Intelligent extraction and auditing method and system for financial data and medium

Abstract

The invention provides an intelligent extraction and auditing method and system for financial data and a medium. The method comprises the steps of obtaining financial data of a target power grid system, performing auditing sampling on the financial data through a specific sampling method to obtain a financial auditing sample, and extracting asset and liability feature data, operation performance feature data and operation feature data according to the financial auditing sample, processing according to the asset and liability characteristic data, the operation performance characteristic data and the operation characteristic data to obtain debt paying capability ratio index data, profit capability ratio index data and operation capability ratio index data, and processing according to the debt paying capability ratio index data, the profit capability ratio index data and the operation capability ratio index data; and a power grid financial comprehensive capability index is obtained and correspondingly adjusted, so that the intelligent extraction and auditing technology of financial data is realized.

Description

Technical Field

[0001] The present application relates to the technical field of financial data, and in particular to an intelligent extraction audit method, system and medium for financial data. Background Art

[0002] The power grid business is extensive, and its financial data comes from many sources and in various data formats. Traditional auditing methods are inefficient and error-prone, making it difficult to quickly and comprehensively audit financial data and to promptly detect potential financial risks and violations. There is an urgent need for an innovative method that can efficiently, accurately and multi-dimensionally audit financial data.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a method, system and medium for intelligent extraction and auditing of financial data. The method can obtain the financial data of the target power grid system and audit and sample the financial data through a specific sampling method to obtain a financial audit sample. Asset and liability characteristic data, business performance characteristic data and operation characteristic data are extracted according to the financial audit sample. The asset and liability characteristic data, business performance characteristic data and operation characteristic data are processed to obtain debt-paying ability ratio index data, profitability ratio index data and operation ability ratio index data respectively. Further processing is performed to obtain the comprehensive financial ability index of the power grid, and corresponding adjustments are made to realize the technology of intelligent extraction and auditing of financial data.

[0005] The present application also provides a method for intelligent extraction and auditing of financial data, comprising the following steps:

[0006] Obtain the financial data of the target power grid system, and audit and sample the financial data using a specific sampling method to obtain a financial audit sample;

[0007] Extracting asset and liability characteristic data, operating performance characteristic data and operation characteristic data based on the financial audit sample;

[0008] Processing the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data respectively;

[0009] The debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data are processed to obtain the comprehensive financial ability index of the power grid, and corresponding adjustments are made.

[0010] Optionally, in the intelligent extraction and auditing method of financial data described in the present application, the step of obtaining the financial data of the target power grid system and performing audit sampling on the financial data by a specific sampling method to obtain a financial audit sample includes:

[0011] Connect with the target power grid's financial system interface to obtain financial data;

[0012] Audit sampling of financial data is conducted through specific sampling methods to obtain financial audit samples;

[0013] The specific sampling methods include risk-based sampling, stratified sampling and cluster sampling.

[0014] Optionally, in the intelligent extraction audit method of financial data described in the present application, the extracting of asset and liability characteristic data, operating performance characteristic data and operation characteristic data according to the financial audit sample includes:

[0015] The asset-liability characteristic data include quick ratio data, asset-liability ratio data and interest coverage multiple data;

[0016] The operating performance characteristic data include gross profit margin data, net profit margin data and return on net assets data;

[0017] The operating characteristic data include accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data.

[0018] Optionally, in the intelligent extraction audit method of financial data described in the present application, the processing according to the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data respectively includes:

[0019] The quick ratio data, debt-to-asset ratio data and interest coverage ratio data are processed by a preset financial debt repayment assessment model to obtain debt repayment capacity ratio indicator data;

[0020] The gross profit rate data, net profit rate data and return on net assets data are processed by a preset financial profit evaluation model to obtain profitability ratio indicator data;

[0021] The accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data are processed through a preset financial operation evaluation model to obtain operating capacity ratio indicator data.

[0022] Optionally, in the intelligent extraction and auditing method of financial data described in the present application, the processing according to the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data to obtain the comprehensive financial ability index of the power grid and make corresponding adjustments includes:

[0023] Processing the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data to obtain a comprehensive power grid financial capability index;

[0024] Comparing the power grid financial comprehensive capability index with a preset power grid financial comprehensive capability threshold to obtain a threshold comparison result;

[0025] Determining whether the financial management capability of the target power grid system meets the standards according to the threshold comparison result;

[0026] If the threshold comparison result is greater than the preset threshold, the financial management capacity of the target power grid system meets the standard;

[0027] Otherwise, it is determined to be not up to standard, and the target power grid system is adjusted accordingly.

[0028] Optionally, the intelligent extraction and auditing method of financial data described in the present application further includes:

[0029] Processing the financial data of the target power grid system respectively through a plurality of preset financial risk assessment models, and recording corresponding model processing characteristic data;

[0030] Performing extraction processing according to the model processing characteristic data to obtain corresponding extraction time data, data processing speed data, abnormality recognition rate data and risk warning time interval data;

[0031] Processing is performed according to the extraction time data, data processing speed data, abnormality recognition rate data, and risk warning time interval data to obtain a risk assessment capability index;

[0032] Sorting in descending order according to the risk assessment capability index and screening to obtain the maximum value data in the risk assessment capability index;

[0033] The preset financial risk assessment model corresponding to the maximum value data is used as the optimal preset financial risk assessment model.

[0034] In a second aspect, the present application provides an intelligent extraction audit system for financial data, the system comprising: a memory and a processor, the memory comprising a program of an intelligent extraction audit method for financial data, and the program of the intelligent extraction audit method for financial data, when executed by the processor, implements the following steps:

[0035] Obtain the financial data of the target power grid system, and audit and sample the financial data using a specific sampling method to obtain a financial audit sample;

[0036] Extracting asset and liability characteristic data, operating performance characteristic data and operation characteristic data based on the financial audit sample;

[0037] Processing the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data respectively;

[0038] The debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data are processed to obtain the comprehensive financial ability index of the power grid, and corresponding adjustments are made.

