Data asset generation method and device

Abstract

The embodiment of the invention discloses a data asset generation method and device which are used for improving data assets. The method comprises the steps that the computing device extracts a target task node set from the workflow based on data asset characteristics, the target task node set comprises one or more task nodes in the workflow, and the one or more task nodes are used for identifying data assets. The computing device performs named entity identification on contextual information of one or more task nodes in the target task node set, and determines one or more data asset fragments, the contextual information including data associated with the data asset fragments in the task nodes. The computing device generates a data asset based on the one or more data asset segments.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computers, and in particular, to a method and device for generating data assets. Background Art

[0002] As various industries pay more attention to data utilization, in today's data-driven business environment, the accumulation of data assets has become a key factor in promoting corporate innovation and enhancing competitiveness. Companies will begin to create their own data assets. However, many companies face the challenge of how to effectively identify and acquire data assets.

[0003] At present, the data asset sedimentation process usually involves multiple complex steps, including the identification, collection, integration and cleaning of data assets. These steps are not only time-consuming and labor-intensive, but also require a lot of human and financial resources. For example, in the current solution for creating data assets, users need to rely on expert experience to manually fill out data forms to complete the creation of data assets. Alternatively, users are required to configure some collection tasks, collect metadata from data sources, and then create data assets. In addition, when users need to create related data assets, they also need to manually enter the relationship between different data assets again.

[0004] Since the current method of creating data assets relies on manual creation by users, when there are many types of assets to be created, it is necessary to switch between different forms, resulting in low efficiency in data asset creation and high maintenance costs. Summary of the invention

[0005] The present application embodiment provides a data asset generation method for improving the efficiency of data asset creation. The present application embodiment also provides a data asset generation device, a computing device, a computing device cluster, a computer-readable storage medium, and a computer program product corresponding to the data asset generation method.

[0006] In the first aspect, an embodiment of the present application provides a method for generating data assets, which can be executed by a computing device, or by a component of the computing device, such as a processor, chip or chip system of the computing device, or by a logic module or software that can realize all or part of the computing device function. The method provided in the first aspect includes: the computing device extracts a target task node set from a workflow based on data asset characteristics, the target task node set includes one or more task nodes in the workflow, and the one or more task nodes are used to identify data assets. The workflows are such as sales workflows and human resource management workflows. The computing device performs named entity recognition on the context information of one or more task nodes in the target task node set to determine one or more data asset fragments, and the context information includes data associated with the data asset fragment in the task node. For example, when the task node is a task node in a sales workflow, the context information includes product information, inventory information, and sales channel information. The computing device generates data assets based on one or more data asset fragments.

[0007] In the embodiment of the present application, the computing device can extract a target task node set from the task nodes of the workflow, and extract data asset fragments from the target task node set to form data assets. Compared with the method in the prior art that requires manual filling in of forms or configuration of collected data and then creating data assets, the data asset generation method provided in the embodiment of the present application can directly generate data assets based on the workflow, thereby improving the efficiency of data asset creation.

[0008] In one possible implementation, when a computing device extracts a target task node set from a workflow based on data asset features, the computing device matches the data asset features with one or more task nodes in the workflow to determine the target task node, where the data asset features include one or more of the following: asset name, asset type, and asset attribute. The computing device obtains a target task node set based on the target task node combination, where the target task node is a task node in the workflow that is associated with the data asset features.

[0009] In the embodiment of the present application, the computing device can match the data asset characteristics with one or more task nodes in the workflow, thereby determining the task nodes that can identify the data assets, improving the feasibility of the computing device to determine the target task node set, and further improving the efficiency of identifying data assets.

[0010] In one possible implementation, in the process of performing named entity recognition on the context information of one or more tasks in the target task node set, the computing device performs named entity recognition on the context information of one or more tasks in the target task node set based on the named entity recognition model to determine one or more data asset fragments. The context information of the task node includes basic information of the task node, document information contained in the task node, and approval information of the task node.

[0011] In the embodiment of the present application, the computing device can perform named entity recognition on the context information of one or more tasks in the target task node set based on the named entity recognition model, thereby improving the accuracy of data asset fragment recognition.

[0012] In one possible implementation, the computing device preprocesses the context information of one or more task nodes in the target task node set, and inputs the preprocessed context information into a trained named entity recognition model. The named entity recognition model performs named entity recognition on the input text, including operations such as entity boundary recognition, entity category judgment, and multi-label classification.

[0013] In the embodiment of the present application, the computing device can perform operations such as entity boundary recognition, entity category judgment, and multi-label classification on the context information of one or more tasks in the target task node set based on the named entity recognition model, thereby improving the accuracy of data asset fragment recognition.

[0014] In one possible implementation, before a computing device identifies data asset segments based on a named entity recognition model, the computing device trains the named entity recognition model. Specifically, the computing device may collect data asset segments and annotate the data asset segments to obtain training data, and then use the annotated training data to train the named entity recognition model.

[0015] In the embodiment of the present application, before the computing device uses the named entity recognition model to identify data asset segments, it is necessary to train the named entity recognition model using labeled data, thereby improving the accuracy of the named entity recognition model in identifying data asset segments.

[0016] In one possible implementation, after the computing device generates the data asset based on one or more data asset segments, the computing device identifies missing data asset segments in the data asset, where the missing data asset segments include missing asset attributes in the data asset. The computing device searches the asset catalog based on the missing data asset segments and determines search results, where the search results include supplementary data asset segments. The computing device supplements the missing data asset segments in the data asset based on the search results.

[0017] In the embodiment of the present application, after the computing device generates data assets, it can retrieve the asset catalog to supplement the missing data asset fragments in the data assets, thereby improving the integrity of the data assets.

[0018] In one possible implementation, in a process of retrieving the asset catalog based on the missing data asset fragment, the computing device determines a search keyword from an upstream task node of the workflow based on the missing data asset fragment, where the upstream task node is a task node outside the target task node set. The computing device retrieves the asset catalog based on the search keyword and determines a search result.

