Method and device for dynamically adjusting product amount, computer equipment and storage medium

Abstract

The invention discloses a method and a device for dynamically adjusting a product amount, computer equipment and a storage medium. The method comprises the following steps: acquiring preset public dimension information and product information; dynamically generating target dimension information based on preset public dimension information and product information; the method comprises the steps of determining a pre-operator according to preset common dimension information, determining a data parameter of the pre-operator according to preset common dimension information, determining a post-operator according to a preset rule expression, target dimension information and the data parameter, dynamically combining the pre-operator and the post-operator to form an operator chain, and dynamically adjusting a product amount corresponding to product information according to the operator chain. According to the scheme, the calculation of the product amount is divided into the related preset public dimension information and the target dimension information, the calculation rule of the product amount is visualized, compared with a personalized development implementation mode, the development time cost and the later maintenance and understanding cost are greatly reduced, the product online and adjustment efficiency is improved, and the product quality is improved. And the interpretability of product commission calculation is enhanced.

Description

Technical Field

[0001] The present invention relates to the fields of data analysis and financial technology, and in particular to a method, device, computer equipment and storage medium for dynamically adjusting product amounts. Background Art

[0002] In the insurance business, the sheer variety and diversity of product types creates numerous complex scenarios and significant discrepancies when calculating commissions for sales representatives. Specifically, the commission calculation logic for different insurance products often varies significantly, potentially involving factors such as premium amount, policy term, payment method, customer age, policy enrollment period, principal and supplementary policy combinations, specific sales channels, promotional rules, and historical performance requirements. This complexity necessitates redevelopment and testing of the unique commission calculation logic for each new insurance product launch or adjustment to an existing one, increasing development costs and time while also leading to high maintenance costs.

[0003] Therefore, it is urgent to find a new technical solution to solve the above problems. Summary of the Invention

[0004] Based on this, a method, device, computer equipment and storage medium for dynamically adjusting product amounts are provided to solve the technical problems in the prior art of calculating amounts, such as high development cost, high time cost and high maintenance cost.

[0005] A method for dynamically adjusting product amounts, comprising: Obtain preset public dimension information and product information; Dynamically generating target dimension information based on the preset common dimension information and product information; The data parameters of the pre-operator are determined according to the preset common dimension information, the post-operator is determined according to the preset rule expression, the target dimension information and the data parameters, the pre-operator and the post-operator are dynamically combined to form the operator chain, and the product amount corresponding to the product information is dynamically adjusted according to the operator chain.

[0006] A device for dynamically calculating an amount, comprising: The acquisition module is used to obtain preset public dimension information and product information; A generation module, configured to dynamically generate target dimension information based on the preset common dimension information and product information; A dynamic adjustment module is used to determine the data parameters of the pre-operator based on the preset common dimension information, determine the post-operator based on the preset rule expression, the target dimension information and the data parameters, dynamically combine the pre-operator and the post-operator to form the operator chain, and dynamically adjust the product amount corresponding to the product information according to the operator chain.

[0007] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for dynamically adjusting product amounts is implemented.

[0008] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for dynamically adjusting product amounts.

[0009] The above-mentioned method, device, computer equipment and storage medium for dynamically adjusting product amounts obtain preset public dimension information and product information; dynamically generate target dimension information based on the preset public dimension information and product information; determine the data parameters of the pre-operator according to the preset public dimension information, determine the post-operator according to the preset rule expression, the target dimension information and the data parameters, dynamically combine the pre-operator and the post-operator to form the operator chain, and dynamically adjust the product amount corresponding to the product information according to the operator chain. This solution greatly improves the reusability of multi-form, multi-scenario and multi-channel product amount calculation by dimension processing and combination by breaking down the calculation of product amounts into relevant preset public dimension information and target dimension information; makes the calculation rules of product amounts visual, greatly reduces the development time cost and the subsequent maintenance and understanding costs compared to the personalized development implementation method, improves the efficiency of product launch and adjustment, and enhances the explainability of product commission calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0011] Figure 1 This is a schematic diagram of an application environment of a method for dynamically adjusting product amounts in one embodiment of the present invention; Figure 2 This is a process diagram of a method for dynamically adjusting product amounts in one embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a device for dynamically calculating an amount in one embodiment of the present invention; Figure 4 FIG. 1 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0013] The method for dynamically adjusting product amount provided by the present invention can be applied in the following situations: Figure 1 In an application environment, a client communicates with a server via a network. The client may include, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices. The server may be implemented as a standalone server or a server cluster consisting of multiple servers. For example, the present invention may be applied to the server corresponding to the data middleware.

