Business data concurrent processing method, device and system

Abstract

The present invention provides a method, device and system for concurrent processing of business data, which belongs to the field of cloud computing technology and can be applied to the financial field or other fields. The method for concurrent processing of business data includes: obtaining business input parameters, determining each rule corresponding to the input parameters and element information of the elements corresponding to each rule; sending the element information of each element to the corresponding cluster client, so that each cluster client obtains the element value of each element information in parallel; according to each element value from each cluster client, concurrently executing the rules corresponding to each element value to obtain each execution result. The present invention can improve system scalability, operational stability and business data processing efficiency, and quickly respond to relevant business needs.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a method, device and system for concurrently processing business data. Background Art

[0002] With the rise of the Internet finance wave, the volume of inclusive finance and personal loan online business is increasing day by day, and the demand for high-time data processing is becoming more and more vigorous. In this situation, higher requirements are required for the system's real-time data processing capabilities. However, at the technical level, due to the microservice transformation and service group splitting caused by system upgrades, data is scattered in various databases as the business fields are split. Business personnel expect to obtain indicator data from different data sources, even external indicator data from third parties outside the bank to achieve comprehensive data management, which greatly reduces the efficiency of the system's factor value acquisition; in addition, the decision engine system is constantly adding rule models to adapt to different business needs. In one scenario, multiple rule models are used at the same time (for example, ordinary rules, rule sets, and scoring card rules are used at the same time), which affects the execution performance of the decision engine for rules. All these have made it difficult for the system to meet the millisecond-level real-time response requirements of the business. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide a method, device and system for concurrent processing of business data to improve system scalability, operational stability and business data processing efficiency and quickly respond to relevant business needs.

[0004] In order to achieve the above object, an embodiment of the present invention provides a method for concurrently processing business data, including:

[0005] Obtain business input parameters, determine the rules corresponding to the input parameters and the element information of the elements corresponding to each rule;

[0006] Sending the element information of each element to the corresponding cluster client so that each cluster client can obtain the element value of each element information in parallel;

[0007] According to each element value from each cluster client, the rules corresponding to each element value are concurrently executed to obtain each execution result.

[0008] The embodiment of the present invention further provides a device for concurrently processing business data, including:

[0009] A rule information determination module is used to obtain business input parameters, determine the rules corresponding to the input parameters and the element information of the elements corresponding to the rules;

[0010] A sending module, used for sending the element information of each element to the corresponding cluster client, so that each cluster client can obtain the element value of each element information in parallel;

[0011] The concurrent execution module is used to concurrently execute the rules corresponding to each element value according to each element value from each cluster client to obtain each execution result.

[0012] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the business data concurrent processing method when executing the computer program.

[0013] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the business data concurrent processing method are implemented.

[0014] The embodiment of the present invention also provides a business data concurrent processing system, including:

[0015] A business data concurrent processing device, used to obtain business input parameters, determine the elements of each rule corresponding to the input parameters and the element information of each element corresponding to each rule; send the element information of each element to the corresponding business cluster client or data middle station; concurrently execute the rules corresponding to each element value according to each element value from the business cluster client or data middle station, and obtain each execution result;

[0016] The business cluster client is used to search for the data acquisition class corresponding to each element information in the preset data structure, and schedule each data acquisition class in parallel to obtain the element value of the corresponding element information;

[0017] The data middle station is used to send the element information of each element to the corresponding external application cluster client, so that the external application cluster client can obtain the element value of each element information in parallel.

[0018] The business data concurrent processing method, device and system of the embodiments of the present invention first determine the rules corresponding to the input parameters and the element information of the elements corresponding to the rules, and then send the element information of each element to the corresponding cluster client, so that each cluster client can obtain the element value of each element information in parallel, and finally execute the rules corresponding to each element value concurrently according to the element value from each cluster client to obtain each execution result, which can improve the system scalability, operation stability and business data processing efficiency, and quickly respond to related business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1is a flow chart of a method for concurrently processing business data in an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of rule relationships in an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of concurrent calling of elements in an embodiment of the present invention;

