Business analysis method and device, electronic equipment and computer readable storage medium

Abstract

The invention relates to the technical field of business analysis, and provides a business analysis method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining business data which carries business scene information; performing conversion processing on the service data according to the service scene information to obtain service index information; performing data mining processing on the business index information based on a preset data mining model to obtain business mining information; performing correlation analysis processing on the business data to obtain data correlation information; performing application analysis processing on the service mining information and the data association information according to a preset application set to obtain data application information; and performing comprehensive analysis processing on the data application information to obtain a service analysis result. By means of the technical scheme, intelligent service analysis processing can be achieved, and the service analysis process can be faster and more accurate.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of business analysis technology, and in particular, to a business analysis method, device, electronic device, and computer-readable storage medium. Background Art

[0002] At present, in the financial industry, spreadsheets are generally used to record relevant financial business data; in the subsequent business data analysis process, business personnel are often required to use spreadsheets to perform data analysis and management analysis; however, due to insufficient development skills or professional data mining skills, limited analysis data, single methods, and relying solely on experience, they may not necessarily reach correct conclusions; and spreadsheets have a high error rate, are difficult to integrate, have low or ineffective data integration effects, and have limited data analysis methods or lack professional statistical analysis support, resulting in the analysis of business data not being fast and accurate enough. Summary of the invention

[0003] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0004] In order to solve the problems mentioned in the above background technology, the embodiments of the present application provide a business analysis method, device, electronic device and computer-readable storage medium, which can realize intelligent business analysis processing, so that the business analysis process can be faster and more accurate.

[0005] In a first aspect, an embodiment of the present application provides a business analysis method, the method comprising:

[0006] Acquire business data, wherein the business data carries business scenario information;

[0007] The business data is converted and processed according to the business scenario information to obtain business indicator information;

[0008] Performing data mining processing on the business indicator information based on a preset data mining model to obtain business mining information;

[0009] Performing correlation analysis on the business data to obtain data correlation information;

[0010] Perform application analysis and processing on the business mining information and the data association information according to a preset application set to obtain data application information;

[0011] Comprehensively analyze and process the data application information to obtain business analysis results.

[0012] According to some embodiments of the present application, converting the business data according to the business scenario information to obtain business indicator information includes:

[0013] Selecting target indicator addition items from a preset indicator addition library according to the business scenario information;

[0014] The business data and the target indicator added item are combined and processed to obtain the business indicator information.

[0015] According to some embodiments of the present application, the indicator addition library includes multiple indicator addition items, each of which carries scenario marking information, and the target indicator addition item is selected from the preset indicator addition library according to the business scenario information, including:

[0016] Matching the business scenario information with the scenario tag information to obtain target scenario tag information;

[0017] The indicator additional item carrying the target scene marking information is used as the target indicator additional item.

[0018] According to some embodiments of the present application, the data mining model includes a preprocessing module, a feature extraction module, a clustering module and an evaluation module. The data mining process is performed on the business indicator information based on the preset data mining model to obtain business mining information, including:

[0019] Preprocessing the business indicator information based on the preprocessing module to obtain preprocessing indicator information;

[0020] Performing feature extraction on the preprocessing indicator information based on the feature extraction module to obtain indicator feature information;

[0021] Performing information clustering processing on the indicator feature information based on the clustering module to obtain indicator clustering information;

[0022] The indicator clustering information is evaluated and processed based on the evaluation module to obtain the business mining information.

[0023] According to some embodiments of the present application, performing correlation analysis on the business data to obtain data correlation information includes:

[0024] Performing data integration processing on the business data to obtain a business data set;

[0025] Performing frequent item set mining on the business data set to obtain multiple business frequent item sets;

[0026] Performing association rule mining on a plurality of the business frequent item sets to obtain association confidences;

[0027] The association confidence is evaluated to obtain the data association information.