[0014] In one embodiment, if Figure 2 As shown, a method for dynamically adjusting product amounts is provided, which is applied in Figure 1 The following steps are used as an example to illustrate the server where the platform is located: S10, obtaining preset public dimension information and product information.

[0015] Understandably, the preset public dimension information may be related to information in the insurance field, which may include policy number, insurance type code, insurance time, payment method, payment period, policy year, premium, insured amount, institution and other dimensions; product information may be the user's customized demand information or the information that the user is most interested in, such as data customized according to the user's own situation, product information of the user's historical insurance purchases, etc.

[0016] S20, dynamically generating target dimension information based on the preset common dimension information and product information.

[0017] Understandably, the target dimension information may refer to the personalized dimension or required dimension required by the user. For example, if the product currently purchased by the user is discounted at a regular amount, the target dimension information can be generated by analyzing the preset data product information and the preset public dimension information. The preset data analysis may include statistical analysis, correlation analysis, cluster analysis, predictive analysis and visualization analysis.

[0018] S30, determining the data parameters of the pre-operator according to the preset common dimension information, determining the post-operator according to the preset rule expression, the target dimension information and the data parameters, dynamically combining the pre-operator and the post-operator to form the operator chain, and dynamically adjusting the product amount corresponding to the product information according to the operator chain.

[0019] Understandably, the operator chain may include pre-operators and post-operators, which have corresponding combination orders. The operator chain can be generated with the help of regular expressions. The regular expressions can parse the post-operators corresponding to the target dimension information based on the pre-operators corresponding to the preset public dimension information. After dynamically obtaining the operator chains of different users and different products, the product amounts will also be dynamically adjusted. Data parameters can be extracted based on the information of the preset public dimension information, such as the customer's age can be extracted from the policy information, and the commission ratio can be extracted from the premium and the insured amount. Specifically, the extraction can be based on the character type and context content of the preset public dimension information. The preset regular expression is used to define the adjustment rules of the post-operator, which can be specifically expressed by #{pol_yr=1 and data parameter: ratio*(target dimension information-amount)}. The data parameters and ratio can change dynamically. The target dimension information represents the adjustment of the commRate commission amount ratio, such as #{pol_yr=1 and age<18:0.3*commRate:commRate}, specifically means that the commission rate for customers under 18 years old is 30% off, etc.; the operator chain can define the execution order of product amount calculation, organizing the pre- and post-operators in sequence. For example, the pre-operator calculates the customer's age and the regular commission rate, and the post-operator adjusts the commission rate based on the customer's age; In this embodiment, the product amount calculation logic can be divided into pre- and post-dimensions, and regular expressions can be used to implement dynamic calculations, thereby improving the maintainability and scalability of the code and being able to flexibly respond to different product amount calculation requirements.

[0020] In the embodiment of steps S10 to S30, by decomposing the calculation of the product amount into relevant preset public dimension information and target dimension information, the dimension processing and combination greatly improves the reusability of multi-form, multi-scenario, and multi-channel product amount calculation; the calculation rules of the product amount are visualized, which greatly reduces the development time cost and the subsequent maintenance and understanding costs compared to the personalized development implementation method, improves the product launch and adjustment efficiency, and enhances the explainability of the product commission calculation; reduces the coupling of the logic between products, and greatly improves the accuracy and stability of the amount calculation results.

[0021] Furthermore, before obtaining the preset public dimension information, the method further includes: Constructing a corresponding public dimension table according to the classification information corresponding to the preset public dimension information; After the classification data corresponding to the classification information matches the corresponding public dimension table, performing data verification on the classification data; After the data verification is successful, the classified data is inserted into the corresponding public dimension table to form a public dimension table containing preset public dimension information.

