Television data query method, system and equipment based on graph calculation and medium

Abstract

The invention relates to a television data query method and system based on graph calculation and a medium, and the method comprises the steps: firstly converting the preprocessed intelligent television data into the format of an on-line analytical processing (OLAP) database; then, on the basis of data in the OLAP database, a graph calculation engine is used for defining entities and relationships thereof, a graph model is constructed, and the graph model is physically stored in a graph database; and finally, querying the current television data (including abnormal data) based on the graph database to obtain an analysis result. According to the method, full-chain processing from original data to structured storage and then to graph calculation and real-time query is realized, the problem of low query efficiency of complex intelligent television and projection equipment data in cross-hardware and software platforms, multiple scenes and multiple fields is solved, and the query efficiency of television data is improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method, system, device, and medium for querying TV data based on graph computing. Background Art

[0002] In the current OTT (Over-the-Top) industry, smart TVs, projection devices, and their complex software ecosystems together constitute a diverse and dynamic data environment. With the development of the product ecosystem where hardware and software are closely integrated, the platform faces huge challenges in data collection and analysis, and each independent unit collects and reports its unique information on its own.

[0003] However, in the face of cross-hardware and software platforms, multiple scenarios, and multiple fields, there is a problem of low efficiency in querying the data of complex smart TVs and projection devices. Summary of the Invention

[0004] Embodiments of this application provide a method, system, and medium for querying TV data based on graph computing, so as to at least solve the problem of low efficiency in querying the data of complex smart TVs and projection devices in the related art in the face of cross-hardware and software platforms, multiple scenarios, and multiple fields.

[0005] In a first aspect, embodiments of this application provide a method for querying TV data based on graph computing, and the method includes:

[0006] Converting the preprocessed smart TV data into the format of an online analytical processing database;

[0007] Based on the data in the online analytical processing database, using a graph computing engine to define entities and the relationships between the entities, and based on the relationships between the entities, constructing a graph model and materializing and storing the graph model data in a graph database;

[0008] Querying the current TV data based on the graph database to obtain a query result, where the current TV data includes abnormal TV data.

[0009] In an embodiment, before converting the preprocessed smart TV data, the method further includes:

[0010] Collecting the raw data of smart TVs and projection devices according to a streaming and batch processing engine, and preprocessing the raw data to obtain the preprocessed smart TV data, where the preprocessing includes cleaning and statistical processing.

[0011] In an embodiment, the step of using a graph computing engine to define entities and the relationships between the entities, and based on the relationships between the entities, constructing a graph model based on the data in the online analytical processing database includes:

[0012] Identify and define entities related to smart TVs based on the data in the online analysis and processing database, where the entities are represented by nodes and include devices, users, and media assets;

[0013] Define the relationships between entities based on the association information of the entities, where the relationships between entities are represented by edges and include the relationships between users, the operation relationships between users and devices, and the operation relationships between devices and media assets;

[0014] Obtain a graph model through a graph computing engine based on the relationships between the entities.

[0015] In one embodiment, the materializing and storing the graph model into a graph database includes:

[0016] Convert the graph model data into a physical storage structure,

[0017] Write the nodes and the edges into the graph database in a batch insertion manner.

[0018] In one embodiment, after the graph model data is materialized and stored into the graph database, the method further includes:

[0019] Store the graph model data into the graph database through real-time and offline graph construction methods, where

[0020] The real-time graph construction method includes: when the graph model data is received in real time, update the corresponding nodes and edges in the graph database, or store the graph model data into the graph database;

[0021] The offline graph construction method includes: storing the historical data or the graph model data generated regularly into the graph database through a batch processing job.

[0022] In one embodiment, the querying the current TV data based on the graph database to obtain a query result includes:

[0023] Perform dynamic threshold calculation based on the historical smart TV data, and real-time judge the abnormal situation of the current TV data based on the dynamic threshold;

[0024] In the case where the current TV data is abnormal, aggregate and frequency-convert the abnormal TV data through Flink SQL, and alarm the abnormal TV data after the aggregation and frequency-conversion processing;

[0025] Perform root cause analysis on the abnormal TV data through the graph database to determine the root cause of the abnormality.

