CN114401448B - Abnormality detection method, device, equipment and storage medium for program forecast information - Google Patents
Abnormality detection method, device, equipment and storage medium for program forecast information Download PDFInfo
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- CN114401448B CN114401448B CN202111610458.5A CN202111610458A CN114401448B CN 114401448 B CN114401448 B CN 114401448B CN 202111610458 A CN202111610458 A CN 202111610458A CN 114401448 B CN114401448 B CN 114401448B
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- 238000001514 detection method Methods 0.000 title claims abstract description 76
- 230000005856 abnormality Effects 0.000 title claims abstract description 54
- 230000002159 abnormal effect Effects 0.000 claims abstract description 165
- 238000000034 method Methods 0.000 claims abstract description 29
- 239000003086 colorant Substances 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000011897 real-time detection Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/458—Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming stream; Updating operations, e.g. for OS modules ; time-related management operations
- H04N21/4586—Content update operation triggered locally, e.g. by comparing the version of software modules in a DVB carousel to the version stored locally
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/482—End-user interface for program selection
- H04N21/4826—End-user interface for program selection using recommendation lists, e.g. of programs or channels sorted out according to their score
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/85—Assembly of content; Generation of multimedia applications
- H04N21/854—Content authoring
- H04N21/8549—Creating video summaries, e.g. movie trailer
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses an anomaly detection method, device and equipment for program forecast information and a storage medium. The detection method comprises the steps of importing program forecast information into a database; acquiring all channel information from a database, reading program forecast information from the database and grouping the program forecast information; detecting according to a set abnormality rule, marking normal program forecast information as a normal state, and marking abnormal program forecast information as an abnormal state, wherein the abnormal state comprises an abnormal state without data, an abnormal state with time conflict, an abnormal state with abnormal duration and an abnormal state with empty name; outputting the detection result in the form of report or text, and if abnormal state exists, correcting and updating the detection result into the database. The method and the device are applied to the technical field of program information detection.
Description
Technical Field
The present invention relates to the technical field of program playing, and in particular, to a method, an apparatus, a device, and a storage medium for detecting abnormality of program forecast information.
Background
The program advance notice information (epg) can browse information of programs played at a future time (up to 7 days or more). The program forecast information needs to be manufactured and imported into an IPTV system or a website, whether the information is correct or not is also checked, and the current manual checking is basically performed, so that time and labor are wasted, errors are easy to occur, and whether the program forecast information is normal or not can not be visually displayed. Therefore, it is required to develop an abnormality detection method, apparatus, device and storage medium for program forecast information which is easy to use, not prone to error and capable of being intuitively displayed.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects of the prior art, and the first aim is to provide an abnormality detection method for program forecast information, which is easy to use, not easy to make mistakes and can be intuitively displayed.
A second object of the present invention is to provide an abnormality detection device for program announcement information.
A third object of the present invention is to provide an abnormality detection apparatus for program announcement information.
A fourth object of the present invention is to provide a computer-readable storage medium.
The technical scheme adopted by the invention is as follows: the abnormality detection method of the program forecast information comprises the following steps:
importing the program forecast information into a database;
acquiring all channel information from a database, reading program forecast information from the database and grouping the program forecast information;
detecting according to a set abnormality rule, marking normal program forecast information as a normal state, and marking abnormal program forecast information as an abnormal state, wherein the abnormal state comprises an abnormal state without data, an abnormal state with time conflict, an abnormal state with abnormal duration and an abnormal state with empty name;
outputting the detection result in the form of report or text, and if abnormal state exists, correcting and updating the detection result into the database.
Further, the marking method in the step of marking the abnormal program forecast information as an abnormal state includes:
marking the program forecast information without data in the day as an abnormal state without data;
marking the program forecast information with time conflict as an abnormal state with time conflict;
marking the program forecast information of which the total time length of the program forecast is less than 20 hours as an abnormal state with abnormal time length;
the program advance notice information with the empty advance notice name in the day is marked as an abnormal state with the empty name.
