Budget comparative analysis method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of data analysis and financial science and technology, and discloses a budget comparative analysis method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining budget data and actual data corresponding to the budget data, the actual data comprises financial data, operation data and business data, performing data preprocessing on the budget data and the actual data to obtain target budget data and target actual data; calculating the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result; performing data statistics on the target budget data and the target actual data to obtain a statistical result, and performing anomaly analysis based on the statistical result and the deviation calculation result to generate an anomaly analysis result; and generating an exception analysis report based on the exception analysis result, and performing visualization processing on the exception analysis report. The efficiency and accuracy of budget comparative analysis can be improved.

Description

Technical Field

[0001] The present application relates to the fields of data analysis and financial technology, and in particular to a budget comparison and analysis method, apparatus, computer equipment and storage medium. Background Art

[0002] In the field of financial management and data analysis, budget-to-actual comparison analysis is a key step in evaluating the overall performance of a project, department, or company. However, existing products have significant deficiencies in performing this task, limiting their effectiveness and efficiency in practical applications. Budget comparison analysis systems can be insurance systems, banking systems, trading systems, and order systems.

[0003] Existing products rely on users to manually input budget and actual data. This manual input method is not only time-consuming and laborious, but also prone to input errors and data inconsistencies. Due to the interference of human factors, the accuracy of the data is difficult to guarantee, which in turn affects the reliability of subsequent analysis. In addition, manual data input also limits the user experience and work efficiency, making the entire analysis process cumbersome and inefficient. Secondly, the analysis functions of existing products are relatively limited. They can usually only provide basic comparison and summary functions, but cannot perform in-depth trend analysis and anomaly detection. This limited analysis capability makes it difficult for users to mine valuable information and insights from the data, making it impossible to make more informed decisions. At the same time, due to the lack of intelligent analysis capabilities, existing products cannot automatically identify key deviations and trends, and users are required to manually find problems, which undoubtedly increases the workload and difficulty of users. There is an urgent need for a budget comparison analysis method to improve the efficiency and accuracy of budget comparison analysis. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a budget comparison and analysis method, apparatus, computer equipment and storage medium to improve the efficiency and accuracy of budget comparison and analysis.

[0005] In order to solve the above technical problems, the present application embodiment provides a budget comparison and analysis method, including:

[0006] Acquire budget data and actual data corresponding to the budget data, and perform data preprocessing on the budget data and the actual data respectively to obtain target budget data and target actual data, wherein the actual data includes financial data, operational data and business data;

[0007] Calculating the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result;

[0008] Performing data statistics on the target budget data and the target actual data to obtain statistical results, and performing abnormal analysis based on the statistical results and the deviation calculation results to generate abnormal analysis results;

[0009] An abnormality analysis report is generated based on the abnormality analysis result, and the abnormality analysis report is visualized.

[0010] In order to solve the above technical problems, the embodiment of the present application provides a budget comparison and analysis device, including:

[0011] A data acquisition unit, used to acquire budget data and actual data corresponding to the budget data, and perform data preprocessing on the budget data and the actual data respectively to obtain target budget data and target actual data, wherein the actual data includes financial data, operational data and business data;

[0012] A deviation calculation unit, used to calculate the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result;

[0013] an abnormality analysis unit, used to perform data statistics on the target budget data and the target actual data to obtain statistical results, and perform abnormality analysis based on the statistical results and the deviation calculation results to generate abnormality analysis results;

[0014] A report generating unit is used to generate an abnormality analysis report based on the abnormality analysis result, and to visualize the abnormality analysis report.

[0015] In order to solve the above technical problems, a technical solution adopted by the present invention is: to provide a computer device, including one or more processors; a memory for storing one or more programs, so that the one or more processors can implement any one of the budget comparison and analysis methods described above.

[0016] In order to solve the above technical problems, a technical solution adopted by the present invention is: a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any one of the budget comparison and analysis methods described above is implemented.