[0028] According to some embodiments of the present application, performing application analysis processing on the business mining information and the data association information according to a preset application set to obtain data application information includes:

[0029] Get application analysis request;

[0030] Selecting target application analysis information from the application set according to the application analysis request;

[0031] The business mining information and the data association information are comprehensively analyzed and processed according to the target application analysis information to obtain the data application information.

[0032] According to some embodiments of the present application, the comprehensive analysis and processing of the data application information to obtain a business analysis result includes:

[0033] Perform query processing on the abnormal flag bit of the data application information to obtain application flag information;

[0034] In a case where the application marking information indicates that the data application information is abnormal, using the service abnormality marking information as the service analysis result;

[0035] In the case where the application mark information indicates that the data application information is normal, the service normal mark information is used as the service analysis result.

[0036] In a second aspect, an embodiment of the present application further provides a service analysis device, the device comprising:

[0037] A first processing module is used to obtain business data, wherein the business data carries business scenario information;

[0038] A second processing module is used to convert the business data according to the business scenario information to obtain business indicator information;

[0039] A third processing module is used to perform data mining processing on the business indicator information based on a preset data mining model to obtain business mining information;

[0040] A fourth processing module, used to perform correlation analysis on the business data to obtain data correlation information;

[0041] A fifth processing module, configured to perform application analysis processing on the business mining information and the data association information according to a preset application set to obtain data application information;

[0042] The sixth processing module is used to perform comprehensive analysis and processing on the data application information to obtain business analysis results.

[0043] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the business analysis method as described in the first aspect above is implemented.

[0044] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the business analysis method described in the first aspect above.

[0045] According to the business analysis method of the embodiment provided by the present application, at least the following beneficial effects are achieved: in the process of business analysis, firstly, business data is obtained, wherein the business data carries business scenario information; then, business indicator information can be obtained by converting the business data according to the business scenario information; then, data mining is performed on the business indicator information based on a preset data mining model to obtain business mining information; then, correlation analysis is performed on the business data to obtain data correlation information; then, application analysis is performed on the business mining information and data correlation information according to a preset application set to obtain data application information; finally, comprehensive analysis is performed on the data application information to obtain business analysis results. Through the above technical solution, intelligent business analysis processing can be realized, so that the business analysis process can be faster and more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0047] Figure 1 is a flow chart of a business analysis method provided by an embodiment of the present application;

[0048] Figure 2 is a specific flow chart of step S200 provided in one embodiment of the present application;

[0049] Figure 3 is a specific flow chart of step S210 provided in one embodiment of the present application;

[0050] Figure 4 is a specific flow chart of step S300 provided in one embodiment of the present application;

[0051] Figure 5 is a specific flow chart of step S400 provided in one embodiment of the present application;

[0052] Figure 6 is a specific flow chart of step S500 provided in one embodiment of the present application;

[0053] Figure 7 is a specific flow chart of step S600 provided in one embodiment of the present application;

[0054] Figure 8 is a schematic diagram of a business analysis device provided by an embodiment of the present application;

[0055] Fig. 9 It is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0058] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0059] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0060] AI is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0061] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0062] Artificial intelligence is AI. AI is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0063] The servers involved in artificial intelligence technology can be independent servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), as well as big data and artificial intelligence platforms.

[0064] The present application provides a business analysis method, device, electronic device and computer-readable storage medium. In the process of business analysis, firstly, business data is obtained, wherein the business data carries business scenario information; then, business indicator information can be obtained by converting the business data according to the business scenario information; then, data mining is performed on the business indicator information based on a preset data mining model to obtain business mining information; then, correlation analysis is performed on the business data to obtain data correlation information; then, application analysis is performed on the business mining information and data correlation information according to a preset application set to obtain data application information; finally, comprehensive analysis is performed on the data application information to obtain business analysis results. Through the above technical solution, intelligent business analysis processing can be realized, so that the business analysis process can be faster and more accurate.