[0039] Optionally, in the intelligent extraction and auditing system for financial data described in the present application, the step of acquiring the financial data of the target power grid system and performing audit sampling on the financial data by a specific sampling method to obtain a financial audit sample includes:

[0040] Connect with the target power grid's financial system interface to obtain financial data;

[0041] Audit sampling of financial data is conducted through specific sampling methods to obtain financial audit samples;

[0042] The specific sampling methods include risk-based sampling, stratified sampling and cluster sampling.

[0043] Optionally, in the intelligent extraction audit system for financial data described in the present application, the extracting of asset and liability characteristic data, operating performance characteristic data and operation characteristic data according to the financial audit sample includes:

[0044] The asset-liability characteristic data include quick ratio data, asset-liability ratio data and interest coverage multiple data;

[0045] The operating performance characteristic data include gross profit margin data, net profit margin data and return on net assets data;

[0046] The operating characteristic data include accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data.

[0047] In a third aspect, the present application also provides a computer-readable storage medium, which stores a program for an intelligent extraction audit method for financial data. When the program for the intelligent extraction audit method for financial data is executed by a processor, the steps of the intelligent extraction audit method for financial data as described in any one of the above items are implemented.

[0048] As can be seen from the above, the intelligent extraction audit method, system and medium of financial data disclosed in the present invention obtain the financial data of the target power grid system, and audit the financial data through a specific sampling method to obtain a financial audit sample, extract asset-liability characteristic data, business performance characteristic data and operation characteristic data according to the financial audit sample, process the asset-liability characteristic data, business performance characteristic data and operation characteristic data, respectively obtain debt-paying ability ratio index data, profitability ratio index data and operation ability ratio index data, process the debt-paying ability ratio index data, profitability ratio index data and operation ability ratio index data, obtain the comprehensive financial capability index of the power grid, and make corresponding adjustments, thereby realizing the technology of intelligent extraction audit of financial data.

[0049] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A flowchart of the intelligent extraction and auditing method of financial data provided in an embodiment of the present application;

[0052] Figure 2 A flowchart of obtaining financial audit samples for the intelligent extraction audit method of financial data provided in an embodiment of the present application;

[0053] Figure 3 A flow chart of obtaining solvency ratio index data, profitability ratio index data and operating capacity ratio index data of the intelligent extraction audit method of financial data provided in an embodiment of the present application;

[0054] Figure 4 A flow chart of obtaining a comprehensive power grid financial capability index according to an intelligent extraction and auditing method for financial data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0056] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0057] Please refer to Figure 1 , Figure 1 1 is a flow chart of a method for intelligently extracting and auditing financial data in some embodiments of the present application. The method for intelligently extracting and auditing financial data is used in a terminal device, such as a computer, a mobile phone terminal, etc. The method for intelligently extracting and auditing financial data comprises the following steps:

[0058] S11. Obtain the financial data of the target power grid system, and perform audit sampling on the financial data through a specific sampling method to obtain a financial audit sample;

[0059] S12, extracting asset and liability characteristic data, operating performance characteristic data and operation characteristic data according to the financial audit sample;

[0060] S13, processing the asset-liability characteristic data, the operating performance characteristic data, and the operation characteristic data to obtain debt-paying ability ratio index data, profitability ratio index data, and operating ability ratio index data respectively;

[0061] S14. Process the debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data to obtain a comprehensive power grid financial capability index, and make corresponding adjustments.

[0062] It should be noted that the power grid has a wide range of business, and its financial data comes from many sources and has various data formats. Traditional auditing methods are inefficient and prone to errors. It is difficult to quickly and comprehensively audit financial data effectively, and it is impossible to timely discover potential financial risks and violations. There is an urgent need for an innovative method that can audit financial data efficiently, accurately and in multiple dimensions. In this embodiment, firstly, the financial data of the target power grid system is obtained, and the financial data is audited and sampled by a specific sampling method to obtain a financial audit sample. According to the financial audit sample, asset and liability characteristic data, operating performance characteristic data and operation characteristic data are extracted, and the asset and liability characteristic data, operating performance characteristic data and operation characteristic data are processed and classified. Obtain debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data respectively, process them according to the debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data, obtain the comprehensive financial ability index of the power grid, and make corresponding adjustments, so as to realize the technology of intelligent extraction and audit of financial data. In addition, this technology is a solution for technical processing of information, data and parameters obtained through the financial data extraction and audit database through external data interaction with a third-party financial data extraction and audit platform. This technology has the means of information acquisition, data interaction and extraction, and data processing technology, and is not a human-set rule or economic law. This technical solution has technical laws.

[0063] Please refer to Figure 2 , Figure 2 The flowchart of obtaining financial audit samples of the intelligent extraction audit method of financial data in some embodiments of the present application. According to the embodiment of the present invention, the financial data of the target power grid system is obtained, and the financial data is audited and sampled by a specific sampling method to obtain the financial audit samples, including:

[0064] S21, performing docking processing through the financial system interface of the target power grid to obtain financial data;

[0065] S22. Audit and sample financial data using a specific sampling method to obtain financial audit samples;

[0066] S23. The specific sampling methods include risk-oriented sampling, stratified sampling and cluster sampling.

[0067] It should be noted that the financial system of the power grid has an external data interface. Auditors can connect to the financial system through the interface, use special interface software or technical support provided by the company's technical personnel to realize data transmission, and can directly obtain relatively complete and accurate financial data, including general ledgers, detailed ledgers, vouchers and other basic financial information. In addition, the focus and scope of sampling can be determined based on the results of risk assessment, and financial data can be stratified or clustered according to certain characteristics, such as business type, amount, time period, etc., and then sampling can be carried out separately in each level or cluster. This can ensure the representativeness and uniformity of the sample and improve the accuracy and reliability of the sampling.

[0068] According to an embodiment of the present invention, extracting asset and liability characteristic data, operating performance characteristic data, and operation characteristic data according to the financial audit sample includes:

[0069] The asset-liability characteristic data include quick ratio data, asset-liability ratio data and interest coverage multiple data;

[0070] The operating performance characteristic data include gross profit margin data, net profit margin data and return on net assets data;

[0071] The operating characteristic data include accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data.

[0072] It should be noted that asset-liability characteristic data, business performance characteristic data and operating characteristic data can be extracted based on the financial audit samples. Among them, asset-liability characteristic data include quick ratio, debt-to-asset ratio and interest coverage ratio data, operating performance characteristic data include gross profit margin, net profit margin data and return on net assets, and operating characteristic data include accounts receivable turnover rate, inventory turnover rate and total asset turnover rate data.

[0073] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of obtaining solvency ratio index data, profitability ratio index data and operating capacity ratio index data of the intelligent extraction audit method of financial data in some embodiments of the present application. According to the embodiment of the present invention, the processing according to the asset-liability characteristic data, the operating performance characteristic data and the operating characteristic data to obtain the solvency ratio index data, the profitability ratio index data and the operating capacity ratio index data respectively includes:

[0074] S31, processing the quick ratio data, the debt-to-asset ratio data, and the interest coverage ratio data through a preset financial debt repayment assessment model to obtain debt repayment capacity ratio indicator data;

[0075] S32, processing the gross profit rate data, net profit rate data and return on net assets data through a preset financial profit evaluation model to obtain profitability ratio indicator data;

[0076] S33. Process the accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data through a preset financial operation evaluation model to obtain operating capacity ratio indicator data.

[0077] It should be noted that the quick ratio data, debt-to-asset ratio data and interest coverage ratio data can be obtained based on the financial audit sample, and the debt-to-asset ratio data can be processed through the preset financial debt repayment assessment model to obtain the debt repayment ability ratio index, wherein the financial debt repayment assessment model belongs to a neural network model, which is to train the initialized financial debt repayment assessment model based on a large amount of historical asset-liability characteristic data to obtain a trained financial debt repayment assessment model, the quick ratio is the quick assets (current assets-inventory) divided by the current liabilities, because the inventory realization speed is relatively slow, so the quick ratio can more accurately reflect the company's immediate debt repayment ability, the debt-to-asset ratio is the total liabilities divided by the total assets, which shows how much of the company's total assets are raised through debt, and is used to measure the company's long-term debt repayment ability, the interest coverage ratio is calculated by dividing the profit before interest and taxes by the interest expense, which reflects the ability of the company's operating income to pay debt interest, and can be processed through the preset financial profit assessment model based on the gross profit margin data, the net profit margin data and the return on net assets data to obtain the profitability ratio index, wherein the financial profit assessment model belongs to a neural network model, which is to train the initialized financial debt repayment assessment model based on a large amount of historical operating performance characteristic data. The financial profit evaluation model is trained to obtain a trained financial profit evaluation model. The gross profit margin is (operating income - operating costs) ÷ operating income × 100%, which reflects the basic profitability of the company's products or services. The net profit margin is calculated by dividing the net profit by the operating income, which reflects the company's ultimate profitability. The return on net assets is the ratio of net profit to average net assets, which measures the profitability of the company's own capital. It can be processed according to the accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data through the preset financial operation evaluation model to obtain the operating capacity. Libi index, among which, the financial operation evaluation model belongs to the neural network model, which is trained on the basis of a large amount of historical operating performance characteristic data to obtain the trained financial operation evaluation model. The accounts receivable turnover rate is the operating income divided by the average balance of accounts receivable, which reflects the speed of accounts receivable turnover. The inventory turnover rate is the operating cost divided by the average inventory balance, which is used to measure the inventory operation efficiency in all aspects of the production and operation of the enterprise. The total asset turnover rate is the operating income divided by the average total assets, which is used to comprehensively evaluate the operating quality and utilization efficiency of all assets of the enterprise.

[0078] Please refer to Figure 4 , Figure 4The flowchart of the method for intelligently extracting and auditing financial data in some embodiments of the present application for obtaining the comprehensive financial capability index of the power grid is shown in FIG. According to the embodiment of the present invention, the processing according to the debt-paying capability ratio index data, the profitability ratio index data and the operating capability ratio index data to obtain the comprehensive financial capability index of the power grid and make corresponding adjustments includes:

[0079] S41, processing the debt-paying ability ratio index data, the profitability ratio index data, and the operating ability ratio index data to obtain a comprehensive power grid financial capability index;

[0080] S42, comparing the power grid financial comprehensive capability index with a preset power grid financial comprehensive capability threshold to obtain a threshold comparison result;

[0081] S43, judging whether the financial management capability of the target power grid system meets the standard according to the threshold comparison result;

[0082] S44. If the threshold comparison result is greater than a preset threshold, the financial management capability of the target power grid system meets the standard;

[0083] S45. Otherwise, it is determined as not meeting the standard, and the target power grid system is adjusted accordingly.

