Business data processing method and device, equipment and medium

Abstract

The invention provides a business data processing method and device, equipment and a medium. The method comprises the following steps: acquiring demand information for processing business data; according to the demand information, determining feature information of a target feature required to be processed in a business data processing process; obtaining a target feature from a preset feature library according to the feature information, and obtaining processing rule information corresponding to the target feature from the preset feature library; extracting initial service sub-data related to the target feature from the service data; and processing the target features and the initial service sub-data according to the processing rule information to obtain a service processing result. Through the method, the feature processing efficiency can be effectively improved, and the feature service performance is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a business data processing method, apparatus, device, and medium. Background Art

[0002] With the continuous evolution and iteration of the algorithmic models used in business processing, from simple linear and tree models to today's complex deep learning models, the prediction results have become increasingly accurate. The platform for producing algorithmic models can be divided into three parts: model serving, model training, and feature platform. Model serving provides online model predictions, model training provides model training outputs, and the feature platform provides data support for features and samples. As the business grows, the volume of features in the feature platform is also growing rapidly, and the challenges and pressures faced are also increasing. The massive number of features can cover various business processing scenarios. Each feature has different processing logic, and of course, there may be common business logic.

[0003] In related technologies, the processing of business-related features is often decentralized within online business systems and tightly coupled with business processing logic. This approach results in low feature processing efficiency and poor feature service performance. Summary of the Invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the present disclosure proposes a business data processing method, device, electronic device, non-transitory computer-readable storage medium storing computer instructions, and computer program product, which can effectively improve feature processing efficiency and improve feature service performance.

[0006] The business data processing method proposed in the embodiment of the first aspect of the present disclosure includes: obtaining demand information for processing business data; determining, based on the demand information, feature information of a target feature required for processing the business data; obtaining the target feature from a preset feature library based on the feature information, and obtaining processing rule information corresponding to the target feature from the preset feature library; extracting initial business sub-data related to the target feature from the business data; and processing the target feature and the initial business sub-data according to the processing rule information to obtain a business processing result.

[0007] The business data processing device proposed in the embodiment of the second aspect of the present disclosure includes: a first acquisition module, used to obtain demand information for processing business data; a determination module, used to determine, based on the demand information, feature information of a target feature required to be processed in the process of processing the business data; a second acquisition module, used to obtain the target feature from a preset feature library based on the feature information, and obtain processing rule information corresponding to the target feature from the preset feature library; an extraction module, used to extract initial business sub-data related to the target feature from the business data; and a processing module, used to process the target feature and the initial business sub-data according to the processing rule information to obtain a business processing result.

[0008] The electronic device proposed in the third embodiment of the present disclosure includes: a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the business data processing method proposed in the first embodiment of the present disclosure.

[0009] The fourth embodiment of the present disclosure proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the business data processing method proposed in the first embodiment of the present disclosure.

[0010] The fifth embodiment of the present disclosure proposes a computer program product. When the instructions in the computer program product are executed by a processor, the business data processing method proposed in the first embodiment of the present disclosure is executed.

[0011] The business data processing method, device, electronic device, non-transitory computer-readable storage medium storing computer instructions, and computer program product provided by the present disclosure obtain demand information for processing business data, and determine, based on the demand information, feature information of target features required for processing business data, obtain target features from a preset feature library based on the feature information, obtain processing rule information corresponding to the target features from the preset feature library, extract initial business sub-data related to the target features from the business data, and process the target features and initial business sub-data according to the processing rule information to obtain business processing results, thereby effectively improving feature processing efficiency and improving feature service performance.

[0012] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0014] Figure 1This is a flowchart of a business data processing method proposed in one embodiment of the present disclosure;

[0015] Figure 2 is a flowchart of a business data processing method proposed in another embodiment of the present disclosure;

[0016] Figure 3 Schematic diagram of the process of constructing a preset feature library in an embodiment of the present disclosure;

[0017] Figure 4 is a flowchart of a business data processing method proposed in another embodiment of the present disclosure;

[0018] Figure 5 is a schematic diagram of the feature calculation process in an embodiment of the present disclosure;

[0019] Figure 6 It is a structural diagram of a business data processing device proposed in one embodiment of the present disclosure;

[0020] Figure 7 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0021] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present disclosure and are not to be construed as limiting the present disclosure. On the contrary, the embodiments of the present disclosure include all variations, modifications, and equivalents that fall within the spirit and scope of the appended claims.

