Methods and apparatus for determining spectra, storage media and electronic devices

Abstract

This invention discloses a method, apparatus, storage medium, and electronic device for determining a knowledge graph, relating to the field of smart home technology. The method for determining the knowledge graph includes: acquiring metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information and task engineering dimension information; creating target nodes based on the metadata information; connecting the target nodes using a preset pattern graph structure, given the logical relationship between the different tasks, to obtain a knowledge graph containing different tasks; and packaging the knowledge graph and the metadata information to obtain a task knowledge graph, which can solve the problem in the prior art of being unable to quickly query data for tasks with complex data links.

Description

Technical Field

[0001] This invention relates to the field of smart homes, and more specifically, to a method and apparatus for determining a map, a storage medium, and an electronic device. Background Technology

[0002] The accuracy and timeliness of data output depend to some extent on whether the task runs smoothly. Job metadata, serving as the guiding principle for the entire data chain, is intricate and complex, making it difficult to handle using traditional processing methods. When the chain is complex, retrieval performance and accuracy will significantly decrease. Furthermore, the intricate task chain, coupled with traditional database storage, results in complex table design and cumbersome querying. With large datasets, retrieval efficiency will be greatly reduced.

[0003] No effective technical solution has yet been proposed to address the problem that related technologies cannot perform fast data queries for tasks with complex data links. Summary of the Invention

[0004] This invention provides a method and apparatus for determining a map, a storage medium, and an electronic device, to at least solve the problem in related technologies that it is impossible to perform fast data queries for tasks with complex data links.

[0005] According to an embodiment of the present invention, a method for determining a knowledge graph is provided, comprising: acquiring metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information, task engineering dimension information; creating target nodes based on the metadata information; connecting the target nodes using a preset pattern graph structure when the logical relationship between the different tasks is determined, thereby obtaining a knowledge graph containing different tasks; and packaging the knowledge graph and the metadata information to obtain a task knowledge graph.

[0006] In an exemplary embodiment, after packaging the graph and the metadata information to obtain a task knowledge graph, the method further includes: storing the task knowledge graph in a database and setting a query entry point for the task knowledge graph; wherein the query entry point includes at least one of the following: a task query entry point, an output table query entry point, and an application query entry point; receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph.

[0007] In an exemplary embodiment, receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph includes: when the task query entry point receives a query instruction, obtaining the running status of different nodes in all task links in the task knowledge graph and the runtime of each node in the different nodes, wherein the task knowledge graph contains data links corresponding to different tasks; generating a task running graph corresponding to the task knowledge graph based on the running status and the runtime; visually marking the task running graph in the task running graph using a preset status identifier, using the marked task running graph as data information, and sending the data information to the visualization display interface.

[0008] In an exemplary embodiment, receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph includes: when the output table query entry point receives a query instruction, determining the query information of the output table to be queried carried by the query instruction; determining the specific position of the current output table to be queried in all task links in the task knowledge graph and the running status of the current output table to be queried based on the query information; generating a visual output table based on the specific position and the running status; using the visual output table as data information; and sending the data information to the visual display interface.

[0009] In an exemplary embodiment, receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph includes: when the application query entry point receives a query instruction, determining the application requirement information corresponding to the query instruction; wherein the application requirement information is used to indicate the acquisition of link information corresponding to the application; determining multiple target data links supporting the application in the task knowledge graph based on the application requirement information, and determining the current running status and runtime of each of the multiple target data links, wherein the target data links are a portion of the data links corresponding to different tasks included in the task knowledge graph; constructing an application support graph using the multiple target data links, the current running status of each of the multiple target data links, and the runtime of each of the multiple target data links, using the application support graph as data information, and sending the data information to a visualization display interface.

[0010] In an exemplary embodiment, after creating a target node based on the metadata information, the method further includes: obtaining update information of the metadata information corresponding to the target node; parsing the update information to obtain attributes to be updated; and using the attributes to be updated to change the attribute information of the target node, wherein the attribute information is used to describe the target node.

[0011] In an exemplary embodiment, after determining the logical relationship between the different tasks and connecting the target nodes using a preset pattern graph structure to obtain a graph containing different tasks, the method further includes: obtaining the current running time and current running result corresponding to the target node; if the current running time is greater than a preset threshold, determining the target node as an excessively long node, and setting an optimization strategy for the excessively long node in the graph; if the current running result indicates that the task has failed, generating a prompt message, wherein the prompt message is used to indicate that the target node in the current graph is an invalid node.