[0023] Figure 4 is a structural block diagram of a device for concurrently processing business data in an embodiment of the present invention;

[0024] Figure 5 is a structural block diagram of a computer device in an embodiment of the present invention;

[0025] Figure 6 It is a schematic diagram of a business data concurrent processing system in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0028] In view of the problems of low timeliness in obtaining factor values, weak extensibility of adding similar factors, and poor rule execution performance of the decision engine in the prior art, an embodiment of the present invention provides a method, device and system for concurrent processing of business data, using the following technical solutions: 1) Parse the rules to obtain all factors, and concurrently call indicator data from multiple groups and sources according to the source classification of the factors; 2) The decision engine concurrently executes all rules according to different rule types, which can improve system scalability, operational stability and business data processing efficiency, and quickly respond to relevant business needs. The present invention can also be applied to the field of risk control to achieve real-time and accurate risk control. The present invention is described in detail below in conjunction with the accompanying drawings.

[0029] The key terms mentioned in this invention are defined as follows:

[0030] Factor (risk control factor): a definition tag used in the process of configuring rules (risk control rules). For example, if cust_001 is defined as the individual customer risk assessment result factor, and the configuration rule is if (cust_001 == '1') return true; return false, it means that if the individual customer risk assessment result of the current customer is 1, there is a risk and the rule is hit, otherwise there is no customer risk.

[0031] Business cluster (credit business cluster): Business cluster (credit business cluster) is mainly divided into different clusters according to different modules of the business (credit business), such as: customer cluster related to customers, basic data cluster related to basic services, and personal loan cluster related to personal loan business, etc. Different business clusters deploy different categories of business elements.

[0032] Relationship between business clusters (credit business clusters) and elements: Elements are configured on different business clusters (credit business clusters) according to their different meanings and sources. One element can only be configured in one cluster, and the same cluster can be configured with multiple different elements. Elements and clusters are many-to-many and one-to-one mapped, and one element uniquely maps to one cluster. Different elements are directly decoupled, and the elements configured on a certain group are coupled with the group, and each group is decoupled.

[0033] Explanation of the extensibility of elements and clusters: If the source of the newly added elements is an existing business group, you can directly add the data acquisition interface to the corresponding business group, and the decision engine does not need to change the program; if the business group has not been connected, you can introduce the public annotation of the decision engine to the new group to register the data acquisition interface service of the public route for service registration, and then add the data acquisition interface of the corresponding element, and the decision engine does not need to change the program. For the business data concurrent processing system, adding new elements and expanding business clusters can complete functional expansion with zero program modification.

[0034] Figure 1 : is a flow chart of a method for concurrently processing business data in an embodiment of the present invention. Figure 1 As shown, the business data concurrent processing method includes:

[0035] S101: Acquire business input parameters, and determine each rule corresponding to the input parameters and element information of elements corresponding to each rule.

[0036] Figure 2 Schematic diagram of the rule relationship in the embodiment of the present invention. Figure 2 As shown, determining the rules corresponding to the input parameters includes: determining the business scenarios corresponding to the input parameters; and determining the rules corresponding to the business scenarios through the connection relationship of the rules under the scenarios. The input parameters are obtained through a custom interface.

[0037] Each rule includes common rules, rule sets and scoring card rules. All of the above rules inherit the parent class Rule, and all of them need to rewrite the execute method (execution method) of the specific rules, and then convert the corresponding rules to the parent class Rule type to prepare for the subsequent execution of the rules.

[0038] In one embodiment, S101 further includes: concurrently calling the expression parsing method of the rule engine to parse each rule and obtain the corresponding element of each rule. The expression parsing method of QLExpress may be concurrently called to parse the element id of each rule and summarize all elements.

[0039] S102: Sending element information of each element to a corresponding cluster client, so that each cluster client obtains element values ​​of each element information in parallel.

[0040] The corresponding element information can be obtained by querying the database according to the resolved element ID. The element information may include the element name, element classification, element value data type, and the application deployment cluster of the element.

