CN112884535A - Fixed asset class asset cost valuation model - Google Patents
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Abstract
The invention provides a fixed asset class asset cost valuation model, which comprises the following steps: acquiring fixed asset information to be evaluated; determining project level public parameters, subject level public parameters and specific asset level valuation data; the method comprises the steps of utilizing a project level public parameter database, a subject level public parameter database and a calculation model database to carry out automatic calculation to obtain to-be-evaluated fixed asset information, outputting an asset evaluation result of the to-be-evaluated fixed asset information according to the reset cost and the comprehensive success rate, and realizing self-calculation according to the information in the database.
Description
Technical Field
The invention relates to the technical field of fixed asset cost estimation, in particular to a fixed asset cost estimation model.
Background
The cost method is also one of the basic methods of asset assessment. The cost method is a general term of an evaluation method for determining the value of an evaluation object by using reconstruction or reset cost as a basis for determining the value of the evaluation object and deducting relevant depreciation according to the idea of reconstructing or resetting the evaluation object. The concept of a cost law from the perspective of reconstruction or reconfiguration of an asset being evaluated is a fundamental idea of a cost law, since any potential investor will be willing to pay a price that does not exceed the current cost of purchasing the asset, if conditions permit it, when deciding to invest in an asset. If the investment object is not brand new, the price which the investor wishes to pay is deducted from various dereferences on the basis of the brand new current purchasing cost of the investment object.
The existing cost method is easy to cause inconsistency of evaluation parameter value standards when fixed asset assets are evaluated, and particularly relates to evaluation projects with more fixed assets. Because the fixed asset type assets have more related parameters, the selection of the parameters is time-consuming and labor-consuming, and has difficulty. In addition, the cost method evaluation calculation methods adopted by different evaluators are not completely the same.
Disclosure of Invention
In order to solve the above problems, the present invention provides a valuation model of fixed asset class asset cost method, comprising:
acquiring fixed asset information to be evaluated;
determining project level public parameters, subject level public parameters and specific asset level valuation data;
and thirdly, automatically calculating by using the project-level public parameter database, the subject-level public parameter database and the calculation model database to obtain the resetting cost and the comprehensive success rate, and outputting the asset evaluation result of the fixed asset information to be evaluated according to the resetting cost and the comprehensive success rate.
Further, the fixed asset information to be evaluated in the first step includes a serial number, a certificate number, a building name, a house source, a structure, a construction year and month, a length, a width, a groove depth, a groove width, a groove thickness, a pipe diameter, a wall thickness, a material, an insulation mode, a metering unit, a building area or a building volume, a cost unit price, an account value raw value and an account value net value.
Further, the item level common parameters in the second step include: the unit evaluates whether the result contains tax, deposit interest rate, loan interest rate, equipment value-added tax rate, real estate value-added tax rate, transportation miscellaneous fee value-added tax rate, installation fee value-added tax rate, basic fee value-added tax rate, early-stage fee value-added tax rate and acquired tax rate.
Further, the comprehensive success rate in the third step is obtained by comprehensively evaluating the theoretical success rate and the scene investigation success rate, and the calculation formula is as follows:
the comprehensive success rate is the theoretical success rate multiplied by 40% + the field investigation success rate multiplied by 60%/the comprehensive success rate determined by other methods
Wherein the theoretical success rate is obtained by data calculation in the specific asset level estimation; the on-site investigation success rate is obtained by scoring through on-site investigation and in combination with the maintenance, repair and modification conditions of the fixed asset to be evaluated and the operation record;
the calculation basis of the asset assessment result of the fixed asset information to be assessed output according to the reset cost and the comprehensive success rate is as follows: fixed asset assessment value is reset cost x integrated new rate.
