Financial data analysis method, system and equipment based on data warehouse and storage medium

Abstract

The invention discloses a financial data analysis method, system and equipment based on a data warehouse, and a storage medium, relates to the technical field of financial data analysis, and aims to predict and analyze financial indexes specified by a user, reduce the difficulty of analyzing financial data by the user, and improve the user experience if a first prediction model is used for prediction. By selecting the key association indexes, the training difficulty can be reduced, the prediction precision can be ensured to the greatest extent, and if the second preset deep learning model is used for prediction, the training time length can be prolonged, but the prediction precision can be improved.

Description

Technical Field

[0001] This invention relates to the field of financial data analysis technology, and in particular to a financial data analysis method, system, device and storage medium based on a data warehouse. Background Technology

[0002] Currently, with the development of technology, the use of natural gas is increasing. Most cities now use natural gas as an energy source, which has brought great convenience to people's lives. The financial data of natural gas pipeline networks is also increasing day by day, which has increased the difficulty for financial personnel to analyze financial data. Summary of the Invention

[0003] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a financial data analysis method, system, device, and storage medium based on a data warehouse, as detailed below: 1) In a first aspect, the present invention provides a financial data analysis method based on a data warehouse, the specific technical solution of which is as follows: From the financial data pre-stored in the data warehouse, obtain the data changes of each related indicator of the preset financial indicator within a first preset time period, and obtain the data changes of the preset financial indicator within the first preset time period; Based on the preset financial indicators and the data changes of each related indicator within a first preset time period, the key related indicators of the preset financial indicators are determined. Based on the preset financial indicators and the data changes of each related indicator within a first preset time period, a first preset deep learning model is trained to obtain a first prediction model corresponding to the preset financial indicators. The preset financial indicators are then predicted based on the first prediction model. And / or, based on the preset financial indicators and the data changes of each key related indicator within a first preset time period, a second preset deep learning model is trained to obtain a second preset deep learning model corresponding to the preset financial indicators. The preset financial indicators are then predicted based on the second preset deep learning model.

[0004] The beneficial effects of the financial data analysis method based on data warehouse provided by this invention are as follows: It can perform predictive analysis on user-specified financial indicators, reducing the difficulty for users to analyze financial data. Moreover, when using the first prediction model, the training difficulty can be reduced by selecting key related indicators, and the prediction accuracy can be guaranteed to the greatest extent. When using the second preset deep learning model, although the training time will be extended, the prediction accuracy can be improved.

[0005] Based on the above solution, the financial data analysis method based on data warehouse of the present invention can be further improved as follows.

[0006] Furthermore, it also includes: Calculate the variance of the data changes of the preset financial indicators and each related indicator within the first preset time period, and determine whether each variance is within the corresponding preset range. If not, issue a prompt message.

[0007] The advantage of adopting the above-mentioned further solution is that by comparing variances, abnormal data can be quickly identified and the user can be alerted.

[0008] Furthermore, it also includes: Each invoice image in the financial data is identified to obtain the issuance information of each invoice. The issuance information of each invoice is then matched with the financial text data in the financial data to identify the missing data information of the invoices.

[0009] The advantage of adopting the above-mentioned further solution is that it can accurately count the missing data information of invoices, which facilitates subsequent processing.

[0010] Furthermore, it also includes: Receive and generate corresponding reports based on the financial indicators and time range selected by the user, and then display them.

[0011] The advantage of adopting the above-mentioned further solutions is that it can further improve the user experience.

[0012] Furthermore, the financial data pre-stored in the data warehouse is the financial data of the natural gas pipeline network.

[0013] Furthermore, it also includes: From the financial data of the natural gas pipeline network, obtain the profit changes of each business in the second preset time period, and sort all businesses in descending order of average profit to obtain the first sequence; Obtain the cost change of each service within the second preset time period, and sort all services in descending order of average cost to obtain the second sequence; The profit margin change of each business within the second preset time period will be obtained, and all businesses will be sorted in descending order of average profit margin to obtain a third sequence. The first sequence, the second sequence, and the third sequence are displayed in a chart format on the display interface.