[0084] It should be noted that the comprehensive financial capability index of the power grid is obtained by processing the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data, and then compared with the preset comprehensive financial capability threshold of the power grid to obtain the threshold comparison result, and whether the financial operating capability of the target power grid system meets the standard is judged according to the threshold comparison result. If the threshold comparison result is greater than the preset threshold, the financial operating capability of the target power grid system meets the standard, otherwise, it is judged as not meeting the standard, and the target power grid system is adjusted accordingly. The corresponding matching preset adjustment application plan is obtained through the gap value between the threshold comparison result and the preset threshold, and the next round of financial adjustment is made to the target power grid system according to the adjustment application plan;

[0085] The calculation formula of the power grid comprehensive financial capability index is:

[0086]

[0087] Among them, Z h is the comprehensive financial capability index of the power grid, C z , Y l , T e They are the debt-paying ability ratio indicator data, profitability ratio indicator data, and operating ability ratio indicator data, respectively; δ, λ, and π are preset characteristic coefficients (the characteristic coefficients are obtained by extracting the preset financial data and querying the audit database).

[0088] According to an embodiment of the present invention, it also includes:

[0089] Processing the financial data of the target power grid system respectively through a plurality of preset financial risk assessment models, and recording corresponding model processing characteristic data;

[0090] Performing extraction processing according to the model processing characteristic data to obtain corresponding extraction time data, data processing speed data, abnormality recognition rate data and risk warning time interval data;

[0091] Processing is performed according to the extraction time data, data processing speed data, abnormality recognition rate data, and risk warning time interval data to obtain a risk assessment capability index;

[0092] Sorting in descending order according to the risk assessment capability index and screening to obtain the maximum value data in the risk assessment capability index;

[0093] Taking the preset financial risk assessment model corresponding to the maximum value data as the optimal preset financial risk assessment model;

[0094] The calculation formula of the risk assessment capability index is:

[0095]

[0096] Among them, F xi is the risk assessment capability index corresponding to the ith one of the n preset financial risk assessment models, x si 、b ji 、v gi ,d ci are the extraction time data, data processing speed data, abnormal recognition rate data, and risk warning time interval data corresponding to the ith one of the n preset financial risk assessment models, η i , β i , μ 1i , μ 2i It is the preset characteristic coefficient (the characteristic coefficient is obtained by extracting the preset financial data and querying the audit database).

[0097] It should be noted that in order to select the most suitable risk assessment model to conduct risk assessment on the financial data of the target power grid system, the financial data of the target power grid system is first subjected to risk assessment processing through multiple preset financial risk assessment models, and the corresponding model processing characteristic data is recorded. Then, extraction processing is performed to obtain the corresponding extraction time data, data processing speed data, abnormal recognition rate data and risk warning time interval data. The data is further processed through the calculation formula to obtain the risk assessment capability index, wherein the extraction time refers to the time taken from the beginning of extracting financial data to the completion of extraction and can be used for analysis, the data processing speed refers to the amount of financial data that can be processed per unit time, and the risk warning time interval data refers to the time interval from the discovery of risks in the data to the issuance of warning signals. A shorter time interval means more timely risk warnings, which helps auditors to take quick measures to deal with risks and reduce losses. Then, one of the maximum values ​​is selected from these risk assessment capability indices. The preset financial risk assessment model corresponding to this maximum value is the optimal preset financial risk assessment model, which can be used as the subsequent risk assessment of the financial data of the target power grid system.

[0098] According to an embodiment of the present invention, it also includes:

[0099] Extracting current asset data and current liability data according to the financial audit sample, and processing to obtain current ratio data;

[0100] Extracting monetary funds data and transactional financial assets data according to the financial audit sample, and processing to obtain cash ratio data;

[0101] According to the current ratio data and the cash ratio data, combined with the quick ratio data, processing is performed to obtain an asset liquidity index;

[0102] Comparing the asset liquidity index with a preset asset liquidity threshold to obtain a threshold comparison result;

[0103] Assessing whether there is a risk in the asset liquidity of the target power grid system according to the threshold comparison result;

[0104] If the threshold comparison result is less than the preset threshold, the asset liquidity of the target power grid system is at risk and needs to be processed accordingly;

[0105] The calculation formula of the asset liquidity index is:

[0106]

[0107] Among them, L d is the asset liquidity index, g e 、h r 、p lThey are current ratio data, cash ratio data, and quick ratio data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by extracting the preset financial data and querying the audit database).

[0108] It should be noted that it is necessary to conduct a liquidity risk assessment on the finances of the target power grid system. The asset liquidity index can be obtained by processing the current ratio data and the cash ratio data in combination with the quick ratio data. The current ratio is based on current assets divided by current liabilities. It is used to measure the company's short-term debt repayment ability and reflects the ability of the company's current assets to be converted into cash for repayment of liabilities before short-term debts mature. The cash ratio is obtained by (cash and cash equivalents + trading financial assets) / current liabilities. It is an important financial indicator for measuring the company's short-term debt repayment ability. It reflects the degree to which the company's cash assets guarantee current liabilities. The asset liquidity index is then compared with the preset asset liquidity threshold. The threshold comparison result is used to assess whether there is a risk in the asset liquidity of the target power grid system. If the threshold comparison result is less than the preset threshold, the asset liquidity of the target power grid system is at risk and needs to be processed accordingly.

[0109] According to an embodiment of the present invention, it also includes:

[0110] Extracting power supply data, power sales data and load characteristic data of the target power grid system within a preset time period according to the financial audit sample;

[0111] Performing statistical processing on the power supply data and the power sales data to obtain line loss rate data;

[0112] The load characteristic data includes the maximum load data of the power grid, the minimum load data of the power grid and the average load data of the power grid, and is processed to obtain the load variation characteristic data of the power grid;

[0113] Processing the line loss rate data and the grid load change characteristic data to obtain a grid operation characteristic index;

[0114] Comparing the power grid operation characteristic index with a preset power grid operation characteristic threshold to obtain a threshold comparison result;

[0115] Evaluate whether the planning, scheduling and equipment selection of the target power grid system are reasonable according to the threshold comparison result;

[0116] If the threshold comparison result is less than the preset threshold, the planning, scheduling and equipment selection of the target power grid system are unreasonable, and corresponding adjustment measures need to be implemented;

[0117] The calculation formula of the power grid load change characteristic data is:

[0118]

[0119] Among them, h b is the grid load variation characteristic data, j u , k m 、s a They are the maximum load data of the power grid, the minimum load data of the power grid, and the average load data of the power grid, respectively; ρ and τ are the preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset financial data extraction audit database);

[0120] The calculation formula of the power grid operation characteristic index is:

[0121]

[0122] Among them, Y t is the power grid operation characteristic index, w q 、h b They are line loss rate data and grid load change characteristic data respectively, and ζ is the preset characteristic coefficient (the characteristic coefficient is obtained by extracting the preset financial data and querying the audit database).

[0123] It should be noted that the line loss rate data is the ratio of line loss power to total power supply, which is calculated as line loss rate = ((power supply - power sales) / power supply) X 100%. It is an important indicator to measure the economic performance and management level of power grid operation. By analyzing the line loss rates of lines in different regions and voltage levels, the weak links in power grid operation can be found. Through the maximum load, minimum load, average load, etc. of the power grid, the load characteristics and changing laws of the power grid can be understood, which provides a basis for power grid planning, dispatching and equipment selection. According to the line loss rate data and the power grid load change characteristic data, the power grid operation characteristic index is obtained, and then compared with the preset power grid operation characteristic threshold to obtain the threshold comparison result. According to the threshold comparison result, it is evaluated whether the planning, dispatching and equipment selection of the target power grid system are reasonable. If the threshold comparison result is less than the preset threshold, the planning, dispatching and equipment selection of the target power grid system are unreasonable, and corresponding adjustment measures need to be implemented.

[0124] In a second aspect, the present invention further discloses an intelligent extraction audit system for financial data, including a memory and a processor, wherein the memory includes an intelligent extraction audit method program for financial data, and when the intelligent extraction audit method program for financial data is executed by the processor, the following steps are implemented:

[0125] Obtain the financial data of the target power grid system, and audit and sample the financial data using a specific sampling method to obtain a financial audit sample;

[0126] Extracting asset and liability characteristic data, operating performance characteristic data and operation characteristic data based on the financial audit sample;

[0127] Processing the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data respectively;

[0128] The debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data are processed to obtain the comprehensive financial ability index of the power grid, and corresponding adjustments are made.

[0129] It should be noted that the power grid has a wide range of business, and its financial data comes from many sources and has various data formats. Traditional auditing methods are inefficient and prone to errors. It is difficult to quickly and comprehensively audit financial data effectively, and it is impossible to timely discover potential financial risks and violations. There is an urgent need for an innovative method that can audit financial data efficiently, accurately and in multiple dimensions. In this embodiment, firstly, the financial data of the target power grid system is obtained, and the financial data is audited and sampled by a specific sampling method to obtain a financial audit sample. According to the financial audit sample, asset and liability characteristic data, operating performance characteristic data and operation characteristic data are extracted, and the asset and liability characteristic data, operating performance characteristic data and operation characteristic data are processed and classified. Obtain debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data respectively, process them according to the debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data, obtain the comprehensive financial ability index of the power grid, and make corresponding adjustments, so as to realize the technology of intelligent extraction and audit of financial data. In addition, this technology is a solution for technical processing of information, data and parameters obtained through the financial data extraction and audit database through external data interaction with a third-party financial data extraction and audit platform. This technology has the means of information acquisition, data interaction and extraction, and data processing technology, and is not a human-set rule or economic law. This technical solution has technical laws.

[0130] According to an embodiment of the present invention, the step of acquiring financial data of the target power grid system and performing audit sampling on the financial data by a specific sampling method to obtain a financial audit sample includes:

[0131] Connect with the target power grid's financial system interface to obtain financial data;

[0132] Audit sampling of financial data is conducted through specific sampling methods to obtain financial audit samples;

[0133] The specific sampling methods include risk-based sampling, stratified sampling and cluster sampling.

[0134] It should be noted that the financial system of the power grid has an external data interface. Auditors can connect to the financial system through the interface, use special interface software or technical support provided by the company's technical personnel to realize data transmission, and can directly obtain relatively complete and accurate financial data, including general ledgers, detailed ledgers, vouchers and other basic financial information. In addition, the focus and scope of sampling can be determined based on the results of risk assessment, and financial data can be stratified or clustered according to certain characteristics, such as business type, amount, time period, etc., and then sampling can be carried out separately in each level or cluster. This can ensure the representativeness and uniformity of the sample and improve the accuracy and reliability of the sampling.

[0135] According to an embodiment of the present invention, extracting asset and liability characteristic data, operating performance characteristic data, and operation characteristic data according to the financial audit sample includes:

[0136] The asset-liability characteristic data include quick ratio data, asset-liability ratio data and interest coverage multiple data;

[0137] The operating performance characteristic data include gross profit margin data, net profit margin data and return on net assets data;

[0138] The operating characteristic data include accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data.

[0139] It should be noted that asset-liability characteristic data, business performance characteristic data and operating characteristic data can be extracted based on the financial audit samples. Among them, asset-liability characteristic data include quick ratio, debt-to-asset ratio and interest coverage ratio data, operating performance characteristic data include gross profit margin, net profit margin data and return on net assets, and operating characteristic data include accounts receivable turnover rate, inventory turnover rate and total asset turnover rate data.

[0140] According to an embodiment of the present invention, the processing according to the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data respectively includes:

[0141] The quick ratio data, debt-to-asset ratio data and interest coverage ratio data are processed by a preset financial debt repayment assessment model to obtain debt repayment capacity ratio indicator data;

[0142] The gross profit rate data, net profit rate data and return on net assets data are processed by a preset financial profit evaluation model to obtain profitability ratio indicator data;

[0143] The accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data are processed through a preset financial operation evaluation model to obtain operating capacity ratio indicator data.

[0144] It should be noted that the quick ratio data, debt-to-asset ratio data and interest coverage ratio data can be obtained based on the financial audit sample, and the debt-to-asset ratio data can be processed through the preset financial debt repayment assessment model to obtain the debt repayment ability ratio index, wherein the financial debt repayment assessment model belongs to a neural network model, which is to train the initialized financial debt repayment assessment model based on a large amount of historical asset-liability characteristic data to obtain a trained financial debt repayment assessment model, the quick ratio is the quick assets (current assets-inventory) divided by the current liabilities, because the inventory realization speed is relatively slow, so the quick ratio can more accurately reflect the company's immediate debt repayment ability, the debt-to-asset ratio is the total liabilities divided by the total assets, which shows how much of the company's total assets are raised through debt, and is used to measure the company's long-term debt repayment ability, the interest coverage ratio is calculated by dividing the profit before interest and taxes by the interest expense, which reflects the ability of the company's operating income to pay debt interest, and can be processed through the preset financial profit assessment model based on the gross profit margin data, the net profit margin data and the return on net assets data to obtain the profitability ratio index, wherein the financial profit assessment model belongs to a neural network model, which is to train the initialized financial debt repayment assessment model based on a large amount of historical operating performance characteristic data. The financial profit evaluation model is trained to obtain a trained financial profit evaluation model. The gross profit margin is (operating income - operating costs) ÷ operating income × 100%, which reflects the basic profitability of the company's products or services. The net profit margin is calculated by dividing the net profit by the operating income, which reflects the company's ultimate profitability. The return on net assets is the ratio of net profit to average net assets, which measures the profitability of the company's own capital. It can be processed according to the accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data through the preset financial operation evaluation model to obtain the operating capacity. Libi index, among which, the financial operation evaluation model belongs to the neural network model, which is trained on the basis of a large amount of historical operating performance characteristic data to obtain the trained financial operation evaluation model. The accounts receivable turnover rate is the operating income divided by the average balance of accounts receivable, which reflects the speed of accounts receivable turnover. The inventory turnover rate is the operating cost divided by the average inventory balance, which is used to measure the inventory operation efficiency in all aspects of the production and operation of the enterprise. The total asset turnover rate is the operating income divided by the average total assets, which is used to comprehensively evaluate the operating quality and utilization efficiency of all assets of the enterprise.

[0145] According to an embodiment of the present invention, the processing according to the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data to obtain the power grid financial comprehensive ability index and make corresponding adjustments includes:

[0146] Processing the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data to obtain a comprehensive power grid financial capability index;

[0147] Comparing the power grid financial comprehensive capability index with a preset power grid financial comprehensive capability threshold to obtain a threshold comparison result;

[0148] Determining whether the financial management capability of the target power grid system meets the standards according to the threshold comparison result;

[0149] If the threshold comparison result is greater than the preset threshold, the financial management capacity of the target power grid system meets the standard;

[0150] Otherwise, it is determined to be not up to standard, and the target power grid system is adjusted accordingly.

[0151] It should be noted that the comprehensive financial capability index of the power grid is obtained by processing the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data, and then compared with the preset comprehensive financial capability threshold of the power grid to obtain the threshold comparison result, and whether the financial operating capability of the target power grid system meets the standard is judged according to the threshold comparison result. If the threshold comparison result is greater than the preset threshold, the financial operating capability of the target power grid system meets the standard, otherwise, it is judged as not meeting the standard, and the target power grid system is adjusted accordingly. The corresponding matching preset adjustment application plan is obtained through the gap value between the threshold comparison result and the preset threshold, and the next round of financial adjustment is made to the target power grid system according to the adjustment application plan;

[0152] The calculation formula of the power grid comprehensive financial capability index is:

[0153]

[0154] Among them, Z h is the comprehensive financial capability index of the power grid, C z , Y l , T e They are the debt-paying ability ratio indicator data, profitability ratio indicator data, and operating ability ratio indicator data, respectively; δ, λ, and π are preset characteristic coefficients (the characteristic coefficients are obtained by extracting the preset financial data and querying the audit database).

[0155] According to an embodiment of the present invention, it also includes:

[0156] Processing the financial data of the target power grid system respectively through a plurality of preset financial risk assessment models, and recording corresponding model processing characteristic data;

[0157] Performing extraction processing according to the model processing characteristic data to obtain corresponding extraction time data, data processing speed data, abnormality recognition rate data and risk warning time interval data;

[0158] Processing is performed according to the extraction time data, data processing speed data, abnormality recognition rate data, and risk warning time interval data to obtain a risk assessment capability index;

[0159] Sorting in descending order according to the risk assessment capability index and screening to obtain the maximum value data in the risk assessment capability index;

[0160] Taking the preset financial risk assessment model corresponding to the maximum value data as the optimal preset financial risk assessment model;

[0161] The calculation formula of the risk assessment capability index is:

[0162]

[0163] Among them, F xi is the risk assessment capability index corresponding to the ith one of the n preset financial risk assessment models, x si 、b ji 、v gi ,d ci are the extraction time data, data processing speed data, abnormal recognition rate data, and risk warning time interval data corresponding to the ith one of the n preset financial risk assessment models, η i , β i , μ 1i , μ 2i It is the preset characteristic coefficient (the characteristic coefficient is obtained by extracting the preset financial data and querying the audit database).