[0041] Figure 3 Schematic diagram of concurrent calling of elements in an embodiment of the present invention. Figure 3 As shown, the elements are divided into business cluster source elements and data middle platform source elements according to their deployment clusters. The data middle platform source elements are further classified according to the data attribution interface (such as external application A cluster elements and external application B cluster elements). The business cluster source elements are classified according to specific business clusters (such as customer cluster elements and basic data cluster elements).

[0042] like Figure 3 As shown, S102 includes:

[0043] 1. When the deployment cluster in the element information is a business cluster, the element information of each element is sent to the corresponding business cluster client, so that the business cluster client obtains the element value of each element information in parallel.

[0044] In one embodiment, sending the element information of each element to the corresponding service cluster client so that the service cluster client obtains the element value of each element information in parallel includes:

[0045] The element information of each element is sent to the corresponding business cluster client, so that the business cluster client searches for the data acquisition class corresponding to each element information in the preset data structure, and schedules each data acquisition class in parallel to acquire the element value of the corresponding element information.

[0046] During the specific implementation, the business cluster client deployed in each business cluster receives the feature information and puts the feature information into the feature information list. According to the feature information list, the decision engine SDK (software development kit) is used to find the data acquisition class corresponding to each feature information in the Map data structure, and the data acquisition interface of each data acquisition class is scheduled in parallel to sort the feature information to obtain the feature value. Among them, the decision engine SDK is used to set the classification, naming and other conditional rules of the feature in the implementation class to standardize the feature. The feature value is stored according to the Map data structure, with the key being the feature id and the value being the feature object.

[0047] 2. When the deployment cluster in the element information is an external application cluster, the element information of each element is sent to the corresponding external application cluster client through the data center, so that the external application cluster client can obtain the element value of each element information in parallel.

[0048] In the specific implementation, the decision engine sends the element information of each element to the unified entry service provided by the data center, and passes the element information of each element to the corresponding List of the data center. <element>List (element list) and corresponding element indicator scenarios. The data center distributes services through the entry routing service, and distributes the element information in the element list to the external application cluster client corresponding to the element indicator scenario. The external application cluster client calls each data acquisition interface in parallel to sort the element information to obtain the element value. Among them, the element value is stored according to the Map data structure, the key is the element id, and the value is the element object.

[0049] S103: Concurrently executing rules corresponding to each element value according to each element value from each cluster client to obtain each execution result.

[0050] Before executing S103, it also includes: summarizing the element values ​​and storing the results in Map<String,Object> The context of the type is saved for subsequent rule execution.

[0051] In one embodiment, S103 includes: concurrently executing execution methods under rules corresponding to each element value according to each element value from each cluster client, obtaining each execution result, and summarizing the execution results and returning them to the caller.

[0052] Figure 1 The execution subject of the business data concurrent processing method shown can be a decision engine. Figure 1 As can be seen from the process shown, the business data concurrent processing method of the embodiment of the present invention first determines the rules corresponding to the input parameters and the element information of the elements corresponding to the rules, and then sends the element information of each element to the corresponding cluster client, so that each cluster client can obtain the element value of each element information in parallel, and finally executes the rules corresponding to each element value concurrently according to the element value from each cluster client to obtain each execution result, which can improve the system scalability, operation stability and business data processing efficiency, and quickly respond to related business needs.

[0053] Figure 4 is a structural block diagram of a device for concurrently processing business data in an embodiment of the present invention. Figure 4 As shown, the business data concurrent processing device includes:

[0054] A rule information determination module is used to obtain business input parameters, determine the rules corresponding to the input parameters and the element information of the elements corresponding to the rules;

[0055] A sending module, used for sending the element information of each element to the corresponding cluster client, so that each cluster client can obtain the element value of each element information in parallel;

[0056] The concurrent execution module is used to concurrently execute the rules corresponding to each element value according to each element value from each cluster client to obtain each execution result.

[0057] In summary, the business data concurrent processing device of the embodiment of the present invention first determines the element information of each rule corresponding to the input parameter and the element corresponding to each rule, and then sends the element information of each element to the corresponding cluster client, so that each cluster client can obtain the element value of each element information in parallel, and finally executes the rules corresponding to each element value concurrently according to the element value from each cluster client to obtain each execution result, which can improve the system scalability, operation stability and business data processing efficiency, and quickly respond to relevant business needs.