[0014] The advantage of adopting the above-mentioned further solutions is that it makes it easier for users to comprehensively analyze the changes in profit, profit margin, and cost of each business, which is more conducive to users carrying out subsequent work.

[0015] Furthermore, when the financial data pre-stored in the data warehouse is financial data of the natural gas pipeline network, the preset financial indicators are profit, cost, revenue or taxes.

[0016] 2) Secondly, the present invention also provides a financial data analysis system based on a data warehouse, the specific technical solution of which is as follows: It includes a data acquisition module, a key correlation indicator determination module, and a training and prediction module; The data acquisition module is used to: acquire the data changes of each related indicator of a preset financial indicator within a first preset time period from the financial data pre-stored in the data warehouse, and acquire the data changes of the preset financial indicator within the first preset time period; The key correlation indicator determination module is used to: determine the key correlation indicators of the preset financial indicators based on the preset financial indicators and the data changes of each correlation indicator within a first preset time period. The training and prediction module is used to: train a first preset deep learning model based on the data changes of the preset financial indicators and each related indicator within a first preset time period to obtain a first prediction model corresponding to the preset financial indicators; predict the preset financial indicators based on the first prediction model; and / or train a second preset deep learning model based on the data changes of the preset financial indicators and each key related indicator within a first preset time period to obtain a second preset deep learning model corresponding to the preset financial indicators; and predict the preset financial indicators based on the second preset deep learning model.

[0017] Based on the above solution, the financial data analysis system based on data warehouse of the present invention can be further improved as follows.

[0018] Furthermore, it also includes a variance matching module, which is used for: Calculate the variance of the data changes of the preset financial indicators and each related indicator within the first preset time period, and determine whether each variance is within the corresponding preset range. If not, issue a prompt message.

[0019] Furthermore, it also includes a statistics module, which is used for: Each invoice image in the financial data is identified to obtain the issuance information of each invoice. The issuance information of each invoice is then matched with the financial text data in the financial data to identify the missing data information of the invoices.

[0020] Furthermore, it also includes a report generation module, which is used for: Receive and generate corresponding reports based on the financial indicators and time range selected by the user, and then display them.

[0021] Furthermore, the financial data pre-stored in the data warehouse is the financial data of the natural gas pipeline network.

[0022] Furthermore, it also includes a data processing and display module, which is used for: From the financial data of the natural gas pipeline network, obtain the profit changes of each business in the second preset time period, and sort all businesses in descending order of average profit to obtain the first sequence; Obtain the cost change of each service within the second preset time period, and sort all services in descending order of average cost to obtain the second sequence; The profit margin change of each business within the second preset time period will be obtained, and all businesses will be sorted in descending order of average profit margin to obtain a third sequence. The first sequence, the second sequence, and the third sequence are displayed in a chart format on the display interface.

[0023] Furthermore, when the financial data pre-stored in the data warehouse is financial data of the natural gas pipeline network, the preset financial indicators are profit, cost, revenue or taxes.

[0024] 3) In a third aspect, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the financial data analysis method based on a data warehouse of the present application.

[0025] 4) In a fourth aspect, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a data warehouse-based financial data analysis method of the present application.

[0026] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here.

[0027] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0028] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating a data warehouse-based financial data analysis method according to an embodiment of the present invention. Figure 2This is a schematic diagram of the structure of a data warehouse-based financial data analysis system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0030] like Figure 1 As shown in the figure, an embodiment of the present invention provides a financial data analysis method based on a data warehouse, which includes the following steps: S1. Obtain data, specifically: From the financial data pre-stored in the data warehouse, obtain the data changes of each related indicator of the preset financial indicator within a first preset time period, and obtain the data changes of the preset financial indicator within the first preset time period; The first preset time period can be set according to the actual situation, such as the first preset time period being last year or the year before last. When the first preset time period is last year, the data changes within the first preset time period specifically refer to the monthly data changes of last year.

[0031] When the financial data pre-stored in the data warehouse is financial data of the natural gas pipeline network, the preset financial indicators are profit, cost, revenue or taxes, etc. Taking cost as a preset financial indicator for explanation, the related indicators of cost include: personnel cost, R&D cost, equipment damage cost, etc. The related indicators of each preset financial indicator can be determined according to the actual situation and in combination with expert experience.