[0012] According to another embodiment of the present invention, a graph determination device is provided, comprising: an acquisition module, configured to acquire metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information, task engineering dimension information; a creation module, configured to create target nodes based on the metadata information; a connection module, configured to connect the target nodes using a preset pattern graph structure, given a determined logical relationship between the different tasks, to obtain a graph containing different tasks; and a packaging module, configured to package the graph and the metadata information to obtain a task knowledge graph.

[0013] In an exemplary embodiment, the above-described apparatus further includes: a query module, configured to store the task knowledge graph in a database and set a query entry point for the task knowledge graph; wherein the query entry point includes at least one of the following: a task query entry point, an output table query entry point, and an application query entry point; receiving query instructions from the front end through the query entry point and obtaining data information matching the query instructions from the task knowledge graph.

[0014] In an exemplary embodiment, the query module is further configured to, upon receiving a query instruction at the task query entry, obtain the running status of different nodes in all task links of the task knowledge graph and the runtime of each node in the different nodes, wherein the task knowledge graph contains data links corresponding to different tasks; generate a task running graph corresponding to the task knowledge graph based on the running status and the runtime; visually identify the task running graph in the task running graph using a preset status identifier, use the identified task running graph as data information, and send the data information to the visualization display interface.

[0015] In an exemplary embodiment, the query module is further configured to, upon receiving a query instruction at the output table query entry, determine the query information of the output table to be queried carried by the query instruction; determine the specific location of the current output table to be queried in all task links in the task knowledge graph and the running status of the current output table to be queried based on the query information; generate a visual output table based on the specific location and the running status; use the visual output table as data information; and send the data information to the visual display interface.

[0016] In an exemplary embodiment, the query module is further configured to, upon receiving a query instruction at the application query entry, determine application requirement information corresponding to the query instruction; wherein the application requirement information is used to indicate the acquisition of link information corresponding to the application; determine multiple target data links supporting the application in the task knowledge graph based on the application requirement information, and determine the current operating status and runtime of each of the multiple target data links, wherein the target data links are a subset of the data links corresponding to different tasks contained in the task knowledge graph; construct an application support graph using the multiple target data links, the current operating status of each of the multiple target data links, and the runtime of each of the multiple target data links, use the application support graph as data information, and send the data information to the visualization display interface.

[0017] In one exemplary embodiment, the above apparatus further includes: an update module, configured to acquire update information of metadata information corresponding to the target node; parse the update information to obtain an attribute to be updated; and use the attribute to be updated to change the attribute information of the target node, wherein the attribute information is used to describe the target node.

[0018] In one exemplary embodiment, the above-described apparatus further includes: an identification module, configured to acquire the current running time and current running result corresponding to the target node; if the current running time is greater than a preset threshold, determine that the target node is an ultra-long node, and set an optimization strategy for the ultra-long node in the graph; if the current running result indicates that the task has failed, generate a prompt message, wherein the prompt message is used to indicate that the target node in the current graph is an invalid node.

[0019] According to yet another embodiment of the present invention, a storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0020] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0021] This invention obtains metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information, task engineering dimension information; creates target nodes based on the metadata information; connects the target nodes using a preset pattern graph structure, given the logical relationship between different tasks, to obtain a graph containing different tasks; packages the graph and metadata information to obtain a task knowledge graph, that is, writes the entire task link relationship corresponding to different tasks into the graph, and uses the pattern graph structure corresponding to the graph to preserve complex task link relationships, enabling fast querying of task-related data using the determined task knowledge graph. Therefore, it can solve the problem of the inability to quickly query data for tasks with complex data links in the prior art. Furthermore, this task knowledge graph supports multi-level relationship queries, and the response speed is significantly improved compared to traditional databases. Combined with the front end, it forms the ability to query tasks and view task links in a timely manner, presenting the entire task network more conveniently on the front end, making it easier for development and maintenance personnel to discover problems in a timely manner. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the hardware environment for a method of determining a spectrum according to an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of a method for determining a spectrum according to an embodiment of the present invention;

[0026] Figure 3 This is a sequence diagram of task metadata management according to an optional embodiment of the present invention;

[0027] Figure 4 This is a structural block diagram of a spectrum determination device according to an embodiment of the present invention;