[0058] The embodiment of the present invention also provides a specific implementation of a computer device that can implement all the steps in the business data concurrent processing method in the above embodiment. Figure 5 is a block diagram of a computer device in an embodiment of the present invention, see Figure 5 , the computer equipment specifically includes the following contents:

[0059] Processor (processor) 501 and memory (memory) 502.

[0060] The processor 501 is used to call the computer program in the memory 502. When the processor executes the computer program, all steps in the method for concurrently processing business data in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0061] Obtain business input parameters, determine the rules corresponding to the input parameters and the element information of the elements corresponding to each rule;

[0062] Sending the element information of each element to the corresponding cluster client so that each cluster client can obtain the element value of each element information in parallel;

[0063] According to each element value from each cluster client, the rules corresponding to each element value are concurrently executed to obtain each execution result.

[0064] In summary, the computer device of the embodiment of the present invention first determines the rules corresponding to the input parameters and the element information of the elements corresponding to the rules, and then sends the element information of each element to the corresponding cluster client, so that each cluster client can obtain the element value of each element information in parallel, and finally executes the rules corresponding to each element value concurrently according to the element value from each cluster client to obtain each execution result, which can improve the system scalability, operation stability and business data processing efficiency, and quickly respond to relevant business needs.

[0065] The embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps of the concurrent processing method for business data in the above embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the concurrent processing method for business data in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0066] Obtain business input parameters, determine the rules corresponding to the input parameters and the element information of the elements corresponding to each rule;

[0067] Sending the element information of each element to the corresponding cluster client so that each cluster client can obtain the element value of each element information in parallel;

[0068] According to each element value from each cluster client, the rules corresponding to each element value are concurrently executed to obtain each execution result.

[0069] In summary, the computer-readable storage medium of an embodiment of the present invention first determines the element information of each rule corresponding to the input parameter and the element corresponding to each rule, and then sends the element information of each element to the corresponding cluster client, so that each cluster client obtains the element value of each element information in parallel, and finally executes the rules corresponding to each element value concurrently according to the element value from each cluster client to obtain each execution result, which can improve the system scalability, operation stability and business data processing efficiency, and quickly respond to relevant business needs.

[0070] Based on the same inventive concept, an embodiment of the present invention also provides a business data concurrent processing system. Since the principle of solving the problem by the system is similar to that of the business data concurrent processing method, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be repeated.

[0071] Figure 6 Schematic diagram of a concurrent processing system for business data in an embodiment of the present invention. Figure 6 As shown, the business data concurrent processing system includes:

[0072] A business data concurrent processing device, used to obtain business input parameters, determine the elements of each rule corresponding to the input parameters and the element information of each element corresponding to each rule; send the element information of each element to the corresponding business cluster client or data middle station; concurrently execute the rules corresponding to each element value according to each element value from the business cluster client or data middle station, and obtain each execution result;

[0073] The business cluster client is used to search for the data acquisition class corresponding to each element information in the preset data structure, and schedule each data acquisition class in parallel to obtain the element value of the corresponding element information;

[0074] The data middle station is used to send the element information of each element to the corresponding external application cluster client, so that the external application cluster client can obtain the element value of each element information in parallel.

[0075] like Figure 6 As shown, the business data concurrent processing system includes a decision engine, a business cluster client and a data middle platform.

[0076] Decision engine: The decision engine is connected to the business cluster client and the data center respectively, and calls the interface of the corresponding factor variable to obtain the value of the factor. The decision engine is deployed in a separate service cluster to provide services to the channel access needs. Different scenarios are distinguished according to the input parameters of the business call, so as to obtain the corresponding rules, parse the rule expressions, obtain the full amount of factors, and then perform concurrent processing according to the different attributes of the factors, obtain the factor value from the corresponding cluster, and execute the full amount of rules concurrently according to the obtained factor value and the parsed rules.

