Business processing method and apparatus, electronic device, and storage medium

Abstract

One or more embodiments of the present disclosure provide a business processing method and device, electronic equipment and storage medium. The method comprises: in response to receiving a rule invocation request, parsing scenario information and activity information according to the invocation request; invoking data rules and / or behavior rules corresponding to the scenario information and the activity information from a rule library to a rule engine; the data rules and the behavior rules are stored in the rule library in layers according to global common or regional difference attributes; determining a data object of the activity according to the data rules and the behavior rules, and invoking business data corresponding to the data rules and the behavior rules according to the data object; executing the data rules and the behavior rules based on the business data to obtain an execution result of the business.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more particularly to a business processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] A rule engine is a software system that extracts business decision-making logic from application code and automatically executes decisions based on predefined rules.

[0003] The current rule engine adopts a definition approach oriented towards single business requirements, and formulates business rules separately for each business requirement. One or more business requirements correspond to the generation of corresponding business rules.

[0004] However, with the development of business, in the architecture mode with the middle platform as the central node, the business rules of each edge node have the characteristics of overall uniformity but also have subtle differences. The existing rule definition method has problems of insufficient universality and difficulty in adaptation. Summary of the Invention

[0005] In view of the above, the purpose of one or more embodiments of this disclosure is to provide a business processing method, apparatus, electronic device and storage medium to solve the problems raised in the background art.

[0006] To achieve the above objectives, the first aspect of this disclosure provides a business processing method, including:

[0007] In response to receiving a rule invocation request, the scenario information and activity information are parsed according to the invocation request; The data rules and / or behavior rules corresponding to the scene information and the activity information are called from the rule base to the rule engine; the data rules and behavior rules are stored in the rule base in a hierarchical manner according to globally common or regionally different attributes; The data object of the activity is determined according to the data rules and the behavior rules, and the business data corresponding to the data rules and the behavior rules is called according to the data object; The data rules and behavior rules are executed based on the business data to obtain the business execution result.

[0008] A second aspect of this disclosure provides a service processing apparatus, comprising: The parsing module is configured to parse scene information and activity information according to a received rule invocation request. The first invocation module is configured to invoke the data rules and / or behavior rules corresponding to the scene information and the activity information from the rule library to the rule engine; the data rules and behavior rules are stored in the rule library in a hierarchical manner according to globally common or regionally different attributes; The second invocation module is configured to determine the data object of the activity based on the data rules and the behavior rules, and to invoke the business data corresponding to the data rules and the behavior rules based on the data object; The execution module is configured to execute the data rules and the behavior rules based on the business data to obtain the execution result of the business.

[0009] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in the first aspect.

[0010] In a fourth aspect, this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in the first aspect.

[0011] As can be seen from the above, the business processing method, apparatus, electronic device and storage medium provided in this disclosure achieve hierarchical management of general rules and dynamic rules by storing data rules and behavioral rules in the rule base according to globally common or regionally different attributes. This not only enables the middle platform of the business system to accumulate data, but also improves the processing efficiency of the rule engine. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in one or more embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only one or more embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a business processing method according to one or more embodiments of this disclosure; Figure 2 This is a schematic diagram of the structure of a rule engine according to one or more embodiments of the present disclosure; Figure 3 This is a schematic diagram of the structure of a service processing apparatus according to one or more embodiments of the present disclosure; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to one or more embodiments of this disclosure. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0015] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in one or more embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0016] As described in the background section, with the continuous expansion of business scale, in order to further improve the operational stability and cross-regional promotion and adaptation capabilities of business systems, and to implement the middle platform construction concept of "global reuse and data fusion and sharing," the industry has generally built a business system architecture supported by the front-end, middle platform, and back-end. The customer service business middle platform and data middle platform built based on the middle platform concept can form a "lively front-end and large middle platform" technical system with multi-terminal collaboration and flexible support, and plans to implement automated operation models and business rule bases.

[0017] However, the relevant business rule base still suffers from problems such as insufficient universality, weak dynamic adjustment capability, and high code coupling in practical applications, making it difficult to support large-scale system promotion and continuous iterative upgrades.