Further, the obtaining of the fixed asset information to be evaluated further includes: the method comprises the following steps of automatically identifying acquired fixed asset information data to be evaluated:
step A1, obtaining past fixed asset information sample data of l determined types, forming a sample data matrix K of the fixed asset information sample data of the l determined types, wherein the sample data matrix K is l rows and d columns, the d columns represent data dimensions of the sample data, the l sample data contain l type identification results, forming a type identification result matrix G of the l type identification results, the type identification result matrix G is l rows and 1 columns, when the fixed asset information sample data is house building type data, the corresponding type identification result is 1, and when the fixed asset information sample data is machine equipment type data, the corresponding type identification result is-1;
step A2, constructing a loss learning function according to the sample data matrix K and the category identification result matrix G:
wherein L (K) represents a constructed loss learning function, Gi,1Values, K, representing the 1 st column of the ith row of the matrix G of the discrimination resultsi,jValues, λ, representing the ith row and jth column of the sample data matrix K0Represents an initial bias, with an initial value of 0, λi,jRepresents the initial parameter values, with an initial value of 0, i-1, 2,3.. m, j-1, 2,3.. n;
step A3, determining the updated parameter values and offsets according to the following formulas:
wherein λ is0' represents the updated offset, λi,j' represents the updated parameter value, alpha represents the learning rate, the initial value is 0.001,
representing the learning function L (K) for the initial parameter value lambdai,jThe partial derivatives are made to the surface of the steel,
representing the loss learning function L (K) versus the initial offset λ0Making a partial derivative;
step A4, converting the acquired fixed asset information data to be evaluated into a data matrix T, and judging the category of the fixed asset information data to be evaluated according to the following formula:
wherein I represents a class judgment value, Ti,jRepresents the value of the ith row and the jth column of the data matrix T, and is 0<And when I is less than or equal to 1, representing that the fixed asset information data to be evaluated is house building data, and when I is less than or equal to-1 and less than or equal to 0, representing that the fixed asset information data to be evaluated is machine equipment data.
Further, the step three is that the reset cost is obtained according to the obtained information of the fixed asset to be evaluated, and includes:
when the obtained fixed asset information data to be evaluated is of a house building type, obtaining the reset cost according to the following formula:
the reset cost is the construction cost of the security project plus the early-stage other expenses plus the capital cost plus the development profit plus the sales tax;
when the obtained fixed asset information data to be evaluated is of a machine device class, obtaining the reset cost according to the following formula:
the reset cost is the purchase price of the equipment, the transportation cost, the installation and debugging cost and other reasonable expenses.
Further, the step three includes a calculation model database for the fixed assets of the building type and a calculation model database for the fixed assets of the machine equipment type, and the calculation model database is automatically selected according to the obtained information of the fixed assets to be evaluated, and includes:
when the obtained fixed asset information data to be evaluated is of a house building type, automatically selecting a house building type fixed asset calculation model database;
and when the acquired fixed asset information data to be evaluated is of a machine equipment type, automatically selecting a machine equipment type fixed asset calculation model database.
Further, the project level public parameter database, the subject level public parameter database and the calculation model database in the third step are built-in databases of the model, and are periodically updated and maintained.
Further, the database content is updated periodically, including:
acquiring a time interval when the database is called next time aiming at the calling of the current project level public parameter database, the subject level public parameter database and the calculation model database;
determining respective corresponding updating priority according to the current project level public parameter database, the subject level public parameter database and the calculation model database;
according to the updating priority, determining the updating cache time corresponding to the current item level public parameter database, the subject level public parameter database and the calculation model database; wherein the updated cache time is less than the time interval when calling;
and the server side regularly updates the contents in the database according to the respective corresponding update cache time and the respective corresponding update priority of the current project level public parameter database, the subject level public parameter database and the calculation model database.
Further, after the outputting the asset assessment result of the fixed asset information to be assessed, the method further includes: verifying the output asset evaluation result value, which comprises the following steps:
acquiring fixed asset information data to be evaluated, and calculating the mean value, standard deviation and obeyed normal distribution of the fixed asset information data to be evaluated according to the fixed asset information data to be evaluated;
verifying the asset evaluation result corresponding to the fixed asset information to be evaluated according to the mean value, the standard deviation and the obeyed normal distribution of the fixed asset information data to be evaluated;
when the asset evaluation result conforms to the mean value, the standard deviation and the obedient normal distribution of the fixed asset information data to be evaluated, the final asset evaluation result is reserved;
and when the asset evaluation result does not accord with the mean value, the standard deviation and the obedient normal distribution of the fixed asset information data to be evaluated, carrying out re-evaluation calculation.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a fixed asset cost valuation model, which comprises a cost method, a market method and a profit method, wherein the model integrates a project-level public parameter database, a subject-level public parameter database and a calculation model database, and valuation can be automatically calculated and completed after project-level public parameters, subject-level public parameters and specific asset-level valuation data are input. The valuation model has wide application range and can be applied to fixed asset evaluation of house buildings, structures, pipeline grooves, machine equipment, vehicles, electronic equipment and the like. The method can be applied to the traditional offline estimation and the online estimation system.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly described below. Wherein the drawings are only for purposes of illustrating some embodiments of the invention and are not to be construed as limiting the invention to all embodiments thereof.
FIG. 1 is a flow chart of a fixed asset class asset cost valuation model according to the present invention.
Detailed Description
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.
As shown in fig. 1, the technical problem to be solved by the present invention is to provide a valuation model of the fixed asset cost method, which includes:
acquiring fixed asset information to be evaluated;
determining project level public parameters, subject level public parameters and specific asset level valuation data;
and thirdly, automatically calculating by using the project-level public parameter database, the subject-level public parameter database and the calculation model database to obtain the resetting cost and the comprehensive success rate, and outputting the asset evaluation result of the fixed asset information to be evaluated according to the resetting cost and the comprehensive success rate.
When the value of the fixed asset information to be evaluated is evaluated, firstly, the fixed asset information to be evaluated is obtained; secondly, determining project level public parameters, subject level public parameters and specific asset level valuation data; finally, the project-level public parameter database, the subject-level public parameter database and the calculation model database are used for automatic calculation to obtain the reset cost and the comprehensive success rate, and the asset evaluation result of the fixed asset information to be evaluated is output according to the reset cost and the comprehensive success rate, wherein the evaluation is carried out by adopting a cost evaluation model, so that the subjectivity of evaluation only according to the personal experience of a professional evaluator is reduced, the problem that the professional evaluator is difficult to collect information during subjective evaluation is effectively avoided by calculating through the information in the database, the technical scheme can realize self-calculation according to the information in the database, the speed of value evaluation is improved, errors are not easy to occur, the process of the fixed asset value evaluation is more efficient and accurate, and the method can be applied to the traditional offline evaluation and can also be applied to an online evaluation system, and the portability of the model is greatly enhanced.
In an embodiment of the present invention, the fixed asset information to be evaluated in the first step includes a serial number, a ticket number, a building name, a house source, a structure, a construction year and month, a length, a width, a groove depth, a groove width, a groove thickness, a pipe diameter, a wall thickness, a material, an insulation manner, a measurement unit, a building area or volume, a cost unit price, an original account value and a net account value.
In the technical scheme, when the information of the fixed assets to be evaluated is obtained, the serial number, the certificate number, the building name, the house source, the structure, the construction year and month, the length, the width, the groove depth, the groove width, the groove thickness, the pipe diameter, the wall thickness, the material, the insulation mode, the metering unit, the building area or volume, the cost unit price, the original account value and the net account value of the fixed assets to be evaluated are obtained, and then the overall condition of the fixed assets to be evaluated can be comprehensively reflected.
In an embodiment provided by the present invention, the item level common parameters in the step two include: the unit evaluates whether the result contains tax, deposit interest rate, loan interest rate, equipment value-added tax rate, real estate value-added tax rate, transportation miscellaneous fee value-added tax rate, installation fee value-added tax rate, basic fee value-added tax rate, early-stage fee value-added tax rate and obtained tax rate;
subject-level common parameters include: project name, charge standard, parameter value, structure, application, layer number, structure part weight, decoration part weight, accessory equipment and other part weights, building classification, equipment category, vehicle type, installation rate, life span, driving mileage, location of the unit to be evaluated, transportation rate, self-definition, basis accessory and data source;
the asset-specific valuation data includes: the method comprises the steps of evaluating method, project name, application, layer number, engineering comprehensive construction cost, engineering comprehensive total price, service life, observation method innovation rate, observation method-structural part scoring weight, observation method-decorative part scoring weight, observation method-supporting facility scoring weight, comprehensive innovation rate determined by other methods, market method or income method evaluation unit price and special equipment reset unit price.
In the technical scheme, the project-level public parameters, the subject-level public parameters and the specific asset-level valuation data are determined, and whether the unit evaluation result contains tax, deposit interest rate, loan interest rate, equipment value-added tax rate, real estate value-added tax rate, transportation and miscellaneous fee value-added tax rate, installation fee value-added tax rate, basic fee value-added tax rate, early-stage fee value-added tax rate and obtained tax rate is determined; determining project names, fee taking standards, parameter values, structures, purposes, layer numbers, structure part weights, decoration part weights, accessory equipment and other part weights, building classification, equipment types, vehicle types, installation rates, life spans, driving mileage, locations of evaluated units, transportation and impurity rates, self-definition, bases and accessories, and data sources; determining an evaluation method, a project name, a purpose, the number of layers, the comprehensive construction cost of the project, the comprehensive total price of the project, the service life, the observation method renewal rate, the observation method-structural part scoring weight, the observation method-decorative part scoring weight, the observation method-supporting facility scoring weight, the comprehensive renewal rate determined by other methods, the market method or income method evaluation unit price and the special equipment resetting unit price, thereby ensuring that the later period utilizes a project level public parameter database, a subject level public parameter database and a calculation model database to automatically calculate to obtain the resetting cost and the comprehensive renewal rate, and leading the calculation result to be more fit with the actual project condition.
In an embodiment provided by the present invention, the comprehensive success rate in the third step is obtained by comprehensively evaluating the theoretical success rate and the on-site exploration success rate, and the calculation formula is as follows:
the comprehensive success rate is the theoretical success rate multiplied by 40% + the field investigation success rate multiplied by 60%/the comprehensive success rate determined by other methods
Wherein the theoretical success rate is obtained by data calculation in the specific asset level estimation; the on-site investigation success rate is obtained by scoring through on-site investigation and in combination with the maintenance, repair and modification conditions of the fixed asset to be evaluated and the operation record;
the calculation basis of the asset assessment result of the fixed asset information to be assessed output according to the reset cost and the comprehensive success rate is as follows: fixed asset assessment value is reset cost x integrated new rate.
According to the technical scheme, when the comprehensive success rate of the fixed asset information to be evaluated is obtained, the theoretical success rate and the field investigation success rate are subjected to weighted comprehensive evaluation, and the theoretical success rate is obtained by data calculation in specific asset-level valuation; the on-site investigation success rate is obtained by on-site investigation and scoring by combining the maintenance, repair and modification conditions of the fixed asset to be evaluated and the operation record, and the comprehensive success rate obtained by the technical scheme not only contains the factors of subjective observation, but also contains the influence of objective factors of the theoretical success rate; the asset assessment result of the fixed asset information to be assessed is the product of the reset cost and the comprehensive success rate, and the product of the reset cost and the comprehensive success rate is used as the asset assessment result of the fixed asset information to be assessed, so that the obtained value assessment result is the value of the detailed condition of the fixed asset information to be assessed relative to the current asset assessment market, the calculation is convenient, and errors are not easy to occur.
In an embodiment provided by the present invention, after acquiring the information of the fixed asset to be evaluated, the method further includes: the method comprises the following steps of automatically identifying acquired fixed asset information data to be evaluated:
step A1, obtaining past fixed asset information sample data of l determined types, forming a sample data matrix K of the fixed asset information sample data of the l determined types, wherein the sample data matrix K is l rows and d columns, the d columns represent data dimensions of the sample data, the l sample data contain l type identification results, forming a type identification result matrix G of the l type identification results, the type identification result matrix G is l rows and 1 columns, when the fixed asset information sample data is house building type data, the corresponding type identification result is 1, and when the fixed asset information sample data is machine equipment type data, the corresponding type identification result is-1;
step A2, constructing a loss learning function according to the sample data matrix K and the category identification result matrix G:
wherein L (K) represents a constructed loss learning function, Gi,1Values, K, representing the 1 st column of the ith row of the matrix G of the discrimination resultsi,jValues, λ, representing the ith row and jth column of the sample data matrix K0Represents an initial bias, with an initial value of 0, λi,jRepresents the initial parameter values, with an initial value of 0, i-1, 2,3.. m, j-1, 2,3.. n;
step A3, determining the updated parameter values and offsets according to the following formulas:
wherein λ is0' represents the updated offset, λi,j' represents the updated parameter value, alpha represents the learning rate, the initial value is 0.001,
representing the learning function L (K) for the initial parameter value lambdai,jThe partial derivatives are made to the surface of the steel,
representing the loss learning function L (K) versus the initial offset λ0Making a partial derivative;
step A4, converting the acquired fixed asset information data to be evaluated into a data matrix T, and judging the category of the fixed asset information data to be evaluated according to the following formula:
wherein I represents a class judgment value, Ti,jRepresents the value of the ith row and the jth column of the data matrix T, and is 0<When I is less than or equal to 1, representing that the fixed asset information data to be evaluated is house building data, and when I is less than or equal to-1 and less than or equal to 0, representing that the fixed asset information data to be evaluated is machine equipment data;
the reset cost is obtained according to the obtained fixed asset information to be evaluated, and the method comprises the following steps:
when the obtained fixed asset information data to be evaluated is of a house building type, obtaining the reset cost according to the following formula:
the reset cost is the construction cost of the security project plus the early-stage other expenses plus the capital cost plus the development profit plus the sales tax;
when the obtained fixed asset information data to be evaluated is of a machine device class, obtaining the reset cost according to the following formula:
the reset cost is the purchase price of the equipment, the transportation cost, the installation and debugging cost and other reasonable costs;
the calculation model database comprises a house building fixed asset calculation model database and a machine equipment fixed asset calculation model database, and the calculation model database is automatically selected according to the acquired information of the fixed assets to be evaluated, and comprises the following steps:
when the obtained fixed asset information data to be evaluated is of a house building type, automatically selecting a house building type fixed asset calculation model database;
and when the acquired fixed asset information data to be evaluated is of a machine equipment type, automatically selecting a machine equipment type fixed asset calculation model database.
In the technical scheme, after the fixed asset information to be evaluated is obtained, automatic identification of a model for obtaining the fixed asset information to be evaluated is also carried out, wherein classification is carried out according to previous sample data for learning, so that a calculation model can intelligently and automatically identify whether the obtained fixed asset information to be evaluated is house building data or machine equipment data, and according to different types of data, calculation model databases corresponding to different types are used for carrying out different modes of calculation and reset cost, so that the calculation and reset cost is more consistent with the actual situation, the final evaluation result has representativeness of different types, the parameter values and bias in the algorithm are updated through learning the class condition of the previous sample data, and the class can be more accurately predicted finally, therefore, the great difference of the final evaluation result caused by the class identification error is avoided.
In an embodiment provided by the invention, the project level public parameter database, the subject level public parameter database and the calculation model database in the third step are built-in databases of the model, and are periodically updated and maintained; wherein, the database content is updated regularly, including:
acquiring a time interval when the database is called next time aiming at the calling of the current project level public parameter database, the subject level public parameter database and the calculation model database;
determining respective corresponding updating priority according to the current project level public parameter database, the subject level public parameter database and the calculation model database;
according to the updating priority, determining the updating cache time corresponding to the current item level public parameter database, the subject level public parameter database and the calculation model database; wherein the updated cache time is less than the time interval when calling;
and the server side regularly updates the contents in the database according to the respective corresponding update cache time and the respective corresponding update priority of the current project level public parameter database, the subject level public parameter database and the calculation model database.
According to the technical scheme, the contents in the project level public parameter database, the subject level public parameter database and the calculation model database are updated regularly, so that the updating of the database contents is completed when the database is called next time, and the data contents acquire the information contents irregularly, so that the evaluation accuracy of the cost method evaluation model is further improved, and the problem that the evaluation value error effect is large due to inaccurate parameters is avoided.