[0019] In the process of supplementing missing data asset fragments in data assets in the embodiment of the present application, the computing device can determine retrieval keywords from upstream task nodes outside the target task node set based on the missing data asset fragments, thereby improving the accuracy of retrieving missing data asset fragments.

[0020] In one possible implementation, the computing device queries the asset catalog for associated assets based on the data asset, where the associated assets are data assets in the asset catalog that have a reference relationship with the data asset. After the computing device queries the asset catalog for associated assets based on the data asset, the computing device may supplement the data asset with the associated asset name associated with the data asset.

[0021] In the embodiment of the present application, after the computing device generates a data asset, the associated assets of the data asset can be queried and the associated relationships can be supplemented in the data asset, thereby further improving the integrity of the data asset.

[0022] In one possible implementation, after the computing device generates the data asset based on the data asset fragment, it updates one or more task nodes in the workflow based on the data asset, and the updated task node in the workflow corresponds to the same data asset as the updated task node. Specifically, the computing device generates a new task node based on the data asset, and replaces one or more task nodes in the workflow with the new task node, and updates the reference relationship between the replaced task node and other task nodes.

[0023] In the embodiment of the present application, the computing device can update one or more task nodes in the workflow based on the generated data assets, thereby optimizing the task node design of the workflow and improving the task processing efficiency of the workflow.

[0024] In a second aspect, an embodiment of the present application provides a device for generating data assets, the device comprising an extraction unit and a processing unit, wherein the extraction unit is used to extract a target task node set from a workflow based on data asset features, the target task node set comprising one or more task nodes in the workflow, and the one or more task nodes are used to identify data assets. The processing unit is used to perform named entity recognition on the context information of one or more task nodes in the target task node set, and determine one or more data asset fragments, the context information comprising data in the task node associated with the data asset fragment. The processing unit is also used to generate data assets based on one or more data asset fragments.

[0025] In one possible implementation, the extraction unit is specifically used to match one or more task nodes in the workflow based on data asset characteristics to determine a target task node, where the data asset characteristics include one or more of the following: asset name, asset type, and asset attribute. The extraction unit is specifically used to obtain a target task node set based on a target task node combination, where the target task node is a task node in the workflow that is associated with the data asset characteristics.

[0026] In a possible implementation, the processing unit is specifically configured to perform named entity recognition on context information of one or more tasks in the target task node set based on a named entity recognition model to determine one or more data asset segments.

[0027] In one possible implementation, the processing unit is also used to identify missing data asset fragments in the data assets, the missing data asset fragments include missing asset attributes in the data assets, retrieve the asset catalog based on the missing data asset fragments, determine the retrieval results, the retrieval results include supplementary data asset fragments, and supplement the missing data asset fragments in the data assets based on the retrieval results.

[0028] In one possible implementation, the processing unit is specifically used to determine the search keywords from the upstream task node of the workflow based on the missing data asset fragment, the upstream task node is a task node outside the target task node set, and search the asset catalog based on the search keywords to determine the search results.

[0029] In a possible implementation, the processing unit is further configured to query an associated asset from an asset catalog based on the data asset, where the associated asset is a data asset in the asset catalog that has a reference relationship with the data asset.

[0030] In a possible implementation, the processing unit is further configured to update one or more task nodes in the workflow based on the data asset, and the updated task node in the workflow corresponds to the same data asset as the updated task node.

[0031] In a third aspect, an embodiment of the present application provides a computing device, comprising a processor, the processor being coupled to a memory, the processor being used to store instructions, and when the instructions are executed by the processor, the computing device executes the method described in the first aspect or any possible implementation of the first aspect.

[0032] In a fourth aspect, an embodiment of the present application provides a computing device cluster, the computing device cluster includes one or more computing devices, the computing device includes a processor, the processor is coupled to a memory, the processor is used to store instructions, when the instructions are executed by the processor, the computing device cluster executes the method described in the first aspect or any possible implementation method of the first aspect.

[0033] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium having instructions stored thereon. When the instructions are executed, the computer executes the method described in the first aspect or any possible implementation manner of the first aspect.

[0034] In a sixth aspect, an embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed, the computer implements the method described in the first aspect or any possible implementation method of the first aspect.

[0035] It can be understood that the beneficial effects that can be achieved by any of the data asset generation devices, computing devices, computing device clusters, computer-readable media or computer program products provided above can refer to the beneficial effects in the corresponding methods and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the system architecture of a data asset system is provided for an embodiment of the present application; Figure 2 A flowchart of a method for generating data assets provided in an embodiment of the present application; Figure 3 A schematic diagram of a workflow provided in an embodiment of the present application; Figure 4 A schematic diagram of extracting a target task node set based on data asset features provided for an embodiment of the application; Figure 5 A schematic diagram of a named entity recognition and determination of data asset segments provided in an embodiment of the present application; Figure 6 A schematic diagram of retrieving missing data asset fragments provided by an embodiment of the present application; Figure 7 A schematic diagram of a data asset update workflow provided in an embodiment of the present application; Figure 8A schematic diagram of another method for generating data assets provided in an embodiment of the present application; Fig. 9 A schematic diagram of a data asset generation device provided in an embodiment of the present application Fig.10 A schematic diagram of the structure of a computing device provided in an embodiment of the present application; Fig.11 A schematic diagram of a computing device cluster provided in an embodiment of the present application; Fig.12 A schematic diagram of computer devices in a computing device cluster connected via a network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The embodiments of the present application provide a method and device for generating data assets, which are used to improve the efficiency of creating data assets.

[0038] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0039] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0040] First, some terms involved in the embodiments of the present application are introduced to facilitate technical personnel in this field to understand the technical solution.

[0041] Data assets refer to a collection of data with value in an organization or enterprise. These data can be structured data, such as table data in a database, or unstructured data, such as documents, images, videos, etc. Data assets can be used to support decision-making, improve efficiency, promote innovation, and create business value.

[0042] Indicators are quantitative criteria used to measure, evaluate and monitor specific activities, processes or results.

