CN116662320A - Media resource data detection method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the disclosure provides a media resource data detection method, a device, electronic equipment and a storage medium. Determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered; detecting task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute data processing tasks; and determining a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data. The scheme solves the problems of fuzzy definition and insufficient quantization of mass real-time media resource data quality, and ensures timeliness and accuracy of the real-time media resource data as much as possible by timely quantizing the real-time media resource data quality.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of data, in particular to a method and a device for detecting media resource data, electronic equipment and a storage medium.
Background
Real-time processing based on massive real-time media resource data is a common scene of big data application at present. The data has the characteristics of large data magnitude and high timeliness and accuracy requirements, and is widely used in various real-time data reports. However, when providing reliable real-time media resource data for users, the quality of the real-time media resource data may have quality problems, and if the real-time media resource data cannot be found in time, the real-time media resource data may not meet the requirements of timeliness and accuracy, so that the use of the real-time media resource data is seriously affected.
Disclosure of Invention
The disclosure provides a method, a device, electronic equipment and a storage medium for detecting media resource data, so as to solve the problem of insufficient data quality quantification of real-time media resource data and realize effective detection of the real-time media resource data.
In a first aspect, an embodiment of the present disclosure provides a method for detecting media resource data, where the method includes:
Determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered;
detecting task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute data processing tasks;
and determining a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data.
In a second aspect, an embodiment of the present disclosure further provides a media resource data detection apparatus, where the apparatus includes:
the determining module is used for determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered;
the first detection module is used for detecting task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute data processing tasks;
and the second detection module is used for determining a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data.
In a third aspect, embodiments of the present disclosure further provide an electronic device, including:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the media asset data detection method described in any of the embodiments of the present disclosure.
In a fourth aspect, the disclosed embodiments also provide a storage medium containing computer-executable instructions that, when executed by a computer processor, are used to perform the media asset data detection method of any of the embodiments of the disclosure.
According to the embodiment of the disclosure, the target real-time media resource data related to the target data full link is obtained, the task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute the data processing task is detected, and the data quality of the target real-time media resource data is quantized according to the task delay time of the target data full link, so that the performance and the availability of the target real-time media resource data are described, the problems of fuzzy definition and insufficient quantization of mass real-time media resource data quality are solved, the timeliness and the accuracy of the real-time media resource data are guaranteed as much as possible, and the reliable real-time media resource data is provided for users.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a flowchart of a media resource data detection method according to an embodiment of the present disclosure;
FIG. 2 is a general schematic flow chart of a real-time media asset data quality quantization provided by an embodiment of the present disclosure;
FIG. 3 is a detailed schematic flow chart of a real-time media asset data quality quantization provided by an embodiment of the present disclosure;
FIG. 4 is a flowchart of another method for detecting media asset data according to an embodiment of the present disclosure;
FIG. 5 is a diagram of statistics of the unavailable time length of a data full link provided by an embodiment of the present disclosure;
FIG. 6 is a graph of task delay trends for a different data full link provided by embodiments of the present disclosure;
fig. 7 is a schematic structural diagram of a media resource data detection device according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device implementing a method for detecting media asset data according to an embodiment of the disclosure.
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.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," "target," "reference," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.
For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.
As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.
It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.
It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.
Fig. 1 is a flowchart of a method for detecting media resource data according to an embodiment of the present disclosure, where the method is applicable to a situation of quantitatively detecting real-time media resource data in a big data system, and the method may be performed by a media resource data detecting device, where the device may be implemented in a form of software and/or hardware, and may be implemented by an electronic device configured in any network communication function, where the electronic device may be a mobile terminal, a PC end, a server, or the like.
As shown in fig. 1, the media resource data detection in the present embodiment may include the following procedures:
s110, determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered.