[0028] Figure 5 This is a structural block diagram of another spectrum determination device according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] According to one aspect of the present invention, a method for determining a map is provided. This method is widely applicable to whole-house intelligent digital control application scenarios such as smart homes, smart home ecosystems, and intelligence house ecosystems. Optionally, in this embodiment, the above-mentioned method for determining the map can be applied to, for example... Figure 1 The hardware environment shown consists of terminal device 102 and server 104. For example... Figure 1 As shown, server 104 is connected to terminal device 102 via a network and can be used to provide services (such as application services) to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for server 104. Cloud computing and / or edge computing services can be configured on the server or independently of the server to provide data processing services for server 104.

[0032] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal device 102 may not be limited to PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projector, smart TV, smart clothes rack, smart curtains, smart audio-visual equipment, smart socket, smart speaker, smart speaker box, smart fresh air equipment, smart kitchen and bathroom equipment, smart bathroom equipment, smart robot vacuum cleaner, smart window cleaning robot, smart mopping robot, smart air purifier, smart steam oven, smart microwave oven, smart water heater, smart air purifier, smart water dispenser, smart door lock, etc.

[0033] This embodiment provides a method for determining a spectrum. Figure 2 This is a flowchart of a method for determining a spectrum according to an embodiment of the present invention, the process including the following steps:

[0034] Step S202: Obtain metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information, task engineering dimension information;

[0035] Step S204: Create a target node based on the metadata information;

[0036] Optionally, given the metadata information, a target node can be generated in the target application. This target node records the task execution status and content information of a specific task at the current time. For example, in a video processing workflow, the task execution status could be the current state of the video being processed, such as 30% of the video data has been processed. The content information could be the data format of the processed video and the corresponding video name. The above is merely an example and does not limit the method described above.

[0037] Step S206: After determining the logical relationship between the different tasks, the target nodes are connected using a preset pattern graph structure to obtain a graph containing different tasks.

[0038] Step S208: Package the graph and the metadata information to obtain a task knowledge graph.

[0039] Through the above steps, metadata information corresponding to different tasks is obtained. This metadata information includes at least one of the following: task runtime information and task engineering dimension information. Target nodes are created based on the metadata information. Given the logical relationships between different tasks, a preset schema graph structure is used to connect the target nodes, resulting in a graph containing different tasks. The graph and metadata information are packaged to obtain a task knowledge graph, which writes the entire task chain relationship corresponding to different tasks into the graph. The schema graph structure corresponding to the graph preserves complex task chain relationships, enabling rapid querying of task-related data using the determined task knowledge graph. Therefore, this solves the problem of existing technologies being unable to quickly query data for tasks with complex data chains. Furthermore, this task knowledge graph supports multi-level relationship queries, and its response speed is significantly improved compared to traditional databases. Combined with the front-end, it enables timely task querying and timely viewing of task chains, presenting the entire task network more conveniently on the front-end, facilitating timely problem detection by development and maintenance personnel.

[0040] As an optional embodiment, in order to improve the efficiency of acquiring metadata information corresponding to different tasks, a distributed data stream engine can be set up to acquire metadata information. This allows the acquisition of arbitrary metadata information to be performed in a data parallel and pipelined manner, thereby enhancing the efficiency of metadata information acquisition. Furthermore, an automatic acquisition process can be configured for the distributed data stream engine, allowing it to directly collect metadata information corresponding to different tasks when the distributed data stream engine starts its reading function.

[0041] In an exemplary embodiment, after packaging the graph and the metadata information to obtain a task knowledge graph, the method further includes: storing the task knowledge graph in a database and setting a query entry point for the task knowledge graph; wherein the query entry point includes at least one of the following: a task query entry point, an output table query entry point, and an application query entry point; receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph.

[0042] For example, when the task knowledge graph is stored in the database, a task query entry point can be set. Specifically, task nodes can be associated with other tasks, workflows, and other nodes to obtain the node status and runtime of the entire task chain. An output table query entry point can also be set. Specifically, output table nodes can be associated with other tasks, workflows, and other nodes to obtain the specific location and runtime of the output table within the entire task chain. An application query entry point can also be set. Specifically, application nodes can be associated with output tables, tasks, workflows, and other nodes to obtain the number of task chains the upper-level application has, the current runtime status of each chain, and its runtime. Ultimately, by supporting querying task runtime status from these three entry points, it is possible to promptly identify nodes with excessively long task execution times, failed task execution nodes, and the current stage of task execution. This facilitates development, operations, and product personnel in querying and identifying problems throughout the entire task chain, enabling timely optimization and adjustments.