[0022] Understandably, the classification information can be different types of the preset public dimension information mentioned above. During the database design phase, it is necessary to create a special public dimension table to store this public dimension information to ensure the structuring and standardization of the data. For example, when creating a policy information table, design a public dimension table called Policy to store the core information of life insurance policies. For example, when creating an agency information table, create an Agency public dimension table to better manage agency information. For example, when creating an insurance type information table, create a Product public dimension table to standardize insurance type information, etc.; collect the classification data entered through the front-end page. - Information such as policy number, insurance type code, insurance application date, payment method, payment period, policy year, premium, insured amount, and institution. The backend service receives data submitted by the frontend and performs data validation to ensure its integrity and accuracy. For example, it verifies that the policy number is unique, the insurance application date cannot be later than the current time, and the premium and insured amount must be positive numbers. After verification, the classified data is inserted into different public dimension tables (after insertion, the preset public dimension information mentioned above is used), and associations are established between different public dimension tables. For example, associations are established with the Product table and the Agency table based on the insurance type code and institution code. In this embodiment, product-related classification information can be effectively abstracted from common dimensions, and data storage, verification, and insertion can be achieved, providing strong support for subsequent business management and decision-making.

[0023] Furthermore, the dynamically generating target dimension information based on the preset common dimension information and product information includes: Obtaining business rules, business processes, and historical data corresponding to the product information; After performing data analysis based on the business rules, business processes, historical data and preset public dimension information according to a preset analysis method, the target dimension information is generated.

[0024] Understandably, the business process can be a special business link, and the product in the business process needs to be analyzed for different links from the standard product, such as some customized products may contain additional health assessment links, at this time, the "health assessment result" target dimension information can be expanded to record the customer's index score in the health assessment, whether to pass the assessment and other information; The business process can follow the regulatory requirements, and different types of products in different regions may be subject to specific regulatory provisions, such as customized products for high net worth customers, which may need to meet more stringent regulatory requirements, at this time, the "review time" target dimension information can be expanded to record the product review; Historical data can be user historical record data, and the standard product data sold in the past is analyzed to find out potential dimensions related to user behavior and risk characteristics, such as through analysis, it is found that the claim rate of customers in some regions is higher in a certain time period, so the "regional risk level" target dimension information can be expanded in the customized product, and the premium and protection range can be adjusted according to the risk level of different regions; In addition, the feedback and suggestions of the user corresponding to the product information can also be included, such as the customer feedback that the product can provide more flexible payment methods, so the "payment frequency adjustment record" target dimension information can be expanded to record the adjustment of the customer's payment frequency; The preset analysis methods may include statistical analysis, correlation analysis, cluster analysis, predictive analysis and visualization analysis. Specifically, frequency analysis: count the frequency of values ​​in each dimension, understand the distribution of data and then determine the appropriate target dimension information (such as determining the target dimension information of health assessment results); mean analysis: calculate the mean of numerical extension dimensions, such as "premium" and "insurance amount", etc., to understand the average premium and insurance amount level of the product. At the same time, different customer groups (such as different ages and genders) can be further analyzed to determine the appropriate target dimension information (such as the target dimension information of the difference in the mean of premium and insurance amount); standard deviation analysis: by calculating the standard deviation, the degree of dispersion of the numerical dimension is measured to determine the appropriate target dimension information (such as the target dimension information of the fluctuation of the compensation amount); inter-correlation: analyze the correlation between different extension dimensions and find the target dimension information of the key factors affecting product risk and pricing; correlation between dimension and target variable: perform correlation analysis on the extension dimension and the target variable of the product (such as claim rate, customer satisfaction, etc.) to find the target dimension information that has a significant impact on the target variable, such as analyzing the "additional protection liability type" and "claims" Customer clustering: Cluster analysis is performed on customers based on dimensions, grouping customers with similar characteristics and needs into the same target dimension information. For example, clustering is performed based on factors such as age, health status, and additional coverage selection, allowing personalized marketing strategies and product design solutions to be developed for different customer groups. Predictive analysis for premium forecasting: Dimensions are used to establish premium forecasting models, predicting product premiums based on customer characteristics and needs. For example, regression analysis and machine learning methods are used to establish premium forecasting models with dimensions such as "age," "gender," "health assessment results," and "additional coverage type" as independent variables and premiums as dependent variables. Predictive analysis for claims forecasting: Dimensions are used to establish claims forecasting models, predicting product claim probability and claim amount target dimension information. For example, historical claims data and dimensions are used to establish logistic regression models or decision tree models to predict customers' claims risks in the future. Graphical presentation: Bar charts, line charts, pie charts, scatter plots, and other graphical forms are used to intuitively display the data distribution, change trends, and correlations of target dimension information. In this embodiment, information is analyzed by at least one preset analysis method to generate target dimension information that best fits the user's intention, thereby improving the accuracy of the analysis.