[0018] Specifically, the relevant technologies lack effective design in decoupling, abstracting, and consolidating regionally differentiated business and general platform capabilities, making it impossible to meet the personalized business needs of different regions while achieving unified encapsulation and global reuse of the core business capabilities of the platform.

[0019] Therefore, one or more embodiments of this disclosure provide a business processing solution. In this solution, the data rules and / or behavioral rules corresponding to the business are divided into globally common and regionally specific rules according to their attributes, and the rules are called from the rule base to the rule engine according to their attributes. Through the solution of this disclosure, the middle platform's data storage capability of the business system can be improved, thereby enhancing the general flexibility and multi-scenario adaptability of the business system.

[0020] refer to Figure 1 The present disclosure discloses a business processing method according to one or more embodiments, including the following steps: Step S101: In response to receiving a rule call request, parse the scene information and activity information according to the call request.

[0021] Step S102: Call the data rules and / or behavior rules corresponding to the scene information and activity information from the rule base to the rule engine; the data rules and / or behavior rules are stored in the rule base in a hierarchical manner according to globally common or regionally different attributes.

[0022] Step S103: Determine the data object of the business according to the data rules and / or behavior rules, and call the business data corresponding to the data rules and / or behavior rules according to the data object.

[0023] Step S104: Execute data rules and / or behavior rules based on business data to obtain the execution results of the business.

[0024] It should be noted that a scenario represents the minimum scope to which data rules and / or behavioral rules apply. It is the context and environment within which the rules apply, and it forms the basic framework and carrier for data objects, activities, behaviors, behavioral rules, and / or data rules. Scenario definition involves the unified management and definition of operations within a business system that can complete an independent business function.

[0025] An activity is one or more sub-processes within a scenario that complete a single business function. An activity definition can be understood as the definition or management of a sub-process. In some embodiments, a scenario contains one activity, in which case the activity can also be understood as a scenario. In other embodiments, a scenario contains multiple activities. However, in any case, one activity cannot complete a full business responsibility. That is, a full business responsibility necessarily includes (corresponds to) multiple activities.

[0026] A behavior is an event in a scenario, such as an input, dropdown, or button press that triggers an operation. The business system then provides corresponding feedback based on the behavior.

[0027] It's understandable that activities and behaviors are deconstructions of a scenario from different dimensions. Activities are deconstructed at the logical level, while behaviors are deconstructed at the operational level. Behaviors can become the trigger points for activities.

[0028] In addition to activities and behaviors, scenarios also carry data objects, behavioral rules, and / or data rules.

[0029] In this context, a data object represents the carrier of all data used in the scenario, and is used to manage and define the data required in the scenario. In some embodiments, data objects can be identified and / or defined as view objects (OV). During definition, they can be referenced from business entity classes, interface service classes, and data service entity classes, or they can be identified and / or defined through the object relationships of business data in the interface. For example, data objects may include application objects, data transfer objects, domain objects, business objects, persistent objects, etc.

[0030] In some embodiments, data objects can be categorized into static data objects and dynamic data objects based on whether computation is required. Static data objects represent data items that can be directly loaded and displayed, while dynamic data objects represent data items that require computation, processing, mapping, or transformation.

[0031] In some embodiments, for static data objects, the business data corresponding to the static data object can be initialized from the backend by parsing scenario information and activity information according to the call request; for dynamic data objects, the business data corresponding to the dynamic data object can be retrieved from the backend when calling data rules and / or behavior rules.

[0032] In some embodiments, because the business data corresponding to a data object needs to be referenced or used by a scenario, the data object can be stored in a data object library and can be referenced (or used) globally. This data object library can be a global public database; that is, a data object can be considered an object of a global public database or an object of a business-class public database. In this embodiment, the data object can be directly referenced without additional definition.

[0033] In some embodiments, the data object library is set up within the rules engine.

[0034] In some embodiments, data objects are classified into dynamic data objects and static data objects based on whether they require processing.