[0032] S2. Identify key related indicators, specifically: Based on the preset financial indicators and the data changes of each related indicator within a first preset time period, the key related indicators of the preset financial indicators are determined. The key correlation indicators can be determined in the following two ways: 1) First implementation method: Analyze the data changes of each related indicator within the first preset time period through expert experience, score each related indicator of the preset financial indicator, rank all related indicators of the preset financial indicator in descending order of score, and then select the top N related indicators as the key related indicators of the preset financial indicator, where N is a positive integer and can be set according to the actual situation.

[0033] 2) Through Principal Component Analysis (PCA), the data changes of the preset financial indicators and each related indicator within the first preset time period are analyzed to determine the degree of influence of each related indicator on the preset financial indicators. All related indicators of the preset financial indicators are ranked in descending order of influence value. Then, the top M related indicators are selected as the key related indicators of the preset financial indicators, where M is a positive integer and can be set according to the actual situation.

[0034] Principal component analysis (PCA) is a multivariate statistical analysis method that uses linear transformations of multiple variables to select a smaller number of important variables. It is also known as principal component analysis.

[0035] S3, Training and Prediction, specifically: Based on the preset financial indicators and the data changes of each related indicator within a first preset time period, a first preset deep learning model is trained to obtain a first prediction model corresponding to the preset financial indicators. The preset financial indicators are then predicted based on the first prediction model. And / or, based on the preset financial indicators and the data changes of each key related indicator within a first preset time period, a second preset deep learning model is trained to obtain a second preset deep learning model corresponding to the preset financial indicators. The preset financial indicators are then predicted based on the second preset deep learning model.

[0036] The first and second preset deep learning models can both be convolutional neural networks, or other deep learning models can be selected according to the actual situation. The specific training process is known to those skilled in the art and will not be described in detail here.

[0037] The present invention provides a data warehouse-based financial data analysis method that can predict and analyze financial indicators specified by users, reducing the difficulty for users to analyze financial data. Moreover, when using the first prediction model for prediction, the training difficulty can be reduced by selecting key related indicators, and the prediction accuracy can be guaranteed to the greatest extent. When using the second preset deep learning model for prediction, although the training time will be extended, the prediction accuracy can be improved.

[0038] Optionally, the above technical solution also includes: S4. Calculate the variance of the data changes of the preset financial indicators and each related indicator within the first preset time period, and determine whether each variance is within the corresponding preset range. If not, issue a prompt message. By comparing the variances, abnormal data can be quickly identified and the user is alerted.

[0039] The preset range of variance for each financial indicator (i.e., the preset financial indicator and each related indicator) can be set according to the actual situation, which will not be elaborated here.

[0040] Optionally, the above technical solution also includes: S5. Recognize each invoice image in the financial data to obtain the issuance information of each invoice, and match the issuance information of each invoice with the financial text data in the financial data to identify missing invoice data. This accurate identification of missing invoice data facilitates subsequent processing.

[0041] Specifically, the identification of each invoice image in the financial data can be achieved in the following two ways: 1) The first implementation method: Use a trained image recognition model to recognize each invoice image in the financial data to obtain the issuance information of each invoice.

[0042] 2) The second implementation method: Use OCR text recognition software to perform OCR text recognition on each invoice image in the financial data to obtain the issuance information of each invoice.

[0043] Optionally, in the above technical solution, when a match is found, a matching success flag can be set, and for invoices and financial text data that do not match successfully, a mismatch flag can be set. The specific forms of the matching success flag and the mismatch flag can be set according to the actual situation.

[0044] Optionally, the above technical solution also includes: S6 receives and generates corresponding reports based on the financial indicators and time range selected by the user, and displays them, which can further improve the user experience.

[0045] For example, if the user selects cost as the financial metric and the time frame is last year, a report will be generated that represents the monthly costs within the last year; if the user selects profit as the financial metric and the time frame is last year, a report will be generated that represents the monthly profits within the last year; if the user selects revenue as the financial metric and the time frame is last year, a report will be generated that represents the monthly revenue within the last year; if the user selects taxes as the financial metric and the time frame is last year, a report will be generated that represents the monthly taxes within the last year.

[0046] Optionally, in the above technical solution, the financial data pre-stored in the data warehouse is the financial data of the natural gas pipeline network.

[0047] Optionally, the above technical solution also includes: S7. Obtain the profit change of each business in the second preset time period from the financial data of the natural gas pipeline network, and sort all businesses in descending order of average profit to obtain the first sequence. S8. Obtain the cost change of each service within the second preset time period, and sort all services in descending order of average cost to obtain the second sequence; S9. Obtain the profit margin change of each business in the second preset time period, and sort all businesses in descending order of average profit margin to obtain a third sequence. S10. Display the first sequence, the second sequence, and the third sequence in chart form on the display interface. This facilitates comprehensive analysis of profit changes, profit margin changes, and cost changes for each business segment, and is more beneficial for users to carry out subsequent work.

[0048] The second preset time period can be the previous year. The profit changes within the second preset time period include: the monthly profit within the previous year, and the average profit is: the average monthly profit within the previous year. The costs within the second preset time period include: the monthly costs within the previous year and the average monthly costs within the previous year. The profit margin within the second preset time period includes: the monthly profit margin within the second preset time period and the monthly profit margin within the previous year.

[0049] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0050] like Figure 2 As shown, an embodiment of the present invention provides a data warehouse-based financial data analysis system 200, which includes a data acquisition module 201, a key correlation indicator determination module 202, and a training and prediction module 203. The data acquisition module 201 is used to: acquire the data changes of each related indicator of a preset financial indicator within a first preset time period from the financial data pre-stored in the data warehouse, and acquire the data changes of the preset financial indicator within the first preset time period. The key correlation indicator determination module 202 is used to: determine the key correlation indicators of the preset financial indicators based on the preset financial indicators and the data changes of each correlation indicator within a first preset time period; The training and prediction module 203 is used to: train a first preset deep learning model based on the data changes of the preset financial indicators and each related indicator within a first preset time period to obtain a first prediction model corresponding to the preset financial indicators; predict the preset financial indicators based on the first prediction model; and / or train a second preset deep learning model based on the data changes of the preset financial indicators and each key related indicator within a first preset time period to obtain a second preset deep learning model corresponding to the preset financial indicators; and predict the preset financial indicators based on the second preset deep learning model.

[0051] Optionally, the above technical solution further includes a variance matching module, which is used for: Calculate the variance of the data changes of the preset financial indicators and each related indicator within the first preset time period, and determine whether each variance is within the corresponding preset range. If not, issue a prompt message.

[0052] Optionally, the above technical solution further includes a statistics module, which is used for: Each invoice image in the financial data is identified to obtain the issuance information of each invoice. The issuance information of each invoice is then matched with the financial text data in the financial data to identify the missing data information of the invoices.

[0053] Optionally, the above technical solution further includes a report generation module, which is used for: Receive and generate corresponding reports based on the financial indicators and time range selected by the user, and then display them.

[0054] Optionally, in the above technical solution, the financial data pre-stored in the data warehouse is the financial data of the natural gas pipeline network.

[0055] Optionally, the above technical solution further includes a data processing and display module, which is used for: From the financial data of the natural gas pipeline network, obtain the profit changes of each business in the second preset time period, and sort all businesses in descending order of average profit to obtain the first sequence; Obtain the cost change of each service within the second preset time period, and sort all services in descending order of average cost to obtain the second sequence; The profit margin change of each business within the second preset time period will be obtained, and all businesses will be sorted in descending order of average profit margin to obtain a third sequence. The first sequence, the second sequence, and the third sequence are displayed in a chart format on the display interface.

[0056] Optionally, in the above technical solution, when the financial data pre-stored in the data warehouse is the financial data of the natural gas pipeline network, the preset financial indicators are profit, cost, revenue or taxes.

[0057] It should be noted that the beneficial effects of the data warehouse-based financial data analysis system 200 provided in the above embodiments are the same as those of the data warehouse-based financial data analysis method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0058] In some embodiments, the data warehouse-based financial data analysis system 200 provided by the present invention can be implemented in a combination of hardware and software. As an example, the data warehouse-based financial data analysis system 200 provided by the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the data warehouse-based financial data analysis method provided by the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0059] In other embodiments, the data warehouse-based financial data analysis system 200 provided in this invention can be implemented in software. Figure 2 A data warehouse-based financial data analysis system 200 is shown, which can be software in the form of programs and plug-ins, and includes a series of modules, including a data acquisition module 201, a key correlation indicator determination module 202, and a training and prediction module 203.

[0060] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0061] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0062] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0063] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0064] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0065] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0066] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0067] Among these, electronic devices can also be terminal devices. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0068] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0069] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0070] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0071] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0072] The computer-readable storage medium provided in this invention can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0073] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0074] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A financial data analysis method based on a data warehouse, characterized in that, include: From the financial data pre-stored in the data warehouse, obtain the data changes of each related indicator of the preset financial indicator within a first preset time period, and obtain the data changes of the preset financial indicator within the first preset time period; Based on the preset financial indicators and the data changes of each related indicator within a first preset time period, the key related indicators of the preset financial indicators are determined. Based on the preset financial indicators and the data changes of each related indicator within a first preset time period, a first preset deep learning model is trained to obtain a first prediction model corresponding to the preset financial indicators. The preset financial indicators are then predicted based on the first prediction model. And / or, based on the preset financial indicators and the data changes of each key related indicator within a first preset time period, a second preset deep learning model is trained to obtain a second preset deep learning model corresponding to the preset financial indicators. The preset financial indicators are then predicted based on the second preset deep learning model.

2. The financial data analysis method based on a data warehouse according to claim 1, characterized in that, Also includes: Calculate the variance of the data changes of the preset financial indicators and each related indicator within the first preset time period, and determine whether each variance is within the corresponding preset range. If not, issue a prompt message.

3. The financial data analysis method based on a data warehouse according to claim 2, characterized in that, Also includes: The system identifies the image of each invoice in the financial data to obtain the issuance information of each invoice, and matches the issuance information of each invoice with the financial text data in the financial data to identify the missing data information of the invoices.

4. The financial data analysis method based on a data warehouse according to claim 3, characterized in that, Also includes: Receive and generate corresponding reports based on the financial indicators and time range selected by the user, and then display them.

5. The financial data analysis method based on a data warehouse according to claim 4, characterized in that, The financial data pre-stored in the data warehouse is the financial data of the natural gas pipeline network.

6. The financial data analysis method based on a data warehouse according to claim 5, characterized in that, Also includes: From the financial data of the natural gas pipeline network, obtain the profit changes of each business in a second preset time period, and sort all businesses in descending order of average profit to obtain the first sequence; Obtain the cost change of each service within the second preset time period, and sort all services in descending order of average cost to obtain the second sequence; The profit margin change of each business within the second preset time period will be obtained, and all businesses will be sorted in descending order of average profit margin to obtain a third sequence. The first sequence, the second sequence, and the third sequence are displayed in a chart format on the display interface.

7. The financial data analysis method based on a data warehouse according to claim 6, characterized in that, When the financial data pre-stored in the data warehouse is financial data of the natural gas pipeline network, the preset financial indicators are profit, cost, revenue or taxes.

8. A financial data analysis system based on a data warehouse, characterized in that, It includes a data acquisition module, a key correlation indicator determination module, and a training and prediction module; The data acquisition module is used to: acquire the data changes of each related indicator of a preset financial indicator within a first preset time period from the financial data pre-stored in the data warehouse, and acquire the data changes of the preset financial indicator within the first preset time period; The key correlation indicator determination module is used to: determine the key correlation indicators of the preset financial indicators based on the preset financial indicators and the data changes of each correlation indicator within a first preset time period. The training and prediction module is used to: train a first preset deep learning model based on the data changes of the preset financial indicators and each related indicator within a first preset time period to obtain a first prediction model corresponding to the preset financial indicators; predict the preset financial indicators based on the first prediction model; and / or train a second preset deep learning model based on the data changes of the preset financial indicators and each key related indicator within a first preset time period to obtain a second preset deep learning model corresponding to the preset financial indicators; and predict the preset financial indicators based on the second preset deep learning model.

9. An electronic device, characterized in that, It includes 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 a data warehouse-based financial data analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a data warehouse-based financial data analysis method according to any one of claims 1 to 7.

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