[0043] In an exemplary embodiment, receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph includes: when the task query entry point receives a query instruction, obtaining the running status of different nodes in all task links in the task knowledge graph and the runtime of each node in the different nodes, wherein the task knowledge graph contains data links corresponding to different tasks; generating a task running graph corresponding to the task knowledge graph based on the running status and the runtime; visually marking the task running graph in the task running graph using a preset status identifier, using the marked task running graph as data information, and sending the data information to the visualization display interface.

[0044] In an exemplary embodiment, receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph includes: when the output table query entry point receives a query instruction, determining the query information of the output table to be queried carried by the query instruction; determining the specific position of the current output table to be queried in all task links in the task knowledge graph and the running status of the current output table to be queried based on the query information; generating a visual output table based on the specific position and the running status; using the visual output table as data information; and sending the data information to the visual display interface.

[0045] In an exemplary embodiment, receiving a query instruction from the front end through the query entry point and obtaining data information matching the query instruction from the task knowledge graph includes: when the application query entry point receives a query instruction, determining the application requirement information corresponding to the query instruction; wherein the application requirement information is used to indicate the acquisition of link information corresponding to the application; determining multiple target data links supporting the application in the task knowledge graph based on the application requirement information, and determining the current running status and runtime of each of the multiple target data links, wherein the target data links are a portion of the data links corresponding to different tasks included in the task knowledge graph; constructing an application support graph using the multiple target data links, the current running status of each of the multiple target data links, and the runtime of each of the multiple target data links, using the application support graph as data information, and sending the data information to a visualization display interface.

[0046] In an exemplary embodiment, after creating a target node based on the metadata information, the method further includes: obtaining update information of the metadata information corresponding to the target node; parsing the update information to obtain attributes to be updated; and using the attributes to be updated to change the attribute information of the target node, wherein the attribute information is used to describe the target node.

[0047] In an exemplary embodiment, after determining the logical relationship between the different tasks and connecting the target nodes using a preset pattern graph structure to obtain a graph containing different tasks, the method further includes: obtaining the current running time and current running result corresponding to the target node; if the current running time is greater than a preset threshold, determining the target node as an excessively long node, and setting an optimization strategy for the excessively long node in the graph; if the current running result indicates that the task has failed, generating a prompt message, wherein the prompt message is used to indicate that the target node in the current graph is an invalid node.

[0048] To better understand the process of determining the above-mentioned spectra, the following describes the process of determining the above-mentioned spectra in conjunction with several optional embodiments.

[0049] As an optional implementation, a knowledge graph-based task metadata management method is proposed. This method leverages the powerful computing capabilities and structural relationships of the knowledge graph to link task metadata into a vast task network, enhancing retrieval and management capabilities. Specifically, to address the intricate nature of task relationships, the entire task chain is written into the graph, forming a task knowledge graph. With the support of the graph structure, complex relationships are preserved, multi-level relationship queries are supported, and response speed is significantly improved. Combined with a front-end, this enables timely task querying and real-time viewing of task chains.

[0050] Optionally, in practical applications, based on the knowledge graph as the storage medium and computing engine, Flink (equivalent to the distributed data stream engine in the embodiment) captures the task metadata database binlog in real time and writes the acquired results into the graph library in real time. Additionally, it calls entity and relationship creation logic scripts to complete the graph structure creation, forming a graph network from the task metadata. This facilitates front-end access to obtain the task chain execution status and the relationships between multiple task chains. The entire task network is presented more conveniently on the front end, allowing development and operations personnel to promptly identify problems and understand the location of task execution.

[0051] Figure 3 This is a sequence diagram of task metadata management according to an optional embodiment of the present invention; it includes the following steps:

[0052] Step 1: Manage objects (actors) and start the program (Flink);

[0053] Step 2.1: The Flink cluster calls the task execution log table in the AZK configuration library of the data server; specifically, it includes: Flink-CDC task execution information: the task execution status is collected in the form of CDC monitoring Binlog; Flink-Jdbc project dimension information: the Flink-Jdbc connection is used to obtain the dimension information of the project corresponding to the task, mainly obtaining the project description information.

[0054] Step 2.2: The AZK configuration library returns the task's running status in real time;

[0055] Step 2.3: The Flink cluster periodically requests project metadata;

[0056] Step 2.4: Return project metadata;

[0057] Step 3.5: Based on logical judgment, create new nodes and update node-relationship status; that is, construct the task knowledge graph: create entity relationships according to the graph pattern to complete the task link network; update the entity attribute information of the graph in a timely manner according to the capture of task metadata.

[0058] Step 3.6: Periodically update the relationships between nodes to the Neo4j graph database;

[0059] Step 4.1: The graph database is organized by task and running status data; optionally, the task query entry of the task knowledge graph is operated as follows: task nodes are associated with task, workflow and other nodes to obtain the running status and running time of the entire task chain.

[0060] Step 4.2: The graph database runs the status data according to the output table; optionally, the operation mode of the task knowledge graph output table query entry is: using the output table node to associate with task, workflow and other nodes, to obtain the specific position and running status of the output table in the entire task chain.

[0061] Step 4.3: The graph database is used to retrieve application status data. Optionally, the application query entry of the task knowledge graph operates as follows: based on application nodes, it associates output tables, tasks, workflows, and other nodes to obtain the number of task links the upper-level application has, as well as the current running status and runtime of each link.

[0062] Step 5: Visualize the results on the front end.

[0063] In other words, using the above method, a knowledge graph is used as both storage medium and computing engine. Flink reads the task metadata database to obtain task execution metadata information, creates entities and relationships according to the graph schema, and writes them into the graph library. After initialization, entities and relationships are updated incrementally. Once the graph is stored, it is made available to the front end through three entry points: the task side, the output table side, and the application side, allowing developers, operations personnel, and product staff to view and use it through the front end. Furthermore, task metadata can be obtained in real time to achieve incremental updates of the graph.

[0064] In summary, the above solution extracts task metadata from complex task chains to form a task knowledge graph. This graph then enables comprehensive management of task metadata monitoring and usage within the task chain. Furthermore, it allows for timely detection of issues within the task chain, providing data support for finding optimization breakthroughs and offering real-time capabilities, improving the processing efficiency of the entire chain to sub-second levels. This approach forms a graph network of task metadata, facilitating front-end access to task chain operation status and relationships between multiple task chains. The entire task network is presented more conveniently on the front-end, enabling developers and operations personnel to promptly identify problems and understand the task's location.

[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the determination of the map described in the various embodiments of the present invention.

[0066] This embodiment also provides a spectrum determination device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0067] Figure 4 This is a structural block diagram of a spectrum determination device according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes:

[0068] The acquisition module 42 is used to acquire metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information, task engineering dimension information;

[0069] Module 44 is used to create a target node based on the metadata information;

[0070] The connection module 46 is used to connect the target nodes using a preset pattern graph structure, based on the logical relationship between the different tasks, to obtain a graph containing different tasks.

[0071] The packaging module 48 is used to package the graph and the metadata information to obtain a task knowledge graph.

[0072] The aforementioned device acquires metadata information corresponding to different tasks, including at least one of the following: task operation information and task engineering dimension information. Target nodes are created based on the metadata information. Given the logical relationships between different tasks, a preset schema graph structure is used to connect the target nodes, resulting in a graph containing different tasks. The graph and metadata information are packaged to obtain a task knowledge graph, i.e., the entire task chain relationship corresponding to different tasks is written into the graph. The schema graph structure corresponding to the graph preserves complex task chain relationships, enabling rapid querying of task-related data using the determined task knowledge graph. Therefore, it solves the problem of existing technologies being unable to quickly query data for tasks with complex data chains. Furthermore, this task knowledge graph supports multi-level relationship queries, and its response speed is significantly improved compared to traditional databases. Combined with the front-end, it enables timely task querying and timely viewing of task chains, presenting the entire task network more conveniently on the front-end, facilitating timely problem detection by development and maintenance personnel.

[0073] Optional, Figure 5 This is a structural block diagram of another spectrum determination device according to an embodiment of the present invention, which includes not only Figure 4 All modules, including: update module 52, identification module 54, and query module 56.

