Intelligent generation method and device of financial statement, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an intelligent generation method and device for a financial statement, computer equipment and a storage medium, and the method comprises the steps: obtaining to-be-generated financial statement information, obtaining multiple pieces of financial data information, obtaining corresponding data processing logic through the correlation information of the financial statement information, and storing the data processing logic in a database; and processing the financial data information to obtain a financial statement. The method has the beneficial effects that the financial statements are quickly and accurately generated by utilizing the financial information in the data processing model and the database, the efficiency of merging and generating the financial statements can be remarkably improved, manual intervention is reduced, the error rate is reduced, the requirement of modern enterprises for quick decision making is met, and the financial management level is favorably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent generation of financial statements, and in particular to a method, device, computer equipment and storage medium for intelligent generation of financial statements. Background Art

[0002] Financial statements are important written reports of a company's financial status, operating results and cash flow, usually including balance sheets, income statements and cash flow statements. They are an important basis for stakeholders (such as investors, creditors and management) to make decisions. As the scale of enterprises expands and transactions become more complex, traditional manual financial statements can no longer meet the needs of efficient, accurate and real-time data. In addition, the traditional financial report production process is time-consuming and error-prone, making it difficult to meet the needs of quick decision-making. Summary of the invention

[0003] Based on this, it is necessary to address the existing problem of intelligent generation of financial statements and propose a method, device, computer equipment and storage medium for intelligent generation of financial statements.

[0004] A method for intelligently generating financial statements, the method comprising:

[0005] Obtain financial statement information to be generated;

[0006] Acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0007] Obtaining association information between the financial data information;

[0008] Inputting the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0009] Processing the financial data information according to the data processing logic to obtain financial statement information;

[0010] The financial statement information is imported into a preset template to obtain the corresponding financial statement.

[0011] Furthermore, the step of obtaining the association information between the financial data information includes:

[0012] Determining whether the preset database contains association information between the financial data information;

[0013] If there is no association information between the financial data information, converting the content in the financial data information into a text string, wherein the text string is composed of a plurality of character strings according to their positions in the financial data information;

[0014] Extracting a key character string from the character string corresponding to one of the financial data information, and performing feature matching on the character string corresponding to the other financial data information by using the BM algorithm;

[0015] The association information between the financial data information is established according to the matching results.

[0016] Furthermore, the step of inputting the association information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic includes:

[0017] Calculate a temporary processing logic based on the associated information;

[0018] By formula Calculating a first matching degree between each of the temporary processing logics and the financial statement information to be generated; wherein, Represents the vector corresponding to the temporary processing logic, Represents the vector corresponding to the financial statement information to be generated, Indicates the first matching degree;

[0019] Determining whether the first matching degree corresponding to each of the temporary processing logics reaches a first preset matching degree value;

[0020] The temporary processing logic that reaches the first preset matching value is used as the data processing logic.

[0021] Furthermore, the step of calculating the temporary processing logic based on the associated information includes:

[0022] Vectorize each of the financial data information to obtain the corresponding n-dimensional vectors;

[0023] The n-dimensional vectors corresponding to the financial data information are weighted to obtain n-dimensional target vectors, which are x1, x2, ..., x n ;

[0024] Multiple temporary processing logics are obtained by calculating G(t)=softmax[Vf(t)]; where f(t)=g[Ux t +Wf(t-1)+b], G(t) is the temporary processing logic obtained by inputting the t-th target vector, x t represents the t-th target vector, f(t) represents the intermediate function obtained by inputting the t-th target vector, f(t-1) represents the intermediate function obtained by inputting the t-1-th target vector, and V, U, W, and b are all preset parameters.

[0025] Furthermore, before the step of obtaining the association information between the financial data information, it also includes:

[0026] According to the formula Calculate the outlier value of each data in each financial data information; where Z ij represents the abnormal value of the i-th data in the j-th financial data information, μ represents the average value of each data in the j-th financial data information, and σ represents the standard deviation of the j financial data information, and x ij represents the i-th data in the j-th financial data information, and n represents the number of data in the j-th financial data information;

[0027] |Z ij |>Z thr The corresponding target data is extracted and compared with the actual information of the financial data information; wherein Z thr is the set parameter value;

[0028] If the comparison result shows that the target data is wrong, the correct actual data will replace the target data.