[0065] The business analysis method provided in the embodiment of the present application relates to the field of business analysis technology. The business analysis method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the business analysis method, etc., but is not limited to the above forms.

[0066] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0067] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0068] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0069] like Figure 1 As shown, Figure 1It is a flowchart of a business analysis method provided by an embodiment of the present application. The business analysis method includes but is not limited to steps S100 to S600.

[0070] Step S100, acquiring business data, wherein the business data carries business scenario information;

[0071] Step S200, converting the business data according to the business scenario information to obtain business indicator information;

[0072] Step S300, performing data mining processing on the business indicator information based on a preset data mining model to obtain business mining information;

[0073] Step S400, performing correlation analysis on the business data to obtain data correlation information;

[0074] Step S500, performing application analysis and processing on the business mining information and the data association information according to a preset application set to obtain data application information;

[0075] Step S600, comprehensively analyzing and processing the data application information to obtain business analysis results.

[0076] It should be noted that in the process of business analysis, first obtain business data, where the business data carries business scenario information; then convert the business data according to the business scenario information to obtain business indicator information; then perform data mining on the business indicator information based on the preset data mining model to obtain business mining information; then perform correlation analysis on the business data to obtain data correlation information; then perform application analysis on the business mining information and data correlation information according to the preset application set to obtain data application information; finally, perform comprehensive analysis on the data application information to obtain business analysis results. Through the above technical solution, intelligent business analysis processing can be realized, making the business analysis process faster and more accurate.

[0077] It is worth noting that in the financial industry, various electronic spreadsheets are generally used to record various financial data. Subsequently, experienced business personnel are required to analyze and process the relevant data tables to obtain the business operation status. However, this business analysis method is relatively inefficient. Therefore, the embodiment of the present application proposes a business analysis method that can realize intelligent business analysis processing, greatly improve the efficiency of business analysis, and can also well meet the business analysis needs of big data.

[0078] It is worth noting that the embodiments of the present application will obtain the user's permission or consent before obtaining business data, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or jump to a confirmation page. After the user's separate permission or consent is clearly obtained, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0079] It should be noted that business data carries business scenario information, and then the business data can be converted and processed according to the business scenario information to obtain business indicator information, thereby making prerequisite preparations for subsequent business analysis and processing.

[0080] It is worth noting that data mining models are a collection of algorithms and techniques that are used to discover patterns and knowledge from large amounts of data. These models can be applied to a variety of fields, such as market analysis, risk management, medical diagnosis, customer segmentation, etc. Here are some common types of data mining models: Classification models: used to predict the category of discrete target variables. For example, determining whether an email is spam. Regression models: used to predict continuous numeric target variables. For example, predicting housing prices. Clustering models: used to divide data into different groups or "clusters" so that data points within the same group are highly similar and different groups are less similar. For example, market segmentation. Association rule learning: used to discover interesting relationships between variables, such as shopping basket analysis to find that customers who buy certain items also tend to buy other items. Anomaly detection models: used to identify anomalies or unusual patterns in data, which is very important in areas such as fraud detection and network security. Time series analysis: used to analyze data points arranged in chronological order to predict future trends or patterns. Neural networks: imitate the way the human brain processes information and can be used for a variety of tasks such as classification, regression, and pattern recognition. Support vector machines: a powerful classification technique that can also be used for regression analysis. Decision tree: Represents the decision-making process through a tree diagram structure, used for classification and regression. Ensemble learning model: Combines multiple models to improve prediction performance, such as random forests and gradient boosting machines. Deep learning model: Uses a multi-layer neural network to learn complex patterns of data, suitable for tasks such as image and speech recognition. The construction of a data mining model generally includes steps such as data preprocessing, feature selection, model training, model evaluation, and model deployment. The selection of appropriate models and algorithms depends on the characteristics of the data, the nature of the problem, and business needs. In an embodiment of the present application, after obtaining the business indicator information, the data mining model can be used to perform data mining processing on the business indicator information to obtain business mining information, in order to prepare for subsequent business analysis.