[0017] The embodiment of the present invention provides a budget comparison analysis method, device, computer equipment and storage medium. The method includes: obtaining budget data and actual data corresponding to the budget data, and performing data preprocessing on the budget data and the actual data respectively to obtain target budget data and target actual data, wherein the actual data includes financial data, operation data and business data; calculating the deviation between the target budget data and the target actual data of each category to obtain the deviation calculation result; performing data statistics on the target budget data and the target actual data to obtain statistical results, and performing abnormal analysis based on the statistical results and the deviation calculation results to generate abnormal analysis results; generating an abnormal analysis report based on the abnormal analysis results, and visualizing the abnormal analysis report. The embodiment of the present invention performs budget analysis by performing deviation calculation and data statistics on the preprocessed budget data and actual data, so as to avoid manual budget comparison analysis, which is conducive to improving the efficiency of budget comparison analysis; performing abnormal analysis based on the statistical results and the deviation calculation results is conducive to improving the accuracy of budget comparison analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a schematic diagram of an application environment of a budget comparison and analysis method in an embodiment of the present invention;

[0020] Figure 2 It is a flowchart of the implementation of the budget comparison and analysis method provided in the embodiment of the present application;

[0021] Figure 3 yes Figure 2 A schematic flow chart of a specific implementation of step S1;

[0022] Figure 4 yes Figure 2 A schematic flow chart of a specific implementation of step S2;

[0023] Figure 5 is a flowchart for implementing a sub-process in a budget comparison and analysis method provided in another embodiment of the present application;

[0024] Figure 6 yes Figure 2 A schematic flow chart of a specific implementation of step S3;

[0025] Figure 7 yes Figure 2 A schematic flow chart of a specific implementation of step S4;

[0026] Figure 8 is a flowchart of implementing a sub-process in a budget comparison and analysis method provided in another embodiment of the present application;

[0027] Fig. 9 is a schematic diagram of a budget comparison and analysis device provided in an embodiment of the present application;

[0028] Fig.10 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0030] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0032] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the budget comparison and analysis method provided in the embodiment of the present application is generally executed by a server, and accordingly, the budget comparison and analysis device is generally configured in the server.

[0034] The budget comparison and analysis method provided by the embodiment of the present invention can be applied in the following aspects: Figure 1In an application environment, the client communicates with the server through a network. The server can receive a budget comparison analysis request through the client, obtain the budget data and the actual data corresponding to the budget data, and perform data preprocessing on the budget data and the actual data respectively to obtain the target budget data and the target actual data, wherein the actual data includes financial data, operational data and business data; calculate the deviation between the target budget data and the target actual data of each category to obtain the deviation calculation result; perform data statistics on the target budget data and the target actual data to obtain the statistical result, and perform abnormal analysis based on the statistical result and the deviation calculation result to generate the abnormal analysis result; generate an abnormal analysis report based on the abnormal analysis result, and perform visualization processing on the abnormal analysis report; at the same time, the server sends the visualized abnormal analysis report to the client so that the user of the client can view the abnormal analysis report. The present invention performs budget analysis by performing deviation calculation and data statistics on the preprocessed budget data and actual data to avoid manual budget comparison analysis, which is conducive to improving the efficiency of budget comparison analysis; at the same time, performing abnormal analysis based on the statistical result and the deviation calculation result is conducive to improving the accuracy of budget comparison analysis. The client may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server may be implemented by an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0035] See also Figure 2 , Figure 2 A specific implementation of the budget comparison analysis method is shown.

[0036] It should be noted that if there are substantially the same results, the method of the present invention is not limited to Figure 2 The process sequence shown is limited to the following steps:

[0037] S1: Obtain budget data and actual data corresponding to the budget data, and perform data preprocessing on the budget data and the actual data respectively to obtain target budget data and target actual data. The actual data includes financial data, operational data and business data.

[0038] Specifically, the budget data and the actual data corresponding to the budget data are obtained from multiple data sources, which can be financial software, ERP system, CRM system, HRMS system, etc. These data sources store the budget data and actual data of the enterprise. Then, the budget data and actual data are preprocessed respectively to obtain the target budget data and target actual data, so as to improve the quality and consistency of the data and provide a reliable basis for subsequent analysis and comparison.

[0039] See also Figure 3 , Figure 3 A specific implementation of step S1 is shown, which is described in detail as follows:

[0040] S11: Establish connections with multiple data sources using a preset connection method.

[0041] S12: extracting budget data from the plurality of data sources and actual data corresponding to the budget data.

[0042] S13: Preprocess the budget data and the actual data respectively to obtain the target budget data and the target actual data, wherein the data preprocessing includes data deduplication, data standardization, outlier processing and data mapping.