[0022] Figure 1 It is a flowchart of a business data processing method proposed in an embodiment of the present disclosure.

[0023] like Figure 1 As shown, the business data processing method includes:

[0024] S101: Obtaining demand information for processing business data.

[0025] Requirement information describes the requirements for processing business data. For example, this information includes the time allowed for processing business data, the required feature processing accuracy, and the result type. Business data refers to business-related data to be processed. Businesses can be any online business, such as shopping, banking, statistics, and online model estimation.

[0026] In some embodiments, an online model estimate can be provided, and the demand information for processing the business data this time can be detected based on the model service involved in the online model estimate. Alternatively, a demand input interface can be provided, and the demand information for processing the business data this time can be detected based on the demand input interface. Of course, the demand information for processing the business data can also be obtained based on any other possible method, and there is no limitation on this.

[0027] S102: Determine, based on the demand information, feature information of target features required for processing business data.

[0028] In embodiments of the present disclosure, support is provided for analyzing the feature requirements during the current business data processing process based on the business data processing requirement information. Information describing the feature requirements during the current business data processing process may be referred to as feature information. Feature information includes information such as a target feature identifier and a target feature type. Target features are features that are required to be processed during the current business data processing process.

[0029] In some embodiments, the demand information can be analyzed to determine the processing method and processing type for processing the business data this time, and the characteristic information of the target characteristics required for processing the business data this time can be determined based on the processing method and processing type; alternatively, the demand information can be input into a characteristic information analysis model to determine the characteristic information of the target characteristics; or the demand information can be processed in combination with any other possible methods to determine the characteristic information of the target characteristics required to process the business data.

[0030] S103: Acquire target features from a preset feature library according to the feature information, and acquire processing rule information corresponding to the target features from the preset feature library.

[0031] After determining the feature information of the target feature to be processed in the process of processing the business data, the target feature can be directly obtained from the preset feature library based on the feature information. For example, the feature information can be used as an index to obtain the target feature from the preset feature library.

[0032] In the embodiment of the present disclosure, the preset feature library may also include processing rule information corresponding to the target feature. The processing rule information is used to describe the processing rules for processing the target feature, such as the processing logic, processing flow, processing type, etc. of processing the target feature.

[0033] The preset feature library can be pre-built. Thus, by pre-extracting and classifying various features and the processing rule information corresponding to each feature, and storing them in the preset feature library, and configuring the feature information as an index of the corresponding features and processing rule information in the preset feature library, when processing business data online, it supports the preset feature library to directly provide target features and processing rule information, without having to perform a full analysis of the business data to determine the target features and processing rule information, thereby achieving decoupling of online business processing and feature processing, effectively improving feature processing efficiency, and improving feature service performance.

[0034] S104: Extracting initial business sub-data related to the target feature from the business data.

[0035] After obtaining the target feature from the preset feature library and the processing rule information corresponding to the target feature from the preset feature library, initial business sub-data related to the target feature can be extracted from the business data. Initial business sub-data refers to the portion of business data involved in processing the target feature. Initial business sub-data can be the partial business data in the business data that is relevant to processing the target feature.

[0036] In some embodiments, the processing logic for processing the target feature can be analyzed to determine part of the business data involved in the process of processing the target feature as the initial business sub-data, or the initial business sub-data related to the target feature can be extracted from the business data based on experience; of course, any other possible method can also be used to extract the initial business sub-data related to the target feature from the business data, and there is no limitation on this.

[0037] S105: Process the target characteristics and the initial business sub-data according to the processing rule information to obtain a business processing result.

[0038] After determining the target characteristics, processing rule information, and initial business sub-data, the target characteristics and initial business sub-data can be processed according to the processing rule information to obtain a business processing result. Since the processing rule information describes the processing rules for processing the target characteristics, the processing rules described in the processing rule information can be implemented to process the target characteristics and initial business sub-data based on the processing rules.

[0039] In this embodiment, by obtaining the demand information for processing business data, and determining the feature information of the target feature required to be processed in the process of processing the business data based on the demand information, obtaining the target feature from the preset feature library based on the feature information, and obtaining the processing rule information corresponding to the target feature from the preset feature library, extracting the initial business sub-data related to the target feature from the business data, and processing the target feature and the initial business sub-data according to the processing rule information to obtain the business processing result, it can effectively improve the feature processing efficiency and improve the feature service performance.

