WO2024045725A1 - 用于目标保单的处理方法、电子设备和可读存储介质 - Google Patents

用于目标保单的处理方法、电子设备和可读存储介质 Download PDF

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Publication number
WO2024045725A1
WO2024045725A1 PCT/CN2023/097491 CN2023097491W WO2024045725A1 WO 2024045725 A1 WO2024045725 A1 WO 2024045725A1 CN 2023097491 W CN2023097491 W CN 2023097491W WO 2024045725 A1 WO2024045725 A1 WO 2024045725A1
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Prior art keywords
calculation
electronic device
data
policy
units
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PCT/CN2023/097491
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English (en)
French (fr)
Inventor
王景龙
施瑜
王嘉杰
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易保网络技术(上海)有限公司
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Publication of WO2024045725A1 publication Critical patent/WO2024045725A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists

Definitions

  • the invention relates to the technical field of financial software, and in particular to a processing method, electronic device and readable storage medium for target insurance policies.
  • the calculation logic involves multiple calculation factors, calculation steps and logical branches, and there will be sequential dependencies between calculation steps. Insurance logic comparison complex.
  • the specified calculation engine is generally called to execute the calculation logic by calling the specified entry calculation node.
  • the execution logic in the computing engine requires configuration personnel to split and configure the algorithm logic. This process requires configuration personnel to spend a lot of time testing the execution logic and burying log output points. The configuration is relatively cumbersome. Moreover, the readability and debuggability of the execution logic completely rely on the manual configuration of the configuration personnel, and the execution performance of the computing engine is difficult to guarantee.
  • the calculation logic changes the execution logic in the calculation engine also needs to be managed and changed. The configuration personnel need to re-split the execution logic of the calculation engine and configure it. The management and change of the calculation engine are relatively cumbersome. It can be seen that the configuration and debugging efficiency of the calculation engine in the current premium calculation scenario is low.
  • embodiments of the present application provide a processing method, electronic device, and readable storage medium for a target policy.
  • embodiments of the present application provide a processing method for a target insurance policy, applied to a system including a first electronic device and a second electronic device, including:
  • the first electronic device determines a plurality of computing units associated with the target policy, and configuration data for the plurality of computing units, wherein the plurality of computing units are used to combine to calculate the premium of the target policy, so
  • the configuration data includes calculation factors of each calculation unit, data object types corresponding to the calculation factors, and calculation rules;
  • the first electronic device sends configuration data of the plurality of computing units to the second electronic device;
  • the second electronic device determines association information of the multiple computing units based on the configuration data of the multiple computing units, wherein the association information is used to describe references to calculation factors of the multiple computing units. relation;
  • the second electronic device determines the calculation graph data of the target policy based on the association information, wherein the calculation graph data uses the plurality of calculation units as nodes and the reference relationship as edges;
  • the second electronic device determines a calculation plan of the target policy based on the calculation graph data, wherein the calculation plan is used to describe the execution sequence of the plurality of calculation units for processing the target policy and the Reference relationship.
  • the processing method for target policies provided by the embodiments of this application does not require configuration personnel to decompose and configure the calculation logic of each policy, and arrange and call the calculation sequence of the decomposed calculation units.
  • the system can be based on the input target policy. , automatically determine the configuration data of each computing unit, and make calculation plans based on the configuration data, which can reduce the configuration complexity of configuration execution logic of configuration personnel and improve the configuration efficiency of the system's calculation execution logic.
  • the embodiment of the present application can quickly and accurately generate the calculation plan, and has lower requirements on the configuration capabilities of the configuration personnel.
  • the calculation factor includes an input calculation factor and an output calculation factor
  • the second electronic device determines the associated information of the multiple computing units based on the configuration data of the multiple computing units, including:
  • the second electronic device determines the input calculation factor and the output calculation factor of each calculation unit according to the configuration data of each calculation unit;
  • the second electronic device determines the associated information based on the input calculation factor and the output calculation factor of each calculation unit.
  • the second electronic device determines the calculation graph data of the target policy based on the associated information, including:
  • the second electronic device generates the calculation graph data based on the reference relationship in the association information and the output calculation factors of each of the calculation units.
  • the above method further includes:
  • the second electronic device Based on the association information, the second electronic device generates verification information when it is determined that a circular reference occurs between some of the computing units among the plurality of computing units.
  • the above method further includes:
  • the first electronic device obtains the user's first query operation, and the first query operation is used to request to query the calculation graph data of the target policy;
  • the first electronic device responds to the first query operation and sends a first query request to the second electronic device;
  • the second electronic device responds to the first query request and sends the calculation graph data and the verification information to the first electronic device;
  • the first electronic device displays the calculation graph data and the verification information to the user.
  • the output display calculation graph data and verification information based on the user's query operation can help the configuration personnel better understand the calculation logic of the policy, and facilitate the configuration personnel to view and modify the configuration data of each computing unit.
  • the second electronic device determines the calculation plan of the target policy based on the calculation graph data, including:
  • the second electronic device parses the calculation graph data and determines the sum of sub-node units in the calculation graph data.
  • Root node unit wherein the computing unit whose number of computing units corresponding to the referenced output computing factors among the plurality of computing units is zero is the root node computing unit, and the computing depth of the root node computing unit is zero; and, The calculation units whose number of calculation units corresponding to the output calculation factors referenced in multiple calculation units is not zero are child node calculation units;
  • the second electronic device determines the calculation plan based on the calculation depth of each calculation unit and the reference relationship in the calculation graph data.
  • the above method further includes:
  • the second electronic device sends the target policy to the second electronic device
  • the second electronic device executes the computing plan based on the target policy and configuration data of the plurality of computing units.
  • the second electronic device executes the calculation plan based on the target policy data and configuration data of the plurality of calculation units, including:
  • the second electronic device determines a value matching the calculation factor from the target policy data according to the calculation factor of the calculation unit, and executes the calculation plan.
  • the second electronic device executes the calculation plan based on the target policy data and the configuration data of the plurality of calculation units, and further includes:
  • the second electronic device executes the calculation plan based on the target policy data and the configuration data of the plurality of calculation units to obtain calculation process data and calculation results of the calculation plan, wherein the calculation process data is The configuration data and corresponding values of each computing unit and the calculation sequence of each computing unit are described below.
  • the above method further includes:
  • the first electronic device obtains the user's second query operation, and the second query operation is used to query the calculation process data and the calculation results;
  • the first electronic device responds to the second query operation and sends a second query request to the second electronic device;
  • the second electronic device responds to the second query request and sends the calculation process data and the calculation result to the first electronic device;
  • the first electronic device displays the calculation process graph data to the user.
  • the calculation process data and the calculation results are represented as calculation process diagram data, wherein the calculation process diagram data is represented by each of the calculation units, each of the calculation units
  • the configuration data and corresponding values are nodes, and the data flow direction of each computing unit is an edge.
  • embodiments of the present application provide a processing method for a target insurance policy, applied to a system including a third electronic device, including:
  • association information of the plurality of computing units according to the configuration data of the plurality of computing units, wherein , the association information is used to describe the reference relationship of the calculation factors of the multiple calculation units;
  • a calculation plan of the target policy is determined, wherein the calculation plan is used to describe the execution sequence of the plurality of calculation units for processing the target policy and the reference relationship.
  • embodiments of the present application provide an electronic device, one or more processors; one or more memories; one or more memories store one or more programs. When one or more programs are processed by one or more When executed by multiple processors, the electronic device is caused to execute the above processing method for the target insurance policy.
  • embodiments of the present application provide a computer-readable storage medium. Instructions are stored on the storage medium. When the instructions are executed on a computer, they cause the computer to execute the above-mentioned processing method for a target insurance policy.
  • inventions of the present application provide a computer program product.
  • the computer program product includes instructions that, when executed, cause the computer to execute the above-mentioned processing method for a target insurance policy.
  • Figure 1 shows a schematic diagram of an application scenario for premium calculation based on multiple computing units provided by an embodiment of the present application
  • Figure 2 shows a schematic structural diagram of a data object provided by an embodiment of the present application
  • Figure 3 shows a hardware structure block diagram of an electronic device provided by an embodiment of the present application
  • Figure 4 shows a schematic flowchart of a processing method for a target insurance policy provided by an embodiment of the present application
  • Figure 5 shows a schematic structural diagram of configuration data of a computing unit provided by an embodiment of the present application
  • Figure 6 shows a schematic structural diagram of a calculation graph data provided by an embodiment of the present application.
  • Figure 7 shows a schematic flowchart of a processing method for a target insurance policy provided by an embodiment of the present application.
  • Figure 1 shows a schematic diagram of an application scenario for premium calculation based on multiple computing units provided by an embodiment of the present application.
  • user 1 provides multiple configured computing units to the application module 101 of the premium system 100.
  • the application module 101 receives the multiple computing units and sends them to the computing engine 102 for computing logic configuration.
  • Application module 101 may also receive policy data from User 2.
  • the calculation engine 102 can configure a premium calculation plan based on multiple calculation units, and can perform calculations based on the calculation plan and policy data.
  • the calculation engine 102 can return the calculated calculation results and the configured calculation plan to the application module 101.
  • the application module 101 performs subsequent processing based on the returned calculation results, or displays the calculation results and calculation logic to the user 1.
  • calculation logic is calculation-related elements required for the premium system 100 to implement business functions, and may include but is not limited to calculation processes, calculation rules, calculation formulas, etc. Specifically, some basic calculation logic such as calculation expressions, expansion functions, variables, filters, decision trees, etc. Calculation logic usually includes data parameters. The calculation logic performs calculations based on the assignment of data parameters to obtain specific calculation logic results.
  • calculation unit is a unit obtained by splitting the calculation logic according to the premium calculation logic and the preset calculation logic decomposition method by user 1, and can realize part of the calculation process.
  • the user 1 can be a system configuration personnel, that is, a personnel who manages and configures the premium system 100, or a business personnel, that is, a business-related personnel who is familiar with software projects.
  • User 2 can be a customer who purchases an insurance policy.
  • the application module 101 is used to obtain multiple computing units provided by user 1 and policy data provided by user 2.
  • the acquisition method can be adopted in a variety of ways, including but not limited to: obtaining through interface, obtaining in file form, and obtaining according to user selection. Get etc.
  • the application module 101 can provide the computing unit configuration interface to the user 1, and the user 11 inputs relevant data in the computing unit configuration interface. After the user 1 completes the input, the computing unit configuration interface will input the user's data.
  • the data is submitted to the application module 101; User 1 can also store multiple computing units in the data file in advance and submit the data file to the application module 101.
  • the application module 101 obtains multiple computing units by parsing the data file; the application module 101 can Multiple preset computing unit templates are provided to user 1, and the computing unit template selected by user 1 or the modified computing unit template is used as the computing unit used. The same applies to the premium calculation stage and will not be repeated here.
  • the premium system 100 can be applied to electronic devices, including but not limited to mobile phones (including folding screen mobile phones), tablet computers, laptop computers, desktop computers, servers, wearable devices, head-mounted displays, mobile phones, etc.
  • electronic devices including but not limited to mobile phones (including folding screen mobile phones), tablet computers, laptop computers, desktop computers, servers, wearable devices, head-mounted displays, mobile phones, etc.
  • Various electronic devices such as email devices, car equipment, portable game consoles, portable music players, reader devices, televisions with one or more processors embedded or coupled therein.
