Enterprise risk calculation method and device based on financial data

Abstract

The invention provides an enterprise risk calculation method and device based on financial data. The method comprises the following steps: triggering a plurality of interface calling services, and obtaining financial data pushed by the plurality of interfaces respectively; executing data cleaning, and storing the cleaned financial data according to a preset specification; in response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table; and displaying an enterprise risk report based on financial data in a preset style according to the detection task table and the calculated risk level. Therefore, the enterprise risk level is efficiently and accurately calculated based on the financial data, and the usability is high.

Description

Technical Field

[0001] The present invention relates to the field of enterprise finance and taxation technology, and in particular to an enterprise risk calculation method and device based on financial data. Background Art

[0002] The third phase of Golden Tax System realizes the merger and unification of national tax and local tax data. The fourth phase of Golden Tax System realizes more comprehensive monitoring of tax business and "non-tax" business, such as the verification of mobile phone numbers of relevant personnel of enterprises, enterprise tax status, and enterprise registration information.

[0003] After the launch of the fourth phase of the Golden Tax System, more corporate data is in the hands of the tax bureau, and monitoring is also comprehensive and three-dimensional, which is conducive to the realization of classified and precise supervision from "tax management by invoice" to "tax management by numbers". In the context of the promotion of the fourth phase of the Golden Tax System, corporate financial and tax compliance is not only related to the survival and development of enterprises, but also a manifestation of social responsibility.

[0004] Currently, there is a lack of efficient and accurate technical solutions for risk calculation based on corporate financial data. Summary of the invention

[0005] In view of this, the present invention proposes a method and device for calculating enterprise risk based on financial data, aiming to solve the problem of lack of efficient and accurate enterprise risk calculation method based on financial data in the prior art.

[0006] In a first aspect, the present invention provides a method for calculating enterprise risk based on financial data, comprising:

[0007] Trigger multiple interface call services to obtain financial data pushed by multiple interfaces;

[0008] Perform data cleaning and store the cleaned financial data in the database according to preset specifications;

[0009] In response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table;

[0010] According to the inspection task table and calculated risk level, the enterprise risk report based on financial data is displayed in a preset style.

[0011] Furthermore, the triggering of multiple interface call services to obtain financial data pushed by multiple interfaces respectively includes:

[0012] Trigger the invoice interface call service and use the invoice data acquisition interface to obtain the pushed invoice data;

[0013] Trigger the finance and taxation interface call service, and use the finance and taxation data acquisition interface to obtain the pushed finance and taxation data;

[0014] Trigger the enterprise basic data interface call service, and use the enterprise basic data acquisition interface to obtain the pushed enterprise basic data;

[0015] Trigger the enterprise risk data interface call service, and use the enterprise risk data acquisition interface to obtain the pushed enterprise risk data.

[0016] Furthermore, the performing of data cleaning includes:

[0017] According to the predetermined length range of each business report field, filter the field data that exceeds the predetermined length range;

[0018] Validate numeric fields according to preset formats and ranges;

[0019] Verify the date field according to the date format logic;

[0020] For numeric fields, fill missing values ​​with zeros; for text fields or categorical fields, fill missing values ​​with null values;

[0021] For fields that contain extra commas, or spaces, remove the extra commas, or spaces.

[0022] Furthermore, in response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table includes:

[0023] In the pre-statistics stage, basic data on invoice amounts and numbers are loaded, and lightweight data structures are used to quickly access the financial data after storage;

[0024] When calculating the risk level of each risk point, use streaming processing or batch loading of financial data after storage to reduce the amount of data resident in memory at the same time;

[0025] When calculating the risk level of each risk point, parallel calculation is used;

[0026] Each risk point calculation module that calculates the risk level of each risk point is dynamically created using the design concept of the factory pattern; each risk point calculation module shares the same code for implementing a public method.

[0027] Furthermore, in response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table includes:

[0028] Generate a detection task table based on the acquired pre-edited risk point configuration information;

[0029] The enterprise risk report based on financial data is displayed in a preset format according to the detection task table and the calculated risk level, including:

[0030] Based on the acquired pre-edited risk point display style information, an enterprise risk report based on financial data is displayed in a preset style.

