CN117829971A

CN117829971A - Cash flow calculation method and device

Info

Publication number: CN117829971A
Application number: CN202311801986.8A
Authority: CN
Inventors: 穆凯敏; 张亲松
Original assignee: Digital China Financial Software Co ltd
Current assignee: Digital China Financial Software Co ltd
Priority date: 2023-12-26
Filing date: 2023-12-26
Publication date: 2024-04-05

Abstract

The invention relates to the technical field of data processing, in particular to a cash flow calculation method, which comprises the following steps: firstly, configuring time step parameters and product parameters; then, a synchronous data set in a system is read by adopting a segmentation task mode to obtain a target data file; secondly, processing the target data file based on the product mapping rule to generate financial tool data, and extracting data from the financial tool data in a segmentation task mode to process the data piece by piece to generate cash lumen fine data; and finally, superposing model parameters on the basis of the cash lumen fine data to obtain an adjusted cash flow, summarizing the adjusted cash flow to generate a target cash flow, and efficiently and accurately calculating the dynamically adjusted cash flow by the method provided by the specification.

Description

Cash flow calculation method and device

Technical Field

The invention relates to the technical field of data processing, in particular to a cash flow calculation method.

Background

The cash flow is adjusted mainly by considering client behavior adjustment and new business adjustment, wherein the client behavior comprises: a demand deposit rate model, a regular deposit advance withdrawal model, a regular deposit rolling model, a loan advance repayment model and a loan reject rate model; the new business adjustment model comprises: a new business increment model, a new business expiration model, and a new business pricing strategy model.

Currently, the adjusted cash flow calculation method is as follows: firstly, acquiring related data files of each business system into the system, processing and storing the files into different information tables, and generating cash lumen fine data by associating the information of each table; then, manually maintaining cash flow adjustment model parameters at the following time points; and secondly, calculating the adjusted cash flow data based on the model parameters and the cash flow fine data.

Thus, existing methods of calculating cash flow have the following disadvantages: firstly, the time point of the model is fixed and cannot be flexibly adjusted; then, the data processing speed is slow and time is consumed due to the large data processing amount; secondly, because the model parameters are manually set according to experience, the subjectivity is stronger, and the data processing accuracy is poorer; finally, the cash flow generation time is not fixed and cannot be flexibly adjusted.

In view of this, how to calculate cash flows efficiently and accurately is a problem to be solved.

Disclosure of Invention

The invention provides a cash flow calculation method, a cash flow calculation device, an electronic device and a computer readable storage medium, which are used for at least solving the problems of low cash flow calculation efficiency and low accuracy after current dynamic adjustment.

According to a first aspect of an embodiment of the present invention, there is provided a cash flow computing method including:

configuring time step parameters and product parameters, wherein the time step parameters are configured with an upper limit, a lower limit, a unit and an option number for each time step, the product parameters are configured with basic information and product mapping rules for each product, and the product mapping rules map a set with specific basic information into the same product;

reading a synchronous data set in a system by adopting a segmentation task mode to obtain a target data file;

processing the target data file based on the product mapping rule to generate financial tool data, and extracting data from the financial tool data in a segmentation task mode to process the data piece by piece to generate cash lumen fine data;

and superposing model parameters on the basis of the cash flow detail data to obtain an adjusted cash flow, and summarizing the adjusted cash flow to generate a target cash flow.

According to a second aspect of an embodiment of the present invention, there is provided a cash flow computing device comprising:

the system comprises a configuration module, a time step parameter and a product parameter, wherein the time step parameter is configured with an upper limit, a lower limit, a unit and an option number for each time step, the product parameter is configured with basic information and a product mapping rule for each product, and the product mapping rule maps a set with specific basic information into the same product;

the reading module is used for reading the synchronous data set in the system by adopting a segmentation task mode to obtain a target data file;

the generation module is used for processing the target data file based on the product mapping rule to generate financial tool data, extracting data from the financial tool data in a segmentation task mode, processing the data piece by piece and generating cash lumen fine data;

and the summarizing module is used for superposing model parameters on the basis of the cash flow detail data to obtain an adjusted cash flow, summarizing the adjusted cash flow and generating a target cash flow.

According to a third aspect of embodiments of the present specification, there is provided an electronic device comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor performs the steps of the cash flow calculation method according to the first aspect of the embodiments of the present specification.

According to a fourth aspect of embodiments of the present specification, there is provided a computer-readable storage medium having stored thereon a program for implementing information transfer, which when executed by a processor, implements the steps of the cash flow calculation method according to the first aspect of embodiments of the present specification.