Real-time processing based on massive real-time media resource data is a common scene of the current big data application, and the real-time media resource data has the characteristics of large data magnitude and high timeliness and accuracy requirements and is widely used in various real-time data reports. When the target media resource demand exists, a target media resource demand event can be triggered, and a data link required for realizing the target media resource demand event can be used as a target data full link; for example, when there is an advertisement information resource demand, an advertisement information resource demand event may be triggered by clicking an advertisement resource icon displayed on a display page, where a data link corresponding to the advertisement information resource demand event is required to provide advertisement resource data.
The target real-time media asset data may be trusted real-time media asset data provided by triggering a target media asset demand event and employing a target data full link. Wherein the real-time media asset data may include, but is not limited to, media asset data in the form of: news asset data, novel asset data, social information asset data, video asset data, and advertising information asset data.
S120, determining task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute the data processing task.
After triggering the target media resource demand event, the target real-time media resource data corresponding to the reliable target media resource demand event can be provided through the target data full link. The target data full link comprises a plurality of layers of task nodes, each layer of task nodes is associated with a preset data processing task, and in the process of providing target real-time media resource data through the target data full link, the target data full link can sequentially reach each layer of task nodes in the target data full link to execute corresponding data processing task logic.
Because the timeliness of the real-time media resource data is high, the delay time of the target real-time media resource data in the data full link can be determined as an index for quantifying the quality of the real-time media resource data. In the real-time media resource data of the big data system, the time for triggering the target real-time media resource data to correspond to the target media resource demand event is increased, and the time of the media resource demand event is utilized to open each layer of task nodes in the data full link.
Furthermore, the time difference between the time when the task nodes of each layer execute the corresponding data processing task and the time when the target media resource demand event is triggered can be determined, and the task delay time from the target real-time media resource data to the current task executing at the target data full link can be obtained. The data processing tasks executed by the task nodes of each layer in the target data full link can comprise one or more of data cleaning, data conversion, data extraction and data calculation.
As an optional but non-limiting implementation manner, determining a task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute the data processing task may include the following steps A1-A2:
and A1, determining target delay time data of target real-time media resource data in a target data full link from a preset storage database, wherein the target delay time data is acquired and reported by task nodes of each layer in the target data full link by adopting a unified preset interface and stored in the preset storage database, and the preset interface is configured by adopting a software development kit.
And A2, determining task delay time of the task nodes in each layer based on the target real-time media resource data from the target delay time data, wherein the task delay time is a time difference value between actual time and reference time when the task nodes execute data processing tasks, and the reference time is time for triggering events corresponding to the target real-time media resource data.
Referring to fig. 2, a schematic flow chart of real-time media resource data quality quantification is shown, the application proposes to use the task delay time of a data full link as an index for measuring the real-time media resource data quality, and in order to realize data quality quantification analysis, target real-time media resource data calculation, target real-time media resource data reporting, target real-time media resource data collection and target real-time media resource data statistics are adopted to realize quantification of the task delay time of each layer of task nodes in the data full link.
Referring to fig. 2 and 3, at each layer node of the target data full link, the actual time when the task node executes the data processing task and the time when the corresponding event of the target real-time media resource data is triggered can be used to obtain the target delay time data of the target real-time media resource data in the target data full link. In order to acquire target delay time data obtained by executing data processing tasks at each layer of task nodes for subsequent unified processing, the target delay time data calculated at each task node may be reported to a database. The data processing task executed at each layer of task nodes can be a Flink task processed in real time.
Optionally, referring to fig. 2 and fig. 3, in order to implement unified reporting of target latency data obtained by executing a data processing task at each layer of task nodes, a unified software development kit SDK is used for configuring the data processing task at each layer of task nodes, so that each layer of task nodes may use a preset interface configured by the SDK to send the target latency data obtained by computing each layer of task nodes to a remote storage database for storage, and meanwhile, a data processing task identifier (such as a task name) corresponding to the target latency data and a data all-link to which the target latency data belongs are attached during storage, so as to perform directional selection during subsequent use.
Optionally, referring to fig. 3, after the target delay time data is reported to a unified database, the reported target delay time data can be collected at regular time according to a preset frequency through a timing service, and the target delay time data is stored in an offline data warehouse tool Hive database after being preprocessed and is used as a data source of follow-up complex data statistics.
