Business product actuarial calculation method based on distributed parallel computing framework

Abstract

The invention relates to a business product actuarial calculation method based on a distributed parallel computing framework. The method comprises the following steps: submitting a newly added attribute value determination task to an attribute value determination task queue through a compilation type application program; the newly added attribute value determination task carries an attribute value determination model of the business product; under the condition that the number of the attribute value determination tasks in the execution process in the attribute value determination task queue is smaller than the number of the servers, an idle server is occupied through a compilation type application program; calling an interpretation type application program through a compiling type application program, determining a model point in the attribute value determination model, splitting the attribute value determination task in the attribute value determination task queue into a plurality of parallel subtasks according to the model point, and distributing the plurality of parallel subtasks to an idle server for parallel processing, and obtaining an attribute value determination result of the business product. By adopting the method, the business product attribute value determination efficiency can be improved, and the calculation delay is reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for actuarial calculation of business products based on a distributed parallel computing framework. Background Art

[0002] With increasingly stringent regulatory requirements, increasingly fierce competition in the financial market, and increasingly diversified customer protection needs, the number of business product developments has gradually increased. Determining the attribute values ​​of business products is a complex and critical business link, involving a large amount of data processing and calculation, especially in the context of product design, risk assessment, market competition and other multi-dimensional contexts. The accuracy and calculation speed of attribute value determination directly affect the market competitiveness and customer satisfaction of financial enterprises.

[0003] Traditional technology mainly relies on manually written VBA (Visual Basic for Applications, an event-driven programming language) programs to traverse attribute values ​​to determine the required model points and drive Excel to serially calculate attribute values.

[0004] However, if we completely rely on Excel software to determine the attribute value, it takes three to five hours for a regular product to complete the attribute value determination, and more than ten hours for a slightly more complex product to complete the attribute value determination. It is difficult to meet the timeliness requirements, and it may cause calculation delays or blockages, affecting business efficiency. Therefore, how to improve the efficiency of business product attribute value determination and reduce calculation delays has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] Based on this, it is necessary to provide a business product actuarial measurement method, device, computer equipment and storage medium based on a distributed parallel computing framework, which can improve the efficiency of determining business product attribute values ​​and reduce calculation delays in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for actuarial calculation of business products based on a distributed parallel computing framework, comprising:

[0007] Submitting a newly added attribute value determination task to an attribute value determination task queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product;

[0008] When the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers, an idle server is occupied by a compiled application;

[0009] The compiled application calls the interpreted application to determine the model point in the attribute value determination model, and splits the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point. The multiple parallel subtasks are allocated to idle servers for parallel processing to obtain the attribute value determination result of the business product.

[0010] In one embodiment, calling the interpreted application program through the compiled application program, determining the model point in the attribute value determination model includes:

[0011] passing the attribute value determination model to the interpreted application via the compiled application;

[0012] An interpretive application is called to parse the attribute value determination model according to the full model point enumeration template to obtain multi-dimensional model points of the attribute value determination model; wherein the multi-dimensional model points include but are not limited to gender and age.

[0013] In one embodiment, multiple parallel subtasks are assigned to idle servers for parallel processing, and the attribute value determination result of the business product is obtained, including:

[0014] Through the distributed parallel computing architecture, multiple parallel subtasks are assigned to idle servers for parallel processing to obtain the subtask results of each parallel subtask;

[0015] The subtask results of multiple parallel subtasks are summarized, and the summary results are output according to a preset format to obtain the attribute value determination result of the business product.

[0016] In one embodiment, the summary result is output according to a preset format, and the attribute value determination result of the business product is obtained, including:

[0017] Output the summary results according to a preset format, and perform consistency check on the summary results in the preset format;

[0018] After passing the consistency check, a compressed file of the attribute value determination result of the business product is generated.