[0043] Specifically, the preset connection methods include API interface, database connection (such as JDBC, ODBC), file transfer (such as FTP, SFTP), etc. The selection of these connection methods depends on the type of data source and the IT environment of the enterprise. When establishing a connection with a data source, it is necessary to configure the corresponding connection parameters, such as server address, port number, database name, user name and password, etc. According to business needs, budget data and corresponding actual data are extracted from the connected data source. These data include financial data (such as income, cost, profit, etc.), operational data (such as sales volume, inventory, customer satisfaction, etc.) and business data (such as project progress, contract amount, employee performance, etc.). The extracted data is then integrated into one or more intermediate storage systems, such as a data warehouse or a data lake, for subsequent data preprocessing and analysis. Finally, the budget data and the actual data are preprocessed to obtain the target budget data and the target actual data.

[0044] Among them, data deduplication is to delete duplicate data records to ensure the uniqueness of the data. Data standardization is to convert data from different data sources into a unified format and unit for comparison and analysis. For example, financial data in different monetary units are converted to the same monetary unit. Outlier processing: Identify and process outliers in the data, such as through statistical methods (such as the 3σ principle) or machine learning algorithms (such as isolation forests) to detect and correct outliers. Data mapping: Establish a mapping relationship between budget data and actual data to ensure that they can correspond correctly. This may involve field matching, data type conversion, etc.

[0045] S2: Calculate the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result.

[0046] The embodiment of the present application introduces a machine learning algorithm to intelligently analyze the budget data and actual data to identify potential deviations and trends. Specifically, an algorithm recognition model is pre-trained, and the deviation between the target budget data and the target actual data of each category is calculated by the trained recognition model to obtain the deviation calculation result, thereby evaluating the budget execution. The categories can be departments, projects, product lines, etc.

[0047] See also Figure 4 , Figure 4 A specific implementation of step S2 is shown, which is described in detail as follows:

[0048] S21: Using the Pandas library, the target budget data and the target actual data are read and merged to obtain merged data.

[0049] Specifically, the Pandas library is used to read the data file (such as CSV, Excel, etc.) storing the target budget data and the target actual data. The read budget data and actual data are merged according to specific keys (such as time, department, etc.) to obtain merged data containing information of both.

[0050] Among them, Pandas is an open source data analysis and processing library for Python, which provides efficient data structures (such as DataFrame) and rich data operation functions.

[0051] S22: identifying potential deviations between the target budget data and the target actual data based on the merged data using a pre-trained recognition model to obtain a recognition result.

[0052] S23: Calculate the deviation between the target budget data and the target actual data of each category based on the recognition result to obtain the deviation calculation result.

[0053] Specifically, the potential deviations between the budget data and the actual data in the merged data are identified through the pre-trained recognition model. Potential deviations include budget overruns, budget savings, and significant inconsistencies between actual and budget data. The recognition model outputs the recognition results of potential deviations, which include information such as the type, size, and location of the deviation. Then, based on the recognition results, the budget data and actual data of each category are compared to calculate the deviation value. The deviation value can be an absolute value (such as a difference) or a relative value (such as a percentage). The deviation calculation results will be used for subsequent analysis and report generation.

[0054] See also Figure 5 , Figure 5 A specific implementation method before step S22 is shown, which is described in detail as follows:

[0055] S221: Collecting initial budget data and initial actual data corresponding to the initial budget data from multiple data sources.

[0056] S222: Perform data preprocessing and data division on the initial budget data and the initial actual data to obtain training data and test data.

[0057] S223: Using the regression algorithm model as a recognition model, and performing model training on the recognition model based on the training data in a cross-validation manner to obtain an initial recognition model.

[0058] S224: Testing and verifying the initial recognition model using the test data to obtain the pre-trained recognition model.

[0059] Specifically, initial budget data and initial actual data are collected from multiple data sources (such as financial software, ERP system, etc.). The initial budget data and initial actual data are stored in different formats, such as CSV, Excel, database table, etc. Then, the collected initial budget data and initial actual data are processed by data cleaning, deduplication, standardization, etc. to improve data quality. Then, the pre-processed data is divided into training data and test data. The training data is used to train the recognition model, and the test data is used to verify the performance of the model.

[0060] Select a regression algorithm as the recognition model, which can be any one of a linear regression algorithm and a decision tree regression algorithm. Use cross-validation to train the model to reduce overfitting and improve the generalization ability of the model. The model obtained after training is called the initial recognition model. Use test data to test and verify the initial recognition model. Evaluate the performance of the model through evaluation indicators (such as accuracy, recall rate, F1 score, etc.). After testing and verification, select the model with the best performance as the pre-trained recognition model for subsequent deviation identification.

[0061] S3: Performing data statistics on the target budget data and the target actual data to obtain statistical results, and performing abnormality analysis based on the statistical results and the deviation calculation results to generate abnormality analysis results.

[0062] Specifically, in-depth data statistics are performed on the target budget data and the target actual data to obtain statistical results. Then, based on the statistical results and the deviation calculation results, abnormal analysis results are identified and generated. Among them, comprehensive data statistics are performed on the target budget data and the target actual data. Data statistics include but are not limited to statistical indicators such as the total amount, average value, standard deviation, maximum value, and minimum value of the data. Through data statistics, the distribution characteristics and basic conditions of the data can be preliminarily understood, providing a basis for subsequent analysis. The data statistics and deviation calculation results are compared with the preset expected values ​​to determine whether there are abnormal events, thereby generating abnormal analysis results.

[0063] See also Figure 6 , Figure 6 A specific implementation of step S3 is shown, which is described in detail as follows:

[0064] S31: Performing data statistics on the target budget data and the target actual data to obtain the statistical results.

[0065] S32: performing association analysis and cluster analysis on the target budget data and the target actual data based on the statistical results to obtain analysis results.

[0066] S33: Compare the analysis result and the deviation calculation result with preset expected values ​​respectively, so as to perform anomaly detection on the statistical result and the deviation calculation result, and obtain the abnormal analysis result.

[0067] Specifically, data statistics are performed on the target budget data and the target actual data to obtain the statistical results. The embodiment of the present application can preliminarily understand the distribution characteristics and basic conditions of the data through data statistics, providing a basis for subsequent analysis. Then, based on the statistical results, the target budget data and the target actual data are subjected to association analysis and cluster analysis to obtain analysis results. Among them, association analysis is to explore the association relationship between the target budget data and the target actual data. For example, analyze which budget items have a significant correlation with actual expenditures, or which factors may cause deviations between the budget and the actual. Cluster analysis is to classify similar data points or items together to form different clusters or groups. This helps to identify potential patterns or anomalies in the data. Through association analysis and cluster analysis, the relationship and pattern between the data can be deeply excavated to provide more in-depth information for anomaly detection. The analysis results (including the results of association analysis and cluster analysis) are compared with preset expected values. The preset expected value can be based on historical data, industry standards or internally set standards. At the same time, the deviation calculation result (i.e., the difference or percentage difference between the budget and the actual) is compared with the preset expected value. Based on the comparison results, data points or patterns that do not match expectations, i.e., outliers, are identified. Based on the presence or absence of outliers, statistical results, and deviation calculation results, anomaly analysis results are generated.

[0068] It should be noted that the preset expected value is set according to the actual situation and is not limited here.

[0069] S4: Generate an abnormality analysis report based on the abnormality analysis result, and visualize the abnormality analysis report.

[0070] Specifically, the abnormal analysis results generated above include statistical results, deviation calculation results and abnormal point conditions. In the embodiment of the present application, the report generation template is determined using the user's preference information, and an abnormal analysis report is generated based on the abnormal analysis results according to the report generation model, and the abnormal analysis report is visualized.

[0071] See also Figure 7 , Figure 7 A specific implementation of step S4 is shown, which is described in detail as follows:

[0072] S41: Obtain user preference information, and determine a report generation template based on the preference information.

[0073] S42: Performing report generation processing based on the abnormality analysis result according to the report generation template to obtain an abnormality analysis report.

[0074] S43: Screening and exporting data of the abnormal analysis report to obtain target data.

[0075] S44: Convert the target data into a visual chart, and display the visual chart.

[0076] Specifically, user preference information includes the style of the report (such as concise, detailed), the structure of the report (such as chronological order, by severity), the format of the report (such as PDF, Excel, web page), etc. These preference information are obtained through user profiles, historical behavior analysis or user interaction interfaces. According to the user's preferences, select or generate a corresponding report generation template. The report generation process includes steps such as data collation, data aggregation, and data interpretation. The report contains detailed information about the abnormal data (such as time, location, type, impact, etc.). Then the abnormal analysis report is subjected to data screening and data export to obtain the target data, and the data screening involves steps such as removing redundant information and retaining key data points; data export involves converting the data in the report into a format suitable for visualization processing (such as CSV, JSON, etc.). The target data should contain all the information that needs to be displayed in a visualization chart. Finally, the target data is converted into a visualization chart, and the visualization chart is displayed. The embodiment of the present application allows users to easily browse data, charts and reports through a user interface, while allowing users to filter, export and share data as needed.

