CN115511644A

CN115511644A - Processing method for target policy, electronic device and readable storage medium

Info

Publication number: CN115511644A
Application number: CN202211041010.0A
Authority: CN
Inventors: 王景龙; 施瑜; 王嘉杰; 许松; 冯逸; 黄河; 陈樟洪; 蔡纯钢; 莫元武
Original assignee: eBaoTech Corp
Current assignee: eBaoTech Corp
Priority date: 2022-08-29
Filing date: 2022-08-29
Publication date: 2022-12-23
Also published as: WO2024045725A1

Abstract

The application relates to the technical field of financial software, in particular to a processing method, an electronic device and a readable storage medium for a target policy, wherein the method is applied to a system comprising a first electronic device and a second electronic device and comprises the following steps: the first electronic device determining a plurality of computing units associated with the target policy and configuration data for the plurality of computing units; the first electronic equipment sends the configuration data of the plurality of computing units to the second electronic equipment; the second electronic equipment determines the associated information of the plurality of computing units according to the configuration data of the plurality of computing units; the second electronic equipment determines calculation graph data of the target policy based on the association information; the second electronic device determines a calculation plan for the target policy based on the calculation map data. The processing method for the target insurance policy provided by the embodiment of the application can quickly and accurately generate the calculation plan, and has lower requirements on the configuration capability of configuration personnel.

Description

Processing method for target policy, electronic device and readable storage medium

Technical Field

The invention relates to the technical field of financial software, in particular to a processing method for a target policy, electronic equipment and a readable storage medium.

Background

In the application scenario of insurance calculation, the insurance calculation method comprises multiple calculation logics such as premium calculation and claim settlement calculation, wherein the calculation logics involve multiple calculation factors, calculation steps and logic branches, the calculation steps have sequential dependency relationship, and the insurance logic is relatively complex.

In current insurance computing scenarios, computing logic is typically executed by invoking a specified compute engine by invoking a specified entry compute node. The execution logic in the computing engine needs a configurator to split and configure the algorithm logic, and the process needs the configurator to spend a lot of time to test the execution logic and output the log to a buried point, so that the configuration is complicated. Furthermore, the readability and the debuggability of the execution logic completely depend on the manual configuration of a configuration person, and the execution performance of the computing engine is difficult to guarantee. When the computing logic is changed, the execution logic in the computing engine also needs to be managed and changed, a configuration worker needs to perform splitting configuration of the execution logic on the computing engine again, and the management and the change of the computing engine are complicated. As can be seen, the configuration and debugging efficiency of the compute engine in the current premium computing scenario is low.

Disclosure of Invention

In order to solve the problem that the configuration, management and change of the computing engine are complicated, embodiments of the present application provide a processing method, an electronic device and a readable storage medium for a target policy.

In a first aspect, an embodiment of the present application provides a processing method for a target policy, which is applied to a system including a first electronic device and a second electronic device, and includes:

the first electronic equipment determines a plurality of computing units associated with a target policy and configuration data for the computing units, wherein the computing units are used for calculating the premium of the target policy in a combined mode, and the configuration data comprises computing factors of the computing units, data object types corresponding to the computing factors and computing rules;

the first electronic device sends the configuration data of the plurality of computing units to the second electronic device;

the second electronic equipment determines the associated information of the plurality of computing units according to the configuration data of the plurality of computing units, wherein the associated information is used for describing the reference relation of the computing factors of the plurality of computing units;

the second electronic device determines computational graph data of the target policy based on the association information, wherein the computational graph data takes the plurality of computing 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 for describing an execution sequence of the plurality of calculation units for processing the target policy and the reference relationship.

According to the processing method for the target policy, the configuration personnel is not required to decompose and configure the calculation logic of each policy, and the calculation sequence of the decomposed calculation units is arranged and called, so that the system can automatically determine the configuration data of each calculation unit according to the input target policy and make a calculation plan according to the configuration data, the configuration complexity of the configuration execution logic of the configuration personnel can be reduced, and the configuration efficiency of the calculation execution logic of the system can be improved. Compared with the scheduling and calling of the calculation sequence of each calculation unit by a user, the calculation plan can be generated quickly and accurately, and the requirement on the configuration capability of configuration personnel is low.

In a possible implementation manner of the first aspect, the calculation factor includes an input calculation factor and an output calculation factor;

the second electronic device determines the associated information of the plurality of computing units according to the configuration data of the plurality of computing units, and the determining includes:

the second electronic equipment determines input calculation factors and output calculation factors of the calculation units according to the configuration data of the calculation units;

the second electronic device determines the association information based on the input calculation factor and the output calculation factor of each of the calculation units.

In a possible implementation manner of the first aspect, the determining, by the second electronic device, calculation map data of the target policy based on the association information includes:

the second electronic device generates the computation graph data based on the reference relationship in the association information and the output computation factor of each computation unit.

In a possible implementation manner of the first aspect, the method further includes:

and the second electronic equipment generates verification information when judging that cyclic reference occurs among part of the computing units in the plurality of computing units based on the association information.

the first electronic equipment acquires a first query operation of a user, wherein the first query operation is used for requesting to query the calculation chart data of the target insurance policy;

the first electronic equipment responds to the first query operation and sends a first query request to the second electronic equipment;

the second electronic device responds to the first query request and sends the calculation graph data and the check information to the first electronic device;

the first electronic device displays the computation graph data and the verification information to the user.

It will be appreciated that the display of the computation graph data and the verification information is output based on a user's query operation, the method can help the configurator to better understand the computational logic of the policy, and is convenient for the configurator to view and modify the configuration data of each computational unit.