[0074] In an exemplary embodiment, the above-described apparatus further includes: a query module, configured to store the task knowledge graph in a database and set a query entry point for the task knowledge graph; wherein the query entry point includes at least one of the following: a task query entry point, an output table query entry point, and an application query entry point; receiving query instructions from the front end through the query entry point and obtaining data information matching the query instructions from the task knowledge graph.

[0075] In an exemplary embodiment, the query module is further configured to, upon receiving a query instruction at the task query entry, obtain the running status of different nodes in all task links of the task knowledge graph and the runtime of each node in the different nodes, wherein the task knowledge graph contains data links corresponding to different tasks; generate a task running graph corresponding to the task knowledge graph based on the running status and the runtime; visually identify the task running graph in the task running graph using a preset status identifier, use the identified task running graph as data information, and send the data information to the visualization display interface.

[0076] In an exemplary embodiment, the query module is further configured to, upon receiving a query instruction at the output table query entry, determine the query information of the output table to be queried carried by the query instruction; determine the specific location of the current output table to be queried in all task links in the task knowledge graph and the running status of the current output table to be queried based on the query information; generate a visual output table based on the specific location and the running status; use the visual output table as data information; and send the data information to the visual display interface.

[0077] In an exemplary embodiment, the query module is further configured to, upon receiving a query instruction at the application query entry, determine application requirement information corresponding to the query instruction; wherein the application requirement information is used to indicate the acquisition of link information corresponding to the application; determine multiple target data links supporting the application in the task knowledge graph based on the application requirement information, and determine the current operating status and runtime of each of the multiple target data links, wherein the target data links are a subset of the data links corresponding to different tasks contained in the task knowledge graph; construct an application support graph using the multiple target data links, the current operating status of each of the multiple target data links, and the runtime of each of the multiple target data links, use the application support graph as data information, and send the data information to the visualization display interface.

[0078] In one exemplary embodiment, the above apparatus further includes: an update module, configured to acquire update information of metadata information corresponding to the target node; parse the update information to obtain an attribute to be updated; and use the attribute to be updated to change the attribute information of the target node, wherein the attribute information is used to describe the target node.

[0079] In one exemplary embodiment, the above-described apparatus further includes: an identification module, configured to acquire the current running time and current running result corresponding to the target node; if the current running time is greater than a preset threshold, determine that the target node is an ultra-long node, and set an optimization strategy for the ultra-long node in the graph; if the current running result indicates that the task has failed, generate a prompt message, wherein the prompt message is used to indicate that the target node in the current graph is an invalid node.

[0080] In the description of this invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," and "right," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0081] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. When a component is referred to as being "fixed to" or "set on" another element, it can be directly on the other component or there may be an intervening component. When a component is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intervening component. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0082] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0083] Embodiments of the present invention also provide a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0084] In one exemplary embodiment, the storage medium described above may be configured to store a computer program for performing the following steps:

[0085] S1, Obtain metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information, task engineering dimension information;

[0086] S2, Create the target node based on the metadata information;

[0087] S3, after determining the logical relationship between the different tasks, the target nodes are connected using a preset pattern diagram structure to obtain a graph containing different tasks;

[0088] S4, the graph and the metadata information are packaged to obtain the task knowledge graph.

[0089] In one exemplary embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0090] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0091] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0092] In one exemplary embodiment, the processor described above may be configured to perform the following steps via a computer program:

[0093] S1, Obtain metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: task operation information, task engineering dimension information;

[0094] S2, Create the target node based on the metadata information;

[0095] S3, after determining the logical relationship between the different tasks, the target nodes are connected using a preset pattern diagram structure to obtain a graph containing different tasks;

[0096] S4, the graph and the metadata information are packaged to obtain the task knowledge graph.

[0097] In an exemplary embodiment, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0098] It will be apparent to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. In one exemplary embodiment, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device. Furthermore, in some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be implemented as a single integrated circuit module. Thus, the present invention is not limited to any particular hardware and software combination.