[0077] See also Figure 8 , Figure 8 A specific implementation after step S4 is shown, which is described in detail as follows:

[0078] S4A: If there is abnormal data in the abnormal analysis result, a warning message is generated.

[0079] S4B: issuing an early warning according to a preset reminder method.

[0080] Specifically, since the abnormal analysis result includes whether an abnormal point exists, the abnormal point is generated by judging the statistical result and the deviation calculation result with the preset expected value. Therefore, in the embodiment of the present application, if there is abnormal data in the abnormal analysis result, an early warning message is generated; the early warning message is warned according to the preset reminder method to remind and help take timely action. Among them, the preset reminder method is at least one of email notification, SMS notification, phone reminder, and system pop-up window.

[0081] In an embodiment of the present application, the budget data and the actual data corresponding to the budget data are obtained, and the budget data and the actual data are respectively preprocessed to obtain target budget data and target actual data, wherein the actual data includes financial data, operational data and business data; the deviation between the target budget data and the target actual data of each category is calculated to obtain a deviation calculation result; data statistics are performed on the target budget data and the target actual data to obtain statistical results, and abnormal analysis is performed based on the statistical results and the deviation calculation results to generate abnormal analysis results; an abnormal analysis report is generated based on the abnormal analysis results, and the abnormal analysis report is visualized. The embodiment of the present invention performs budget analysis by performing deviation calculation and data statistics on the preprocessed budget data and actual data, so as to avoid manual budget comparison analysis, which is conducive to improving the efficiency of budget comparison analysis; at the same time, abnormal analysis is performed based on the statistical results and the deviation calculation results, which is conducive to improving the accuracy of budget comparison analysis.

[0082] The embodiments of the present application can automate data processing and cleaning to reduce the possibility of data errors and ensure the accuracy and consistency of the data. The intelligent comparative analysis of the embodiments of the present application can accurately identify deviations and trends, helping users to find problems and make adjustments in a timely manner. Timely detection of anomalies and trends helps companies avoid risks such as budget overruns and revenue declines. Provide intelligent reminders and early warning functions to help users take timely actions to reduce financial risks. The embodiments of the present application improve the efficiency and accuracy of budget management, enabling companies to better grasp their financial status and improve their overall competitiveness.

[0083] Please refer to Fig. 9 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a budget comparison and analysis device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0084] like Fig. 9 As shown, the budget comparison and analysis device of this embodiment includes: a data acquisition unit 51, a deviation calculation unit 52, an abnormality analysis unit 53 and a report generation unit 54, wherein:

[0085] A data acquisition unit 51 is used to acquire budget data and actual data corresponding to the budget data, and perform data preprocessing on the budget data and the actual data to obtain target budget data and target actual data, wherein the actual data includes financial data, operation data and business data;

[0086] A deviation calculation unit 52, used to calculate the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result;

[0087] The abnormality analysis unit 53 is used to perform data statistics on the target budget data and the target actual data to obtain statistical results, and perform abnormality analysis based on the statistical results and the deviation calculation results to generate abnormality analysis results;

[0088] The report generating unit 54 is used to generate an abnormality analysis report based on the abnormality analysis result, and to perform visualization processing on the abnormality analysis report.

[0089] Further, the deviation calculation unit 52 includes:

[0090] A data merging unit, used to read and merge the target budget data and the target actual data using a Pandas library to obtain merged data;

[0091] an identification result unit, configured to identify potential deviations between the target budget data and the target actual data based on the merged data by using a pre-trained identification model, and obtain an identification result;

[0092] A calculation result unit is used to calculate the deviation between the target budget data and the target actual data of each category based on the recognition result to obtain the deviation calculation result.

[0093] Furthermore, the recognition result unit includes:

[0094] A data collection unit, used for collecting initial budget data and initial actual data corresponding to the initial budget data from multiple data sources;

[0095] A data preprocessing unit, used for performing data preprocessing and data division on the initial budget data and the initial actual data to obtain training data and test data;

[0096] A model training unit, used to use the regression algorithm model as a recognition model, and to perform model training on the recognition model based on the training data in a cross-validation manner to obtain an initial recognition model;

[0097] The model testing unit is used to test and verify the initial recognition model through the test data to obtain the pre-trained recognition model.