  • this application provides a The processing method used for target policies.
  • the processing method for the target policy includes: the calculation engine 102 determines the dependency relationship of each computing unit based on the acquired configuration data of each computing unit of the policy, and determines the dependency relationship of the policy based on the dependency relationship of each computing unit.
  • Calculation plan The configuration data of the computing unit includes data objects, calculation factors, and calculation rules of the computing unit.
  • the data objects may include each policy element and its corresponding data object type.
  • the calculation engine 102 can execute the calculation plan based on the acquired policy data and the preset correspondence between policy elements and data objects, and return the obtained calculation results to the application module 101 for data processing or show.
  • the calculation engine 102 can also return the calculation graph data generated based on the dependency relationship of each calculation unit to the application module 101 for output and display.
  • the processing method for the target policy provided by the embodiment of this application does not require the configuration personnel to decompose the calculation logic of each policy and arrange and call the calculation sequence of the decomposed calculation units.
  • the configuration personnel only need to input each calculation unit.
  • the computing engine 102 can automatically determine the computing sequence of each computing unit and make a computing plan, which can reduce the configuration complexity of the configuration execution logic of the configuration personnel and improve the configuration of the execution logic of the computing engine 102 efficiency.
  • the embodiment of the present application can quickly and accurately generate the calculation plan, and has lower requirements on the configuration capabilities of the configuration personnel.
  • calculation graph data output display generated based on the dependency relationship of each computing unit can help the configuration personnel better understand the calculation logic of the policy, and facilitate the configuration personnel to view and modify the configuration data of each computing unit.
  • the computing engine 102 can optimize the computing sequence and computing strategy of each computing unit based on the dependencies of each computing unit to improve the execution performance of the computing engine 102 .
  • the calculation engine 102 can store the calculation process data generated during the execution of the calculation plan, and can represent the calculation process data in a graphical form. Configuration personnel can view the calculation process based on the calculation process data and adjust the configuration data of the calculation unit to improve the efficiency and quality of configuration.
  • the policy data is input to the calculation engine 102 in a tree-like data structure, and the tree-like data structure may be, for example, as shown in FIG. 2 .
  • the tree data structure is a tree data structure of data objects.
  • Each data object can include data object types and attribute fields of policy elements.
  • data object types can include insurance policies, subject matter, liabilities, and secondary liabilities.
  • the policy elements corresponding to the policy can be such as effective date, expiration date, etc.
  • the policy elements corresponding to the subject matter can be such as gender, date of birth, occupation, etc.
  • the policy elements corresponding to liability and secondary liability can include various insurance amounts, such as for vehicles.
  • Insurance policies, liability, and secondary liability can include vehicle damage insurance amount, third-party liability insurance amount, glass breakage insurance amount, etc.
  • the attribute field of the policy element does not need to exist in the tree data structure, and can be expressed as the code corresponding to the policy element in the product definition of the policy.
  • Level liability 203001 indicates that the data object type is secondary liability, and the corresponding policy element is the policy element numbered 203001 in the product definition of the policy.
  • different policy types may correspond to different calculation engines 102 .
  • the application programming interface Application Programming Interface, API
  • the calculation engine 102 may include multiple calculation numbers, and each calculation number may correspond to the generation of a calculation plan and premium calculation for a policy. This application does not limit this.
  • the method provided by the embodiment of the present application can be applied to an electronic device 300 including a server, where the server can be, for example, the computing engine 102 in Figure 1. Specifically, it can be through The calculation engine generates and executes calculation plans. In other embodiments, the method provided by the embodiment of the present application can be applied to an electronic device 300 including a server and a client.
  • the server can be, for example, the computing engine 102 in Figure 1
  • the client can be, for example, the application module in Figure 1 101.
  • the client can obtain data input by User 1 or User 2, such as configuration data, policy data, etc., and the client can generate a calculation plan based on the input configuration data, and combine the calculation plan and policy data Sent to the server, the server can execute the calculation plan based on the policy data and return the calculation results to the client.
  • the client can obtain the data input by user 1 or user 2, such as configuration data, policy data, etc., and the client can generate calculation graph data based on the input configuration data, and combine the calculation graph data and policy data Sent to the server, the server can generate a calculation plan based on the calculation graph data, execute the calculation plan based on the policy data, and then return the calculation results to the client.
  • the client may only be used to obtain configuration data and policy data, and the server may be used to generate and execute the calculation plan. This application does not limit this.
  • the application module 101 and the computing engine 102 in the embodiment of the present application may correspond to the same electronic device or may correspond to different electronic devices.
  • the application modules 101 corresponding to the data input by User 1 and User 2 can be applied to the same electronic device or to different electronic devices. This application does not limit this.
  • FIG. 3 shows a hardware structure block diagram of an electronic device 300 for implementing a processing method for a target insurance policy provided by an embodiment of the present application.
  • the electronic device 300 may include one or more processors 301 , a system control logic 302 connected to at least one of the processors 301 , a system memory 303 connected to the system control logic 302 , and A non-volatile memory (Non-Volatile Memory, NVM) 304 connected to the system control logic 302, and a network interface 306 connected to the system control logic 302.
  • NVM Non-Volatile Memory
  • processor 301 may include one or more single-core or multi-core processors.
  • processor 301 may include any combination of general-purpose processors and special-purpose processors (eg, graphics processors, applications processors, baseband processors, etc.).
  • the electronic device 300 adopts an enhanced base station (Evolved Node B, eNB) or a radio access network (Radio Access Network, RAN) controller
  • the processor 301 may be configured to execute various conforming embodiments.
  • processor 301 may be configured to execute a processing method for a target policy.
  • system control logic 302 may include any suitable interface controller to provide any suitable interface to at least one of processors 301 and/or any suitable device or component in communication with system control logic 302 .
  • system control logic 302 may include one or more memory controllers to provide an interface to system memory 303 .
  • System memory 303 may be used to load and store data and/or instructions.
  • the system memory 303 can load the instructions for analyzing the calculation logic in the embodiment of the present application, and can also save input data and configuration data, etc.
  • the system memory 303 of the electronic device 300 may include any suitable volatile memory, such as a suitable dynamic random access memory (Dynamic Random Access Memory, DRAM).
  • DRAM Dynamic Random Access Memory
  • NVM memory 304 may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions.
  • NVM memory 304 may include any suitable non-volatile memory such as flash memory and/or any suitable non-volatile storage device such as a hard disk drive (Hard Disk Drive, At least one of a HDD), a compact disc (Compact Disc, CD) drive, and a digital versatile disc (Digital Versatile Disc, DVD) drive.
  • the NVM memory 304 can be used to store input data and configuration data obtained by the application module.
  • NVM memory 304 may comprise a portion of storage resources on the device on which electronic device 300 is installed, or it may be accessed by the device but is not necessarily part of the device. For example, NVM memory 304 may be accessed over the network via network interface 306 .
  • system memory 303 and NVM memory 304 may include temporary copies and permanent copies of instructions 305, respectively.
  • the instructions 305 may include instructions that, when executed by at least one of the processors 301, cause the electronic device 300 to implement the method shown in FIG. 3 .
  • instructions 305, hardware, firmware, and/or software components thereof may additionally/alternatively be placed in system control logic 302, network interface 306, and/or processor 301.
  • Network interface 306 may include a transceiver for providing a radio interface for electronic device 300 to communicate with any other suitable device (eg, front-end module, antenna, etc.) over one or more networks.
  • network interface 306 may be integrated with other components of electronic device 300 .
  • network interface 306 may be integrated with at least one of processor 301 , system memory 303 , NVM memory 304 , and a firmware device (not shown) with instructions that when at least one of processor 301 executes the instructions When, the electronic device 300 implements the method as shown in the method embodiment.
  • the network interface 306 may be used to receive input data and configuration data sent by the application module.
  • Network interface 306 may further include any suitable hardware and/or firmware to provide a multiple-input multiple-output radio interface.
  • network interface 306 may be a network adapter, a wireless network adapter, a telephone modem, and/or a wireless modem.
  • At least one of the processors 301 may be packaged with logic for one or more controllers of the system control logic 302 to form a system package (System In a Package, SIP). In some embodiments, at least one of the processors 301 may be integrated on the same die with logic for one or more controllers for the system control logic 302 to form a system on chip (SOC).
  • SIP System In a Package
  • SOC system on chip
  • Electronic device 300 may further include an input/output (I/O) device 307 .
  • the I/O device 307 may include a user interface that enables a user to interact with the electronic device 300; the peripheral component interface is designed to enable peripheral components to also interact with the electronic device 300.
  • FIG. 3 does not constitute a specific limitation on the electronic device 300 .
  • the electronic device 300 may include more or fewer components than shown in the figures, or combine some components, or separate some components, or arrange different components.
  • the components illustrated may be implemented by hardware or software, or a combination of software and hardware.
  • Figure 4 shows a schematic flowchart of a processing method for a target insurance policy provided by an embodiment of the present application. It can be understood that the execution subject of this process can be any electronic device including a client, and the client can be the application module 101.
  • the method includes:
  • configuration data of each computing unit can be the basic information of each computing unit that represents the computing logic, Can include calculation factors, calculation rules, and applicable data objects for calculation units.
  • the data object may include attributes of policy elements and their corresponding data object types.
  • the data object type is a data object type pre-divided by the configurer based on the usage of multiple policy elements involved in the premium calculation logic in the product definition of the target policy.
  • data object types can include insurance policies, subject matter, liabilities, and secondary liabilities.
  • the policy elements included in the policy may include the policy's effective date, expiration date, etc.
  • the subject matter may include policy elements in the target policy that represent the insured’s basic information, such as the insured’s gender, date of birth, occupation, etc.
  • Liability or secondary liability can include policy elements that represent the amount of insurance in the target policy.
  • liability or secondary liability can include policy elements such as vehicle damage insurance amount, third party liability insurance amount, glass breakage insurance amount. wait.
  • attributes of the policy element of the data object may be represented by attribute fields of the policy element.
  • the “total insurance amount” can be expressed as “TOTAL_SI”
  • the “total insurance amount” can also be expressed as other attribute fields, and this application does not limit this.
  • the attributes of the policy element of the data object can also be identified by the code corresponding to the policy element in the product definition of the target policy, such as the data object "Liability 201" in Figure 2, where “Liability” represents data The object type, "201” represents the encoding of the policy element in the product definition of the target policy.
  • calculation factor is the policy element or the identification of the policy element.
  • the calculation factor may include: an input calculation factor input into the calculation unit when the calculation unit performs calculation, and an output calculation factor output when the calculation unit completes calculation.
  • calculation rules may include calculation formulas, calculation functions or calculation conditions for calculation factors, etc.
  • Each calculation factor in the calculation rule can be represented by a calculation factor identifier.
  • different policies correspond to different computing units.
  • the policy number can be used as the identifier of the policy, and different policy numbers can correspond to different computing units.
  • Different computing units may include different numbers of computing units and different configuration data of at least one of the computing units, which is not limited by this application.
  • different types of insurance policies correspond to different computing units.
  • different types can include insurance policies with different insured persons and insured objects.