[0026] In one embodiment, the attribution analysis of the abnormal TV data through the graph database to determine the cause of the anomaly includes:

[0027] Obtain incremental data on metrics, alarms, task / service change records, weather, and traffic;

[0028] Perform graph calculations on the incremental data and the full - volume data in the graph database to obtain an updated graph database;

[0029] Based on the updated graph database, perform attribution analysis on the abnormal TV data to determine the cause of the anomaly.

[0030] In a second aspect, an embodiment of the present application provides a TV data query system based on graph calculation. The system includes a conversion format module, a graph model construction module, and a query module; where:

[0031] The conversion format module is used to convert the pre - processed smart TV data into the format of an online analytical processing database;

[0032] The graph model construction module is used to define entities and the relationships between the entities based on the data in the online analytical processing database by using a graph calculation engine, construct a graph model based on the relationships between the entities, and physically store the graph model data in the graph database;

[0033] The query module is used to query the current TV data based on the graph database to obtain a query result, where the current TV data includes abnormal TV data.

[0034] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the TV data query method based on graph calculation as described in the first aspect above.

[0035] In a fourth aspect, an embodiment of the present application provides a computer - readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the TV data query method based on graph calculation as described in the first aspect above.

[0036] The TV data query method, system, and medium provided by the embodiments of the present application have at least the following technical effects.

[0037] First, convert the preprocessed smart TV data into the format of an online analytical processing (OLAP) database. Then, based on the data in the OLAP database, define entities and their relationships using a graph computing engine, construct a graph model, and materialize and store the graph model in a graph database. Finally, query the current TV data (including abnormal data) based on the graph database to obtain the analysis results. This realizes the full-chain processing from raw data to structured storage, then to graph computing and real-time query, solves the problem of low efficiency in querying complex smart TV and projection device data across hardware and software platforms, multiple scenarios, and multiple fields, and improves the query efficiency of TV data.

[0038] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects, and advantages of this application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of this application, form a part of this application, and the illustrative embodiments and descriptions thereof are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0040] Figure 1 is a flowchart of a TV data query method based on graph computing;

[0041] Figure 2 is a schematic diagram of the overall structure of TV data query shown according to an exemplary embodiment;

[0042] Figure 3 is a schematic diagram of the process of step S102 shown according to an exemplary embodiment;

[0043] Figure 4 is a schematic diagram of the process of step S103 shown according to an exemplary embodiment;

[0044] Figure 5 is a block diagram of the structure of a TV data query system based on graph computing shown according to an exemplary embodiment;

[0045] Figure 6 is a block diagram of the structure of an electronic device shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the objectives, technical solutions, and advantages of this application clearer, the following describes and explains this application with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in this application without creative efforts fall within the scope of protection of this application.

[0047] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in such a development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing, or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as the content disclosed in the present application being insufficient.

[0048] When the term "embodiment" is mentioned in the present application, it means that the specific features, structures, or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0049] Unless otherwise defined, the technical terms or scientific terms involved in the present application should have the ordinary meaning understood by those of ordinary skill in the technical field to which the present application belongs. The terms "a", "an", "one", "the", and the like involved in the present application do not indicate a quantity limitation and can represent a singular or plural number. The terms "comprising", "including", "having", and any variations thereof involved in the present application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products, or devices. The terms "connected", "coupled", and the like involved in the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" involved in the present application refers to two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates an "or" relationship between the associated objects before and after. The terms "first", "second", "third", etc. involved in the present application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0050] In this context, it should be understood that the terms involved can be technical means for implementing a part of the present invention or other summary technical terms. For example, the terms may include:

[0051] OLAP (Online Analytical Processing) database: It refers to an online analytical processing database.

[0052] Tugraph-Analytics: It is a high-performance graph computing engine used to process analysis and computing tasks for large-scale graph data.

[0053] Incremental Data: It refers to the data newly added or modified after a certain point in time. It only contains the changed part relative to the last update, rather than the entire dataset.

[0054] Full Data: It refers to the data set that contains all historical records and the current latest status. It covers all relevant data existing within a specific time period or system.

[0055] Media assets, namely Media Asset, refer to all digital content and its related metadata involved in the production, management, and distribution of multimedia content. These digital contents can be in various forms such as videos, audios, images, documents, etc.