[0019] In one embodiment, the method further comprises:

[0020] When the number of attribute value determination tasks in the execution process in the attribute value determination task queue is greater than or equal to the number of servers, polling the latest status of the attribute value determination tasks in the execution process;

[0021] When the latest status is task completion, obtaining a compressed file of the attribute value determination result of the attribute value determination task in the execution process;

[0022] The compressed file of the attribute value determination result is uploaded to the file management platform, and the task status of the attribute value determination task in the execution process is updated, and the server status identification of the server corresponding to the attribute value determination task in the execution process is updated, and the attribute value determination task with the latest status of task completion is removed from the attribute value determination task queue.

[0023] In one embodiment, the method further comprises:

[0024] In the case where the business product is of a preset product type, in the process of allocating multiple parallel subtasks to idle servers for parallel processing, planning and solving are performed on target resource data in the multiple parallel subtasks.

[0025] In one embodiment, the method further comprises:

[0026] Establish a channel between product development data and product launch data;

[0027] The attribute value determination results of the business product are transferred to the product online end through the channel.

[0028] In a second aspect, the present application also provides a business product actuarial calculation device based on a distributed parallel computing framework, comprising:

[0029] A task submission module is used to submit a newly added attribute value determination task to an attribute value determination task queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product;

[0030] A server resource determination module, used for occupying idle servers through compiled applications when the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers;

[0031] The attribute value determination module is used to call the interpreted application through the compiled application, determine the model point in the attribute value determination model, and split the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point, and allocate the multiple parallel subtasks to the idle servers for parallel processing to obtain the attribute value determination result of the business product.

[0032] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Submitting a newly added attribute value determination task to an attribute value determination task queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product;

[0034] When the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers, an idle server is occupied by a compiled application;

[0035] The compiled application calls the interpreted application to determine the model point in the attribute value determination model, and splits the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point. The multiple parallel subtasks are allocated to idle servers for parallel processing to obtain the attribute value determination result of the business product.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Submitting a newly added attribute value determination task to an attribute value determination task queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product;

[0038] When the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers, an idle server is occupied by a compiled application;

[0039] The compiled application calls the interpreted application to determine the model point in the attribute value determination model, and splits the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point. The multiple parallel subtasks are allocated to idle servers for parallel processing to obtain the attribute value determination result of the business product.

[0040] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0041] Submitting a newly added attribute value determination task to an attribute value determination task queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product;

[0042] When the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers, an idle server is occupied by a compiled application;

[0043] The compiled application calls the interpreted application to determine the model point in the attribute value determination model, and splits the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point. The multiple parallel subtasks are allocated to idle servers for parallel processing to obtain the attribute value determination result of the business product.

[0044] The above-mentioned business product actuarial measurement method, device, computer equipment, storage medium and computer program product based on the distributed parallel computing framework, the newly added attribute value determination task is received by the terminal at the product front-end interface. By introducing distributed parallel computing technology, the traditional method of manually writing VBA programs to drive Excel serial calculations is transformed into an automatic program drive from page to compiled application to interpretive application and then to Excel. Simple task queuing and queue scheduling are used to fully utilize server performance, which can effectively improve the calculation speed of business product attribute value determination, improve the efficiency of business product attribute value determination, and reduce calculation delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 It is an application environment diagram of a business product actuarial calculation method based on a distributed parallel computing framework in one embodiment;

[0047] Figure 2 A schematic diagram of the architecture of a business product actuarial calculation system based on a distributed parallel computing framework in one embodiment;

[0048] Figure 3 A schematic diagram of a process flow of a business product actuarial calculation method based on a distributed parallel computing framework in one embodiment;

[0049] Figure 4 A schematic diagram of a task submission interface in an embodiment;

[0050] Figure 5 is a flowchart of processing steps in a case where there are no idle servers in one embodiment;

[0051] Figure 6 It is a structural block diagram of a business product actuarial calculation device based on a distributed parallel computing framework in one embodiment;

[0052] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] The business product actuarial calculation method based on the distributed parallel computing framework provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the business product actuarial measurement system based on the distributed parallel computing framework includes a terminal 102 and a server 104. The terminal 102 communicates with the server 104 through a network. The server 104 may include multiple servers. Each server can be used as a computing node to determine the attribute value. The terminal 102 uploads the newly added attribute value determination task to the server 104, and the server 104 submits the newly added attribute value determination task to the attribute value determination task queue queue through a compiled application; the newly added attribute value determination task carries the attribute value determination model of the business product. When the number of attribute value determination tasks in the attribute value determination task queue queue that is in the execution process is less than the number of servers, the idle server is occupied by the compiled application. The model point in the attribute value determination model is determined by calling the interpreted application through the compiled application, and the attribute value determination task in the attribute value determination task queue queue is split into multiple parallel subtasks according to the model point, and the multiple parallel subtasks are assigned to the idle servers for parallel processing to obtain the attribute value determination result of the business product. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones and tablet computers. The server end 104 may be implemented by a server cluster consisting of multiple servers.

[0055] In an exemplary embodiment, the architecture diagram of the business product actuarial calculation system based on the distributed parallel computing framework is as follows: Figure 2 As shown, it includes the display layer formed by the front-end interface of the terminal product, the service layer on the server side, the data layer and the system layer. The display layer is implemented based on vue (a JavaScript framework for building user interfaces) to provide the functions of adding new tasks, task management and data management. The service layer is implemented based on the SpringBoot framework, the FastAPI framework and Excel. It calls the python computing service through java to drive Excel to perform parallel calculations to achieve the purpose of accelerating the actuarial calculation of business products. In the data layer, OceanBase is used to define and store the relevant data for attribute value determination. In the system layer, the attribute value determination task is separated from other logics by combining the Linux and Windows systems to maximize the use of server performance and accelerate the attribute value determination process to the greatest extent.

[0056] In an exemplary embodiment, Figure 3 As shown, a business product actuarial calculation method based on a distributed parallel computing framework is provided. Figure 1The server side in the example is used to illustrate, including the following steps 302 to 306. Among them:

[0057] Step 302: submitting the newly added attribute value determination task to the attribute value determination task queuing queue through the compiled application; the newly added attribute value determination task carries the attribute value determination model of the business product.

[0058] Among them, business products refer to financial products whose attribute values ​​are to be determined. Business product attribute values ​​refer to the prices of business products. Business product actuarial calculation refers to pricing of business products. Compiled application refers to Java applications in Linux systems. Attribute value determination model refers to pricing model.

[0059] Optionally, the terminal receives a task submission operation on the product front-end interface, and in response to the task submission operation, displays a task submission interface, such as Figure 4 As shown, the attribute value determination model of the business product is received in the task submission interface, and the measurement data type is received, that is, the selection operation of the pricing file, and the remark data entered by the user can also be obtained to meet the personalized needs in the product development process. For example, the pricing file may include a rate table, a surcharge table, etc. The terminal obtains the trigger operation of the submission task component, generates a new attribute value determination task, and uploads it to the server. The server side performs a format check on the attribute value determination model of the business product carried in the new attribute value determination task through a compiled application. After the check is passed, the new attribute value determination task is submitted to the attribute value determination task queue.

[0060] Optionally, the input and output of the attribute value determination model of the business product are standardized and normalized. Since the attribute value determination between model points of the attribute value determination model of the business product is relatively independent, the input and output of the attribute value determination model can be standardized and normalized, which can achieve isolation of product attribute value determination and parallel operation while maintaining the degree of freedom of the core algorithm of the product attribute value determination model.

[0061] Step 304: When the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers, idle servers are occupied by compiled applications.

[0062] Optionally, the server side obtains the number of attribute value determination tasks in the attribute value determination task queue queue that are in the process of being executed. The attribute value determination tasks in the process of being executed refer to the attribute value determination tasks being processed. The number of attribute value determination tasks in the process of being executed is compared with the number of servers. When the number of attribute value determination tasks in the attribute value determination task queue queue that are in the process of being executed is less than the number of servers, idle servers are occupied by compiled applications, and the server status identifier of the occupied idle servers is updated to in use, and attribute value determination is started.

[0063] Step 306, calling the interpreted application through the compiled application, determining the model point in the attribute value determination model, and splitting the attribute value determination task in the attribute value determination task queue queue into multiple parallel subtasks according to the model point, and assigning the multiple parallel subtasks to idle servers for parallel processing to obtain the attribute value determination result of the business product.

[0064] The interpreted application refers to a Python application in a Windows system. The model point in the attribute value determination model refers to a model point that affects the attribute value determination result.

[0065] Optionally, when the attribute value determination is started, the server-side interpretive application is called to parse the attribute value determination model to obtain the model points in the attribute value determination model. For example, the model points may include insurance period, gender, age, etc. At the same time, the attribute value determination task in the attribute value determination task queue is refined and split according to the complexity and scale of the attribute value determination task through the interpretive application to obtain multiple subtasks that can be executed in parallel, that is, multiple parallel subtasks. Then, through the distributed parallel computing architecture, the computing resources of the idle server are dynamically scheduled to drive Excel to process multiple parallel subtasks in parallel to obtain the attribute value determination result of the business product.

[0066] In the above-mentioned business product actuarial calculation method based on the distributed parallel computing framework, the newly added attribute value determination task is received by the terminal on the product front-end interface. By introducing distributed parallel computing technology, the traditional method of manually writing VBA programs to drive Excel serial calculations is transformed into an automatic program drive from page to compiled application to interpretive application and then to Excel. Simple task queuing and queue scheduling are used to fully utilize server performance, which can effectively improve the calculation speed of business product attribute value determination, improve the efficiency of business product attribute value determination, and reduce calculation delays.

[0067] In an exemplary embodiment, calling an interpreted application through a compiled application to determine the model points in the attribute value determination model includes: passing the attribute value determination model to the interpreted application through the compiled application; calling the interpreted application to parse the attribute value determination model according to a full model point enumeration template to obtain multidimensional model points of the attribute value determination model; wherein the multidimensional model points include but are not limited to gender and age.

[0068] Optionally, when the compiled application starts to determine the attribute value, the operation serial number and the attribute value determination model of the business product are passed to the interpreted application. The full model point enumeration template is obtained through the interpreted application. The full model point enumeration template includes pre-determined multidimensional model points that affect the attribute value determination result. The attribute value determination model is parsed by the interpreted application according to the full model point enumeration template to obtain the multidimensional model points of the attribute value determination model.

[0069] In this embodiment, the multi-dimensional model points of the attribute value determination model are determined by a pre-constructed full model point enumeration template. The multi-dimensional model points are all model points that affect the attribute value determination results, which can improve the accuracy of the attribute value determination results of the business products.

[0070] In an exemplary embodiment, multiple parallel subtasks are assigned to idle servers for parallel processing to obtain attribute value determination results of business products, including: through a distributed parallel computing architecture, multiple parallel subtasks are assigned to idle servers for parallel processing to obtain subtask results of each parallel subtask; the subtask results of multiple parallel subtasks are summarized, and the summarized results are output in a preset format to obtain attribute value determination results of the business product.

[0071] Optionally, through a distributed parallel computing architecture, the computing resources of idle servers are dynamically scheduled. There may be multiple idle servers, and each server may serve as a computing node. Through an interpreted application, multiple parallel subtasks are assigned to different computing nodes for parallel processing to obtain the subtask results of each computing node.

[0072] Furthermore, based on the actuarial principle, the attribute values ​​in determining the result data have a circular dependency, that is, multiple types of result data must be calculated simultaneously in the process of determining the attribute values ​​of a set of model points, so as to realize parallel reading of the result data, reduce repeated calculation processes, and greatly reduce the overall time consumption of pricing calculation.

[0073] After the calculation is completed, the subtask results of multiple computing nodes are summarized, the summary results are stored, and the summary results are output in a preset format. The preset format refers to the result output format determined according to the current work specifications and regulatory filing file format requirements, which can be a formatted table, that is, the summary results are output as a formatted table. For example, the preset format table may include but is not limited to a rate table, a cash value table, a reserve table, etc.

[0074] In this embodiment, through a distributed parallel computing architecture, multiple parallel subtasks are assigned to multiple computing nodes corresponding to idle servers for parallel processing, which effectively solves the problems of low computing efficiency and computing resource bottlenecks in traditional methods. At the same time, by outputting the summary results in a preset format, the output is standardized, which can reduce manual operations, reduce error rates, and effectively improve product data quality.

[0075] Furthermore, the summary results are output in a preset format to obtain the attribute value determination results of the business product, including: outputting the summary results in a preset format, performing a consistency check on the summary results in the preset format; after passing the consistency check, generating a compressed file of the attribute value determination results of the business product.

[0076] After the summary result in the preset format is output, a consistency check is performed on the summary result in the preset format. The consistency check can be to compare the summary result in the preset format with the subtask result of each computing node. After passing the consistency check, a compressed file of the attribute value determination result of the business product is generated. Through the consistency check, the accuracy of the distributed computing results can be ensured.

[0077] In an exemplary embodiment, Figure 5 As shown, the above method also includes processing steps when there is no idle server, including steps 502 to 506. Among them:

[0078] Step 502: When the number of attribute value determination tasks in the execution process in the attribute value determination task queue is greater than or equal to the number of servers, the latest status of the attribute value determination tasks in the execution process is polled.

[0079] Step 504: when the latest status is task completion, obtain a compressed file of the attribute value determination result of the attribute value determination task in the execution process.

[0080] Step 506, upload the compressed file of the attribute value determination result to the file management platform, update the task status of the attribute value determination task in the execution process, update the server status identifier of the server corresponding to the attribute value determination task in the execution process, and remove the attribute value determination task whose latest status is task completion from the attribute value determination task queue.

[0081] Optionally, when the number of attribute value determination tasks in the execution process in the attribute value determination task queuing queue is greater than or equal to the number of servers, it indicates that there are no server resources, that is, there are no idle servers, and the polling task status is updated and the server status is updated.

[0082] The polling task status update and server status update process may include: obtaining the latest status of the attribute value determination task in the execution process. Calling the server-side interface to identify whether the attribute value determination result compressed file of the attribute value determination task in the execution process is generated, and in the case where the attribute value determination result compressed file of the attribute value determination task in the execution process is generated, obtaining the attribute value determination result compressed file of the attribute value determination task in the execution process from the server-side. Uploading the attribute value determination result compressed file to the file management platform, and updating the task status of the attribute value determination task in the execution process, and updating the server status identification of the server corresponding to the attribute value determination task in the execution process, and removing the attribute value determination task whose latest status is task completion from the attribute value determination task queue. Among them, the server corresponding to the attribute value determination task in the execution process refers to the server used to process the attribute value determination task. In this way, the addition, calculation and result acquisition of pricing tasks can be realized.

[0083] In this embodiment, in the absence of server resources, the task status update and server status update process are polled to obtain the attribute value determination result compressed file.

[0084] In an exemplary embodiment, the method further includes: when the business product is of a preset product type, in the process of allocating multiple parallel subtasks to idle servers for parallel processing, planning and solving target resource data in the multiple parallel subtasks.

[0085] Optionally, the preset product type refers to the product type that needs to apply planning and solving, such as special scenario products such as reduced payment. In the case where the business product is of the preset product type, in the process of allocating multiple parallel subtasks to idle servers for parallel processing, the target resource data in the multiple parallel subtasks are planned and solved according to the determining factors and specific solution conditions of the planning and solving process. Among them, the target resource data can be the insurance amount. Through the above method, the reduced payment planning and solving of the insurance amount can be realized, and the planning and solving can be nested in the process of multiple attribute value determination of the model point, so as to achieve the effect of completing the determination and solution of multiple sets of attribute values ​​with one click.

[0086] In an exemplary embodiment, the method further includes: establishing a channel between product development data and product launch data; and transferring the attribute value determination result of the business product to the product launch end through the channel.

[0087] Establishing channels for product development data and R&D product launch data, and realizing the automatic flow of business product attribute value determination results from the business production end to the use end, can reduce manual operations, reduce manual operation costs, reduce error rates, and effectively improve product data quality and product launch efficiency.

[0088] Exemplarily, when the Java application obtains a new task for single product pricing, it performs a format check on the pricing model in the task, and after passing the format check, submits the task to the pricing task queue. When the number of pricing tasks being processed is less than the number of servers, idle servers are occupied and the corresponding server status is updated to be marked as in use. After that, the operation serial number and pricing model file are passed, and the pricing acceleration service is called, that is, the Python application is called to parse the pricing model and generate a full model point. Through the distributed parallel computing architecture, multiple parallel subtasks are assigned to idle servers. Multithreading is enabled to process multiple parallel subtasks in parallel to obtain the subtask results of each parallel subtask. The subtask results of each parallel subtask are summarized, the calculation results are stored, and a formatted table is output. The summary results are verified for consistency to ensure the accuracy of the distributed computing results, and a compressed package of the calculation results is generated.

[0089] When the number of pricing tasks being processed is greater than or equal to the number of servers, the latest status of the tasks being processed is obtained. The server-side interface is called to identify whether the task result compressed file corresponding to the task is generated. If it is generated, the result compressed package is obtained from the server and uploaded to the file management platform, i.e., the content cloud. The task status of the tasks being processed is updated, and the server status identifier of the server corresponding to the tasks being processed is updated. The tasks whose latest status is task completion are dequeued.

[0090] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0091] Based on the same inventive concept, the embodiment of the present application also provides a business product actuarial measurement device based on a distributed parallel computing framework for implementing the above-mentioned business product actuarial measurement method based on a distributed parallel computing framework. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the business product actuarial measurement device based on a distributed parallel computing framework provided below can be found in the above limitations on the business product actuarial measurement method based on a distributed parallel computing framework, and will not be repeated here.

[0092] In an exemplary embodiment, Figure 6 As shown, a business product actuarial calculation device based on a distributed parallel computing framework is provided, including: a task submission module 602, a server resource determination module 604 and an attribute value determination module 606, wherein:

[0093] The task submission module 602 is used to submit a newly added attribute value determination task to the attribute value determination task queuing queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product.

[0094] The server resource determination module 604 is used to occupy idle servers through the compiled application when the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers.

[0095] The attribute value determination module 606 is used to call the interpreted application through the compiled application to determine the model point in the attribute value determination model, and split the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point, and allocate the multiple parallel subtasks to the idle server for parallel processing to obtain the attribute value determination result of the business product.

[0096] In an exemplary embodiment, the attribute value determination module 606 is also used to pass the attribute value determination model to the interpreted application through the compiled application; call the interpreted application, parse the attribute value determination model according to the full model point enumeration template, and obtain the multidimensional model points of the attribute value determination model; wherein the multidimensional model points include but are not limited to gender and age.

[0097] In an exemplary embodiment, the attribute value determination module 606 is also used to distribute multiple parallel subtasks to the idle servers for parallel processing through a distributed parallel computing architecture to obtain subtask results of each parallel subtask; summarize the subtask results of multiple parallel subtasks, and output the summary results in a preset format to obtain the attribute value determination result of the business product.

[0098] In an exemplary embodiment, the attribute value determination module 606 is further used to output the summary result in a preset format, perform consistency check on the summary result in the preset format, and generate a compressed file of the attribute value determination result of the business product after passing the consistency check.

[0099] In an exemplary embodiment, the above device further includes:

[0100] A polling module is used to poll the latest status of the attribute value determination tasks in the execution process when the number of attribute value determination tasks in the attribute value determination task queue is greater than or equal to the number of servers; obtain the attribute value determination result compressed file of the attribute value determination task in the execution process when the latest status is task completion; upload the attribute value determination result compressed file to the file management platform, update the task status of the attribute value determination tasks in the execution process, update the server status identifier of the server corresponding to the attribute value determination tasks in the execution process, and remove the attribute value determination tasks whose latest status is task completion from the attribute value determination task queue.