[0025] Furthermore, the data analysis is performed based on the business rules, business processes, historical data and preset public dimension information according to the preset analysis method, including: Determining at least one corresponding preset data analysis method according to different preset common dimension information; Determine, based on a preset analysis method and under the business rules and business processes, target dimension information that is not currently included in the preset public dimension information; According to the preset analysis method, target dimension information corresponding to user behavior and risk characteristics is determined based on the historical data.

[0026] It can be understood that the different data analysis methods listed above can process different preset public dimension information. For example, data-related information can use frequency analysis, mean analysis and standard deviation analysis, and premium information can use premium prediction in predictive analysis. The specific analysis process will not be repeated here; the target dimension information that is not currently available in the preset public dimension information can be a newly expanded dimension, and user behavior and risk characteristics can be relevant data for customers in certain regions within a specific time period, according to different risk levels in different regions.

[0027] Furthermore, the dynamically combining the pre-operator and the post-operator to form the operator chain includes: Input the pre-operator and the post-operator as input parameters into a preset operator interface; The operator chain is automatically assembled according to the order of the current scenario through the preset operator interface.

[0028] Understandably, a general operator interface is defined, such as a class containing multiple input parameters, where each operator can serve as an input parameter. The order of the current scenario corresponds to the process corresponding to the current business. The corresponding process can be automatically matched based on the characters in all operators. For example, after matching the commission algorithm rules for a product, the base commission can be calculated for that product first, then adjusted in steps based on sales volume, and then product category discounts, promotional bonuses, and customer level discounts can be considered. Each of the above processes is an operator, and each operator has its own business adjustment rules: In this embodiment, independent operators are determined for complex business processes and freely combined and arranged into operator chains according to business needs, thereby improving the scalability and flexibility of the operator chain combination.

[0029] Furthermore, dynamically calculating the product amount corresponding to the product information based on the operator chain includes: Determining a calculation strategy based on the current mode type, executing the preceding operator in the operator chain under the preset common dimension information according to the calculation strategy, and calculating a first amount; The post-operator in the operator chain under the target dimension information is executed according to the calculation strategy to calculate the second amount under the first amount.

[0030] Understandably, the current model types can be divided into the first premium: using a fixed commission rate; additional premiums: using a weighted average algorithm to calculate the commission rate. Product amount calculation strategy: Different commission calculation algorithms are selected based on the premium type (first premium or additional premium). The pre-operator is responsible for extracting and calculating preset common dimension information, such as calculating the first amount of the basic data of the regular commission rate; the post-operator dynamically adjusts the regular commission rate (such as a 30% discount for those under 18) to obtain the adjusted second amount. Regular expressions can be parsed through EL expressions to parse dynamic rules. In this embodiment, by calculating the regular ratio of the front dimension, dynamically adjusting the back dimension, distinguishing the type of strategy mode, and parsing the rule expression, a flexible and scalable product ratio calculation logic is implemented, which can easily cope with changes in different business rules.

[0031] Furthermore, the dynamically combining an operator chain based on the target dimension information and the preset common dimension information includes: Add annotations to each pre-operator corresponding to the preset common dimension information and each post-operator corresponding to the target dimension information; An operator chain list is created, and the annotated pre-operator and post-operator are dynamically loaded through a reflection mechanism, and are added to the operator chain list in the order of the operators to form the operator chain.

[0032] Understandably, in Java, using custom annotations (such as @Function) to mark operator classes and configuring these operators into an operator chain can effectively manage and execute commission calculation logic. The specific steps are to define a custom annotation @Function to mark the operator class, implement each operator class, add the @Function annotation to each class, create an operator chain, and dynamically load operator classes with the @Function annotation through the reflection mechanism. Operator classes are added to the chain in sequence. Finally, in the main program, the operator chain can be loaded using the OperatorChain.loadOperators() method and the product amount calculation can be performed. In this embodiment, a custom annotation is defined to mark the operator class, and these operator classes are dynamically loaded into the operator linked list through the reflection mechanism. As a result, the product amount calculation logic is more modular and configurable, which is convenient for management and expansion. If a new operator needs to be added, it is only necessary to implement the new operator class and add the annotation. The system will automatically load and execute the new operator class.

[0033] In summary, the above provides a method for dynamically adjusting product amounts, obtaining preset public dimension information and product information; dynamically generating target dimension information based on the preset public dimension information and product information; determining the data parameters of the pre-operator according to the preset public dimension information, determining the post-operator according to the preset rule expression, the target dimension information and the data parameters, dynamically combining the pre-operator and the post-operator to form the operator chain, and dynamically adjusting the product amount corresponding to the product information according to the operator chain. In this solution, by decomposing the calculation of the product amount into relevant preset public dimension information and target dimension information, the dimension processing and combination greatly improves the reusability of multi-form, multi-scenario, and multi-channel product amount calculation; making the calculation rules of the product amount visual, compared with the personalized development implementation method, greatly reduces the development time cost and the subsequent maintenance and understanding cost, improves the efficiency of product launch and adjustment, and enhances the explainability of product commission calculation; reduces the coupling of logic between products, and greatly improves the accuracy and stability of the amount calculation results.

[0034] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0035] In one embodiment, a dynamic amount calculation device is provided, which corresponds to the method for dynamically adjusting the product amount in the above embodiment. Figure 3 As shown, the dynamic amount calculation device includes an acquisition module 11, a generation module 12 and a dynamic adjustment module 13. The functional modules are described in detail as follows: An acquisition module 11 is used to acquire preset common dimension information and product information; A generating module 12, configured to dynamically generate target dimension information based on the preset common dimension information and product information; The dynamic adjustment module 13 is used to determine the data parameters of the pre-operator based on the preset common dimension information, determine the post-operator based on the preset rule expression, the target dimension information and the data parameters, dynamically combine the pre-operator and the post-operator to form the operator chain, and dynamically adjust the product amount corresponding to the product information according to the operator chain.

[0036] Furthermore, the dynamic amount calculation device also includes: A construction module, configured to construct a corresponding public dimension table according to the classification information corresponding to the preset public dimension information; A data verification module, configured to perform data verification on the classification data after the classification data corresponding to the classification information matches the corresponding public dimension table; The inserting module is used to insert the classified data into the corresponding public dimension table after the data verification is successful, so as to form a public dimension table containing preset public dimension information.

[0037] Furthermore, the generation module includes: An acquisition submodule, configured to acquire business rules, business processes, and historical data corresponding to the product information; The first forming submodule is used to generate the target dimension information after performing data analysis based on the business rules, business processes, historical data and preset public dimension information according to a preset analysis method.

[0038] Furthermore, the generation submodule includes: A first determination submodule is configured to determine at least one corresponding preset data analysis method according to different preset common dimension information; A second determination submodule is configured to determine, based on a preset analysis method and under the business rules and business processes, target dimension information that is not currently included in the preset public dimension information; The third determination submodule is used to determine target dimension information corresponding to user behavior and risk characteristics based on the historical data according to a preset analysis method.

[0039] Furthermore, the dynamic adjustment module includes: An input submodule, configured to input the pre-operator and the post-operator as input parameters into a preset operator interface; The combination submodule is used to automatically combine the operator chain according to the order of the current scene through the preset operator interface.

[0040] Furthermore, the dynamic adjustment module includes: a first calculation submodule, configured to determine a calculation strategy according to a current mode type, execute a preceding operator in the operator chain under the preset common dimension information according to the calculation strategy, and calculate a first amount; The second calculation submodule is used to execute the post-operator in the operator chain under the target dimension information according to the calculation strategy, and calculate the second amount under the first amount.

[0041] Furthermore, the dynamic adjustment module includes: An adding submodule, used to add annotations to each pre-operator corresponding to the preset common dimension information and each post-operator corresponding to the target dimension information; The second forming submodule is used to create an operator chain list, and dynamically load the annotated pre-operator and post-operator through the reflection mechanism, and add them to the operator chain list in the order of the operators to form the operator chain.

[0042] The specific limitations of the dynamic amount calculation device can be found in the limitations of the method for dynamically adjusting product amounts described above and will not be further elaborated here. Each module in the dynamic amount calculation device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the modules described above can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0043] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. 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 computer program in the non-volatile storage medium. The database of the computer device is used to store data involved in the method for dynamically adjusting product amounts. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a method for dynamically adjusting product amounts.