[0081] It is worth noting that after using the data mining model to perform data mining on business indicator information to obtain business mining information, the business data can be first analyzed for correlation to obtain data association information; subsequently, the business mining information and data association information can be analyzed and processed according to the preset application set to obtain data application information, and finally the data application information can be analyzed and processed comprehensively to obtain the corresponding business analysis results. Among them, the application set mainly carries various functional applications, including scoring, delineation, stratification and prediction; Scoring: quantitative scoring of specified objects and the function of managing scoring rules. For objects that need to be quantitatively scored, the fields, conditions, benchmark scores, etc. are combined into scoring rules to quantitatively score the objects, record the scoring results, and give the results and output fields to downstream business system applications. Delineation: the function of screening and delineating specified objects and managing delineation rules. For objects that need to be screened and delineated, within the specified delineation range, the objects are screened and delineated by two methods: custom rules and fixed rules, the delineation results are recorded, and the results and output fields are given to downstream business system applications. Layering: the function of layering specified objects and managing layering thresholds and rules. For objects that need to be stratified, select the appropriate stratification method according to the specified number of stratification layers, priority and division basis, stratify the objects, record the stratification thresholds and rules, and give the results to downstream business system applications. Prediction: The function of predicting the specified objects and recording the prediction results. For objects that need to be predicted, obtain the prediction results from the data modeling, record the results and push them to downstream business system applications.

[0082] In some embodiments, Figure 2 As shown, the above step S200 may include but is not limited to steps S210 to S220.

[0083] Step S210, selecting a target indicator addition item from a preset indicator addition library according to the business scenario information;

[0084] Step S220, combining the business data and the target indicator addition item to obtain business indicator information.

[0085] It should be noted that in the process of converting business data according to business scenario information to obtain business indicator information, first, the target indicator addition item is selected from the preset indicator addition library according to the business scenario information; then the business data and the target indicator addition item are combined to obtain the business indicator information, which is the prerequisite for subsequent business analysis and processing.

[0086] It is worth noting that the indicator addition library includes multiple indicator addition items. According to the business scenario information, the corresponding target indicator addition items can be selected from the pre-set indicator addition library; subsequently, the business data and the selected target indicator addition items can be combined to obtain the corresponding business indicator information.

[0087] In some embodiments, Figure 3 As shown, the indicator addition library includes multiple indicator addition items, each indicator addition item carries scene marking information, and the above step S210 may include but is not limited to steps S211 to S212.

[0088] Step S211, matching the business scenario information with the scenario tag information to obtain target scenario tag information;

[0089] Step S212: taking the indicator addition item carrying the target scene marking information as the target indicator addition item.

[0090] It should be noted that in the process of selecting the target indicator addition item from the preset indicator addition library according to the business scenario information, the business scenario information and the scenario marking information are first matched and processed to obtain the target scenario marking information; then the indicator addition item carrying the target scenario marking information is used as the target indicator addition item; after determining that the target indicator addition item is obtained, the prerequisite preparations can be made for the subsequent business analysis.

[0091] It is worth noting that the indicator addition library includes multiple indicator addition items, and each indicator addition item carries scene marking information. Therefore, in the process of selecting the target indicator addition item, the business scenario information and the scene marking information can be matched first, and then the indicator addition item corresponding to the matched target scene marking information can be used as the target indicator addition item. The whole process is simple, safe and reliable.

[0092] In some embodiments, Figure 4 As shown, the data mining model includes a preprocessing module, a feature extraction module, a clustering module and an evaluation module, and the above step S300 may include but is not limited to steps S310 to S340.

[0093] Step S310, preprocessing the business indicator information based on the preprocessing module to obtain preprocessed indicator information;

[0094] Step S320, extracting features from the preprocessed index information based on the feature extraction module to obtain index feature information;

[0095] Step S330, performing information clustering processing on the indicator feature information based on the clustering module to obtain indicator clustering information;

[0096] Step S340: evaluating the indicator clustering information based on the evaluation module to obtain business mining information.