[0035] Specifically, for static data objects, the corresponding business data can be initialized from the backend after parsing the scene and activity information according to the call request; for dynamic data objects, the corresponding business data can be retrieved from the backend when calling data rules and behavior rules. By distinguishing between static and dynamic data objects, business data can be loaded on demand, improving rule execution efficiency and system resource utilization.

[0036] In some embodiments, after determining the data object of the activity, the method further includes: retrieving the validation rules corresponding to the data object; and validating the current scenario, activity, data rules, and business rules according to the validation rules. Validation can prevent data from being missed or errors from occurring.

[0037] In this embodiment of the disclosure, the rule invocation request is used to retrieve the behavior rules and / or data rules corresponding to a certain scenario of the target's business function, so as to carry out the activities included in that scenario under the target's business function.

[0038] A rule refers to the requirements for all data objects and activities in a scenario. A rule definition refers to the function of configuring and managing single rules and / or combinations of rules based on the rule parser's rule system standard, based on the requirements for the aforementioned data objects and activities.

[0039] As mentioned above, the relevant technologies unify the configuration of rules corresponding to various business functions in the business system within a central platform. The inventors of this disclosure have discovered that most business functions are highly similar, with only some personalized configuration differences based on the needs of each front-end. In other words, the business system has multiple business functions corresponding to multiple scenarios with essentially the same activities, differing only in some rule configurations.

[0040] Therefore, in some embodiments of this disclosure, rules are stored hierarchically in the rule base according to globally common or regionally different attributes, thereby enabling personalized retrieval of different rules. For example, globally common rules are encapsulated for easier database management; regionally different attributes are stored in a cache for fast retrieval.

[0041] In some embodiments, the aforementioned data rules and / or behavioral rules can be stored in a rule base. This rule base can manage and define the data rules and / or behavioral rules defined in the scenario, and has the ability to manage, configure, parse, and generate rules designed and defined according to a preset rule system.

[0042] The business processing methods provided in one or more embodiments of this disclosure are based on, for example, Figure 2 The rule engine implementation shown can include a loader, scheduler, pre-compiler, rule parser, and executor.

[0043] The loader can receive rule invocation requests and parse them to obtain the scene information and activity information corresponding to the request; and retrieve the corresponding data rules and / or behavior rules based on the scene information and activity information. In some embodiments, scene information may include scene context data, and activity information may include corresponding data rules, behavior rules, etc. The rules can be converted into tasks and sent to the scheduler.

[0044] The scheduler is used to allocate and schedule the pre-compiler, rule parser, and executor according to the execution order of the tasks corresponding to the data rules and / or behavior rules, based on the dependencies between data rules and / or behavior rules, and the execution status of the executors. At the same time, it allocates computing resources, with the goal of enabling the business system to achieve preset goals, such as maximizing throughput or minimizing latency.

[0045] The pre-compiler is used to receive tasks corresponding to the above data rules and / or the above behavior rules, and bind the above tasks to the above execution functions according to the relationship between the above data rules and / or the above behavior rules and the rule execution functions. When the triggering conditions of the above data rules and / or the above behavior rules are met, the corresponding execution functions are triggered to execute the corresponding data rules and / or behavior rules when the triggering conditions are met.

[0046] A rule parser is used to parse tasks issued by a pre-compiler into executable functions and return computation results. In some embodiments, a rule parser includes a parsing model, a pattern recognition model, a policy pattern model, a logic programming model, and a computation model.

[0047] In some embodiments, the computation model performs computations based on algorithms such as dependency task ordering algorithms or JSON path algorithms (JSONPath).

[0048] The executor constructs the business data context and passes this business data to the executable function generated by the rule parser, providing the data foundation for the execution of data rules and behavioral rules, and executing the corresponding rules. That is, the executor can retrieve the data objects of the activities corresponding to the data rules and behavioral rules, as well as the business data corresponding to the data objects; and execute the aforementioned data rules and behavioral rules based on this business data.