[0077] Business cluster client: includes multiple business cluster data acquisition interfaces. The service group split brought about by microservice transformation has split the huge service system into multiple modules and multiple service groups according to business functions, which greatly improves the scalability and maintainability of the program. At the same time, business elements are also allocated to different service clusters according to the different affiliations of functional modules, and then the corresponding data acquisition interface is customized for each element under the same service group to ensure the reliability, orderliness and efficiency of indicator element acquisition.

[0078] Data center: includes multiple data center data acquisition interfaces. The business expects to use internal factor data and also wants to use indicator elements of various external data companies. The data center uniformly connects to external data formulas, external application customer data or external company customer data. Different data center data acquisition interfaces encapsulate different data acquisition interface services for use by the decision engine. The data is located differently and the actual meaning of the data architecture is different. The decision engine connects to the data center data acquisition interface to obtain the corresponding indicator element values.

[0079] The specific process of the embodiment of the present invention is as follows:

[0080] 1. The business data concurrent processing device applied to the decision engine obtains business input parameters, determines the rules corresponding to the input parameters and the element information of the elements corresponding to the rules.

[0081] 2. When the deployment cluster in the element information is a business cluster, the business data concurrent processing device sends the element information of each element to the corresponding business cluster client.

[0082] When the deployment cluster in the element information is an external application cluster, the business data concurrent processing device sends the element information of each element to the corresponding external application cluster client through the data middle station.

[0083] 3. The business cluster client searches for the data acquisition class corresponding to each element information in the preset data structure, schedules each data acquisition class in parallel to obtain the element value of the corresponding element information, and returns the element value to the business data concurrent processing device.

[0084] 4. The data center sends the element information of each element to the corresponding external application cluster client, so that the external application cluster client can obtain the element value of each element information in parallel, and return the element value from the external application cluster client to the business data concurrent processing device.

[0085] 5. The business data concurrent processing device concurrently executes the execution method under the rules corresponding to each element value according to each element value from the business cluster client or the data middle station to obtain each execution result.

[0086] In summary, the present invention is supported by the decision engine capability, and multiple rule models are implemented according to business needs, supporting efficient concurrent execution in the same business scenario. The decision engine stores the source information of the elements when initializing the elements. After the elements are obtained by parsing the rules, the elements are classified into the data acquisition interfaces under their respective clusters according to the source of the elements, and all the element values ​​are obtained concurrently, which greatly improves the execution efficiency of the system. The present invention can simultaneously cope with the business element indicator requirements or third-party external data indicator requirements that are continuously added to the business in the future, and the execution time of the program will not increase synchronously due to the multiple increase of the data source; in addition, the program of the present invention has strong scalability, and for the newly added indicators, the stock code has almost achieved zero changes, effectively ensuring the stability and security of the system operation, and the business caller can customize the data acquisition logic of the elements, and the decision engine and the element business logic are isolated in both code and running state, supporting the business caller to flexibly configure and realize more professional indicator elements, and the ease of use and transparency of the business indicator elements are greatly enhanced, which can more accurately and effectively assist business personnel to improve the risk identification ability in the business handling stage.

[0087] In summary, the business data concurrent processing system provided by the embodiment of the present invention has the following beneficial effects:

[0088] (1) Based on the distributed technology system, efficient and stable engine services are achieved by concurrently calling the data acquisition interface and concurrently executing rules based on the classification of indicator elements. Compared with the original loose serial solution for obtaining indicator elements, the system scalability, operation stability and execution efficiency have been greatly improved;

[0089] (2) Applied in the field of risk control, it can quickly respond to the risk control variable requirements raised by the business without adjusting the decision-making engine, thereby achieving real-time and precise risk control.

[0090] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0091] Those skilled in the art may also understand that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention may be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly demonstrate the interchangeability of hardware and software, the various illustrative components, units, and steps described above have generally described their functions. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be understood as exceeding the scope of protection of the embodiments of the present invention.

[0092] The various illustrative logic blocks, or units, or devices described in the embodiments of the present invention can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or any combination of the above. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0093] The steps of the method or algorithm described in the embodiments of the present invention can be directly embedded in hardware, a software module executed by a processor, or a combination of the two. The software module can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or other storage media of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a user terminal. Optionally, the processor and the storage medium can also be arranged in different components in the user terminal.

