CN112529675A

CN112529675A - Asset estimation method and device based on financial data

Info

Publication number: CN112529675A
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: 2021-03-19

Abstract

In the embodiment of the invention, financial data in a financial system is obtained; the financial data comprises an initial asset data for a target time period; determining the expected parameter data of the target time period, and calculating the expected asset data of the target time period according to the expected parameter data of the target time period; and estimating the end-term 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. Therefore, the method and the system can establish the relation between the historical data and the future financial condition in the financial data, effectively predict the condition of the assets according to the historical data, reasonably reflect the future asset condition of the company in the target time period, provide reference for important decisions of the enterprise, and are also beneficial for managers to clearly and comprehensively master the asset development condition 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

The asset estimation is an important ring in the financial accounting work, the asset estimation 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 for managers to clearly and comprehensively master the asset development condition of the enterprise.

The existing ERP software can only record financial data of an enterprise, the accounting of the enterprise in an accounting period is delayed, the accounting in the accounting period in the current period cannot timely and truly reflect the financial condition of the company due to the problems of account entry time and the like, and meanwhile, the existing financial accounting technology does not consider the condition between the existing financial data and the future asset condition of the enterprise, so that the asset estimation of the enterprise cannot be carried out.

Disclosure of Invention

The invention aims to solve the technical problem of providing an asset estimation method and device based on financial data, which can establish the relationship between historical data and future financial conditions in the financial data, effectively predict the asset conditions according to the historical data, reasonably reflect the future asset conditions of a company in a target time period, provide reference for important decisions of an enterprise, and facilitate managers to clearly and comprehensively master the asset development conditions of the enterprise.

In order to solve the technical problem, the 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 comprises an initial asset data for a target time period;

determining the expected parameter data of the target time period, and calculating the expected asset data of the target time period according to the expected parameter data of the target time period;

and estimating the end-term 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.

As an alternative embodiment, 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 expected parameter data of the target time period and calculating the expected asset data of the target time period according to the expected parameter data of the target time period comprise:

determining the expected production warehousing amount and the expected sales ex-warehouse amount of the target time period;

calculating a projected change inventory amount for the target time period based on the projected production inventory amount and the projected sales inventory amount for the target time period according to the following formula:

S20＝S1-S2；

wherein S20 is the expected change inventory amount of the target time period, S1 is the expected production warehousing amount of the target time period, and S2 is the expected sales ex-warehouse amount of the target time period;

estimating end-of-term asset estimation data of the target time period according to the initial-of-term asset data of the target time period and the calculated estimated asset data of the target time period, comprising:

calculating end-of-term inventory amount estimate data for the target time period according to the following formula:

S＝S10+S20；

wherein S is estimated data of the end inventory amount of the target time period, S10 is the initial inventory amount of the target time period, and S20 is the expected change inventory amount of the target time period.

As an optional implementation manner, in the first aspect of the present invention, the determining the expected production warehousing amount and the expected sales warehousing amount of the target time period includes:

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

S1＝P1*P2+P3；

s1 is the estimated production warehousing amount of the target time period, P1 is the estimated production warehousing quantity of the target time period, P2 is the production cost coefficient of the target time period, and P3 is the estimated purchase warehousing inventory value of the target time period;

determining the values of the wholesale channel estimated sales inventory, the retail channel estimated sales inventory, the e-commerce channel estimated sales inventory and the new shop estimated stock, and calculating the estimated sales outbound amount of the target time period according to the following formula:

S2＝S21+S22+S23+S24；

wherein S2 is the estimated sales outbound 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;

and the determining of the wholesale channel projected sales inventory, retail channel projected sales inventory, e-commerce channel projected sales inventory of the target time period comprises:

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

S21＝S211*S212/S213*S214；

the step S21 is to predict sales inventory for the wholesale channel of the target time period, the step S211 is to budget for the wholesale channel of the target time period, the step S212 is to add value tax for wholesale sales of the target time period, the step S213 is to unit price of historical wholesale products of the target time period, and the step S214 is to coefficient of wholesale sales price of the target time period;

determining the budget of the retail channel, the value-added tax of retail sales, the unit price of historical retail products and the coefficient of retail sales price of the target time period, and calculating the expected sales inventory of the retail channel of the target time period according to the following formula:

S22＝S221*S222/S223*S224；

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

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

S23＝S231*S232/S233*S234；

the step S23 is to estimate sales inventory for the e-commerce channel of the target time period, the step S231 is to estimate the e-commerce channel budget of the target time period, the step S232 is to sell the value-added tax for the e-commerce of the target time period, the step S233 is to obtain the historical e-commerce product unit price of the target time period, and the step S234 is to obtain the e-commerce sales price coefficient of the target time period.

As an alternative embodiment, in the first aspect of the invention, the initial asset of the target time period comprises an initial fixed asset amount of the target time period; the determining the expected parameter data of the target time period and calculating the expected asset data of the target time period according to the expected parameter data of the target time period comprise:

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

calculating the expected fixed asset amount of the target time period according to the fixed asset prepayment, the fixed asset cost, the fixed asset depreciation, the other asset cost and the other asset depreciation of the target time period;

calculating end fixed asset amount estimate data for the target time period according to the following formula:

FA＝FA1+FA2；

and FA is estimation data of the final fixed asset amount of the target time period, FA1 is the initial fixed asset amount of the target time period, and FA2 is the estimated fixed asset amount of the target time period.

As an alternative embodiment, in the first aspect of the present invention, the calculating the expected fixed asset amount 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 includes:

calculating the expected fixed asset amount for the target time period according to the following formula:

FA2＝FAP+FAC–FAD+OAC–OAD；

the FA2 is the expected amount of the fixed assets in the target time period, the FAP prepays the fixed assets in the target time period, the FAC is the fixed assets cost in the target time period, the FAD is the depreciation of the fixed assets in the target time period, the OAC is the rest assets cost in the target time period, and the OAD is the rest assets depreciation in the target time period.

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

according to the data type of the amount data in the financial data table and corresponding financial information, establishing a mapping relation between the amount data related to the same data type or financial information in all the financial data tables based on a preset data mapping rule so as to obtain processed financial data;

when an inquiry command of a user is received, target financial data or end-of-term asset estimation data corresponding to the inquiry command is pushed to the user; the query command is used for indicating one or more of a data category to be queried, financial information and end-of-term asset estimation data; the target financial data is money amount data mapped with the data category and/or financial information indicated by the query command in the processed financial data.

As an optional implementation manner, in the first aspect of the present invention, when 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 includes:

when receiving a query command of a user, acquiring user information of the user;

judging whether the user has the authority to inquire the target financial data or the end-of-term 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 when the user is judged to have the right to inquire the target financial data or the end-of-term asset estimation data corresponding to the inquiry command, pushing the target financial data or the end-of-term asset estimation data corresponding to the inquiry command to the user.

In a second aspect, the invention discloses 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 comprises an initial asset data for a target time period;

the calculation module is used for determining the expected parameter data of the target time period and calculating the expected asset data of the target time period according to the expected parameter data of the target time period;

and the estimation module is used for estimating the estimation data of the end-of-term assets of the target time period according to the initial-term asset data of the target time period and the calculated estimated 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 the expected parameter data of the target time period, and calculates the specific mode of the expected asset data of the target time period according to the expected parameter data of the target time period, wherein the specific mode comprises the following steps:

S20＝S1-S2；

wherein S20 is the expected change inventory amount of the target time period, S1 is the expected production warehousing amount of the target time period, and S2 is the expected sales ex-warehouse amount of the target time period; the specific way of estimating the end-of-term asset estimation data of the target time period by the estimation module according to the initial-term asset data of the target time period and the calculated estimated asset data of the target time period comprises:

S＝S10+S20；

As an optional implementation manner, in the second aspect of the present invention, a specific manner of determining the expected production warehousing amount and the expected sales export amount of the target time period by the calculation module includes:

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 the expected parameter data of the target time period, and calculates the specific mode of the expected asset data of the target time period according to the expected parameter data of the target time period, wherein the specific mode comprises the following steps:

the specific way of estimating the end-of-term asset estimation data of the target time period by the estimation module according to the initial-term asset data of the target time period and the calculated estimated asset data of the target time period comprises:

FA＝FA1+FA2；

As an alternative embodiment, in the second aspect of the present invention, the calculating the expected fixed asset amount 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 includes:

FA2＝FAP+FAC–FAD+OAC–OAD；

As an alternative embodiment, in the 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 of different data categories and corresponding financial information; the device further comprises:

the mapping module is used for establishing a mapping relation between the money data related to the same data type or financial information in all the financial data tables based on a preset data mapping rule according to the data type of the money data in the financial data tables and the corresponding financial information so as to obtain the processed financial data;

the system comprises a pushing module, a storage module and a processing module, wherein the pushing module is used for pushing target financial data or end-of-term asset estimation data corresponding to a query command to a user when the query command of the user is received; the query command is used for indicating one or more of a data category to be queried, financial information and end-of-term asset estimation data; the target financial data is money amount data mapped with the data category and/or financial information indicated by the query command in the processed financial data.

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

the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring user information of a user when receiving a query command of the user;

the judging unit is used for judging whether the user has the authority to inquire the target financial data or the end-of-term 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-term asset estimation data corresponding to the query command to the user when the user is judged to have the authority of querying the target financial data or the end-of-term asset estimation data corresponding to the query command.

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

a memory storing executable program code;

a processor coupled with the memory;

the processor calls the executable program code stored in the memory to execute part or all of the steps of the financial data-based asset estimation method disclosed in the first aspect of the embodiment of the present invention.

A fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are configured to perform some or all of the steps of the method for asset estimation based on financial data disclosed in the first aspect 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 obtained; the financial data comprises an initial asset data for a target time period; determining the expected parameter data of the target time period, and calculating the expected asset data of the target time period according to the expected parameter data of the target time period; and estimating the end-term 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. Therefore, the method can calculate the predicted asset data of the target time period, estimate the estimated asset data of the end of the target time period by combining the initial asset data of the target time period in the financial data in the financial system, establish the relation between the historical data and the future financial condition in the financial data, effectively predict the asset condition according to the historical data, reasonably reflect the future asset condition of a company of the target time period, provide reference for important decisions of enterprises, and be beneficial for managers to clearly and comprehensively master the asset development condition of the enterprises.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram of a method for asset estimation based on financial data according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart diagram of another method for financial data based asset estimation according to an embodiment of the present disclosure;

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 another financial data based asset estimation device according to an embodiment of the present disclosure;

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

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.

The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively 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 can be included in at least one embodiment of the invention. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The invention discloses an asset estimation method and device based on financial data, which can establish the relationship between historical data and future financial conditions in the financial data, effectively predict the asset conditions according to the historical data, reasonably reflect the future asset conditions of a company in a target time period, provide reference for important decisions of an enterprise, and be beneficial for managers to clearly and comprehensively master the asset development conditions of the enterprise. The following are detailed below.

Example one

Referring to fig. 1, fig. 1 is a schematic flowchart illustrating 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, a computing device, or a server, and the server may be a local server or a cloud server, and the embodiment of the present invention is not limited thereto. As shown in FIG. 1, the method for financial data based asset estimation may include the operations of:

101. financial data in a financial system is obtained.

In an embodiment of the invention, the financial data comprises initial asset data for the target time period. Optionally, the financial data includes the initial asset data may be an initial inventory amount, an initial fixed asset amount, or an initial payable capital expenditure for the target time period. Specifically, 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, specifically, the manner of obtaining the financial data in the financial system may be to send a data request to the financial system through a pre-established communication connection, and receive the financial data sent by the financial system in response to the data request, or may be to directly extract the stored financial data from a storage device of the financial data, and in this scenario, the stored financial data should be updated in time, so as to prevent the timeliness of subsequent calculations from being lost due to an over-old version.

102. And determining the expected parameter data of the target time period, and calculating the expected asset data of the target time period according to the expected parameter data of the target time period.

In the embodiment of the invention, the forecast parameter data is used for calculating the forecast asset data, and the forecast parameter data can be acquired by manually inputting the data into a computer by an operator or by historical data. Specifically, the predicted parameter data includes one or more of a predicted production warehousing amount, a predicted sales ex-warehousing amount, a fixed asset prepayment, a fixed asset cost, a fixed asset depreciation, a remaining asset cost and a remaining asset depreciation for a target time period, and the predicted asset data includes one or more of a predicted change inventory amount, a predicted fixed asset amount and a predicted fixed asset payment amount for the target time period.

103. And estimating the end-of-term 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.

Therefore, by implementing the method described by the embodiment of the invention, the end-of-term asset estimation data of the target time period can be estimated according to the beginning asset data of the target time period and the calculated estimated asset data of the target time period, so that the relation between the historical data in the financial data and the future financial condition can be established, the condition of the asset can be effectively predicted according to the historical data, the future asset condition of a company in the target time period can be reasonably reflected, a reference is provided for important decisions of an enterprise, and managers can clearly and comprehensively master the asset development condition 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 the expected parameter data of the target time period in step 102, and calculating the expected asset data of the target time period according to the expected parameter data of the target time period, including:

determining the expected production warehousing amount and the expected sales ex-warehousing amount in a target time period;

S20＝S1-S2；

wherein, S20 is the expected change inventory amount of the target time period, S1 is the expected production warehousing amount of the target time period, and S2 is the expected sales ex-warehouse amount of the target time period.

Therefore, by implementing the optional embodiment, the expected change inventory amount of the target time period can be calculated according to the expected production inventory amount and the expected sales inventory amount of the target time period, so that a more accurate calculation result can be obtained, and a reliable data base is provided for subsequent further asset estimation.

In another alternative embodiment, determining the projected production warehousing amount and the projected sales outbound amount for the target time period comprises:

S1＝P1*P2+P3；

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

determining the values of the wholesale channel estimated sales inventory, the retail channel estimated sales inventory, the E-commerce channel estimated sales inventory and the new shop estimated shop inventory of the target time period, and calculating the estimated sales outbound amount of the target time period according to the following formula:

S2＝S21+S22+S23+S24；

wherein S2 is the estimated sales shipment amount of the target time slot, S21 is the estimated sales inventory of the wholesale channel of the target time slot, S22 is the estimated sales inventory of the retail channel of the target time slot, S23 is the estimated sales inventory of the e-commerce channel of the target time slot, and S24 is the estimated stock value of the new shop of the target time slot.

Further, determining a wholesale channel projected sales inventory, a retail channel projected sales inventory, and an e-commerce channel projected sales inventory for the target time period includes:

determining the budget of a wholesale channel, the wholesale sales value-added tax, the unit price of historical wholesale products and the wholesale sales price coefficient of a target time period, and calculating the estimated sales inventory of the wholesale channel 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 a 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 the budget of the retail channel, the value-added tax of retail sales, the unit price of historical retail products and the coefficient of retail sales price in the target time period, and calculating the expected sales inventory of the retail channel in the target time period according to the following formula:

S22＝S221*S222/S223*S224；

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

S23＝S231*S232/S233*S234；

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

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

Therefore, by implementing the optional embodiment, the expected production warehousing amount of the target time period can be calculated according to the expected production warehousing quantity, the production cost coefficient and the expected purchasing warehousing inventory value, the expected sales inventory of the wholesale, retail and e-commerce channels can be calculated according to the channel budget, the sales added value tax, the historical product unit price, the sales price coefficient and other information corresponding to different channels, so that more accurate expected sales inventory can be obtained, the expected change inventory amount of the target time period can be determined according to the expected sales inventory and the expected production warehousing amount of all sales channels, more accurate calculation results can be obtained, and a reliable data base can be provided for subsequent further asset estimation.

In yet another alternative embodiment, the estimating end-of-term asset valuation data for the target time period based on the initial asset data for the target time period and the calculated projected asset data for the target time period in step 103 comprises:

S＝S10+S20；

wherein S is estimated data of the end stock amount of the target time period, S10 is the initial stock amount of the target time period, and S20 is the expected change stock amount of the target time period.

Therefore, by implementing the optional embodiment, the estimation data of the end-of-term inventory amount can be calculated according to the sum of the initial asset data and the expected change inventory amount in the target time period, the estimation data of the end-of-term inventory amount can be accurately and quickly estimated by using the historical data and the estimation data, the method is beneficial to reasonably reflecting the inventory amount of a company in the target time period in the future, and provides reference for important decisions of enterprises.

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

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

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

Optionally, the expected sales inventory of the wholesale channel of the target time period may be obtained by calculating a relation or a relation curve between the historical wholesale channel sales inventory data 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 according to the time period length or the time interval of the target time period by the calculated relation or the calculated relation curve.

Optionally, 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 trained model is used to predict a planned sales inventory of a wholesale channel of a target time period. Specifically, the time period parameters of the target historical time period include, but are not limited to, information such as a time period length, a time period interval, and sales activities in the time period.

Optionally, the target historical time period may be selected, and the historical time period which meets a preset time period characteristic rule with the target time period may be selected, so as to improve the correlation between the historical wholesale channel sales inventory data of the selected target historical time period and the wholesale channel estimated sales inventory of the target time period to be predicted, thereby improving the accuracy of prediction. Optionally, the preset time period characteristic rules may include, but are not limited to, rules belonging to the same quarter, month, business, starting month and/or ending month, and equal time period length.

In yet another alternative embodiment, determining a retail channel projected sales inventory for a target time period comprises:

historical retail channel selling inventory data in a plurality of target historical time periods are obtained, and the expected selling inventory of the retail channel in the target time period is determined according to the historical retail channel selling inventory data in the plurality of target historical time periods.

Alternatively, the expected sales inventory of the retail channel for the target time period may be determined by calculating an average or median of historical retail channel sales inventory data for a plurality of target historical time periods, and determining the calculated average or median of historical retail channel sales inventory data.

Optionally, the expected sales inventory of the retail channel of the target time period may be obtained by calculating a relational expression or a relational curve between the historical retail channel sales inventory data of the multiple target historical time periods and the time period length or the time interval of the corresponding target historical time period, and predicting according to the time period length or the time interval of the target time period by using the calculated relational expression or the calculated relational curve.

Optionally, 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 trained model is used to predict a predicted sales inventory of a retail channel of the target time period. Specifically, the time period parameters of the target historical time period include, but are not limited to, information such as a time period length, a time period interval, and sales activities in the time period.

Optionally, the target historical time period may be selected, and the historical time period which meets a preset time period characteristic rule with the target time period may be selected, so as to improve the correlation between the historical retail channel sales inventory data of the selected target historical time period and the retail channel estimated sales inventory of the target time period to be predicted, thereby improving the accuracy of prediction. Optionally, the preset time period characteristic rules may include, but are not limited to, rules belonging to the same quarter, month, business, starting month and/or ending month, and equal time period length.

In yet another alternative embodiment, determining an e-commerce channel projected sales inventory for a target time period comprises:

obtaining historical e-commerce channel selling inventory data in a plurality of target historical time periods, and determining the e-commerce channel predicted selling inventory of the target time periods according to the historical e-commerce channel selling inventory data of 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 a plurality of target historical time periods can be calculated, and the calculated average value or the median value of the historical e-commerce channel sales inventory data is determined as the e-commerce channel estimated sales inventory of the target time period.

Optionally, the expected sales inventory of the e-commerce channel of the target time period can be obtained by calculating a relational expression or a relational curve between the historical e-commerce channel sales inventory data of the multiple target historical time periods and the time period length or the time interval of the corresponding target historical time period, and predicting the expected sales inventory of the e-commerce channel of the target time period according to the time period length or the time interval of the target time period by using the calculated relational expression or the calculated relational curve.

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 trained model is used to predict e-commerce channel expected sales inventory of the target time periods. Specifically, the time period parameters of the target historical time period include, but are not limited to, information such as a time period length, a time period interval, and sales activities in the time period.

Optionally, the target historical time period is selected, and the historical time period which accords with the preset time period characteristic rule with the target time period can be selected, so that the 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 is improved, and the prediction accuracy is improved. Optionally, the preset time period characteristic rules may include, but are not limited to, rules belonging to the same quarter, month, business, starting month and/or ending month, and equal time period length.

In yet another alternative embodiment, the initial asset of the target time period comprises an initial fixed asset amount of the target time period. Determining the expected parameter data of the target time period in step 102, and calculating the expected asset data of the target time period according to the expected parameter data of the target time period, including:

determining a fixed asset prepayment, a fixed asset cost, a fixed asset depreciation, a remaining asset cost, and a remaining asset depreciation for a target time period;

and calculating the expected fixed asset amount of the target time period according to the fixed asset prepayment, the fixed asset cost, the fixed asset depreciation, the rest asset cost and the rest asset depreciation of the target time period.

In this alternative embodiment, the information of the pre-paid fixed asset, the cost of the fixed asset, the depreciation of the fixed asset, the cost of the remaining assets, and the depreciation of the remaining assets may be manually entered into the system by the operator, or may be calculated by the system based on historical data.

Therefore, the optional embodiment can calculate the estimated fixed asset amount in the target time period according to the fixed asset prepayment, the fixed asset cost, the fixed asset depreciation, the other asset cost and the other asset depreciation in the target time period, can improve the accuracy of the determined estimated fixed asset amount, and provides a reliable data base for further asset estimation in the follow-up process.

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

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

FA2＝FAP+FAC–FAD+OAC–OAD；

the FA2 is the expected fixed asset amount in the target time period, FAP is the prepayment of the fixed asset in the target time period, FAC is the fixed asset cost in the target time period, FAD is the depreciation of the fixed asset in the target time period, OAC is the cost of the rest assets in the target time period, and OAD is the depreciation of the rest assets in the target time period.

The above formula calculates the estimated fixed asset amount for the target time slot in combination with the fixed asset advance payment, the fixed asset cost, the fixed asset depreciation, the remaining asset cost, and the remaining asset depreciation for the target time slot, and this calculation is aimed at judging the stability and liquidity of the financial situation reflected by the budget in combination with each calculated estimated value including the estimation data of the last term inventory amount calculated before, thereby contributing to improvement of the financial situation even if the relevant prediction is revised.

