Disclosure of Invention
In order to solve the problem of complicated configuration, management and modification of the computing engine, the embodiment of the application provides a processing method for a target policy, electronic equipment and a readable storage medium.
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 device 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 configuration data of the plurality of computing units to the second electronic device;
The second electronic equipment determines the 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 the reference relation of the computing factors of the plurality of computing units;
the second electronic device determines calculation map data of the target policy based on the association information, wherein the calculation map data takes the plurality of calculation units as nodes and the reference relationship as an edge;
The second electronic device determines a calculation plan of the target policy based on the calculation map 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 relation.
According to the processing method for the target policy, provided by the embodiment of the application, a configurator is not required to decompose and configure the computation logic of each policy, and the calculation sequence of the decomposed computation units is arranged and called, so that the system can automatically determine the configuration data of each computation unit according to the input target policy, and make a computation plan according to the configuration data, the configuration complexity of the configurator to configure the execution logic can be reduced, and the configuration efficiency of the computation execution logic of the system is improved. In addition, compared with the scheduling call of the user to the calculation sequence of each calculation unit, the embodiment of the application can quickly and accurately generate the calculation plan and has lower requirement on the configuration capability of configuration personnel.
In a possible implementation manner of the first aspect, the calculation factors include an input calculation factor and an output calculation factor;
The second electronic device determining association information of the plurality of computing units according to the configuration data of the plurality of computing units, including:
The second electronic equipment determines input calculation factors and output calculation factors of the calculation units according to 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 calculation map data based on the reference relation in the association information and the output calculation factors of the calculation units.
In a possible implementation manner of the first aspect, the method further includes:
and the second electronic equipment generates check information when judging that cyclic reference occurs among part of the computing units in the plurality of computing units based on the association information.
In a possible implementation manner of the first aspect, the method further includes:
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 map data of the target 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 map data and the verification information to the first electronic device;
The first electronic device displays the calculation map data and the verification information to the user.
It can be appreciated that outputting and displaying the calculation graph data and the verification information based on the query operation of the user can help the configurator to better understand the calculation logic of the policy, and is convenient for the configurator to check and modify the configuration data of each calculation unit.
In a possible implementation manner of the first aspect, the determining, by the second electronic device, the calculation plan of the target policy based on the calculation map data includes:
The second electronic device analyzes the calculation map data, determines child node units and root node units in the calculation map data, wherein the calculation units with zero numbers of calculation units corresponding to output calculation factors cited in the plurality of calculation units are root node calculation units, and the calculation depth of the root node calculation units is zero;
Determining the number of edges with the largest number between the child node computing units and the root node computing units corresponding to the child node computing units as the computing depth of the child node computing units;
The second electronic device determines the calculation plan according to the calculation depth of each calculation unit and the reference relation in the calculation map data.
In a possible implementation manner of the first aspect, the 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.
In a possible implementation manner of the first aspect, 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.
In a possible implementation manner of the first aspect, the second electronic device executes the computing plan based on the target policy data and configuration data of the plurality of computing units, and further includes:
The second electronic device executes the calculation plan based on the target policy data and 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 calculation sequences of each calculation unit.
In a possible implementation manner of the first aspect, the method further includes:
the first electronic device obtains 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 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 computational 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 map data, wherein the calculation process map data takes configuration data and corresponding values of each calculation unit as nodes and takes a 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, applied to a system including a third electronic device, including:
Determining a plurality of computing units associated with a target policy, and configuration data for the plurality of computing units, wherein the plurality of computing units are used for calculating the premium of the target policy in a combined manner, and the configuration data comprises computing factors of the computing units, data object types corresponding to the computing factors and computing rules;
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 calculation map data of the target policy based on the association information, wherein the calculation map data takes the plurality of calculation units as nodes and the reference relationship as an edge;
And determining a calculation plan of the target policy based on the calculation map data, wherein the calculation plan is used for describing the execution sequence of the plurality of calculation units for processing the target policy and the reference relation.
In a third aspect, an embodiment of the present application provides an electronic device, one or more processors, one or more memories, and one or more programs stored in the one or more memories, where the one or more programs, when executed by the one or more processors, cause the electronic device to perform the above-described processing method for a target policy.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where instructions are stored, which when executed on a computer, cause the computer to perform the above-mentioned processing method for a target policy.
In a fifth aspect, embodiments of the present application provide a computer program product comprising instructions that, when executed, cause a computer to perform the above-described processing method for a target policy.
Detailed Description
The present invention is 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 in detail. The present invention will be fully understood by those skilled in the art without the details described herein. Well-known methods, procedures, flows, components and circuits have not been described in detail so as not to obscure the nature of the invention.
Moreover, those of ordinary skill in the art will appreciate that the drawings are provided herein for illustrative purposes and that the drawings are not necessarily drawn to scale.
Unless the context clearly requires otherwise, the words "comprise," comprising, "and the like throughout the specification are to be construed as including, rather than being exclusive or exhaustive, that is to say, as" including but not limited to.
In the description of the present invention, it should 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. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Before describing the solution in the present application, in order to facilitate understanding of the solution in the present application, an application scenario of the present application is described below with reference to fig. 1.
Fig. 1 is a schematic diagram of an application scenario for performing premium calculation based on a plurality of calculation units according to an embodiment of the present application.
As shown in fig. 1, a user 1 provides a plurality of configured computing units to an application module 101 of a premium system 100, and the application module 101 receives the plurality of computing units and transmits the same to a computing engine 102 for computing logic configuration. The application module 101 may also receive policy data from the user 2. The calculation engine 102 may configure a calculation plan of the 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 calculated result and the configured calculation plan to the application module 101, where the application module 101 performs subsequent processing according to the returned calculation result, or presents the calculation result and the calculation logic to the user 1.
It is understood that the computing logic may include, but is not limited to, computing processes, computing rules, computing formulas, etc., as computing-related elements required by the premium system 100 to implement business functions. Specifically, some basic computational logic such as computational expressions, spread functions, variables, filters, decision trees, and the like. The calculation logic generally comprises data parameters, and calculates according to assignment of the data parameters to obtain a specific calculation logic result.
It can be understood that the calculating unit is a unit which is obtained by splitting the calculating logic according to the calculating logic of the premium and a preset calculating logic splitting method by the user 1 and can realize a part of the calculating process.
It will be appreciated that the user 1 may be a system configurator, i.e. a person managing the premium system 100 and related to the configuration, or a business person, i.e. a person related to a business familiar with the software project. The 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 in a variety of manners, and may include, but not limited to, obtaining in an interface manner, obtaining in a file form, obtaining according to a user selection, and so on. For example, in the computing logic configuration stage, 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, 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 store a plurality of computing units in a data file in advance, submit the data file to the application module 101, the application module 101 obtains a plurality of computing units by analyzing the data file, and the application module 101 may provide a plurality of preset computing unit templates to the user 1 and use the computing unit template selected by the user 1 or the modified computing unit template as a used computing unit. The same is true in the premium calculation stage, and will not be described in detail here.
It is understood that the premium system 100 may be applied to electronic devices including, but not limited to, cell phones (including folding screen cell phones), tablet computers, laptop computers, desktop computers, servers, wearable devices, head mounted displays, mobile email devices, car set devices, portable gaming devices, portable music players, reader devices, televisions with one or more processors embedded therein or coupled thereto, and the like.
As described in the foregoing background, the present decomposing and arranging of premium computing logic depends on the operation and execution of the configurator, and in order to solve the problem of complicated configuration, management and modification of the computing engine in the insurance computing scene, the present application provides a processing method for the target policy.
The processing method for the target policy provided by the embodiment of the application comprises the steps that the calculation engine 102 determines the dependency relationship of each calculation unit based on the obtained 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 a data object of the computing unit, a computing factor and a computing rule, and the data object may include each policy element and a corresponding data object type thereof. Further, in the premium calculation stage, the calculation engine 102 may execute a calculation plan based on the acquired policy data and the corresponding relationship between the preset policy element and the data object, and return the obtained calculation result to the application module 101 for data processing or display. The calculation engine 102 may also return calculation map data generated based on the dependency relationship of each calculation unit to the application module 101 for output display.
According to the processing method for the target policy, provided by the embodiment of the application, a configurator is not required to decompose the computation logic of each policy, and the computation sequence of the decomposed computation units is arranged and called, the configurator only needs to input the configuration data of each computation unit, the computation engine 102 can automatically determine the computation sequence of each computation unit based on the configuration data of each computation unit, a computation plan is made, the configuration complexity of the configurator for configuring the execution logic can be reduced, and the configuration efficiency of the execution logic of the computation engine 102 is improved. In addition, compared with the scheduling call of the user to the calculation sequence of each calculation unit, the embodiment of the application can quickly and accurately generate the calculation plan and has lower requirement on the configuration capability of configuration personnel.
In addition, the calculation graph data generated based on the dependency relationship of each calculation unit is output and displayed, so that configuration personnel can be helped to better understand the calculation logic of the policy, and the configuration personnel 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 of each computing unit, improving the performance of the computing engine 102.
In some embodiments, the computing engine 102 may store the computing process data generated during execution of the computing plan and may represent the computing process data in a graphical form. The 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 configuration efficiency and quality are improved.
In some embodiments, policy data is input to the compute engine 102 in a tree data structure, such as that 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 an attribute field of a policy element. The data object types may include policy, target, liability, secondary liability, among others. The policy element corresponding to the policy may be, for example, an effective date, an expiration date, etc., the policy element corresponding to the target may be, for example, gender, birth date, occupation, etc., the policy element corresponding to the responsibility and the secondary responsibility may include various insurance amounts, for example, for a vehicle policy, the responsibility and the secondary responsibility may be, for example, a vehicle loss insurance amount, a third party responsibility insurance amount, a glass breaking insurance amount, etc. 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, the corresponding policy element is the policy element numbered 201 in the product definition of the policy, for example, for the data object "secondary responsibility 203001" in FIG. 2, it indicates that the data object type is secondary responsibility, and the corresponding policy element is the policy element numbered 203001 in the product definition of the policy.
In some embodiments, different policy types may correspond to different compute engines 102. For example, an application programming interface (Application Programming Interface, API) of the compute engine 102 corresponding to the policy type may be invoked based on the policy type of the policy, enabling invocation of the compute engine 102 corresponding to the policy type. In some embodiments, different policy types may correspond to the same computing engine 102, and the computing engine 102 may include multiple computing numbers, each of which may correspond to the generation of a computing plan for one policy and premium computations. The application is not limited in this regard.
It will be appreciated that in some embodiments, the method provided by embodiments of the present application may be applied to an electronic device 300 that includes a server, such as the computing engine 102 of fig. 1, and in particular, may generate and execute a computing plan by the computing engine. In other embodiments, the method provided by the embodiments of the present application may be applied to an electronic device 300 including a server, a client, and 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 the 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, etc., and the client may generate calculation map data according to the input configuration data and send the calculation map data and the policy data to the server, and the server may generate a calculation plan based on the calculation map data and execute the calculation plan based on the policy data, and then return the calculation result to the client. In other embodiments, the client may be configured to obtain only configuration data and policy data, and the server may be configured to generate and execute a computing plan. The application is not limited in this regard.
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 input data of the user 1 and the user 2 may be applied to the same electronic device or may be applied to different electronic devices. The application is not limited in this regard.
Before describing the processing method for the target policy provided by 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 of 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 embodiment shown in fig. 3, an electronic device 300 may include one or more processors 301, system control logic 302 coupled to at least one of the processors 301, a system Memory 303 coupled to the system control logic 302, a Non-Volatile Memory (NVM) 304 coupled to the system control logic 302, and a network interface 306 coupled to the system control logic 302.
In some embodiments, processor 301 may include one or more single-core or multi-core processors. In some embodiments, processor 301 may include any combination of general-purpose and special-purpose processors (e.g., graphics processor, application processor, baseband processor, etc.). In embodiments where electronic device 300 employs an enhanced Node B (eNB) or radio access network (Radio Access Network, RAN) controller, processor 301 may be configured to perform various conforming 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 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.
In some embodiments, system control logic 302 may include one or more memory controllers to provide an interface to system memory 303. The system memory 303 may be used to load and store data and/or instructions. For example, the system memory 303 may load instructions for resolving computation logic in the embodiment of the present application, and may also store input data, configuration data, and the like.
The system memory 303 of the electronic device 300 may include any suitable volatile memory in some embodiments, such as a suitable dynamic random access memory (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 nonvolatile memory, such as flash memory, and/or any suitable nonvolatile storage device, such as at least one of a hard disk drive (HARD DISK DRIVE, HDD), compact Disc (CD) drive, digital versatile Disc (DIGITAL VERSATILE DISC, DVD) drive. In an embodiment of the present application, NVM memory 304 may be used to store input data and configuration data acquired by an application module.
NVM memory 304 may include a portion of a memory resource on the device in which electronic apparatus 300 is installed, or it may be accessed by, but not necessarily a part of, the apparatus. 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 include temporary 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 as shown in fig. 3. In some embodiments, instructions 305, hardware, firmware, and/or software components thereof may additionally/alternatively be disposed in system control logic 302, network interface 306, and/or 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 which, when executed by at least one of processor 301, implement a method as shown in the method embodiments. In an embodiment of the present application, the network interface 306 may be configured 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 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 of the System control logic 302 to form a System On Chip (SOC).
Electronic device 300 may further include 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, and a peripheral component interface designed to enable peripheral components to also interact with the electronic device 300.
It will be appreciated that the configuration illustrated in fig. 3 does not constitute a particular limitation of the electronic device 300. In other embodiments of the application, electronic device 300 may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware or software, or a combination of software and hardware.
The following describes a processing method for a target policy provided by the embodiment of the present application with an implementation subject as a client in conjunction with fig. 4.
Fig. 4 is a schematic flow chart of a processing method for a target policy according to an embodiment of the present application. It will be appreciated that the execution subject of the process may be any electronic device that includes a client, which may be the application module 101.
As shown in fig. 4, the method includes:
And 401, acquiring configuration data of a plurality of computing units corresponding to the target policy.
It will be appreciated that the configuration data for each computing unit may be the basic information characterizing each computing unit of the computing logic, and may include the computing factors, computing rules, and applicable data objects for the computing unit.
It is to be appreciated that the data object can include attributes of the policy element and its corresponding data object type. The data object types are divided in advance according to the purposes of a plurality of policy elements participating in the premium calculation logic in the product definition of the target policy by the configurator. For example, in FIG. 2, the data object types may include policy, target, responsibility, secondary responsibility. The policy includes policy elements such as the effective date, expiration date, etc. of the policy. The target may include a policy element in the target policy that characterizes the essential information of the insured life, such as the insured life's gender, date of birth, occupation, etc. The responsibility or secondary responsibility may include policy elements representing insurance amounts in the target policy, e.g., for a car insurance policy, the responsibility or secondary responsibility may include policy elements such as a car loss insurance amount, a third party responsibility insurance amount, a glass break insurance amount, etc.
In some embodiments, the attributes of the policy element of the data object may be represented by attribute fields of the policy element. For example, "insurance sum" may be represented as "total_si" and "insurance sum" may also be represented as other attribute fields, as the application is not limited in this regard. In other embodiments, the attributes of the policy elements of the data object may also be identified by the corresponding encodings of the policy elements in the product definition of the target policy, such as the data object "responsibilities 201" in FIG. 2, where "responsibilities" represent the data object type and "201" represent the encodings of the policy elements in the product definition of the target policy.
It will be appreciated that the calculation factors, i.e. the identity of the policy element or elements, may include an input calculation factor that is input to the calculation unit when the calculation unit performs the calculation, and an output calculation factor that is output when the calculation unit completes the calculation.
It is understood that the calculation rule may include a calculation formula, a calculation function, a calculation condition, or the like for the calculation factor. For example, for a calculation unit for calculating the risk hair premium, the calculation rule includes that the risk Mao Baofei =standard year premium is a guaranteed period rate, and if the guaranteed time is less than 6 months, the guaranteed period rate adopts a short period rate. Wherein each computation factor in the computation rule may be represented by a computation factor identification.
In some embodiments, different policies correspond to different computing units. For example, the policy number may be utilized as an identification of the policy, and different policy numbers may correspond to different computing units. Wherein different computing units may include different numbers of computing units, different configuration data of at least one of the computing units, and the application is not limited in this regard.
In other embodiments, different types of policies correspond to different computing units. Wherein the different types may be e.g. different insured persons, the policy of the insured object. Different types of insurance policies correspond to different computing units, and may, for example, correspond to different computing units for vehicle insurance policies, personal accident insurance, and the like. Therefore, when acquiring the configuration data of the computing unit of the target policy, the policy type of the target policy may be determined first, and then the configuration data of a plurality of computing units corresponding to the policy type may be acquired according to the determined policy type, or the calculation plan generated based on the configuration data may be directly acquired.
In some embodiments, the obtained configuration data may be the computing unit associated with the target policy and its configuration data determined by the application module 101 among the computing units of the plurality of policies and the corresponding configuration data.
And 402, determining the association information of a plurality of computing units according to the configuration data of each computing unit.
Specifically, the association information of the plurality of computing units may be determined based on the computing factors in the configuration data of each computing unit.
It is understood that the association information may be understood as a reference relationship in which each computing unit needs to refer to the output of the computing factor by the other computing unit when performing the computation. For example, the input calculation factor a in the calculation unit a refers to the value of the output calculation factor B calculated by the calculation unit B, and then the association information characterizing that the calculation unit a refers to the output calculation factor B is generated.
It will be appreciated that the generated association information may be stored in the system memory 303 of the electronic device 300 for use in subsequent generation of computational graphs and computational plans. 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 factors in the configuration data may be represented by calculation factor identifications, and the step 402 of determining the association information based on the calculation factors of the calculation units may specifically include determining an input calculation factor identification and an input calculation factor identification of each calculation unit, and for any two calculation units a and B, determining whether the input calculation factor identifications of the calculation unit a match the output calculation factor identifications of the calculation unit B, and if so, indicating that the calculation unit a references the output calculation factors of the calculation unit B.
And 403, generating calculation map data based on the association information of the plurality of calculation units and the configuration data.
Specifically, the calculation map data may be generated based on the association information and the calculation factors in the configuration data. It will be appreciated that the computation factors in the configuration data may include output computation factors of the computation unit, and the association information may include output computation factors referenced by the computation unit.
It is understood that the application module 101 may determine the computation graph data characterizing the dependency relationship between the plurality of computation units based on the output computation factors of the computation units, the output computation factors of the other computation units referenced.
It is to be understood that the calculation map data may be a description of map data having a calculation unit as a node and a reference relationship of a calculation factor as an edge, and the calculation map data may also be map data having a calculation unit as a node and a reference relationship of a calculation factor as an edge, and stored in the form of a vector, for example, map data in the form of a mind map. I.e. the generated computational graph data may be characterized as image data or as text data, as the application is not limited in this regard.
Referring now to fig. 5 and 6, calculation map data of one embodiment of the present application will be described.
Fig. 5 is a schematic structural diagram of configuration data of a computing unit according to an embodiment of the present application.
Fig. 6 is a schematic diagram of a calculation map according to an embodiment of the present application.
As shown in fig. 5, the calculation unit in the configuration data may be named according to "data object type_output calculation factor_ (calculation rule, applicable condition, policy element identification, or the like)". For example, for a calculation unit "liability_standard year premium_code 201", the data object type of the calculation unit is liability, the output calculation factor is standard year premium, and the code of the output standard year premium is 201. For example, for "policy_risk Mao Baofei _calculation and accumulation" of the calculation unit, the data object type of the calculation unit is policy, the output calculation factor is risk Mao Baofei, the calculation rule is calculation and accumulation, i.e. the calculation rule is calculated according to a preset calculation formula, and the calculation result is accumulated.
The computation factors in the configuration data may include input computation factors and output computation factors of the respective computation units. For example. The calculation factors of the calculation unit 'policy_risk Mao Baofei _calculation and accumulation' can comprise the input of standard annual premium and guaranteed period rate after accumulation of the calculation factors and the output of the calculation factor risk Mao Baofei.
It will be appreciated that the configuration data shown in fig. 5 is an example of an embodiment of the present application, and in some embodiments, more or fewer computing units, computing factors, configuration data types, etc. may be included in the configuration data than shown in fig. 5, which is not limiting in this respect.
It is understood that the application module 101 may generate association information of a 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 calculation map data is represented in the form of a mind map, and the dotted arrows therein may represent the reference relationships of the respective calculation units, for example. The direction of the arrow represents the source of the referenced computation factor.
For example, for the calculation unit "responsible_standard annual premium_code 201" having an input calculation factor of insurance amount and an output calculation factor of standard annual premium_code 201, the calculation unit may refer to the output calculation factor insurance amount of the calculation unit "policy_insurance amount_accumulation" and the calculation unit "policy_post-accumulation standard annual premium_accumulation" may refer to the output calculation factor standard annual premium_code 201 of the calculation unit.
It will be appreciated that in other embodiments, the computational graph data may be in a representation other than that shown in fig. 6, such as in other graphical representations, textual representations, tabular representations, etc., other than those shown in fig. 6, to which the application is not limited.
In some embodiments, after the application module 101 generates the computation graph data, it may further determine whether there is a loop reference between the multiple computation units based on the reference relationship of each computation unit in the computation graph data. If the judgment result is yes, generating check information.
It will be appreciated that a loop reference occurs closed-loop for a reference relationship between parts of the computing units, e.g. computing unit a references computing unit B, computing unit B references computing unit C, which in turn references computing unit a. The loop reference may generate a calculation closed loop, and the application module 101 may generate a calculation plan, and may not stop, so that the calculation engine 102 cannot output the premium calculation result.
It is understood that the verification information may characterize the inclusion of circular references in the computational graph data. In some embodiments, the check information may be "Error," i.e., the application module 101 may report an Error when detecting the loop reference.
And 404, determining a calculation plan corresponding to the target policy based on the calculation map data.
It is understood that a computing plan may include an order of execution of a plurality of computing units, and a reference relationship of the plurality of computing units. The execution sequence may include a calculation unit in which a part of the plurality of calculation units may be executed in parallel, an execution sequence of the plurality of calculation 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 application module 101 may determine that the computing units with the number of computing units corresponding to the output computing factors cited in each computing unit being zero 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 units is zero, and the application module 101 may determine that other computing units other than the root node computing units are child node computing units, and may determine the computing depth of the child node computing units according to the number of edges between the child node computing units and the root node computing units corresponding thereto.
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 the "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 computing unit "policy_adjusted Mao Baofei _allocated" is a child node computing unit, where the corresponding root node computing unit includes "policy_insurance amount_accumulation", "target_target rate X", and "policy_guarantee period rate", and the maximum number of sides included between the "policy_adjusted Mao Baofei _allocated" and the corresponding root node computing unit is 5, and the computing depth of the child node computing unit "policy_adjusted Mao Baofei _allocated" is 5.
Further, after determining the calculation depths of the respective calculation units, the execution order of the respective calculation units may be determined according to the order in which the calculation depths of the respective calculation units are from small to large. Partial calculation units of the same calculation depth may be executed in parallel.
According to the processing method for the target policy, provided by the embodiment of the application, a configurator is not required to decompose the calculation logic of each policy, and the arrangement and the call are carried out on 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, a calculation plan is made, 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 is improved. In addition, compared with the scheduling call of the user to the calculation sequence of each calculation unit, the embodiment of the application can quickly and accurately generate the calculation plan and has lower requirement on the configuration capability of configuration personnel.
In addition, the calculation graph data generated based on the dependency relationship of each calculation unit is output and displayed, so that configuration personnel can be helped to better understand the calculation logic of the policy, and the configuration personnel can conveniently check and modify the configuration data of each calculation unit.
The following describes a processing method for a target policy according to an embodiment of the present application with an implementation subject as a client and a server with reference to fig. 7.
Fig. 7 is a schematic flow chart of 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 and a server. Specifically, the description is given taking a client as an application module 101 and a server as a calculation engine 102 as an example.
As shown in fig. 7, the method includes:
701 the application module 101 obtains configuration data of a plurality of computing units of a target policy. Step 701 is the same as step 401 in fig. 4, and will not be described herein.
The application module 101 determines association information for a plurality of computing units based on the configuration data 702. Step 702 is the same as step 402 in fig. 4, and will not be described herein.
The application module 101 generates calculation map data of the target policy according to the association information and the configuration data of the plurality of calculation units 703. Step 703 is the same as step 403 in fig. 4, and will not be described herein.
In some embodiments, the application module 101 may output display computational graph data in a form similar to a mind map at a display interface in response to a computational graph query request by a user. Further, the application module 101 may further obtain graph data adjustment information for adjusting the computation graph data by a configurator, or configuration data adjustment information for adjusting the configuration data of the computation unit, to obtain computation graph data more conforming to the computation logic.
704, The application module 101 sends the computational graph data and configuration data for each computational unit to the computational engine 102.
It will be appreciated that the calculation map data sent by the application module 101 may be reference relation data of calculation factors of each calculation unit, or may be imaging data representing the reference relation. The configuration data may include calculation factors, calculation rules, and applicable data objects for each calculation unit.
It will be appreciated that in some embodiments, step 704 may specifically include the application module 101 may call the calculation engine 102 via an API, trigger a premium operation of the target policy, and upon call of the calculation engine 102, may pass calculation map data characterizing matching conditions (i.e., reference relationships) of multiple calculation units, as well as configuration data including data objects.
The computing engine 102 generates a computing plan and corresponding computing number for the target policy from the received computing map data 705. The process of generating the calculation plan in 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 the target policy data.
It is understood that the target policy data may include policy elements related to premium calculation and values corresponding to the policy elements in the target policy. In some embodiments, the target policy data may be represented as attribute fields or encodings defined by individual policy elements in the product structure of the target policy, as well as corresponding values.
In some embodiments, the target policy data obtained by the application module 101 may be tree structure data, such as the target policy data shown in fig. 2. The target policy data may include a plurality of data objects, each of which 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 obtained 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 plurality of extracted policy elements and the corresponding relationship between the preset policy elements and the data object types, and send the tree structure data as 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 related to premium calculation, and may generate tree structure data as shown in fig. 2 according to the plurality of policy elements acquired and the corresponding relationship between the preset policy elements and the data object types, and send the tree structure data as input of premium calculation to the calculation engine 102 for premium calculation.
It will be appreciated that the target policy data in step 706 may be any form of data, as the application is not limited in this regard.
707 The application module 101 sends the target policy data to the calculation engine 102.
The computing engine 102 executes the computing plan based on the received target policy data and configuration data for the plurality of computing units to obtain computing process data and computing results 708.
The computing process data may be a data object matched by each computing unit and a generated computing result in a computing process, and specifically may include a data object input to each computing unit, a value of the data object, a computing output factor generated by the computing unit executing a computing rule, and a corresponding value. Wherein 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 can comprise the execution process of a calculation plan, data objects matched by all calculation units, values of all calculation factors, reference relations of the calculation factors and the like. Specifically, the calculation process data and the calculation result may be characterized as calculation process map data that is bounded by each of the calculation units, the configuration data of each of the calculation units, and the corresponding values, and the data flow direction of each of the calculation units. For example, the computing process data may be displayed in a form similar to a computing graph as in FIG. 6 on a display interface, and the user may click on each computing unit to view the data that the computing unit matches or generates during the computing process.
In some embodiments, where the data objects are characterized as tree-structured data as shown in FIG. 2, the calculation engine 102 performs the process of calculating the plan, each calculation unit may take a value corresponding to the corresponding data from the tree-structured data according to the applicable data object type, and assign the value as a calculation factor for calculating the DNA element. When the computing unit needs to refer to the output computing factors of other computing units, the process of the computing factor value may specifically include:
The computing unit determines that the input computing factor needs to refer to the output computing factors of other computing units, and then 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 judging result is the same, the computing unit can take the value from the output computing factors included in the data object type suitable for the computing unit, otherwise, the matching is needed from the tree-shaped data structure of the suitable data object type along the direction from the father node to the root node until the matching is carried out on the output computing factors quoted by the computing unit, and the value is taken from the matched output computing factors.
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, can perform further data processing or data storage based on the service management requirement, and can output and display the received calculation number, calculation process data and calculation result.
In some embodiments, the application module 101 may obtain a query operation of the user, where the query operation may include a calculation number to be queried, and the application module 101 may display the calculation number, the calculation process data, and the calculation result in response to the query operation of the user.
In some embodiments, the computing engine 102 may generate a data file characterizing the correspondence of the computing numbers, computing process data, and computing results, storing the computing numbers, computing process data, and computing results in different database tables, respectively. The computing engine 102 may send a data file characterizing the correspondence of the computing number, the computing process data, the computing results to the application module 102. Further, when the application module 101 obtains the query operation of the user, it may determine, according to the data file, the calculation process data and the calculation result that match the calculation number to be queried, and then query the database for the matched calculation process data and calculation result, and output and display the calculation process data and the calculation result together with the calculation number.
In other embodiments, the computing engine 102 may store the computing numbers, the computing process data, the computing results in the same database table, and the application module 101 may query the corresponding computing process data and computing results based on the computing numbers.
The processing method for the target policy provided by the embodiment of the application can display the data or the calculation graph generated when the calculation plan is executed to the user in a graphical form, is convenient for the user to understand and adjust the calculation plan, and can improve the configuration efficiency of the calculation plan. Moreover, the calculation engine 102 can generate and optimize a calculation plan based on the calculation map data, manual configuration by a user is not needed, and the configuration efficiency and quality are higher.
Embodiments of the disclosed mechanisms may be implemented in hardware, software, firmware, or a combination of these implementations. Embodiments of the application may be implemented as a computer program or program code that is executed on a programmable system 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 the 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. Whereas for languages such as c++, java, the storage will be transformed based on some differences in the application of the data processing method in embodiments of the present application, those skilled in the art can transform based on a specific high-level language without departing from the scope of embodiments 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 over a network or through 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 tangible machine-readable memory for transmitting information (e.g., carrier waves, infrared signal digital signals, etc.) in an electrical, optical, acoustical or other form of propagated signal using the internet. 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 structural or methodological features may be shown in a particular arrangement and/or order. However, it should be understood that such a particular arrangement and/or ordering may not be required. Rather, in some embodiments, these features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of structural or methodological features in a particular figure is not meant to imply that such features are 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 present application, each unit/module mentioned in each device is a logic unit/module, and in physical terms, one logic unit/module may be one physical unit/module, or may be a part of one physical unit/module, or may be implemented by a combination of multiple physical units/modules, where the physical implementation manner of the logic unit/module itself is not the most important, and the combination of functions implemented by the logic unit/module is only a key for solving the technical problem posed by the present application. Furthermore, in order to highlight the innovative part of the present application, the above-described device embodiments of the present application do not introduce units/modules that are less closely related to solving the technical problems posed by the present application, which does not indicate that the above-described device embodiments do not have other units/modules.
It should be noted that in the examples and descriptions of this 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. Moreover, 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, an element defined by the phrase "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
While the application has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the application.