[0056] In a first aspect, the embodiments of the present application provide a method for querying TV data based on graph computing. Figure 1 It is a flowchart of the method for querying TV data based on graph computing. Figure 2 It is a schematic diagram of the overall structure of TV data query shown according to an exemplary embodiment, as Figure 1 and 2 shown, a method for querying TV data based on graph computing, the method includes:

[0057] Step S101, convert the preprocessed smart TV data into the format of an online analytical processing database.

[0058] Step S102, based on the data in the online analytical processing database, use the graph computing engine to define entities and the relationships between entities, and based on the relationships between entities, construct a graph model and physically store the graph model data in the graph database.

[0059] Step S103, based on the graph database, query the current TV data to obtain a query result, where the current TV data includes abnormal TV data.

[0060] In summary, the method for querying TV data based on graph computing provided by the embodiments of the present application first converts the preprocessed smart TV data into the format of an online analytical processing (OLAP) database; then, based on the data in the OLAP database, uses a graph computing engine to define entities and their relationships, constructs a graph model, and materializes and stores the graph model in a graph database; finally, queries the current TV data (including abnormal data) based on the graph database to obtain analysis results. It realizes the full-chain processing from raw data to structured storage, then to graph computing and real-time query, solves the problem of low efficiency in querying complex smart TV and projection device data in the face of cross-hardware and software platforms, multiple scenarios and multiple fields, and improves the query efficiency of TV data.

[0061] In one embodiment, step S101 is to convert the preprocessed smart TV data into the format of an online analytical processing database. Specifically, it includes:

[0062] Organize the preprocessed data into dimensions (such as time, location, device type, etc.) and measures (such as viewing duration, click count, etc.) to form a multi-dimensional data model suitable for OLAP processing.

[0063] After step S101 converts the data into the OLAP format, it can support efficient multi-dimensional queries and complex analyses, significantly improving the query response speed.

[0064] In one embodiment, before converting the preprocessed smart TV data, the method further includes:

[0065] Collect the raw data of smart TVs and projection devices according to the streaming and batch processing engines, and preprocess the raw data to obtain the preprocessed smart TV data, where the preprocessing includes cleaning and statistical processing.

[0066] Optionally, the streaming engine (such as Apache Flink): consumes the raw data streams from smart TVs and projection devices in real time, such as user clicks, viewing duration, device status changes, etc. The batch processing engine (such as Apache Spark): processes batches of historical data regularly, such as daily summarized user behavior records. In the data processing pipeline of the smart TV platform, the raw data usually comes from multiple heterogeneous sources, such as user behavior logs, device status reports, etc. These data often contain noise and inconsistencies and need to be preprocessed to ensure their quality and usability. The raw data is cleaned and statistically processed through the streaming engine and the batch processing engine. Cleaning can be removing obviously incorrect or illogical data points, such as abnormally high viewing duration or invalid device IDs. Statistics can be performing preliminary aggregation calculations on the raw data, such as counting the total viewing duration and click count of users by time dimensions such as hours, days, weeks, etc.

[0067] The quality of the data is effectively improved through cleaning and statistical processing in the preprocessing step, reducing noise and inconsistencies and ensuring the accuracy of subsequent analysis.

[0068] Figure 3 It is a schematic flowchart of step S102 shown according to an exemplary embodiment. As Figure 3 shown, in step S102, based on the data in the online analytical processing database, use a graph computing engine to define entities and the relationships between entities. Based on the relationships between entities, construct a graph model and materialize and store the graph model data in a graph database. Specifically, it includes:

[0069] Step S1021: According to the data in the online analytical processing database, identify and define entities related to smart TVs. Among them, entities are represented by nodes, and entities include devices, users, and media assets.

[0070] Step S1022: Based on the association information of entities, define the relationships between entities. Among them, the relationships between entities are represented by edges, and the relationships between entities include the relationships between users, the operation relationships between users and devices, and the operation relationships between devices and media assets.

[0071] Step S1023: Through the graph computing engine, obtain a graph model based on the relationships between entities.

[0072] Optionally, first, use a graph to define the relationships between entities. In the graph, nodes are used to represent entities, and edges are used to represent relationships. For example, in device, user (member), and media asset operation behaviors, there are three types of entities: devices, users, and media assets, corresponding to three points in the graph. In addition, the relationships between users, the operation clicks between users and devices, and the operation behaviors between devices and media assets are the edges in the graph. By defining the nodes and edges, the relationships between entities can be defined using the graph.