[0029] Furthermore, before the step of importing the financial statement information into a preset template to obtain a corresponding financial statement, the step further includes:

[0030] Send multiple financial report templates to corresponding users;

[0031] The financial report template selected by the user is obtained as the preset template.

[0032] Furthermore, the step of processing the financial data information according to the data processing logic to obtain financial statement information includes:

[0033] Cleaning, classifying and summarizing the financial data to obtain target financial data;

[0034] Extracting preset key indicator data from the target financial data;

[0035] The financial statement information is obtained by analyzing and predicting the key indicator data.

[0036] An intelligent device for generating financial statements, the device comprising:

[0037] A first acquisition module, used to acquire financial statement information to be generated;

[0038] A second acquisition module is used to acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0039] A third acquisition module is used to acquire association information between the financial data information;

[0040] An input module, used to input the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0041] A processing module, used to process the financial data information according to the data processing logic to obtain financial statement information;

[0042] The import module is used to import the financial statement information into a preset template to obtain the corresponding financial statement.

[0043] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0044] Obtain financial statement information to be generated;

[0045] Acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0046] Obtaining association information between the financial data information;

[0047] Inputting the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0048] Processing the financial data information according to the data processing logic to obtain financial statement information;

[0049] The financial statement information is imported into a preset template to obtain the corresponding financial statement.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the following steps:

[0051] Obtain financial statement information to be generated;

[0052] Acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0053] Obtaining association information between the financial data information;

[0054] Inputting the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0055] Processing the financial data information according to the data processing logic to obtain financial statement information;

[0056] The financial statement information is imported into a preset template to obtain the corresponding financial statement.

[0057] The beneficial effects of the present invention are as follows: by acquiring the financial statement information to be generated and acquiring multiple financial data information, using the associated information to acquire the corresponding data processing logic, and processing the financial data information to obtain financial statements, thereby realizing the use of data processing models and financial information in a database to achieve rapid and accurate generation of financial statements, which can significantly improve the efficiency of consolidated generation of financial statements, reduce manual intervention, and reduce the error rate, meet the needs of modern enterprises for rapid decision-making, and help improve the level of financial management. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] in:

[0060] Figure 1 An application environment diagram of a method for intelligently generating financial statements in one embodiment;

[0061] Figure 2 is a flow chart of a method for intelligently generating financial statements in one embodiment;

[0062] Figure 3 is a structural block diagram of an intelligent generation device for financial statements in one embodiment;

[0063] Figure 4 FIG. 4 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Figure 1 This is a diagram of an application environment for intelligent generation of financial statements in an embodiment. Figure 1, the intelligent generation method of financial statements is applied to the intelligent generation system of financial statements. The intelligent generation system of financial statements includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network. The terminal 110 can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, a tablet computer, a laptop computer, etc. The server 120 can be implemented as an independent server or a server cluster composed of multiple servers. The terminal 110 is used to obtain financial data information, and the server 120 is used to store financial data information.

[0066] like Figure 2 As shown, in one embodiment, a method for intelligently generating financial statements is provided. The method can be applied to both a terminal and a server. This embodiment is described by applying to a terminal as an example. The method for intelligently generating financial statements specifically includes the following steps:

[0067] S1: Obtain the financial statement information to be generated;

[0068] S2: acquiring corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0069] S3: Obtaining association information between the financial data information;

[0070] S4: inputting the association information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0071] S5: Processing the financial data information according to the data processing logic to obtain financial statement information;

[0072] S6: Import the financial statement information into a preset template to obtain a corresponding financial statement.

[0073] As described in step S1 above, the financial statement information to be generated is obtained. It is necessary to identify the type of financial statement that the user needs to generate (such as a balance sheet, an income statement, a cash flow statement, etc.), as well as the required specific time period and any other relevant parameters (such as a company or department). The method of obtaining the information can be through manual uploading, or some financial statement information can be customized and then selected by the user.

[0074] As described in step S2 above, corresponding multiple financial data information is obtained from a preset database according to the financial statement information to be generated. A preset database (such as an ERP system, financial software or a data warehouse) is accessed and relevant financial data is extracted therefrom. The relevant financial data may include sales data, cost data, expense data, asset and liability data, etc. The financial data information may be obtained from a preset database by crawling or other methods.

[0075] As described in step S3 above, the association information between the financial data information is obtained. The extracted financial data is analyzed to identify the association between them. The association information is the associated part between two financial data information. For example, one of the financial data information is a list of various expenditures, and the other financial data information is the specific information of an expenditure. Therefore, the corresponding association information is to find the specific information of the expenditure, that is, the content of one financial data information can be associated with the other financial data information through one financial data information.

[0076] As described in step S4 above, the association information and the financial statement information to be generated are input into a preset data processing model to obtain the corresponding data processing logic; the preset data processing model (which can be a machine learning model or a rule-based model) analyzes the association information and the report information to be generated. This model will generate the required data processing logic, specifically a string of processing codes, which can be data capture, or data addition, modification, deletion and other processing logic, so it is input into the preset data processing model, and the data processing logic can be obtained according to the corresponding association relationship.

[0077] As described in step S5 above, the financial data information is processed according to the data processing logic to obtain financial statement information. The financial data is calculated and processed by the data processing logic. This processing may include aggregation, classification, averaging, ratio calculation, etc., and finally generates financial statement information that complies with accounting standards.

[0078] As described in step S6 above, the financial statement information is imported into a preset template to obtain the corresponding financial statement. The processed financial statement information is imported into a specific report template according to a preset format (such as Excel, PDF or other report formats). The generated financial statements have a clear structure and are easy to read and analyze. The data processing model and the financial information in the database are used to achieve fast and accurate generation of financial statements, which can significantly improve the efficiency of the combined generation of financial statements, reduce manual intervention, and reduce the error rate, meet the needs of modern enterprises for rapid decision-making, and help improve the level of financial management.

[0079] In one embodiment, the step S3 of obtaining the association information between the financial data information includes:

[0080] S301: Determine whether the preset database contains association information between the financial data information;

[0081] S302: If there is no association information between the financial data information, convert the content in the financial data information into a text string, wherein the text string is composed of multiple character strings according to their positions in the financial data information;

[0082] S303: extracting a key character string from a character string corresponding to one of the financial data information, and performing feature matching on the character strings corresponding to other financial data information by using the BM algorithm;

[0083] S304: Establish association information between the financial data information according to the matching results.

[0084] As described in the above steps S301-S304, since some financial data information is not associated with other financial data information before entering the preset database, it does not have associated information, so that the specific data content cannot be obtained based on the associated information later. Therefore, it is possible to detect whether the selected target financial data information has associated information in the preset database. When there is no associated information between the financial data information, an associated relationship can be established between the preset databases as the associated information between the target medical databases. The specific establishment method is to first convert the content in the financial data information into strings, and then extract the key strings therein according to the machine learning model based on natural language processing, and then calculate the feature matching between the preset databases through the BM (Boyer-Moore) algorithm. The BM algorithm (Boyer-Moore algorithm) is an efficient algorithm for string search, which is particularly suitable for finding pattern strings in text. Establish the associated information between the target financial data information. It should be noted that each financial data information generally has complex associated information, and only relatively simple associated information can be established through the above method.

[0085] In one embodiment, the step S4 of inputting the association information and the financial statement information to be generated into a preset data processing model to obtain the corresponding data processing logic includes:

[0086] S401: Calculate and obtain temporary processing logic based on the associated information;

[0087] S402: By formula Calculating a first matching degree between each of the temporary processing logics and the financial statement information to be generated; wherein, Represents the vector corresponding to the temporary processing logic, Represents the vector corresponding to the financial statement information to be generated, Indicates the first matching degree;

[0088] S403: Determine whether the first matching degree corresponding to each of the temporary processing logics reaches a first preset matching degree value;

[0089] S404: Using the temporary processing logic that reaches the first preset matching value as the data processing logic.

[0090] As described in the above steps S401-S404, the acquisition of data processing logic is realized, and all corresponding temporary processing logics can be calculated based on the association information. The calculation method can be training based on big data, and the sample data of the training is generated by multiple association relationships and operation logic built based on the association relationships. The sample data can be acquired through big data. In order to simplify the number of temporary processing logics generated, temporary processing logics that are not related to the financial statement can be eliminated. Specifically, the first matching degree between each temporary processing logic and the financial statement information to be generated can be calculated by a formula. When the first matching degree reaches a first preset matching degree value, the corresponding temporary processing logic can be used as the data processing logic, wherein the first preset matching degree is a pre-set value, and the matching degree algorithm can be any algorithm, such as a WMD algorithm (word mover's degree), an algorithm based on cosine similarity, a simhash algorithm, a vector model based on SVM (Support Vector Machine, SVM), etc., preferably through Calculation is performed. When the calculation result approaches 1, it indicates that the temporary processing logic is more relevant to the financial statement information to be generated. When the calculation result approaches 0, it indicates that the temporary processing logic is less relevant to the financial statement information to be generated. The temporary processing logic whose calculation result reaches the first preset matching degree is used as the data processing logic to participate in the subsequent calculation and analysis. Therefore, the temporary processing logic that is not the data processing logic can be omitted and does not need to participate in the subsequent calculation process, thus saving calculation time.

[0091] In one embodiment, the step S401 of calculating and obtaining temporary processing logic based on the association information includes:

[0092] S4011: vectorizing each of the financial data information to obtain respective corresponding n-dimensional vectors;

[0093] S4012: Perform weighted calculation on the n-dimensional vectors corresponding to the financial data information to obtain n-dimensional target vectors, which are x1, x2, ..., xn ;

[0094] S4013: Calculate multiple temporary processing logics through G(t)=softmax[Vf(t)]; where f(t)=g[Ux t +Wf(t-1)+b], G(t) is the temporary processing logic obtained by inputting the t-th target vector, x t represents the t-th target vector, f(t) represents the intermediate function obtained by inputting the t-th target vector, f(t-1) represents the intermediate function obtained by inputting the t-1-th target vector, and V, U, W, and b are all preset parameters.

[0095] As described in the above steps S4011-S4013, the specific formula for obtaining the temporary processing logic can be: first vectorize the financial data information separately, and then perform weighted calculation, wherein the weighted calculation method can be through obtaining the mathematical mean or the geometric mean, and then adding them, that is, integrating the related information, so that the generated n-dimensional target vector integrates the content of the financial data information, and the weighted budget method is not limited, and can be used to integrate the information of the two, and then calculate multiple temporary processing logics through the formula G(t)=softmax[Vf(t)], G(t) is the temporary processing logic obtained by inputting the t-th target vector, softmax is the softmax function, wherein the temporary processing logic is related to the dimension of the vector, that is, the more complex the financial data information is, the more temporary processing logics are generated, and V, U, W, and b are all parameter values ​​obtained through training. When the t-th target vector is input, the t-th operation logic can be obtained.

[0096] In one embodiment, before the step S3 of obtaining the association information between the financial data information, the method further includes:

[0097] S201: According to the formula Calculate the outlier value of each data in each financial data information; where Z ij represents the abnormal value of the i-th data in the j-th financial data information, μ represents the average value of each data in the j-th financial data information, and σ represents the standard deviation of the j financial data information, and x ij represents the i-th data in the j-th financial data information, and n represents the number of data in the j-th financial data information;

[0098] S202: Will | Z ij |>Z thr The corresponding target data is extracted and compared with the actual information of the financial data information; wherein Z thris the set parameter value;

[0099] S203: If the comparison result shows that the target data is wrong, the target data is replaced by the correct actual data.

[0100] As described in the above steps S201-S203, since some data may have some errors in the statistical process, after obtaining the financial data information, it is necessary to calculate the data in the financial data information and calculate the abnormal values ​​of each data in the financial data information. When the abnormal value exceeds the set parameter value, the data can be considered abnormal. Of course, it is not ruled out that the data is a normal data value. For example, the business department made a large profit in a certain month, while other months are far below the normal value. Therefore, this application only extracts the abnormal value. Whether it is a normal value still requires subsequent judgment, that is, comparing the actual information with the target data. When the comparison is incorrect, the correct actual data will replace the target data to ensure the reliability of the generated financial statements.

[0101] In one embodiment, before the step S6 of importing the financial statement information into a preset template to obtain a corresponding financial statement, the method further includes:

[0102] S501: Send multiple financial report templates to corresponding users;

[0103] S502: Acquire the financial report template selected by the user as the preset template.

[0104] As described in steps S501-S502 above, multiple preset financial report template options are provided to users (e.g., financial personnel or management). These templates can be based on some standard financial report formats, or customized according to user needs. Template sending can be implemented in various ways such as email, internal system notification, or real-time application program interface (API) to ensure easy access for users. By showing a brief description of the sample or template, help users understand the main features and applicable scenarios of each template. The user selects a financial report template that best meets his or her needs from the multiple templates provided. In some embodiments, the system records the user's selection to ensure that subsequent financial report generation operations use the template selected by the user. This selection process can be performed through a graphical user interface (GUI) or other interactive methods.

[0105] In one embodiment, the step S5 of processing the financial data information according to the data processing logic to obtain financial statement information includes:

[0106] S511: Clean, classify and summarize the financial data to obtain target financial data;

[0107] S512: extracting preset key indicator data from the target financial data;

[0108] S513: Analyze and predict based on the key indicator data to obtain the financial statement information.

[0109] As described in steps S511-S513 above, data cleaning includes checking the extracted financial data, identifying and correcting erroneous or incomplete data records. For example, deleting duplicates, filling missing values, correcting format errors, etc. Data classification includes organizing financial data according to predefined categories (such as assets, liabilities, income, expenses, etc.) for further processing. Ensure the structure and manageability of the data. Data aggregation includes aggregating the classified data, such as calculating totals, averages, or other statistical indicators. This process helps convert large amounts of data into a target data format that is easy to analyze.

[0110] Reference Figure 3 The present invention also provides a device for intelligently generating financial statements, the device comprising:

[0111] A first acquisition module 10, used to acquire financial statement information to be generated;

[0112] A second acquisition module 20 is used to acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0113] A third acquisition module 30 is used to acquire association information between the financial data information;

[0114] An input module 40, used to input the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0115] A processing module 50, configured to process the financial data information according to the data processing logic to obtain financial statement information;

[0116] The import module 60 is used to import the financial statement information into a preset template to obtain a corresponding financial statement.

[0117] In one embodiment, the third acquisition module 30 includes:

[0118] A correlation information determination submodule, used to determine whether the preset database contains correlation information between the financial data information;

[0119] A text string conversion submodule, for converting the content in the financial data information into a text string if there is no association information between the financial data information, wherein the text string is composed of a plurality of character strings according to their positions in the financial data information;

[0120] A string extraction submodule, used to extract a key string from a string corresponding to one of the financial data information, and perform feature matching with the string corresponding to other financial data information through the BM algorithm;

[0121] The association information establishment submodule is used to establish association information between the financial data information according to the matching results.

[0122] In one embodiment, the input module 40 includes:

[0123] A first calculation submodule, configured to calculate a temporary processing logic based on the associated information;

[0124] The second calculation submodule is used to calculate the Calculating a first matching degree between each of the temporary processing logics and the financial statement information to be generated; wherein, Represents the vector corresponding to the temporary processing logic, Represents the vector corresponding to the financial statement information to be generated, Indicates the first matching degree;

[0125] A matching degree judgment submodule, used for judging whether the first matching degree corresponding to each of the temporary processing logics reaches a first preset matching degree value;

[0126] As a submodule, it is used to use the temporary processing logic that reaches the first preset matching value as the data processing logic.