[0164] It should be noted that in order to select the most suitable risk assessment model to conduct risk assessment on the financial data of the target power grid system, the financial data of the target power grid system is first subjected to risk assessment processing through multiple preset financial risk assessment models, and the corresponding model processing characteristic data is recorded. Then, extraction processing is performed to obtain the corresponding extraction time data, data processing speed data, abnormal recognition rate data and risk warning time interval data. The data is further processed through the calculation formula to obtain the risk assessment capability index, wherein the extraction time refers to the time taken from the beginning of extracting financial data to the completion of extraction and can be used for analysis, the data processing speed refers to the amount of financial data that can be processed per unit time, and the risk warning time interval data refers to the time interval from the discovery of risks in the data to the issuance of warning signals. A shorter time interval means more timely risk warnings, which helps auditors to take quick measures to deal with risks and reduce losses. Then, one of the maximum values ​​is selected from these risk assessment capability indices. The preset financial risk assessment model corresponding to this maximum value is the optimal preset financial risk assessment model, which can be used as the subsequent risk assessment of the financial data of the target power grid system.

[0165] According to an embodiment of the present invention, it also includes:

[0166] Extracting current asset data and current liability data according to the financial audit sample, and processing to obtain current ratio data;

[0167] Extracting monetary funds data and transactional financial assets data according to the financial audit sample, and processing to obtain cash ratio data;

[0168] According to the current ratio data and the cash ratio data, combined with the quick ratio data, processing is performed to obtain an asset liquidity index;

[0169] Comparing the asset liquidity index with a preset asset liquidity threshold to obtain a threshold comparison result;

[0170] Assessing whether there is a risk in the asset liquidity of the target power grid system according to the threshold comparison result;

[0171] If the threshold comparison result is less than the preset threshold, the asset liquidity of the target power grid system is at risk and needs to be processed accordingly;

[0172] The calculation formula of the asset liquidity index is:

[0173]

[0174] Among them, L d is the asset liquidity index, g e 、h r 、p lThey are current ratio data, cash ratio data, and quick ratio data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by extracting the preset financial data and querying the audit database).

[0175] It should be noted that it is necessary to conduct a liquidity risk assessment on the finances of the target power grid system. The asset liquidity index can be obtained by processing the current ratio data and the cash ratio data in combination with the quick ratio data. The current ratio is based on current assets divided by current liabilities. It is used to measure the company's short-term debt repayment ability and reflects the ability of the company's current assets to be converted into cash for repayment of liabilities before short-term debts mature. The cash ratio is obtained by (cash and cash equivalents + trading financial assets) / current liabilities. It is an important financial indicator for measuring the company's short-term debt repayment ability. It reflects the degree to which the company's cash assets guarantee current liabilities. The asset liquidity index is then compared with the preset asset liquidity threshold. The threshold comparison result is used to assess whether there is a risk in the asset liquidity of the target power grid system. If the threshold comparison result is less than the preset threshold, the asset liquidity of the target power grid system is at risk and needs to be processed accordingly.

[0176] According to an embodiment of the present invention, it also includes:

[0177] Extracting power supply data, power sales data and load characteristic data of the target power grid system within a preset time period according to the financial audit sample;

[0178] Performing statistical processing on the power supply data and the power sales data to obtain line loss rate data;

[0179] The load characteristic data includes the maximum load data of the power grid, the minimum load data of the power grid and the average load data of the power grid, and is processed to obtain the load variation characteristic data of the power grid;

[0180] Processing the line loss rate data and the grid load change characteristic data to obtain a grid operation characteristic index;

[0181] Comparing the power grid operation characteristic index with a preset power grid operation characteristic threshold to obtain a threshold comparison result;

[0182] Evaluate whether the planning, scheduling and equipment selection of the target power grid system are reasonable according to the threshold comparison result;

[0183] If the threshold comparison result is less than the preset threshold, the planning, scheduling and equipment selection of the target power grid system are unreasonable, and corresponding adjustment measures need to be implemented;

[0184] The calculation formula of the power grid load change characteristic data is:

[0185]

[0186] Among them, h b is the grid load variation characteristic data, j u , k m 、s a They are the maximum load data of the power grid, the minimum load data of the power grid, and the average load data of the power grid, respectively; ρ and τ are the preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset financial data extraction audit database);

[0187] The calculation formula of the power grid operation characteristic index is:

[0188]

[0189] Among them, Y t is the power grid operation characteristic index, w q 、h b They are line loss rate data and grid load change characteristic data respectively, and ζ is the preset characteristic coefficient (the characteristic coefficient is obtained by extracting the preset financial data and querying the audit database).

[0190] It should be noted that the line loss rate data is the ratio of line loss power to total power supply, which is calculated as line loss rate = ((power supply - power sales) / power supply) X 100%. It is an important indicator to measure the economic performance and management level of power grid operation. By analyzing the line loss rates of lines in different regions and voltage levels, the weak links in power grid operation can be found. Through the maximum load, minimum load, average load, etc. of the power grid, the load characteristics and changing laws of the power grid can be understood, which provides a basis for power grid planning, dispatching and equipment selection. According to the line loss rate data and the power grid load change characteristic data, the power grid operation characteristic index is obtained, and then compared with the preset power grid operation characteristic threshold to obtain the threshold comparison result. According to the threshold comparison result, it is evaluated whether the planning, dispatching and equipment selection of the target power grid system are reasonable. If the threshold comparison result is less than the preset threshold, the planning, dispatching and equipment selection of the target power grid system are unreasonable, and corresponding adjustment measures need to be implemented.

[0191] A third aspect of the present invention provides a readable storage medium, which stores a program for an intelligent extraction audit method for financial data. When the program for the intelligent extraction audit method for financial data is executed by a processor, the steps of the intelligent extraction audit method for financial data as described in any one of the above items are implemented.

[0192] The intelligent extraction audit method, system and medium of financial data disclosed in the present invention obtain the financial data of the target power grid system, and audit and sample the financial data through a specific sampling method to obtain a financial audit sample, extract asset-liability characteristic data, business performance characteristic data and operation characteristic data according to the financial audit sample, process the asset-liability characteristic data, business performance characteristic data and operation characteristic data to obtain debt-paying ability ratio index data, profitability ratio index data and operation ability ratio index data respectively, process the debt-paying ability ratio index data, profitability ratio index data and operation ability ratio index data to obtain a comprehensive power grid financial capability index, and make corresponding adjustments, thereby realizing the technology of intelligent extraction audit of financial data.

[0193] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0194] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0195] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0196] Those skilled in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0197] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for intelligent extraction and auditing of financial data, characterized in that: The following steps are involved: Obtain the financial data of the target power grid system, and audit and sample the financial data using a specific sampling method to obtain a financial audit sample; Extracting asset and liability characteristic data, operating performance characteristic data and operation characteristic data based on the financial audit sample; Processing the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data respectively; The debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data are processed to obtain the comprehensive financial ability index of the power grid, and corresponding adjustments are made.

2. The intelligent extraction and auditing method of financial data according to claim 1 is characterized in that: The step of obtaining the financial data of the target power grid system and performing audit sampling on the financial data by a specific sampling method to obtain a financial audit sample includes: Connect with the target power grid's financial system interface to obtain financial data; Audit sampling of financial data is conducted through specific sampling methods to obtain financial audit samples; The specific sampling methods include risk-based sampling, stratified sampling and cluster sampling.

3. The intelligent extraction and auditing method of financial data according to claim 2 is characterized in that: The extracting of asset and liability characteristic data, operating performance characteristic data and operation characteristic data according to the financial audit sample includes: The asset-liability characteristic data include quick ratio data, asset-liability ratio data and interest coverage multiple data; The operating performance characteristic data include gross profit margin data, net profit margin data and return on net assets data; The operating characteristic data include accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data.

4. The intelligent extraction and auditing method of financial data according to claim 3 is characterized in that: The processing according to the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data respectively includes: The quick ratio data, debt-to-asset ratio data and interest coverage ratio data are processed by a preset financial debt repayment assessment model to obtain debt repayment capacity ratio indicator data; The gross profit rate data, net profit rate data and return on net assets data are processed by a preset financial profit evaluation model to obtain profitability ratio indicator data; The accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data are processed through a preset financial operation evaluation model to obtain operating capacity ratio indicator data.

5. The intelligent extraction and auditing method of financial data according to claim 4 is characterized in that: The processing according to the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data is performed to obtain the comprehensive financial ability index of the power grid, and the corresponding adjustments are made, including: Processing the debt-paying ability ratio index data, the profitability ratio index data and the operating ability ratio index data to obtain a comprehensive power grid financial capability index; Comparing the power grid financial comprehensive capability index with a preset power grid financial comprehensive capability threshold to obtain a threshold comparison result; Determining whether the financial management capability of the target power grid system meets the standards according to the threshold comparison result; If the threshold comparison result is greater than the preset threshold, the financial management capacity of the target power grid system meets the standard; Otherwise, it is determined to be not up to standard, and the target power grid system is adjusted accordingly.

6. The intelligent extraction and auditing method of financial data according to claim 5 is characterized in that: Also includes: Processing the financial data of the target power grid system respectively through a plurality of preset financial risk assessment models, and recording corresponding model processing characteristic data; Performing extraction processing according to the model processing characteristic data to obtain corresponding extraction time data, data processing speed data, abnormality recognition rate data and risk warning time interval data; Processing is performed according to the extraction time data, data processing speed data, abnormality recognition rate data, and risk warning time interval data to obtain a risk assessment capability index; Sorting in descending order according to the risk assessment capability index and screening to obtain the maximum value data in the risk assessment capability index; The preset financial risk assessment model corresponding to the maximum value data is used as the optimal preset financial risk assessment model.

7. An intelligent extraction and auditing system for financial data, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of an intelligent extraction audit method for financial data, and when the program of the intelligent extraction audit method for financial data is executed by the processor, the following steps are implemented: Obtain the financial data of the target power grid system, and audit and sample the financial data using a specific sampling method to obtain a financial audit sample; Extracting asset and liability characteristic data, operating performance characteristic data and operation characteristic data based on the financial audit sample; Processing the asset-liability characteristic data, the operating performance characteristic data and the operation characteristic data to obtain debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data respectively; The debt-paying ability ratio index data, profitability ratio index data and operating ability ratio index data are processed to obtain the comprehensive financial ability index of the power grid, and corresponding adjustments are made.

8. The intelligent extraction and auditing system for financial data according to claim 7 is characterized in that: The step of obtaining the financial data of the target power grid system and performing audit sampling on the financial data by a specific sampling method to obtain a financial audit sample includes: Connect with the target power grid's financial system interface to obtain financial data; Audit sampling of financial data is conducted through specific sampling methods to obtain financial audit samples; The specific sampling methods include risk-based sampling, stratified sampling and cluster sampling.

9. The intelligent extraction and auditing system for financial data according to claim 8 is characterized in that: The extracting of asset and liability characteristic data, operating performance characteristic data and operation characteristic data according to the financial audit sample includes: The asset-liability characteristic data include quick ratio data, asset-liability ratio data and interest coverage multiple data; The operating performance characteristic data include gross profit margin data, net profit margin data and return on net assets data; The operating characteristic data include accounts receivable turnover rate data, inventory turnover rate data and total asset turnover rate data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a program for an intelligent extraction and auditing method for financial data. When the program for an intelligent extraction and auditing method for financial data is executed by a processor, the steps of the intelligent extraction and auditing method for financial data as described in any one of claims 1 to 6 are implemented.

Images