[0101] In an exemplary embodiment, the above device further includes:

[0102] The planning and solving module is used to plan and solve the target resource data in the multiple parallel subtasks in the process of allocating the multiple parallel subtasks to the idle servers for parallel processing when the business product is of a preset product type.

[0103] In an exemplary embodiment, the above device further includes:

[0104] The data flow module is used to establish a channel between product development data and product launch data; the attribute value determination result of the business product is transferred to the product launch end through the channel.

[0105] Each module in the above-mentioned business product actuarial calculation device based on the distributed parallel computing framework can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0106] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as newly added attribute value determination tasks and attribute value determination results of business products. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a business product actuarial measurement method based on a distributed parallel computing framework is implemented.

[0107] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0108] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0109] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0110] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0111] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0113] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A business product actuarial calculation method based on a distributed parallel computing framework, characterized in that: The method comprises: Submitting a newly added attribute value determination task to an attribute value determination task queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product; When the number of attribute value determination tasks in the attribute value determination task queue being executed is less than the number of servers, occupying idle servers by the compiled application; The compiled application calls the interpreted application to determine the model point in the attribute value determination model, and split the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point, and allocate the multiple parallel subtasks to the idle server for parallel processing to obtain the attribute value determination result of the business product.

2. The method according to claim 1, characterized in that: The calling of the interpreted application program by the compiled application program to determine the model point in the attribute value determination model comprises: passing the attribute value determination model to an interpreted application via the compiled application; The interpretive application is called to parse the attribute value determination model according to the full model point enumeration template to obtain multidimensional model points of the attribute value determination model; wherein the multidimensional model points include but are not limited to gender and age.

3. The method according to claim 1, characterized in that The allocating multiple parallel subtasks to the idle server for parallel processing to obtain the attribute value determination result of the business product includes: Through a distributed parallel computing architecture, multiple parallel subtasks are assigned to the idle servers for parallel processing to obtain subtask results of each parallel subtask; The subtask results of the multiple parallel subtasks are aggregated, and the aggregated results are output according to a preset format to obtain a result of determining the attribute value of the business product.

4. The method according to claim 3, characterized in that Outputting the summary result in a preset format to obtain the attribute value determination result of the business product includes: Output the summary results according to a preset format, and perform consistency check on the summary results in the preset format; After passing the consistency check, a compressed file of the attribute value determination result of the business product is generated.

5. The method according to claim 1, characterized in that The method further comprises: When the number of attribute value determination tasks in the execution process in the attribute value determination task queuing queue is greater than or equal to the number of servers, polling the latest status of the attribute value determination tasks in the execution process; When the latest status is task completion, obtaining a compressed file of the attribute value determination result of the attribute value determination task in the execution process; The compressed file of the attribute value determination result is uploaded to the file management platform, and the task status of the attribute value determination task in the execution process is updated, and the server status identification of the server corresponding to the attribute value determination task in the execution process is updated, and the attribute value determination task with the latest status of task completion is removed from the attribute value determination task queue.

6. The method according to claim 1, characterized in that The method further comprises: In the case that the business product is of a preset product type, in the process of allocating multiple parallel subtasks to the idle servers for parallel processing, planning and solving are performed on target resource data in the multiple parallel subtasks.

7. The method according to claim 1, characterized in that The method further comprises: Establish a channel between product development data and product launch data; The attribute value determination result of the business product is transferred to the product online end through the channel.

8. A business product actuarial calculation device based on a distributed parallel computing framework, characterized in that: The device comprises: A task submission module, used to submit a newly added attribute value determination task to an attribute value determination task queue through a compiled application; the newly added attribute value determination task carries an attribute value determination model of a business product; A server resource determination module, configured to occupy idle servers through the compiled application program when the number of attribute value determination tasks in the execution process in the attribute value determination task queuing queue is less than the number of servers; The attribute value determination module is used to call the interpreted application through the compiled application to determine the model point in the attribute value determination model, and split the attribute value determination task in the attribute value determination task queue into multiple parallel subtasks according to the model point, and allocate the multiple parallel subtasks to the idle server for parallel processing to obtain the attribute value determination result of the business product.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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