As an alternative but not limiting implementation, the target real-time media resource data is selected from the real-time media resource data processed by the target data full link according to a reference sampling percentage, and the reference sampling percentage is determined according to different orders of magnitude of the real-time media resource data processed by the target data full link.
Referring to fig. 3, in a big data system scenario, the data magnitude of the real-time media resource data is big, so the amount of the real-time media resource data processed through the target data full link is very large, if the delay time is calculated for each piece of real-time media resource data through the target data full link, the processing performance loss is likely to occur when the data processing task is performed on the target data full link. Therefore, the application adopts a random sampling mode, and selects a certain reference sampling percentage according to different orders of magnitude of the real-time media resource data processed by the target data full link, and then selects the target real-time media resource data from the real-time media resource data processed by the target data full link according to the sampling percentage.
As an optional but non-limiting implementation manner, each layer of task node in the target data full link is encapsulated with a target structured query language logic, and the target structured query language logic is used for calculating, reporting and storing task delay time when the task node executes a data processing task based on target real-time media resource data.
Referring to fig. 3, on each layer of task nodes of the target data full link, task delay time calculation logic based on target real-time media resource data when the task nodes execute data processing tasks can be packaged into general target Structured Query Language (SQL) logic, and the target structured query language logic is embedded into the data processing tasks of each layer of task nodes, so that the task delay time of the data processing tasks of each layer of task nodes can be calculated by adopting the general target structured query language logic.
S130, determining a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data.
The task delay time of the target real-time media resource data in the target data full link comprises a first task delay time or a second task delay time, wherein the first task delay time can be the task delay time of each layer of task node when the target real-time media resource data sequentially passes through each layer of task node in the target data full link to execute data tasks, and the second task delay time can be the sum of the task delay time when the target real-time media resource data respectively executes data tasks at each layer of task node.
After the task delay time of the target real-time media resource data in the target data full link is determined, the data delay condition of the real-time media resource data in the target data full link can be quantitatively analyzed by the task delay time, and the problems that the definition of the quality of the real-time media resource data is fuzzy and cannot be quantized can be solved by utilizing the quantitative analysis of the data delay condition of the target data full link to reflect the quality of the real-time media resource data.
The availability index of the real-time media resource data is defined based on the task delay time of the target data full link and is used as an SLA measurement standard. The SLA (Service-Level agent) is to agree on the data quality from the system index Level, and compared with the offline SLA definition, the SLA of the real-time media resource data is more complex and difficult to define, but is also an essential index for externally displaying the data quality as a standard for evaluating the real-time data quality. The SLA is also required to be quantifiable, and the scheme calculates related indexes of the SLA based on task delay time of the target data full link, and further judges whether the requirement of the preset SLA is met based on the calculated data quality detection result.
According to the embodiment of the disclosure, the target real-time media resource data related to the target data full link can be obtained, the task delay time when the target real-time media resource data sequentially passes through all layers of task nodes in the target data full link to execute data processing tasks is detected, and the data quality of the target real-time media resource data is quantized according to the task delay time of the target data full link so as to describe the performance and the availability of the target real-time media resource data, all layers of task nodes in the link are opened based on the core field of the task delay time of the data full link of the real-time media resource data, the unified flow of task delay time from calculation, reporting, collection, statistics and visualization is completed, the problems of fuzzy definition and insufficient quantization of mass real-time media resource data quality are solved, and the timeliness and the accuracy of the real-time media resource data are ensured as much as possible by quantizing the real-time media resource data in time, so that reliable real-time media resource data is provided for users.
Fig. 4 is a flowchart of another media resource data detection method according to an embodiment of the present disclosure, where the process of determining the data quality detection result of the target real-time media resource data according to the task delay time of the target data full link in the foregoing embodiment is further optimized based on the foregoing embodiment, and the present embodiment may be combined with each of the alternatives in one or more embodiments.