[0127] In one embodiment, the first computing submodule includes:

[0128] A vectorization unit, used to vectorize each of the financial data information to obtain the corresponding n-dimensional vectors;

[0129] The weighted calculation unit is used to perform weighted calculation on the n-dimensional vectors corresponding to the financial data information to obtain n-dimensional target vectors, which are x1, x2, ..., x n ;

[0130] A temporary processing logic calculation unit is used to calculate multiple temporary processing logics through G(t)=softmax[Vf(t)]; wherein f(t)=g[Ux t +Wf(t-1)+b], G(t) is the temporary processing logic obtained by inputting the t-th target vector, x trepresents the t-th target vector, f(t) represents the intermediate function obtained by inputting the t-th target vector, f(t-1) represents the intermediate function obtained by inputting the t-1-th target vector, and V, U, W, and b are all preset parameters.

[0131] In one embodiment, the device further comprises:

[0132] The outlier calculation module is used to calculate the outlier value according to the formula Calculate the outlier value of each data in each financial data information; where Z ij represents the abnormal value of the i-th data in the j-th financial data information, μ represents the average value of each data in the j-th financial data information, and σ represents the standard deviation of the j financial data information, and x ij represents the i-th data in the j-th financial data information, and n represents the number of data in the j-th financial data information;

[0133] Target data extraction module, used to convert |Z ij |>Z thr The corresponding target data is extracted and compared with the actual information of the financial data information; wherein Z thr is the set parameter value;

[0134] The replacement module is used to replace the target data with the correct actual data if the comparison result shows that the target data is wrong.

[0135] In one embodiment, the device further comprises:

[0136] A financial report template sending module is used to send multiple financial report templates to corresponding users;

[0137] The financial report template acquisition module is used to acquire the financial report template selected by the user as the preset template.

[0138] In one embodiment, the processing module 50 includes:

[0139] A cleaning submodule is used to clean, classify and summarize the financial data to obtain target financial data;

[0140] A key indicator data extraction submodule is used to extract the key indicator data preset in the target financial data;

[0141] The analysis submodule is used to perform analysis and prediction based on the key indicator data to obtain the financial statement information.

[0142] The beneficial effects of the present invention are as follows: by acquiring the financial statement information to be generated and acquiring multiple financial data information, using the associated information to acquire the corresponding data processing logic, and processing the financial data information to obtain financial statements, thereby realizing the use of data processing models and financial information in a database to achieve rapid and accurate generation of financial statements, which can significantly improve the efficiency of consolidated generation of financial statements, reduce manual intervention, and reduce the error rate, meet the needs of modern enterprises for rapid decision-making, and help improve the level of financial management.

[0143] Figure 4 FIG. 1 shows an internal structure diagram of a computer device in an embodiment. The computer device may be a terminal or a server. Figure 4 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement the intelligent generation method of financial statements. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement the intelligent generation method of financial statements. Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0144] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0145] Obtain financial statement information to be generated;

[0146] Acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0147] Obtaining association information between the financial data information;

[0148] Inputting the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0149] Processing the financial data information according to the data processing logic to obtain financial statement information;

[0150] The financial statement information is imported into a preset template to obtain the corresponding financial statement.

[0151] Utilizing data processing models and financial information in the database to quickly and accurately generate financial statements can significantly improve the efficiency of consolidated financial statement generation, reduce manual intervention, and lower the error rate, adapting to the needs of modern enterprises for rapid decision-making and helping to improve the level of financial management.

[0152] In one embodiment, a computer-readable storage medium is provided, storing a computer program, wherein when the computer program is executed by a processor, the processor performs the following steps:

[0153] Obtain financial statement information to be generated;

[0154] Acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated;

[0155] Obtaining association information between the financial data information;

[0156] Inputting the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic;

[0157] Processing the financial data information according to the data processing logic to obtain financial statement information;

[0158] The financial statement information is imported into a preset template to obtain the corresponding financial statement.

[0159] Utilizing data processing models and financial information in the database to quickly and accurately generate financial statements can significantly improve the efficiency of consolidated financial statement generation, reduce manual intervention, and lower the error rate, adapting to the needs of modern enterprises for rapid decision-making and helping to improve the level of financial management.