In a possible implementation manner of the first aspect, the determining, by the second electronic device, a calculation plan of the target policy based on the calculation graph data includes:

the second electronic device analyzes the calculation graph data and determines child node units and root node units in the calculation graph data, wherein the calculation units with the number of zero corresponding to the output calculation factors quoted in the calculation units are root node calculation units, and the calculation depth of each root node calculation unit is zero; the computing units with the number not equal to zero corresponding to the output computing factors quoted in the plurality of computing units are child node computing units;

determining the number of the edges with the largest number between the child node calculation units and the root node calculation units corresponding to the child node calculation units as the calculation depth of the child node calculation units;

and the second electronic equipment determines the calculation plan according to the calculation depth of each calculation unit and the reference relation in the calculation map data.

the second electronic equipment sends the target insurance policy to the second electronic equipment;

the second electronic device executes the calculation plan based on the target policy and the configuration data of the plurality of calculation units.

In a possible implementation manner of the first aspect, the executing, by the second electronic device, the calculation plan based on the target policy data and the configuration data of the plurality of calculation units includes:

and the second electronic equipment determines a value matched with the calculation factor from the target policy data according to the calculation factor of the calculation unit and executes the calculation plan.

In a possible implementation manner of the first aspect, the executing, by the second electronic device, the calculation plan based on the target policy data and the configuration data of the plurality of calculation units further includes:

and the second electronic equipment 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 used for describing the configuration data and corresponding values of each calculation unit and the calculation sequence of each calculation unit.

the first electronic equipment acquires a second query operation of a user, wherein the second query operation is used for querying the calculation process data and the calculation result;

the first electronic equipment responds to the second query operation and sends a second query request to the second electronic equipment;

the second electronic equipment responds to the second query request and sends the calculation process data and the calculation result to the first electronic equipment;

the first electronic device displays the computed process map data to the user.

In a possible implementation manner of the first aspect, the calculation process data and the calculation result are characterized as calculation process graph data, where the calculation process graph data uses the configuration data of each calculation unit, and the corresponding value as nodes, and uses the data flow direction of each calculation unit as an edge.

In a second aspect, an embodiment of the present application provides a processing method for a target policy, which is applied to a system including a third electronic device, and includes:

determining a plurality of computing units associated with a target policy and configuration data for the computing units, wherein the computing units are used for calculating the premium of the target policy in a combined manner, and the configuration data comprises a computing factor of each computing unit, a data object type corresponding to the computing factor and a computing rule;

determining 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 for describing reference relations of computing factors of the plurality of computing units;

determining computational graph data of the target policy based on the association information, wherein the computational graph data takes the plurality of computing units as nodes and the reference relation as an edge;

determining a calculation plan of the target policy based on the calculation graph data, wherein the calculation plan is used for describing an execution sequence and the reference relation of the plurality of calculation units for processing the target policy.

In a third aspect, embodiments of the present application provide an electronic device, one or more processors; one or more memories; the one or more memories store one or more programs that, when executed by the one or more processors, cause the electronic device to perform the processing method for a target policy described above.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having instructions stored thereon, which when executed on a computer cause the computer to perform the above-mentioned processing method for a target policy.

In a fifth aspect, the present application provides a computer program product, which includes instructions that, when executed, cause a computer to execute the processing method for a target policy.

Drawings

Fig. 1 is a schematic view of an application scenario of premium calculation based on multiple calculation units according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a data object according to an embodiment of the present application;

fig. 3 is a block diagram illustrating a hardware structure of an electronic device according to an embodiment of the present disclosure;

FIG. 4 is a schematic flow chart illustrating a processing method for a target policy according to an embodiment of the present application;

fig. 5 is a schematic structural diagram illustrating configuration data of a computing unit according to an embodiment of the present application;

fig. 6 is a schematic structural diagram illustrating computational graph data according to an embodiment of the present application;

fig. 7 is a flowchart illustrating a processing method for a target policy according to an embodiment of the present application.

Detailed Description

The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, flows, components and circuits have not been described in detail so as not to obscure the present invention.

Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.

Unless the context clearly requires otherwise, throughout the description, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including but not limited to".

In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

Before describing the scheme in the present application, in order to facilitate understanding of the scheme in the present application, an application scenario of the present application is described below with reference to fig. 1.

Fig. 1 is a schematic view of an application scenario of premium calculation based on multiple calculation units according to an embodiment of the present application.

As shown in FIG. 1, the user 1 provides the configured plurality of computing units to the application module 101 of the premium system 100, and the application module 101 receives the plurality of 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 may configure a calculation plan of a premium according to a plurality of calculation units, and may perform calculation based on the calculation plan and policy data. The calculation engine 102 may return the calculation result obtained by the calculation and the configured calculation plan to the application module 101, and the application module 101 performs subsequent processing according to the returned calculation result, or displays the calculation result and the calculation logic to the user 1.

It is to be understood that the computational-related elements required by the computational logic to implement the business functions for the premium system 100 may include, but are not limited to, computational processes, computational rules, computational formulas, and the like. In particular, some basic computational logic such as computational expressions, extended functions, variables, filters, decision trees, etc. The computation logic usually includes data parameters, and the computation logic performs computation according to assignment of the data parameters to obtain a specific computation logic result.

It can be understood that the calculation unit is a unit which is obtained by splitting the calculation logic according to the calculation logic of the premium and a preset calculation logic decomposition method by the user 1 and can realize a part of the calculation process.