[0049] In some embodiments, the computational model may be an algorithm for ranking dependent tasks, a decision tree algorithm, a divide-and-conquer strategy, or bracket matching, etc. This disclosure does not limit it in this regard.

[0050] In some embodiments, the rules engine may also include a metadata database for storing scenario information, activity information, data objects, data rules, and behavioral rules.

[0051] In some embodiments, a rule engine can be implemented using the logical programming concept of "algorithm = logic + control".

[0052] Decision trees, as a machine learning classification and prediction method, are primarily suitable for classifying discrete data. After input data undergoes conditional judgments at each node, it is passed down along the true branches, ultimately forming a unique path from the root node to the result node. Therefore, decision trees not only classify input data but also provide interpretability of the classification criteria. The implementation process mainly includes: feature selection, decision tree model training and construction, and model prediction of new data.

[0053] The core idea of ​​the divide-and-conquer strategy is: for a problem of size N, if it can be solved directly, then solve it directly; otherwise, decompose it into K independent subproblems that are identical in form to the original problem, solve each subproblem recursively, and then combine the solutions of the subproblems to obtain the final result of the original problem.

[0054] Based on the above ideas, the rule invocation request can be regarded as a decision tree problem of size N, and the depth traversal of the decision tree problem can be achieved by using a divide-and-conquer strategy.

[0055] Formula parsing can employ a recursive parsing approach based on a divide-and-conquer strategy, relying on a modified bracket matching algorithm. In some embodiments, when the stack is empty, a maximum parsing unit can be identified, and the entire formula can be split into several independent segments (as in the example above, it can be split into two segments), and then each segment can be processed separately using the divide-and-conquer and recursive approaches.

[0056] Therefore, the business data extraction process of the rule engine can refer to the feature selection logic, corresponding to the data fields used for condition judgment in the hard-coded data; the decision tree model can be constructed based on the actual experience rules of business personnel; the input data processing process can be analogous to the model prediction stage, and is completed by the model execution class.

[0057] For example, in the business system of the power network, the three types of business judgments—regional affiliation, voltage level, and electricity price adjustment conditions—can be abstracted into independent decision logics. The regional logic is used to determine whether the target region is J or Z, the voltage level logic is used to determine whether the voltage includes 10kV / 25kV, and the electricity price adjustment logic determines whether to implement an electricity price adjustment based on conditions such as electricity price threshold and power factor. The three types of logic form a complete rule through the combination relationship of "{'regional logic' or 'voltage level logic'} and 'electricity price adjustment logic'", and the logic part is defined by business personnel based on practical experience.

[0058] At the execution level, the control part is implemented by the rule engine: the loader loads the above decision logic and combination rules, and the executor uses a divide-and-conquer strategy to traverse each logic branch. It controls the execution process in the order of first judging the results of each sub-logic independently, and then performing the logic combination operation. Finally, it outputs the rule judgment result, realizing the division of labor between business personnel to focus on rule building and the engine to be responsible for execution control, and achieving the goal of configurable business rules.

[0059] In some embodiments, based on the above ideas, the construction of the rule base can be achieved by performing the following operations: obtaining the configuration operation input by the user through the configuration interface; determining the scenario and activity corresponding to the configuration operation, and determining the business process and data object corresponding to the activity; the business process includes at least one node, each node corresponds to at least one data object and at least one behavioral rule, and the data object and behavioral rule satisfy a preset object behavior mapping relationship; determining the data attributes and data rules corresponding to the data object; storing the data rules and behavioral rules in the rule base in a hierarchical manner according to globally common or regionally different attributes; performing a rule-based description of the business requirements, data rules, and behavioral rules corresponding to the data attributes in a natural language-like manner; and constructing the rule base according to the scenario, activity, data rules, and behavioral rules.

[0060] In some embodiments, business personnel can propose business rules, which are then converted into executable configuration files by the interpreter. The interpreter or compiler then parses and executes these rules. This achieves a division of labor where business personnel focus on rule building, developers focus on logical expression, and the execution process is uniformly managed by the interpreter or compiler. After the caller selects a rule model and passes in initialization data, the loader loads the corresponding model, and the executor controls the execution flow according to the decision path and returns the rule results, thus enabling the configurability of some business processes.