[0031] In a second aspect, the present invention provides an enterprise risk calculation device based on financial data, comprising:

[0032] A data acquisition unit is used to trigger multiple interface call services and obtain financial data pushed by multiple interfaces respectively;

[0033] The data cleaning unit is used to perform data cleaning and store the cleaned financial data in the warehouse according to preset specifications;

[0034] A risk point calculation unit, configured to calculate the risk level of each risk point recorded in the detection task table in response to the generated detection task table;

[0035] The enterprise risk report display unit is used to display the enterprise risk report based on financial data in a preset style according to the detection task table and the calculated risk level.

[0036] Furthermore, the data acquisition unit triggers multiple interface call services to acquire financial data pushed by multiple interfaces respectively, including:

[0037] Trigger the invoice interface call service and use the invoice data acquisition interface to obtain the pushed invoice data;

[0038] Trigger the finance and taxation interface call service, and use the finance and taxation data acquisition interface to obtain the pushed finance and taxation data;

[0039] Trigger the enterprise basic data interface call service, and use the enterprise basic data acquisition interface to obtain the pushed enterprise basic data;

[0040] Trigger the enterprise risk data interface call service, and use the enterprise risk data acquisition interface to obtain the pushed enterprise risk data.

[0041] Furthermore, the data cleaning unit performs data cleaning, including:

[0042] According to the predetermined length range of each business report field, filter the field data that exceeds the predetermined length range;

[0043] Validate numeric fields according to preset formats and ranges;

[0044] Verify the date field according to the date format logic;

[0045] For numeric fields, fill missing values ​​with zeros; for text fields or categorical fields, fill missing values ​​with null values;

[0046] For fields that contain extra commas, or spaces, remove the extra commas, or spaces.

[0047] Furthermore, the risk point calculation unit, in response to the generated detection task table, calculates the risk level of each risk point recorded in the detection task table, including:

[0048] In the pre-statistics stage, basic data on invoice amounts and numbers are loaded, and lightweight data structures are used to quickly access the financial data after storage;

[0049] When calculating the risk level of each risk point, use streaming processing or batch loading of financial data after storage to reduce the amount of data resident in memory at the same time;

[0050] When calculating the risk level of each risk point, parallel calculation is used;

[0051] Each risk point calculation module for calculating the risk level of each risk point is dynamically created using the design concept of the factory pattern; each risk point calculation module has the same code for implementing a public device.

[0052] Furthermore, the risk point calculation unit, in response to the generated detection task table, calculates the risk level of each risk point recorded in the detection task table, including:

[0053] Generate a detection task table based on the acquired pre-edited risk point configuration information;

[0054] The enterprise risk report display unit displays the enterprise risk report based on financial data in a preset format according to the detection task table and the calculated risk level, including:

[0055] Based on the acquired pre-edited risk point display style information, an enterprise risk report based on financial data is displayed in a preset style.

[0056] In a third aspect, the present invention provides a terminal, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0057] In a fourth aspect, the present invention provides a computer storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the method described in the first aspect.

[0058] Additional aspects and advantages of the present invention will be set forth in part in the following description and, in part, will be obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0060] Figure 1 A schematic diagram of a process of calculating enterprise risk based on financial data according to an embodiment of the present invention;

[0061] Figure 2 Another schematic diagram of a flow chart of a method for calculating enterprise risk based on financial data according to an embodiment of the present invention;

[0062] Figure 3 A schematic diagram of an interface for collecting tax and fee type identification information in a method for calculating enterprise risk based on financial data according to an embodiment of the present invention;

[0063] Figure 4 A schematic diagram of an interface for editing risk points in a method for calculating enterprise risk based on financial data according to an embodiment of the present invention;

[0064] Figure 5 It is a schematic diagram of the composition of the enterprise risk calculation device based on financial data according to an embodiment of the present invention;

[0065] Figure 6 The present invention is a schematic diagram of the components of a terminal to which the method of the present invention is applied. DETAILED DESCRIPTION

[0066] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0067] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0068] In the context of the promotion of the fourth phase of the Golden Tax System, the accuracy, completeness and timeliness of corporate financial and tax data have become the cornerstones of tax compliance. Accurate data processing can ensure the accuracy of tax declarations and avoid tax risks caused by data errors.