[0043] Named entity recognition (NER) is a key technology in natural language processing (NLP), which aims to identify and classify named entities from text. Named entities usually include names of people, places, organizations, dates, times, currencies, etc.

[0044] Workflow, also known as work electronic flow, refers to a series of orderly steps, activities and processes designed to complete specific tasks or business goals in an organization or system. These steps usually involve multiple participants, tools and resources, and are designed to improve efficiency, standardize operations and ensure the smooth progress of tasks.

[0045] In order to make the technical solution of the present application clearer and easier to understand, the system architecture of the present application is introduced below with reference to the accompanying drawings.

[0046] See also Figure 1 , Figure 1 A schematic diagram of the system architecture of a data asset system is provided for an embodiment of the present application. Figure 1 In the example shown, the data asset system 10 includes an application server 100, a switch 110, and a database server 120, wherein the database server 120 includes an engine 121 and a hard disk enclosure 130. The specific functions of each part of the data asset system 10 are described below.

[0047] The application server 100 may be a physical machine or a virtual machine. Physical application servers include but are not limited to computing devices such as desktop computers, servers, and laptops. The application server 100 runs an application, which may be a workflow application. Users may generate data assets through the workflow application in the application server 100.

[0048] The switch 110 is used for the application server 100 to access the database server 120 to store and store data. The switch 110 may be an Ethernet switch, an InfiniBand switch, a switch using RDMA over converged Ethernet (RoCE), or the like.

[0049] It is understandable that the switch 110 Figure 1 The system shown is an optional device, and the application server 100 can also communicate with the database server 120 directly through the network.

[0050] The database server 120 is used to store data assets generated by the application server 100. The database server 120 can be a centralized storage system. The centralized storage system is characterized by having a unified entrance through which all data from external devices must pass. This entrance is the engine 121 of the centralized storage system. The engine 121 is the core component of the centralized storage system, and many advanced functions of the storage system are implemented in it.

[0051] The engine 121 has one or more controllers. Figure 1 The example shown is described by taking the engine including two controllers as an example. There is a mirror channel between the first controller 0 and the second controller 1. When the first controller 0 writes a copy of data into its memory 124, a copy of the data can be sent to the second controller 1 through the mirror channel, and the second controller 1 stores the copy in its local memory 124.

[0052] Thus, the first controller 0 and the second controller 1 are backed up for each other. When the first controller 0 fails, the second controller 1 can take over the business of the first controller 0. When the second controller 1 fails, the first controller 0 can take over the business of the second controller 1, thereby avoiding the unavailability of the entire database server 120 due to hardware failure. When four controllers are deployed in the engine 121, there is a mirror channel between any two controllers, so any two controllers are backed up for each other.

[0053] The engine 121 also includes a front-end interface 125 and a back-end interface 126, wherein the front-end interface 125 is used to communicate with the application server 100, thereby providing storage services for the application server 100. The back-end interface 126 is used to communicate with the hard disk 134 to expand the capacity of the storage system. Through the back-end interface 126, the engine 121 can connect more hard disks 134 to form a storage resource pool.

[0054] The first controller 0 includes at least a processor 123 and a memory 124. The processor 123 is a central processing unit (CPU) used to process data access requests from outside the storage system (server or other storage system), and also used to process requests generated inside the storage system. Exemplarily, when the processor 123 receives a write data request sent by the application server 100 through the front-end interface 125, the data in the write data request will be temporarily stored in the memory 124. When the total amount of data in the memory 124 reaches a certain threshold, the processor 123 sends the data stored in the memory 124 to the hard disk 134 through the back-end interface for persistent storage.

[0055] Memory 124 refers to an internal memory that directly exchanges data with the processor. It can read and write data at any time and at a very high speed. It serves as a temporary data storage for the operating system or other running programs. Memory 124 includes at least two types of memories. For example, the memory can be either a random access memory or a read-only memory (ROM). For example, the random access memory is a dynamic random access memory (DRAM) or a storage class memory (SCM). Among them, DRAM is a semiconductor memory, which, like most random access memories (RAM), is a volatile memory device. SCM is a composite storage technology that combines the characteristics of traditional storage devices and memories. Storage class memory can provide faster read and write speeds than hard disks, but the access speed is slower than DRAM, and the cost is also cheaper than DRAM.

[0056] However, DRAM and SCM are only exemplary in this embodiment, and the memory 124 may also include other random access memories, such as static random access memory (SRAM), etc. As for the read-only memory, for example, it may be a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), etc.

[0057] In addition, the memory 124 may also be a dual in-line memory module or a dual-line memory module (DIMM), that is, a module composed of a dynamic random access memory DRAM, or a solid state disk (SSD). In practical applications, multiple memories 124 and different types of memories 124 may be configured in the first controller 0. This embodiment does not limit the number and type of the memory 124. In addition, the memory 124 may be configured to have a power-saving function. The power-saving function means that when the system loses power and then powers on again, the data stored in the memory 124 will not be lost. A memory with a power-saving function is called a non-volatile memory.

[0058] The memory 124 stores software programs, and the processor 123 runs the software programs in the memory 124 to manage the hard disk. For example, the hard disk is abstracted into a storage resource pool, which is then divided into LUNs and provided to the server. The LUN here can be a hard disk in the server. Of course, some centralized storage systems are also file servers themselves, which can provide shared file services for the server.

[0059] It should be noted that Figure 1 The figure shows a centralized storage system with separated disk and controller. In this system, the engine 121 may not have a hard disk slot, and the hard disk 134 needs to be placed in the hard disk frame 130, and the back-end interface 126 communicates with the hard disk frame 130. The back-end interface 126 exists in the engine 121 in the form of an adapter card. Two or more back-end interfaces 126 can be used on one engine 121 to connect multiple hard disk frames. Alternatively, the adapter card can also be integrated on the motherboard, in which case the adapter card can communicate with the hard disk frame 130 via the PCIE bus. In addition, Figure 1 Only one engine 121 is shown in the figure. However, in actual applications, the storage system may include two or more engines 121, and redundancy or load balancing is performed between the multiple engines 121.