  • Different types of insurance policies correspond to different computing units. For example, for automobile insurance policies, personal accident insurance, etc., they may correspond to different computing units. Therefore, when obtaining the configuration data of the computing unit of the target policy, you can first determine the policy type of the target policy, and then obtain the configuration data of multiple computing units corresponding to the policy type based on the determined policy type, or obtain it directly and based on the configuration Computational plan for data generation.
  • the obtained configuration data may be the computing unit and its configuration data associated with the target policy determined by the application module 101 among the computing units and corresponding configuration data of multiple policies.
  • the associated information of multiple computing units can be determined based on the computing factors in the configuration data of each computing unit.
  • association information can be understood as the reference relationship between each calculation unit that needs to reference the output calculation factors of other calculation units when performing calculations. For example, if the input calculation factor a in the calculation unit A refers to the value of the output calculation factor b calculated and output by the calculation unit B, then the associated information indicating that the calculation unit A refers to the output calculation factor b is generated.
  • the generated correlation information can be stored in the system memory 303 of the electronic device 300 for use in subsequent generation of calculation graphs and calculation plans.
  • the generated correlation information can also be stored in the non-volatile memory 304 of the electronic device 300, and the electronic device 300 can perform subsequent data processing based on the stored correlation information, or the electronic device 300 can output and display the correlation information to help configuration personnel adjust. Configuration data of the compute unit.
  • the calculation factors in the configuration data can be represented by calculation factor identifiers.
  • the associated information is determined based on the calculation factors of each calculation unit. Specifically, it may include: determining the input calculation factor identification and input calculation of each calculation unit. Factor identifier, for any two computing unit A and computing unit B, determine whether the input computing factor identifier of computing unit A matches the output computing factor identifier of computing unit B. If it matches, it means that computing unit A refers to the output computing factor of computing unit B. .
  • calculation graph data can be generated based on the correlation information and calculation factors in the configuration data. It can be understood that the calculation factor in the configuration data may include the output calculation factor of the calculation unit, and the associated information may include the output calculation factor referenced by the calculation unit.
  • the application module 101 can determine the calculation graph data representing the dependency relationship between multiple calculation units based on the output calculation factors of each calculation unit and the output calculation factors of other referenced calculation units.
  • calculation graph data can be a description of graph data with calculation units as nodes and reference relationships of calculation factors as edges.
  • the calculation graph data can also be a description of graph data with calculation units as nodes and reference relationships of calculation factors as Edge graph data is stored in the form of vectors, such as graph data in the form of mind maps. That is, the generated calculation graph data can be characterized as image data or text data, and this application does not limit this.
  • FIG. 5 shows a schematic structural diagram of configuration data of a computing unit provided by an embodiment of the present application.
  • Figure 6 shows a schematic structural diagram of calculation graph data provided by an embodiment of the present application.
  • the calculation units in the configuration data can be named according to "data object type_output calculation factor_(calculation rules, applicable conditions or policy element identification, etc.)".
  • the data object type of the calculation unit is liability
  • the output calculation factor is the standard annual premium
  • the code of the output standard annual premium is 201.
  • the calculation unit "Policy_Risk Gross Premium_Calculation and Accumulation” the data object type of this calculation unit is policy
  • the output calculation factor is Risk Gross Premium
  • the calculation rule is calculation and accumulation, that is, the calculation rule is based on the preset calculation The formula is calculated and the calculation results are accumulated.
  • the calculation factors in the configuration data may include input calculation factors and output calculation factors of each calculation unit.
  • the calculation factors of the calculation unit "Policy_Gross Risk Premium_Calculation and Accumulation" can include: input the standard annual premium after accumulation of calculation factors and the insurance period rate, and output the calculation factor Gross Risk Premium.
  • configuration data shown in Figure 5 is an example in the embodiment of the present application.
  • the configuration data may include more or less computing units, computing factors, and configurations than those shown in Figure 5 Data types, etc., this application does not limit this.
  • the application module 101 can generate correlation information of multiple computing units according to the calculation factors in the configuration data shown in Figure 5, and can generate the correlation information of multiple computing units according to the generated correlation information and the calculation factors of each computing unit in Figure 5.
  • the dotted arrows therein can represent the reference relationships of each calculation unit.
  • the direction of the arrow indicates the source of the referenced calculation factor.
  • the input calculation factor is the insurance amount
  • the output calculation factor is Standard Annual Premium_Code 201
  • the associated information can include a reference to the calculation unit "Policy_Insurance”
  • the output of "amount_accumulate” calculates the factor insurance amount
  • the calculation unit “policy_accumulate standard annual premium_accumulate” refers to the output of this calculation unit calculation factor standard annual premium_code 201.
  • calculation graph data can be in a representation form different from that shown in Figure 6, such as using other graphical representation forms, text representation forms, table representation forms, etc. other than Figure 6. This application does not cover this. limit.
  • the application module 101 after the application module 101 generates the calculation graph data, it can also determine whether there is a circular reference between multiple calculation units based on the reference relationship of each calculation unit in the calculation graph data. If the judgment result is yes, verification information is generated.
  • a circular reference is a closed loop in the reference relationship between some computing units.
  • computing unit A refers to computing unit B
  • computing unit B refers to computing unit C
  • computing unit C refers to computing unit A.
  • Circular references will create a closed calculation loop, and the calculation plan generated by the application module 101 may appear in an infinite loop and will not stop, so the calculation engine 102 cannot output the premium calculation result.
  • the check information can indicate that the calculation graph data includes circular references.
  • the verification information may be "Error", that is, the application module 101 will report an error when detecting a circular reference.
  • calculation plan may include the execution sequence of multiple calculation units and the reference relationship of multiple calculation units.
  • the execution sequence may include some of the multiple computing units that can be executed in parallel, the order in which the multiple computing units are executed, etc.
  • the application module 101 can determine the computing depth of each computing unit based on the computing graph data, and determine the execution order of each computing unit based on the computing depth.
  • the application module 101 can determine that the calculation unit whose number of calculation units corresponding to the output calculation factor referenced in each calculation unit is zero is the root node calculation unit, for example, the calculation unit "Policy_Insurance Amount_Accumulation" in Figure 6, "Target_Target Rate is a child node calculation unit, and the calculation depth of the child node calculation unit can be determined based on the number of edges between the child node calculation unit and its corresponding root node calculation unit.
  • the calculation unit "Liability_Standard Annual Premium_Code 203" in Figure 6 is a child node calculation unit, and its corresponding root node calculation unit is "Target_Target Rate X", and "Liability_Standard Annual Premium_ There is an edge between “Code 203" and "Target_Target Rate X”, then the calculation depth of the sub-node calculation unit "Liability_Standard Annual Premium_Code 203" is 1.
  • the calculation unit "Policy_Adjusted Gross Premium_Amortization” in Figure 6 is a sub-node calculation unit, and its corresponding root node calculation unit includes "Policy_Insurance Amount_Accumulation”, “Target_Target Rate X” and The maximum number of edges between "Policy_Guarantee Period Rate”, "Policy_Adjusted Gross Premium_Amortization” and the corresponding root node calculation unit is 5, then the child node calculation unit "Policy_Adjusted Gross Premium_Amortization” "The calculation depth is 5.
  • the execution order of each calculation unit can be determined according to the order of the calculation depth of each calculation unit from small to large. Some computing units with the same computing depth can be executed in parallel.
  • the processing method for the target policy provided by the embodiment of this application does not require the configuration personnel to decompose the calculation logic of each policy and arrange and call the calculation sequence of the decomposed calculation units.
  • the configuration personnel only need to input each calculation unit.
  • the application module 101 can automatically determine the calculation sequence of each computing unit and make a calculation plan, which can reduce the configuration complexity of the execution logic of configuration personnel and improve the configuration of the execution logic of the application module 101 efficiency.
  • the embodiment of the present application can quickly and accurately generate the calculation plan, and has lower requirements on the configuration capabilities of the configuration personnel.
  • the output display of calculation graph data generated based on the dependencies of each computing unit can help configuration personnel better understand the calculation logic of the policy, and facilitate configuration personnel to view and modify the configuration data of each computing unit.
  • Figure 7 shows a schematic flowchart of a processing method for a target insurance policy provided by an embodiment of the present application.
  • the execution subject of this process can be any electronic device including a client and a server.
  • the introduction is made by taking the client as the application module 101 and the server as the computing engine 102 as an example.
  • the method includes:
  • Step 701 The application module 101 obtains the configuration data of multiple computing units of the target policy. Step 701 is the same as step 401 in Figure 4 and will not be described again.
  • Step 702 The application module 101 determines the associated information of multiple computing units based on the configuration data. Step 702 is the same as step 402 in Figure 4 and will not be described again.
  • step 703 The application module 101 generates the calculation graph data of the target policy based on the associated information and the configuration data of multiple computing units. Among them, step 703 is the same as step 403 in Figure 4 and will not be described again.
  • the application module 101 can respond to the user's calculation graph query request and output and display the calculation graph data in a form similar to a mind map on the display interface. Furthermore, the application module 101 can also obtain the graph data adjustment information used by the configuration personnel to adjust the calculation graph data, or the configuration data adjustment information used to adjust the configuration data of the computing unit, to obtain calculation graph data that is more consistent with the calculation logic.
  • the application module 101 sends the calculation graph data and the configuration data of each calculation unit to the calculation engine 102.
  • calculation graph data sent by the application module 101 may be reference relationship data of the calculation factors of each calculation unit, or may be graphical data representing the reference relationship.
  • the configuration data can include calculation factors, calculation rules, and applicable data objects of each calculation unit.
  • step 704 may specifically include: the application module 101 may call the calculation engine 102 through the API to trigger the premium calculation of the target policy.
  • the calculation engine 102 may transfer multiple calculation unit matching conditions (That is, the calculation graph data of the reference relationship) and the configuration data including the data object.
  • Step 705 The calculation engine 102 generates the calculation plan and the corresponding calculation number of the target policy based on the received calculation graph data.
  • Step 705 generates a calculation plan and the process of generating the calculation plan may be the same as step 404 in FIG. 4 , except that the execution subject of step 705 is the calculation engine 102 and the execution subject of step 404 is the application module 101 .
  • the application module 101 obtains target policy data.
  • the target policy data may include policy elements in the target policy that are related to premium calculation and values corresponding to the policy elements.
  • the target policy data may be represented as policy elements in the target The attribute fields or codes defined in the product structure of the policy, and the corresponding values.
  • the target policy data obtained by the application module 101 may be tree-structured data, such as the target policy data shown in Figure 2 .
  • the target policy data may include multiple data objects, and each data object may include data object types, attribute fields or codes of policy elements, values of policy elements, etc.
  • the target policy data obtained by the application module 101 may be the target policy, and the application module 101 may extract multiple policy elements related to premium calculation in the target policy, and based on the extracted multiple policy elements, As well as the preset corresponding relationship between policy elements and data object types, the tree structure data shown in Figure 2 is generated, and the tree structure data is used as input for premium calculation and sent to the calculation engine 102 for premium calculation.
  • the target policy data obtained by the application module 101 may be multiple policy elements related to premium calculation of the target policy, and may be based on the multiple acquired policy elements, as well as preset policy elements and data. According to the corresponding relationship between the object types, the tree structure data as shown in Figure 2 is generated, and the tree structure data is used as input for premium calculation and sent to the calculation engine 102 for premium calculation.
  • target policy data in step 706 can be data in any form, and this application does not limit this.