By applying the method provided by the embodiment of the specification, the data set is processed in a segmented task mode to acquire transaction data, so that the processing speed of a program can be improved, and the problem of low efficiency when a liquidity risk system carries out complex operation on a large data volume is solved; updating transaction data according to the mapping rule of the flowable product to generate financial tool data, and processing the financial tool data to generate cash lumen fine data, so that the accuracy of the cash lumen fine data can be improved; the time step parameters and the product parameters are configured in the algorithm for calculating the cash flow by the superposition model, so that the flexibility and the expandability of the data are greatly enhanced, and the cash flow report can be flexibly switched through different dimensions.

The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:

FIG. 1 is a flow chart of a cash flow computing method provided in an embodiment of the present disclosure;

FIG. 2 is a block diagram of a cash flow computing device provided by embodiments of the present description;

fig. 3 is a block diagram of a computing device provided by an embodiment of the present description.

Detailed Description

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

Metering of cash flows can generally be categorized into static cash flow metering and dynamic adjustment cash flow metering. The static cash flow metering is based on the contract cash flow, future business increment and behavior adjustment are not considered, the output cash flow can reflect contract attributes of the cash flows of the items in and out of the bank table, and time distribution and mismatch conditions of the contract cash flows of all businesses on the reporting day can be provided. However, since banking business is continuously developed in a variable market, the liquidity decision needs to consider not only the mismatch condition of the current static gap, but also the influence of the customer behavior mode and the newly added business on cash flow in a variable financial environment, so that the adjusted dynamic cash flow also needs to be measured.

The cash flow is adjusted mainly by considering client behavior adjustment and new business adjustment, wherein the client behavior adjustment comprises: the demand deposit rate model, the regular deposit advance taking model, the regular deposit rolling model, the loan advance repayment model and the loan reject rate model, and the new business adjustment model comprises: the embodiment of the specification describes an adjusted liquidity risk cash flow calculation method based on client behaviors.

In the present specification, a cash flow computing method, a cash flow computing device, a computing apparatus, and a computer-readable storage medium are provided, which are described in detail in the following embodiments one by one.

Referring to fig. 1, fig. 1 is a flowchart of a cash flow calculation method according to an embodiment of the present disclosure, and specifically includes the following steps:

step S102: and configuring time step parameters and product parameters, wherein the time step parameters are configured with an upper limit, a lower limit, a unit and an option number for each time step, the product parameters are configured with basic information and product mapping rules for each product, and the product mapping rules map a set with specific basic information into the same product.

It should be noted that, the flexible configuration of the time step parameters can realize flexible display of the cash flow report based on different time granularity, and specifically, the upper limit, the lower limit, the unit and the period number of each time step in the time step parameters can be accurately obtained during calculation. In the embodiment of the present disclosure, the configuration of the time step parameters may configure a common report time sequence, for example: next day, 2 to 7 days, 8 to 30 days, 31 to 90 days, 91 to 1 year, 1 to 5 years … …; on the other hand, a finer-grained time series may be configured, for example: next day, 2 days, 3 days, 4 days, 5 days … … days, 30 days, 31 days to 40 days … …; yet another aspect may also flexibly configure the point in time of the model, for example: 1 day, 7 days, 14 days, 1 month, 3 months, 6 months and 1 year … …, the configuration of the time step parameters in the specification can be flexibly adjusted according to the actual needs of users.

The configuration of product parameters is that basic information of products in product information is configured on one hand, and mapping rules of the products are configured on the other hand, namely, a set of specific attributes in a financial tool table are mapped to the same product.

Step S104: and reading the synchronous data set in the system by adopting a segmentation task mode to obtain the target data file.

It should be noted that the target data is a data file read from a data set of the system based on a segmentation task, wherein the data set is synchronized with the system and includes, but is not limited to, account data and other related data files.

Specifically, the method for reading the synchronous data set in the system by adopting the segmentation task mode to obtain the target data file includes:

configuring corresponding file names for data to be analyzed, and configuring data quantity of each piece of data;

determining the number of segments of data to be analyzed according to the data quantity of each segment of data;

and reading the synchronous data set in the system according to the file name and the segmentation number to obtain a target data file.

The method for reading the synchronous data set in the system by adopting the segmentation task mode to obtain the target data file further comprises the following steps:

configuring a corresponding segmentation number for the data to be analyzed, and automatically determining the data quantity of each segment of data based on the segmentation number;

and reading the synchronous data set in the system according to the number of the segments and the data quantity of each segment of data to obtain a target data file.