As shown in fig. 4, the media resource data detection method of the present embodiment may include the following procedures:
s410, determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered.
S420, determining task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute the data processing task.
S430, determining the target duration of the target data full link according to the task delay time of the target data full link, wherein the target duration is the duration of the target data full link in an unavailable state when the task node executes the data processing task in the target data full link.
Referring to fig. 5, when a target data full link fails, a delay of a data processing task occurs at each layer task node of the target data full link, resulting in a task delay time. When the task delay time of each layer of task node is longer, the poor availability of the task node in the target data full link can be considered to be in an impossible state even directly, so that the duration of the target data full link in the unavailable state when the task node performs the data processing task in the target data full link can be counted based on the task delay time of each layer of task node in the target data full link.
In the above process, the duration of the unavailable state of the target data full link when the task nodes of each layer execute the data processing task is counted, because the real-time data processing task of the real-time media resource data of the task nodes of each layer often generates transient stability jitter caused by transient faults of a network, a dependent component and the like, and some jitter can cause a short time delay in processing the real-time media resource data, but the real-time media resource data can be quickly and automatically recovered, which is basically not perceived in the situation, if the delay time of the task nodes of each layer is simply used for measuring the data quality, more noise without attention is introduced, and the data quality detection result is inconsistent with the real sense of body. To this end, the data quality of the real-time media asset data may be measured by counting the duration of time that the target data full link is in an unavailable state.
As an alternative but not limiting implementation, determining the target duration of the target data full link according to the task delay time at the target data full link may include steps B1-B3:
and B1, determining a starting time point of the target data full link in an unavailable state according to the task delay time of the target data full link, wherein the starting time point is a time point when the task delay time of the target data full link starts to rise and is larger than a preset delay time threshold.
And B2, determining an ending time point of the target data full link in an unavailable state according to the task delay time of the target data full link, wherein the ending time point is a time point when the task delay time of the target data full link begins to decrease and is smaller than a preset delay time threshold.
And B3, determining the target duration of the target data full link based on the starting time point and the ending time point.
Referring to fig. 5, taking the target data full link as the data link a as an example, for the target data full link, it may be defined that when the task delay time of the target data full link exceeds the preset delay time threshold, the target data full link is considered to be in an unavailable state; and when the task delay time of the target data full link is lower than the preset delay time threshold, the target data full link is considered to be in an available state, so that the duration of the target data full link in an unavailable state is counted.
Referring to fig. 5, when the target data full link fails, the task delay time at the target data full link starts to increase, and when the task delay time at the target data full link is greater than a preset delay time threshold, the target data full link becomes in an unavailable state, and a time point when the target data full link has just become in an unavailable state at this time may be recorded as a start time point.
Referring to fig. 5, when the failure recovery starts after the failure of the target data full link, the task delay time of the target data full link starts to decrease, and in the case that the task delay time of the target data full link is not less than the preset delay time threshold, the target data full link still continues to be in an unavailable state even though the task delay time starts to decrease. Only when the task delay time of the target data full link continuously decreases by less than the preset delay time threshold, the target data full link becomes available, and the time point when the target data full link just becomes available from the unavailable state can be recorded as the end time point. Further, the duration from the start time point to the end time point may be calculated as the target duration of the target data full link.
For example, referring to fig. 5, taking the target data full link as the data link a as an example, the threshold value of the task delay time of the data link a is 30 minutes, when the link a fails, the task delay time of the full link thereof increases due to the processing performance being damaged, when it exceeds 30 minutes, the link a is considered to be in an unavailable state, the time point when the link a becomes in an unavailable state is set as a start time point, the processing performance of the link a is restored after the failure is repaired, the task delay time of the full link is reduced, when it is less than 30 minutes, the link a is considered to be in an available state, the time point when the link a becomes in an available state is set as an end time point, and the duration between the start time point and the end time point is calculated as the unavailable duration of the target data full link.
S440, determining a data quality detection result of the target real-time media resource data according to the target duration of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data.