[0094] In one or more exemplary designs, the above functions described in the embodiments of the present invention can be implemented in hardware, software, firmware or any combination of the three. If implemented in software, these functions can be stored on a computer-readable medium, or transmitted in the form of one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media that facilitate the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by any general or special computer. For example, such computer-readable media can include but are not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store program codes in the form of instructions or data structures and other forms that can be read by general or special computers, or general or special processors. In addition, any connection can be appropriately defined as a computer-readable medium, for example, if the software is transmitted from a website site, server or other remote resource through a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wirelessly, such as infrared, wireless and microwave, it is also included in the defined computer-readable medium. The disk and disc include compact disk, laser disk, optical disk, DVD, floppy disk and blue-ray disk. Disks usually copy data magnetically, while discs usually copy data optically with lasers. The above combination can also be included in computer readable media.< / element>

Claims

1. A method for concurrently processing business data, characterized in that: include: Obtain business input parameters, determine the rules corresponding to the input parameters and the element information of the elements corresponding to the rules; wherein the elements are definition tags used in the rule configuration process; the element information includes: element name, element classification, element value data type and application deployment cluster of the element; Sending the element information of each element to the corresponding cluster client so that each cluster client can obtain the element value of each element information in parallel; Concurrently executing rules corresponding to each element value according to each element value from each cluster client to obtain each execution result; Sending the element information of each element to the corresponding cluster client so that each cluster client obtains the element value of each element information in parallel includes: When the deployment cluster in the element information is a business cluster, the element information of each element is sent to the corresponding business cluster client, so that the business cluster client searches for the data acquisition class corresponding to each element information in the preset data structure, and schedules each data acquisition class in parallel to obtain the element value of the corresponding element information; The relationship between business clusters and elements includes: elements are configured on different business clusters according to their different meanings and sources. One element can only be configured in one cluster, and the same cluster can be configured with multiple different elements; elements and clusters are many-to-many and one-to-one mapped, and one element uniquely maps to one cluster; different elements are directly decoupled, and elements configured on a certain group are coupled with the group, and each group is decoupled; When the deployment cluster in the element information is an external application cluster, the element information of each element is sent to the corresponding external application cluster client through the data center, so that the external application cluster client obtains the element value of each element information in parallel; Among them, the element information of each element is sent to the unified entry service provided by the data center, and the element information of each element is passed into the element list and the corresponding element indicator scenario corresponding to the data center; the data center distributes services through the entry routing service, and distributes the element information in the element list to the external application cluster client corresponding to the element indicator scenario, and the external application cluster client calls each data acquisition interface in parallel to sort out the element information to obtain the element value; The rules for determining the input parameters include: Determine the business scenario corresponding to the input parameters; Determine the rules corresponding to the business scenario, specifically: The rules corresponding to the business scenario are determined through the connection relationship of the rules under the scenario; the input parameters are obtained through a custom interface; Each rule includes common rules, rule sets and scoring card rules. The common rules, rule sets and scoring card rules all inherit the parent class Rule, and all need to rewrite the execution method of specific rules, and then convert the corresponding rules to the parent class Rule type to prepare for subsequent rule execution.

2. The method for concurrently processing business data according to claim 1, characterized in that: Also includes: The expression parsing method of the rule engine is concurrently called to parse each rule and obtain the corresponding elements of each rule.

3. The method for concurrently processing business data according to claim 1, characterized in that: According to the element values ​​from each cluster client, the rules corresponding to each element value are concurrently executed, and the execution results obtained include: According to each element value from each cluster client, an execution method under a rule corresponding to each element value is concurrently executed to obtain each execution result.