[0097] It should be noted that, in the process of performing data mining processing on business indicator information based on a preset data mining model to obtain business mining information, the business indicator information is first preprocessed based on the preprocessing module to obtain preprocessed indicator information; then, feature extraction is performed on the preprocessed indicator information based on the feature extraction module to obtain indicator feature information; then, information clustering processing is performed on the indicator feature information based on the clustering module to obtain indicator clustering information; finally, the indicator clustering information can be evaluated based on the evaluation module to obtain business mining information; through the above technical scheme, business indicator information can be quickly data mined through the data mining model to obtain business mining information, and the whole process is stable and reliable.

[0098] It is worth noting that the data mining model includes a preprocessing module, a feature extraction module, a clustering module and an evaluation module. The preprocessing module can preprocess the business indicator information; the feature extraction module can extract features from the preprocessed indicator information to obtain indicator feature information; the clustering module can cluster the indicator feature information to obtain indicator clustering information; and the evaluation module can evaluate the indicator clustering information to obtain business mining information.

[0099] In some embodiments, Figure 5 As shown, the above step S400 may include but is not limited to step S410 to step S440.

[0100] Step S410, performing data integration processing on the business data to obtain a business data set;

[0101] Step S420, performing frequent item set mining processing on the business data set to obtain multiple business frequent item sets;

[0102] Step S430, performing association rule mining on multiple business frequent item sets to obtain association confidence;

[0103] Step S440: Evaluate the association confidence to obtain data association information.

[0104] It should be noted that in the process of performing correlation analysis on business data to obtain data correlation information, the business data is firstly subjected to data integration processing to obtain a business data set; then the business data set is subjected to frequent item set mining processing to obtain multiple business frequent item sets; then the multiple business frequent item sets are subjected to association rule mining processing to obtain correlation confidence; finally, the correlation confidence is evaluated and processed to obtain data correlation information; through the above technical solution, correlation analysis and processing of business data can be performed quickly and accurately.

[0105] It is worth noting that in order to analyze and process the correlation of business data, the business data can first be integrated to obtain a business data set, and then the business data set can be processed for frequent item set mining to obtain multiple business frequent item sets; then the multiple business frequent item sets can be processed for association rule mining to obtain association confidence; finally, the size of the association confidence can be evaluated to determine the correlation information of the business data in preparation for subsequent business analysis.

[0106] In some embodiments, Figure 6 As shown, the above step S500 may include but is not limited to step S510 to step S530.

[0107] Step S510, obtaining an application analysis request;

[0108] Step S520, selecting target application analysis information from the application set according to the application analysis request;

[0109] Step S530, comprehensively analyzing and processing the business mining information and the data association information according to the target application analysis information to obtain data application information.

[0110] It should be noted that, in the process of performing application analysis on the business mining information and data association information according to a preset application set to obtain data application information, first, an application analysis request is obtained, and then the target application analysis information is selected from the application set according to the application analysis request; finally, the business mining information and data association information can be comprehensively analyzed and processed according to the target application analysis information to obtain the data application information; in an embodiment of the present application, in the process of performing application analysis on the business mining information and data association information based on the application set, it is first necessary to determine the target application analysis information, and then the business mining information and data association information can be comprehensively analyzed and processed according to the target application analysis information to obtain the data application information.

[0111] It is worth noting that in the process of comprehensive analysis and processing of business mining information and data association information according to target application analysis information, it can be achieved based on a pre-trained neural network. Based on this neural network, the business mining information and data association information can be well comprehensively analyzed and processed to finally obtain data application information.

[0112] In some embodiments, Figure 7 As shown, the above step S600 may include but is not limited to step S610 to step S630.