[0098] Furthermore, the abnormality analysis unit 53 includes:

[0099] A data statistics unit, used for performing data statistics on the target budget data and the target actual data to obtain the statistical result;

[0100] An analysis unit, configured to perform association analysis and cluster analysis on the target budget data and the target actual data based on the statistical results to obtain analysis results;

[0101] The comparison unit is used to compare the analysis result and the deviation calculation result with preset expected values ​​respectively, so as to perform anomaly detection on the statistical result and the deviation calculation result to obtain the abnormal analysis result.

[0102] Furthermore, the data acquisition unit 51 includes:

[0103] A connection unit, used to establish connections with multiple data sources using a preset connection method;

[0104] An extraction unit, used for extracting budget data from a plurality of data sources and actual data corresponding to the budget data;

[0105] The target data generating unit is used to perform data preprocessing on the budget data and the actual data respectively to obtain the target budget data and the target actual data, wherein the data preprocessing includes data deduplication, data standardization, outlier processing and data mapping.

[0106] Further, the report generating unit 54 includes:

[0107] A target generation unit, configured to obtain user preference information and determine a report generation template based on the preference information;

[0108] An abnormality analysis report generating unit, used for performing report generation processing based on the abnormality analysis result according to the report generation template to obtain an abnormality analysis report;

[0109] A data export unit, used for screening and exporting data of the abnormal analysis report to obtain target data;

[0110] The data display unit is used to convert the target data into a visual chart and display the visual chart.

[0111] Furthermore, the report generating unit 54 further includes:

[0112] A warning information generating unit, configured to generate a warning information if abnormal data exists in the abnormal analysis result;

[0113] The early warning unit is used to warn the early warning information according to a preset reminder method.

[0114] In an embodiment of the present application, the budget data and the actual data corresponding to the budget data are obtained, and the budget data and the actual data are respectively preprocessed to obtain target budget data and target actual data, wherein the actual data includes financial data, operational data and business data; the deviation between the target budget data and the target actual data of each category is calculated to obtain a deviation calculation result; data statistics are performed on the target budget data and the target actual data to obtain statistical results, and abnormal analysis is performed based on the statistical results and the deviation calculation results to generate abnormal analysis results; an abnormal analysis report is generated based on the abnormal analysis results, and the abnormal analysis report is visualized. The embodiment of the present invention performs budget analysis by performing deviation calculation and data statistics on the preprocessed budget data and actual data, so as to avoid manual budget comparison analysis, which is conducive to improving the efficiency of budget comparison analysis; at the same time, abnormal analysis is performed based on the statistical results and the deviation calculation results, which is conducive to improving the accuracy of budget comparison analysis.

[0115] To solve the above technical problems, the present application also provides a computer device. Fig.10 , Fig.10 This is a basic structural block diagram of the computer device in this embodiment.

[0116] The computer device 7 includes a memory 71, a processor 72, and a network interface 73 which are interconnected and communicated with each other through a system bus. It should be noted that: Fig.10 Only a computer device 7 having three components, a memory 71, a processor 72, and a network interface 73, is shown in the figure, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0117] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0118] The memory 71 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 71 can be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 can also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 7. Of course, the memory 71 can also include both the internal storage unit of the computer device 7 and its external storage device. In this embodiment, the memory 71 is generally used to store the operating system and various application software installed on the computer device 7, such as the program code of the budget comparison and analysis method, etc. In addition, the memory 71 can also be used to temporarily store various data that have been output or are to be output.

[0119] The processor 72 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 72 is generally used to control the overall operation of the computer device 7. In this embodiment, the processor 72 is used to run the program code stored in the memory 71 or process data, such as running the program code of the above-mentioned budget comparison and analysis method to implement various embodiments of the budget comparison and analysis method.

[0120] The network interface 73 may include a wireless network interface or a wired network interface, and the network interface 73 is generally used to establish a communication connection between the computer device 7 and other electronic devices.

[0121] The present application also provides another implementation, namely, providing a computer-readable storage medium, which stores a computer program. The computer program can be executed by at least one processor to enable the at least one processor to perform the steps of a budget comparison and analysis method as described above.

[0122] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0123] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to replace some of the technical features therein with equivalents. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A budget comparison analysis method, characterized in that: include: Acquire budget data and actual data corresponding to the budget data, and perform data preprocessing on the budget data and the actual data respectively to obtain target budget data and target actual data, wherein the actual data includes financial data, operational data and business data; Calculating the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result; Performing data statistics on the target budget data and the target actual data to obtain statistical results, and performing abnormal analysis based on the statistical results and the deviation calculation results to generate abnormal analysis results; An abnormality analysis report is generated based on the abnormality analysis result, and the abnormality analysis report is visualized.

2. The budget comparison and analysis method according to claim 1, characterized in that: The step of calculating the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result includes: The target budget data and the target actual data are read and merged using the Pandas library to obtain merged data; Identifying potential deviations between the target budget data and the target actual data based on the merged data using a pre-trained recognition model to obtain a recognition result; The deviation between the target budget data and the target actual data of each category is calculated based on the recognition result to obtain the deviation calculation result.

3. The budget comparison and analysis method according to claim 2, characterized in that: Before the potential deviation between the target budget data and the target actual data is identified based on the merged data by using the pre-trained recognition model and obtaining the recognition result, the method includes: Collecting initial budget data and initial actual data corresponding to the initial budget data from multiple data sources; Performing data preprocessing and data division on the initial budget data and the initial actual data to obtain training data and test data; The regression algorithm model is used as a recognition model, and the recognition model is trained based on the training data in a cross-validation manner to obtain an initial recognition model; The initial recognition model is tested and verified using the test data to obtain the pre-trained recognition model.

4. The budget comparison and analysis method according to claim 1, characterized in that: The performing of data statistics on the target budget data and the target actual data to obtain statistical results, and performing abnormal analysis based on the statistical results and the deviation calculation results to generate abnormal analysis results, includes: Performing data statistics on the target budget data and the target actual data to obtain the statistical results; Based on the statistical results, association analysis and cluster analysis are performed on the target budget data and the target actual data to obtain analysis results; The analysis result and the deviation calculation result are respectively compared with preset expected values ​​to perform anomaly detection on the statistical result and the deviation calculation result to obtain the abnormal analysis result.

5. The budget comparison and analysis method according to claim 1, characterized in that: The acquiring of budget data and actual data corresponding to the budget data, and performing data preprocessing on the budget data and the actual data respectively to obtain target budget data and target actual data, includes: Use preset connection methods to establish connections with multiple data sources; Extracting budget data from a plurality of data sources and actual data corresponding to the budget data; The budget data and the actual data are respectively preprocessed to obtain the target budget data and the target actual data, wherein the data preprocessing includes data deduplication, data standardization, outlier processing and data mapping.

6. The budget comparison and analysis method according to any one of claims 1 to 5, characterized in that: The generating an abnormality analysis report based on the abnormality analysis result and visualizing the abnormality analysis report includes: Acquiring user preference information, and determining a report generation template based on the preference information; Performing report generation processing based on the abnormality analysis result according to the report generation template to obtain an abnormality analysis report; Performing data screening and data export on the abnormal analysis report to obtain target data; The target data is converted into a visual chart, and the visual chart is displayed.

7. The budget comparison and analysis method according to any one of claims 1 to 5, characterized in that: After generating an abnormality analysis report based on the abnormality analysis result and visualizing the abnormality analysis report, the method further includes: If there is abnormal data in the abnormal analysis result, a warning message is generated; The warning information is warned according to a preset reminder method.

8. A budget comparison and analysis device, characterized in that: include: A data acquisition unit, used to acquire budget data and actual data corresponding to the budget data, and perform data preprocessing on the budget data and the actual data respectively to obtain target budget data and target actual data, wherein the actual data includes financial data, operational data and business data; A deviation calculation unit, used to calculate the deviation between the target budget data and the target actual data of each category to obtain a deviation calculation result; an abnormality analysis unit, used to perform data statistics on the target budget data and the target actual data to obtain statistical results, and perform abnormality analysis based on the statistical results and the deviation calculation results to generate abnormality analysis results; A report generating unit is used to generate an abnormality analysis report based on the abnormality analysis result, and to visualize the abnormality analysis report.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the budget comparison and analysis method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the budget comparison and analysis method according to any one of claims 1 to 7 is implemented.

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