As an optional but non-limiting implementation manner, determining the data quality detection result of the target real-time media resource data according to the target duration of the target data full link may include steps C1-C3:
And C1, determining the target time length of the target data full link in a single statistical period from the target time length of the target data full link.
And C2, determining a target duty ratio based on the period total duration of the single statistical period and the target duration in the single statistical period, wherein the target duty ratio is the time duty ratio of the unavailable state of the target data full link in the single statistical period.
And C3, determining a data quality detection result of the target real-time media resource data in a single statistical period according to the target duty ratio.
Referring to fig. 5, if the delay time of each layer of task node is simply used to measure the data quality, more noise without attention is introduced, resulting in inconsistent data quality detection results with real sense of body, so that different statistics periods are divided according to different statistics periods on the basis of the duration time of the unavailable state of the target data full link, and further the duration time of the unavailable state of the target data full link can be calculated in each statistics period, and further the time ratio of the unavailable state of the target data full link in a single statistics period is counted to agree with the value of the SLA, so as to obtain the data quality detection results of the target real-time media resource data, and the SLA statistics of the different data full links are shown in table 1.
TABLE 1 SLA statistics for different data full links
| SLA benchmark | SLA percentage (%) | Duration (minutes) of unavailability | Link maximum delay time (minutes) |
| 98 | 98.16 | 25 | 70.1000 |
| 98 | 98.89 | 15 | 86.05000 |
| 98 | 98.89 | 15 | 47.6167 |
| 98 | 99.26 | 10 | 40.6833 |
Alternatively, the SLA calculation formula for a single statistical period is as follows:
wherein, SLA represents the performance and availability of the target real-time media resource data, A1 represents the duration of the target data full link in the unavailable state of the target data full link in a single statistical period, and A2 represents the total duration of the single statistical period.
As an optional but non-limiting implementation, after determining the data quality detection result of the target real-time media resource data, it may further include:
and determining the change trend information of the real-time media resource data quality under the full link of the target data according to the data quality detection results of the target real-time media resource data corresponding to each statistical period.
The change trend information of the real-time media resource data quality is used for describing the change situation of the performance and the availability of the target real-time media resource data processed by the target data full link along with the time. If the performance and the availability of the target real-time media resource data gradually become satisfactory, the fault of each layer of task node in the surface target data full link is only required to wait for recovery in slow recovery, and the stability fault duration of the task is not prolonged based on the performance and the availability of the target real-time media resource data; if the performance and availability of the target real-time media resource data are not changed in the direction of gradually meeting the requirements, the faults of task nodes of each layer in the surface target data all-link are not relieved all the time, and at the moment, the task nodes of each layer are required to be optimized to optimize the task nodes of each layer, and MTTR (Mean Time To Restoration average time before recovery) is optimized as much as possible, so that the faults of the target data all-link can be recovered quickly.
As an optional but non-limiting implementation, after determining the data quality detection result of the target real-time media resource data, it may further include:
based on the data quality detection result of the target real-time media resource data, a visual report of the target data full-link for real-time media resource data processing is generated and pushed and displayed.
The real-time media resource data is deeply processed based on the performance and availability of the target real-time media resource data to obtain a data structure capable of being visually displayed, and finally put into production and used for serving a visual report for monitoring the quality of the media resource data.
As an optional but non-limiting implementation, after determining the data quality detection result of the target real-time media resource data, it may further include:
and optimizing the performance of each layer of task node in the target data full link based on the data quality detection result of the target real-time media resource data so as to enhance the recovery speed of the target full link after data delay.
The method is characterized in that the stability of the task is guaranteed based on the performance and the availability of the target real-time media resource data, the fault duration is reduced, meanwhile, task nodes of each layer are optimized based on the performance and the availability of the target real-time media resource data, MTTR is optimized as much as possible, and the fault of the target data full link can be recovered quickly.
As an alternative but not limiting implementation, the media asset data detection in this embodiment may further include the following steps D1-D2 process
And D1, determining task delay time of different target real-time media resource data associated with each task node in the target data full link according to the task delay time of the target data full link.
