
before step S4, the online course inspection method further includes:
s401: acquiring parameters of a system execution inspection plan, and executing the inspection plan at regular time;
s402: obtaining a patrol conclusion of a patrol plan based on the patrol conclusion template;
s403: if the manual review selection parameter input by the system foreground page is received, the step S402 is repeatedly executed, and the manually input review result information is acquired for file retention and storage.
2. The online course inspection method of claim 1, wherein: the step S2 comprises the following steps:
S21: receiving inspection task parameters input by a foreground page of a system, wherein the inspection task parameters comprise inspection time and inspection frequency;
S22: selecting a tour inspection mode associated with the course object and reserving a file for storage; wherein the inspection mode includes: screen capturing, screen recording and recording.
3. The online course inspection method of claim 1, wherein: the step S3 comprises the following steps:
s31: acquiring inspection conclusion template selection parameters input by a system foreground page, and determining an inspection target and a corresponding inspection conclusion template, wherein the inspection conclusion template comprises: an automatic inspection template and a manual inspection template;
S32: based on the determined inspection conclusion template, performing system inspection, and collecting course inspection information in the inspection process for file reservation and storage;
S33: and analyzing and comparing a plurality of course inspection information of the same course to obtain inspection analysis information, and outputting an abnormal early warning signal by the system if the inspection analysis information is larger than a preset threshold value.
4. The online course inspection method of claim 2, wherein: step S22 further includes:
s221: analyzing the course live page of the online live platform to obtain a corresponding course teaching mode, wherein the course teaching mode comprises the following steps: on-line video teaching and on-line voice teaching;
s222: based on the course teaching mode, selecting a tour inspection mode associated with a course object and reserving files for storage, wherein the tour inspection mode comprises the following steps: screen capturing, screen recording and recording.
5. An online course inspection system, which is applied to an online course inspection method as claimed in claim 1, wherein the system comprises:
And an acquisition adaptation module: the online live broadcast platform comprises a course object data model, a course live broadcast page and a course live broadcast page, wherein the course object data model is used for acquiring course objects of various online live broadcast platforms, and inspection checkpoints which are adapted to the various online live broadcast platforms are correspondingly arranged one by one corresponding to the data models of the various online live broadcast platforms, and the course live broadcast page of the online live broadcast platform can be accessed after the inspection checkpoints are passed; the data model of the course object is a data structure tree composed of a plurality of course information; the inspection checkpoint includes a plurality of different course information;
Selecting a patrol mode module: the method comprises the steps of selecting a patrol mode associated with a course object;
Executing a patrol module: the system inspection method comprises the steps of determining an inspection target and a corresponding inspection conclusion template based on the inspection mode, executing system inspection based on the inspection conclusion template, and outputting an inspection conclusion; the inspection conclusion template comprises an automatic inspection template and a manual inspection template;
a specified time statistics module: based on the inspection conclusion, counting online live courses in the system in a specified time period to obtain an inspection statistical result in the specific time period;
The acquisition adaptation module is specifically used for acquiring characteristic description fields of data models of course objects of various different online live broadcasting platforms to obtain course description information of the course objects;
setting inspection checkpoints adapting to data models of different course objects based on course description information of the course objects, wherein each data model of the course objects is respectively provided with a plurality of inspection checkpoints;
and locating and searching the inspection points by the data model of the course object, and sequentially passing through a plurality of inspection points one by one from the first inspection point, thereby accessing the course live page of the online live broadcast platform.
6. Computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps for an online course inspection method according to any of claims 1 to 4 when the computer program is executed.
7. A computer readable storage medium storing a computer program, which when executed by a processor implements the steps for an online course inspection method as claimed in any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310143565.4A CN116128453B (en) | 2023-02-18 | 2023-02-18 | Online course inspection method, system, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310143565.4A CN116128453B (en) | 2023-02-18 | 2023-02-18 | Online course inspection method, system, equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116128453A CN116128453A (en) | 2023-05-16 |
| CN116128453B true CN116128453B (en) | 2024-05-03 |
Family
ID=86299058
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310143565.4A Active CN116128453B (en) | 2023-02-18 | 2023-02-18 | Online course inspection method, system, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116128453B (en) |
Citations (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106358039A (en) * | 2016-09-07 | 2017-01-25 | 深圳Tcl数字技术有限公司 | Sound and image synchronization test method and device |
| CN107682719A (en) * | 2017-09-05 | 2018-02-09 | 广州数沃信息科技有限公司 | A kind of monitoring and assessing method and device of live content health degree |
| CN108154134A (en) * | 2018-01-11 | 2018-06-12 | 天格科技（杭州）有限公司 | Internet live streaming pornographic image detection method based on depth convolutional neural networks |
| CN108513298A (en) * | 2018-02-27 | 2018-09-07 | 大同煤矿集团有限责任公司 | A kind of method for inspecting |
| CN110365996A (en) * | 2019-07-25 | 2019-10-22 | 深圳市元征科技股份有限公司 | Management method, live streaming management platform, electronic equipment and storage medium is broadcast live |
| CN110830763A (en) * | 2019-09-25 | 2020-02-21 | 视联动力信息技术股份有限公司 | Monitoring video inspection method and device |
| CN110839162A (en) * | 2019-12-04 | 2020-02-25 | 湖南快乐阳光互动娱乐传媒有限公司 | Play state detection method and device |
| CN111553323A (en) * | 2020-05-22 | 2020-08-18 | 北京市商汤科技开发有限公司 | Video processing method and device, electronic equipment and storage medium |
| CN111627273A (en) * | 2020-06-01 | 2020-09-04 | 河南优观大数据科技有限公司 | Cloud live broadcast cloud recording and broadcasting method based on interconnection and intercommunication of Internet and IPTV television network |
| CN112055230A (en) * | 2020-09-03 | 2020-12-08 | 北京中润互联信息技术有限公司 | Live broadcast monitoring method and device, computer equipment and readable storage medium |
| CN112651866A (en) * | 2020-12-31 | 2021-04-13 | 重庆工业职业技术学院 | Classroom auxiliary teaching system and method |
| CN113470211A (en) * | 2021-08-12 | 2021-10-01 | 京东科技信息技术有限公司 | Routing inspection track management method and device based on beacon identification |
| CN113593069A (en) * | 2021-07-05 | 2021-11-02 | 北京瑞博众成科技有限公司 | Intelligent inspection system for mathematics twin |
| CN113781270A (en) * | 2021-07-30 | 2021-12-10 | 杭州迈杰教育科技有限公司 | Intelligent campus comprehensive management system and method |
| CN113965769A (en) * | 2021-10-19 | 2022-01-21 | 创盛视联数码科技（北京）有限公司 | Live broadcast system for online education |
| KR102365858B1 (en) * | 2020-11-02 | 2022-02-22 | 현대로템 주식회사 | Automated Inspection System for Railway Vehicle M.F. Output Broadcasting System |
| CN114187563A (en) * | 2021-12-13 | 2022-03-15 | 张家港聚智慧文化科技有限公司 | Online education course identification system and identification method |
| CN114219233A (en) * | 2021-11-27 | 2022-03-22 | 作业帮教育科技（北京）有限公司 | Evaluation method and evaluation device for live broadcast quality of live broadcast course system and storage medium |
| CN114845130A (en) * | 2022-04-26 | 2022-08-02 | 武汉景冠科技有限公司 | Method for intelligently examining self-media audio and video contents |
| CN114842567A (en) * | 2022-03-31 | 2022-08-02 | 浙江大华技术股份有限公司 | Video inspection method, device, equipment and medium |
| CN114979690A (en) * | 2022-05-18 | 2022-08-30 | 中国广电重庆网络股份有限公司 | Live channel signal control inspection method based on big data analysis |
| CN114998844A (en) * | 2022-08-08 | 2022-09-02 | 北京师范大学 | Online class patrol and supervision system and method based on AI classroom |
| CN115086746A (en) * | 2022-07-19 | 2022-09-20 | 北京微吼时代科技有限公司 | Video polling method for live system, live system and electronic equipment |
| CN115103215A (en) * | 2022-06-16 | 2022-09-23 | 招商银行股份有限公司 | Live broadcast quality inspection method, system, Web server and storage medium |
| CN115134617A (en) * | 2022-06-16 | 2022-09-30 | 招商银行股份有限公司 | Live broadcast quality inspection system, method, Web server and storage medium |
| CN115278276A (en) * | 2022-06-23 | 2022-11-01 | 麦苗(广东)云科技有限公司 | Remote online teaching live broadcast method and system based on 5G communication |
| TWI793025B (en) * | 2022-05-26 | 2023-02-11 | 艾樂維教育科技股份有限公司 | Live teaching system |
-
2023
- 2023-02-18 CN CN202310143565.4A patent/CN116128453B/en active Active
Patent Citations (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106358039A (en) * | 2016-09-07 | 2017-01-25 | 深圳Tcl数字技术有限公司 | Sound and image synchronization test method and device |
| CN107682719A (en) * | 2017-09-05 | 2018-02-09 | 广州数沃信息科技有限公司 | A kind of monitoring and assessing method and device of live content health degree |
| CN108154134A (en) * | 2018-01-11 | 2018-06-12 | 天格科技（杭州）有限公司 | Internet live streaming pornographic image detection method based on depth convolutional neural networks |
| CN108513298A (en) * | 2018-02-27 | 2018-09-07 | 大同煤矿集团有限责任公司 | A kind of method for inspecting |
| CN110365996A (en) * | 2019-07-25 | 2019-10-22 | 深圳市元征科技股份有限公司 | Management method, live streaming management platform, electronic equipment and storage medium is broadcast live |
| CN110830763A (en) * | 2019-09-25 | 2020-02-21 | 视联动力信息技术股份有限公司 | Monitoring video inspection method and device |
| CN110839162A (en) * | 2019-12-04 | 2020-02-25 | 湖南快乐阳光互动娱乐传媒有限公司 | Play state detection method and device |
| CN111553323A (en) * | 2020-05-22 | 2020-08-18 | 北京市商汤科技开发有限公司 | Video processing method and device, electronic equipment and storage medium |
| CN111627273A (en) * | 2020-06-01 | 2020-09-04 | 河南优观大数据科技有限公司 | Cloud live broadcast cloud recording and broadcasting method based on interconnection and intercommunication of Internet and IPTV television network |
| CN112055230A (en) * | 2020-09-03 | 2020-12-08 | 北京中润互联信息技术有限公司 | Live broadcast monitoring method and device, computer equipment and readable storage medium |
| KR102365858B1 (en) * | 2020-11-02 | 2022-02-22 | 현대로템 주식회사 | Automated Inspection System for Railway Vehicle M.F. Output Broadcasting System |
| CN112651866A (en) * | 2020-12-31 | 2021-04-13 | 重庆工业职业技术学院 | Classroom auxiliary teaching system and method |
| CN113593069A (en) * | 2021-07-05 | 2021-11-02 | 北京瑞博众成科技有限公司 | Intelligent inspection system for mathematics twin |
| CN113781270A (en) * | 2021-07-30 | 2021-12-10 | 杭州迈杰教育科技有限公司 | Intelligent campus comprehensive management system and method |
| CN113470211A (en) * | 2021-08-12 | 2021-10-01 | 京东科技信息技术有限公司 | Routing inspection track management method and device based on beacon identification |
| CN113965769A (en) * | 2021-10-19 | 2022-01-21 | 创盛视联数码科技（北京）有限公司 | Live broadcast system for online education |
| CN114219233A (en) * | 2021-11-27 | 2022-03-22 | 作业帮教育科技（北京）有限公司 | Evaluation method and evaluation device for live broadcast quality of live broadcast course system and storage medium |
| CN114187563A (en) * | 2021-12-13 | 2022-03-15 | 张家港聚智慧文化科技有限公司 | Online education course identification system and identification method |
| CN114842567A (en) * | 2022-03-31 | 2022-08-02 | 浙江大华技术股份有限公司 | Video inspection method, device, equipment and medium |
| CN114845130A (en) * | 2022-04-26 | 2022-08-02 | 武汉景冠科技有限公司 | Method for intelligently examining self-media audio and video contents |