[0040] Figure 2 It is a flowchart of a business data processing method proposed in another embodiment of the present disclosure.

[0041] like Figure 2 As shown, the business data processing method includes:

[0042] S201: Receive an initial processing expression, wherein the initial processing expression is used to process candidate features, and the initial processing expression has a corresponding processing type.

[0043] In some embodiments, a platform for generating a preset feature library may be provided to support the generation of processing rules for various features based on the feature processing requirements of developers.

[0044] In some embodiments, an initial processing expression may be received based on the platform that generates the preset feature library. The initial processing expression may be, for example, a Structured Query Language (SQL) expression that describes the processing rules and processing type for processing candidate features. The initial processing expression may be input by a developer in the platform that generates the preset feature library. The processing type may also be customized in the platform that generates the preset feature library.

[0045] S202: Pre-processing the initial processing expression according to the processing type to obtain a target processing expression.

[0046] In some embodiments, in order to effectively reduce the storage resource consumption of the preset feature library and support the rapid retrieval of target features and corresponding processing rule information during online business processing, the initial processing expression can also be optimized and preprocessed, and the processing expression obtained by processing can be called the target processing expression.

[0047] In some embodiments, when performing the step of pre-processing the initial processing expression according to the processing type to obtain the target processing expression, verification rules and constraints may be obtained based on the processing type, and the initial processing expression may be verified according to the verification rules. If the verification passes, the initial processing expression may be configured according to the constraints to obtain the target processing expression. This can effectively improve the processing accuracy and efficiency of the target processing expression and avoid introducing too much redundant information into the preset feature library.

[0048] S203: Decompose the target processing expression to obtain multiple candidate operators for processing candidate features.

[0049] In some embodiments, after obtaining the target processing expression as described above, the target processing expression can be decomposed to obtain multiple candidate operators for processing candidate features. In computer science and mathematics, an operator generally refers to a symbol, a combination of symbols, or a function that operates or calculates an input value. An operator can be unary (with only one input) or binary (with two inputs). The target processing expression can be decomposed to determine one or more candidate operators contained in the target processing expression.

[0050] S204: Select some candidate operators from the plurality of candidate operators, and use the candidate rule type corresponding to each candidate operator in the part of the candidate operators as candidate rule information corresponding to the candidate feature.

[0051] After decomposing the target processing expression to obtain multiple candidate operators for processing candidate features, the candidate operators may be further de-redundant to improve the construction effect of the preset feature library.

[0052] In some embodiments, some candidate operators may be selected from a plurality of candidate operators, and the candidate rule type corresponding to each candidate operator in the part of the candidate operators may be used as candidate rule information corresponding to the candidate feature.

[0053] Among these, some candidate operators can be selected from multiple candidate operators, such as for deduplication and merging. The candidate rule type corresponding to each candidate operator can be determined based on the processing type of the target processing expression to which the candidate operator belongs. The candidate rule type indicates the processing rule classification or category of the corresponding candidate operator.

[0054] In some embodiments, when performing the step of selecting some candidate operators from multiple candidate operators, a candidate operator syntax corresponding to each candidate operator may be determined, and an initial syntax tree may be formed based on the multiple candidate operator syntaxes, wherein the initial syntax tree includes multiple tree nodes, and at least some of the tree nodes have connecting edges. At least some of the connecting edges in the initial syntax tree are pruned and / or merged to obtain a target syntax tree, and the candidate operators corresponding to the candidate operator syntaxes included in the target syntax tree are selected as the candidate operators. This can effectively improve the accuracy and rationality of the candidate operator selection.

[0055] A syntax tree is an abstract representation of the grammatical structure of source code. It represents the grammatical structure of a programming language in a tree-like format, with each node representing a structure in the source code. This is also called an abstract syntax tree.

[0056] S205: forming a preset feature library based on the candidate features and the candidate rule information corresponding to the candidate features.

[0057] After the candidate features and the candidate rule information corresponding to the candidate features are determined, a preset feature library may be formed based on the candidate features and the candidate rule information corresponding to the candidate features.