And D2, generating task delay time change trend information of each task node according to task delay time of different target real-time media resource data associated with each task node, and performing performance optimization on each task node.
For target real-time media resource data processed at different times on the target data full link, a task delay time at each task node that varies over time may be obtained. And generating a time-dependent change trend of the task delay time of each task node by utilizing the task delay time of different target real-time media resource data associated with each task node, and further obtaining the time-dependent change trend of the task delay time of the target data link to which each task node belongs by combining the time-dependent change trend of the task delay time of each task node, wherein the time-dependent change trend of the task delay time of the target data link to which each task node belongs is shown in FIG. 6.
According to the embodiment of the disclosure, based on the core field of the task delay time of the data full link of the real-time media resource data, all layers of task nodes in the link are opened, the unified flow of task delay time from calculation, reporting, collection, statistics and visualization is completed, the problems of fuzzy definition and insufficient quantization of mass real-time media resource data quality are solved, timeliness and accuracy of the real-time media resource data are guaranteed as much as possible by timely quantizing the real-time media resource data, and a guarantee is provided for users to reliably provide the real-time media resource data. In addition, unlike a statistical mode based on a fixed time point, real-time SLA quantitative calculation based on data availability is innovatively defined, the statistical rule of the real-time SLA is given in detail, MTTR is taken into consideration of a real-time data quality model, and meanwhile the problem of data noise caused by sporadic negligible delay due to the network jitter problem is eliminated.
Fig. 7 is a block diagram of a media resource data detection apparatus provided by an embodiment of the present disclosure, where the embodiment of the present disclosure is applicable to a situation of performing quantization detection on real-time media resource data in a big data system, and the apparatus may be implemented in a form of software and/or hardware, and may be implemented by being configured in any electronic device having a network communication function, where the electronic device may be a mobile terminal, a PC end, a server, or the like.
As shown in fig. 7, the media resource data detection apparatus of the present embodiment may include: a determination module 710, a first detection module 720, and a second detection module 730. Wherein:
a determining module 710, configured to determine target real-time media resource data associated with a target data full link, where the target data full link is a data link used when a target media resource demand event is triggered;
the first detection module 720 is configured to detect a task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute a data processing task;
and a second detection module 730, configured to determine a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, where the data quality detection result is used to describe performance and availability of the target real-time media resource data.
On the basis of the foregoing embodiment, optionally, the target real-time media resource data is selected from the real-time media resource data processed by the target data full link according to a reference sampling percentage, where the reference sampling percentage is determined according to different orders of magnitude of the real-time media resource data processed by the target data full link.
On the basis of the foregoing embodiment, optionally, determining a task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute a data processing task includes:
determining target delay time data of target real-time media resource data in a target data full link from a preset storage database, wherein the target delay time data is acquired and reported by task nodes of each layer in the target data full link by adopting a uniform preset interface and stored in the preset storage database, and the preset interface is configured by adopting a software development kit;
and determining task delay time of the task nodes in each layer based on the target real-time media resource data from the target delay time data, wherein the task delay time is a time difference value between actual time and reference time when the task nodes execute data processing tasks, and the reference time is time for triggering events corresponding to the target real-time media resource data.
On the basis of the above embodiment, optionally, each layer of task node in the target data full link is encapsulated with a target structured query language logic, where the target structured query language logic is configured to calculate, report and store a task delay time when the task node executes a data processing task based on target real-time media resource data.
On the basis of the foregoing embodiment, optionally, determining the data quality detection result of the target real-time media resource data according to the task delay time of the target data full link includes:
determining a target time length of a target data full link according to a task delay time of the target data full link, wherein the target time length is a time length of the target data full link in an unavailable state when a task node in the target data full link executes a data processing task;
and determining a data quality detection result of the target real-time media resource data according to the target duration of the target data full link.