  • the application module 101 sends the target policy data to the calculation engine 102.
  • the calculation engine 102 executes the calculation plan based on the received target policy data and the configuration data of the multiple calculation units, and obtains calculation process data and calculation results.
  • the calculation process data can be the data objects matched by each calculation unit and the calculation results generated during the calculation process. Specifically, it can include the data objects input to each calculation unit, the values of the data objects, and the calculation output generated by the calculation rules executed by the calculation units. factors and corresponding values. Among them, the calculation result is the calculation result output after executing the calculation plan.
  • the computational process data is represented in a graphical form.
  • the calculation process data may include the execution process of the calculation plan, as well as the data objects matched by each calculation unit, the values of each calculation factor, the reference relationships of the calculation factors, etc.
  • the calculation process data and calculation results can be characterized as a calculation process graph with each of the calculation units, the configuration data of each of the calculation units, and the corresponding values as nodes, and with the data flow direction of each of the calculation units as edges. data.
  • the calculation process data can be displayed on the display interface in a form similar to the calculation diagram in Figure 6, and the user can click on each calculation unit to view the data matched or generated by the calculation unit during the calculation process.
  • the data object is represented as tree-structured data as shown in Figure 2.
  • the calculation engine 102 executes the process of calculating the plan.
  • Each computing unit can obtain corresponding data from the tree-structured data according to the applicable data object type.
  • the corresponding value is assigned as the calculation factor for calculating DNA elements.
  • the calculation factor value process may specifically include:
  • the calculation unit can first determine whether the data object type of the output calculation factor is the same as the data object type applicable to the calculation unit. If the judgment results are the same, the calculation unit can obtain the value from the output calculation factor included in the data object type applicable to the calculation unit; otherwise, it needs to be obtained from the tree data structure of the applicable data object type along the parent node to The direction of the root node is matched until it matches the output calculation factor referenced by the calculation unit, and the value is taken from the matched output calculation factor.
  • the calculation engine 102 sends the calculation number, calculation process data and calculation results of the target policy to the application module 101.
  • the application module can perform further data processing or data storage based on business management requirements, and can also output and display the received calculation number, calculation process data and calculation results.
  • the application module 101 can obtain the user's query operation, which can include the calculation number to be queried. In response to the user's query operation, the application module 101 can display the calculation number, calculation process data, and calculation results.
  • the calculation engine 102 can generate a data file representing the correspondence between calculation numbers, calculation process data, and calculation results, and store the calculation numbers, calculation process data, and calculation results in different database tables respectively.
  • the calculation engine 102 may send a data file representing the correspondence between calculation numbers, calculation process data, and calculation results to the application module 102 .
  • the application module 101 obtains the user's query operation, it can determine the calculation process data and calculation results that match the calculation number to be queried based on the data file, and then query the matching calculation process data and calculation results from the database. , and output and display it together with the calculation number.
  • the calculation engine 102 can store the calculation number, calculation process data, and calculation results in the same database table, and the application module 101 can query the corresponding calculation process data and calculation results based on the calculation number.
  • the processing method for target insurance policies provided by the embodiments of this application can display the data or calculation diagram generated when executing the calculation plan to the user in a graphical form, which facilitates the user to understand and adjust the calculation plan, and can improve the configuration efficiency of the calculation plan. .
  • the calculation engine 102 can generate and optimize calculation plans based on calculation graph data, without the need for manual configuration by users, and the configuration efficiency and quality are higher.
  • Embodiments of the mechanisms disclosed in this application may be implemented in hardware, software, firmware, or a combination of these implementation methods.
  • Embodiments of the present application may be implemented as a computer program or program code executing on a programmable system including at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements) , at least one input device and at least one output device.
  • Program code may be applied to input instructions to perform the functions described herein and to generate output information.
  • Output information can be applied to one or more output devices in a known manner.