| CN114979690A (en) * | 2022-05-18 | 2022-08-30 | 中国广电重庆网络股份有限公司 | Live channel signal control inspection method based on big data analysis |
| TWI793025B (en) * | 2022-05-26 | 2023-02-11 | 艾樂維教育科技股份有限公司 | Live teaching system |
| CN115103215A (en) * | 2022-06-16 | 2022-09-23 | 招商银行股份有限公司 | Live broadcast quality inspection method, system, Web server and storage medium |
| CN115134617A (en) * | 2022-06-16 | 2022-09-30 | 招商银行股份有限公司 | Live broadcast quality inspection system, method, Web server and storage medium |
| CN115278276A (en) * | 2022-06-23 | 2022-11-01 | 麦苗(广东)云科技有限公司 | Remote online teaching live broadcast method and system based on 5G communication |
| CN115086746A (en) * | 2022-07-19 | 2022-09-20 | 北京微吼时代科技有限公司 | Video polling method for live system, live system and electronic equipment |
| CN114998844A (en) * | 2022-08-08 | 2022-09-02 | 北京师范大学 | Online class patrol and supervision system and method based on AI classroom |
Non-Patent Citations (2)
| Title |
|---|
| 基于实时交互模式的在线课程直播教学平台的研究;商娟叶;;微型电脑应用;20201020(10);全文 * |
| 高校在线课程教学复合式授课模式与多方连动巡查机制研究――以湖南中医药大学为例;张丹;常小荣;卓海燕;胡以仁;陈楚淘;;中国多媒体与网络教学学报(上旬刊);20200518(06);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116128453A (en) | 2023-05-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111079113B (en) | Teaching system with artificial intelligent control and use method thereof | |
| CN111949822B (en) | Intelligent education video service system based on cloud computing and mobile terminal and operation method thereof | |
| CN109034036A (en) | A kind of video analysis method, Method of Teaching Quality Evaluation and system, computer readable storage medium | |
| Yusof et al. | Real-time internet based attendance using face recognition system | |
| CN115062675A (en) | Full-spectrum pollution tracing method based on neural network and cloud system | |
| AU2012204061A1 (en) | Methods and systems for object based data management | |
| CN108376158B (en) | Student mobile terminal behavior log analysis method, device and system | |
| CN116128453B (en) | Online course inspection method, system, equipment and medium | |
| CN117474353A (en) | Decision automatic generation method and device based on online education | |
| CN115481941B (en) | Multifunctional area combined intelligent security management method and system | |
| CN116824311A (en) | Performance detection method, device, equipment and storage medium of crowd analysis algorithm | |
| CN114005325B (en) | Teaching training method, device and medium based on big data | |
| CN114998844A (en) | Online class patrol and supervision system and method based on AI classroom | |
| CN114926152A (en) | Power grid construction safety examination and training method and system and electronic equipment | |
| CN114842567A (en) | Video inspection method, device, equipment and medium | |
| CN115034938A (en) | Experiment teaching monitoring and evaluating system and method | |
| KR20220070904A (en) | Online Consultation Service System | |
| Ünal et al. | Investigating the impact of forgetting in software development | |
| Purba | Models Of Sustainable Quality To Improve Implementation Of Quality System Of Qualified Enterprise | |
| CN112990848A (en) | Classroom teaching analysis and evaluation system and method based on big data | |
| CN112148948A (en) | Public opinion monitoring system and method | |
| CN111223341A (en) | Teaching training intelligent evaluation inspection method and system based on virtualization | |
| CN111161113A (en) | Method and system for quickly assisting in entering score information | |
| CN116797841A (en) | House source picture auditing and model training automation method based on deep learning | |
| Pollard et al. | Research design and establishing causality |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |