CN112529675B

CN112529675B - Asset estimation method and device based on financial data

Info

Publication number: CN112529675B
Application number: CN202011521425.9A
Authority: CN
Inventors: 菲利普·普雷特
Original assignee: Apm Monaco LLC; Beride Jewelry Guangzhou Co ltd
Current assignee: Apm Monaco LLC; Beride Jewelry Guangzhou Co ltd
Priority date: 2020-12-21
Filing date: 2020-12-21
Publication date: 2024-10-15
Anticipated expiration: 2040-12-21
Also published as: CN112529675A

Abstract

In the embodiment of the invention, financial data in a financial system is acquired; the financial data includes initial-period asset data for a target time period; determining predicted parameter data of the target time period, and calculating predicted asset data of the target time period according to the predicted parameter data of the target time period; and estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period. Therefore, the invention can establish the relation between the historical data in the financial data and the future financial situation, effectively predict the situation of the asset according to the historical data, reasonably reflect the future asset situation of the company in the target time period, provide reference for important decisions of the enterprise, and be beneficial to the management personnel to clearly and comprehensively master the asset development situation of the enterprise.

Description

Asset estimation method and device based on financial data

Technical Field

The invention relates to the technical field of computers, in particular to an asset estimation method and device based on financial data.

Background

Asset estimation is an important ring in financial accounting work, can reasonably reflect the asset condition of a company in a future target time period, can provide reference for important decisions of the enterprise, and is also beneficial to management staff to clearly and comprehensively master the asset development condition of the enterprise.

The existing ERP software can only record financial data of an enterprise, has postponement for accounting during accounting, cannot reflect financial conditions of the enterprise timely and truly due to the problems of account entry time and the like during accounting during the period, and cannot estimate assets for the enterprise because the existing financial accounting technology does not consider the conditions between the existing financial data and future asset conditions of the enterprise.

Disclosure of Invention

The invention aims to solve the technical problem of providing the asset estimation method and the device based on the financial data, which can establish the relation between the historical data in the financial data and the future financial situation, effectively predict the situation of the asset according to the historical data, reasonably reflect the future asset situation of the company in the target time period, provide reference for important decisions of the enterprise, and are also beneficial to the management personnel to clearly and comprehensively master the asset development situation of the enterprise.

To solve the above technical problem, a first aspect of the present invention discloses an asset estimation method based on financial data, the method comprising:

acquiring financial data in a financial system; the financial data includes initial-period asset data for a target time period;

Determining predicted parameter data of the target time period, and calculating predicted asset data of the target time period according to the predicted parameter data of the target time period;

and estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period.

As an optional implementation manner, in the first aspect of the present invention, the initial asset data of the target time period includes an initial inventory amount of the target time period; the determining the predicted parameter data of the target time period, calculating the predicted asset data of the target time period according to the predicted parameter data of the target time period, comprising:

Determining an estimated production warehouse-in amount and an estimated sales warehouse-out amount for the target time period;

Calculating an estimated variation inventory amount for the target time period based on the estimated production warehouse entry amount and the estimated sales warehouse exit amount for the target time period according to the following formula:

S20＝S1-S2；

Wherein, S20 is the estimated change inventory amount of the target time period, S1 is the estimated production warehouse-in amount of the target time period, and S2 is the estimated sales warehouse-out amount of the target time period;

the estimating the end-of-period asset estimation data of the target time period according to the initial-period asset data of the target time period and the calculated expected asset data of the target time period comprises the following steps:

end-of-period inventory amount estimation data for the target time period is calculated according to the following formula:

S＝S10+S20；

Wherein S is end-of-period inventory amount estimation data of the target time period, S10 is initial inventory amount of the target time period, and S20 is estimated change inventory amount of the target time period.

As an optional implementation manner, in the first aspect of the present invention, the determining the estimated production warehouse-in amount and the estimated sales warehouse-out amount of the target time period includes:

Determining the estimated production and warehousing quantity, the production cost coefficient and the estimated purchasing and warehousing inventory value of the target time period, and calculating the estimated production and warehousing amount of the target time period according to the following formula;

S1＝P1*P2+P3；

S1 is the estimated production and warehousing amount of the target time period, P2 is the production cost coefficient of the target time period, and P3 is the estimated purchasing and warehousing inventory value of the target time period;

determining wholesale channel estimated sales inventory, retail channel estimated sales inventory, e-commerce channel estimated sales inventory and new store estimated stock value for the target time period, and calculating an estimated sales outlet amount for the target time period according to the following formula:

S2＝S21+S22+S23+S24；

Wherein S2 is the estimated sales amount of the target time period, S21 is the estimated sales inventory of the wholesale channel of the target time period, S22 is the estimated sales inventory of the retail channel of the target time period, S23 is the estimated sales inventory of the e-commerce channel of the target time period, and S24 is the estimated stock value of the new store of the target time period;

And determining wholesale channel projected sales inventory, retail channel projected sales inventory, electronic commerce channel projected sales inventory for the target time period, comprising:

Determining wholesale channel budget, wholesale sales value-added tax, historical wholesale product unit price and wholesale sales price coefficient of the target time period, and calculating wholesale channel estimated sales inventory of the target time period according to the following formula:

S21＝S211*S212/S213*S214；

Wherein S21 is a wholesale channel estimated sales inventory of the target time period, S211 is a wholesale channel budget of the target time period, S212 is a wholesale sales value-added tax of the target time period, S213 is a historical wholesale product unit price of the target time period, and S214 is a wholesale sales price coefficient of the target time period;

determining a retail channel budget, a retail sales value-added tax, a historical retail product price, and a retail sales price coefficient for the target time period, and calculating a retail channel projected sales inventory for the target time period according to the following formula:

S22＝S221*S222/S223*S224；

Wherein S22 is a retail channel estimated sales inventory of the target time period, S221 is a retail channel budget of the target time period, S222 is a retail sales value-added tax of the target time period, S223 is a historical retail product unit price of the target time period, and S224 is a retail sales price coefficient of the target time period;

determining the e-commerce channel budget, the e-commerce sales value-added tax, the historical e-commerce product unit price and the e-commerce sales price coefficient of the target time period, and calculating the e-commerce channel estimated sales inventory of the target time period according to the following formula:

S23＝S231*S232/S233*S234；

wherein, S23 is an e-commerce channel estimated sales inventory of the target time period, S231 is an e-commerce channel budget of the target time period, S232 is an e-commerce sales value-added tax of the target time period, S233 is a historical e-commerce product unit price of the target time period, and S234 is an e-commerce sales price coefficient of the target time period.

As an optional implementation manner, in the first aspect of the present invention, the initial asset of the target time period includes an initial fixed asset amount of the target time period; the determining the predicted parameter data of the target time period, calculating the predicted asset data of the target time period according to the predicted parameter data of the target time period, comprising:

Determining fixed asset prepayment, fixed asset cost, fixed asset depreciation, remaining asset cost and remaining asset depreciation for the target time period;

Calculating a fixed asset estimated amount for the target time period based on the fixed asset pre-payment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time period;

calculating end-of-term fixed asset amount estimation data for the target time period according to the following formula:

FA＝FA1+FA2；

wherein, FA is the fixed asset amount estimation data of the end of the period of the target time, FA1 is the fixed asset amount of the beginning of the period of the target time, and FA2 is the fixed asset estimated amount of the target time.

As an optional implementation manner, in the first aspect of the present invention, the calculating the predicted amount of the fixed asset for the target period according to the fixed asset prepayment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost and the remaining asset depreciation for the target period includes:

calculating a predicted amount of the fixed asset for the target time period according to the following formula:

FA2＝FAP+FAC–FAD+OAC–OAD；

The FAP is the expected amount of the fixed asset in the target time period, the FAP is the prepayment of the fixed asset in the target time period, the FAC is the cost of the fixed asset in the target time period, the FAD is the depreciation of the fixed asset in the target time period, the OAC is the cost of the rest of the assets in the target time period, and the OAD is the depreciation of the rest of the assets in the target time period.

As an alternative embodiment, in the first aspect of the present invention, the financial data includes a plurality of financial data tables; each financial data table comprises a plurality of amount data with different data types and corresponding financial information; the method further comprises the steps of:

According to the data types of the amount data in the financial data table and the corresponding financial information, establishing a mapping relation between the amount data which are associated to the same data types or financial information in all the financial data table based on a preset data mapping rule so as to obtain processed financial data;

When a query command of a user is received, pushing target financial data or end-of-term asset estimation data corresponding to the query command to the user; the inquiry command is used for indicating one or more of data types to be inquired, financial information and end-of-term asset estimation data; the target financial data is amount data of the processed financial data, which is mapped with the data category and/or the financial information indicated by the query command.

In an optional implementation manner, in the first aspect of the present invention, when a query command of a user is received, pushing target financial data or end-of-term asset estimation data corresponding to the query command to the user includes:

when a query command of a user is received, acquiring user information of the user;

Judging whether the user has permission to inquire target financial data or terminal asset estimation data corresponding to the inquiry command based on a preset permission rule according to the user information of the user;

And pushing the target financial data or the end-of-period asset estimation data corresponding to the query command to the user when the user has the right of querying the target financial data or the end-of-period asset estimation data corresponding to the query command.

In a second aspect of the invention, there is disclosed an asset estimation device based on financial data, the device comprising:

the acquisition module is used for acquiring financial data in the financial system; the financial data includes initial-period asset data for a target time period;

A calculation module for determining predicted parameter data of the target time period, and calculating predicted asset data of the target time period according to the predicted parameter data of the target time period;

And the estimation module is used for estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period.

As an alternative embodiment, in the second aspect of the present invention, the initial asset data of the target time period includes an initial inventory amount of the target time period; the calculation module determines predicted parameter data of the target time period, calculates a specific mode of predicted asset data of the target time period according to the predicted parameter data of the target time period, and comprises the following steps:

S20＝S1-S2；

Wherein, S20 is the estimated change inventory amount of the target time period, S1 is the estimated production warehouse-in amount of the target time period, and S2 is the estimated sales warehouse-out amount of the target time period; the estimating module estimates a specific mode of the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period, and the specific mode comprises the following steps:

S＝S10+S20；

In a second aspect of the present invention, the calculating module determines the estimated production warehouse entry amount and the estimated sales warehouse exit amount for the target time period, including:

S1＝P1*P2+P3；

S2＝S21+S22+S23+S24；

S21＝S211*S212/S213*S214；

S22＝S221*S222/S223*S224；

S23＝S231*S232/S233*S234；

As an alternative embodiment, in the second aspect of the present invention, the initial asset of the target time period includes an initial fixed asset amount of the target time period; the calculation module determines predicted parameter data of the target time period, calculates a specific mode of predicted asset data of the target time period according to the predicted parameter data of the target time period, and comprises the following steps:

The estimating module estimates a specific mode of the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period, and the specific mode comprises the following steps:

FA＝FA1+FA2；

As an alternative embodiment, in the second aspect of the present invention, the calculating the predicted amount of the fixed asset for the target time period according to the fixed asset prepayment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time period includes:

FA2＝FAP+FAC–FAD+OAC–OAD；

As an alternative embodiment, in a second aspect of the invention, the financial data comprises a plurality of financial data tables; each financial data table comprises a plurality of amount data with different data types and corresponding financial information; the apparatus further comprises:

The mapping module is used for establishing a mapping relation between the amount data which are associated to the same data category or financial information in all the financial data tables based on a preset data mapping rule according to the data category of the amount data and the corresponding financial information in the financial data tables so as to obtain processed financial data;

the pushing module is used for pushing the target financial data or the terminal asset estimation data corresponding to the query command to the user when the query command of the user is received; the inquiry command is used for indicating one or more of data types to be inquired, financial information and end-of-term asset estimation data; the target financial data is amount data of the processed financial data, which is mapped with the data category and/or the financial information indicated by the query command.

As an optional implementation manner, in the second aspect of the present invention, the pushing module includes:

the acquisition unit is used for acquiring user information of the user when receiving a query command of the user;

the judging unit is used for judging whether the user has the authority for inquiring the target financial data or the terminal asset estimation data corresponding to the inquiry command or not based on a preset authority rule according to the user information of the user;

and the pushing unit is used for pushing the target financial data or the end-of-period asset estimation data corresponding to the query command to the user when the user has the right of querying the target financial data or the end-of-period asset estimation data corresponding to the query command.

In a third aspect the invention discloses another asset estimation device based on financial data, said device comprising:

a memory storing executable program code;

a processor coupled to the memory;

the processor invokes the executable program code stored in the memory to perform some or all of the steps in the financial data based asset estimation method disclosed in the first aspect of the embodiments of the invention.

A fourth aspect of the embodiments of the present invention discloses a computer storage medium storing computer instructions which, when invoked, are adapted to perform part or all of the steps of the financial data based asset estimation method disclosed in the first aspect of the embodiments of the present invention.

Compared with the prior art, the embodiment of the invention has the following beneficial effects:

In the embodiment of the invention, financial data in a financial system is acquired; the financial data includes initial-period asset data for a target time period; determining predicted parameter data of the target time period, and calculating predicted asset data of the target time period according to the predicted parameter data of the target time period; and estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period. Therefore, the invention can calculate the predicted asset data of the target time period, and estimate the estimated asset data of the end-of-period asset of the target time period by combining the initial asset data of the target time period in the financial data in the financial system, thereby establishing the relation between the historical data in the financial data and the future financial situation, effectively predicting the asset according to the historical data, reasonably reflecting the future asset situation of the company of the target time period, providing reference for important decisions of enterprises, and being beneficial to management staff to clearly and comprehensively master the asset development situation of the enterprises.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method of asset estimation based on financial data disclosed by an embodiment of the invention;

FIG. 2 is a flow chart of another method of asset estimation based on financial data disclosed by embodiments of the invention;

FIG. 3 is a schematic diagram of an asset estimation device based on financial data according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of an asset estimation device based on financial data according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of an asset estimation device based on financial data according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or article.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

The invention discloses an asset estimation method and device based on financial data, which can establish the relation between historical data in the financial data and future financial conditions, effectively predict the conditions of assets according to the historical data, reasonably reflect the future asset conditions of a company in a target time period, provide references for important decisions of the enterprise, and are also beneficial to management staff to clearly and comprehensively master the asset development conditions of the enterprise. The following will describe in detail.

Example 1

Referring to fig. 1, fig. 1 is a flow chart of an asset estimation method based on financial data according to an embodiment of the present invention. The method described in fig. 1 is applied to an asset estimation device based on financial data, where the computing device may be a corresponding computing terminal, computing device or server, and the server may be a local server or a cloud server, which is not limited by the embodiment of the present invention. As shown in FIG. 1, the asset estimation method based on financial data may include the following operations:

101. financial data in a financial system is acquired.

In an embodiment of the invention, the financial data includes initial-period asset data for the target time period. Optionally, the financial data may include initial asset data that may be an initial inventory amount, an initial fixed asset amount, or an initial due capital expenditure for the target time period. In particular, in the embodiment of the present invention, the financial system may be a financial accounting system in an enterprise, or may be an ERP system having a financial accounting function, and in particular, a manner of acquiring financial data in the financial system may be a manner of sending a data request to the financial system through a pre-established communication connection, receiving the financial data sent by the financial system in response to the data request, or may be a manner of directly extracting the stored financial data from a storage device of the financial data, where the stored financial data should be updated in time so as to avoid a subsequent calculation from losing timeliness due to an old version.

102. The predicted parameter data of the target time period is determined, and predicted asset data of the target time period is calculated according to the predicted parameter data of the target time period.

In the embodiment of the invention, the predicted parameter data is used for calculating the predicted asset data, and the predicted asset data can be obtained by manually inputting the predicted asset data into a computer by an operator or obtaining the predicted asset data from historical data. Specifically, the projected parameter data includes one or more of a projected production warehouse entry amount for a target time period, a projected sales warehouse exit amount, a fixed asset pre-payment, a fixed asset cost, a fixed asset depreciation, a remaining asset cost, and a remaining asset depreciation, and the projected asset data includes one or more of a projected changing inventory amount for a target time period, a fixed asset projected amount, and a fixed asset projected required payment amount.

103. And estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period.

Therefore, by implementing the method described by the embodiment of the invention, the end-of-period asset estimation data of the target time period can be estimated according to the initial asset data of the target time period and the calculated expected asset data of the target time period, so that the relation between the historical data in the financial data and the future financial situation can be established, the future asset situation of the company in the target time period can be reasonably reflected according to the situation of the historical data, the reference is provided for important decisions of the enterprise, and the management personnel can clearly and comprehensively master the asset development situation of the enterprise.

In an alternative embodiment, the initial asset data for the target time period includes an initial inventory amount for the target time period. Determining projected parameter data for the target time period in step 102, calculating projected asset data for the target time period from the projected parameter data for the target time period, comprising:

Determining the estimated production warehouse-in amount and the estimated sales warehouse-out amount of the target time period;

S20＝S1-S2；

wherein S20 is the estimated change stock amount of the target time period, S1 is the estimated production warehouse-in amount of the target time period, and S2 is the estimated sales warehouse-out amount of the target time period.

It will be seen that by implementing this alternative embodiment, the estimated change inventory amount for the target time period can be calculated based on the estimated production warehouse entry amount and the estimated sales warehouse exit amount for the target time period, so that a more accurate calculation result can be obtained, providing a reliable data basis for subsequent further asset estimation.

In another alternative embodiment, determining the projected production warehouse entry amount and the projected sales warehouse exit amount for the target time period includes:

S1＝P1*P2+P3；

S1 is the estimated production and warehousing amount of a target time period, P1 is the estimated production and warehousing quantity of the target time period, P2 is the production cost coefficient of the target time period, and P3 is the estimated purchasing and warehousing value of the target time period;

determining wholesale channel estimated sales inventory, retail channel estimated sales inventory, electronic commerce channel estimated sales inventory and new store estimated stock value for the target time period, and calculating an estimated sales outlet amount for the target time period according to the following formula:

S2＝S21+S22+S23+S24；

Wherein, S2 is the estimated sales amount of the target time period, S21 is the estimated sales inventory of the wholesale channel of the target time period, S22 is the estimated sales inventory of the retail channel of the target time period, S23 is the estimated sales inventory of the e-commerce channel of the target time period, and S24 is the estimated shop inventory value of the new store of the target time period.

Further, determining wholesale channel estimated sales inventory, retail channel estimated sales inventory, electronic commerce channel estimated sales inventory for the target time period includes:

S21＝S211*S212/S213*S214；

Wherein, S21 is the wholesale channel estimated sales inventory of the target time period, S211 is the wholesale channel budget of the target time period, S212 is the wholesale sales value-added tax of the target time period, S213 is the historical wholesale product unit price of the target time period, and S214 is the wholesale sales price coefficient of the target time period;

determining a retail channel budget, a retail sales value-added tax, a historical retail product price and a retail sales price coefficient for a target time period, and calculating a retail channel estimated sales inventory for the target time period according to the following formula:

S22＝S221*S222/S223*S224；

wherein, S22 is the estimated sales inventory of the retail channel in the target time period, S221 is the budget of the retail channel in the target time period, S222 is the retail sales value-added tax in the target time period, S223 is the unit price of the historical retail product in the target time period, and S224 is the retail sales price coefficient in the target time period;

S23＝S231*S232/S233*S234；

Wherein, S23 is the estimated sales inventory of the e-commerce channel in the target time period, S231 is the budget of the e-commerce channel in the target time period, S232 is the e-commerce sales value-added tax in the target time period, S233 is the historical e-commerce product unit price in the target time period, and S234 is the e-commerce sales price coefficient in the target time period.

In this alternative embodiment, the data such as the wholesale channel budget, wholesale sales value-added tax, historical wholesale product unit price, wholesale sales price coefficient, retail channel budget, retail sales value-added tax, historical retail product unit price, retail sales price coefficient, e-commerce channel budget, e-commerce sales value-added tax, historical e-commerce product unit price and e-commerce sales price coefficient in the target time period may be input into the system by an operator through an interaction device, or may be predicted by historical data through a prediction algorithm or obtained from a data plan in the operation plan in the target time period.

It can be seen that by implementing the alternative embodiment, the estimated production warehouse entry amount in the target time period can be calculated according to the estimated production warehouse entry amount, the production cost coefficient and the estimated purchase warehouse entry value, and the estimated sales inventory of the wholesale, retail and electronic commerce channels can be calculated according to the channel budget, the sales value-added tax, the historical product unit price, the sales price coefficient and other information corresponding to different channels, so that the more accurate estimated sales inventory can be obtained, the estimated change inventory amount in the target time period can be determined according to the estimated sales inventory and the estimated production warehouse entry amount of all sales channels, the more accurate calculation result can be obtained, and a reliable data basis can be provided for the subsequent further asset estimation.

In yet another alternative embodiment, estimating end-of-period asset estimation data for the target time period from the initial-of-period asset data for the target time period and the calculated projected asset data for the target time period in step 103 includes:

End-of-term inventory amount estimation data for the target time period is calculated according to the following formula:

S＝S10+S20；

Wherein S is the end-of-period inventory amount estimation data of the target time period, S10 is the initial inventory amount of the target time period, and S20 is the estimated change inventory amount of the target time period.

Therefore, by implementing the alternative embodiment, the end-of-period inventory amount estimation data can be calculated according to the sum of the initial-period asset data and the expected change inventory amount of the target time period, the end-of-period data can be accurately and rapidly estimated by utilizing the historical data and the estimation data, the inventory amount of a company in the future target time period can be reasonably reflected, and a reference is provided for important decisions of the enterprise.

In yet another alternative embodiment, determining a wholesale channel projected sales inventory for a target period of time includes:

historical wholesale channel sales inventory data in a plurality of target historical time periods is obtained, and wholesale channel estimated sales inventory in the target time periods is determined according to the historical wholesale channel sales inventory data in the plurality of target historical time periods.

Alternatively, the average value or the median value of the historical wholesale channel sales inventory data of the plurality of target historical time periods may be calculated, and the calculated average value or median value of the historical wholesale channel sales inventory data may be determined as the wholesale channel estimated sales inventory of the target time period.

Alternatively, the expected sales inventory of the wholesale channel in the target time period can be obtained by calculating a relation or a relation curve between the sales inventory data of the historical wholesale channels in the plurality of target historical time periods and the time period length or the time interval of the corresponding target historical time period and predicting the obtained wholesale channel in the target time period according to the calculated relation or relation curve.

Alternatively, model training may be performed by using historical wholesale channel sales inventory data of a plurality of target historical time periods and time period parameters of the target historical time periods as training sets through a neural network algorithm, and the model obtained by training is used for predicting wholesale channel estimated sales inventory of the target time periods. Specifically, the time period parameters of the target history time period include, but are not limited to, information such as a time period length, a time period interval, and sales activities within the time period.

Optionally, the selection of the target historical time period may select a historical time period meeting a preset time period feature rule between the selected historical time period and the target time period, so as to improve the correlation between the sales inventory data of the historical wholesale channel of the selected target historical time period and the expected sales inventory of the wholesale channel of the target time period to be predicted, thereby improving the accuracy of the prediction. Alternatively, the preset time period characterization rules may include, but are not limited to, rules that are identical in quarter, month, business season, beginning month and/or ending month and time period length.

In yet another alternative embodiment, a retail outlet for determining a target time period predicts sales inventory, comprising:

Historical retail channel sales inventory data in a plurality of target historical time periods is acquired, and retail channel estimated sales inventory in the target time period is determined according to the historical retail channel sales inventory data in the plurality of target historical time periods.

Alternatively, the average or median of the historical retail channel sales inventory data for the plurality of target historical time periods may be calculated, and the calculated average or median of the historical retail channel sales inventory data may be determined as the retail channel projected sales inventory for the target time period.

Alternatively, the retail channel estimated sales inventory of the target time period can be obtained by calculating a relation or a relation curve between the sales inventory data of the historical retail channels of the plurality of target historical time periods and the time period length or the time interval of the corresponding target historical time period, and predicting the retail channel estimated sales inventory of the target time period according to the time period length or the time interval of the target time period by the calculated relation or relation curve.

Alternatively, model training may be performed by using historical retail channel sales inventory data of a plurality of target historical time periods and time period parameters of the target historical time periods as training sets through a neural network algorithm, and the model obtained by training is used for predicting retail channel estimated sales inventory of the target time periods. Specifically, the time period parameters of the target history time period include, but are not limited to, information such as a time period length, a time period interval, and sales activities within the time period.

Optionally, the selection of the target historical time period may select a historical time period meeting a preset time period characteristic rule between the selected historical time period and the target time period, so as to improve correlation between sales inventory data of a historical retail channel of the selected target historical time period and expected sales inventory of a retail channel of the target time period to be predicted, thereby improving accuracy of prediction. Alternatively, the preset time period characterization rules may include, but are not limited to, rules that are identical in quarter, month, business season, beginning month and/or ending month and time period length.

In yet another alternative embodiment, determining the e-commerce channel projected sales inventory for the target time period includes:

Historical e-commerce channel sales inventory data in a plurality of target historical time periods is obtained, and the e-commerce channel estimated sales inventory in the target time period is determined according to the historical e-commerce channel sales inventory data in the plurality of target historical time periods.

Alternatively, the average value or the median value of the historical e-commerce channel sales inventory data of the plurality of target historical time periods may be calculated, and the calculated average value or median value of the historical e-commerce channel sales inventory data may be determined as the e-commerce channel estimated sales inventory of the target time period.

Alternatively, the estimated sales inventory of the e-commerce channel in the target time period can be obtained by calculating a relation or a relation curve between the historical e-commerce channel sales inventory data in the plurality of target historical time periods and the time period length or the time interval of the corresponding target historical time period and predicting the calculated relation or relation curve according to the time period length or the time interval of the target time period.

Optionally, model training may be performed by using historical e-commerce channel sales inventory data of a plurality of target historical time periods and time period parameters of the target historical time periods as training sets through a neural network algorithm, and the model obtained by training is used for predicting e-commerce channel estimated sales inventory of the target time periods. Specifically, the time period parameters of the target history time period include, but are not limited to, information such as a time period length, a time period interval, and sales activities within the time period.

Optionally, the selecting of the target historical time period may select a historical time period meeting a preset time period feature rule between the selected historical time period and the target time period, so as to improve correlation between the historical e-commerce channel sales inventory data of the selected target historical time period and the e-commerce channel estimated sales inventory of the target time period to be predicted, thereby improving accuracy of prediction. Alternatively, the preset time period characterization rules may include, but are not limited to, rules that are identical in quarter, month, business season, beginning month and/or ending month and time period length.

In yet another alternative embodiment, the initial asset of the target time period includes an initial fixed asset amount of the target time period. Determining projected parameter data for the target time period in step 102, calculating projected asset data for the target time period from the projected parameter data for the target time period, comprising:

the fixed asset estimated amount for the target time period is calculated based on the fixed asset prepayment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time period.

In this alternative embodiment, information such as fixed asset prepayment, fixed asset cost, fixed asset depreciation, remaining asset cost, and remaining asset depreciation may be manually entered into the system by an operator or may be calculated by the system based on historical data.

It can be seen that this alternative embodiment can calculate the predicted amount of the fixed asset for the target time period based on the fixed asset prepayment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time period, and can improve the accuracy of the determined predicted amount of the fixed asset, providing a reliable data base for subsequent further asset estimation.

In yet another alternative embodiment, calculating the projected amount of the fixed asset for the target time period based on the fixed asset pre-payment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time period includes:

the predicted amount of fixed asset for the target time period is calculated according to the following formula:

FA2＝FAP+FAC–FAD+OAC–OAD；

Wherein FA2 is the predicted amount of the fixed asset in the target time period, FAP is the fixed asset prepayment in the target time period, FAC is the fixed asset cost in the target time period, FAD is the fixed asset depreciation in the target time period, OAC is the remaining asset cost in the target time period, and OAD is the remaining asset depreciation in the target time period.

The above formula combines fixed asset pre-payment, fixed asset cost, fixed asset depreciation, remaining asset cost and remaining asset depreciation of the target time period to calculate a fixed asset estimated amount of the target time period, the purpose of this calculation being to determine the stability and liquidity of the financial condition reflected by the budget in combination with each calculated estimated value, including the end-of-period inventory amount estimation data calculated previously, thereby contributing to an improvement in the financial condition even if the relevant predictions are revised.

In this alternative embodiment, the expected fixed asset amount in the target time period may be calculated according to the above formula, so that the accuracy of the determined expected fixed asset amount may be improved, and a reliable data base may be provided for further asset estimation.

In yet another alternative embodiment, estimating end-of-period asset estimation data for the target time period from the initial-of-period asset data for the target time period and the calculated predicted asset data for the target time period in step 103 includes:

calculating fixed asset amount estimation data at the end of the target time period according to the following formula:

FA＝FA1+FA2；

wherein, FA is the end fixed asset amount estimation data of the target time period, FA1 is the initial fixed asset amount of the target time period, and FA2 is the predicted fixed asset amount of the target time period.

Therefore, by implementing the above optional embodiment, the sum of the initial fixed asset amount of the target time period and the fixed asset estimated amount of the target time period can be determined as the end fixed asset amount estimation data of the target time period, which is favorable for improving the accuracy of the determined fixed asset estimated amount, reasonably reflecting the fixed asset estimated amount of the target time period company in the future, and providing a reference for important decisions of the enterprise.

In yet another alternative embodiment, the initial asset of the target time period includes an initial due capital expenditure of the target time period, the projected asset data of the target time period includes a fixed asset projected required payment amount of the target time period, and estimating the end-of-period asset estimation data of the target time period based on the initial asset data of the target time period and the calculated projected asset data of the target time period in step 103 includes:

the end of the target time period due capital expenditure is calculated according to the following formula:

CP＝CP1+CP2；

Wherein CP is the end of the target time period due capital expenditure, CP1 is the beginning of the target time period due capital expenditure, CP2 is the fixed asset of the target time period for which the required payment amount is expected.

It can be seen that, by implementing the above-mentioned alternative embodiment, the sum of the initial payable capital expenditure of the target time period and the expected payment amount of the fixed asset of the target time period can be determined as the final payable capital expenditure of the target time period, which is beneficial to improving the accuracy of the determined final payable capital expenditure, and is beneficial to reasonably reflecting the final payable capital expenditure amount of the company in the future target time period, and providing a reference for important decisions of the enterprise.

Example two

Referring to FIG. 2, FIG. 2 is a flow chart of another asset estimation method based on financial data according to an embodiment of the present invention. The method described in fig. 2 is applied to a computing device of a commodity sales attribute, where the computing device may be a corresponding computing terminal, computing device or server, and the server may be a local server or a cloud server, which is not limited by the embodiment of the present invention. As shown in FIG. 2, the financial data based asset estimation method may include the following operations:

201. Financial data in a financial system is acquired.

202. The predicted parameter data of the target time period is determined, and predicted asset data of the target time period is calculated according to the predicted parameter data of the target time period.

203. And estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period.

In the embodiment of the present invention, specific implementation details of steps 201 to 203 and explanation of corresponding technical terms may refer to the descriptions of steps 101 to 103 in the first embodiment, and the technical details already described in the first embodiment are not described in detail in this embodiment.

In an embodiment of the present invention, the financial data includes a plurality of financial data tables; each financial data table includes monetary data of a plurality of different data categories and corresponding financial information. Optionally, the data categories of the amount data include, but are not limited to, one or more of prepayment, accounts receivable, stock, payout amount, income amount, deposit, fixed asset value, borrowing, profit, and tax. Optionally, the financial information of the amount data includes, but is not limited to, one or more of company information, account information, transaction information, time information, store information, region information, currency information, accounting subject information, manager information, data creator information, and computer data attribute information corresponding thereto.

204. And establishing a mapping relation between the amount data associated to the same data category or financial information in all financial data tables based on a preset data mapping rule according to the data category of the amount data in the financial data tables and the corresponding financial information, so as to obtain the processed financial data.

205. And when receiving the query command of the user, pushing the target financial data or the final asset estimation data corresponding to the query command to the user.

In the embodiment of the invention, the query command is used for indicating one or more of data types to be queried, financial information and end-of-term asset estimation data, and specifically, the target financial data is amount data mapped with the data types and/or the financial information indicated by the query command in the processed financial data.

Optionally, pushing the target financial data or the end-of-period asset estimation data corresponding to the query command to the user may be implemented by a visual interface, for example, by providing the user with a visual query interface, receiving the query command input by the user through an interactive device such as a keyboard or a mouse, and pushing the target financial data or the end-of-period asset estimation data corresponding to the query command to the visual interface for displaying, so as to display the query result for the user.

Therefore, the embodiment of the invention can establish the mapping relation between the amount data which are related to the same data category or financial information in all financial data tables according to the data category of the amount data in the financial data tables and the corresponding financial information, so that the vast and complicated financial data are carded and optimized, an effective mapping relation is established between the data, further, the target financial data or asset estimation data can be provided for the user according to the query instruction of the user, and the previously established mapping relation is combined, so that the query result can be accurately and quickly provided for the user.

In an alternative embodiment, when a query command of the user is received in step 205, pushing target financial data or end-of-term asset estimation data corresponding to the query command to the user includes:

When a query command of a user is received, user information of the user is obtained;

Judging whether the user has permission to inquire target financial data or end-of-term asset estimation data corresponding to the inquiry command based on a preset permission rule according to user information of the user;

And pushing the target financial data or the end-of-period asset estimation data corresponding to the query command to the user when judging that the user has the authority to query the target financial data or the end-of-period asset estimation data corresponding to the query command.

In this alternative embodiment, the preset authority rule may include user authority levels corresponding to different user information, and data query ranges corresponding to different user authority levels, where the data query ranges may be directly set to include one or both of the target financial data or the end-of-period asset estimation data, or may be specifically set to include part of the target financial data or the end-of-period asset estimation data. Alternatively, the user information of different users may be associated with the query account or the query device of the user in advance, and then the user information of the user may be determined by the source account or the source device of the query command when the query command of the user is received.

Therefore, by implementing the alternative embodiment, the authority of the user desiring to inquire the data can be judged, and the inquiry result is provided for the user when the user is judged to have the inquiry authority of the corresponding data, so that the safety of data inquiry is ensured, and important financial data is prevented from being leaked.

In another alternative embodiment, the method further comprises:

According to the data types of the amount data in the financial data table and the corresponding financial information, classifying the amount data which are associated to the same data type or financial information in all the financial data tables based on a preset data classification rule, so as to obtain a plurality of classified data tables, and storing the plurality of classified data tables.

In this alternative embodiment, the preset data classification rules include, but are not limited to, one or more of monthly summary classification, accounting code classification, primary or secondary classification by accounting subject, classification by corporate information, classification by subject balance or subject change, classification by account, and classification by asset type.

It can be seen that by implementing this alternative embodiment, the amount data associated with the same data category or financial information in all the financial data tables can be classified, so that existing financial data can be further sorted and summarized, so that the local financial data can be stored more reasonably and orderly, and convenience and high efficiency in the process of subsequent query or data analysis are facilitated.

In yet another alternative embodiment, the method further comprises:

And when receiving the data comparison command of the user, pushing the two or more target financial data corresponding to the data comparison command to the user.

In this alternative embodiment, the user's data comparison command is used to indicate the two or more target financial data selected by the user, alternatively this may be accomplished through a visual interface, for example, the user may select two or more financial data to be compared on a visual interface on which a plurality of financial data has been displayed, the visual interface generating a data comparison command upon receipt of the user's selection, the server or local processor pushing the corresponding two or more target financial data to the user upon receipt of such data comparison command.

Further, the data comparison command may also indicate a user selected data comparison rule, which may include, but is not limited to, one or more of data difference calculation, data growth rate calculation, data analysis table generation, and data analysis graph generation. Further, according to the received data comparison rule indicated by the data comparison command of the user, corresponding data comparison operations may be performed on two or more target financial data corresponding to the data comparison command.

Therefore, by implementing the alternative embodiment, two or more target financial data corresponding to the data comparison command can be pushed to the user, so that the user can conveniently check the data to be compared, and further, the financial data can be compared and analyzed according to the data comparison rule appointed by the user, so that the user can more intuitively perceive the relation between related data, the efficiency of data analysis by the user is improved, and the smooth progress of financial work is facilitated.

Example III

Referring to fig. 3, fig. 3 is a schematic structural diagram of an asset estimation device based on financial data according to an embodiment of the present invention. The apparatus described in fig. 3 may be applied to a corresponding computing terminal, computing device, or server, and the server may be a local server or a cloud server, which is not limited by the embodiment of the present invention. As shown in fig. 3, the apparatus may include:

an acquisition module 301, configured to acquire financial data in a financial system;

A calculation module 302, configured to determine predicted parameter data of a target time period, and calculate predicted asset data of the target time period according to the predicted parameter data of the target time period;

And the estimating module 303 is configured to estimate end-of-period asset estimation data of the target period according to the initial-period asset data of the target period and the calculated estimated asset data of the target period.

Therefore, by implementing the embodiment of the invention, the end-of-period asset estimation data of the target time period can be estimated according to the initial-period asset data of the target time period and the calculated expected asset data of the target time period, so that the relation between the historical data in the financial data and the future financial situation can be established, the future asset situation of the company in the target time period can be reasonably reflected according to the situation of the historical data, the reference is provided for important decisions of the enterprise, and the manager can clearly and comprehensively master the asset development situation of the enterprise.

In an alternative embodiment, the initial asset data for the target time period includes an initial inventory amount for the target time period; the computing module 302 determines projected parameter data for the target time period, and computes a concrete manner of projected asset data for the target time period based on the projected parameter data for the target time period, including:

S20＝S1-S2；

In another alternative embodiment, the specific manner in which the calculation module 302 determines the projected production warehouse entry amount and the projected sales warehouse exit amount for the target time period includes:

S1＝P1*P2+P3；

S2＝S21+S22+S23+S24；

Further, the computing module 302 determines a specific manner of wholesale channel projected sales inventory, retail channel projected sales inventory, electronic commerce channel projected sales inventory for the target time period, including:

S21＝S211*S212/S213*S214；

S22＝S221*S222/S223*S224；

S23＝S231*S232/S233*S234；

In yet another alternative embodiment, the estimating module 303 estimates the concrete way of estimating the end-of-period asset estimation data of the target period according to the initial-period asset data of the target period and the calculated estimated asset data of the target period, including:

S＝S10+S20；

In yet another alternative embodiment, the calculation module 302 determines a particular manner in which the wholesale channel of the target time period is to estimate sales inventory, including:

In yet another alternative embodiment, the computing module 302 determines a particular manner in which the retail outlet for the target time period is to estimate sales inventory, including:

In yet another alternative embodiment, the calculation module 302 determines a particular manner in which the e-commerce channel of the target time period is to estimate sales inventory, including:

In yet another alternative embodiment, the initial asset of the target time period includes an initial fixed asset amount of the target time period. The computing module 302 determines projected parameter data for the target time period, and computes a concrete manner of projected asset data for the target time period based on the projected parameter data for the target time period, including:

In yet another alternative embodiment, the calculating module 302 calculates the particular manner in which the projected amount of the fixed asset for the target time period based on the fixed asset pre-payment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time period, includes:

FA2＝FAP+FAC–FAD+OAC–OAD；

FA＝FA1+FA2；

In yet another alternative embodiment, the initial period asset of the target period includes an initial due capital expenditure of the target period, the projected asset data of the target period includes a fixed asset projected required payment amount of the target period, and the estimating module 303 estimates the concrete manner of the final asset estimation data of the target period based on the initial period asset data of the target period and the calculated projected asset data of the target period, including:

CP＝CP1+CP2；

It can be seen that, by implementing the above-mentioned alternative embodiment, the sum of the initial payable capital expenditure of the target time period and the expected payment amount of the fixed asset of the target time period can be determined as the final payable capital expenditure of the target time period, which is beneficial to improving the accuracy of the determined final payable capital expenditure, and is beneficial to reasonably reflecting the final payable capital expenditure amount of the company of the target time period in the future, and providing reference for important decisions of the enterprise.

In yet another alternative embodiment, as shown in fig. 4, the apparatus further comprises:

And the mapping module 304 is configured to establish a mapping relationship between the amount data associated to the same data category or financial information in all the financial data tables based on a preset data mapping rule according to the data category of the amount data in the financial data tables and the corresponding financial information, so as to obtain the processed financial data.

And the pushing module 305 is configured to, when receiving a query command of the user, push target financial data or end-of-term asset estimation data corresponding to the query command to the user.

Optionally, the pushing module 305 pushes the target financial data or the end-of-period asset estimation data corresponding to the query command to the user, which may be implemented by a visual interface, for example, by providing the user with a visual query interface, receiving the query command input by the user through an interactive device such as a keyboard or a mouse, and pushing the target financial data or the end-of-period asset estimation data corresponding to the query command to the visual interface for displaying, so as to show the query result for the user.

Therefore, the optional embodiment can establish a mapping relation between the amount data which is related to the same data category or financial information in all financial data tables according to the data category of the amount data in the financial data tables and the corresponding financial information, so that the vast financial data is carded and optimized, an effective mapping relation is established between the data, further, target financial data or asset estimation data can be provided for the user according to the query instruction of the user, and the previously established mapping relation is combined, so that a query result can be accurately and rapidly provided for the user.

In yet another alternative embodiment, as shown in fig. 4, the pushing module 305 includes:

an acquiring unit 3051, configured to acquire user information of a user when receiving a query command of the user;

The judging unit 3052 is configured to judge, according to user information of a user, whether the user has authority to query target financial data or end-of-term asset estimation data corresponding to the query command based on a preset authority rule;

And the pushing unit 3053 is configured to push the target financial data or the end-of-term asset estimation data corresponding to the query command to the user when the determining unit 3052 determines that the user has the authority to query the target financial data or the end-of-term asset estimation data corresponding to the query command.

In yet another alternative embodiment, the apparatus further comprises:

the classifying module is used for classifying the amount data which are associated to the same data category or financial information in all the financial data tables based on a preset data classifying rule according to the data category of the amount data in the financial data tables and the corresponding financial information so as to obtain a plurality of classified data tables and storing the plurality of classified data tables.

In yet another alternative embodiment, the apparatus further comprises:

And the data comparison module is used for pushing the two or more target financial data corresponding to the data comparison command to the user when the data comparison command of the user is received.

Example IV

Referring to fig. 5, fig. 5 is a schematic structural diagram of an asset estimation device based on financial data according to an embodiment of the present invention. As shown in fig. 5, the apparatus may include:

a memory 401 storing executable program codes;

A processor 402 coupled with the memory 401;

Processor 402 invokes executable program code stored in memory 401 to perform some or all of the steps in the financial data based asset estimation method disclosed in either embodiment one or embodiment two of the present invention.

Example five

The embodiment of the invention discloses a computer storage medium which stores computer instructions for executing part or all of the steps in the asset estimation method based on financial data disclosed in the first or second embodiment of the invention when the computer instructions are called.

The apparatus embodiments described above are merely illustrative, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.

Finally, it should be noted that: the embodiment of the invention discloses an asset estimation method and device based on financial data, which are only disclosed in the preferred embodiment of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. A method of asset estimation based on financial data, the method comprising:

Estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated estimated asset data of the target time period;

The end-of-period asset estimation data is determined through initial-period asset data of the target time period and first type information of the target time period, wherein when the initial-period asset data of the target time period comprises initial-period inventory amount of the target time period, the first type information comprises estimated production warehouse-in amount and estimated sales warehouse-out amount; when the initial period asset data of the target time period includes an initial fixed asset amount of the target time period, the first type of information includes fixed asset prepayment, fixed asset cost, fixed asset depreciation, remaining asset cost, and remaining asset depreciation;

When the initial period asset data of the target time period comprises the initial period inventory amount of the target time period, the first type information is determined through the second type information; the second type of information is determined through an average value or a median value of third type of information of a plurality of target historical time periods, or through a relational expression or a relational curve between the third type of information of the plurality of target historical time periods and the time period length or the time period of the corresponding target historical time period and the time period length or the time period of the target time period, or through a model obtained by training a neural network algorithm through the third type of information of the plurality of target historical time periods and the time period parameters of the target historical time period as a training set, or through correlation between the third type of information of the historical time period which accords with a preset time period characteristic rule and fourth type of information of the target time period to be predicted;

The third type of information comprises one of historical wholesale channel sales inventory data, historical retail channel estimated sales inventory and historical electronic commerce channel estimated sales inventory, the fourth type of information is the same as the third type of information, and the fourth type of information is one of wholesale channel estimated sales inventory, retail channel estimated sales inventory and electronic commerce channel estimated sales inventory;

The financial data includes a plurality of financial data tables; each financial data table comprises a plurality of amount data with different data types and corresponding financial information; the method further comprises the steps of:

When a query command of a user is received, pushing target financial data or end-of-term asset estimation data corresponding to the query command to the user; the inquiry command is used for indicating one or more of data types to be inquired, financial information and end-of-term asset estimation data; the target financial data is amount data which is mapped with the data category and/or the financial information indicated by the query command in the processed financial data;

The method further comprises the steps of:

Classifying the amount data which are related to the same data category or financial information in all financial data tables based on preset data classification rules according to the data category of the amount data in the financial data tables and the corresponding financial information, so as to obtain a plurality of classified data tables, and storing the data tables after the classification, wherein the preset data classification rules comprise one or more of monthly summarization classification, accounting code classification, primary or secondary subject classification according to accounting subjects, company information classification, subject balance or subject change amount classification, account classification and asset type classification.

2. The financial data based asset estimation method of claim 1 wherein the initial asset data for the target time period comprises an initial inventory amount for the target time period; the determining the predicted parameter data of the target time period, calculating the predicted asset data of the target time period according to the predicted parameter data of the target time period, comprising:

S20＝S1-S2；

And estimating end-of-period asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period, wherein the estimating end-of-period asset estimation data of the target time period comprises the following steps:

S＝S10+S20；

3. A method of estimating a property based on financial data as claimed in claim 2 wherein said determining an estimated production warehouse entry amount and an estimated sales warehouse exit amount for said target time period comprises:

S1＝P1*P2+P3；

S2＝S21+S22+S23+S24；

S21＝S211*S212/S213*S214；

S22＝S221*S222/S223*S224；

S23＝S231*S232/S233*S234；

4. The financial data based asset estimation method of claim 1, wherein the initial asset data for the target time period comprises an initial fixed asset amount for the target time period; the determining the predicted parameter data of the target time period, calculating the predicted asset data of the target time period according to the predicted parameter data of the target time period, comprising:

FA＝FA1+FA2；

5. The financial data based asset estimation method of claim 4, wherein calculating the fixed asset estimated amount for the target time period based on the fixed asset pre-payment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time period comprises:

FA2＝FAP+FAC–FAD+OAC–OAD；

6. A method of estimating a financial data based asset according to any of claims 1-5, wherein upon receiving a query command from a user, pushing target financial data or end-of-term asset estimation data corresponding to the query command to the user comprises:

7. A financial data based asset estimation device for performing a financial data based asset estimation method as claimed in any one of claims 1 to 6, and comprising:

the estimating module is used for estimating the final asset estimation data of the target time period according to the initial asset data of the target time period and the calculated expected asset data of the target time period;

The third type of information comprises one of historical wholesale channel sales inventory data, historical retail channel estimated sales inventory and historical electronic commerce channel estimated sales inventory, the fourth type of information is the same as the third type of information, and the fourth type of information is one of wholesale channel estimated sales inventory, retail channel estimated sales inventory and electronic commerce channel estimated sales inventory.

8. An asset estimation device based on financial data, the device comprising:

a memory storing executable program code;

a processor coupled to the memory;

The processor invokes the executable program code stored in the memory to perform the financial data based asset estimation method as claimed in any one of claims 1 to 5.

9. A computer storage medium storing computer instructions which, when invoked, are operable to perform a financial data based asset estimation method as claimed in any one of claims 1 to 5.