Therefore, in the optional embodiment, the expected fixed asset amount in the target time period can be calculated according to the formula, so that the accuracy of the determined expected fixed asset amount can be improved, and a reliable data base is provided for further asset estimation in the following process.

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

FA＝FA1+FA2；

the FA is estimation data of the final fixed asset amount of the target time period, the FA1 is the initial fixed asset amount of the target time period, and the FA2 is the estimated fixed asset amount of the target time period.

Therefore, by implementing the optional embodiment, the sum of the initial fixed asset amount in the target time period and the estimated fixed asset amount in the target time period can be determined as the estimated data of the final fixed asset amount in the target time period, so that the accuracy of the determined estimated fixed asset amount can be improved, the estimated fixed asset amount of a company in the target time period in the future can be reasonably reflected, and reference can be provided for important decisions of enterprises.

In yet another alternative embodiment, the initial asset of the target time period comprises the initial payable capital expenditure of the target time period, the projected asset data of the target time period comprises the projected required payment amount of the fixed asset of the target time period, and the estimated end asset data of the target time period is estimated in step 103 based on the initial asset data of the target time period and the calculated projected asset data of the target time period, comprising:

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

CP＝CP1+CP2；

where CP is the end due capital expenditure for the target time period, CP1 is the beginning due capital expenditure for the target time period, and CP2 predicts the required payment for the fixed asset for the target time period.

Therefore, by implementing the optional embodiment, the sum of the initial due capital expenditure in the target time period and the expected required payment amount of the fixed asset in the target time period can be determined as the final due capital expenditure in the target time period, so that the accuracy of the determined final due capital expenditure can be improved, the final due capital expenditure amount of the company in the target time period in the future can be reasonably reflected, and a reference is provided for important decisions of the enterprise.

Example two

Referring to fig. 2, fig. 2 is a schematic flow chart illustrating another method for asset estimation 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 volume attribute, where the computing device may be a corresponding computing terminal, a computing device, or a server, and the server may be a local server or a cloud server, and the embodiment of the present invention is not limited thereto. As shown in FIG. 2, the method for financial data based asset estimation may include the operations of:

201. financial data in a financial system is obtained.

202. And determining the expected parameter data of the target time period, and calculating the expected asset data of the target time period according to the expected parameter data of the target time period.

203. And estimating the end-of-term 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.

In the embodiment of the present invention, the specific implementation details of

steps

201 and 203 and the explanation of the corresponding technical terms may refer to the description related to

steps

101 and 103 in the first embodiment, and the technical details already described in the first embodiment are not repeated in this embodiment.

In the embodiment of the invention, the financial data comprises a plurality of financial data tables; each financial data table comprises a plurality of different data types of money data and corresponding financial information. Optionally, the data categories of the amount data include, but are not limited to, one or more of pre-paid, accounts receivable, equity, expenditure amount, income amount, deposit, fixed asset value, debit, 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 information, passerby information, data creator information, and computer data attribute information corresponding thereto.

204. And establishing a mapping relation between the amount data related to the same data type or financial information in all the financial data tables based on a preset data mapping rule according to the data type 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 a query command of the user is received, pushing target financial data or end-of-term asset estimation data corresponding to the query command to the user.

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

Optionally, the target financial data or the end-of-term asset estimation data corresponding to the query command is pushed to the user, and may be implemented in a visual interface, for example, a visual query interface is provided for the user, the query command input by the user through an interactive device such as a keyboard or a mouse is received, and the target financial data or the end-of-term asset estimation data corresponding to the query command is pushed to the visual interface to be displayed, so as to display the query result for the user.

Therefore, according to the embodiment of the invention, the mapping relation can be established between the amount data related to the same data type or financial information in all the financial data tables according to the data type and the corresponding financial information of the amount data in the financial data tables, so that the numerous and complicated financial data are sorted and optimized, an effective mapping relation is established between the data, further, the target financial data or asset estimation data can be provided for a user according to the query instruction of the user, the mapping relation established before is combined, the query result can be accurately and quickly provided for the user, and compared with the condition that the existing financial system only can store the data or perform simple query, the method can obviously provide better query experience for the user and is beneficial to improving the efficiency of financial work.

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

In this alternative embodiment, the preset authority rules 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 and the end-of-term asset estimation data, or may be specifically set to include part of the target financial data or the end-of-term asset estimation data. Optionally, the user information of different users may be associated with the query account or the query device of the user in advance, and the user information of the user may be determined by querying the source account or the source device of the command when the query command of the user is received.

Therefore, by implementing the optional embodiment, the authority of the user 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 the important financial data is prevented from being leaked.

In another optional embodiment, the method further comprises:

and classifying the amount data related to the same data category or financial information in all the financial data tables based on a preset data classification rule according to the data category of the amount data in the financial data tables and the corresponding financial information to obtain a plurality of classified data tables, and storing the plurality of classified data tables.

In this alternative embodiment, the predetermined data classification rules include, but are not limited to, one or more of a monthly summary classification, an accounting number classification, a primary subject or secondary subject classification based on accounting subject, a company information classification, a subject balance or subject change amount classification, an account classification, and an asset type classification.

Therefore, by implementing the optional embodiment, the money amount data related to the same data category or financial information in all the financial data tables can be classified, so that the existing financial data is further sorted and summarized and grouped, the local financial data is stored more reasonably and orderly, and convenience and high efficiency in subsequent query or data analysis are facilitated.

In yet another optional embodiment, the method further comprises:

and when a data comparison command of the user is received, pushing two or more target financial data corresponding to the data comparison command to the user.

In this alternative embodiment, the data comparison command of the user is used to indicate two or more target financial data selected by the user, and optionally, this operation may be implemented through a visual interface, for example, the user may select two or more financial data to be compared in the visual interface on which the plurality of financial data are displayed, the visual interface generates the data comparison command when receiving the selection operation of the user, and the server or the local processor pushes the corresponding two or more target financial data to the user when receiving the 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 a data difference calculation, a data growth rate calculation, a data analysis table generation, and a data analysis graph generation. Further, according to the data comparison rule indicated by the received 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 optional 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 specified by the user, so that the user can more intuitively sense the relationship between the related data, the efficiency of data analysis performed by the user is improved, and the financial work can be promoted to be smoothly performed.

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, a corresponding computing device, or a corresponding server, where the server may be a local server or a cloud server, and the embodiment of the present invention is not limited thereto. As shown in fig. 3, the apparatus may include:

an obtaining module 301, configured to obtain financial data in a financial system;

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

And the estimation module 303 is configured to estimate the end-of-term 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.

Therefore, by implementing the embodiment of the invention, the end-of-term 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 estimated asset data of the target time period, so that the relation between the historical data and the future financial condition in the financial data can be established, the condition of the asset can be effectively predicted according to the historical data, the future asset condition of a company in the target time period can be reasonably reflected, reference is provided for important decisions of an enterprise, and managers can clearly and comprehensively master the asset development condition 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 calculation module 302 determines the expected parameter data of the target time period, and calculates the expected asset data of the target time period according to the expected parameter data of the target time period, including:

S20＝S1-S2；

In another alternative embodiment, the calculation module 302 determines the specific manner of the expected production warehousing amount and the expected sales export amount for the target time period, including:

S1＝P1*P2+P3；

S2＝S21+S22+S23+S24；

Further, the calculation module 302 determines specific ways of the wholesale channel projected sales inventory, the retail channel projected sales inventory, and the e-commerce channel projected sales inventory of 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 specific manner of estimating the end-of-term asset estimation data of the target time period by the estimation module 303 according to the initial asset data of the target time period and the calculated expected asset data of the target time period includes:

S＝S10+S20；

In yet another alternative embodiment, the calculation module 302 determines the specific way that the wholesale channel of the target time period is expected to sell the inventory, including:

In yet another alternative embodiment, the calculation module 302 determines the specific manner in which the retail channel of the target time period is expected to sell inventory, including:

In yet another alternative embodiment, the calculation module 302 determines the specific way for the e-commerce channel to anticipate selling inventory for the target time period, including:

In yet another alternative embodiment, the initial asset of the target time period comprises an initial fixed asset amount of the target time period. The calculation module 302 determines the expected parameter data of the target time period, and calculates the expected asset data of the target time period according to the expected parameter data of the target time period, including:

In yet another alternative embodiment, the calculation module 302 calculates the estimated amount of the fixed asset for the target time period according to the pre-paid fixed asset, the cost of the fixed asset, the depreciation of the fixed asset, the cost of the remaining asset and the depreciation of the remaining asset for the target time period by:

FA2＝FAP+FAC–FAD+OAC–OAD；

In another alternative embodiment, the specific manner of estimating the end-of-term asset estimation data of the target time period by the estimation module 303 according to the initial asset data of the target time period and the calculated expected asset data of the target time period includes:

