Digital asset data processing method and device, and electronic equipment

Abstract

The embodiment of the invention is suitable for the technical field of block chains, and provides a digital asset data processing method and device, and electronic equipment, and the method comprises the steps: obtaining to-be-processed data corresponding to digital assets; determining a target rule instance matched with the to-be-processed data from a preset rule instance set; the rule instance set comprises general rule instances and self-defined rule instances set for the digital assets; generating a calculation task according to the to-be-processed data and the target rule instance; and executing the calculation task to obtain a calculation result. According to the embodiment of the invention, the operation and maintenance cost in a corresponding service scene for providing various different digital assets can be reduced.

Description

Technical Field

[0001] The embodiments of the present application belong to the field of blockchain technology, and in particular, relate to a digital asset data processing method, device, and electronic device. Background Art

[0002] RWA (Real World Asset) is a tangible or intangible asset in the real world that is digitized on the blockchain. RWA's off-chain calculation is one of the key links in the RWA life cycle, including compliance verification, pricing calculation, data privacy protection, data availability proof and other complex business logics that require the RWA platform to interact with off-chain data. Moreover, for different RWA products, the specific implementation of the above business logic is different.

[0003] Currently, every time a general RWA platform releases a new RWA product, it needs to develop customized computing logic for it, and then upgrade the original platform. This approach obviously requires very high operation and maintenance costs.

[0004] There is an urgent need to reduce operation and maintenance costs when providing a variety of RWA-related services on the RWA platform. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a digital resource data processing method, device, and electronic device to reduce the operation and maintenance costs in the scenario of providing a variety of different digital asset corresponding services.

[0006] A first aspect of an embodiment of the present application provides a digital asset data processing method, including:

[0007] Obtaining data to be processed corresponding to digital assets;

[0008] Determine a target rule instance that matches the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset;

[0009] Generate a computing task according to the data to be processed and the target rule instance;

[0010] The computing task is executed to obtain a computing result.

[0011] In some implementations of the first aspect, determining a target rule instance that matches the data to be processed includes:

[0012] In a case where the data to be processed includes general computing event information, determining at least one general rule instance corresponding to the general computing event information as the target rule instance;

[0013] In a case where the data to be processed includes customized computing event information of a digital asset, at least one custom rule instance corresponding to the digital asset is determined as the target rule instance.

[0014] In some implementations of the first aspect, determining at least one custom rule instance corresponding to the digital asset as the target rule instance includes:

[0015] Extracting one or more asset identifiers from the customized computing event information as target identifiers; one asset identifier corresponds to one digital asset;

[0016] A custom rule instance corresponding to the target identifier is determined from the rule instance set as a target rule instance.

[0017] In some implementations of the first aspect, the custom rule instance includes at least one of a WASM rule instance and a Lua rule instance;

[0018] The WASM rule instance includes a WASM bytecode generated for a first digital asset and an asset identifier corresponding to the first digital asset.

[0019] The Lua rule instance includes generating a Lua script for the second digital asset and an asset identifier corresponding to the digital asset.

[0020] In some implementations of the first aspect, generating a computing task according to the data to be processed and the target rule instance includes:

[0021] Determining privacy protection feature information corresponding to the data to be processed;

[0022] Determine the target task type according to the privacy feature information;

[0023] According to the target task type, a computing task is generated based on the data to be processed and the target rule instance.

[0024] In some implementations of the first aspect, executing the computing task to obtain a computing result includes:

[0025] If the computing task includes the general rule instance, the native runtime environment is used to execute the computing task and generate a computing result;

[0026] If the computing task includes the custom rule instance, a virtual machine matching the computing task is used to execute the computing task and generate a computing result.

[0027] In some implementations of the first aspect, the method further includes:

[0028] Receive a new custom rule instance;

[0029] Determining meta information corresponding to the new custom rule instance;

[0030] After registering in a preset blockchain using the meta information, the new custom rule instance is added to the rule instance set.

[0031] A second aspect of an embodiment of the present application provides a digital asset data processing device, including:

[0032] A data acquisition module, used to acquire the data to be processed corresponding to the digital assets;

[0033] A target rule instance determination module, used to determine a target rule instance matching the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset;

[0034] A computing task generation module, used to generate a computing task according to the data to be processed and the target rule instance;

[0035] The calculation processing module is used to execute the calculation task to obtain the calculation result.

[0036] A third aspect of an embodiment of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements the digital asset data processing method as described in the first aspect above.

[0037] A fourth aspect of an embodiment of the present application provides a computer program product, including a computer program, which, when executed, enables the digital asset data processing method described in the first aspect to be executed.

[0038] A fifth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the digital asset data processing method as described in the first aspect above is implemented.

[0039] The embodiments of the present application have the following beneficial effects:

[0040] In an embodiment of the present application, by acquiring data to be processed corresponding to a digital asset; determining a target rule instance matching the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset; generating a computing task based on the data to be processed and the target rule instance; and executing the computing task to obtain a computing result, it is achieved that when a single platform receives data to be processed corresponding to a digital asset, a general rule instance and / or a hot-swappable custom rule instance matching the data to be processed can be determined as a target rule instance according to the data to be processed, and a computing task is generated using the target rule instance matching the data to be processed, and a computing result corresponding to the data to be processed is obtained by executing the computing task, thereby implementing deployment of different types of rule instances in the platform for performing calculations related to digital assets, and the hot-swappable custom rule instance corresponds to an RWA, so that when the platform adds or removes RWA-related services, there is no need to update the platform version, and only needs to add or delete the corresponding rule instance, thereby reducing the operation and maintenance cost of the platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or prior art descriptions. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a schematic diagram of a digital asset data processing method provided in an embodiment of the present application;

[0043] Figure 2 It is a schematic diagram of the architecture of a computing engine provided in an embodiment of the present application;

[0044] Figure 3 is a schematic diagram of a digital asset data processing device provided in an embodiment of the present application;

[0045] Figure 4 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0047] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0048] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0049] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0050] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0051] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0052] The RWA (Real World Asset) platform can provide services related to the RWA life cycle, such as RWA verification, transaction, and replacement services. In the scenario of providing related services for RWA, it is usually necessary to involve multiple services and process-related calculations, including calculations for the platform to interact with the off-chain data of the blockchain. The calculation logic of different RWAs may not be the same. In related technologies, when the platform needs to provide a new RWA, it needs to develop new calculation logic and upgrade the version of the original platform. During the upgrade process, it cannot process related calculations in time. When it provides multiple new RWAs, the platform needs to develop new calculation logic for each new RWA, which requires huge operation and maintenance costs. In order to reduce operation and maintenance costs, existing platforms only maintain a fixed number of RWAs.

[0053] The technical solution of the present application is described below through specific embodiments.

[0054] Reference Figure 1 , shows a schematic diagram of a digital asset data processing method provided in an embodiment of the present application, which may specifically include the following steps:

[0055] Step 101, obtaining the data to be processed corresponding to the digital asset;

[0056] The embodiment of the present application can be applied to the RWA platform, and the digital asset is a product object provided by the RWA platform. The digital asset described below includes at least RWA. The RWA platform can receive a variety of different data and identify whether the received data is the pending data corresponding to the digital asset. The pending data is data related to the digital asset and needs to be calculated.

[0057] The embodiment of the present application does not limit the data source object that sends the data to be processed. The data source object may include but is not limited to the digital asset issuer, blockchain, terminal device, etc.

[0058] As an example, the RWA platform may be provided with a business layer, which is used to monitor the data to be processed related to the digital assets.

[0059] Step 102, determining a target rule instance that matches the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset;

[0060] A rule instance set is pre-set in the RWA platform, which includes a general rule instance and one or more custom rule instances corresponding to digital assets.

[0061] The life cycle of each product object includes multiple calculation links (the "verification" related to the product object is also considered a calculation link). For different digital assets, some calculation logics are the same, such as the verification of the identity information of the product object development organization; some calculation logics are different, such as the different pricing and valuation models for each product object.

[0062] Common rule instances are obtained during the compilation phase of the RWA platform. Custom rule instances can be hot-swapped after the compilation phase of the RWA platform, that is, the addition, modification, and deletion of custom rule instances do not affect the operation of the RWA platform. Custom rule instances can be written in different computer languages, including but not limited to Rust, Go, C++, Python, Lua, etc. For example, custom rule instance 1 is written in the computer language Go, and custom rule instance 2 is written in the computer language Lua.

[0063] Different data to be processed may correspond to different calculation logics and need to be processed by different rule instances. For example, for data to be processed related to general calculations, it is necessary to call general rule instances for calculation processing; for data to be processed related to customized (customized) calculations of digital assets, it is necessary to call custom rule instances that match the data to be processed for processing.

[0064] Step 103, generating a computing task according to the data to be processed and the target rule instance;

[0065] After determining the target rule instance, information to be calculated related to the target rule instance in the data to be processed is determined, and a calculation task is generated using the target rule instance and the related information to be calculated.

[0066] In a specific implementation, the data to be processed may involve customized calculations of multiple digital assets, or involve customized digital assets and general calculations at the same time. Therefore, in practice, one or more target rule instances can be determined for the data to be processed. Each information to be calculated in the data to be processed and each target rule instance corresponding to each information to be calculated are used to generate calculation tasks respectively, that is, multiple calculation tasks can be generated for the data to be processed.

[0067] Step 104: execute the computing task to obtain a computing result.

[0068] Since the computing task needs to be processed by using an execution environment that matches the rule instance, after the computing task is generated, the execution environment that matches the computing task can be determined, and the computing task is processed in the execution environment to obtain the computing result.

[0069] After the calculation result is obtained, the calculation result can be fed back to the source object of the data to be processed so that the source object can perceive the calculation result.

[0070] As an example, the calculation results can also be processed on the chain.

[0071] In an embodiment of the present application, by acquiring data to be processed corresponding to a digital asset; determining a target rule instance matching the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset; generating a computing task based on the data to be processed and the target rule instance; executing the computing task to obtain a computing result, so as to achieve receiving data to be processed corresponding to a digital asset on a single RWA platform, determining a general rule instance and / or a hot-swappable custom rule instance matching the data to be processed as a target rule instance according to the data to be processed, and generating a computing task using the target rule instance matching the data to be processed, and obtaining a computing result corresponding to the data to be processed by executing the computing task, so as to achieve deployment of different types of rule instances in the RWA platform for digital asset-related computing, and a hot-swappable custom rule instance corresponding to a digital asset, so that when the RWA platform adds or removes digital asset-related services, it is not necessary to update the version of the RWA platform, but only needs to add or delete the corresponding rule instance, thereby reducing the operation and maintenance cost of the RWA platform.

[0072] In some implementations of the embodiments of the present application, determining the target rule instance that matches the data to be processed includes: when the data to be processed contains general computing event information, determining at least one general rule instance corresponding to the general computing event information as the target rule instance; when the data to be processed contains customized computing event information of a digital asset, determining at least one custom rule instance corresponding to the digital asset as the target rule instance.

[0073] The general computing event information may include, but is not limited to, feature information related to general computing (e.g., keywords, characters, fields, tags, etc. related to general computing), and information to be calculated for general computing (e.g., calculation parameters, calculation time, etc.). In the case where the data to be processed includes general computing event information, the general rule instance corresponding to the general event information is determined as the target rule instance in the preset rule instance set.

[0074] In actual applications, there may be multiple general computing logics related to digital assets. Therefore, multiple general rule instances can be deployed in the RWA platform, and the general rule instance that matches the general computing event information is determined as the target rule instance.

[0075] Customized calculation event information may include, but is not limited to, characteristic information related to the customized calculation (e.g., keywords, characters, fields, tags, etc. related to the customized calculation), and information to be calculated for the customized calculation (e.g., calculation parameters, calculation time, etc.). In the case where the data to be processed contains customized calculation event information, the customized rule instance corresponding to the customized event information is determined as the target rule instance in the preset rule instance set.

[0076] In practical applications, the customized computing event information in the data to be processed may correspond to multiple digital assets, for example, the data to be processed involves the replacement of multiple digital assets. In this case, the customized rule instance corresponding to each digital asset may be determined as the target rule instance.

[0077] In some implementations of the embodiments of the present application, determining at least one custom rule instance corresponding to the digital asset as the target rule instance includes: extracting one or more asset identifiers as target identifiers from the customized computing event information; one asset identifier corresponds to one digital asset; and determining the custom rule instance corresponding to the target identifier from the rule instance set as the target rule instance.

[0078] A custom rule instance matches a digital asset and can extract one or more asset identifiers for distinguishing different digital assets from the customized computing event information, and identify the asset as a target identifier.

[0079] The custom rule instance that matches the target identifier is determined from the rule instance set as the target rule instance, thereby realizing matching by asset identifier. When the data to be processed contains customized computing event information, the custom rule instance corresponding to the data to be processed is determined, thereby avoiding using the wrong rule instance to process the data to be processed.

[0080] In some implementations of the embodiments of the present application, the custom rule instance includes at least one of a WASM rule instance and a Lua rule instance;

[0081] The WASM rule instance includes a WASM bytecode generated for a first digital asset and an asset identifier corresponding to the first digital asset.

[0082] The Lua rule instance includes generating a Lua script for the second digital asset and an asset identifier corresponding to the digital asset.

[0083] The first digital asset and the second digital asset mentioned above are only used to distinguish that different rule instances are generated for different digital assets. In actual applications, multiple WASM rule instances and multiple Lua rule instances can be set, each WASM rule instance corresponds to a digital asset, and each Lua rule instance corresponds to a digital asset.

[0084] The embodiment of the present application does not limit the types of the first digital asset and the second digital asset.

[0085] After the calculation model is written using an unrestricted computer, it is compiled into WASM field code through a compiler, and the WASM field code is associated with an asset identifier to generate a WASM rule instance.

[0086] After the calculation model is written in Lua language, a Lua script corresponding to the calculation model can be generated, and a Lua rule instance can be generated by associating the Lua script with an asset identifier.

[0087] In some implementations of the embodiments of the present application, generating a computing task based on the data to be processed and the target rule instance includes: determining privacy protection feature information corresponding to the data to be processed; determining a target task type according to the privacy feature information; and generating a computing task based on the data to be processed and the target rule instance according to the target task type.

[0088] The RWA platform can also determine the privacy protection feature information corresponding to the data to be processed through the data source object, and the privacy protection requirements of the data source object can be determined through the privacy protection feature information. According to the privacy feature information, the target task type can be determined as a non-privacy task type or a privacy task type.

[0089] According to the target task type, the computing task is initialized based on the data to be processed and the target rule instance.

[0090] For example: if the target task type is a non-privacy task type, the computing task can be a local computing task; if the target task type is a privacy task type, the computing task can be a secure multi-party computing task.

[0091] The computing task may include a target rule instance and information to be computed that is extracted from the data to be processed and matches the target rule instance. When the computing task is executed, the target rule instance uses the information to be computed that matches it to perform computing and generates a computing result.

[0092] The embodiment of the present application decouples the rule instance from the computing task, so that multiple computing tasks can be initialized by the same computing rule instance.

[0093] In some implementations of the embodiments of the present application, executing the computing task to obtain a computing result includes:

[0094] If the computing task includes the general rule instance, the native runtime environment is used to execute the computing task and generate a computing result;

[0095] If the computing task includes the custom rule instance, a virtual machine matching the computing task is used to execute the computing task and generate a computing result.

[0096] A virtual machine (VM) is a computer system emulator that simulates one or more computer systems running on a computer with complete hardware system functions through software. Native Runtime refers to an operating environment that does not require a virtual machine or interpreter.

[0097] Different rule instances require different execution environments. For computing tasks whose target rule instances are general rule instances, the general rule instances are obtained during the compilation phase of the RWA platform. The computer language used to write general rules is consistent with the language used to write the RWA platform. Therefore, the native runtime can be used to process computing tasks that match the general rule instances, thereby quickly processing computing tasks without introducing a virtual machine.

[0098] Since custom rule instances are written in different computer languages, when a computing task includes a custom rule instance, it is necessary to first determine that the virtual machine corresponding to the custom rule instance included in the computing task is the target virtual machine, and call the target virtual machine to execute the computing task to obtain the computing result.

[0099] For example, if the custom rule instance included in the computing task is a WASM rule instance, the target virtual machine is determined to be a WASM virtual machine; if the custom rule instance included in the computing task is a Lua rule instance, the target virtual machine is determined to be a Lua virtual machine.

[0100] In some implementations of the embodiments of the present application, the method further includes: receiving a new custom rule instance;

[0101] Determine meta information corresponding to the new custom rule instance; and after registering in a preset blockchain using the meta information, add the new custom rule instance to the rule instance set.

[0102] In the embodiment of the present application, the RWA platform operation and maintenance personnel can update the above-mentioned general rule instance on the RWA platform, and input new custom rule instances to the RWA platform. The RWA platform can also receive new custom rule instances input from outside.

[0103] After receiving a new custom rule instance, the RWA platform first determines the meta information corresponding to the new custom rule instance. The meta information represents the characteristics of the new custom rule instance. For example, the meta information may include a description of the product object matched by the new custom rule instance, a description of the service applicable to the custom rule instance, etc.

[0104] By registering the new custom rule instance on the chain, users in the blockchain can know the characteristics of the custom rule instance, and then understand the relevant information of the new custom rule instance and product object, thereby increasing the transparency of the custom rule instance used by the RWA platform.

[0105] It is understandable that the RWA platform can verify the custom rule instance before using the meta information to register it in the blockchain, for example: determine whether there is an anomaly or a risk, and then register it on the chain after the verification is passed, and whether the source of the new custom rule instance meets the specified conditions.

[0106] After completing the registration of the custom rule instance in the blockchain, the RWA platform can add the new custom rule instance to the above rule instance set, and the new rule instance can be called by the RWA platform and initialize the computing task in the future.

[0107] In a practical application, a computing engine can be constructed, through which the above-mentioned digital asset data processing method can be implemented.

[0108] Reference Figure 2 , shows a schematic diagram of the architecture of a computing engine provided in an embodiment of the present application, the computing engine includes three components: a task scheduler, a rule manager and an executor.

[0109] The rule manager is used to store rule instances. After receiving a new rule instance, the rule manager registers the meta information corresponding to the new rule instance to the rule management contract on the chain to make the meta information corresponding to the new rule instance public.

[0110] The rule manager contains three types of rule instances: (1) built-in rule instances (general rule instances), (2) WASM rule instances, and (3) Lua rule instances.

[0111] Built-in rule instances: Calculation rule instances natively integrated into the engine, written in the native computer language of the engine, mainly for the general calculation logic of RWA-related businesses. For example: security verification rules executed on the issuing institution during the issuance of digital assets, transaction rule instances executed on both parties during the asset transaction process, etc. The RWA platform is responsible for the development and maintenance of built-in rule instances. Built-in rule instances can be used as pre-verification conditions for subsequent services. They can support fewer types of service functions, but they are executed faster.

[0112] Both WASM rule instances and Lua rule instances are flexible and pluggable custom rule instances. For the customized computing logic of RWA products, one of the computing scenarios is the pricing calculation of RWA. Custom rule instances can be provided by the platform or by other parties, and can support a variety of service functions. For WASM rule instances, the rule instance generator can use different types of computer languages ​​such as Rust, Go, C++, Python, etc. to write the calculation model, and then compile it into WASM bytecode through the WASM compiler. Each WASM bytecode will be associated with an RWA asset identifier to form a WASM rule instance; for Lua rule instances, the rule instance generator can use the Lua computer language to write the calculation model, and the corresponding Lua script is associated with an RWA asset identifier to form a Lua rule instance.

[0113] The task scheduler is used to manage the life cycle of computing tasks. According to the privacy protection requirements of the data source (data to be processed), the task scheduler can initialize multi-party computing tasks or local computing tasks. The task scheduler responds to the trigger event of the platform business layer, matches a specific computing rule instance from the rule instance manager according to the event information of the trigger event, and then initializes a computing task.

[0114] By decoupling rule instances from tasks, multiple computing tasks can be initialized by the same computing rule instance. For non-built-in rule instances, hot upgrades and hot deployments of computing rule instances can be achieved. The process of adding, modifying, and deleting rule instances is real-time and dynamic, and will not affect the normal operation of the engine.

[0115] The executor supports three types of execution environments:

[0116] Native runtime: for built-in rule instances. Built-in rule instances are determined during the engine compilation phase and do not need to be compiled again during the engine runtime. Computation tasks initialized by built-in rule instances are executed natively during the engine runtime, so the execution speed is the fastest.