[0113] Step S610, querying the abnormal flag bit of the data application information to obtain application flag information;

[0114] Step S620, when the application mark information indicates that the data application information is abnormal, the business abnormality mark information is used as the business analysis result;

[0115] Step S630: When the application mark information indicates that the data application information is normal, the normal service mark information is used as the service analysis result.

[0116] It should be noted that in the process of comprehensively analyzing and processing the data application information to obtain the business analysis result, the abnormal flag bit of the data application information is first queried and processed to obtain the application flag information; then, when the application flag information indicates that the data application information is abnormal, the business abnormal flag information is used as the business analysis result; when the application flag information indicates that the data application information is normal, the business normal flag information is used as the business analysis result. Through the above technical solution, the business analysis result can be determined simply and quickly.

[0117] It is worth noting that by querying the abnormal flag bit of the data application information, the application marking information can be obtained, and then the corresponding business analysis results can be determined according to the different situations of the application marking information. Through the above method, the determination of the business analysis results can be simpler and faster.

[0118] In addition, if Figure 8 As shown, an embodiment of the present application further provides a service analysis device 10, comprising:

[0119] The first processing module 100 is used to obtain business data, wherein the business data carries business scenario information;

[0120] The second processing module 200 is used to convert the business data according to the business scenario information to obtain business indicator information;

[0121] The third processing module 300 is used to perform data mining processing on the business indicator information based on a preset data mining model to obtain business mining information;

[0122] The fourth processing module 400 is used to perform correlation analysis on the business data to obtain data correlation information;

[0123] A fifth processing module 500 is used to perform application analysis processing on the business mining information and the data association information according to a preset application set to obtain data application information;

[0124] The sixth processing module 600 is used to perform comprehensive analysis and processing on the data application information to obtain business analysis results.

[0125] It should be noted that in the process of business analysis, first obtain business data, where the business data carries business scenario information; then convert the business data according to the business scenario information to obtain business indicator information; then perform data mining on the business indicator information based on the preset data mining model to obtain business mining information; then perform correlation analysis on the business data to obtain data correlation information; then perform application analysis on the business mining information and data correlation information according to the preset application set to obtain data application information; finally, perform comprehensive analysis on the data application information to obtain business analysis results. Through the above technical solution, intelligent business analysis processing can be realized, making the business analysis process faster and more accurate.

[0126] It is worth noting that in the financial industry, various electronic spreadsheets are generally used to record various financial data. Subsequently, experienced business personnel are required to analyze and process the relevant data tables to obtain the business operation status. However, this business analysis method is relatively inefficient. Therefore, the embodiment of the present application proposes a business analysis method that can realize intelligent business analysis processing, greatly improve the efficiency of business analysis, and can also well meet the business analysis needs of big data.

[0127] It is worth noting that the embodiments of the present application will obtain the user's permission or consent before obtaining business data, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or jump to a confirmation page. After the user's separate permission or consent is clearly obtained, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0128] It should be noted that business data carries business scenario information, and then the business data can be converted and processed according to the business scenario information to obtain business indicator information, thereby making prerequisite preparations for subsequent business analysis and processing.

[0129] The specific implementation of the business analysis device 10 is substantially the same as the specific implementation of the above-mentioned business analysis method, and will not be described in detail herein.

[0130] In addition, if Fig. 9 As shown, an embodiment of the present application further provides an electronic device 700 , which includes: a memory 720 , a processor 710 , and a computer program stored in the memory 720 and executable on the processor 710 .

[0131] The processor 710 and the memory 720 may be connected via a bus or in other ways.

[0132] The non-transient software programs and instructions required to implement the business analysis methods of the above embodiments are stored in the memory 720 , and when executed by the processor 710 , the business analysis methods of the above embodiments are executed.

[0133] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor 710 or a controller, for example, by a processor 710 in the above-mentioned device embodiment, so that the above-mentioned processor 710 can execute the business analysis method in the above-mentioned embodiment.

[0135] The above embodiments may be used in combination, and modules with the same name in different embodiments may be the same or different.

[0136] The above describes specific embodiments of the present application, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0138] The apparatus, device, computer-readable storage medium and method provided in the embodiments of the present application correspond to each other. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be repeated here.

[0139] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0140] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0141] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0142] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0144] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0147] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0148] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash memory (Flash RAM). The memory is an example of a computer-readable medium.

[0149] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0150] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0151] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0152] Embodiments of the present application may be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Embodiments of the present application may also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0153] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0154] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A business analysis method, characterized in that: The method comprises: Acquire business data, wherein the business data carries business scenario information; The business data is converted and processed according to the business scenario information to obtain business indicator information; Performing data mining processing on the business indicator information based on a preset data mining model to obtain business mining information; Performing correlation analysis on the business data to obtain data correlation information; Perform application analysis and processing on the business mining information and the data association information according to a preset application set to obtain data application information; Comprehensively analyze and process the data application information to obtain business analysis results.

2. The business analysis method according to claim 1, characterized in that: The converting and processing the business data according to the business scenario information to obtain business indicator information includes: Selecting target indicator addition items from a preset indicator addition library according to the business scenario information; The business data and the target indicator added item are combined and processed to obtain the business indicator information.

3. The business analysis method according to claim 2, characterized in that: The indicator addition library includes a plurality of indicator addition items, each of which carries scenario tag information, and the target indicator addition item is selected from the preset indicator addition library according to the business scenario information, including: Matching the business scenario information with the scenario tag information to obtain target scenario tag information; The indicator additional item carrying the target scene marking information is used as the target indicator additional item.

4. The business analysis method according to claim 1, characterized in that: The data mining model includes a preprocessing module, a feature extraction module, a clustering module and an evaluation module. The business indicator information is subjected to data mining processing based on the preset data mining model to obtain business mining information, including: Preprocessing the business indicator information based on the preprocessing module to obtain preprocessing indicator information; Performing feature extraction on the preprocessing indicator information based on the feature extraction module to obtain indicator feature information; Performing information clustering processing on the indicator feature information based on the clustering module to obtain indicator clustering information; The indicator clustering information is evaluated and processed based on the evaluation module to obtain the business mining information.

5. The business analysis method according to claim 1, characterized in that: The performing correlation analysis on the business data to obtain data correlation information includes: Performing data integration processing on the business data to obtain a business data set; Performing frequent item set mining on the business data set to obtain multiple business frequent item sets; Performing association rule mining on a plurality of the business frequent item sets to obtain association confidences; The association confidence is evaluated to obtain the data association information.

6. The business analysis method according to claim 1, characterized in that: The performing application analysis and processing on the business mining information and the data association information according to the preset application set to obtain data application information includes: Get application analysis request; Selecting target application analysis information from the application set according to the application analysis request; The business mining information and the data association information are comprehensively analyzed and processed according to the target application analysis information to obtain the data application information.

7. The business analysis method according to claim 1, characterized in that: The comprehensive analysis and processing of the data application information to obtain the business analysis results includes: Perform query processing on the abnormal flag bit of the data application information to obtain application flag information; In a case where the application marking information indicates that the data application information is abnormal, using the service abnormality marking information as the service analysis result; In the case where the application mark information indicates that the data application information is normal, the service normal mark information is used as the service analysis result.

8. A business analysis device, characterized in that: include: A first processing module is used to obtain business data, wherein the business data carries business scenario information; A second processing module is used to convert the business data according to the business scenario information to obtain business indicator information; A third processing module is used to perform data mining processing on the business indicator information based on a preset data mining model to obtain business mining information; A fourth processing module, used to perform correlation analysis on the business data to obtain data correlation information; A fifth processing module, configured to perform application analysis processing on the business mining information and the data association information according to a preset application set to obtain data application information; The sixth processing module is used to perform comprehensive analysis and processing on the data application information to obtain business analysis results.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the business analysis method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the business analysis method described in any one of claims 1 to 7.

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