FA＝FA1+FA2；

In yet another alternative embodiment, the initial asset of the target time period includes the initial payable capital expenditure of the target time period, the expected asset data of the target time period includes the expected required payment amount of the fixed asset of the target time period, and the estimating module 303 estimates the estimated data of the end asset 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, including:

CP＝CP1+CP2；

Therefore, by implementing the optional embodiment, the sum of the initial due capital expenditure in the target time period and the expected required payment amount of the fixed asset in the target time period can be determined as the final due capital expenditure in the target time period, so that the accuracy of the determined final due capital expenditure can be improved, the final due capital expenditure amount of a company in the target time period in the future can be reasonably reflected, and a reference is provided for important decisions of the company.

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

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 table 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 the target financial data or the 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-term asset estimation data corresponding to the query command to the user, which may be implemented by a visual interface, for example, providing a visual query interface for the user, 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-term asset estimation data corresponding to the query command to the visual interface for displaying a query result for the user.

It can be seen that, in the optional embodiment, a mapping relationship can be established between the amount data associated to the same data category or financial information in all the financial data tables according to the data category and the corresponding financial information of the amount data in the financial data tables, so that the numerous and complicated financial data are sorted and optimized, an effective mapping relationship is established between the data, further, target financial data or asset estimation data can be provided for a user according to a query instruction of the user, the mapping relationship established before is combined, a query result can be accurately and quickly provided for the user, and compared with the condition that the existing financial system only can store the data or perform simple query, better query experience can be obviously provided for the user, and the efficiency of financial work is improved.

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

the obtaining unit 3051, configured to obtain user information of a user when receiving a query command of the user;

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

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

In yet another alternative embodiment, the apparatus further comprises:

and the classification module is used for classifying the amount data related to the same data category or financial information in all the financial data tables based on a preset data classification 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 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 four

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

a memory 401 storing executable program code;

a processor 402 coupled with the memory 401;

the processor 402 calls executable program code stored in the memory 401 to perform some or all of the steps of the financial data based asset estimation method disclosed in one or both embodiments of the present invention.

EXAMPLE five

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

The above-described embodiments of the apparatus are merely illustrative, and the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, may be located in one place, or may be distributed on 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 the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above detailed description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, where the storage medium includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc-Read-Only Memory (CD-ROM), or other disk memories, CD-ROMs, or other magnetic disks, A tape memory, or any other medium readable by a computer that can be used to carry or store data.

Finally, it should be noted that: the asset estimation method and device based on financial data disclosed in the embodiments of the present invention are only disclosed as preferred embodiments of the present invention, and are only used for illustrating the technical solutions of the present invention, not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

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

2. A financial data based asset estimation method according to claim 1 wherein the initial asset data for said target time period includes an initial inventory amount for said target time period; the determining the expected parameter data of the target time period and calculating the expected asset data of the target time period according to the expected parameter data of the target time period comprise:

S20＝S1-S2；

the estimating of the end-of-term asset estimation data of the target time period according to the end-of-term asset data of the target time period and the calculated estimated asset data of the target time period includes:

S＝S10+S20；

3. The financial data-based asset estimation method according to claim 2, wherein said determining a projected production warehousing amount and a projected sales export 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. A method of asset estimation based on financial data according to claim 1 wherein the initial asset of the target time period comprises an initial fixed asset amount of the target time period; the determining the expected parameter data of the target time period and calculating the expected asset data of the target time period according to the expected parameter data of the target time period comprise:

FA＝FA1+FA2；

5. The method of claim 4, wherein calculating the projected amount of fixed assets for the target time period based on the pre-paid fixed assets, the cost of fixed assets, the depreciation of fixed assets, the cost of remaining assets, and the depreciation of remaining assets for the target time period comprises:

FA2＝FAP+FAC–FAD+OAC–OAD；

6. A method of asset estimation based on financial data according to claim 1 wherein said financial data includes a plurality of financial data tables; each financial data table comprises a plurality of amount data of different data categories and corresponding financial information; the method further comprises the following steps:

7. The method of claim 6, wherein the step of pushing target financial data or end-of-term asset valuation data corresponding to a query command to a user upon receiving the query command comprises:

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

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

a memory storing executable program code;

a processor coupled with the memory;

the processor invokes the executable program code stored in the memory to perform the financial data based asset estimation method of any of claims 1-7.

10. A computer storage medium storing computer instructions which, when invoked, perform a method of financial data based asset estimation according to any of claims 1 to 7.