[0160] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0161] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for intelligently generating financial statements, characterized in that: The method comprises: Obtain financial statement information to be generated; Acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated; Obtaining association information between the financial data information; Inputting the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic; Processing the financial data information according to the data processing logic to obtain financial statement information; The financial statement information is imported into a preset template to obtain the corresponding financial statement.

2. The intelligent generation method of financial statements according to claim 1, characterized in that: The step of obtaining the association information between the financial data information includes: Determining whether the preset database contains association information between the financial data information; If there is no association information between the financial data information, converting the content in the financial data information into a text string, wherein the text string is composed of a plurality of character strings according to their positions in the financial data information; Extracting a key character string from the character string corresponding to one of the financial data information, and performing feature matching on the character string corresponding to the other financial data information by using the BM algorithm; The association information between the financial data information is established according to the matching results.

3. The intelligent generation method of financial statements according to claim 1, characterized in that: The step of inputting the association information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic includes: Calculate a temporary processing logic based on the associated information; By formula Calculating a first matching degree between each of the temporary processing logics and the financial statement information to be generated; wherein, Represents the vector corresponding to the temporary processing logic, Represents the vector corresponding to the financial statement information to be generated, Indicates the first matching degree; Determining whether the first matching degree corresponding to each of the temporary processing logics reaches a first preset matching degree value; The temporary processing logic that reaches the first preset matching value is used as the data processing logic.

4. The intelligent generation method of financial statements according to claim 3 is characterized in that: The step of calculating the temporary processing logic based on the associated information includes: Vectorize each of the financial data information to obtain the corresponding n-dimensional vectors; The n-dimensional vectors corresponding to the financial data information are weighted to obtain n-dimensional target vectors, which are x1, x2, ..., x n ; Multiple temporary processing logics are obtained by calculating G(t)=softmax[Vf(t)]; where f(t)=g[Ux t +Wf(t-1)+b], G(t) is the temporary processing logic obtained by inputting the t-th target vector, x t represents the t-th target vector, f(t) represents the intermediate function obtained by inputting the t-th target vector, f(t-1) represents the intermediate function obtained by inputting the t-1-th target vector, and V, U, W, and b are all preset parameters.

5. The intelligent generation method of financial statements according to claim 1, characterized in that: Before the step of obtaining the association information between the financial data information, the method further includes: According to the formula Calculate the outlier value of each data in each financial data information; where Z ij represents the abnormal value of the i-th data in the j-th financial data information, μ represents the average value of each data in the j-th financial data information, and σ represents the standard deviation of the j financial data information, and x ij represents the i-th data in the j-th financial data information, and n represents the number of data in the j-th financial data information; |Z ij |>Z thr The corresponding target data is extracted and compared with the actual information of the financial data information; wherein Z thr is the set parameter value; If the comparison result shows that the target data is wrong, the correct actual data will replace the target data.

6. The intelligent generation method of financial statements according to claim 1, characterized in that: Before the step of importing the financial statement information into a preset template to obtain a corresponding financial statement, the method further includes: Send multiple financial report templates to corresponding users; The financial report template selected by the user is obtained as the preset template.

7. The intelligent generation method of financial statements according to claim 1, characterized in that: The step of processing the financial data information according to the data processing logic to obtain financial statement information includes: Cleaning, classifying and summarizing the financial data to obtain target financial data; Extracting preset key indicator data from the target financial data; The financial statement information is obtained by analyzing and predicting the key indicator data.

8. An intelligent generation device for financial statements, characterized in that: The device comprises: A first acquisition module, used to acquire financial statement information to be generated; A second acquisition module is used to acquire corresponding multiple financial data information from a preset database according to the financial statement information to be generated; A third acquisition module is used to acquire association information between the financial data information; An input module, used to input the associated information and the financial statement information to be generated into a preset data processing model to obtain corresponding data processing logic; A processing module, used to process the financial data information according to the data processing logic to obtain financial statement information; The import module is used to import the financial statement information into a preset template to obtain the corresponding financial statement.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor executes the steps of the intelligent generation method of financial statements as claimed in any one of claims 1 to 7.

10. A computer device, characterized in that: The device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for intelligently generating financial statements as claimed in any one of claims 1 to 7.

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