[0058] In some embodiments, an association relationship can be established between the feature information of a candidate feature, the candidate feature, and the candidate rule information corresponding to the candidate feature, and the association relationship can be saved in a preset feature library to support online business processing to effectively and quickly retrieve target features and target rule information.

[0059] The process of building a preset feature library for this embodiment can be illustrated as follows:

[0060] like Figure 3 As shown, Figure 3 It is a flowchart of building a preset feature library in an embodiment of the present disclosure. After the business personnel submit the feature processing expression (an optional example of the initial processing expression) on the interface, the back-end will parse the expression, first perform format verification, and after the format verification passes, perform expression parsing and construct a syntax tree. Then, various optimizations will be performed on the syntax tree, and some unreachable nodes will be pruned or optimized and merged by traversing the syntax tree to obtain an optimized syntax tree. The optimized syntax tree is then converted into feature processing code through code generation technology, and the generated feature processing code is compiled and loaded into the Java Virtual Machine (JVM) through hot loading technology, and the metadata of the newly added features is registered in the feature library. Specifically, the following steps are included:

[0061] Receiving feature processing expressions: Business personnel submit feature processing expressions to the back-end system through the interface.

[0062] Format Verification: The backend system first performs format verification to ensure that the submitted expression conforms to the predetermined syntax specifications and constraints. Only expressions that pass format verification can be processed further.

[0063] Expression parsing and syntax tree construction: By parsing the expression, the backend system converts it into a syntax tree for subsequent optimization and code generation.

[0064] Syntax tree optimization: The backend system traverses the syntax tree and performs pruning or merging optimization on unreachable nodes to reduce computational complexity and improve operational efficiency.

[0065] Optimized syntax tree generation: The optimized syntax tree is further converted into feature processing code. During the code generation process, considering flexibility and scalability, the system adopts various code generation technologies, including template engines, metaprogramming, and dynamic code generation.

[0066] Code loading and compilation: The generated feature processing code uses hot loading technology, leveraging the JVM's capabilities to dynamically load the code onto the target platform, eliminating the overhead of restarting the application. At the same time, the code is compiled into executable machine code on the fly for higher execution efficiency.

[0067] Register metadata: The metadata of the newly added feature will be registered in the feature library for use by other modules or applications.

[0068] S206: Obtaining demand information for processing business data.

[0069] S207: Determine, based on the demand information, feature information of the target feature required for processing the business data.

[0070] S208: Acquire target features from a preset feature library according to the feature information, and acquire processing rule information corresponding to the target features from the preset feature library.

[0071] S209: Extracting initial business sub-data related to the target feature from the business data.

[0072] S210: Process the target characteristics and the initial business sub-data according to the processing rule information to obtain a business processing result.

[0073] For the description of S206 - S210 , please refer to the above embodiment for details, which will not be repeated here.

[0074] In this embodiment, by obtaining the demand information for processing business data, and determining the feature information of the target feature required for processing the business data based on the demand information, obtaining the target feature from the preset feature library based on the feature information, and obtaining the processing rule information corresponding to the target feature from the preset feature library, extracting the initial business sub-data related to the target feature from the business data, and processing the target feature and the initial business sub-data based on the processing rule information to obtain the business processing result, it is possible to effectively improve the feature processing efficiency and improve the feature service performance. By receiving the initial processing expression, wherein the initial processing expression is used to process the candidate feature, the initial processing expression has a corresponding processing type, and pre-processing the initial processing expression according to the processing type to obtain the target processing expression, decomposing the target processing expression to obtain multiple candidate operators for processing the candidate feature, selecting some candidate operators from the multiple candidate operators, and using the candidate rule type corresponding to each candidate operator in the part of the candidate operators as the candidate rule information corresponding to the candidate feature, and forming a preset feature library based on the candidate feature and the candidate rule information corresponding to the candidate feature. By pre-extracting and classifying various features and the processing rule information corresponding to each feature, and storing them in a preset feature library, and configuring the feature information as an index of the corresponding features and processing rule information in the preset feature library, when processing business data online, it supports the preset feature library to directly provide target features and processing rule information, without having to conduct a full analysis of the business data to determine the target features and processing rule information, thereby achieving decoupling of online business processing and feature processing, effectively improving feature processing efficiency, and improving feature service performance.

[0075] Figure 4 It is a flowchart of a business data processing method proposed in another embodiment of the present disclosure.