[0073] Subsequently, after the definition of the graph, convert the graph model data into a physical storage structure, and write the nodes and edges into the graph database in a batch insertion manner. Specifically, the graph can be used to materialize this relationship, thereby accelerating the query. Materializing the relationship is to convert the logically graph into a physical graph through graph construction. For example, write data such as users, media assets, transactions, and relationships into the graph storage through a graph construction engine. Then, graph computing can be performed through OLAP services or graph computing jobs. The beneficial effects are that the relationships between physicalized points and edges can be done in real time, and the performance of the entire query can be improved using the graph physicalized structure. Real-time queries of multi-table associations can be performed. In addition, compared with wide tables, points and edges can be flexibly added, and no changes need to be made to the original structure.

[0074] There are two ways to build a graph model and use the graph to define a relationship model. First, manual modeling, where one manually analyzes which entities and relationships exist in the system and defines the graph. For example, in a user-media asset graph, there are two types of nodes: users and media assets, and two types of edges: trade and relation. Second, automatic modeling, where the associated relationships are analyzed and modeled automatically based on the relevance of statements and existing models in the data warehouse.

[0075] Step S102 constructs a complete graph model by defining entities and their relationships, making the associations between different types of entities clearer and facilitating complex association analysis. Materializing and storing the graph model in a graph database not only improves query efficiency but also ensures data consistency and integrity, facilitating subsequent real-time queries and complex analysis. Materializing and storing the graph model in a graph database not only improves query efficiency but also ensures data consistency and integrity, facilitating subsequent real-time queries and complex analysis.

[0076] In one embodiment, after materializing and storing the graph model data in the graph database, the method further includes:

[0077] Storing the graph model data in the graph database through real-time and offline graph construction methods, where

[0078] The real-time graph construction method includes: when receiving graph model data in real time, updating the corresponding nodes and edges in the graph database or storing the graph model data in the graph database;

[0079] The offline graph construction method includes: storing historical data or periodically generated graph model data in the graph database through batch processing jobs.

[0080] Optionally, after building the graph model, the data in the data warehouse table can be written into the graph using both real-time and offline graph construction methods, and writing from multiple external data sources is also supported. A data warehouse table is a structured table used to store and manage data in a Data Warehouse (DW). SQL can be automatically converted to graph queries. First, a graph model is required. For example, there are three nodes V1, V2, V3 and three edges e1, e2, e3. The SQL statement is v1 join el join v2. First, it will be converted into relational algebra, such as in the form of LogicalJoin. Then, it is optimized and rewritten by an optimizer to convert the join into a match and convert the SQL statement into the ability to perform graph queries.

[0081] Figure 4 It is a flowchart of step S103 shown according to an exemplary embodiment, as Figure 4As shown, when the current TV data is abnormal TV data, in step S103, query the current TV data based on the graph database to obtain the query result, where the current TV data includes abnormal TV data. Specifically, it includes the following steps:

[0082] Step S1031: Calculate the dynamic threshold based on the historical smart TV data, and judge the abnormality of the current TV data in real time based on the dynamic threshold.

[0083] Optionally, the anomaly detection includes two modules. One is the dynamic threshold calculation module, which consumes the data in the data warehouse, then calculates the dynamic threshold based on the historical data, and then stores the dynamic threshold in MySQL. The other module consumes the data in the data warehouse, combines the dynamic threshold in MySQL to judge the anomaly. When it is judged that the data is abnormal, the abnormal data is sent to Kafka for the graph construction of tugraph-analytics.

[0084] Specifically, first, the dynamic threshold calculation module uses a batch processing engine (such as Apache Spark) to regularly extract historical smart TV data from the data warehouse (such as Hive, Presto). The data includes user behavior data (such as viewing duration, click times), device status data (such as startup time, shutdown time), etc. Statistically analyze the extracted historical data to calculate the dynamic threshold for each user. The calculation results include the normal behavior range for each user or device (such as the maximum viewing duration, the minimum operation interval, etc.). Store the calculated dynamic threshold in the MySQL database to ensure that the threshold information is easy to access and query. Subsequently, the anomaly judgment module uses a streaming processing engine (such as Apache Flink) to consume the new data stream from smart TVs and projection devices in real time, such as user clicks, plays, pauses, etc. Read the pre-calculated dynamic threshold from the MySQL database to ensure that the latest threshold information is available for each judgment. Finally, compare each new piece of received data with the pre-calculated dynamic threshold to judge whether it is abnormal. If the new data exceeds the set threshold, it is marked as abnormal and the corresponding alarm mechanism is triggered. Send the detected abnormal data to the Kafka (message queue) topic for subsequent use by the graph calculation engine (such as Tugraph-Analytics).