[0060] The hard disk enclosure 130 includes a control unit 131 and a plurality of hard disks 134. The control unit 131 may have various forms. In one embodiment, the hard disk enclosure 130 is an intelligent disk enclosure, such as Figure 1 As shown, the control unit 131 includes a CPU and a memory. The CPU is used to perform operations such as address conversion and reading and writing data. The memory is used to temporarily store data to be written to the hard disk 134, or read from the hard disk 134 to be sent to the controller. In another case, the control unit 131 is a programmable electronic component, such as a data processing unit (DPU). The DPU has the versatility and programmability of the CPU, but is more specialized and can run efficiently on network data packets, storage requests or analysis requests. The DPU is distinguished from the CPU by a greater degree of parallelism (a large number of requests need to be processed). Optionally, the DPU here can also be replaced with a processing chip such as a graphics processing unit (GPU) or an embedded neural-network processing unit (NPU).

[0061] The number of control units 131 may be one, two or more, and is not specifically limited. When the hard disk frame 130 includes at least two control units 131, the hard disk 134 and the control unit 131 may have an ownership relationship. If the hard disk 134 and the control unit 131 have an ownership relationship, each control unit 131 can only access the hard disk that belongs to it. In addition, if the storage space is insufficient, when adding a new hard disk 134 to the hard disk frame 130, it is necessary to rebind the ownership relationship between the hard disk 134 and the control unit 131, which is complicated to operate and leads to poor scalability of the storage space.

[0062] In another embodiment, the functions of the control unit 131 can be offloaded to the network card 104. In other words, in this embodiment, the hard disk frame 130 does not have a control unit 131 inside, but the network card 104 performs data reading and writing, address conversion and other computing functions. At this time, the network card 104 is an intelligent network card. It can include a CPU and a memory. In some application scenarios, the network card 104 may also have a persistent memory medium, such as a persistent memory (PM), or a non-volatile random access memory (NVRAM), or a phase change memory (PCM). The CPU is used to perform operations such as address conversion and reading and writing data. The memory is used to temporarily store data to be written to the hard disk 134, or data read from the hard disk 134 to be sent to the controller.

[0063] The CPU can also be another programmable electronic component, such as a data processing unit (DPU). The DPU has the versatility and programmability of the CPU, but is more specialized and can run efficiently on network data packets, storage requests, or analysis requests. The DPU is distinguished from the CPU by a greater degree of parallelism (needing to process a large number of requests). Optionally, the DPU here can also be replaced with processing chips such as graphics processing units (GPUs), embedded neural-network processing units (NPUs), etc.

[0064] There is no ownership relationship between the network card 104 and the hard disk 134 in the hard disk frame 130. The network card 104 can access any hard disk 134 in the hard disk frame 130, so it is more convenient to expand the hard disk when the storage space is insufficient. According to the type of communication protocol between the engine 121 and the hard disk frame 130, the hard disk frame 130 may be a SAS hard disk frame, or it may be an NVMe hard disk frame, an IP hard disk frame, and other types of hard disk frames. Among them, the SAS hard disk frame adopts the SAS3.0 protocol, and each frame supports 25 SAS hard disks. The engine 121 is connected to the hard disk frame 130 via an onboard SAS interface or a SAS interface module. The NVMe hard disk frame is more like a complete computer system. The NVMe hard disk is inserted into the NVMe hard disk frame, and the NVMe hard disk frame is connected to the engine 121 via the RDMA port.

[0065] In the embodiment of the present application, the application server 100 and the database server 120 in the data asset system 10 can also be collectively referred to as computing devices. When the data asset system 10 includes multiple application servers 100 or multiple database servers 120, they can also be collectively referred to as a computing device cluster, without specific limitation.

[0066] based on Figure 1 The data asset system 10 shown in the present application also provides a method for generating data assets. The method for generating data assets provided in the present application embodiment is introduced below in conjunction with an embodiment.

[0067] See also Figure 2 , Figure 2 A flowchart of a method for generating data assets provided in an embodiment of the present application. Figure 2 In the example shown, the method includes the following steps: 201. The computing device extracts a target task node set from the workflow based on data asset characteristics, where the target task node set includes one or more task nodes in the workflow, and the one or more task nodes are used to identify data assets.

[0068] In an embodiment of the present application, a data asset source is provided as a workflow, and the workflow includes one or more task nodes. Since these task nodes in the workflow involve specific tasks or operations in the business process, the data of these task nodes are valuable data that fits the business and can be used as the source of data assets.

[0069] For example, in a sales workflow, a task node may contain product information, inventory information, and sales channel information, which can be used to generate data assets. For another example, in a human resources management workflow, a task node may contain employee information, training plans, and performance evaluation information, which can also be used to generate data assets.

[0070] In an embodiment of the present application, a computing device extracts a target task node set from a workflow based on data asset characteristics, wherein the data asset characteristics indicate a data structure of a data asset that needs to be extracted, and the target task node set includes one or more task nodes, and one or more task nodes in the target task node set are used to identify data asset fragments.

[0071] The data asset features in the embodiments of the present application include one or more of the following: asset name, asset type, and asset attribute, where different asset types correspond to different asset attributes, and different asset attributes include description, dimension, measurement, unit, cycle, threshold, and data table, etc. For example, the asset name is human resources annual financial year-on-year, the asset type is indicator, and the asset attributes include dimension, measurement, and data table, such as dimension, such as year, measurement, such as year-on-year, and data table, such as human resources financial table.

[0072] See also Figure 3 , Figure 3 A schematic diagram of a workflow provided in an embodiment of the present application. Figure 3 In the example shown, the workflow is a part of the sales data analysis workflow. In this part of the sales data analysis workflow, multiple task nodes are included, and the task nodes include a global shipment volume query node, a pull-up feasibility analysis node, a demand analysis node, a demand satisfaction judgment node, an acquisition pull quantity node, an acquisition global minimum shipment quantity node, and a node for the ratio of global average monthly shipment volume.