[0069] Risk calculation is one of the keys to corporate tax compliance management. Through scientific risk calculation methods, companies can quantitatively assess the degree of tax risk they face and provide strong support for the formulation of risk response strategies. The intelligent risk warning function of the Golden Tax Phase IV system also requires companies to have scientific risk calculation capabilities.

[0070] like Figure 1 As shown, the enterprise risk calculation method based on financial data in an embodiment of the present invention includes the following steps:

[0071] S100: trigger multiple interface call services to obtain financial data pushed by multiple interfaces respectively;

[0072] S200: Perform data cleaning and store the cleaned financial data in the database according to preset specifications;

[0073] S300: In response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table;

[0074] S400: According to the detection task table and the calculated risk level, an enterprise risk report based on financial data is displayed in a preset format.

[0075] Under the background of the promotion of the fourth phase of the Golden Tax System, various corporate financial and tax data can be collected through the electronic tax bureau. Figure 2 As shown, the triggering of multiple interface call services to obtain financial data pushed by multiple interfaces respectively includes:

[0076] Trigger the invoice interface call service and use the invoice data acquisition interface to obtain the pushed invoice data;

[0077] Trigger the finance and taxation interface call service, and use the finance and taxation data acquisition interface to obtain the pushed finance and taxation data;

[0078] Trigger the enterprise basic data interface call service, and use the enterprise basic data acquisition interface to obtain the pushed enterprise basic data;

[0079] Trigger the enterprise risk data interface call service, and use the enterprise risk data acquisition interface to obtain the pushed enterprise risk data.

[0080] Specifically, enterprise risk data can be various data used for risk point calculation, such as risk abbreviation, risk category, report chapter, registration type, system risk abbreviation, risk subcategory, risk point ID, risk module, such as tax risk; risk point description, detection order, taxpayer qualification type, such as general taxpayer; risk point class name, risk point style, such as style one to style six; version used:, such as free version, paid version; whether to count; whether enabled, such as enabled or disabled; industry to which it belongs; risk level, such as high, medium, low, none; response suggestions, etc.

[0081] Furthermore, the performing of data cleaning includes:

[0082] According to the predetermined length range of each business report field, filter the field data that exceeds the predetermined length range;

[0083] Validate numeric fields according to preset formats and ranges;

[0084] Verify the date field according to the date format logic;

[0085] For numeric fields, fill missing values ​​with zeros; for text fields or categorical fields, fill missing values ​​with null values;

[0086] For fields that contain extra commas, or spaces, remove the extra commas, or spaces.

[0087] Furthermore, in response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table includes:

[0088] In the pre-statistics stage, basic data on invoice amounts and numbers are loaded, and lightweight data structures are used to quickly access the financial data after storage;

[0089] When calculating the risk level of each risk point, use streaming processing or batch loading of financial data after storage to reduce the amount of data resident in memory at the same time;

[0090] When calculating the risk level of each risk point, parallel calculation is used;

[0091] Each risk point calculation module that calculates the risk level of each risk point is dynamically created using the design concept of the factory pattern; each risk point calculation module shares the same code for implementing a public method.

[0092] In the corporate tax compliance, tax-related risk points include value-added tax, corporate income tax, personal income tax, property tax, land use tax, stamp tax, etc. Among them, value-added tax is mainly checked from the output tax and input tax, corporate income tax is mainly checked from the taxable income and cost, and personal income tax is mainly checked from whether personal income tax is missed.

[0093] Specifically, each risk point calculation module automatically calculates and compares the company's various financial indicators and tax indicators to identify potential tax risks. The calculation of various indicators is carried out in accordance with tax laws to ensure the compliance and accuracy of the calculation results.

[0094] Furthermore, in response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table includes:

[0095] Generate a detection task table based on the acquired pre-edited risk point configuration information;

[0096] The enterprise risk report based on financial data is displayed in a preset format according to the detection task table and the calculated risk level, including:

[0097] Based on the acquired pre-edited risk point display style information, an enterprise risk report based on financial data is displayed in a preset style.

[0098] The risk calculation technology solution based on enterprise financial data in the embodiment of the present invention designs a comprehensive enterprise risk calculation solution, which can efficiently and flexibly evaluate the financial risk assessment results of the enterprise. In this way, financial and tax data processing and risk calculation are conducive to ensuring the compliance, accuracy and scientificity of enterprise tax management. In this way, it can not only reduce the financial and tax risk level of the enterprise and improve tax compliance, but also win more social reputation and competitive advantages for the enterprise. Integrating the entire process of financial data collection, data analysis and risk management, providing a comprehensive and efficient enterprise financial risk management solution, it helps enterprises to manage and control financial risks more effectively.

[0099] like Figure 2 As shown, another embodiment of the present invention is a risk calculation method based on enterprise financial data that autonomously detects enterprise financial and tax risks, generates a task list, and detects the task list; after the detection business is completed, the enterprise financial and tax risk report is displayed. Specifically, a service is provided or called. After the user logs in through the electronic tax bureau interface, or after logging in to the electronic tax bureau, the detection business begins; after the task list is generated, the risk point calculation service is triggered to execute the risk point calculation. The risk point calculation uses the stored enterprise basic data, financial and tax data, invoice data and other data.

[0100] Specifically, data collection includes: triggering the invoice interface call service, using the invoice data acquisition interface to obtain the pushed invoice data; performing data cleaning on the acquired invoice data, and storing the cleaned data in the warehouse; triggering the finance and taxation interface call service, using the finance and taxation data acquisition interface, obtaining the pushed finance and taxation data; performing data cleaning on the acquired finance and taxation data, and storing the cleaned data in the warehouse; triggering the enterprise basic data interface call service, using the enterprise basic data acquisition interface, obtaining the pushed enterprise basic data; performing data cleaning on the acquired enterprise basic data, and storing the cleaned data in the warehouse; triggering the enterprise risk data interface call service, using the enterprise risk data acquisition interface, obtaining the pushed enterprise risk data; performing data cleaning on the acquired enterprise risk data, and storing the cleaned data in the warehouse;

[0101] After data collection is completed, the collected financial data will be processed and the audited data will be stored in the database.

[0102] During the data processing phase, the first priority is to ensure the integrity and accuracy of the data. During the data verification process, it is crucial to ensure the accuracy and validity of the fields.

[0103] Specifically, define and confirm the reasonable length range of each business report field as the predetermined length; for each field of the business report, check the field length of the field data one by one; for the case where the report field length is insufficient, fill in the missing value; for field data whose field length exceeds the predetermined length, refuse to enter the database. In this way, the verification mechanism is implemented, and field data whose field length exceeds the predetermined length is refused to be entered or updated, so as to prevent data truncation or error accumulation, thereby ensuring data integrity and accuracy.

[0104] Specifically, field type verification includes: numeric fields, date fields, and character fields. Verify the legitimacy of numeric fields and adjust numeric fields to meet the expected storage format and value range for storage, and avoid mixing non-numeric characters. For example, store the transaction amount of 100 yuan in the default format of 100.00.

[0105] Use precise date format verification logic to verify each date field, and adjust the date fields that do not meet the preset date format to ensure the accuracy of date data. For example, November 2024 is stored in the default format of 202411.

[0106] The above guarantees data integrity, accuracy and consistency, which helps prevent data from being unable to be parsed or parsed incorrectly due to format errors. The above double-check strategy of field length check and field type check helps improve data processing efficiency and significantly enhances data reliability and availability.

[0107] For missing field values, a customized filling strategy is adopted to confirm the fields according to the financial and tax business needs. Specifically, for numeric fields, missing field values ​​are filled with zeros to maintain the validity of statistical calculations to ensure that subsequent analysis will not be affected by incomplete data. For text fields or categorical fields, missing field values ​​are filled with null values ​​to avoid data skew. This helps to ensure that subsequent analysis will not be affected by incomplete data.

[0108] In the above, for the case of missing field values, a customized supplement strategy is adopted to confirm the fields according to the financial and tax business needs. According to the characteristics of each field, the missing field values ​​are filled by filling zeros or null values.

[0109] Specifically, data cleaning focuses on identifying and correcting known errors in data for confirmed field value errors (type errors or length errors) to ensure accuracy and implement precise cleaning strategies. For example, for character fields, remove redundant or unnecessary characters contained in field values, such as commas "," or spaces; in this way, removing unnecessary characters and restoring the original appearance of the data is conducive to improving the quality and reliability of the data. It plays a vital role in subsequent data analysis and business decisions.

[0110] Furthermore, before calculating the risk points, the integrity of the business data is also verified. Figure 3 As shown, before calculating the risk point, verify the tax type identification information. In this way, it can be ensured that all necessary reports have been fully collected, and the data types returned by each collection channel are complete and can meet the needs of subsequent risk point calculations to ensure the integrity and comprehensiveness of business data. Tax type identification information is one of the basic bases for enterprises to handle tax declarations, allowing enterprises to clarify the specific taxes and fees that need to be declared monthly, quarterly or annually.

[0111] Specifically, verifying the integrity of business data may also include: verifying reports with higher risk point priorities.

[0112] Furthermore, the risk points are calculated using the report data after storage. The risk point list may involve more than 200 risk point items. Therefore, a large number of enterprises will generate a large number of risk point items, and a huge memory usage requirement will be generated in the process of calculating risk points. To reduce memory usage, lightweight data structures (such as hash tables) are used in the pre-statistical stage to quickly access the stored report data and invoice data, such as financial statements, such as invoice amounts and numbers. To reduce memory usage, streaming processing or batch loading is used for calculation in the risk point calculation stage. In this way, the amount of data residing in the memory at the same time can be reduced.

[0113] Stream processing, as a data processing method, allows data to be processed when it is generated, rather than stored and then processed. Therefore, it is suitable for scenarios that require real-time processing. The advantage of stream processing is that it can quickly respond to and process continuously arriving data streams, which may contain a large amount of data, but only a small part of it will be stored in limited memory.

[0114] Batch processing is a delayed processing method, where data is first stored and then analyzed and processed. MapReduce is a very important model in batch processing, which divides data into several small blocks for parallel processing and finally merges the results. In contrast, stream processing pays more attention to the real-time nature of data. It does not need to wait for all data to arrive before starting processing, but starts processing as soon as the data arrives, which can achieve efficient data stream processing. Mainstream stream processing components include Storm, Spark Streaming, Kafka, Flume, Flink, S3, etc. The above uses optimized data structures and parallel algorithms to reduce memory usage and ensure high performance under high load.

[0115] Specifically, through financial data and industry knowledge, the financial risk point values ​​of the enterprise are calculated risk point by risk point; all financial risk point values ​​are integrated to determine the financial risk assessment results of the enterprise, analyze and predict potential financial risks, and display them in the risk report.

[0116] In view of the calculation requirements of more than 200 risk points, the parallel computing strategy is adopted, combined with the design concept of the factory mode. Through the factory mode, the dynamic creation and flexible management of risk point calculation modules are realized; each module focuses on the analysis of its own risk point, thereby improving the calculation efficiency and the maintainability of the system.

[0117] For similar codes commonly found in risk point calculation, common methods are extracted through abstraction and encapsulation. The common methods should cover the common logic in various risk calculation scenarios, which is conducive to reducing code redundancy and improving code reusability. At the same time, the common methods should have good flexibility and scalability to adapt to the calculation needs of new risk points in the future, so as to optimize the code structure and improve development efficiency and code quality.

[0118] As described above, the risk calculation method based on enterprise financial data of the embodiment of the present invention brings significant beneficial effects by integrating financial data collection, data analysis and risk management processes. First, at the data processing level, a strict and standardized data cleaning and conversion mechanism ensures the accuracy and completeness of the data, laying a solid foundation for subsequent analysis. By processing missing values ​​and erroneous data through customized strategies, the impact of data quality issues on analysis results can be effectively avoided, and the reliability and availability of data can be improved.

[0119] Furthermore, the innovative design of risk calculation methods, especially the combination of parallel computing and factory mode, greatly improves the efficiency and flexibility of risk point calculation. By dynamically creating and managing risk point calculation modules, it can achieve parallel processing of more than 200 risk points, significantly shorten the calculation time, and provide enterprises with more timely and accurate financial risk assessment results.

[0120] The Factory Pattern is a common design pattern in Java and is a type of creational pattern. It is used to create objects, but unlike using the new keyword to create objects directly in the code, the Factory Pattern creates objects through a common interface, thereby separating the object creation process from the specific client code. In the Factory Pattern, the Abstract Factory is responsible for defining an interface for creating objects, but is not responsible for the specific object creation process. It is usually an interface or an abstract class, which defines one or more methods for creating objects. The Concrete Factory is responsible for implementing the Abstract Factory interface and is responsible for actually creating specific objects. Each concrete factory corresponds to a specific object type. The Product is the object type created by the factory. It can be an interface, an abstract class, or a concrete class. The Concrete Product is a specific object that implements the product interface. Each concrete factory in the Factory Pattern is responsible for creating a specific concrete product. The main purpose of the Factory Pattern is to encapsulate the object creation process in the factory class. The client code only needs to care about the process of obtaining objects from the factory, without having to understand the details of object creation. This can reduce the coupling of the code and improve the maintainability and scalability of the code.

[0121] like Figure 4 As shown, the risk calculation method based on enterprise financial data in the embodiment of the present invention can configure the interface for more than 200 risk points and also enhance the adaptability and customizability of the system by introducing a flexible configuration mechanism for the background management of risk point thresholds. Enterprises can flexibly adjust risk thresholds according to their own business needs and market changes to better respond to potential risks and achieve more refined risk management.

[0122] In summary, the present invention performs well in improving data processing efficiency, optimizing risk assessment models, and enhancing system flexibility, and can efficiently and flexibly improve the efficiency of enterprise financial risk management.

[0123] like Figure 5 As shown, the enterprise risk calculation device 1000 based on financial data according to the embodiment of the present invention includes:

[0124] The data acquisition unit 10 is used to trigger multiple interface call services and acquire financial data pushed by multiple interfaces respectively;

[0125] The data cleaning unit 20 is used to perform data cleaning and store the cleaned financial data in the warehouse according to preset specifications;

[0126] The risk point calculation unit 30 is used to calculate the risk level of each risk point recorded in the detection task table in response to the generated detection task table;

[0127] The enterprise risk report display unit 40 is used to display the enterprise risk report based on financial data in a preset format according to the detection task table and the calculated risk level.

[0128] Furthermore, the data acquisition unit triggers multiple interface call services to acquire financial data pushed by multiple interfaces respectively, including:

[0129] Trigger the invoice interface call service and use the invoice data acquisition interface to obtain the pushed invoice data;

[0130] Trigger the finance and taxation interface call service, and use the finance and taxation data acquisition interface to obtain the pushed finance and taxation data;

[0131] Trigger the enterprise basic data interface call service, and use the enterprise basic data acquisition interface to obtain the pushed enterprise basic data;

[0132] Trigger the enterprise risk data interface call service, and use the enterprise risk data acquisition interface to obtain the pushed enterprise risk data.

[0133] Furthermore, the data cleaning unit performs data cleaning, including:

[0134] According to the predetermined length range of each business report field, filter the field data that exceeds the predetermined length range;

[0135] Validate numeric fields according to preset formats and ranges;

[0136] Verify the date field according to the date format logic;

[0137] For numeric fields, fill missing values ​​with zeros; for text fields or categorical fields, fill missing values ​​with null values;

[0138] For fields that contain extra commas, or spaces, remove the extra commas, or spaces.

[0139] Furthermore, the risk point calculation unit, in response to the generated detection task table, calculates the risk level of each risk point recorded in the detection task table, including:

[0140] In the pre-statistics stage, basic data on invoice amounts and numbers are loaded, and lightweight data structures are used to quickly access the financial data after storage;

[0141] When calculating the risk level of each risk point, use streaming processing or batch loading of financial data after storage to reduce the amount of data resident in memory at the same time;

[0142] When calculating the risk level of each risk point, parallel calculation is used;

[0143] Each risk point calculation module for calculating the risk level of each risk point is dynamically created using the design concept of the factory pattern; each risk point calculation module has the same code for implementing a public device.

[0144] Furthermore, the risk point calculation unit, in response to the generated detection task table, calculates the risk level of each risk point recorded in the detection task table, including:

[0145] Generate a detection task table based on the acquired pre-edited risk point configuration information;

[0146] The enterprise risk report display unit displays the enterprise risk report based on financial data in a preset format according to the detection task table and the calculated risk level, including:

[0147] Based on the acquired pre-edited risk point display style information, an enterprise risk report based on financial data is displayed in a preset style.

[0148] The present invention also provides a terminal to execute the method. Figure 5 It shows a schematic diagram of a terminal provided by some embodiments of the present invention. Figure 5 As shown, the terminal 8 includes: a processor 800, a memory 801, a bus 802 and a communication interface 803, wherein the processor 800, the communication interface 803 and the memory 801 are connected via the bus 802; the memory 801 stores a computer program that can be run on the processor 800, and the processor 800 executes the method provided by any embodiment of the present invention when running the computer program.

[0149] The memory 801 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 803 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0150] The bus 802 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store programs, and the processor 800 executes the programs after receiving execution instructions. The method disclosed in any implementation of the embodiment of the present invention may be applied to the processor 800, or implemented by the processor 800.

[0151] The processor 800 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the method can be completed by the hardware integrated logic circuit in the processor 800 or the instruction in the form of software. The processor 800 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention may be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 801, and the processor 800 reads the information in the memory 801 and completes the steps of the method in combination with its hardware.

[0152] The terminal provided by the embodiment of the present invention and the method provided by the embodiment of the present invention are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by them.

[0153] An embodiment of the present invention also provides a computer-readable storage medium corresponding to the method provided by the embodiment, and the computer-readable storage medium is a CD, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the method provided by any of the embodiments.

[0154] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0155] The computer-readable storage medium provided by the embodiment of the present invention is based on the same inventive concept as the method of the embodiment of the present invention, and has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0156] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these adjustments and modifications of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these changes and modifications.

Claims

1. A method for calculating enterprise risk based on financial data, characterized in that: include: Trigger multiple interface call services to obtain financial data pushed by multiple interfaces; Perform data cleaning and store the cleaned financial data in the database according to preset specifications; In response to the generated detection task table, calculating the risk level of each risk point recorded in the detection task table; According to the inspection task table and calculated risk level, the enterprise risk report based on financial data is displayed in a preset style.

2. The enterprise risk calculation method based on financial data according to claim 1, characterized in that: The triggering of multiple interface call services to obtain financial data pushed by multiple interfaces respectively includes: Trigger the invoice interface call service and use the invoice data acquisition interface to obtain the pushed invoice data; Trigger the finance and taxation interface call service, and use the finance and taxation data acquisition interface to obtain the pushed finance and taxation data; Trigger the enterprise basic data interface call service, and use the enterprise basic data acquisition interface to obtain the pushed enterprise basic data; Trigger the enterprise risk data interface call service, and use the enterprise risk data acquisition interface to obtain the pushed enterprise risk data.

3. The enterprise risk calculation method based on financial data according to claim 1, characterized in that: The execution data cleaning includes: According to the predetermined length range of each business report field, filter the field data that exceeds the predetermined length range; Validate numeric fields according to preset formats and ranges; Verify the date field according to the date format logic; For numeric fields, fill missing values ​​with zeros; for text fields or categorical fields, fill missing values ​​with null values; For fields that contain extra commas, or spaces, remove the extra commas, or spaces.

4. The enterprise risk calculation method based on financial data according to claim 1, characterized in that: The step of calculating the risk level of each risk point recorded in the detection task table in response to the generated detection task table includes: In the pre-statistics stage, basic data on invoice amounts and numbers are loaded, and lightweight data structures are used to quickly access the financial data after storage; When calculating the risk level of each risk point, use streaming processing or batch loading of financial data after storage to reduce the amount of data resident in memory at the same time; When calculating the risk level of each risk point, parallel calculation is used; Each risk point calculation module that calculates the risk level of each risk point is dynamically created using the design concept of the factory pattern; each risk point calculation module shares the same code for implementing a public method.

5. The enterprise risk calculation method based on financial data according to claim 1, characterized in that: The step of calculating the risk level of each risk point recorded in the detection task table in response to the generated detection task table includes: Generate a detection task table based on the acquired pre-edited risk point configuration information; The enterprise risk report based on financial data is displayed in a preset format according to the detection task table and the calculated risk level, including: Based on the acquired pre-edited risk point display style information, an enterprise risk report based on financial data is displayed in a preset style.

6. An enterprise risk calculation device based on financial data, characterized in that: include: A data acquisition unit is used to trigger multiple interface call services and obtain financial data pushed by multiple interfaces respectively; The data cleaning unit is used to perform data cleaning and store the cleaned financial data in the warehouse according to preset specifications; A risk point calculation unit, configured to calculate the risk level of each risk point recorded in the detection task table in response to the generated detection task table; The enterprise risk report display unit is used to display the enterprise risk report based on financial data in a preset style according to the detection task table and the calculated risk level.

7. The enterprise risk calculation device based on financial data according to claim 6, characterized in that: The data acquisition unit triggers multiple interface call services to acquire financial data pushed by multiple interfaces respectively, including: Trigger the invoice interface call service and use the invoice data acquisition interface to obtain the pushed invoice data; Trigger the finance and taxation interface call service, and use the finance and taxation data acquisition interface to obtain the pushed finance and taxation data; Trigger the enterprise basic data interface call service, and use the enterprise basic data acquisition interface to obtain the pushed enterprise basic data; Trigger the enterprise risk data interface call service, and use the enterprise risk data acquisition interface to obtain the pushed enterprise risk data.

8. The enterprise risk calculation device based on financial data according to claim 6, characterized in that: The data cleaning unit performs data cleaning, including: According to the predetermined length range of each business report field, filter the field data that exceeds the predetermined length range; Validate numeric fields according to preset formats and ranges; Verify the date field according to the date format logic; For numeric fields, fill missing values ​​with zeros; for text fields or categorical fields, fill missing values ​​with null values; For fields that contain extra commas, or spaces, remove the extra commas, or spaces.

9. The enterprise risk calculation device based on financial data according to claim 6, characterized in that: The risk point calculation unit calculates the risk level of each risk point recorded in the detection task table in response to the generated detection task table, including: In the pre-statistics stage, basic data on invoice amounts and numbers are loaded, and lightweight data structures are used to quickly access the financial data after storage; When calculating the risk level of each risk point, use streaming processing or batch loading of financial data after storage to reduce the amount of data resident in memory at the same time; When calculating the risk level of each risk point, parallel calculation is used; Each risk point calculation module for calculating the risk level of each risk point is dynamically created using the design concept of the factory pattern; each risk point calculation module has the same code for implementing a public device.

10. The enterprise risk calculation device based on financial data according to claim 6, characterized in that: The risk point calculation unit calculates the risk level of each risk point recorded in the detection task table in response to the generated detection task table, including: Generate a detection task table based on the acquired pre-edited risk point configuration information; The enterprise risk report display unit displays the enterprise risk report based on financial data in a preset format according to the detection task table and the calculated risk level, including: Based on the acquired pre-edited risk point display style information, an enterprise risk report based on financial data is displayed in a preset style.

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