On the basis of the foregoing embodiment, optionally, determining the target duration of the target data full link according to the task delay time of the target data full link includes:
determining a starting time point of the target data full link in an unavailable state according to the task delay time of the target data full link, wherein the starting time point is a time point when the task delay time of the target data full link starts to rise and is larger than a preset delay time threshold;
determining an ending time point of the target data full link in an unavailable state according to the task delay time of the target data full link, wherein the ending time point is a time point when the task delay time of the target data full link begins to decline and is smaller than a preset delay time threshold;
And determining the target duration of the target data full link based on the starting time point and the ending time point.
On the basis of the foregoing embodiment, optionally, determining a data quality detection result of the target real-time media resource data according to a target duration of the target data full link includes:
determining the target time length of the target data full link in a single statistical period from the target time length of the target data full link;
and determining a target duty ratio based on the period total duration of the single statistical period and the target duration in the single statistical period, wherein the target duty ratio is the time duty ratio of the target data full link in the single statistical period in an unavailable state.
And determining a data quality detection result of the target real-time media resource data in a single statistical period according to the target duty ratio.
On the basis of the foregoing embodiment, optionally, after determining the data quality detection result of the target real-time media resource data, the method further includes:
and determining the change trend information of the real-time media resource data quality under the full link of the target data according to the data quality detection results of the target real-time media resource data corresponding to each statistical period.
On the basis of the foregoing embodiment, optionally, after determining the data quality detection result of the target real-time media resource data, the method further includes:
and generating a visual report of real-time media resource data processing of the target data full link based on the data quality detection result of the target real-time media resource data, and pushing and displaying the visual report.
On the basis of the foregoing embodiment, optionally, after determining the data quality detection result of the target real-time media resource data, the method further includes:
and optimizing the performance of each layer of task node in the target data full link based on the data quality detection result of the target real-time media resource data so as to enhance the recovery speed of the target full link after data delay.
On the basis of the foregoing embodiment, optionally, the media resource data detection apparatus of this embodiment further includes:
determining task delay time of different target real-time media resource data associated with each task node in the target data full link according to the task delay time of the target data full link;
and generating task delay time change trend information of each task node according to task delay time of different target real-time media resource data associated with each task node, so as to optimize performance of each task node.
The media resource data detection device provided in the embodiment of the present disclosure may perform the media resource data detection method provided in any embodiment of the present disclosure, and has the corresponding functions and beneficial effects of performing the media resource data detection method, and the detailed process refers to the related operations of the media resource data detection method in the foregoing embodiment.
It should be noted that each unit and module included in the above apparatus are only divided according to the functional logic, but not limited to the above division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for convenience of distinguishing from each other, and are not used to limit the protection scope of the embodiments of the present disclosure.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. Referring now to fig. 8, a schematic diagram of an electronic device (e.g., a terminal device or server in fig. 8) 800 suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 8, the electronic device 800 may include a processing means (e.g., a central processor, a graphics processor, etc.) 801, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An edit/output (I/O) interface 805 is also connected to the bus 804.
In general, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, etc.; storage 808 including, for example, magnetic tape, hard disk, etc.; communication means 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 shows an electronic device 800 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication device 809, or installed from storage device 808, or installed from ROM 802. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 801.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The electronic device provided by the embodiment of the present disclosure and the method for detecting media resource data provided by the foregoing embodiment belong to the same inventive concept, and technical details not described in detail in the present embodiment may be referred to the foregoing embodiment, and the present embodiment has the same beneficial effects as the foregoing embodiment.
The present disclosure provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the media asset data detection method provided by the above embodiments.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered; determining task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute data processing tasks; and determining a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (14)
1. A method for detecting media asset data, the method comprising:
determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered;
determining task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute data processing tasks;
and determining a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data.
2. The method of claim 1, wherein the target real-time media asset data is selected from the real-time media asset data processed by the target data full link based on a reference sampling percentage, the reference sampling percentage being determined based on different orders of magnitude of the real-time media asset data processed by the target data full link.