[0073] exist Figure 3 In the example shown, the computing device can query these task nodes based on the data asset characteristics and extract the target task node set. For example, the computing device extracts the task nodes in the workflow based on the asset type "indicator". The extracted target task node set includes 3 task nodes, namely, the node for obtaining the pull quantity, the node for obtaining the global minimum shipment quantity, and the node for the ratio of the global average monthly shipment quantity. For another example, the target task node set extracted from the workflow by the computing device based on the asset attribute "scale" includes 1 task node, which is the node for obtaining the pull quantity.

[0074] In one possible implementation, in the process of extracting a target task node set from a workflow based on data asset features, the computing device matches the data asset features with one or more task nodes in the workflow to determine the target task node, wherein the data asset features include one or more of the following: asset name, asset type, and asset attribute. The computing device obtains a target task node set based on the target task node combination, and the target task node is a task node in the workflow that is associated with the data asset features.

[0075] See also Figure 4 , Figure 4A schematic diagram of extracting a target task node set based on data asset features is provided for an embodiment of the application. Figure 4 In the example shown, the computing device extracts a set of target task nodes in the workflow based on the asset type and asset attributes in the data asset characteristics, wherein the asset type in the data asset characteristics is an indicator, and the asset attributes include the indicator name, description, dimension, measurement, unit, cycle, threshold, and data set.

[0076] exist Figure 4 In the example shown, the computing device can extract the target task node from the workflow, and the computing device matches the above-mentioned asset type and asset attribute with one or more task nodes in the workflow. If the context information of one or more tasks in the workflow can match the above-mentioned asset type and asset attribute, the computing device determines these nodes as target task nodes, and uses the target task nodes to combine to obtain a target task node set. For example, in the process of matching the task nodes in the workflow, the computing device determines that the node for obtaining the pull quantity, the node for obtaining the global minimum shipment quantity, and the node for the ratio of the global average monthly shipments can all match the asset type indicator "indicator", then the computing device determines the three task nodes of obtaining the pull quantity node, obtaining the global minimum shipment quantity node, and the node for the ratio of the global average monthly shipments as target task nodes, and combines the three target task nodes into a target task node set.

[0077] 202. The computing device performs named entity recognition on context information of one or more task nodes in the target task node set to determine one or more data asset fragments, wherein the context information includes data in the task node associated with the data asset fragment.

[0078] After the computing device determines the target task node set, it performs named entity recognition on the context information of one or more task nodes in the target task node set to determine one or more data asset fragments. The one or more data asset fragments are used to form a completed data asset, and the context information includes data in the task node associated with the data asset fragment.

[0079] Among them, the context information of the task node includes the basic information of the task node, the document information contained in the task node and the approval information of the task node, etc. The basic information of the task node includes the task node name, task node description and task node status, etc. The document information contained in the task node includes the document name, document summary, submission time and author, etc. The approval information of the task node includes the approval role and approval operation content, etc.

[0080] In one possible implementation, in a process of performing named entity recognition on context information of one or more tasks in a target task node set, a computing device performs named entity recognition on the context information of one or more task nodes in the target task node set based on a named entity recognition (NER) model to determine one or more data asset fragments.

[0081] Specifically, the computing device preprocesses the context information of one or more task nodes in the target task node set, and inputs the preprocessed context information into a trained named entity recognition model. The named entity recognition model performs named entity recognition on the input text, including operations such as entity boundary recognition, entity category judgment, and multi-label classification. The named entity recognition model recognizes and outputs one or more entities and the category label of each entity, one or more entities such as data asset fragments, and the category label such as the asset attribute corresponding to the data asset fragment.

[0082] See also Figure 5 , Figure 5 A schematic diagram of a named entity recognition method for determining a data asset segment provided in an embodiment of the present application. Figure 5 In the example shown, the set of target task nodes extracted by the computing device from the workflow includes a node for querying global shipment volume, a node for analyzing and pulling feasibility, a node for demand analysis, a node for determining demand satisfaction, a node for obtaining the pulling quantity, a node for obtaining the global minimum shipment quantity, and a node for the ratio of the global average monthly shipment volume.

[0083] exist Figure 5 In the example shown, the computing device extracts task nodes that all contain their own context information, and the computing device performs named entity recognition on the context information based on a named entity recognition model. For example, the computing device performs named entity recognition on the node for obtaining global shipment volume, and determines four data asset fragments. The asset types (type) of the four data asset fragments are all metrics. The four data asset fragments are batch product, minimum value (min), proportion of total volume, and number of pulls. Among them, the asset attribute of batch product is dataset, the asset attribute of minimum value is measure, the asset attribute of proportion of total volume is formula, and the asset attribute of number of pulls is dimension.

[0084] It can be understood that before the computing device identifies data asset fragments based on the named entity recognition model, the named entity recognition model is trained. Specifically, the computing device collects data asset fragments and labels the data asset fragments to obtain training data, and then uses the labeled training data to train the named entity recognition model.

[0085] 203. The computing device generates a data asset based on one or more data asset fragments.

[0086] After the computing device performs named entity recognition on the context information of one or more task nodes in the target task node set to determine one or more data asset fragments, it generates a data asset based on the one or more data asset fragments. The data asset includes one or more asset attributes, wherein each data asset fragment can serve as the content of the asset attribute.

[0087] It should be noted that the computing device can also directly output data assets based on the named entity recognition model, that is, the computing device can combine data asset fragments into data assets based on the named entity recognition model, wherein the combined data assets may contain some missing data asset fragments.

[0088] Please continue to refer to 5. Figure 5 In the example shown, the computing device generates a data asset based on a named entity recognition model, the asset type of the data asset is an indicator type, the data asset includes multiple asset attributes, and different asset attributes correspond to different data asset fragments. For example, the asset attributes of the data asset include a name, a dimension, a measurement type, a filter, a time limit, and a data set, where the name is the ratio of global monthly average shipments, the dimension is shipments, the measurement type is a cycle, the filter is the minimum shipment, the time limit is monthly, and the data set is a missing data asset fragment.

[0089] In one possible implementation, after the computing device generates the data asset based on one or more data asset segments, it identifies missing data asset segments in the data asset, where the missing data asset segments include missing asset attributes in the data asset. The computing device searches the asset catalog based on the missing data asset segments, determines the search results, where the search results include supplementary data asset segments, and the computing device supplements the missing data asset segments in the data asset based on the search results.

[0090] Specifically, in the process of the computing device retrieving the asset catalog based on the missing data asset fragment, the computing device determines the retrieval keyword from the upstream task node of the workflow based on the missing data asset fragment, the upstream task node is a task node outside the target task node set, and the computing device retrieves the asset catalog based on the retrieval keyword to determine the retrieval result.

[0091] See also Figure 6 , Figure 6 A schematic diagram of retrieving missing data asset fragments provided in an embodiment of the present application. Figure 6 In the example shown, after the computing device generates the data asset, it identifies the missing data asset segments in the data asset, for example, Figure 6In the data assets shown, the computing device identifies the missing data asset fragments and determines that the asset attribute data set is missing, so the computing device needs to retrieve the asset catalog and supplement the data asset fragments corresponding to the asset attribute data set.

[0092] exist Figure 6 In the example shown, the computing device first determines the search keyword during the process of retrieving the asset catalog. The computing device matches the search keyword from the upstream task node based on the missing data asset fragment. For example, the computing device determines that the asset attribute corresponding to the missing asset fragment is a data set. The computing device searches the upstream task node based on the data set and other data asset fragments of the data asset to match the search keyword. The matched search keyword is "shipment batch". The computing device uses the search keyword to search the asset catalog to determine the missing data asset fragment. The missing data asset fragment is the shipment batch table. The computing device uses the shipment batch table to supplement the missing data asset fragment corresponding to the data set in the data asset.

[0093] In one possible implementation, the computing device queries the asset catalog for associated assets based on the data asset, where the associated assets are data assets in the asset catalog that have a reference relationship with the data asset. After the computing device queries the asset catalog for associated assets based on the data asset, the computing device may supplement the data asset with the associated asset name associated with the data asset.

[0094] It is understandable that the computing device may also query the asset directory for associated assets based on the data asset fragment. The associated assets may be data asset fragments or complete data assets, without specific limitation.

[0095] Please continue reading Figure 6 ,exist Figure 6 In the example shown, the computing device queries the asset catalog based on the data asset fragment "shipment batch table" to determine the associated assets. The associated assets include "shipment batch table [data table]" and "query global shipment volume [data service API]". The computing device can supplement the above associated assets in the data asset "ratio of global average monthly shipment volume".

[0096] It should be noted that since the data asset fragments identified by the computing device are determined based on the above information of the target task node set, the data asset fragments in the embodiments of the present application can be traced back to the original context information, that is, the data assets in the embodiments of the present application are interpretable. Similarly, the associated assets corresponding to the data assets in the embodiments of the present application are also interpretable.

[0097] In one possible implementation, after the computing device generates a data asset based on a data asset fragment, the computing device updates one or more task nodes in the workflow based on the data asset, and the updated work node in the workflow corresponds to the same data asset as the updated task node. Specifically, the computing device replaces one or more task nodes in the workflow with the new task node, and updates the reference relationship between the replaced task node and other task nodes.

[0098] See also Figure 7 , Figure 7 A schematic diagram of a data asset update workflow provided in an embodiment of the present application. Figure 7 In the example shown, after the computing device generates a data asset, the computing device can generate a new task node based on the data asset, and use the new task node to update the task node in the workflow, that is, the computing device can use the new task node to replace the updated task node in the workflow, and the updated task node in the workflow corresponds to the same data asset as the new task node.

[0099] For example, in Figure 7 In the example shown, the computing device can generate a new task node "Indicator - Ratio of global average monthly shipments" based on data assets. This task node has the same data assets as the task nodes "Get the pull quantity", "Get the global minimum shipment quantity" and "Ratio of global average monthly shipments" in the original workflow, and the new task node has the same input and output as the task fragment composed of the above three task nodes. Therefore, the computing device can use the task node "Indicator - Ratio of global average monthly shipments" to replace the task node fragment composed of the task nodes "Get the pull quantity", "Get the global minimum shipment quantity" and "Ratio of global average monthly shipments", thereby completing the update of the workflow.

[0100] In the embodiment of the present application, the process of the computing device updating one or more task nodes in the workflow based on the data asset can also be referred to as the process of the computing device updating the task node to the data asset node. In addition, after the computing device generates the data asset, the data asset can be stored in the data lake.

[0101] See also Figure 8 , Figure 8 A schematic diagram of another method for generating data assets provided in an embodiment of the present application. Figure 8In steps 1 to 3 of the example shown, when the computing device generates data assets, the computing device first extracts a target task node set from the workflow based on the data asset characteristics, that is, the computing device determines the scope of data asset identification. The computing device then identifies data asset fragments for one or more task nodes in the target task node set, and generates data assets based on the data asset fragments, wherein the data assets contain missing data asset fragments, and the computing device further supplements the missing data asset fragments. Finally, the computing device can supplement the associated assets of the data asset, extract the association relationship between different data assets, and store the data assets in the data lake. The computing device supplementing the associated assets can also be called data asset derivation.

[0102] exist Figure 8 In step 4 of the example shown, after the computing device generates the data asset, the data asset can be used to update the workflow. Specifically, the computing device generates a data asset node based on the data asset, and determines a workflow segment in the original workflow that has the same input and output as the data asset node. The workflow segment can include one or more task nodes. The computing device replaces the workflow segment with the data asset node, thereby updating the workflow.

[0103] It can be seen from the above embodiments that the computing device in the embodiments of the present application can extract a set of target task nodes from the task nodes of the workflow, and extract data asset fragments from the set of target task nodes to form data assets. That is, the data asset generation method provided in the embodiments of the present application can directly identify valuable data from the workflow, thereby generating data assets, thereby improving the creation efficiency of data assets and the practicality of data assets.

[0104] Based on the above method embodiment, the embodiment of the present application also provides a data asset generation device. The data asset generation device provided by the embodiment of the present application is specifically introduced below.

[0105] See also Fig. 9 , Fig. 9 A schematic diagram of the structure of a data asset generation device provided in an embodiment of the present application. Fig. 9 In the example shown, the data asset generation device 900 is used to implement the various steps performed by the data asset generation system in the above-mentioned embodiments. The data asset generation device 900 includes an extraction unit 901 and a processing unit 902 .

[0106] The extraction unit 901 is used to extract a target task node set from the workflow based on data asset features, the target task node set includes one or more task nodes in the workflow, and the one or more task nodes are used to identify data assets. The processing unit 902 is used to perform named entity recognition on the context information of one or more task nodes in the target task node set to determine one or more data asset fragments, the context information includes data associated with the data asset fragment in the task node. The processing unit 902 is also used to generate data assets based on one or more data asset fragments.

[0107] In a possible implementation, the extraction unit 901 is specifically used to match one or more task nodes in the workflow based on data asset characteristics to determine the target task node, where the data asset characteristics include one or more of the following: asset name, asset type, and asset attribute. The extraction unit 901 is specifically used to obtain a target task node set based on the target task node combination, where the target task node is a task node in the workflow that is associated with the data asset characteristics.

[0108] In a possible implementation, the processing unit 902 is specifically configured to perform named entity recognition on context information of one or more tasks in the target task node set based on a named entity recognition model to determine one or more data asset segments.

[0109] In one possible implementation, the processing unit 902 is also used to identify missing data asset fragments in the data assets, the missing data asset fragments include missing asset attributes in the data assets, retrieve the asset catalog based on the missing data asset fragments, determine the retrieval results, the retrieval results include supplementary data asset fragments, and supplement the missing data asset fragments in the data assets based on the retrieval results.

[0110] In a possible implementation, the processing unit 902 is specifically used to determine the search keywords from the upstream task node of the workflow based on the missing data asset fragment, the upstream task node is a task node outside the target task node set, and search the asset catalog based on the search keywords to determine the search results.

[0111] In a possible implementation, the processing unit 902 is further configured to query an associated asset from the asset catalog based on the data asset, where the associated asset is a data asset in the asset catalog that has a reference relationship with the data asset.

[0112] In a possible implementation, the processing unit 902 is further configured to update one or more task nodes in the workflow based on the data asset, and the updated task node in the workflow corresponds to the same data asset as the updated task node.

[0113] It is understandable that the extraction unit 901 and the processing unit 902 in the data asset generation device 900 can be used as functional modules and Figure 1 There is a mapping between each module in the data asset system 10, so as to realize the function of each module in the data asset system 10.

[0114] It should be understood that the division of the units in the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And the units in the device can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some units can also be implemented in the form of software calling through processing elements, and some units can be implemented in the form of hardware. For example, each unit can be a separately established processing element, or it can be integrated in a certain chip of the device for implementation. In addition, it can also be stored in the memory in the form of a program, and called and executed by a certain processing element of the device. The function of the unit. In addition, all or part of these units can be integrated together, or they can be implemented independently. The processing element described here can also be a processor, which can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each unit above can be implemented by an integrated logic circuit of hardware in the processor element or in the form of software calling through a processing element.

[0115] It is worth noting that, for the above method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required for the present application.

[0116] Other reasonable step combinations that can be thought of by those skilled in the art based on the above description also fall within the scope of protection of this application. Secondly, those skilled in the art should also be familiar with the fact that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this application.

[0117] See also Fig.10 , Fig.10 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. Fig.10 As shown, the computing device 1000 includes: a processor 1001, a memory 1002, a communication interface 1003 and a bus 1004. The processor 1001, the memory 1002 and the communication interface 1003 are coupled via a bus (not marked in the figure). The memory 1002 stores instructions. When the execution instructions in the memory 1002 are executed, the computing device 1000 executes the method executed by the data asset system in the above method embodiment.

[0118] The computing device 1000 may be one or more integrated circuits configured to implement the above method, such as one or more application specific integrated circuits (ASIC), or one or more microprocessors (digital signal processors, DSP), or one or more field programmable gate arrays (FPGA), or a combination of at least two of these integrated circuit forms. For another example, when a unit in the device can be implemented in the form of a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0119] The processor 1001 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0120] The memory 1002 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0121] The memory 1002 stores executable program codes, and the processor 1001 executes the executable program codes to respectively implement the functions of the aforementioned units or modules, thereby implementing the aforementioned data asset generation method. That is, the memory 1002 stores instructions for executing the aforementioned data asset generation method.

[0122] The communication interface 1003 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device 1000 and other devices or a communication network.

[0123] In addition to the data bus, the bus 1004 may also include a power bus, a control bus, a status signal bus, etc. The bus may be a peripheral component interconnect express (PCIe) bus, or an extended industry standard architecture (EISA) bus, a unified bus (Ubus or UB), a compute express link (CXL), a cache coherent interconnect for accelerators (CCIX), etc. The bus may be divided into an address bus, a data bus, a control bus, etc.

[0124] See also Fig.11 , Fig.11 A schematic diagram of a computing device cluster provided in an embodiment of the present application. Fig.11 As shown, the computing device cluster 1100 includes at least one computing device 1000 .

[0125] like Fig.11 As shown, the computing device cluster 1100 includes at least one computing device 1000. The memory 1002 in one or more computing devices 1000 in the computing device cluster 1100 may store the same instructions for executing the above-mentioned data asset generation method.

[0126] In some possible implementations, the memory 1002 of one or more computing devices 1000 in the computing device cluster 1100 may also respectively store partial instructions for executing the above-mentioned data asset generation method. In other words, the combination of one or more computing devices 1000 may jointly execute instructions for executing the above-mentioned data asset generation method.

[0127] It should be noted that the memory 1002 in different computing devices 1000 in the computing device cluster 1100 can store different instructions, which are respectively used to execute part of the functions of the above-mentioned node load control device. That is, the instructions stored in the memory 1002 in different computing devices 1000 can realize the functions of one or more modules in the extraction unit and the processing unit.