[0076] like Figure 4 As shown, the business data processing method includes:

[0077] S401: Obtaining demand information for processing business data.

[0078] S402: Determine, based on the demand information, feature information of target features required for processing business data.

[0079] S403: Acquire target features from a preset feature library according to the feature information, and acquire processing rule information corresponding to the target features from the preset feature library.

[0080] For the description of S401 - S403 , please refer to the above embodiment for details, which will not be repeated here.

[0081] S404: Extracting initial business sub-data related to the target feature from the business data.

[0082] In some embodiments, in order to improve the accuracy and efficiency of initial business sub-data extraction, when executing the step of extracting initial business sub-data related to the target feature from the business data, multiple data extraction rules related to the target feature can also be obtained, wherein each data extraction rule is used to extract a type of initial business sub-data, and multiple data extraction rules are executed in parallel to extract multiple types of initial business sub-data from the business data respectively.

[0083] S405: Determine at least one processing rule type according to the processing rule information.

[0084] For example, the processing rule information may be parsed to determine one or more processing rule types, where the processing rule type represents the type, classification, etc. of the corresponding processing operator.

[0085] S406: Acquire a processing operator corresponding to the processing rule type.

[0086] In some embodiments, after determining at least one processing rule type based on the processing rule information, a corresponding processing operator can be retrieved based on the processing rule type to support subsequent processing of target features and initial business sub-data based on the processing operator.

[0087] S407: Process the target feature and the initial business sub-data according to at least one processing operator to obtain a business processing result.

[0088] After obtaining one or more processing operators, the target feature and the initial business sub-data can be processed according to at least one processing operator to obtain a business processing result.

[0089] In some embodiments, different processing operators have corresponding processing orders. Therefore, when executing the step of processing the target features and initial business sub-data according to multiple processing operators to obtain a business processing result, the multiple processing operators may be called according to the multiple processing orders. When calling a corresponding processing operator, the target features and initial business sub-data are processed based on the processing rules of the corresponding processing operator to obtain an intermediate processing result. The business processing result is then obtained based on the multiple intermediate processing results. This helps improve processing accuracy.

[0090] In some embodiments, there are multiple target features. Before processing the target features and initial business sub-data according to at least one processing operator to obtain the business processing results, the feature dependencies between different target features can be determined, and the various types of initial business sub-data can be converted and processed according to the feature dependencies. The business sub-data obtained by the conversion processing can be used as the target business sub-data, so as to ensure that the processing efficiency and effect are improved.

[0091] In some embodiments, the target feature and the target business sub-data are processed according to at least one processing operator to obtain a business processing result.

[0092] An example of the above process can be as follows:

[0093] like Figure 5 As shown, Figure 5 It is a schematic diagram of the feature calculation process in the embodiment of the present disclosure. In the feature calculation process, the feature processing part of the business processing process is implemented. When an online request arrives or offline backtracking begins, the features that need to be processed for the business are first found from the preset feature library, and then through a general data extractor, that is, the data is first converted into an object tree, and the required data is automatically extracted in parallel through the tree path, and then the processing of the required features is completed, and some active optimization is performed in the execution stage, including vectorized calculations, parallel calculations, etc. Finally, it is necessary to verify whether the results are correct. This can be done through manual verification, unit testing, or automated testing. Specifically, the following steps are included:

[0094] Receive request: The system receives a trigger signal for an online request or an offline backtracking task.

[0095] Preset feature library query: Based on business needs, the system finds the features to be processed (an optional example of target features) from the preset feature library. The preset feature library stores metadata (an optional example of feature information) and related processing rules (an optional example of processing rule information) for each type of feature.

[0096] Data Extractor: The system uses a general data extractor to convert input data (an optional example of business data) into an object tree. The data extractor extracts the required data in parallel along a specific path and converts it into a high-order matrix (an optional example of initial business sub-data).

[0097] Feature processing: According to the processing rules in the preset feature library, the system calculates and transforms the high-order matrix based on feature dependencies and processing algorithms to generate new features (an optional example of target business sub-data).

[0098] Execution phase optimization: During feature processing, the system performs active optimization, including vectorized calculations and parallel calculations, to improve computing efficiency and processing speed.

[0099] Result Verification: The system verifies the feature results after processing to ensure their correctness. Verification can be performed through manual verification, unit testing, or automated testing.