3. The method of claim 1, wherein determining a task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to perform a data processing task comprises:
determining target delay time data of target real-time media resource data in a target data full link from a preset storage database, wherein the target delay time data is acquired and reported by task nodes of each layer in the target data full link by adopting a uniform preset interface and stored in the preset storage database, and the preset interface is configured by adopting a software development kit;
and determining task delay time of the task nodes in each layer based on the target real-time media resource data from the target delay time data, wherein the task delay time is a time difference value between actual time and reference time when the task nodes execute data processing tasks, and the reference time is time for triggering events corresponding to the target real-time media resource data.
4. The method of claim 1, wherein each layer of task nodes in the target data full link is encapsulated with target structured query language logic, and the target structured query language logic is configured to calculate and report and store a task delay time when the task nodes perform a data processing task based on target real-time media resource data.
5. The method of claim 1, wherein determining the data quality detection result of the target real-time media resource data based on the task delay time at the target data full link comprises:
determining a target time length of a target data full link according to a task delay time of the target data full link, wherein the target time length is a time length of the target data full link in an unavailable state when a task node in the target data full link executes a data processing task;
and determining a data quality detection result of the target real-time media resource data according to the target duration of the target data full link.
6. The method of claim 5, wherein determining the target duration of the target data full link based on the task delay time at the target data full link comprises:
determining a starting time point of the target data full link in an unavailable state according to the task delay time of the target data full link, wherein the starting time point is a time point when the task delay time of the target data full link starts to rise and is larger than a preset delay time threshold;
determining an ending time point of the target data full link in an unavailable state according to the task delay time of the target data full link, wherein the ending time point is a time point when the task delay time of the target data full link begins to decline and is smaller than a preset delay time threshold;
And determining the target duration of the target data full link based on the starting time point and the ending time point.
7. The method of claim 5, wherein determining the data quality detection result of the target real-time media resource data according to the target duration of the target data full link comprises:
determining the target time length of the target data full link in a single statistical period from the target time length of the target data full link;
and determining a target duty ratio based on the period total duration of the single statistical period and the target duration in the single statistical period, wherein the target duty ratio is the time duty ratio of the target data full link in the single statistical period in an unavailable state.
And determining a data quality detection result of the target real-time media resource data in a single statistical period according to the target duty ratio.
8. The method of claim 7, further comprising, after determining the data quality detection result of the target real-time media asset data:
and determining the change trend information of the real-time media resource data quality under the full link of the target data according to the data quality detection results of the target real-time media resource data corresponding to each statistical period.
9. The method according to any one of claims 1 to 8, further comprising, after determining the data quality detection result of the target real-time media asset data:
and generating a visual report of real-time media resource data processing of the target data full link based on the data quality detection result of the target real-time media resource data, and pushing and displaying the visual report.
10. The method according to any one of claims 1 to 8, further comprising, after determining the data quality detection result of the target real-time media asset data:
and optimizing the performance of each layer of task node in the target data full link based on the data quality detection result of the target real-time media resource data so as to enhance the recovery speed of the target full link after data delay.
11. The method according to claim 1, wherein the method further comprises:
determining task delay time of different target real-time media resource data associated with each task node in the target data full link according to the task delay time of the target data full link;
and generating task delay time change trend information of each task node according to task delay time of different target real-time media resource data associated with each task node, so as to optimize performance of each task node.
12. A media asset data detection device, the device comprising:
the determining module is used for determining target real-time media resource data associated with a target data full link, wherein the target data full link is a data link adopted when a target media resource demand event is triggered;
the first detection module is used for detecting task delay time when the target real-time media resource data sequentially passes through each layer of task nodes in the target data full link to execute data processing tasks;
and the second detection module is used for determining a data quality detection result of the target real-time media resource data according to the task delay time of the target data full link, wherein the data quality detection result is used for describing the performance and the availability of the target real-time media resource data.
13. An electronic device, the electronic device comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the media asset data detection method of any of claims 1-11.
14. A storage medium containing computer executable instructions which, when executed by a computer processor, are for performing the media asset data detection method of any of claims 1-11.
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