CN116091106A - Multifunctional financial cost evaluation system - Google Patents
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Abstract
The invention relates to the technical field of data evaluation, and particularly discloses a multifunctional financial cost evaluation system which is used for solving the problems that the existing evaluation method can only singly analyze, has inaccuracy and surface property for judging financial cost and is difficult to carry out scientific and accurate analysis and early warning on the financial cost; the evaluation condition of the financial cost is determined in a multi-dimensional mode by combining the cost evaluation information, the income evaluation information, the asset evaluation information, the performance evaluation information and the risk evaluation information of the production enterprise, a proper financial cost evaluation model is selected according to the data acquired from the inside and the outside of the enterprise by the production enterprise, the financial condition and the cost benefit of the enterprise are evaluated and analyzed according to the actual requirement of the enterprise, and early warning is given.
Description
Technical Field
The invention relates to the technical field of data evaluation, in particular to a multifunctional financial cost evaluation system.
Background
In a strong market competition, businesses face a variety of challenges, one of which is how to effectively conduct financial cost assessments. Especially in a production enterprise, the traditional financial cost evaluation method has single index considered, ignores complex relations inside the enterprise, cannot comprehensively evaluate financial conditions and cost benefits of the enterprise, and the decision of the enterprise lacks the favorable support provided by financial cost evaluation.
Because the factors involved in the financial cost evaluation of the production enterprises are more, the existing evaluation method can only analyze the financial cost singly, has inaccuracy and surface property for the judgment of the financial cost, and is difficult to carry out scientific and accurate analysis and early warning on the financial cost.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, the embodiment of the present invention provides a multifunctional financial cost assessment system, which is configured to combine cost assessment information, income assessment information, asset assessment information, performance assessment information and risk assessment information of a manufacturing enterprise, to determine the assessment situation of financial cost in a multi-dimensional manner, to select a suitable financial cost assessment model according to data collected by the manufacturing enterprise from the inside and the outside of the enterprise, to assess and analyze financial situation and cost benefit of the enterprise according to actual requirements of the enterprise, and to make early warning, to ensure effective assessment of financial and cost of the enterprise, to facilitate comprehensive grasp of financial situation and cost benefit of the enterprise, and to provide decision support for the enterprise, so as to solve the problems raised in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a multifunctional financial cost assessment system comprises a processor, and a data acquisition module, a data analysis module, a financial cost assessment module and a data storage module which are in communication connection with the processor;
the financial cost evaluation module selects an adaptive financial cost evaluation model according to the received data and the actual demands of the enterprises, evaluates and analyzes the financial condition and the cost benefit of the enterprises, generates an evaluation report, provides decision support for the enterprises, and is a sum of linear adjustment of a dynamic prediction model, a nonlinear control model, a data-driven optimization model, a non-financial index performance model and a probability risk model, wherein the formula of the financial cost evaluation model is as follows:
wherein:
for financial cost assessment model->
For dynamic predictive model, ++>
For nonlinear control model +.>
Data-driven optimization model,/->
For non-financial performance model, +.>
For probabilistic risk model->
、/>
、/>
、/>
And +.>
Respectively a dynamic prediction model, a nonlinear control model and a numberAnd according to the selected coefficients of the driven optimization model, the non-financial index performance model and the probability risk model, the value is 1 when the model is selected, and the value is 0 when the model is not selected.
As a further scheme of the invention, in the financial cost evaluation module, a dynamic prediction model is based on historical data and market trend, and a long-term and short-term memory model is adopted to predict future financial conditions and cost benefits by combining a time sequence prediction technology and a machine learning technology, and the formula of the dynamic prediction model is as follows:
wherein:
for the dynamic prediction model value at the current moment, < >>
And +.>
Respectively, the input at the current moment and the hidden state at the previous moment, < >>
And +.>
Weight matrix of input and weight matrix of hidden state respectively, < ->
For the activation function of the dynamic predictive model, the expression of the activation function is +.>
And->
One of them.
As a further scheme of the invention, in the financial cost evaluation module, the control strategy of continuously adjusting the financial cost by adopting the self-adaptive neural fuzzy control model through monitoring and analyzing the financial data, the requirements of different businesses and clients are met, and the formula of the nonlinear control model is as follows:
wherein:
for the number of fuzzy logic rules in the financial data, < >>
Is->
The ∈of the fuzzy logic rule>
Input variables->
Is->
Weights corresponding to fuzzy logic rules, +.>
Is->
Membership function value of fuzzy logic rule, wherein the membership function value is about ∈ ->
The values of the individual input variables are mapped to +.>
The membership value between the two values,
is->
The membership value corresponding to each fuzzy logic rule is obtained by data analysis according to the production requirements and market demands of the production enterprises, the weight of each rule is set according to expert opinion and experience, and representative financial data is generated by utilizing the acquired financial data generation countermeasure network technology.
As a further scheme of the invention, in the financial cost evaluation module, the data-driven optimization model analyzes financial data by utilizing a big data analysis and deep reinforcement learning technology to obtain the optimal financial strategy and scheme, and the formula of the data-driven optimization model is as follows:
wherein:
evaluation value entered for the current state of the current action of the data-driven optimization model, < >>
To reward the value in time, the user is provided with->
For discounts factor->
For the status action function->
Data analysis is carried out based on brand value, innovation capability and social requirement of a production enterprise, and a multi-input state action function expression is obtained, namely ∈>
For the current state +.>
For the current action +.>
For the next state +.>
For the next action.
As a further scheme of the invention, in the financial cost estimation module, a non-financial index performance model combines various performance indexes and performance factors, and the collection, the processing, the traceable storage and the safe sharing of financial data are realized by using the blockchain, the performance of a production enterprise is estimated and analyzed, problems and advantages are found, and the formula of the non-financial index performance model is as follows:
wherein:
for category number->
Evaluation weight of performance indicators of +.>
For category number->
Performance indicator actual value of +.>
For category number->
Performance indicator target values of (c).
As a further scheme of the invention, in the financial cost estimation module, a probability risk model evaluates and analyzes financial cost risks of a production enterprise through collected data, probability of occurrence of financial cost double-risk events in probability risk evaluation data of risk generation financial cost is utilized, data feature grabbing and learning are carried out on the financial cost double-risk events by combining a machine learning technology, potential risks and hidden dangers are found, corresponding measures are adopted for control and management, and a formula of the probability risk model is as follows:
wherein:
for the risk probability value of the financial cost risk event a under the condition of occurrence of the financial cost risk event B +.>
To determine the probability of occurrence of financial cost risk event B under the condition that financial cost risk event a occurs,
for the prior probability of occurrence of financial cost risk event a, +.>
Is the prior probability of the occurrence of financial cost risk event B.
As a further aspect of the invention, the processor is configured to process data from at least one component of the multi-functional financial cost assessment system;
the data acquisition module is used for acquiring internal and external related financial cost data of a production enterprise, introducing market data, customer data and competition data, utilizing deep learning and natural language processing technology to mine and analyze the data, sending the acquired information to the data analysis module for analysis and processing, and sending the acquired information to the data storage module for storage;
after the data analysis module receives the information sent by the data acquisition module, the processor calls the data stored in the data storage module to clean, analyze and classify the acquired data, and sends the analyzed data to the financial cost evaluation module;
the data storage module is used for storing historical acquisition data, analysis data and evaluation data of the manufacturing enterprises.
A multi-functional financial cost assessment method is used for realizing the multi-functional financial cost assessment system, and comprises the following steps:
step S1, acquiring internal and external related financial cost data of a production enterprise, introducing market data, customer data and competition data, and mining and analyzing the data by utilizing deep learning and natural language processing technology;
step S2, after receiving the information sent by the data acquisition module, calling the data stored in the data storage module to clean, analyze and classify the acquired data;
s3, selecting an adaptive financial cost assessment model according to the received data and the actual demands of enterprises;
and S4, selecting an adaptive financial cost assessment model according to the received data and the actual demands of enterprises, assessing and analyzing the financial condition and cost benefit of the enterprises, generating an assessment report, providing decision support for the enterprises, wherein the financial cost assessment model is the sum of a dynamic prediction model, a nonlinear control model, a data-driven optimization model, a non-financial index performance model and linear adjustment of a probability risk model, the value of each selected coefficient is 1 when the model is selected, the value of each selected coefficient is 0 when the model is not selected, fuzzy logic rules in the financial data are obtained by data analysis according to the production demands and market demands of the manufacturing enterprises, the weight of each rule is set according to expert opinion and experience, and generating representative financial data by utilizing an countermeasure network technology.
The invention relates to a technical effect and advantages of a multifunctional financial cost evaluation system, which are as follows:
according to the invention, the evaluation condition of financial cost is determined in a multi-dimensional manner by combining the cost evaluation information, the income evaluation information, the asset evaluation information, the performance evaluation information and the risk evaluation information of a production enterprise, a proper financial cost evaluation model is selected according to the data acquired from the inside and the outside of the enterprise by the production enterprise, the financial condition and the cost benefit of the enterprise are evaluated and analyzed according to the actual requirement of the enterprise, early warning is made, the effective evaluation of the financial condition and the cost of the enterprise is ensured, the financial cost evaluation model combined by a plurality of evaluation models is provided, the adjustment and customization can be carried out according to the requirements of the production enterprise, the requirements of different enterprises are met, the financial condition and the cost benefit of the enterprise are comprehensively mastered, the financial management level of the enterprise is effectively improved, and decision support is provided for the enterprise.
Drawings
FIG. 1 is a flow chart of a multi-functional financial cost assessment method of the present invention;
FIG. 2 is a schematic diagram of a multi-functional financial cost assessment system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The invention relates to a multifunctional financial cost evaluation system, which is characterized in that the evaluation condition of financial cost is determined in a multi-dimensional way by combining cost evaluation information, income evaluation information, asset evaluation information, performance evaluation information and risk evaluation information of a production enterprise, a proper financial cost evaluation model is selected according to data acquired from the inside and the outside of the enterprise by the production enterprise and the actual demand of the enterprise, the financial condition and the cost benefit of the enterprise are evaluated and analyzed, early warning is made, the effective evaluation of the financial condition and the cost of the enterprise is ensured, the financial condition and the cost benefit of the enterprise are comprehensively mastered, and decision support is provided for the enterprise.
FIG. 1 presents a flow chart of the multi-functional financial cost assessment method of the present invention, comprising the steps of:
step S1, acquiring internal and external related financial cost data of a production enterprise, introducing market data, customer data and competition data, and mining and analyzing the data by utilizing deep learning and natural language processing technology;
step S2, after receiving the information sent by the data acquisition module, calling the data stored in the data storage module to clean, analyze and classify the acquired data;
s3, selecting an adaptive financial cost assessment model according to the received data and the actual demands of enterprises;
and S4, selecting an adaptive financial cost assessment model according to the received data and the actual demands of enterprises, assessing and analyzing the financial condition and cost benefit of the enterprises, generating an assessment report, providing decision support for the enterprises, wherein the financial cost assessment model is the sum of a dynamic prediction model, a nonlinear control model, a data-driven optimization model, a non-financial index performance model and linear adjustment of a probability risk model, the value of each selected coefficient is 1 when the model is selected, the value of each selected coefficient is 0 when the model is not selected, fuzzy logic rules in the financial data are obtained by data analysis according to the production demands and market demands of the manufacturing enterprises, the weight of each rule is set according to expert opinion and experience, and generating representative financial data by utilizing an countermeasure network technology.
Specifically, the detailed process of each step of the invention is as follows:
step S1:
the invention collects and acquires the internal and external relevant financial cost data of the production enterprises, introduces market data, client data and competition data, and utilizes deep learning and natural language processing technology to mine and analyze the data.
In the above description, the deep learning technology and the natural language processing technology are utilized to perform mining analysis on the data collected by the data collection module, so that the characteristics of the data can be automatically learned, manual intervention is reduced, further, the accuracy of the data and the efficiency of mining and classifying the data are improved, the classification and the preliminary analysis of the data are conveniently realized, and the result can be used for prediction and control of financial cost.
Step S2:
after the information sent by the data acquisition module is received, the data stored in the data storage module is called to clean, analyze and classify the acquired data.
It should be noted that the data can be cleaned, analyzed and categorized to improve the validity and reliability of the data, thereby improving the accuracy of the overall financial cost assessment model assessment and prediction results.
Step S3:
the invention selects the adaptive financial cost assessment model according to the received data and the actual demands of the enterprises.
The financial cost assessment model is the sum of linear adjustment of a dynamic prediction model, a nonlinear control model, a data driven optimization model, a non-financial index performance model and a probability risk model, and the formula of the financial cost assessment model is as follows:
wherein:
for financial cost assessment model->
For dynamic predictive model, ++>
For nonlinear control model +.>
Data-driven optimization model,/->
For non-financial performance model, +.>
For probabilistic risk model->
、/>
、/>
、/>
And +.>
The coefficients selected for the dynamic prediction model, the nonlinear control model, the data driven optimization model, the non-financial index performance model and the probability risk model are respectively 1 when the model is selected and 0 when the model is not selected.
Further, in the financial cost evaluation module, the nonlinear control model monitors and analyzes financial data, timely discovers problems and risks, adjusts and optimizes by adopting corresponding measures, realizes flexible financial control by combining a neural network and a control theory, continuously adjusts a control strategy of financial cost by adopting a self-adaptive neural fuzzy control model, and meets the requirements of different businesses and clients, and the formula of the nonlinear control model is as follows:
wherein:
for the number of fuzzy logic rules in the financial data, < >>
Is->
The ∈of the fuzzy logic rule>
Input variables->
Is->
Weights corresponding to fuzzy logic rules, +.>
Is->
Membership function value of fuzzy logic rule, wherein the membership function value is about ∈ ->
The values of the individual input variables are mapped to +.>
The membership value between the two values,
is->
The membership value corresponding to each fuzzy logic rule is obtained by data analysis according to the production requirements and market demands of the production enterprises, the weight of each rule is set according to expert opinion and experience, and representative financial data is generated by utilizing the acquired financial data generation countermeasure network technology.
After collecting financial cost and independent variables affecting the financial cost, the data is divided into a training set and a test set, regression analysis is performed by using the training set data, regression coefficients are calculated, the test set data is predicted, errors are calculated, and finally the prediction performance of the model is evaluated.
Further, in the financial cost evaluation module, the data-driven optimization model analyzes financial data by using big data analysis and deep reinforcement learning technology to obtain an optimal financial strategy and scheme, and the formula of the data-driven optimization model is as follows:
in the middle of：
Evaluation value entered for the current state of the current action of the data-driven optimization model, < >>
To reward the value in time, the user is provided with->
For discounts factor->
For the status action function->
Data analysis is carried out based on brand value, innovation capability and social requirement of a production enterprise, and a multi-input state action function expression is obtained, namely ∈>
For the current state +.>
For the current action +.>
For the next state +.>
For the next action.
It should be noted that, according to the deep intensity learning technology, the data driving optimization model value input by the current state of the current action is calculated, a proper timely rewarding value and a discount factor are set to realize constraint condition setting of constraint financial cost data, finally an inequality constraint is established based on the constraint condition, the inequality is solved, feasibility and optimality of the solution are checked, and the optimal financial strategy and scheme under the current state of the current action are obtained.
Further, in the financial cost estimation module, the non-financial index performance model combines various performance indexes and performance factors, and the block chain is utilized to realize the collection, the processing, the traceable storage and the safe sharing of financial data, so as to evaluate and analyze the performance of a production enterprise, find problems and advantages, and the formula of the non-financial index performance model is as follows:
wherein:
for category number->
Evaluation weight of performance indicators of +.>
For category number->
Performance indicator actual value of +.>
For category number->
Performance indicator target values of (c).
Specifically, each performance index is weighted and summed according to a formula of a non-financial performance evaluation model to obtain comprehensive evaluation performance, and the comprehensive evaluation performance is helped to obtain the predicted performance obtained by the financial cost strategy and the scheme in the current state.
Further, in the financial cost estimation module, the probability risk model evaluates and analyzes financial cost risks of the manufacturing enterprises through collected data, probability of occurrence of financial cost double-risk events in probability risk evaluation data of financial cost of risk is utilized, data feature grabbing and learning are carried out on the financial cost double-risk events by combining machine learning technology, potential risks and hidden dangers are found, corresponding measures are taken for control and management, and a formula of the probability risk model is as follows:
wherein:
for the risk probability value of the financial cost risk event a under the condition of occurrence of the financial cost risk event B +.>
To determine the probability of occurrence of financial cost risk event B under the condition that financial cost risk event a occurs,
for the prior probability of occurrence of financial cost risk event a, +.>
Is the prior probability of the occurrence of financial cost risk event B.
It should be noted that, according to the current double financial cost risk probability model, the risk evaluation value of the current financial cost risk event can be calculated on the premise that the expected benefits and the standard deviation of benefits of the asset coexist, so that the high risk cost investment is reduced according to the risk evaluation value, and the stable low risk financial cost investment is increased.
Step S4:
in step S4, an adaptive financial cost assessment model is selected mainly according to the received data and the actual needs of the enterprise, the financial situation and the cost benefit of the enterprise are assessed and analyzed, an assessment report is generated, decision support is provided for the enterprise, the financial cost assessment model is a sum of a dynamic prediction model, a nonlinear control model, a data-driven optimization model, a non-financial index performance model and linear adjustment of a probability risk model, each selected coefficient has a value of 1 when the model is selected, a value of 0 when the model is not selected, fuzzy logic rules in financial data are obtained by data analysis according to the production needs and market needs of the manufacturing enterprise, the weight of each rule is set according to expert opinion and experience, and representative financial data is generated by using an countermeasure network technology on the acquired financial data.
It should be noted that, the financial cost evaluation module selects an adaptive financial cost evaluation model according to the received data and the actual requirement of the enterprise, evaluates and analyzes the financial condition and the cost benefit of the enterprise, and generates an evaluation report to provide decision support for the enterprise, the financial cost evaluation model is a sum of a dynamic prediction model, a nonlinear control model, a data-driven optimization model, a non-financial index performance model and linear adjustment of a probability risk model, and the formula of the financial cost evaluation model is:
wherein:
for financial cost assessment model->
For dynamic predictive model, ++>
For nonlinear control model +.>
Data-driven optimization model,/->
For non-financial performance model, +.>
For probabilistic risk model->
、/>
、/>
、/>
And +.>
The coefficients selected for the dynamic prediction model, the nonlinear control model, the data driven optimization model, the non-financial index performance model and the probability risk model are respectively 1 when the model is selected and 0 when the model is not selected.
Specifically, the dynamic performance of the dynamic prediction model is combined to contact the change trend of the market, clients and competition market in real time, so that the future financial cost can be predicted more accurately; the nonlinear control model is combined, and representative financial data can be generated by combining the generation countermeasure network in the deep reinforcement learning technology, so that different financial cost evaluation scenes, such as production cost, labor cost and research and development cost, can be better simulated to support different financial cost evaluation scenes; the data-driven optimization model is combined, so that main factors in evaluation indexes can be extracted, performance and risk of enterprises are better reflected, multi-objective financial cost evaluation and optimization are realized, influences of different factors on financial cost are reflected, and a more personalized financial cost scheme and strategy are further formulated; and the probability risk model is combined to carry out diversified evaluation on the double-risk financial cost event, so that the support of the performance management strategy is realized, and meanwhile, the safety and traceability of the information technology can be ensured by setting the blockchain technology.
Example 2
Embodiment 2 of the present invention differs from embodiment 1 in that this embodiment is presented as a multi-functional financial cost assessment system.
FIG. 2 is a schematic diagram of a multi-functional financial cost assessment system of the present invention, including a processor and a data acquisition module, a data analysis module, a financial cost assessment module, and a data storage module communicatively coupled to the processor.
The processor may be used to process data and/or information from at least one component of the multi-functional financial cost assessment system or an external data source, such as a cloud data center. In some embodiments, the processor may be local or remote. For example, the processor may access information and/or data from the data storage device, the terminal device, and/or the data acquisition device via a network. As another example, the processor may be directly connected to the data storage device, the terminal device, and/or the data acquisition device to access information and/or data. In some embodiments, the processor may be implemented on a cloud platform. For example, the cloud platform includes a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof.
The data acquisition module is used for acquiring internal and external related financial cost data of a production enterprise, introducing market data, customer data and competition data, utilizing deep learning and natural language processing technology to mine and analyze the data, sending the acquired information to the data analysis module for analysis and processing, and sending the acquired information to the data storage module for storage;
after the data analysis module receives the information sent by the data acquisition module, the processor calls the data stored in the data storage module to clean, analyze and classify the acquired data, and sends the analyzed data to the financial cost evaluation module;
the financial cost evaluation module selects an adaptive financial cost evaluation model according to the received data and the actual demands of the enterprise, evaluates and analyzes the financial condition and the cost benefit of the enterprise, and generates an evaluation report;
the data storage module is used for storing historical acquisition data, analysis data and evaluation data of the manufacturing enterprises.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The multifunctional financial cost evaluation system is characterized by comprising a processor, and a data acquisition module, a data analysis module, a financial cost evaluation module and a data storage module which are in communication connection with the processor;
the financial cost evaluation module selects an adaptive financial cost evaluation model according to the received data and the actual demands of the enterprises, evaluates and analyzes the financial condition and the cost benefit of the enterprises, generates an evaluation report, provides decision support for the enterprises, and is a sum of linear adjustment of a dynamic prediction model, a nonlinear control model, a data-driven optimization model, a non-financial index performance model and a probability risk model, wherein the formula of the financial cost evaluation model is as follows:
wherein:
for financial cost assessment model->
For dynamic predictive model, ++>
In the case of a non-linear control model,/>
data-driven optimization model,/->
For non-financial performance model, +.>
For probabilistic risk model->
、/>
、/>
、/>
And +.>
The coefficients selected for the dynamic prediction model, the nonlinear control model, the data driven optimization model, the non-financial index performance model and the probability risk model are respectively 1 when the model is selected and 0 when the model is not selected.
2. A multi-functional financial cost assessment system according to claim 1, wherein: in the financial cost evaluation module, a dynamic prediction model is based on historical data and market trend, and a long-term and short-term memory model is adopted to predict future financial conditions and cost benefits by combining a time sequence prediction technology and a machine learning technology, wherein the dynamic prediction model has the following formula:
wherein:
for the dynamic prediction model value at the current moment, < >>
And +.>
Respectively, the input at the current moment and the hidden state at the previous moment, < >>
And +.>
Weight matrix of input and weight matrix of hidden state respectively, < ->
For the activation function of the dynamic predictive model, the expression of the activation function is +.>
And->
One of them.
3. A multi-functional financial cost assessment system according to claim 1, wherein: in the financial cost evaluation module, the nonlinear control model monitors and analyzes financial data, and adopts a control strategy of continuously adjusting financial cost by adopting a self-adaptive neural fuzzy control model to meet the requirements of different businesses and clients, and the formula of the nonlinear control model is as follows:
wherein:
for the number of fuzzy logic rules in the financial data, < >>
Is->
The ∈of the fuzzy logic rule>
Input variables->
Is->
Weights corresponding to fuzzy logic rules, +.>
Is->
Membership function value of fuzzy logic rule, wherein the membership function value is about ∈ ->
The values of the individual input variables are mapped to +.>
The membership value between the two values,
is->
Membership value corresponding to each fuzzy logic rule, wherein the fuzzy logic rule in the financial data is obtained by data analysis according to the production requirement and the market requirement of a manufacturing enterprise, the weight of each rule is set according to expert opinion and experience, and the acquired financial data is obtained by the fuzzy logic ruleThe data utilization generation antagonism network technology generates representative financial data.
4. A multi-functional financial cost assessment system according to claim 1, wherein: in the financial cost evaluation module, the data-driven optimization model analyzes financial data by utilizing big data analysis and deep reinforcement learning technology to obtain an optimal financial strategy and scheme, and the formula of the data-driven optimization model is as follows:
wherein:
evaluation value entered for the current state of the current action of the data-driven optimization model, < >>
To reward the value in time, the user is provided with->
For discounts factor->
For the status action function->
Data analysis is carried out based on brand value, innovation capability and social requirement of a production enterprise, and a multi-input state action function expression is obtained, namely ∈>
For the current state +.>
For the current action +.>
For the next state +.>
For the next action.
5. A multi-functional financial cost assessment system according to claim 1, wherein: in the financial cost estimation module, a non-financial index performance model combines various performance indexes and performance factors, and the collection, the processing, the traceable storage and the safe sharing of financial data are realized by using the blockchain, so that the performance of a manufacturing enterprise is estimated and analyzed, problems and advantages are found, and the formula of the non-financial index performance model is as follows:
wherein:
for category number->
Evaluation weight of performance indicators of +.>
For category number->
Is used to determine the actual value of the performance indicator,
for category number->
Performance indicator target values of (c).
6. A multi-functional financial cost assessment system according to claim 1, wherein: in the financial cost estimation module, the probability risk model evaluates and analyzes financial cost risks of a production enterprise through collected data, probability of occurrence of financial cost double-risk events in probability risk evaluation data of financial cost of risk is utilized, data feature grabbing and learning are carried out on the financial cost double-risk events by combining a machine learning technology, potential risks and hidden dangers are found, corresponding measures are adopted for control and management, and a formula of the probability risk model is as follows:
wherein:
for the risk probability value of the financial cost risk event a under the condition of occurrence of the financial cost risk event B +.>
For the probability of occurrence of financial cost risk event B under the condition of occurrence of financial cost risk event a +.>
For the prior probability of occurrence of financial cost risk event a, +.>
Is the prior probability of the occurrence of financial cost risk event B.
7. A multi-functional financial cost assessment system according to claim 1, wherein:
a processor for processing data from at least one component of the multi-functional financial cost assessment system;
the data acquisition module is used for acquiring internal and external related financial cost data of a production enterprise, introducing market data, customer data and competition data, utilizing deep learning and natural language processing technology to mine and analyze the data, sending the acquired information to the data analysis module for analysis and processing, and sending the acquired information to the data storage module for storage;
after the data analysis module receives the information sent by the data acquisition module, the processor calls the data stored in the data storage module to clean, analyze and classify the acquired data, and sends the analyzed data to the financial cost evaluation module;
the data storage module is used for storing historical acquisition data, analysis data and evaluation data of the manufacturing enterprises.
8. A multi-functional financial cost assessment method for implementing a multi-functional financial cost assessment system according to any one of claims 1-7, comprising the steps of:
step S1, acquiring internal and external related financial cost data of a production enterprise, introducing market data, customer data and competition data, and mining and analyzing the data by utilizing deep learning and natural language processing technology;
step S2, after receiving the information sent by the data acquisition module, calling the data stored in the data storage module to clean, analyze and classify the acquired data;
s3, selecting an adaptive financial cost assessment model according to the received data and the actual demands of enterprises;
and S4, selecting an adaptive financial cost assessment model according to the received data and the actual demands of enterprises, assessing and analyzing the financial condition and cost benefit of the enterprises, generating an assessment report, providing decision support for the enterprises, wherein the financial cost assessment model is the sum of a dynamic prediction model, a nonlinear control model, a data-driven optimization model, a non-financial index performance model and linear adjustment of a probability risk model, the value of each selected coefficient is 1 when the model is selected, the value of each selected coefficient is 0 when the model is not selected, fuzzy logic rules in the financial data are obtained by data analysis according to the production demands and market demands of the manufacturing enterprises, the weight of each rule is set according to expert opinion and experience, and generating representative financial data by utilizing an countermeasure network technology.
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