  • a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • Program code may be implemented in a high-level procedural language or an object-oriented programming language to communicate with the processing system. Including but not limited to OpenCL, C language, C++, Java, etc. For languages such as C++ and Java, since they will convert storage, there will be some differences based on the application of the data processing methods in the embodiments of the present application. Those skilled in the art can perform conversion based on specific high-level languages without departing from it. scope of the embodiments of this application.
  • the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof.
  • the disclosed embodiments may also be implemented as instructions carried on or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be operated by one or more processors Read and execute.
  • instructions may be distributed over a network or through other computer-readable media.
  • machine-readable media may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., computer), including but not Limited to, floppy disks, optical disks, CD-ROMs, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROM), random-access memories (RAM), erasable programmable read-only memories (EPROM), electrically erasable Except for programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or tangible devices used to transmit information (for example, carrier waves, infrared signals, digital signals, etc.) using electrical, optical, acoustic or other forms of propagation signals over the Internet. of machine-readable storage.
  • machine-readable media includes any type of machine-readable media suitable for storing or transmitting electronic instructions or information in a form readable by a machine (eg, computer).
  • each unit/module mentioned in each device embodiment of this application is a logical unit/module.
  • a logical unit/module can be a physical unit/module, or it can be a physical unit/module.
  • Part of the module can also be implemented as a combination of multiple physical units/modules.
  • the physical implementation of these logical units/modules is not the most important.
  • the combination of functions implemented by these logical units/modules is what solves the problem of this application. Key technical issues raised.
  • the above-mentioned equipment embodiments of this application do not introduce units/modules that are not closely related to solving the technical problems raised by this application. This does not mean that the above-mentioned equipment embodiments do not exist. Other units/modules.

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Abstract

本申请涉及金融软件技术领域，特别涉及一种用于目标保单的处理方法、电子设备和可读存储介质，该方法应用于包括第一电子设备和第二电子设备的系统，包括：第一电子设备确定与目标保单关联的多个计算单元，以及用于多个计算单元的配置数据；第一电子设备将多个计算单元的配置数据发送至第二电子设备；第二电子设备根据多个计算单元的配置数据，确定多个计算单元的关联信息；第二电子设备基于关联信息，确定目标保单的计算图数据；第二电子设备基于计算图数据，确定目标保单的计算计划。本申请实施例提供的用于目标保单的处理方法，可以快速、准确地生成计算计划，对于配置人员的配置能力要求较低。

Description

用于目标保单的处理方法、电子设备和可读存储介质
本申请要求2022年08月29日提交中国专利局、申请号为202211041010.0、申请名称为“用于目标保单的处理方法、电子设备和可读存储介质”的中国专利申请的优先权，上述申请的全部内容通过引用结合在本申请中。
技术领域
本发明涉及金融软件技术领域，具体涉及一种用于目标保单的处理方法、电子设备和可读存储介质。
背景技术
在保险计算的应用场景中，包括保费计算、赔付理算等多种计算逻辑，计算逻辑中涉及多个计算因子、计算步骤和逻辑分支，且计算步骤之间会有顺序依赖关系，保险逻辑比较复杂。
目前的保险计算场景中，一般通过调用指定入口计算节点来调用指定的计算引擎执行计算逻辑。计算引擎中的执行逻辑需要配置人员对算法逻辑进行拆分、配置，此过程需要配置人员花费大量时间，以对执行逻辑进行测试以及日志输出埋点等，配置比较繁琐。并且，执行逻辑的可读性、可调试性完全依赖于配置人员的人工配置，计算引擎的执行性能难以保证。当计算逻辑发生变更，计算引擎中的执行逻辑也需要进行管理和变更，配置人员需要对计算引擎重新进行执行逻辑的拆分配置，计算引擎的管理和变更比较繁琐。可见，目前的保费计算场景中的计算引擎的配置和调试效率较低。
发明内容
为解决上述计算引擎的配置、管理和变更繁琐的问题，本申请实施例提供了一种用于目标保单的处理方法、电子设备和可读存储介质。
第一方面，本申请实施例提供了一种用于目标保单的处理方法，应用于包括第一电子设备和第二电子设备的系统，包括：
所述第一电子设备确定与目标保单关联的多个计算单元，以及用于所述多个计算单元的配置数据，其中，所述多个计算单元用于组合计算所述目标保单的保费，所述配置数据包括各所述计算单元的计算因子、所述计算因子对应的数据对象类型以及计算规则；
所述第一电子设备将所述多个计算单元的配置数据发送至所述第二电子设备；
所述第二电子设备根据所述多个计算单元的所述配置数据，确定所述多个计算单元的关联信息，其中，所述关联信息用于描述所述多个计算单元的计算因子的引用关系；
所述第二电子设备基于所述关联信息，确定所述目标保单的计算图数据，其中，所述计算图数据以所述多个计算单元为节点，以所述引用关系为边；
所述第二电子设备基于所述计算图数据，确定所述目标保单的计算计划，其中，所述计算计划用于描述所述多个计算单元用于处理所述目标保单的执行顺序以及所述引用关系。
本申请实施例提供的用于目标保单的处理方法，不需要配置人员对每个保单的计算逻辑进行分解配置，并对分解后的计算单元的计算顺序进行编排调用，系统可以根据输入的目标保单，自动确定各计算单元的配置数据，并根据配置数据作出计算计划，可以降低配置人员配置执行逻辑的配置复杂度，提高系统的计算执行逻辑的配置效率。并且，相比于用户对各计算单元的计算顺序的编排调用，本申请实施例可以快速、准确地生成计算计划，对于配置人员的配置能力要求较低。
在上述第一方面的一种可能的实现方式中，所述计算因子包括输入计算因子和输出计算因子；
所述第二电子设备根据所述多个计算单元的所述配置数据，确定所述多个计算单元的关联信息，包括：
所述第二电子设备根据各所述计算单元的配置数据，确定各所述计算单元的输入计算因子和输出计算因子；
所述第二电子设备基于各所述计算单元的所述输入计算因子和所述输出计算因子，确定所述关联信息。
在上述第一方面的一种可能的实现方式中，所述第二电子设备基于所述关联信息，确定所述目标保单的计算图数据，包括：
所述第二电子设备基于所述关联信息中的所述引用关系，以及各所述计算单元的输出计算因子，生成所述计算图数据。
在上述第一方面的一种可能的实现方式中，上述方法还包括：
所述第二电子设备基于所述关联信息，在判断出所述多个计算单元中的部分所述计算单元之间出现循环引用时，生成校验信息。
在上述第一方面的一种可能的实现方式中，上述方法还包括：
所述第一电子设备获取用户的第一查询操作，所述第一查询操作用于请求查询所述目标保单的计算图数据；
所述第一电子设备响应于所述第一查询操作，向所述第二电子设备发送第一查询请求；
所述第二电子设备响应于所述第一查询请求，向所述第一电子设备发送所述计算图数据和所述校验信息；
所述第一电子设备向所述用户显示所述计算图数据和所述校验信息。
可以理解，基于用户的查询操作输出显示计算图数据和校验信息，可以帮助配置人员更好地理解保单的计算逻辑，便于配置人员查看修改各计算单元的配置数据。
在上述第一方面的一种可能的实现方式中，所述第二电子设备基于所述计算图数据，确定所述目标保单的计算计划，包括：
所述第二电子设备解析所述计算图数据，确定所述计算图数据中的子节点单元和 根节点单元，其中所述多个计算单元中引用的输出计算因子对应的计算单元的数量为零的计算单元为根节点计算单元，所述根节点计算单元的计算深度为零；以及，所述多个计算单元中引用的输出计算因子对应的计算单元的数量不为零的计算单元为子节点计算单元；
确定所述子节点计算单元与所述子节点计算单元对应的根节点计算单元之间的数量最多的边的数量为所述子节点计算单元的计算深度；
所述第二电子设备根据各所述计算单元的计算深度和所述计算图数据中的所述引用关系，确定所述计算计划。
在上述第一方面的一种可能的实现方式中，上述方法还包括：
所述第二电子设备将所述目标保单发送至所述第二电子设备；
所述第二电子设备基于所述目标保单以及所述多个计算单元的配置数据，执行所述计算计划。
在上述第一方面的一种可能的实现方式中，所述第二电子设备基于所述目标保单数据以及所述多个计算单元的配置数据，执行所述计算计划，包括：
所述第二电子设备根据所述计算单元的计算因子，从所述目标保单数据中确定匹配于所述计算因子的值，并执行所述计算计划。
在上述第一方面的一种可能的实现方式中，所述第二电子设备基于所述目标保单数据以及所述多个计算单元的配置数据，执行所述计算计划，还包括：
所述第二电子设备基于所述目标保单数据以及所述多个计算单元的配置数据，执行所述计算计划，得到所述计算计划的计算过程数据和计算结果，其中，所述计算过程数据用于描述各所述计算单元的所述配置数据及对应的值、各所述计算单元的计算顺序。
在上述第一方面的一种可能的实现方式中，上述方法还包括：
所述第一电子设备获取用户的第二查询操作，所述第二查询操作用于查询所述计算过程数据和所述计算结果；
所述第一电子设备响应于所述第二查询操作，向所述第二电子设备发送第二查询请求；
所述第二电子设备响应于所述第二查询请求，向所述第一电子设备发送所述计算过程数据和所述计算结果；
所述第一电子设备向所述用户显示所述计算过程图数据。
在上述第一方面的一种可能的实现方式中，所述计算过程数据和所述计算结果表征为计算过程图数据，其中所述计算过程图数据以各所述计算单元、各所述计算单元的配置数据以及对应的值为节点，以各所述计算单元的数据流动方向为边。
第二方面，本申请实施例提供了一种用于目标保单的处理方法，应用于包括第三电子设备的系统，包括：
确定与目标保单关联的多个计算单元，以及用于所述多个计算单元的配置数据，其中，所述多个计算单元用于组合计算所述目标保单的保费，所述配置数据包括各所述计算单元的计算因子、所述计算因子对应的数据对象类型以及计算规则；