Further, the method for marking the program forecast information with time conflict as an abnormal state with time conflict comprises the following steps:
the ending time of the previous program forecast information is longer than the starting time of the next program forecast information, and the two pieces of program forecast information are marked as abnormal states with time conflict;
the ending time of the previous program forecast information is discontinuous with the starting time of the next program forecast information, and the two pieces of program forecast information are marked as abnormal states with time conflict;
the start time of the same piece of program forecast information is equal to or later than the end time, and the program forecast information is marked as an abnormal state of time conflict.
Further, the step of reading the program forecast information from the database and grouping the program forecast information includes:
reading channels, languages and dates in the program forecast information from a database;
grouping is carried out by taking channels, languages and dates as minimum units.
Further, in the step of marking the abnormal program forecast information as an abnormal state, when a plurality of abnormal states exist, by setting the weight values for all the abnormal states, the abnormal state with a large weight value covers the abnormal state with a small weight value, leaving one abnormal state with a maximum weight value.
Further, in the step of marking the abnormal program forecast information as an abnormal state, when a plurality of abnormal states exist, a plurality of abnormal states may coexist.
Further, in the step of outputting the detection result in the form of a report or text, the normal state and the abnormal state in the report or text are respectively distinguished by different colors.
The invention also provides an abnormality detection device of the program forecast information, which comprises:
the importing module is used for importing the program forecast information into the database;
the reading module is used for acquiring all channel information from the database, reading program forecast information from the database and grouping the program forecast information;
the detection module is used for detecting according to a set abnormality rule, marking normal program forecast information as a normal state, and marking abnormal program forecast information as an abnormal state, wherein the abnormal state comprises an abnormal state without data, an abnormal state with time conflict, an abnormal state with abnormal duration and an abnormal state with an empty name;
and the output module is used for outputting the detection result in the form of a report or text, and correcting and updating the detection result into the database if the abnormal state exists.
The invention also provides an abnormality detection device for the program forecast information, which comprises:
the program comprises a memory, a processor and an abnormality detection program of the program forenotice information, wherein the abnormality detection program of the program forenotice information is stored in the memory and can run on the processor, and the abnormality detection method of the program forenotice information is realized when the abnormality detection program of the program forenotice information is executed by the processor.
The invention also provides a computer readable storage medium, wherein the computer storage medium stores an abnormality detection program of the program forenotice information, and the abnormality detection program of the program forenotice information realizes the steps of the abnormality detection method of the program forenotice information when being executed by a processor.
The beneficial effects of the invention are as follows:
the invention provides an abnormality detection method, device, equipment and storage medium of program forecast information, which can detect the program forecast information, search abnormal program forecast information and mark, so that when an output result has an abnormal state, the program forecast information in the abnormal state can be rapidly positioned, updated into a database after correction, and the abnormal program forecast information is covered; in addition, the detection result is output in the form of a report or a text, and the state of the daily forecast information of each program and whether the detection result is in a normal state can be intuitively displayed, so that time and labor are saved, errors are avoided, and the method has the advantages of easiness in use, difficulty in error and capability of being intuitively displayed.
Drawings
Fig. 1 is a flowchart of an abnormality detection method of program advance notice information;
FIG. 2 is a flowchart of an abnormality detection method of program advance notice information;
fig. 3 is a schematic diagram of an abnormality detection device of program advance notice information;
FIG. 4 is a table diagram showing the detection results of program forecast information;
fig. 5 is a table of program advance notice information for the day of the channel.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
As shown in fig. 1, the present invention provides a method for detecting abnormality of program forecast information, the method for detecting abnormality of program forecast information comprising:
s1, importing program forecast information into a database;
s2, acquiring all channel information from a database, reading program forecast information from the database and grouping the program forecast information;
specifically, channels, languages and dates in the program forecast information are sequentially read from the database and are grouped, so that the grouped program forecast information can be displayed more intuitively.