[0128] In some possible implementations, one or more computing devices 1000 in the computing device cluster 1100 may be connected via a network, which may be a wide area network or a local area network.

[0129] See also Fig.12 , Fig.12 A schematic diagram of a computer device cluster connected via a network provided in an embodiment of the present application. Fig.12 As shown, the first computing device 1000A and the second computing device 1000B are connected via a network. Specifically, they are connected to the network via a communication interface in each computing device.

[0130] In a possible implementation, the memory in the first computing device 1000A stores instructions for executing the function of the extraction unit, and the memory in the second computing device 1000B stores instructions for executing the function of the processing unit.

[0131] It should be understood that Fig.12 The functions of the first computing device 1000A shown in FIG. 1000A may also be completed by multiple computing devices. Similarly, the functions of the second computing device 1000B may also be completed by multiple computing devices.

[0132] In another embodiment of the present application, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the processor of the device executes the computer-executable instructions, the device executes the method executed by the data asset system in the above method embodiment.

[0133] In another embodiment of the present application, a computer program product is provided, the computer program product includes computer executable instructions, the computer executable instructions are stored in a computer readable storage medium. When the processor of the device executes the computer executable instructions, the device executes the method executed by the data asset system in the above method embodiment.

[0134] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0135] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0136] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A method for generating data assets, characterized in that: include: Extracting a target task node set from the workflow based on the data asset feature, the target task node set including one or more task nodes in the workflow, the one or more task nodes being used to identify the data asset; Performing named entity recognition on context information of one or more task nodes in the target task node set to determine one or more data asset segments, wherein the context information includes data in the task node associated with the data asset segment; The data asset is generated based on one or more of the data asset fragments.

2. The method according to claim 1, characterized in that The extracting of a target task node set from the workflow based on data asset features includes: Determine a target task node based on matching the data asset feature with one or more task nodes in the workflow, wherein the data asset feature includes one or more of the following: asset name, asset type, and asset attribute; The target task node set is obtained based on the target task node combination, and the target task node is a task node in the workflow that is associated with the data asset feature.

3. The method according to claim 1, characterized in that The performing named entity recognition on the context information of one or more tasks in the target task node set includes: Based on the named entity recognition model, named entity recognition is performed on the context information of one or more tasks in the target task node set to determine one or more data asset segments.

4. The method according to any one of claims 1 to 3, characterized in that After generating the data asset based on one or more data asset fragments, the method further includes: identifying missing data asset segments in the data asset, the missing data asset segments comprising missing asset attributes in the data asset; searching the asset catalog based on the missing data asset segment to determine a search result, the search result including a supplemental data asset segment; The missing data asset segments in the data assets are supplemented based on the search results.

5. The method according to claim 4, characterized in that The retrieving the asset catalog based on the missing data asset fragment comprises: Determining a search keyword from an upstream task node of the workflow based on the missing data asset fragment, the upstream task node being a task node outside the target task node set; The asset catalog is searched based on the search keyword to determine the search result.

6. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Based on the data asset, an associated asset is queried from the asset catalog, where the associated asset is a data asset in the asset catalog that has a reference relationship with the data asset.

7. The method according to any one of claims 1 to 3, characterized in that After generating the data asset based on the data asset fragment, the method further includes: One or more task nodes in the workflow are updated based on the data asset, wherein the updated task nodes in the workflow correspond to the same data assets as the updated task nodes.

8. A data asset generation device, characterized in that: include: An extraction unit, configured to extract a target task node set from a workflow based on data asset features, wherein the target task node set includes one or more task nodes in the workflow, and the one or more task nodes are used to identify data assets; A processing unit, configured to perform named entity recognition on context information of one or more task nodes in the target task node set to determine one or more data asset segments, wherein the context information includes data in the task node associated with the data asset segment; The processing unit is further configured to generate the data asset based on one or more of the data asset fragments.

9. The device according to claim 8, characterized in that The extraction unit is specifically used for: Determine a target task node based on matching the data asset feature with one or more task nodes in the workflow, wherein the data asset feature includes one or more of the following: asset name, asset type, and asset attribute; The target task node set is obtained based on the target task node combination, and the target task node is a task node in the workflow that is associated with the data asset feature.

10. The device according to claim 8, characterized in that The processing unit is specifically used for: Based on the named entity recognition model, named entity recognition is performed on the context information of one or more tasks in the target task node set to determine one or more data asset segments.

11. The device according to any one of claims 8 to 10, characterized in that The processing unit is also used for: identifying missing data asset segments in the data asset, the missing data asset segments comprising missing asset attributes in the data asset; searching the asset catalog based on the missing data asset segment to determine a search result, the search result including a supplemental data asset segment; The missing data asset segments in the data assets are supplemented based on the search results.

12. The device according to claim 11, characterized in that The processing unit is specifically used for: Determining a search keyword from an upstream task node of the workflow based on the missing data asset fragment, the upstream task node being a task node outside the target task node set; The asset catalog is searched based on the search keyword to determine the search result.

13. The device according to any one of claims 8 to 10, characterized in that The processing unit is also used for: Based on the data asset, an associated asset is queried from the asset catalog, where the associated asset is a data asset in the asset catalog that has a reference relationship with the data asset.

14. The device according to any one of claims 8 to 10, characterized in that The processing unit is also used for: One or more task nodes in the workflow are updated based on the data asset, wherein the updated task nodes in the workflow correspond to the same data assets as the updated task nodes.

15. A computing device, characterized in that: The device comprises a processor coupled to a memory, wherein the processor is used to store instructions. When the instructions are executed by the processor, the computing device performs the method according to any one of claims 1 to 7.

16. A computing device cluster, characterized in that: The method comprises at least one computing device, wherein the computing device comprises a processor, wherein the processor is coupled to a memory, and wherein the processor is used to store instructions. When the instructions are executed by the processor, the computing device cluster performs the method according to any one of claims 1 to 7.

17. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed, the computer is caused to perform the method according to any one of claims 1 to 7.

18. A computer program product, comprising instructions, characterized in that: When the instructions are executed, the computer implements the method according to any one of claims 1 to 7.

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