[0044] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for dynamically adjusting the product amount in the above embodiment are implemented, for example: Figure 2 Steps S10 to S30 are shown; Alternatively, when the processor executes the computer program, the functions of the modules / units of the apparatus for dynamically calculating the amount in the above embodiment are realized, for example Figure 3 The functions of modules 11 to 13 are shown in FIG.

[0045] 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 of the method for dynamically adjusting the product amount in the above embodiment are implemented, for example: Figure 2 Steps S10 to S30 are shown; Alternatively, when the computer program is executed by the processor, the functions of the modules / units of the apparatus for dynamically calculating the amount in the above embodiment are realized, for example Figure 3 The functions of modules 11 to 13 are shown in FIG.

[0046] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0047] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0048] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for dynamically adjusting product amounts, characterized in that: include: Obtain preset public dimension information and product information; Dynamically generating target dimension information based on the preset common dimension information and product information; The data parameters of the pre-operator are determined according to the preset common dimension information, the post-operator is determined according to the preset rule expression, the target dimension information and the data parameters, the pre-operator and the post-operator are dynamically combined to form the operator chain, [Chen 1] and the product amount corresponding to the product information is dynamically adjusted according to the operator chain.

2. The method for dynamically adjusting product amounts according to claim 1, characterized in that: Before obtaining the preset public dimension information, the method further includes: Constructing a corresponding public dimension table according to the classification information corresponding to the preset public dimension information; After the classification data corresponding to the classification information matches the corresponding public dimension table, performing data verification on the classification data; After the data verification is successful, the classified data is inserted into the corresponding public dimension table to form a public dimension table containing preset public dimension information.

3. The method for dynamically adjusting product amounts according to claim 1, characterized in that: The dynamically generating target dimension information based on the preset common dimension information and product information includes: Obtaining business rules, business processes, and historical data corresponding to the product information; After performing data analysis based on the business rules, business processes, historical data and preset public dimension information according to a preset analysis method, the target dimension information is generated.

4. The method for dynamically adjusting product amounts according to claim 3, characterized in that: The data analysis is performed based on the business rules, business processes, historical data and preset public dimension information according to the preset analysis method, including: Determining at least one corresponding preset data analysis method according to different preset common dimension information; Determine, based on a preset analysis method and under the business rules and business processes, target dimension information that is not currently included in the preset public dimension information; According to the preset analysis method, target dimension information corresponding to user behavior and risk characteristics is determined based on the historical data.

5. The method for dynamically adjusting product amounts according to claim 1, characterized in that: The dynamically combining the pre-operator and the post-operator to form the operator chain includes: Input the pre-operator and the post-operator as input parameters into a preset operator interface; The operator chain is automatically assembled according to the order of the current scenario through the preset operator interface.

6. The method for dynamically adjusting product amounts according to claim 1, characterized in that: The dynamically calculating the product amount corresponding to the product information according to the operator chain includes: Determining a calculation strategy based on the current mode type, executing the preceding operator in the operator chain under the preset common dimension information according to the calculation strategy, and calculating a first amount; The post-operator in the operator chain under the target dimension information is executed according to the calculation strategy to calculate the second amount under the first amount.

7. The method for dynamically adjusting product amounts according to claim 5 or 6, characterized in that: The dynamically combining an operator chain based on the target dimension information and the preset common dimension information includes: Add annotations to each pre-operator corresponding to the preset common dimension information and each post-operator corresponding to the target dimension information; An operator chain list is created, and the annotated pre-operator and post-operator are dynamically loaded through a reflection mechanism, and are added to the operator chain list in the order of the operators to form the operator chain.

8. A device for dynamically calculating an amount, characterized in that: include: The acquisition module is used to obtain preset public dimension information and product information; A generation module, configured to dynamically generate target dimension information based on the preset common dimension information and product information; A dynamic adjustment module is used to determine the data parameters of the pre-operator based on the preset common dimension information, determine the post-operator based on the preset rule expression, the target dimension information and the data parameters, dynamically combine the pre-operator and the post-operator to form the operator chain, and dynamically adjust the product amount corresponding to the product information according to the operator chain.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for dynamically adjusting the product amount as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for dynamically adjusting product amounts as claimed in any one of claims 1 to 7 is implemented.

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