Step S3, detecting according to a set abnormality rule, marking normal program forecast information as a normal state, and marking abnormal program forecast information as an abnormal state, wherein the abnormal state comprises an abnormal state without data, an abnormal state with time conflict, an abnormal state with abnormal duration and an abnormal state with an empty name;
specifically, the program forecast information conforming to the anomaly rule is found to be marked as a corresponding anomaly state, when the anomaly state is marked, for example, the epg of 2021-08-11 days of English in the channel cctv is detected, if no epg exists, the detected state of the date is marked as a data-free anomaly state, one or more anomaly states can be marked, when various anomaly states exist, a weight value can be set for the anomaly state, a larger one can cover a smaller anomaly state, so that the anomaly state with the largest weight value is reserved, or a plurality of anomaly states coexist, and some epgs exist in a cross-day condition, and the starting time of the program forecast is required to be used for marking the anomaly state; meanwhile, the flag that the abnormal state does not exist is set as a normal state.
And S4, outputting a detection result in a report form or a text form, and correcting and updating the detection result into a database if an abnormal state exists.
Specifically, outputting the detection result report chart shown in fig. 4, so as to intuitively display the abnormal state, and correcting the marked erroneous program information according to the program forecast information list chart of the channel, language and date in which the abnormal state is displayed and positioned to the channel shown in fig. 5 on the day when correcting; when each examination is finished, the detection result is stored in a detection report or text of the corresponding channel language date in the database, and the old detection result is covered by the new detection result; the program of any language of a channel on any day is predicted to be abnormal, and the state of the channel is marked as abnormal state.
The detection can be divided into two types according to the requirement, one is real-time detection, and the other is timing detection. When real-time performance is required for the detection result, the method can be switched to real-time detection, namely, when the input of the No. 11 epg of 8 months of English under the channel cctv is completed, the epg of the day is detected; timing detection can be adopted when the real-time requirement on the detection result is not high, and the detection of the epg of all channels can be performed by setting a specific time point.
The abnormal detection method of the program forecast information can detect the program forecast information, search abnormal program forecast information and mark, so that when an output result has an abnormal state, the program forecast information in the abnormal state can be rapidly positioned, updated into a database after correction, and the abnormal program forecast information is covered; in addition, the detection result is output in the form of a report or a text, and the state of the daily forecast information of each program and whether the detection result is in a normal state can be intuitively displayed, so that time and labor are saved, errors are avoided, and the abnormal detection method of the program forecast information has the advantages of easiness in use, difficulty in error and capability of being intuitively displayed.
In the step S3, the marking method in the step of marking the abnormal program forecast information as an abnormal state includes:
no data: marking the program forecast information without data in the day as an abnormal state without data;
time conflict: marking the program forecast information with time conflict as an abnormal state with time conflict;
abnormal duration: marking the program forecast information of which the total time length of the program forecast is less than 20 hours as an abnormal state with abnormal time length;
the name is empty: the program advance notice information with the empty advance notice name in the day is marked as an abnormal state with the empty name.
Specifically, the method of marking an abnormal state includes, but is not limited to, the above-described method, and the above-described marking method need not be sequentially performed.
The method for marking the program forecast information with time conflict as the abnormal state with time conflict comprises the following steps:
the ending time of the previous program forecast information is longer than the starting time of the next program forecast information, and the two pieces of program forecast information are marked as abnormal states with time conflict;
the ending time of the previous program forecast information is discontinuous with the starting time of the next program forecast information, and the two pieces of program forecast information are marked as abnormal states with time conflict;
the start time of the same piece of program forecast information is equal to or later than the end time, and the program forecast information is marked as an abnormal state of time conflict.
Specifically, the method of marking the abnormal state as the time conflict includes, but is not limited to, the above method, and the above marking method need not be sequentially performed.