[0061] In some embodiments, all nodes in the above business process can be divided into globally common nodes and regionally different nodes according to globally common or regionally different attributes. Regionally different nodes additionally define a difference factor, which can be understood as a parameter or personalized extension template in the rule engine, possessing the characteristics of being loadable, parsable, and assignable. In the above embodiments, personalized extensions of rules are achieved through difference factors.

[0062] It is understandable that the data rules and / or behavioral rules corresponding to the regional difference nodes only involve configuration modifications for the difference factors, while the other parts of the rules can be encapsulated and directly called.

[0063] The business processing method provided by one or more embodiments of this disclosure can realize centralized management of rules and support the construction of a business system platform; by improving the versatility of the platform rule engine, the accumulation capability of the business system platform can be improved, thereby improving the processing efficiency of the business system.

[0064] It is understandable that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities.

[0065] It should be noted that the methods of one or more embodiments of this disclosure can be executed by a single device, such as a computer or server. The methods of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to complete the process. In such a distributed scenario, one of these devices may execute only one or more steps of the methods of one or more embodiments of this disclosure, and the multiple devices will interact with each other to complete the method described.

[0066] It should be noted that the above description pertains to specific embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0067] Based on the same inventive concept, corresponding to any of the methods in the above embodiments, this disclosure also provides a business processing apparatus. For example... Figure 3 As shown, the device includes: Parsing module 11 is configured to respond to receiving rule call requests and parse scene information and activity information according to the call requests; The first calling module 12 is configured to call the data rules and / or behavior rules corresponding to the scene information and activity information from the rule base to the rule engine; the data rules and behavior rules are stored in the rule base in a hierarchical manner according to globally common or regionally different attributes; The second calling module 13 is configured to determine the data object of the activity based on the data rules and behavior rules, and to call the business data corresponding to the data rules and behavior rules based on the data object; Execution module 14 is configured to execute data rules and behavior rules based on business data to obtain the execution results of the business.

[0068] Optionally, the following steps can be performed to build the rule base: Obtain the configuration operations entered by the user through the configuration interface; Determine the scenario and activity corresponding to the configuration operation, and determine the business process and data object corresponding to the activity; the business process includes at least one node, each node corresponds to at least one data object and at least one behavior rule, and the data object and behavior rule satisfy the preset object behavior mapping relationship; Determine the data attributes and data rules corresponding to the data objects; data rules and behavioral rules are stored in the rule base in a hierarchical manner according to globally common or regionally different attributes; The business requirements, data rules, and behavioral rules corresponding to data attributes are described in a rule-based manner similar to natural language. Build a rule base based on scenarios, activities, data rules, and behavioral rules.

[0069] Optionally, it is also configured as follows: Based on the attributes of all nodes in the business process, they are divided into globally common nodes and regionally different nodes. Extract the difference factors from the regional difference nodes and reserve rule configuration entry points for the difference factors; Extract the standardized execution framework from the regional difference nodes; A general decision chain is constructed based on a standardized execution framework and globally common nodes, and then the general decision chain is encapsulated.

[0070] Optionally, the second calling module 13 is specifically configured as follows: Data objects are categorized into dynamic data objects and static data objects based on whether they require processing. For static data objects, after parsing the scene information and activity information according to the call request, the business data corresponding to the static data object is initialized from the backend; For dynamic data objects, when calling data rules and behavior rules, the corresponding business data of the dynamic data object is retrieved from the backend.

[0071] Optionally, it is also configured as follows: The rule engine stores scene information, activity information, data objects, data rules, and behavior rules.

[0072] Optionally, it is also configured as follows: Also includes: Data objects are stored in the data object library of the rules engine.

[0073] Optionally, the data object corresponds to a validation rule; The aforementioned device is also configured as follows: Retrieve the validation rules corresponding to the data object; The current scenario, activity, data rules, and business rules are validated according to the validation rules.