[0117] WASM virtual machine: for WASM rule instances. The WASM virtual machine loads the WASM bytecode and input data corresponding to the WASM rule instance from the computing task instance and executes it, and returns the execution result to the engine.

[0118] Lua virtual machine: for Lua rule instances. Lua virtual machine loads the Lua script and input data corresponding to the Lua rule instance from the computing task instance and executes it, and returns the execution result to the engine.

[0119] The computing engine can be deployed on the platform to initialize and execute computing tasks related to RWA, and apply WASM and virtual machine technology to RWA's off-chain computing scenarios, which can not only support the computing needs of different RWA products, but also reduce the development and operation costs of the RWA platform.

[0120] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0121] Reference Figure 3 , shows a schematic diagram of a digital asset data processing device provided by an embodiment of the present application, which may specifically include a data acquisition module, a target rule instance determination module, a computing task generation module, and a computing processing module, wherein:

[0122] The data acquisition module 301 is used to acquire the data to be processed corresponding to the digital assets;

[0123] A target rule instance determination module 302 is used to determine a target rule instance that matches the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset;

[0124] A computing task generating module 303, used to generate a computing task according to the data to be processed and the target rule instance;

[0125] The calculation processing module 304 is used to execute the calculation task to obtain the calculation result.

[0126] In some implementations of the embodiments of the present application, the target rule instance determination module includes:

[0127] a general rule instance matching submodule, configured to determine, when the data to be processed includes general computing event information, at least one general rule instance corresponding to the general computing event information as the target rule instance;

[0128] The custom rule instance matching submodule is used to determine at least one custom rule instance corresponding to the digital asset as the target rule instance when the data to be processed includes customized computing event information of the digital asset.

[0129] In some implementations of the embodiments of the present application, the custom rule instance matching submodule includes:

[0130] A target identifier determination unit, configured to extract one or more asset identifiers from the customized computing event information as target identifiers; one asset identifier corresponds to one digital asset;

[0131] The target rule instance determining unit is used to determine the custom rule instance corresponding to the target identifier from the rule instance set as the target rule instance.

[0132] In some implementations of the embodiments of the present application, the custom rule instance includes at least one of a WASM rule instance and a Lua rule instance;

[0133] The WASM rule instance includes a WASM bytecode generated for a first digital asset and an asset identifier corresponding to the first digital asset.

[0134] The Lua rule instance includes generating a Lua script for the second digital asset and an asset identifier corresponding to the digital asset.

[0135] In some implementations of the embodiments of the present application, the computing task generation module 303 includes:

[0136] A privacy protection characteristic information determination submodule, used to determine the privacy protection characteristic information corresponding to the data to be processed;

[0137] A target task type determination submodule, used to determine the target task type according to the privacy feature information;

[0138] The computing task generation submodule is used to generate a computing task according to the target task type, the data to be processed and the target rule instance.

[0139] In some implementations of the embodiments of the present application, the calculation processing module 304 includes:

[0140] A first execution submodule, configured to execute the computing task and generate a computing result by using a native runtime environment if the computing task includes the general rule instance;

[0141] The second execution submodule is used to use a virtual machine matching the computing task to execute the computing task and generate a computing result if the computing task includes the custom rule instance.

[0142] In some implementations of the embodiments of the present application, the device further includes:

[0143] A rule instance receiving module, used for receiving a new custom rule instance;

[0144] A meta information determination module, used to determine the meta information corresponding to the new custom rule instance;

[0145] A rule instance adding module is used to add the new custom rule instance to the rule instance set after registering in a preset blockchain using the meta information.

[0146] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment part.

[0147] Reference Figure 4 , shows a schematic diagram of an electronic device provided by an embodiment of the present application. Figure 4 As shown, the electronic device 400 in the embodiment of the present application includes: a processor 410, a memory 420, and a computer program 421 stored in the memory 420 and executable on the processor 410. When the processor 410 executes the computer program 421, the steps in each embodiment of the above-mentioned digital asset data processing method are implemented, such as Figure 1 Alternatively, when the processor 410 executes the computer program 421, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 3 Functions of modules 301 to 304 are shown.