In the above step S2, as shown in fig. 2, the step of reading the program forecast information from the database and grouping the program forecast information includes:
step S201, reading channels, languages and dates in the program forecast information from a database;
step S202, grouping is carried out by taking the channel, the language and the date as the minimum unit.
In the step S3, when a plurality of abnormal states exist in the step of marking the abnormal program forecast information as the abnormal states, the abnormal state with the large weight value is covered by the abnormal state with the small weight value by setting the weight value for all the abnormal states, and one abnormal state with the largest weight value is left;
in the step S3, in the step of marking the abnormal program forecast information as an abnormal state, there is another embodiment in which when a plurality of abnormal states exist, a plurality of abnormal states may coexist.
In the step S4, in the step of outputting the detection result in the form of a report or text, the normal state and the abnormal state in the report or text are respectively distinguished by different colors.
Specifically, as shown in fig. 4, the ground color of the normal state in the report or the text is marked as green, and the ground colors of the other abnormal states are displayed as red and yellow according to the balance of the abnormal types, so that the abnormal condition can be intuitively and clearly displayed, and the abnormal program forecast can be conveniently and rapidly positioned.
As shown in fig. 3, the present invention further provides an abnormality detection device for program announcement information, the abnormality detection device for program announcement information including:
an importing module 10 for importing program forecast information into a database;
a reading module 20, configured to obtain all channel information from the database, read program forecast information from the database, and group the program forecast information;
the detection module 30 is configured to detect according to a set anomaly rule, mark normal program forecast information as a normal state, and mark abnormal program forecast information as an abnormal state, where the abnormal state includes an abnormal state without data, an abnormal state with time conflict, an abnormal state with abnormal duration, and an abnormal state with an empty name;
and the output module 40 is used for outputting the detection result in the form of a report or text, and correcting and updating the detection result into the database if the abnormal state exists.
The device provided by the embodiment is used for importing the program forecast information into a database; the method comprises the steps of acquiring all channel information from a database, reading program forecast information from the database and grouping the program forecast information; the method comprises the steps of detecting according to a set abnormality rule, marking normal program forecast information as a normal state, marking abnormal program forecast information as an abnormal state, wherein the abnormal state comprises an abnormal state without data, an abnormal state with time conflict, an abnormal state with abnormal duration and an abnormal state with empty name; and the system is used for outputting the detection result in the form of a report or text, and if the abnormal state exists, correcting and updating the detection result into a database, so that a user intuitively pairs the program forecast information for locating the abnormal state according to the output detection result, updating the corrected program forecast information into the database, and covering the abnormal program forecast information.
It should be noted that, this embodiment is an apparatus embodiment corresponding to the above-mentioned method embodiment, and this embodiment may be implemented in cooperation with the above-mentioned method embodiment. The related technical details mentioned in the above method embodiments are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the related technical details mentioned in the present embodiment may also be applied in the above-described method item embodiments.
The invention also provides an abnormality detection device for the program forecast information, which comprises:
the program comprises a memory, a processor and an abnormality detection program of the program forenotice information, wherein the abnormality detection program of the program forenotice information is stored in the memory and can run on the processor, and the abnormality detection method of the program forenotice information is realized when the abnormality detection program of the program forenotice information is executed by the processor.
In addition, the invention also provides a computer readable storage medium, wherein the computer storage medium stores an abnormality detection program of the program forenotice information, and the abnormality detection program of the program forenotice information realizes the steps of the abnormality detection method of the program forenotice information when being executed by a processor.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
While the embodiments of this invention have been described in terms of practical aspects, they are not to be construed as limiting the meaning of this invention, and modifications to the embodiments and combinations with other aspects thereof will be apparent to those skilled in the art from this description.