[0099] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of determining a map, characterized by, The method comprises: acquiring metadata information corresponding to different tasks, wherein the metadata information comprises at least one of the following: running information of the task, engineering dimension information of the task; creating a target node according to the metadata information; in a case where a logical relationship between the different tasks is determined, connecting the target node using a preset schema graph structure to obtain a graph containing different tasks; packaging the graph and the metadata information to obtain a task knowledge graph; The method further comprises: in a case where a query instruction is received at an application query entrance of the task knowledge graph, determining application requirement information corresponding to the query instruction; wherein the application requirement information is used to indicate acquisition of link information corresponding to an application; determining, according to the application requirement information, a plurality of target data links in the task knowledge graph that support the application, and determining a current running condition of each data link in the plurality of target data links and a running duration of each data link in the plurality of target data links, the target data link being a part of a link corresponding to different tasks contained in the task knowledge graph; constructing an application support graph using the plurality of target data links, the current running condition of each link in the plurality of target data links, and the running duration of each link in the plurality of target data links, taking the application support graph as data information, and sending the data information to a visual display interface.

2. The method of claim 1, wherein, After the graph and the metadata information are packaged to obtain the task knowledge graph, the method further comprises: storing the task knowledge graph in a database and setting a query entrance of the task knowledge graph; wherein the query entrance comprises at least one of the following: a task query entrance, an output table query entrance, and an application query entrance; receiving a query instruction from a front end through the query entrance and acquiring data information matched with the query instruction from the task knowledge graph.

3. The method of claim 2, wherein, Receiving a query instruction from a front end through the query entrance and acquiring data information matched with the query instruction from the task knowledge graph comprises: in a case where a query instruction is received at the task query entrance, acquiring a running state of different nodes in all task links in the task knowledge graph and a running duration corresponding to each of the different nodes, wherein the task knowledge graph contains data links corresponding to different tasks; generating a task running graph corresponding to the task knowledge graph based on the running state and the running duration; visually identifying the task running graph in the task running graph using a preset state identifier, taking the task running graph with the completion identifier as data information, and sending the data information to a visual display interface.

4. The method of claim 2, wherein, Receiving a query instruction from a front end through the query entrance and acquiring data information matched with the query instruction from the task knowledge graph comprises: in a case where a query instruction is received at the output table query entrance, determining query information of a to-be-queried output table carried by the query instruction; determine a specific position of a current to-be-queried output table in all task links in the task knowledge graph and a running condition of the current to-be-queried output table according to the query information; generate a visual output table based on the specific position and the running condition, use the visual output table as data information, and send the data information to a visual display interface.

5. The method of claim 1, wherein, After the target node is created according to the metadata information, the method further includes: obtaining update information of the metadata information corresponding to the target node; parsing the update information to obtain to-be-updated attributes; using the to-be-updated attributes to change attribute information of the target node, wherein the attribute information is used to describe the target node.

6. The method of claim 1, wherein, After the logical relationship between the different tasks is determined and the target node is connected using a preset mode graph structure to obtain a graph containing different tasks, the method further includes: obtaining a current running time and a current running result corresponding to the target node; in a case where the current running time is greater than a preset threshold, determining that the target node is an ultra-long node and setting an optimization strategy for the ultra-long node in the graph; in a case where the current running result indicates that a task fails to run, generating a prompt information, wherein the prompt information is used to indicate that the target node in the current graph is an invalid node.

7. An atlas determination apparatus, characterized by includes: an obtaining module, configured to obtain metadata information corresponding to different tasks, wherein the metadata information includes at least one of the following: running information of a task, engineering dimension information of a task; a creating module, configured to create a target node according to the metadata information; a connecting module, configured to, in a case where a logical relationship between the different tasks is determined, connect the target node using a preset mode graph structure to obtain a graph containing different tasks; a packaging module, configured to package the graph and the metadata information to obtain a task knowledge graph; a querying module, configured to, in a case where a query instruction is received at an application query entrance of the task knowledge graph, determine application requirement information corresponding to the query instruction; wherein the application requirement information is used to indicate that link information corresponding to an application is obtained; determine, according to the application requirement information, a plurality of target data links in the task knowledge graph that support the application, and determine a current running condition of each data link in the plurality of target data links and a running time length of each data link in the plurality of target data links, the target data link being a part of a data link corresponding to different tasks included in the task knowledge graph; construct an application support graph using the plurality of target data links, the current running condition of each link in the plurality of target data links, and the running time length of each link in the plurality of target data links, use the application support graph as data information, and send the data information to a visual display interface.

8. A computer readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program is executed by a processor to perform the method of any one of claims 1 to 6. 9.An electronic device comprising a memory and a processor, the electronic device characterized by, The memory stores a computer program, and the processor is configured to execute the method of any one of claims 1 to 6 by using the computer program.

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