In an embodiment of the present invention, after outputting the asset assessment result of the fixed asset information to be assessed, the method further includes: verifying the output asset evaluation result value, which comprises the following steps:
acquiring fixed asset information data to be evaluated, and calculating the mean value, standard deviation and obeyed normal distribution of the fixed asset information data to be evaluated according to the fixed asset information data to be evaluated;
verifying the asset evaluation result corresponding to the fixed asset information to be evaluated according to the mean value, the standard deviation and the obeyed normal distribution of the fixed asset information data to be evaluated;
when the asset evaluation result conforms to the mean value, the standard deviation and the obedient normal distribution of the fixed asset information data to be evaluated, the final asset evaluation result is reserved;
and when the asset evaluation result does not accord with the mean value, the standard deviation and the obedient normal distribution of the fixed asset information data to be evaluated, carrying out re-evaluation calculation.
According to the technical scheme, the verification of the output asset evaluation result value is realized, so that the asset evaluation result value can meet the distribution trend of the fixed asset information data to be evaluated, the final evaluation value is more accurate and representative, when the distribution trend of the fixed asset information data to be evaluated is not met, the re-evaluation of the cost method model is carried out until the distribution trend of the fixed asset information data to be evaluated is met, the loss caused by evaluation with larger error of the cost method model is avoided, and the cost method model is more comprehensive.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle scope of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A fixed asset class asset cost valuation model, comprising:
acquiring fixed asset information to be evaluated;
determining project level public parameters, subject level public parameters and specific asset level valuation data;
and thirdly, automatically calculating by using the project-level public parameter database, the subject-level public parameter database and the calculation model database to obtain the information of the fixed assets to be evaluated, and outputting the asset evaluation result of the information of the fixed assets to be evaluated according to the reset cost and the comprehensive success rate.
2. The fixed asset cost valuation model of claim 1, wherein the fixed asset information to be evaluated in the first step comprises serial number, certificate number, building name, house source, structure, construction year and month, length, width, groove depth, groove width, groove thickness, pipe diameter, wall thickness, material, insulation mode, measurement unit, building area or volume, cost unit price, account value raw value and account value net value.
3. The fixed-asset class asset cost valuation model of claim 1 wherein said item-level common parameters of step two comprise: the unit evaluates whether the result contains tax, deposit interest rate, loan interest rate, equipment value-added tax rate, real estate value-added tax rate, transportation miscellaneous fee value-added tax rate, installation fee value-added tax rate, basic fee value-added tax rate, early-stage fee value-added tax rate and acquired tax rate.
4. The fixed-asset cost valuation model of claim 1, wherein said integrated new-forming rate in step three is obtained by comprehensively evaluating a theoretical new-forming rate and a field survey new-forming rate, and the calculation formula is as follows:
the comprehensive success rate is the theoretical success rate multiplied by 40% + the field investigation success rate multiplied by 60%/the comprehensive success rate determined by other methods
Wherein the theoretical success rate is obtained by data calculation in the specific asset level estimation; the on-site investigation success rate is obtained by scoring through on-site investigation and in combination with the maintenance, repair and modification conditions of the fixed asset to be evaluated and the operation record;
the calculation basis of the asset assessment result of the fixed asset information to be assessed output according to the reset cost and the comprehensive success rate is as follows: fixed asset assessment value is reset cost x integrated new rate.
5. The fixed asset class asset cost valuation model of claim 1, wherein said obtaining fixed asset information to be evaluated further comprises: the method comprises the following steps of automatically identifying acquired fixed asset information data to be evaluated:
step A1, obtaining past fixed asset information sample data of l determined types, forming a sample data matrix K of the fixed asset information sample data of the l determined types, wherein the sample data matrix K is l rows and d columns, the d columns represent data dimensions of the sample data, the l sample data contain l type identification results, forming a type identification result matrix G of the l type identification results, the type identification result matrix G is l rows and 1 columns, when the fixed asset information sample data is house building type data, the corresponding type identification result is 1, and when the fixed asset information sample data is machine equipment type data, the corresponding type identification result is-1;
step A2, constructing a loss learning function according to the sample data matrix K and the category identification result matrix G:
wherein L (K) represents a constructed loss learning function, Gi,1Values, K, representing the 1 st column of the ith row of the matrix G of the discrimination resultsi,jValues, λ, representing the ith row and jth column of the sample data matrix K0Represents an initial bias, with an initial value of 0, λi,jRepresents the initial parameter values, with an initial value of 0, i-1, 2,3.. m, j-1, 2,3.. n;
step A3, determining the updated parameter values and offsets according to the following formulas:
wherein λ is0' represents the updated offset, λi,j' represents the updated parameter value, alpha represents the learning rate, the initial value is 0.001,
representing the learning function L (K) for the initial parameter value lambdai,jThe partial derivatives are made to the surface of the steel,
representing the loss learning function L (K) versus the initial offset λ0Making a partial derivative;
step A4, converting the acquired fixed asset information data to be evaluated into a data matrix T, and judging the category of the fixed asset information data to be evaluated according to the following formula:
wherein I represents a class judgment value, Ti,jRepresents the value of the ith row and the jth column of the data matrix T, and is 0<And when I is less than or equal to 1, representing that the fixed asset information data to be evaluated is house building data, and when I is less than or equal to-1 and less than or equal to 0, representing that the fixed asset information data to be evaluated is machine equipment data.
6. The fixed asset class asset cost valuation model of claim 1, wherein said step three reset costs are obtained from obtained fixed asset information to be evaluated, comprising:
when the obtained fixed asset information data to be evaluated is of a house building type, obtaining the reset cost according to the following formula:
the reset cost is the construction cost of the security project plus the early-stage other expenses plus the capital cost plus the development profit plus the sales tax;
when the obtained fixed asset information data to be evaluated is of a machine device class, obtaining the reset cost according to the following formula:
the reset cost is the purchase price of the equipment, the transportation cost, the installation and debugging cost and other reasonable expenses.
7. The fixed asset class asset cost valuation model of claim 1, wherein said computational model database in step three comprises a building class fixed asset computational model database and a machine equipment class fixed asset computational model database, said computational model database being automatically selected according to the obtained fixed asset information to be evaluated, comprising:
when the obtained fixed asset information data to be evaluated is of a house building type, automatically selecting a house building type fixed asset calculation model database;
and when the acquired fixed asset information data to be evaluated is of a machine equipment type, automatically selecting a machine equipment type fixed asset calculation model database.
8. The fixed asset class asset cost valuation model of claim 1 wherein said project level common parameter database, subject level common parameter database and computational model database in step three are built-in databases and are periodically updated and maintained.
9. The fixed-asset class asset cost valuation model of claim 8 wherein said database content is updated periodically comprising:
acquiring a time interval when the database is called next time aiming at the calling of the current project level public parameter database, the subject level public parameter database and the calculation model database;
determining respective corresponding updating priority according to the current project level public parameter database, the subject level public parameter database and the calculation model database;
according to the updating priority, determining the updating cache time corresponding to the current item level public parameter database, the subject level public parameter database and the calculation model database; wherein the updated cache time is less than the time interval when calling;
and the server side regularly updates the contents in the database according to the respective corresponding update cache time and the respective corresponding update priority of the current project level public parameter database, the subject level public parameter database and the calculation model database.
10. The fixed asset class asset cost valuation model of claim 1, wherein said outputting the asset valuation result of the fixed asset information to be assessed further comprises: verifying the output asset evaluation result value, which comprises the following steps:
acquiring fixed asset information data to be evaluated, and calculating the mean value, standard deviation and obeyed normal distribution of the fixed asset information data to be evaluated according to the fixed asset information data to be evaluated;
verifying the asset evaluation result corresponding to the fixed asset information to be evaluated according to the mean value, the standard deviation and the obeyed normal distribution of the fixed asset information data to be evaluated;
when the asset evaluation result conforms to the mean value, the standard deviation and the obedient normal distribution of the fixed asset information data to be evaluated, the final asset evaluation result is reserved;
and when the asset evaluation result does not accord with the mean value, the standard deviation and the obedient normal distribution of the fixed asset information data to be evaluated, carrying out re-evaluation calculation.
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