It will be appreciated that the user 1 may be a system configurator, i.e. a person managing and configuring the premium system 100, or a service person, i.e. a person familiar with the service of the software project. User 2 may be a customer who purchases a policy.

The application module 101 is configured to obtain a plurality of computing units provided by the user 1 and policy data provided by the user 2, where the obtaining manner may be a variety of manners, which may include but is not limited to: interface mode, file form, according to user selection, etc. For example, in the stage of computing logic configuration, the application module 101 may provide a computing unit configuration interface to the user 1, the user 11 inputs related data in the computing unit configuration interface, and after the user 1 finishes inputting, the computing unit configuration interface submits the data input by the user to the application module 101; the user 1 may also store the plurality of computing units in a data file in advance, submit the data file to the application module 101, and the application module 101 obtains the plurality of computing units by analyzing the data file; the application module 101 may provide the user 1 with a plurality of preset calculation unit templates, and use the calculation unit template selected by the user 1 or the modified calculation unit template as a calculation unit to be used. The same process is repeated in the premium calculation stage.

It is to be appreciated that the premium system 100 may be applied to electronic devices including, but not limited to, cell phones (including folding screen cell phones), tablets, laptops, desktops, servers, wearable devices, head-mounted displays, mobile email devices, car-mounted devices, portable games, portable music players, reader devices, televisions with one or more processors embedded or coupled therein, and the like.

As described in the foregoing background, the decomposition, arrangement, and the like of the existing premium calculation logic depend on the operation and execution of a configurator, and in order to solve the problem that the configuration, management, and change of a calculation engine in an insurance calculation scenario are tedious, the present application provides a processing method for a target policy.

The processing method for the target policy provided by the embodiment of the application comprises the following steps: the calculation engine 102 determines the dependency relationship of each calculation unit based on the acquired configuration data of each calculation unit of the policy, and determines the calculation plan of the policy according to the dependency relationship of each calculation unit. The configuration data of the computing unit includes data objects, computing factors and computing rules of the computing unit, and the data objects may include each policy element and its corresponding data object type. Further, in the charge-conserving calculation stage, the calculation engine 102 may execute the calculation plan based on the obtained policy data and the preset corresponding relationship between policy elements and data objects, and return the obtained calculation result to the application module 101 for data processing or display. The calculation engine 102 may also return calculation graph data generated based on the dependency relationship of each calculation unit to the application module 101 for output and display.

According to the processing method for the target policy, a configurator is not required to decompose the calculation logic of each policy, and arrange and call the calculation sequence of the decomposed calculation units, the configurator only needs to input the configuration data of each calculation unit, the calculation engine 102 can automatically determine the calculation sequence of each calculation unit based on the configuration data of each calculation unit to make a calculation plan, the configuration complexity of the configurator for configuring the execution logic can be reduced, and the configuration efficiency of the execution logic of the calculation engine 102 can be improved. In addition, compared with the scheduling and calling of the calculation sequence of each calculation unit by a user, the calculation plan can be generated quickly and accurately, and the requirement on the configuration capability of configuration personnel is low.

In addition, the output display of the data of the calculation graph generated based on the dependency relationship of each calculation unit can help a configurator to better understand the calculation logic of the policy, and the configurator can conveniently check and modify the configuration data of each calculation unit.

In some embodiments, the computing engine 102 may optimize the computing order and the computing policy of each computing unit based on the dependency relationship of each computing unit, so as to improve the execution performance of the computing engine 102.

In some embodiments, the calculation engine 102 may store the calculation process data generated during execution of the calculation plan, and may represent the calculation process data in an imaged form. Configuration personnel can check the calculation process based on the calculation process data and adjust the configuration data of the calculation unit, so that the efficiency and the quality of configuration are improved.

In some embodiments, policy data is input to the calculation engine 102 in a tree data structure, which may be, for example, as shown in FIG. 2.

As shown in FIG. 2, the tree data structure is a tree data structure of data objects, each of which may include a data object type and attribute fields of policy elements. Data object types may include policy, objective, responsibility, secondary responsibility, among others. The policy elements corresponding to the policy can be, for example, effective date, expiration date and the like, the policy elements corresponding to the target can be, for example, gender, birth date, occupation and the like, and the policy elements corresponding to the responsibility and the secondary responsibility can comprise various insurance amounts, for example, for a vehicle policy, the responsibility and the secondary responsibility can be, for example, vehicle loss insurance amount, third party responsibility insurance amount, glass breakage insurance amount and the like. The attribute field of the policy element may not exist in the tree data structure, and may be represented as a code corresponding to the policy element in the product definition of the policy.

For example, for the data object "responsibility 201" in FIG. 2, it indicates that the data object type is responsibility and the corresponding policy element is the policy element numbered 201 in the product definition for which the policy is policy; for example, for data object "secondary responsibility 203001" in fig. 2, it is indicated that the data object type is secondary responsibility and the corresponding policy element is the policy element, numbered 203001, in the product definition for which the policy is applied.

In some embodiments, different policy types may correspond to different compute engines 102. For example, based on the policy type of the policy, an Application Programming Interface (API) of the calculation engine 102 corresponding to the policy type may be called, so as to implement the call to the calculation engine 102 corresponding to the policy type. In some embodiments, different policy types may correspond to the same calculation engine 102, and the calculation engine 102 may include multiple calculation numbers, each calculation number may correspond to the generation of a calculation plan for a policy and the calculation of a premium. This is not limited by the present application.

It is understood that, in some embodiments, the method provided by the embodiments of the present application may be applied to the electronic device 300 including a server, where the server may be, for example, the computing engine 102 in fig. 1, and specifically, the computing plan may be generated and executed by the computing engine. In other embodiments, the method provided by the embodiment of the present application may be applied to the electronic device 300 including a server and a client, where the server may be, for example, the computing engine 102 in fig. 1, and the client may be, for example, the application module 101 in fig. 1. Further, in some embodiments, the client may obtain data input by the user 1 or the user 2, such as configuration data, policy data, and the like, and the client may generate a calculation plan according to the input configuration data and send the calculation plan and the policy data to the server, and the server may execute the calculation plan based on the policy data and return a calculation result to the client. In other embodiments, the client may obtain data input by the user 1 or the user 2, such as configuration data, policy data, and the like, and the client may generate computational graph data according to the input configuration data and send the computational graph data and the policy data to the server, and the server may generate a computation plan based on the computational graph data and execute the computation plan based on the policy data, and then return a computation result to the client. In other embodiments, the client may also be used only to obtain configuration data and policy data, and the server may be used to generate and execute the calculation plan. This is not limited by the present application.

It is understood that 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 the user 1 and the user 2 may be applied to the same electronic device or different electronic devices. This is not limited by the present application.

Before describing the processing method for the target policy provided in the embodiment of the present application, a hardware structure of an electronic device to which the embodiment of the present application is applied is described with reference to fig. 3.

Fig. 3 is a block diagram illustrating a hardware structure of an electronic device 300 for implementing a processing method for a target policy according to an embodiment of the present application. In the illustrated embodiment of FIG. 3, electronic device 300 may include one or more processors 301, system control logic 302 coupled to at least one of processors 301, system Memory 303 coupled to system control logic 302, non-Volatile Memory (NVM) 304 coupled to system control logic 302, and a network interface 306 coupled to system control logic 302.

In some embodiments, processor 301 may include one or more single-core or multi-core processors. In some embodiments, the processor 301 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In embodiments where the electronic device 300 employs an enhanced Node B (eNB) or a Radio Access Network (RAN) controller, the processor 301 may be configured to perform various consistent embodiments. For example, the processor 301 may be configured to execute a processing method for a target policy.

In some embodiments, system control logic 302 may include any suitable interface controllers to provide any suitable interface to at least one of processors 301 and/or to any suitable device or component in communication with system control logic 302.

In some embodiments, 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. For example, the system memory 303 may load an instruction of the parsing calculation logic in the embodiment of the present application, and may also store input data, configuration data, and the like.

In some embodiments, system Memory 303 of electronic device 300 may include any suitable volatile Memory, such as suitable Dynamic Random Access Memory (DRAM).

NVM memory 304 may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions. In some embodiments, NVM memory 304 may include any suitable non-volatile memory, such as flash memory, and/or any suitable non-volatile storage device, such as at least one of a Hard Disk Drive (HDD), a Compact Disc (CD) Drive, and a Digital Versatile Disc (DVD) Drive. In the present embodiment, the NVM memory 304 may be used to store input data and configuration data obtained by an application module.

NVM memory 304 may comprise a portion of a storage resource on the device on which electronic device 300 is installed, or it may be accessible by, but not necessarily a part of, the device. For example, NVM memory 304 may be accessed over a network via network interface 306.

In particular, system memory 303 and NVM memory 304 may each include: a temporary copy and a permanent copy of the instruction 305. 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 as shown in fig. 3. In some embodiments, the instructions 305, hardware, firmware, and/or software components thereof may additionally/alternatively be disposed in the system control logic 302, the network interface 306, and/or the processor 301.

The network interface 306 may include a transceiver to provide a radio interface for the electronic device 300 to communicate with any other suitable device (e.g., front end module, antenna, etc.) over one or more networks. In some embodiments, the network interface 306 may be integrated with other components of the electronic device 300. For example, 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) having instructions that, when executed by at least one of processors 301, electronic device 300 implements a method as shown in method embodiments. In this embodiment, the network interface 306 may be used to receive input data and configuration data sent by the application module.

The network interface 306 may further include any suitable hardware and/or firmware to provide a multiple-input multiple-output radio interface. For example, network interface 306 may be a network adapter, a wireless network adapter, a telephone modem, and/or a wireless modem.

In some embodiments, at least one of the processors 301 may be packaged together with logic for one or more controllers of the System control logic 302 to form a 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 of the System control logic 302 to form a System On Chip (SOC).

The electronic device 300 may further include: input/output (I/O) devices 307. The I/O device 307 may include a user interface to enable a user to interact with the electronic device 300; the design of the peripheral component interface enables peripheral components to also interact with the electronic device 300.

It is to be understood that the configuration illustrated in fig. 3 does not constitute a specific limitation on the electronic device 300. In other embodiments of the present application, electronic device 300 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented by hardware or software, or a combination of software and hardware.

The processing method for the target policy provided by the embodiment of the present application is described below with reference to fig. 4, where the implementation subject is a client.

Fig. 4 is a flowchart illustrating a processing method for a target policy according to an embodiment of the present application. It is understood that the execution subject of the process may be any electronic device including a client, which may be the application module 101.

As shown in fig. 4, the method includes:

401: and acquiring configuration data of a plurality of computing units corresponding to the target policy.

It is understood that the configuration data of each computing unit may be the basic information of each computing unit characterizing the computing logic, and may include the computing factors, computing rules, and applicable data objects of the computing unit.

It will be appreciated that the data object may include attributes of policy elements and their corresponding data object types. The data object type is divided in advance by a configurator according to the usage of a plurality of policy elements participating in the premium calculation logic in the product definition of the target policy. For example, in FIG. 2, the data object types may include policy, objective, responsibility, secondary responsibility. The policy may include policy elements such as the policy's expiration date, etc. The target may include policy elements in the target policy that characterize the insured person's basic information, such as the insured person's gender, date of birth, occupation, etc. The liability or secondary liability may comprise policy elements in the target policy characterizing the amount of the insurance, for example for a vehicle insurance policy, the policy elements comprised by the liability or secondary liability may for example be the amount of vehicle loss insurance, the amount of third party liability insurance, the amount of glass breakage insurance, etc.

In some embodiments, attributes of the policy element of the data object may be represented by an attribute field of the policy element. For example, the "sum of insurance amounts" may be represented as "TOTAL _ SI", and the "sum of insurance amounts" may also be represented as other attribute fields, which is not limited in this application. In other embodiments, the attributes of the policy elements of the data object may also be identified by the corresponding codes of the policy elements in the product definition of the target policy, such as data object "responsibility 201" in FIG. 2, where "responsibility" denotes the data object type and "201" denotes the code of the policy element in the product definition of the target policy.

It is to be understood that the calculation factor, i.e., the policy element or the identification of the policy element, may include: the input calculation factor of the input calculation unit when the calculation unit performs the calculation, and the output calculation factor output when the calculation unit completes the calculation.

It is understood that the calculation rule may include a calculation formula, a calculation function or a calculation condition, and the like performed on the calculation factor. For example, for a calculation unit that calculates a risk gross premium, the calculation rule includes: risk gross premium = standard annual premium guaranteed period rate, and if the guaranteed time is less than 6 months, the guaranteed period rate adopts a short-term rate. Wherein, each calculation factor in the calculation rule can be represented by a calculation factor identifier.

In some embodiments, different policies correspond to different computing units. For example, a policy number may be used as an identification of the policy, and different policy numbers may 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 in this application.

In other embodiments, different types of policies correspond to different computing units. Wherein the different types may be, for example, different insureds, policies of insured objects. Different types of insurance policies correspond to different computing units, and may correspond to different computing units, for example, for vehicle insurance policies, personal accident insurance, and the like. Therefore, when acquiring the configuration data of the calculation unit of the target policy, the policy type of the target policy may be determined, and then the configuration data of the plurality of calculation units corresponding to the policy type may be acquired according to the determined policy type, or the calculation plan may be directly acquired and generated based on the configuration data.

In some embodiments, the acquired configuration data may be the computing units and their configuration data associated with the target policy determined by application module 101 among the computing units of the plurality of policies and the corresponding configuration data.

402: and determining the associated information of the plurality of computing units according to the configuration data of each computing unit.

Specifically, the association information of the plurality of calculation units may be determined based on the calculation factor in the configuration data of each calculation unit.

It can be understood that the associated information can be understood as a reference relation that each computing unit needs to refer to other computing units to output computing factors when computing. For example, 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, and then the associated information representing that the calculation unit a refers to the output calculation factor B is generated.

It is understood that the generated association information may be stored in the system memory 303 of the electronic device 300 for subsequent generation of the computation graph and the computation plan. The generated association information may also be stored in the non-volatile memory 304 of the electronic device 300, the electronic device 300 may perform subsequent data processing based on the stored association information, or the electronic device 300 may output display association information to assist a configurator in adjusting configuration data of the computing unit.

In some embodiments, the calculation factor in the configuration data may be represented by a calculation factor identifier, and the determining of the association information based on the calculation factor of each calculation unit in step 402 may specifically include: and determining the input calculation factor identification and the input calculation factor identification of each calculation unit, determining whether the input calculation factor identification of the calculation unit A is matched with the output calculation factor identification of the calculation unit B or not for any two calculation units A and B, and if so, indicating that the calculation unit A refers to the output calculation factor of the calculation unit B.

403: the calculation graph data is generated based on the association information and the configuration data of the plurality of calculation units.

Specifically, the computation graph data may be generated based on the correlation information and the computation factors in the configuration data. It is to be understood that the calculation factor in the configuration data may include an output calculation factor of the calculation unit, and the correlation information may include an output calculation factor referenced by the calculation unit.

It is understood that the application module 101 may determine the computation graph data characterizing the dependency relationship between the plurality of computing units based on the output computing factors of each computing unit, the output computing factors of the referenced other computing units.

It is to be understood that the computation graph data may be a description of graph data in which the computation units are nodes and the reference relationships of the computation factors are edges, and the computation graph data may also be graph data in which the computation units are nodes and the reference relationships of the computation factors are edges, and stored in a vector form, for example, graph data in the form of a thought-leading graph. That is, the generated computation graph data may be represented by image data or text data, which is not limited in this application.

Next, a description will be given of a kind of computation graph data in the embodiment of the present application with reference to fig. 5 and 6.

Fig. 5 is a schematic structural diagram illustrating configuration data of a computing unit according to an embodiment of the present disclosure.

Fig. 6 is a schematic structural diagram of computational graph data according to an embodiment of the present disclosure.

As shown in fig. 5, the calculation units in the configuration data may be named according to "data object type _ output calculation factor _ (calculation rule, applicable condition, or policy element identification, etc.)". For example, for a computing unit "responsibility _ standard annual premium _ code 201", the data object type for that computing unit is responsibility, the output computing factor is the standard annual premium, and the code for the output standard annual premium is 201. For example, for a calculation unit "policy _ risk Mao Baofei _ calculation and accumulation", the data object type of the calculation unit is policy, the output calculation factor is risk Mao Baofei, and the calculation rule is calculation and accumulation, that is, the calculation rule is calculation according to a preset calculation formula and accumulation of the calculation result.

The calculation factors in the configuration data may include input calculation factors and output calculation factors of the respective calculation units. For example. The calculation factors for the calculation unit "policy _ risk Mao Baofei _ calculation and accumulation" may include: inputting standard annual premium and rate during guarantee after calculation factor accumulation, and outputting calculation factor risk Mao Baofei.

It is to be understood that the configuration data shown in fig. 5 is an example in the embodiment of the present application, and in some embodiments, the configuration data may include more or less computing units, computing factors, types of configuration data, and the like than those shown in fig. 5, which is not limited in the present application.

It is understood that the application module 101 may generate the association information of the plurality of computing units according to the computing factors in the configuration data shown in fig. 5, and may generate the computation graph data shown in fig. 6 according to the generated association information and the computing factors of the computing units in fig. 5.

As shown in fig. 6, the computation graph data may represent the reference relationship of each computation unit by a dotted arrow if the computation graph data is represented in the form of a mind graph. The direction of the arrows represents the source of the referenced calculation factors.

For example, if the input calculation factor of the calculation unit "responsibility _ standard annual premium _ code 201" is the insurance amount and the output calculation factor is the standard annual premium _ code 201, the associated information may include an output calculation factor insurance amount of the calculation unit "policy _ insurance amount _ accumulation" referred by the calculation unit, and the output calculation factor standard annual premium _ code 201 of the calculation unit is referred by the calculation unit "standard annual premium _ accumulation after policy _ accumulation".

It is understood that, in other embodiments, the computation graph data may be in a representation form different from that shown in fig. 6, for example, in an imaging representation form, a text representation form, a table representation form and the like other than fig. 6, which is not limited in this application.

In some embodiments, after the application module 101 generates the computation graph data, it may further determine whether a cyclic reference exists between multiple computation units based on a reference relationship of each computation unit in the computation graph data. And if the judgment result is yes, generating verification information.

It is understood that cyclic referencing presents a closed loop for the referencing relationship between parts of the computing units, e.g., computing unit a references computing unit B, computing unit B references computing unit C, and computing unit C references computing unit a. The loop reference may generate a closed loop of computation, and the application module 101 may generate a dead loop in the generated computation plan, and thus the computation engine 102 cannot output the premium computation result.

It is to be appreciated that the check information can characterize the inclusion of a circular reference in the computational graph data. In some embodiments, the check information may be "Error", that is, the application module 101 may report an Error when detecting the loop reference.

404: and determining a calculation plan corresponding to the target insurance policy based on the calculation chart data.

It is understood that the calculation plan may include an execution order of the plurality of calculation units, and a reference relationship of the plurality of calculation units. The execution sequence may include some computation units that may be executed in parallel in the multiple computation units, the execution sequence of the multiple computation units, and the like.

In some embodiments, the application module 101 may determine a computation depth of each computation unit based on the computation graph data, and determine an execution order of each computation unit based on the computation depth.

Specifically, the method comprises the following steps: the application module 101 may determine that the computing units with the number of zero corresponding to the output computing factors quoted in each computing unit are root node computing units, for example, the computing units "policy _ insurance amount _ accumulation", "target _ target rate X", and "policy _ guarantee period rate" in fig. 6 are root node computing units, and determine that the computing depth of the root node computing unit is zero; the application module 101 may determine that other computing units except the root node computing unit are child node computing units, and may determine the computation depth of the child node computing unit according to the number of edges between the child node computing unit and its corresponding root node computing unit.

For example, the computing unit "responsibility _ standard year premium _ code 203" in fig. 6 is a child node computing unit, the corresponding root node computing unit is "target _ target rate X", and an edge is included between "responsibility _ standard year premium _ code 203" and "target _ target rate X", so that the computing depth of the child node computing unit "responsibility _ standard year premium _ code 203" is 1. For another example, in fig. 6, the calculation unit "after policy _ adjustment Mao Baofei _ apportionment" is a child node calculation unit, and the root node calculation unit corresponding thereto includes "policy _ insurance amount _ accumulation", "target _ target rate X", and "policy _ guarantee period rate", and the maximum edge number included between the policy _ after adjustment Mao Baofei _ apportionment "and the root node calculation unit corresponding thereto is 5, and then the calculation depth of the child node calculation unit" after policy _ adjustment Mao Baofei _ apportionment "is 5.

Further, after the calculation depth of each calculation unit is determined, the execution order of each calculation unit may be determined according to the order of the calculation depth of each calculation unit from small to small. Parts of the computation units of the same computation depth may be executed in parallel.

According to the processing method for the target policy, a configurator is not required to decompose the calculation logic of each policy, and arrange and call the calculation sequence of the decomposed calculation units, the configurator only needs to input the configuration data of each calculation unit, the application module 101 can automatically determine the calculation sequence of each calculation unit based on the configuration data of each calculation unit to make a calculation plan, the configuration complexity of the configurator for configuring the execution logic can be reduced, and the configuration efficiency of the execution logic of the application module 101 can be improved. Compared with the scheduling and calling of the calculation sequence of each calculation unit by a user, the calculation plan can be generated quickly and accurately, and the requirement on the configuration capability of configuration personnel is low.

The processing method for the target policy provided by the embodiment of the present application is further described below with reference to fig. 7, where the implementation subject is a client and a server.

Fig. 7 is a schematic flowchart illustrating a processing method for a target policy according to an embodiment of the present application. It is understood that the execution subject of the process can be any electronic device including a client and a server. Specifically, a client is taken as the application module 101, and a server is taken as the computing engine 102 for example.

As shown in fig. 7, the method includes:

701: the application module 101 obtains configuration data for a plurality of computing units of the target policy. Step 701 is the same as step 401 in fig. 4, and is not described herein again.

702: the application module 101 determines association information of a plurality of computing units according to the configuration data. Step 702 is the same as step 402 in fig. 4, and is not described herein again.

703: the application module 101 generates calculation graph data of the target policy according to the association information and the configuration data of the plurality of calculation units. Step 703 is the same as step 403 in fig. 4, and is not described herein again.

In some embodiments, the application module 101 may output and display the computational graph data in a form similar to a mind graph on the display interface in response to a computational graph query request of a user. Further, the application module 101 may also obtain graph data adjustment information for a configurator to adjust the computational graph data, or obtain configuration data adjustment information for adjusting the configuration data of the computational unit, so as to obtain the computational graph data that better conforms to the computational logic.

704: the application module 101 sends the computation graph data and the configuration data of each computation unit to the computation engine 102.

It is understood that the computation graph data sent by the application module 101 may be reference relationship data of the computation factors of each computation unit, or may be imaging data representing the reference relationship. The configuration data may include the calculation factors, calculation rules, and applicable data objects for each calculation unit.

It is understood that, in some embodiments, step 704 may specifically include: the application module 101 may invoke the calculation engine 102 through the API to trigger the premium operation of the target policy, and may transfer calculation graph data representing a plurality of calculation unit matching conditions (i.e., reference relationships) and configuration data including data objects when invoking the calculation engine 102.

705: the calculation engine 102 generates a calculation plan and a corresponding calculation number of the target policy based on the received calculation graph data. The process of generating the calculation plan by step 705 and 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.

706: the application module 101 obtains target policy data.

It will be appreciated that the target policy data may include policy elements and corresponding values for policy elements in the target policy that are relevant to the premium calculations. In some embodiments, the target policy data may be represented as attribute fields or encodings defined for individual policy elements in the product structure of the target policy, and corresponding numerical values.

In some embodiments, the target policy data retrieved by the application module 101 may be tree structured data, such as the target policy data shown in fig. 2. The target policy data may include a plurality of data objects, and each data object may include a data object type, an attribute field or code of a policy element, a value of a policy element, and the like.

In other embodiments, the target policy data acquired by the application module 101 may be a target policy, and the application module 101 may extract a plurality of policy elements related to premium calculation in the target policy, generate tree structure data as shown in fig. 2 according to the extracted policy elements and a preset corresponding relationship between policy elements and data object types, and send the tree structure data as an input of premium calculation to the calculation engine 102 for premium calculation.

In other embodiments, the target policy data acquired by the application module 101 may be a plurality of policy elements of the target policy and related to premium calculation, and may generate tree-structured data as shown in fig. 2 according to the acquired policy elements and a preset correspondence between policy elements and data object types, and send the tree-structured data to the calculation engine 102 as an input of premium calculation to perform premium calculation.

It is to be appreciated that the target policy data in step 706 can be any form of data, and is not limited by the present application.

707: application module 101 sends the target policy data to compute engine 102.

708: the calculation engine 102 executes the calculation plan based on the received target policy data and the configuration data of the plurality of calculation units, and obtains calculation process data and calculation results.

The calculation process data may be data objects matched with each calculation unit and generated calculation results in the calculation process, and specifically may include data objects input to each calculation unit, values of the data objects, calculation output factors generated by the calculation unit executing the calculation rules, and corresponding values. And the calculation result is output after the calculation plan is executed.

In some embodiments, the computational process data is represented in a graphical form. The calculation process data may include an execution process of the calculation plan, and data objects matched by each calculation unit, values of each calculation factor, reference relationships of the calculation factors, and the like. Specifically, the calculation process data and the calculation result may be characterized as calculation process graph data that takes each of the calculation units, the configuration data of each of the calculation units, and the corresponding value as nodes and takes the data flow direction of each of the calculation units as edges. For example, the calculation process data may be displayed in a display interface in a form similar to the calculation graph in fig. 6, and the user may click on each calculation unit to view the data matched or generated by the calculation unit in the calculation process.

In some embodiments, the data objects are represented by tree-structured data as shown in fig. 2, the calculation engine 102 performs a process of calculating a plan, and each calculation unit can take a value corresponding to corresponding data from the tree-structured data according to an applicable data object type and assign the value as a calculation factor for calculating DNA elements. When the computing unit needs to refer to the output computing factors of other computing units, the evaluation process of the computing factors may specifically include:

if the computing unit determines that the input computing factor needs to refer to the output computing factors of other computing units, the computing unit may first determine whether the data object type of the output computing factor is the same as the data object type applicable to the computing unit. If the judgment results are the same, the calculation unit can take values from output calculation factors included in the data object types applicable to the calculation unit; otherwise, matching is performed from the tree data structure where the applicable data object type is located along the direction from the parent node to the root node until the output calculation factor quoted by the calculation unit is matched, and a value is taken from the matched output calculation factor.

709: the calculation engine 102 sends the calculation number, the calculation process data, and the calculation result of the target policy to the application module 101.

It can be understood that the application module receives the calculation number, the calculation process data and the calculation result, may perform further data processing or data storage based on the service management requirement, and may also output and display the received calculation number, the calculation process data and the calculation result.

In some embodiments, the application module 101 may obtain a query operation of a user, where the query operation may include a calculation number to be queried, and the application module 101 may display the calculation number, calculation process data, and calculation result in response to the query operation of the user.

In some embodiments, the calculation engine 102 may generate a data file representing the corresponding relationship between the calculation number, the calculation process data, and the calculation result, and store the calculation number, the calculation process data, and the calculation result in different database tables, respectively. The calculation engine 102 may send a data file representing a correspondence relationship between the calculation number, the calculation process data, and the calculation result to the application module 102. Further, when acquiring the query operation of the user, the application module 101 may determine, according to the data file, the calculation process data and the calculation result that are matched with the calculation number to be queried, then query the database for the matched calculation process data and calculation result, and output and display the data and the calculation number together.

In other embodiments, the calculation engine 102 may store the calculation number, the calculation process data, and the calculation result in the same database table, and the application module 101 may query the corresponding calculation process data and calculation result based on the calculation number.

According to the processing method for the target policy, the data or the calculation graph generated when the calculation plan is executed can be displayed to the user in a graphical mode, so that the user can understand and adjust the calculation plan conveniently, and the configuration efficiency of the calculation plan can be improved. Moreover, the calculation engine 102 can generate and optimize the calculation plan based on the calculation graph data, and the manual configuration by the user is not needed, so that the configuration efficiency and the quality are higher.

Embodiments of the mechanisms disclosed herein may be implemented in hardware, software, firmware, or a combination of these implementations. Embodiments of the application may be implemented as computer programs or program code executing on programmable systems comprising 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 generate output information. The output information may be applied to one or more output devices in a known manner. For purposes of this application, 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.

The program code may be implemented in a high level procedural or object oriented programming language to communicate with a processing system. Including but not limited to OpenCL, C language, C + +, java, etc. For languages such as C + +, java, etc., since they convert the storage, those skilled in the art may make the conversion based on the specific high-level language, which may be different from the application of the data processing method in the embodiment of the present application, without departing from the scope of the embodiment of the present application.

In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy diskettes, optical disks, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), random Access Memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or a tangible machine-readable storage device for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the internet in an electrical, optical, acoustical or other form of propagated signal. Thus, a machine-readable medium includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

In the drawings, some features of structures or methods may be shown in a particular arrangement and/or order. However, it is to be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, the features may be arranged in a manner and/or order different from that shown in the illustrative figures. In addition, the inclusion of a structural or methodical feature in a particular figure is not meant to imply that such feature is required in all embodiments, and in some embodiments, may not be included or may be combined with other features.

It should be noted that, in the embodiments of the apparatuses in the present application, each unit/module is a logical unit/module, and physically, one logical unit/module may be one physical unit/module, or may be a part of one physical unit/module, and may also be implemented by a combination of multiple physical units/modules, where the physical implementation manner of the logical unit/module itself is not the most important, and the combination of the functions implemented by the logical unit/module is the key to solve the technical problem provided by the present application. Furthermore, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units/modules which are not so closely related to solve the technical problems presented in the present application, which does not indicate that no other units/modules exist in the above-mentioned device embodiments.

It is noted that, in the examples and description of the present patent, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element.

While the present application has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.

Claims

1. A processing method for a target policy, applied to a system including a first electronic device and a second electronic device, comprising:

the second electronic device determines a calculation plan of the target policy based on the calculation graph data, wherein the calculation plan is used for describing an execution sequence and the reference relation of the plurality of calculation units for processing the target policy.

2. The process for a target policy according to claim 1, wherein said calculation factor comprises an input calculation factor and an output calculation factor;

the second electronic device determines, according to the configuration data of the plurality of computing units, associated information of the plurality of computing units, including:

3. The process for a target policy according to claim 2, wherein said second electronic device determines calculation map data of said target policy based on said association information, comprising:

4. A process for a target policy according to claim 3, further comprising:

5. The process for target policy according to claim 4, further comprising:

6. The process for a target policy according to claim 2, wherein said second electronic device determines a calculation plan for said target policy based on said calculation map data, comprising:

the second electronic device analyzes the calculation graph data and determines child node units and root node units in the calculation graph data, wherein the calculation units with the number of zero corresponding to the output calculation factors quoted in the plurality of calculation units are root node calculation units, and the calculation depth of each root node calculation unit is zero; the computing units with the number not equal to zero corresponding to the output computing factors quoted in the plurality of computing units are child node computing units;

7. The process for target policy according to claim 1, further comprising:

8. The process for target warranty according to claim 7 wherein said second electronic device executes said calculation plan based on said target warranty data and configuration data of said plurality of calculation units, comprising:

9. The process for target warranty of claim 7 wherein the second electronic device executes the calculation plan based on the target warranty data and the configuration data of the plurality of calculation units, further comprising:

10. The process for target policy according to claim 9, further comprising:

the first electronic device displays the computed process map data to the user.

11. The process for target warranty of claim 10 wherein the computational process data and the computational results are characterized as computational process graph data, wherein the computational process graph data is bounded by the nodes of the computational units, the configuration data of the computational units, and the corresponding values, and by the data flow direction of the computational units.

12. A processing method for a target policy, applied to a system including a third electronic device, comprising:

13. An electronic device, comprising:

a memory for storing instructions for execution by one or more processors of the electronic device, an

A processor, being one of the processors of the electronic device, for controlling execution of the processing method for the target policy of any one of claims 1 to 11 or the processing method for the target policy of claim 12.

14. A computer-readable storage medium having stored thereon instructions which, when executed on a computer, cause the computer to perform the processing method for a target policy of any one of claims 1 to 11 or the processing method for a target policy of claim 12.

15. A computer program product, characterized in that it comprises instructions which, when executed, cause a computer to carry out the processing method for a target policy of any one of claims 1 to 11 or the processing method for a target policy of claim 12.