根据所述多个计算单元的所述配置数据，确定所述多个计算单元的关联信息，其 中，所述关联信息用于描述所述多个计算单元的计算因子的引用关系；
基于所述关联信息，确定所述目标保单的计算图数据，其中，所述计算图数据以所述多个计算单元为节点，以所述引用关系为边；
基于所述计算图数据，确定所述目标保单的计算计划，其中，所述计算计划用于描述所述多个计算单元用于处理所述目标保单的执行顺序以及所述引用关系。
第三方面，本申请实施例提供了一种电子设备，一个或多个处理器；一个或多个存储器；一个或多个存储器存储有一个或多个程序，当一个或者多个程序被一个或多个处理器执行时，使得电子设备执行上述用于目标保单的处理方法。
第四方面，本申请实施例提供了一种计算机可读存储介质，存储介质上存储有指令，指令在计算机上执行时使所述计算机执行上述用于目标保单的处理方法。
第五方面，本申请实施例提供了一种计算机程序产品，计算机程序产品包括指令，该指令在执行时使计算机执行上述用于目标保单的处理方法。
附图说明
图1所示为本申请实施例提供的一种基于多个计算单元进行保费计算的应用场景示意图；
图2所示为本申请实施例提供的一种数据对象的结构示意图；
图3所示为本申请实施例提供的一种电子设备的硬件结构框图；
图4所示为本申请实施例提供的一种用于目标保单的处理方法的流程示意图；
图5所示为本申请实施例提供的一种计算单元的配置数据的结构示意图；
图6所示为本申请实施例提供的一种计算图数据的结构示意图；
图7所示为本申请实施例提供的一种用于目标保单的处理方法的流程示意图。
具体实施方式
以下基于实施例对本发明进行描述，但是本发明并不仅仅限于这些实施例。在下文对本发明的细节描述中，详尽描述了一些特定的细节部分。对本领域技术人员来说没有这些细节部分的描述也可以完全理解本发明。为了避免混淆本发明的实质，公知的方法、过程、流程、元件和电路并没有详细叙述。
此外，本领域普通技术人员应当理解，在此提供的附图都是为了说明的目的，并且
附图不一定是按比例绘制的。
除非上下文明确要求，否则整个说明书中的“包括”、“包含”等类似词语应当解释
为包含的含义而不是排他或穷举的含义；也就是说，是“包括但不限于”的含义。
在本发明的描述中，需要理解的是，术语“第一”、“第二”等仅用于描述目的，而不能理解为指示或暗示相对重要性。此外，在本发明的描述中，除非另有说明，“多个”的含义是两个或两个以上。
为使本申请的目的、技术方案和优点更加清楚，下面将结合附图对本申请的实施方式作进一步地详细描述。
在介绍本申请中的方案之前，为了便于理解本申请中的方案，下面结合图1对本申请的应用场景进行介绍。
图1所示为本申请实施例提供的一种基于多个计算单元进行保费计算的应用场景示意图。
如图1所示，用户1将配置的多个计算单元提供给保费系统100的应用模块101，应用模块101接收多个计算单元并发送给计算引擎102进行计算逻辑配置。应用模块101还可以接收来自用户2的保单数据。计算引擎102可以根据多个计算单元，配置保费的计算计划，并可以基于计算计划以及保单数据进行计算。计算引擎102可以将计算后得到的计算结果以及配置的计算计划返回给应用模块101，应用模块101根据返回的计算结果进行后续处理，或将计算结果以及计算逻辑展示给用户1。
可以理解，计算逻辑为保费系统100实现业务功能所需的与计算相关的元素，可以包括但不限于计算过程、计算规则、计算公式等。具体来说，一些基本的计算逻辑例如计算表达式、扩展函数、变量、过滤器、决策树等。计算逻辑中通常包括数据参数，计算逻辑根据数据参数的赋值进行计算，得到具体的计算逻辑结果。
可以理解，计算单元为用户1根据保费的计算逻辑，以及预设的计算逻辑分解方法，对计算逻辑进行拆分得到的，能够实现部分计算过程的单元。
可以理解，用户1可以为系统配置人员，即对保费系统100进行管理与相关配置的人员，也可以为业务人员，即熟悉软件项目的业务相关的人员。用户2可以为购买保单的客户。
应用模块101用于获取用户1提供的多个计算单元，以及用户2提供的保单数据，获取方式可以采用多种方式，可以包括但不限于：以界面方式获取、以文件形式获取、根据用户选择获取等。例如，在计算逻辑配置阶段，应用模块101可向用户1提供计算单元配置界面，用户11在输计算单元配置界面中进行相关数据的输入，在用户1输入完毕后，计算单元配置界面将用户输入的数据提交给应用模块101；用户1也可以预先将多个计算单元存储在数据文件中，将数据文件提交给应用模块101，应用模块101通过解析数据文件得到多个计算单元；应用模块101可以向用户1提供多个预设的计算单元模板，并将用户1选择的计算单元模板或修改后的计算单元模板作为使用的计算单元。在保费计算阶段同理，在此不作赘述。
可以理解，保费系统100可以应用于电子设备上，电子设备包括但不限于手机(包括折叠屏手机)、平板电脑、膝上型计算机、台式计算机、服务器、可穿戴设备、头戴式显示器、移动电子邮件设备、车机设备、便携式游戏机、便携式音乐播放器、阅读器设备、其中嵌入或耦接有一个或多个处理器的电视机等各类电子设备。
如前文背景技术所述，现有的保费计算逻辑的分解、编排等，依赖于配置人员操作执行，为解决保险计算场景中计算引擎的配置、管理和变更繁琐的问题，本申请提供了一种用于目标保单的处理方法。
本申请实施例提供的用于目标保单的处理方法包括：计算引擎102基于获取到的保单的各计算单元的配置数据，确定各计算单元的依赖关系，并根据各计算单元的依赖关系确定保单的计算计划。其中，计算单元的配置数据包括计算单元的数据对象、计算因子以及计算规则，数据对象可以包括各保单元素及其对应的数据对象类型。进 一步的，在保费计算阶段，计算引擎102可以基于获取到的保单数据以及预设的保单元素与数据对象的对应关系，执行计算计划，并将得到的计算结果返回给应用模块101进行数据处理或显示。计算引擎102还可以将基于各计算单元的依赖关系生成的计算图数据，返回给应用模块101输出显示。
本申请实施例提供的用于目标保单的处理方法，不需要配置人员对每个保单的计算逻辑进行分解，并对分解后的计算单元的计算顺序进行编排调用，配置人员只需要输入各计算单元的配置数据，计算引擎102可以基于各计算单元的配置数据，自动确定各计算单元的计算顺序，作出计算计划，可以降低配置人员配置执行逻辑的配置复杂度，提高计算引擎102的执行逻辑的配置效率。并且，相比于用户对各计算单元的计算顺序的编排调用，本申请实施例可以快速、准确地生成计算计划，对于配置人员的配置能力要求较低。
此外，基于各计算单元的依赖关系生成的计算图数据输出显示，可以帮助配置人员更好地理解保单的计算逻辑，便于配置人员查看修改各计算单元的配置数据。
在一些实施例中，计算引擎102可以基于各计算单元的依赖关系，优化各计算单元的计算顺序和计算策略，提高计算引擎102的执行性能。
在一些实施例中，计算引擎102可以对执行计算计划过程中产生的计算过程数据进行存储，并可以即将计算过程数据以图像化的形式表示。配置人员可以基于计算过程数据查看计算过程，并调整计算单元的配置数据，提高配置的效率和质量。
在一些实施例中，保单数据以树状数据结构输入至计算引擎102，树状数据结构可例如图2所示。
如图2所示，树状数据结构为数据对象的树状数据结构，每个数据对象可以包括数据对象类型以及保单元素的属性字段。其中，数据对象类型可以包括保单、标的、责任、二级责任。其中，保单对应的保单元素可以例如生效日期、失效日期等，标的对应的保单元素可以例如性别、出生日期、职业等，责任、二级责任对应的保单元素可以包括各种保险金额，例如对于车辆保单，责任、二级责任可以例如车辆损失险金额、第三者责任险金额、玻璃碎裂险金额等。其中，保单元素的属性字段可以不存在于树状数据结构，可以表示为保单的产品定义中保单元素对应的编码。
例如，对于图2中的数据对象“责任201”，表示数据对象类型为责任，对应的保单元素为保单的产品定义中，编号为201的保单元素；例如，对于图2中的数据对象“二级责任203001”，表示数据对象类型为二级责任，对应的保单元素为保单的产品定义中，编号为203001的保单元素。
在一些实施例中，不同的保单类型可以对应不同的计算引擎102。例如，可以基于保单的保单类型，调用对应该保单类型的计算引擎102的应用程序编程接口(Application Programming Interface，API)，实现对该保单类型对应的计算引擎102的调用。在一些实施例中，不同的保单类型可以对应同一个计算引擎102，并且计算引擎102可以包括多个计算号，每个计算号可以对应一种保单的计算计划的生成以及保费计算。本申请对此不作限制。
可以理解的是，在一些实施例中，本申请实施例提供的方法可以应用于包括服务端的电子设备300，其中的服务端可以例如图1中的计算引擎102，具体地，可以通过 计算引擎生成并执行计算计划。在另一些实施例中，本申请实施例提供的方法可以应用于包括服务端、客户端的电子设备300，其中服务端可例如图1中的计算引擎102，客户端可例如图1中的应用模块101。进一步地，在一些实施例中，客户端可以获取用户1或用户2输入的数据，例如配置数据、保单数据等，并且客户端可以根据输入的配置数据生成计算计划，并将计算计划以及保单数据发送至服务端，服务端可以基于保单数据执行计算计划，并向客户端返回计算结果。在另一些实施例中，客户端可以获取用户1或用户2输入的数据，例如配置数据、保单数据等，并且客户端可以根据输入的配置数据生成计算图数据，并将计算图数据以及保单数据发送至服务端，服务端可以基于计算图数据生成计算计划，并基于保单数据执行计算计划，然后向客户端返回计算结果。在其他实施例中，客户端还可以只用于获取配置数据和保单数据，服务端用于生成并执行计算计划。本申请对此不作限制。
可以理解的是，本申请实施例中的应用模块101和计算引擎102可以对应于同一电子设备，也可以对应于不同电子设备。用户1和用户2输入数据对应的应用模块101可以应用于同一电子设备，也可以应用于不同电子设备。本申请对此不作限制。
下面在对本申请实施例提供的用于目标保单的处理方法进行介绍之前，先结合图3对应用本申请实施例的电子设备的硬件结构进行介绍。
图3所示为本申请实施例提供的一种用于实现用于目标保单的处理方法的电子设备300的硬件结构框图。在图3所示的实施例中，电子设备300可以包括一个或多个处理器301，与处理器301中的至少一个连接的系统控制逻辑302，与系统控制逻辑302连接的系统内存303，与系统控制逻辑302连接的非易失性存储器(Non-Volati le Memory，NVM)304，以及与系统控制逻辑302连接的网络接口306。
在一些实施例中，处理器301可以包括一个或多个单核或多核处理器。在一些实施例中，处理器301可以包括通用处理器和专用处理器(例如，图形处理器，应用处理器，基带处理器等)的任意组合。在电子设备300采用增强型基站(Evolved Node B，eNB)或无线接入网(Radio Access Network，RAN)控制器的实施例中，处理器301可以被配置为执行各种符合的实施例。例如，处理器301可以用于执行用于目标保单的处理方法。
在一些实施例中，系统控制逻辑302可以包括任意合适的接口控制器，以向处理器301中的至少一个和/或与系统控制逻辑302通信的任意合适的设备或组件提供任意合适的接口。
在一些实施例中，系统控制逻辑302可以包括一个或多个存储器控制器，以提供连接到系统内存303的接口。系统内存303可以用于加载以及存储数据和/或指令。例如，系统内存303可以加载本申请实施例中解析计算逻辑的指令，也可以保存输入数据和配置数据等。
在一些实施例中电子设备300的系统内存303可以包括任意合适的易失性存储器，例如合适的动态随机存取存储器(Dynamic Random Access Memory，DRAM)。
NVM存储器304可以包括用于存储数据和/或指令的一个或多个有形的、非暂时性的计算机可读介质。在一些实施例中，NVM存储器304可以包括闪存等任意合适的非易失性存储器和/或任意合适的非易失性存储设备，例如硬盘驱动器(Hard Di sk Drive， HDD)，光盘(Compact Di sc，CD)驱动器，数字通用光盘(Digital Versatile Disc，DVD)驱动器中的至少一个。在本申请实施例中，NVM存储器304可以用于存储应用模块获取的输入数据和配置数据。
NVM存储器304可以包括安装电子设备300的装置上的一部分存储资源，或者它可以由设备访问，但不一定是设备的一部分。例如，可以经由网络接口306通过网络访问NVM存储器304。
特别地，系统内存303和NVM存储器304可以分别包括：指令305的暂时副本和永久副本。指令305可以包括：由处理器301中的至少一个执行时导致电子设备300实施如图3所示的方法的指令。在一些实施例中，指令305、硬件、固件和/或其软件组件可另外地/替代地置于系统控制逻辑302，网络接口306和/或处理器301中。
网络接口306可以包括收发器，用于为电子设备300提供无线电接口，进而通过一个或多个网络与任意其他合适的设备(如前端模块，天线等)进行通信。在一些实施例中，网络接口306可以集成于电子设备300的其他组件。例如，网络接口306可以集成于处理器301的，系统内存303，NVM存储器304，和具有指令的固件设备(未示出)中的至少一种，当处理器301中的至少一个执行所述指令时，电子设备300实现如方法实施例中示出的方法。在本申请实施例中，网络接口306可以用于接收应用模块发送的输入数据和配置数据。
网络接口306可以进一步包括任意合适的硬件和/或固件，以提供多输入多输出无线电接口。例如，网络接口306可以是网络适配器，无线网络适配器，电话调制解调器和/或无线调制解调器。
在一些实施例中，处理器301中的至少一个可以与用于系统控制逻辑302的一个或多个控制器的逻辑封装在一起，以形成系统封装(System In a Package，SIP)。在一些实施例中，处理器301中的至少一个可以与用于系统控制逻辑302的一个或多个控制器的逻辑集成在同一管芯上，以形成片上系统(System on Chip，SOC)。
电子设备300可以进一步包括：输入/输出(I/O)设备307。I/O设备307可以包括用户界面，使得用户能够与电子设备300进行交互；外围组件接口的设计使得外围组件也能够与电子设备300交互。
可以理解的是，图3示意的结构并不构成对电子设备300的具体限定。在本申请另外一些实施例中电子设备300可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以由硬件或软件，或软件和硬件的组合实现。
下面结合图4，以实施主体为客户端，对本申请实施例提供的用于目标保单的处理方法进行介绍。
图4所示为本申请实施例提供的一种用于目标保单的处理方法的流程示意图。可以理解，该流程的执行主体可以是任意包括客户端的电子设备，客户端可以为应用模块101。
如图4所示，该方法包括：
401：获取目标保单对应的多个计算单元的配置数据。
可以理解，各计算单元的配置数据可以为表征计算逻辑的各计算单元的基础信息， 可以包括计算单元的计算因子、计算规则以及适用的数据对象。
可以理解，数据对象可以包括保单元素的属性及其对应的数据对象类型。其中，数据对象类型是配置人员根据目标保单的产品定义中，参与保费计算逻辑的多个保单元素的用途，预先划分好的数据对象类型。例如，图2中，数据对象类型可以包括保单、标的、责任、二级责任。保单包括的保单元素可以例如保单的生效日期、失效日期等。标的可以包括目标保单中表征被保人基本信息的保单元素，例如被保人性别、出生日期、职业等。责任或二级责任可以包括目标保单中表征保险金额的保单元素，例如对于车险保单，责任或二级责任包括的保单元素可以例如车辆损失险金额、第三者责任险金额、玻璃碎裂险金额等。
在一些实施例中，数据对象的保单元素的属性可以通过保单元素的属性字段表示。例如，“保险金额总和”可以表示为“TOTAL_SI”，“保险金额总和”还可以表示为其他属性字段，本申请对此不作限制。在另一些实施例中，数据对象的保单元素的属性还可以通过目标保单的产品定义中，保单元素对应的编码进行标识，例如图2中的数据对象“责任201”，其中“责任”表示数据对象类型、“201”表示保单元素在目标保单的产品定义中的编码。
可以理解，计算因子即保单元素或保单元素的标识，计算因子可以包括：计算单元进行计算时输入计算单元的输入计算因子，以及计算单元完成计算时输出的输出计算因子。
可以理解，计算规则可以包括对计算因子进行的计算公式、计算函数或计算条件等。例如，对于计算风险毛保费的计算单元，计算规则包括：风险毛保费＝标准年保费*保障期间费率，若保障时间低于6个月，保障期间费率采用短期费率。其中，计算规则中的各计算因子可以通过计算因子标识表示。
在一些实施例中，不同的保单对应不同的计算单元。例如，可以利用保单号作为保单的标识，不同的保单号可以对应不同的计算单元。其中，不同的计算单元可以包括计算单元的数量不同、计算单元的至少之一的配置数据不同，本申请对此不作限制。
在另一些实施例中，不同类型的保单对应不同的计算单元。其中，不同类型可以例如不同被保人、被保对象的保单。不同类型的保单对应不同的计算单元，可以例如，对于车险保单、人身意外险等，可以对应不同的计算单元。因此，在获取目标保单的计算单元的配置数据时，可以先确定目标保单的保单类型，然后根据确定的保单类型，获取该保单类型对应的多个计算单元的配置数据，或者直接获取以及基于配置数据生成的计算计划。
在一些实施例中，获取的配置数据可以为应用模块101在多个保单的计算单元以及对应的配置数据中确定的与目标保单关联的计算单元及其配置数据。
402：根据各计算单元的配置数据，确定多个计算单元的关联信息。
具体地，可以基于各计算单元的配置数据中的计算因子，确定多个计算单元的关联信息。
可以理解，关联信息可以理解为各计算单元在进行计算时需要引用其他计算单元输出计算因子的引用关系。例如计算单元A中的输入计算因子a引用计算单元B计算输出的输出计算因子b的值，则生成表征计算单元A引用输出计算因子b的关联信息。
可以理解，生成的关联信息可以存储于电子设备300的系统内存303中，用于后续计算图以及计算计划的生成。生成的关联信息还可以存储于电子设备300的非易失性存储器304中，电子设备300可以基于存储的关联信息进行后续数据处理，或者电子设备300可以输出显示关联信息，以助于配置人员调整计算单元的配置数据。
在一些实施例中，配置数据中的计算因子可以通过计算因子标识表示，步骤402中基于各计算单元的计算因子，确定关联信息，具体可以包括：确定各计算单元的输入计算因子标识和输入计算因子标识，对于任意两个计算单元A和计算单元B，确定计算单元A的输入计算因子标识是否计算单元B的输出计算因子标识匹配，若匹配则表明计算单元A引用计算单元B的输出计算因子。
403：基于多个计算单元的关联信息以及配置数据，生成计算图数据。
具体地，可以基于关联信息以及配置数据中的计算因子，生成计算图数据。可以理解，配置数据中的计算因子可以包括计算单元的输出计算因子，关联信息中可以包括计算单元引用的输出计算因子。
可以理解，应用模块101可以基于各计算单元的输出计算因子、引用的其他计算单元的输出计算因子，确定表征多个计算单元之间的依赖关系的计算图数据。
可以理解的是，计算图数据可以为对以计算单元为节点，以计算因子的引用关系为边的图数据的描述，计算图数据还可以为以计算单元为节点，以计算因子的引用关系为边的图数据，以向量的形式存储，例如思维导图形式的图数据。即生成的计算图数据可以表征为图像数据，也可以表征为文本数据，本申请对此不作限制。
下面结合图5和图6，对本申请实施例中的一种计算图数据进行介绍。
图5所示为本申请实施例提供的一种计算单元的配置数据的结构示意图。
图6所示为本申请实施例提供的一种计算图数据的结构示意图。
如图5所示，配置数据中的计算单元可以根据“数据对象类型_输出计算因子_(计算规则、适用条件或保单元素标识等)”进行命名。例如，对于计算单元“责任_标准年保费_编码201”，该计算单元的数据对象类型为责任，输出计算因子为标准年保费，且输出的标准年保费的编码为201。例如对于计算单元“保单_风险毛保费_计算及累加”，该计算单元的数据对象类型为保单，输出计算因子为风险毛保费，计算规则为计算及累计，即计算规则为根据预设的计算公式进行计算，并对计算结果进行累加。
配置数据中的计算因子可以包括各计算单元的输入计算因子和输出计算因子。例如。对于计算单元“保单_风险毛保费_计算及累加”的计算因子中可以包括：输入计算因子累加后标准年保费、保障期间费率，输出计算因子风险毛保费。
可以理解，图5所示的配置数据为本申请实施例中的一种示例，在一些实施例中，配置数据中可以包括比图5所示更多或更少的计算单元、计算因子、配置数据种类等，本申请对此不作限制。
可以理解，应用模块101可以根据图5所示的配置数据中的计算因子，生成多个计算单元的关联信息，并且可以根据生成的关联信息以及图5中各计算单元的计算因子，生成如图6所示的计算图数据。
如图6所示，示例性地，计算图数据以思维导图的形式来表示的话，其中的虚线箭头可以表示各计算单元的引用关系。箭头的方向表示引用的计算因子来源。
例如，对于计算单元“责任_标准年保费_编码201”的输入计算因子为保险金额，输出计算因子为标准年保费_编码201，则关联信息中可以包括该计算单元引用计算单元“保单_保险金额_累加”的输出计算因子保险金额，并且计算单元“保单_累加后标准年保费_累加”引用该计算单元的输出计算因子标准年保费_编码201。
可以理解，在其他实施例中，计算图数据可以为不同于图6所示的表示形式，例如采用图6外的其他图像化表示形式、文本表示形式、表格表示形式等，本申请对此不作限制。
在一些实施例中，应用模块101生成计算图数据后，还可以基于计算图数据中各计算单元的引用关系，判断多个计算单元之间是否存在循环引用。若判断结果为是，则生成校验信息。
可以理解，循环引用为部分计算单元之间的引用关系出现闭环，例如计算单元A引用计算单元B，计算单元B引用计算单元C，计算单元C又引用计算单元A。循环引用会产生计算闭环，应用模块101在生成的计算计划可能会出现死循环，不会停止，因而计算引擎102无法输出保费计算结果。
可以理解，校验信息可以表征计算图数据中包括循环引用。在一些实施例中，校验信息可以为“Error”，即应用模块101在检测到循环引用时，会进行报错。
404：基于计算图数据，确定目标保单对应的计算计划。
可以理解，计算计划可以包括多个计算单元的执行顺序、多个计算单元的引用关系。其中，执行顺序中可以包括多个计算单元中部分可以并行执行的计算单元、多个计算单元执行的先后顺序等。
在一些实施例中，应用模块101可以基于计算图数据，确定各计算单元的计算深度，并基于计算深度确定各计算单元的执行顺序。
具体地：应用模块101可以确定各计算单元中引用的输出计算因子对应的计算单元的数量为零的计算单元为根节点计算单元，例如图6中的计算单元“保单_保险金额_累加”、“标的_标的费率X”和“保单_保障期间费率”为根节点计算单元，并确定根节点计算单元的计算深度为零；应用模块101可以确定根节点计算单元之外的其他计算单元为子节点计算单元，并可以根据该子节点计算单元与其对应的根节点计算单元之间的边的数量，确定该子节点计算单元的计算深度。
例如，图6中的计算单元“责任_标准年保费_编码203”，为子节点计算单元，其对应的根节点计算单元为“标的_标的费率X”，且“责任_标准年保费_编码203”与“标的_标的费率X”之间包括一条边，则子节点计算单元“责任_标准年保费_编码203”的计算深度为1。再例如，图6中计算单元“保单_调整后毛保费_分摊”为子节点计算单元，其对应的根节点计算单元包括“保单_保险金额_累加”、“标的_标的费率X”和“保单_保障期间费率”，“保单_调整后毛保费_分摊”与对应的根节点计算单元之间包括的最多边数为5，则子节点计算单元“保单_调整后毛保费_分摊”的计算深度为5。
进一步地，在确定各计算单元的计算深度后，可以根据各计算单元的计算深度从小到的顺序，确定各计算单元的执行顺序。相同计算深度的部分计算单元可以并行执行。
本申请实施例提供的用于目标保单的处理方法，不需要配置人员对每个保单的计算逻辑进行分解，并对分解后的计算单元的计算顺序进行编排调用，配置人员只需要输入各计算单元的配置数据，应用模块101可以基于各计算单元的配置数据，自动确定各计算单元的计算顺序，作出计算计划，可以降低配置人员配置执行逻辑的配置复杂度，提高应用模块101的执行逻辑的配置效率。并且，相比于用户对各计算单元的计算顺序的编排调用，本申请实施例可以快速、准确地生成计算计划，对于配置人员的配置能力要求较低。
此外，基于各计算单元的依赖关系生成的计算图数据输出显示，可以帮助配置人员更好地理解保单的计算逻辑，便于配置人员查看修改各计算单元的配置数据。
下面结合图7，以实施主体为客户端和服务端，对本申请实施例提供的用于目标保单的处理方法进行进一步介绍。
图7所示为本申请实施例提供的一种用于目标保单的处理方法的流程示意图。可以理解，该流程的执行主体可以是任意包括客户端和服务端的电子设备。具体地，以客户端为应用模块101，服务端为计算引擎102为例，进行介绍。
如图7所示，该方法包括：
701：应用模块101获取目标保单的多个计算单元的配置数据。其中，步骤701与图4中的步骤401相同，在此不作赘述。
702：应用模块101根据配置数据，确定多个计算单元的关联信息。其中，步骤702与图4中的步骤402相同，在此不作赘述。
703：应用模块101根据关联信息以及多个计算单元的配置数据，生成目标保单的计算图数据。其中，步骤703与图4中的步骤403相同，在此不作赘述。
在一些实施例中，应用模块101可以响应于用户的计算图查询请求，在显示界面，以类似思维导图的形式输出显示计算图数据。进一步地，应用模块101还可以获取配置人员对计算图数据进行调整的图数据调整信息，或对计算单元的配置数据进行调整的配置数据调整信息，得到更加符合计算逻辑的计算图数据。
704：应用模块101向计算引擎102发送计算图数据以及各计算单元的配置数据。
可以理解，应用模块101发送的计算图数据，可以为各计算单元的计算因子的引用关系数据，也可以为表征引用关系的图像化数据。配置数据中可以包括各计算单元的计算因子、计算规则以及适用的数据对象。
可以理解，在一些实施例中，步骤704具体可以包括：应用模块101可以通过API调用计算引擎102，触发目标保单的保费运算，在调用计算引擎102时，可以传递表征多个计算单元匹配条件(即引用关系)的计算图数据以及包括数据对象的配置数据。
705：计算引擎102根据接收到的计算图数据，生成目标保单的计算计划以及对应的计算号。步骤705生成计算计划以及生成计算计划的过程可以与图4中的步骤404相同，区别在于步骤705的执行主体为计算引擎102，步骤404的执行主体为应用模块101。
706：应用模块101获取目标保单数据。
可以理解，目标保单数据可以包括目标保单中的，与保费计算相关的保单元素以及保单元素对应的值。在一些实施例中，目标保单数据可以表示为个保单元素在目标 保单的产品结构中定义的属性字段或编码，以及对应的数值。
在一些实施例中，应用模块101获取到的目标保单数据可以为树状结构数据，例如图2所示的目标保单数据。目标保单数据中可以包括多个数据对象，各数据对象可以包括数据对象类型、保单元素的属性字段或编码、保单元素的取值等。
在另一些实施例中，应用模块101获取到的目标保单数据可以为目标保单，应用模块101可以提取目标保单中的与保费计算相关的多个保单元素，并根据提取出的多个保单元素，以及预设的保单元素与数据对象类型的对应关系，生成如图2所示的树状结构数据，并将该树状结构数据作为保费计算的输入，发送给计算引擎102进行保费计算。
在其他实施例中，应用模块101获取到的目标保单数据可以为目标保单的、与保费计算相关的多个保单元素，并可以根据获取出的多个保单元素，以及预设的保单元素与数据对象类型的对应关系，生成如图2所示的树状结构数据，并将该树状结构数据作为保费计算的输入，发送给计算引擎102进行保费计算。