[0100] In this embodiment, by obtaining service data processing requirements and, based on the requirements, determining the feature information of the target feature required for processing the service data, obtaining the target feature from a preset feature library based on the feature information, and obtaining processing rule information corresponding to the target feature from the preset feature library, extracting initial service sub-data related to the target feature from the service data, and processing the target feature and initial service sub-data according to the processing rule information to obtain a service processing result, the efficiency of feature processing and the performance of feature services can be effectively improved. By determining at least one processing rule type based on the processing rule information, obtaining a processing operator corresponding to the processing rule type, and processing the target feature and initial service sub-data according to the at least one processing operator to obtain a service processing result, the efficiency and effectiveness of feature processing can be effectively improved, supporting improvements in the efficiency and effectiveness of overall online service processing. Feature-configured processing capabilities improve feature iteration efficiency and reduce service access costs. By receiving and parsing feature processing expressions, flexible definition and expansion of feature processing rules are achieved. Syntax tree optimization technology improves feature processing execution efficiency through pruning and merging optimization. Hot loading and just-in-time compilation technologies enable dynamic loading and high-performance feature processing code generation and execution.

[0101] Figure 6 It is a structural diagram of a business data processing device proposed in one embodiment of the present disclosure.

[0102] like Figure 6 As shown, the business data processing device 60 includes:

[0103] The first acquisition module 601 is used to acquire the demand information for processing business data.

[0104] The determination module 602 is used to determine feature information of target features required for processing business data according to the demand information.

[0105] The second acquisition module 603 is configured to acquire target features from a preset feature library according to the feature information, and acquire processing rule information corresponding to the target features from the preset feature library.

[0106] The extraction module 604 is configured to extract initial business sub-data related to the target feature from the business data.

[0107] The processing module 605 is used to process the target characteristics and the initial business sub-data according to the processing rule information to obtain a business processing result.

[0108] It should be noted that the above explanation of the business data processing method is also applicable to the business data processing device of this embodiment and will not be repeated here.

[0109] In this embodiment, by obtaining the demand information for processing business data, and determining the feature information of the target feature required to be processed in the process of processing the business data based on the demand information, obtaining the target feature from the preset feature library based on the feature information, and obtaining the processing rule information corresponding to the target feature from the preset feature library, extracting the initial business sub-data related to the target feature from the business data, and processing the target feature and the initial business sub-data according to the processing rule information to obtain the business processing result, it can effectively improve the feature processing efficiency and improve the feature service performance.

[0110] Figure 7 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 7 The electronic device 12 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0111] like Figure 7 As shown, electronic device 12 is implemented as a general purpose computing device. Components of electronic device 12 may include, but are not limited to, one or more processors or processing units 16, memory 28, and a bus 18 that connects various system components (including memory 28 and processing unit 16).

[0112] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of such architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnection (PCI) bus.

[0113] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0114] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 7 Not shown, often called a "hard drive").

[0115] although Figure 7 Although not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a Compact Disc Read Only Memory (hereinafter referred to as: CD-ROM), a Digital Video Disc Read Only Memory (hereinafter referred to as: DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

[0116] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally implement the functions and / or methods of the embodiments described herein.

[0117] The electronic device 12 can also communicate with one or more external devices 14 (e.g., a keyboard, pointing device, display 24, etc.), one or more devices that enable human interaction with the electronic device 12, and / or any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). This communication can occur via an input / output (I / O) interface 22. Furthermore, the electronic device 12 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with the electronic device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0118] The processing unit 16 executes various functional applications and data processing by running the programs stored in the memory 28, such as implementing the business data processing method mentioned in the above embodiment.

[0119] In order to implement the above embodiments, the present disclosure further proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the business data processing method proposed in the above embodiments of the present disclosure is implemented.

[0120] In order to implement the above embodiments, the present disclosure further proposes a computer program product. When the instructions in the computer program product are executed by a processor, the business data processing method proposed in the above embodiments of the present disclosure is executed.

[0121] It should be noted that, in the description of this disclosure, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of this disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0123] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0124] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0125] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0126] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0127] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0128] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are illustrative and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A business data processing method, characterized in that: The method comprises: Obtaining information on business data processing requirements; Determining, based on the demand information, feature information of target features required for processing the business data; Acquire the target feature from a preset feature library according to the feature information, and acquire processing rule information corresponding to the target feature from the preset feature library; Extracting initial business sub-data related to the target feature from the business data; and The target feature and the initial service sub-data are processed according to the processing rule information to obtain a service processing result.

2. The method according to claim 1, wherein The method further comprises: determining at least one processing rule type according to the processing rule information; Obtaining a processing operator corresponding to the processing rule type; The processing of the target feature and the initial service sub-data according to the processing rule information to obtain a service processing result includes: The target feature and the initial business sub-data are processed according to at least one of the processing operators to obtain a business processing result.

3. The method according to claim 2, wherein Different processing operators have corresponding processing orders; The target feature and the initial business sub-data are processed according to the plurality of processing operators to obtain a business processing result, including: Invoking the plurality of processing operators respectively according to the plurality of processing orders, wherein, when a corresponding processing operator is invoked, the target feature and the initial business sub-data are processed based on the processing rules of the corresponding processing operator to obtain an intermediate processing result; The business processing result is obtained according to the multiple intermediate processing results.

4. The method according to claim 2, wherein The extracting initial business sub-data related to the target feature from the business data includes: Acquire a plurality of data extraction rules related to the target feature, wherein each of the data extraction rules is used to extract a type of initial business sub-data; The multiple data extraction rules are executed in parallel to respectively extract multiple types of initial business sub-data from the business data.

5. The method according to claim 4, wherein There are multiple target features; before processing the target features and the initial business sub-data according to at least one processing operator to obtain a business processing result, the method further includes: determining feature dependencies between different target features; performing conversion processing on the multiple types of initial business sub-data according to the feature dependency relationship, and using the business sub-data obtained from the conversion processing as target business sub-data; The step of processing the target feature and the initial business sub-data according to at least one of the processing operators to obtain a business processing result includes: The target feature and the target business sub-data are processed according to at least one of the processing operators to obtain the business processing result.

6. The method according to any one of claims 1 to 5, wherein: The preset feature library is constructed based on the following method: receiving an initial processing expression, wherein the initial processing expression is used to process the candidate feature, and the initial processing expression has a corresponding processing type; Preprocessing the initial processing expression according to the processing type to obtain a target processing expression; Decomposing the target processing expression to obtain multiple candidate operators for processing the candidate features; Selecting some candidate operators from the multiple candidate operators, and using the candidate rule type corresponding to each candidate operator in the part of the candidate operators as candidate rule information corresponding to the candidate feature; The preset feature library is formed according to the candidate features and the candidate rule information corresponding to the candidate features.

7. The method according to claim 6, wherein The preprocessing of the initial processing expression according to the processing type to obtain a target processing expression includes: Obtaining verification rules and constraints based on the processing type; The initial processing expression is verified according to the verification rule, and if the verification passes, the initial processing expression is configured according to the constraint condition to obtain the target processing expression.

8. The method according to claim 6, wherein The selecting some candidate operators from the multiple candidate operators includes: Determining a candidate operator grammar corresponding to each candidate operator; forming an initial syntax tree according to the plurality of candidate operator grammars, wherein the initial syntax tree comprises a plurality of tree nodes, and at least some of the tree nodes have connecting edges; Pruning and / or merging at least some of the connection edges in the initial syntax tree to obtain a target syntax tree; The candidate operator corresponding to the candidate operator syntax contained in the target syntax tree is used as the selected candidate operator.

9. The method according to claim 6, wherein The forming of the preset feature library according to the candidate features and the candidate rule information corresponding to the candidate features includes: Establishing an association relationship between the feature information of the candidate feature, the candidate feature, and the candidate rule information corresponding to the candidate feature; The association relationship is saved in the preset feature library.

10. A business data processing device, characterized in that: The device comprises: The first acquisition module is used to obtain the demand information for processing business data; A determination module, configured to determine, based on the demand information, feature information of target features required for processing the business data; A second acquisition module is configured to acquire the target feature from a preset feature library according to the feature information, and acquire processing rule information corresponding to the target feature from the preset feature library; an extraction module, configured to extract initial business sub-data related to the target feature from the business data; and A processing module is used to process the target feature and the initial business sub-data according to the processing rule information to obtain a business processing result.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: in, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 9.

13. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.

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