In the embodiment of the present disclosure, the processing of the data set by using the segmentation task includes two modes: firstly, a file name to be analyzed, the size of the segmented data volume and the like are required to be configured, secondly, the number of the segmented data stripes is set according to the size of the data volume and the size of the segmented data volume, and finally, the data set is processed according to the number of the segmented data stripes, so that segmented data of the data set are obtained. In practical application, assuming that 1000 ten thousand data sizes are corresponding to a certain deposit of a certain enterprise, setting the size of the segmented data size to be 100 ten thousand, 1000/100=10 segmented data sizes can be obtained, so that the scheduler can call an executor by 10 subtasks, and 10 threads can process the segmented tasks simultaneously at the moment, thereby improving the reading rate of the file.

The other is to automatically calculate the number of data pieces per segment by setting the number of segments. In practical application, 1000 ten thousand pieces of data are provided, the number of segments is set to 20, at this time, according to the calculation result of 1000/2=50, the system will process the data by each segment subtask of 20, and the data amount processed by each subtask is 50 ten thousand pieces.

Step S106: and processing the target data file based on the product mapping rule to generate financial tool data, and extracting data from the financial tool data in a segmentation task mode to process the data piece by piece to generate cash lumen fine data.

The financial instrument data herein includes, but is not limited to, demand deposit, regular deposit, public loan, retail loan, business-to-business, bond transaction, bill transaction, etc., and all information required in all cash flow calculation processes, including, but not limited to, account information, customer information, product information, subjects, currencies, institutions, balances, raw deadlines, remaining deadlines, interest rate information, re-pricing information, etc., are stored in the financial instrument data herein.

Specifically, the processing the target data file based on the product mapping rule to generate financial tool data includes:

according to the product type, filtering account data corresponding to at least one financial tool in the data set to obtain deposit data of the at least one financial tool, wherein the data set comprises the account data and related account data;

determining expiration dates of deposits corresponding to different product types in at least one financial tool, and determining a remaining period according to the expiration dates and the current date;

correlating the remaining term with the related account data to obtain correlated data, and updating initial financial tool data based on the correlated data to obtain financial tool updating data;

updating the financial tool updating data according to the product mapping rule to generate financial tool data.

The relevant account data is data other than basic information related to the product, including but not limited to customer information, balance information, etc., and the obtaining of the relevant data is used for perfect updating of corresponding information in a financial tool table, wherein the financial tool table is the above-mentioned flowable product mapping rule, and it is noted that parameters of the flowable product mapping rule can be flexibly configured according to actual requirements.

In practice, taking a regular deposit as an example, expiration dates for regular deposits of different product types are determined, including but not limited to: informing of deposit date (e.g., 1 day, 7 days), processing interest information, etc., after determining the expiration date of the regular deposit, the remaining term=expiration date-current date.

In the embodiment of the present disclosure, for the processing of the financial tool data, a segmentation task manner is still adopted, so as to improve the efficiency of data processing. Specifically, the method for extracting data from the financial tool data piece by adopting a segmentation task mode to generate cash lumen fine data comprises the following steps:

extracting data from the financial instrument data piece by piece, and registering basic information related to the data;

calculating principal cash flow and interest cash flow in the financial instrument data by distinguishing the types of the amounts;

acquiring current date rate information, and converting the money amount according to the current date rate information;

obtaining a remaining period of each data in the financial instrument data, wherein the remaining period is obtained by subtracting a current date from an expiration date;

generating time series data of all cash flows which need to be generated in the cash flow time series according to the cash flow time series;

cash register data is generated based on the basis information, the principal cash flow and the interest cash flow, the conversion amount, the remaining deadline, and the time-series data.

It should be noted that, the basic data related to the extracted data herein includes, but is not limited to: basic information such as legal person, date, currency, institution, product number, interest rate type, expiration date, original term and the like is registered, wherein the basic information can be directly obtained from financial data.

Obtaining a deposit remaining period in the financial tool data, which may include two cases, wherein one is that the remaining period can be directly extracted in the case of the remaining period; alternatively, in the case where there is no remaining period, the remaining period may be calculated by subtracting the current date from the expiration date.

In practical application, according to the cash flow time sequence, generating cash lumen fine data corresponding to the cash flow time sequence, and assuming that 3 time sequences needing to generate cash flow exist, the data can finally generate 3 pieces of cash lumen fine data, and the data are matched with time periods of different time sequences.