[0148] Exemplarily, the computer program 421 may be divided into one or more modules / units, which are stored in the memory 420 and executed by the processor 410 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which may be used to describe the execution process of the computer program 421 in the electronic device 400. For example, the computer program 421 may be divided into a data acquisition module, a target rule instance determination module, a computing task generation module, and a computing processing module, and the specific functions of each module are as follows:

[0149] A data acquisition module, used to acquire the data to be processed corresponding to the digital assets;

[0150] A target rule instance determination module, used to determine a target rule instance matching the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset;

[0151] A computing task generation module, used to generate a computing task according to the data to be processed and the target rule instance;

[0152] The calculation processing module is used to execute the calculation task to obtain the calculation result.

[0153] The electronic device 400 may include, but is not limited to, a processor 410 and a memory 420. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 400 and does not constitute a limitation of the electronic device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 400 may also include input and output devices, network access devices, buses, etc.

[0154] The processor 410 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, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0155] The memory 420 may be an internal storage unit of the electronic device 400, such as a hard disk or memory of the electronic device 400. The memory 420 may also be an external storage device of the electronic device 400, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 400. Further, the memory 420 may also include both an internal storage unit of the electronic device 400 and an external storage device. The memory 420 is used to store the computer program 421 and other programs and data required by the electronic device 400. The memory 420 may also be used to temporarily store data that has been output or is to be output.

[0156] An embodiment of the present application further discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the digital asset data processing method as described in the aforementioned embodiments is implemented.

[0157] An embodiment of the present application also discloses a computer program product, including a computer program. When the computer program is run, the digital asset data processing method described in the above embodiments is executed.

[0158] As an example, the computer program is a computing engine.

[0159] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them. Although the present application is described in detail with reference to the above-mentioned embodiments, a person skilled in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A digital asset data processing method, characterized in that: Obtaining data to be processed corresponding to digital assets; Determine a target rule instance that matches the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset; Generate a computing task according to the data to be processed and the target rule instance; The computing task is executed to obtain a computing result.

2. The method according to claim 1, characterized in that The determining of a target rule instance matching the data to be processed includes: In a case where the data to be processed includes general computing event information, determining at least one general rule instance corresponding to the general computing event information as the target rule instance; In a case where the data to be processed includes customized computing event information of a digital asset, at least one custom rule instance corresponding to the digital asset is determined as the target rule instance.

3. The method according to claim 2, characterized in that The determining at least one custom rule instance corresponding to the digital asset as the target rule instance includes: Extracting one or more asset identifiers from the customized computing event information as target identifiers; one asset identifier corresponds to one digital asset; A custom rule instance corresponding to the target identifier is determined from the rule instance set as a target rule instance.

4. The method according to claim 1, characterized in that: The custom rule instance includes at least one of a WASM rule instance and a Lua rule instance; The WASM rule instance includes a WASM bytecode generated for a first digital asset and an asset identifier corresponding to the first digital asset. The Lua rule instance includes generating a Lua script for the second digital asset and an asset identifier corresponding to the digital asset.

5. The method according to claim 1, characterized in that The generating a computing task according to the data to be processed and the target rule instance includes: Determining privacy protection feature information corresponding to the data to be processed; Determine the target task type according to the privacy feature information; According to the target task type, a computing task is generated based on the data to be processed and the target rule instance.

6. The method according to any one of claims 1 to 5, characterized in that: The performing of the computing task to obtain a computing result includes: If the computing task includes the general rule instance, the native runtime environment is used to execute the computing task and generate a computing result; If the computing task includes the custom rule instance, a virtual machine matching the computing task is used to execute the computing task and generate a computing result.

7. The method according to claim 1, characterized in that The method further comprises: Receive a new custom rule instance; Determining meta information corresponding to the new custom rule instance; After registering in a preset blockchain using the meta information, the new custom rule instance is added to the rule instance set.

8. A digital asset data processing device, characterized in that: include: A data acquisition module, used to acquire the data to be processed corresponding to the digital assets; A target rule instance determination module, used to determine a target rule instance matching the data to be processed from a preset rule instance set; the rule instance set includes a general rule instance and a custom rule instance set for the digital asset; A computing task generation module, used to generate a computing task according to the data to be processed and the target rule instance; The calculation processing module is used to execute the calculation task to obtain the calculation result.

9. An electronic device, characterized in that: The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The invention comprises a computer program, which, when being executed, enables the method according to any one of claims 1 to 7 to be performed.

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