Claims (8)
1. An anomaly detection method for program forecast information is characterized in that: the abnormality detection method of the program forecast information comprises the following steps:
importing the program forecast information into a database;
all channel information is obtained from a database, program forecast information is read from the database and grouped, wherein the step of reading the program forecast information from the database and grouping comprises the following steps:
reading channels, languages and dates in the program forecast information from a database;
grouping by taking the channel, language and date as the minimum unit;
detecting according to a set abnormality rule, marking normal program forecast information as a normal state, marking abnormal program forecast information as an abnormal state, wherein the abnormal state comprises a data-free abnormal state, a time-conflict abnormal state, a time-duration abnormal state and an empty-named abnormal state, and the marking method of the abnormal state comprises the steps of marking the program forecast information without data in the day as a data-free abnormal state, marking the program forecast information with time conflict as a time-conflict abnormal state, marking the program forecast information with total time length smaller than 20 hours in the day as a time-duration abnormal state and marking the program forecast information with empty-named in the day as an empty-named abnormal state;
outputting the detection result in the form of report or text, and if abnormal state exists, correcting and updating the detection result into the database.
2. The abnormality detection method for program announcement information according to claim 1, characterized in that: the method for marking the program forecast information with time conflict as the abnormal state with time conflict comprises the following steps:
the ending time of the previous program forecast information is longer than the starting time of the next program forecast information, and the two pieces of program forecast information are marked as abnormal states with time conflict;
the ending time of the previous program forecast information is discontinuous with the starting time of the next program forecast information, and the two pieces of program forecast information are marked as abnormal states with time conflict;
the start time of the same piece of program forecast information is equal to or later than the end time, and the program forecast information is marked as an abnormal state of time conflict.
3. The abnormality detection method for program announcement information according to claim 1, characterized in that: in the step of marking the abnormal program forecast information as the abnormal state, when a plurality of abnormal states exist, the abnormal state with the large weight value is covered by setting the weight value for all the abnormal states, and one abnormal state with the largest weight value is left.
4. The abnormality detection method for program announcement information according to claim 1, characterized in that: in the step of marking the abnormal program forecast information as an abnormal state, when a plurality of abnormal states exist, a plurality of abnormal states can coexist.
5. The abnormality detection method for program announcement information according to claim 1, characterized in that: in the step of outputting the detection result in the form of report or text, the normal state and the abnormal state in the report or text are respectively distinguished by different colors.
6. An abnormality detection device for program announcement information, characterized in that: the abnormality detection device of the program forecast information includes:
the importing module is used for importing the program forecast information into the database;
the reading module is used for acquiring all channel information from the database, reading the program forecast information from the database and grouping the program forecast information, wherein the step of reading the program forecast information from the database and grouping the program forecast information comprises the following steps:
reading channels, languages and dates in the program forecast information from a database;
grouping by taking the channel, language and date as the minimum unit;
the detection module is used for detecting according to a set abnormality rule, marking normal program forecast information as a normal state, marking abnormal program forecast information as an abnormal state, wherein the abnormal state comprises a data-free abnormal state, a time-conflict abnormal state, a time-duration abnormal state and a null-named abnormal state, and the marking method of the abnormal state comprises the steps of marking the program forecast information without data in the day as a data-free abnormal state, marking the time-conflict program forecast information as a time-conflict abnormal state, marking the program forecast information with total time of less than 20 hours in the day as a time-duration abnormal state and marking the program forecast information with a pre-name null in the day as a null-named abnormal state;
and the output module is used for outputting the detection result in the form of a report or text, and correcting and updating the detection result into the database if the abnormal state exists.
7. An abnormality detection apparatus for program announcement information, characterized by: the abnormality detection device of the program forecast information includes:
a memory, a processor, and an abnormality detection program of program announcement information stored in the memory and executable on the processor, the abnormality detection program of program announcement information realizing the steps of the abnormality detection method of program announcement information according to any one of claims 1 to 5 when executed by the processor.
8. A computer-readable storage medium, characterized by: the computer storage medium has stored thereon an abnormality detection program for program announcement information, which when executed by a processor, implements the abnormality detection method for program announcement information according to any one of claims 1 to 5.
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