By using the method provided by the embodiment of the specification, the segmentation task is processed in the file reading and data processing processes in a segmentation task mode, the segmentation number can be dynamically calculated by utilizing the transverse expansion capability of the micro-service, the processing efficiency is solved in a multi-task and multi-thread mode, in the processing process, after the segmentation number is calculated by a scheduler, the scheduler obtains available executors, the available executors are obtained by the registry, the files are distributed to the specific executors, the scheduler is called up each executor again to process tasks, heartbeat check and task state check are continuously performed in the process until each executor task execution is completed, the tasks are calculated to be completed, and task state management is performed by matching with a task abnormality processing mechanism, so that the processing speed of a program can be greatly improved, and the problem of low efficiency when a mobility risk system performs complex operation on a large amount of data is solved.

Step S108: and superposing model parameters on the basis of the cash flow detail data to obtain an adjusted cash flow, and summarizing the adjusted cash flow to generate a target cash flow.

In practical application, taking regular deposit as an example, superposing model parameters on the basis of the cash flow detail data to obtain an adjusted cash flow, and the method comprises the following steps:

calculating the advance withdrawal rate of the cash flow in the current time period according to the advance withdrawal rate of each time point in the model, wherein the advance withdrawal rate is obtained based on the upper limit advance withdrawal rate and the lower limit advance withdrawal rate;

and calculating the cash flow time sequence according to the advanced cash flow withdrawal rate of the current time period, and withdrawing the adjusted cash flow in advance by the regular deposit in each time period.

In practical applications, based on historical data, the system generates an advanced extraction model, wherein the advanced extraction model records the advanced extraction rates of different products with different original periods at various time points.

Along the above example, calculating the advance rate of cash flow in the current time period according to the advance rate of each time point in the model includes:

calculating the upper limit advance withdrawal rate and the lower limit advance withdrawal rate of the time steps, and calculating the advance withdrawal rate of the cash flow in the current time period according to the upper limit advance withdrawal rate and the lower limit advance withdrawal rate by an interpolation method.

According to the above example, according to the advance rate of cash flow in the current time period, calculating the time sequence of cash flow, and the regular deposit in each time period advances to draw the adjusted cash flow, including:

and calculating the cash flow after the adjustment of the advance cash flow of the regular deposit according to the original amount of the regular deposit and the advance cash flow withdrawal rate of the cash flow in the time period.

It should be noted that, according to the advance withdrawal rate of each time point in the advance withdrawal model, the advance withdrawal rate of the current time period of the cash flow is calculated, that is, the advance withdrawal rate values of the upper and lower limits of the time steps are calculated respectively. Specifically, the period advance rate=upper limit advance rate value-lower limit advance rate value.

The present embodiment employs interpolation for calculation of the advance extraction rate. The time node for setting the upper and lower limits of the time steps comprises: the time point D is the first time point of the time step, the time point E is the time point with the maximum time step, the time point A and the time point B are positioned between the time point D and the time point E, and the current time period is the time point C, so that when the time point C falls between the time point A and the next time point B, the calculation mode of the advance extraction rate of the time point C is as follows:

dataC＝(dataB-dataA)*(C-A)/(B-A)+dataA；

when the time point C is smaller than the first time point D of the time step, the calculation mode of the advance extraction rate of the time point C is as follows:

dataC＝(dataD-0)*(C-0)/(D-0)+0；

when the time point C is larger than the time point E with the maximum time step, the calculation mode of the advance extraction rate of the time point C is as follows:

dataC＝(0-dataE)*(C-E)/(0-E)+dataE。

the dataC herein is not more than 100%.

In practical applications, all time periods of the cash flow time sequence need to be acquired, wherein the adjusted cash flow = original amount is paid out in advance in each time period. Assuming that the time sequence has 10 time periods, the one piece of detail data becomes 10 pieces of detail data.

By applying the method provided by the embodiment of the specification, the data set is processed in a segmented task mode to acquire transaction data, so that the processing speed of a program can be improved, and the problem of low efficiency when a liquidity risk system carries out complex operation on a large data volume is solved; updating transaction data according to the mapping rule of the flowable product to generate financial tool data, and processing the financial tool data to generate cash lumen fine data, so that the accuracy of the cash lumen fine data can be improved; the time step parameters and the product parameters are configured in the algorithm for calculating the cash flow by the model, so that the flexibility and the expandability of the data are greatly enhanced, and the cash flow report can be flexibly switched through different dimensions.

Referring to fig. 2, fig. 2 is a block diagram of a cash flow computing device according to an embodiment of the present disclosure, and specifically includes the following modules as shown in fig. 2.