[0085] The dynamic threshold calculated based on historical data in step S1031 can more accurately reflect the normal behavior pattern, reducing false alarms and missed detections. Combining with the streaming processing engine, it realizes the anomaly detection of real-time data and ensures timely response.

[0086] Step S1032: When the current TV data is abnormal, the abnormal TV data is aggregated and frequency-converted through Flink SQL, and an alarm is issued for the abnormal TV data after aggregation and frequency conversion.

[0087] Optionally, first, after the abnormal data is detected by the abnormal judgment module, the data is sent to the Kafka topic for subsequent processing. Use Flink SQL to consume the abnormal data stream from the Kafka topic to ensure real-time data processing. Subsequently, the received abnormal data is aggregated, for example, the number and duration of abnormal events are counted by dimensions such as user ID, device ID or content ID. The abnormal data is grouped and aggregated through a window mechanism (such as a rolling window or a sliding window) to ensure the accuracy and timeliness of the aggregation results. Next, different window sizes and trigger conditions are set according to business needs to achieve variable frequency alarms. During business peaks (such as prime time), a shorter time window and a higher alarm threshold are set to ensure timely response. During business off-peak periods (such as late at night), a longer time window and a lower alarm threshold are set to reduce unnecessary alarms. Finally, the abnormal data after aggregation and frequency conversion processing is sent to the alarm system to trigger the corresponding notification mechanism. The notification method can include email, SMS, instant messaging tools, etc. to ensure that relevant personnel receive the alarm information in a timely manner.

[0088] Step S1032 performs aggregation and frequency conversion through Flink SQL to achieve the effect of noise reduction. The new process has three major advantages. One is that the streaming engine is more timely, and currently all operations are implemented through SQL or GQL, which also improves development efficiency to a certain extent; the second advantage is the introduction of a dynamic threshold algorithm, which calculates the alarm threshold based on historical data, which is more accurate than the static threshold method; the third is the implementation of frequency conversion alarms, which uses windows to perform aggregation and frequency conversion. The alarm sensitivity requirements for indicators during business peaks or some core scenarios are higher, while the alarm sensitivity requirements for off-peak periods or non-core scenarios are relatively low. We have implemented flexible window frequency conversion notifications through Flink, which has achieved the effect of noise reduction.

[0089] Step S1033: Perform attribution analysis on abnormal TV data through the graph database to determine the cause of the abnormality.

[0090] Optionally, first, for incremental data stream processing, the newly incoming abnormal TV data is used as an incremental update and is loaded into the graph database in real time. The incrementally updated data will be subject to graph calculation together with the full-volume data to ensure the latest state of the graph model. Subsequently, for multi-source data fusion, combined with metrics, alarms, task / service change records, and external information such as weather and traffic, a comprehensive graph model is constructed. Complex association analysis is performed through a graph calculation engine (such as Tugraph-Analytics) to analyze the complex patterns and relationships in the data. Immediately afterwards, for path finding and community discovery, graph algorithms (such as the shortest path algorithm) are used to identify the potential associations between abnormal data and other entities. For example, the association path between an abnormal device and the most recent service change is found through the shortest path algorithm, or the affected user groups are identified through the community discovery algorithm. Then, causal reasoning is carried out. Based on the entity relationships in the graph model, causal reasoning is performed to determine the specific cause of the abnormality. For example, if the failure rate of devices in a certain area suddenly increases, it can be inferred whether it is caused by bad weather in combination with weather data; or whether it is caused by a system upgrade in combination with service change records. Finally, for result output and update, the results of the attribution analysis are output into a table for subsequent query and analysis. At the same time, the incremental data is updated to the full-volume graph to maintain the latest state of the graph data.