4. A device for concurrently processing business data, characterized in that: include: A rule information determination module is used to obtain business input parameters, determine the rules corresponding to the input parameters and the element information of the elements corresponding to the rules; wherein the elements are definition tags used in the rule configuration process; the element information includes: element name, element classification, element value data type and element application deployment cluster; A sending module, used for sending the element information of each element to the corresponding cluster client, so that each cluster client can obtain the element value of each element information in parallel; A concurrent execution module, used for concurrently executing rules corresponding to each element value according to each element value from each cluster client, to obtain each execution result; The sending module is specifically used for: When the deployment cluster in the element information is a business cluster, the element information of each element is sent to the corresponding business cluster client, so that the business cluster client searches for the data acquisition class corresponding to each element information in the preset data structure, and schedules each data acquisition class in parallel to obtain the element value of the corresponding element information; The relationship between business clusters and elements includes: elements are configured on different business clusters according to their different meanings and sources. One element can only be configured in one cluster, and the same cluster can be configured with multiple different elements; elements and clusters are many-to-many and one-to-one mapped, and one element uniquely maps to one cluster; different elements are directly decoupled, and elements configured on a certain group are coupled with the group, and each group is decoupled; When the deployment cluster in the element information is an external application cluster, the element information of each element is sent to the corresponding external application cluster client through the data center, so that the external application cluster client obtains the element value of each element information in parallel; Among them, the element information of each element is sent to the unified entry service provided by the data center, and the element information of each element is passed into the element list and the corresponding element indicator scenario corresponding to the data center; the data center distributes services through the entry routing service, and distributes the element information in the element list to the external application cluster client corresponding to the element indicator scenario, and the external application cluster client calls each data acquisition interface in parallel to sort out the element information to obtain the element value; The rules for determining the input parameters include: Determine the business scenario corresponding to the input parameters; Determine the rules corresponding to the business scenario, specifically: The rules corresponding to the business scenario are determined through the connection relationship of the rules under the scenario; the input parameters are obtained through a custom interface; Each rule includes common rules, rule sets and scoring card rules. The common rules, rule sets and scoring card rules all inherit the parent class Rule, and all need to rewrite the execution method of specific rules, and then convert the corresponding rules to the parent class Rule type to prepare for subsequent rule execution.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the business data concurrent processing method described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the business data concurrent processing method according to any one of claims 1 to 3 are implemented.

7. A business data concurrent processing system, characterized in that: include: A concurrent business data processing device, used to obtain business input parameters, determine each rule corresponding to the input parameters and element information of each element corresponding to each rule; Send the element information of each element to the corresponding business cluster client or data middle station; concurrently execute the rules corresponding to each element value according to each element value from the business cluster client or data middle station to obtain each execution result; wherein the element is a definition label used in the rule configuration process; the element information includes: element name, element classification, element value data type and element application deployment cluster; The business cluster client is used to find the data acquisition class corresponding to each element information in the preset data structure, and schedule each data acquisition class in parallel to obtain the element value of the corresponding element information; The relationship between business clusters and elements includes: elements are configured on different business clusters according to their different meanings and sources. One element can only be configured in one cluster, and the same cluster can be configured with multiple different elements; elements and clusters are many-to-many and one-to-one mapped, and one element uniquely maps to one cluster; different elements are directly decoupled, and elements configured on a certain group are coupled with the group, and each group is decoupled; The data middle station is used to send the element information of each element to the corresponding external application cluster client, so that the external application cluster client can obtain the element value of each element information in parallel; Among them, the element information of each element is sent to the unified entry service provided by the data center, and the element information of each element is passed into the element list and the corresponding element indicator scenario corresponding to the data center; the data center distributes services through the entry routing service, and distributes the element information in the element list to the external application cluster client corresponding to the element indicator scenario, and the external application cluster client calls each data acquisition interface in parallel to sort out the element information to obtain the element value; The rules for determining the input parameters include: Determine the business scenario corresponding to the input parameters; Determine the rules corresponding to the business scenario, specifically: The rules corresponding to the business scenario are determined through the connection relationship of the rules under the scenario; the input parameters are obtained through a custom interface; Each rule includes common rules, rule sets and scoring card rules. The common rules, rule sets and scoring card rules all inherit the parent class Rule, and all need to rewrite the execution method of specific rules, and then convert the corresponding rules to the parent class Rule type to prepare for subsequent rule execution.

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