A configuration module 202, configured to configure a time step parameter and a product parameter, where the time step parameter configures an upper limit, a lower limit, a unit, and an option for each time step, and the product parameter configures basic information and a product mapping rule for each product, and the product mapping rule maps a set with specific basic information into the same product;

a reading module 204, configured to read the synchronized data set in the system by adopting a segmentation task manner, so as to obtain a target data file;

a generating module 206, configured to process the target data file based on the product mapping rule, generate financial tool data, and extract data from the financial tool data in a segment task manner, and process the data piece by piece, so as to generate cash lumen fine data;

and a summarizing module 208, configured to superimpose model parameters on the basis of the cash flow detail data, obtain an adjusted cash flow, and summarize the adjusted cash flow to generate a target cash flow.

In an alternative embodiment, the reading module 202 is further configured to:

In an alternative embodiment, the generating module 206 is further configured to:

determining deposit expiration dates corresponding to different product types in at least one financial tool, and determining a remaining period according to the expiration dates and the current date;

By applying the device provided by the embodiment of the specification, the data set is processed in a segmented task mode to acquire transaction data, so that the processing speed of a program can be improved, and the problem of low efficiency when a liquidity risk system carries out complex operation on a large data volume is solved; updating transaction data according to the mapping rule of the flowable product to generate financial tool data, and processing the financial tool data to generate cash lumen fine data, so that the accuracy of the cash lumen fine data can be improved; the time step parameters and the product parameters are configured in the algorithm for calculating the cash flow by the superposition model, so that the flexibility and the expandability of the data are greatly enhanced, and the cash flow report can be flexibly switched through different dimensions.

Fig. 3 is a block diagram of a computing device provided by an embodiment of the present description. The components of the computing device 300 include, but are not limited to, a memory 310 and a processor 320. Processor 320 is coupled to memory 310 via bus 330 and database 350 is used to hold data.

Computing device 300 also includes an access device 340, access device 340 enabling computing device 300 to communicate via one or more networks 360. Examples of such networks include public switched telephone networks (PSTN, public Switched Telephone Network), local area networks (LAN, localAreaNetwork), wide area networks (WAN, wideAreaNetwork), personal area networks (PAN, personalAreaNetwork), or combinations of communication networks such as the internet. The access device 340 may include one or more of any type of network interface, wired or wireless, such as a network interface card (NIC, network interface controller), such as an IEEE802.7 wireless local area network (WLAN, wireless LocalAreaNetwork) wireless interface, a worldwide interoperability for microwave access (Wi-MAX, worldwide Interoperability for Microwave Access) interface, an ethernet interface, a universal serial bus (USB, universal Serial Bus) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.

In one embodiment of the present application, the above-described components of computing device 300, as well as other components not shown in FIG. 3, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device illustrated in FIG. 3 is for exemplary purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as desired.

Computing device 300 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smart phone), wearable computing device (e.g., smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or personal computer (PC, personal Computer). Computing device 300 may also be a mobile or stationary server.

Wherein the processor 320 is configured to execute computer-executable instructions that, when executed by the processor, perform the steps of the method described above.

The foregoing is a schematic illustration of a computing device of this embodiment. It should be noted that, the technical solution of the computing device and the technical solution of the method belong to the same conception, and details of the technical solution of the computing device, which are not described in detail, can be referred to the description of the technical solution of the method.

An embodiment of the present disclosure also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the steps of the above-described method.

The above is an exemplary version of a computer-readable storage medium of the present embodiment. It should be noted that, the technical solution of the storage medium and the technical solution of the method belong to the same conception, and details of the technical solution of the storage medium which are not described in detail can be referred to the description of the technical solution of the method.

It should be noted that, in the embodiments of the present disclosure, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), including several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.

The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the protection of the claims, which fall within the protection of the present application.

Claims

1. A cash flow computing method, comprising:

2. The method according to claim 1, wherein the step of reading the synchronized data set in the system by means of the segmentation task to obtain the target data file includes:

3. The method according to claim 1, wherein the step of reading the synchronized data set in the system by means of the segmentation task to obtain the target data file includes:

4. The method of claim 1, wherein processing the target data file based on the product mapping rules to generate financial instrument data comprises:

according to the product type, filtering account data corresponding to at least one financial tool in the data set to obtain data of the at least one financial tool, wherein the data set comprises account data and related account data;

5. The method of claim 1, wherein the step of extracting data from the financial instrument data piece by piece using a segmented task to generate cash lumen fine data comprises:

6. A cash flow computing device, comprising: