CN115577879A - Cash flow prediction method and device, electronic equipment and storage medium - Google Patents
Cash flow prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to a cash flow prediction method, a cash flow prediction device, electronic equipment and a storage medium. The cash flow prediction method comprises the following steps: acquiring the total predicted refund income per month and the total actual refund income per month in a first statistical period; determining a forecast income deviation proportion in a first statistical period according to the forecast total income of each month and the actual total income of each month; acquiring actual total income of money returned per month and actual purchasing expenditure per month in a second statistical period; determining a predicted income-expenditure deviation proportion in a second statistical period according to the actual total income of the refund in each month and the actual purchasing expenditure in each month; acquiring a predicted total revenue of reimbursements for a next month based on a current month; and determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the return of the next month. The method can predict the predicted cash flow in the next month and provide a data basis for enterprise decision making.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a cash flow prediction method and apparatus, an electronic device, and a storage medium.
Background
The enterprise purchasing is a complex and difficult work, and the purchasing work can be ensured to be smoothly carried out only by establishing a perfect purchasing decision prediction mechanism in combination with the production and operation states of the enterprise, so that the working efficiency of the purchasing work and the utilization rate of capital are improved.
In the purchasing work, because the uncertainty of the fund collection and expenditure causes that the difference between the predicted cash flow and the actual cash flow is large, the situation that the purchasing budget has to be changed along with the actual situation and an enterprise has random increase and decrease of the purchasing budget often occurs, so that the purchasing budget does not have due effect, and the situations of low purchasing work efficiency, serious fund waste and the like are caused. Therefore, how to accurately predict cash flow becomes a problem to be urgently solved.
Disclosure of Invention
In order to solve the technical problem of how to accurately predict cash flow, the application provides a cash flow prediction method, a cash flow prediction device, electronic equipment and a storage medium.
In a first aspect, the present application provides a cash flow prediction method, including:
acquiring the total predicted refund income per month and the total actual refund income per month in a first statistical period; the first statistical period comprises M months, wherein M is greater than or equal to one;
determining a forecast income deviation proportion in the first statistical period according to the forecast total income of each month and the actual total income of each month;
acquiring actual total income of money returned per month and actual purchasing expenditure per month in a second statistical period; the second statistical period comprises N months, wherein N is greater than or equal to one;
determining a predicted income-expenditure deviation proportion in the second statistical period according to the actual total income of the refund in each month and the actual purchasing expenditure in each month;
acquiring a predicted total revenue of reimbursements for a next month based on a current month;
determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the next month;
optionally, determining the forecast income deviation proportion in the first statistical period according to the forecast total income of each month and the actual total income of each month, including:
respectively calculating M income deviation proportions according to the predicted total income of the money withdrawals in each month and the actual total income of the money withdrawals in each month; wherein, for any one income deviation proportion, the income deviation proportion is obtained by dividing the actual total income of the refund and the predicted total income of the refund in any one month;
calculating an average of the M revenue deviation ratios to obtain the predicted revenue deviation ratio within the first statistical period;
optionally, M is greater than or equal to three; calculating an average of the M revenue bias proportions to obtain the predicted revenue bias proportion within the first statistical period, including:
determining a first value; the first value is a sum of the M revenue deviation proportions;
determining a second value and a third value; the second value is a maximum of the M revenue deviation ratios and the third value is a minimum of the M revenue deviation ratios;
subtracting the second numerical value and the third numerical value from the first numerical value respectively, and then averaging to obtain the forecast income deviation ratio;
optionally, determining the predicted revenue and expenditure deviation ratio in the second statistical period according to the actual total revenue of the refund per month and the actual purchasing expenditure per month, including:
respectively calculating to obtain N deviation proportions of income and expenditure according to the actual total income of the refund in each month and the actual purchasing expenditure in each month; wherein, for any one of the deviation ratios of the income and the expense, the actual purchasing expenditure and the actual total income of the return of any one month are divided to obtain;
calculating an average value of the N receiving and dispatching deviation ratios to obtain the predicted receiving and dispatching deviation ratio in the second statistical period;
optionally, N is greater than or equal to three; calculating an average value of the N break-even ratios to obtain the predicted break-even ratio in the second statistical period, including:
determining a fourth value; the fourth value is the sum of the N break-even ratios;
determining a fifth numerical value and a sixth numerical value; the fifth value is a maximum value of the N break-even ratios, and the sixth value is a minimum value of the N break-even ratios;
subtracting the fifth numerical value and the sixth numerical value from the fourth numerical value respectively, and then averaging to obtain the predicted revenue and expenditure deviation ratio;
optionally, determining a predicted cash flow for the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the return for the next month, including:
calculating the product of the forecast income deviation proportion, the forecast income deviation proportion and the forecast total income of the next month, and taking the product as the forecast cash flow of the next month;
optionally, the month corresponding to the first statistical period is the same as the month corresponding to the second statistical period.
In a second aspect, the present application provides a cash flow prediction apparatus, the apparatus comprising:
the first acquisition module is used for acquiring the predicted total income of the refund of each month and the actual total income of the refund of each month in a first statistical period; the first statistical period comprises M months, wherein M is greater than or equal to one;
the first determining module is used for determining the forecast income deviation proportion in the first statistical period according to the forecast total income of each month and the actual total income of each month;
the second acquisition module is used for acquiring the actual total income of the refund per month and the actual purchasing expenditure per month in the second statistical period; the second statistical period comprises N months, wherein N is greater than or equal to one;
the second determination module is used for determining the predicted income-expenditure deviation proportion in the second statistical period according to the actual total income of the refund in each month and the actual purchasing expenditure in each month;
a third obtaining module for obtaining a predicted total revenue of reimbursement for a next month based on a current month;
and the third determining module is used for determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the next month.
In a third aspect, the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
a processor, configured to implement the steps of a cash flow prediction method according to any one of the embodiments of the first aspect when executing a program stored in the memory.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a cash flow prediction method as described in any one of the embodiments of the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
the method provided by the embodiment of the application obtains the predicted total income of the refund per month and the actual total income of the refund per month in the first statistical period; the first statistical period comprises M months, wherein M is greater than or equal to one; determining a forecast income deviation proportion in the first statistical period according to the forecast total income of each month and the actual total income of each month; acquiring actual total income of money returned per month and actual purchasing expenditure per month in a second statistical period; the second statistical period comprises N months, wherein N is greater than or equal to one; determining a predicted income-expenditure deviation proportion in the second statistical period according to the actual total income of the refund in each month and the actual purchasing expenditure in each month; acquiring a predicted total revenue of reimbursements for a next month based on a current month; and determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the next month. The method determines the forecast income deviation proportion in the first statistic period and the forecast income deviation proportion in the second statistic period, determines the forecast cash flow of the next month according to the forecast income deviation proportion, the forecast income deviation proportion and the forecast total income of the next month, can improve the forecast accuracy of the forecast cash flow of the next month, and provides a data basis for enterprise decision making.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a system architecture diagram of a cash flow prediction method according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating a cash flow prediction method according to an embodiment of the present application;
FIG. 3 is a schematic illustration of a method of determining a forecast revenue bias ratio according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating a method for determining a predicted revenue and expenditure bias ratio according to an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating a procurement decision recommendation provided by one embodiment of the application;
fig. 6 is a schematic structural diagram of a cash flow predicting apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The first embodiment of the present application provides a cash flow prediction method, which may be applied to a system architecture as shown in fig. 1, where the system architecture includes at least an acquisition unit 101 and a computing unit 102, and the acquisition unit 101 and the computing unit 102 establish a communication connection.
The collecting unit 101 is used for collecting the total predicted refund income per month and the total actual refund income per month in the first statistical period, collecting the total actual refund income per month and the actual purchasing expenditure per month in the second statistical period, collecting the total predicted income of the next month based on the current month, and sending the collected data to the calculating unit 102, and the calculating unit 102 can calculate and determine the predicted cash flow of the next month according to the data collected by the collecting unit 101.
The method can be applied to the computing unit 102 in the system architecture, where the acquiring unit 101 may be a terminal, or a component having a data acquiring function in the terminal, and the computing unit 102 may be a terminal, or a component having a data processing function in the terminal, or a server, without limitation.
It should be noted that the above terminals may include an intelligent terminal (a mobile phone, a tablet computer, etc.), a notebook computer, a desktop computer, etc., and a component having a data acquisition function in the terminal may be a component supporting data input or entry in the terminal, such as a keyboard, a touch screen, etc., without limitation.
The purchasing decision prediction mechanism needs to carry out cost prediction, and the cost prediction refers to that a certain scientific calculation method is applied to scientifically estimate the future cost level and the change trend of the future cost level. The method provides purchasing decision recommendation by combining the production and operation states of enterprises based on the withdrawal condition of each month of a contract, setting a income counting period, counting estimated withdrawal income and actual income conditions within a certain time, then setting a collection and payment counting period, counting actual income and actual expenditure conditions within a certain time and dynamically calculating and predicting cash flow according to counted data.
The method of the embodiment can replace the static purchasing decision with the dynamic purchasing decision, make a relatively accurate prediction on the whole cost and provide a basis for the subsequent decision of the enterprise.
Next, a cash flow prediction method is described in detail based on the system architecture, as shown in fig. 2, the method includes:
The first statistical period may be counted according to an integral multiple of a month, for example, any integral month greater than or equal to one such as the past 12 months, 6 months, or 3 months may be counted, and because the actual income needs to be counted, the first statistical period is a month before the current date, and does not include a month that has not yet arrived.
The predicted total income of the refund of each month in the first statistical period can be obtained through the refund condition in the contract corresponding to each month, and the actual total income of each month is calculated according to the actual income condition corresponding to each month. For example, if the current date is 2022 years, 8 months and 1 day, the first statistical period may be six months of the first half year of 2022 years, and the predicted total income of the refund for each of the months 1 to 6 may be calculated from the corresponding income situation for each of the months 1 to 6 in the contract and the actual total income for each of the months 1 to 6 may be calculated from the actual income situation in the account, respectively.
It should be noted that, the above description is only an example that the first statistical period is 6 months, and the first statistical period may be other number of months in the past, and this embodiment does not limit this. It should be noted that, for convenience of statistics, the total income of the predicted repayment of each month may correspond to a natural month, or may correspond to a fixed deadline of each month as a deadline, for example, 25 days per month may be set as the fixed deadline, and then a contract deadline from 26 days per month to 25 days per month in 3 to 4 may be counted as data of month 4 as needed, and so on for data statistics intervals of other months, which are not described again.
And step 202, determining a forecast income deviation proportion in the first statistical period according to the forecast total income of the money return in each month and the actual total income of the money return in each month.
The forecast income deviation proportion in the first statistic period can be calculated by the total forecast total income of the months and the total actual total income of the refunds included in the first statistic period, or the income deviation proportion of each month can be independently calculated firstly, and then the forecast income deviation proportion in the first statistic period can be calculated according to the income deviation proportion of each month.
In one embodiment, determining a predicted revenue deviation ratio for the first statistical period based on the predicted total revenue returned per month and the actual total revenue returned per month comprises:
respectively calculating M income deviation proportions according to the predicted total income of the money withdrawals in each month and the actual total income of the money withdrawals in each month; wherein, for any income deviation proportion, the income deviation proportion is obtained by dividing the actual total income of the refund and the predicted total income of the refund in any month;
and calculating the average value of the M income deviation ratios to obtain the predicted income deviation ratio in the first statistical period.
In this embodiment, it is assumed that the first statistical period is α, α includes M months, the total revenue collected for predicted payback per month is p, the total revenue collected for actual payback per month is a1, the revenue deviation ratio per month is t, and the revenue deviation ratio is γ.
The income deviation ratio t of each month is respectively calculated,
it should be noted that the above formula needs to be calculated by using the numerical value corresponding to the same month, for example, calculating the income deviation ratio t of march, and dividing the actual total income a1 of march by the predicted total income p of march.
After the income deviation ratio t of each month is obtained, the average value of all income deviation ratios t can be calculated to be used as the predicted income deviation ratio in the first statistical period, or a median can be taken, or the average value of the residual data after a maximum value and a minimum value are removed.
In one embodiment, M is greater than or equal to three because data for a month corresponding to a maximum value and data for a month corresponding to a minimum value need to be removed. Calculating an average of the M revenue deviation ratios to obtain a predicted revenue deviation ratio over a first statistical period, comprising:
determining a first value; the first value is the sum of the M revenue deviation proportions;
determining a second value and a third value; the second value is a maximum of the M incoming deviation ratios and the third value is a minimum of the M incoming deviation ratios;
and subtracting the second value and the third value from the first value respectively, and then averaging to obtain the forecast income deviation ratio.
In this embodiment, a schematic diagram for determining the deviation ratio of the prediction income is shown in fig. 3, and the income in the statistical period α is calculated, and includes the total prediction income and the total actual income.
Passing all income deviation ratios t in the first statistical period alpha through the formula: sum 1= ∑ t, and the summation yields sum1 (first numerical value).
And then through the formula:
and calculating a forecast income deviation proportion gamma, wherein sum1 is a first numerical value, max1 is a second numerical value, min1 is a third numerical value, and M is the number of months included in the first statistic cycle alpha.
In the embodiment, the numerical values of the individual months with obvious fluctuation can be removed, so that the calculated forecast income deviation proportion is more accurate, more accurate basic data is provided for forecasting cash flow, and the forecasting accuracy is improved.
It should be noted that, in the present embodiment, the prediction income deviation ratio γ is calculated by averaging the remaining data after removing one maximum value and one minimum value, which is only an example, the prediction income deviation ratio γ may also be calculated by directly averaging or calculating a median of income deviation ratios of M months, and the present application is not limited thereto.
The second statistical period may be counted according to an integral multiple of a month, for example, any integer month greater than or equal to one such as the past 12 months, 6 months or 3 months may be counted, because the actual income and the actual purchase expenditure need to be counted, the second statistical period is a month before the current date and does not include a month that has not yet arrived.
In the second statistical period, the actual total income of the refund of the month is calculated according to the actual income condition corresponding to each month, and the actual purchasing expense of the month is calculated according to the actual purchasing expense condition corresponding to each month, for example, if the current date is 2022, 8, 1 month, the second statistical period can be six months of the last half year of 2022, the actual total income of each month in1 to 6 months can be respectively calculated according to the actual income condition of the account, and the actual purchasing expense in each month in1 to 6 months can be respectively calculated according to the actual purchasing expense condition of the account.
It should be noted that, the second statistical period of 6 months is only an example, and may be other number of months in the past, for example, any number of months greater than or equal to one, and this embodiment does not limit this.
And step 204, determining the predicted income-expenditure deviation proportion in the second statistical period according to the actual total income of the refund in each month and the actual purchasing expenditure in each month.
The predicted revenue and expenditure deviation proportion in the second statistical period can be calculated through the total actual total income of the refund and the total actual purchasing expenditure of the month included in the second statistical period, or the predicted revenue and expenditure deviation proportion in the second statistical period can be calculated according to the revenue and expenditure deviation proportion of each month.
In one embodiment, determining the predicted revenue and expenditure deviation ratio within the second statistical period based on the actual total revenue and the actual purchase expenditure per month comprises:
respectively calculating N receiving and distributing deviation proportions according to the actual total income of the refund in each month and the actual purchasing expenditure in each month; wherein, for any one of the income-expense deviation proportion, the income-expense deviation proportion is obtained by dividing the actual purchasing expense and the actual refund total income of any one month;
and calculating the average value of the N receiving and dispatching deviation ratios to obtain the predicted receiving and dispatching deviation ratio in the second statistical period.
In this embodiment, the second statistical period is β, β includes N months, the actual total income of money returned per month is a1, the actual purchasing expense per month is a2, the deviation ratio of income and expense per month is s, and the predicted deviation ratio of income and expense is θ.
Respectively calculating the balance deviation ratio s of each month,
it should be noted that, in the above formula, the numerical value corresponding to the same month is used for calculation, for example, the balance deviation ratio s of april is calculated, and the actual purchasing expenditure a2 of april is divided by the actual return total income of april to be a1.
It should be noted that, when a certain month included in the first statistical period α and the second statistical period β is the same month, the actual total income a1 of the month corresponds to the same value. The specific values of a1, a2 and p in the first statistical period and the second statistical period are calculated by actually generated data, for example, the total income of actual money returns of march and april is represented by a1, but the specific values can correspond to different specific values.
After the receiving and distributing deviation ratio s of each month is obtained, the average value of the receiving and distributing deviation ratios s can be calculated to be used as the predicted receiving and distributing deviation ratio in the second statistical period, or the median can be taken, or the average value of the residual data after a maximum value and a minimum value are removed.
In one embodiment, since the data of the month corresponding to the maximum value and the data of the month corresponding to the minimum value need to be removed, N is greater than or equal to three. Calculating the average value of the N receiving and distributing deviation ratios to obtain a predicted receiving and distributing deviation ratio in a second statistical period, wherein the method comprises the following steps:
determining a fourth value; the fourth value is the sum of N receiving-distributing deviation proportions;
determining a fifth numerical value and a sixth numerical value; the fifth value is the maximum value of the N recentandoff deviation ratios, and the sixth value is the minimum value of the N recentandoff deviation ratios;
and subtracting the fifth numerical value and the sixth numerical value from the fourth numerical value respectively, and then averaging to obtain the predicted receiving and dispatching deviation ratio.
In this embodiment, a schematic diagram of determining the predicted revenue and expenditure deviation ratio is shown in fig. 4, and the revenue and expenditure in the second statistical period β is calculated, where the revenue and expenditure includes the actual total revenue of the reimbursement and the actual purchase expenditure.
And (3) passing all the break-even deviation ratios s in the second statistical period beta through a formula: sum 2= Σ s, and the summation results in sum2 (fourth numerical value).
And then through the formula:
the statistical calculation is a predicted balance deviation ratio theta, wherein sum2 is a fourth numerical value, max2 is a fifth numerical value, min2 is a sixth numerical value, and N is the number of months included in the second statistical period beta.
In the embodiment, the numerical values of the individual months with obvious fluctuation can be removed, so that the calculated forecast balance deviation proportion is more accurate, more accurate basic data is provided for forecasting cash flow, and the forecasting accuracy is improved.
The current month, that is, the month in which the current date is located, or referred to as this month, for example, the current date is 2022 years, 8 month and 1 day, and this month is august, the predicted total income p of payback of the next month, that is, september, can be obtained, and the predicted total income p of payback of september is obtained through the corresponding income situation of september in the contract.
And step 206, determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the next month.
In one embodiment, determining a predicted cash flow for the next month based on the predicted income deviation ratio, and the predicted total income for the return for the next month comprises: and calculating the product of the forecast income deviation proportion, the forecast income deviation proportion and the forecast total income of the next month, and taking the product as the forecast cash flow of the next month.
In this embodiment, the predicted income deviation ratio γ, the predicted income deviation ratio θ, and the predicted total income p of the next month are substituted into a formula: c = p × γ × θ the predicted cash flow c for the next month is dynamically calculated. As shown in fig. 5, according to the contract money return situation, the total predicted money return income p of the next month can be determined in the present month, and the predicted cash flow c of the next month can be accurately predicted by combining the predicted income deviation proportion gamma and the predicted income/expense deviation proportion theta, so as to recommend the purchasing decision of the enterprise.
In the embodiment, based on the withdrawal condition of each month of the contract, the income counting period is dynamically set by combining the production and operation state of the enterprise, the estimated withdrawal income and the actual income condition of each month in a certain time range are counted, the income counting period is dynamically set again, the actual income and the actual expenditure condition of each month in a certain time range are counted, and finally, the purchasing decision recommendation is provided by a method for dynamically calculating and predicting the cash flow according to the counted data, so that the problems of low purchasing work efficiency, serious fund waste and the like caused by uncertainty of enterprise fund collection and expenditure or over-random purchasing decision are solved, and a basis is provided for the enterprise to correctly perform the production and operation decision.
In the embodiment, the predicted cash flow of the next month can be automatically predicted according to the collected data such as the predicted total income of the cash withdrawal, the actual total income of the cash withdrawal, the predicted total income of the cash withdrawal of the next month and the like, the prediction result is accurate, and accurate basic data support is provided for enterprise decision making.
In one embodiment, the month corresponding to the first statistical period is the same as the month corresponding to the second statistical period.
In this embodiment, not only the values of M and N are the same, but also the month corresponding to the first statistical period α and the month corresponding to the second statistical period β are the same, for example, the month corresponding to the first statistical period α is 1 month to 6 months in 2022 years, and the month corresponding to the second statistical period β is 1 month to 6 months in 2022 years, and the correlation between the prediction income deviation ratio γ and the prediction income deviation ratio θ calculated in the same statistical period is stronger, so that the result is more accurate when predicting the cash flow in the next month.
The calculation method of the present embodiment is strictly contract-based, and strict control on the discipline of the operating units and the contract compliance is required. The embodiment has dependency on the refund date, the estimated refund date and the actual refund date of each contract have a relatively accurate rule, and the income statistical period and the fixed deadline for monthly counting the actual income amount in the current month are set according to the refund rule, so that the method achieves the optimal stability. Meanwhile, for enterprises with great variation in the statistical period, the statistical period of expenditure needs to be dynamically adjusted according to the production and management conditions of the enterprises, the income statistical period is not influenced in principle, but the enterprises still need to judge whether part of distortion data needs to be abandoned or not by themselves so as to ensure the authenticity and stability of the final prediction result of the method.
Based on the same technical concept, a second embodiment of the present application provides a cash flow predicting apparatus, as shown in fig. 6, the apparatus including:
a first obtaining module 601, configured to obtain a predicted total income of money withdrawal per month and an actual total income of money withdrawal per month in a first statistical period; the first statistical period comprises M months, wherein M is greater than or equal to one;
a first determining module 602, configured to determine a prediction income deviation ratio in the first statistical period according to the predicted total income of the refund per month and the actual total income of the refund per month;
a second obtaining module 603, configured to obtain an actual total income of money withdrawal per month and an actual purchasing expense per month in a second statistical period; the second statistical period comprises N months, wherein N is greater than or equal to one;
a second determining module 604, configured to determine a predicted revenue and expenditure deviation ratio in the second statistical period according to the actual total revenue of the refund per month and the actual purchasing expenditure per month;
a third obtaining module 605 for obtaining a predicted total revenue of the refund based on the next month of the current month;
a third determining module 606, configured to determine a predicted cash flow in the next month according to the predicted income deviation proportion, and the predicted total income for the return in the next month.
The device determines the forecast income deviation proportion in the first statistic period and the forecast income deviation proportion in the second statistic period, determines the forecast cash flow of the next month according to the forecast income deviation proportion, the forecast income deviation proportion and the forecast total income of the next month, can improve the forecast accuracy of the forecast cash flow of the next month, and provides a data basis for enterprise decision making.
As shown in fig. 7, a third embodiment of the present application provides an electronic device, which includes a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, the memory 113 complete mutual communication through the communication bus 114,
a memory 113 for storing a computer program;
in one embodiment, the processor 111, when executing the program stored in the memory 113, is configured to implement the cash flow prediction method provided by any one of the foregoing method embodiments, including:
acquiring the total predicted refund income of each month and the total actual refund income of each month in a first statistical period; the first statistical period comprises M months, wherein M is greater than or equal to one;
determining a forecast income deviation proportion in the first statistical period according to the forecast total income of each month and the actual total income of each month;
acquiring actual total income of money returned per month and actual purchasing expenditure per month in a second statistical period; the second statistical period comprises N months, wherein N is greater than or equal to one;
determining a predicted income-expenditure deviation proportion in the second statistical period according to the actual total income of the refund in each month and the actual purchasing expenditure in each month;
acquiring a predicted total income of the refund of the next month based on the current month;
and determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the next month.
The communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the terminal and other equipment.
The Memory may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
A fourth embodiment of the present application further provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the cash flow prediction method as provided in any one of the method embodiments described above.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the description, suffixes such as "module", "part", or "unit" used to denote elements are used only for facilitating the description of the present invention, and have no specific meaning in themselves. Thus, "module", "component" or "unit" may be used mixedly.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A cash flow prediction method, the method comprising:
acquiring the total predicted refund income of each month and the total actual refund income of each month in a first statistical period; the first statistical period comprises M months, wherein M is greater than or equal to one;
determining a forecast income deviation proportion in the first statistical period according to the forecast total income of each month and the actual total income of each month;
acquiring actual total income of money returned per month and actual purchasing expenditure per month in a second statistical period; the second statistical period comprises N months, wherein N is greater than or equal to one;
determining a predicted income-expenditure deviation proportion in the second statistical period according to the actual total income of the refund of each month and the actual purchasing expense of each month;
acquiring a predicted total income of the refund of the next month based on the current month;
and determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the next month.
2. The method of claim 1, wherein determining a predicted revenue deviation ratio for the first statistical period based on the predicted total revenue returned per month and the actual total revenue returned per month comprises:
respectively calculating M income deviation proportions according to the predicted total income of the money withdrawals in each month and the actual total income of the money withdrawals in each month; wherein, for any one income deviation proportion, the income deviation proportion is obtained by dividing the actual total income of the refund and the predicted total income of the refund in any one month;
calculating an average of the M revenue deviation ratios to obtain the predicted revenue deviation ratio within the first statistical period.
3. The method of claim 2, wherein M is greater than or equal to three; calculating an average of the M revenue deviation ratios to obtain the predicted revenue deviation ratio over the first statistical period, comprising:
determining a first value; the first value is a sum of the M revenue deviation proportions;
determining a second value and a third value; the second value is a maximum of the M revenue deviation ratios and the third value is a minimum of the M revenue deviation ratios;
and subtracting the second numerical value and the third numerical value from the first numerical value respectively, and then averaging to obtain the prediction income deviation ratio.
4. The method of claim 1, wherein determining the predicted revenue and expenditure deviation ratio for the second statistical period based on the actual total revenue returned per month and the actual procurement expenditure per month comprises:
respectively calculating to obtain N deviation proportions of income and expenditure according to the actual total income of the refund in each month and the actual purchasing expenditure in each month; wherein, for any one of the deviation ratios of the income and the expense, the actual purchasing expenditure and the actual total income of the return of any one month are divided to obtain;
and calculating the average value of the N receiving and distributing deviation ratios to obtain the predicted receiving and distributing deviation ratio in the second statistical period.
5. The method of claim 4, wherein N is greater than or equal to three; calculating an average value of the N break-even ratios to obtain the predicted break-even ratio in the second statistical period, including:
determining a fourth value; the fourth value is the sum of the N break-even ratios;
determining a fifth numerical value and a sixth numerical value; the fifth value is a maximum value of the N break-even ratios, and the sixth value is a minimum value of the N break-even ratios;
and subtracting the fifth numerical value and the sixth numerical value from the fourth numerical value respectively, and then averaging to obtain the predicted revenue and expenditure deviation ratio.
6. The method of claim 1, wherein determining the predicted cash flow for the next month based on the predicted income deviation ratio, and the predicted total income for the return for the next month comprises:
and calculating the product of the forecast income deviation proportion, the forecast income deviation proportion and the forecast total income of the next month, and taking the product as the forecast cash flow of the next month.
7. The method of any of claims 1-6, wherein the month to which the first statistical period corresponds is the same as the month to which the second statistical period corresponds.
8. A cash flow prediction apparatus, the apparatus comprising:
the first acquisition module is used for acquiring the predicted total income of the refund of each month and the actual total income of the refund of each month in a first statistical period; the first statistical period comprises M months, wherein M is greater than or equal to one;
the first determining module is used for determining the forecast income deviation proportion in the first statistical period according to the forecast total income of each month and the actual total income of each month;
the second acquisition module is used for acquiring the actual total income of the refund of each month and the actual purchase expenditure of each month in the second statistical period; the second statistical period comprises N months, wherein N is greater than or equal to one;
the second determination module is used for determining the predicted income-expenditure deviation proportion in the second statistical period according to the actual total income of the refund in each month and the actual purchasing expenditure in each month;
a third obtaining module for obtaining a predicted total revenue of reimbursement for a next month based on a current month;
and the third determining module is used for determining the predicted cash flow of the next month according to the predicted income deviation proportion, the predicted income deviation proportion and the predicted total income of the next month.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the steps of a cash flow prediction method according to any one of claims 1 to 7 when executing a program stored in a memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of a cash flow prediction method according to any one of claims 1 to 7.
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