[0091] It should be noted that for the query of non-abnormal TV data, the query can be carried out through the following steps. First, the current TV data (including user behavior, device status, content operations, etc.) is loaded from the data warehouse to ensure the latestness and integrity of the data. Subsequently, using the graph model pre-constructed in the graph database, complex queries are performed based on nodes (such as users, devices, media assets) and edges (such as operation relationships, time relationships). The queries can be multi-dimensional, for example, by time period, user group, device type, etc., to meet different business requirements. Finally, detailed reports or statistical data are generated, such as users' viewing habits, device usage frequencies, popular content, etc. Combining historical data and external information (such as weather, traffic, etc.) provides richer background support.

[0092] Step S103 efficiently queries the current TV data (including abnormal TV data) through the graph database. Using the entity relationships stored in the graph database and the pre-constructed graph model, detailed query results can be quickly obtained. It can not only reflect the complex associations between data, but also combine historical data and various external information (such as alarms, task changes, weather, etc.), thus effectively supporting real-time monitoring, anomaly detection, and attribution analysis. When dealing with the parsing of complex data relationships across hardware and software platforms, multiple scenarios, and multiple domains, the accuracy of anomaly detection and anomaly attribution is improved.

[0093] In summary, for the TV data query method based on graph computing provided by the embodiments of the present application, first, the preprocessed smart TV data is converted into the format of an online analytical processing (OLAP) database; then, based on the data in the OLAP database, a graph computing engine is used to define entities and their relationships, a graph model is constructed, and the graph model is physically stored in a graph database; finally, based on the graph database, the current TV data (including abnormal data) is queried to obtain the analysis result. The full-chain processing from raw data to structured storage, then to graph computing and real-time query is realized, solving the problem of low efficiency in querying complex smart TV and projection device data in the face of cross-hardware and software platforms, multiple scenarios, and multiple fields, and improving the query efficiency of TV data.

[0094] In a second aspect, the embodiments of the present application provide a TV data query system based on graph computing. Figure 5 It is a block diagram of a TV data query system based on graph computing shown according to an exemplary embodiment. As Figure 5 shown, the system includes a format conversion module 510, a graph model construction module 520, and a query module 530; among them:

[0095] The format conversion module 510 is configured to convert the preprocessed smart TV data into the format of an online analytical processing database;

[0096] The graph model construction module 520 is configured to define entities and the relationships between entities based on the data in the online analytical processing database, construct a graph model based on the relationships between entities, and physically store the graph model data in a graph database;

[0097] The query module 530 is configured to query the current TV data based on the graph database to obtain a query result, where the current TV data includes abnormal TV data.

[0098] In summary, the TV data query system based on graph computing provided by the present application realizes the full-chain processing from raw data to structured storage, then to graph computing and real-time query through the format conversion module 510, the graph model construction module 520, and the query module 530, solves the problem of low efficiency in querying complex smart TV and projection device data in the face of cross-hardware and software platforms, multiple scenarios, and multiple fields, and improves the query efficiency of TV data.

[0099] It should be noted that the TV data query system based on graph computing provided in this embodiment is used to implement the above-mentioned implementation manners, and those that have been described will not be repeated. As used above, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can implement a predetermined function. Although the devices described in the above embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0100] In a third aspect, an embodiment of the present application provides an electronic device, Figure 6 which is a block diagram of an electronic device shown according to an exemplary embodiment. As Figure 6 shown, the electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0101] Specifically, the above-mentioned processor 61 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be one or more integrated circuits configured to implement the embodiments of the present application.

[0102] Among them, the memory 62 may include a mass memory for data or instructions. By way of example and not limitation, the memory 62 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 62 may include removable or non-removable (or fixed) media. In appropriate cases, the memory 62 may be internal or external to the data processing device. In a particular embodiment, the memory 62 is a non-volatile memory. In a particular embodiment, the memory 62 includes a read-only memory (ROM) and a random access memory (RAM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable read-only memory (EAROM), or a flash memory, or a combination of two or more of these. In appropriate cases, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0103] The memory 62 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 61.

[0104] By reading and executing the computer program instructions stored in the memory 62, the processor 61 implements any of the above-described graph-computation-based TV data query methods in the embodiments.

[0105] In one embodiment, the graph-computation-based TV data query device may further include a communication interface 63 and a bus 60. Among them, as Figure 6 shown, the processor 61, the memory 62, and the communication interface 63 are connected through the bus 60 and complete communication with each other.

[0106] The communication interface 63 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication port 63 can also implement data communication with other components, such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations, etc.

[0107] The bus 60 includes hardware, software, or both, and couples the components of the graph-computing-based TV data query device to each other. The bus 60 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, the bus 60 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. In a suitable case, the bus 60 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0108] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, the graph-computing-based TV data query method provided in the first aspect is implemented.

[0109] Among them, more specifically, the readable storage medium may include, but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0110] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps of implementing the graph-computing-based TV data query method provided in the first aspect.

[0111] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, executed as an independent software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0112] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0113] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A television data query method based on graph computing, characterized in that: The method comprises: Convert the pre-processed smart TV data into the format of an online analysis and processing database; Based on the data in the online analytical processing database, using a graph computing engine to define entities and relationships between the entities, building a graph model based on the relationships between the entities, and materializing and storing the data of the graph model in the graph database; Based on the graph database, current television data is queried to obtain query results, wherein the current television data includes abnormal television data.

2. The method according to claim 1, characterized in that Based on the data in the online analytical processing database, defining entities and relationships between the entities using a graph computing engine, and constructing a graph model based on the relationships between the entities, including: According to the data in the online analysis and processing database, identifying and defining entities related to the smart TV, wherein the entities are represented by nodes and the entities include devices, users and media assets; Based on the association information of the entities, define the relationship between the entities, wherein the relationship between the entities is represented by an edge, and the relationship between the entities includes the relationship between users, the operation relationship between users and devices, and the operation relationship between devices and media assets; A graph model is obtained through a graph computing engine based on the relationship between the entities.

3. The method according to claim 2, characterized in that The materializing and storing the data of the graph model in the graph database includes: Convert the data of the graph model into a physical storage structure, The nodes and the edges are written into the graph database by batch insertion.

4. The method according to claim 1, characterized in that: The querying of the current television data based on the graph database to obtain the query result includes: According to the historical smart TV data, dynamic threshold calculation is performed, and abnormal conditions of current TV data are judged in real time based on the dynamic threshold; In the case where the current TV data is abnormal, the abnormal TV data is aggregated and frequency-converted through Flink SQL, and an alarm is issued for the abnormal TV data after the aggregation and frequency-conversion processing; The abnormal TV data is subjected to attribution analysis through the graph database to determine the cause of the abnormality.

5. The method according to claim 4, characterized in that The attribution analysis of the abnormal TV data is performed through the graph database to determine the abnormal cause, including: Get incremental data on indicators, alarms, task change records, and service change records; Perform graph calculation on the incremental data and the full data in the graph database to obtain an updated graph database; Based on the updated graph database, an attribution analysis is performed on the abnormal television data to determine the cause of the abnormality.

6. The method according to claim 1, characterized in that Before the pre-processed smart TV data is transmitted, the method further includes: According to the streaming and batch processing engines, the original data of the smart TV and the projection device are collected, and the original data are preprocessed to obtain the preprocessed smart TV data, wherein the preprocessing includes cleaning and statistical processing.

7. The method according to claim 1, characterized in that After materializing and storing the graph model data in the graph database, the method further includes: The graph model data is stored in the graph database in a real-time and offline mapping manner, wherein: The real-time graphing method includes: in response to receiving the graph model data in real time, updating corresponding nodes and edges in the graph database, or storing the graph model data in the graph database; The offline mapping method includes: storing historical data or the graph model data generated regularly into the graph database through a batch processing job.

8. A television data query system based on graph computing, characterized in that: The system includes a format conversion module, a graph model construction module and a query module; wherein: The format conversion module is used to convert the pre-processed smart TV data into the format of the online analysis and processing database; The graph model building module is used to define entities and relationships between the entities based on the data in the online analytical processing database using a graph computing engine, build a graph model based on the relationships between the entities, and materialize and store the graph model data in the graph database; The query module is used to query the current television data based on the graph database to obtain query results, wherein the current television data includes abnormal television data.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, a computer program stored in the memory and executable on the processor, and the processor implements a television data query method based on graph computing as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a television data query method based on graph computing as claimed in any one of claims 1 to 7 is implemented.

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