[0074] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, when implementing one or more embodiments of this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0075] The apparatus described above is used to implement the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0076] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0077] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0078] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this disclosure are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0079] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0080] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0081] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0082] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this disclosure, and not necessarily all the components shown in the figures.

[0083] The electronic devices described above are used to implement the corresponding methods in the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0084] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0085] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0086] Additionally, to simplify the description and discussion, and to avoid obscuring one or more embodiments of this disclosure, the provided drawings may or may not show well-known power / ground connections to integrated circuit (IC) chips and other components. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring one or more embodiments of this disclosure, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which one or more embodiments of this disclosure will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuitry) are set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that one or more embodiments of this disclosure may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0087] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0088] This disclosure includes one or more embodiments intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A business processing method, characterized in that, include: In response to receiving a rule invocation request, the scenario information and activity information are parsed according to the invocation request; The data rules and / or behavioral rules corresponding to the scene information and the activity information are retrieved from the rule base and sent to the rule engine; The data rules and the behavioral rules are stored in the rule base in a hierarchical manner according to globally common or regionally different attributes; The data object of the activity is determined according to the data rules and the behavior rules, and the business data corresponding to the data rules and the behavior rules is called according to the data object; The data rules and behavior rules are executed based on the business data to obtain the business execution result.

2. The method according to claim 1, characterized in that, The rule base is constructed by performing the following operations: Obtain the configuration operations entered by the user through the configuration interface; The scenario and activity corresponding to the configuration operation are determined, and the business process and data object corresponding to the activity are determined; the business process includes at least one node, each node corresponds to at least one data object and at least one behavior rule, and the data object and the behavior rule satisfy a preset object behavior mapping relationship; The data attributes and data rules corresponding to the data object are determined; the data rules and the behavioral rules are stored in the rule base in a hierarchical manner according to globally common or regionally different attributes; The business requirements, data rules, and behavioral rules corresponding to the data attributes are described in a rule-based manner similar to natural language. The rule base is constructed based on the scenario, the activity, the data rules, and the behavior rules.

3. The method according to claim 2, characterized in that, Also includes: Based on the attributes of all nodes in the business process, they are divided into globally common nodes and regionally different nodes according to their globally common or regionally different attributes. Extract the difference factors from the regional difference nodes and reserve rule configuration entry points for the difference factors; Extract the standardized execution framework from the regional difference nodes; A general decision chain is constructed based on the standardized execution framework and the global general nodes, and the general decision chain is encapsulated.

4. The method according to claim 1, characterized in that, According to the data object, the business data corresponding to the data rule and the behavior rule are invoked, including: The data objects are divided into dynamic data objects and static data objects according to whether they need to be processed. For the static data object, after parsing the scene information and activity information according to the call request, the business data corresponding to the static data object is initialized from the background; When the data rules and behavior rules are invoked for the dynamic data object, the business data corresponding to the dynamic data object is retrieved from the background.

5. The method according to claim 2, characterized in that, Also includes: The scene information, the activity information, the data object, the data rules, and the behavior rules are stored in the rule engine.

6. The method according to claim 5, characterized in that, Also includes: The data objects are stored in the data object library of the rule engine.

7. The method according to claim 1, characterized in that, The data object corresponds to the verification rules; After determining the data object of the activity, the process also includes: Retrieve the validation rules corresponding to the data object; The scenario, the activity, the data rules, and the business rules are validated according to the validation rules.

8. A business processing apparatus, characterized in that, include: The parsing module is configured to parse scene information and activity information according to a received rule invocation request. The first invocation module is configured to invoke the data rules and / or behavior rules corresponding to the scene information and the activity information from the rule base to the rule engine; The data rules and the behavioral rules are stored in the rule base in a hierarchical manner according to globally common or regionally different attributes; The second invocation module is configured to determine the data object of the activity based on the data rules and the behavior rules, and to invoke the business data corresponding to the data rules and the behavior rules based on the data object; The execution module is configured to execute the data rules and the behavior rules based on the business data to obtain the execution result of the business.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executed by the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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