可以理解的是，步骤706中的目标保单数据可以是任意形式的数据，本申请对此不作限制。
707：应用模块101向计算引擎102发送目标保单数据。
708：计算引擎102基于接收到的目标保单数据，以及多个计算单元的配置数据，执行计算计划，得到计算过程数据以及计算结果。
其中，计算过程数据可以为计算过程中，各计算单元匹配的数据对象以及生成的计算结果，具体可以包括输入各计算单元的数据对象、数据对象的取值、计算单元执行计算规则生成的计算输出因子以及对应的值。其中，计算结果执行计算计划后输出的计算结果。
在一些实施例中，计算过程数据以图形化的形式表示。该计算过程数据中可以包括计算计划的执行过程，以及各计算单元匹配的数据对象、各计算因子的取值、计算因子的引用关系等。具体地，计算过程数据和计算结果可以表征为以各所述计算单元、各所述计算单元的配置数据以及对应的值为节点，以各所述计算单元的数据流动方向为边的计算过程图数据。例如，计算过程数据可以在显示界面显示为类似如图6中的计算图的形式，并且用户可以点击各计算单元查看该计算单元在计算过程中匹配到的或产生的数据。
在一些实施例中，数据对象表征为图2所示的树状结构数据，计算引擎102执行计算计划的过程，各计算单元可以根据适用的数据对象类型，从树状结构数据上取对应的数据对应的值，并把该值赋值为计算DNA元的计算因子。当计算单元需要引用其他计算单元的输出计算因子时，的计算因子的取值过程具体可以包括：
计算单元确定输入计算因子需要引用其他计算单元的输出计算因子，则计算单元可以先判断该输出计算因子的数据对象类型是否与该计算单元适用的数据对象类型相同。若判断结果为相同，则计算单元可以从该计算单元适用的数据对象类型中包括的输出计算因子中取值；否则，需要从适用的数据对象类型所处的树状数据结构中沿父节点至根节点的方向进行匹配，直至匹配至该计算单元引用的输出计算因子，并从匹配的输出计算因子中取值。
709：计算引擎102向应用模块101发送目标保单的计算号、计算过程数据和计算结果。
可以理解，应用模块接收到计算号、计算过程数据和计算结果，可以基于业务管理需求进行进一步数据处理或数据存储，也可以将接收到的计算号、计算过程数据和计算结果进行输出显示。
在一些实施例中，应用模块101可以获取用户的查询操作，查询操作中可以包括待查询的计算号，应用模块101响应于用户的查询操作，可以显示计算号、计算过程数据和计算结果。
在一些实施例中，计算引擎102可以生成表征计算号、计算过程数据、计算结果的对应关系的数据文件，将计算号、计算过程数据和计算结果分别存储到不同的数据库表中。计算引擎102可以将表征计算号、计算过程数据、计算结果的对应关系的数据文件发送至应用模块102。进一步地，应用模块101在获取到用户的查询操作时，可以根据该数据文件，确定匹配于待查询的计算号的计算过程数据以及计算结果，然后从数据库中查询匹配的计算过程数据和计算结果，并将其与计算号一起输出显示。
在另一些实施例中，计算引擎102可以将计算号、计算过程数据、计算结果存储至同一个数据库表中，并且应用模块101可以基于计算号查询到对应的计算过程数据和计算结果。
本申请实施例提供的用于目标保单的处理方法，可以将执行计算计划时生成的数据或计算图以图形化的形式向用户展示，便于用户理解、调整计算计划，可以提高计算计划的配置效率。并且，计算引擎102可以基于计算图数据，生成、优化计算计划，无需用户手动配置，配置效率和质量更高。
本申请公开的机制的各实施例可以被实现在硬件、软件、固件或这些实现方法的组合中。本申请的实施例可实现为在可编程系统上执行的计算机程序或程序代码，该可编程系统包括至少一个处理器、存储系统(包括易失性和非易失性存储器和/或存储元件)、至少一个输入设备以及至少一个输出设备。
可将程序代码应用于输入指令，以执行本申请描述的各功能并生成输出信息。可以按已知方式将输出信息应用于一个或多个输出设备。为了本申请的目的，处理系统包括具有诸如例如数字信号处理器(DSP)、微控制器、专用集成电路(ASIC)或微处理器之类的处理器的任何系统。
程序代码可以用高级程序化语言或面向对象的编程语言来实现，以便与处理系统通信。包括但不局限于OpenCL、C语言、C++、Java等。而对于C++、Java之类语言，由于其会将存储进行转换，基于对于本申请实施例中的数据处理方法的应用会有些差异，本领域技术人员可以基于具体地高级语言进行变换，均不脱离本申请实施例的范围。
在一些情况下，所公开的实施例可以以硬件、固件、软件或其任何组合来实现。所公开的实施例还可以被实现为由一个或多个暂时或非暂时性机器可读(例如，计算机可读)存储介质承载或存储在其上的指令，其可以由一个或多个处理器读取和执行。例如，指令可以通过网络或通过其他计算机可读介质分发。因此，机器可读介质可以包括用于以机器(例如，计算机)可读的形式存储或传输信息的任何机制，包括但不 限于，软盘、光盘、光碟、只读存储器(CD-ROMs)、磁光盘、只读存储器(ROM)、随机存取存储器(RAM)、可擦除可编程只读存储器(EPROM)、电可擦除可编程只读存储器(EEPROM)、磁卡或光卡、闪存、或用于利用因特网以电、光、声或其他形式的传播信号来传输信息(例如，载波、红外信号数字信号等)的有形的机器可读存储器。因此，机器可读介质包括适合于以机器(例如，计算机)可读的形式存储或传输电子指令或信息的任何类型的机器可读介质。
在附图中，可以以特定布置和/或顺序示出一些结构或方法特征。然而，应该理解，可能不需要这样的特定布置和/或排序。而是，在一些实施例中，这些特征可以以不同于说明性附图中所示的方式和/或顺序来布置。另外，在特定图中包括结构或方法特征并不意味着暗示在所有实施例中都需要这样的特征，并且在一些实施例中，可以不包括这些特征或者可以与其他特征组合。
需要说明的是，本申请各设备实施例中提到的各单元/模块都是逻辑单元/模块，在物理上，一个逻辑单元/模块可以是一个物理单元/模块，也可以是一个物理单元/模块的一部分，还可以以多个物理单元/模块的组合实现，这些逻辑单元/模块本身的物理实现方式并不是最重要的，这些逻辑单元/模块所实现的功能的组合才是解决本申请所提出的技术问题的关键。此外，为了突出本申请的创新部分，本申请上述各设备实施例并没有将与解决本申请所提出的技术问题关系不太密切的单元/模块引入，这并不表明上述设备实施例并不存在其它的单元/模块。
需要说明的是，在本专利的示例和说明书中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
虽然通过参照本申请的某些优选实施例，已经对本申请进行了图示和描述，但本领域的普通技术人员应该明白，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。

Claims (15)

  1. 一种用于目标保单的处理方法，应用于包括第一电子设备和第二电子设备的系统，其特征在于，包括：
    所述第一电子设备确定与目标保单关联的多个计算单元，以及用于所述多个计算单元的配置数据，其中，所述多个计算单元用于组合计算所述目标保单的保费，所述配置数据包括各所述计算单元的计算因子、所述计算因子对应的数据对象类型以及计算规则；
    所述第一电子设备将所述多个计算单元的配置数据发送至所述第二电子设备；
    所述第二电子设备根据所述多个计算单元的所述配置数据，确定所述多个计算单元的关联信息，其中，所述关联信息用于描述所述多个计算单元的计算因子的引用关系；
    所述第二电子设备基于所述关联信息，确定所述目标保单的计算图数据，其中，所述计算图数据以所述多个计算单元为节点，以所述引用关系为边；
    所述第二电子设备基于所述计算图数据，确定所述目标保单的计算计划，其中，所述计算计划用于描述所述多个计算单元用于处理所述目标保单的执行顺序以及所述引用关系。
  2. 根据权利要求1所述的用于目标保单的处理方法，其特征在于，所述计算因子包括输入计算因子和输出计算因子；
    所述第二电子设备根据所述多个计算单元的所述配置数据，确定所述多个计算单元的关联信息，包括：
    所述第二电子设备根据各所述计算单元的配置数据，确定各所述计算单元的输入计算因子和输出计算因子；
    所述第二电子设备基于各所述计算单元的所述输入计算因子和所述输出计算因子，确定所述关联信息。
  3. 根据权利要求2所述的用于目标保单的处理方法，其特征在于，所述第二电子设备基于所述关联信息，确定所述目标保单的计算图数据，包括：
    所述第二电子设备基于所述关联信息中的所述引用关系，以及各所述计算单元的输出计算因子，生成所述计算图数据。
  4. 根据权利要求3所述的用于目标保单的处理方法，其特征在于，还包括：
    所述第二电子设备基于所述关联信息，在判断出所述多个计算单元中的部分所述计算单元之间出现循环引用时，生成校验信息。
  5. 根据权利要求4所述的用于目标保单的处理方法，其特征在于，还包括：
    所述第一电子设备获取用户的第一查询操作，所述第一查询操作用于请求查询所述目标保单的计算图数据；
    所述第一电子设备响应于所述第一查询操作，向所述第二电子设备发送第一查询请求；
    所述第二电子设备响应于所述第一查询请求，向所述第一电子设备发送所述计算图数据和所述校验信息；
    所述第一电子设备向所述用户显示所述计算图数据和所述校验信息。
  6. 根据权利要求2所述的用于目标保单的处理方法，其特征在于，所述第二电子设备基于所述计算图数据，确定所述目标保单的计算计划，包括：
    所述第二电子设备解析所述计算图数据，确定所述计算图数据中的子节点单元和根节点单元，其中所述多个计算单元中引用的输出计算因子对应的计算单元的数量为零的计算单元为根节点计算单元，所述根节点计算单元的计算深度为零；以及，所述多个计算单元中引用的输出计算因子对应的计算单元的数量不为零的计算单元为子节点计算单元；
    确定所述子节点计算单元与所述子节点计算单元对应的根节点计算单元之间的数量最多的边的数量为所述子节点计算单元的计算深度；
    所述第二电子设备根据各所述计算单元的计算深度和所述计算图数据中的所述引用关系，确定所述计算计划。
  7. 根据权利要求1所述的用于目标保单的处理方法，其特征在于，还包括：
    所述第二电子设备将所述目标保单发送至所述第二电子设备；
    所述第二电子设备基于所述目标保单以及所述多个计算单元的配置数据，执行所述计算计划。
  8. 根据权利要求7所述的用于目标保单的处理方法，其特征在于，所述第二电子设备基于所述目标保单数据以及所述多个计算单元的配置数据，执行所述计算计划，包括：
    所述第二电子设备根据所述计算单元的计算因子，从所述目标保单数据中确定匹配于所述计算因子的值，并执行所述计算计划。
  9. 根据权利要求7所述的用于目标保单的处理方法，其特征在于，所述第二电子设备基于所述目标保单数据以及所述多个计算单元的配置数据，执行所述计算计划，还包括：
    所述第二电子设备基于所述目标保单数据以及所述多个计算单元的配置数据，执行所述计算计划，得到所述计算计划的计算过程数据和计算结果，其中，所述计算过程数据用于描述各所述计算单元的所述配置数据及对应的值、各所述计算单元的计算顺序。
  10. 根据权利要求9所述的用于目标保单的处理方法，其特征在于，还包括：
    所述第一电子设备获取用户的第二查询操作，所述第二查询操作用于查询所述计算过程数据和所述计算结果；
    所述第一电子设备响应于所述第二查询操作，向所述第二电子设备发送第二查询请求；
    所述第二电子设备响应于所述第二查询请求，向所述第一电子设备发送所述计算过程数据和所述计算结果；
    所述第一电子设备向所述用户显示所述计算过程图数据。
  11. 根据权利要求10所述的用于目标保单的处理方法，其特征在于，所述计算过程数据和所述计算结果表征为计算过程图数据，其中所述计算过程图数据以各所述计算单元、各所述计算单元的配置数据以及对应的值为节点，以各所述计算单元的数据流动方向为边。
  12. 一种用于目标保单的处理方法，应用于包括第三电子设备的系统，其特征在于，包括：
    确定与目标保单关联的多个计算单元，以及用于所述多个计算单元的配置数据，其中，所述多个计算单元用于组合计算所述目标保单的保费，所述配置数据包括各所述计算单元的计算因子、所述计算因子对应的数据对象类型以及计算规则；
    根据所述多个计算单元的所述配置数据，确定所述多个计算单元的关联信息，其中，所述关联信息用于描述所述多个计算单元的计算因子的引用关系；
    基于所述关联信息，确定所述目标保单的计算图数据，其中，所述计算图数据以所述多个计算单元为节点，以所述引用关系为边；
    基于所述计算图数据，确定所述目标保单的计算计划，其中，所述计算计划用于描述所述多个计算单元用于处理所述目标保单的执行顺序以及所述引用关系。
  13. 一种电子设备，其特征在于，包括：
    存储器，用于存储由电子设备的一个或多个处理器执行的指令，以及
    处理器，是电子设备的处理器之一，用于控制执行权利要求1至11中任一项所述的用于目标保单的处理方法或权利要求12中所述的用于目标保单的处理方法。
  14. 一种计算机可读存储介质，其特征在于，所述存储介质上存储有指令，所述指令在计算机上执行时使所述计算机执行权利要求1至11中任一项所述的用于目标保单的处理方法或权利要求12中所述的用于目标保单的处理方法。
  15. 一种计算机程序产品，其特征在于，所述计算机程序产品包括指令，该指令在执行时使计算机执行权利要求1至11中任一项所述的用于目标保单的处理方法或权利要求12中所述的用于目标保单的处理方法。
PCT/CN2023/097491 2022-08-29 2023-05-31 用于目标保单的处理方法、电子设备和可读存储介质 WO2024045725A1 (zh)

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